Look at the following regression output.

Y = Dealer List Price and X = Dealer Invoice Price

Regression Statistics

Multiple R 0.90482

R Square 0.81871

Adjusted R Square 0.773392

Standard Error 0.618233

Observations 6

Coefficient Standard Error t stat P-value

Intercept - 3.8844 4.16062 - 0.9336 0.4036

Invoice Prices 1.41776 0.33357 4.2502 0.0136

How much of the variability in Dealer List Prices is explained by Dealer Invoice prices?

a.

82%

b.

1%

c.

40%

d.

90%

a. 82%

Explanation:

From the question, the R-squared of 0.81871, or 82% approximately reveals the level of variability in Dealer List Prices that is explained by Dealer Invoice prices.

In econometric and statistical analysis, R-squared statistic shows the percentage of variation or variability in the dependent variable that is explained by or accounted for by the explanatory variables in a model. Thus, if the explanatory power of the model is high (e. g. 80%), it implies that the included explanatory variables are good predictors of the dependent variable.

In this question, Dealer List Prices which is the dependent variable is therefore explained by 82% by Dealer Invoice prices which is the explanatory or independent variable.

The 82% which is high also shows that Dealer Invoice prices are good predictors of Dealer List Prices.