732G21/732G28/732A35 Lecture 6
Example second-order model with one predictor 2 Electricity consumption (Y)Home size (X)
Regression Analysis: Electricity cons versus X(cent), X(cent)sq The regression equation is Electricity consumption (Y) = X(cent) X(cent)sq Predictor Coef SE Coef T P VIF Constant X(cent) X(cent)sq S = R-Sq = 98.2% R-Sq(adj) = 97.7% Analysis of Variance Source DF SS MS F P Regression Residual Error Total Source DF Seq SS X(cent) X(cent)sq 3
4
5 RegionSales volume Y ($10,000)TV advertising X1 ($1000)Newspaper advertising X2 ($1000)
6 RegionSales volume ($10,000) YTV advertising ($1000) X1Newspaper advertising ($1000) X2Interaction term X1X
Regression Analysis: Sales volume versus TV advertisi, Newspaper ad,... The regression equation is Sales volume ($10,000) = TV advertising ($1000) Newspaper advertising ($1000) Interaction term (X1X2) Predictor Coef SE Coef T P VIF Constant TV advertising ($1000) Newspaper advertising ($1000) Interaction term (X1X2) S = R-Sq = 98.6% R-Sq(adj) = 98.4% Analysis of Variance Source DF SS MS F P Regression Residual Error Total Source DF Seq SS TV advertising ($1000) Newspaper advertising ($1000) Interaction term (X1X2)
8
Salary (Y) Highschool points (X1) Art/Engineering (X2)
Regression Analysis: Salary (Y) versus Highschool p, Filfak/tekfa The regression equation is Salary (Y) = Highschool points (X1) Filfak/tekfak (X2) Predictor Coef SE Coef T P Constant Highschool points (X1) Filfak/tekfak (X2) S = R-Sq = 85.8% R-Sq(adj) = 78.8% Analysis of Variance Source DF SS MS F P Regression Residual Error Total Source DF Seq SS Highschool points (X1) Filfak/tekfak (X2)
11
12