In today's data-driven world, statistical analysis plays a critical role in uncovering insights, validating hypotheses, and driving decision-making across industries. R, a powerful programming ...

Before you start analyzing, you might want to take a look at your data object’s structure and a few row entries. If it’s a 2-dimensional table of data stored in an R data frame object with rows and ...

The table below shows my favorite go-to R packages for data import, wrangling, visualization and analysis — plus a few miscellaneous tasks tossed in. The package names in the table are clickable if ...

Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

This workshop is hands-on and intended for beginners; no previous knowledge of data analysis and/or R is required. This session will cover the following topics for R: data preparation, descriptive ...

Predictive analysis refers to the use of historical data and analyzing it using statistics to predict future events. It takes place in seven steps, and these are: defining the project, data collection ...

The R programming language is an important tool for development in the numeric analysis and machine learning spaces. With machines becoming more important as data generators, the popularity of the ...

Forbes contributors publish independent expert analyses and insights. Rachel Wells is a writer who covers leadership, AI, and upskilling. For the next four years, big data analytics is expected to be ...