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makvit [3.9K]
2 years ago
6

In 1983, a microwave oven cost $78. 75. Today, a microwave oven costs $92. 66. If the CPI is 177, what is the relation of the ac

tual price of a microwave oven to the expected price, to the nearest cent? a. The actual price is $46. 73 lower than the expected price. B. The actual price is $5. 59 lower than the expected price. C. The actual price is $13. 91 higher than the expected price. D. The actual price is $71. 35 higher than the expected price.
Mathematics
1 answer:
mamaluj [8]2 years ago
3 0

The actual price is $46.73 lower than the expected price.

<h3>What is the Formula for Consumer Price Index?</h3>

CPI_t=\frac{C_t}{C_0} * 100

CPI=consumer price index in the current period

Ct= expected cost in current period

C₀ =cost in the base period

It is given that

Price in base period ie 1983 C₀= $78.75

The actual cost of the microwave oven = $92.66

Consumer Price Index= 177

So, expected price of microwave oven in the current period

Ct= CPIt * C₀ /100 = 177*78.75/100 = $139.39

Difference between the actual price and expected price = 139.39-92.66=$46.73

Therefore, The actual price is $46.73 lower than the expected price.

To get more about CPI visit:

brainly.com/question/5992428

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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.

Population:

We have that \mu = 7.19, \sigma = 0.86

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

By the Central Limit Theorem

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

Z = \frac{7 - 7.19}{0.2483}

Z = -0.765

Z = -0.765 has a pvalue of 0.222

1 - 0.222 = 0.778

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