Supply Chain Management

STAT 3331

Statistics for Business Applications

Data-Driven Management

Course Overview

STAT 3331 equips students with the analytical tools and statistical reasoning needed to make data-driven decisions in today’s complex business environment. Designed especially for future Supply Chain Management professionals, this course introduces the three pillars of business analytics — descriptive, predictive, and prescriptive analysis — and applies them to real-world business problems involving uncertainty, variability, and risk.

Course Purpose

This course builds a strong foundation in statistical thinking and quantitative analysis, preparing students to interpret data, evaluate business performance, and support strategic decision-making. Emphasis is placed on the ethical use of data, the communication of analytical insights, and the application of statistical methods across business functions such as supply chain, marketing, finance, and operations.

Students will learn to:

• Analyze and visualize data using descriptive statistics
• Model relationships and forecast outcomes using predictive techniques
• Optimize decisions using prescriptive tools
• Apply statistical thinking to real-world business scenarios using modern software tools

This course is a critical component of the data analytics foundation emphasized in Gartner-ranked supply chain programs and is essential for students pursuing careers in operations, logistics, sourcing, planning, and business intelligence.

Learning Objectives

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

• Analyze data using appropriate statistical methods and interpret results in a business context
• Identify and avoid common ethical pitfalls in data analysis and reporting
• Communicate statistical findings clearly and effectively to business stakeholders
• Apply descriptive statistics to summarize and explore data distributions
• Use probability models to assess risk and uncertainty in business decisions
• Conduct statistical inference through confidence intervals and hypothesis testing
• Build and evaluate linear regression models for business forecasting
• Analyze time series data and apply forecasting techniques
• Explore and model large datasets using data mining techniques such as clustering, association rules, and regression trees
• Apply logistic regression and classification models for predictive decision-making
• Use prescriptive analytics to support optimization and decision-making under constraints

Topics Covered

• Descriptive Statistics and Data Visualization
• Probability Theory and Distributions
• Sampling, Estimation, and Hypothesis Testing
• Big Data Applications and Modeling Relationships
• Linear and Multiple Regression
• Time Series Analysis and Forecasting
• Data Mining: Clustering, Association, and Classification
• Logistic Regression and Predictive Modeling
• Prescriptive and Predictive Analytics and Optimization
• Ethical Use of Data and Statistical Integrity
• Applications in Supply Chain, Finance, Marketing, and Operations

Experiential Learning & Course Pedagogy

Students will engage with:

• Real-world business datasets and case studies
• Hands-on exercises using statistical software and spreadsheet tools
• Practice problems and simulations to reinforce quantitative skills
• Opportunities to explore how analytics supports supply chain and business strategy

Course Format & Assessment

Students are provided with course notes, textbook resources, and lectures. Grades are typically based on:

• Homework assignments and applied exercises that progressively cover the course material
• Three exams assessing conceptual understanding and applied skills