'Regression assumptions' presentation slideshows

Multiple Regression Assumptions & Diagnostics

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Unit 4: Regression assumptions: Evaluating their tenability

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Unit 4: Regression assumptions: Evaluating their tenability

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Unit 4: Regression assumptions: Evaluating their tenability

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AP Statistics

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Chapter 13

Chapter 13. Simple Linear Regression Analysis. Simple Linear Regression Analysis. 13.1 The Simple Linear Regression Model and the Least Square Point Estimates 13.2 Model Assumptions and the Standard Error 13.3 Testing the Significance of Slope and y-Intercept

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Statistics in WR: Lecture 11

Statistics in WR: Lecture 11. Key Themes Simple Linear Regression Derivation of the normal equations Multiple Linear Regression Reading: Helsel and Hirsch Chapter 7 Comparing several independent groups Reading: Barnett, Environmental Statistics Chapter 10 Time series methods

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Statistics in WR: Lecture 11

Statistics in WR: Lecture 11. Key Themes Simple Linear Regression Derivation of the normal equations Multiple Linear Regression Reading: Helsel and Hirsch Chapter 7 Comparing several independent groups Reading: Barnett, Environmental Statistics Chapter 10 Time series methods

206 views • 12 slides

Lecture 11

Lecture 11. Multivariate Regression A Case Study . Other topics: Multicollinearity. Assuming that all the regression assumptions hold how good are our estimators? The answer is pretty good if the multicollinearity effect is not present. So what is multicollinearity ?

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Inference About Prediction and Checking the Regression Assumptions

Inference About Prediction and Checking the Regression Assumptions. Sections 14.2 and 14.3. LSRL. We use the LSRL to predict a response to a specific value of x by substituting. We can give a confidence interval to describe how accurate a prediction is. Depends on whether:

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