3130.18 - Statistics
Mathematics 1 (3103) and Probability theory (3035)
To give the participants an introduction to statistical thinking and methods and to provide them with an understanding of random variation and of the use of statistical models. Important statistical principles for collection and analysis of data are introduced, and methods for model choice, estimation, testing and model verification are presented. The participants learn to handle a number of elementary problems which occur frequently in engineering practice and are thus enabled to critically assess statistical (empirical) investigations and references.
Simple methods for graphical and tabular assessments of collected or measured data: empirical distributions, histogram, normal probability plot, box plot. Model formulation. Model control: control of distribution function. Application of especially Poisson-, binomial-, exponential- and normal distribution. Estimation and test of parameters and construction of confidence intervals in frequently occurring situations (e.g. means, variances, proportions). Regression analysis with one independent variable and introduction to the analysis of variance and to the analysis of contingency tables, estimation theory and simulation based methods.
Learning and teaching approaches
Online activities and lectures. Exercise classes. Project work with feedback.
By the end of the course the student is expected to be able to: • Estimate and interpret simple summary statistics, such as mean, standard deviation, variance, median and quartiles. • Apply simple graphical techniques, including histograms, normal-score plots, and box plots. • Identify and describe probability distributions, including Poisson, binomial, exponential and the Normal distribution. • Compare different statistical methods. • Apply and interpret important statistical concepts, such as the formulation of models, parameter estimation, construction of confidence intervals and hypothesis testing. • Apply and interpret simulation based statistical methods. • Apply and interpret simple statistical methods within one- and two sample situations and for count data. • Apply and interpret simple statistical methods within simple linear regression and analysis of variance. • Understand and interpret output from some commonly used statistical software • Perform sample size calculations in simplified and standard setups • Debate and criticize empirically based information.
There will be 1 mini project that must both be handed in and approved in order to be allowed to go to the final exam. The final exam is a 4-hours written multiple choice test. The course is coordinated with DTU and will follow any changes made there.
Online teaching material and textbook from DTU.
Toke Meier Carlsen