Last year, five randomly selected students took a math aptitude test before they began their statistics course. The Statistics Department would like to analyze the relationship of the data, make...

Answer only ( d , e ,f)

Extracted text: Last year, five randomly selected students took a math aptitude test before they began their statistics course. The Statistics Department would like to analyze the relationship of the data, make predictions using the regression equation and validate the regression equation. The data of the math aptitude test score and corresponding statistics grade are shown in Table 4. Table 4 Score on math aptitude test (x)Statistics grade (y) 95 85 85 80 80 75 70 75 60 65 Answers at 2 decimal places. (α) Calculate Συ, ΣΥy Σεy and Σα?. Ex = Ey = Exy = Ex² = %3D (b) Calculate the value of b and bo. b = bo = %3D (c) Based on your answers in (a) and (b), what linear regression equation best predicts statistics performance, based on math aptitude scores? ŷ = (d) If a student made an 65 on the aptitude test, what grade would we expect her to make in statistics? Grade in Stat = (e) Find the values of SSR, SST and R2. SSR = SST = R² = (f) Based on your answer in (e), how well does the regression equation fit the data? A coefficient of determination equal to 0.93 indicates that about 93% of the variation in statistics grades (the dependent variable) can be explained by the relationship to math aptitude scores (the independent variable). This would be considered a bad fit to the data / god fit to the data in the sense that it would substantially improve an educator's ability to predict student performance in statistics class. +