STATS22221B (HAM)
Principles of Probability and Statistics
15 Points
Staff
Convenor(s)
Han Gan
G.3.28
han.gan@waikato.ac.nz

Lecturer(s)
Chaitanya Joshi
4019
G.3.22
chaitanya.joshi@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 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.
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.
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 maximum expected workload for this paper is upto 10 hours per week, including the scheduled times for lectures and tutorials.
Linkages to Other Papers
Prerequisite(s)
Prerequisite papers: At least one of MATHS101, MATHS102, MATHS165, STATS111, STATS121, or minimum B grade in ENGEN102.
Restriction(s)
Restricted papers: STATS226, STAT226