Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental DesignsSome basic results in probability and statistics. basic regression analysis. Linear regression with one independent variable. Inferences in regression analysis. Aptness of model and remedial measures. Topics in regression analysis - I. General regression and correlation analysis. Matrix appreach to simple regression analysis. Multiple regression. Polymonial regression. Indicator variables. Topics in regression analysis - II. Search for "best" set of independent variables. Normal correlation models. Basic analysis of variance. Single - factor analysis of variance. Analysis of factor effects. Implementation of ANOVA model. Topics in analysis of variance - I. Multifactor analysis of variance. Two factor analysis of variance. Analysis of two - factor studies. To pics in analysis of variance - II. Multifactor studies. Experimental designs. Completely randomized designs. Analysis of covariance for completely randomized designs. Randomized block designs. Latin square designs. |
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Page 136
... plotted the residuals against the fitted values Ŷ , he found no relation . Why is there this difference , and which is the more meaningful plot ? 4.4 . Refer to Problem 2.12 . Obtain the residuals and make all appropriate residual plots ...
... plotted the residuals against the fitted values Ŷ , he found no relation . Why is there this difference , and which is the more meaningful plot ? 4.4 . Refer to Problem 2.12 . Obtain the residuals and make all appropriate residual plots ...
Page 502
... Residual Plots Residual plots for an analysis of variance model are shown in Figure 15.1 , which contains the residuals of Table 13.2 for the Kenton study , plotted against observation number . It is helpful to plot the residuals for ...
... Residual Plots Residual plots for an analysis of variance model are shown in Figure 15.1 , which contains the residuals of Table 13.2 for the Kenton study , plotted against observation number . It is helpful to plot the residuals for ...
Page 516
... residual plots enables you to diagnose that in one case the error variance changes over time whereas in the other case the effect is of a different nature ? 15.7 . Refer to Figure 15.3 . How would you modify the basic analysis of ...
... residual plots enables you to diagnose that in one case the error variance changes over time whereas in the other case the effect is of a different nature ? 15.7 . Refer to Figure 15.3 . How would you modify the basic analysis of ...
Contents
Some Basic Results in Probability and Statistics | 1 |
Linear Regression with One Independent Variable | 21 |
Inferences in Regression Analysis | 53 |
Copyright | |
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5-color 95 percent analysis of variance ANOVA B₁ B₂ Bonferroni C₁ Company example completely randomized design conclude C₂ confidence interval correlation covariance decision rule degrees of freedom denoted equal error sum error terms experimental units F test factor effects factor level means family confidence coefficient Figure fitted follows Hence independent variables indicator variables interval estimate Kenton Food Company level of significance linear regression main effects matrix mean response mean sales mean squares means µ multiple comparison normally distributed Note observations obtain parameters probability distribution procedure Refer to Problem regression analysis regression coefficients regression function regression line regression model residual plots response function sample sizes Scheffé single-factor specific SSAB SSE(F ẞo SSTO SSTR standard deviation sum of squares test statistic transformation treatment means two-factor study Type I error variance model X₁ Y₁ Y₂ zero σ²