Multiple Regression: A PrimerSAGE Publications, 29 Ara 1998 - 224 sayfa Multiple regression is at the heart of social science data analysis, because it deals with explanations and correlations. This book is a complete introduction to this statistical method. This textbook is designed for the first social statistics course a student takes and, unlike other titles aimed at a higher level, has been specifically written with the undergraduate student in mind. |
İçindekiler
Series Foreword | |
How Do I interpret Multiple Regression Results? | |
What Can Go Wrong With Multiple Regression? | |
How Do I Run a Multiple Regression? | |
How Does Bivariate Regression Work? | |
What Are the Assumptions of Multiple Regression? | |
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assumptions average bivariate regression calculate causal confidence intervals data set deleted demoralization dependent devaluation-discrimination dummy variables equation models estimates example extreme multicollinearity Figure gender heteroscedasticity homoscedasticity Income on Age increase independent variables intercept interpret least squares regression linear equation linear model logarithm logit male marital status married mean independence measurement error method missing data multicollinearity Multilevel Models multiple regression near-extreme multicollinearity nonlinear number of children observed omitted variables ordinary least squares possible predicted values prediction errors probability sample problem produce quadratic random regression analysis regression coefficients regression equation regression model Regression of Income regression packages regression programs relationship residuals SAT scores SAT training scale self-rated health SPSS squares regression line standard deviation standard error standardized coefficients statistical package statistically significant studentized residuals sum of squared Suppose Table tell there’s tolerance transformation unstandardized urban variance inflation factor violations What’s zero