This learning path provides a comprehensive introduction to statistics, focusing on both theoretical concepts and practical application using R. It covers central tendency measures, variance, standard deviation, various probability distributions (Bernoulli, Binomial, Poisson, Normal), hypothesis testing, regression, and correlation. Developed with the contribution of the OEduverse Erasmus Plus Project. www.oeduverse.eu

1. Understand and Apply Measures of Central Tendency: Learners will be able to explain the concepts of mean, median, and mode and apply them to describe data sets.

2. Calculate and Interpret Measures of Dispersion: Learners will be able to calculate variance and standard deviation and interpret their significance for data analysis.

3. Identify and Apply Various Probability Distributions: Learners will be able to describe the characteristics of Bernoulli, Binomial, Poisson, and Normal distributions and apply them to appropriate scenarios.

4. Conduct Hypothesis Tests: Learners will be able to formulate, conduct, and interpret the results of basic hypothesis tests.

5. Fundamentals of Regression and Correlation: Learners will be able to explain linear regression and correlation and demonstrate their application in analyzing relationships between variables.

6. Practical Application of Statistical Methods with R: Learners will be able to implement and interpret the learned statistical concepts and methods using the R programming language.