This procedure creates a 2x2 table from 2 variables with dichotomous data, a test variable (the variable for which you want to obtain the test characteristics) and a classification variable (a …
How to calculate test characteristics such as sensitivity, specificity, positive and negative likelihood ratio, disease prevalence as well as positive and negative predictive power, from a …
ROC curve is a plot of sensitivity (the ability of the model to predict an event correctly) versus 1-specificity for the possible cut-off classification probability values π 0. For logistic regression …
Classification using this threshold point can be summarized in a 2X2 table, where the columns summarize the data with respect to true disease status and the row summarize data with …
Which of several candidate 2x2 tables -- each defined by a different cut-off for a positive test (e.g., >10, >15, or >20) -- should be used for which patients? Which strata raise pre-test probability …
ROC summary uses a confusion matrix, which is a 2x2 table that reports the outcomes of a test compared to their true state. This produces four values within the table : True positive and …
The Receiver Operating Characteristic (ROC) curve of a diagnostic test is a plot of test sensitivity (the probability of a \true" positive) against 1.0 minus test speci city (the probability of a \false" …
The Tests menu includes statistical tests on tabulated or summarized data. These tests are useful when you do not have the raw data available in the spreadsheet, e.g. when you want to …