Mat 510 Case Study 2 Improving E-Mail Marketing Response

Case Study 2: Improving E-Mail Marketing ResponseYour name:Institution name:Date:Case Study 2: Improving E-Mail Marketing ResponseModel results discussionThe model summary showed an R-square value equal to 0.740. This implied that 74% of the variations in the response variable were explained by the predictors in the model. Ideally, the linear model fit was good. Table 1.1: Model SummaryModelRR SquareAdjusted R SquareStd. E of the Estimate1.860a.740.64610.847The ANOVA table results showed that jointly, the predictor variables were effective. This is because the F-statistic was equal to 7.833 with a significance value of 0.003<0.05. This led to a rejection of the null hypothesis that the predictor variables were insignificant CITATION Jos121 \l 2057 (Schmee & Oppenlander, 2012). Table 1.2: ANOVAModelS of SD fMean SquareFSig.1Regression3686.2504921.5637.833.003bResidual1294.18711117.653Total4980.43815The overall model for the fixed effects wasy=48.188+3.875x1+12.125x2-27.125x3+4.875x4Where y was the response, x1 the heading, x2 the email open, x3 the body and x4 the replicate (blocking factor).Table 1.3: CoefficientsModelUnstandardized Coeff.Standardized Coeff.tSig.BStd. EBeta1(Constant)48.18816.4952.921.014Heading3.8755.423.110.714.490Email open12.1255.423<span