I am trying to run a linear regression with a Dummy Variable on NBA statistics with NBA Salaries as the y variable, and different performance statistics as the x variables. I have already ran a linear regression and found PPG and RPG are the only 2 significant results in determining player salary. However looking at my graphs, there only seems to be correlation between increased PPG and higher salary after a player scores over 10 PPG, before this there is just a large chaotic cluster of data points.
To look at if there is differing determinants before and after the point of 10 PPG, i used a dummy variable Called PPGDummy which = 1 when players PPG is bigger than 10 and =0 when it is less than 10.
I have run the regression for this but have no clue how to interpret the results from this regression?
Here is my code for the regression:
lm2 <- lm(log(Salary) ~ PPGDummy + PPG + APG + RPG + SPG + BPG + FG + THREEPG + FT + Age, data = Econ_III_Data_Set)
Here is the section of results that it produces:
Estimate Std. Error t value Pr(>|t|) (Intercept) 15.239385 0.555064 27.455 < 2e-16 *** PPGDummy1 0.317359 0.110765 2.865 0.00431 ** PPG 0.052420 0.011527 4.548 6.55e-06 *** APG 0.038682 0.025772 1.501 0.13390 RPG 0.084863 0.021474 3.952 8.67e-05 *** SPG 0.143407 0.111059 1.291 0.19710 BPG -0.125260 0.118076 -1.061 0.28919 FG 0.267770 0.669408 0.400 0.68929 THREEPG -0.029914 0.328201 -0.091 0.92741 FT -0.556308 0.368705 -1.509 0.13187 Age -0.009873 0.011326 -0.872 0.38371 ``` Unsure how to interpret the P value and estimate fr the PPGDummy variable in the results. Thanks in Advance for your help