![]() \begin (the point on the linear regression line). It is important to note that the line-of-best-fit only models the linear relationship between the independent and dependent variables. When implementing simple linear regression, you typically start with a given set of input-output (. The following figure illustrates simple linear regression: Example of simple linear regression. Simple linear regression is a modeling technique in which the linear relationship between one independent variable x and one dependent variable y is approximated by a straight line, called the line-of-best-fit or least squares line. Simple or single-variate linear regression is the simplest case of linear regression, as it has a single independent variable. When a linear relationship exists between an independent and dependent variable, we can build a linear model of that relationship, and then we can use that model to make predictions about the dependent variable. Linear regression shows the linear relationship between two variables. For example, we might want to use the amount a business spends on advertising each quarter to make a prediction about the revenue the business will generate that quarter. Linear Regression Equation Linear Regression Formula. ![]() We often want to use values of the independent variable to make predictions about the value of the dependent variable. Use the line-of-best-fit to make predictions.Usually you would use software like Microsoft Excel, SPSS, or a graphing calculator to actually find the equation for this line. ![]() This line is known as the least squares regression line and it can be used to help us understand the relationships between weight and height. It turns out that the line of best fit has the equation: y a + bx. When you make the SSE a minimum, you have determined the points that are on the line of best fit.
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