Course Overview
The course introduces descriptive and inferential statistics. The descriptive statistics will concentrate on using these methods for exploratory data analysis. Basic probability and probability distributions are used with an emphasis on their use in computer simulation methods. Inferential statistical methods and regression are introduced to lay the groundwork for decision making and prediction. In all parts of the course, current computer applications (i.e. R) will be used to perform statistical analyses and simulation programming.
Prerequisite(s)
Credits
4.0
- Not offered this term
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Learning Outcomes
Upon successful compleltion of this course, the student will be able to:
- Summarize data using standard numerical statistical measures.
- Visualize data with statistical graphical methods.
- Apply the basic rules of probability and Bayes' theorem.
- Perform stochastic simulations using discrete and continuous probability distributions.
- Apply inferential techniques (e.g. hypothesis testing, confidence intervals) to make a decision.
- Assess data validity.
- Apply regression methods on data to make predictions.
- Use current computer tools to aid in statistical enquiries.
- Explain basic Bayesian statistical methods.
Effective as of Fall 2018
Related Programs
Applied Probability and Statistics (MATH 3042) is offered as a part of the following programs:
- Indicates programs accepting international students.
- Indicates programs eligible for students to apply for Post-graduation Work Permit (PGWP).
School of Computing and Academic Studies
- Computer Systems Technology
Diploma Full-time
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