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jok3333 [9.3K]
3 years ago
15

Can someone help..? thanks ​

Mathematics
1 answer:
Serjik [45]3 years ago
4 0

Answer:

2.2*10^5

Step-by-step explanation:

to write a # in scientific notation change the # to decimal form.

so 2.2

then move the decimal point to the right until you get the original #

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Pls answer correctly I’ll give you brainliest
baherus [9]

Answer:

y=-1x+-3

Step-by-step explanation:

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2 years ago
Determine the equation of the line perpendicular to the line y = –8 through the point (–4, –2).
sesenic [268]
The perpendicular line is x=-4
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the graph-related the number of gallon of white paint to the number of gallon of red paint jess to make the perfect pink. ​
attashe74 [19]

Answer:

y= 3/4x

Step-by-step explanation:

Points on the graph

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the equation based on the difference in points:

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3 years ago
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g Annual starting salaries in a certain region of the U. S. for college graduates with an engineering major are normally distrib
algol13

Answer:

The probability that the sample mean would be at least $39000 is of 0.8665 = 86.65%.

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 $39725 and standard deviation $7320.

This means that \mu = 39725, \sigma = 7320

Sample of 125

This means that n = 125, s = \frac{7320}{\sqrt{125}}

The probability that the sample mean would be at least $39000 is about?

This is 1 subtracted by the pvalue of Z when X = 39000. So

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

By the Central Limit Theorem

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

Z = \frac{39000 - 39725}{\frac{7320}{\sqrt{125}}}

Z = -1.11

Z = -1.11 has a pvalue of 0.1335

1 - 0.1335 = 0.8665

The probability that the sample mean would be at least $39000 is of 0.8665 = 86.65%.

4 0
2 years ago
The following graphs have a scale assigned to them: The area of each grid
dezoksy [38]

The graphs that are density curves for a continuous random variable are: Graph A, C, D and E.

<h3>How to determine the density curves?</h3>

In Geometry, the area of the density curves for a continuous random variable must always be equal to one (1). Thus, we would test this rule in each of the curves:

Area A = (1 × 5 + 1 × 3 + 1 × 2) × 0.1

Area A = 10 × 0.1

Area A = 1 sq. units (True).

For curve B, we have:

Area B = (3 × 3) × 0.1

Area B = 9 × 0.1

Area B = 0.9 sq. units (False).

For curve C, we have:

Area C = (3 × 4 - 2 × 1) × 0.1

Area C = 10 × 0.1

Area C = 1 sq. units (False).

For curve D, we have:

Area D = (1 × 4 + 1 × 3 + 1 × 2 + 1 × 1) × 0.1

Area D = 10 × 0.1

Area D = 1 sq. units (True).

For curve E, we have:

Area E = (1/2 × 4 × 5) × 0.1

Area E = 10 × 0.1

Area E = 1 sq. units (True).

Read more on density curves here: brainly.com/question/26559908

#SPJ1

6 0
2 years ago
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