| Linear Regression |
| For a given dataset from a real time system or a process, the data is non-linear when plotted on a graph as shown below. For analysis, five data points are used. The data represents 13k, 5k, .. etc. factored as 13, 5, respectively, from a financial system skewed for illustration. |
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| By computing the values of slope m and intercept c, in the equation y = mx + c, a linear relationship is derived as y = 11.9 - 1.7x. The yp is the predicted value of y for each value of x. |
| ypi = 11.9 - 1.7 xi. |
| Linear regression is a simple way to predict outcome of a dependent variable y by deriving a linear equation. |
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| The Root Mean Square Error (RMSE) represents a factor, which is the difference between the actual value and the predicted value. In the above graph, it is clear that three predicted values are close the actual value. |
| Linear Regression |
| Machine Learning |
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