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M1 Core Courses

In our data era, statistics is much needed in particular to evaluate data quality: whether a given data set may lead, or not, to a given conclusion. In this course, we will focus on inferential statistics: how a sample data set may or may not lead to some better knowledge of the underlying population (confidence intervals, hypothesis testing). We will also briefly study a simple model-making method based on data called multiple linear regression.

Formulas will be presented and briefly discussed, but no mathematical proof of their derivation will be provided. This course has a strong business focus: much of the efforts will be on writing nice-to-read and enjoyable (lack of) conclusions. Most of the class time will be devoted to solving exercises illustrating management situations or dealing with other fields (sociology, politics, etc.).


Descriptive statistics, modeling of data
Concept 1: Confidence intervals
Concept 2: One‐sample tests (comparison of a mean or a proportion to a reference value)
Concept 3: Two‐sample tests (comparison of means or proportions for independent or paired data)
Concept 4: Chi‐square tests (of independence or of goodness of fit)
Concept 5: Linear regression (simple and multiple linear regression)
Numerous applications on real data with the SPSS software will be performed.
No mathematical proof or details will be provided.