Sometimes, learning about statistics can be a bit like peering into a crystal ball. You think you’ve got it, only to read one more article/paper/blog, and suddenly it’s as clear as mud again, and you’re further away from the answer than when you first started.
Choosing the correct statistic to use in any given situation can seem like a daunting task at times. When there are numerous possible statistical calculations you can do with your data, there is not usually one correct answer – in most cases there is a spectrum of approaches, some of which are more appropriate than others.
In Errors of Regression Models you’ll learn how to choose the most appropriate statistics to measure the accuracy of your regression-based prediction model.
You’ll discover that there is a family of related statistics, each member of which has their own set of dedicated fans.
Nevertheless, in this case there is one correct statistic to use, while all the other measures – while useful in their own way – give only partial answers as to how to select the most accurate predictive model.
Each of these family members will be introduced, and Errors of Regression Models will explain all their advantages and disadvantages, and show you precisely how to calculate and interpret all of them. Finally, Errors of Regression Models will explain exactly why one particular family member is The Daddy!
Written in plain English with no technical jargon, Errors of Regression Models is perfect for beginners!
Discover how to measure the accuracy of your regression models quickly and effectively.
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