Week 2 / Module 1 — Statistics for Engineers

An outline of the module's learning topics spanning introductory statistics concepts, descriptive statistics (central tendency, spread, shape), and data variability measures; used by instructors to see scope and by learners to preview what will be covered.

A landing page linking to the Module 0 introductory PowerPoint slides on probability and statistics fundamentals; learners use it to access the lecture slides for background context before the main module content.

A landing page linking to the Module 1 PowerPoint slides on the role of statistics in engineering; learners use it to access the core lecture presentation for this module.

A reading that explains how statistics supports evidence-based engineering decision-making under uncertainty, referencing both Module 0 and Module 1 lectures as supporting material; learners read it to understand the practical relevance of statistics in engineering design and testing.

This reading introduces the role of statistics in engineering practice, explaining why statistical thinking is essential across engineering disciplines such as civil and mechanical engineering, and serves as a conceptual foundation for learners beginning the module.

This reading covers descriptive statistics concepts including measures of center, spread, and distribution shape, giving learners the mathematical tools needed to summarize and interpret engineering datasets before conducting deeper analysis.

This reading explains data variability and distribution shape—including how spread, center, and visual form of data are analyzed—and is used by learners to understand how datasets behave in engineering and quality management contexts.

This reading covers probability distributions and their mathematical frameworks for modeling uncertainty in engineering scenarios such as manufacturing defects, dimensional variation, and network traffic, helping learners connect probability theory to real engineering decisions.

This reading covers statistical inference concepts—drawing conclusions about populations from samples—including methods engineers use when it is impractical to measure entire populations, serving as a guide for learners interpreting sample-based engineering data.

This reading covers applied statistical methods in engineering analysis and quality control, showing learners how to interpret real-world manufacturing and process data using statistical tools to support engineering decision-making and quality assurance.

A 26-question quiz bank covering identification of populations vs. samples, use of Venn diagrams to distinguish population from sample, and classification of statements as descriptive or inferential statistics; instructors use it to generate assessments and learners use it to test their grasp of foundational statistical concepts.

A getting-started assignment requiring learners to demonstrate readiness by submitting a report with two screen captures showing they can run Python (or R) in Google Colab and write reports; used to verify students have the necessary software skills before tackling later assignments.

A graded discussion assignment in which learners query an AI tool about why data collection matters in engineering and how data is used, then post and cite the AI's response; used to introduce AI-assisted statistical inquiry and prompt reflection on data's role in engineering processes.