APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Y
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Y (hat)
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X
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X (hat)
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a
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Detailed explanation-1: -In linear regression, the residual refers to the difference between the predicted Y and actual Y values.
Detailed explanation-2: -The predicted value of y (” “) is sometimes referred to as the “fitted value” and is computed as y ^ i = b 0 + b 1 x i . Below, we’ll look at some of the formulas associated with this simple linear regression method. In this course, you will be responsible for computing predicted values and residuals by hand.
Detailed explanation-3: -We can use the regression line to predict values of Y given values of X. For any given value of X, we go straight up to the line, and then move horizontally to the left to find the value of Y. The predicted value of Y is called the predicted value of Y, and is denoted Y’.
Detailed explanation-4: -Therefore, the formula for calculation is Y = a + bX + E, where Y is the dependent variable, X is the independent variable, a is the intercept, b is the slope, and E is the residual. Regression is a statistical tool to predict the dependent variable with the help of one or more independent variables.