Canvas -> X Redwood scenario. A forest management company wished to develop a model to assist in estimating the heights of the trees in a tract of Califomia redwood trees. For a random sample of 21 redwood trees in the tract were determined the height (in feet), diameter (in inches) five feet from the base of the tree, and the bark thickness (in inches) five feet from the base of the tree. Below are the results of a regression analysis on this data using a model specification of Height = Bo+B,Diameter + ,Bark Thickness + E. [source: Adapted from Berenson et al (2006)] Regression Statistics Multiple R 0.8864 R Square 0.7858 Adjusted R Squ 0.7620 Standard Error 22.5982 Observations 21 ANOVA df SS Regression 2 33718.478 MS 16859.24 F Significance F 33.0135 9.4991E-07 Residual 18 9192.189 510.6772 Total 20 42910.667 Coefficients Standard Error Intercept Diameter Bark Thickness 62.141 2.057 15.642 13.503 0.443 7.148 t Stat P-value 4.602 0.000221 4.645 0.000201 2.188 0.042087 Lower 95% Upper 95% 33.772 90.510 1.126 0.624 2.987 30.660 12 The appropriate test statistic and p-value for assessing whether there is evidence that bark thickness does aid in predicting height when used along with diameter are: