'F distribution' diaporamas de présentation

IE241: Introduction to Hypothesis Testing

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Hypothesis tests for slopes in multiple linear regression model

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Multiple Linear Regression and Correlation Analysis

Multiple Linear Regression and Correlation Analysis. Chapter 14. GOALS. Describe the relationship between several independent variables and a dependent variable using multiple regression analysis . Set up, interpret, and apply an ANOVA table

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Chapter 12 Simple Linear Regression

Chapter 12 Simple Linear Regression. Simple Linear Regression Model Least Squares Method Coefficient of Determination Model Assumptions Testing for Significance Using the Estimated Regression Equation for Estimation and Prediction Computer Solution

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Chapter 3 Multiple Linear Regression

Chapter 3 Multiple Linear Regression. Ray-Bing Chen Institute of Statistics National University of Kaohsiung. 3.1 Multiple Regression Models. Multiple regression model: involve more than one regressor variable.

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Qualitative predictor variables

Qualitative predictor variables. Examples of qualitative predictor variables. Gender (male, female) Smoking status (smoker, nonsmoker) Socioeconomic status (poor, middle, rich). An example with one qualitative predictor. On average, do smoking mothers have babies with lower birth weight?.

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Factorial Models

Factorial Models. Random Effects Gauge R&R studies (Repeatability and Reproducibility) have been an expanding area of application Mixed Effects Models. One-way Random Effects. The one-way random effects model is quite different from the one-way fixed effects model

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Statistical Inference and Regression Analysis: Stat-GB.3302.30, Stat-UB.0015.01

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Chapter 2 Simple Comparative Experiments

Chapter 2 Simple Comparative Experiments. 2.1 Introduction. Consider experiments to compare two conditions Simple comparative experiments Example: The strength of portland cement mortar Two different formulations: modified v.s. unmodified Collect 10 observations for each formulations

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Chapter 2 Simple Comparative Experiments

Chapter 2 Simple Comparative Experiments. 2.1 Introduction. Consider experiments to compare two conditions Simple comparative experiments Example: The strength of portland cement mortar Two different formulations: modified v.s. unmodified Collect 10 observations for each formulations

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Chapter 13 Experimental Design and Analysis of Variance

Chapter 13 Experimental Design and Analysis of Variance . 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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Part IVA

Part IVA. Analysis of Variance (ANOVA). Dr. Stephen H. Russell. Weber State University. Introduction to the concept of ANOVA.

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NEED TO GO BACK TO CLT - LECTURE 4 Hypothesis Testing in the Multiple regression model

NEED TO GO BACK TO CLT - LECTURE 4 Hypothesis Testing in the Multiple regression model. Testing that individual coefficients take a specific value such as zero or some other value is done in exactly the same way as with the simple two variable regression model.

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ANOVA Single Factor Models

ANOVA Single Factor Models. ANOVA. ANOVA ( AN alysis O f VA riance) is a natural extension used to compare the means more than 2 populations.

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Two-sample problems for population means

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Two-sample problems for population means

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Chi-square and F Distributions

Chi-square and F Distributions. Children of the Normal. Distributions. There are many theoretical distributions, both continuous and discrete. We use 4 of these a lot: z (unit normal), t, chi-square, and F.

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Inferences about Variances ( Chapter 7)

Inferences about Variances ( Chapter 7). In this Lecture we will:. Develop point estimates for the population variance Construct confidence intervals for the population variance. Perform one-sample tests for the population variance. Perform two-sample tests for the population variance.

375 views • 16 slides