Course Description
An overview of regression analysis techniques to build predictive models and evaluate the contribution of a variable to change in output.
Topics Covered
- Simple and multiple linear regression
+ Prediction from a regression model
+ Interpretation of regression coefficients
+ Correlation and the coefficient of determination (R-square)
+ Model assumptions
+ Model validation
- Model building and variable selection techniques
+ Parsimonious models
+ Multicollinearity
+ Forward, backwards and stepwise variable selection
- Categorical predictors
+ Creating dummy (indicator) variables
+ Interpretation of coefficients of dummy variables
- Non-linear regression
+ Polynomial regression
+ Interactions - Graphical methods for data display
+ Bar charts, histograms, scatterplots, box plots etc. What are their differences?
+ Matching graphics to your data type
+ Comparing groups - Scaling techniques
+ Adjusting for variance inflation
+ Adjusting for skewed data - Residual analysis
+ Identifying outliers
+ Identifying leverage points
Assumed Knowledge
Attendees should have an understanding of SPSS equivalent to that taught in the Fast Track to SPSS course and knowledge of statistics equivalent to the Statistics I courses.
Attendee Requirements
Course attendees are required to bring their own laptop installed with a licensed copy of SPSS Base version 10 or above.
Laptops are available for hire at an additional cost.
Class Sizes
Class are limited to a maximum of 8 attendees.
Course Duration & Cost
1 day, 9:30am – 4pm
Cost: $850.00 per person
Schedule & Bookings
Please see the Public Training Schedule for current course dates.
Bookings can be made via the Bookings page.