Article in English: 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. For example, if you wanted to find out how many standard deviations 7.5 was from the mean in our example of tree heights, you would plug in 7.5 for X in the equation. In the formula, μ stands for the mean. In our sample of tree heights the mean was 7.9. In the formula, σ stands for the standard deviation. In our sample of tree heights the standard deviation was 0.74. This will start out the calculations for a z-score.   For example, in our sample of tree heights we want to find out how many standard deviations 7.5 is from the mean of 7.9. Therefore, you would perform the following: 7.5 - 7.9. 7.5 - 7.9 = -0.4. Double check that you have the correct mean and subtraction figure before you proceed. This calculation will provide you with your z-score.   In our sample of tree heights, we want the z-score for the data point 7.5. We already subtracted the mean from 7.5, and came up with a figure of -0.4. Remember, the standard deviation from our sample of tree heights was 0.74. - 0.4 / 0.74 = - 0.54 Therefore the z-score in this case is -0.54. This z-score means that 7.5 is -0.54 standard deviations away from the mean in our sample of tree heights. Z-scores can be both positive and negative numbers. A negative z-score indicates that the data point is less than the mean, and a positive z-score indicates the data point in question is larger than the mean.

Summary in Indonesian:
Gunakan format ini untuk mencari z-score: Mulai perhitungan dengan mengurangi mean dari poin data yang ingin Anda uji. Bagi hasil pengurangan dengan standar deviasi.