Id: 008463 Credits Min: 3 Credits Max: 3 Description Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of ...

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.

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.

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 ...

The mode of a distribution provides an important summary of data and is often estimated on the basis of some non-parametric kernel density estimator. This article develops a new data analysis tool ...

For a simple linear regression model, this typically entails finding the slope and intercept of the line that best fits the data. In more complex scenarios, the process becomes more intricate but ...

We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of ...