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"Least-squares" in the term "least-squares regression line" refers to a. minimizing the products of each value of the response variable and the predicted value based on the regression equation. X b. minimizing the sum of the squares of all values of the explanatory variable. c. minimizing the squares of the differences between each value of the response variable and each value of the explanatory variable. d. minimizing the sum of the squares of all values of the response variable. e. minimizing the sum of the squares of the residuals.

"Least-squares" in the term "least-squares regression line" refers to a. minimizing the products of each value of the response variable and the predicted value based on the regression equation. X b. minimizing the sum of the squares of all values of the explanatory variable. c. minimizing the squares of the differences between each value of the response variable and each value of the explanatory variable. d. minimizing the sum of the squares of all values of the response variable. e. minimizing the sum of the squares of the residuals.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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"Least-squares" in the term "least-squares regression line" refers to a. minimizing the products of each value of the response variable and the predicted value based on the regression equation. X b. minimizing the sum of the squares of all values of the explanatory variable. c. minimizing the squares of the differences between each value of the response variable and each value of the explanatory variable. d. minimizing the sum of the squares of all values of the response variable. e. minimizing the sum of the squares of the residuals.
Transcribed Image Text:"Least-squares" in the term "least-squares regression line" refers to a. minimizing the products of each value of the response variable and the predicted value based on the regression equation. X b. minimizing the sum of the squares of all values of the explanatory variable. c. minimizing the squares of the differences between each value of the response variable and each value of the explanatory variable. d. minimizing the sum of the squares of all values of the response variable. e. minimizing the sum of the squares of the residuals.
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