Variables Entered/Removed Variables Entered Variables Removed Model Method X17 A

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Variables Entered/Removed Variables Entered Variables Removed Model Method X17 Attractive Interior, X15 Fresh Food X16-- Reasonable Prices, X12- Friendly Employees Enter a. Dependent Variable: X22-- Satisfaction b. All requested variables entered Model Summary Adjusted R Square Std. Error of the Estimate Model R Square 675 455 450 829 a. Predictors: (Constant), X17- Attractive Interior, X15 Fresh Food, X16 - Reasonable Prices, X12- Friendly Employees ANOVAa Sum of Squares Model df Mean Square Si Regression Residual Total 230.019 275.225 505.244 57.50583.575 400 688 404 a. Dependent Variable: X22.- Satisfaction b. Predictors: (Constant), X17 -Attractive Interior, X15 Fresh Food, X16- Reasonable Prices, X12 Friendly Employees Coefficients Standardized Coefficients Beta Unstandardized Coefficients Model Std. Error Si Ig 298 429 Constant) X12- Friendly Employees X15 Fresh Food X16 Reasonable Prices X17 - Attractive Interior 128 668 281 037 304 7.596 390 038 035 042 417 10.346 197 178 5.041 4.617 a. Dependent Variable: X22- Satisfaction Explain fully the concepts of predictive modeling and regression analysis. For Santa Fe Grill, what does the multiple regressioneveal about the ability of fresh food and friendly service to predict customer satisfaction? Don't guess. What are the beta coefficients for each? Explain fully

Explanation / Answer

Dependent variable is satisfaction and independent variables are friendly employees, fresh food, reasonable prices and attractive interiors.

We have given the regression output.

First we have to set difference between predictive modeling and regression analysis.

Predictive modeling is a commonly used statistical technique to predict future behavior. Predictive modeling solutions are a form of data-mining technology that works by analyzing historical and current data and generating a model to help predict future outcomes

Simply put, predictive analytics uses past trends and applies them to future. For example, if a customer purchases a smart phone from a e-commerce website, he might be interested in it’s accessories immediately. He might be a potential customer for phone battery a few years down the line. Currently, chances of him buying accessory of a competitor smartphone are relatively bleak

Regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed.

Regression output description :

The regression equation is,

Satisfaction = -0.128 + 0.281*friendly employees + 0.390*fresh food + 0.178*reasonable prices + 0.195*attractive interior

Overall significance :

Here we have to test the hypothesis that,

H0 : Bj = 0 Vs H1 : Bj not= 0

where Bj is population slope for jth independent variable.

Assume alpha = level of significance = 0.05

Test statistic follows F-distribution.

F = 83.575

P-value = 0.000

P-value < alpha

Reject H0 at 5% level of significance.

Conclusion : Atleast one of the slope is differ than 0.

Individual significance :

Hypothesis for the test is,

H0 : B = 0 Vs H1 : B not= 0

where B is population slope.

Test statistic follows t-distribution.

Decision rule :

If P-value < alpha then reject H0 at 5% level of significance.

Conclusion : COrresponding variable is significant and population slope is differ than 0.

Or viceversa.

Now we can see that,

All the variables are significant because p-value for each variable is less than 0.05.

Rsq = 0.455

It expresses the proportion of variation in dependent variable which is expressed by variation in independent variables.

R = 0.675

There is positive relationship between dependent variable and independent variables.

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