Question

only part II is needed

Regardless of your answer to (a), you come up with the following multiple regression model. b. Coefficients: Estimate Std. Error t value Pr>lt (Intercept) 72.2285 1.2697 56.89 2e-16 X2 X3 Residual standard error: 7.25 on 191 degrees of freedom Multiple R-squared: 0.494, Adjusted R-squared: 0.489 F-statistic: 93.3 on 2 and 191 DF, p-value: <2e-16 0.4590 0.0524-8.76 1.1e-15 0.4146 0.1290 3.21 0.0015** I) What percentage of the total variation in Life Expectancy can you explain with your regression model, what is this number called? Can you explain the variation in your dependent variable, better than what the average value of your dependent variable does (justification is necessary)?

Answer 1

I can explain 49.4 % of the total variation in life expectancy by this regression model, by R-squared,

which is better than what average of dependent variable does because R-squared gives explained variation by the usage of this regression model of predicting y values by the x values whereas the average value of x doesnt take account of x values while calculating the varaition.