COM 2503 Business Statistics


This course introduces students to the collection, analysis, and graphic presentation of data and the application of statistical methods to the solution of practical business problems. The course covers descriptive statistics, probability Theory, and statistical inference. The major topics cover under descriptive statistics include, collection, organization and presentation of data, and statistical summary measures. Probability theory includes topics such as approaches to probability theory, basic probability rules, Bayes’ theorem and theoretical distributions. Statistical inference covers sampling distributions, estimation, confidence intervals and hypothesis testing. Two important data analysis techniques regression analysis and time series forecasting are also included in the course.

  • To provides a conceptual framework of statistical reasoning by which larger conclusions are drawn from sample evidence.
  • To equip students with basic concepts, techniques, and tools of statistical analysis so that they can understand the strategic importance of statistics in contemporary business environments, and apply statistical methods to problems pertaining to business.

Upon completion of this course, the students will be able to:

  • describe the nature and usefulness of statistics in business
  • identify various types and sources of data, and explain methods of data collection
  • present data graphically and correctly interpret graphic presentations
  • acquire raw data and make frequency distributions
  • calculate interpret basic measures of central tendency, dispersion and skewness
  • solve probability problems using basic probability rules and Bayes’ theorem
  • use binomial, Poisson and normal distributions to solve business problems
  • describe probability and non-probability sampling methods
  • derive sampling distributions of sample mean and sample proportion
  • develop and interpret confidence interval estimates for the mean and the proportion
  • describe basic principles of hypothesis testing and test hypotheses concerning a mean and a proportion, and perform chi-square test of independence
  • use regression analysis to predict the value of a dependent variable based on one independent variable when the relationship is linear
  • explain the components of a time series and estimate trend and seasonal components
  • forecast using multiplicative model

1. Introduction
1.1 Definition of Statistics
1.2 Nature and Scope of Statistics
1.3 Importance of Statistics in Business and Management
1.4 Limitations of Statistics

2. Collection of Data
2.1 Primary and Secondary Data
2.2 Types of data
2.3 Methods of Collecting Primary data
2.3 Designing a Questionnaire

3. Presentation of Data in Tables and Charts
3.1 Organization of Numerical Data
3.1.1 Ordered Array
3.1.2 Frequency Distributions
3.1.3 General Rules for Forming Frequency Distributions
3.1.4 Relative Frequency Distributions and Percentage Distributions
3.1.5 Cumulative Frequency Distributions
3.2 Charts for Numerical Data
3.2.1 Histogram, Frequency Polygon
3.2.2 Cumulative Frequency Polygon (Ogive)
3.2.3 Scatter Diagram (Graph for Bivariate Numerical Data)

3.3 Tables and Charts for Categorical Data
3.3.1 Summary Table
3.3.2 Bar Chart, Pie Chart
3.4 Tables and Charts for Bivariate Categorical Data
3.4.1 Contingency Table
3.4.2 Component Bar Chart

4. Statistical Summery Measures
4.1 Measures of Central Tendency (Averages)
4.1.1 Arithmetic Mean, Median, Mode, Geometric Mean
4.1.2 Quartiles
4.2 Measures of Dispersion
4.2.1 Range, Interquartile Range,
4.2.2 Variance and Standard Deviation
4.2.3 Coefficient of Determination
4.3 Measures of Skewness

5. Probability Theory
5.1 The Concept of Probability
5.2 Basic Definitions
5.2.1 Sets, Set Operations, Venn Diagram
5.2.2 Sample Space and Events
5.2.3 Permutations and Combinations
5.3 Probability Approaches
5.3.1 Classical Approach
5.3.2 Relative Frequency Approach
5.3.3 Subjective Approach
5.4 Basic Probability Rules
5.5 Conditional Probability
5.6 Statistical independence and Multiplication Rule
5.7 Joint-Probability Table
5.7.1 Marginal (Simple) Probabilities
5.7.2 Joint Probabilities
5.8 The Law of Total Probability and Probability Tree Diagrams
5.9 Bays’ Theorem
6. Random Variables and Probability Distributions
6.1 Discrete and Continuous Random Variables
6.2 Probability Distribution for a Discrete Random Variable
6.2.1 Expected Value of a Discrete Random Variable
6.2.2 Variance and Standard Deviation of a Discrete Random Variable
6.3 Binomial Distribution
6.3.1 Conditions for a Binomial Random Variable
6.3.2 Applications of Binomial Distribution
6.4 Poisson Distribution
6.4.1 Applications of Poisson Distribution
6.4.2 Poisson Approximation to Binomial Distribution
6.5 Probability Distributions for a Continuous Random Variable
6.6 Normal Probability Distribution
6.6.1 Properties of Normal Distribution
6.6.2 The Standard Normal Distribution
6.6.3 Normal Approximation to Binomial Distribution

7. Sampling and Sampling Distributions
7.1 Census and Sample surveys
7.2 Sampling and Non-Sampling Errors
7.3 Methods of Sampling
7.3.1 Probability Sampling Methods
7.3.2 Non-probability Sampling Methods
7.4 Sampling Distributions
7.4.1 Sampling Distribution of Sample Mean
7.4.2 Central Limit Theorem
7.4.3 Sampling Distribution of Sample Proportion
7.4.4 Finite Population Correction

8. Statistical Estimation
8.1 Point Estimation
8.1.1 Point Estimators for Population Mean Variance and Proportion
8.1.2 Properties of a Good Point Estimator
8.2 Interval Estimation
8.2.1 Confidence Intervals for Population Mean (When σ is Known)
8.2.2 T Distribution
8.2.3 Confidence Intervals for Population Mean (When σ is Unknown)
8.2.4 Confidence Intervals for Population Proportion

9. Hypothesis Testing
9.1 Null Hypothesis and Alternative Hypothesis
9.2 Concepts of Hypothesis Testing
9.2.1 Type I and Type II Errors
9.2.2 The Significance Level
9.2.3 The Test Statistic
9.2.4 Critical Region, Critical Value and P Value
9.2.5 One-Tailed and Two-Tailed Tests
9.3 Testing Hypothesis Concerning Population Mean
9.4 Testing Hypothesis Concerning Population Proportion P
9.5 Chi-square Test of Independence

10. Correlation and Simple Linear Regression
10.1 Scatter Diagram and Correlation
10.2 Simple Linear Regression Model
10.3 The Method of Least Squares
10.4 Coefficient of Determination
10.5 Standard error of the Estimate

11. Time Series Forecasting
11.1 The importance of Time Series Forecasting
11.2 Components of a Time Series
11.3 Mathematical Models for Time Series
11.4 Estimation of Linear Trend
11.4.1 Method of Least Squares
11.4.2 Method of Moving Averages
11.5 Estimation of the Seasonal Component
11.5.1 Seasonal Indices
11.5.2 Ratio to Moving average Method
11.6 Deseasonalized Data
11.7 Forecasting a Multiplicative Series

Lectures, seminars, course manuals, workshops, assignments, self study.

  1. Amir D.A. and Sounderpandian, J (2005). Complete Business Statistics, Sixth edition, Irwin/ McGraw-Hill.
  2. Berenson, M. L., Levine, D. M., and Krehbiel, T. C., Basic Business Statistics: (2006). Concepts and Applications, 10th edition, Prentice-Hall,
  3. Bowerman, B.L., O’Connell, R.T., and Michael H.L. (2010). Business Statistics in Practice, Fifth Edition, McGraw-Hill.
  4. Keller, G. (2009) Statistics for Management and Economics, Seventh Edition, Thomson South-Western.
  5. Levine, D. M Timothy C. Krehbiel, Berenson, M.L and. Viswanathan. P. K (2009). Business Statistics-A First Course, Fourth Edition, Pearson Education, Inc.
  6. Ronald, M. W. (2008). Introduction to Business Statistics, Sixth Edition, McGraw-Hill