idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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101 | Why square the difference instead of taking the absolute value in standard deviation? | This is an old thread, but most answers focus on analytical simplicity, which IMO is a weak argument in times of computers (although numerical stability might be an issue when using absolute values in optimization routines). Here are some more fundamental arguments in favor of the variance.
The statistical mean minimi... | Why square the difference instead of taking the absolute value in standard deviation? | This is an old thread, but most answers focus on analytical simplicity, which IMO is a weak argument in times of computers (although numerical stability might be an issue when using absolute values in | Why square the difference instead of taking the absolute value in standard deviation?
This is an old thread, but most answers focus on analytical simplicity, which IMO is a weak argument in times of computers (although numerical stability might be an issue when using absolute values in optimization routines). Here are ... | Why square the difference instead of taking the absolute value in standard deviation?
This is an old thread, but most answers focus on analytical simplicity, which IMO is a weak argument in times of computers (although numerical stability might be an issue when using absolute values in |
102 | Why square the difference instead of taking the absolute value in standard deviation? | Squaring amplifies larger deviations.
If your sample has values that are all over the chart then to bring the 68.2% within the first standard deviation your standard deviation needs to be a little wider. If your data tended to all fall around the mean then σ can be tighter.
Some say that it is to simplify calculation... | Why square the difference instead of taking the absolute value in standard deviation? | Squaring amplifies larger deviations.
If your sample has values that are all over the chart then to bring the 68.2% within the first standard deviation your standard deviation needs to be a little wi | Why square the difference instead of taking the absolute value in standard deviation?
Squaring amplifies larger deviations.
If your sample has values that are all over the chart then to bring the 68.2% within the first standard deviation your standard deviation needs to be a little wider. If your data tended to all f... | Why square the difference instead of taking the absolute value in standard deviation?
Squaring amplifies larger deviations.
If your sample has values that are all over the chart then to bring the 68.2% within the first standard deviation your standard deviation needs to be a little wi |
103 | Why square the difference instead of taking the absolute value in standard deviation? | My guess is this: Most populations (distributions) tend to congregate around the mean. The farther a value is from the mean, the rarer it is. In order to adequately express how "out of line" a value is, it is necessary to take into account both its distance from the mean and its (normally speaking) rareness of occurren... | Why square the difference instead of taking the absolute value in standard deviation? | My guess is this: Most populations (distributions) tend to congregate around the mean. The farther a value is from the mean, the rarer it is. In order to adequately express how "out of line" a value i | Why square the difference instead of taking the absolute value in standard deviation?
My guess is this: Most populations (distributions) tend to congregate around the mean. The farther a value is from the mean, the rarer it is. In order to adequately express how "out of line" a value is, it is necessary to take into ac... | Why square the difference instead of taking the absolute value in standard deviation?
My guess is this: Most populations (distributions) tend to congregate around the mean. The farther a value is from the mean, the rarer it is. In order to adequately express how "out of line" a value i |
104 | The Two Cultures: statistics vs. machine learning? | I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JASA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, and neural networks, although this area is less active now. Statisticians have appropriated the work of Valiant and Vapn... | The Two Cultures: statistics vs. machine learning? | I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JASA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, | The Two Cultures: statistics vs. machine learning?
I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JASA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, and neural networks, although this area is less active now. Statistic... | The Two Cultures: statistics vs. machine learning?
I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JASA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, |
105 | The Two Cultures: statistics vs. machine learning? | The biggest difference I see between the communities is that statistics emphasizes inference, whereas machine learning emphasized prediction. When you do statistics, you want to infer the process by which data you have was generated. When you do machine learning, you want to know how you can predict what future data ... | The Two Cultures: statistics vs. machine learning? | The biggest difference I see between the communities is that statistics emphasizes inference, whereas machine learning emphasized prediction. When you do statistics, you want to infer the process by | The Two Cultures: statistics vs. machine learning?
The biggest difference I see between the communities is that statistics emphasizes inference, whereas machine learning emphasized prediction. When you do statistics, you want to infer the process by which data you have was generated. When you do machine learning, you... | The Two Cultures: statistics vs. machine learning?
The biggest difference I see between the communities is that statistics emphasizes inference, whereas machine learning emphasized prediction. When you do statistics, you want to infer the process by |
106 | The Two Cultures: statistics vs. machine learning? | Bayesian: "Hello, Machine Learner!"
Frequentist: "Hello, Machine Learner!"
Machine Learning: "I hear you guys are good at stuff. Here's some data."
F: "Yes, let's write down a model and then calculate the MLE."
B: "Hey, F, that's not what you told me yesterday! I had some univariate data and I wanted to estimate the... | The Two Cultures: statistics vs. machine learning? | Bayesian: "Hello, Machine Learner!"
Frequentist: "Hello, Machine Learner!"
Machine Learning: "I hear you guys are good at stuff. Here's some data."
F: "Yes, let's write down a model and then calcula | The Two Cultures: statistics vs. machine learning?
Bayesian: "Hello, Machine Learner!"
Frequentist: "Hello, Machine Learner!"
Machine Learning: "I hear you guys are good at stuff. Here's some data."
F: "Yes, let's write down a model and then calculate the MLE."
B: "Hey, F, that's not what you told me yesterday! I ha... | The Two Cultures: statistics vs. machine learning?
Bayesian: "Hello, Machine Learner!"
Frequentist: "Hello, Machine Learner!"
Machine Learning: "I hear you guys are good at stuff. Here's some data."
F: "Yes, let's write down a model and then calcula |
107 | The Two Cultures: statistics vs. machine learning? | In such a discussion, I always recall the famous Ken Thompson quote
When in doubt, use brute force.
In this case, machine learning is a salvation when the assumptions are hard to catch; or at least it is much better than guessing them wrong. | The Two Cultures: statistics vs. machine learning? | In such a discussion, I always recall the famous Ken Thompson quote
When in doubt, use brute force.
In this case, machine learning is a salvation when the assumptions are hard to catch; or at least | The Two Cultures: statistics vs. machine learning?
In such a discussion, I always recall the famous Ken Thompson quote
When in doubt, use brute force.
In this case, machine learning is a salvation when the assumptions are hard to catch; or at least it is much better than guessing them wrong. | The Two Cultures: statistics vs. machine learning?
In such a discussion, I always recall the famous Ken Thompson quote
When in doubt, use brute force.
In this case, machine learning is a salvation when the assumptions are hard to catch; or at least |
108 | The Two Cultures: statistics vs. machine learning? | What enforces more separation than there should be is each discipline's lexicon.
There are many instances where ML uses one term and Statistics uses a different term--but both refer to the same thing--fine, you would expect that, and it doesn't cause any permanent confusion (e.g., features/attributes versus expectatio... | The Two Cultures: statistics vs. machine learning? | What enforces more separation than there should be is each discipline's lexicon.
There are many instances where ML uses one term and Statistics uses a different term--but both refer to the same thing | The Two Cultures: statistics vs. machine learning?
What enforces more separation than there should be is each discipline's lexicon.
There are many instances where ML uses one term and Statistics uses a different term--but both refer to the same thing--fine, you would expect that, and it doesn't cause any permanent con... | The Two Cultures: statistics vs. machine learning?
What enforces more separation than there should be is each discipline's lexicon.
There are many instances where ML uses one term and Statistics uses a different term--but both refer to the same thing |
109 | The Two Cultures: statistics vs. machine learning? | The largest differences I've been noticing in the past year are:
Machine learning experts do not spend enough time on fundamentals, and many of them do not understand optimal decision making and proper accuracy scoring rules. They do not understand that predictive methods that make no assumptions require larger sampl... | The Two Cultures: statistics vs. machine learning? | The largest differences I've been noticing in the past year are:
Machine learning experts do not spend enough time on fundamentals, and many of them do not understand optimal decision making and prop | The Two Cultures: statistics vs. machine learning?
The largest differences I've been noticing in the past year are:
Machine learning experts do not spend enough time on fundamentals, and many of them do not understand optimal decision making and proper accuracy scoring rules. They do not understand that predictive me... | The Two Cultures: statistics vs. machine learning?
The largest differences I've been noticing in the past year are:
Machine learning experts do not spend enough time on fundamentals, and many of them do not understand optimal decision making and prop |
110 | The Two Cultures: statistics vs. machine learning? | Machine Learning seems to have its basis in the pragmatic - a Practical observation or simulation of reality. Even within statistics, mindless "checking of models and assumptions" can lead to discarding methods that are useful.
For example, years ago, the very first commercially available (and working) Bankruptcy mode... | The Two Cultures: statistics vs. machine learning? | Machine Learning seems to have its basis in the pragmatic - a Practical observation or simulation of reality. Even within statistics, mindless "checking of models and assumptions" can lead to discard | The Two Cultures: statistics vs. machine learning?
Machine Learning seems to have its basis in the pragmatic - a Practical observation or simulation of reality. Even within statistics, mindless "checking of models and assumptions" can lead to discarding methods that are useful.
For example, years ago, the very first c... | The Two Cultures: statistics vs. machine learning?
Machine Learning seems to have its basis in the pragmatic - a Practical observation or simulation of reality. Even within statistics, mindless "checking of models and assumptions" can lead to discard |
111 | The Two Cultures: statistics vs. machine learning? | I disagree with this question as it suggests that machine learning and statistics are different or conflicting sciences.... when the opposite is true!
machine learning makes extensive use of statistics... a quick survey of any Machine learning or data mining software package will reveal Clustering techniques such as k-... | The Two Cultures: statistics vs. machine learning? | I disagree with this question as it suggests that machine learning and statistics are different or conflicting sciences.... when the opposite is true!
machine learning makes extensive use of statistic | The Two Cultures: statistics vs. machine learning?
I disagree with this question as it suggests that machine learning and statistics are different or conflicting sciences.... when the opposite is true!
machine learning makes extensive use of statistics... a quick survey of any Machine learning or data mining software p... | The Two Cultures: statistics vs. machine learning?
I disagree with this question as it suggests that machine learning and statistics are different or conflicting sciences.... when the opposite is true!
machine learning makes extensive use of statistic |
112 | The Two Cultures: statistics vs. machine learning? | The real problem is that this question is misguided. It is not machine learning vs statistics, it is machine learning against real scientific advance. If a machine learning device gives the right predictions 90% of the time but I cannot understand "why", what is the contribution of machine learning to science at large?... | The Two Cultures: statistics vs. machine learning? | The real problem is that this question is misguided. It is not machine learning vs statistics, it is machine learning against real scientific advance. If a machine learning device gives the right pred | The Two Cultures: statistics vs. machine learning?
The real problem is that this question is misguided. It is not machine learning vs statistics, it is machine learning against real scientific advance. If a machine learning device gives the right predictions 90% of the time but I cannot understand "why", what is the co... | The Two Cultures: statistics vs. machine learning?
The real problem is that this question is misguided. It is not machine learning vs statistics, it is machine learning against real scientific advance. If a machine learning device gives the right pred |
113 | The Two Cultures: statistics vs. machine learning? | I have spoken on this at a different forum the ASA Statistical Consulting eGroup. My response was more specifically to data mining but the two go hand in hand. We statisticians have snubbed our noses at data miners, computer scientists, and engineers. It is wrong. I think part of the reason it happens is because we s... | The Two Cultures: statistics vs. machine learning? | I have spoken on this at a different forum the ASA Statistical Consulting eGroup. My response was more specifically to data mining but the two go hand in hand. We statisticians have snubbed our nose | The Two Cultures: statistics vs. machine learning?
I have spoken on this at a different forum the ASA Statistical Consulting eGroup. My response was more specifically to data mining but the two go hand in hand. We statisticians have snubbed our noses at data miners, computer scientists, and engineers. It is wrong. I ... | The Two Cultures: statistics vs. machine learning?
I have spoken on this at a different forum the ASA Statistical Consulting eGroup. My response was more specifically to data mining but the two go hand in hand. We statisticians have snubbed our nose |
114 | The Two Cultures: statistics vs. machine learning? | Statistical learning (AKA Machine Learning) has its origins in the quest to create software by "learning from examples". There are many tasks that we would like computers to do (e.g., computer vision, speech recognition, robot control) that are difficult to program but for which it is easy to provide training examples... | The Two Cultures: statistics vs. machine learning? | Statistical learning (AKA Machine Learning) has its origins in the quest to create software by "learning from examples". There are many tasks that we would like computers to do (e.g., computer vision | The Two Cultures: statistics vs. machine learning?
Statistical learning (AKA Machine Learning) has its origins in the quest to create software by "learning from examples". There are many tasks that we would like computers to do (e.g., computer vision, speech recognition, robot control) that are difficult to program bu... | The Two Cultures: statistics vs. machine learning?
Statistical learning (AKA Machine Learning) has its origins in the quest to create software by "learning from examples". There are many tasks that we would like computers to do (e.g., computer vision |
115 | The Two Cultures: statistics vs. machine learning? | Ideally one should have a thorough knowledge of both statsitics and machine learning before attempting to answer his question. I am very much a neophyte to ML, so forgive me if wat I say is naive.
I have limited experience in SVMs and regression trees. What strikes me as lacking in ML from a stats point of view is a we... | The Two Cultures: statistics vs. machine learning? | Ideally one should have a thorough knowledge of both statsitics and machine learning before attempting to answer his question. I am very much a neophyte to ML, so forgive me if wat I say is naive.
I h | The Two Cultures: statistics vs. machine learning?
Ideally one should have a thorough knowledge of both statsitics and machine learning before attempting to answer his question. I am very much a neophyte to ML, so forgive me if wat I say is naive.
I have limited experience in SVMs and regression trees. What strikes me ... | The Two Cultures: statistics vs. machine learning?
Ideally one should have a thorough knowledge of both statsitics and machine learning before attempting to answer his question. I am very much a neophyte to ML, so forgive me if wat I say is naive.
I h |
116 | The Two Cultures: statistics vs. machine learning? | This question can also be extended to the so-called super-culture of data science in 2015 David Donoho paper 50 years of Data Science, where he confronts different points of view from statistics and computer science (including machine learning), for instance direct standpoints (from different persons) such that:
Why D... | The Two Cultures: statistics vs. machine learning? | This question can also be extended to the so-called super-culture of data science in 2015 David Donoho paper 50 years of Data Science, where he confronts different points of view from statistics and c | The Two Cultures: statistics vs. machine learning?
This question can also be extended to the so-called super-culture of data science in 2015 David Donoho paper 50 years of Data Science, where he confronts different points of view from statistics and computer science (including machine learning), for instance direct sta... | The Two Cultures: statistics vs. machine learning?
This question can also be extended to the so-called super-culture of data science in 2015 David Donoho paper 50 years of Data Science, where he confronts different points of view from statistics and c |
117 | The Two Cultures: statistics vs. machine learning? | I don't really know what the conceptual/historical difference between machine learning and statistic is but I am sure it is not that obvious... and I am not really interest in knowing if I am a machine learner or a statistician, I think 10 years after Breiman's paper, lots of people are both...
Anyway, I found interes... | The Two Cultures: statistics vs. machine learning? | I don't really know what the conceptual/historical difference between machine learning and statistic is but I am sure it is not that obvious... and I am not really interest in knowing if I am a machin | The Two Cultures: statistics vs. machine learning?
I don't really know what the conceptual/historical difference between machine learning and statistic is but I am sure it is not that obvious... and I am not really interest in knowing if I am a machine learner or a statistician, I think 10 years after Breiman's paper, ... | The Two Cultures: statistics vs. machine learning?
I don't really know what the conceptual/historical difference between machine learning and statistic is but I am sure it is not that obvious... and I am not really interest in knowing if I am a machin |
118 | The Two Cultures: statistics vs. machine learning? | Clearly, the two fields clearly face similar but different problems, in similar but not identical ways with analogous but not identical concepts, and work in different departments, journals and conferences.
When I read Cressie and Read's Power Divergence Statistic it all snapped into place for me. Their formula genera... | The Two Cultures: statistics vs. machine learning? | Clearly, the two fields clearly face similar but different problems, in similar but not identical ways with analogous but not identical concepts, and work in different departments, journals and confer | The Two Cultures: statistics vs. machine learning?
Clearly, the two fields clearly face similar but different problems, in similar but not identical ways with analogous but not identical concepts, and work in different departments, journals and conferences.
When I read Cressie and Read's Power Divergence Statistic it ... | The Two Cultures: statistics vs. machine learning?
Clearly, the two fields clearly face similar but different problems, in similar but not identical ways with analogous but not identical concepts, and work in different departments, journals and confer |
119 | The Two Cultures: statistics vs. machine learning? | You run a fancy computer algorithm once -- and you get a CS conference presentation/statistics paper (wow, what a fast convergence!). You commercialize it and run it 1 million times -- and you go broke (ouch, why am I getting useless and irreproducible results all the time???) unless you know how to employ probability ... | The Two Cultures: statistics vs. machine learning? | You run a fancy computer algorithm once -- and you get a CS conference presentation/statistics paper (wow, what a fast convergence!). You commercialize it and run it 1 million times -- and you go brok | The Two Cultures: statistics vs. machine learning?
You run a fancy computer algorithm once -- and you get a CS conference presentation/statistics paper (wow, what a fast convergence!). You commercialize it and run it 1 million times -- and you go broke (ouch, why am I getting useless and irreproducible results all the ... | The Two Cultures: statistics vs. machine learning?
You run a fancy computer algorithm once -- and you get a CS conference presentation/statistics paper (wow, what a fast convergence!). You commercialize it and run it 1 million times -- and you go brok |
120 | The Two Cultures: statistics vs. machine learning? | There is an area of application of statistics where focus on the data generating model makes a lot of sense. In designed experiments, e.g., animal studies, clinical trials, industrial DOEs, statisticians can have a hand in what the data generating model is. ML tends not to spend much time on this very important problem... | The Two Cultures: statistics vs. machine learning? | There is an area of application of statistics where focus on the data generating model makes a lot of sense. In designed experiments, e.g., animal studies, clinical trials, industrial DOEs, statistici | The Two Cultures: statistics vs. machine learning?
There is an area of application of statistics where focus on the data generating model makes a lot of sense. In designed experiments, e.g., animal studies, clinical trials, industrial DOEs, statisticians can have a hand in what the data generating model is. ML tends no... | The Two Cultures: statistics vs. machine learning?
There is an area of application of statistics where focus on the data generating model makes a lot of sense. In designed experiments, e.g., animal studies, clinical trials, industrial DOEs, statistici |
121 | The Two Cultures: statistics vs. machine learning? | I think machine learning needs to be a sub-branch under statistics, just like, in my view, chemistry needs to be a sub-branch under physics.
I think physics-inspired view into chemistry is pretty solid (I guess). I don't think there is any chemical reaction whose equivalent is not known in physical terms. I think physi... | The Two Cultures: statistics vs. machine learning? | I think machine learning needs to be a sub-branch under statistics, just like, in my view, chemistry needs to be a sub-branch under physics.
I think physics-inspired view into chemistry is pretty soli | The Two Cultures: statistics vs. machine learning?
I think machine learning needs to be a sub-branch under statistics, just like, in my view, chemistry needs to be a sub-branch under physics.
I think physics-inspired view into chemistry is pretty solid (I guess). I don't think there is any chemical reaction whose equiv... | The Two Cultures: statistics vs. machine learning?
I think machine learning needs to be a sub-branch under statistics, just like, in my view, chemistry needs to be a sub-branch under physics.
I think physics-inspired view into chemistry is pretty soli |
122 | The Two Cultures: statistics vs. machine learning? | From the Coursera course "Data Science in real life" by Brian Caffo
Machine Learning
Emphasize predictions
Evaluates results via prediction performance
Concern for overfitting but not model complexity per se
Emphasis on performance
Generalizability is obtained through performance on novel datasets
Usually, no superpop... | The Two Cultures: statistics vs. machine learning? | From the Coursera course "Data Science in real life" by Brian Caffo
Machine Learning
Emphasize predictions
Evaluates results via prediction performance
Concern for overfitting but not model complexit | The Two Cultures: statistics vs. machine learning?
From the Coursera course "Data Science in real life" by Brian Caffo
Machine Learning
Emphasize predictions
Evaluates results via prediction performance
Concern for overfitting but not model complexity per se
Emphasis on performance
Generalizability is obtained through... | The Two Cultures: statistics vs. machine learning?
From the Coursera course "Data Science in real life" by Brian Caffo
Machine Learning
Emphasize predictions
Evaluates results via prediction performance
Concern for overfitting but not model complexit |
123 | The Two Cultures: statistics vs. machine learning? | As as Computer Scientist, I am always intrigued when looking to statistical approaches. To me many times it looks like the statistical models used in the statistical analysis are way too complex for the data in many situations!
For example there is a strong link between data compression and statistics. Basically one ne... | The Two Cultures: statistics vs. machine learning? | As as Computer Scientist, I am always intrigued when looking to statistical approaches. To me many times it looks like the statistical models used in the statistical analysis are way too complex for t | The Two Cultures: statistics vs. machine learning?
As as Computer Scientist, I am always intrigued when looking to statistical approaches. To me many times it looks like the statistical models used in the statistical analysis are way too complex for the data in many situations!
For example there is a strong link betwee... | The Two Cultures: statistics vs. machine learning?
As as Computer Scientist, I am always intrigued when looking to statistical approaches. To me many times it looks like the statistical models used in the statistical analysis are way too complex for t |
124 | How to understand the drawbacks of K-means | While I like David Robinson's answer here a lot, here's some additional critique of k-means.
Clustering non-clustered data
Run k-means on uniform data, and you will still get clusters! It doesn't tell you when the data just does not cluster, and can take your research into a dead end this way.
Sensitive to scale
Res... | How to understand the drawbacks of K-means | While I like David Robinson's answer here a lot, here's some additional critique of k-means.
Clustering non-clustered data
Run k-means on uniform data, and you will still get clusters! It doesn't te | How to understand the drawbacks of K-means
While I like David Robinson's answer here a lot, here's some additional critique of k-means.
Clustering non-clustered data
Run k-means on uniform data, and you will still get clusters! It doesn't tell you when the data just does not cluster, and can take your research into a... | How to understand the drawbacks of K-means
While I like David Robinson's answer here a lot, here's some additional critique of k-means.
Clustering non-clustered data
Run k-means on uniform data, and you will still get clusters! It doesn't te |
125 | How to understand the drawbacks of K-means | What a great question- it's a chance to show how one would inspect the drawbacks and assumptions of any statistical method. Namely: make up some data and try the algorithm on it!
We'll consider two of your assumptions, and we'll see what happens to the k-means algorithm when those assumptions are broken. We'll stick t... | How to understand the drawbacks of K-means | What a great question- it's a chance to show how one would inspect the drawbacks and assumptions of any statistical method. Namely: make up some data and try the algorithm on it!
We'll consider two o | How to understand the drawbacks of K-means
What a great question- it's a chance to show how one would inspect the drawbacks and assumptions of any statistical method. Namely: make up some data and try the algorithm on it!
We'll consider two of your assumptions, and we'll see what happens to the k-means algorithm when ... | How to understand the drawbacks of K-means
What a great question- it's a chance to show how one would inspect the drawbacks and assumptions of any statistical method. Namely: make up some data and try the algorithm on it!
We'll consider two o |
126 | How to understand the drawbacks of K-means | Logically speaking, the drawbacks of K-means are :
needs linear separability of the clusters
need to specify the number of clusters
Algorithmics : Loyds procedure does not converge to the true global maximum even with a good initialization when there are many points or dimensions
But K-means is better than we usually... | How to understand the drawbacks of K-means | Logically speaking, the drawbacks of K-means are :
needs linear separability of the clusters
need to specify the number of clusters
Algorithmics : Loyds procedure does not converge to the true global | How to understand the drawbacks of K-means
Logically speaking, the drawbacks of K-means are :
needs linear separability of the clusters
need to specify the number of clusters
Algorithmics : Loyds procedure does not converge to the true global maximum even with a good initialization when there are many points or dimens... | How to understand the drawbacks of K-means
Logically speaking, the drawbacks of K-means are :
needs linear separability of the clusters
need to specify the number of clusters
Algorithmics : Loyds procedure does not converge to the true global |
127 | How to understand the drawbacks of K-means | I would just like to add to @DavidRobinson's answer that clustering to minimal total cluster variance is actually a combinatorial optimization problem, of which k-Means is just one technique - and given the latter's "one shot", local "steepest descent" nature, a pretty bad one too. Also, trying to substantially improve... | How to understand the drawbacks of K-means | I would just like to add to @DavidRobinson's answer that clustering to minimal total cluster variance is actually a combinatorial optimization problem, of which k-Means is just one technique - and giv | How to understand the drawbacks of K-means
I would just like to add to @DavidRobinson's answer that clustering to minimal total cluster variance is actually a combinatorial optimization problem, of which k-Means is just one technique - and given the latter's "one shot", local "steepest descent" nature, a pretty bad one... | How to understand the drawbacks of K-means
I would just like to add to @DavidRobinson's answer that clustering to minimal total cluster variance is actually a combinatorial optimization problem, of which k-Means is just one technique - and giv |
128 | How to understand the drawbacks of K-means | To understand the drawbacks of K-means, I like to think of what the model behind it is.
K-means is a special case of Gaussian Mixture Models (GMM). GMM assumes that the data comes from a mixture of $K$ Gaussian distributions. In other words, there is a certain probability that the data comes from one of $K$ of the Ga... | How to understand the drawbacks of K-means | To understand the drawbacks of K-means, I like to think of what the model behind it is.
K-means is a special case of Gaussian Mixture Models (GMM). GMM assumes that the data comes from a mixture of $ | How to understand the drawbacks of K-means
To understand the drawbacks of K-means, I like to think of what the model behind it is.
K-means is a special case of Gaussian Mixture Models (GMM). GMM assumes that the data comes from a mixture of $K$ Gaussian distributions. In other words, there is a certain probability th... | How to understand the drawbacks of K-means
To understand the drawbacks of K-means, I like to think of what the model behind it is.
K-means is a special case of Gaussian Mixture Models (GMM). GMM assumes that the data comes from a mixture of $ |
129 | Bayesian and frequentist reasoning in plain English | Here is how I would explain the basic difference to my grandma:
I have misplaced my phone somewhere in the home. I can use the phone locator on the base of the instrument to locate the phone and when I press the phone locator the phone starts beeping.
Problem: Which area of my home should I search?
Frequentist Reasonin... | Bayesian and frequentist reasoning in plain English | Here is how I would explain the basic difference to my grandma:
I have misplaced my phone somewhere in the home. I can use the phone locator on the base of the instrument to locate the phone and when | Bayesian and frequentist reasoning in plain English
Here is how I would explain the basic difference to my grandma:
I have misplaced my phone somewhere in the home. I can use the phone locator on the base of the instrument to locate the phone and when I press the phone locator the phone starts beeping.
Problem: Which a... | Bayesian and frequentist reasoning in plain English
Here is how I would explain the basic difference to my grandma:
I have misplaced my phone somewhere in the home. I can use the phone locator on the base of the instrument to locate the phone and when |
130 | Bayesian and frequentist reasoning in plain English | Tongue firmly in cheek:
A Bayesian defines a "probability" in exactly the same way that most non-statisticians do - namely an indication of the plausibility of a proposition or a situation. If you ask them a question about a particular proposition or situation, they will give you a direct answer assigning probabilitie... | Bayesian and frequentist reasoning in plain English | Tongue firmly in cheek:
A Bayesian defines a "probability" in exactly the same way that most non-statisticians do - namely an indication of the plausibility of a proposition or a situation. If you as | Bayesian and frequentist reasoning in plain English
Tongue firmly in cheek:
A Bayesian defines a "probability" in exactly the same way that most non-statisticians do - namely an indication of the plausibility of a proposition or a situation. If you ask them a question about a particular proposition or situation, they ... | Bayesian and frequentist reasoning in plain English
Tongue firmly in cheek:
A Bayesian defines a "probability" in exactly the same way that most non-statisticians do - namely an indication of the plausibility of a proposition or a situation. If you as |
131 | Bayesian and frequentist reasoning in plain English | Very crudely I would say that:
Frequentist: Sampling is infinite and decision rules can be sharp. Data are a repeatable random sample - there is a frequency. Underlying parameters are fixed i.e. they remain constant during this repeatable sampling process.
Bayesian: Unknown quantities are treated probabilistically and... | Bayesian and frequentist reasoning in plain English | Very crudely I would say that:
Frequentist: Sampling is infinite and decision rules can be sharp. Data are a repeatable random sample - there is a frequency. Underlying parameters are fixed i.e. they | Bayesian and frequentist reasoning in plain English
Very crudely I would say that:
Frequentist: Sampling is infinite and decision rules can be sharp. Data are a repeatable random sample - there is a frequency. Underlying parameters are fixed i.e. they remain constant during this repeatable sampling process.
Bayesian: ... | Bayesian and frequentist reasoning in plain English
Very crudely I would say that:
Frequentist: Sampling is infinite and decision rules can be sharp. Data are a repeatable random sample - there is a frequency. Underlying parameters are fixed i.e. they |
132 | Bayesian and frequentist reasoning in plain English | Let us say a man rolls a six sided die and it has outcomes 1, 2, 3, 4, 5, or 6. Furthermore, he says that if it lands on a 3, he'll give you a free text book.
Then informally:
The Frequentist would say that each outcome has an equal 1 in 6 chance of occurring. She views probability as being derived from long run freque... | Bayesian and frequentist reasoning in plain English | Let us say a man rolls a six sided die and it has outcomes 1, 2, 3, 4, 5, or 6. Furthermore, he says that if it lands on a 3, he'll give you a free text book.
Then informally:
The Frequentist would sa | Bayesian and frequentist reasoning in plain English
Let us say a man rolls a six sided die and it has outcomes 1, 2, 3, 4, 5, or 6. Furthermore, he says that if it lands on a 3, he'll give you a free text book.
Then informally:
The Frequentist would say that each outcome has an equal 1 in 6 chance of occurring. She vie... | Bayesian and frequentist reasoning in plain English
Let us say a man rolls a six sided die and it has outcomes 1, 2, 3, 4, 5, or 6. Furthermore, he says that if it lands on a 3, he'll give you a free text book.
Then informally:
The Frequentist would sa |
133 | Bayesian and frequentist reasoning in plain English | Just a little bit of fun...
A Bayesian is one who, vaguely expecting a horse, and catching a glimpse of a donkey, strongly believes he has seen a mule.
From this site:
http://www2.isye.gatech.edu/~brani/isyebayes/jokes.html
and from the same site, a nice essay...
"An Intuitive Explanation of Bayes' Theorem"
http://yudk... | Bayesian and frequentist reasoning in plain English | Just a little bit of fun...
A Bayesian is one who, vaguely expecting a horse, and catching a glimpse of a donkey, strongly believes he has seen a mule.
From this site:
http://www2.isye.gatech.edu/~bra | Bayesian and frequentist reasoning in plain English
Just a little bit of fun...
A Bayesian is one who, vaguely expecting a horse, and catching a glimpse of a donkey, strongly believes he has seen a mule.
From this site:
http://www2.isye.gatech.edu/~brani/isyebayes/jokes.html
and from the same site, a nice essay...
"An ... | Bayesian and frequentist reasoning in plain English
Just a little bit of fun...
A Bayesian is one who, vaguely expecting a horse, and catching a glimpse of a donkey, strongly believes he has seen a mule.
From this site:
http://www2.isye.gatech.edu/~bra |
134 | Bayesian and frequentist reasoning in plain English | The Bayesian is asked to make bets, which may include anything from which fly will crawl up a wall faster to which medicine will save most lives, or which prisoners should go to jail. He has a big box with a handle. He knows that if he puts absolutely everything he knows into the box, including his personal opinion, an... | Bayesian and frequentist reasoning in plain English | The Bayesian is asked to make bets, which may include anything from which fly will crawl up a wall faster to which medicine will save most lives, or which prisoners should go to jail. He has a big box | Bayesian and frequentist reasoning in plain English
The Bayesian is asked to make bets, which may include anything from which fly will crawl up a wall faster to which medicine will save most lives, or which prisoners should go to jail. He has a big box with a handle. He knows that if he puts absolutely everything he kn... | Bayesian and frequentist reasoning in plain English
The Bayesian is asked to make bets, which may include anything from which fly will crawl up a wall faster to which medicine will save most lives, or which prisoners should go to jail. He has a big box |
135 | Bayesian and frequentist reasoning in plain English | In plain english, I would say that Bayesian and Frequentist reasoning are distinguished by two different ways of answering the question:
What is probability?
Most differences will essentially boil down to how each answers this question, for it basically defines the domain of valid applications of the theory. Now you c... | Bayesian and frequentist reasoning in plain English | In plain english, I would say that Bayesian and Frequentist reasoning are distinguished by two different ways of answering the question:
What is probability?
Most differences will essentially boil dow | Bayesian and frequentist reasoning in plain English
In plain english, I would say that Bayesian and Frequentist reasoning are distinguished by two different ways of answering the question:
What is probability?
Most differences will essentially boil down to how each answers this question, for it basically defines the do... | Bayesian and frequentist reasoning in plain English
In plain english, I would say that Bayesian and Frequentist reasoning are distinguished by two different ways of answering the question:
What is probability?
Most differences will essentially boil dow |
136 | Bayesian and frequentist reasoning in plain English | In reality, I think much of the philosophy surrounding the issue is just grandstanding. That's not to dismiss the debate, but it is a word of caution. Sometimes, practical matters take priority - I'll give an example below.
Also, you could just as easily argue that there are more than two approaches:
Neyman-Pearson ... | Bayesian and frequentist reasoning in plain English | In reality, I think much of the philosophy surrounding the issue is just grandstanding. That's not to dismiss the debate, but it is a word of caution. Sometimes, practical matters take priority - I' | Bayesian and frequentist reasoning in plain English
In reality, I think much of the philosophy surrounding the issue is just grandstanding. That's not to dismiss the debate, but it is a word of caution. Sometimes, practical matters take priority - I'll give an example below.
Also, you could just as easily argue that ... | Bayesian and frequentist reasoning in plain English
In reality, I think much of the philosophy surrounding the issue is just grandstanding. That's not to dismiss the debate, but it is a word of caution. Sometimes, practical matters take priority - I' |
137 | Bayesian and frequentist reasoning in plain English | Bayesian and frequentist statistics are compatible in that they can be understood as two limiting cases of assessing the probability of future events based on past events and an assumed model, if one admits that in the limit of a very large number of observations, no uncertainty about the system remains, and that in th... | Bayesian and frequentist reasoning in plain English | Bayesian and frequentist statistics are compatible in that they can be understood as two limiting cases of assessing the probability of future events based on past events and an assumed model, if one | Bayesian and frequentist reasoning in plain English
Bayesian and frequentist statistics are compatible in that they can be understood as two limiting cases of assessing the probability of future events based on past events and an assumed model, if one admits that in the limit of a very large number of observations, no ... | Bayesian and frequentist reasoning in plain English
Bayesian and frequentist statistics are compatible in that they can be understood as two limiting cases of assessing the probability of future events based on past events and an assumed model, if one |
138 | Bayesian and frequentist reasoning in plain English | I would say that they look at probability in different ways. The Bayesian is subjective and uses a priori beliefs to define a prior probability distribution on the possible values of the unknown parameters. So he relies on a theory of probability like deFinetti's. The frequentist see probability as something that ha... | Bayesian and frequentist reasoning in plain English | I would say that they look at probability in different ways. The Bayesian is subjective and uses a priori beliefs to define a prior probability distribution on the possible values of the unknown para | Bayesian and frequentist reasoning in plain English
I would say that they look at probability in different ways. The Bayesian is subjective and uses a priori beliefs to define a prior probability distribution on the possible values of the unknown parameters. So he relies on a theory of probability like deFinetti's. ... | Bayesian and frequentist reasoning in plain English
I would say that they look at probability in different ways. The Bayesian is subjective and uses a priori beliefs to define a prior probability distribution on the possible values of the unknown para |
139 | Bayesian and frequentist reasoning in plain English | The simplest and clearest explanation I've seen, from Larry Wasserman's notes on Statistical Machine Learning (with disclaimer: "at the risk of oversimplifying"):
Frequentist versus Bayesian Methods
In frequentist inference, probabilities are interpreted as long run frequencies. The goal is to create procedures with ... | Bayesian and frequentist reasoning in plain English | The simplest and clearest explanation I've seen, from Larry Wasserman's notes on Statistical Machine Learning (with disclaimer: "at the risk of oversimplifying"):
Frequentist versus Bayesian Methods
| Bayesian and frequentist reasoning in plain English
The simplest and clearest explanation I've seen, from Larry Wasserman's notes on Statistical Machine Learning (with disclaimer: "at the risk of oversimplifying"):
Frequentist versus Bayesian Methods
In frequentist inference, probabilities are interpreted as long run... | Bayesian and frequentist reasoning in plain English
The simplest and clearest explanation I've seen, from Larry Wasserman's notes on Statistical Machine Learning (with disclaimer: "at the risk of oversimplifying"):
Frequentist versus Bayesian Methods
|
140 | Bayesian and frequentist reasoning in plain English | I've attempted a side-by-side comparison of the two schools of thought here and have more background information here. | Bayesian and frequentist reasoning in plain English | I've attempted a side-by-side comparison of the two schools of thought here and have more background information here. | Bayesian and frequentist reasoning in plain English
I've attempted a side-by-side comparison of the two schools of thought here and have more background information here. | Bayesian and frequentist reasoning in plain English
I've attempted a side-by-side comparison of the two schools of thought here and have more background information here. |
141 | Bayesian and frequentist reasoning in plain English | The way I answer this question is that frequentists compare the data they see to what they expected. That is, they have a mental model on how frequent something should happen, and then see data and how often it did happen. i.e. how likely is the data they have seen given the model they chose.
Bayesian people, on the ot... | Bayesian and frequentist reasoning in plain English | The way I answer this question is that frequentists compare the data they see to what they expected. That is, they have a mental model on how frequent something should happen, and then see data and ho | Bayesian and frequentist reasoning in plain English
The way I answer this question is that frequentists compare the data they see to what they expected. That is, they have a mental model on how frequent something should happen, and then see data and how often it did happen. i.e. how likely is the data they have seen gi... | Bayesian and frequentist reasoning in plain English
The way I answer this question is that frequentists compare the data they see to what they expected. That is, they have a mental model on how frequent something should happen, and then see data and ho |
142 | Bayesian and frequentist reasoning in plain English | In short plain English as follows:
In Bayesian, parameters vary and data are fixed
In Bayesian, $P(\theta|X)=\frac{P(X|\theta)P(\theta)}{P(X)}$ where $P(\theta|X)$ means parameters vary and data are fixed.
In frequentist, parameters are fixed and data vary
In frequentist, $P(\theta|X)=P(X|\theta)$ where $P(X|\theta... | Bayesian and frequentist reasoning in plain English | In short plain English as follows:
In Bayesian, parameters vary and data are fixed
In Bayesian, $P(\theta|X)=\frac{P(X|\theta)P(\theta)}{P(X)}$ where $P(\theta|X)$ means parameters vary and data are | Bayesian and frequentist reasoning in plain English
In short plain English as follows:
In Bayesian, parameters vary and data are fixed
In Bayesian, $P(\theta|X)=\frac{P(X|\theta)P(\theta)}{P(X)}$ where $P(\theta|X)$ means parameters vary and data are fixed.
In frequentist, parameters are fixed and data vary
In freq... | Bayesian and frequentist reasoning in plain English
In short plain English as follows:
In Bayesian, parameters vary and data are fixed
In Bayesian, $P(\theta|X)=\frac{P(X|\theta)P(\theta)}{P(X)}$ where $P(\theta|X)$ means parameters vary and data are |
143 | What is the difference between fixed effect, random effect and mixed effect models? | Statistician Andrew Gelman says that the terms 'fixed effect' and 'random effect' have variable meanings depending on who uses them. Perhaps you can pick out which one of the 5 definitions applies to your case. In general it may be better to either look for equations which describe the probability model the authors are... | What is the difference between fixed effect, random effect and mixed effect models? | Statistician Andrew Gelman says that the terms 'fixed effect' and 'random effect' have variable meanings depending on who uses them. Perhaps you can pick out which one of the 5 definitions applies to | What is the difference between fixed effect, random effect and mixed effect models?
Statistician Andrew Gelman says that the terms 'fixed effect' and 'random effect' have variable meanings depending on who uses them. Perhaps you can pick out which one of the 5 definitions applies to your case. In general it may be bett... | What is the difference between fixed effect, random effect and mixed effect models?
Statistician Andrew Gelman says that the terms 'fixed effect' and 'random effect' have variable meanings depending on who uses them. Perhaps you can pick out which one of the 5 definitions applies to |
144 | What is the difference between fixed effect, random effect and mixed effect models? | There are good books on this such as Gelman and Hill. What follows is essentially a summary of their perspective.
First of all, you should not get too caught up in the terminology. In statistics, jargon should never be used as a substitute for a mathematical understanding of the models themselves. That is especially tr... | What is the difference between fixed effect, random effect and mixed effect models? | There are good books on this such as Gelman and Hill. What follows is essentially a summary of their perspective.
First of all, you should not get too caught up in the terminology. In statistics, jarg | What is the difference between fixed effect, random effect and mixed effect models?
There are good books on this such as Gelman and Hill. What follows is essentially a summary of their perspective.
First of all, you should not get too caught up in the terminology. In statistics, jargon should never be used as a substit... | What is the difference between fixed effect, random effect and mixed effect models?
There are good books on this such as Gelman and Hill. What follows is essentially a summary of their perspective.
First of all, you should not get too caught up in the terminology. In statistics, jarg |
145 | What is the difference between fixed effect, random effect and mixed effect models? | I have written about this in a book chapter on mixed models (chapter 13 in Fox, Negrete-Yankelevich, and Sosa 2014); the relevant pages (pp. 311-315) are available on Google Books. I think the question reduces to "what are the definitions of fixed and random effects?" (a "mixed model" is just a model that contains bot... | What is the difference between fixed effect, random effect and mixed effect models? | I have written about this in a book chapter on mixed models (chapter 13 in Fox, Negrete-Yankelevich, and Sosa 2014); the relevant pages (pp. 311-315) are available on Google Books. I think the questi | What is the difference between fixed effect, random effect and mixed effect models?
I have written about this in a book chapter on mixed models (chapter 13 in Fox, Negrete-Yankelevich, and Sosa 2014); the relevant pages (pp. 311-315) are available on Google Books. I think the question reduces to "what are the definiti... | What is the difference between fixed effect, random effect and mixed effect models?
I have written about this in a book chapter on mixed models (chapter 13 in Fox, Negrete-Yankelevich, and Sosa 2014); the relevant pages (pp. 311-315) are available on Google Books. I think the questi |
146 | What is the difference between fixed effect, random effect and mixed effect models? | Fixed effect: Something the experimenter directly manipulates and is often repeatable, e.g., drug administration - one group gets drug, one group gets placebo.
Random effect: Source of random variation / experimental units e.g., individuals drawn (at random) from a population for a clinical trial.
Random effects estima... | What is the difference between fixed effect, random effect and mixed effect models? | Fixed effect: Something the experimenter directly manipulates and is often repeatable, e.g., drug administration - one group gets drug, one group gets placebo.
Random effect: Source of random variatio | What is the difference between fixed effect, random effect and mixed effect models?
Fixed effect: Something the experimenter directly manipulates and is often repeatable, e.g., drug administration - one group gets drug, one group gets placebo.
Random effect: Source of random variation / experimental units e.g., individ... | What is the difference between fixed effect, random effect and mixed effect models?
Fixed effect: Something the experimenter directly manipulates and is often repeatable, e.g., drug administration - one group gets drug, one group gets placebo.
Random effect: Source of random variatio |
147 | What is the difference between fixed effect, random effect and mixed effect models? | Econometric perspective
I came to this question from here, a possible duplicate.
There are several excellent answers already, but as stated in the accepted answer, there are many different (but related) uses of the term, so it might be valuable to give the perspective as employed in econometrics, which does not yet see... | What is the difference between fixed effect, random effect and mixed effect models? | Econometric perspective
I came to this question from here, a possible duplicate.
There are several excellent answers already, but as stated in the accepted answer, there are many different (but relate | What is the difference between fixed effect, random effect and mixed effect models?
Econometric perspective
I came to this question from here, a possible duplicate.
There are several excellent answers already, but as stated in the accepted answer, there are many different (but related) uses of the term, so it might be ... | What is the difference between fixed effect, random effect and mixed effect models?
Econometric perspective
I came to this question from here, a possible duplicate.
There are several excellent answers already, but as stated in the accepted answer, there are many different (but relate |
148 | What is the difference between fixed effect, random effect and mixed effect models? | The distinction is only meaningful in the context of non-Bayesian statistics. In Bayesian statistics, all model parameters are "random". | What is the difference between fixed effect, random effect and mixed effect models? | The distinction is only meaningful in the context of non-Bayesian statistics. In Bayesian statistics, all model parameters are "random". | What is the difference between fixed effect, random effect and mixed effect models?
The distinction is only meaningful in the context of non-Bayesian statistics. In Bayesian statistics, all model parameters are "random". | What is the difference between fixed effect, random effect and mixed effect models?
The distinction is only meaningful in the context of non-Bayesian statistics. In Bayesian statistics, all model parameters are "random". |
149 | What is the difference between fixed effect, random effect and mixed effect models? | In econometrics, the terms are typically applied in generalized linear models, where the model is of the form
$$y_{it} = g(x_{it} \beta + \alpha_i + u_{it}). $$
Random effects: When $\alpha_i \perp u_{it}$,
Fixed effects: When $\alpha_i \not \perp u_{it}$.
In linear models, the presence of a random effect does not r... | What is the difference between fixed effect, random effect and mixed effect models? | In econometrics, the terms are typically applied in generalized linear models, where the model is of the form
$$y_{it} = g(x_{it} \beta + \alpha_i + u_{it}). $$
Random effects: When $\alpha_i \perp | What is the difference between fixed effect, random effect and mixed effect models?
In econometrics, the terms are typically applied in generalized linear models, where the model is of the form
$$y_{it} = g(x_{it} \beta + \alpha_i + u_{it}). $$
Random effects: When $\alpha_i \perp u_{it}$,
Fixed effects: When $\alpha... | What is the difference between fixed effect, random effect and mixed effect models?
In econometrics, the terms are typically applied in generalized linear models, where the model is of the form
$$y_{it} = g(x_{it} \beta + \alpha_i + u_{it}). $$
Random effects: When $\alpha_i \perp |
150 | What is the difference between fixed effect, random effect and mixed effect models? | Not really a formal definition, but I like the following slides: Mixed models and why sociolinguists should use them, from Daniel Ezra Johnson. A brief recap' is offered on slide 4. Although it mostly focused on psycholinguistic studies, it is very useful as a first step. | What is the difference between fixed effect, random effect and mixed effect models? | Not really a formal definition, but I like the following slides: Mixed models and why sociolinguists should use them, from Daniel Ezra Johnson. A brief recap' is offered on slide 4. Although it mostly | What is the difference between fixed effect, random effect and mixed effect models?
Not really a formal definition, but I like the following slides: Mixed models and why sociolinguists should use them, from Daniel Ezra Johnson. A brief recap' is offered on slide 4. Although it mostly focused on psycholinguistic studies... | What is the difference between fixed effect, random effect and mixed effect models?
Not really a formal definition, but I like the following slides: Mixed models and why sociolinguists should use them, from Daniel Ezra Johnson. A brief recap' is offered on slide 4. Although it mostly |
151 | What is the difference between fixed effect, random effect and mixed effect models? | Another very practical perspective on random and fixed effects models comes from econometrics when doing linear regressions on panel data. If you’re estimating the association between an explanatory variable and an outcome variable in a dataset with multiple samples per individual / group, this is the framework you wan... | What is the difference between fixed effect, random effect and mixed effect models? | Another very practical perspective on random and fixed effects models comes from econometrics when doing linear regressions on panel data. If you’re estimating the association between an explanatory v | What is the difference between fixed effect, random effect and mixed effect models?
Another very practical perspective on random and fixed effects models comes from econometrics when doing linear regressions on panel data. If you’re estimating the association between an explanatory variable and an outcome variable in a... | What is the difference between fixed effect, random effect and mixed effect models?
Another very practical perspective on random and fixed effects models comes from econometrics when doing linear regressions on panel data. If you’re estimating the association between an explanatory v |
152 | Explaining to laypeople why bootstrapping works | fwiw the medium length version I usually give goes like this:
You want to ask a question of a population but you can't. So you take a sample and ask the question of it instead. Now, how confident you should be that the sample answer is close to the population answer obviously depends on the structure of population. ... | Explaining to laypeople why bootstrapping works | fwiw the medium length version I usually give goes like this:
You want to ask a question of a population but you can't. So you take a sample and ask the question of it instead. Now, how confident yo | Explaining to laypeople why bootstrapping works
fwiw the medium length version I usually give goes like this:
You want to ask a question of a population but you can't. So you take a sample and ask the question of it instead. Now, how confident you should be that the sample answer is close to the population answer ob... | Explaining to laypeople why bootstrapping works
fwiw the medium length version I usually give goes like this:
You want to ask a question of a population but you can't. So you take a sample and ask the question of it instead. Now, how confident yo |
153 | Explaining to laypeople why bootstrapping works | +1 to @ConjugatePrior, I just want to bring out one point which is implicit in his answer. The question asks, "if we are resampling from our sample, how is it that we are learning something about the population rather than only about the sample?" Resampling is not done to provide an estimate of the population distrib... | Explaining to laypeople why bootstrapping works | +1 to @ConjugatePrior, I just want to bring out one point which is implicit in his answer. The question asks, "if we are resampling from our sample, how is it that we are learning something about the | Explaining to laypeople why bootstrapping works
+1 to @ConjugatePrior, I just want to bring out one point which is implicit in his answer. The question asks, "if we are resampling from our sample, how is it that we are learning something about the population rather than only about the sample?" Resampling is not done... | Explaining to laypeople why bootstrapping works
+1 to @ConjugatePrior, I just want to bring out one point which is implicit in his answer. The question asks, "if we are resampling from our sample, how is it that we are learning something about the |
154 | Explaining to laypeople why bootstrapping works | This is probably a more technical explanation aimed at people who understand some statistics and mathematics (calculus, at least). Here's a slide from a course on survey bootstraps that I taught some while ago:
Some explanations are needed, of course. $T$ is the procedure to obtain the statistic from the existing data... | Explaining to laypeople why bootstrapping works | This is probably a more technical explanation aimed at people who understand some statistics and mathematics (calculus, at least). Here's a slide from a course on survey bootstraps that I taught some | Explaining to laypeople why bootstrapping works
This is probably a more technical explanation aimed at people who understand some statistics and mathematics (calculus, at least). Here's a slide from a course on survey bootstraps that I taught some while ago:
Some explanations are needed, of course. $T$ is the procedu... | Explaining to laypeople why bootstrapping works
This is probably a more technical explanation aimed at people who understand some statistics and mathematics (calculus, at least). Here's a slide from a course on survey bootstraps that I taught some |
155 | Explaining to laypeople why bootstrapping works | I am answering this question because I agree that this is a difficult thing to do and there are many misconceptions. Efron and Diaconis attempted to do that in their 1983 Scientific American article and in my view they failed. There are several books out now devoted to the bootstrap that do a good job. Efron and Tibs... | Explaining to laypeople why bootstrapping works | I am answering this question because I agree that this is a difficult thing to do and there are many misconceptions. Efron and Diaconis attempted to do that in their 1983 Scientific American article | Explaining to laypeople why bootstrapping works
I am answering this question because I agree that this is a difficult thing to do and there are many misconceptions. Efron and Diaconis attempted to do that in their 1983 Scientific American article and in my view they failed. There are several books out now devoted to... | Explaining to laypeople why bootstrapping works
I am answering this question because I agree that this is a difficult thing to do and there are many misconceptions. Efron and Diaconis attempted to do that in their 1983 Scientific American article |
156 | Explaining to laypeople why bootstrapping works | Through bootstrapping you are simply taking samples over and over again from the same group of data (your sample data) to estimate how accurate your estimates about the entire population (what really is out there in the real world) is.
If you were to take one sample and make estimates on the real population, you might ... | Explaining to laypeople why bootstrapping works | Through bootstrapping you are simply taking samples over and over again from the same group of data (your sample data) to estimate how accurate your estimates about the entire population (what really | Explaining to laypeople why bootstrapping works
Through bootstrapping you are simply taking samples over and over again from the same group of data (your sample data) to estimate how accurate your estimates about the entire population (what really is out there in the real world) is.
If you were to take one sample and ... | Explaining to laypeople why bootstrapping works
Through bootstrapping you are simply taking samples over and over again from the same group of data (your sample data) to estimate how accurate your estimates about the entire population (what really |
157 | Explaining to laypeople why bootstrapping works | I realize this is an old question with an accepted answer, but I'd like to provide my view of the bootstrap method. I'm in no ways an expert (more of a statistics user, as the OP) and welcome any corrections or comments.
I like to view bootstrap as a generalization of the jackknife method. So, let's say you have a samp... | Explaining to laypeople why bootstrapping works | I realize this is an old question with an accepted answer, but I'd like to provide my view of the bootstrap method. I'm in no ways an expert (more of a statistics user, as the OP) and welcome any corr | Explaining to laypeople why bootstrapping works
I realize this is an old question with an accepted answer, but I'd like to provide my view of the bootstrap method. I'm in no ways an expert (more of a statistics user, as the OP) and welcome any corrections or comments.
I like to view bootstrap as a generalization of th... | Explaining to laypeople why bootstrapping works
I realize this is an old question with an accepted answer, but I'd like to provide my view of the bootstrap method. I'm in no ways an expert (more of a statistics user, as the OP) and welcome any corr |
158 | Explaining to laypeople why bootstrapping works | A finite sampling of the population approximates the distribution the same way a histogram approximates it. By re-sampling, each bin count is changed and you get a new approximation. Large count values fluctuate less that small count values both in the original population and in the sampled set. Since you are explainin... | Explaining to laypeople why bootstrapping works | A finite sampling of the population approximates the distribution the same way a histogram approximates it. By re-sampling, each bin count is changed and you get a new approximation. Large count value | Explaining to laypeople why bootstrapping works
A finite sampling of the population approximates the distribution the same way a histogram approximates it. By re-sampling, each bin count is changed and you get a new approximation. Large count values fluctuate less that small count values both in the original populatio... | Explaining to laypeople why bootstrapping works
A finite sampling of the population approximates the distribution the same way a histogram approximates it. By re-sampling, each bin count is changed and you get a new approximation. Large count value |
159 | Explaining to laypeople why bootstrapping works | Paraphrasing Fox, I would start by saying that the process of repeatedly resampling from your observed sample has been shown to mimic the process of the original sampling from the whole population. | Explaining to laypeople why bootstrapping works | Paraphrasing Fox, I would start by saying that the process of repeatedly resampling from your observed sample has been shown to mimic the process of the original sampling from the whole population. | Explaining to laypeople why bootstrapping works
Paraphrasing Fox, I would start by saying that the process of repeatedly resampling from your observed sample has been shown to mimic the process of the original sampling from the whole population. | Explaining to laypeople why bootstrapping works
Paraphrasing Fox, I would start by saying that the process of repeatedly resampling from your observed sample has been shown to mimic the process of the original sampling from the whole population. |
160 | Explaining to laypeople why bootstrapping works | Note that in classic inferential statistics the theoretical entity that connects a sample to the population as a good estimator of the population is the sampling distribution (all the possible samples that could be drawn from the population). The bootstrap method is creating a kind of sampling distribution (a distribut... | Explaining to laypeople why bootstrapping works | Note that in classic inferential statistics the theoretical entity that connects a sample to the population as a good estimator of the population is the sampling distribution (all the possible samples | Explaining to laypeople why bootstrapping works
Note that in classic inferential statistics the theoretical entity that connects a sample to the population as a good estimator of the population is the sampling distribution (all the possible samples that could be drawn from the population). The bootstrap method is crea... | Explaining to laypeople why bootstrapping works
Note that in classic inferential statistics the theoretical entity that connects a sample to the population as a good estimator of the population is the sampling distribution (all the possible samples |
161 | Explaining to laypeople why bootstrapping works | When explaining to beginners I think it helps to take a specific example...
Imagine you've got a random sample of 9 measurements from some population. The mean of the sample is 60. Can we be sure that the average of the whole population is also 60? Obviously not because small samples will vary, so the estimate of 60 is... | Explaining to laypeople why bootstrapping works | When explaining to beginners I think it helps to take a specific example...
Imagine you've got a random sample of 9 measurements from some population. The mean of the sample is 60. Can we be sure that | Explaining to laypeople why bootstrapping works
When explaining to beginners I think it helps to take a specific example...
Imagine you've got a random sample of 9 measurements from some population. The mean of the sample is 60. Can we be sure that the average of the whole population is also 60? Obviously not because ... | Explaining to laypeople why bootstrapping works
When explaining to beginners I think it helps to take a specific example...
Imagine you've got a random sample of 9 measurements from some population. The mean of the sample is 60. Can we be sure that |
162 | Explaining to laypeople why bootstrapping works | My point is a very tiny one.
Bootstrap works because it computationally intensively exploits the main premise of our research agenda.
To be more specific, in statistics or biology, or most non-theoretical sciences, we study individuals, thus collecting samples.
Yet, from such samples, we want to make inferences on ot... | Explaining to laypeople why bootstrapping works | My point is a very tiny one.
Bootstrap works because it computationally intensively exploits the main premise of our research agenda.
To be more specific, in statistics or biology, or most non-theore | Explaining to laypeople why bootstrapping works
My point is a very tiny one.
Bootstrap works because it computationally intensively exploits the main premise of our research agenda.
To be more specific, in statistics or biology, or most non-theoretical sciences, we study individuals, thus collecting samples.
Yet, fr... | Explaining to laypeople why bootstrapping works
My point is a very tiny one.
Bootstrap works because it computationally intensively exploits the main premise of our research agenda.
To be more specific, in statistics or biology, or most non-theore |
163 | When conducting multiple regression, when should you center your predictor variables & when should you standardize them? | In regression, it is often recommended to center the variables so that the predictors have mean $0$. This makes it easier to interpret the intercept term as the expected value of $Y_i$ when the predictor values are set to their means. Otherwise, the intercept is interpreted as the expected value of $Y_i$ when the predi... | When conducting multiple regression, when should you center your predictor variables & when should y | In regression, it is often recommended to center the variables so that the predictors have mean $0$. This makes it easier to interpret the intercept term as the expected value of $Y_i$ when the predic | When conducting multiple regression, when should you center your predictor variables & when should you standardize them?
In regression, it is often recommended to center the variables so that the predictors have mean $0$. This makes it easier to interpret the intercept term as the expected value of $Y_i$ when the predi... | When conducting multiple regression, when should you center your predictor variables & when should y
In regression, it is often recommended to center the variables so that the predictors have mean $0$. This makes it easier to interpret the intercept term as the expected value of $Y_i$ when the predic |
164 | When conducting multiple regression, when should you center your predictor variables & when should you standardize them? | You have come across a common belief. However, in general, you do not need to center or standardize your data for multiple regression. Different explanatory variables are almost always on different scales (i.e., measured in different units). This is not a problem; the betas are estimated such that they convert the u... | When conducting multiple regression, when should you center your predictor variables & when should y | You have come across a common belief. However, in general, you do not need to center or standardize your data for multiple regression. Different explanatory variables are almost always on different | When conducting multiple regression, when should you center your predictor variables & when should you standardize them?
You have come across a common belief. However, in general, you do not need to center or standardize your data for multiple regression. Different explanatory variables are almost always on different... | When conducting multiple regression, when should you center your predictor variables & when should y
You have come across a common belief. However, in general, you do not need to center or standardize your data for multiple regression. Different explanatory variables are almost always on different |
165 | When conducting multiple regression, when should you center your predictor variables & when should you standardize them? | In addition to the remarks in the other answers, I'd like to point out that the scale and location of the explanatory variables does not affect the validity of the regression model in any way.
Consider the model $y=\beta_0+\beta_1x_1+\beta_2x_2+\ldots+\epsilon$.
The least squares estimators of $\beta_1, \beta_2,\ldots$... | When conducting multiple regression, when should you center your predictor variables & when should y | In addition to the remarks in the other answers, I'd like to point out that the scale and location of the explanatory variables does not affect the validity of the regression model in any way.
Conside | When conducting multiple regression, when should you center your predictor variables & when should you standardize them?
In addition to the remarks in the other answers, I'd like to point out that the scale and location of the explanatory variables does not affect the validity of the regression model in any way.
Consid... | When conducting multiple regression, when should you center your predictor variables & when should y
In addition to the remarks in the other answers, I'd like to point out that the scale and location of the explanatory variables does not affect the validity of the regression model in any way.
Conside |
166 | When conducting multiple regression, when should you center your predictor variables & when should you standardize them? | In case you use gradient descent to fit your model, standardizing covariates may speed up convergence (because when you have unscaled covariates, the corresponding parameters may inappropriately dominate the gradient). To illustrate this, some R code:
> objective <- function(par){ par[1]^2+par[2]^2} #quadratic functio... | When conducting multiple regression, when should you center your predictor variables & when should y | In case you use gradient descent to fit your model, standardizing covariates may speed up convergence (because when you have unscaled covariates, the corresponding parameters may inappropriately domin | When conducting multiple regression, when should you center your predictor variables & when should you standardize them?
In case you use gradient descent to fit your model, standardizing covariates may speed up convergence (because when you have unscaled covariates, the corresponding parameters may inappropriately domi... | When conducting multiple regression, when should you center your predictor variables & when should y
In case you use gradient descent to fit your model, standardizing covariates may speed up convergence (because when you have unscaled covariates, the corresponding parameters may inappropriately domin |
167 | When conducting multiple regression, when should you center your predictor variables & when should you standardize them? | I prefer "solid reasons" for both centering and standardization (they exist very often). In general, they have more to do with the data set and the problem than with the data analysis method.
Very often, I prefer to center (i.e. shift the origin of the data) to other points that are physically/chemically/biologically/.... | When conducting multiple regression, when should you center your predictor variables & when should y | I prefer "solid reasons" for both centering and standardization (they exist very often). In general, they have more to do with the data set and the problem than with the data analysis method.
Very oft | When conducting multiple regression, when should you center your predictor variables & when should you standardize them?
I prefer "solid reasons" for both centering and standardization (they exist very often). In general, they have more to do with the data set and the problem than with the data analysis method.
Very of... | When conducting multiple regression, when should you center your predictor variables & when should y
I prefer "solid reasons" for both centering and standardization (they exist very often). In general, they have more to do with the data set and the problem than with the data analysis method.
Very oft |
168 | When conducting multiple regression, when should you center your predictor variables & when should you standardize them? | To illustrate the numerical stability issue mentioned by @cbeleites, here is an example from Simon Wood on how to "break" lm(). First we'll generate some simple data and fit a simple quadratic curve.
set.seed(1); n <- 100
xx <- sort(runif(n))
y <- .2*(xx-.5)+(xx-.5)^2 + rnorm(n)*.1
x <- xx+100
b <- lm(y ~ x+I(x^2))
pl... | When conducting multiple regression, when should you center your predictor variables & when should y | To illustrate the numerical stability issue mentioned by @cbeleites, here is an example from Simon Wood on how to "break" lm(). First we'll generate some simple data and fit a simple quadratic curve.
| When conducting multiple regression, when should you center your predictor variables & when should you standardize them?
To illustrate the numerical stability issue mentioned by @cbeleites, here is an example from Simon Wood on how to "break" lm(). First we'll generate some simple data and fit a simple quadratic curve.... | When conducting multiple regression, when should you center your predictor variables & when should y
To illustrate the numerical stability issue mentioned by @cbeleites, here is an example from Simon Wood on how to "break" lm(). First we'll generate some simple data and fit a simple quadratic curve.
|
169 | When conducting multiple regression, when should you center your predictor variables & when should you standardize them? | I doubt seriously whether centering or standardizing the original data could really mitigate the multicollinearity problem when squared terms or other interaction terms are included in regression, as some of you, gung in particular, have recommend above.
To illustrate my point, let's consider a simple example.
Suppose... | When conducting multiple regression, when should you center your predictor variables & when should y | I doubt seriously whether centering or standardizing the original data could really mitigate the multicollinearity problem when squared terms or other interaction terms are included in regression, as | When conducting multiple regression, when should you center your predictor variables & when should you standardize them?
I doubt seriously whether centering or standardizing the original data could really mitigate the multicollinearity problem when squared terms or other interaction terms are included in regression, as... | When conducting multiple regression, when should you center your predictor variables & when should y
I doubt seriously whether centering or standardizing the original data could really mitigate the multicollinearity problem when squared terms or other interaction terms are included in regression, as |
170 | What happens if the explanatory and response variables are sorted independently before regression? | I'm not sure what your boss thinks "more predictive" means. Many people incorrectly believe that lower $p$-values mean a better / more predictive model. That is not necessarily true (this being a case in point). However, independently sorting both variables beforehand will guarantee a lower $p$-value. On the other ... | What happens if the explanatory and response variables are sorted independently before regression? | I'm not sure what your boss thinks "more predictive" means. Many people incorrectly believe that lower $p$-values mean a better / more predictive model. That is not necessarily true (this being a ca | What happens if the explanatory and response variables are sorted independently before regression?
I'm not sure what your boss thinks "more predictive" means. Many people incorrectly believe that lower $p$-values mean a better / more predictive model. That is not necessarily true (this being a case in point). Howeve... | What happens if the explanatory and response variables are sorted independently before regression?
I'm not sure what your boss thinks "more predictive" means. Many people incorrectly believe that lower $p$-values mean a better / more predictive model. That is not necessarily true (this being a ca |
171 | What happens if the explanatory and response variables are sorted independently before regression? | If you want to convince your boss, you can show what is happening with simulated, random, independent $x,y$ data. With R:
n <- 1000
y<- runif(n)
x <- runif(n)
linearModel <- lm(y ~ x)
x_sorted <- sort(x)
y_sorted <- sort(y)
linearModel_sorted <- lm(y_sorted ~ x_sorted)
par(mfrow = c(2,1))
plot(x,y, main = "Random... | What happens if the explanatory and response variables are sorted independently before regression? | If you want to convince your boss, you can show what is happening with simulated, random, independent $x,y$ data. With R:
n <- 1000
y<- runif(n)
x <- runif(n)
linearModel <- lm(y ~ x)
x_sorted <- | What happens if the explanatory and response variables are sorted independently before regression?
If you want to convince your boss, you can show what is happening with simulated, random, independent $x,y$ data. With R:
n <- 1000
y<- runif(n)
x <- runif(n)
linearModel <- lm(y ~ x)
x_sorted <- sort(x)
y_sorted <- s... | What happens if the explanatory and response variables are sorted independently before regression?
If you want to convince your boss, you can show what is happening with simulated, random, independent $x,y$ data. With R:
n <- 1000
y<- runif(n)
x <- runif(n)
linearModel <- lm(y ~ x)
x_sorted <- |
172 | What happens if the explanatory and response variables are sorted independently before regression? | Your intuition is correct: the independently sorted data have no reliable meaning because the inputs and outputs are being randomly mapped to one another rather than what the observed relationship was.
There is a (good) chance that the regression on the sorted data will look nice, but it is meaningless in context.
In... | What happens if the explanatory and response variables are sorted independently before regression? | Your intuition is correct: the independently sorted data have no reliable meaning because the inputs and outputs are being randomly mapped to one another rather than what the observed relationship was | What happens if the explanatory and response variables are sorted independently before regression?
Your intuition is correct: the independently sorted data have no reliable meaning because the inputs and outputs are being randomly mapped to one another rather than what the observed relationship was.
There is a (good) ... | What happens if the explanatory and response variables are sorted independently before regression?
Your intuition is correct: the independently sorted data have no reliable meaning because the inputs and outputs are being randomly mapped to one another rather than what the observed relationship was |
173 | What happens if the explanatory and response variables are sorted independently before regression? | Actually, let's make this really obvious and simple. Suppose I conduct an experiment in which I measure out 1 liter of water in a standardized container, and I look at the amount of water remaining in the container $V_i$ as a function of time $t_i$, the loss of water due to evaporation:
Now suppose I obtain the follow... | What happens if the explanatory and response variables are sorted independently before regression? | Actually, let's make this really obvious and simple. Suppose I conduct an experiment in which I measure out 1 liter of water in a standardized container, and I look at the amount of water remaining i | What happens if the explanatory and response variables are sorted independently before regression?
Actually, let's make this really obvious and simple. Suppose I conduct an experiment in which I measure out 1 liter of water in a standardized container, and I look at the amount of water remaining in the container $V_i$... | What happens if the explanatory and response variables are sorted independently before regression?
Actually, let's make this really obvious and simple. Suppose I conduct an experiment in which I measure out 1 liter of water in a standardized container, and I look at the amount of water remaining i |
174 | What happens if the explanatory and response variables are sorted independently before regression? | It is a real art and takes a real understanding of psychology to be able to convince some people of the error of their ways. Besides all the excellent examples above, a useful strategy is sometimes to show that a person's belief leads to an inconsistency with herself. Or try this approach. Find out something your bo... | What happens if the explanatory and response variables are sorted independently before regression? | It is a real art and takes a real understanding of psychology to be able to convince some people of the error of their ways. Besides all the excellent examples above, a useful strategy is sometimes t | What happens if the explanatory and response variables are sorted independently before regression?
It is a real art and takes a real understanding of psychology to be able to convince some people of the error of their ways. Besides all the excellent examples above, a useful strategy is sometimes to show that a person'... | What happens if the explanatory and response variables are sorted independently before regression?
It is a real art and takes a real understanding of psychology to be able to convince some people of the error of their ways. Besides all the excellent examples above, a useful strategy is sometimes t |
175 | What happens if the explanatory and response variables are sorted independently before regression? | This technique is actually amazing. I'm finding all sorts of relationships that I never suspected. For instance, I would have not have suspected that the numbers that show up in Powerball lottery, which it is CLAIMED are random, actually are highly correlated with the opening price of Apple stock on the same day! Fo... | What happens if the explanatory and response variables are sorted independently before regression? | This technique is actually amazing. I'm finding all sorts of relationships that I never suspected. For instance, I would have not have suspected that the numbers that show up in Powerball lottery, w | What happens if the explanatory and response variables are sorted independently before regression?
This technique is actually amazing. I'm finding all sorts of relationships that I never suspected. For instance, I would have not have suspected that the numbers that show up in Powerball lottery, which it is CLAIMED ar... | What happens if the explanatory and response variables are sorted independently before regression?
This technique is actually amazing. I'm finding all sorts of relationships that I never suspected. For instance, I would have not have suspected that the numbers that show up in Powerball lottery, w |
176 | What happens if the explanatory and response variables are sorted independently before regression? | One more example. Imagine that you have two variables, one connected with eating chocolate and second one connected to overall well-being. You have a sample of two and your data looks like below:
$$
\begin{array}{cc}
\text{chocolate} & \text{no happiness} \\
\text{no chocolate} & \text{happiness} \\
\end{array}... | What happens if the explanatory and response variables are sorted independently before regression? | One more example. Imagine that you have two variables, one connected with eating chocolate and second one connected to overall well-being. You have a sample of two and your data looks like below:
$$
\ | What happens if the explanatory and response variables are sorted independently before regression?
One more example. Imagine that you have two variables, one connected with eating chocolate and second one connected to overall well-being. You have a sample of two and your data looks like below:
$$
\begin{array}{cc}
\t... | What happens if the explanatory and response variables are sorted independently before regression?
One more example. Imagine that you have two variables, one connected with eating chocolate and second one connected to overall well-being. You have a sample of two and your data looks like below:
$$
\ |
177 | What happens if the explanatory and response variables are sorted independently before regression? | A simple example that maybe your manager could understand:
Let's say you have Coin Y and Coin X, and you flip each of them 100 times. Then you want to predict whether getting a heads with Coin X (IV) can increase the chance of getting a heads with Coin Y (DV).
Without sorting, the relationship will be none, because C... | What happens if the explanatory and response variables are sorted independently before regression? | A simple example that maybe your manager could understand:
Let's say you have Coin Y and Coin X, and you flip each of them 100 times. Then you want to predict whether getting a heads with Coin X (IV) | What happens if the explanatory and response variables are sorted independently before regression?
A simple example that maybe your manager could understand:
Let's say you have Coin Y and Coin X, and you flip each of them 100 times. Then you want to predict whether getting a heads with Coin X (IV) can increase the cha... | What happens if the explanatory and response variables are sorted independently before regression?
A simple example that maybe your manager could understand:
Let's say you have Coin Y and Coin X, and you flip each of them 100 times. Then you want to predict whether getting a heads with Coin X (IV) |
178 | What happens if the explanatory and response variables are sorted independently before regression? | Plenty of good counter examples in here. Let me just add a paragraph about the heart of the problem.
You are looking for a correlation between $X_i$ and $Y_i$. That means that $X$ and $Y$ both tend to be large for the same $i$ and small for the same $i$. So a correlation is a property of $X_1$ linked with $Y_1$, $X_2$ ... | What happens if the explanatory and response variables are sorted independently before regression? | Plenty of good counter examples in here. Let me just add a paragraph about the heart of the problem.
You are looking for a correlation between $X_i$ and $Y_i$. That means that $X$ and $Y$ both tend to | What happens if the explanatory and response variables are sorted independently before regression?
Plenty of good counter examples in here. Let me just add a paragraph about the heart of the problem.
You are looking for a correlation between $X_i$ and $Y_i$. That means that $X$ and $Y$ both tend to be large for the sam... | What happens if the explanatory and response variables are sorted independently before regression?
Plenty of good counter examples in here. Let me just add a paragraph about the heart of the problem.
You are looking for a correlation between $X_i$ and $Y_i$. That means that $X$ and $Y$ both tend to |
179 | What happens if the explanatory and response variables are sorted independently before regression? | Actually, the test that is described (i.e. sort the X values and the Y values independently and regress one against the other) DOES test something, assuming that the (X,Y) are sampled as independent pairs from a bivariate distribution. It just isn't a test of what your manager wants to test. It is essentially checkin... | What happens if the explanatory and response variables are sorted independently before regression? | Actually, the test that is described (i.e. sort the X values and the Y values independently and regress one against the other) DOES test something, assuming that the (X,Y) are sampled as independent p | What happens if the explanatory and response variables are sorted independently before regression?
Actually, the test that is described (i.e. sort the X values and the Y values independently and regress one against the other) DOES test something, assuming that the (X,Y) are sampled as independent pairs from a bivariate... | What happens if the explanatory and response variables are sorted independently before regression?
Actually, the test that is described (i.e. sort the X values and the Y values independently and regress one against the other) DOES test something, assuming that the (X,Y) are sampled as independent p |
180 | What happens if the explanatory and response variables are sorted independently before regression? | Strange that the most obvious counterexample is still not present among the answers in its simplest form.
Let $Y = -X$.
If you sort the variables separately and fit a regression model on such data, you should obtain something like $\hat Y \approx X$ (because when the variables are sorted, larger values of one must corr... | What happens if the explanatory and response variables are sorted independently before regression? | Strange that the most obvious counterexample is still not present among the answers in its simplest form.
Let $Y = -X$.
If you sort the variables separately and fit a regression model on such data, yo | What happens if the explanatory and response variables are sorted independently before regression?
Strange that the most obvious counterexample is still not present among the answers in its simplest form.
Let $Y = -X$.
If you sort the variables separately and fit a regression model on such data, you should obtain somet... | What happens if the explanatory and response variables are sorted independently before regression?
Strange that the most obvious counterexample is still not present among the answers in its simplest form.
Let $Y = -X$.
If you sort the variables separately and fit a regression model on such data, yo |
181 | What happens if the explanatory and response variables are sorted independently before regression? | It's a QQ-plot, isn't it? You'd use it to compare the distribution of x vs. y. If you'd plot sorted outcomes of a relationship like $x \sim x^2$, the plot would be crooked, which indicates that $x$ and $x^2$ for some sampling of $x$s have different distributions.
The linear regression is usually less reasonable (except... | What happens if the explanatory and response variables are sorted independently before regression? | It's a QQ-plot, isn't it? You'd use it to compare the distribution of x vs. y. If you'd plot sorted outcomes of a relationship like $x \sim x^2$, the plot would be crooked, which indicates that $x$ an | What happens if the explanatory and response variables are sorted independently before regression?
It's a QQ-plot, isn't it? You'd use it to compare the distribution of x vs. y. If you'd plot sorted outcomes of a relationship like $x \sim x^2$, the plot would be crooked, which indicates that $x$ and $x^2$ for some samp... | What happens if the explanatory and response variables are sorted independently before regression?
It's a QQ-plot, isn't it? You'd use it to compare the distribution of x vs. y. If you'd plot sorted outcomes of a relationship like $x \sim x^2$, the plot would be crooked, which indicates that $x$ an |
182 | What happens if the explanatory and response variables are sorted independently before regression? | You are right. Your manager would find "good" results! But they are meaningless. What you get when you sort them independently is that the two either increase or decrease similarly and this gives a semblance of a good model. But the two variables have been stripped of their actual relationship and the model is incorrec... | What happens if the explanatory and response variables are sorted independently before regression? | You are right. Your manager would find "good" results! But they are meaningless. What you get when you sort them independently is that the two either increase or decrease similarly and this gives a se | What happens if the explanatory and response variables are sorted independently before regression?
You are right. Your manager would find "good" results! But they are meaningless. What you get when you sort them independently is that the two either increase or decrease similarly and this gives a semblance of a good mod... | What happens if the explanatory and response variables are sorted independently before regression?
You are right. Your manager would find "good" results! But they are meaningless. What you get when you sort them independently is that the two either increase or decrease similarly and this gives a se |
183 | What happens if the explanatory and response variables are sorted independently before regression? | I have a simple intuition why this is actually a good idea if the function is monotone:
Imagine you know the inputs $x_1, x_2,\cdots, x_n$ and they are ranked, i.e. $x_i<x_{i+1}$ and assume $f:\Re\mapsto\Re$ is the unknown function we want to estimate. You can define a random model $y_i = f(x_i) + \varepsilon_i$ where... | What happens if the explanatory and response variables are sorted independently before regression? | I have a simple intuition why this is actually a good idea if the function is monotone:
Imagine you know the inputs $x_1, x_2,\cdots, x_n$ and they are ranked, i.e. $x_i<x_{i+1}$ and assume $f:\Re\ma | What happens if the explanatory and response variables are sorted independently before regression?
I have a simple intuition why this is actually a good idea if the function is monotone:
Imagine you know the inputs $x_1, x_2,\cdots, x_n$ and they are ranked, i.e. $x_i<x_{i+1}$ and assume $f:\Re\mapsto\Re$ is the unkno... | What happens if the explanatory and response variables are sorted independently before regression?
I have a simple intuition why this is actually a good idea if the function is monotone:
Imagine you know the inputs $x_1, x_2,\cdots, x_n$ and they are ranked, i.e. $x_i<x_{i+1}$ and assume $f:\Re\ma |
184 | What happens if the explanatory and response variables are sorted independently before regression? | Say you have these points on a circle of radius 5. You calculate the correlation:
import pandas as pd
s1 = [(-5, 0), (-4, -3), (-4, 3), (-3, -4), (-3, 4), (0, 5), (0, -5), (3, -4), (3, 4), (4, -3), (4, 3), (5, 0)]
df1 = pd.DataFrame(s1, columns=["x", "y"])
print(df1.corr())
x y
x 1 0
y 0 1
Then you sort your ... | What happens if the explanatory and response variables are sorted independently before regression? | Say you have these points on a circle of radius 5. You calculate the correlation:
import pandas as pd
s1 = [(-5, 0), (-4, -3), (-4, 3), (-3, -4), (-3, 4), (0, 5), (0, -5), (3, -4), (3, 4), (4, -3), (4 | What happens if the explanatory and response variables are sorted independently before regression?
Say you have these points on a circle of radius 5. You calculate the correlation:
import pandas as pd
s1 = [(-5, 0), (-4, -3), (-4, 3), (-3, -4), (-3, 4), (0, 5), (0, -5), (3, -4), (3, 4), (4, -3), (4, 3), (5, 0)]
df1 = p... | What happens if the explanatory and response variables are sorted independently before regression?
Say you have these points on a circle of radius 5. You calculate the correlation:
import pandas as pd
s1 = [(-5, 0), (-4, -3), (-4, 3), (-3, -4), (-3, 4), (0, 5), (0, -5), (3, -4), (3, 4), (4, -3), (4 |
185 | What happens if the explanatory and response variables are sorted independently before regression? | Let me play Devil's Advocate here. I think many answers have made convincing cases that the boss' procedure is fundamentally mistaken. At the same time, I offer a counter-example that illustrates that the boss may have actually seen results improve with this mistaken transformation.
I think that acknowledging that this... | What happens if the explanatory and response variables are sorted independently before regression? | Let me play Devil's Advocate here. I think many answers have made convincing cases that the boss' procedure is fundamentally mistaken. At the same time, I offer a counter-example that illustrates that | What happens if the explanatory and response variables are sorted independently before regression?
Let me play Devil's Advocate here. I think many answers have made convincing cases that the boss' procedure is fundamentally mistaken. At the same time, I offer a counter-example that illustrates that the boss may have ac... | What happens if the explanatory and response variables are sorted independently before regression?
Let me play Devil's Advocate here. I think many answers have made convincing cases that the boss' procedure is fundamentally mistaken. At the same time, I offer a counter-example that illustrates that |
186 | What happens if the explanatory and response variables are sorted independently before regression? | Sorting the columns of the following table independently also makes it look "better":
name,country
Alice,DE
Daniel,US
Christian,DE
Bernadette,US
->
name,country
Alice,DE
Bernadette,DE
Christian,US
Daniel,US
Now, all females are from Germany, and all males are from the US.
We can now much more nicely predict the gende... | What happens if the explanatory and response variables are sorted independently before regression? | Sorting the columns of the following table independently also makes it look "better":
name,country
Alice,DE
Daniel,US
Christian,DE
Bernadette,US
->
name,country
Alice,DE
Bernadette,DE
Christian,US
Da | What happens if the explanatory and response variables are sorted independently before regression?
Sorting the columns of the following table independently also makes it look "better":
name,country
Alice,DE
Daniel,US
Christian,DE
Bernadette,US
->
name,country
Alice,DE
Bernadette,DE
Christian,US
Daniel,US
Now, all fem... | What happens if the explanatory and response variables are sorted independently before regression?
Sorting the columns of the following table independently also makes it look "better":
name,country
Alice,DE
Daniel,US
Christian,DE
Bernadette,US
->
name,country
Alice,DE
Bernadette,DE
Christian,US
Da |
187 | What happens if the explanatory and response variables are sorted independently before regression? | If he has preselected the variables to be monotone, it actually is fairly robust. Google "improper linear models" and "Robin Dawes" or "Howard Wainer." Dawes and Wainer talk about alternate wayes of choosing coefficients. John Cook has a short column (http://www.johndcook.com/blog/2013/03/05/robustness-of-equal-weight... | What happens if the explanatory and response variables are sorted independently before regression? | If he has preselected the variables to be monotone, it actually is fairly robust. Google "improper linear models" and "Robin Dawes" or "Howard Wainer." Dawes and Wainer talk about alternate wayes of | What happens if the explanatory and response variables are sorted independently before regression?
If he has preselected the variables to be monotone, it actually is fairly robust. Google "improper linear models" and "Robin Dawes" or "Howard Wainer." Dawes and Wainer talk about alternate wayes of choosing coefficients... | What happens if the explanatory and response variables are sorted independently before regression?
If he has preselected the variables to be monotone, it actually is fairly robust. Google "improper linear models" and "Robin Dawes" or "Howard Wainer." Dawes and Wainer talk about alternate wayes of |
188 | What happens if the explanatory and response variables are sorted independently before regression? | I thought about it, and thought there is some structure here based on order statistics. I checked, and seems manager's mo is not as nuts as it sounds
Order Statistics Correlation Coefficient as a Novel Association Measurement With Applications to Biosignal Analysis
http://www.researchgate.net/profile/Weichao_Xu/publica... | What happens if the explanatory and response variables are sorted independently before regression? | I thought about it, and thought there is some structure here based on order statistics. I checked, and seems manager's mo is not as nuts as it sounds
Order Statistics Correlation Coefficient as a Nove | What happens if the explanatory and response variables are sorted independently before regression?
I thought about it, and thought there is some structure here based on order statistics. I checked, and seems manager's mo is not as nuts as it sounds
Order Statistics Correlation Coefficient as a Novel Association Measure... | What happens if the explanatory and response variables are sorted independently before regression?
I thought about it, and thought there is some structure here based on order statistics. I checked, and seems manager's mo is not as nuts as it sounds
Order Statistics Correlation Coefficient as a Nove |
189 | How to normalize data to 0-1 range? | If you want to normalize your data, you can do so as you suggest and simply calculate the following:
$$z_i=\frac{x_i-\min(x)}{\max(x)-\min(x)}$$
where $x=(x_1,...,x_n)$ and $z_i$ is now your $i^{th}$ normalized data. As a proof of concept (although you did not ask for it) here is some R code and accompanying graph to ... | How to normalize data to 0-1 range? | If you want to normalize your data, you can do so as you suggest and simply calculate the following:
$$z_i=\frac{x_i-\min(x)}{\max(x)-\min(x)}$$
where $x=(x_1,...,x_n)$ and $z_i$ is now your $i^{th}$ | How to normalize data to 0-1 range?
If you want to normalize your data, you can do so as you suggest and simply calculate the following:
$$z_i=\frac{x_i-\min(x)}{\max(x)-\min(x)}$$
where $x=(x_1,...,x_n)$ and $z_i$ is now your $i^{th}$ normalized data. As a proof of concept (although you did not ask for it) here is so... | How to normalize data to 0-1 range?
If you want to normalize your data, you can do so as you suggest and simply calculate the following:
$$z_i=\frac{x_i-\min(x)}{\max(x)-\min(x)}$$
where $x=(x_1,...,x_n)$ and $z_i$ is now your $i^{th}$ |
190 | How to normalize data to 0-1 range? | The general one-line formula to linearly rescale data values having observed min and max into a new arbitrary range min' to max' is
newvalue= (max'-min')/(max-min)*(value-max)+max'
or
newvalue= (max'-min')/(max-min)*(value-min)+min'. | How to normalize data to 0-1 range? | The general one-line formula to linearly rescale data values having observed min and max into a new arbitrary range min' to max' is
newvalue= (max'-min')/(max-min)*(value-max)+max'
or
newvalue= | How to normalize data to 0-1 range?
The general one-line formula to linearly rescale data values having observed min and max into a new arbitrary range min' to max' is
newvalue= (max'-min')/(max-min)*(value-max)+max'
or
newvalue= (max'-min')/(max-min)*(value-min)+min'. | How to normalize data to 0-1 range?
The general one-line formula to linearly rescale data values having observed min and max into a new arbitrary range min' to max' is
newvalue= (max'-min')/(max-min)*(value-max)+max'
or
newvalue= |
191 | How to normalize data to 0-1 range? | Here is my PHP implementation for normalisation:
function normalize($value, $min, $max) {
$normalized = ($value - $min) / ($max - $min);
return $normalized;
}
But while I was building my own artificial neural networks, I needed to transform the normalized output back to the original data to get good readable output ... | How to normalize data to 0-1 range? | Here is my PHP implementation for normalisation:
function normalize($value, $min, $max) {
$normalized = ($value - $min) / ($max - $min);
return $normalized;
}
But while I was building my own artifi | How to normalize data to 0-1 range?
Here is my PHP implementation for normalisation:
function normalize($value, $min, $max) {
$normalized = ($value - $min) / ($max - $min);
return $normalized;
}
But while I was building my own artificial neural networks, I needed to transform the normalized output back to the origin... | How to normalize data to 0-1 range?
Here is my PHP implementation for normalisation:
function normalize($value, $min, $max) {
$normalized = ($value - $min) / ($max - $min);
return $normalized;
}
But while I was building my own artifi |
192 | How to normalize data to 0-1 range? | Division by zero
One thing to keep in mind is that max - min could equal zero. In this case, you would not want to perform that division.
The case where this would happen is when all values in the list you're trying to normalize are the same. To normalize such a list, each item would be 1 / length.
// JavaScript
functi... | How to normalize data to 0-1 range? | Division by zero
One thing to keep in mind is that max - min could equal zero. In this case, you would not want to perform that division.
The case where this would happen is when all values in the lis | How to normalize data to 0-1 range?
Division by zero
One thing to keep in mind is that max - min could equal zero. In this case, you would not want to perform that division.
The case where this would happen is when all values in the list you're trying to normalize are the same. To normalize such a list, each item would... | How to normalize data to 0-1 range?
Division by zero
One thing to keep in mind is that max - min could equal zero. In this case, you would not want to perform that division.
The case where this would happen is when all values in the lis |
193 | How to normalize data to 0-1 range? | Try this. It is consistent with the function scale
normalize <- function(x) {
x <- as.matrix(x)
minAttr=apply(x, 2, min)
maxAttr=apply(x, 2, max)
x <- sweep(x, 2, minAttr, FUN="-")
x=sweep(x, 2, maxAttr-minAttr, "/")
attr(x, 'normalized:min') = minAttr
attr(x, 'normalized:max') = maxAttr
return (x)
... | How to normalize data to 0-1 range? | Try this. It is consistent with the function scale
normalize <- function(x) {
x <- as.matrix(x)
minAttr=apply(x, 2, min)
maxAttr=apply(x, 2, max)
x <- sweep(x, 2, minAttr, FUN="-")
x=sweep | How to normalize data to 0-1 range?
Try this. It is consistent with the function scale
normalize <- function(x) {
x <- as.matrix(x)
minAttr=apply(x, 2, min)
maxAttr=apply(x, 2, max)
x <- sweep(x, 2, minAttr, FUN="-")
x=sweep(x, 2, maxAttr-minAttr, "/")
attr(x, 'normalized:min') = minAttr
attr(x, 'norm... | How to normalize data to 0-1 range?
Try this. It is consistent with the function scale
normalize <- function(x) {
x <- as.matrix(x)
minAttr=apply(x, 2, min)
maxAttr=apply(x, 2, max)
x <- sweep(x, 2, minAttr, FUN="-")
x=sweep |
194 | How to normalize data to 0-1 range? | The answer is right but I have a suggestion, what if your training data face some number out of range?
you could use the squashing technique. it will be guaranteed never to go out of range. rather than this
I recommend using this
with squashing like this in min and max of the range
and the size of the expected out-... | How to normalize data to 0-1 range? | The answer is right but I have a suggestion, what if your training data face some number out of range?
you could use the squashing technique. it will be guaranteed never to go out of range. rather th | How to normalize data to 0-1 range?
The answer is right but I have a suggestion, what if your training data face some number out of range?
you could use the squashing technique. it will be guaranteed never to go out of range. rather than this
I recommend using this
with squashing like this in min and max of the rang... | How to normalize data to 0-1 range?
The answer is right but I have a suggestion, what if your training data face some number out of range?
you could use the squashing technique. it will be guaranteed never to go out of range. rather th |
195 | How to normalize data to 0-1 range? | Select a cumulative probability distribution F. Then F(x) is between 0 and 1 for every x. | How to normalize data to 0-1 range? | Select a cumulative probability distribution F. Then F(x) is between 0 and 1 for every x. | How to normalize data to 0-1 range?
Select a cumulative probability distribution F. Then F(x) is between 0 and 1 for every x. | How to normalize data to 0-1 range?
Select a cumulative probability distribution F. Then F(x) is between 0 and 1 for every x. |
196 | Difference between logit and probit models | They mainly differ in the link function.
In Logit:
$\Pr(Y=1 \mid X) = [1 + e^{-X'\beta}]^{-1} $
In Probit:
$\Pr(Y=1 \mid X) = \Phi(X'\beta)$ (Cumulative standard normal pdf)
In other way, logistic has slightly flatter tails. i.e the probit curve approaches the axes more quickly than the logit curve.
Logit has easier ... | Difference between logit and probit models | They mainly differ in the link function.
In Logit:
$\Pr(Y=1 \mid X) = [1 + e^{-X'\beta}]^{-1} $
In Probit:
$\Pr(Y=1 \mid X) = \Phi(X'\beta)$ (Cumulative standard normal pdf)
In other way, logistic h | Difference between logit and probit models
They mainly differ in the link function.
In Logit:
$\Pr(Y=1 \mid X) = [1 + e^{-X'\beta}]^{-1} $
In Probit:
$\Pr(Y=1 \mid X) = \Phi(X'\beta)$ (Cumulative standard normal pdf)
In other way, logistic has slightly flatter tails. i.e the probit curve approaches the axes more quic... | Difference between logit and probit models
They mainly differ in the link function.
In Logit:
$\Pr(Y=1 \mid X) = [1 + e^{-X'\beta}]^{-1} $
In Probit:
$\Pr(Y=1 \mid X) = \Phi(X'\beta)$ (Cumulative standard normal pdf)
In other way, logistic h |
197 | Difference between logit and probit models | A standard linear model (e.g., a simple regression model) can be thought of as having two 'parts'. These are called the structural component and the random component. For example:
$$
Y=\beta_0+\beta_1X+\varepsilon \\
\text{where } \varepsilon\sim\mathcal{N}(0,\sigma^2)
$$
The first two terms (that is, $\beta_0+\beta_... | Difference between logit and probit models | A standard linear model (e.g., a simple regression model) can be thought of as having two 'parts'. These are called the structural component and the random component. For example:
$$
Y=\beta_0+\beta_1 | Difference between logit and probit models
A standard linear model (e.g., a simple regression model) can be thought of as having two 'parts'. These are called the structural component and the random component. For example:
$$
Y=\beta_0+\beta_1X+\varepsilon \\
\text{where } \varepsilon\sim\mathcal{N}(0,\sigma^2)
$$
Th... | Difference between logit and probit models
A standard linear model (e.g., a simple regression model) can be thought of as having two 'parts'. These are called the structural component and the random component. For example:
$$
Y=\beta_0+\beta_1 |
198 | Difference between logit and probit models | In addition to vinux’ answer, which already tells the most important:
the coefficients $\beta$ in the logit regression have natural interpretations in terms of odds ratio;
the probistic regression is the natural model when you think that your binary outcome depends of a hidden gaussian variable $Z = X' \beta + \epsilo... | Difference between logit and probit models | In addition to vinux’ answer, which already tells the most important:
the coefficients $\beta$ in the logit regression have natural interpretations in terms of odds ratio;
the probistic regression is | Difference between logit and probit models
In addition to vinux’ answer, which already tells the most important:
the coefficients $\beta$ in the logit regression have natural interpretations in terms of odds ratio;
the probistic regression is the natural model when you think that your binary outcome depends of a hidde... | Difference between logit and probit models
In addition to vinux’ answer, which already tells the most important:
the coefficients $\beta$ in the logit regression have natural interpretations in terms of odds ratio;
the probistic regression is |
199 | Difference between logit and probit models | Regarding your statement
I'm more interested here in knowing when to use logistic regression, and when to use probit
There are already many answers here that bring up things to consider when choosing between the two but there is one important consideration that hasn't been stated yet: When your interest is in looking a... | Difference between logit and probit models | Regarding your statement
I'm more interested here in knowing when to use logistic regression, and when to use probit
There are already many answers here that bring up things to consider when choosing | Difference between logit and probit models
Regarding your statement
I'm more interested here in knowing when to use logistic regression, and when to use probit
There are already many answers here that bring up things to consider when choosing between the two but there is one important consideration that hasn't been sta... | Difference between logit and probit models
Regarding your statement
I'm more interested here in knowing when to use logistic regression, and when to use probit
There are already many answers here that bring up things to consider when choosing |
200 | Difference between logit and probit models | An important point that has not been addressed in the previous (excellent) answers is the actual estimation step. Multinomial logit models have a PDF that is easy to integrate, leading to a closed-form expression of the choice probability. The density function of the normal distribution is not so easily integrated, so ... | Difference between logit and probit models | An important point that has not been addressed in the previous (excellent) answers is the actual estimation step. Multinomial logit models have a PDF that is easy to integrate, leading to a closed-for | Difference between logit and probit models
An important point that has not been addressed in the previous (excellent) answers is the actual estimation step. Multinomial logit models have a PDF that is easy to integrate, leading to a closed-form expression of the choice probability. The density function of the normal di... | Difference between logit and probit models
An important point that has not been addressed in the previous (excellent) answers is the actual estimation step. Multinomial logit models have a PDF that is easy to integrate, leading to a closed-for |
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