Simple Linear Regression: The most basic linear regression model describes the correlation between an independent and dependent variable using a regression line to convey the linear relationship ...

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, ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

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.

A linear model which uses age to predict net wealth, i.e., Wealth = α + β Age + ε , fails to capture this phenomenon. A better model might be Wealth = α + β 1 Age + β 2 Age 2 + ε . A quadratic ...

Prediction is often the primary goal of data analysis. In this work, we propose a novel model averaging approach to the prediction of a functional response variable. We develop a crossvalidation model ...

The random forest model significantly outperformed all other models, including the logistic regression model that the entire paper focuses on, with an eventual AUC of 0.936 and an accuracy of 0.918.

Elise Dusseldorp, Claudio Conversano, Bart Jan Van Os, Combining an Additive and Tree-Based Regression Model Simultaneously: STIMA, Journal of Computational and Graphical Statistics, Vol. 19, No. 3 ...