“Only do a few things from part B”PartB Conduct multiple linear regression with gender, program type and the interaction between them as predictors. Explain the similarities with anova results reported in parta. Interpret R-squared within context.Here is my process:> mlr.fit <- lm(writing ~ gender + programtype + programtype:gender, data = hsb2)> summary(mlr.fit)Call:lm(formula = writing ~ gender + programtype + programtype:gender, data = hsb2)Residuals: Min 1Q Median 3Q Max -21.617 -5.143 1.037 6.123 21.174 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 57.586 1.085 53.070 < 2e-16 ***gendermale -2.969 1.622 -1.831 0.068676 . programtypegeneral -4.336 2.006 -2.162 0.031849 * programtypevocation -6.623 1.925 -3.440 0.000712 ***gendermale:programtypegeneral -1.138 2.954 -0.385 0.700521 gendermale:programtypevocation -6.168 2.851 -2.163 0.031745 * ---Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1Residual standard error: 8.264 on 194 degrees of freedomMultiple R-squared: 0.259,Adjusted R-squared: 0.2399 F-statistic: 13.56 on 5 and 194 DF, p-value: 2.382e-11#R-squared is 0.259, it means the 25.9% errors of prediction of writing score can be explained by gender, program type and the interaction between them.Please do the "Explain the similarities with anova results reported in parta." for me, thanksAttachment 1Attachment 2
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Conduct multiple linear regression
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