Educational Material for Mathematics and Statistics
Educational material for mathematics and statistics available for download for private use.
Main Coursebook
Introduction to Statistical Methodology
Comprehensive lecture notes for a course on introductory statistics. A separate book of practice exercises to accompany these lecture notes can be found on this page. [490 pp.]
Exercises with Solutions
Exercises for Introduction to Statistical Methodology
Extensive set of practice exercises with solutions. These range from technical exercises to extended project-style applications involving real data. These are cross-tabulated with the main coursebook. [305 pp.]
Main Coursebook
Intermediate Statistical Methodology
Comprehensive lecture notes for a course on intermediate statistical methodology. The coursebook contains an extensive set of practice exercises with solutions. These range from technical exercises to extended project-style applications involving real data. A separate archive of demonstration software to accompany these lecture notes can be found on this page. [604 pp.]
Software Files
Software files for Intermediate Statistical Methodology
An archive of R software files to accompany Intermediate Statistical Methodology. These contain fully coded applications of the methods described in the main coursebook. [zipped archive of 20 R files]
Study Notes
A compact summary of introductory topology. [49 pp.]
Study Notes on Topology
Study Notes
Operations Research
Includes dynamic programming and utility theory. These are fairly old, but I hope still useful. [61 pp.]
Study Notes
Causal Inference and Bayesian Networks
Summary of the basic theory of Bayesian networks, and their relationship to causal inference. [55 pp.]
Lecture Slides
Causal Inference and Bayesian Networks
A version of the Causal Inference and Bayesian Networks study notes in lecture slide format. [80 pp.]
Lecture Slides
Advanced Theory of Statistical Inference
Lecture slides for a graduate level course 0n the theory of statistical inference (large sample inference is not included in this course) [zipped archive contains 22 files of lecture slides, with a separate legend file]
Abstract— Currently, work injury compensation boards in Canada track injury information using a standard system of codes (under the National Work Injury Statistics Program (NWISP)). These codes capture the medical nature and original cause of the injury in some detail, hence they potentially contain information which may be used to predict the severity of an injury and the resulting time loss from work. Claim duration measurements and forecasts are central to the operation of a work injury compensation program. However, due to the complexity of the codes traditional statistical modelling techniques are of limited value.
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We will describe an artificial neural network implementation of Cox proportional hazards regression due to Ripley (1998 thesis) which is used as the basis for a model for the prediction of claim duration within a work injury compensation environment. The model accepts as input the injury codes, as well as basic demographic and workplace information. The output consists of a claim duration prediction in the form of a distribution. The input represents information available when a claim is first filed, and may therefore be used in a claims management setting. We will describe the model selection procedure, as well as a procedure for accepting inputs with missing covariates.
Almudevar (2006) Using Artificial Neural Networks to Predict Claim Duration in a Work Injury Compensation
Environment