Engineering Data Analysis

Overview of statistics in engineering, data collection methods, observational studies versus experiments, and sampling techniques.

Measures of central tendency, dispersion, position, skewness, and kurtosis, including grouped data.

Basic probability theory, sample spaces, events, counting rules, and probability rules.

Understanding how probabilities change when new information is available, including Bayes' Theorem and Independence.

Expected value, Binomial, Poisson, Negative Binomial, Geometric, and Hypergeometric distributions.

Probability density functions, Normal, Uniform, Exponential, Gamma, Weibull, and Lognormal distributions.

Joint probability mass/density functions, marginal and conditional distributions, covariance, and correlation.

How sample statistics behave, the Central Limit Theorem, t-distribution, Chi-square, and F-distribution.

Point estimation, confidence intervals for means, proportions, and variances, and prediction/tolerance intervals.

Null and alternative hypotheses, Type I/II errors, P-values, and tests for means, proportions, variances, and Goodness-of-Fit.

Simple and multiple linear regression, correlation coefficients, least squares method, and residual analysis.

One-way ANOVA, Randomized Complete Block Design (RCBD), and post-hoc tests for comparing multiple means.

Control charts for variables and attributes, process capability indices, and natural tolerance limits.