Search results
Results From The WOW.Com Content Network
Excel maintains 15 figures in its numbers, but they are not always accurate; mathematically, the bottom line should be the same as the top line, in 'fp-math' the step '1 + 1/9000' leads to a rounding up as the first bit of the 14 bit tail '10111000110010' of the mantissa falling off the table when adding 1 is a '1', this up-rounding is not undone when subtracting the 1 again, since there is no ...
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
The problem with using "less than" to define negative is that it is a circular definition. The definition of "less than", in most books, is that a < b if and only if a - b is negative. We need some idea of what a negative number means before we can understand how it is possible for anything to be less than zero, an idea that is counterintuitive.
In some applications and programming languages, notably Microsoft Excel, PlanMaker (and other spreadsheet applications) and the programming language bc, unary operations have a higher priority than binary operations, that is, the unary minus has higher precedence than exponentiation, so in those languages −3 2 will be interpreted as (−3) 2 ...
The negative predictive value is defined as: = + = where a "true negative" is the event that the test makes a negative prediction, and the subject has a negative result under the gold standard, and a "false negative" is the event that the test makes a negative prediction, and the subject has a positive result under the gold standard.
The #ifeq function selects one of two alternatives based on whether two test strings are equal to each other. {{#ifeq: string 1 | string 2 | value if equal | value if not equal}} If both strings are valid numerical values, they are compared as numbers, rather than as literal strings: {{#ifeq: 01 | 1 | equal | not equal }} → equal
In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).
Negative result may refer to: Proof of impossibility, a proof that a particular problem cannot be solved; Null result, a result which shows no evidence of the intended effect; Null hypothesis, a hypothesis that there is no relationship between two measured phenomena