MGSC 312 is a second-level introductory course in statistics, designed as a continuation of MGSC 301. Together, these courses provide students with basic concepts and methods of statistical analysis. The course and the textbook are tailored to meet the needs of students in administrative studies. Accordingly, all the applications problems are borrowed from business and economics, with many exercises based on real data.
Lesson 1: Tests of Goodness of Fit and Independence
Lesson 2: Analysis of Variance and Experimental Design
Lesson 3: Simple Linear Regression
Lesson 4: Multiple Regression
Lesson 5: Forecasting
Lesson 6: Nonparametric Methods
Lesson 7: Index Numbers
Lesson 8: Statistical Methods for Quality Control
Learning outcomes
After you have completed this course, you should be able to:
Determine whether to reject a hypothesized probability distribution for a multinomial, Poisson, and/or normal distribution, using tests for goodness of fit and independence.
Study an analysis of variance (ANOVA) procedure to determine whether means of two or more populations are equal.
Use test statistics to examine differences in treatment means and differences in interactions between factors.
Analyze and measure the strength of linear relationship between two variables.
Estimate a dependent variable based on independent variables using digital analysis via MS-Excel.
Determine various index numbers in which the weight of each item is based on quantities in the base and current periods.
Predict the value of a variable in future time periods based on past data.
Apply statistical methods to determine differences between two populations involving ordinal, interval/ratio scale, or rank order data.
Determine whether quality standards are being met and whether production processes are in control using control limits and MS-Excel charts.
Evaluation
To receive credit for this course, you must submit all assignments, achieve a minimum grade of 50 percent on each of the two examinations (midterm and final), and achieve a minimum overall grade of D (50 percent) for the entire course.
Completing all assignments is mandatory. You are strongly encouraged to complete the assignments before writing the exams. If you do not complete an assignment, you will not be able to finish the course until you do so.
The following chart summarizes the evaluation activities:
Activity
Weight
Assignment 1
10%
Assignment 2
10%
Midterm Exam
30%
Assignment 3
10%
Assignment 4
10%
Final Examination
30%
Total
100%
The midterm and final examinations for this course must be requested in advance and written under the supervision of an AU-approved exam invigilator. Invigilators include either ProctorU or an approved in-person invigilation centre that can accommodate online exams. Students are responsible for payment of any invigilation fees. Information on exam request deadlines, invigilators, and other exam-related questions, can be found at the Exams and grades section of the Calendar.
To learn more about assignments and examinations, please refer to Athabasca University’s online Calendar.
Materials
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. J., Fry, M. J., & Ohlmann, J. W. (2020). Statistics for business and economics (14th ed.). Cengage. ISBN: 978-1-337-90106-2 (eText)
Students will access all other course materials online.
Note: You must have Microsoft Excel installed on your computer in order to complete course requirements.
Challenge for credit
Overview
The challenge for credit process allows you to demonstrate that you have acquired a command of the general subject matter, knowledge, intellectual and/or other skills that would normally be found in a university-level course.
Full information about challenge for credit can be found in the Undergraduate Calendar.
Evaluation
To receive creditfor the MGSC 312 challenge registration, you must achieve a grade of at least D (50 percent) on the examination.
Athabasca University reserves the right to amend course outlines occasionally and without notice. Courses offered by other delivery methods may vary from their individualized study counterparts.