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test H0: 1 0. Minitab performs this test. The P-value is 0.04, indicating that temperature has a signi cant effect on the probability of O-ring failure. The odds ratio is 0.84, so every one degree increase in temperature reduces the odds of failure by 0.84. Figure S11-9 shows the tted logistic regression model. The sharp increase in the probability of O-ring failure is very evident in this graph. The actual temperature at the Challenger launch was 31 F . This is well outside the range of other launch temperatures, so our logistic regression model is not likely to provide highly accurate predictions at that temperature, but it is clear that a launch at 31 F is almost certainly going to result in O-ring failure. It is interesting to note that all of these data were available prior to launch. However, engineers were unable to effectively analyze the data and use them to provide a convincing argument against launching Challenger to NASA managers. Yet a simple regression analysis of the data would have provided a strong quantitative basis for this argument. This is one of the more dramatic instances that points out why engineers and scientists need a strong background in basic statistical techniques.

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12-1 MULTIPLE LINEAR REGRESSION MODEL 12-1.1 Introduction 12-1.2 Least Squares Estimation of the Parameters 12-1.3 Matrix Approach to Multiple Linear Regression 12-1.4 Properties of the Least Squares Estimators 12-2 HYPOTHESIS TESTS IN MULTIPLE LINEAR REGRESSION 12-2.1 Test for Significance of Regression 12-2.2 Tests on Individual Regression Coefficients and Subsets of Coefficients 12-2.3 More About the Extra Sum of Squares Method (CD Only) 12-3 CONFIDENCE INTERVALS IN MULTIPLE LINEAR REGRESSION 12-3.1 Confidence Intervals on Individual Regression Coefficients

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12-4 PREDICTION OF NEW OBSERVATIONS 12-5 MODEL ADEQUACY CHECKING 12-5.1 Residual Analysis 12-5.2 Influential Observations 12-6 ASPECTS OF MULTIPLE REGRESSION MODELING 12-6.1 Polynomial Regression Models 12-6.2 Categorical Regressors and Indicator Variables 12-6.3 Selection of Variables and Model Building 12-6.4 Multicollinearity 12-6.5 Ridge Regression (CD Only) 12-6.6 Nonlinear Regression Models (CD Only)

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After careful study of this chapter, you should be able to do the following: 1. Use multiple regression techniques to build empirical models to engineering and scientific data 2. Understand how the method of least squares extends to fitting multiple regression models

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3. Assess regression model adequacy 4. Test hypotheses and construct confidence intervals on the regression coefficients 5. Use the regression model to estimate the mean response and to make predictions and to construct confidence intervals and prediction intervals 6. Build regression models with polynomial terms 7. Use indicator variables to model categorical regressors 8. Use stepwise regression and other model building techniques to select the appropriate set of variables for a regression model CD MATERIAL 9. Understand how ridge regression provides an effective way to estimate model parameters where there is multicollinearity. 10. Understand the basic concepts of fitting a nonlinear regression model. Answers for many odd numbered exercises are at the end of the book. Answers to exercises whose numbers are surrounded by a box can be accessed in the e-Text by clicking on the box. Complete worked solutions to certain exercises are also available in the e-Text. These are indicated in the Answers to Selected Exercises section by a box around the exercise number. Exercises are also available for some of the text sections that appear on CD only. These exercises may be found within the e-Text immediately following the section they accompany.

PowerPoint is a presentation program you can use to convey all kinds of messages to an audience. You can use PowerPoint to create slide shows to present ideas to clients, explain a concept or procedure to employees or teach a class. In this part, you learn how to create slide shows; add text, tables, charts, video clips and pictures to your slide show; and package your show on a CD-ROM or output it as a movie file. You also learn how to add special effects, including animations, sound effects, and transitions to make your slide show lively and engaging.

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Many applications of regression analysis involve situations in which there are more than one regressor variable. A regression model that contains more than one regressor variable is called a multiple regression model. As an example, suppose that the effective life of a cutting tool depends on the cutting speed and the tool angle. A multiple regression model that might describe this relationship is Y

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