A You have been asked to do an analysis of the pricing structure of properties the Midcity dataset. Recall that the data set had the following attributes: PRICE = selling price in thousands of dollars OFFERS = the number of offers made on the house SQFT = total size of the residence, measured in square feet BEDROOMS = number of bedrooms BATHROOMS = number of bathrooms BRICK = YES; 1 if property has brickfront, 0 otherwise. You decide to build a multiple regression model using a random sample of 100 properties, and using PRICE as the dependent variable and SQFT, BEDROOMS, BATHROOMS, OFFERS and BRICK=YES as independent variables. Part of the regression output is shown below. Notice that some cells have missing values. You do not need to run this in JMP all answers are to be given based on the given output. A Summary of Fit RSquare 0.807506 RSquare Adj 0.797267 Root Mean Square Error 12583.29 Mean of Response 132698 Observations (or Sum Wgts) 100 AlAnalvsis of Variance 4 Analysis of Variance Sum of Source DF Squares Mean Square F Ratio Model 5 6.2437e+10 1.249e+10 78.8653 Error 94 1.4884e+10 158339201 Prob > F C. Total 99 7.7321e+10 <.0001 parameter estimates term estimate std error t ratio prob> |t| Intercept -19095.2 11671.44 -1.64 0.1052 SqFt 7.763049 7.71 Brick Yes 18751.31 2765.508 6.78 <.0001 bedrooms bathrooms offers the pairwise correlations between independent variables are shown below: sqft brick_yes price points what is degrees of freedom for unexplained variation in this regression model confidence interval on coefficient its interpretation p-value test t.stat standard error based output evaluate model. there any evidence that suggests weakness and why your friend house was one used characteristics his following: offer square feet bedroooms has brickfront. does predict if he bought dollars value residual an unexpected career opportunity just come up friend. will be relocating to another state sell soon. before selling could add foot bedroom increasing by it cost bedroom. should go ahead with project purpose answering question assume trusts predictive power same who made above suggestion runs a different these houses using only as variable price. claims r do you have reason believe number wrong j use predictor bedsooms brick="YES)" which would explain r-square simple linear chose part>
