Course Description:

This introductory course explores the principles and applications of statistics in business and economics. Students will study both descriptive and inferential statistical methods, with an emphasis on data analysis, interpretation, and practical application using statistical software. Topics include data collection, probability theory, random variables, estimation, hypothesis testing, and regression analysis. The course prepares students to make data-driven decisions and critically evaluate statistical information in real-world business contexts.

 

Course Objectives:

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

• Understand the role and importance of statistics in business decision-making.

• Collect, organize, summarize, and interpret business data using statistical methods.

• Distinguish between descriptive and inferential statistics and apply appropriate techniques for each.

• Perform probability calculations and analyze probability distributions.

• Conduct hypothesis testing and make inferences based on sample data.

• Apply regression and correlation analysis for prediction and interpretation of relationships.

• Utilize statistical software for data analysis and presentation of results.

 

Course Learning Outcomes:

By the end of the course, students will be able to:

• Explain the basic concepts and applications of statistics in business environments.

• Create and interpret graphical and numerical summaries of data.

• Use probability theory to assess risk and support business decisions.

• Analyze discrete and continuous random variables using appropriate probability distributions.

• Conduct sampling and understand sampling distributions in the context of business research.

• Estimate population parameters and construct confidence intervals.

• Formulate and test hypotheses using both manual methods and statistical software.

• Interpret and evaluate regression and correlation output for business forecasting.

• Apply statistical tools and software to solve real-world business problems.

 

Recommended Course Materials:

• Textbook: Business Statistics: A First Course by David M. Levine, Timothy C. Krehbiel, and Mark L. Berenson (Latest Edition)

• Software: Microsoft Excel or SPSS; Instructor-approved statistical software

• Calculator (with statistical functions)

• Additional readings and case studies as provided by the instructor

 

Course Content:
 

1. Elements and Scope of Statistics

• Role of business statistics

• Types and sources of data

• Data collection methods

• Levels of measurement

 

2. Organizing and Summarizing Data

• Frequency distributions and graphs

• Box-and-whisker plots, stem-and-leaf displays

• Measures of central tendency and dispersion

• Interpreting computer-generated statistical output

 

3. Probability Concepts

• Classical and empirical probability

• Contingency tables: joint and marginal probabilities

• Rules of probability

 

4. Discrete Probability Distributions

• Binomial distribution

• Poisson distribution and Poisson approximation

• Statistical software application and interpretation

 

5. Continuous Probability Distributions

• Normal distribution

• Central Limit Theorem

• Normal approximation to the binomial

• Computer-assisted analysis

 

6. Sampling and Sampling Distributions

• Probability vs. non-probability sampling

• Sampling distribution of the sample mean

• Application of sampling methods using software

 

7. Estimation (Statistical Inference I)

• Point and interval estimates

• Confidence intervals for known and unknown variance

• Use of Student’s t-distribution

• Determining appropriate sample size

• Software-supported analysis

 

8. Hypothesis Testing (Statistical Inference II)

• Types of errors in hypothesis testing

• One- and two-tailed tests for means

• p-values and test power

• Computer-aided hypothesis testing and interpretation

 

9. Regression and Correlation Analysis

• Scatterplots and linear relationships

• Simple linear regression model

• Hypothesis testing on regression coefficients

• Correlation analysis and assumptions

• Residual analysis and normality testing

• Computer-assisted regression and correlation interpretation

 

Course Assessment & Evaluation:

• Class Participation and Attendance – 10%

• Midterm Examination – 30%

• Final Examination – 60%