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THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING INFORMATION

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THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING INFORMATION:
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 (x1),the cholesterol level (x2),and the number of points that the individual's blood pressure exceeded the recommended value (x3).A random sample of 40 individuals was selected.The computer output of the multiple regression model is shown below.
THE REGRESSION EQUATION IS: THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING INFORMATION: 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:    = 55.8 + 1.79x<sub>1</sub> - 0.021x<sub>2</sub> - 0.016x<sub>3</sub>     S = 9.47 R-Sq = 22.5% ANALYSIS OF VARIANCE    -Is there enough evidence at the 1% significance level to infer that the average number of hours of exercise per week and the age at death are linearly related?
= 55.8 + 1.79x1 - 0.021x2 - 0.016x3
THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING INFORMATION: 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:    = 55.8 + 1.79x<sub>1</sub> - 0.021x<sub>2</sub> - 0.016x<sub>3</sub>     S = 9.47 R-Sq = 22.5% ANALYSIS OF VARIANCE    -Is there enough evidence at the 1% significance level to infer that the average number of hours of exercise per week and the age at death are linearly related?
S = 9.47 R-Sq = 22.5%
ANALYSIS OF VARIANCE
THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING INFORMATION: 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:    = 55.8 + 1.79x<sub>1</sub> - 0.021x<sub>2</sub> - 0.016x<sub>3</sub>     S = 9.47 R-Sq = 22.5% ANALYSIS OF VARIANCE    -Is there enough evidence at the 1% significance level to infer that the average number of hours of exercise per week and the age at death are linearly related?
-Is there enough evidence at the 1% significance level to infer that the average number of hours of exercise per week and the age at death are linearly related?


Definitions:

Inflation

The rate at which the general level of prices for goods and services is rising, eroding purchasing power.

Consumer Price Index

A measure that examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food, and medical care, used to estimate cost of living changes.

Market Basket

A selected group of goods and services used to track inflation and other economic indicators through price changes.

Base Year

A specific year against which economic growth is measured, serving as a standard point of comparison for real economic calculations such as GDP.

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