In traditional models like linear regression and ANOVA, assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals are foundational. These assumptions ...

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Often, regression models that appear nonlinear upon first glance are actually linear. The curve estimation procedure can be used to identify the nature of the functional relationships at play in ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example. Microsoft Excel and other software ...

Linear forecasting models can be used in both types of forecasting methods. In the case of causal methods, the causal model may consist of a linear regression with several explanatory variables.

Model-combining (i.e., mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been ...

To illustrate the interaction between feature selection and linear regression, I scraped 500 rows of game logs from stats.nba.com, placed them in a .csv file on one of our test Hadoop clusters ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...