Understanding Regression Assumptions, Issue 92Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation. |
Contents
A Formal Presentation of the Regression Assumptions | 3 |
A Weighty Illustration | 13 |
The Substantive Meaning of Regression Assumptions | 22 |
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Common terms and phrases
assume autocorrelation B₁X₁ B₂ bias biased CALORIES correlated denotes dent variables dichotomous disturbance term effect Equation 2.2 Equation 3.1 error of exclusion error term estimation model Estimator Bias example excluded variables expected value fat intake Figure frame of reference Gauss-Markov assumptions graph Gujarati held constant heteroscedasticity homoscedasticity human weight included income increase independent variables intercept least squares linear and additive mators measurement error METABOLISM negative nonlinear and/or nonadditive nonsmokers normally distributed NRME observations OLS estimators OLS regression ordered discrete variable parameter estimators partial slope coefficient perfect multicollinearity positive pounds proxy quantitative random variable reference model regression analysis regression assumptions regression equation regression model regressors relationship resulting sample saturated fat slope coefficient estimators SMOKER specification error substantive meaning theory time-series tion true model true score unbiased estimators uncorrelated vacation value in Column vari variables are held variance weight model X₁ X₂ Y₁ zero
References to this book
Discovering Statistics Using SPSS for Windows: Advanced Techniques for the ... Andy P. Field No preview available - 2000 |
Discovering Statistics Using SPSS: (and Sex, Drugs and Rock 'n' Roll) Andy P. Field No preview available - 2005 |