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Survey Factors that affect on shopping centers selection

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Y=ß0+ ß1X1+ ß2X2+ ß3x3+ ß4x4+ ß5x5+ ß6x6+ ß7x7+ ß8x8+ ß9x9 There are: Y-your favorite shopping center X1-age X2-location X3-raiting X4-number of boutiques X5-advice from friends X6-design

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Regression Source | SS df MS Number of obs = 51 -------------+------------------------------ F( 3, 47) = 1.23 Model | 7.26303807 3 2.42101269 Prob > F = 0.3089 Residual | 92.4232364 47 1.96645184 R-squared = 0.0729 -------------+------------------------------ Adj R-squared = 0.0137 Total | 99.6862745 50 1.99372549 Root MSE = 1.4023

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Y= 4.694907 +0.204*X2+0,133*X5+0,271*X8 When all the independent variables are equal to zero, the intercept of the model is 4.694907 When 1 increase in X2 and hold second independent constant, satisfaction rate will increase by 0.2046206 When 1 increase in X5 and hold another independent constant, dependent variable will increase by 0,1337833 When 1 increase in X6 and hold second independent constant,satisfaction rate will increase by 0,2717567.

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T-test a)H0: β2=0 no linear relationship H1: β2≠0 linear relationship does exist between x and y t= (β2-0)/se(β2)= 0.2046206/0.2961503= 0.69 T=(0,025,3)=3,182 t

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T-test b) H0: β5=0 no linear relationship H1: β3≠0 linear relationship does exist between xj and y t= | 0.1337833/0.1293437= 1,0343 T(0,025,2)=3,182 t

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T-test c) H0: β8=0 no linear relationship H1: β6≠0 linear relationship does exist between x and y t= 0.2717567/0.1853627= 1,466088082424 t

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F-test H0: β2=β5=β8=0 H1: at least one of the βi is not equal to zero f-statistics=1.23 F( 3, 47) =2.201

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R-Square, R2. The value of R2 is 0,01 means that 1% of the variation in satisfaction rate can be explained by the variation of reputation, social life rate, building, feedback, accreditation.

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Auto Correlation Breusch-Godfrey LM test for autocorrelation --------------------------------------------------------------------------- lags(p) | chi2 df Prob > chi2 -------------+------------------------------------------------------------- 1 | 0.142 1 0.7067 --------------------------------------------------------------------------- H0: no serial correlation

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Hetrocodeceticity test Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of Y chi2(1) = 0.04 Prob > chi2 = 0.8364

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Durbin-Watson test Durbin-Watson d-statistic( 4, 51) = 1.857508 0-----------------dl(1.206)---------------------du(1.537)------------4-du(2.463)--------------4-dl(2.79)--------------4 P ? Nope ? Negative No autocorrelation

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Normality test Jarque-Bera normality test: 3.129 Chi(2) 0.2092

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Multicolenarity test Variable | VIF 1/VIF -------------+---------------------- X5 | 1.07 0.937834 X2 | 1.04 0.957690 X8 | 1.02 0.978161 -------------+---------------------- Mean VIF | 1.04