The kurtosis of a distribution is defined as follows: a. $\frac { E \left[ \left( Y - \mu _ { Y } \right) ^ { 4 } \right) } { \sigma _ { Y } ^ { 4 } }$
b. $\frac { E \left[ \left( Y ^ { 4 } - \mu _ { Y } ^ { 4 } \right) \right) } { \sigma _ { Y } ^ { 2 } }$
c. $\frac { \text { skewness } } { \operatorname { var } ( Y ) }$
d. $E \left[ \left( Y - \mu _ { Y } \right) ^ { 4 } \right)$

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The Answer of The kurtosis of a distribution is defined as follows: a. $\frac { E \left[ \left( Y - \mu _ { Y } \right) ^ { 4 } \right) } { \sigma _ { Y } ^ { 4 } }$
b. $\frac { E \left[ \left( Y ^ { 4 } - \mu _ { Y } ^ { 4 } \right) \right) } { \sigma _ { Y } ^ { 2 } }$
c. $\frac { \text { skewness } } { \operatorname { var } ( Y ) }$
d. $E \left[ \left( Y - \mu _ { Y } \right) ^ { 4 } \right)$

The Kurtosis of a Distribution Is Defined as Follows: A \frac