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Luba_88 [7]
3 years ago
15

What are some of the applications for exponential functions

Mathematics
2 answers:
Ganezh [65]3 years ago
7 0
Exponential functions help us in reducing higher dimensional equivalents into smaller ones , simply , we have an advantage over using exponents , indices , simultaneously ,

we can substitute x² = t in any equation which has raised to the power 4 , it'll result in t² for sure ,

SO , the above illustration is the most basic and common application of exponential functions , rest is utilized in reducing logarithmic , trigonometric functions and so on more
scoundrel [369]3 years ago
3 0

Answer:

The special matter about exponential functions is that they can be very beneficial in day to day situations.

Exponential functions are beneficial to carbon date artifacts, compute investments, help coroners determine the death time, model populations as well as many other applications.

Three of the very common applications for exponential functions:

  1. population growth
  2. exponential decay
  3. compound interest.

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Step-by-step explanation:

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What is the equation of the line that is perpendicular to f(x) and passes through the point (4, –6)? a.4x + 3y = –2 b. 3x + 4y =
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(2/11x-3-5y)+(-3/11x+5y+5.5) what is the answer for this I need help quick I'm in math
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3 years ago
Toll Brothers is a luxury home builder that would like to test the hypothesis that the average size of new homes exceeds 2,400 s
boyakko [2]

Answer:

Since the pvalue of the test is 0.09 > 0.01, we do not reject the null hypothesis that the average size of new homes is of 2400 square feet.

Step-by-step explanation:

Toll Brothers is a luxury home builder that would like to test the hypothesis that the average size of new homes exceeds 2,400 square feet. Test to see if the average size of new homes exceeds 2,400 square feet.

At the null hypothesis, we test if the average is of 2400 square feet, that is:

H_o: \mu = 2400

At the alternate hypothesis, we test if the average is greater than 2400 square feet, that is:

H_a: \mu > 2400

The test statistic is:

Since we have the standard deviation for the sample, we use the t-distribution.

t = \frac{X - \mu}{\frac{s}{\sqrt{n}}}

In which X is the sample mean, \mu is the value tested at the null hypothesis, s is the standard deviation of the sample and n is the size of the sample.

2400 is tested at the null hypothesis:

This means that \mu = 2400

A random sample of 36 newly constructed homes had an average of 2,510 square feet with a sample standard deviation of 480 square feet.

This means that n = 36, X = 2510, s = 480

Test statistic:

t = \frac{X - \mu}{\frac{s}{\sqrt{n}}}

t = \frac{2510 - 2400}{\frac{480}{\sqrt{36}}}

t = 1.375

Pvalue of the test and decision:

The pvalue of the test is the probability of finding a sample mean of at least 2510, which is the pvalue of t = 1.375 with 36 - 1 = 35 degrees of freedom, using a one-tailed test.

With the help of a calculator, this pvalue is of 0.09

Since the pvalue of the test is 0.09 > 0.01, we do not reject the null hypothesis that the average size of new homes is of 2400 square feet.

6 0
2 years ago
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