AIMS AND SCOPE
The aim of the course is introduce the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
Lectures and practical activities and data analysis in R.
Use of the statistical software in R regarding:
Descriptive Statistics. Simple Statistical Distributions. Data tables. Frequency distributions. Main summary statistics: arithmetic mean, geometric mean, harmonic mean. Median and percentiles. Variance, standard deviation, relative variation. Graphical representations. Multiple Statistical Distributions. Contingency Tables. Joint distributions, marginal and conditional distributions. Covariance and correlation.
Probability. Random number generation and data modeling according to different probability distributions: uniform, binomial, Poisson, Gaussian.
Statistical inference. Sample distributions: Student-t, chi-square. Confidence estimation. Confidence level. Confidence bounds for means, variances, proportions. Hypothesis testing. Null hypotheses and alternative hypotheses. P-values. Statistical tests for means, variances, proportions, comparison of means, comparison of proportions.
Statistical models. The simple regression model. Goodness of fit. Residual analysis. Inference on the parameters of a linear regression model.
Documents available on the web page of The R Project for Statistical Computing: https://www.r-project.org and other resources available on the web