Multiple regression. The nbaallelo_slr. csv data base contains information on 126315 NBA games between 1947 and 2015. The columns report the points made by one team, the Elo rating of that team coming into the game, the Elo rating of the team after the game, and the points made by the opposing team. Load the data set into a data frame.
Use the ols function to perform a multiple linear regression with pts as the response variable and opp_pts and elo_i as the predictor variables.
Create an analysis of variance table using the results of the multiple regression.
Result must be:
If the Elo rating of the team after the game, elo_n, is used instead of elo_i, the output is:
sum_sq df F PR(>F)
elo_n 1.481593e+06 1.0 11335.413055 0.0
opp_pts 1.080327e+07 1.0 82653.915172 0.0
Residual 1.650946e+07 126311.0 NaN NaN
Default template for coding:
# Import the necessary modules
nba = # Code to read in nbaallelo_slr. csv
# Perform multiple linear regression on pts, elo_i, and opp_pts
results = # Code to perform multiple regression using statsmodels ols
# Create an analysis of variance table
aov_table = # Code to create ANOVA table
# Print the analysis of variance table
print(aov_table)