## 10.1 An Introduction to Inference for One Variance (Assuming a Normally Distributed Population)

I discuss confidence intervals and hypothesis tests for a variance when sampling from a normally distributed population. I discuss the logic behind the procedures, discuss some characteristics of the sampling distribution of the sample variance, and give the appropriate formulas. I briefly discuss the results for an example problem, but I don’t work through any … Read more

## 10.2 Inference for One Variance: An Example of a Confidence Interval and a Hypothesis Test

I work through an example of a confidence interval and a hypothesis test for a variance (using chi-square methods that are appropriate when sampling from a normally distributed population). The cereal data is from a sample of 15 bags of cereal I collected and weighed. I have a video introduction to these procedures available at: … Read more

## 10.3 Deriving a Confidence Interval for a Variance (Assuming a Normally Distributed Population)

I derive the appropriate formula for a confidence interval for a population variance (when we are sampling from a normally distributed population). I do not do any calculations or look at any examples in this video, I simply derive the appropriate confidence interval formula. I work through an example at http://youtu.be/tsLGbpu_NPk

## 10.4 Hypothesis Tests for Equality of Two Variances

A discussion of the sampling distribution of the sample variance. I begin by discussing the sampling distribution of the sample variance when sampling from a normally distributed population, and then illustrate, through simulation, the sampling distribution of the sample variance for a few other distributions.

## 10.5 Inference for a Variance: How Robust are These Procedures?

A discussion of the effect of violations of the normality assumption on confidence intervals for a variance. The effects of different violations of the normality assumption are investigated through simulation. The quick summary: These procedures are very sensitive to violations of the normality assumption, and often perform very poorly when the normality assumption is violated.

## 10.6 An Introduction to Inference for the Ratio of Two Variances

An introduction to confidence intervals and hypothesis tests for the ratio of two population variances (using F procedures based on the assumption of normally distributed populations). I introduce the methods, and take a quick look at an example and discuss the results. I don’t work through any calculations in this video, but I have another … Read more

## 10.7 Inference for Two Variances: An Example of a Confidence Interval and a Hypothesis Test

I work through an example of a confidence interval and a hypothesis test for the ratio of population variances, using F procedures that are based on the assumption of normally distributed populations. The example in this video involves tail lengths of male and female lizards. The summary statistics and distributions of the tail lengths were … Read more

## 10.8 Deriving a Confidence Interval for the Ratio of Two Variances

I derive the appropriate formula for a confidence interval for the ratio of two population variances (when we are sampling from normally distributed populations). I do not do any calculations or look at any examples in this video, I simply derive the appropriate confidence interval formula.

## 10.9 The Sampling Distribution of the Ratio of Sample Variances

A discussion of the sampling distribution of the ratio of sample variances. I begin by discussing the sampling distribution of the ratio of sample variances when sampling from normally distributed populations, and then illustrate, through simulation, the sampling distribution of the ratio of sample variances for two other distributions.

## 10.10 Inference for the Ratio of Variances: How Robust are These Procedures?

A discussion of the effect of violations of the normality assumption on confidence intervals for the ratio of variances. The effects of different violations of the normality assumption are investigated through simulation. The quick summary: These procedures are very sensitive to violations of the normality assumption, and often perform very poorly when the normality assumption … Read more