The data represents 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 negative given that she or he has the disease  the probability is approximately

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The data represents 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 negative given that she or he has the disease 

the probability is approximately 

**Confusion Matrix for Disease Diagnosis Test Results**

This table displays the actual condition of individuals regarding the presence of a disease versus the test results. It is an example of a confusion matrix commonly used in evaluating diagnostic tests.

|                           | The Individual Actually Had the Disease |
|---------------------------|----------------------------------------|
|                           | **Yes**                                | **No**                             |
| **Test Result**           |                                        |                                    |
| **Positive**              | 144                                    | 11                                 |
| **Negative**              | 7                                      | 138                                |

**Explanation:**

- **True Positives (TP):** 144 cases where the test correctly identified the disease.
- **False Positives (FP):** 11 cases where the test incorrectly indicated the presence of the disease.
- **False Negatives (FN):** 7 cases where the test failed to detect the disease.
- **True Negatives (TN):** 138 cases where the test correctly identified the absence of the disease.

This matrix helps in calculating important metrics such as sensitivity, specificity, accuracy, and predictive values, which are crucial for understanding the performance of the diagnostic test.
Transcribed Image Text:**Confusion Matrix for Disease Diagnosis Test Results** This table displays the actual condition of individuals regarding the presence of a disease versus the test results. It is an example of a confusion matrix commonly used in evaluating diagnostic tests. | | The Individual Actually Had the Disease | |---------------------------|----------------------------------------| | | **Yes** | **No** | | **Test Result** | | | | **Positive** | 144 | 11 | | **Negative** | 7 | 138 | **Explanation:** - **True Positives (TP):** 144 cases where the test correctly identified the disease. - **False Positives (FP):** 11 cases where the test incorrectly indicated the presence of the disease. - **False Negatives (FN):** 7 cases where the test failed to detect the disease. - **True Negatives (TN):** 138 cases where the test correctly identified the absence of the disease. This matrix helps in calculating important metrics such as sensitivity, specificity, accuracy, and predictive values, which are crucial for understanding the performance of the diagnostic test.
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