The course is designed to give non-intimidating presentation of statistical concepts, principals and techniques most useful for students in business management. The main objectives of the course are to enhance students’ competency in application of statistics to solve business management problems and to improve their level of quantitative sophistication for further advanced business analysis. This course uses a problem solving approach that focuses on proper interpretation and use of statistical information, while developing necessary understanding of the underlying theory and techniques. Topics include the role of statistics in modern business environments and for management information, data collection, data tabulation, probability concepts and probability distributions, sampling distribution, interval estimation and hypothesis testing, correlation and regression analysis.
At the conclusion of this course, the student will be:
Week 01: INTRODUCTION
• Definition
• Descriptive Statistics & Inferential Statistics
• Statistics Applications in Business
Week 02: DATA CONDENSATION AND PRESENTATION
• The Data Array and Frequency Distribution
• Graphical Representation
Week 03: MEASURES OF CENTRAL TENDENCY
• Means: (Arithmetic, Geometric, Harmonic)
• The Median
• The Mode
• Quartiles
Week 04: MEASURES OF DISPERSION
• Range
• The Semi-Inter-quartiles Range
• The Mean Deviation
• The Variance and Standard Deviation
Week 05: INDEX NUMBERS
• Defining an Index Number
• Un-weighted Aggregates Index
• Weighted Aggregates Index
• Average of Relative Methods
• Quantity and Value Indices
Week 06: SETS &PROBABILITY
• Sets
• Basic concepts
• Types and Rules
• Conditional Probabilities
Week 07: MATHEMATICAL EXPECTATION
• Expected value.
• Expected monetary value.
• Decision criterion utility & Expected Utility.
Week 08: PROBABILITY DISTRIBUTION
• Basic concept
• Types of Probability Distribution.
• Random variables
• The Binomial distribution
• The Poisson distribution
• The Normal Distribution
• Distribution of continues Random variable.
• Sample regression and correlation