Overview

Linear regression is a way of predicting an unknown variable using results that you do know. If you have a set of x and y values, you can use a regression equation to make a straight line relating the x and y. The reason you might want to do this is if you know some information, and want to estimate other information. For instance, you might have measured the fuel economy in your car when you were driving 30 miles per hour, when you were driving 40 miles per hour, and when you were driving 75 miles per hour. What Is In This Book? There are a number of examples shown in this book, they include: -How to do a correlation calculation -An example of correlation on the stock price of 10 different big-name stocks, such as Coke and Pepsi -How having uncorrelated investments can give you better returns at lower risk. -How to do linear regression with two variables -How to do multiple linear regression with any number of independent variables -A regression analysis to predict the number of viewers in future episodes of the television show 'Modern Family' -How to evaluate the quality of your regression analysis using R-squared or adjusted R-squared -How to do regression on exponential data, and recreate Moore's law

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