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Table Of Contents
Data Analysis Using SQL and Excel - Second Edition
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The previous chapter introduced data mining ideas using various data mining techniques well suited to databases, such as look-alike models, lookup tables, and naïve Bayesian models. This chapter extends these ideas to the realm of the most traditional statistical modeling technique: linear regression and best-fit lines.
Unlike the techniques in the previous chapter, linear regression requires that input and target variables all be numeric. The results of the regression are coefficients in a mathematical formula. A formal treatment of linear regression involves lots of mathematics and proofs. This chapter steers away from an overly theoretical approach.
In addition to providing a basis for statistical modeling, linear regression has many applications. Regressions—especially best-fit lines—are a great way to investigate relationships between different numeric quantities. The examples in this chapter include estimating...
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