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Typically, to standardize variables, you calculate the mean and standard deviation for a variable. Then, for each observed value of the variable, you subtract the mean and divide by the standard deviation.
Standardized values (also called standard scores or normal deviates) are the same thing as z-scores. A standardized value is what you get when you take a data point and scale it by population data. It tells us how far from the mean we are in terms of standard deviations.
Subtract mean and divide by standard deviation: Center the data and change the units to standard deviations. Subtract mean: Center the data. Divide by standard deviation: Standardize the scale for each variable that you specify, so that you can compare them on a similar scale.
Subtract the mean,, from the value you want to convert, X. Divide the result from Step 1 by the standard deviation,.
Subtract mean and divide by standard deviation: Center the data and change the units to standard deviations. Subtract mean: Center the data. Divide by standard deviation: Standardize the scale for each variable that you specify, so that you can compare them on a similar scale.
We subtract off the mean of X, which is, and divide through by the standard deviation of X, which is n, to obtain a standardized version of the sample mean: X/n.
When you standardize a random variable, its mean becomes zero and its standard deviation becomes one. Therefore, if the Z score of x is zero, then the value of x is equal to the mean. If the Z score of x is one, then the value of x is one standard deviation above the mean.
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