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 |
|---|---|---|---|---|---|---|
9,801 | Pdf of the square of a general normal random variable | You have stumbled upon one of the most famous results of probability theory and statistics. I'll write an answer, although I am certain this question has been asked (and answered) before on this site.
First, note that the pdf of $Y = X^2$ cannot be the same as that of $X$ as $Y$ will be nonnegative. To derive the distr... | Pdf of the square of a general normal random variable | You have stumbled upon one of the most famous results of probability theory and statistics. I'll write an answer, although I am certain this question has been asked (and answered) before on this site. | Pdf of the square of a general normal random variable
You have stumbled upon one of the most famous results of probability theory and statistics. I'll write an answer, although I am certain this question has been asked (and answered) before on this site.
First, note that the pdf of $Y = X^2$ cannot be the same as that ... | Pdf of the square of a general normal random variable
You have stumbled upon one of the most famous results of probability theory and statistics. I'll write an answer, although I am certain this question has been asked (and answered) before on this site. |
9,802 | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Hypothesis tests are still used because they are motivated by a different need in statistical inference than interval estimators are motivated by.
The purpose of a hypothesis test is to make a decision as to whether there is evidence for the alternative hypothesis' expression of the population parameter.
Confidence int... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Hypothesis tests are still used because they are motivated by a different need in statistical inference than interval estimators are motivated by.
The purpose of a hypothesis test is to make a decisio | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Hypothesis tests are still used because they are motivated by a different need in statistical inference than interval estimators are motivated by.
The purpose of a hypothesis test is to make a decision as to whether there is evide... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Hypothesis tests are still used because they are motivated by a different need in statistical inference than interval estimators are motivated by.
The purpose of a hypothesis test is to make a decisio |
9,803 | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | One reason to use traditional hypothesis testing methods (when they can be used) is that it is computationally efficient to do so compared to bootstrap sampling. Depending upon the number of dimensions in your data, the number of bootstrap samples required to estimate p values (or confidence intervals) can be very larg... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | One reason to use traditional hypothesis testing methods (when they can be used) is that it is computationally efficient to do so compared to bootstrap sampling. Depending upon the number of dimension | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
One reason to use traditional hypothesis testing methods (when they can be used) is that it is computationally efficient to do so compared to bootstrap sampling. Depending upon the number of dimensions in your data, the number of ... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
One reason to use traditional hypothesis testing methods (when they can be used) is that it is computationally efficient to do so compared to bootstrap sampling. Depending upon the number of dimension |
9,804 | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Let's see what actually happened with your example
You started with a mixture of three normal distributions, where the probability of exceeding $\$80k$ was about $0.32576$
You used this to construct a population of $300000$ with $97751$ cases exceeding $\$80k$, a proportion of about $0.32587$
You sampled $15000$ witho... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Let's see what actually happened with your example
You started with a mixture of three normal distributions, where the probability of exceeding $\$80k$ was about $0.32576$
You used this to construct | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Let's see what actually happened with your example
You started with a mixture of three normal distributions, where the probability of exceeding $\$80k$ was about $0.32576$
You used this to construct a population of $300000$ with ... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Let's see what actually happened with your example
You started with a mixture of three normal distributions, where the probability of exceeding $\$80k$ was about $0.32576$
You used this to construct |
9,805 | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Furthermore, the Non-Parametric Bootstrap also allows you to evaluate population inference and confidence intervals - regardless of the population's true distribution. Thus, why do we still use classical hypothesis testing methods? The only reason I can think of, is when there are smaller sample sizes. But are there an... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Furthermore, the Non-Parametric Bootstrap also allows you to evaluate population inference and confidence intervals - regardless of the population's true distribution. Thus, why do we still use classi | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Furthermore, the Non-Parametric Bootstrap also allows you to evaluate population inference and confidence intervals - regardless of the population's true distribution. Thus, why do we still use classical hypothesis testing methods... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Furthermore, the Non-Parametric Bootstrap also allows you to evaluate population inference and confidence intervals - regardless of the population's true distribution. Thus, why do we still use classi |
9,806 | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | The bootstrap cannot resample events that didn't occur in the dataset. If the probability of some event was very low and ends up not occuring -- e.g., the expected number of events in some range or bin is less than 1, and indeed zero are obtained -- the bootstrap procedure will of course never be able to produce (re)sa... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | The bootstrap cannot resample events that didn't occur in the dataset. If the probability of some event was very low and ends up not occuring -- e.g., the expected number of events in some range or bi | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
The bootstrap cannot resample events that didn't occur in the dataset. If the probability of some event was very low and ends up not occuring -- e.g., the expected number of events in some range or bin is less than 1, and indeed z... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
The bootstrap cannot resample events that didn't occur in the dataset. If the probability of some event was very low and ends up not occuring -- e.g., the expected number of events in some range or bi |
9,807 | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Take many random samples from any distribution
But your example only has a single sample that you're resampling. You are not taking samples from the population, you are resampling your sample, so you actually just evaluate your sample's parameters that you could've found directly.
The distribution of thee means wil... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Take many random samples from any distribution
But your example only has a single sample that you're resampling. You are not taking samples from the population, you are resampling your sample, so yo | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Take many random samples from any distribution
But your example only has a single sample that you're resampling. You are not taking samples from the population, you are resampling your sample, so you actually just evaluate your ... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Take many random samples from any distribution
But your example only has a single sample that you're resampling. You are not taking samples from the population, you are resampling your sample, so yo |
9,808 | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Classical methods, designed experiments analyzed with analysis of variance, were designed to work with small samples and complex design structure. Complex design structure includes Latin Squares, Balanced Incomplete Block Designs, Repeated Measures Designs, and others. All of these are used regularly in biological and ... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | Classical methods, designed experiments analyzed with analysis of variance, were designed to work with small samples and complex design structure. Complex design structure includes Latin Squares, Bala | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Classical methods, designed experiments analyzed with analysis of variance, were designed to work with small samples and complex design structure. Complex design structure includes Latin Squares, Balanced Incomplete Block Designs,... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
Classical methods, designed experiments analyzed with analysis of variance, were designed to work with small samples and complex design structure. Complex design structure includes Latin Squares, Bala |
9,809 | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | There are scenarios, though I will grant that many of them are a bit esoteric, where the bootstrap is not consistent.
For instance: parameters involving ranks, or when a parameter is at the boundary of a space.
The bootstrap can be tricky, or inefficient to implement with non-iid data. For instance, with clustered dat... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem? | There are scenarios, though I will grant that many of them are a bit esoteric, where the bootstrap is not consistent.
For instance: parameters involving ranks, or when a parameter is at the boundary o | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
There are scenarios, though I will grant that many of them are a bit esoteric, where the bootstrap is not consistent.
For instance: parameters involving ranks, or when a parameter is at the boundary of a space.
The bootstrap can b... | Why are hypothesis tests still used when we have the bootstrap and central limit theorem?
There are scenarios, though I will grant that many of them are a bit esoteric, where the bootstrap is not consistent.
For instance: parameters involving ranks, or when a parameter is at the boundary o |
9,810 | What's wrong with Bonferroni adjustments? | What is wrong with the Bonferroni correction besides the conservatism mentioned by others is what's wrong with all multiplicity corrections. They do not follow from basic statistical principles and are arbitrary; there is no unique solution to the multiplicity problem in the frequentist world. Secondly, multiplicity ... | What's wrong with Bonferroni adjustments? | What is wrong with the Bonferroni correction besides the conservatism mentioned by others is what's wrong with all multiplicity corrections. They do not follow from basic statistical principles and a | What's wrong with Bonferroni adjustments?
What is wrong with the Bonferroni correction besides the conservatism mentioned by others is what's wrong with all multiplicity corrections. They do not follow from basic statistical principles and are arbitrary; there is no unique solution to the multiplicity problem in the f... | What's wrong with Bonferroni adjustments?
What is wrong with the Bonferroni correction besides the conservatism mentioned by others is what's wrong with all multiplicity corrections. They do not follow from basic statistical principles and a |
9,811 | What's wrong with Bonferroni adjustments? | He summarized saying that Bonferroni adjustment have, at best, limited applications in biomedical research and should not be used when assessing evidence about specific hypothesis.
The Bonferroni correction is one of the simplest and most conservative multiple comparisons technique. It is also one of the oldest and ha... | What's wrong with Bonferroni adjustments? | He summarized saying that Bonferroni adjustment have, at best, limited applications in biomedical research and should not be used when assessing evidence about specific hypothesis.
The Bonferroni cor | What's wrong with Bonferroni adjustments?
He summarized saying that Bonferroni adjustment have, at best, limited applications in biomedical research and should not be used when assessing evidence about specific hypothesis.
The Bonferroni correction is one of the simplest and most conservative multiple comparisons tech... | What's wrong with Bonferroni adjustments?
He summarized saying that Bonferroni adjustment have, at best, limited applications in biomedical research and should not be used when assessing evidence about specific hypothesis.
The Bonferroni cor |
9,812 | What's wrong with Bonferroni adjustments? | Thomas Perneger is not a statistician and his paper is full of mistakes. So I wouldn't take it too seriously. It's actually been heavily criticized by others.
For example, Aickin said Perneger's paper "consists almost entirely of errors": Aickin, "Other method for adjustment of multiple testing exists", BMJ. 1999 Jan 9... | What's wrong with Bonferroni adjustments? | Thomas Perneger is not a statistician and his paper is full of mistakes. So I wouldn't take it too seriously. It's actually been heavily criticized by others.
For example, Aickin said Perneger's paper | What's wrong with Bonferroni adjustments?
Thomas Perneger is not a statistician and his paper is full of mistakes. So I wouldn't take it too seriously. It's actually been heavily criticized by others.
For example, Aickin said Perneger's paper "consists almost entirely of errors": Aickin, "Other method for adjustment of... | What's wrong with Bonferroni adjustments?
Thomas Perneger is not a statistician and his paper is full of mistakes. So I wouldn't take it too seriously. It's actually been heavily criticized by others.
For example, Aickin said Perneger's paper |
9,813 | What's wrong with Bonferroni adjustments? | A nice discussion of Bonferroni correction and effect size http://beheco.oxfordjournals.org/content/15/6/1044.full.pdf+html
Also, Dunn-Sidak correction and Fisher's combined probabilities approach are worth considering as alternatives. Regardless of the approach, it is worth reporting both adjusted and raw p-values plu... | What's wrong with Bonferroni adjustments? | A nice discussion of Bonferroni correction and effect size http://beheco.oxfordjournals.org/content/15/6/1044.full.pdf+html
Also, Dunn-Sidak correction and Fisher's combined probabilities approach are | What's wrong with Bonferroni adjustments?
A nice discussion of Bonferroni correction and effect size http://beheco.oxfordjournals.org/content/15/6/1044.full.pdf+html
Also, Dunn-Sidak correction and Fisher's combined probabilities approach are worth considering as alternatives. Regardless of the approach, it is worth re... | What's wrong with Bonferroni adjustments?
A nice discussion of Bonferroni correction and effect size http://beheco.oxfordjournals.org/content/15/6/1044.full.pdf+html
Also, Dunn-Sidak correction and Fisher's combined probabilities approach are |
9,814 | What's wrong with Bonferroni adjustments? | Maybe it's good to explain the ''reasoning behind'' multiple testing corrections like the one of Bonferroni. If that is clear then you will be able to judge yourself whether you should apply them or not.
In a hypothesis test one tries to find evidence for some known or assumed fact about the real world. It is simil... | What's wrong with Bonferroni adjustments? | Maybe it's good to explain the ''reasoning behind'' multiple testing corrections like the one of Bonferroni. If that is clear then you will be able to judge yourself whether you should apply them or | What's wrong with Bonferroni adjustments?
Maybe it's good to explain the ''reasoning behind'' multiple testing corrections like the one of Bonferroni. If that is clear then you will be able to judge yourself whether you should apply them or not.
In a hypothesis test one tries to find evidence for some known or assum... | What's wrong with Bonferroni adjustments?
Maybe it's good to explain the ''reasoning behind'' multiple testing corrections like the one of Bonferroni. If that is clear then you will be able to judge yourself whether you should apply them or |
9,815 | What's wrong with Bonferroni adjustments? | For one, it's extremely conservative. The Holm-Bonferroni method accomplishes what the Bonferonni method accomplishes (controlling the Family Wise Error Rate) while also being uniformly more powerful. | What's wrong with Bonferroni adjustments? | For one, it's extremely conservative. The Holm-Bonferroni method accomplishes what the Bonferonni method accomplishes (controlling the Family Wise Error Rate) while also being uniformly more powerful. | What's wrong with Bonferroni adjustments?
For one, it's extremely conservative. The Holm-Bonferroni method accomplishes what the Bonferonni method accomplishes (controlling the Family Wise Error Rate) while also being uniformly more powerful. | What's wrong with Bonferroni adjustments?
For one, it's extremely conservative. The Holm-Bonferroni method accomplishes what the Bonferonni method accomplishes (controlling the Family Wise Error Rate) while also being uniformly more powerful. |
9,816 | What's wrong with Bonferroni adjustments? | One should look at the "False Discovery Rate" methods as a less conservative alternative to Bonferroni. See
John D. Storey, "THE POSITIVE FALSE DISCOVERY RATE: A BAYESIAN
INTERPRETATION AND THE q-VALUE,"
The Annals of Statistics
2003, Vol. 31, No. 6, 2013–2035. | What's wrong with Bonferroni adjustments? | One should look at the "False Discovery Rate" methods as a less conservative alternative to Bonferroni. See
John D. Storey, "THE POSITIVE FALSE DISCOVERY RATE: A BAYESIAN
INTERPRETATION AND THE q-VA | What's wrong with Bonferroni adjustments?
One should look at the "False Discovery Rate" methods as a less conservative alternative to Bonferroni. See
John D. Storey, "THE POSITIVE FALSE DISCOVERY RATE: A BAYESIAN
INTERPRETATION AND THE q-VALUE,"
The Annals of Statistics
2003, Vol. 31, No. 6, 2013–2035. | What's wrong with Bonferroni adjustments?
One should look at the "False Discovery Rate" methods as a less conservative alternative to Bonferroni. See
John D. Storey, "THE POSITIVE FALSE DISCOVERY RATE: A BAYESIAN
INTERPRETATION AND THE q-VA |
9,817 | What's wrong with Bonferroni adjustments? | Suppose we have 20 null hypotheses, all of which happen to be true. Consider two cases:
If 20 scientists each independently pick and test one of the null hypotheses at p=0.05, on average they will correctly accept 19 of the hypotheses and incorrectly reject 1.
If a single scientist does one big study testing all 20 n... | What's wrong with Bonferroni adjustments? | Suppose we have 20 null hypotheses, all of which happen to be true. Consider two cases:
If 20 scientists each independently pick and test one of the null hypotheses at p=0.05, on average they will co | What's wrong with Bonferroni adjustments?
Suppose we have 20 null hypotheses, all of which happen to be true. Consider two cases:
If 20 scientists each independently pick and test one of the null hypotheses at p=0.05, on average they will correctly accept 19 of the hypotheses and incorrectly reject 1.
If a single sci... | What's wrong with Bonferroni adjustments?
Suppose we have 20 null hypotheses, all of which happen to be true. Consider two cases:
If 20 scientists each independently pick and test one of the null hypotheses at p=0.05, on average they will co |
9,818 | Is there any advantage of SVD over PCA? | As @ttnphns and @nick-cox said, SVD is a numerical method and PCA is an analysis approach (like least squares). You can do PCA using SVD, or you can do PCA doing the eigen-decomposition of $X^T X$ (or $X X^T$), or you can do PCA using many other methods, just like you can solve least squares with a dozen different algo... | Is there any advantage of SVD over PCA? | As @ttnphns and @nick-cox said, SVD is a numerical method and PCA is an analysis approach (like least squares). You can do PCA using SVD, or you can do PCA doing the eigen-decomposition of $X^T X$ (or | Is there any advantage of SVD over PCA?
As @ttnphns and @nick-cox said, SVD is a numerical method and PCA is an analysis approach (like least squares). You can do PCA using SVD, or you can do PCA doing the eigen-decomposition of $X^T X$ (or $X X^T$), or you can do PCA using many other methods, just like you can solve l... | Is there any advantage of SVD over PCA?
As @ttnphns and @nick-cox said, SVD is a numerical method and PCA is an analysis approach (like least squares). You can do PCA using SVD, or you can do PCA doing the eigen-decomposition of $X^T X$ (or |
9,819 | Is there any advantage of SVD over PCA? | The question is really asking if you should do Z-score normalization of the columns before applying the SVD. This is because PCA is the above transformation followed by the SVD. Sometimes doing the normalization is quite harmful. If your data is for example (transformed) word counts which are positive, subtracting the ... | Is there any advantage of SVD over PCA? | The question is really asking if you should do Z-score normalization of the columns before applying the SVD. This is because PCA is the above transformation followed by the SVD. Sometimes doing the no | Is there any advantage of SVD over PCA?
The question is really asking if you should do Z-score normalization of the columns before applying the SVD. This is because PCA is the above transformation followed by the SVD. Sometimes doing the normalization is quite harmful. If your data is for example (transformed) word cou... | Is there any advantage of SVD over PCA?
The question is really asking if you should do Z-score normalization of the columns before applying the SVD. This is because PCA is the above transformation followed by the SVD. Sometimes doing the no |
9,820 | Why and when create a R package? | I don't program in R, but I program otherwise, and I see no R-specific issue here.
I imagine that most people first write something because they really want it for themselves. Conversely, any feeling that one should be publishing software because it is the thing to do should be resisted strongly. Smart people can be l... | Why and when create a R package? | I don't program in R, but I program otherwise, and I see no R-specific issue here.
I imagine that most people first write something because they really want it for themselves. Conversely, any feeling | Why and when create a R package?
I don't program in R, but I program otherwise, and I see no R-specific issue here.
I imagine that most people first write something because they really want it for themselves. Conversely, any feeling that one should be publishing software because it is the thing to do should be resiste... | Why and when create a R package?
I don't program in R, but I program otherwise, and I see no R-specific issue here.
I imagine that most people first write something because they really want it for themselves. Conversely, any feeling |
9,821 | Why and when create a R package? | This is an important and practical question. Let's start by distinguishing between writing a package and publishing it on CRAN.
Reasons not to write a package:
Cost efficiency.
Lack of experience.
Reasons to write an R package:
Sharing with people and platforms.
Forces a tidy code and work process.
Ease of use (e... | Why and when create a R package? | This is an important and practical question. Let's start by distinguishing between writing a package and publishing it on CRAN.
Reasons not to write a package:
Cost efficiency.
Lack of experience.
| Why and when create a R package?
This is an important and practical question. Let's start by distinguishing between writing a package and publishing it on CRAN.
Reasons not to write a package:
Cost efficiency.
Lack of experience.
Reasons to write an R package:
Sharing with people and platforms.
Forces a tidy code... | Why and when create a R package?
This is an important and practical question. Let's start by distinguishing between writing a package and publishing it on CRAN.
Reasons not to write a package:
Cost efficiency.
Lack of experience.
|
9,822 | Why and when create a R package? | Remember that there is option #3; you may ask the maintainer of a relevant package to include your code or data. | Why and when create a R package? | Remember that there is option #3; you may ask the maintainer of a relevant package to include your code or data. | Why and when create a R package?
Remember that there is option #3; you may ask the maintainer of a relevant package to include your code or data. | Why and when create a R package?
Remember that there is option #3; you may ask the maintainer of a relevant package to include your code or data. |
9,823 | Why and when create a R package? | My personal triggers for packaging are:
I find I'm again using some code that I once wrote for another data analysis project.
I think I'll need the method I just wrote again.
A colleague asks me for code. A substantial part of the code I write is at least as much on request of colleagues (who use R but do not program... | Why and when create a R package? | My personal triggers for packaging are:
I find I'm again using some code that I once wrote for another data analysis project.
I think I'll need the method I just wrote again.
A colleague asks me for | Why and when create a R package?
My personal triggers for packaging are:
I find I'm again using some code that I once wrote for another data analysis project.
I think I'll need the method I just wrote again.
A colleague asks me for code. A substantial part of the code I write is at least as much on request of colleag... | Why and when create a R package?
My personal triggers for packaging are:
I find I'm again using some code that I once wrote for another data analysis project.
I think I'll need the method I just wrote again.
A colleague asks me for |
9,824 | Why and when create a R package? | I'd say create a package whenever you are doing a large enough set of similar tasks in R that you would benefit from a package in which you can put things in a namespace (to avoid conflicts with similarly named functions), where you can write documentation. I even have a package on github for bundling up a grab bag of ... | Why and when create a R package? | I'd say create a package whenever you are doing a large enough set of similar tasks in R that you would benefit from a package in which you can put things in a namespace (to avoid conflicts with simil | Why and when create a R package?
I'd say create a package whenever you are doing a large enough set of similar tasks in R that you would benefit from a package in which you can put things in a namespace (to avoid conflicts with similarly named functions), where you can write documentation. I even have a package on gith... | Why and when create a R package?
I'd say create a package whenever you are doing a large enough set of similar tasks in R that you would benefit from a package in which you can put things in a namespace (to avoid conflicts with simil |
9,825 | Why and when create a R package? | I agree with everything I read so far. All those reasons are good programming practice and do not apply to R in particular. However I find myself writing R packages most of the time, and for yet another reason. So I will add:
R-specific reason to write an R package:
because you write in C
Any time you use foreign lan... | Why and when create a R package? | I agree with everything I read so far. All those reasons are good programming practice and do not apply to R in particular. However I find myself writing R packages most of the time, and for yet anoth | Why and when create a R package?
I agree with everything I read so far. All those reasons are good programming practice and do not apply to R in particular. However I find myself writing R packages most of the time, and for yet another reason. So I will add:
R-specific reason to write an R package:
because you write i... | Why and when create a R package?
I agree with everything I read so far. All those reasons are good programming practice and do not apply to R in particular. However I find myself writing R packages most of the time, and for yet anoth |
9,826 | Why and when create a R package? | One reason not mentioned in the other excellent answers: You have a large or complex data analysis project. Packaging, first, the data as a package, and then extending with useful functions to transform, plot, or compute specific analyses. This way you get a documented version of the data complete with all the functi... | Why and when create a R package? | One reason not mentioned in the other excellent answers: You have a large or complex data analysis project. Packaging, first, the data as a package, and then extending with useful functions to trans | Why and when create a R package?
One reason not mentioned in the other excellent answers: You have a large or complex data analysis project. Packaging, first, the data as a package, and then extending with useful functions to transform, plot, or compute specific analyses. This way you get a documented version of the ... | Why and when create a R package?
One reason not mentioned in the other excellent answers: You have a large or complex data analysis project. Packaging, first, the data as a package, and then extending with useful functions to trans |
9,827 | How much lung cancer is really caused by smoking? [closed] | For the US data:
You are confusing two important but different concepts in epidemiology: prevalence and incidence. A Wikipedia page describes the difference.
The anti-smoking warning that you show says that 9 of every 10 lung cancers that occur are caused by smoking. That's the incidence of smoking-related lung cancers... | How much lung cancer is really caused by smoking? [closed] | For the US data:
You are confusing two important but different concepts in epidemiology: prevalence and incidence. A Wikipedia page describes the difference.
The anti-smoking warning that you show say | How much lung cancer is really caused by smoking? [closed]
For the US data:
You are confusing two important but different concepts in epidemiology: prevalence and incidence. A Wikipedia page describes the difference.
The anti-smoking warning that you show says that 9 of every 10 lung cancers that occur are caused by sm... | How much lung cancer is really caused by smoking? [closed]
For the US data:
You are confusing two important but different concepts in epidemiology: prevalence and incidence. A Wikipedia page describes the difference.
The anti-smoking warning that you show say |
9,828 | How much lung cancer is really caused by smoking? [closed] | What you're asking about is called the "Population Attributable Fraction"—the number of cases in the entire population that can be attributed to the exposure (in this case, smoking). The formula for this is:
$$
PAF = \frac{P_{{\rm pop}}\times (RR-1)}{P_{{\rm pop}}\times (RR-1)+1}
$$
Here, $P_{{\rm pop}}$ is the proport... | How much lung cancer is really caused by smoking? [closed] | What you're asking about is called the "Population Attributable Fraction"—the number of cases in the entire population that can be attributed to the exposure (in this case, smoking). The formula for t | How much lung cancer is really caused by smoking? [closed]
What you're asking about is called the "Population Attributable Fraction"—the number of cases in the entire population that can be attributed to the exposure (in this case, smoking). The formula for this is:
$$
PAF = \frac{P_{{\rm pop}}\times (RR-1)}{P_{{\rm po... | How much lung cancer is really caused by smoking? [closed]
What you're asking about is called the "Population Attributable Fraction"—the number of cases in the entire population that can be attributed to the exposure (in this case, smoking). The formula for t |
9,829 | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)? | Yes.
All hypothesis tests have two salient properties: their size (or "significance level"), a number which is directly related to confidence and expected false positive rates, and their power, which expresses the chance of false negatives. When sample sizes are small and you continue to insist on a small size (high c... | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)? | Yes.
All hypothesis tests have two salient properties: their size (or "significance level"), a number which is directly related to confidence and expected false positive rates, and their power, which | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)?
Yes.
All hypothesis tests have two salient properties: their size (or "significance level"), a number which is directly related to confidence and expected false positive rates, and their power, which expresses the chance of false negati... | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)?
Yes.
All hypothesis tests have two salient properties: their size (or "significance level"), a number which is directly related to confidence and expected false positive rates, and their power, which |
9,830 | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)? | As @whuber asked in the comments, a validation for my categorical NO. edit : with the shapiro test, as the one-sample ks test is in fact wrongly used. Whuber is correct: For correct use of the Kolmogorov-Smirnov test, you have to specify the distributional parameters and not extract them from the data. This is however ... | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)? | As @whuber asked in the comments, a validation for my categorical NO. edit : with the shapiro test, as the one-sample ks test is in fact wrongly used. Whuber is correct: For correct use of the Kolmogo | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)?
As @whuber asked in the comments, a validation for my categorical NO. edit : with the shapiro test, as the one-sample ks test is in fact wrongly used. Whuber is correct: For correct use of the Kolmogorov-Smirnov test, you have to specif... | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)?
As @whuber asked in the comments, a validation for my categorical NO. edit : with the shapiro test, as the one-sample ks test is in fact wrongly used. Whuber is correct: For correct use of the Kolmogo |
9,831 | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)? | Question posed here have some misconception that why Normality check is required for a sample size of 6. Here the main objective is “to test whether the time spent in the code version A is differ from the time spent in the code version B or not (This is my H1)”. When the word “differ” is used, is it one tail test?. ... | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)? | Question posed here have some misconception that why Normality check is required for a sample size of 6. Here the main objective is “to test whether the time spent in the code version A is differ fr | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)?
Question posed here have some misconception that why Normality check is required for a sample size of 6. Here the main objective is “to test whether the time spent in the code version A is differ from the time spent in the code versio... | Is it meaningful to test for normality with a very small sample size (e.g., n = 6)?
Question posed here have some misconception that why Normality check is required for a sample size of 6. Here the main objective is “to test whether the time spent in the code version A is differ fr |
9,832 | How to interpret coefficient standard errors in linear regression? | Parameter estimates, like a sample mean or an OLS regression coefficient, are sample statistics that we use to draw inferences about the corresponding population parameters. The population parameters are what we really care about, but because we don't have access to the whole population (usually assumed to be infinite... | How to interpret coefficient standard errors in linear regression? | Parameter estimates, like a sample mean or an OLS regression coefficient, are sample statistics that we use to draw inferences about the corresponding population parameters. The population parameters | How to interpret coefficient standard errors in linear regression?
Parameter estimates, like a sample mean or an OLS regression coefficient, are sample statistics that we use to draw inferences about the corresponding population parameters. The population parameters are what we really care about, but because we don't ... | How to interpret coefficient standard errors in linear regression?
Parameter estimates, like a sample mean or an OLS regression coefficient, are sample statistics that we use to draw inferences about the corresponding population parameters. The population parameters |
9,833 | How to test the autocorrelation of the residuals? | There are probably many ways to do this but the first one that comes to mind is based on linear regression. You can regress the consecutive residuals against each other and test for a significant slope. If there is auto-correlation, then there should be a linear relationship between consecutive residuals. To finish the... | How to test the autocorrelation of the residuals? | There are probably many ways to do this but the first one that comes to mind is based on linear regression. You can regress the consecutive residuals against each other and test for a significant slop | How to test the autocorrelation of the residuals?
There are probably many ways to do this but the first one that comes to mind is based on linear regression. You can regress the consecutive residuals against each other and test for a significant slope. If there is auto-correlation, then there should be a linear relatio... | How to test the autocorrelation of the residuals?
There are probably many ways to do this but the first one that comes to mind is based on linear regression. You can regress the consecutive residuals against each other and test for a significant slop |
9,834 | How to test the autocorrelation of the residuals? | Use the Durbin-Watson test, implemented in the lmtest package.
dwtest(prices[,1] ~ prices[,2]) | How to test the autocorrelation of the residuals? | Use the Durbin-Watson test, implemented in the lmtest package.
dwtest(prices[,1] ~ prices[,2]) | How to test the autocorrelation of the residuals?
Use the Durbin-Watson test, implemented in the lmtest package.
dwtest(prices[,1] ~ prices[,2]) | How to test the autocorrelation of the residuals?
Use the Durbin-Watson test, implemented in the lmtest package.
dwtest(prices[,1] ~ prices[,2]) |
9,835 | How to test the autocorrelation of the residuals? | The DW Test or the Linear Regression test are not robust to anomalies in the data. If you have Pulses, Seasonal Pulses , Level Shifts or Local Time Trends these tests are useless as these untreated components inflate the variance of the errors thus downward biasing the tests causing you ( as you have found out ) to inc... | How to test the autocorrelation of the residuals? | The DW Test or the Linear Regression test are not robust to anomalies in the data. If you have Pulses, Seasonal Pulses , Level Shifts or Local Time Trends these tests are useless as these untreated co | How to test the autocorrelation of the residuals?
The DW Test or the Linear Regression test are not robust to anomalies in the data. If you have Pulses, Seasonal Pulses , Level Shifts or Local Time Trends these tests are useless as these untreated components inflate the variance of the errors thus downward biasing the ... | How to test the autocorrelation of the residuals?
The DW Test or the Linear Regression test are not robust to anomalies in the data. If you have Pulses, Seasonal Pulses , Level Shifts or Local Time Trends these tests are useless as these untreated co |
9,836 | How to test the autocorrelation of the residuals? | 2021: R Provides an Autocorrelation Function - acf
I'm assuming that the other answers posted were created before the acf function existed in R.
However, in 2021, there is a dedicated function for calculating the autocorrelation.
Here's how to use it:
# calculate the ACF for lags between 1 and 20 (inclusive)
autocorrel... | How to test the autocorrelation of the residuals? | 2021: R Provides an Autocorrelation Function - acf
I'm assuming that the other answers posted were created before the acf function existed in R.
However, in 2021, there is a dedicated function for cal | How to test the autocorrelation of the residuals?
2021: R Provides an Autocorrelation Function - acf
I'm assuming that the other answers posted were created before the acf function existed in R.
However, in 2021, there is a dedicated function for calculating the autocorrelation.
Here's how to use it:
# calculate the AC... | How to test the autocorrelation of the residuals?
2021: R Provides an Autocorrelation Function - acf
I'm assuming that the other answers posted were created before the acf function existed in R.
However, in 2021, there is a dedicated function for cal |
9,837 | Why do linear regression and ANOVA give different $p$-value in case of considering interaction between variable? | The fit for lm() and aov() are identical but the reporting is different. The t tests are the marginal impact of the variables in question, given the presence of all the other variables. The F tests are sequential - so they test for the importance of time in the presence of nothing but the intercept, of treat in the p... | Why do linear regression and ANOVA give different $p$-value in case of considering interaction betwe | The fit for lm() and aov() are identical but the reporting is different. The t tests are the marginal impact of the variables in question, given the presence of all the other variables. The F tests | Why do linear regression and ANOVA give different $p$-value in case of considering interaction between variable?
The fit for lm() and aov() are identical but the reporting is different. The t tests are the marginal impact of the variables in question, given the presence of all the other variables. The F tests are seq... | Why do linear regression and ANOVA give different $p$-value in case of considering interaction betwe
The fit for lm() and aov() are identical but the reporting is different. The t tests are the marginal impact of the variables in question, given the presence of all the other variables. The F tests |
9,838 | Why do linear regression and ANOVA give different $p$-value in case of considering interaction between variable? | Peter Ellis' answer is excellent, but there is another point to be made. The $t$-test statistic (and its $p$-value) is a test of whether $\beta = 0$. The $F$-test on the anova() printout is whether the added variable significantly reduces the residual sum of squares.
The $t$-test is order-independent, while the $F$-tes... | Why do linear regression and ANOVA give different $p$-value in case of considering interaction betwe | Peter Ellis' answer is excellent, but there is another point to be made. The $t$-test statistic (and its $p$-value) is a test of whether $\beta = 0$. The $F$-test on the anova() printout is whether th | Why do linear regression and ANOVA give different $p$-value in case of considering interaction between variable?
Peter Ellis' answer is excellent, but there is another point to be made. The $t$-test statistic (and its $p$-value) is a test of whether $\beta = 0$. The $F$-test on the anova() printout is whether the added... | Why do linear regression and ANOVA give different $p$-value in case of considering interaction betwe
Peter Ellis' answer is excellent, but there is another point to be made. The $t$-test statistic (and its $p$-value) is a test of whether $\beta = 0$. The $F$-test on the anova() printout is whether th |
9,839 | Why do linear regression and ANOVA give different $p$-value in case of considering interaction between variable? | The above two answers are great, but thought I'd add a bit more. Another nugget of information can be gleaned from here.
When you report the lm() results with the interaction term, you're saying something like: "treat 1 is different than treat 0 (beta != 0, p=0.0925), when time is set to the base value of 1". Where... | Why do linear regression and ANOVA give different $p$-value in case of considering interaction betwe | The above two answers are great, but thought I'd add a bit more. Another nugget of information can be gleaned from here.
When you report the lm() results with the interaction term, you're saying so | Why do linear regression and ANOVA give different $p$-value in case of considering interaction between variable?
The above two answers are great, but thought I'd add a bit more. Another nugget of information can be gleaned from here.
When you report the lm() results with the interaction term, you're saying something... | Why do linear regression and ANOVA give different $p$-value in case of considering interaction betwe
The above two answers are great, but thought I'd add a bit more. Another nugget of information can be gleaned from here.
When you report the lm() results with the interaction term, you're saying so |
9,840 | Why do linear regression and ANOVA give different $p$-value in case of considering interaction between variable? | The difference has to do with the type pairwise comparisons of cascading models.
Also, the aov() function has an issue with how it chooses the degrees of freedom. It seems to mix two concepts: 1) the sum of squares from the stepwise comparisons, 2) the degrees of freedom from an overall picture.
PROBLEM REPRODUCTION
>... | Why do linear regression and ANOVA give different $p$-value in case of considering interaction betwe | The difference has to do with the type pairwise comparisons of cascading models.
Also, the aov() function has an issue with how it chooses the degrees of freedom. It seems to mix two concepts: 1) the | Why do linear regression and ANOVA give different $p$-value in case of considering interaction between variable?
The difference has to do with the type pairwise comparisons of cascading models.
Also, the aov() function has an issue with how it chooses the degrees of freedom. It seems to mix two concepts: 1) the sum of ... | Why do linear regression and ANOVA give different $p$-value in case of considering interaction betwe
The difference has to do with the type pairwise comparisons of cascading models.
Also, the aov() function has an issue with how it chooses the degrees of freedom. It seems to mix two concepts: 1) the |
9,841 | Extreme Value Theory - Show: Normal to Gumbel | An indirect way, is as follows:
For absolutely continuous distributions, Richard von Mises (in a 1936 paper "La distribution de la plus grande de n valeurs", which appears to have been reproduced -in English?- in a 1964 edition with selected papers of his), has provided the following sufficient condition for the maximu... | Extreme Value Theory - Show: Normal to Gumbel | An indirect way, is as follows:
For absolutely continuous distributions, Richard von Mises (in a 1936 paper "La distribution de la plus grande de n valeurs", which appears to have been reproduced -in | Extreme Value Theory - Show: Normal to Gumbel
An indirect way, is as follows:
For absolutely continuous distributions, Richard von Mises (in a 1936 paper "La distribution de la plus grande de n valeurs", which appears to have been reproduced -in English?- in a 1964 edition with selected papers of his), has provided the... | Extreme Value Theory - Show: Normal to Gumbel
An indirect way, is as follows:
For absolutely continuous distributions, Richard von Mises (in a 1936 paper "La distribution de la plus grande de n valeurs", which appears to have been reproduced -in |
9,842 | Extreme Value Theory - Show: Normal to Gumbel | The question asks two things: (1) how to show that the maximum $X_{(n)}$ converges, in the sense that $(X_{(n)}-b_n)/a_n$ converges (in distribution) for suitably chosen sequences $(a_n)$ and $(b_n)$, to the Standard Gumbel distribution and (2) how to find such sequences.
The first is well-known and documented in the o... | Extreme Value Theory - Show: Normal to Gumbel | The question asks two things: (1) how to show that the maximum $X_{(n)}$ converges, in the sense that $(X_{(n)}-b_n)/a_n$ converges (in distribution) for suitably chosen sequences $(a_n)$ and $(b_n)$, | Extreme Value Theory - Show: Normal to Gumbel
The question asks two things: (1) how to show that the maximum $X_{(n)}$ converges, in the sense that $(X_{(n)}-b_n)/a_n$ converges (in distribution) for suitably chosen sequences $(a_n)$ and $(b_n)$, to the Standard Gumbel distribution and (2) how to find such sequences.
T... | Extreme Value Theory - Show: Normal to Gumbel
The question asks two things: (1) how to show that the maximum $X_{(n)}$ converges, in the sense that $(X_{(n)}-b_n)/a_n$ converges (in distribution) for suitably chosen sequences $(a_n)$ and $(b_n)$, |
9,843 | Extreme Value Theory - Show: Normal to Gumbel | Here is a "direct" approach. Let $a_n > 0$, $b_n$ to be determined so that $a_nx+b_n \rightarrow +\infty$ for all $x$.
From L'Hospital's rule,
$$
\underset{A \rightarrow \infty }{lim} \frac{\int_A^{+\infty} e^{-u^2/2} du}{A^p e^{-A^2/2}} = 1
$$
when $p=-1$, so we have:
$$
F(a_n x + b_n) = 1 - \frac{1}{\sqrt{2\pi}}\, ... | Extreme Value Theory - Show: Normal to Gumbel | Here is a "direct" approach. Let $a_n > 0$, $b_n$ to be determined so that $a_nx+b_n \rightarrow +\infty$ for all $x$.
From L'Hospital's rule,
$$
\underset{A \rightarrow \infty }{lim} \frac{\int_A^{+ | Extreme Value Theory - Show: Normal to Gumbel
Here is a "direct" approach. Let $a_n > 0$, $b_n$ to be determined so that $a_nx+b_n \rightarrow +\infty$ for all $x$.
From L'Hospital's rule,
$$
\underset{A \rightarrow \infty }{lim} \frac{\int_A^{+\infty} e^{-u^2/2} du}{A^p e^{-A^2/2}} = 1
$$
when $p=-1$, so we have:
$$... | Extreme Value Theory - Show: Normal to Gumbel
Here is a "direct" approach. Let $a_n > 0$, $b_n$ to be determined so that $a_nx+b_n \rightarrow +\infty$ for all $x$.
From L'Hospital's rule,
$$
\underset{A \rightarrow \infty }{lim} \frac{\int_A^{+ |
9,844 | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | Update 3 (May, 2013): Another really good paper on mixed models in Psychology was released in the Journal of Memory and Language (although I do not agree with the authors conclusions on how to obtain p-values, see package afex instead). It very nicely discusses on how to specify the random effects structure. Go read it... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | Update 3 (May, 2013): Another really good paper on mixed models in Psychology was released in the Journal of Memory and Language (although I do not agree with the authors conclusions on how to obtain | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
Update 3 (May, 2013): Another really good paper on mixed models in Psychology was released in the Journal of Memory and Language (although I do not agree with the authors conclusions on how to obtain p-values, see package afex inst... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
Update 3 (May, 2013): Another really good paper on mixed models in Psychology was released in the Journal of Memory and Language (although I do not agree with the authors conclusions on how to obtain |
9,845 | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | This is a highly-cited paper on mixed models for ecology and evolution:
Bolker et al. (2009) Generalized linear mixed models: a practical guide for ecology and evolution Trends in Ecology & Evolution Vol. 24 pp127-135 (PDF) (from ScienceDirect with links to Supplementary Content). | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | This is a highly-cited paper on mixed models for ecology and evolution:
Bolker et al. (2009) Generalized linear mixed models: a practical guide for ecology and evolution Trends in Ecology & Evolution | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
This is a highly-cited paper on mixed models for ecology and evolution:
Bolker et al. (2009) Generalized linear mixed models: a practical guide for ecology and evolution Trends in Ecology & Evolution Vol. 24 pp127-135 (PDF) (from ... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
This is a highly-cited paper on mixed models for ecology and evolution:
Bolker et al. (2009) Generalized linear mixed models: a practical guide for ecology and evolution Trends in Ecology & Evolution |
9,846 | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | The following article endeavours to promote the use of multilevel modelling in social science settings:
Bliese, P. D. & Ployhart, R. E. (2002). Growth Modeling Using Random
Coefficient Models: Model Building, Testing, and Illustrations, Organizational Research Methods, Vol. 5 No. 4, October 2002 362-387. PDF
To quote... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | The following article endeavours to promote the use of multilevel modelling in social science settings:
Bliese, P. D. & Ployhart, R. E. (2002). Growth Modeling Using Random
Coefficient Models: Model | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
The following article endeavours to promote the use of multilevel modelling in social science settings:
Bliese, P. D. & Ployhart, R. E. (2002). Growth Modeling Using Random
Coefficient Models: Model Building, Testing, and Illustra... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
The following article endeavours to promote the use of multilevel modelling in social science settings:
Bliese, P. D. & Ployhart, R. E. (2002). Growth Modeling Using Random
Coefficient Models: Model |
9,847 | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | An excellent example of using mixed models in ecology is:
"Demography and management of the invasive species Hypericum
perforatum. I. Using multi-level mixed-effects models for
characterizing growth, survival and fecundity in a long-term data
set" (Buckely, Briese and Rees 2003).
Unfortunately it uses older R librar... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | An excellent example of using mixed models in ecology is:
"Demography and management of the invasive species Hypericum
perforatum. I. Using multi-level mixed-effects models for
characterizing growth | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
An excellent example of using mixed models in ecology is:
"Demography and management of the invasive species Hypericum
perforatum. I. Using multi-level mixed-effects models for
characterizing growth, survival and fecundity in a l... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
An excellent example of using mixed models in ecology is:
"Demography and management of the invasive species Hypericum
perforatum. I. Using multi-level mixed-effects models for
characterizing growth |
9,848 | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | I am reading Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York, NY: Springer Science+Business Media, LLC. It is written for ecologists, so the stats are fairly easy to follow; I think it would be useful for people from other dis... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine? | I am reading Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York, NY: Springer Science+Business Media, LLC. It | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
I am reading Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York, NY: Springer Science+Business Media, LLC. It is written for ecologists, so ... | Example reports for mixed-model analysis using lmer in biology, psychology and medicine?
I am reading Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York, NY: Springer Science+Business Media, LLC. It |
9,849 | What is the Bayesian justification for privileging analyses conducted earlier than other analyses? | Bayes' theorem says the posterior is equal to prior * likelihood after rescaling (so the probability sums to 1). Each observation has a likelihood which can be used to update the prior and create a new posterior:
posterior_1 = prior * likelihood_1
posterior_2 = posterior_1 * likelihood_2
...
posterior_n = posterior_{n-... | What is the Bayesian justification for privileging analyses conducted earlier than other analyses? | Bayes' theorem says the posterior is equal to prior * likelihood after rescaling (so the probability sums to 1). Each observation has a likelihood which can be used to update the prior and create a ne | What is the Bayesian justification for privileging analyses conducted earlier than other analyses?
Bayes' theorem says the posterior is equal to prior * likelihood after rescaling (so the probability sums to 1). Each observation has a likelihood which can be used to update the prior and create a new posterior:
posterio... | What is the Bayesian justification for privileging analyses conducted earlier than other analyses?
Bayes' theorem says the posterior is equal to prior * likelihood after rescaling (so the probability sums to 1). Each observation has a likelihood which can be used to update the prior and create a ne |
9,850 | What is the Bayesian justification for privileging analyses conducted earlier than other analyses? | First I should point out that:
In your significance-testing approach, you followed up a negative result with a different model that gave you another chance to get a positive result. Such a strategy increases your project-wise type-I error rate. Significance-testing requires choosing your analytic strategy in advance f... | What is the Bayesian justification for privileging analyses conducted earlier than other analyses? | First I should point out that:
In your significance-testing approach, you followed up a negative result with a different model that gave you another chance to get a positive result. Such a strategy i | What is the Bayesian justification for privileging analyses conducted earlier than other analyses?
First I should point out that:
In your significance-testing approach, you followed up a negative result with a different model that gave you another chance to get a positive result. Such a strategy increases your project... | What is the Bayesian justification for privileging analyses conducted earlier than other analyses?
First I should point out that:
In your significance-testing approach, you followed up a negative result with a different model that gave you another chance to get a positive result. Such a strategy i |
9,851 | What is the Bayesian justification for privileging analyses conducted earlier than other analyses? | I thought I might make a series of graphs with a different, but stylized problem, to show you why it can be dangerous to go from Frequentist to Bayesian methods and why using summary statistics can create issues.
Rather than use your example, which is multidimensional, I am going to cut it down to one dimension with tw... | What is the Bayesian justification for privileging analyses conducted earlier than other analyses? | I thought I might make a series of graphs with a different, but stylized problem, to show you why it can be dangerous to go from Frequentist to Bayesian methods and why using summary statistics can cr | What is the Bayesian justification for privileging analyses conducted earlier than other analyses?
I thought I might make a series of graphs with a different, but stylized problem, to show you why it can be dangerous to go from Frequentist to Bayesian methods and why using summary statistics can create issues.
Rather t... | What is the Bayesian justification for privileging analyses conducted earlier than other analyses?
I thought I might make a series of graphs with a different, but stylized problem, to show you why it can be dangerous to go from Frequentist to Bayesian methods and why using summary statistics can cr |
9,852 | Did Statistics.com publish the wrong answer? | I believe that you and your colleague are correct. Statistics.com has the correct line of thinking, but makes a simple mistake. Out of the 90 "OK" claims, we expect 20% of them to be incorrectly classified as fraud, not 80%. 20% of 90 is 18, leading to 9 correctly identified claims and 18 incorrect claims, with a ratio... | Did Statistics.com publish the wrong answer? | I believe that you and your colleague are correct. Statistics.com has the correct line of thinking, but makes a simple mistake. Out of the 90 "OK" claims, we expect 20% of them to be incorrectly class | Did Statistics.com publish the wrong answer?
I believe that you and your colleague are correct. Statistics.com has the correct line of thinking, but makes a simple mistake. Out of the 90 "OK" claims, we expect 20% of them to be incorrectly classified as fraud, not 80%. 20% of 90 is 18, leading to 9 correctly identified... | Did Statistics.com publish the wrong answer?
I believe that you and your colleague are correct. Statistics.com has the correct line of thinking, but makes a simple mistake. Out of the 90 "OK" claims, we expect 20% of them to be incorrectly class |
9,853 | Did Statistics.com publish the wrong answer? | You are correct. The solution that the website posted is based on a misreading of the problem in that 80% of the nonfraudulent claims are classified as fraudulent instead of the given 20%. | Did Statistics.com publish the wrong answer? | You are correct. The solution that the website posted is based on a misreading of the problem in that 80% of the nonfraudulent claims are classified as fraudulent instead of the given 20%. | Did Statistics.com publish the wrong answer?
You are correct. The solution that the website posted is based on a misreading of the problem in that 80% of the nonfraudulent claims are classified as fraudulent instead of the given 20%. | Did Statistics.com publish the wrong answer?
You are correct. The solution that the website posted is based on a misreading of the problem in that 80% of the nonfraudulent claims are classified as fraudulent instead of the given 20%. |
9,854 | Scikit correct way to calibrate classifiers with CalibratedClassifierCV | There are two things mentioned in the CalibratedClassifierCV docs that hint towards the ways it can be used:
base_estimator: If cv=prefit, the classifier must have been fit already on data.
cv: If “prefit” is passed, it is assumed that base_estimator has been fitted already and all data is used for calibration.
I may... | Scikit correct way to calibrate classifiers with CalibratedClassifierCV | There are two things mentioned in the CalibratedClassifierCV docs that hint towards the ways it can be used:
base_estimator: If cv=prefit, the classifier must have been fit already on data.
cv: If “p | Scikit correct way to calibrate classifiers with CalibratedClassifierCV
There are two things mentioned in the CalibratedClassifierCV docs that hint towards the ways it can be used:
base_estimator: If cv=prefit, the classifier must have been fit already on data.
cv: If “prefit” is passed, it is assumed that base_estima... | Scikit correct way to calibrate classifiers with CalibratedClassifierCV
There are two things mentioned in the CalibratedClassifierCV docs that hint towards the ways it can be used:
base_estimator: If cv=prefit, the classifier must have been fit already on data.
cv: If “p |
9,855 | Scikit correct way to calibrate classifiers with CalibratedClassifierCV | I am interested in this question as well and wanted to add some experiments to better understand CalibratedClassifierCV (CCCV).
As has already been said, there are two ways to use it.
#Method 1, train classifier within CCCV
model = CalibratedClassifierCV(my_clf)
model.fit(X_train_val, y_train_val)
#Method 2, train c... | Scikit correct way to calibrate classifiers with CalibratedClassifierCV | I am interested in this question as well and wanted to add some experiments to better understand CalibratedClassifierCV (CCCV).
As has already been said, there are two ways to use it.
#Method 1, tra | Scikit correct way to calibrate classifiers with CalibratedClassifierCV
I am interested in this question as well and wanted to add some experiments to better understand CalibratedClassifierCV (CCCV).
As has already been said, there are two ways to use it.
#Method 1, train classifier within CCCV
model = CalibratedClas... | Scikit correct way to calibrate classifiers with CalibratedClassifierCV
I am interested in this question as well and wanted to add some experiments to better understand CalibratedClassifierCV (CCCV).
As has already been said, there are two ways to use it.
#Method 1, tra |
9,856 | How do I fit a set of data to a Pareto distribution in R? | Well, if you have a sample $X_1, ..., X_n$ from a pareto distribution with parameters $m>0$ and $\alpha>0$ (where $m$ is the lower bound parameter and $\alpha$ is the shape parameter) the log-likelihood of that sample is:
$$n \log(\alpha) + n \alpha \log(m) - (\alpha+1) \sum_{i=1}^{n} \log(X_i) $$
this is a monotonica... | How do I fit a set of data to a Pareto distribution in R? | Well, if you have a sample $X_1, ..., X_n$ from a pareto distribution with parameters $m>0$ and $\alpha>0$ (where $m$ is the lower bound parameter and $\alpha$ is the shape parameter) the log-likeliho | How do I fit a set of data to a Pareto distribution in R?
Well, if you have a sample $X_1, ..., X_n$ from a pareto distribution with parameters $m>0$ and $\alpha>0$ (where $m$ is the lower bound parameter and $\alpha$ is the shape parameter) the log-likelihood of that sample is:
$$n \log(\alpha) + n \alpha \log(m) - (... | How do I fit a set of data to a Pareto distribution in R?
Well, if you have a sample $X_1, ..., X_n$ from a pareto distribution with parameters $m>0$ and $\alpha>0$ (where $m$ is the lower bound parameter and $\alpha$ is the shape parameter) the log-likeliho |
9,857 | How do I fit a set of data to a Pareto distribution in R? | You can use the fitdist function provided in fitdistrplus package:
library(MASS)
library(fitdistrplus)
library(actuar)
# suppose data is in dataPar list
fp <- fitdist(dataPar, "pareto", start=list(shape = 1, scale = 500))
#the mle parameters will be stored in fp$estimate | How do I fit a set of data to a Pareto distribution in R? | You can use the fitdist function provided in fitdistrplus package:
library(MASS)
library(fitdistrplus)
library(actuar)
# suppose data is in dataPar list
fp <- fitdist(dataPar, "pareto", start=list(sh | How do I fit a set of data to a Pareto distribution in R?
You can use the fitdist function provided in fitdistrplus package:
library(MASS)
library(fitdistrplus)
library(actuar)
# suppose data is in dataPar list
fp <- fitdist(dataPar, "pareto", start=list(shape = 1, scale = 500))
#the mle parameters will be stored in f... | How do I fit a set of data to a Pareto distribution in R?
You can use the fitdist function provided in fitdistrplus package:
library(MASS)
library(fitdistrplus)
library(actuar)
# suppose data is in dataPar list
fp <- fitdist(dataPar, "pareto", start=list(sh |
9,858 | In a Poisson model, what is the difference between using time as a covariate or an offset? | Offsets can be used in any regression model, but they are much more common when working with count data for your response variable. An offset is just a variable that is forced to have a coefficient of $1$ in the model. (See also this excellent CV thread: When to use an offset in a Poisson regression?)
When used cor... | In a Poisson model, what is the difference between using time as a covariate or an offset? | Offsets can be used in any regression model, but they are much more common when working with count data for your response variable. An offset is just a variable that is forced to have a coefficient o | In a Poisson model, what is the difference between using time as a covariate or an offset?
Offsets can be used in any regression model, but they are much more common when working with count data for your response variable. An offset is just a variable that is forced to have a coefficient of $1$ in the model. (See als... | In a Poisson model, what is the difference between using time as a covariate or an offset?
Offsets can be used in any regression model, but they are much more common when working with count data for your response variable. An offset is just a variable that is forced to have a coefficient o |
9,859 | In a Poisson model, what is the difference between using time as a covariate or an offset? | Time offsets can usually be viewed as your model estimating the rate an event occurs per unit time, with the offset controlling for how long you observed different subjects.
In poisson models you are always estimating a rate that something happens, but you never get to observe this rate directly. You do get to observe... | In a Poisson model, what is the difference between using time as a covariate or an offset? | Time offsets can usually be viewed as your model estimating the rate an event occurs per unit time, with the offset controlling for how long you observed different subjects.
In poisson models you are | In a Poisson model, what is the difference between using time as a covariate or an offset?
Time offsets can usually be viewed as your model estimating the rate an event occurs per unit time, with the offset controlling for how long you observed different subjects.
In poisson models you are always estimating a rate that... | In a Poisson model, what is the difference between using time as a covariate or an offset?
Time offsets can usually be viewed as your model estimating the rate an event occurs per unit time, with the offset controlling for how long you observed different subjects.
In poisson models you are |
9,860 | If linear regression is related to Pearson's correlation, are there any regression techniques related to Kendall's and Spearman's correlations? | There's a very straightforward means by which to use almost any correlation measure to fit linear regressions, and which reproduces least squares when you use the Pearson correlation.
Consider that if the slope of a relationship is $\beta$, the correlation between $y-\beta x$ and $x$ should be expected to be $0$.
Inde... | If linear regression is related to Pearson's correlation, are there any regression techniques relate | There's a very straightforward means by which to use almost any correlation measure to fit linear regressions, and which reproduces least squares when you use the Pearson correlation.
Consider that if | If linear regression is related to Pearson's correlation, are there any regression techniques related to Kendall's and Spearman's correlations?
There's a very straightforward means by which to use almost any correlation measure to fit linear regressions, and which reproduces least squares when you use the Pearson corre... | If linear regression is related to Pearson's correlation, are there any regression techniques relate
There's a very straightforward means by which to use almost any correlation measure to fit linear regressions, and which reproduces least squares when you use the Pearson correlation.
Consider that if |
9,861 | If linear regression is related to Pearson's correlation, are there any regression techniques related to Kendall's and Spearman's correlations? | The proportional odds (PO) model generalizes Wilcoxon and Kruskal-Wallis tests. Spearman's correlation when $X$ is binary is the Wilcoxon test statistic simply translated. So you could say that the PO model is a unifying method. Since the PO model can have as many intercepts as there are unique values of $Y$ (less o... | If linear regression is related to Pearson's correlation, are there any regression techniques relate | The proportional odds (PO) model generalizes Wilcoxon and Kruskal-Wallis tests. Spearman's correlation when $X$ is binary is the Wilcoxon test statistic simply translated. So you could say that the | If linear regression is related to Pearson's correlation, are there any regression techniques related to Kendall's and Spearman's correlations?
The proportional odds (PO) model generalizes Wilcoxon and Kruskal-Wallis tests. Spearman's correlation when $X$ is binary is the Wilcoxon test statistic simply translated. So... | If linear regression is related to Pearson's correlation, are there any regression techniques relate
The proportional odds (PO) model generalizes Wilcoxon and Kruskal-Wallis tests. Spearman's correlation when $X$ is binary is the Wilcoxon test statistic simply translated. So you could say that the |
9,862 | If linear regression is related to Pearson's correlation, are there any regression techniques related to Kendall's and Spearman's correlations? | Aaron Han (1987 in econometrics) proposed the Maximum Rank Correlation estimator that fits regression models by maximizing tau. Dougherty and Thomas (2012 in the psychology literature) recently proposed a very similar algorithm. There is an abundance of work on the MRC illustrating its properties.
Aaron K. Han, Non-par... | If linear regression is related to Pearson's correlation, are there any regression techniques relate | Aaron Han (1987 in econometrics) proposed the Maximum Rank Correlation estimator that fits regression models by maximizing tau. Dougherty and Thomas (2012 in the psychology literature) recently propos | If linear regression is related to Pearson's correlation, are there any regression techniques related to Kendall's and Spearman's correlations?
Aaron Han (1987 in econometrics) proposed the Maximum Rank Correlation estimator that fits regression models by maximizing tau. Dougherty and Thomas (2012 in the psychology lit... | If linear regression is related to Pearson's correlation, are there any regression techniques relate
Aaron Han (1987 in econometrics) proposed the Maximum Rank Correlation estimator that fits regression models by maximizing tau. Dougherty and Thomas (2012 in the psychology literature) recently propos |
9,863 | What if interaction wipes out my direct effects in regression? | I think this one is tricky; as you hint, there's 'moral hazard' here: if you hadn't looked at the interaction at all, you'd be free and clear, but now that you have there is a suspicion of data-dredging if you drop it.
The key is probably a change in the meaning of your effects when you go from the main-effects-only to... | What if interaction wipes out my direct effects in regression? | I think this one is tricky; as you hint, there's 'moral hazard' here: if you hadn't looked at the interaction at all, you'd be free and clear, but now that you have there is a suspicion of data-dredgi | What if interaction wipes out my direct effects in regression?
I think this one is tricky; as you hint, there's 'moral hazard' here: if you hadn't looked at the interaction at all, you'd be free and clear, but now that you have there is a suspicion of data-dredging if you drop it.
The key is probably a change in the me... | What if interaction wipes out my direct effects in regression?
I think this one is tricky; as you hint, there's 'moral hazard' here: if you hadn't looked at the interaction at all, you'd be free and clear, but now that you have there is a suspicion of data-dredgi |
9,864 | What if interaction wipes out my direct effects in regression? | Are you sure the variables have been appropriately expressed? Consider two independent variables $X_1$ and $X_2$. The problem statement asserts that you are getting a good fit in the form
$$Y = \beta_0 + \beta_{12} X_1 X_2 + \epsilon$$
If there is some evidence that the variance of the residuals increases with $Y$, ... | What if interaction wipes out my direct effects in regression? | Are you sure the variables have been appropriately expressed? Consider two independent variables $X_1$ and $X_2$. The problem statement asserts that you are getting a good fit in the form
$$Y = \bet | What if interaction wipes out my direct effects in regression?
Are you sure the variables have been appropriately expressed? Consider two independent variables $X_1$ and $X_2$. The problem statement asserts that you are getting a good fit in the form
$$Y = \beta_0 + \beta_{12} X_1 X_2 + \epsilon$$
If there is some e... | What if interaction wipes out my direct effects in regression?
Are you sure the variables have been appropriately expressed? Consider two independent variables $X_1$ and $X_2$. The problem statement asserts that you are getting a good fit in the form
$$Y = \bet |
9,865 | What if interaction wipes out my direct effects in regression? | This may be a problem of interpretation, a misunderstanding of what a so-called "direct effect" coefficient really is.
In regression models with continuous predictor variables and no interaction terms -- that is, with no terms that are constructed as the product of other terms -- each variable's coefficient is the slop... | What if interaction wipes out my direct effects in regression? | This may be a problem of interpretation, a misunderstanding of what a so-called "direct effect" coefficient really is.
In regression models with continuous predictor variables and no interaction terms | What if interaction wipes out my direct effects in regression?
This may be a problem of interpretation, a misunderstanding of what a so-called "direct effect" coefficient really is.
In regression models with continuous predictor variables and no interaction terms -- that is, with no terms that are constructed as the pr... | What if interaction wipes out my direct effects in regression?
This may be a problem of interpretation, a misunderstanding of what a so-called "direct effect" coefficient really is.
In regression models with continuous predictor variables and no interaction terms |
9,866 | What if interaction wipes out my direct effects in regression? | In a regular multiple regression with two quantitative predictor variables, including their interaction just means including their observation-wise product as an additional predictor variable: $Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 (X_1 \cdot X_2) = (b_0 + b_2 X_2) + (b_1 + b_3 X_2) X_1$
This typically int... | What if interaction wipes out my direct effects in regression? | In a regular multiple regression with two quantitative predictor variables, including their interaction just means including their observation-wise product as an additional predictor variable: $Y = \b | What if interaction wipes out my direct effects in regression?
In a regular multiple regression with two quantitative predictor variables, including their interaction just means including their observation-wise product as an additional predictor variable: $Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 (X_1 \cdot X_... | What if interaction wipes out my direct effects in regression?
In a regular multiple regression with two quantitative predictor variables, including their interaction just means including their observation-wise product as an additional predictor variable: $Y = \b |
9,867 | How to perform Student's t-test having only sample size, sample average and population average are known? | This may surprise many, but to solve this problem you don't necessarily need to estimate s. In fact, you don't need to know anything about the spread of the data (although that would be helpful, of course). For instance, Wall, Boen, and Tweedie in a 2001 article describe how to find a finite confidence interval for t... | How to perform Student's t-test having only sample size, sample average and population average are k | This may surprise many, but to solve this problem you don't necessarily need to estimate s. In fact, you don't need to know anything about the spread of the data (although that would be helpful, of c | How to perform Student's t-test having only sample size, sample average and population average are known?
This may surprise many, but to solve this problem you don't necessarily need to estimate s. In fact, you don't need to know anything about the spread of the data (although that would be helpful, of course). For i... | How to perform Student's t-test having only sample size, sample average and population average are k
This may surprise many, but to solve this problem you don't necessarily need to estimate s. In fact, you don't need to know anything about the spread of the data (although that would be helpful, of c |
9,868 | How to perform Student's t-test having only sample size, sample average and population average are known? | This does look to be a slightly contrived question. 49 is an exact square of 7. The value of a t-distribution with 48 DoF for a two-sided test of p<0.05 is very nearly 2 (2.01).
We reject the null hypothesis of equality of means if |sample_mean - popn_mean| > 2*StdError, i.e. 200-112 > 2*SE so SE < 44, i.e. SD < 7*44 =... | How to perform Student's t-test having only sample size, sample average and population average are k | This does look to be a slightly contrived question. 49 is an exact square of 7. The value of a t-distribution with 48 DoF for a two-sided test of p<0.05 is very nearly 2 (2.01).
We reject the null hyp | How to perform Student's t-test having only sample size, sample average and population average are known?
This does look to be a slightly contrived question. 49 is an exact square of 7. The value of a t-distribution with 48 DoF for a two-sided test of p<0.05 is very nearly 2 (2.01).
We reject the null hypothesis of equ... | How to perform Student's t-test having only sample size, sample average and population average are k
This does look to be a slightly contrived question. 49 is an exact square of 7. The value of a t-distribution with 48 DoF for a two-sided test of p<0.05 is very nearly 2 (2.01).
We reject the null hyp |
9,869 | How to perform Student's t-test having only sample size, sample average and population average are known? | Suppose there are 999 workers at ACME north factory each making a wage of 112, and 1 CEO making 88112. The population mean salary is $\mu = 0.999 * 112 + 0.001 * 88112 = 200.$ The probability of drawing the CEO from a sample of 49 people at the factory is $49 / 1000 < 0.05$ (this is from the hypergeometric distribution... | How to perform Student's t-test having only sample size, sample average and population average are k | Suppose there are 999 workers at ACME north factory each making a wage of 112, and 1 CEO making 88112. The population mean salary is $\mu = 0.999 * 112 + 0.001 * 88112 = 200.$ The probability of drawi | How to perform Student's t-test having only sample size, sample average and population average are known?
Suppose there are 999 workers at ACME north factory each making a wage of 112, and 1 CEO making 88112. The population mean salary is $\mu = 0.999 * 112 + 0.001 * 88112 = 200.$ The probability of drawing the CEO fro... | How to perform Student's t-test having only sample size, sample average and population average are k
Suppose there are 999 workers at ACME north factory each making a wage of 112, and 1 CEO making 88112. The population mean salary is $\mu = 0.999 * 112 + 0.001 * 88112 = 200.$ The probability of drawi |
9,870 | How to perform Student's t-test having only sample size, sample average and population average are known? | I presume you are referring to a one sample t test. Its goal is to compare the mean of your sample with a hypothetical mean. It then computes (assuming your population is Gaussian) a P value that answers this question: If the population mean really was the hypothetical value, how unlikely would it be to draw a sample w... | How to perform Student's t-test having only sample size, sample average and population average are k | I presume you are referring to a one sample t test. Its goal is to compare the mean of your sample with a hypothetical mean. It then computes (assuming your population is Gaussian) a P value that answ | How to perform Student's t-test having only sample size, sample average and population average are known?
I presume you are referring to a one sample t test. Its goal is to compare the mean of your sample with a hypothetical mean. It then computes (assuming your population is Gaussian) a P value that answers this quest... | How to perform Student's t-test having only sample size, sample average and population average are k
I presume you are referring to a one sample t test. Its goal is to compare the mean of your sample with a hypothetical mean. It then computes (assuming your population is Gaussian) a P value that answ |
9,871 | Continuous generalization of the negative binomial distribution | That's an interesting question. My research group has been using the distribution you refer to for some years in our publicly available bioinformatics software. As far as I know, the distribution does not have a name and there is no literature on it. While the paper by Chandra et al (2012) cited by Aksakal is closely r... | Continuous generalization of the negative binomial distribution | That's an interesting question. My research group has been using the distribution you refer to for some years in our publicly available bioinformatics software. As far as I know, the distribution does | Continuous generalization of the negative binomial distribution
That's an interesting question. My research group has been using the distribution you refer to for some years in our publicly available bioinformatics software. As far as I know, the distribution does not have a name and there is no literature on it. While... | Continuous generalization of the negative binomial distribution
That's an interesting question. My research group has been using the distribution you refer to for some years in our publicly available bioinformatics software. As far as I know, the distribution does |
9,872 | Continuous generalization of the negative binomial distribution | Look at this paper: Chandra, Nimai Kumar, and Dilip Roy. A continuous version of the negative binomial distribution. Statistica 72, no. 1 (2012): 81.
It's defined in the paper as the survival function, which is a natural approach since neg binomial was introduced in reliability analysis:
$$S_r(x)=\begin{cases}q^x & \... | Continuous generalization of the negative binomial distribution | Look at this paper: Chandra, Nimai Kumar, and Dilip Roy. A continuous version of the negative binomial distribution. Statistica 72, no. 1 (2012): 81.
It's defined in the paper as the survival functio | Continuous generalization of the negative binomial distribution
Look at this paper: Chandra, Nimai Kumar, and Dilip Roy. A continuous version of the negative binomial distribution. Statistica 72, no. 1 (2012): 81.
It's defined in the paper as the survival function, which is a natural approach since neg binomial was in... | Continuous generalization of the negative binomial distribution
Look at this paper: Chandra, Nimai Kumar, and Dilip Roy. A continuous version of the negative binomial distribution. Statistica 72, no. 1 (2012): 81.
It's defined in the paper as the survival functio |
9,873 | Statistics and data mining software tools for dealing with large datasets | I'll second @suncoolsu comment: The dimensionality of your data set is not the only criterion that should orient you toward a specific software. For instance, if you're just planning to do unsupervised clustering or use PCA, there are several dedicated tools that cope with large data sets, as commonly encountered in ge... | Statistics and data mining software tools for dealing with large datasets | I'll second @suncoolsu comment: The dimensionality of your data set is not the only criterion that should orient you toward a specific software. For instance, if you're just planning to do unsupervise | Statistics and data mining software tools for dealing with large datasets
I'll second @suncoolsu comment: The dimensionality of your data set is not the only criterion that should orient you toward a specific software. For instance, if you're just planning to do unsupervised clustering or use PCA, there are several ded... | Statistics and data mining software tools for dealing with large datasets
I'll second @suncoolsu comment: The dimensionality of your data set is not the only criterion that should orient you toward a specific software. For instance, if you're just planning to do unsupervise |
9,874 | Statistics and data mining software tools for dealing with large datasets | Most of the algorithms on Apache Mahout scale way beyond 20M records, even with high-dimensional data. If you only need to build a prediction model, there are specific tools like Vowpal Wabbit (http://hunch.net/~vw/) that can easily scale to billions of records on a single machine. | Statistics and data mining software tools for dealing with large datasets | Most of the algorithms on Apache Mahout scale way beyond 20M records, even with high-dimensional data. If you only need to build a prediction model, there are specific tools like Vowpal Wabbit (http:/ | Statistics and data mining software tools for dealing with large datasets
Most of the algorithms on Apache Mahout scale way beyond 20M records, even with high-dimensional data. If you only need to build a prediction model, there are specific tools like Vowpal Wabbit (http://hunch.net/~vw/) that can easily scale to bill... | Statistics and data mining software tools for dealing with large datasets
Most of the algorithms on Apache Mahout scale way beyond 20M records, even with high-dimensional data. If you only need to build a prediction model, there are specific tools like Vowpal Wabbit (http:/ |
9,875 | Statistics and data mining software tools for dealing with large datasets | There is the RHIPE package (R-Hadoop integration). It is can make it very easy (with exceptions) to analyze large amounts of data in R. | Statistics and data mining software tools for dealing with large datasets | There is the RHIPE package (R-Hadoop integration). It is can make it very easy (with exceptions) to analyze large amounts of data in R. | Statistics and data mining software tools for dealing with large datasets
There is the RHIPE package (R-Hadoop integration). It is can make it very easy (with exceptions) to analyze large amounts of data in R. | Statistics and data mining software tools for dealing with large datasets
There is the RHIPE package (R-Hadoop integration). It is can make it very easy (with exceptions) to analyze large amounts of data in R. |
9,876 | Statistics and data mining software tools for dealing with large datasets | It is hard to give a good answer without knowing what kind of models you have in mind.
For linear regression, I have successfully used the biglm package in R. | Statistics and data mining software tools for dealing with large datasets | It is hard to give a good answer without knowing what kind of models you have in mind.
For linear regression, I have successfully used the biglm package in R. | Statistics and data mining software tools for dealing with large datasets
It is hard to give a good answer without knowing what kind of models you have in mind.
For linear regression, I have successfully used the biglm package in R. | Statistics and data mining software tools for dealing with large datasets
It is hard to give a good answer without knowing what kind of models you have in mind.
For linear regression, I have successfully used the biglm package in R. |
9,877 | Statistics and data mining software tools for dealing with large datasets | Since you are building predictive models from large datasets you might benefit from Google's BigQuery (a hosted version of the technology from Google's research paper on massive dataset analysis with Dremel). You can export the query results as CSV for ingestion into a predictive classifier, for example.
BigQuery has a... | Statistics and data mining software tools for dealing with large datasets | Since you are building predictive models from large datasets you might benefit from Google's BigQuery (a hosted version of the technology from Google's research paper on massive dataset analysis with | Statistics and data mining software tools for dealing with large datasets
Since you are building predictive models from large datasets you might benefit from Google's BigQuery (a hosted version of the technology from Google's research paper on massive dataset analysis with Dremel). You can export the query results as C... | Statistics and data mining software tools for dealing with large datasets
Since you are building predictive models from large datasets you might benefit from Google's BigQuery (a hosted version of the technology from Google's research paper on massive dataset analysis with |
9,878 | Statistics and data mining software tools for dealing with large datasets | We trained 3.5M observations and 44 features using 64-bit R on an EC2 instance with 32GB ram and 4 cores. We used random forests and it worked well. Note that we had to preprocess/manipulate the data before training. | Statistics and data mining software tools for dealing with large datasets | We trained 3.5M observations and 44 features using 64-bit R on an EC2 instance with 32GB ram and 4 cores. We used random forests and it worked well. Note that we had to preprocess/manipulate the data | Statistics and data mining software tools for dealing with large datasets
We trained 3.5M observations and 44 features using 64-bit R on an EC2 instance with 32GB ram and 4 cores. We used random forests and it worked well. Note that we had to preprocess/manipulate the data before training. | Statistics and data mining software tools for dealing with large datasets
We trained 3.5M observations and 44 features using 64-bit R on an EC2 instance with 32GB ram and 4 cores. We used random forests and it worked well. Note that we had to preprocess/manipulate the data |
9,879 | Statistics and data mining software tools for dealing with large datasets | SAS Enterprise Miner version 6.2 would have no problem handling 20 million observations, and a variety of models which can be adapted to your situation. The issue with SAS is usually the cost however. Here's a summary of what SAS EM can do:
SAS EM 6.2: What's New | Statistics and data mining software tools for dealing with large datasets | SAS Enterprise Miner version 6.2 would have no problem handling 20 million observations, and a variety of models which can be adapted to your situation. The issue with SAS is usually the cost however. | Statistics and data mining software tools for dealing with large datasets
SAS Enterprise Miner version 6.2 would have no problem handling 20 million observations, and a variety of models which can be adapted to your situation. The issue with SAS is usually the cost however. Here's a summary of what SAS EM can do:
SAS E... | Statistics and data mining software tools for dealing with large datasets
SAS Enterprise Miner version 6.2 would have no problem handling 20 million observations, and a variety of models which can be adapted to your situation. The issue with SAS is usually the cost however. |
9,880 | Statistics and data mining software tools for dealing with large datasets | Can you look at ScaVis (http://jwork.org/scavis)? I did not look at 20M, but you may try to check it. | Statistics and data mining software tools for dealing with large datasets | Can you look at ScaVis (http://jwork.org/scavis)? I did not look at 20M, but you may try to check it. | Statistics and data mining software tools for dealing with large datasets
Can you look at ScaVis (http://jwork.org/scavis)? I did not look at 20M, but you may try to check it. | Statistics and data mining software tools for dealing with large datasets
Can you look at ScaVis (http://jwork.org/scavis)? I did not look at 20M, but you may try to check it. |
9,881 | Statistics and data mining software tools for dealing with large datasets | RHIPE is a great solution, and I would probably choose this one, if having this issue! but have you considered NCSS? As far as I know, the newest version 10 can build these models. The full ver. is very expensive, but on several remote desktop services you can run the app only for a small fee but I dunno.. rather check... | Statistics and data mining software tools for dealing with large datasets | RHIPE is a great solution, and I would probably choose this one, if having this issue! but have you considered NCSS? As far as I know, the newest version 10 can build these models. The full ver. is ve | Statistics and data mining software tools for dealing with large datasets
RHIPE is a great solution, and I would probably choose this one, if having this issue! but have you considered NCSS? As far as I know, the newest version 10 can build these models. The full ver. is very expensive, but on several remote desktop se... | Statistics and data mining software tools for dealing with large datasets
RHIPE is a great solution, and I would probably choose this one, if having this issue! but have you considered NCSS? As far as I know, the newest version 10 can build these models. The full ver. is ve |
9,882 | What are alternatives to broken axes? | I am very wary of using logarithmic axes on bar graphs. The problem is that you have to choose a starting point of the axis, and this is almost always arbitrary. You can choose to make two bars have very different heights, or almost the same height, merely by changing the minimum value on the axis. These three graphs a... | What are alternatives to broken axes? | I am very wary of using logarithmic axes on bar graphs. The problem is that you have to choose a starting point of the axis, and this is almost always arbitrary. You can choose to make two bars have v | What are alternatives to broken axes?
I am very wary of using logarithmic axes on bar graphs. The problem is that you have to choose a starting point of the axis, and this is almost always arbitrary. You can choose to make two bars have very different heights, or almost the same height, merely by changing the minimum v... | What are alternatives to broken axes?
I am very wary of using logarithmic axes on bar graphs. The problem is that you have to choose a starting point of the axis, and this is almost always arbitrary. You can choose to make two bars have v |
9,883 | What are alternatives to broken axes? | Some additional ideas:
(1) You needn't confine yourself to a logarithmic transformation. Search this site for the "data-transformation" tag, for example. Some data lend themselves well to certain transformations like a root or a logit. (Such transformations--even logs--are usually to be avoided when publishing graph... | What are alternatives to broken axes? | Some additional ideas:
(1) You needn't confine yourself to a logarithmic transformation. Search this site for the "data-transformation" tag, for example. Some data lend themselves well to certain tr | What are alternatives to broken axes?
Some additional ideas:
(1) You needn't confine yourself to a logarithmic transformation. Search this site for the "data-transformation" tag, for example. Some data lend themselves well to certain transformations like a root or a logit. (Such transformations--even logs--are usual... | What are alternatives to broken axes?
Some additional ideas:
(1) You needn't confine yourself to a logarithmic transformation. Search this site for the "data-transformation" tag, for example. Some data lend themselves well to certain tr |
9,884 | What are alternatives to broken axes? | I'd separate the problem of log axes from the problem of bar charts.
Logarithmic axes IMHO are best suited for things that come or happen in multiples (... increased by a factor of 20 when treated with ...).
In that case, 1 = 10⁰ is the natural origin. There is a whole range of physical/chemical values which are in f... | What are alternatives to broken axes? | I'd separate the problem of log axes from the problem of bar charts.
Logarithmic axes IMHO are best suited for things that come or happen in multiples (... increased by a factor of 20 when treated wit | What are alternatives to broken axes?
I'd separate the problem of log axes from the problem of bar charts.
Logarithmic axes IMHO are best suited for things that come or happen in multiples (... increased by a factor of 20 when treated with ...).
In that case, 1 = 10⁰ is the natural origin. There is a whole range of p... | What are alternatives to broken axes?
I'd separate the problem of log axes from the problem of bar charts.
Logarithmic axes IMHO are best suited for things that come or happen in multiples (... increased by a factor of 20 when treated wit |
9,885 | What are alternatives to broken axes? | Maybe it can be classified as lattice, but I'll try; plot all the bars scaled to the highest in one panel and put another panel showing zoom on lower ones. I used this technique once in case of a scatterplot, and the result was quite nice. | What are alternatives to broken axes? | Maybe it can be classified as lattice, but I'll try; plot all the bars scaled to the highest in one panel and put another panel showing zoom on lower ones. I used this technique once in case of a scat | What are alternatives to broken axes?
Maybe it can be classified as lattice, but I'll try; plot all the bars scaled to the highest in one panel and put another panel showing zoom on lower ones. I used this technique once in case of a scatterplot, and the result was quite nice. | What are alternatives to broken axes?
Maybe it can be classified as lattice, but I'll try; plot all the bars scaled to the highest in one panel and put another panel showing zoom on lower ones. I used this technique once in case of a scat |
9,886 | What are alternatives to broken axes? | Two ideas that were alluded to, but not explicitly described in when I looked at the excellent answers and comments were that you are using a bar chart "in a manner inconsistent with labelling" and normalized/dimensionless data.
Plot type:
The star/spider/radar-style chart (link)(link) is often very good for comparin... | What are alternatives to broken axes? | Two ideas that were alluded to, but not explicitly described in when I looked at the excellent answers and comments were that you are using a bar chart "in a manner inconsistent with labelling" and | What are alternatives to broken axes?
Two ideas that were alluded to, but not explicitly described in when I looked at the excellent answers and comments were that you are using a bar chart "in a manner inconsistent with labelling" and normalized/dimensionless data.
Plot type:
The star/spider/radar-style chart (link)... | What are alternatives to broken axes?
Two ideas that were alluded to, but not explicitly described in when I looked at the excellent answers and comments were that you are using a bar chart "in a manner inconsistent with labelling" and |
9,887 | What are alternatives to broken axes? | The broken-axis solution works best when there is a clear break right across the plot and the ordinate is labeled so that the gap is obvious. The advantage of this is that the scale is preserved across the two sets of values. Panel plots with different scales may not convey the relative variation within the low and hig... | What are alternatives to broken axes? | The broken-axis solution works best when there is a clear break right across the plot and the ordinate is labeled so that the gap is obvious. The advantage of this is that the scale is preserved acros | What are alternatives to broken axes?
The broken-axis solution works best when there is a clear break right across the plot and the ordinate is labeled so that the gap is obvious. The advantage of this is that the scale is preserved across the two sets of values. Panel plots with different scales may not convey the rel... | What are alternatives to broken axes?
The broken-axis solution works best when there is a clear break right across the plot and the ordinate is labeled so that the gap is obvious. The advantage of this is that the scale is preserved acros |
9,888 | Why use Monte Carlo method instead of a simple grid? | I found chapter 1 and 2 of these lecture notes helpful when I asked the same question myself a few years ago. A short summary: A grid with $N$ points in 20 dimensional space will demand $N^{20}$ function evaluations. That is a lot. By using Monte Carlo simulation, we dodge the curse of dimensionality to some extent. Th... | Why use Monte Carlo method instead of a simple grid? | I found chapter 1 and 2 of these lecture notes helpful when I asked the same question myself a few years ago. A short summary: A grid with $N$ points in 20 dimensional space will demand $N^{20}$ funct | Why use Monte Carlo method instead of a simple grid?
I found chapter 1 and 2 of these lecture notes helpful when I asked the same question myself a few years ago. A short summary: A grid with $N$ points in 20 dimensional space will demand $N^{20}$ function evaluations. That is a lot. By using Monte Carlo simulation, we... | Why use Monte Carlo method instead of a simple grid?
I found chapter 1 and 2 of these lecture notes helpful when I asked the same question myself a few years ago. A short summary: A grid with $N$ points in 20 dimensional space will demand $N^{20}$ funct |
9,889 | Why use Monte Carlo method instead of a simple grid? | Sure it does; however it comes with much larger CPU usage. The problem increases especially in many dimensions, where grids become effectively unusable. | Why use Monte Carlo method instead of a simple grid? | Sure it does; however it comes with much larger CPU usage. The problem increases especially in many dimensions, where grids become effectively unusable. | Why use Monte Carlo method instead of a simple grid?
Sure it does; however it comes with much larger CPU usage. The problem increases especially in many dimensions, where grids become effectively unusable. | Why use Monte Carlo method instead of a simple grid?
Sure it does; however it comes with much larger CPU usage. The problem increases especially in many dimensions, where grids become effectively unusable. |
9,890 | Why use Monte Carlo method instead of a simple grid? | Previous comments are right in that simulation is easier to use in multidimensional problems. However, there are ways to address your concern - take a look at http://en.wikipedia.org/wiki/Halton_sequence and
http://en.wikipedia.org/wiki/Sparse_grid. | Why use Monte Carlo method instead of a simple grid? | Previous comments are right in that simulation is easier to use in multidimensional problems. However, there are ways to address your concern - take a look at http://en.wikipedia.org/wiki/Halton_seque | Why use Monte Carlo method instead of a simple grid?
Previous comments are right in that simulation is easier to use in multidimensional problems. However, there are ways to address your concern - take a look at http://en.wikipedia.org/wiki/Halton_sequence and
http://en.wikipedia.org/wiki/Sparse_grid. | Why use Monte Carlo method instead of a simple grid?
Previous comments are right in that simulation is easier to use in multidimensional problems. However, there are ways to address your concern - take a look at http://en.wikipedia.org/wiki/Halton_seque |
9,891 | Why use Monte Carlo method instead of a simple grid? | While one typically things of rejection sampling when considering Monte Carlo, Markov Chain Monte Carlo allows one to explore a multi-dimensional parameter space more efficiently than with a grid (or rejection sampling for that matter). How MCMC can be used for integration is clearly stated in this tutorial- http://bio... | Why use Monte Carlo method instead of a simple grid? | While one typically things of rejection sampling when considering Monte Carlo, Markov Chain Monte Carlo allows one to explore a multi-dimensional parameter space more efficiently than with a grid (or | Why use Monte Carlo method instead of a simple grid?
While one typically things of rejection sampling when considering Monte Carlo, Markov Chain Monte Carlo allows one to explore a multi-dimensional parameter space more efficiently than with a grid (or rejection sampling for that matter). How MCMC can be used for integ... | Why use Monte Carlo method instead of a simple grid?
While one typically things of rejection sampling when considering Monte Carlo, Markov Chain Monte Carlo allows one to explore a multi-dimensional parameter space more efficiently than with a grid (or |
9,892 | Why use Monte Carlo method instead of a simple grid? | Two things -
Faster convergence by avoiding curse of dimensionality. Because most points in a grid lie on the same hyper plane without contributing significantly extra information. Random points fill the N-dimensional space evenly. LDS is even better.
Sometimes for Monte carlo methods we need statistically random poi... | Why use Monte Carlo method instead of a simple grid? | Two things -
Faster convergence by avoiding curse of dimensionality. Because most points in a grid lie on the same hyper plane without contributing significantly extra information. Random points fil | Why use Monte Carlo method instead of a simple grid?
Two things -
Faster convergence by avoiding curse of dimensionality. Because most points in a grid lie on the same hyper plane without contributing significantly extra information. Random points fill the N-dimensional space evenly. LDS is even better.
Sometimes for... | Why use Monte Carlo method instead of a simple grid?
Two things -
Faster convergence by avoiding curse of dimensionality. Because most points in a grid lie on the same hyper plane without contributing significantly extra information. Random points fil |
9,893 | How to calculate the p-value of parameters for ARIMA model in R? | The "t value" is the ratio of the coefficient to the standard error. The degrees of freedom (ndf) would be the number of observations minus the max order of difference in the model minus the number of estimated coefficients. The "F value " would be the square of the "t value" In order to exactly compute probability you... | How to calculate the p-value of parameters for ARIMA model in R? | The "t value" is the ratio of the coefficient to the standard error. The degrees of freedom (ndf) would be the number of observations minus the max order of difference in the model minus the number of | How to calculate the p-value of parameters for ARIMA model in R?
The "t value" is the ratio of the coefficient to the standard error. The degrees of freedom (ndf) would be the number of observations minus the max order of difference in the model minus the number of estimated coefficients. The "F value " would be the sq... | How to calculate the p-value of parameters for ARIMA model in R?
The "t value" is the ratio of the coefficient to the standard error. The degrees of freedom (ndf) would be the number of observations minus the max order of difference in the model minus the number of |
9,894 | How to calculate the p-value of parameters for ARIMA model in R? | Since arima uses maximum likelihood for estimation, the coefficients are assymptoticaly normal. Hence divide coefficients by their standard errors to get the z-statistics and then calculate p-values. Here is the example with in R with the first example from arima help page:
> aa <- arima(lh, order = c(1,0,0))
> aa
Ca... | How to calculate the p-value of parameters for ARIMA model in R? | Since arima uses maximum likelihood for estimation, the coefficients are assymptoticaly normal. Hence divide coefficients by their standard errors to get the z-statistics and then calculate p-values. | How to calculate the p-value of parameters for ARIMA model in R?
Since arima uses maximum likelihood for estimation, the coefficients are assymptoticaly normal. Hence divide coefficients by their standard errors to get the z-statistics and then calculate p-values. Here is the example with in R with the first example fr... | How to calculate the p-value of parameters for ARIMA model in R?
Since arima uses maximum likelihood for estimation, the coefficients are assymptoticaly normal. Hence divide coefficients by their standard errors to get the z-statistics and then calculate p-values. |
9,895 | How to calculate the p-value of parameters for ARIMA model in R? | You could also use coeftest from lmtestpackage:
> aa <- arima(lh, order = c(1,0,0))
> coeftest(aa)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
ar1 0.57393 0.11614 4.9417 7.743e-07 ***
intercept 2.41329 0.14661 16.4602 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’... | How to calculate the p-value of parameters for ARIMA model in R? | You could also use coeftest from lmtestpackage:
> aa <- arima(lh, order = c(1,0,0))
> coeftest(aa)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
ar1 0.57393 | How to calculate the p-value of parameters for ARIMA model in R?
You could also use coeftest from lmtestpackage:
> aa <- arima(lh, order = c(1,0,0))
> coeftest(aa)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
ar1 0.57393 0.11614 4.9417 7.743e-07 ***
intercept 2.41329 0.1... | How to calculate the p-value of parameters for ARIMA model in R?
You could also use coeftest from lmtestpackage:
> aa <- arima(lh, order = c(1,0,0))
> coeftest(aa)
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
ar1 0.57393 |
9,896 | Inference after using Lasso for variable selection | To add to the previous responses. You should definitely check out the recent work by Tibshirani and colleagues. They have developed a rigorous framework for inferring selection-corrected p-values and confidence intervals for lasso-type methods and also provide an R-package.
See:
Lee, Jason D., et al. "Exact post-select... | Inference after using Lasso for variable selection | To add to the previous responses. You should definitely check out the recent work by Tibshirani and colleagues. They have developed a rigorous framework for inferring selection-corrected p-values and | Inference after using Lasso for variable selection
To add to the previous responses. You should definitely check out the recent work by Tibshirani and colleagues. They have developed a rigorous framework for inferring selection-corrected p-values and confidence intervals for lasso-type methods and also provide an R-pac... | Inference after using Lasso for variable selection
To add to the previous responses. You should definitely check out the recent work by Tibshirani and colleagues. They have developed a rigorous framework for inferring selection-corrected p-values and |
9,897 | Inference after using Lasso for variable selection | Generally, refitting using no penalty after having done variable selection via the Lasso is considered "cheating" since you have already looked at the data and the resulting p-values and confidence intervals are not valid in the usual sense.
This very recent paper looks at exactly what you want to do, and explains co... | Inference after using Lasso for variable selection | Generally, refitting using no penalty after having done variable selection via the Lasso is considered "cheating" since you have already looked at the data and the resulting p-values and confidence in | Inference after using Lasso for variable selection
Generally, refitting using no penalty after having done variable selection via the Lasso is considered "cheating" since you have already looked at the data and the resulting p-values and confidence intervals are not valid in the usual sense.
This very recent paper lo... | Inference after using Lasso for variable selection
Generally, refitting using no penalty after having done variable selection via the Lasso is considered "cheating" since you have already looked at the data and the resulting p-values and confidence in |
9,898 | Inference after using Lasso for variable selection | I wanted to add some papers from the orthogonal/double machine learning literature that is becoming popular in the Applied Econometrics literature.
Belloni, Alexandre, Victor Chernozhukov, and Christian Hansen. "Inference on treatment effects after selection among high-dimensional controls." The Review of Economic Stu... | Inference after using Lasso for variable selection | I wanted to add some papers from the orthogonal/double machine learning literature that is becoming popular in the Applied Econometrics literature.
Belloni, Alexandre, Victor Chernozhukov, and Christ | Inference after using Lasso for variable selection
I wanted to add some papers from the orthogonal/double machine learning literature that is becoming popular in the Applied Econometrics literature.
Belloni, Alexandre, Victor Chernozhukov, and Christian Hansen. "Inference on treatment effects after selection among hig... | Inference after using Lasso for variable selection
I wanted to add some papers from the orthogonal/double machine learning literature that is becoming popular in the Applied Econometrics literature.
Belloni, Alexandre, Victor Chernozhukov, and Christ |
9,899 | Difference between regression analysis and analysis of variance? | Suppose your data set consists of a set $(x_i,y_i)$ for $i=1,\ldots,n$ and you want to look at the dependence of $y$ on $x$.
Suppose you find the values $\hat\alpha$ and $\hat\beta$ of $\alpha$ and $\beta$ that minimize the residual sum of squares
$$
\sum_{i=1}^n (y_i - (\alpha+\beta x_i))^2.
$$
Then you take $\hat y =... | Difference between regression analysis and analysis of variance? | Suppose your data set consists of a set $(x_i,y_i)$ for $i=1,\ldots,n$ and you want to look at the dependence of $y$ on $x$.
Suppose you find the values $\hat\alpha$ and $\hat\beta$ of $\alpha$ and $\ | Difference between regression analysis and analysis of variance?
Suppose your data set consists of a set $(x_i,y_i)$ for $i=1,\ldots,n$ and you want to look at the dependence of $y$ on $x$.
Suppose you find the values $\hat\alpha$ and $\hat\beta$ of $\alpha$ and $\beta$ that minimize the residual sum of squares
$$
\sum... | Difference between regression analysis and analysis of variance?
Suppose your data set consists of a set $(x_i,y_i)$ for $i=1,\ldots,n$ and you want to look at the dependence of $y$ on $x$.
Suppose you find the values $\hat\alpha$ and $\hat\beta$ of $\alpha$ and $\ |
9,900 | Difference between regression analysis and analysis of variance? | The main difference is the response variable. While logistic regression deals with a binary response in linear regression analysis and also nonlinear regression the response variable is continuous. You have a variable(s) (aka covariate(s)) that have a functional relationship to the continuous response variable. In th... | Difference between regression analysis and analysis of variance? | The main difference is the response variable. While logistic regression deals with a binary response in linear regression analysis and also nonlinear regression the response variable is continuous. Y | Difference between regression analysis and analysis of variance?
The main difference is the response variable. While logistic regression deals with a binary response in linear regression analysis and also nonlinear regression the response variable is continuous. You have a variable(s) (aka covariate(s)) that have a fu... | Difference between regression analysis and analysis of variance?
The main difference is the response variable. While logistic regression deals with a binary response in linear regression analysis and also nonlinear regression the response variable is continuous. Y |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.