STATS222-21B (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)

Lecturer(s)

Administrator(s)

: maria.admiraal@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: debby.dada@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 the foundations of modern Frequentist and Bayesian statistics. Topics will include estimators and estimation, likelihood theory, Baye's theorem, prior and posterior distributions, confidence and credible intervals.


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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
    Linked to the following assessments:
  • Understand and explain theory behing Frequentist inference
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  • Understand and explain the theory behind Bayesian inference
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  • Understand the similarities and differences between Bayesian and Frequentist approaches
    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.

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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
19 Aug 2021
3:00 PM
15
  • In Class: In Lecture
2. Test 2
14 Oct 2021
3:00 PM
15
  • In Class: In Lecture
3. Assignment 1
29 Jul 2021
3:00 PM
7.5
4. Assignment 2
12 Aug 2021
3:00 PM
7.5
5. Assignment 3
23 Sep 2021
3:00 PM
7.5
6. Assignment 4
7 Oct 2021
3:00 PM
7.5
7. Exam
40
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 maximum expected workload for this paper is upto 10 hours per week, including the scheduled times for lectures and tutorials.

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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: At least one of MATHS101, MATHS102, MATHS165, STATS111, STATS121, or minimum B grade in ENGEN102.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: STATS226, STAT226

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