BASIC STATISTICS APPLICATION
Basic Statistics Application in Python has many libraries that allow us to apply various statistical concepts discussed in the Theory section. Libraries such as Numpy or Pandas can be used to calculate Measures Of Frequency, Central Tendency, and Variability. Matplotlib allows for the creation of various graphs. A variety of libraries are available for Inferential Statistics. The most important is scipy.stats, which allows users to perform different types of t-Tests and F-Tests. This section has a number of Hypothetical datasets to illustrate the use of these libraries. This section also covers Univariate and Bivariate Analysis. It is covered only in the Theory Section of Data Exploration and Preparation.
DESCRIPTIVE STATISTICS IN PYTHON
Simple statistics explain the different aspects of our information. These characteristics can be identified by using four types of descriptive statistics: Measures of Frequency, Central Tendency, Measures of Variability, Measures of Form where each type of descriptive statistic describes a particular aspect of the dataset. Each of these statistics plays a significant role in data analysis. It is called the A, B, and C of Statistics.
INFERENTIAL STATISTICS IN PYTHON
Certain statistical methods are employed to help explain the information in the sample and determine the general population, which is from where the sample was taken. A variety of Statistical methods are employed to answer various questions. The following section will discuss fundamental concepts essential to understanding the many statistical tools used to draw inferences from the information which described.