Ephraim Nii Amon Poncho-Kotey
Quantitative Methods & Business Stats
This course reviews descriptive statistics, exploratory data, and probability distributions. We will then examine the theory and methods of statistical inference, emphasizing those applications most useful in modeling business problems. Topics include sampling theory, estimation, hypothesis testing, linear regression, analysis of variance, and several advanced applications of the general linear model.
Text: Business Statistics for Contemporary Decision Making (6th Edition) by Black, K. Wiley Publishers, ISBN-10: 0470409010; ISBN-13: 978-0470409015.
A FIRST COURSE IN PROBABILITY Eighth Edition by Sheldon Ross
Microsoft Excel, SPSS
Understand the basic concept of both descriptive and inferential statistics.
Appreciate the usefulness and limitations of inferential methods widely used in management analysis.
Demonstrate the ability to analyze data using statistical methods.
Demonstrate the ability to build and test explanatory models.
Understand how to build a case for causation based on correlational data, and appreciate the limitations of using correlation methods to test theories of causation.
Understand some common biases in interpreting statistical results (Why they occur and how they can be prevented).
Be skilled at interpreting statistical results presented in professional reports and journals.
Be skilled at organizing and presenting statistical information in a format that will facilitate informed management judgements.
Review of fundamental concept
- Descriptive Statistics/Exploratory Data Analysis Visualization of Data
- Probability Distributions
Statistical Inference (Basics)
- Sampling Distributions/Sampling
- Error of Estimation: Means
- Estimations: Proportion
- Hypothesis Testing: Single Population
- Sample Size Determination
- Sampling Methods
- Managing Total Survey
- Error of Statistical Power
Statistical Inference: Comparing Two Populations
- Hypothesis Testing: Comparing Two Related Populations
- Hypothesis Testing: Comparing Two Independent Populations
Multiple Regression Analysis
- Statistical Model and Assumptions
- Statistical Inference in Multiple Regression
- Correlation and Causation
- Interpreting Regression Results
- Modeling Techniques (linear, curvilinear)
- Variable Selection and Model Refinement
- Time-series Regress (trends, lagged effects, seasonal effects
Experimental Design and Analysis of Variance
- Experimental Design
- One-way Analysis of Variance
- Class Start: 2020-09-01
- Course Duration: 2 Months
- Student Capacity: Max 45 Students
- Class Schedule: Monday: 10am-12pm, Wednesday: 12pm-2pm