# How to interpret Linear Regression Summary with Dummy Variable R

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.
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