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A method analogous to piece-wise linear approximation but using only arithmetic instead of algebraic equations, uses the multiplication tables in reverse: the square root of a number between 1 and 100 is between 1 and 10, so if we know 25 is a perfect square (5 × 5), and 36 is a perfect square (6 × 6), then the square root of a number greater than or equal to 25 but less than 36, begins with ...
If all 1,000 take the test, 82 of those with the disease will get a true positive result (sensitivity of 90.1%), 9 of those with the disease will get a false negative result (false negative rate of 9.9%), 827 of those without the disease will get a true negative result (specificity of 91.0%), and 82 of those without the disease will get a false ...
The specificity of the test is equal to 1 minus the false positive rate. In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false ...
Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a " gold standard test " which is assumed correct.
For example, the following algorithm is a direct implementation to compute the function A(x) = (x−1) / (exp(x−1) − 1) which is well-conditioned at 1.0, [nb 12] however it can be shown to be numerically unstable and lose up to half the significant digits carried by the arithmetic when computed near 1.0. [58]
For the example above, diatomic nitrogen (approximating air) at 300 K, = [note 2] and = % % /, the true value for air can be approximated by using the average molar weight of air (29 g/mol), yielding 347 m/s at 300 K (corrections for variable humidity are of the order of 0.1% to 0.6%).
In the case where X takes random values from a finite data set x 1, x 2, ... 1 / 10 1.959 964 σ: 95% 5% 1 / 20 2 ... s 1, s 2 can be used at any time to compute the ...
2–3 million 1–1.6% of Russian population [14] 1918–1922 Russia: 13 Cocoliztli epidemic of 1576: Cocoliztli 2–2.5 million 50% of Mexican population [12] 1576–1580 Mexico 14 1772–1773 Persian Plague: Bubonic plague 2 million – 1772–1773 Persia: 15 735–737 Japanese smallpox epidemic: Smallpox 2 million 33% of Japanese population ...