Supply Chain Management

STAT 3331

Statistics for Business Applications

Course Purpose

This course develops future Supply Chain Management majors’ understanding of the three pillars of business analytics: descriptive, predictive, and prescriptive analyses. Through a variety of technology resources, students utilize statistical methods to analyze and answer business questions containing uncertainty.

Expected Learning Objectives

Upon completion of the course students will:

• Analyze data using proper statistical methods.
• Ethical Considerations: Learn to identify when data is manipulated, inappropriate assumptions made, or incorrect statistical methods used
• Display proficiency using statistical procedures on data from a variety of business disciplines
• Learn to effectively communicate the meaning of numerical results achieved by appropriate statistical analysis

Selection of Topics Covered:

• Descriptive Statistics including analyzing distributions, measures of location and variability and association.
• Probability Analyses including conditional probability, random variables, discrete probability distributions, and continuous probability distributions.
• Statistical inference including sample selection and distributions, interval estimation, and hypothesis tests
• Big data and practical significance
• Linear regression models and inference
• Modeling relationships using big data
• Time series analysis and forecasting including patterns, accuracy, and analysis techniques
• Descriptive data mining: cluster analysis, association, and text mining
• Predictive data mining: performance measures, logistic regression, and regression trees
• Prescriptive statistics

Course Pedagogy and Immersive/Experiential Activities

Students are provided with course notes, textbook resources, lectures. Extensive practice problems are provided to students.

Grades are typically determined by performance in a series of homework assignments that progressively cover the course material and three Exams.