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Archive for the ‘Regression Models’ Category

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This digital document is a journal article from Analytica Chimica Acta, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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An algorithm that calculates the sensitivity to the systematic error of the fitted parameters of a least-squares regression model, with respect to the known parameters, i… More >>

Analysis of the sensitivity to the systematic error in least-squares regression models

A simple (two-variable) regression has three standard errors: one for each coefficient (slope, intercept) and one for the predicted Y (standard error of regression). While the population regression function (PRF) is singular, sample regression functions (SRF) are plural. Each sample produces a (slightly?) different SRF. So, the coefficients exhibit dispersion (sampling distribution). The standard error is the measure of this dispersion: it is the standard deviation of the coefficient….

SANJEEV SHARMA: B.Tech. 3rd Year. IIT Roorkee. 3rd November 2009. Part -1 of Lecture 7 in Machine Learning. Non-Linear Regression. In this lecture I explained the non-linear basis function. Then I also explained the underlying concept of linear dependency on parameters. Then I derived the analytical solution and discussed the concept of under and over fitting. Then I provided the hint of L1 Norm Regularization. For all videos go to – searching-eye.com

I need to use linear regression on my calculator to find a linear function that can be used to predict the sales x years after 2003!

The following table shows the sales of ipods in the US for the years 1996-2000

Year Sales
03 $1132
04 $1482
05 $1469
06 $1502
07 $1638

Then predict the sales in the year 2010

THANKS!

Video showing how to perform a non-linear regression analysis with SPSS 17.

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A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how models relate to linear regression models whenever possible. … More >>

Regression Models for Categorical and Limited Dependent Variables

SANJEEV SHARMA: B.Tech. 3rd Year. IIT Roorkee. 3rd November 2009. Part -2 of Lecture 7 in Machine Learning. Non-Linear Regression. In this lecture I explained the non-linear basis function. Then I also explained the underlying concept of linear dependency on parameters. Then I derived the analytical solution and discussed the concept of under and over fitting. Then I provided the hint of L1 Norm Regularization. For all videos go to – searching-eye.com