View Regression table output results and use it to answer questions a, b, and c a) Check and explain the R square value, what does it mean in terms of model quality? The R square value is a measure of how well the model explains variations in the sampled essentially the percentage of variations that follow the model's hypotethical correlation. Check the Intercept and slope [regression coefficients], and associated P-value, decide if t The high P-value for the intercept indicates that it does not hold statistical significance in model. While the slope has a much lower P-value, the coefficient is very close to zero, ind very little (if any) correlation between the variables. In this situation, we would accept the hypothesis. b) Producing Residue table and plot can help you analyze residuals Check Residual Plot, and compare data points to zero line to determine outliers The output for the predicted unemployment was initially in decimal form. After changing percentage (the original unit), outliers were much easier to find. I have highlighted outlier residual table