Could you help me solving this problem ? Also, Assume the following values: the simple, bivariate correlation between age and price is r=0.60, the multiple R^2 for your multiple regression with all 3 predictions is R^2=0.56, SSy =2500, and n=25. What is the value of r^2additional for addicting weight and quality to the age-only model? What is the value of SSadditional ? MSadditional? What is the value of SSresidual? df residual? What is the value of F? What is the critical value of F, if ?=.05?Given the above details , does adding 'weight' and 'quality' significantly improve our predictions? Yes or No?Thank you very very much!

Imagine again you own that antique shop and you have a hunch that you can predict the price (Y measured in dollars) a piece will sell at not only knowing its age (X ] measured in years), but also its weight (X2 measured in pounds), and condition (X3 measured on a 1-10 interval-level scale where 10 is perfect and 1 is terrible). Your estimate the best fitting multiple regression equation to be: Y = 4.53x1 +. 5x2 + 75x3 +20 On your next trip to antique country you spot a 220-year-old handcarved wooden table that weighs 80 pounds and you rate at a 9 for condition. According to your model, what is the predicted price you could sell this for