Question

please help me with this question!!

The table below summarizes nested multiple regression models used to predict a person's quality of life score. Model 1 Model 2 Model 3 est. sig. est. | sig. est. intercept 17.17 1 <.001 8.87 1 <.001 9.56 sig. <.001 0.075 0.66 0.033 0.43 0.47 3.11 0.085 0.013 3.22 0.030 size of social network college degree time (yrs) at current job # of siblings 1.49 0.154 -0.64 0.047 R 3.89% 8.98% 10.81% AR? 3.89% 5.08% 1.84% F (for AR) 4.66(1,115) 0.033 6.37(1,114) 0.013 1.15(2,112) 0.319 Using the model information (parameter estimates and comparative model fit), provide a prediction for the quality of life score for a person with a social network of size 11, no college degree, 6 years at current job, and 8 siblings. QOL = ġ = 18.11 Report answer accurate to 2 decimal places. Be prepared to briefly justify your model choice.

Answer 1

To predict the value of person's quality of life score we will use model 2 because of following reasons:

i) although 4 parameters are taken into consideration in model 3 but it does not explain the quality of life score very much that is the even though it is having 4 independent variables the value of R^2 is just 0.1081.

ii) The value of F(for delta R^2) in case of model 3 is 1.15 and the p -value is 0.319 which is quite high and hence not significant that is adding an independent variable does not increase the R^2 value significantly.

iii) in case of model 2 the value of F(for delta R^2) is 6.37 and the p-value is 0.013 which is quite low which implies adding a regressor to model helps increase R^2 value significantly.

The predicted value of quality of life score using model 2 is

y = 8.87 +0.47 * 11 +3.11 *0

y = 14.04