1. Introducction

In this section, a brief introduction to the R and/or Python environment will be provided. In the case of R, it is an environment designed for data processing, computation, graphical development, and programming. It allows easy manipulation of vectors and matrices and offers various tools for data analysis and statistics in general (both descriptive and inferential).

Information related to R is available on the website http://www.r-project.org/index.html. R can be installed through CRAN (Comprehensive R Archive Network). Additionally, R is an environment in which various statistical techniques have been implemented. Some of these are part of the base R package, while many others are available as add-on packages. These packages can be found on the website http://cran.au.r-project.org/.

For ease and practicality, if you prefer not to install R, it is recommended to use the RStudio Cloud environment by signing in with a Gmail account.

In the case of Python, it is another open-source programming language, very similar or equivalent to R. It is an object-oriented and interpreted language, meaning that the program code is converted to bytecode and then executed by the interpreter, which in this case is the Python virtual machine. It is widely used for scripting and automation tasks. In the following link, you can find all the relevant information about Python, including its installation, download, and some basic usage examples: https://ellibrodepython.com/descargar-instalar-python.

1.1 Probability

One of the primary objectives of probability is to quantify, in some manner, the degree of uncertainty we have regarding a specific phenomenon. In this section, we present the axiomatic and fundamental elements that will allow us to compute a value indicating the likelihood of the occurrence of the given phenomenon.

Key elements include set theory and its properties, which logically extend to the theory underpinning probability and its calculation. This broadens the scope of the theory’s application, which in turn facilitates the generalization of other principles, such as universal theorems that are generally applicable to real world phenomena, such as Bayes’ theorem.

There are tools that simplify the learning and application of probability calculation, among which are the properties of counting, which are central to combinatorial theory, permutations, and variations. All these tools focus on fundamental properties that are crucial for developing probability calculations in the analysis of specific phenomena. Additionally, the use of software such as R and/or Python is recommended, as these allow for the handling and analysis of real data efficiently, as well as facilitating more complex calculations.

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Unit One

Unit Two

Unit Three

Unit Four

Unit Five

Unit Conclusions

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