If you mean the lines above, I am simply invented some data to work with. The details are not important but I will explain them briefly. I made a data frame with a column named X with values that run from 1 to 100. The column named Y is calculated as
Y = 2.3 * X + 6.97
Instead of writing X, I wrote 1:100 again, which is how I defined X. To prevent the relationship being perfectly linear, leaving no residuals in the model, I added some Gaussian noise using the rnorm() function. Writing
rnorm(100) produces 100 values randomly drawn from a normal distribution that has a mean of zero and a standard deviation of one. It is equivalent to writing
rnorm(100, mean = 0, sd = 1). All of that together produces a Y column that is linearly related to the X column but has some noise around the linear relationship. In the usual case, you get your data from some measurements and do not have to bother to invent a data frame.
DF[23, 2] <- 0
substitutes a zero for the number in the 23rd row, 2nd column, that it, the 23 value of Y. I wanted to put in one value that would be very far from the linearly derived values so it would fall outside of the 3-sigma limits of the residuals.