1)The SPSS output give the selling price, square footage, number of bedrooms, and ageof houses that have sold in a neighborhood in the past 6 months.Model SummaryStd. Error ofAdjusted RSquareModelRR Squarethe Estimate932.86883715231.904a. Predictors: (Constant), Age, Bedrooms, Square_FootageANOVASum ofModelSquaresdfMean SquareFSig.19794476008„0001Regression3659815866928.439Residual301614163913232010895.3Total2281061764716a. Dependent Variable: Selling_Priceb. Predictors: (Constant), Age, Bedrooms, Square_FootageCoefficientsStandardizedUnstandardized CoefficientsCoefficientsStd. Error26076.890ModelBBetaSig.(Constant)91446.4933.507.004Square_Footage29.85810.8614902.749.017Bedrooms2116.85510003.009034212836Age-1504.766370.820-.520-4.058.001a. Dependent Variable: Selling_Price(a) Identify dependent variable and independent variables.(b) Develop a regression model.(c) Predict the selling price of a 10-year old house, with 2000 square foot house, andhave 3 bedrooms.(d) Determine which factors/independent variables that is significant in predictingselling price of a house? Use p-value method at 0.05 level of significance.

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1) The SPSS output give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Model Summary Adjusted R Std. Error of R Square Square 868 a. Predictors: (Constant), Age, Bedrooms, Square_Footage Model. R 932 the Estimate 837 15231.904 ANOVA Sum of Squares Mean Square 6598158669 13 232010895.3 df F Sig 000 Model Regression 19794476008 3. 28.439 Residual 3016141639 Total a. Dependent Variable: Selling_Price b. Predictors: (Constant), Age, Bedrooms, Square_Footage 22810617647 16 Coeficients Standardized Coeficients Model (Constant Unstandardized Coefmicients Std. Error 26076.890 Beta Sig 91446.493 3.507 .004 Square_Footage 29.858 10.861 490 2.749 017 Bedrooms 2116.855 10003.009 034 212 836 -1504.766 370.820 520 4.058 001 Age a. Dependent Variable: Selling_Price (a) Identify dependent variable and independent variables. (b) Develop a regression model. (c) Predict the selling price of a 10-year old house, with 2000 square foot house, an have 3 bedrooms. (d) Determine which factors/independent variables that is significant in predicting selling price of a house? Use p-value method at 0.05 level of significance.

1) The SPSS output give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Model Summary Adjusted R Std. Error of R Square Square 868 a. Predictors: (Constant), Age, Bedrooms, Square_Footage Model. R 932 the Estimate 837 15231.904 ANOVA Sum of Squares Mean Square 6598158669 13 232010895.3 df F Sig 000 Model Regression 19794476008 3. 28.439 Residual 3016141639 Total a. Dependent Variable: Selling_Price b. Predictors: (Constant), Age, Bedrooms, Square_Footage 22810617647 16 Coeficients Standardized Coeficients Model (Constant Unstandardized Coefmicients Std. Error 26076.890 Beta Sig 91446.493 3.507 .004 Square_Footage 29.858 10.861 490 2.749 017 Bedrooms 2116.855 10003.009 034 212 836 -1504.766 370.820 520 4.058 001 Age a. Dependent Variable: Selling_Price (a) Identify dependent variable and independent variables. (b) Develop a regression model. (c) Predict the selling price of a 10-year old house, with 2000 square foot house, an have 3 bedrooms. (d) Determine which factors/independent variables that is significant in predicting selling price of a house? Use p-value method at 0.05 level of significance.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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