Abstract
PART ONE: PRELIMINARIES Statistics and Social Science What Is Regression Analysis? Examining Data Transforming Data PART TWO: LINEAR MODELS AND LEAST SQUARES Linear Least-Squares Regression Statistical Inference for Regression Dummy-Variable Regression Analysis of Variance Statistical Theory for Linear Models The Vector Geometry of Linear Models PART THREE: LINEAR-MODEL DIAGNOSTICS Unusual and Influential Data Diagnosing Nonlinearity, Nonconstant Error Variance, and Nonnormality Collinearity and Its Purported Remedies PART FOUR: BEYOND LINEAR LEAST SQUARES Extending Linear Least Squares Time Series, Nonlinear, Robust, and Nonparametric Regression Logit and Probit Models Assessing Sampling Variation Bootstrapping and Cross-Validation
Keywords
MathematicsStatisticsRegression diagnosticLinear modelProper linear modelGeneralized least squaresLinear regressionHeteroscedasticityOrdinary least squaresLinear predictor functionVariance functionRegression analysisEconometricsLocal regressionRobust regressionPolynomial regressionEstimator
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Publication Info
- Year
- 1998
- Type
- article
- Volume
- 40
- Issue
- 2
- Pages
- 156-156
- Citations
- 1015
- Access
- Closed
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Cite This
J. Brian Gray,
John Fox
(1998).
Applied Regression Analysis, Linear Models, and Related Methods.
Technometrics
, 40
(2)
, 156-156.
https://doi.org/10.2307/1270653
Identifiers
- DOI
- 10.2307/1270653