Real life example for linear regression: different training regimens and player performance

 


Data scientists for professional sports teams often use linear regression to measure the effect that different training regimens have on player performance.

For example, data scientists in the NBA might analyze how different amounts of weekly yoga sessions and weightlifting sessions affect the number of points a player scores. They might fit a multiple linear regression model using yoga sessions and weightlifting sessions as the predictor variables and total points scored as the response variable. The regression model would take the following form:

points scored = β0 + β1(yoga sessions) + β2(weightlifting sessions)

The coefficient β0 would represent the expected points scored for a player who participates in zero yoga sessions and zero weightlifting sessions.

The coefficient β1 would represent the average change in points scored when weekly yoga sessions is increased by one, assuming the number of weekly weightlifting sessions remains unchanged.

The coefficient β2 would represent the average change in points scored when weekly weightlifting sessions is increased by one, assuming the number of weekly yoga sessions remains unchanged.

Depending on the values of β1 and β2, the data scientists may recommend that a player participates in more or less weekly yoga and weightlifting sessions in order to maximize their points scored.

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