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Regression and Nonparametric Inference (On-campus)
Undergraduate | CUR-STAT1006 | 2021
Course information for 2021 intake
- Study method
- On-campus
- Assessments
- Subject may require attendance
- Entry requirements
- No ATAR needed,
- No prior study
- Duration
- 17 weeks
HECS-HELP available
Regression and Nonparametric Inference (On-campus)
About this subject
- use non-parametric tests for carrying out hypothesis testing
- formulate, fit, assess goodness-of-fit and predictive ability of simple and multiple linear regression models
- explain and use contemporary variable selection methods for constructing multiple linear regression models
- use a statistical computing environment to carry out and document statistical analyses
- 1. Introduction and overview
- 2. Nonparametric methods
- 3. Bootstrap methods and Permutation Tests
- 4. Correlation
- 5. Simple Linear Regression (SLR)
- 6. Multiple Linear Regression (MLR)
This unit begins with an introduction to nonparametric methods for hypothesis testing. These methods are distribution-free analogues of parametric methods such as one- and two-sample t-tests that have been presented in introductory statistics units. The main part of the unit is devoted to linear regression, and it will include topics such as simple and multiple linear regressions, estimation by the method of least squares, diagnostic checking of models, analysis of variance, conventional as well as more contemporary methods of variable selection. Throughout, students will be using the R language statistical computing environment to carry out and document analyses in a reproducible manner.
1. Test Portfolio, 20 percent, ULOs assessed 1,2,4;
2. Technical Report, 30 percent, ULOs assessed 2,3,4;
3. Final Exam, 50 percent, ULOs assessed 1,2,3.- Test Portfolio (20%)
- Final Exam (50%)
- Technical Report (30%)
For textbook details check your university's handbook, website or learning management system (LMS).
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