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Mathematical Statistics with Applications
Linear Models and Estimation by Least Squares
Mathematical Statistics with Applications
Dennis D. Wackerly, William Mendenhall III, Richard L. Scheaffer
Chapter 11
Linear Models and Estimation by Least Squares - all with Video Answers
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Section 1
Introduction
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11.1: Introduction
11.2: Linear Statistical Models
11.3: The Method of Least Squares
11.4: Properties of the Least-Squares Estimators: Simple Linear Regression
11.5: Inferences Concerning the Parameters $\beta_{i}$
11.6: Inferences Concerning Linear Functions of the Model Parameters: Simple Linear Regression
11.7: Predicting a Particular Value of $Y$ by Using Simple Linear Regression
11.8: Correlation
11.9: Some Practical Examples
11.10: Fitting the Linear Model by Using Matrices
11.11: Linear Functions of the Model Parameters: Multiple Linear Regression
11.12: Inferences Concerning Linear Functions of the Model Parameters: Multiple Linear Regression
11.13: Predicting a Particular Value of $Y$ by Using Multiple Regression
11.14: A Test for $H_{0}: \beta_{g+1}=\beta_{g+2}=\cdots=\beta_{k}=0$
11.15: Summary and Concluding Remarks