## Curriculum Guideline

Effective Date:
Course
Discontinued
No
Course Code
BUSN 3431
Descriptive
Department
Faculty
Credits
3.00
Start Date
End Term
Not Specified
PLAR
No
Semester Length
15 Weeks X 4 Hours per Week = 60 Hours
Max Class Size
35
Contact Hours
Lecture: 3 Hours Seminar: 1 Hour Total: 4 Hours
Method Of Instruction
Lecture
Seminar
Methods Of Instruction

Lectures and computer seminars.

Course Description
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.
Course Content
1. Review of Statistics: sampling methods, interval estimation and hypothesis testing, 1 and 2 populations
2. Chi-square applications and pivot tables
3. Non-parametric techniques for analysis of categorical and ranked data
4. ANOVA and basic principles of experimental design
5. Linear Regression, Correlation and Scatterplots: interpreting r and R2, t and F tests, examining residuals, estimation and prediction, computer solutions
6. Multiple Regression and Model Building: meetings assumptions and conditions, examining residuals and diagnostics, adding qualitative variables, log and other transformations
7. Forecasting and Time Series:  components, smoothing, trend projection, seasonality, accuracy, projection using regression.
8. Decision Analysis:  structuring the problem, decision-making under certainty and risk, expected value, graphical sensitivity analysis
9. Index numbers and more Linear Programming applications (if time permits)
Learning Outcomes

The student will be able to:

1. create interval estimates and conduct hypothesis tests of means and proportions to assess statistical and practical significance;
2. analyze categorical data using pivot tables and chi-square analysis;
3. build and apply regression models for estimation and prediction;
4. develop forecasts using smoothing techniques and regression;
5. analyze decisions using probability theory;
6. use computer spreadsheets and statistical software in solving statistical problems.
7. assess validity and appropriateness of statistical techniques and study design.
Means of Assessment
 Final Examination 30% Term Tests 20%-40%* Research Project(s) 10%-30% Assignments/Quizzes 10%-20% Participation 0%-5% 100%

*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.

Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.

Textbook Materials

Textbooks and Materials to be Purchased by Students

Sharpe, DeVeaux, Velleman and Wright. Business Statistics, latest Cdn ed., Pearson Canada

or Donnelly, Robert A. Jr..; Business Statistics, latest ed., Pearson

or similar Business Statistics textbook as approved by department