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Life Expectancy an Actuary Wanted to Develop a Model to Predict

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Life Expectancy An actuary wanted to develop a model to predict how long individuals will live. After consulting a number of physicians, she collected the age at death ( y ), the average number of hours of exercise per week ( x 1), the cholesterol level ( x 2), and the number of points that the individual's blood pressure exceeded the recommended value ( x 3). A random sample of 40 individuals was selected. The computer output of the multiple regression model is shown below. THE REGRESSION EQUATION IS y = 55.8 + 1.79 x 1 - 0.021 x 2 - 0.061 x 3 Life Expectancy An actuary wanted to develop a model to predict how long individuals will live. After consulting a number of physicians, she collected the age at death ( y ), the average number of hours of exercise per week ( x <sub>1</sub>), the cholesterol level ( x <sub>2</sub>), and the number of points that the individual's blood pressure exceeded the recommended value ( x <sub>3</sub>). A random sample of 40 individuals was selected. The computer output of the multiple regression model is shown below. THE REGRESSION EQUATION IS y = 55.8 + 1.79 x <sub>1</sub> - 0.021 x <sub>2</sub> - 0.061 x <sub>3</sub> S = 9.47 R - Sq = 22.5% ANALYSIS OF VARIANCE {Life Expectancy Narrative} Is there enough evidence at the 5% significance level to infer that the cholesterol level and the age at death are negatively linearly related?
S = 9.47 R - Sq = 22.5%
ANALYSIS OF VARIANCE Life Expectancy An actuary wanted to develop a model to predict how long individuals will live. After consulting a number of physicians, she collected the age at death ( y ), the average number of hours of exercise per week ( x <sub>1</sub>), the cholesterol level ( x <sub>2</sub>), and the number of points that the individual's blood pressure exceeded the recommended value ( x <sub>3</sub>). A random sample of 40 individuals was selected. The computer output of the multiple regression model is shown below. THE REGRESSION EQUATION IS y = 55.8 + 1.79 x <sub>1</sub> - 0.021 x <sub>2</sub> - 0.061 x <sub>3</sub> S = 9.47 R - Sq = 22.5% ANALYSIS OF VARIANCE {Life Expectancy Narrative} Is there enough evidence at the 5% significance level to infer that the cholesterol level and the age at death are negatively linearly related? {Life Expectancy Narrative} Is there enough evidence at the 5% significance level to infer that the cholesterol level and the age at death are negatively linearly related?

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blured image vs. blured image Rejection region: t - t 0.05,36 » - 1.69 ...

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