The answer depends on the relationship between X and z. This may not be linear, in which case the conversion may need to take account of turning points.
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z=x-mean / sd
z = (x - mean of x)/ std dev of x I thought this website was pretty good: http://www.jrigol.com/Statistics/TandZStatistics.htm
You use the z-transformation.For any variable X, with mean m and standard error s,Z = (X - m)/s is distributed as N(0, 1).You use the z-transformation.For any variable X, with mean m and standard error s,Z = (X - m)/s is distributed as N(0, 1).You use the z-transformation.For any variable X, with mean m and standard error s,Z = (X - m)/s is distributed as N(0, 1).You use the z-transformation.For any variable X, with mean m and standard error s,Z = (X - m)/s is distributed as N(0, 1).
z = (x - μ) / σ is the formula where x is the raw score and z is the z-score. μ and σ are the mean and standard deviations and must be known numbers. Multiply both sides by σ zσ = x-μ Add μ to both sides μ + zσ = x x = μ + zσ You calculate the raw score x , given the z-score, μ and σ by using the above formula.
z = (x - u)/(standard dev)The z score expresses the difference of the experimental result x from the most probable result u as a number of standard deviations. The probability can then be calculated from the cumulative standard normal distribution. ie sigma(z)