Question

A researcher working for an insurance company that sells life insurance would like to use regression analysis to predict life expectancy of his clients. He knows that there are several factors that contribute to life expectancy: some are genetic, some are related to life style, some are related to biological factors, and some are related to environment (access to health care, cleanliness of air, etc.) . He selects these candidate variables to develop his regression equation: gender, number of cigarettes smoked per day, cholesterol level, systolic blood pressure, and height-to-weight index: (actual weight / appropriate weight given gender, build, and height) * 100. Use the datasheet life expectancy in datasetsRM.xls to develop a regression equation to predict how long a person should live (for gender: 0=female, 1=male): 1. First, plot each non-categorical predictor variable against the dependent variable (age of death) and examine the plot to see if the relationship is linear. What’s your assessment? 2. Perform a multiple regression analysis and write up the results of your regression analysis in APA style. 3. For a male who does not smoke cigarettes at all, has a systolic blood pressure of 130, a height-to-weight index of 110, and a cholesterol level of 200, what is his life expectancy? NumCigsDay WtHtIndex Gender Cholesterol BloodPres AgeOfDeath 0 98 0 179 120 90 0 90 0 186 100 98 0 140 0 190 130 90 3 96 0 191 120 87 0 120 0 200 120 90 0 100 0 187 120 94 0 130 0 190 110 96 4 92 0 191 110 83 5 110 0 200 110 79 5 193 0 210 120 79 10 107 0 215 130 77 0 117 0 227 140 80 0 128 0 240 130 99 15 179 0 230 150 68 10 150 0 240 160 70 5 100 0 245 120 79 8 112 0 260 130 76 10 150 0 275 140 67 8 121 0 280 130 72 0 90 1 210 120 85 0 100 1 187 100 94 0 130 1 179 130 88 10 92 1 183 120 72 0 119 1 184 120 89 0 110 1 189 120 80 2 120 1 192 110 87 6 100 1 196 110 69 4 140 1 204 110 73 10 128 1 215 120 65 0 107 1 216 140 85 0 98 1 219 130 75 8 119 1 220 140 68 3 117 1 222 130 89 11 193 1 232 150 62 12 179 1 245 160 66 8 150 1 246 120 78 12 96 1 261 130 67 0 121 1 269 130 70 8 112 1 279 140 64 0 150 1 280 130 74

Answer 1

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