The main objective is to provide a set of basic knowledge that will enable the student to perform a descriptive analysis on a set of data.

The student should be able to apply appropriate descriptive measures to the data being studied, understand how to sample correctly, and identify the constraints and limits of representativeness of a sample.

Introduction to Statistics: Population, sample, variables. Classification of variables. Transformation of scales. - 1 Week

Univariate Descriptive Statistics: frequency tables; descriptive measures of location and dispersion; skewness and kurtosis coefficients; evaluation of sample dispersion; graphical representations of discrete, continuous, and qualitative data: histogram; bar graphs; pictogram; pie chart; stem-and-leaf, box-plotwith analysis of outliers; comparison of samples; other charts. - 3.5 Week

Bivariate Descriptive Statistics: cross-tables and contingency tables; scatter diagram; Pearson correlation and Spearman's rank correlation; simple linear regression. - 2,5 Weeks

Software to support the Univariate and Bivariate Descriptive Statistics classes: R or similar. - 3 Weeks

Probabilities: set theory; random experiments; sample space and events; operations on events; frequency approximation of probability; classical or Laplace's definition; axioms of probability; conditional probability; independent events and Bayes' formula; - 3 weeks

Relative frequency distributions and probability distributions: random variable; probability mass function and probability density function; mean vs expected value; sample standard deviation vs. population standard deviation. - 2 weeks