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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Some Basic Results in Probability and Statistics | 1 |
Inferences in Regression Analysis | 3 |
Linear Regression with One Independent Variable | 21 |
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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 terms experimental units F test factor effects factor level means family confidence coefficient Figure fitted follows Hence independent variables indicator variables interval estimate ith level Kenton Food Company level of significance linear regression main effects matrix mean response mean sales mean squares means µ multiple comparison normally distributed observations obtained parameters population probability distribution random variables 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 sum of squares test statistic transformation treatment means two-factor study Type I error variance model X₁ Y₁ Y₂ zero σ²