A realtor used the regression model, y betao +beta1x1 +beta2x2 + epsilon, to pre

Question

A realtor used the regression model, y betao +beta1x1 +beta2x2 + epsilon, to predict selling prices of homes (in thousands of $) in a Columbus suburb The variable x1 represents the home size (square feet), and x2 represents the number of bedrooms. The following Excel partial output is available ANOVA df Regression 2 Residual 10 12 5800.44 312.99 6113.43 MS 2900.22 92.66 31.30 Total Coefficients Standard t Stat Intercept 27.13 Size Bedrooms 20.22 Error 22.80 0.03 6.42 1.19 5.33 3.15 0.16 Which of the two independent variables seems to have a stronger impact on the selling price?

Explanation / Answer

here as we know that test statsitic is measure of relatrive significance among independent variables

Higher the absolute value of test statistic t ; greater the significance of independent variable

Here as Size has higher absolute value of t-stat

therefore size seems to have a sronger impact on selling price.

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