Regression Inference in R

here we will discuss how to perform standard inference in regression models.

Setup

We continue the example we started in R_Regression#A first example and which is replicated here:

    # This is my first R regression!
    setwd("T:/ECLR/R/FirstSteps")              # This sets the working directory
    mydata <- read.csv("mroz.csv")  # Opens mroz.csv from working directory
     
    # Now convert variables with "." to num with NA
    mydata$wage <- as.numeric(as.character(mydata$wage))
    mydata$lwage <- as.numeric(as.character(mydata$lwage))

Before we run our initial regression model we shall restrict the dataframe mydata to those data that do not have missing wage information, using the following subset command:

   mydata <- subset(mydata, wage!="NA")  # select non NA data

Now we can run our initial regression:

    # Run a regression
    reg_ex1 <- lm(lwage~exper+log(huswage),data=mydata)
    reg_ex1_sm <- summary(reg_ex1)

We will introduce inference in this model.

t-tests

We use t-tests to test simple coefficient restrictions on regression coefficients.

F-tests

F-tests are used to test multiple coefficient restrictions on regression coefficients.

Let's say we are interested whether two additional variables age and educ should be included into the model. As a good econometrics student, or even master, you know that to calculate a F-test you need residual sum of squares from a restricted model