Which of the following statements about Least Squares Regression is not true ? Least -squares regression is a mathematical technique to fit a cost -estimating equation to observed data . In least -squares regression , a statistical measure (R -squared ) can be used to determine how well the equation fits the data . Least -squares regression is considered superior to the high -low method because it uses more data points than just the high and low points None of the listed choices are the correct answer because all of the listed choices are true . Least -squares regression maximizes the vertical squared difference between the estimated and actual costs at each data point .
Which of the following statements about Least Squares Regression is not true ? Least -squares regression is a mathematical technique to fit a cost -estimating equation to observed data . In least -squares regression , a statistical measure (R -squared ) can be used to determine how well the equation fits the data . Least -squares regression is considered superior to the high -low method because it uses more data points than just the high and low points None of the listed choices are the correct answer because all of the listed choices are true . Least -squares regression maximizes the vertical squared difference between the estimated and actual costs at each data point .
Chapter1: Financial Statements And Business Decisions
Section: Chapter Questions
Problem 1Q
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Which of the following statements about Least Squares Regression is not true ?
Least -squares regression is a mathematical technique to fit a cost -estimating equation to observed data .
In least -squares regression , a statistical measure (R -squared ) can be used to determine how well the equation fits the data .
Least -squares regression is considered superior to the high -low method because it uses more data points than just the high and low points
None of the listed choices are the correct answer because all of the listed choices are true .
Least -squares regression maximizes the vertical squared difference between the estimated and actual costs at each data point .
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