I need to fill this out for notes .

Mathematics

1 answer:

docker41 [41]3 years ago

5 0

Answer:

not an answer but how do you attach images because it would be more useful

Step-by-step explanation:

You might be interested in

Please help me with this question

Zinaida [17]

Tree C is the proper tree. The first game has the possibility of a win, a loss, or a tie which the first row represents. The second game could also win, lose or tie, meaning each result would have each possibilty for that game.

4 0

4 years ago

Make V the subject of the formula E=mv2/2

Dima020 [189]

Answer:

v = ± $\sqrt{\frac{2E}{m} }$

Step-by-step explanation:

Given

E = $\frac{mv^2}{2}$ ( multiply both sides by 2 to clear the fraction )

2E = mv² ( divide both sides by m )

$\frac{2E}{m}$ = v² ( take the square root of both sides )

v = ± $\sqrt{\frac{2E}{m} }$

5 0

4 years ago

Can you plz give me more of an anser

Aleks04 [339]

Answer:

peepeepoopoo check

Step-by-step explanation:

6 0

3 years ago

The GPAs of all students enrolled at a large university have an approximately normal distribution with a mean of 3.02 and a stan

IgorLugansk [536]

Answer:

10.93% probability that the mean GPA of a random sample of 20 students selected from this university is 3.10 or higher.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .