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This course is for anyone who has worked with SPSS for Windows and wants to become better versed in the more advanced statistical capabilities of SPSS for Windows. Anyone who has a solid understanding of statistics and wants to expand their knowledge of appropriate statistical procedures and how to set them up using SPSS.

Introduction and Overview
Goals of the Course
Taxonomy of Methods
General Approach

Discriminant Analysis
•How Does Discriminant Analysis Work?
•The Elements of Discriminant Analysis
•The Discriminant Model
•How Cases are Classified
•Assumptions of Discriminant Analysis
•A Two-Group Discriminant Example
•Checking Variance Assumptions
•Running a Discriminant Analysis
•The Discriminant Coefficients
•Classification Statistics 2- 18 Prediction
•The Assumption of Equal Covariance
•Modifying the List of Predictors
•Casewise Statistics and Outliers
•Adjusting Prior Probabilities
•Validating the Discriminant Model
•Stepwise Model Selection
•Three-Group Discriminant Analysis

Binary Logistic Regression
•How Does Logistic Regression Work?
•The Logistic Equation
•The Elements of Logistic Regression
•Assumptions of Logistic Regression
•A First Example of Logistic Regression
•Interpreting Logistic Regression Coefficients
•Making Predictions
•The Accuracy of Prediction
•Estimated Probabilities
•Checking Classifications
•Residual Analysis
•Stepwise Logistic Regression

Multinomial Logistic Regression
•Multinomial Logistic Model
•Assumptions of Multinomial Logistic Regression
•A Multinomial Logistic Analysis: Predicting Credit Risk
•Interpreting Coefficients
•Classification Table
•Making Predictions
•Appendix: Multinomial Logistic with a Two-Category Outcome

Survival Analysis (Kaplan-Meier)
•What is Survival Analysis
•What to Look for in Survival Analysis
•Survival Procedures in SPSS
•An Example: Kaplan-Meier

Cluster Analysis
•How Does Cluster Analysis Work?
•Types of Data Used for Clustering
•What to Look at When Clustering
•Distance and Standardization
•Overall Recommendations
•Example I: Hierarchical Cluster Analysis
•Cluster Results
•Obtaining Mean Profiles of Clusters
•Relating Clusters to Other Variables
•Summary of First Cluster Example
•Example II: K-Means Clustering
•Running K-Means Clustering
Factor Analysis
•Uses of Factor Analysis
•What to Look for When Running Factor Analysis
•The Idea of a Principal Component
•Factor Analysis Versus Principal Components
•Number of Factors
•Factor Scores & Sample Size
•An Example: 1988 Olympic Decathlon Scores
•Looking at Correlations
•Principal Components Analysis with an Orthogonal Rotation
•Principal Axis Factoring with an Oblique Rotation
Loglinear Analysis
•What are Loglinear Models
•Relations Among Loglinear, Logit Models and Logistic Regression
•What to Look for in Loglinear and Logit Analysis
•Procedures in SPSS that Run Loglinear or Logit Analysis
•Analysis of Location Preference (Model Selection)
•Running the Analysis
•Significance Tests
•Coefficient Interpretation
Multivariate Analysis of Variance
•Why Perform MANOVA
•Assumptions of MANOVA
•What to Look for in MANOVA
•An Example: Memory Influences
•Examining the Output
•Post Hoc Tests
Repeated measures Analysis of Variance
•Why do a Repeated Measures Study
•The Logic of Repeated Measures
•Example: One Factor Drug Study
•Examining Results
•Further Analysis
•Planned Comparisons
•Ad Viewing with Pre-Post Brand Ratings
•Examining Results
•Tests of Assumptions
•Profile Plots

Times Series and Forecasting
The basics of forecasting
Smoothing time series data
Outliers and error in time series data
Automatic forecasting with the Expert Modeler
Assessing model performance
Fitting curves to time series data
Regression with time series data
Exponential smoothing models
ARIMA models
Applying a model to new data
Seasonal decomposition
Modeling seasonality
Intervention analysis
Transfer functions in ARIMA
Automatic forecasting of several time series