Linked Questions

1
vote
1answer
1k views

Covariance Matrix vs. Pairwise Covariance Matrix?

I found this equation here to calculate a covariance matrix of any number of variables using matrix algebra. $$\frac1{N} (X - 1\bar{x})^T(X - 1\bar{x}^T) $$ For a given matrix $X$ with $N$ samples. ...
0
votes
1answer
38 views

Confused when to use Population vs Sample standard deviation in engineering testing

When I run an test for something (say 10 trials) and want to find the standard deviation of all 10 trials, I am getting confused if I should use the sample or population standard deviation. My initial ...
2
votes
2answers
43 views

“… because sample mean gets different values from sample to sample and it is a random variable with mean $\mu$ and variance $\frac{\sigma^2}{n}$.”

This answer by user "sevenkul" says the following: The sample mean $\overline{X}$ also deviates from $\mu$ with variance $\frac{\sigma^2}{n}$ because sample mean gets different values from ...
1
vote
0answers
23 views

Different sample covariance formulae (conventions) [duplicate]

Page 358 of Introduction to probability, second edition, by Blitzstein and Hwang, defines the sample covariance as $$r = \dfrac{1}{n} \sum_{i = 1}^n (x_i - \bar{x})(y_i - \bar{y}),$$ where $\bar{x} = \...
0
votes
0answers
16 views

Degree of freedom [duplicate]

Why do we use degree of freedom while dealing with samples(e.g. while calculating t statistic and sample variance, we divide by $ n-1 $ degree of freedom, but while calculating z score and population ...
1
vote
0answers
19 views

Is there ever a right time to not use Bessel's Correction? [duplicate]

I can not find a clear explanation for when NOT to use Bessel's correction and use N instead of N-1. As I understand it, Bessel's correction would apply to things such as clinical trials, sampling ...
1
vote
0answers
29 views

Explanation for Bessel's correction [duplicate]

In textbooks and online the explanation for Bessel's correction is that the sample points are closer to the sample mean than the population mean.This is true for sampling with replacement as well. So, ...
0
votes
0answers
17 views

Show estimator is unbiased [duplicate]

Consider the estimator of the variance given by the formula: $(S')^2 = \frac{1}{n} \sum_{i=1}^{n}(Y_i − µ)^2$ Is this a biased or unbiased estimator? I'm not sure if it is possible to prove ...
1
vote
1answer
827 views

The derivation of standard deviation [duplicate]

So, if there are $N$ data points in my sample space of any randomly distributed variable $X$. The standard deviation, $\sigma$, (from my understanding) is the root mean squared of the error (from the ...
4
votes
2answers
230 views

Sample Variance and Dividing by $n-1$

In this video... https://www.youtube.com/watch?v=sHRBg6BhKjI ...and in many others, the explanation for why when calculating the sample variance we divide by $n-1$ instead of by $n$ is the following:...
0
votes
1answer
115 views

Why Standard deviation formula has n-1 in the denominator but Variance only n? [duplicate]

If is true that SQRT(Variance) = SD then one cannot have n-1 in the denominator and the other not but should be same? SD formula: Variance formula:
3
votes
1answer
143 views

Why is $\frac{\sum^n_{i=1}(X_i-\bar{X})^2}{\sigma^2}$chi-square distributed with $n-1$ degrees of freedom?

On Wikipedia, it says If $X_{i};i=1,\ldots ,n$ are independent normal $(\mu ,\sigma ^{2})$ random variables, the statistic $$\frac{\sum\limits^n_{i=1}(X_i-\bar{X})^2}{\sigma^2}$$ ...
0
votes
0answers
132 views

What are biased and inefficient estimators?

I’m studying statistics from Schaum’s Outline, which gives the following: ...
8
votes
3answers
2k views

When estimating variance, why do unbiased estimators divide by n-1 yet maximum likelihood estimates divide by n?

I am totally confused: On the one hand you can read all kinds of explanations why you have to divide by n-1 to get an unbiased estimator for the (unknown) population variance (degrees of freedom, not ...
2
votes
1answer
95 views

Why do I need to rounding x and y values to the nearest 0.5 when manually calculating correlation?

I'm trying to calculate correlation using a formula in Statistics 4th Edition by Freedman: r = average of (x in standard units) * (y in standard units) If I try this out ... ...
0
votes
1answer
228 views

Estimates of variance from an iid sample [duplicate]

There are two kinds of estimates of variance from an iid sample $X1, \dots, X_n$ $1/n * \sum_i (X_i - \bar{X})^2$, which is MLE $1/(n-1) * \sum_i (X_i - \bar{X})^2$, which is unbiased. The unbiased ...
1
vote
1answer
55 views

Trouble understanding some r code

I have this code here (it's out of a book called "An Introduction to Bootstrap Methods with Applications to R") We are working with the estimator: $S_n^2=\frac{\sum_{i=1}^{n}(X_i-X_b)^2}{n}$ where $...
8
votes
1answer
157 views

Alternate (?) definition of sample variance [duplicate]

The variance of a sample can be defined as $$s^2 = \frac{1}{2}\frac{1}{n(n-1)}\sum_{i}\sum_{j\ne i}\left(x_i - x_j\right)^2$$ Apart from the factor of $1/2$, this can be paraphrased verbally as ...
3
votes
1answer
534 views

Dividing by degrees of freedom [duplicate]

When estimating parameters such as (I don't care about this specific instance particularly) Variance of a random variable X, one usually adopts Bessel's correction, i.e. using the formula $\hat{Var}{(...
4
votes
1answer
229 views

An unbiased estimator of σ³

As it was suggested in the linked answer, $s_n = \sqrt{\frac{\sum_{i = 1}^n (x_i - \bar{x})^2}{n - 1}}$ is not an unbiased estimator of $\sigma$. I suspect neither $s_n^3 = \sqrt{\frac{\sum_{i = 1}^n ...
0
votes
0answers
107 views

Standardizing dependent and independent variable formula for ridge regression

For ridge I now that I have to standardize the the predictors before applying ridge regression. I want to standardize them like in the glmnet function. And I don't know wich formula is used for ...
1
vote
0answers
71 views

What is the story of “$n-1$” in the denominator of an estimated variance? [duplicate]

In the book "Numerical Recipes in C" there is said that there is a long story about why the denominator of variance is $N-1$ instead of $N$. If you have never heard that story, you may consult any ...
73
votes
5answers
39k views

How exactly did statisticians agree to using (n-1) as the unbiased estimator for population variance without simulation?

The formula for computing variance has $(n-1)$ in the denominator: $s^2 = \frac{\sum_{i=1}^N (x_i - \bar{x})^2}{n-1}$ I've always wondered why. However, reading and watching a few good videos about "...
0
votes
1answer
148 views

A/B test and SRS

I was reading about Simple Random Sampling (SRS) from this lecture notes. It says that SRS, which actually is a sampling without replacement makes $x_i$ not independent. Then I thought about A/B ...
0
votes
0answers
140 views

Which are the expressions for the variance and standard deviation of a sample?

I have checked several offline and online resources, and they are conflictive. Some define the sample variance as $$s_n^2 = \frac{\sum_{i}^{n}(x_i-\overline{x})^2}{n}$$ That is just the second ...
0
votes
0answers
137 views

Intuition behind Besel's correction for an unbiased estimate of the variance [duplicate]

We have two estimators for samples dispersion: $\frac{1}{n}\sum_1^n (X_i - \overline{X_n})^2$ and $\frac{1}{n - 1}\sum_1^n (X_i - \overline{X_n})^2$. The second estimator unbiased - it has ...
1
vote
1answer
1k views

Variance estimator of Bernoulli RV (in CI)

Assume n samples from Bernoulli distribution with unknown parameter p, i. e. $x_{1},....,x_{n}\underset{iid}{\sim}Ber\left(p\right)$ It is known that the confidence interval is given by: $CI=\hat{...
15
votes
3answers
4k views

Why does one have to use REML (instead of ML) for choosing among nested var-covar models?

Various descriptions on model selection on random effects of Linear Mixed Models instruct to use REML. I know difference between REML and ML at some level, but I don't understand why REML should be ...
1
vote
1answer
113 views

Statistical analysis for replication in very small experimental datasets

A colleague does replication on a quite coslty experiments. There are four different conditions, each one duplicated. The outcome with $4\times 2 = 8$ points is illustrated below: The analysis is ...
4
votes
1answer
6k views

Estimating the covariance matrix in linear discriminant analysis

I was trying to derive the equations from page 109 in "elements of statistical learning" (image below) To be honest, I am not sure how the covariance $\Sigma$ is estimated (the third bullet point in ...

15 30 50 per page