Temario y contenidos
Qué se trabaja en la asignatura
El programa combina inferencia estadística en una y dos poblaciones, diseño de experimentos y análisis de varianza, análisis de datos categóricos y pruebas no paramétricas. La práctica se realiza con problemas, casos y R.
1
Sampling distributions
- Random samples, statistics and sampling distributions
- Sample mean, Central Limit Theorem and linear combinations
- Simulation experiments and sampling distributions with R
2
Inference: single population
- Confidence intervals for means, proportions and variances
- Hypothesis testing with z, Student’s t, proportion and variance tests
- p-values, Type II error and power of the test
3
Inference: two populations
- Dependent and independent samples
- Confidence intervals and tests for differences in means, proportions and variances
- Two-population inference with R
4
Analysis of Variance
- Designed experiments and required sample size
- Completely randomized and randomized block designs
- Single-factor, multiple-comparison and two-factor analyses with aov in R
5
Categorical data and non-parametric tests
- Goodness-of-fit, normality and contingency-table tests
- Sign, Wilcoxon, Mann-Whitney, Spearman and runs tests
- Categorical-data analysis with VCD, table(), prop.table() and chisq.test()