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This course provides the theoretical foundations for statistical estimation and testing at an introductory level. These are essential for more advanced studies in statistics at higher levels because they facilitate a deeper understanding of statistical techniques and their applications.
To illuminate key ideas in estimation and testing, the course will focus mainly on inference for independent and identically distributed univariate data. Topics that are usually covered include:• Fundamentals of probabilistic modelling: Probabilities, distribution functions, densities, expectations, quantiles• Expected values, Moment-generating functions• Likelihood function, Maximum likelihood principle, Score function, Fisher Information• Sufficient statistics• Estimation: Method of Moments, Maximum likelihood estimation• Properties of estimators: Unbiasedness, Efficiency, Consistency• Sampling distributions, the Law of large numbers, Central limit theorem• Interval estimation
On completion of the course, you will be able to:Apply various discrete and continuous univariate probability distributions in modelling statistical processes.Understand the concept of sampling distributions and how to apply them.Estimate unknown parameters of a given probability distribution using standard estimation techniques.
(1) one of MATH102, MATH199 or EMTH118; and (2) one of STAT101, DATA101, STAT211, EMTH119, orEMTH210
Students must attend one activity from each section.
To obtain a clear pass in this course, you must both pass the course as a whole (≥ 50%) and also obtain at least 40% in the final examination.
Course materials will be provided and no textbook is needed. After enrolling in the course, you will be able to access materials from the course web page in Learn at: http://www.learn.canterbury.ac.nz/
General information for students
Domestic fee $847.00
International fee $4,988.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics