zvalue
(noun)Definition of zvalue
the standardized value of an observation found by subtracting the mean from the observed value, and then dividing that value by the standard deviation; also called zscore
Source: Boundless Learning  CC BYSA 3.0
Examples of zvalue in the following topics:

Using the Normal Curve
 The zvalue tells us how many standard deviations, or "how many sigmas," the variable is from its respective mean.Areas Under the CurveTo calculate the probability that a variable is within a range, we have to find the area under the curve.
 The intersection of the 6th row and 2nd column is 0.6950, which tells us that there is a 69.50% percent chance that a variable is less than 0.51 sigmas (or standard deviations) above the mean.A common mistake is to look up a zvalue in the table and simply report the corresponding entry, regardless of whether the problem asks for the area to the left or to the right of the zvalue.
 The table only gives the probabilities to the left of the zvalue.
 Since the total area under the curve is 1, all we need to do is subtract the value found in the table from 1.
 What if we wished instead to find out the probability that a value falls below a zvalue of 0.51, or 0.51 standard deviations below the mean?
 The normal curve is used to find the probability that a value falls within a certain standard deviation away from the mean.

Estimating a Population Proportion
 This is computed slightly differently, using the formula $\hat{p}\pm z^{*}\sqrt{\frac{\hat{p}(1\hat{p})}{n}}$, where z* is the upper critical value of the standard normal distribution.
 In the above example, if we wished to calculate p with a confidence of 95%, we would use a Zvalue of 1.960 (found using a critical value table: ), and we would find p to be estimated as 0.52$\pm$0.04896.

Change of Scale
 This conversion process is called standardizing or normalizing.Standard scores are also called zvalues, zscores, normal scores, and standardized variables.
 The use of "Z" is because the normal distribution is also known as the "Z distribution".
 They are most frequently used to compare a sample to a standard normal deviate (standard normal distribution, with μ = 0 and σ = 1).The zscore is only defined if one knows the population parameters.
 The absolute value of z represents the distance between the raw score and the population mean in units of the standard deviation. z is negative when the raw score is below the mean, positive when above.A key point is that calculating z requires the population mean and the population standard deviation, not the sample mean or sample deviation.
 In cases where it is impossible to measure every member of a population, a random sample may be used.The Z value measures the sigma distance of actual data from the average and provides an assessment of how offtarget a process is operating.