The North Valley Real Estate data reports information on homes on the market. Use the selling price of the home as the d

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The North Valley Real Estate Data Reports Information On Homes On The Market Use The Selling Price Of The Home As The D 1 (79.23 KiB) Viewed 14 times

The North Valley Real Estate data reports information on homes on the market. Use the selling price of the home as the dependent variable and determine the regression equation using the size of the house, number of bedrooms, days on the market, and number of bathrooms as independent variables. Click here for the Excel Data File a-1. Develop a correlation matrix. (Negative amounts should be indicated by a minus sign. Round your answers to 3 decimal places.) Price Size (Square feet) Bedrooms Baths Days on Market Price There is a Size (Square feet) strong, positive Bedrooms 1 Baths a-2. Which independent variables have strong or weak correlations with the dependent variable? Days on Market correlation between "Price" and the independent variables "Bedrooms", "Size", and "Baths".

a-3. Do you see any propiems with multicollinearity? O Yes b-1. Use a statistical software package to determine the multiple regression equation. (Negative amounts should be indicated by a minus sign. Round your answers to 3 decimal places.) Price = O No R² b-2. What is the value of R2. (Round your answer to 3 decimal places.) Size. c. Evaluate the addition of the variables to the regression equation Adding garage New Tab to the regression equation increases the R-square.

U-1. Develop a miswyram on the residuals in the mannegression equation, which mistogram is connect 35 30 25 20 15 10 5 0 -112.50 -77.50 -42.50 -7.50 27.50 62.50 97.50 Residuals Histogram of the residuals 1 Frequency of Residuals Histogram of the residuals 1 150000 100000 Yes No 35 30 Residuals vs Fits 1 Residuals vs. Predicted 25 20 15 10 d-2. Is it reasonable to conclude that the normality assumption has been met? -112.50 -77.50 -42.50 Histogram of the residuals 2 Frequency of Residuals Histogram of the residuals 2. 150000 -7.50 27.50 62.50 97.50 Residuals 100000 35 30 e-1. Plot the residuals against the fitted values from the final regression equation. Which plot is correct? Residuals vs Fits 2 Residuals vs. Predicted 25 20 15 10 Histogram of the residuals 3 5 0 -112.50 Histogram of the residuals 3 Frequency of Residuals 150000 100000 ub -77.50 -42.50 -7.50 27.50 62.50 97.50 Residuals Residuals vs Fits 3 Residuals vs. Predicted

d-2. Is it reasonable to conclude that the normality assumption has been met? 150000 e-1. Plot the residuals against the fitted values from the final regression equation. Which plot is correct? 100000 50000 0 -100000 O Yes 150000 O No • Residuals vs Fits 1 . Residuals vs. Predicted 100000 2000 300000 400000 500000 600000 700000 800000 900000 O Residuals vs Fits 1 Residuals vs Fits 2 150000 100000 50000 100000 -150000 100000 Residuals vs Fits 2 Residuals vs. Predicted 400000 500000 600000 700000 800000 900000 O Residuals vs Fits 3 150000 100000 50000 0 -100000 -150000 100000 Residuals vs Fits 3. Residuals vs. Predicted 400000 500000 600000 700000 800000 900000