In this screencast we'll demonstrate frequency tables and proportion tables for analyzing the relationship between two factors (qualitative or categorical variables).

Here we'll build on the last video by showing how a frequency table can be used to calculate a \(Chi^2\) test for independence between two variables.

Here we will consider a couple of ways to visualize the relationship between two factors.

Here we'll begin with visualizing the relationship between two continuous variables.

Now we'll introduce the function cor() for correlations, and show how to derive both Pearson and Spearman correlations, and how to specify how missing values should be treated.

Now that we've explored correlation we'll take a brief look at using the "linear model" function lm() to fit regressions. We'll also look at how we can examine the residuals (stored inside the "lm object" created by lm()) to see if their distribution is approximagely normal. Note that we'll we explore regression in much more detail in a later chapter.

Here we'll briefly introduce the built-in regression diagnsitcs available by calling plot() on an lm object. The residual plot and the normal q-q plots are the most helpful.

We'll wind up this chapter by demonstraing comparison of group means using t-tests and one-way tests of means, and ANOVA.