Regression Slope - R Programming

A regression workflow starts by estimating how much the response changes with one predictor.

Program

Play the script to adjust the last score and watch the fitted slope change.

regression_slope.R

bonus <- 
hours <- c(1, 2, 3, 4)
score <- c(52, 61, 70, 79) + c(0, 0, 0, bonus)
fit <- lm(score ~ hours)
slope <- round(coef(fit)[["hours"]], 1)
label <- paste("slope", slope, sep = ":")
cat(label, "\n", sep = "")

bonus <- 
hours <- c(1, 2, 3, 4)
score <- c(52, 61, 70, 79) + c(0, 0, 0, bonus)
fit <- lm(score ~ hours)
slope <- round(coef(fit)[["hours"]], 1)
label <- paste("slope", slope, sep = ":")
cat(label, "\n", sep = "")

bonus <- 
hours <- c(1, 2, 3, 4)
score <- c(52, 61, 70, 79) + c(0, 0, 0, bonus)
fit <- lm(score ~ hours)
slope <- round(coef(fit)[["hours"]], 1)
label <- paste("slope", slope, sep = ":")
cat(label, "\n", sep = "")

linear model `lm(score ~ hours)` estimates an intercept and a slope.

coefficient `coef(fit)[["hours"]]` reads the predictor slope.

sensitivity Changing one observation can change the fitted trend.