Question: Case Study: Calculating the Accuracy of the Major Depression Inventory\\r\\n \\r\\nDepression Inventory\\r\\nThe same logic used in the previous example can be applied to multiple situations. Let’s consider the accuracy of a psychological test. According to a study by the National Institute of Mental Health (NIMH, 2019) approximately 7.1% of adults in the U.S. suffer from depression (by self-report, having had at least one major depressive episode during 2017).\\r\\nThe World Health Organization (WHO) has developed the Major Depression Inventory (MDI) to aid in the diagnosis of depression. It was found by Bech, et al. (2001) to have about 90% accuracy in identifying cases of depression among adults.\\r\\nLab 5 Assignment - Case Study: Calculating the Accuracy of the MD Inventory\\r\\nUse a computer program or an online Bayesian calculator to calculate the conditional probability of being clinically depressed given a positive test result. Report the results, including a copy or screenshot of the calculator or computer output.\\r\\nThere is a Bayes Theorem calculator free for use.\\r\\nPaste a screenshot of the output into a Word document.\\r\\nInclude a short paragraph describing the results and anything notable about them.\\r\\nGive the probability of a false positive and interpret that outcome.\\r\\nGive the probability of a false negative and interpret that outcome.\\r\\nIf you were a clinical psychologist, would you be comfortable using only a positive result on a single administration of the inventory to confirm a diagnosis of depression? Why or why not?\\r\\n  Edit