The kurtosis of a distribution is defined as follows: A) $\frac { E \left[ \left( Y - \mu _ { \gamma } \right) ^ { 4 } \right] } { \sigma _ { \gamma } ^ { 4 } }$ B) $\frac { E \left[ \left( Y ^ { 4 } - \mu _ { y } ^ { 4 } \right) \right] } { \sigma _ { y } ^ { 2 } }$ C) $\frac { \text { skewness } } { \operatorname { vat } ( Y ) }$ D)E[(Y - ${ } ^ { \mu } \gamma$ ⁴)

Question

Quizplus · Accepted Answer

The kurtosis of a distribution is defined as the fourth moment about the mean, divided by the fourth power of the standard deviation. This is given by the formula $\frac { E \left[ \left( Y - \mu _ { \gamma } \right) ^ { 4 } \right] } { \sigma _ { \gamma } ^ { 4 } }$.

The Kurtosis of a Distribution Is Defined as Follows: A) E[(Y−μγ)4]σγ4\frac { E \left[ \left( Y - \mu _ { \gamma } \right) ^ { 4 } \right] } { \sigma _ { \gamma } ^ { 4 } }σγ4​E[(Y−μγ​)4]​

The Kurtosis of a Distribution Is Defined as Follows:
A) $\frac { E \left[ \left( Y - \mu _ { \gamma } \right) ^ { 4 } \right] } { \sigma _ { \gamma } ^ { 4 } }$