Shalini Shalini - Academia.edu (original) (raw)
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University of California, Berkeley
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Papers by Shalini Shalini
Journal of Laboratory Physicians
Discriminant function analysis is the statistical analysis used to analyze data when the dependen... more Discriminant function analysis is the statistical analysis used to analyze data when the dependent variable or outcome is categorical and independent variable or predictor variable is parametric. It is a parametric technique to determine which weightings of quantitative variables or predictors best discriminates between two or more than two categories of dependent variables and does so better than chance. Discriminant analysis is used to find out the accuracy of a given classification system in predicting the sample into a particular group. Discriminant analysis includes the development of discriminant functions for each sample and deriving a cutoff score that is used for classifying the samples into different groups. Discriminant function analysis is a statistical analysis used to find out the accuracy of a given classification system or predictor variables. This article explains the basic assumptions, uses, and necessary requirements of discriminant analysis with a real-life clini...
Journal of Laboratory Physicians, 2021
Diagnostic tests are pivotal in modern medicine due to their applications in statistical decision... more Diagnostic tests are pivotal in modern medicine due to their applications in statistical decision-making regarding confirming or ruling out the presence of a disease in patients. In this regard, sensitivity and specificity are two most important and widely utilized components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard test. Other diagnostic indices like positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, accuracy of a diagnostic test, and the effect of prevalence on various diagnostic indices have also been discussed. We have tried to present the performance of a classification model at all classification thresholds by reviewing the receiver operating characteristic (ROC) curve and the depiction of the tradeoff between sensitivity and (1–specificity) across a series of cutoff points when the diagnostic test is on a continuous scale. The area under the ROC (AUROC) and...
Journal of Laboratory Physicians
Discriminant function analysis is the statistical analysis used to analyze data when the dependen... more Discriminant function analysis is the statistical analysis used to analyze data when the dependent variable or outcome is categorical and independent variable or predictor variable is parametric. It is a parametric technique to determine which weightings of quantitative variables or predictors best discriminates between two or more than two categories of dependent variables and does so better than chance. Discriminant analysis is used to find out the accuracy of a given classification system in predicting the sample into a particular group. Discriminant analysis includes the development of discriminant functions for each sample and deriving a cutoff score that is used for classifying the samples into different groups. Discriminant function analysis is a statistical analysis used to find out the accuracy of a given classification system or predictor variables. This article explains the basic assumptions, uses, and necessary requirements of discriminant analysis with a real-life clini...
Journal of Laboratory Physicians, 2021
Diagnostic tests are pivotal in modern medicine due to their applications in statistical decision... more Diagnostic tests are pivotal in modern medicine due to their applications in statistical decision-making regarding confirming or ruling out the presence of a disease in patients. In this regard, sensitivity and specificity are two most important and widely utilized components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard test. Other diagnostic indices like positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, accuracy of a diagnostic test, and the effect of prevalence on various diagnostic indices have also been discussed. We have tried to present the performance of a classification model at all classification thresholds by reviewing the receiver operating characteristic (ROC) curve and the depiction of the tradeoff between sensitivity and (1–specificity) across a series of cutoff points when the diagnostic test is on a continuous scale. The area under the ROC (AUROC) and...