NOTE: For registration please contact Bob Gaglardi School of Business and Economics
General Program Inquiries (Email: firstname.lastname@example.org or Tel:
Students examine the statistical methods and tools required for decision making in today's
business environment. Topics include descriptive statistics and numerical measures, statistical
inferences with two populations, hypothesis tests and nonparametric methods, analysis of
variance, simple regression models, multiple regression models, regression and the model building
process, regression models with categorical dependent variables, applied models with categorical
- Summarize, tabulate, plot, and professionally present raw data for a target audience
including measures of location, relative location, variability, and association between two
- Apply a variety of probability distributions in different areas of business.
- Construct interval estimates for a variety of hypothesized parameters.
- Set up, develop, and test hypothesized parameters including distribution-free methods.
- Analyze and test data out of experimental designs and observational data.
- Build, estimate, test, and interpret possible existing relationships between two variables,
using simple regression analysis.
- Build, estimate, test, and interpret possible existing relationships among more than two
variables, using multiple regression analysis.
- Deal with major issues including transformations and interactions in regression models.
- Build and estimate regression models with qualitative (categorical) dependent variable.
Unit 1 – Introduction to Statistics and Probability
- Module 1: Graphical and Numerical Descriptive Statistics
- Module 2: Probability and Probability Distributions
Unit 2 – Statistical Inference
- Module 3: Sampling Distributions
- Module 4: Interval Estimation for a Single Population
- Module 5: Hypothesis Testing for a Single Population
Unit 3 – Analysis of Multiple Groups
- Module 6: Statistical Inference for Two Populations
- Module 7: Categorical Data Analysis
- Module 8: Analysis of Variance (ANOVA)
Unit 4 – Regression Modeling
- Module 9: Simple Linear Regression
- Module 10: Residual Analysis
- Module 11: Multiple Regression
- Module 12: Categorical Variables in Regression
Required text and materials
Online students are responsible for sourcing and ordering their own textbooks. Please see the
list of required textbooks here: https://www.tru.ca/distance/courses/MBA_Textbook_List.pdf.
Please note that publishers may offer several package options that include additional resource
material not required in your course. You may purchase a package of your choice as long as it
includes the correct author, title and edition listed for your course.
If you have any questions about obtaining the correct textbook, please contact OLMaterials@tru.ca. They will be happy to assist you.
Please be aware that should your course have a final exam, you are responsible for the fee to the online proctoring service, ProctorU, or to the in-person approved Testing Centre. Please contact email@example.com with any questions about this.
To successfully complete this course, students must achieve a passing grade of 70% or higher on
the overall course, and 50% or higher on the final mandatory exam.
|Quizzes (best 10 of 12)
|Assignment 1: Introduction to Statistics and Probability
|Assignment 2: Statistical Interference
|Assignment 3: Analysis of Multiple Groups
|Assignment 4: Regression Modeling
|Final Exam (mandatory)
Open Learning Faculty Member Information
An Open Learning Faculty Member is available to assist students. Students will receive the necessary contact information at the start of the course.