Could you please interpret the R square, adjusted R squared, t value for each va
ID: 3225012 • Letter: C
Question
Could you please interpret the R square, adjusted R squared, t value for each variable, p value for each variable, F value for each variable. And lastly, overall what are these results saying? Please and thank you.
Regression Statistics Multiple R 0.64573493 R Square 0.4169736 Adjusted R Square 0.365149031 Standard Error 1.124670994 Observations 50 ANOVA df SS MS F Significance F Regression 4 40.70838199 10.1770955 8.04586721 5.5496E-05 Residual 45 56.91981801 1.264884845 Total 49 97.6282 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3.745525338 1.880659747 1.9916018 0.05250505 -0.042317833 7.533368509 -0.042317833 7.533368509 ADULTS WITH DIABETES,OVERWEIGHT OR OBESITY X1 0.007552918 0.033102959 0.228164448 0.820552027 -0.059119864 0.074225701 -0.059119864 0.074225701 ADULTS WITH DIABETES - PHYSICAL INACTIVITY X2 0.06977475 0.079502772 0.877639207 0.384801338 -0.090352053 0.229901552 -0.090352053 0.229901552 NUMBER, ADULTS WITH DIABETES - MALE X3 0.011256043 0.028783328 0.391061211 0.697597468 -0.046716556 0.069228642 -0.046716556 0.069228642 NUMBER, ADULTS WITH DIABETES - FEMALE X4 0.068333555 0.053324717 1.281461173 0.206595744 -0.039067939 0.175735048 -0.039067939 0.175735048Explanation / Answer
R square:
It is degree of determination. Here 64.57% (multiple R-sq) of total variability in dependent variable is explained by the model comprising independent variables X's.
Adjusted R squared:
It is degree of determination considering the model complexity. Here 36.51% of total variability in dependent variable is explained by the model comprising independent variables X's, even after penalising for 4 independent variables.
t-value for each variable:
It is the indication of significance of the coefficient for the respective variables. More the value of t-more significant the variable is, further given by p-value.
p value for each variable:
It is the indication of significance of the coefficient for the respective variables. Less the p-value more chance of significant the variable is. If p-value<0.05 then variable is significance.In the given regression model, no variable is significant at 5% level.
F value :
It indicates if regression model is useful or not based on its p-value. Here model is significant.