Statistics is a discipline that deals with the collection, analysis and interpretation of data. Through the use of statistical methods, it is possible to extract meaningful information from data, draw conclusions and make informed decisions. R is a programming and statistical analysis environment that enjoys great popularity in the scientific and business community. Its versatility and the wide range of statistical packages available make it a powerful tool for exploring and analyzing data efficiently. In this section we will see how to delve into the world of statistics by working with R through a series of in-depth articles.
R: an essential tool for statistics
R is open-source software, which means it is freely accessible and modifiable by anyone. This fosters collaboration and ongoing community development. Additionally, R has a large ecosystem of packages that provide specific functionality for different needs. These packages are developed by industry experts and can be easily installed and used. Finally, R offers powerful tools for creating high-quality graphs, allowing for effective data visualization.
IN-DEPTH ARTICLE
IN-DEPTH ARTICLE
Fundamental Statistical Concepts
The Population and the Sample
- Population: In statistics, the population is the complete set of elements that satisfy a certain characteristic. For example, the population of all people in a city.
- Sample: A sample is a representative subset of the population. Statistical studies are often conducted on samples, as analyzing the entire population may be impractical.
IN-DEPTH ARTICLE
Descriptive and Inferential Statistics
Descriptive Statistics: Provide a summary description of the data, such as the mean, median and standard deviation.
Inferential Statistics: They allow you to make inferences about the population based on sample data. Includes hypothesis tests and interval estimates.
This is just the beginning of our journey into statistics with R. We are working on adding many more articles on the topic