Quantitative Analysis for Management

Course Aims

and Objectives:

The main aim of this course is to provide students with an introductory yet comprehensive overview of quantitative tools that can be used to analyze management related problems. It also provides an opportunity to learn the application of some of the basic models used for business forecasting. Relationship with Other Courses: The students enrolling in this course must have taken statistics courses. However, the basic issues of the course require a good understanding of principles of microeconomics, management and math courses.

Course Content:

This course aims at introducing various quantitative tools that can be used in the analysis of various types of probability theory in making statistical inferences. Models for business forecasting and criteria for decision-making under conditions of uncertainty and risk.

Course Learning Outcomes (CLOs):

(Knowledge, skills and competencies expected to be achieved at the end of the course)

A) On the successful completion of the course, students will be able to:

  1. Apply fundamental concepts of probability, statistics, and quantitative analysis to managerial decision-making.
  2. Analyze different types of probability distributions and apply them to real-world business scenarios.
  3. Use decision analysis techniques—including decision trees, Bayesian analysis, and utility theory—to make informed decisions under uncertainty and risk.
  4. Develop and interpret simple and multiple regression models to identify relationships between business variables.
  5. Construct and evaluate forecasting models to predict future business trends and improve planning.
  6. Employ computer-based tools and quantitative software (e.g., Excel QM) to solve management and forecasting problems.

B) On successful completion of the course, students will have developed their skills in:

  1. Quantitative reasoning and analytical problem-solving for managerial applications.
  2. Data interpretation and model evaluation using statistical and forecasting tools.
  3. Applying structured decision-making processes to complex and uncertain business environments.

C) On successful completion of the course, students will have developed their appreciation of values related to:

  1. The importance of data-driven and evidence-based decision-making in management.
  2. Ethical use of quantitative tools and data in business problem-solving.
  3. Continuous improvement and critical evaluation of models and forecasts to support responsible management decisions.
  4. Collaboration and professional integrity in the use of quantitative analysis for organizational success.

Responsible Use of AI

Students are encouraged to use artificial intelligence responsibly and in ways that support their learning. They are expected to familiarize themselves with, and comply with, the University’s Regulation on the Responsible Use of AI and the AI-use categories provided at the end of this course outline.