In this video, we will implement Multiple Linear Regression in Python from Scratch on a Real World House Price dataset. We will not use built-in model, but we will make our own model. This can be a ...

The following is a summary of “Association between the serum alpha-1-acid glycoprotein concentrations and depression in US ...

which can aid you in the development of your model. In a “forward” stepwise regression analysis, the computer will begin by examining every possible simple linear regression model, and will show you ...

Regression models with intractable normalizing constants are valuable tools for analyzing complex data structures, yet ...

The main distinction between different types of regression model is that they are used for different types of outcome (eg, linear regression for a continuous outcomes and a logistic regression for a ...

In this module, we will learn how to diagnose issues with the fit of a linear regression model. In particular, we will use formal tests and visualizations to decide whether a linear model is ...

The standard linear regression model does not apply when the effect of one explanatory variable on the dependent variable depends on the value of another explanatory variable. In this case, the ...

Ordinary regression analysis is based on several statistical assumptions. One key assumption is that the errors are independent of each other. However, with time series data, the ordinary regression ...

By the end of the video, students will be able to (1) recognize the major steps involved in an OpenMx model and (2) translate implementation of a linear regression between a statistical equation, ...

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.