A marketing researcher has developed a formal linear regression model to predict Consumer Buying Behaviour of fashion products.

The estimated model is: Y = b0 + b1X1+ b2X2+ b3X3

where, Y = Consumer buying behaviour,

X1 = Brand love,

X2 = Brand romance,

X3 = Price,

The following SPSS output was produced.

a) How much of the variation in Consumer buying behaviour is explained by the combined effects of Brand love, Brand romance, Price? Explain it. b) Consider the model fit, is this linear regression model valid? Explain. c) State null and alternative hypotheses from the above output and comment on the significance of SPSS results.

Regression Model Summary Adjusted R Std. Error of Model R R Square the Estimate Square 1 .645 .491 .441 1.990 Predictors: (Constant), Brand love, Brand romance, Price F Sig. 6.79 1.039 ANOVA Model Sum of df Mean Squares Square 1 Regression 150.00 10 15.0 Residual 250.00 100 2.50 Total 400.00 110 Dependent Variable: Consumer buying behavior Predictors: (Constant), Brand love, Brand romance, Price t Sig. .056 Coefficients Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta 1 (Constant) 3.21 2.01 Brand love -0.98 .333 |-.930 Brand 0.23 .203 .120 romance Price |-0.563 .570 |-.562 Dependent Variable: Consumer buying behavior Predictors: (Constant), Brand love, Brand romance, Price 1.931 2.24 -1.15 1.04 .08 -2.134 1.007