medical tests. Task Compute the requested probabilities using the contingency table. A group of 7500 individuals take part in a survey about a rare disease and medical test. The data for the group is given in the table below. A person listed as "Sick" has the disease, while someone listed as "Not Sick does not "Tested Positive means the test indicated the person was sick, and "Tested Negative" means the test indicated they were not sick. The test is believed to be around 99% accurate. Sick Not Sick Tested Positive 59 75 Tested Negative 1 7365 h One person at random is chosen from the group (3 pts) Find the probability the person is sick. b. (3 pts) Find the probability the test is positive given that the person is sick (3 pts) Find the probability the test is negative given that the person is not sick. d. (pts) Find the probability the person is sick given that the test is positive (3 pts) Find the probability the person is not sick given that the test is negative. f. (5 pts) When we say a test is 99% accurate, to what is that referring? Given vour calculations, should information other than a single test be considered when diagnosing a rare condition? If so, what other information might be considered? Criteria for Success

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

(a) Probability that a person is sick

From the table, we have:

[tex]\mathbf{Sick = 59+1 = 60}[/tex]

So, the probability that a person is sick is:

[tex]\mathbf{Pr = \frac{Sick}{Total}}[/tex]

This gives

[tex]\mathbf{Pr = \frac{60}{7500}}[/tex]

[tex]\mathbf{Pr = 0.008}[/tex]

The probability that a person is sick is: 0.008

(b) Probability that a test is positive, given that the person is sick

From the table, we have:

[tex]\mathbf{Positive\ and\ Sick=59}[/tex]

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

[tex]\mathbf{Pr = \frac{Positive\ and\ Sick}{Sick}}[/tex]

This gives

[tex]\mathbf{Pr = \frac{59}{60}}[/tex]

[tex]\mathbf{Pr = 0.9833}[/tex]

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

(c) Probability that a test is negative, given that the person is not sick

From the table, we have:

[tex]\mathbf{Negative\ and\ Not\ Sick=7365}[/tex]

[tex]\mathbf{Not\ Sick = 75 + 7365 = 7440}[/tex]

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

[tex]\mathbf{Pr = \frac{Negative\ and\ Not\ Sick}{Not\ Sick}}[/tex]

This gives

[tex]\mathbf{Pr = \frac{7365}{7440}}[/tex]

[tex]\mathbf{Pr = 0.9899}[/tex]

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

(d) Probability that a person is sick, given that the test is positive

From the table, we have:

[tex]\mathbf{Positive\ and\ Sick=59}[/tex]

[tex]\mathbf{Positive=59 + 75 = 134}[/tex]

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

[tex]\mathbf{Pr = \frac{Positive\ and\ Sick}{Positive}}[/tex]

This gives

[tex]\mathbf{Pr = \frac{59}{134}}[/tex]

[tex]\mathbf{Pr = 0.4403}[/tex]

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

(e) Probability that a person is not sick, given that the test is negative

From the table, we have:

[tex]\mathbf{Negative\ and\ Not\ Sick=7365}[/tex]

[tex]\mathbf{Negative = 1+ 7365 = 7366}[/tex]

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

[tex]\mathbf{Pr = \frac{Negative\ and\ Not\ Sick}{Negative}}[/tex]

This gives

[tex]\mathbf{Pr = \frac{7365}{7366}}[/tex]

[tex]\mathbf{Pr = 0.9998}[/tex]

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

(f) When a test is 99% accurate

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.