Course description:

An overview of regression techniques.

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
  •  
• 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

AAttendees should be enrolled or intending to enrol in a data analysis or statistics course at a tertiary institution and have knowledge similar to that taught in the Probability Theory and Hypothesis Testing courses.

Course attendees are required to bring their own scientific calculator. Attendees should bring exercises from their university course to work on during problem solving time.