Descriptive Statistics

But du cours

1. Descriptive statistics for a single variable

• Definitions and vocabulary.

• Statistical variable and nature of a variable.

• Frequencies.

• Graphical representations of a distribution.

• Measures of central tendency: mean, median.

• Measures of dispersion: variance, quartiles.

1. Descriptive statistics for a pair of variables

• Two-dimensional statistical series.

• Correlation: covariance, correlation coefficient, analysis of the relationship between two variables.

• Linear (affine) fitting:

• Scatter plot.

• Mayer’s line.

• Least squares method.

• Non-linear regressions.

• Contingency table.

Acquis d'apprentissage visés

• Understand and define the fundamental concepts of descriptive statistics: population, sample, variable, types of data (quantitative and qualitative).

• Calculate and interpret measures of central tendency and measures of dispersion.

• Describe the shape of a distribution using symmetry, skewness, and kurtosis.

• Know and use different types of graphical representations: histograms, bar charts, box plots, scatter plots, pie charts.

• Organize and present data in the form of frequency tables and cross-tabulations.

• Use statistical software (Python / R) to perform descriptive analyses and generate graphical representations.

• Analyze and interpret results and formulate relevant conclusions based on data analysis.

• Understand the fundamental concepts of linear regression and the relationship between dependent and independent variables.

• Calculate and interpret regression coefficients (slope and intercept) using the least squares method.

• Calculate and interpret the coefficient of determination $R^2$.

• Use a linear regression model to make predictions.

• Use a numerical tool to perform regression.

Modalités d'évaluation

Lab work: 2.0h - Coefficient: 1.0 Written exam: 2.0h - Coefficient: 1.0 Written exam: 2.0h - Coefficient: 1.0

Supports

Materials available on the Moodle platform.