Objectif
The objective of this course is to provide a solid background in statistical thinking, more precisely regarding estimation and inference. At the end of this course, a student should be able to use the tools presented in class in similar situations. He/she should also be able to follow a more specialized course in statistics (e.g. regression analysis or multivariate analysis).
Descriptif
In the first part of the course we introduce exploratory data analysis, either graphically or by numerical summaries, which will allow to discuss the notion of sampling distribution. We then move on to estimation and statistical inference. For estimation, we consider several techniques for point estimation (method of moments, method of maximum likelihood, least squares) and their properties. We also introduce interval estimation (confidence intervals). For statistical inference, we consider hypothesis testing in different settings: one-sample situations (parametric and nonparametric location tests, goodness-of-fit test), two-sample situation (parametric and nonparametric location tests, comparison of proportions, test of independence) and several sample situation (ANOVA and multiple comparisons). Each technique is presented with real data examples. Additional examples and applications are studied during the exercise sessions.