Problem Solving Questions (Chapter 7: Linear Regression] A realtor wishes to investigate what type of homes would attract residents in the neighborhood. The realtor collected a sample data that contains the following information about some single-family houses recently sold in the neighborhood. y = price in $1,000s (PRICE) x1 = size of the house in square feet (SIZE) X2 = = number of rooms (ROOMS) = number of bedrooms (BEDROOMS) X4 = age of the house in years (AGE) X3 = The realtor conducted a regression analysis of the data using the following model: y = Bo + B1 x1 + B2 x2 + B3 x3 + 34 x4 + E Below is the Excel output of the regression analysis. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.86 0.74 0.71 18.57 50 ANOVA . ANOVA df SS MS 10917.89 Significance F 1.46E-12 43671.56 31.65 Regression Residual Total 4 45 344.93 15521.77 59193.33 49 Intercept SIZE ROOMS BEDROOMS Coefficients Standard Error 15.54 12.67 0.05 0.01 4.14 3.04 -10.77 6.34 -0.35 0.13 t Stat 1.23 6.10 1.36 -1.70 -2.68 P-value 0.23 2.24E-07 0.18 0.10 0.01 Lower 9596 -9.98 0.04 -1.99 -23.54 Upper 9596 41.06 0.07 10.27 1.99 -0.09 AGE -0.61 Answer the following questions based on the Excel output. For fill-in-the-blank questions, write your final answer only (without intermediate steps). Question 40 (4 points) Saved Based on the p-value and the level of significance a, we do not reject Ho reject Ho cannot tell; more information is needed Question 41 (4 points) Saved Based on the sample data, there is sufficient evidence that a linear relationship between PRICE and BEDROOMS. cannot tell; more information is needed Question 41 (4 points) Saved Based on the sample data, there is sufficient evidence that a linear relationship between PRICE and BEDROOMS. cannot tell; more information is needed does not exist exists Previous Page Next Page Page 29 of 29 Problem Solving Questions (Chapter 7: Linear Regression] A realtor wishes to investigate what type of homes would attract residents in the neighborhood. The realtor collected a sample data that contains the following information about some single-family houses recently sold in the neighborhood. y = price in $1,000s (PRICE) x1 = size of the house in square feet (SIZE) X2 = = number of rooms (ROOMS) = number of bedrooms (BEDROOMS) X4 = age of the house in years (AGE) X3 = The realtor conducted a regression analysis of the data using the following model: y = Bo + B1 x1 + B2 x2 + B3 x3 + 34 x4 + E Below is the Excel output of the regression analysis. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.86 0.74 0.71 18.57 50 ANOVA . ANOVA df SS MS 10917.89 Significance F 1.46E-12 43671.56 31.65 Regression Residual Total 4 45 344.93 15521.77 59193.33 49 Intercept SIZE ROOMS BEDROOMS Coefficients Standard Error 15.54 12.67 0.05 0.01 4.14 3.04 -10.77 6.34 -0.35 0.13 t Stat 1.23 6.10 1.36 -1.70 -2.68 P-value 0.23 2.24E-07 0.18 0.10 0.01 Lower 9596 -9.98 0.04 -1.99 -23.54 Upper 9596 41.06 0.07 10.27 1.99 -0.09 AGE -0.61 Answer the following questions based on the Excel output. For fill-in-the-blank questions, write your final answer only (without intermediate steps). Question 40 (4 points) Saved Based on the p-value and the level of significance a, we do not reject Ho reject Ho cannot tell; more information is needed Question 41 (4 points) Saved Based on the sample data, there is sufficient evidence that a linear relationship between PRICE and BEDROOMS. cannot tell; more information is needed Question 41 (4 points) Saved Based on the sample data, there is sufficient evidence that a linear relationship between PRICE and BEDROOMS. cannot tell; more information is needed does not exist exists Previous Page Next Page Page 29 of 29