Economics and Management Statistics

Teacher(s)

Heuchenne Cédric;

Language

French

Prerequisites

The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.

Main themes

Part 1: Basic methods of statistical analysis. After an introduction to statistical models (population and sampling models), students are shown how statistical sampling distributions form the basis for inferencing. These properties make it possible to check the precision of specific estimators, to construct confidence intervals and to check the risks of error in a hypothesis testing procedure. Part 2: Application to some standard problems. In this part, the basic methods taught in Part 1 are adapted to analyzing useful application issues in Economics and Management: Variance analysis (comparison of several averages); inter-variable relation modelling (linear models); Studies of categorical variables including an inter-variable independence test. Students will also be introduced, through simple examples, to the maximum likelihood estimation method, which is particularly useful in the more complex models analysed in later Econometrics courses. We consider finally the problem of poor specification of the model and the case a non-linear regression.

Learning outcomes

At the end of this learning unit, the student is able to :
1	The aim of this course is to introduce the types of reasoning and basic methods used in statistical analysis, and examine how they are used to solve simple statistical problems in the field of Economics and Management. This course also aims to teach the core subject-matter developed in the Statistics and Econometrics courses which students will take later in their degree course. By the end of the course students should be able to understand basic mechanisms of statistical inferencing and provide practical solutions to standard problems of estimation, confidence interval construction and hypothesis-testing on averages, variances and proportions. They should also be able to model inter-variable relations using simple linear regression models, with a basic introduction to multivariate aspects.

Content

Content : Statistical model and sampling distribution, point and interval estimation, hypothesis testing, linear model (including matrix rating), Methods of Estimation including Maximum likelihood, Properties estimators, Inference in the simple regression, non linear regression Method: The course comprises: - lectures (on the basis of videos watched by the students before the lesson, the teacher re-introduces the concepts before beginning the debate with the students who annswer questions they prepared before the lesson -independent problem solving-), - practical exercise sessions (the teacher gives students applications/problems and suggests ways of solving them).

Teaching methods

See the introduction of the course LECGE1224 on moodle.
Face-to-face and distance teachning

Evaluation methods

First session evaluation
1) If the student passes a test during the courses (random tests during the courses), he gets a grade for the continuous evaluation that corresponds to 25% of the final first session grade, the other 75% corresponding to the final written exam (MCQ) schedulded during the exams first session.
2) If the student does not pass any test (the student is not selected) during the courses, 100% of his final grade (MCQ) correspond to the final written exam schedulded during the exams first session.
Second session evaluation
100% of the final second session grade correspond to the final written exam (MCQ) schedulded during the exams second session.
Language of the evaluation: French

Other information

Prerequisite: LECGE1114 Statistics in Economics and Management I or equivalent course.

Online resources

Course LECGE1224 on moodle.

Bibliography

Mathematical Statistics with Applications, 7ème édition. Wackerly, Mendenhall, Scheaffer.

Teaching materials

• vidéos et documents (transcriptions, exercices, démonstrations sur logiciel) relatifs sur moodle/videos and related documents (transcriptions, exercices, software demonstrations) on moodle

Faculty or entity

> ESPO