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Areas Served: Guide to Using Descriptive Statistics in Data Science
Author(s): Paula Maldonado Originally published on Towards AI. Understand the key concepts to summarize data This member-only story is on us. Upgrade to access all of Medium. Photo by Cathryn Lavery on Unsplash Statistics are at the heart of data science and …
Understanding Type-I and Type-II Errors in Hypothesis Testing
Author(s): Deepak Chopra | Talking Data Science Originally published on Towards AI. An under-the-hood look into the concepts that underpin the all-important statistical hypothesis testing. This member-only story is on us. Upgrade to access all of Medium. (image by author) Knowingly or …
ML Algorithms from scratch in Python
Author(s): Ravi Shankar Originally published on Towards AI. Self notes for behind the scenes mathematical understanding This member-only story is on us. Upgrade to access all of Medium. Goal: To build Machine Learning algorithms in python without using any over-the-shelf ML library …
Ljung-Box or Durbin Watson — Which test is more powerful
Author(s): Tanveer Hurra Originally published on Towards AI. Statistics Source: Pixabay Durbin Watson is more powerful but there is a catch. Read on to know more. When it comes to statistical testing, one of the most important factors that we look for …
Population Pyramid — Interesting Visualization of Population Statistics using R
Author(s): Supriya Ghosh Originally published on Towards AI. Data Visualization Pic on Unsplash by Eugene Tkachenko The proverb “ A picture is worth a thousand words” is always put to test when we make an attempt to address and communicate technical complexities …
Analysis on Biodiversity in National Parks Projects
Author(s): Karthik Bhandary Originally published on Towards AI. Data Analysis Photo by Tania Malréchauffé on Unsplash In this blog, we are going to be performing an analysis on the data set “Biodiversity in National Parks Projects”, which is available in Kaggle. We …
What is sampling and why it’s important?
Author(s): Ibrahim Israfilov Originally published on Towards AI. Statistics Intro This article will cover the basic notions of econometrics and OLS which include the first steps before starting the analysis, namely testing the assumptions for the unbiasedness of OLS. No matter whether …
Laws of Probability — A Primer for Data Scientists and Machine Learning Engineers
Author(s): Supriya Ghosh Originally published on Towards AI. Probability Image by Author Real-life is a book that has probability in all its chapters. What is probability? Probability is a measure of uncertainty. Real-world involves taking decisions at every step, but many times …
Generalized Linear Mixed Models in SAS
Author(s): Dr. Marc Jacobs Originally published on Towards AI. Statistics Distributions, link functions, scales, overdispersion, and normality of residuals. All statistical models have a systematic and a random part. In ANOVA and GLM models, the systematic part is the fixed effect and …
Skewness and Kurtosis — Explanation in detail along with Cheat-sheet…
Author(s): Supriya Ghosh Originally published on Towards AI. Machine Learning Image by Author Before we start with the Skewness and Kurtosis, let me explain in brief about Moments since we will need moments for in and out understanding of Skewness and Kurtosis. …
Exploratory Data Analysis on Mercedes Benz Car Models
Author(s): Sarvesh Talele Originally published on Towards AI. Analysis of Mercedes Benz Models and some additional insights which assist in making business decisions. Tableau Dashboard (Image by Author) This article will analyze the important variables which go into determining a Mercedes Benz …
Chi-Square Test Examples with R
Author(s): Amit Chauhan Originally published on Towards AI. Analysis of variables association and distribution This member-only story is on us. Upgrade to access all of Medium. Image Source Introduction When we talk of chi-square tests, basically we study two types: Chi-square for …
Introduction to Bayesian Inference
Author(s): ___ Originally published on Towards AI. A Distribution With No Constraints Top highlight In this article, I will explain what the maximum entropy principle is, how to apply it and why it’s useful in the context of Bayesian inference. The code …
Regression Line with Mathematics for the Linear Regression
Author(s): Gul Ershad Originally published on Towards AI. The equation of Intercept a: Regression is a prediction where the target is continuous and its applications are several. It is the simplest parametric model. Every data-set is given in a pair consisting of …
Introduction
Author(s): ___ Originally published on Towards AI. In this article, I will explain why when building a linear model, we add an x₁x₂ term if we think the variables x₁ and x₂ interact and conclude with a principled method to add interaction …
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