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Daniel [21]
3 years ago
15

Find the difference. -6 2 1411 1-6 (-1)5 10.-9.18][ 3 14 ol -51]

Mathematics
1 answer:
scoray [572]3 years ago
8 0

Answer: This problem is all mixed up. There is no way to solve it with it like this.

Step-by-step explanation:

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Help asap!!!!!! answer 4 5 and 6 please thx TvT
Yuliya22 [10]

Answer:

4. 9

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Step-by-step explanation:

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What’s would be the answer to 95 plus 95
podryga [215]
90 + 90 = 180
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8 0
3 years ago
medical tests. Task Compute the requested probabilities using the contingency table. A group of 7500 individuals take part in a
uysha [10]

Probabilities are used to determine the chances of an event

  • The probability that a person is sick is: 0.008
  • The probability that a test is positive, given that the person is sick is 0.9833
  • The probability that a test is negative, given that the person is not sick is: 0.9899
  • The probability that a person is sick, given that the test is positive is: 0.4403
  • The probability that a person is not sick, given that the test is negative is: 0.9998
  • A 99% accurate test is a correct test

<u />

<u>(a) Probability that a person is sick</u>

From the table, we have:

\mathbf{Sick = 59+1 = 60}

So, the probability that a person is sick is:

\mathbf{Pr = \frac{Sick}{Total}}

This gives

\mathbf{Pr = \frac{60}{7500}}

\mathbf{Pr = 0.008}

The probability that a person is sick is: 0.008

<u>(b) Probability that a test is positive, given that the person is sick</u>

From the table, we have:

\mathbf{Positive\ and\ Sick=59}

So, the probability that a test is positive, given that the person is sick is:

\mathbf{Pr = \frac{Positive\ and\ Sick}{Sick}}

This gives

\mathbf{Pr = \frac{59}{60}}

\mathbf{Pr = 0.9833}

The probability that a test is positive, given that the person is sick is 0.9833

<u>(c) Probability that a test is negative, given that the person is not sick</u>

From the table, we have:

\mathbf{Negative\ and\ Not\ Sick=7365}

\mathbf{Not\ Sick = 75 + 7365 = 7440}

So, the probability that a test is negative, given that the person is not sick is:

\mathbf{Pr = \frac{Negative\ and\ Not\ Sick}{Not\ Sick}}

This gives

\mathbf{Pr = \frac{7365}{7440}}

\mathbf{Pr = 0.9899}

The probability that a test is negative, given that the person is not sick is: 0.9899

<u>(d) Probability that a person is sick, given that the test is positive</u>

From the table, we have:

\mathbf{Positive\ and\ Sick=59}

\mathbf{Positive=59 + 75 = 134}

So, the probability that a person is sick, given that the test is positive is:

\mathbf{Pr = \frac{Positive\ and\ Sick}{Positive}}

This gives

\mathbf{Pr = \frac{59}{134}}

\mathbf{Pr = 0.4403}

The probability that a person is sick, given that the test is positive is: 0.4403

<u>(e) Probability that a person is not sick, given that the test is negative</u>

From the table, we have:

\mathbf{Negative\ and\ Not\ Sick=7365}

\mathbf{Negative = 1+ 7365 = 7366}

So, the probability that a person is not sick, given that the test is negative is:

\mathbf{Pr = \frac{Negative\ and\ Not\ Sick}{Negative}}

This gives

\mathbf{Pr = \frac{7365}{7366}}

\mathbf{Pr = 0.9998}

The probability that a person is not sick, given that the test is negative is: 0.9998

<u>(f) When a test is 99% accurate</u>

The accuracy of test is the measure of its sensitivity, prevalence and specificity.

So, when a test is said to be 99% accurate, it means that the test is correct, and the result is usable; irrespective of whether the result is positive or negative.

Read more about probabilities at:

brainly.com/question/11234923

4 0
2 years ago
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