The data represent the results for a test for a certain disease. Assume one individual from the group is randomly selected. Find the probability of getting someone who tests positive, given that he or she had the disease. The individual actually had the disease Yes 130 23 No Positive Negative 13 134 The probability is approximately (Round to three decimal places as needed.)

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### Disease Test Results Analysis

In this section, we will analyze the results of a test for a certain disease. The data provided will help us determine the probability of randomly selecting an individual who tests positive for this disease, given that he or she actually has the disease.

#### Dataset
The data below shows results for individuals who tested either positive or negative for the disease and whether they actually had the disease.

|                          | The individual actually had the disease |
|--------------------------|----------------------------------------|
|                          | Yes                                    | No                    |
| **Positive**             | 130                                    | 13                    |
| **Negative**             | 23                                     | 134                   |

#### Analysis

To find the probability that an individual who tests positive actually has the disease, we need to use the concept of conditional probability. The conditional probability is given by the formula:

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

Where:
- \( P(A|B) \) is the probability of event A occurring given that event B has occurred.
- \( P(A \cap B) \) is the probability of both events A and B occurring.
- \( P(B) \) is the probability of event B occurring.

In our context:
- Event A is "the individual actually has the disease."
- Event B is "the individual tests positive."

From the table:
- \( P(A \cap B) \) = Number of individuals who tested positive and actually had the disease = 130.
- \( P(B) \) = Total number of individuals who tested positive = 130 (Yes) + 13 (No) = 143.

Therefore, the probability is:

\[ P(\text{Has disease | Positive}) = \frac{130}{143} \approx 0.909 \]

#### Conclusion

The probability of selecting an individual at random who tests positive and actually has the disease is approximately **0.909** (rounded to three decimal places). This means that there is about a 90.9% chance that an individual who tests positive truly has the disease.
Transcribed Image Text:### Disease Test Results Analysis In this section, we will analyze the results of a test for a certain disease. The data provided will help us determine the probability of randomly selecting an individual who tests positive for this disease, given that he or she actually has the disease. #### Dataset The data below shows results for individuals who tested either positive or negative for the disease and whether they actually had the disease. | | The individual actually had the disease | |--------------------------|----------------------------------------| | | Yes | No | | **Positive** | 130 | 13 | | **Negative** | 23 | 134 | #### Analysis To find the probability that an individual who tests positive actually has the disease, we need to use the concept of conditional probability. The conditional probability is given by the formula: \[ P(A|B) = \frac{P(A \cap B)}{P(B)} \] Where: - \( P(A|B) \) is the probability of event A occurring given that event B has occurred. - \( P(A \cap B) \) is the probability of both events A and B occurring. - \( P(B) \) is the probability of event B occurring. In our context: - Event A is "the individual actually has the disease." - Event B is "the individual tests positive." From the table: - \( P(A \cap B) \) = Number of individuals who tested positive and actually had the disease = 130. - \( P(B) \) = Total number of individuals who tested positive = 130 (Yes) + 13 (No) = 143. Therefore, the probability is: \[ P(\text{Has disease | Positive}) = \frac{130}{143} \approx 0.909 \] #### Conclusion The probability of selecting an individual at random who tests positive and actually has the disease is approximately **0.909** (rounded to three decimal places). This means that there is about a 90.9% chance that an individual who tests positive truly has the disease.
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