The “least squares” method of fitting the best straight line in simple linear regression does which of the following: a. Minimizes the standard deviation of the independent variable b. Minimizes the sum of squared deviations between the fitted line and the dependent variable c. Maximizes the sum of the absolute deviations between the fitted line and the dependent variable d. Minimizes the standard deviation of the dependent variable
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Step 1: The least squares method in simple linear regression aims to minimize the sum of squared deviations between the fitted line and the dependent variable. Show more…
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Sri K.
The least squares (best fitting) line is one where the residual sum of squares is closest to zero. intercept of the regression equation, a, is closest to zero. slope of the regression equation, b, is closest to zero. variance of Y is large.
Madhur L.
Which of the following is not a characteristic of the least-squares regression line? (a) The slope of the least-squares regression line is always between -1 and 1 (b) The least-squares regression line always goes through the point $(\bar{x}, \bar{y})$ (c) The least-squares regression line minimizes the sum of squared residuals. (d) The slope of the least-squares regression line will always have the same sign as the correlation. (e) The least-squares regression line is not resistant to outliers.
Md.Daniyal A.
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