This course covers advanced topics in quantitative analysis including: analysis of variance, time series and forecasting, linear and multiple regression, and decision analysis. The focus is to develop understanding of the use of data, data analysis, statistical inference and model building as applied to business decisions and to be able to assess the validity and interpret the meaning of statistical information. Spreadsheets and statistical software will be utilized in problem-solving. Students are expected to already have basic Excel skills.
- Review of Statistics: sampling methods, interval estimation and hypothesis testing, 1 and 2 populations
- Chi-square applications and pivot tables
- Non-parametric techniques for analysis of categorical and ranked data
- ANOVA and basic principles of experimental design
- Linear Regression, Correlation and Scatterplots: interpreting r and R2, t and F tests, examining residuals, estimation and prediction, computer solutions
- Multiple Regression and Model Building: meetings assumptions and conditions, examining residuals and diagnostics, adding qualitative variables, log and other transformations
- Forecasting and Time Series: components, smoothing, trend projection, seasonality, accuracy, projection using regression.
- Decision Analysis: structuring the problem, decision-making under certainty and risk, expected value, graphical sensitivity analysis
- Index numbers and more Linear Programming applications (if time permits)
Methods of Instruction
Lectures and computer seminars.
Means of Assessment
*includes at least 5% related to statistical analysis using computers
Students must obtain a grade of at least 50% on the combined examinations/tests to obtain credit for the course.
The student will be able to:
- create interval estimates and conduct hypothesis tests of means and proportions to assess statistical and practical significance;
- analyze categorical data using pivot tables and chi-square analysis;
- build and apply regression models for estimation and prediction;
- develop forecasts using smoothing techniques and regression;
- analyze decisions using probability theory;
- use computer spreadsheets and statistical software in solving statistical problems.
- assess validity and appropriateness of statistical techniques and study design.
(BUSN 2429 or MATH 1160) with a minimum grade of C
Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester/year of the course, consider the previous version as the applicable version.
Below shows how this course and its credits transfer within the BC transfer system.
A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.
For more information on transfer visit the BC Transfer Guide and BCCAT websites.
If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.