Thursday, December 21, 2023

MBA Quantitative Methods Syllabus

Table of Contents

  1. Descriptive Statistics
  2. Probability Theory
  3. Sampling Techniques
  4. Regression Analysis
  5. Hypothesis Testing
  6. Time Series Analysis
  7. Decision Analysis

1. Descriptive Statistics

This section provides an overview of descriptive statistics, including measures of central tendency and dispersion. Explore techniques such as mean, median, mode, standard deviation, and variance to summarize and analyze data.

2. Probability Theory

Learn the fundamental concepts of probability theory and its applications in business decision-making. Topics covered include probability distributions, expected values, and probability calculations using various techniques like permutations and combinations.

Welcome to the Probability Theory section of the MBA Quantitative Methods syllabus. This module will introduce you to the fundamental concepts and principles of Probability Theory in the context of business decision-making.

Module Objectives

  • Understand the basic principles and terminology of Probability Theory
  • Apply Probability Theory in business scenarios
  • Explore the concept of random variables and their applications
  • Examine the different types of probability distributions

Module Topics

During this module, we will cover the following topics:

  1. Introduction to Probability Theory
  2. Probability Laws and Rules
  3. Conditional Probability and Independence
  4. Random Variables and Probability Distributions
  5. Normal Distribution and Central Limit Theorem
  6. Sampling and Estimation


Your understanding and application of Probability Theory will be assessed through a combination of assignments, quizzes, and a final examination. It is important to actively participate in class discussions and complete all assigned tasks to succeed in this module.

Additional Resources

To enhance your understanding of Probability Theory, we recommend utilizing the following resources:

  • Textbooks: Recommended readings will be provided throughout the module.
  • Online Materials: Access to online resources such as articles, videos, and tutorials.
  • Discussion Forums: Engage in online discussions with fellow students to exchange knowledge and clarify concepts.

We hope you find this module on Probability Theory valuable for your MBA journey. If you have any questions or need assistance, please don't hesitate to reach out to your instructor. Best of luck!

2. Probability Theory

3. Sampling Techniques

Understand different sampling techniques used to gather data and analyze its characteristics. Topics covered include simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Explore how to determine appropriate sample sizes for accurate results.

The study of sampling techniques is an essential component of the MBA Quantitative Methods syllabus. This course introduces students to the various methods used to gather representative samples from populations in business research.

Course Objectives

  • Understand the importance of sampling in business research
  • Learn different sampling techniques and their applications
  • Gain knowledge on sampling errors and how to minimize them
  • Develop skills to design effective sample surveys

Topics Covered

  1. Introduction to Sampling
    • Definition and significance of sampling
    • Types of populations and samples
  2. Probability Sampling Methods
    • Simple Random Sampling
    • Stratified Sampling
    • Cluster Sampling
    • Systematic Sampling
    • Multi-stage Sampling
  3. Non-Probability Sampling Methods
    • Convenience Sampling
    • Purposive Sampling
    • Quota Sampling
    • Snowball Sampling
  4. Sampling Errors and Sample Size Determination
    • Types of sampling errors
    • Calculating sample size
    • Margin of error and confidence level
  5. Data Collection Methods
    • Questionnaires
    • Interviews
    • Observations
    • Secondary data sources
  6. Sampling Applications in Business Research
    • Market research
    • Consumer behavior studies
    • Opinion surveys
    • Data analysis and interpretation


Students will be evaluated through a combination of assignments, quizzes, a mid-term examination, and a final project that involves designing a sample survey and analyzing the collected data.

3. Sampling Techniques

4. Regression Analysis

Dive into regression analysis, a powerful tool to examine relationships between variables. Topics covered include simple linear regression, multiple regression, model building, and interpreting regression results. Learn how to use regression analysis to make predictions and draw meaningful conclusions.

Regression analysis is an important statistical technique used in MBA Quantitative Methods courses. It is used to model and analyze the relationship between a dependent variable and one or more independent variables.

Course Overview

This course will provide students with an in-depth understanding of regression analysis and its applications in business. Students will learn how to perform regression analysis, interpret the results, and make informed business decisions based on the findings.

Topics Covered

The course syllabus will cover the following topics:

  1. Introduction to Regression Analysis
  2. Simple Linear Regression
  3. Multiple Linear Regression
  4. Assumptions of Regression Analysis
  5. Model Specification and Estimation
  6. Hypothesis Testing in Regression Analysis
  7. Model Evaluation and Interpretation
  8. Variable Selection and Model Building
  9. Time Series Analysis

Course Objectives

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

  • Understand the concept of regression analysis and its relevance in business decision-making.
  • Apply regression analysis techniques to real-world business problems.
  • Interpret regression analysis results and draw meaningful conclusions.
  • Effectively communicate the findings of regression analysis to stakeholders.
  • Utilize regression analysis as a tool for forecasting and predicting future outcomes.


Assessment in this course will be based on a combination of assignments, quizzes, and a final examination. Students will also be required to complete a regression analysis project, where they will apply the concepts learned to a real-world business problem.


Some recommended references for this course include:

  1. Applied Regression Analysis: A Research Tool by John O. Rawlings, Sastry G. Pantula, and David A. Dickey
  2. Regression Analysis by Example by Samprit Chatterjee and Ali S. Hadi
  3. Introduction to Linear Regression Analysis by Douglas C. Montgomery, Elizabeth A. Peck, and G. Geoffrey Vining
4. Regression Analysis

5. Hypothesis Testing

Discover the concept of hypothesis testing and its significance in making data-driven decisions. Explore null and alternative hypotheses, type I and type II errors, confidence intervals, and t-tests. Gain practical knowledge in conducting hypothesis tests and interpreting results.

Hypothesis testing is a crucial concept covered in the MBA Quantitative Methods syllabus. It involves using statistical methods to test the validity of hypotheses or claims made about population parameters.

In this section, students will learn about the different steps involved in hypothesis testing, including formulating null and alternative hypotheses, selecting an appropriate test statistic, determining the level of significance, conducting the test, and making conclusions based on the test results.

Moreover, students will be introduced to various types of hypothesis tests commonly used in business and management research, such as t-tests, chi-square tests, and ANOVA (Analysis of Variance).

By understanding hypothesis testing, MBA students will gain the ability to analyze data, make informed decisions, and draw meaningful conclusions based on statistical evidence. This skill is crucial in various business scenarios, such as market research, forecasting, and performance evaluation.

Overall, this topic plays a significant role in the MBA Quantitative Methods syllabus, equipping students with the necessary tools to apply statistical techniques in their future careers.

5. Hypothesis Testing

6. Time Series Analysis

Learn about time series analysis, which focuses on studying patterns and trends in time-based data. Topics covered include forecasting techniques, decomposition, smoothing methods, and evaluating forecast accuracy. Gain the skills to make informed predictions based on historical data.

The course on Time Series Analysis is a part of the MBA Quantitative Methods syllabus. It focuses on studying and analyzing data points collected at regular intervals over a period of time.

Course Objectives

  • Understand the concept of time series and its importance in business forecasting
  • Learn various techniques for data collection, data cleaning, and data preparation for time series analysis
  • Apply statistical methods to analyze and interpret time series data
  • Explore different models for time series forecasting
  • Gain hands-on experience using statistical software for time series analysis

Course Topics

  1. Introduction to Time Series Analysis
  2. Time Series Components
  3. Time Series Visualization and Data Cleaning
  4. Time Series Decomposition
  5. Stationarity and Differencing
  6. Autocorrelation and Partial Autocorrelation Functions
  7. Forecasting Methods: Moving Average, Exponential Smoothing, ARIMA
  8. Model Selection and Evaluation
  9. Seasonal Time Series Analysis
  10. Introduction to Panel Data Analysis

Assessment and Grading

The assessment for this course will consist of quizzes, assignments, a mid-term exam, and a final project. The grading will be based on the overall performance in these assessments.


Students are expected to have a basic understanding of statistics and regression analysis. Familiarity with statistical software such as R or Python will be an advantage.


The course will provide study materials, lecture notes, and recommended textbooks for reference. Students will also have access to relevant online resources and software for conducting time series analysis.

By the end of this course, students will have gained the knowledge and skills required to analyze and forecast time series data, which can be valuable in making informed business decisions.

6. Time Series Analysis

7. Decision Analysis

Explore the principles of decision analysis and decision-making under uncertainty. Learn techniques such as decision trees, expected value analysis, and sensitivity analysis. Understand how to evaluate different decision-making scenarios and select the best course of action.

Course Overview

The Decision Analysis course is part of the MBA Quantitative Methods curriculum. This course focuses on teaching students the fundamentals of decision-making and analytical techniques that can be used to make effective business decisions. The syllabus covers various decision models and tools, along with practical applications and case studies.

Course Objectives

  • Understand the importance of decision analysis in managerial decision-making processes
  • Develop skills to apply quantitative techniques to support decision-making
  • Explore decision trees and decision-making under uncertainty
  • Analyze and interpret various decision models
  • Learn techniques to evaluate decision alternatives and select the most optimal solution

Course Topics

  1. Introduction to Decision Analysis
  2. Decision Trees and Tree Analysis
  3. Decision-Making Under Uncertainty
  4. Utility Theory
  5. Multi-Criteria Decision Analysis
  6. Risk Analysis and Sensitivity Analysis
  7. Decision Support Systems

Assessment and Grading

Assessment in this course will be based on various methods including assignments, quizzes, class participation, and a final project. The grading criteria will be communicated at the beginning of the course and will be based on a comprehensive evaluation of the student's performance throughout the semester.

Recommended Resources

  • Textbook: "Decision Analysis for Managers" by Peter T. Paul
  • Additional readings and case studies will be provided during the course
  • Online resources and simulation tools related to decision analysis

Contact Information

For any questions or clarifications regarding the syllabus or course content, please feel free to contact the course instructor or visit the department's website.

7. Decision Analysis

Key Takeaways

  • Develop a strong foundation in descriptive statistics and probability theory.
  • Understand various sampling techniques and determine appropriate sample sizes.
  • Gain expertise in regression analysis to analyze relationships between variables.
  • Learn hypothesis testing techniques to make data-driven decisions.
  • Master time series analysis for accurate forecasting and trend identification.
  • Apply decision analysis tools to make optimal business decisions under uncertainty.

Frequently Asked Questions

Q: What are the prerequisites for the MBA Quantitative Methods course?

A: There are no specific prerequisites for this course; however, basic mathematical knowledge and familiarity with statistics will be beneficial.

Q: How is the course assessed?

A: The course assessment includes assignments, quizzes, exams, and possibly a project. The grading criteria will be provided at the beginning of the course.

Q: Can this course be taken online?

A: Yes, the course is designed to be offered online, allowing flexibility for students to study at their convenience.

mba quantitative methods syllabus

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