Activity #6: Intro to Linear Regression: First Base Hitting Stats and Salary Predictions

# Read in data
firstbase = read.csv("firstbasestats.csv")
str(firstbase)

summary(firstbase)

# Linear Regression (one variable)
model1 = lm(Payroll.Salary2023 ~ RBI, data=firstbase)
#RBI is out independent variable(feature,explanatory variable)
#Payroll.Salary is our dependent variable (target, response variable)
summary(model1)

Since the absolute value of t is greater than2, the RBI independent variable is significant at 5% significance level. You may use p value as well, in this case<=0.05.

Either|t|>=2 or pr=<0.05 the corresponding feature is significant at a 5% significance level.

For each additional RBI a 1stBase player get $ 157088 more.

RBI explains 36.57% of the model

SSE = sum(model1$residuals^2)
SSE

# Linear Regression (two variables)
model2 = lm(Payroll.Salary2023 ~ AVG + RBI, data=firstbase)
summary(model2)


AVG is not significant at a 5% significance level
RBI is significance at 5% significance level
Adjusted R Squared went up !

The model is significant at 1% level

# Sum of Squared Errors
SSE = sum(model2$residuals^2)

Error: object 'model2' not found

# Linear Regression (all variables)
model3 = lm(Payroll.Salary2023 ~ HR + RBI + AVG + OBP+ OPS, data=firstbase)
summary(model3)