What  Customers Are Saying  About  the Excel Statistical Master "We just started building statistical excel spreadsheets for our direct mail and online marketing campaigns, I purchased Excel Statistical Master to help fill in some of the blanks. Little did I know, this book has everything I could ever want to know about business statistics. Easy to follow and written so even a child could understand some of the most complex statistical theories. Thanks Mark!" Brandon Congleton Marketing Director www.worldprinting.com "After years of searching for a simplified statistics book, I found the Excel Statistical Master. Unlike the indecipherable jargon in the countless books I have wasted money on, the language in this book is plain and easy to understand. This is the best \$40 I have ever spent. " Mahdi Raghfar "I really like the Excel Statistical Master. It is incredibly useful. The explanations and videos in the manual are excellent. It has really made my work with statistics a LOT easier. I'm really glad that I came across the manual. If you're a student of business statistics, this e-manual is worth WAY more it's priced. I will use your manual as a reference for my MBA course this summer." Dr. Yan Qin Co-Director Nankai-Grossman Center for Health Economics and Medical Insurance
Excel
STATISTICAL
Master

Regression Error - Extrapolation

Clear and Complete - WITH LOTS OF SOLVED PROBLEMS

# Linear Regression Error of  Extrapolation

One of the most common errors in the use of linear regression analysis is that of extrapolation. Extrapolation occurs when input data outside the range of original input data is used. The original input data (the X values) are the data used to create the linear regression equation. Using regression for prediction is correct only if the new input is from within the same range as the original input data. One major regression assumption is that the input data is from within the range of original data. Regression forecasting, that is, using data outside of the range of original input data can produce some unbelievable results.

An example of the regression error of using regression to extrapolate the linear regression line outside the range of original data would be to create a linear regression equation describing the relation between a child's age and weight and then to apply that linear regression equation to input data from adults. That would produce completely spurious results. Once again, one of the major regression assumptions is that all input data must come from within the range of original input data. Regression forecasting is a common regression error.

If You Like This, Then Share It...