Descriptive Statistics

This course introduces descriptive statistics as a method to gather, organize, and interpret numerical data from large sets. Key concepts include:

  1. Statistical Concepts: The course covers basic terms such as population, individuals (or statistical units), characteristics, and modalities, explaining quantitative (discrete and continuous) and qualitative variables.

  2. Distribution of Variables: Data are grouped into tables based on observed characteristics, covering discrete and continuous quantitative variables. It introduces frequency, cumulative frequency, and relative frequency calculations.

  3. Graphical Representations: Techniques for visualizing data include bar charts, cumulative distribution functions, histograms, and cumulative curves for both discrete and continuous data.

  4. Position Parameters: Key measures include the arithmetic mean, mode, median, and quantiles, providing ways to summarize and understand data distributions.

  5. Exercises: Practical exercises support mastery of descriptive statistics concepts, including calculating frequencies, means, variances, standard deviations, and graph interpretation.


Correlation and linear regression

This course covers statistical methods in correlation and linear regression, focusing on analyzing relationships between two simultaneously measured variables. Key points include:

  1. Bivariate Statistical Series: Study of correlated quantitative variables. Data are presented as pairs (X, Y) and visualized through scatter plots.

  2. Correlation: Measurement of the strength and direction of the relationship between variables, using Pearson's linear correlation coefficient to assess linear dependence.

  3. Linear Regression: Modeling the relationship between a dependent and an explanatory variable through the least squares method, allowing for prediction.

  4. Regression Lines: Calculation and interpretation of regression lines of Y in terms of X and X in terms of Y, explaining their differences.

  5. Practical Applications: Exercises on calculating correlation coefficients and estimating Y values based on X.