Course Content (Syllabus)
Descriptive statistics: data summary and presentation, frequency distribution, histogram, characteristic values.
Probability and probability distributions: basic concepts, events, conditional probability and Bayes theorem. Probability distributions, discrete and continuous random variables, expected value, variance and standard deviation, moment generating function. Important distributions: binomial, geometric, Poisson, uniform, exponential, gamma, normal distribution and the central limit theorem, Student, X2 and F distributions.
Statistical estimation: sampling distributions, point estimation, properties of estimators, confidence intervals, required sample size.
Statistical hypotheses: type I and type II errors, hypotheses on parameters, goodness of fit tests.
Simple linear regression.
Additional bibliography for study
- Montgomery, D.C., Runger, G.C., Applied Statistics and Probability for Engineers, Wiley, 2006.
- Montgomery, D.C., Runger, G.C., Hubele, N.F., Engineering Statistics, Wiley, 2007.