Simple and multiple linear regression, comparing models using the extra sum of squares and AIC, model validation, the use of qualitative predictor variables, analysis of covariance; factorial experiments up to three factors, interaction interpretation, missing data and loss of balance; sequential data and interruptive time series; logistic regression and the comparison of models using deviance differences and AIC; adjusted odds ratios and probability calculations from a chosen model; binomial regression and overdispersion; multinomial logistic regression.
Regression and modelling, the "core techniques" of modern statistical analysis, appear regularly in the research journals of many fields ranging from the health sciences, nutrition, epidemiology, ecology, environmental science, zoology and botany to sociology, marketing, economics and finance. In many settings there is a response or outcome being researched and a number, often quite large, of potential causal factors. A regression analysis develops equations that assist in identifying important influences on an outcome measure. The regression procedures allow correction for potential confounding effects, as well as suggesting hypotheses for future investigation with designed experiments. The paper uses the statistical packages SPSS and R.This paper is central for the advanced study of statistics and biostatistics. It is the first in a regression sequence that extends from second to fourth year in statistics. It is a prerequisite for most of the higher-level STAT papers.
|Paper title||Regression and Modelling 1|
|Teaching period||First Semester|
|Domestic Tuition Fees (NZD)||$868.95|
|International Tuition Fees (NZD)||$3,656.70|
- STAT 110 or STAT 115 or BSNS 102 or BSNS 112
- ECON 210, FINC 203, HASC 415
- Schedule C
- Arts and Music, Science
- All students who intend to major in Statistics or Biostatistics should take this paper. It is a key paper for a minor in Statistics, for the DipGrad endorsed in Statistics or for a double major. The paper is useful as an advanced-service statistics paper for all students majoring in any of the subjects listed above.
- More information link
- View more information about STAT 241
- Teaching staff
- To be confirmed.
- Teaching Arrangements
- 32 lectures and 12 tutorials.
- Textbooks are not required for this paper.
- Paper Structure
- Main topics:
- Simple linear regression
- Multiple linear regression
- Model building and model diagnostics
- Outliers and influential points
- Dummy variables, categorical predictors
- Factorial experiments and interactions
- Two factor and unbalanced experiments
- Sequential data and interrupted time series
- Logistic regression for binary data
- Comparison of logistic models
- Confidence intervals for odds ratios
- Adjustments for covariates and confounders
- Binomial responses and overdispersion
- Multinomial logistic regression
- Course outline
- View course outline for STAT 241
- Graduate Attributes Emphasised
- Critical thinking, Information literacy.
View more information about Otago's graduate attributes.
- Learning Outcomes
- Students who successfully complete the paper will develop
- An understanding of key concepts for more advanced training in statistics
- An appreciation of the critical appraisal of published research