JB 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.
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
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
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.
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.
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
A brief introduction to one-way Analysis of Variance (ANOVA). I discuss the null and alternative hypotheses and conclusions of the test. I also illustrate the difference between and within group variance using a visual example. In other related videos, I discuss the ANOVA formulas in detail and work through a real-world example.
I look at the calculation formulas and the meaning of the terms in the one-way ANOVA table. In other related videos, I have a brief introduction to one-way ANOVA, and work through a real world example.
The example and data used here is based on information and summary statistics from: Grattan-Miscio, K. and Vogel-Sprott, M. (2005). Alcohol, intentional control, and inappropriate behavior: Regulation by caffeine or an incentive. Experimental and Clinical Psychopharmacology, 13:48-55.
Finding significance in a one-way ANOVA F test means there is strong evidence that not all population means are equal. Which are different? We can explore that with a multiple comparison procedure. Here we look at the simplest one: Fisher’s LSD confidence intervals.