'Predictor coef se coef' diaporamas de présentation

Residuals

Residuals. A continuation of regression analysis. Lesson Objectives. Continue to build on regression analysis . Learn how residual plots help identify problems with the analysis. Case X Y 1 73 175 2 68 158 3 67 140 4 72 207 5 62 115. ^.

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Model selection and model building

Model selection and model building. Model selection. Selection of predictor variables. Statement of problem. A common problem is that there is a large set of candidate predictor variables.

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Multiple Regression Analysis

Multiple Regression Analysis. Ram Akella University of California Berkeley Silicon Valley Center/SC Lecture 2 January 26, 2011. Introduction.

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Announcements:

Announcements:. Next Homework is on the Web Due next Tuesday. Mosquito repellent experiment: 30 people were recruited for an experiment. Groups of 10 were randomly assigned to one of three repellent types.

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Multiple Regression Models: Interactions and Indicator Variables

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Design and Analysis of Experiments (5) Fitting Regression Models

Design and Analysis of Experiments (5) Fitting Regression Models. Kyung-Ho Park. In many problems one or more variables are related, it is of interest to model and explore this relationship. The model can be used for prediction, process optimization, or process control. Modelling.

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Lecture 13. Inference for regression

Lecture 13. Inference for regression. Objectives. Inference for regression (NHST Regression Inference Award)[B level award] The regression model Confidence interval for the regression slope ? Testing the hypothesis of no linear relationship Inference for prediction

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Lecture 10

Lecture 10. Review of Lecture 9 Cases of F-test Problems with Examples. Review of Lecture 9 : Three Basic Concepts : Full Model : contain ALL coefficients of interest Reduced Model : contain PART of the coefficients of interest

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MARE 250 Dr. Jason Turner

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Linear Regression and Correlation

CHAPTER 12. Linear Regression and Correlation. Example. The table shows the math achievement test scores for a random sample of n = 10 college freshmen, along with their final calculus grades. Example. The ANOVA Table. The Calculus Example:. Least squares regression line.

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Inference with computer printouts

Inference with computer printouts. Leaning Tower - Excell. Leaning Tower - Minitab. Predictor Coef SE Coef T P Constant -42.41 32.95 -1.29 0.234 Year 9.0925 0.4054 22.43 0.000 S = 4.57997 R-Sq = 98.4% R-Sq(adj) = 98.2%.

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Analysis - 1 st with Minitab

Analysis - 1 st with Minitab. Regression Analysis: Capacity versus Age, Height, ... The regression equation is Capacity = - 6.17 - 0.0140 Age + 0.149 Height + 0.00636 Weight - 0.0087 Chest - 0.0220 Waist + 0.343 Activity - 0.109 Smoke - 0.409 Gender

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Interpreting Computer Output

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