Fundamentals of Multiple Regression

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Note re laptops:

Participants are required to bring a laptop to this course with their choice of either SPSS, SAS or Stata installed. In most cases university staff and students have access to at least one of these packages via their university. If so, it is important to double check that it works while you are not on campus as sometimes the settings may require access to your university's VPN, particularly for SPSS. If the package works while you are at home, then there is nothing to worry about. If not, you may need to contact the relevant IT department at your institution. 

If you do not have access to any of these packages, temporary trial versions of SPSS and SAS are available online (care should be taken not to download these too early). ACSPRI staff will be in touch with all course participants a few weeks prior to the course to make sure there are no issues and/or to help offer solutions.

A wireless network will be available during the course.

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Based on the General Linear Model, least-squares multiple regression is a general technique through which one can analyse the relationship between a dependent variable and a set of independent or predictor variables.  The most important uses of the technique are:
1. to find the best linear prediction equation and evaluate its accuracy;
2. to control for other confounding variations in order to evaluate the contribution of a specific variable or set of variables;
3. to find  structural relations and provide explanations for them; and
4. to estimate population parameters and test hypotheses about the population.
 

This course provides an introduction to, and the fundamentals of multiple regression, covering enough of the statistical material for the intelligent use of the technique.  The approach is informal and applied rather than emphasising proofs of relevant theorems.  The course begins with a review of bivariate regression and extends the relevant principles to the case of multiple regression.  Particular attention is given to the application of multiple regression to substantive problems in the social sciences. By the end of the course, the student will have a knowledge of the principles of multiple regression, and the ability to conduct regression analyses, interpret the results, and to inspect elementary regression diagnostics to test the underlying model assumptions.  This course provides the foundations necessary for progression to ‘Applied Multiple Regression Analysis’, and to subsequent advanced-level courses in structural equation modelling and log-linear modelling.