What is the answer?

Mathematics

1 answer:

andreev551 [17]3 years ago

7 0

$\bf \begin{array}{cll} \cfrac{1}{3}\\\\\cline{1-1}\\ \cfrac{1}{9}~~\cfrac{1}{9}~~\cfrac{1}{9} \end{array}\qquad \implies\qquad \cfrac{1}{3}=\cfrac{1+1+1}{9}\implies \cfrac{1}{3}= \cfrac{3}{9} \\\\[-0.35em] \rule{34em}{0.25pt}\\\\ \begin{array}{cll} \cfrac{1}{4}\\\\\cline{1-1}\\ \cfrac{1}{8}~~\cfrac{1}{8} \end{array}\qquad \implies\qquad \cfrac{1}{4}=\cfrac{1+1}{8}\implies \cfrac{1}{4}= \cfrac{2}{8}$

You might be interested in

She wants to save 3/5 of 180.00 and spend the rest hpw much would she have left

Anastasy [175]

3/5 of 180 is 108
180-108=72
she would have 72$ left

6 0

3 years ago

Read 2 more answers

We have n = 100 many random variables Xi ’s, where the Xi ’s are independent and identically distributed Bernoulli random variab

777dan777 [17]

Answer:

(a) The distribution of $X=\sum\limits^{n}_{i=1}{X_{i}}$ is a Binomial distribution.

(b) The sampling distribution of the sample mean will be approximately normal.

Step-by-step explanation:

It is provided that random variables $X_{i}$ are independent and identically distributed Bernoulli random variables with p = 0.50.

The random sample selected is of size, n = 100.

(a)

Theorem:

Let $X_{1},\ X_{2},\ X_{3},...\ X_{n}$ be independent Bernoulli random variables, each with parameter p, then the sum of of thee random variables, $X=X_{1}+X_{2}+X_{3}...+X_{n}$ is a Binomial random variable with parameter n and p.

Thus, the distribution of $X=\sum\limits^{n}_{i=1}{X_{i}}$ is a Binomial distribution.

(b)

According to the Central Limit Theorem if we have an unknown population with mean μ and standard deviation σ and appropriately huge random samples (n > 30) are selected from the population with replacement, then the distribution of the sample mean will be approximately normally distributed.

The sample size is large, i.e. n = 100 > 30.

So, the sampling distribution of the sample mean will be approximately normal.

The mean of the distribution of sample mean is given by,

$\mu_{\bar x}=\mu=p=0.50$