Temario de la asignatura
Estos bloques orientan el estudio. Adaptamos las clases al programa y al material que estés trabajando.
1
Data collection and descriptive statistics
- Sampling, studies, experiments and variable types
- Charts, summary measures, covariance and correlation
2
Python tools for statistical data analysis
- Colab, pandas and NumPy
- Descriptive visualisation with Matplotlib
3
Random variables and probability distributions
- Binomial, Poisson, uniform, normal and exponential models
- Mass, density, cumulative and inverse distribution functions
4
Confidence intervals and foundations of inference
- Population and sample distributions
- Interval estimation for unknown parameters
5
Hypothesis testing for one population
- Means and proportions
- Large and small samples, errors and p-values
6
Hypothesis testing for two populations
- Paired and independent samples
- Differences of means and proportions with Python
7
One-way analysis of variance
- ANOVA assumptions
- Post-hoc analysis and implementation with statsmodels