List questions from statistical-inference

Published on 30 Mar 2026 - 16:05

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integral support for a density function

Published on 21 Jan 2014 - 12:59

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Justifying the Normal Approx to the Binomial Distribution through MGFs

Published on 29 Mar 2026 - 23:58

129

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$QQ$-plot - Why do we choose the empirical distribution $F_n(x) = \frac {\#\{y \in S \mid y \le x\}} n$, $S$ is sample, for comparison with normal?

Published on 23 Jan 2014 - 19:35

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Standardizing Sum of Uniform Distributions

Published on 30 Mar 2026 - 0:04

306

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$Z$~$N(0,1)$ relating to the chi square distribution

Published on 30 Mar 2026 - 0:04

5.4k

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Showing that the MLE doesn't exist for $e^{\theta-x}$

Published on 29 Mar 2026 - 19:27

422

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Distribution of the sample variance

Published on 26 Jan 2014 - 3:58

59

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Let $Y=B_1 + B_2 +...+B_n$ where the R.V. $B_1...B_n$ are i.i.d. and each has a Bern(p) distribution.

Published on 30 Mar 2026 - 0:05

524

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Most powerful test for discrete variable

Published on 30 Mar 2026 - 22:52

207

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Show that the co-variance is zero between $\bar{Y}$ and $Y_i - \bar{Y}$

Published on 26 Jan 2014 - 18:39

53

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Adjusting regression for small sample bias

Published on 03 Apr 2026 - 7:27

228

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Show that as $d$ goes to $\infty$, a standardized version of $X$ has the STD Normal Dist

Published on 30 Mar 2026 - 1:51

77

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Multicollinearity Check in statistics

Published on 02 Feb 2014 - 7:40

232

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What is the relationship between Multicollinearity and Standard error?

Published on 02 Feb 2014 - 12:48

List Question

Sampling Distributions: Sample size of 1 vs Sample Size of m

integral support for a density function

Justifying the Normal Approx to the Binomial Distribution through MGFs

$QQ$-plot - Why do we choose the empirical distribution $F_n(x) = \frac {\#\{y \in S \mid y \le x\}} n$, $S$ is sample, for comparison with normal?

Standardizing Sum of Uniform Distributions

$Z$~$N(0,1)$ relating to the chi square distribution

Showing that the MLE doesn't exist for $e^{\theta-x}$

Distribution of the sample variance

Let $Y=B_1 + B_2 +...+B_n$ where the R.V. $B_1...B_n$ are i.i.d. and each has a Bern(p) distribution.

Most powerful test for discrete variable

Show that the co-variance is zero between $\bar{Y}$ and $Y_i - \bar{Y}$

Adjusting regression for small sample bias

Show that as $d$ goes to $\infty$, a standardized version of $X$ has the STD Normal Dist

Multicollinearity Check in statistics

What is the relationship between Multicollinearity and Standard error?

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