One of the factors that affect life insurance premiums is longevity (age at death). An insurance company wants to predict the longevity of its clients to compute an optimum life insurance premium. The company gathered the following data from 100 recently deceased clients Age at death of the customer (Longevity) . Age at death of his/her mother (Mother) . Age at death of his/her father (Father) Age at death of his/her grandmother (Gmother) Age at death of his/her grandfather (Gfather) Smoker (1 = yes and 0 = no) Female (1 = female and 0 = male) Having Children (1 = yes and 0 = no) Rural Area (1 = rural and 0 = urban) Hereditary Diseases (1 = yes and 0 = no) The results of regression analysis are provided below. Based on these results answer the following questions: Regression equation: Longevity = 26.1 + 0.282 Mother + 0.287 Father + 0. 0437 Gmother + 0. 0589 Gfather - 3.22 Smoker - 0.369 Female - 0.511 HavingChildren + 0. 171 RuralArea + 1.41 HereditaryDiseases Regression table: Predictor Coef SE Coef T P VIF Constant 26 . 127 6. 115 4. 27 0 . 000 Mother 0. 28170 0 . 0549 5 . 13 0 . 000 2. 117 Father 0. 28734 0 . 0480 5 . 99 0 . 000 1. 586 Gmothers 0. 04372 0 . 0573 0 . 76 0 . 448 1. 390 Gfather 0 . 05895 0 . 0571 1. 03 0 . 305 1. 446 Smoker - 3 . 2241 0. 6925 -4. 66 0 . 000 2 . 096 Female -0. 3693 0 . 4870 -0. 76 0 . 450 1. 093 Children - 0 . 5109 0. 4671 -1 . 09 0. 277 1. 048 Rural Area |0. 1712 0 . 4965 0 . 34 0. 731 1. 176 Hereditary 1 . 4147 0. 5737 2. 47 0 . 016 1. 578 disease S = 2. 28147 R- Sq = 82 . 0% R- Sq (adj ) = 80.2% Analysis of Variance: Source DF SS MS F P Regression 9 2135.30 237.26 |? ? 0 . 000 Residual Error 90 468 . 46 5. 21 Total 99 2603 .76