#
Elements of statistical learning with R
(TDAESLR)

Learning about probability distributions, hypothesis test, regression and classification by using R, the best open statical tool for data scientist. The theoretical part of the course is based on the famous "Probability and Statistics for Engineers and Scientists" by Sheldon M. Ross, while the practical one follows the exercises shown in "Understanding Statistics Using R" by R. Schumacker and S. Tomek.

**Audience**

Anyone with no prerequisites who would like to develop statistical analysis with R also as an introductory step to Machine Learning

__Approaches (Objective)__

**R fundamentals**

- Running R programs with R Studio
- Dataframes and lists
- Basic syntax with tidyverse
- Data wrangling with dplyr
- Data visualization with ggplot2

**Probability and statistical theory**

- Descriptive statistics
- Element of probability
- Random variables
- Distributions of sampling statistics

**Statistical estimation and testing**

- Distribution of sampling statistics
- Confidence intervals
- Hypothesis testing

**Regression and analysis of variance**

- Simple regression
- Analysis of residuals
- Polynomial regression
- Logistic regression teaser
- One-Way Analysis of Variance
- Two-Factor Analysis of Variance