Design and Analysis of ExperimentsThis book describes the methods and techniques used in the design and analysis of experiments. It emphasizes the connection between the experiment and the model that the experimenter can develop from the results of the experiment and features a new chapter on experiments with random factors. |
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Results 1-3 of 57
Page 38
... ( Equal Vars . ) Sample 1 Sample 2 ( Unequal Vars . ) Sample 1 Sample 2 - Ratio of Variances = 1.62926 Conf . Interval for Ratio of Variances : Sample 1 Sample 2 - Hypothesis Test for HO : Diff = 0 vs Alt : NE at Alpha = 0.05 95 Percent ...
... ( Equal Vars . ) Sample 1 Sample 2 ( Unequal Vars . ) Sample 1 Sample 2 - Ratio of Variances = 1.62926 Conf . Interval for Ratio of Variances : Sample 1 Sample 2 - Hypothesis Test for HO : Diff = 0 vs Alt : NE at Alpha = 0.05 95 Percent ...
Page 79
... equal variances for the a treatments if the sample sizes are equal . This is not the case for unequal sample sizes . Second , the power of the test is maximized if the samples are of equal size . 3-4 MODEL ADEQUACY CHECKING The ...
... equal variances for the a treatments if the sample sizes are equal . This is not the case for unequal sample sizes . Second , the power of the test is maximized if the samples are of equal size . 3-4 MODEL ADEQUACY CHECKING The ...
Page 335
... equal . To illustrate the calculations , the value of F is S2 ( B + ) F * = ln S2 ( B- ) ( 2.72 ) 2 = In ( 0.83 ) 2 = = 2.37 Table 7-13 presents the complete set of contrasts for the 24 design along with the residuals for each run from ...
... equal . To illustrate the calculations , the value of F is S2 ( B + ) F * = ln S2 ( B- ) ( 2.72 ) 2 = In ( 0.83 ) 2 = = 2.37 Table 7-13 presents the complete set of contrasts for the 24 design along with the residuals for each run from ...
Contents
Simple Comparative Experiments | 20 |
The Analysis | 63 |
More About SingleFactor Experiments | 126 |
Copyright | |
16 other sections not shown
Common terms and phrases
23 design ABCD aliased analysis of variance Analyze the data average B₁ block design central composite design column computed confidence interval confounded Consider covariance defining relation degrees of freedom effect estimates engineer error expected mean squares experimental design factor levels factorial experiment fractional factorial design Latin square least squares linear main effects method normal equations normal probability plot null hypothesis observations obtained orthogonal P-value parameter percent confidence interval Problem procedure quadratic random variables regression coefficients regression model replicates residuals versus response surface response variable runs sample shown in Figure shown in Table significant Source of Variation Square Fo P-Value SSAB standard deviation sum of squares Suppose temperature tensile strength test statistic three-factor treatment combinations treatment means two-factor interactions usually variance components x₁ y₁ yield Yijk σ² στ