

Critical points occur where the gradient is zero. This is guaranteed whenever

and either

or

.
The Hessian matrix for this function looks like

and has determinant

Maxima occur whenever the determinant is positive and

. Minima occur whenever both the determinant and

are positive. Saddle points occur whenever the determinant is negative.
At

, you have a saddle point since the determinant reduces to -324, so

is the saddle point.
At

, the determinant is

and

, so

is a local maximum.
No other critical points remain, so you're done.
Answer:
This is a statement not a question
Answer:
![(D)E[ X ] =np.](https://tex.z-dn.net/?f=%28D%29E%5B%20X%20%5D%20%3Dnp.)
Step-by-step explanation:
Given a binomial experiment with n trials and probability of success p,


Since each term of the summation is multiplied by x, the value of the term corresponding to x = 0 will be 0. Therefore the expected value becomes:

Now,

Substituting,

Factoring out the n and one p from the above expression:

Representing k=x-1 in the above gives us:

This can then be written by the Binomial Formula as:
![E[ X ] = (np) (p +(1 - p))^{n -1 }= np.](https://tex.z-dn.net/?f=E%5B%20X%20%5D%20%3D%20%28np%29%20%28p%20%2B%281%20-%20p%29%29%5E%7Bn%20-1%20%7D%3D%20np.)
Answer: 0.16
Step-by-step explanation:
Given that the run times provided are normally distributed ;
Mean(x) of distribution = 3 hours 50 minutes
Standard deviation(s) = 30 minutes
The probability that a randomly selected runner has a time less than or equal to 3 hours 20 minutes
3 hours 20 minutes = (3 hrs 50 mins - 30 mins):
This is equivalent to :
[mean(x) - 1 standard deviation]
z 1 standard deviation within the mean = 0.84
z, 1 standard deviation outside the mean equals:
P(1 - z value , 1standard deviation within the mean)
1 - 0.8413 = 0.1587
= 0.16
Answer:
ezz multiply and see what it s if its wrong then txt me
Step-by-step explanation: