## 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 572

First we shall obtain a decomposition of the total deviation Yijk — F by viewing

the study as consisting of ab treatments : (17.29) Yijk-Y,.. = Yij^J^ + l^Jii. Total

Deviation of Deviation deviation

...

First we shall obtain a decomposition of the total deviation Yijk — F by viewing

the study as consisting of ab treatments : (17.29) Yijk-Y,.. = Yij^J^ + l^Jii. Total

Deviation of Deviation deviation

**treatment mean**around around overall treatment...

Page 573

This is the ordinary ANOVA table treating the study as a single-factor one with ab

= r = 6 treatments. The sums of ... + (46 - 44)2 = 62 One could test at this point, by

means of (13.65), whether or not the six

This is the ordinary ANOVA table treating the study as a single-factor one with ab

= r = 6 treatments. The sums of ... + (46 - 44)2 = 62 One could test at this point, by

means of (13.65), whether or not the six

**treatment means**are equal. If they are ...Page 778

Remember that each

Next, the analyst found the T multiple in (24.10b): T = 4- ?(-90; 5, 12) = 4= (3.92) =

2.77 so that: Ts(D) = 2.77(2.51) = 7.0 Using the

the ...

Remember that each

**treatment mean**Y k is based on five observations here.Next, the analyst found the T multiple in (24.10b): T = 4- ?(-90; 5, 12) = 4= (3.92) =

2.77 so that: Ts(D) = 2.77(2.51) = 7.0 Using the

**treatment means**in Table 24.2,the ...

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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 blocking variable Bonferroni column Company example completely randomized design conclude C2 confidence interval correlation covariance analysis 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 illustration independent variables indicator variables interval estimate latin square latin square design 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 shown significance of 05 Source of Variation SSAB SSE(F SSE(R SSTO SSTR sum of squares test statistic three-factor transformation treatment effects treatment means two-factor study Type I error variance model vector Westwood Company zero