Consider the following multiple regression results. Using a= .05 as the significance level, identify all statistically significant predictors. In a relative sense, which variable has the strongest impact? What is the interpretation of the “constant” term?
Independent Variables Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
(Constant) .991 .106 9.362 .000
Variable 1 .045 .101 .015 .445 .657
Variable 2 -.051 .084 -.020 -.607 .544
Variable 3 .299 .107 .062 2.786 .005
Variable 4 -.341 .329 -.023 -1.039 .299
Variable 5 .000 .000 -.011 -.284 .776
Variable 6 .000 .000 .009 .251 .802
Variable 7 .000 .000 -.011 -.264 .792
Variable 8 .000 .000 -.001 -.022 .983
Variable 9 -.003 .005 -.015 -.704 .482
Variable 10 .031 .036 .019 .870 .384
Variable 11 -.023 .041 -.013 -.558 .577
Variable 12 -.001 .011 -.003 -.117 .907
Variable 13 .000 .000 .050 2.310 .021
Variable 14 .003 .000 .145 6.631 .000
Variable 15 .000 .000 -.008 -.380 .704
Variable 16 .053 .016 .075 3.403 .001
Variable 17 .006 .014 .430 .458 .647
Variable 18 -.006 .013 -.425 -.450 .653
Variable 19 -.136 .156 -.019 -.869 .385
How can I get this homework help on Mon, sept 5, 2011.
To identify the statistically significant predictors, we look at the Sig. column. The significance level is given as a= .05, meaning that any predictor with a p-value less than .05 is considered statistically significant.
Looking at the Sig. column in the given table, we can see that Variable 3 (Sig. = .005), Variable 13 (Sig. = .021), and Variable 14 (Sig. = .000) are all statistically significant predictors since their p-values are less than .05.
In a relative sense, to determine which variable has the strongest impact, we can look at the absolute values of the standardized coefficients (Beta) for each variable. The variable with the largest absolute value of the standardized coefficient has the strongest impact. In this case, Variable 14 has the largest absolute value of the standardized coefficient (.145), indicating that it has the strongest impact among the predictors.
The "constant" term represents the intercept in the multiple regression model. In this case, the constant term has a coefficient of .991. This means that when all the predictor variables are set to zero, the predicted value of the dependent variable is .991. It can be interpreted as the baseline value of the dependent variable when all the predictors have no influence.