STATS22222B (HAM)
Principles of Probability and Statistics
15 Points
Staff
Convenor(s)
Bob Durrant
8334
G.3.31
bob.durrant@waikato.ac.nz

Lecturer(s)
David Chan
9068
G.3.09
david.chan@waikato.ac.nz

Administrator(s)
Librarian(s)
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Paper Description
In this paper we will tackle the question of: How do we quantify the idea of randomness and chance? To achieve this we will carefully construct an intuitive, logical and consistent theory of probability, and then explore its use as the basis for modern statistics.
The paper is structured in two halves. In the first half, we focus on foundational probability theory, which includes the topics of probability axioms, conditional probability, random variables, discrete and continuous distributions and expectations and variances. In the second half of the paper, we apply the ideas and concepts from the first half to derive elements of the foundations of modern Frequentist and Bayesian statistics. Topics will include probability as the basis for inference, estimators and their properties, and parameter estimation and inference from both a Frequentist and Bayesian perspective.
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The learning outcomes for this paper are linked to Washington Accord graduate attributes WA1WA11. Explanation of the graduate attributes can be found at: https://www.ieagreements.org/
Paper Structure
Learning Outcomes
Students who successfully complete the paper should be able to:
Assessment
The internal assessment for this course will consist of:
Two tests, each worth 15% of your final mark,
Four assignments, each worth 7.5% of your final mark.
The external assessment for this course will consist of:
Final exam , worth 40% of your final mark.
If you are enrolled in a BE (Hons) degree: Samples of your work may be required as part of the Engineering New Zealand accreditation process for BE (Hons) degrees. Any samples taken will have the student name and ID redacted. If you do not want samples of your work collected, then please email the engineering administrator, Natalie Shaw (natalie.shaw@waikato.ac.nz ), to opt out.
Assessment Components
The internal assessment/exam ratio (as stated in the University Calendar) is 60:40. The final exam makes up 40% of the overall mark.
Required and Recommended Readings
Recommended Readings
Other Resources
We may be (if required) making use of the R statistical software package in this course. R is available in the Rblock computer labs. R is opensource software which is freely available for personal use. You can download your own copy of R from cran.rproject.org, along with any accompanying Rpackages you desire.
In addition, you might also like to download the RStudio software. This provides a more userfriendly interface to the R program (you will also need to download R itself to use RStudio). RStudio is also opensource and freely available: www.rstudio.com
Online Support
All information relating to this paper, including your internal assessment marks, will be posted to the STATS222 Moodle page (elearn.waikato.ac.nz).
All material and lecture recordings will be available online for remote access. Online Zoom help sessions will also be organised as needed.Workload
Your expected workload for this paper is up to 10 hours per week, including the scheduled contact time for lectures and tutorials.
Students frequently find (some aspects of) probability challenging, highly counterintuitive, or both. You should therefore expect that in some weeks you will need to dedicate a full 10 hours to your study on this paper.
Linkages to Other Papers
Prerequisite(s)
Prerequisite papers: MATHS101 or MATHS102 or MATHS165 or STATS111 or STATS121 or minimum B grade in ENGEN102.
Restriction(s)
Restricted papers: STATS226