'Mstr mse' diaporamas de présentation

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Chapter 13 Experimental Design and Analysis of Variance

Chapter 13 Experimental Design and Analysis of Variance. An Introduction to Experimental Design and Analysis of Variance. Analysis of Variance and the Completely Randomized Design. Multiple Comparison Procedures. An Introduction to Experimental Design

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SLIDES . BY

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Analysis of Variance

Analysis of Variance. Chapter 13 BA 303. An Introduction to Analysis of Variance. A factor is a variable that the experimenter has selected for investigation. A treatment is a level of a factor. Example:

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Chapter 10: Comparisons Involving Means

Chapter 10: Comparisons Involving Means. Introduction to Analysis of Variance. Analysis of Variance: Testing for the Equality of k Population Means. Analysis of Variance. Analysis of Variance (ANOVA) is used to test for the equality of three or more population means.

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ANOVA Randomized Block Models 2-Factor Without Replication

ANOVA Randomized Block Models 2-Factor Without Replication. Reducing Variability. Remember -- we are more sure of our results if we can reduce variability. Also we may feel that more than one factor affects the outcome of the experiment.

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Test for the Equality of k Population Means

Test for the Equality of k Population Means. Hypotheses . H 0 : ? 1 ? = ? ? 2 ? = ? ? 3 ? = ? . . . = ? k ? H a : Not all population means are equal. F = MSTR/MSE. Test Statistic . Rejection Rule. p -value Approach:. Reject H 0 if p -value < a. Critical Value Approach:.

344 views • 16 slides

ANOVA Randomized Block Models 2-Factor Without Replication

ANOVA Randomized Block Models 2-Factor Without Replication. Reducing Variability. Remember -- we are more sure of our results if we can reduce variability. Also we may feel that more than one factor affects the outcome of the experiment.

359 views • 15 slides

ANOVA Randomized Block Models 2-Factor Without Replication

ANOVA Randomized Block Models 2-Factor Without Replication. Reducing Variability. Remember -- we are more sure of our results if we can reduce variability. Also we may feel that more than one factor affects the outcome of the experiment.

232 views • 15 slides