00:01
The data set on body fat contains 15 body measurements on 250 men and 22 from 22 to 81 years old.
00:08
Is the average percent of body fat related to the weight? here's a scatterplots as you can see and the simple regression.
00:15
The dependent variable is the percent of body fat.
00:19
The r squared is 38 .1 percent and so on we see a lot of information.
00:25
So is the coefficient of body fat on weight statistically distinguishable from zero, performing? the hypothesis test and we could say that the null hypothesis is that the slope is equal to zero and that the alternative hypothesis is that it's not equal to zero and we can see that the t ratio here for weight is 12 .4 and then if you go to the p value it is less than 0 .0001 and so we reject the null hypothesis.
01:29
So the coefficient of weight is statistically discernible from 0.
01:39
B.
01:45
What does the slope coefficient mean in this regression? we saw before the slopes of both waist size and height are statistically significant when it entered into a multiple regression equation.
01:54
What happens if we add weight to that regression? recall that we've already checked the assumptions and conditions for regression on waist size and height in the chapter.
02:02
So here's the output from the ration on all three variables.
02:10
Well, from this we would expect for the weight, we would expect the percent of body fat to increase by the 0 .18937...