Multiple Linear Regression: Multiple linear regression describes the correlation between two or more independent variables and a dependent variable, also using a straight regression line.

A test for autocorrelated errors in the linear model is introduced and shown to have, in general, greater power than the Durbin and Watson test for high values of autocorrelation. Journal Information ...

Rothenberg's (1984) Edgeworth size correction is applied to tests of linear hypotheses in the linear regression model with AR(1) errors. Previous simulation findings on the effect of autocorrelation ...

The attenuation factors estimated in a linear regression calibration model were 0.368 (0.228, 0.508) for nitrogen and 0.500 (0.361, 0.639) for potassium intakes; only the former was different from ...

Course TopicsIn many applications, the response variable is not Normally distributed. GLM can be used to analyze data from various non-Normal distributions. In this short course, we will introduce two ...

Building a linear regression model So far, I have explored the dataset in detail and got familiar with it. Now it is time to create the model and see if I can predict Yearly Amount Spent.

If the variance of the errors around the regression line varies much, the regression model may be poorly defined. The opposite of homoskedasticity is heteroskedasticity (just as the opposite of ...

Specialization: Statistical Modeling for Data Science Applications Instructor: Brian Zaharatos, Director, Professional Master’s Degree in Applied Mathematics Prior knowledge needed: Basic calculus ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...