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‫ –ניתוחי רגרסיה‬SPSS ‫ניתוחים מתקדמים ב‬
‫תוכן הקורס‬

1. Introduction to regression                                        6. Influential points and
 A simple regression analysis                                       multicollinearity
 Fitting lines to data                                               Influential points
 Goodness-of fit                                                     Multicollinearity
 How a line is fit                                                   Requesting the diagnostic
 Residual and influential points                                     Regression output
 What does linear model mean?                                        Influence measures
 Assumption of the general linear model                              Using explore to locate extremes
 What to do about unusual points
2. Examining the data
 Effects of large samples
 Univariate exploration – independent
 What if a cluster of points is unusual
variables
 Revisiting multicollinearity
 Measures of central tendency
 Variability measures                                               7. Dummy variables
 Shape of the distribution                                           Dummy variable coding
 Univariate exploration – dependent                                  A simple example
variables                                                           Error distribution
 Relations with the dependent variable                               Using variables with more than to
categories
3. Simple regression: Fit and
Assumption                                                            Dummy variables and missing data
 Running simple regression                                           Regression with a three-category
dummy variable
 Using two categorical variables
 Assumption of the analysis
 Regression with continuous and dummy
4. Multiple Regression: Fit and                                        variables
Assumption                                                            Appendix: Other dummy variable coding
 Running multiple regression                                          schemes
 Regression results
8. Logistic regression
 Residual analysis
 Introduction to logistic regression
 Diagnostic plots
 A first example of logistic regression
 The need of a substantive model of
 Stepwise logistic regression
causation
 ROC curves
5. Stepwise regression
9. Multinomial logistic
 Methods of selection
regression
 Evaluating fit
 Multinomial logistic model
 Running stepwise regression
 A multinomial logistic analysis:
 Stepwise output
Predicting credit risk
 Statistical significance and practical
 Appendix: multinomial logistic with a
importance
two-category outcome
 Over fitting

www.genius.co.il  03 -9222204  07174 ‫ הסיבים 7, קרית מטלון ת .ד . 7777 פתח תקוה‬ ‫ בישראל‬SPSS ‫ג ' ניוס מערכות בע" מ , נציגת‬
10. Modeling interactions
 Defining interactions
 Interactions of dummy variables
 Graphing interactions
 Interactions between categorical and continuous variables
 Centering interval variables
11. Polynomial regression
 Curvilinear regression
 Fitting a quadratic model with the Curve estimation procedure
 Polynomial regression using the linear regression procedure
 Further advice on polynomial models
12. Non linear regression
 What does nonlinear mean
 Assumption of nonlinear regression
 An example: Oxygen concentration over time
 Extensions: Constrained nonlinear regression

www.genius.co.il  03 -9222204  07174 ‫ הסיבים 7, קרית מטלון ת .ד . 7777 פתח תקוה‬ ‫ בישראל‬SPSS ‫ג ' ניוס מערכות בע" מ , נציגת‬
‫ –ניתוחי שונות‬SPSS ‫ניתוחים מתקדמים ב‬
‫תוכן הקורס‬

1. Introduction to ANOVA                                             5. Multivariate analysis of
 Why do analysis of variance                                        variance
 Visualizing analysis of variance                                    Introduction to MANOVA
 What is analysis of variance                                        MANOVA assumptions
 Variance of means                                                   Example: memory influences
 A formal statement of ANOVA                                        6. Within-Subject design:
assumptions                                                        repeated measures
2. Examining data and testing                                         Introduction to repeated measures
assumptions                                                            analysis
 Exploratory data analysis                                           One factor repeated measures analysis
 Measures of central tendency                                         example
 Variability measures                                                Planned comparison
 A look at the groups                                               7. Between and within subjects
 Effects of violations of assumptions in                            ANOVA
ANOVA                                                               A split-plot example
3. One factor ANOVA                                                   Split-plot analysis
 Logic of testing for means differences                              Appendix: Ad viewing with Pre-Post
 Running one-factor ANOVA                                             brand ratings
 One-factor ANOVA results                                           8. Mixed models ANOVA
 Post-Hoc testing                                                    Mixed models with complex covariance
 Planned comparisons                                                  structures
 One-factor nonparametric analysis                                   Data organization for linear mixed
4. Multi-Way univariate ANOVA                                          models
 Introduction to multi-way ANOVA                                     Linear mixed models analysis
 Logic of testing and assumptions                                    Mixed      models     with   alternative
 Interactions                                                         covariance structures
 Exploring the data                                                 9. Analysis of covariance
 Two-factor ANOVA                                                    Introduction to ANCOVA
 Post Hoc and simple effect test                                     ANCOVA analysis
 Unequal samples and unbalanced                                      Repeated measures ANCOVA with a
designs                                                              single covariate
 s
10. Special topics
 Latin square designs
 Random effects models
   Hierarchical linear models

www.genius.co.il  03 -9222204  07174 ‫ הסיבים 7, קרית מטלון ת .ד . 7777 פתח תקוה‬ ‫ בישראל‬SPSS ‫ג ' ניוס מערכות בע" מ , נציגת‬

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