STATS222-22B (HAM)

Principles of Probability and Statistics

15 Points

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Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences
Department of Mathematics and Statistics

Staff

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Convenor(s)

Bob Durrant

Bob Durrant
8334
G.3.31
See Moodle
bob.durrant@waikato.ac.nz

Lecturer(s)

David Chan

David Chan
9068
G.3.09
See Moodle
david.chan@waikato.ac.nz

Administrator(s)

: maria.admiraal@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: alistair.lamb@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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Paper Description

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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 WA1-WA11. Explanation of the graduate attributes can be found at: https://www.ieagreements.org/

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Paper Structure

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There are four contact hours per week. There will be three lectures a week, and one workshop.
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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • Understand the basics of mathematical statistics and probability (WA1)
    Linked to the following assessments:
  • Understand and explain theory behind Frequentist inference (WA1)
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  • Understand and explain the theory behind Bayesian inference (WA1)
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  • Understand the similarities and differences between Bayesian and Frequentist approaches (WA1, WA5)
    Linked to the following assessments:
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Assessment

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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.

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 60:40. There is no final exam. The final exam makes up 40% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 60:40 or 0:0, whichever is more favourable for the student. The final exam makes up either 40% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Test 1
25 Aug 2022
1:00 PM
15
  • In Class: In Lecture
2. Test 2
20 Oct 2022
1:00 PM
15
  • In Class: In Lecture
3. Assignment 1
5 Aug 2022
5:00 PM
7.5
4. Assignment 2
19 Aug 2022
5:00 PM
7.5
5. Assignment 3
30 Sep 2022
5:00 PM
7.5
6. Assignment 4
14 Oct 2022
5:00 PM
7.5
7. Exam
40
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Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Recommended Readings

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Introduction to Bayesian Statistics - 2nd Edition, by William M. Bolstad
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Other Resources

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We may be (if required) making use of the R statistical software package in this course. R is available in the R-block computer labs. R is open-source software which is freely available for personal use. You can download your own copy of R from cran.r-project.org, along with any accompanying R-packages you desire.

In addition, you might also like to download the R-Studio software. This provides a more user-friendly interface to the R program (you will also need to download R itself to use R-Studio). R-Studio is also open-source and freely available: www.rstudio.com

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Online Support

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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.
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Workload

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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.

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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: MATHS101 or MATHS102 or MATHS165 or STATS111 or STATS121 or minimum B grade in ENGEN102.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: STATS226

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