Linear Regression with One Regressor (FRM Part 1 – Book 2 – Chapter 6)
Skip to main content. Regression Models. Scott Long. In Stock. If you still cannot understand regression models for categorical data after you've read this book, you would better off considering another line of job. Statistics doesn't fit you! Add to cart.
Statistics came well before computers. It would be very different if it were the other way around. The stats most people learn in high school or college come from the time when computations were done with pen and paper.
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Regression is a technique that allows one to determine the value of one or more quantities based on the values of other quantities. Linear regression is a type of regression that assumes this determination can be made based upon a simple, linear relationship. Linear regression is very significant for didactic and practical reasons. Linear regression is important from a didactic perspective because pretty much any important concept in statistics or machine learning is a facet of linear regression analysis, so it is frequently used as a simple illustration of such concepts. Linear regression is also very widely used in practice because the underlying models are very interpretable, they don't require much data to use, and many real relationships are approximately linear. Since linear regression has such foundational importance and practical utility, it is a subject worthy of its own book or books. Despite its apparent simplicity, linear regression has so many applications and associated pitfalls that it requires careful study.