Linear regression is one of those old-school statistical modeling approaches that are still popular. With the development of new languages and libraries, it is now in a much-improved version and…
A linear relationship between two variables is very common. So, a lot of mathematical and statistical models have been developed to use this phenomenon and extract more information about the…
Probability and the probability distribution is the base for mot statistical inference techniques, therefore machine learning, artificial intelligence, and data analytics. I wrote an overview of the discrete probability distribution…
Our end goal is to draw inferences from the population. We first need to learn about probability because it is the underlying base for statistical inferences, predictive models, and machine…
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…
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…
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,…
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…
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 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…