Everything Data Analytics-A Beginner's Guide to Data Literacy: Understanding the Processes That Turn Data Into Insights by Elizabeth Clarke

Author:Elizabeth Clarke [Clarke, Elizabeth]
Language: eng
Format: azw, pdf
Published: 2022-05-23T00:00:00+00:00

Linear regression predicts an outcome based on continuous features. It establishes a relationship between dependant and independent variables by fitting a “best line,”, commonly called the regression line. The variable you are predicting is the dependent variable (aka the response variable.) The variable you are using to predict the value of that variable is called the independent variable (aka explanatory or predictor variable.) The simple formula to remember for a linear regression line is Y = a + bX.

Y is the Dependant variable (that represents the Y-axis,) and X is the independent variable (which is plotted on the X-axis.) “b” represents the slope line, thus determining its steepness, and the “a” is the intercept (value of “Y” when “X” = 0.) Let's look at an example. In this case, we are comparing advertising cost (independent variable) to the number of conversions (dependant variable.) Let's plot this data.

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