Older than the average bear: The Canadian Journal of Zoology published data on the age (in years) and weight (in kg) of black bears. One bear was much older than all of the others in the data set.

MODEL 1 (Bear):

1.What is the R Square for the Model? The Adjusted R² (Effect Size)? The Standard Error of the Estimate (SEE)?2.What is the t value for Age? What is the t(Sig)? Should you reject the null?3.Use a sentence to describe the effect size for this model, using the percentage of the variance in Y that is accounted for by X.

MODEL 2 (Bear Modified):

1.What is the R Square for the Model? The Adjusted R²? The Standard Error of the Estimate (SEE)?2.What is the t value for Age? What is the t(Sig)? Should you reject the null?3.Use a sentence to describe the effect size for this model.4.Compare the two outputs. In the second scatterplot, what is different about the Line of Best Fit, or regression line? Why? Which MODEL is better?

Bear Modified, w/out old bear 29 65 R- Linear = 0.356 60 R Linear = 0.246 60 y=44.51+0.68*x 55 55 Weight y=38.08+1.51*x Weight 50 50 45 45 40 40 5.0 10.0 15.0 20.0 25.0 30.0 6.0 8.0 10.0 12.0 Age Age Model Summary Adjusted R Std. Error of R Square the Estimate Model Summary Model R Square 1 597 292 6.021 Std. Error of 356 Adjusted R Model R R Square Square the Estimate a. Predictors: (Constant), Age 1 .4962 246 162 6.022 Coefficients* a. Predictors: (Constant), Age Unstandardized Standardized Coefficients Coefficients Coefficientsa Model B Std. Error Beta Sig. Unstandardized Standardized 1 Constant) 44.511 3.291 13.5 000 Coefficients Coefficients Age 680 .289 .597 2.352 .041 B Std. Error Beta Sig. a. Dependent Variable: Weight onstant) 38.082 7.239 5.260 .001 Age 1.510 881 496 1.714 .121 a. Dependent Variable: Weight