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- catalog abstract ""Through the use of careful explanations and examples, Berry shows the reader 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 textbooks, Berry moves on to explore in detail the "substantive" meaning of each assumption (such as lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of autocorrelation). Aimed at improving social science applications of regression, this volume is a must for every student's and researcher's library."--Pub. desc.".
- catalog contributor b4178258.
- catalog created "c1993.".
- catalog date "1993".
- catalog date "c1993.".
- catalog dateCopyrighted "c1993.".
- catalog description ""Through the use of careful explanations and examples, Berry shows the reader 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 textbooks, Berry moves on to explore in detail the "substantive" meaning of each assumption (such as lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of autocorrelation). Aimed at improving social science applications of regression, this volume is a must for every student's and researcher's library."--Pub. desc.".
- catalog description "1. Introduction -- 2. A formal presentation of the regression assumptions -- The regression surface -- The role of the error term -- Other regression assumptions -- 3. A "weighty" illustration -- 4. The consequences of the regression assumptions being satisfied -- 5. The substantive meaning of regression assumptions -- Drawing dynamic inferences from cross-sectional regressions -- The assumption of the absence of perfect multicollinearity -- The assumption that the error term is uncorrelated with each of the independent variables -- Specification error: using the wrong independent variables -- The assumption that the mean of the error term is zero -- Assumptions about level of measurement -- The assumption of measurement without error -- Random measurement error -- Nonrandom measurement error -- Proxy variables -- The assumptions of linearity and additivity -- The assumption of homoscedasticity and lack of autocorrelation -- The substantive meaning of autocorrelation -- The substantive meaning of homoscedasticity -- The consequences of heteroscedasticity and autocorrelation -- The assumption that the error term is normally distributed -- 6. Conclusion.".
- catalog description "Includes bibliographical references (p. 89-90).".
- catalog extent "vii, 91 p. :".
- catalog hasFormat "Understanding regression assumptions.".
- catalog identifier "080394263X".
- catalog isFormatOf "Understanding regression assumptions.".
- catalog isPartOf "Quantitative applications in the social sciences ; no. 07-092.".
- catalog isPartOf "Sage university papers series. Quantitative applications in the social sciences ; no. 07-092".
- catalog issued "1993".
- catalog issued "c1993.".
- catalog language "eng".
- catalog publisher "Newbury Park : Sage Publications,".
- catalog relation "Understanding regression assumptions.".
- catalog subject "300/.1/519536 20".
- catalog subject "Error analysis (Mathematics)".
- catalog subject "HA31.3 .B47 1993".
- catalog subject "Regression analysis.".
- catalog subject "Social sciences Statistical methods.".
- catalog tableOfContents "1. Introduction -- 2. A formal presentation of the regression assumptions -- The regression surface -- The role of the error term -- Other regression assumptions -- 3. A "weighty" illustration -- 4. The consequences of the regression assumptions being satisfied -- 5. The substantive meaning of regression assumptions -- Drawing dynamic inferences from cross-sectional regressions -- The assumption of the absence of perfect multicollinearity -- The assumption that the error term is uncorrelated with each of the independent variables -- Specification error: using the wrong independent variables -- The assumption that the mean of the error term is zero -- Assumptions about level of measurement -- The assumption of measurement without error -- Random measurement error -- Nonrandom measurement error -- Proxy variables -- The assumptions of linearity and additivity -- The assumption of homoscedasticity and lack of autocorrelation -- The substantive meaning of autocorrelation -- The substantive meaning of homoscedasticity -- The consequences of heteroscedasticity and autocorrelation -- The assumption that the error term is normally distributed -- 6. Conclusion.".
- catalog title "Understanding regression assumptions / William D. Berry.".
- catalog type "text".