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3 years ago

Y + 2 = -3(x-4) complete the missing value in the solution to the equation

Mathematics

1 answer:

FinnZ [79.3K]3 years ago

3 0

Answer:

y = -3x +10 Step-by-step explanation:

y+2 = -3(x-4)

y+2 = (-3*x) + (-3*-4)

y+2 = -3x + 12

^ -2 = . ^ -2

y = -3x +10

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The cut out part on the right side is "the tree"

solniwko [45]

Answer:

C, just C

Step-by-step explanation:

Demonstrating all calculations would be too much word right now.

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4 years ago

How can you tell that a function is linear or nonlinear

STatiana [176]

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4 years ago

21 = -4h +13-h Send help:(

kogti [31]

Answer:

h = -8/5

Step-by-step explanation:

21 = -4h +13-h

simplify the right side

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3 years ago

A=5b+8 Rearrange to make subject

Mumz [18]

AnswThe method is to separate the variable from everything else. That means getting the variable on one side of the equals sign and everything else on the other side. A=5B+8.

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Step-by-step explanation:

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3 years ago

The amount of time that a 4th grader reads comic books in a week is normally distributed with a mean of 6 hours and a standard d

KATRIN_1 [288]

Answer:

0.9544 = 95.44% probability that the sample mean time for reading comic books per week is between 5 and 7 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 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 $s = \frac{\sigma}{\sqrt{n}}$ .