Prove that the least squares general straight-line model gives the absolute minimum of the sum of the squares of the residuals?

Posted on September 3, 2012 | 1 Comment

Many regression models when derived in books only show the first derivative test to find the formulas for the constants of a linear regression model.  Here we show a thoroughly explained derivation.



___________________________________________

This post is brought to you by Holistic Numerical Methods: Numerical Methods for the STEM undergraduate at http://numericalmethods.eng.usf.edu, the textbook on Numerical Methods with Applications available from the lulu storefront, the textbook on Introduction to Programming Concepts Using MATLAB, and the YouTube video lectures available at http://numericalmethods.eng.usf.edu/videos.  Subscribe to the blog via a reader or email to stay updated with this blog. Let the information follow you.

About these ads
This entry was posted in Numerical Methods, Regression and tagged , . Bookmark the permalink.

One Response to Prove that the least squares general straight-line model gives the absolute minimum of the sum of the squares of the residuals?

  1. Pingback: Proving the denominator of the linear regression formula for its constants is greater than zero. | The Numerical Methods Guy

Leave a Reply

Fill in your details below or click an icon to log in:

Gravatar
WordPress.com Logo

You are commenting using your WordPress.com account. ( Log Out / Change )

Twitter picture

You are commenting using your Twitter account. ( Log Out / Change )

Facebook photo

You are commenting using your Facebook account. ( Log Out / Change )

Cancel

Connecting to %s