The principle of least squares results in values of $\hat { \beta } _ { 0 } \text { and } \hat { \beta } _ { 1 }$ that minimizes the sum of squared deviations between
A) the observed values of the explanatory variable x and the estimated values $\hat { x }$
B) the observed values of the response variable y and the estimated values $\hat { y }$
C) the observed values of the explanatory variable x and the response variable y
D) the observed values of the explanatory variable x and the response values $\hat { y }$
E) the estimated values of the explanatory variable x and the observed values of the response variable y

Question

Quizplus · Accepted Answer

The principle of least squares is used to minimize the sum of squared deviations between the observed values of the response variable y and the estimated values $\hat { y }$, which are predicted by the regression line.

The Principle of Least Squares Results in Values Of β^0 and β^1\hat { \beta } _ { 0 } \text { and } \hat { \beta } _ { 1 }β^​0​ and β^​1​

The Principle of Least Squares Results in Values Of $\hat { \beta } _ { 0 } \text { and } \hat { \beta } _ { 1 }$