An outline of the module's topics, spanning descriptive statistics fundamentals, measures of central tendency (mean, median, mode), and measures of variability (range, variance), used by learners to preview what the module covers.
A reading that defines and explains key descriptive statistics concepts — mean, median, mode, and measures of variability — with formulas and engineering-context examples, used by learners as the primary reference text for the module.
A landing page that provides learners with links to the Module 2 PowerPoint presentation and YouTube lecture on descriptive statistics, serving as a navigation hub for the module's multimedia resources.
This reading introduces descriptive statistics as the practice of summarising, organising, and presenting data, giving real-world examples of its use and establishing the conceptual foundation for the measures covered later in the module.
This reading explains the three main measures of central tendency — mean, median, and mode — describing what each represents, how each is calculated, and when each is the most appropriate summary of a typical value in a dataset.
This reading covers measures of variability — including range, variance, and standard deviation — explaining how each quantifies the spread of data around a central value and why spread matters even when two datasets share the same mean.
This reading describes how data values form distributional patterns, covering concepts such as frequency distributions, histogram shapes, skewness, and symmetry so learners can recognise and interpret the overall structure of a dataset.
This reading guides learners and instructors on how to choose the correct descriptive statistics for a given dataset by matching measures of central tendency and variability to the appropriate level of measurement and data type to avoid misleading conclusions.
A 40-question quiz bank featuring engineering-scenario problems (e.g., concrete strength, voltage measurements) that test learners' ability to select and apply the correct descriptive statistic, used by instructors to assess comprehension of module concepts.
A programming-based assignment requiring learners to choose a real-world dataset, perform descriptive statistical analysis using R or Python, and write a report summarizing the results, assessing their practical ability to apply module concepts.
A graded discussion in which learners query an AI about which descriptive statistics every engineer should know, reflect on their current understanding of those statistics, and respond to at least one peer's post.