COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Estimators
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Parameters
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Random errors
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Variables
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Detailed explanation-1: -The regression model is given by: Y = + X + where is the y intercept (the value of Y where X = 0), is the slope of the line, and is a random error term. It may also be given as: Y = 0 + 1X + where 0 is the y intercept, 1 is the slope of the line, and is a random error term.
Detailed explanation-2: -In statistics, standardized (regression) coefficients, also called beta coefficients or beta weights, are the estimates resulting from a regression analysis where the underlying data have been standardized so that the variances of dependent and independent variables are equal to 1.
Detailed explanation-3: -A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable.
Detailed explanation-4: -0 is the value of y when x = 0, and 1 is the change in y when x increases by 1 unit.