Which of the Following Statements Are Not True?
A) the Slope

Multiple Choice

Which of the following statements are not true?

A) The slope $\hat { \beta } _ { 1 }$
Of the least squares line is an unbiased estimator of the slope coefficient $\beta _ { 1 }$
Of the true regression line.
B) The variance $\hat { \beta } _ { 1 }$
Of the least squares line equals the variance $\sigma ^ { 2 }$
Of the random error $\varepsilon$

Divided by $\sqrt { S _ { xx } }$
, where $S _ {xx } = \sum \left( x _ { i } - \bar { x } \right) ^ { 2 }$
C) Values of $x _ { i}$
All close to one another imply a highly variable estimator $\hat { \beta } _ { 1 }$
Of the slope $\beta _ { 1 }$
Of the true regression line.
D) Values of $x _ { i }$
That are quite spread out results in a more precise estimator $\hat { \beta } _ { 1 }$
Of the slope $\beta _ { 1 }$
Of the true regression line
E) All of the above statements are true