BUSINESS ADMINISTRATION
BUSINESS ANALYTICS
Question
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differences between actual and predicted y values


absolute deviations between actual and predicted y values


absolute deviations between actual and predicted x values


squared differences between actual and predicted y values

Detailed explanation1: The main objective of the regression analysis is to find the line of best fit that minimizes the sum of the squares of the residuals. In other words, its goal is to minimize the sum of the squared differences of the actual and the predicted value of the dependent variable.
Detailed explanation2: The Least Squares Regression Line is the line that minimizes the sum of the residuals squared. In other words, for any other line other than the LSRL, the sum of the residuals squared will be greater. This is what makes the LSRL the sole bestfitting line.
Detailed explanation3: The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. It’s called a “least squares” because the best line of fit is one that minimizes the variance (the sum of squares of the errors).
Detailed explanation4: This method is called the leastsquares computation procedure because it aims to minimize the squared distances between each of the points and the line. Least Squares Regression Line (LSRL): The line that minimizes the sum of the squares of the vertical distances of the data points from the line.
Detailed explanation5: Properties of the Regression Line The line minimizes the sum of squared differences between observed values (the y values) and predicted values (the ŷ values computed from the regression equation). The regression line passes through the mean of the x values (x) and through the mean of the y values (y).