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klasskru [66]
3 years ago
5

How do you graph y = x + 2

Mathematics
1 answer:
Ghella [55]3 years ago
7 0

Answer:

Basically you first start with having a graph with positive and negative numbers on four sides. Then, you start with 2. You have to graph 2 on the  positive y section.

x = 1/1 so you go 1 up which means put another dot at 3 on the y section, then go 1 on the right. And thats it.

Please brainliest

Step-by-step explanation:

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Triangle HIJ has vertices H(1, 7) , I(−4, −2) , and J(−3, 5) . Write the coordinate notation for a translation of 3 units right
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Step-by-step explanation:

(x, y) -> (x+3, y+4)

that is what 3 units to the right (3 units into the outsource x direction) and 4 units up (4 units into the posits y directing) mean.

so, all points go through this translation

(1, 7) -> (4, 11)

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Step-by-step explanation:

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7.) 18.86m

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The Office of Student Services at a large western state university maintains information on the study habits of its full-time st
Vera_Pavlovna [14]

Answer:

0.8254 = 82.54% probability that the mean of this sample is between 19.25 hours and 21.0 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}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

Mean of 20 hours, standard deviation of 6:

This means that \mu = 20, \sigma = 6

Sample of 150:

This means that n = 150, s = \frac{6}{\sqrt{150}}

What is the probability that the mean of this sample is between 19.25 hours and 21.0 hours?

This is the p-value of Z when X = 21 subtracted by the p-value of Z when X = 19.5. So

X = 21

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

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{21 - 20}{\frac{6}{\sqrt{150}}}

Z = 2.04

Z = 2.04 has a p-value of 0.9793

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Z = \frac{X - \mu}{s}

Z = \frac{19.5 - 20}{\frac{6}{\sqrt{150}}}

Z = -1.02

Z = -1.02 has a p-value of 0.1539

0.9793 - 0.1539 = 0.8254

0.8254 = 82.54% probability that the mean of this sample is between 19.25 hours and 21.0 hours

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An equation with more than one variable is called a "literal Equation"
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