Multiple Linear Regression
Chapter Outline
12-1 Multiple Linear Regression Model
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
12-3 Confidence Intervals in Multiple Linear Regression
12-3.1 Confidence Intervals on Individual Regression Coefficients
12-3.2 Confidence Interval on the Mean Response
12-4 Prediction of New Observations
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
This chapter generalizes the simple linear regression to a situation that has more than one predictor or regressor variable. This situation occurs frequently in science and engineering; for example, in Chapter 1, we provided data on the pull strength of a wire bond on a semiconductor package and illustrated its relationship to the wire length and the die height. Understanding ...
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