Use t-tests to assess the contribution of each regressor to the model. Discuss y
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Use t-tests to assess the contribution of each regressor to the model. Discuss your findings b. t. testy,x1+x2+x3+x4+X5 +X6+x7, data chem) welch Two Sample t-test data: y and x1 X2 x3 t x4 x5 x6 + x7 t--9. 6408, df 26.012, p-value 4.495 e-10 alternative hypothesis: true difference in means is not equal to o 95 percent confidence interval 2536.361 1644.890 sample estimates: mean of x mean of y 24. 73037 2115. 35593 Fit a multiple regression model relating CO2 product (y) to total solvent (x6) and hydrogen consumption (x7). Test for significance of regression. Calculate R and R2d- Interpret these statistics. c. fit2=1n(y-x6+x7,data=chen) > > summary(fitz) Call: 1m(formula = y ~ x6 + x7, data-chen) Residuals: -23. 2035 -4. 3713 0.2513 4.9339 21. 9682 Coefficients: Min 1Q Median 30 Max Estimate std. Error t value Pr(>It!) (Intercept) 2.526460 3-6100550-7000.4908 x6 x7 o.018522 0.002747 6.742 5 66e-07 2.185753 0.972696 2.247 0. 0341 Residual standard error: 9. 924 on 24 degrees of freedom Multiple R-squared: o. 6996, Adjusted R-squared: 0.6746 F-statistic: 27.95 on 2 and 24 DF, I p-value: 5.391e-07 Construct 95% confidence intervals for associated with x6 and x7, respectively). d. and (these are the regression coefficients s confint (fit2,"x6", 1evel-0.95) 2.5 % 97.5 % x6 0.01285196 0.02419204Explanation / Answer
Use t-tests to assess the contribution of each regressor to the model. Discuss your findings b. t. testy,x1+x2+x3+x4+X5 +X6+x7, data chem) welch Two Sample t-test data: y and x1 X2 x3 t x4 x5 x6 + x7 t--9. 6408, df 26.012, p-value 4.495 e-10 alternative hypothesis: true difference in means is not equal to o 95 percent confidence interval 2536.361 1644.890 sample estimates: mean of x mean of y 24. 73037 2115. 35593 Fit a multiple regression model relating CO2 product (y) to total solvent (x6) and hydrogen consumption (x7). Test for significance of regression. Calculate R and R2d- Interpret these statistics. c. fit2=1n(y-x6+x7,data=chen) > > summary(fitz) Call: 1m(formula = y ~ x6 + x7, data-chen) Residuals: -23. 2035 -4. 3713 0.2513 4.9339 21. 9682 Coefficients: Min 1Q Median 30 Max Estimate std. Error t value Pr(>It!) (Intercept) 2.526460 3-6100550-7000.4908 x6 x7 o.018522 0.002747 6.742 5 66e-07 2.185753 0.972696 2.247 0. 0341 Residual standard error: 9. 924 on 24 degrees of freedom Multiple R-squared: o. 6996, Adjusted R-squared: 0.6746 F-statistic: 27.95 on 2 and 24 DF, I p-value: 5.391e-07 Construct 95% confidence intervals for associated with x6 and x7, respectively). d. and (these are the regression coefficients s confint (fit2,"x6", 1evel-0.95) 2.5 % 97.5 % x6 0.01285196 0.02419204
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