Search results
Results from the WOW.Com Content Network
X-bar chart. In industrial statistics, the X-bar chart is a type of variable control chart [1] that is used to monitor the arithmetic means of successive samples of constant size, n. This type of control chart is used for characteristics that can be measured on a continuous scale, such as weight, temperature, thickness etc. For example, one ...
x̅. and s chart. In statistical quality control, the and s chart is a type of control chart used to monitor variables data when samples are collected at regular intervals from a business or industrial process. [1] This is connected to traditional statistical quality control (SQC) and statistical process control (SPC).
In probability theory and statistics, the exponential distribution or negative exponential distribution is the probability distribution of the distance between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate; the distance parameter could be any meaningful mono-dimensional measure of the process, such as time ...
x̅ and R chart. x̅. and R chart. In statistical process control (SPC), the and R chart is a type of scheme, popularly known as control chart, used to monitor the mean and range of a normally distributed variables simultaneously, when samples are collected at regular intervals from a business or industrial process. [1]
Mean. A mean is a numeric quantity representing the "center" of a collection of numbers and is intermediate to the extreme values of the set of numbers. [1] There are several kinds of means (or "measures of central tendency ") in mathematics, especially in statistics.
Standard normal table. In statistics, a standard normal table, also called the unit normal table or Z table, [1] is a mathematical table for the values of Φ, the cumulative distribution function of the normal distribution. It is used to find the probability that a statistic is observed below, above, or between values on the standard normal ...
Therefore, a naïve algorithm to calculate the estimated variance is given by the following: Let n ← 0, Sum ← 0, SumSq ← 0. For each datum x: n ← n + 1. Sum ← Sum + x. SumSq ← SumSq + x × x. Var = (SumSq − (Sum × Sum) / n) / (n − 1) This algorithm can easily be adapted to compute the variance of a finite population: simply ...
where s x 2 and s y 2 are the variances of the x and y variates respectively, m x and m y are the means of the x and y variates respectively and s xy is the covariance of x and y. Although the approximate variance estimator of the ratio given below is biased, if the sample size is large, the bias in this estimator is negligible.