Search results
Results from the WOW.Com Content Network
The formula for the difference of two squares can be used for factoring polynomials that contain the square of a first quantity minus the square of a second quantity. For example, the polynomial x 4 − 1 {\displaystyle x^{4}-1} can be factored as follows:
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 ...
Describe the differences in proportions using the rule of thumb criteria set out by Cohen. [1] Namely, h = 0.2 is a "small" difference, h = 0.5 is a "medium" difference, and h = 0.8 is a "large" difference. [2] [3] Only discuss differences that have h greater than some threshold value, such as 0.2. [4]
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...
It is the mean divided by the standard deviation of a difference between two random values each from one of two groups. It was initially proposed for quality control [1] and hit selection [2] in high-throughput screening (HTS) and has become a statistical parameter measuring effect sizes for the comparison of any two groups with random values. [3]
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.
If the mean =, the first factor is 1, and the Fourier transform is, apart from a constant factor, a normal density on the frequency domain, with mean 0 and variance /. In particular, the standard normal distribution φ {\textstyle \varphi } is an eigenfunction of the Fourier transform.
The values B n (1) are the Bernoulli numbers B n. Notice that for n ≠ 1 we have = = (), and for n = 1, = = (). The functions P n agree with the Bernoulli polynomials on the interval [0, 1] and are periodic with period 1. Furthermore, except when n = 1, they are also continuous.