![]() For instance, an analysis found that the amount of study time by a student was correlated with the student’s score on the test, so a simple linear regression line was created. ![]() In reality, there are almost always limits. Mathematically, the line extends in either direction to infinity. While this line is valuable for both investigating root causes and for predicting performance when designing a solution, there are some limitations. In real life, the actual data points are seldom exactly on the line, but when there is high correlation, the data points will be close to the line. This line is the best fit plot of the data points. Finally, b is the y intercept for the line and is needed to establish the correct values in order to use the line for prediction. Also, m is the slope of the line and represents the actual correlation relationship. ![]() In this equation, x is the independent variable and y is the dependent variable. ![]() Once correlation has been established between two continuous variables, then a simple linear regression line can be determined. This formula can be used to determine the effect of the independent variable on the dependent variable during the Analyze Phase and for predicting process performance when designing a solution during the Improve phase. Once correlation is established between two factors, if those factors are continuous variables, a simple linear regression line formula can be created. One variable is the independent variable and the other is the dependent variable. Simple linear regression is the creation of a formula that shows the straight line relationship between two correlated variable. ![]()
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