Residuals: Min 1Q Median 3Q Max -7388.9 -2030.8 -539.2 2421.8 8545.0 Coefficient

Question

Residuals:

Min 1Q Median 3Q Max

-7388.9 -2030.8 -539.2 2421.8 8545.0

Coefficients:

Estimate Std. Error t value Pr(>|t|)   

(Intercept) 124375.0 33431.3 3.720 0.003380 **

CPI.Energy 502.4 252.5 1.990 0.072044 .  

CPI 2336.9 432.1 5.408 0.000214 ***

Inflation -2343.7 794.4 -2.950 0.013199 *  

UnEmployment.Rate -10034.5 1839.4 -5.455 0.000199 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4913 on 11 degrees of freedom

(50 observations deleted due to missingness)

Multiple R-squared: 0.9947, Adjusted R-squared: 0.9928

F-statistic: 516.6 on 4 and 11 DF, p-value: 1.96e-12

why is my r^2 so high

Explanation / Answer

R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or thecoefficient of multiple determination for multipleregression. 0% indicates that the model explains none of the variability of the response data around its mean. 100% indicates that the model explains all the variability of the response data around its mean.In general, the higher the R-squared, the better the model fits your data.

Given r^2 = 0.9974 = 99.74% of variations in the repondent variable can be explained by the all predictors variable

Here  p-value: 1.96e-12 which is very low that means the regression equation is best fit to the given data and also all regression coefficients are significant since their p-values are < 0.05. So all regression coefficients effect on regression equation. so it produce R^2 value is maximized

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