Linear Regression | Course Paper

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Introduction to linear regression

1.Choose another traditional variable from mlb11 that you think might be a good predictor

of runs. Produce a scatterplot of the two variables and fit a linear model. At a glance, does there

seem to be a linear relationship?

CODE:

predict_runs <- lm(runs ~ hits, data = mlb11)

predict_runs

plot(mlb11$runs ~ mlb11$hits)

abline(predict_runs)

plot(predict_runs$residuals ~ mlb11$hits)

abline(h = 0, lty = 3)

OUTPUT:

> predict_runs <- lm(runs ~ hits, data = mlb11)

> predict_runs

Call:

lm(formula = runs ~ hits, data = mlb11)

Coefficients:

(Intercept) hits

-375.5600 0.7589

> plot(mlb11$runs ~ mlb11$hits)

> abline(predict_runs)

SCREENSHOT:

INFERENCE:

Yes, there seems to be a linear relationship between the two.

2. How does this relationship compare to the relationship between runs and at_bats? Use the

R2 values from the two model summaries to compare. Does your variable seem to

predict runs better than at_bats? How can you tell?

CODE:

m1 <– lm(runs ~ at_bats, data = mlb11)