The expected length of code for one encoded symbol is

where
is the probability of picking the letter
, and
is the length of code needed to encode
.
is given to us, and we have

so that we expect a contribution of

bits to the code per encoded letter. For a string of length
, we would then expect
.
By definition of variance, we have
![\mathrm{Var}[L]=E\left[(L-E[L])^2\right]=E[L^2]-E[L]^2](https://tex.z-dn.net/?f=%5Cmathrm%7BVar%7D%5BL%5D%3DE%5Cleft%5B%28L-E%5BL%5D%29%5E2%5Cright%5D%3DE%5BL%5E2%5D-E%5BL%5D%5E2)
For a string consisting of one letter, we have

so that the variance for the length such a string is

"squared" bits per encoded letter. For a string of length
, we would get
.
This is equals 3 and 18/56 which reduces to 3 and 9/28 all you have to do is plug it the calculator like it is shown and then you will get this answer
Answer:
#3 1/6, 1/6, 1/2, 1/3, 5/6.
#4 3/12, 4/12, 5/12, 8/12, 9/12
Step-by-step explanation:
#3 is numbers out of six (fractions).
#4 is the same thing. You add all of the numbers (4, 3, and 5) to get the total and then you subtract to get the probability.
These are all I know.
Answer:
Step-by-step explanation:
Given that:
Population Mean = 7.1
sample size = 24
Sample mean = 7.3
Standard deviation = 1.0
Level of significance = 0.025
The null hypothesis:

The alternative hypothesis:

This test is right-tailed.

Rejection region: at ∝ = 0.025 and df of 23, the critical value of the right-tailed test 
The test statistics can be computed as:



t = 0.980
Decision rule:
Since the calculated value of t is lesser than, i.e t = 0.980 <
, then we do not reject the null hypothesis.
Conclusion:
We conclude that there is insufficient evidence to claim that the population mean is greater than 7.1 at 0.025 level of significance.
Answer: i don't see the problem
Step-by-step explanation: