STAT2001 (v.1) Mathematical Statistics
Area: | Department of Mathematics and Statistics |
---|---|
Credits: | 25.0 |
Contact Hours: | 4.0 |
TUITION PATTERNS: | The tuition pattern provides details of the types of classes and their duration. This is to be used as a guide only. Precise information is included in the unit outline. |
Lecture: | 1 x 2 Hours Weekly |
Tutorial: | 1 x 1 Hours Weekly |
Workshop: | 1 x 1 Hours Weekly |
Equivalent(s): |
302315 (v.2)
Mathematical Statistics 202
or any previous version
|
Prerequisite(s): |
7062 (v.6)
Mathematics 101
or any previous version
OR 10926 (v.5) Mathematics 103 or any previous version OR MATH1010 (v.1) Advanced Mathematics or any previous version OR MATH1004 (v.1) Mathematics 1 or any previous version AND 310532 (v.1) Statistical Data Analysis 102 or any previous version OR STAT1001 (v.1) Statistical Probability or any previous version AND 7063 (v.6) Mathematics 102 or any previous version OR 7492 (v.5) Mathematics 104 or any previous version OR MATH1011 (v.1) Mathematics 2 or any previous version |
UNIT REFERENCES, TEXTS, OUTCOMES AND ASSESSMENT DETAILS: | The most up-to-date information about unit references, texts and outcomes, will be provided in the unit outline. |
Syllabus: | This unit will cover the probabilistic framework for modelling real world process. Students will acquire practical skills in developing mathematical models for processes, compute various probabilities of interest and estimate parameters of the underlying models. There will be particular emphasis on mathematical statistical techniques and how these are used to compute probabilities of interest for both discrete and continuous process. Topics covered include; review of probability axioms and probability rules; special univariate distributions; random variables and expectations; multivariate distributions; covariance and correlation; marginal and conditional distributions; conditional expectation; transformations; functions of random variables including random sums and order statistics; moment generating functions; probability generating functions and cumulant generating functions; convergence of random sequences; distributions derived from normal distribution; distribution of sample mean and the sample variance; methods of estimation and properties of estimators. |
Field of Education: | 010103 Statistics |
Result Type: | Grade/Mark |
Availability
Year | Location | Period | Internal | Partially Online Internal | Area External | Central External | Fully Online |
---|---|---|---|---|---|---|---|
2016 | Bentley Campus | Semester 1 | Y |
Area External refers to external course/units run by the School or Department or offered by research.
Central External refers to external and online course/units run through the Curtin Bentley-based Distance Education Area
Partially Online Internal refers to some (a portion of) learning provided by interacting with or downloading pre-packaged material from the Internet but with regular and ongoing participation with a face-to-face component retained. Excludes partially online internal course/units run through the Curtin Bentley-based Distance Education Area which remain Central External
Fully Online refers to the main (larger portion of) mode of learning provided via Internet interaction (including the downloading of pre-packaged material on the Internet). Excludes online course/units run through the Curtin Bentley-based Distance Education Area which remain Central External
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