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Margaret [11]
2 years ago
13

A machine produces small cans that are used for baked beans. The probabil- ity that the can is in perfect shape is 0.9. The prob

ability of the can having an unnoticeable dent is 0.02. The probability that the can is obviously dented is 0.08. Produced cans get passed through an automatic inspection machine, which is able to detect obviously dented cans and discard them. What is the probability that a can that gets shipped for use will be of perfect shape?
Mathematics
1 answer:
Elis [28]2 years ago
8 0

Answer:

0.9783 = 97.83% probability that a can that gets shipped for use will be of perfect shape

Step-by-step explanation:

Conditional Probability

We use the conditional probability formula to solve this question. It is

P(B|A) = \frac{P(A \cap B)}{P(A)}

In which

P(B|A) is the probability of event B happening, given that A happened.

P(A \cap B) is the probability of both A and B happening.

P(A) is the probability of A happening.

In this question:

Event A: Shipped for use

Event B: Perfect shape

Probability of being shipped for use:

Perfect shape(0.9 probability) or unnoticeable dent(0.02 probability). So

P(A) = 0.9 + 0.02 = 0.92

Being shipped for use and being in perfect shape.

0.9 probability, so P(A \cap B) = 0.9

What is the probability that a can that gets shipped for use will be of perfect shape?

P(B|A) = \frac{P(A \cap B)}{P(A)} = \frac{0.9}{0.92} = 0.9783

0.9783 = 97.83% probability that a can that gets shipped for use will be of perfect shape

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FV=PV(1+r)ⁿ

FV=Future Value, or the $2,600 that you want.
PV=Present Value, or the value that you are depositing.
APR: .06

2,600=2,000(1.06)ⁿ
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It would take about a year for Nicole to get $2600.

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3 years ago
The mean life of a television set is 119119 months with a standard deviation of 1414 months. If a sample of 7474 televisions is
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Answer:

0.5034 = 50.34% probability that the sample mean would differ from the true mean by less than 1.1 months

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 normally distributed samples can be 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, the sample means with size n of at least 30 can be approximated to a normal distribution with mean

In this problem, we have that:

\mu = 119, \sigma = 14, n = 74, s = \frac{14}{\sqrt{74}} = 1.6275

What is the probability that the sample mean would differ from the true mean by less than 1.11 months?

This is the pvalue of Z when X = 119 + 1.1 = 120.1 subtracted by the pvalue of Z when X = 119 - 1.1 = 117.9. So

X = 120.1

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By the Central Limit Theorem

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Z = -0.68

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0.7517 - 0.2483 = 0.5034

0.5034 = 50.34% probability that the sample mean would differ from the true mean by less than 1.1 months

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