Week 3/Module 2 - Descriptive Statistics

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.

An opening reading that explains what descriptive statistics are and why engineers summarise raw measurement data into typical values, spread, and visual pictures, used to orient learners before they tackle specific summary measures.

A lesson page defining the mean, median, and mode as answers to the question "what is a typical value?", which learners use to compute and compare each measure on engineering data such as test results, failure times, and sensor readings.

A reading on quantifying spread — range, variance, standard deviation, and related measures — that shows why datasets with identical means can behave very differently and helps learners judge whether a process or measurement system is acceptable.

A topic page on the shape of data distributions, covering how values cluster or thin out, symmetry versus skew, and reading histograms or dot plots, so learners inspect data visually before computing summary numbers.

A decision-focused reading on matching the statistical measure to the data type and distribution, using examples like means on skewed cost data or modes on continuous readings to teach learners how to choose measures that will not mislead an audience.

The consolidated module page that strings together all the Week 3 descriptive statistics topics — introduction, central tendency, variability, distributions, and measure selection — for learners who want to read or review the full module in one place.

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.

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.