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 165
... Type I error at a is : ( 5.45 ) - If F * ≤ F ( 1 − a ; 2 , n , + n2 − 4 ) , conclude C1 - - If F * > F ( 1 − a ; 2 , n , + n2 − 4 ) , conclude C2 Example . For our soap production lines illustration , the test statistic is ...
... Type I error at a is : ( 5.45 ) - If F * ≤ F ( 1 − a ; 2 , n , + n2 − 4 ) , conclude C1 - - If F * > F ( 1 − a ; 2 , n , + n2 − 4 ) , conclude C2 Example . For our soap production lines illustration , the test statistic is ...
Page 493
... error process , however , would be required with these charts . Fortunately , other charts are available which ... Type I error is to be controlled . 2. The value of o ' at which the risk of making a Type II error is to be con- trolled ...
... error process , however , would be required with these charts . Fortunately , other charts are available which ... Type I error is to be controlled . 2. The value of o ' at which the risk of making a Type II error is to be con- trolled ...
Page 580
... Type I error at a is : ( 17.42 ) If F * ≤ F ( 1 − x ; a 1 , ( n − 1 ) ab ) , conclude C1 - If F * > F ( 1 − x ; a − 1 , ( n − 1 ) ab ) , conclude C2 - - Test for Factor B Effects This test is similar to the one for factor A ...
... Type I error at a is : ( 17.42 ) If F * ≤ F ( 1 − x ; a 1 , ( n − 1 ) ab ) , conclude C1 - If F * > F ( 1 − x ; a − 1 , ( n − 1 ) ab ) , conclude C2 - - Test for Factor B Effects This test is similar to the one for factor A ...
Contents
Some Basic Results in Probability and Statistics | 1 |
Inferences in Regression Analysis | 3 |
3 | 13 |
Copyright | |
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95 percent analysis of variance ANOVA appropriate b₁ B₂ block design blocking variable Bonferroni C₁ column Company example completely randomized design conclude C₂ confidence interval correlation decision rule degrees of freedom denoted error sum error terms error variance experimental units factor effects factor level means family confidence coefficient Figure fixed effects follows Hence independent variables indicator variables interval estimate latin square level of significance linear regression main effects matrix mean response normally distributed observations obtain parameters prediction prediction interval probability distribution random variables Refer to Problem regression analysis regression coefficients regression function regression line residual plots response function sample sizes Source of Variation ẞ₁ SSAB SSE(F ẞo SSTO SSTR sum of squares test statistic transformation treatment effects two-factor study Type I error variance model Variation SS df Westwood Company X₁ X₂ Y₁ Y₂ zero σ²