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MGMT 290 - Quantitative Methods for Business
Course Code:
MGMT 290

Credits:
3

Calendar Description:
The course addresses a wide range of issues and questions using statistical techniques such as collection, analysis and interpretation of data, measures of central tendency and dispersion, probability and probability distributions, sampling and sampling distributions, estimation, statistical hypotheses testing, test of goodness of fit, regression and correlation. Problem solving for business and economics, social and physical sciences, engineering, etc. are used.

Date First Offered:
2006-09-01

Hours:
Total Hours: 60
Lecture Hours: 45
Laboratory Hours: 15


Total Weeks: 15

This course is offered online: Yes
Depending on semester schedules, this course may be available online. Please consult the Academic Timetables.

Pre-Requisites:
One of Principles of Mathematics 11, Pre-Calculus 11, Foundations of Mathematics 12, Math 040, Math 108, or Management 111

Non-Course Pre-Requisites: None

Co-Requisites: None

Rearticulation Submission: No

Course Content:

- Introduction to Statistics
- Describing, Exploring and Comparing Data
- Probability
- Probability Distributions
- Normal Probability Distributions
- Estimates and Sample Sizes
- Hypothesis Testing
- Inferences from Two Samples
- Correlation and Regression
- Analysis of Variance
- Non parametric Statistics


Learning Outcomes:
Upon successful completion of this course, the student should be able to:
1. Define the terms statistics, population, sample, parameter, statistic, descriptive statistics, and statistical inference.
2. Outline the basic steps in the statistical problem-solving methodology.
3. Identify methods of obtaining samples.
4. Discuss the types of tables and charts used to analyze and present numerical facts.
5. Construct and interpret a frequency polygon, frequency distribution, and histogram.
6. Compute central tendency measures such as arithmetic mean, median, and mode by formulas and with the aid of a
financial calculator.
7. Compute measures of dispersion such as the range, standard deviation, and mean absolute deviation.
8. Define probability and explain how sample probabilities are assigned.
9. Differentiate between discrete and continuous random variables.
10. Find expected value of a discrete random variable.
11. Explain the concepts of conditional, independent and mutually exclusive events.
12. Identify and compute binomial and Poisson Distributions
13. Determine and utilize the properties of a normal distribution including calculating the probabilities and using z scores.
14. State the steps required to
a. Produce a sampling distribution of sample means;
b. Compute the mean; and
c. Calculate the standard deviation of this sampling distribution.
15. Compute estimates of population means, percentages, and variances using different levels of confidence.
16. Determine the appropriate sample size used to estimate the population mean at different levels of confidence.
17. Outline the steps of the hypothesis-testing procedure.
18. Conduct one and two-sample hypothesis tests of means, and percentages for testing situations.
19. Use the Anova testing procedure and F-distribution to arrive at statistical decisions.
20. Perform Chi-sequare and contingency table tests.
21. Calculate, use and interpret regression and correlation analysis.
22. Identify and use nonparametric statistical methods.


Grading System:
Letters

Passing Grade:
D (50%)

Grading Weight:
Final Exam: 40 %
Midterm Exam: 30 %
Quizzes and Tests: 30 %


Percentage of Individual Work:
100

Course Offered in Other Programs:
Yes

Other Programs:
Elementary Education, Associate of Arts Diploma - Fine Arts, Associate of Arts Degree
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