You take your x-value, subtract the mean , and then divide this difference by the standard deviation. This gives you the corresponding standard score (z-value or z-score).

z = (x – μ) / (σ / √n) This z-score will tell you how many standard errors there are between the sample mean and the population mean.

Z Score for sample proportion: z = (p̄ – p) / SE.

To find a z score, subtract the mean of a population from the particular value in question, then divide the result by the population’s standard deviation.

The formula for calculating a z-score is is z = (x-μ)/σ, where x is the raw score, μ is the population mean, and σ is the population standard deviation.

In other words, the process of transforming X values into z-scores serves two useful purposes: – Each z-score tells the exact location of the original X value within the distribution.

Use the following format to find a z-score: z = X – μ / σ. This formula allows you to calculate a z-score for any data point in your sample. Remember, a z-score is a measure of how many standard deviations a data point is away from the mean. In the formula X represents the figure you want to examine.

The formula for calculating a z-score is is z = (x-μ)/σ, where x is the raw score, μ is the population mean, and σ is the population standard deviation. As the formula shows, the z-score is simply the raw score minus the population mean, divided by the population standard deviation.

Solving for the data value, x, gives the formula x = z*sigma + mu. So the data value equals the z-score times the standard deviation, plus the mean.

The equation for z-score of a data point is calculated by subtracting the population mean from the data point (referred to as x) and then the result is divided by the population standard deviation. Mathematically, it is represented as, Z Score Formula = (x – μ) / ơ.

When you calculate a z-score you are converting a raw data value to a standardized score on a standardized normal distribution. The z-score allows you to compare data from different samples because z-scores are in terms of standard deviations.

The formula for calculating a z-score is is z = (x-μ)/σ, where x is the raw score, μ is the population mean, and σ is the population standard deviation. As the formula shows, the z-score is simply the raw score minus the population mean, divided by the population standard deviation.

Using the z score, as well as the mean and the standard deviation, we can compute the raw score value by the formula, x= µ + Zσ, where µ equals the mean, Z equals the z score, and σ equals the standard deviation.