# Engineering Statistics Modules

• Developer: Dr. Steven Jiang at NC A&T
• Course: Engineering statistics course that is primarily taken by sophomores and juniors
• Knowledge Prerequisites: None
• Data Tool(s): Github Classroom and Google Colab - is scaffolded such that students who have no programming experience are able to leverage statistical analysis libraries
• Module Focus:
• Introducing students to these data science tools and data science topics, such as descriptive statistics, inferential statistics, and
visualizations, so that the students can leverage their knowledge to solve engineering statistics related problems. In both modules, students analyze real-world data such energy use in buildings (Basic Statistics module) and stormwater runoff (Hypothesis Testing module).
• Keywords (Disciplinary-Specific): engineering statistics, energy use in buildings, stormwater runoff
• Keywords (Data Science): descriptive statistics, histograms, scatter plots, boxplots, confidence intervals, hypothesis testing
Basic Statistics Module Hypothesis Testing Module

Targeted Data Science Topics and Competencies

• Data Use (Statistical Analysis):
• Demonstrate an ability to use statistical methods and/or software pertaining to an analysis goal
• Demonstrate an ability to identify appropriate subset of data for an analysis goal
• Data Visualization (Data Communication):
• Demonstrate an ability to create a graphical or tabular representation of data
• Data Visualization (Data Interpretation):
• Demonstrate an ability to interpret graphical and tabular representations

Targeted Data Science Topics and Competencies

• Data Preprocessing (Data cleaning)
• Demonstrate an ability to detect and deal with outliers in data
• Data Visualization (Data Communication)
• Demonstrate an ability to create a graphical or tabular representation of data
• Data Visualization (Data Interpretation)
• Demonstrate an ability to communicate concepts and findings using graphical or tabular representations of data
• Data Use (Statistical Analysis)
• Demonstrate an ability to formulate a hypothesis, prediction, or goal to be targeted using data analysis
• Demonstrate an ability to identify appropriate statistical methods and/or software for an analysis goal
• Demonstrate an ability to identify appropriate subset of data for an analysis goal
• Demonstrate an ability to use statistical methods and/or software pertaining to an analysis goal
• Demonstrate an ability to compare results of analysis with other findings
• Data Use (Data communication)
• Demonstrate an ability to explain results to an appropriate audience

Module Materials

Module Materials