Marketing. Question below

A marketing researcher has developed a formal linear regression model to predict Consumer Buying Behaviour of fashion products. The estimated model is: Y = bo+ biXi+ b2X2+ b3X3 where, Y = Consumer buying behaviour, X1 = Brand love, X2 = Brand romance, X3 = Price, The following SPSS output was produced. Regression Model Summary Adjusted R Std. Error of Model R R Square the Estimate Square 645 491 441 1.990 Predictors: (Constant), Brand love, Brand romance, Price ANOVA Model Sum of df Mean Sig. Squares Square 1 Regression 150.00 10 15.0 6.79 039 Residual 250.00 100 2.50 Total 400.00 110 Dependent Variable: Consumer buying behavior Predictors: (Constant), Brand love, Brand romance, Price Coefficients Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta Sig. 1 (Constant) 3.21 2.01 1.931 056 Brand love -0.98 333 .930 2.24 04 Brand 0.23 .203 120 -1.15 08 romance Price 0.563 570 -.562 -2.134 007 Dependent Variable: Consumer buying behavior Predictors: (Constant), Brand love, Brand romance, Pricea) 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