Changing the units of measurement obviously will have an effect on the slope of your regression function. For example, let $Y ^ { * } = a Y \text { and } X ^ { * } = b X \text {. }$
Then it is easy
but tedious to show that $\widehat { \beta } _ { 1 } ^ { * } = \frac { \sum _ { i = 1 } ^ { n } x _ { i } ^ { * } y _ { i } ^ { * } } { \sum _ { i = 1 } ^ { n } x _ { i } ^ { * 2 } } = \frac { a } { b } \widehat { \beta } _ { 1 }$ Given this result, how do you think
the standard errors and the regression $R ^ { 2 }$ will change?

Question

Changing the units of measurement obviously will have an effect on the slope of your regression function. For example, let $Y ^ { * } = a Y \text { and } X ^ { * } = b X \text {. }$
Then it is easy
but tedious to show that $\widehat { \beta } _ { 1 } ^ { * } = \frac { \sum _ { i = 1 } ^ { n } x _ { i } ^ { * } y _ { i } ^ { * } } { \sum _ { i = 1 } ^ { n } x _ { i } ^ { * 2 } } = \frac { a } { b } \widehat { \beta } _ { 1 }$ Given this result, how do you think
the standard errors and the regression  $R ^ { 2 }$ will change?

Quizplus · Accepted Answer

The Answer of Changing the units of measurement obviously will have an effect on the slope of your regression function. For example, let $Y ^ { * } = a Y \text { and } X ^ { * } = b X \text {. }$
Then it is easy
but tedious to show that $\widehat { \beta } _ { 1 } ^ { * } = \frac { \sum _ { i = 1 } ^ { n } x _ { i } ^ { * } y _ { i } ^ { * } } { \sum _ { i = 1 } ^ { n } x _ { i } ^ { * 2 } } = \frac { a } { b } \widehat { \beta } _ { 1 }$ Given this result, how do you think
the standard errors and the regression  $R ^ { 2 }$ will change?

Changing the Units of Measurement Obviously Will Have an Effect Y∗=aY and X∗=bX. Y ^ { * } = a Y \text { and } X ^ { * } = b X \text {. }Y∗=aY and X∗=bX.

Changing the Units of Measurement Obviously Will Have an Effect $Y ^ { * } = a Y \text { and } X ^ { * } = b X \text {. }$