What is Statistics?
Definition and History
Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, and presentation of masses of numerical data. It is the science of learning from data and encompasses everything from planning for the collection of data and data management, to end-of-the-line activities such as drawing inferences from data and presentation of results.
The term "statistics" was first used in the 18th century, with its meaning initially restricted to information about states, particularly demographics such as population. This was later extended to include all collections of information of all types, and even later it was extended to include the analysis and interpretation of such data. The relationship between statistics and probability theory developed rather late, however. In the 19th century, statistics increasingly used probability theory, whose initial results were found in the 17th and 18th centuries, particularly in the analysis of games of chance (gambling).
Real-World Example
One common real-world application of statistics is in weather forecasting. Probability is used by weather forecasters to assess how likely it is that there will be rain, snow, clouds, etc., on a given day in a certain area. For instance, forecasters might say things like "there is a 90% chance of rain today after 5PM" to indicate that there's a high likelihood of rain during certain hours.
Statistical Programming Languages
Statistical programming refers to computation techniques that help in data analysis. Making sense of data by using statistical concepts/methodology is usually achieved by writing a code, and the programming language used to perform this task is called statistical programming. Some languages come with statistical programming packages/libraries that offer a wide variety of statistical and graphical techniques to explore large data sets and create graphical displays of them for better and quick understanding.
Python for Statistics
Python is a popular language for statistical analysis. The built-in Python library, statistics, provides functions for calculating mathematical statistics of numeric data. It's not intended to be a competitor to third-party libraries such as NumPy, SciPy, or proprietary full-featured statistics packages aimed at professional statisticians such as Minitab, SAS and Matlab.
Python's NumPy is a third-party library for numerical computing, optimized for working with single- and multi-dimensional arrays. Other libraries like pandas provide flexible data structures which allow you to work with labeled and relational data in an easy and intuitive manner. SciPy complements these tools with a high-level command set for managing and visualizing data.
Python also has libraries like Matplotlib, Seaborn, and Plotly that provide a flexible platform for creating rich visualizations to effectively explore data. And with machine learning libraries like scikit-learn, Python has become a staple in machine learning and artificial intelligence applications.
Julia for Statistics
Julia is another language that's gaining popularity in the statistics and data science community. The JuliaStats project provides easy-to-use tools for statistics and machine learning, offering extensible and reusable models and algorithms with efficient and scalable implementations.
The standard library in Julia includes basic statistics functionality, such as computing the sample standard deviation of a collection, calculating the sample variance of a collection, and more. The StatsBase.jl package provides basic support for statistics, implementing a variety of statistics-related functions such as scalar statistics, high-order moment computation, counting, ranking, covariances, sampling, and empirical density estimation.
Julia also has packages for more specific statistical needs. For example, the GLM.jl package supports generalized linear models with a friendly API for fitting GLM to data. The Clustering.jl package provides several common clustering algorithms.
R
R is a statistical computing language and graphics environment, created in 1992 by statistician Ross Ihaka, that is free to use. It is a domain-specific language that aims to solve data analytics problems. It builds and operates on a broad range of UNIX, Windows, and macOS systems. R is extremely extensible and offers a wide range of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and graphical) tools.
Importance of Statistics
Statistics plays a vital role in every field of human activity. It helps us understand the general trends and patterns in a given data set. It can be used for making predictions about future events and behaviors, determining the existing position of per capita income, unemployment, population growth rates, housing, schooling medical facilities, etc., in a country.
Moreover, as technology becomes more present in our daily lives, more data is being generated and collected now than ever before in human history. Statistics is the field that can help us understand how to use this data to gain a better understanding of the world around us.
In conclusion, statistics is an essential tool in our modern world that allows us to understand complex sets of data and make informed decisions based on that understanding. Whether it's predicting tomorrow's weather or planning for a nation's future, statistics provides the tools we need to navigate our increasingly data-driven world.
