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Could you please help interpret this SPSS output? a) Determine the regression equation.

Could you please help interpret this SPSS output? a) Determine the regression equation.

MATLAB: An Introduction with Applications
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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Could you please help interpret this SPSS output?

a) Determine the regression equation.

vegression I Variables Entered/Ramovad Variables Removed Variables Model Entered Method Floor area in sq mts, Branches of multiple stere, 1 Enter median housing cost in $k within 5km radius, Number of employees in branch, Distance in kms from nearest competitor, Number of carparking a. Dependent Variable: Value of monthly sales in $k in 2007 b. All requested variables entered. Model Summery Adjusted R Square .968 Std. Error of R Square 991 983 Model R the Estimate 1 537.69721 a. Predictors: (Constant), Floor area in sq mts, Branches of multiple stere, median housing cost in $k within 5km radius, Number of employees in branch, Distance in kms from nearest competitor, Number of carparking spaces b. Dependent Variable: Value of monthly sales in $k in 2007 ANOVA: Sum of Model Squares df Mean Square F Sig. <.001 Regression 114716546.93 6 19119424.49 66.130 7 Residual 2023827.991 7 7 289118.284 Total 13 116740374.92 9 a. Dependent Variable: Value of monthly sales in $k in 2007 b. Predictors: (Constant), Floor area in sq mts, Branches of multiple stere, median housing cost in Sk within 5km radius, Number of employees in branch, Distance in kms from nearest competitor, Number of carparking spaces Coofficiente: Unstandardized Standardized Coefficients Coefficients B Std. Error 505.676 1215.593 Model B Beta t (Constant) Sig 690 1 .416 Number of employees in 83.206 .131 91.408 1.099 .308 branch Number of carparking 28.442 11.355 .521 2.505 .041 spaces Distance in kms from -9.787 145.917 -.008 -.067 .948 nearest competitor -.995 3.910 -.022 -.254 .807 median housing cost in Sk within 5km radius Branches of multiple 4.302 53.438 .006 .081 .938 store Floor area in sa mts 700 258 396 2.713 030 Coafficiente: Collinearity Statistics Tolerance Correlations Zero-order Partial Model 1 Part VIF (Constant) Number of employees in .892 .383 .055 .174 5.747 branch Number of carparking .975 .687 .125 .057 17.441 spaces Distance in kms from .764 -.025 -.003 .170 5.898 nearest competitor median housing cost in Sk within 5km radius Branches of multiple .681 -.096 -.013 .335 2.988 320 .030 .004 445 2.247 store Floor area in sq mts .954 716 .135 .116 8.596
Transcribed Image Text:vegression I Variables Entered/Ramovad Variables Removed Variables Model Entered Method Floor area in sq mts, Branches of multiple stere, 1 Enter median housing cost in $k within 5km radius, Number of employees in branch, Distance in kms from nearest competitor, Number of carparking a. Dependent Variable: Value of monthly sales in $k in 2007 b. All requested variables entered. Model Summery Adjusted R Square .968 Std. Error of R Square 991 983 Model R the Estimate 1 537.69721 a. Predictors: (Constant), Floor area in sq mts, Branches of multiple stere, median housing cost in $k within 5km radius, Number of employees in branch, Distance in kms from nearest competitor, Number of carparking spaces b. Dependent Variable: Value of monthly sales in $k in 2007 ANOVA: Sum of Model Squares df Mean Square F Sig. <.001 Regression 114716546.93 6 19119424.49 66.130 7 Residual 2023827.991 7 7 289118.284 Total 13 116740374.92 9 a. Dependent Variable: Value of monthly sales in $k in 2007 b. Predictors: (Constant), Floor area in sq mts, Branches of multiple stere, median housing cost in Sk within 5km radius, Number of employees in branch, Distance in kms from nearest competitor, Number of carparking spaces Coofficiente: Unstandardized Standardized Coefficients Coefficients B Std. Error 505.676 1215.593 Model B Beta t (Constant) Sig 690 1 .416 Number of employees in 83.206 .131 91.408 1.099 .308 branch Number of carparking 28.442 11.355 .521 2.505 .041 spaces Distance in kms from -9.787 145.917 -.008 -.067 .948 nearest competitor -.995 3.910 -.022 -.254 .807 median housing cost in Sk within 5km radius Branches of multiple 4.302 53.438 .006 .081 .938 store Floor area in sa mts 700 258 396 2.713 030 Coafficiente: Collinearity Statistics Tolerance Correlations Zero-order Partial Model 1 Part VIF (Constant) Number of employees in .892 .383 .055 .174 5.747 branch Number of carparking .975 .687 .125 .057 17.441 spaces Distance in kms from .764 -.025 -.003 .170 5.898 nearest competitor median housing cost in Sk within 5km radius Branches of multiple .681 -.096 -.013 .335 2.988 320 .030 .004 445 2.247 store Floor area in sq mts .954 716 .135 .116 8.596
a. Dependent Variable: Value of monthly sales in $k in 2007 Collinearity Diagrastice. Variance Proportions Number of Number of Eigenvalu Condition employees in carparking Model Dimension Index (Constant) branch 6.492 spaces .00 1.000 .00 .00 2 .239 5.217 .01 .00 .01 3 .166 6.246 .02 .01 .00 .062 10.194 .00 .04 .00 5 .026 15.955 .02 .37 .01 .010 24.880 22 34 .59 .004 38.306 .73 .24 .38 Collinearity Diegnastice: Variance Proportions Distance in median kms from housing cost in $k within 5km radius multiple store .00 Branches of Floor area in nearest Model Dimension competitor sq mts .00 .00 .00 2 00 .00 .13 .02 3 .02 .01 .27 .01 .38 .00 .10 .06 .01 .01 .11 .46 .07 .09 .11 .18 7 .51 .88 .28 .27 a. Dependent Variable: Value of monthly sales in $k in 2007 Residuals Stetistice Std. Mean 2652.0798 11307.1230 5826,9286 2970.58071 Minimum Maximum Deviation Predicted Value 14 Residual -707.65735 765.92017 .00000 394.56188 14 Std. Predicted -1.069 1.845 .000 1.000 14 Value Std. Residual -1.316 1.424 .000 .734 14 a. Dependent Variable: Value of monthly sales in $k in 2007 Charts Histogram Dependent Variable: Value of monthly sales in Sk in 2007 S. Desgr N14 -15 00 Regression Standardized Residual Scatterplot Dependent Variable: Value of monthly sales in $k in 2007 18 Regression Standardized Predicted Value Regression Standardized Residual
Transcribed Image Text:a. Dependent Variable: Value of monthly sales in $k in 2007 Collinearity Diagrastice. Variance Proportions Number of Number of Eigenvalu Condition employees in carparking Model Dimension Index (Constant) branch 6.492 spaces .00 1.000 .00 .00 2 .239 5.217 .01 .00 .01 3 .166 6.246 .02 .01 .00 .062 10.194 .00 .04 .00 5 .026 15.955 .02 .37 .01 .010 24.880 22 34 .59 .004 38.306 .73 .24 .38 Collinearity Diegnastice: Variance Proportions Distance in median kms from housing cost in $k within 5km radius multiple store .00 Branches of Floor area in nearest Model Dimension competitor sq mts .00 .00 .00 2 00 .00 .13 .02 3 .02 .01 .27 .01 .38 .00 .10 .06 .01 .01 .11 .46 .07 .09 .11 .18 7 .51 .88 .28 .27 a. Dependent Variable: Value of monthly sales in $k in 2007 Residuals Stetistice Std. Mean 2652.0798 11307.1230 5826,9286 2970.58071 Minimum Maximum Deviation Predicted Value 14 Residual -707.65735 765.92017 .00000 394.56188 14 Std. Predicted -1.069 1.845 .000 1.000 14 Value Std. Residual -1.316 1.424 .000 .734 14 a. Dependent Variable: Value of monthly sales in $k in 2007 Charts Histogram Dependent Variable: Value of monthly sales in Sk in 2007 S. Desgr N14 -15 00 Regression Standardized Residual Scatterplot Dependent Variable: Value of monthly sales in $k in 2007 18 Regression Standardized Predicted Value Regression Standardized Residual
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