'Ss error' presentation slideshows

STT 511-STT411: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE Dr. Cuixian Chen

STT 511-STT411: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE Dr. Cuixian Chen. Chapter 3: Experiments with a single factor: ANOVA. Portland Cement Formulation (page 26). Review: Two (independent) sample scenario.

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Basic concept of statistics

Basic concept of statistics. Measures of central tendency. Measures of dispersion & variability. Measures of tendency central. Arithmetic mean (= simple average). Best estimate of population mean is the sample mean, X. measurement in population. summation. sample size.

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INC341 Design with Root Locus

INC341 Design with Root Locus. Lecture 9. 2 objectives for desired response. Improving transient response Percent overshoot, damping ratio, settling time, peak time Improving steady-state error Steady state error. Gain adjustment.

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ANALYSIS OF VARIANCE

ANALYSIS OF VARIANCE. t-test refresher. In chapter 7 we talked about analyses that could be conducted to test whether pairs of means were significantly different.

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Repeated Measures Design

Repeated Measures Design. Repeated Measures ANOVA. Instead of having one score per subject, experiments are frequently conducted in which multiple scores are gathered for each case Repeated Measures or Within-subjects design. Advantages.

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Repeated Measures Design

Repeated Measures Design. Repeated Measures ANOVA. Instead of having one score per subject, experiments are frequently conducted in which multiple scores are gathered for each case Repeated Measures or Within-subjects design. Advantages.

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Repeated-Measures ANOVA

Repeated-Measures ANOVA. Old and New. One-Way ANOVA looks at differences between different samples exposed to different manipulations (or different samples that may come from different groups) within a single factor. That is, 1 factor, k levels ? k separate groups are compared.

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Warsaw Summer School 2019, OSU S tudy A broad P rogram

Warsaw Summer School 2019, OSU S tudy A broad P rogram. Regression. Linear Relationship. The line = a mathematical function that can be expressed through the formula Y = a + bX, where Y & X are our variables.

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Chapter Seventeen

Chapter Seventeen. One-way Analysis of Variance (ANOVA). One Sample. Two Independent Samples. Paired Samples. More Than Two Samples. Means. Means. Means. Means. Proportions. Proportions. Proportions. Proportions. Hypothesis Tests Related to Differences. Tests of Differences.

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One-Factor Analysis of Variance

One-Factor Analysis of Variance. A method to compare two or more (normal) population means. Does distance it takes to stop car at 60 mph depend on tire brand ?. Brand1 Brand2 Brand3 Brand4 Brand5 194 189 185 183 195 184 204 183 193 197 189 190 186 184 194

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Chapter Seventeen

Chapter Seventeen. Chapter 17. Figure 17.1 Relationship to the Previous Chapters & The Marketing Research Process. Figure 17.1 Relationship of Hypothesis Testing Related to Differences to the Previous Chapter and the Marketing Research Process. Focus of This Chapter. Relationship to

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Regression & Prediction

Regression & Prediction. Linear Regression . Finding the best fitting straight line for a set of data This line is represented by the equation Y = bX + a, where a & b are fixed constants. Y=bX+a. Least Squares Regression. Most commonly used regression line

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Chapter 14: Repeated-Measures Analysis of Variance

Chapter 14: Repeated-Measures Analysis of Variance. The Logical Background for a Repeated-Measures ANOVA. Chapter 14 extends analysis of variance to research situations using repeated?measures (or related?samples) research designs.

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Moderator Analyses

Moderator Analyses. Testing a categorical moderator Testing a continuous moderator Examining relations among moderators Testing a multiple regression model. Testing a Categorical Moderator. Parallels one-way ANOVA Between-group heterogeneity Qb

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