As the titles states, I would like to compare two coefficients in my multiple regression model but I'm not quite sure how.
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.9483 29.7439 2.318 0.024493 *
Shots.PG -0.5074 1.4696 -0.345 0.731334
Shots.OT.PG 7.4992 3.1410 2.388 0.020707 *
Dribbles.PG 0.6081 0.8121 0.749 0.457401
Fouled.PG -0.9856 0.8783 -1.122 0.267031
Offsides.PG 1.0520 3.0728 0.342 0.733477
Tackles.PG 0.2705 0.6721 0.402 0.689016
Fouls.PG -0.4230 0.7893 -0.536 0.594329
Ints.PG 0.3414 0.5962 0.573 0.569451
Shots.Allowed.PG -3.3604 0.8063 -4.167 0.000119 ***
Above are the results I've obtained. At first glance I thought it was interesting Shots OT has double the impact of Shots Allowed but I see that their standard errors are significantly different so that worries me.
How would I go about comparing these two values?
Using linear.hypothesis() I get:
Linear hypothesis test
Hypothesis:
Shots.OT.PG + 2 Shots.Allowed.PG = 0
Model 1: restricted model
Model 2: Points ~ Shots.PG + Shots.OT.PG + Dribbles.PG + Fouled.PG + Offsides.PG +
Tackles.PG + Fouls.PG + Ints.PG + Shots.Allowed.PG
Res.Df RSS Df Sum of Sq F Pr(>F)
1 52 4488.5
2 51 4484.2 1 4.2107 0.0479 0.8277
How do I interpret this? Does this mean they are not different due to its large P Value. I am trying to find out whether or not the Shots OT has a larger effect on the Points total than the Shots Allowed PG