In calculating the regression coefficients we square the errors of prediction because
A) statisticians square everything.
B) the sum of the errors would always be 0 for a great many lines we could draw.
C) squaring makes the errors more striking.
D) little errors are more important than big errors.
Correct Answer:
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Q2: The "best fitting line" is that regression
Q3: In the equation for a straight line
Q4: When we think in terms of standardized
Q5: When the slope of the regression line
Q6: In the equation Ŷ = 12.6 X
Q7: If the correlation between X and Y
Q8: Suppose that you sell ice cream from
Q9: If we have a regression line predicting
Q10: When we standardize paired data we
A) divide
Q11: The notation ( Y - Ŷ )
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