Course Details

  • Teacher
    Abdul Kudus
  • Category
  • Course Price
    GHC 199

Programming with VB

Course Details

Course Description

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

 Learning Objectives

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.

Topic Examined:

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

Course Information

  • Class Start: 2020-10-01
  • Course Duration: 3 Months
  • Student Capacity: Max 45 Students
  • Class Schedule: Monday 2:30 to 3:30, Wednesday 5:30 to 7:30

Course Teacher

Abdul Kudus