Answer:
We know that n = 50 and p =0.78.
We need to check the conditions in order to use the normal approximation.
Since both conditions are satisfied we can use the normal approximation and the distribution for the proportion is given by:

With the following parameters:


Step-by-step explanation:
Previous concepts
The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".
Solution to the problem
We know that n = 50 and p =0.78.
We need to check the conditions in order to use the normal approximation.
Since both conditions are satisfied we can use the normal approximation and the distribution for the proportion is given by:

With the following parameters:


Answer:

Step-by-step explanation:
No it isn't, because:
![{1.08}^{ \frac{1}{5} } = { \frac{108}{100} }^{ \frac{1}{5} } = { \frac{27}{25} }^{ \frac{1}{5} } = \sqrt[5]{ \frac{27}{25} } = 1.01551](https://tex.z-dn.net/?f=%20%7B1.08%7D%5E%7B%20%5Cfrac%7B1%7D%7B5%7D%20%7D%20%20%3D%20%20%7B%20%5Cfrac%7B108%7D%7B100%7D%20%7D%5E%7B%20%5Cfrac%7B1%7D%7B5%7D%20%7D%20%20%3D%20%20%7B%20%5Cfrac%7B27%7D%7B25%7D%20%7D%5E%7B%20%5Cfrac%7B1%7D%7B5%7D%20%7D%20%20%3D%20%20%5Csqrt%5B5%5D%7B%20%5Cfrac%7B27%7D%7B25%7D%20%7D%20%20%3D%201.01551)
The ten thousandth number in 14,620 is 1
the number to the right of 1 is 4
4>5
The answer is 10,000
Answer:
Therefore, the probability is P=1/84.
Step-by-step explanation:
We have a bag contains 3 red and 6 white tokens. Tokens are randomly selected and removed one at a time until the bag is empty.
We conclude that in a bag have 9 tokens.
We calculate the probability that the red tokens are drawn consecutively.
We calculate the number of possible combinations:

Number of favorable combinations is 1.
Therefore, the probability is P=1/84.
Youre answer is D, you’re welcome