STATISTICS I - SAMPLING AND INFERENCE
Learning outcomes of the course unit
The course is the second statistics module and sets out to introduce the basic tools for understanding sampling and inference methods, starting from knowledge acquired by students during the Data Analysis module. In many business and economic applications, in fact, available information refers to a sample (of consumers, businesses, etc.), therefore the descriptive information must be extended from the sample taken to a wider population. This extension is the process of statistical inference. During the course attention is focused on a restricted, yet widely used, set of techniques. In particular, after an introduction to the basic concepts of calculation of probabilities and of sampling, methods of estimation and hypothesis testing are considered. The logical foundations and knowledge purposes of each technique are illustrated, while technical details and mathematical derivations take second place. Each technique is introduced with reference to the business and economic problems that it can help to solve, and include statistical quality control, study of market shares and analysis of relations between economic variables. An important aspect regards the use of computers to carry out statistical analyses. In particular, the power of spreadsheets widely used in business such as Microsoft Excel is illustrated, for the purpose of actual applications of the methods illustrated during the lessons.
Students are advised to take the exam in Statistics – Sampling and Inference after having taken the exam in Statistics – Data Analysis.
Course contents summary
Concepts of probability
Random variables: general aspects and applications
Sampling distributions of statistical indices
Point estimate of the mean and of relative frequency
Interval estimate of the mean in large and small samples
Interval estimate of relative frequency in large samples
Introduction to statistical hypothesis testing; observed significance value (P-value)
Mean hypothesis testing in large and small samples
Hypothesis testing for relative frequency in large samples
Hypothesis testing for two universes in large samples
Model significance and relations with the regression line
Problems of estimation and hypothesis testing on model parameters
Testing the good fit of the model; analysis of variance table
A. Cerioli, M.A. Milioli, Introduzione all’inferenza statistica senza (troppo) sforzo, 2nd edition, Uni.Nova, Parma, 2004 (except Sections 1.2 and 1.3).
A. Cerioli, M.A. Milioli, Esercizi di statistica, 2nd edition, Uni.Nova, Parma, 2005 (Chapters 6 - 10).