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zzz [600]
3 years ago
11

In a poll of 745 randomly selected adults, 591 said that it is morally wrong to not report all income on tax returns. Use a 0.01

significance level to test the claim that 75% of adults say that it is morally wrong to not report all income on tax returns.
Mathematics
1 answer:
Marianna [84]3 years ago
4 0

Answer:

The calculated value Z = 2.734 > 2.576 at 0.01 level of significance

Null hypothesis is rejected

The claim that 75% of adults say that it is morally wrong to report all income on tax returns.

Step-by-step explanation:

<u><em>Step(i):-</em></u>

Given random sample size 'n' = 745

Given In a poll of 745 randomly selected adults, 591 said that it is morally wrong to not report all income on tax returns

sample proportion

   p^{-}  = \frac{x}{n} = \frac{591}{745} = 0.7932

Given Population proportion P = 0.75

                                           Q = 1-P

                                          Q = 1 - 0.75 = 0.25

Null Hypothesis :H₀: P=0.75

Alternative Hypothesis :H₁:P≠ 0.75

<u><em>Step(ii):-</em></u>

<em>Test statistic</em>

                          Z = \frac{p^{-}-P }{\sqrt{\frac{P Q}{n} } }

                          Z = \frac{0.7932-0.75 }{\sqrt{\frac{0.75 X 0.25}{745} } }

                          Z  = 2.734

Level of significance ∝ = 0.01

The critical value Z =2.576

The calculated value Z = 2.734 > 2.576 at 0.01 level of significance

Null hypothesis is rejected

The claim that 75% of adults say that it is morally wrong to report all income on tax returns.

                       

                         

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Step-by-step explanation:

If the equation is :

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Keep the terms you need the way they are, and move the terms you need on the other side of equal sign with changed sign.

4.2(9 - x) + 36 = 102 -2.5(2x + 2)

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In a given year, the average annual salary of a NFL football player was $189,000 with a standard deviation of $20,500. If a samp
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15.15% probability that the sample mean will be $192,000 or more.

Step-by-step explanation:

To solve this problem, 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 random variable X, with mean \mu and standard deviation \sigma, a large sample size can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}

In this problem, we have that:

\mu = 189000, \sigma = 20500, n = 50, s = \frac{20500}{\sqrt{50}} = 2899.14

The probability that the sample mean will be $192,000 or more is

This is 1 subtracted by the pvalue of z when X = 192000. So

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

By the Central Limit Theorem

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

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Z = 1.03 has a pvalue of 0.8485.

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