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An Introduction to Computational Statistics: Regression Analysis

Robert L. Schaefer, Robert I. Jennrich

Open publisher page 65 citations

Abstract

1. A Quick Look at a Typical Regression Program. 2. Simple Linear Regression. 3. Applying Simple Linear Regression. 4. Multiple Linear Regression. 5. Computer Assisted Model Building. 6. The General Linear Model. 7. Analysis of Variance and Covariance. 8. Nonlinear Regression. 9. Maximum Likelihood Analysis and Robust Estimation.

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What this paper is about

1. A Quick Look at a Typical Regression Program. 2. Simple Linear Regression. 3. Applying Simple Linear Regression. 4. Multiple Linear Regression. 5. Computer Assisted Model Building. 6. The General Linear Model. 7. Analysis of Variance and Covariance. 8. Nonlinear Regression. 9. Maximum Likelihood Analysis and Robust Estimation.

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OpenAlex reports 65 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

1. A Quick Look at a Typical Regression Program. 2. Simple Linear Regression. 3. Applying Simple Linear Regression. 4. Multiple Linear Regression. 5. Computer Assisted Model Building. 6. The General Linear Model. 7. Analysis of Variance and Covariance. 8. Nonlinear Regression. 9. Maximum Likelihood Analysis and Robust Estimation.

Key concepts: Proper linear model, Statistics, Simple linear regression, Linear regression, Regression diagnostic, Regression analysis, Analysis of covariance, Bayesian multivariate linear regression

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