Statistics

Learning from engineering data. Statistics for engineers: descriptive statistics and plots, correlation and least-squares regression (and why correlation is not causation), confidence intervals and hypothesis tests, and quality, reliability and design-of-experiments methods.

• Descriptive Statistics — Summarise and visualise data before analysing it. Learn mean, median and mode, range, variance and standard deviation, percentiles, quartiles and IQR, outliers, skewness and kurtosis, histograms, box plots, scatter plots and probability plots.
• Correlation & Regression — Relationships between variables. Learn covariance and the correlation coefficient r, simple linear regression by least squares, slope and intercept, residuals and R², prediction vs extrapolation, multiple and non-linear regression, and why correlation is not causation.
• Sampling, Confidence & Hypothesis Tests — Draw conclusions about a population from a sample. Learn sampling distributions and the central limit theorem, standard error, confidence intervals (z and t), hypothesis testing, p-values, Type I and II errors, power and sample size, and common tests (t, paired, two-sample, chi-square).
• Quality, Reliability & Design of Experiments — Statistics that run factories and test labs. Learn statistical process control (x̄-R charts, control limits, rules), process capability Cp and Cpk, tolerance analysis, reliability statistics (MTBF, hazard, bathtub curve), and design of experiments (factorial designs, main effects and interactions).