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just olya [345]
2 years ago
9

You get GPS units from two manufacturers, A and B. You get 43% of your units from A and 57% of your units from B. In the past, 2

% of the units from A have been defective, and 1.5% of the units from B have been defective. Assuming this holds true, if a GPS unit is found to be defective what is the probability that it came from manufacturer A (think Bayes Theorem AND round to two decimal places)
Mathematics
1 answer:
defon2 years ago
4 0

Answer:

0.5015 = 50.15% probability that it came from manufacturer A.

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: Defective

Event B: From manufacturer A.

Probability a unit is defective:

2% of 43%(from manufacturer A)

1.5% of 57%(from manufacturer B). So

P(A) = 0.02*0.43 + 0.015*0.57 = 0.01715

Probability a unit is defective and from manufacturer A:

2% of 43%. So

P(A \cap B) = 0.02*0.43 = 0.0086

What is the probability that it came from manufacturer A?

P(B|A) = \frac{P(A \cap B)}{P(A)} = \frac{0.0086}{0.01715} = 0.5015

0.5015 = 50.15% probability that it came from manufacturer A.

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A machine that fills beverage cans is supposed to put 12 ounces of beverage in each can. The standard deviation of the amount in
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Now we can calculate the p value using the alternative hypothesis:

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Since the p value is higher than the signficance level assumed of 0.05 we have enough evidence to FAIL to reject the null hypothesis and there is no evidence to conclude that the true deviation differs from 0.12 ounces

Step-by-step explanation:

Assuming the following data:"12.14 12.05 12.27 11.89 12.06

12.14 12.05 12.38 11.92 12.14"

We can calculate the sample deviation with this formula:

s =\sqrt{\frac{\sum_{i=1}^n (X_i -\bar X)^2}{n-1}}

n=10 represent the sample size

\alpha=0.05 represent the confidence level  

s^2 =0.0217 represent the sample variance

\sigma^2_0 =0.12^2= 0.0144 represent the value to verify

Null and alternative hypothesis

We want to determine whether the standard deviation differs from 0.12 ounce, so the system of hypothesis would be:

Null Hypothesis: \sigma^2 = 0.0144

Alternative hypothesis: \sigma^2 \neq 0.0144

The statistic can be calculated like this;

\chi^2 =\frac{n-1}{\sigma^2_0} s^2

\chi^2 =\frac{10-1}{0.0144} 0.0217 =13.54

The degrees of freedom are:

df=n-1=10-1=9

Now we can calculate the p value using the alternative hypothesis:

p_v =2*P(\chi^2 >13.54)=0.279

Since the p value is higher than the signficance level assumed of 0.05 we have enough evidence to FAIL to reject the null hypothesis and there is no evidence to conclude that the true deviation differs from 0.12 ounces

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3 years ago
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