Statistic

Learn how to interpret statistical and machine learning models using the marginaleffects package for R and Python. Compute marginal effects, marginal means, contrasts, odds ratios, hypothesis tests, equivalence tests, slopes, and more. Free software, book, tutorials, and documentation available.

The **Gaussian distribution**, or **normal distribution** is a key subject in statistics, machine learning, physics, and pretty much any other field that deals with data and probability.

An introduction to applied Bayesian modeling.

Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.

Whilst I am embarking upon my data science studies, I am endeavouring to learn as much as I can about time series analysis. I have…

Hello Everyone, Hope you all are doing good. Today I have come up with a post which would help us to do multivariate variable time series…

Time series forecasting is used in multiple business domains, such as pricing, capacity planning, inventory management, etc. Forecasting with techniques such as ARIMA requires the user to correctly determine and validate the model parameters (p,q,d). This is a multistep process that requires the user to interpret the Autocorrelation Function (ACF) and Partial Autocorrelation (PACF) plots correctly. Using the wrong model can easily lead to erroneous results.

The series of data points plotted against time is known as time series. It is a de-facto analysis technique used in market evaluation and…

Poisson, quasi-Poisson, and negative binomial regression - when to do them and how you should choose the method. What are overdispersion and underdispersion,...

Learn how to interpret the output from a regression analysis including p-values, confidence intervals prediction intervals and the RSquare statistic.

by Sharad Vijalapuram How to read a Regression TablePhoto by Isaac Smith on UnsplashWhat is regression?Regression is one of the most important and commonly used data analysis processes. Simply put, it is a statistical method that explains the strength of the relationship between a dependent variable and one or

Single, double, or triple exponential smoothing — this article has you covered.

Creating Quality Control Charts using matplotlib library

A technical manual of inferential statistics and regression modeling in the people and social sciences

How to do statistical testing of the Net Promoter Score. An informative article by Displayr's Justin Yap, including practical examples and code.

Enroll today at Penn State World Campus to earn an accredited degree or certificate in Statistics.

Enroll today at Penn State World Campus to earn an accredited degree or certificate in Statistics.

Enroll today at Penn State World Campus to earn an accredited degree or certificate in Statistics.

Recently we argued that confidence intervals are a poor choice for analyzing staged rollout experiments. In this article, we show a better way: a Bayesian approach that gives decision-makers the answers they really need. Check out our interactive Streamlit app first, then the article for the details.

This is an absolute gem of a book. McElreath has found an elusive combination: Statistical Rethinking is not only one of the best intro textbooks for both causal and Bayesian modeling, it's also highly readable, even entertaining.

Survival analysis has been a standard tool for decades in clinical research, but data scientists in other domains have mostly ignored it. Here are some applications for which you might use survival analysis, to jump-start your creative data science engine.

IBM Documentation.

A guide to building the Poisson Regression Model for counts based data sets and a tutorial on Poisson regression using Python

Vose Software specializes in providing software systems for assessing and managing risks using the most precise information possible.

Enroll today at Penn State World Campus to earn an accredited degree or certificate in Statistics.