## Curriculum Guideline

Effective Date:
Course
Discontinued
No
Course Code
BUSN 2429
Descriptive
Department
Faculty
Credits
3.00
Start Date
End Term
201420
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 seminars.

Course Description
This course will provide students with an introduction to statistics. Students will learn to solve problems using computer spreadsheets. Topics include measures of central tendency and dispersion, probability, sampling, normal and binomial distributions, confidence intervals and hypothesis testing and regression analysis. Students will not receive credit for BUSN 2429 and BUSN 430.
Course Content
1. Descriptive Statistics:  frequency distributions, graphical displays, measures of central tendency, measures of dispersion.
2. Probability:  experiments, counting rules, assigning probabilities, events, complement, exclusion, intersection, union, addition law, conditional probability.
3. Discrete Probability Distributions:  expected value and variance, binomial distribution.
4. Continuous Probability Distributions:  uniform and normal probability distributions.
5. Sampling Distributions:  random sampling, sampling distribution of sample mean and sample proportion.
6. Interval Estimation:  means and proportions, small and large samples, determining sample size.
7.  Hypothesis Testing:  formulating and testing a research hypothesis, 1 and 2 tailed tests about sample mean and proportion, Type 1 and 2 error.
8. Statistical Inference with Two Populations (independent samples):  interval estimation and hypothesis tests for difference between two means and between two proportions.
9. Computer Analysis with Excel Spreadsheets:  creation of spreadsheets, histograms, frequency tables, scatter charts, interval estimates, and use of probability distribution functions.
10. Simple Linear Regression:  least squares, model and assumption, R-Squared, prediction.
Learning Outcomes

At the end of the course, the successful student should be able to:

1. collect statistical data using appropriate sampling techniques;
2. organize statistical data and calculate measures of central tendency and variation;
3. calculate the probability of events when they are mutually exclusive, independent and dependent;
4. use binomial and normal distribution to make probability estimates;
5. set up confidence intervals for population means and proportions;
6. use sample information to test statements or claims about parameters;
7. use computer spreadsheets to solve statistical problems;
8. use simple regression to determine significance of relationship between two variables.
Means of Assessment
 Final Exam 30% Term Examinations (2-3) 40% - 50% Computer Lab Test 5% - 10% Assignments (6-12) 15% - 25% Participation 0% -  5% 100%
Textbook Materials

Textbooks and Materials to be Purchased by Students

Anderson, D.R., Sweeney et al.  Statistics for Business and Economics, Latest Ed.  South-Western (Thomson).