## Introduction to the Descriptive Statistics

Descriptive statistics summarize, show, and analyze the data and make it more understandable. If the dataset is large, it is hard to make any sense from the raw data. Using…

## Univariate and Bivariate Gaussian Distribution: Clear explanation with Visuals

Gaussian distribution is the most important probability distribution in statistics and it is also important in machine learning. Because a lot of natural phenomena such as the height of a…

## A Complete Guide to Hypothesis Testing and Examples in Python

Hypothesis testing is an important part of statistics and data analysis. Most of the time it is practically not possible to take data from a total population. In that case,…

## A Complete Guide to Confidence Interval and Calculation in Python

Confidence Interval(CI) is essential in statistics and very important for data scientists. In this article, I will explain it thoroughly with necessary formulas and also demonstrate how to calculate it…

## Logistic Regression Model, Analysis, Visualization, And Prediction

This article will explain a statistical modeling technique with an example. I will explain a logistic regression modeling for binary outcome variables here. That means the outcome variable can have…

## Linear Regression Model Fitting, Inference and Visualization

Linear regression is the most commonly used regression model. The reason is it is simple to use, it can infer good information and it is easy to understand. In this…

## Understand the p-test, Characteristics, and Calculation with Example

A p-test is a statistical method to test the validity of a commonly accepted claim about a population. That commonly accepted claim is called a null hypothesis. Based on the…

## Confidence Interval, Calculation, and Characteristics

The Confidence Interval (CI) is very important in statistics and data science. In this article, I am going to explain the confidence interval, how to calculate it, and the important…