Editing Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 7

* correlations
** cor(): works with two variables, or with more!
** cor(method="spearman") is useful if you have non-normally distributed data because it is simply rank correlations)
* fitting a linear model with one variable: lm()
** module formulae, which we've already seen! 
** looking at model objects: summary(); m$<tab> or names(m)
*** m$fitted.values; m$residuals
*** also functions: coefficients(m) (or coef), predict(m), residuals(m) (or resid); confint(m)
*** we can also do these by hand:
**** residuals: mtcars$mpg - m$fitted.values 
**** confint: est + 1.96 * c(-1, 1) * se
* plotting residuals:
** hist(residuals(m))
** plot against our x: plot(mtcars$hp, residuals(m)
** QQ-plots with qqnorm(residuals(m))
** doing a plot with ggplot just involves making a dataset: d.fig <- data.frame(hp=mtcars$hp, resids=residuals(m))
* adding controls: just make our formula more complex
** update.formula()
** or just a write a new one
** adding logical variables: no problem!
** adding categorical variables: no problem! (I'll explain this later!)
* generating nice regression plots:
** one of many options: stargazer(m1, m2, type="text") or type="html"
* interpreting linear models with anova() — i'm not going to walk through the details but the important thing to keep in mind is that although the statistics are different, the p-values are identical!