Shift absolute value graphs

Mathematics

2 answers:

grin007 [14]3 years ago

5 0

Answer:

down 1 and right 9

Step-by-step explanation:

Ksivusya [100]3 years ago

3 0

Answer

up 5 left 3

Step-by-step explanation:

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Midpoint of (-2,-4) and (2,1)

Mice21 [21]

I got it =4/5 hope it’s right

7 0

3 years ago

3^-2 expanded form

Zanzabum

3^(-2) = 1/(3^2) = 1/(3*3) = 1/9

The rule used here is x^(-y) = 1/(x^y) to make the exponent positive.

3^2 turns into 3*3 because the exponent 2 tells us how many copies of the base '3' to multiply out.

5 0

3 years ago

Power +, Inc. produces AA batteries used in remote-controlled toy cars. The mean life of these batteries follows the normal prob

Novay_Z [31]

Answer:

a) By the Central Limit Theorem, it is approximately normal.

b) The standard error of the distribution of the sample mean is 1.8333.

c) 0.1379 = 13.79% of the samples will have a mean useful life of more than 38 hours.

d) 0.7939 = 79.39% of the samples will have a mean useful life greater than 34.5 hours

e) 0.656 = 65.6% of the samples will have a mean useful life between 34.5 and 38 hours

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 normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score 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 p-value, 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 $s = \frac{\sigma}{\sqrt{n}}$ .