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oksano4ka [1.4K]
3 years ago
14

Graph the equation-5x-2y=-10 by plotting the points

Mathematics
1 answer:
bearhunter [10]3 years ago
4 0

Answer:

y = -2.5x + 5

Explanation:

Convert -5x - 2y = -10 into slope-intercept form: y = -2.5x + 5

You can start solving for and graphing points by putting numbers in for 'x' in the equation, and the answer you get out will be y.

Ex: -2.5(7) + 5 = -12.5

7 is the number we inserted for x, so it is the x value. -12.5 is the number we got out of the equation, so it is the y value.

The points will now look like this: (x, y) but for the example they would look like (7, -12.5)

Plot the numbers, x being on the horizontal axis, y being on the vertical axis.

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Furkat [3]

Answer:

Step-by-step explanation:

5 0
3 years ago
The mean amount purchased by a typical customer at Churchill's Grocery Store is $27.50 with a standard deviation of $7.00. Assum
Schach [20]

Answer:

a) 0.0016 = 0.16% probability that the sample mean is at least $30.00.

b) 0.8794 = 87.94% probability that the sample mean is greater than $26.50 but less than $30.00

c) 90% of sample means will occur between $26.1 and $28.9.

Step-by-step explanation:

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

Normal probability distribution

When the distribution is normal, we use 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}}.

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

In this question, we have that:

\mu = 27.50, \sigma = 7, n = 68, s = \frac{7}{\sqrt{68}} = 0.85

a. What is the likelihood the sample mean is at least $30.00?

This is 1 subtracted by the pvalue of Z when X = 30. So

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

By the Central Limit Theorem, we have that:

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

Z = \frac{30 - 27.5}{0.85}

Z = 2.94

Z = 2.94 has a pvalue of 0.9984

1 - 0.9984 = 0.0016

0.0016 = 0.16% probability that the sample mean is at least $30.00.

b. What is the likelihood the sample mean is greater than $26.50 but less than $30.00?

This is the pvalue of Z when X = 30 subtracted by the pvalue of Z when X = 26.50. So

From a, when X = 30, Z has a pvalue of 0.9984

When X = 26.5

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

Z = \frac{26.5 - 27.5}{0.85}

Z = -1.18

Z = -1.18 has a pvalue of 0.1190

0.9984 - 0.1190 = 0.8794

0.8794 = 87.94% probability that the sample mean is greater than $26.50 but less than $30.00.

c. Within what limits will 90 percent of the sample means occur?

Between the 50 - (90/2) = 5th percentile and the 50 + (90/2) = 95th percentile, that is, Z between -1.645 and Z = 1.645

Lower bound:

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

-1.645 = \frac{X - 27.5}{0.85}

X - 27.5 = -1.645*0.85

X = 26.1

Upper Bound:

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

1.645 = \frac{X - 27.5}{0.85}

X - 27.5 = 1.645*0.85

X = 28.9

90% of sample means will occur between $26.1 and $28.9.

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3 years ago
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Kryger [21]
21, 18 1/2, 6.2, 1.75, -1/4, -1/2, -4,-5.9
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Use the tally chart to answer the question.
Semenov [28]

Answer:

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3 years ago
Read 2 more answers
Please help me with this​
kolbaska11 [484]

Answer:

c

Step-by-step explanation:

but its a negative 2 so but it would be 4 times 2 its 4 times negative 4

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