The course will consist of 5 2-hour sessions, one per week. Notes summarising the material to be covered each week will be made available over the world wide web before the class. . Sample problems will be included in the notes. Students will be expected to have read these notes and worked on the examples before the session, and to come to the class with questions about them. In addition I shall be available for consultation by email. Interesting questions raised over the email will be reflected back to the entire class.
At the end of the course, a test paper will be distributed. It will include short questions testing students' knowledge of various kinds of analytic technique, together with questions about some sample data sets. Students will be expected to complete this test paper in their own time, using books, notes and other resources. Completed test papers should include explanations of why a particular technique is appropriate for that example, computer output, and interpretations of the results.
The course aims to introduce students to the more common multivariate techniques that use manifest variables, that is, the observed variables themselves, not inferred underlying latent variables as in, for example, factor analysis. The commonest manifest variables analysis is multiple regression but there are numerous others. The aim of the course is not to teach the underlying mathematics but to put students in a position to (a) choose when to use one of these kinds of analysis and (b) use the appropriate computer statistics package to carry it out.
Topics to be covered each week
1. Multiple regression: introduction/revision. R2-adjusted, F, regression coefficients, standardized beta-weights, t-values. Using Minitab and SPSS to carry out multiple regression.
2. Multiple regression continued. Dummy variables techniques. Stepwise and Best Regression procedures. Choosing a regression model. Problems: Outliers, Heteroscedasticity, Multicollinearity, Identification problems.
3. Path analysis. Input and output path diagrams. Direct, indirect, and overall impacts of independent variables on dependent variables.
4. Dichotomous dependent variables. Discriminant analysis. Relation between discriminant analysis, multiple regression and manova. Discriminant analysis with 2 and k groups. Logistic regression. The logistic and logit transformations. Goodness of fit versus accuracy of classification. Logits and probits. SPSS DISCRIMINANT, LOGISTIC REGRESSION and PROBIT ANALYSIS commands.
5. Additional techniques. Analysis of covariance. Ordered logit analysis. Use of LIMDEP for ordered logit analysis.
From time to time students and colleagues from outside Exeter mail in asking if it is OK for them to be using these notes. The answer is yes, of course, we are very pleased if our teaching materials can help you. All that we ask is that you respect our authorship and copyright - so please don't reproduce our notes without asking us first, and if you use them in a way which would usually cause you to cite your source, don't forget to cite us. If you send us questions, we will answer them if time permits - some of the entries in our FAQ file have come from people who were not members of the course. If you spot errors, we will be very grateful indeed if you let us know. We are always glad to hear if you do find our materials useful, and also if you have comparable material on the web which we could make links to.
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