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Which of the following statements about least-squares regression involving two quantitative variables, ? and ?, is FALSE? A change of 1 standard deviation in x corresponds to a change of ? standard deviations in ?. The least-squares regression line always passes through the point (). The least-squares regression line of ? on ? is the line that makes the sum of the squares of the vertical distances of the data points from the line as small as possible. When the least-squares regression line is a perfect fit to the data, the correlation coefficient will be exactly 0.

Which of the following statements about least-squares regression involving two quantitative variables, ? and ?, is FALSE? A change of 1 standard deviation in x corresponds to a change of ? standard deviations in ?. The least-squares regression line always passes through the point (). The least-squares regression line of ? on ? is the line that makes the sum of the squares of the vertical distances of the data points from the line as small as possible. When the least-squares regression line is a perfect fit to the data, the correlation coefficient will be exactly 0.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
6th Edition
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Bruce Crauder, Benny Evans, Alan Noell
Chapter3: Straight Lines And Linear Functions
Section3.4: Linear Regression
Problem 12SBE: Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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B9. Which of the following statements about least-squares regression involving two quantitative variables, ? and ?, is FALSE?

  1. A change of 1 standard deviation in x corresponds to a change of ? standard deviations in ?.
  2. The least-squares regression line always passes through the point ().
  3. The least-squares regression line of ? on ? is the line that makes the sum of the squares of the vertical distances of the data points from the line as small as possible.
  4. When the least-squares regression line is a perfect fit to the data, the correlation coefficient will be exactly 0.

 

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