Question: > summary (fit) Call: lm(formula = mpg ~ wt + disp, data = mtcars) Residuals: Min 1Q Median 3Q Max -3.4087 -2.3243 -0.7683 1.7721 6.3484

> summary (fit) Call: lm(formula = mpg ~ wt + disp, data = mtcars) Residuals: Min 1Q Median 3Q Max -3.4087 -2.3243 -0.7683 1.7721 6.3484 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 34.96055 2.16454 16.151 4.91e-16 *** wt -3.35082 1.16413 -2.878 0.00743 ** disp -0.01773 0.00919 -1.929 0.06362 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.917 on 29 degrees of freedom Multiple R-squared: 0.7809, Adjusted R-squared: 0.7658 F-statistic: 51.69 on 2 and 29 DF, p-value: 2.744e-10" Based on this data, please help me to answer this question "Explain which estimated coefficients appear to be statistically significant and how you know (reference the appropriate p-value). How much faith would you have in these regression estimates, and why? (Base your answer on the R-squared value)

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