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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Contents
Some Basic Results in Probability and Statistics | 1 |
Linear Regression with One Independent Variable | 21 |
Inferences in Regression Analysis | 53 |
Copyright | |
22 other sections not shown
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Common terms and phrases
95 percent analysis of variance ANOVA appropriate block design blocking variable Bonferroni column Company example completely randomized design conclude C2 confidence interval correlation decision rule degrees of freedom denoted equal error sum error terms error variance experimental units factor effects factor level means family confidence coefficient Figure follows Hence independent variables indicator variables interval estimate latin square level of significance linear regression main effects matrix mean response method normally distributed Note observations obtain parameters percent confidence prediction prediction interval probability distribution procedure random variables Refer to Problem regression analysis regression approach regression coefficients regression function regression line residual plots response function sample sizes significance of 05 Source of Variation SSAB SSE(F SSE(R SSTO SSTR standard deviation sum of squares test statistic three-factor transformation treatment effects treatment means two-factor study Type I error variance model Variation SS df vector Westwood Company zero
Bibliographic information
| Title | Applied linear statistical models: regression, analysis of variance, and experimental designs |
| Authors | John Neter, William Wasserman |
| Publisher | R. D. Irwin, 1974 |
| Original from | the University of Michigan |
| Digitized | Feb 3, 2010 |
| ISBN | 0256014981, 9780256014983 |
| Length | 842 pages |
| Subjects | › › Analysis of variance Experimental design Linear models (Statistics) Mathematics / Probability & Statistics / General Mathematics / Probability & Statistics / Regression Analysis Regression analysis Regressional analysis Science / Research & Methodology System analysis |
| Export Citation | BiBTeX EndNote RefMan |