About This Course
This is an introductory course in statistics with applications to business and economics. Students are introduced to the analysis and interpretation of data using fundamental statistical techniques.
Course Objectives
Upon successful completion of this course, students will be able to
- Explain the nature and scope of statistics;
- Apply statistical procedures and techniques to organise and summarise data;
- Utilise probability theories to analyse discrete and continuous random variables;
- Conduct hypothesis tests to analyse data;
- Apply regression and correlation analysis to analyse data; and
- Demonstrate the use of statistical software to manipulate raw data.
Material Includes
- Anderson D., Sweeney D., Williams T., Camm J., & Cochram J. (2012). Sta-tistics for business and economics (12th ed.). Mason, OH: South–Western Cengage Learning. SUPPLEMENTARY READINGS/MATERIALS Aczel, A., & Sounderpandian, J. (2012).
- Mason, OH: South–Western Cengage Learning. Brenson, M., & Levine, D. (2011). Basic statistics (11th ed.).
- Upper Saddle River, NJ: Prentice Hall. McLave J., & Benson, G. (2005). Statistics for business and economics (11th ed.).
- San Francisco, CA: Dellen Publishing Company. Mendenhall, W., & Sincich, T. (2002). A second course in Basic statistics (7th ed.). Upper Sad-dle River, NJ: Prentice Hall. Stephan, L., & Berenson, K. (2010). Statistics for managers (6th ed.).
- Upper Saddle River, NJ: Prentice Hall. Weirs, R. (2005). Introduction to bi-ostatistics statistics (6th ed.). Mason, OH: South–Western Cengage Learn.
Curriculum
Week 1: Data and Scope of Statistics
A.Role
B.Methods of data collection
i. Surveys
ii. Experimental studies
C. Types of data
i. Qualitative
ii. Quantitative
Week 2: Organising and Summarising Data
A. Graphical descriptions
i. Bar chart
ii. Pie chart
iii. Dot plot
iv. Histogram
v. Box-Whisker Plot
vi. Stem and leaf
B. Numerical descriptive measures
i. Central tendency
ii. Variability
C. Measures of distribution and relative frequencies
D. Measures of association between two variables
Week 3: Elementary Probability Concepts and Applications
A. Classical and empirical theory
i. Counting rules
ii. Addition law
iii. Multiplication law
iv. Conditional probability
B. Contingency table of joint and marginal probability.
Week 4: Discrete Random Variables
A. Uniform probability distribution
B. Binomial probability distribution
C. Poisson probability distribution
D. Hypergeometric distribution
Week 5: Continuous Random Variables
A. Uniform probability distribution
B. Exponential distribution
C. Normal probability distribution
D. Central Limit Theorem
E. Normal approximation to Binomial
Week 6: Sampling Distribution
A. Finite and infinite population
B. Mean
C. Proportions
D. Properties of estimators
i. Un-biased
ii. Efficiency
iii. Consistency
Week 7: Revision and Midterm
Week 8: Statistical Inference
A. Point estimates
B. Interval estimates for mean with infinite and finite population
i. Population variance known
ii. Population variance un-known
C. Sample size determination
Week 9: Hypothesis Testing
A. Type I and Type II errors
B. One-tailed testing of a mean
i. Population variance known
ii. Population variance unknown
C. Two-tailed testing of a mean
i. Population variance known
ii. Population variance unknown
D. Power of a test
E. Methods of hypothesis testing
i. Critical value approach
ii. P-value approach
iii. Critical mean approach
iv. Confidence interval approach
Week 10: Simple Linear Regression and Correlation
A. Scatter diagram
B. Relationship between variables
C. Assumptions and model development
D. Estimation and hypothesis testing of the regression line
E. Correlation analysis
Week 11 – 14: Revision
Free
Enrollment validity:
190 days
