‫ –ניתוחי רגרסיה‬SPSS ‫ניתוחים מתקדמים ב‬
‫תוכן הקורס‬
1. Introduction to regression
 A simple regression analysis
 Fitting lines to data
 Goodness-of fit
 How a line is fit
 Residual and influential points
 What does linear model mean?
 Assumption of the general linear model
2. Examining the data
 Univariate exploration – independent
variables
 Measures of central tendency
 Variability measures
 Shape of the distribution
 Univariate exploration – dependent
variables
 Relations with the dependent variable
3. Simple regression: Fit and
Assumption
 Running simple regression
 Information about residuals
 Assumption of the analysis
4. Multiple Regression: Fit and
Assumption
 Running multiple regression
 Regression results
 Residual analysis
 Diagnostic plots
 The need of a substantive model of
causation
5. Stepwise regression
 Methods of selection
 Evaluating fit
 Running stepwise regression
 Stepwise output
 Statistical significance and practical
importance
 Over fitting
6. Influential points and
multicollinearity
 Influential points
 Multicollinearity
 Requesting the diagnostic
 Regression output
 Influence measures
 Using explore to locate extremes
 What to do about unusual points
 Effects of large samples
 What if a cluster of points is unusual
 Revisiting multicollinearity
7. Dummy variables
 Dummy variable coding
 A simple example
 Error distribution
 Using variables with more than to
categories
 Dummy variables and missing data
 Regression with a three-category
dummy variable
 Using two categorical variables
 Regression with continuous and dummy
variables
 Appendix: Other dummy variable coding
schemes
8. Logistic regression
 Introduction to logistic regression
 A first example of logistic regression
 Stepwise logistic regression
 ROC curves
9. Multinomial logistic
regression
 Multinomial logistic model
 A multinomial logistic analysis:
Predicting credit risk
 Appendix: multinomial logistic with a
two-category outcome
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10. Modeling interactions
 Defining interactions
 Interactions of dummy variables
 Adding a continuous variable
 Graphing interactions
 Interactions between categorical and continuous variables
 Centering interval variables
 Adding additional 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  49170 ‫ פתח תקוה‬7796 .‫ד‬.‫ קרית מטלון ת‬, 7 ‫ הסיבים‬ ‫ בישראל‬SPSS ‫ נציגת‬,‫ג'ניוס מערכות בע"מ‬
‫ –ניתוחי שונות‬SPSS ‫ניתוחים מתקדמים ב‬
‫תוכן הקורס‬
1. Introduction to ANOVA
 Why do analysis of variance
 Visualizing analysis of variance
 What is analysis of variance
 Variance of means
 A formal statement of ANOVA
assumptions
2. Examining data and testing
assumptions
 Exploratory data analysis
 Measures of central tendency
 Variability measures
 A look at the groups
 Effects of violations of assumptions in
ANOVA
3. One factor ANOVA
 Logic of testing for means differences
 Running one-factor ANOVA
 One-factor ANOVA results
 Post-Hoc testing
 Planned comparisons
 One-factor nonparametric analysis
4. Multi-Way univariate ANOVA
 Introduction to multi-way ANOVA
 Logic of testing and assumptions
 Interactions
 Exploring the data
 Two-factor ANOVA
 Post Hoc and simple effect test
 Unequal samples and unbalanced
designs
5. Multivariate analysis of
variance
 Introduction to MANOVA
 MANOVA assumptions
 Example: memory influences
6. Within-Subject design:
repeated measures
 Introduction to repeated measures
analysis
 One factor repeated measures analysis
example
 Planned comparison
7. Between and within subjects
ANOVA
 A split-plot example
 Split-plot analysis
 Appendix: Ad viewing with Pre-Post
brand ratings
8. Mixed models ANOVA
 Mixed models with complex covariance
structures
 Data organization for linear mixed
models
 Linear mixed models analysis
 Mixed
models
with
alternative
covariance structures
9. Analysis of covariance
 Introduction to ANCOVA
 ANCOVA analysis
 Repeated measures ANCOVA with a
single covariate
 s
10. Special topics
 Latin square designs
 Random effects models

Hierarchical linear models
www.genius.co.il  03 -9222204  49170 ‫ פתח תקוה‬7796 .‫ד‬.‫ קרית מטלון ת‬, 7 ‫ הסיבים‬ ‫ בישראל‬SPSS ‫ נציגת‬,‫ג'ניוס מערכות בע"מ‬