MN3040 Data Management and Statistics

The course introduces students to basic concepts and procedures in descriptive and inferential statistics and prepares them for subsequent statistical courses such as Multivariate Data Analysis (MN4110, MN4111, MN4912), Data Analytics and Visualization for Policy Evaluation (MN4128), Advanced Model Building for Causal Inference and Prediction (MN4913), Applications of Data Analytics in Defense Management (MN4914), Applied Manpower modeling (MN4761), and beyond.

This course focuses on the descriptive and inferential statistical concepts and practical analytical skills useful for conducting managerial and policy analysis. This course bridges the gap between theoretical concepts and applied work in statistics in the context of answering manpower, financial management and resource allocation related policy questions. The course starts with pivot tables, data visualization, data storage and retrieval. The course introduces probability theory as a background for understanding inferential statistics. It then covers statistical distributions and methods for deriving, describing, and summarizing single-variable statistics. Finally, methods are presented for drawing inferences from research samples to populations, including hypothesis testing and confidence intervals. Prerequisites: None.

Lecture Hours

Lab Hours

Course Learning Outcomes

At the end of the course, you should know how to:

Apply core concepts in probability and statistics used for decision-making.
Demonstrate the ability to turn data into usable, managerially relevant, information.
Recognize patterns and statistical measures from examining charts and data.
Interpret and scrutinize commonly reported statistics.
Test the validity of a hypothesis using evidence from data.
Assess the relationship between two variables.
Critically evaluate and effectively communicate the findings in professional and managerial contexts.