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Curricular information is subject to change
On successful completion of this module students should be able to demonstrate familiarity with standard nonparametric methods. They should be able to carry out these procedures on data sets using the statistical software package R. They should be able to understand the difference between nonparametric methods and classical methods and have the knowledge to make informed judgements as to what method is appropriate in a given problem. Students will be familiar with bootstrap techniques and with permutation/randomization procedures. Students will be able to construct in addition to standard density estimators such as histograms, smooth kernel density estimators. Students will also be able to perform regression smoothing using loess and conduct kernel and spline regression
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 5 |
Computer Aided Lab | 6 |
Specified Learning Activities | 30 |
Autonomous Student Learning | 48 |
Total | 113 |
A knowledge of probability and statistical inference to the level of Probability Theory STAT20110 and Inferential Statistics STAT20100 courses is required. Knowledge of calculus and linear algebra at First Science level is required.
Learning Recommendations:Knowledge of linear models to the level of STAT30240 is desirable
Resit In | Terminal Exam |
---|---|
Summer | Yes - 2 Hour |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Thurs 11:00 - 11:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Thurs 12:00 - 12:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Tues 11:00 - 11:50 |
Computer Aided Lab | Offering 1 | Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Wed 14:00 - 14:50 |