Standardization Calculator
Standardize a value by subtracting the mean and dividing by the standard deviation (z-score standardization).
Standardized Value (z)
1.500000
Standardized Value (z) vs Value to Standardize
Formula
## How to Standardize Data (Z-Score) ### Formula **Z = (X - Mean) / SD** Standardization (z-score transformation) converts data to have mean 0 and standard deviation 1. Each standardized value tells you how many standard deviations the original value is from the mean. This is the most common preprocessing step in statistics and machine learning.
Esempio Risolto
Dataset has mean 60 and SD 10. Standardize the value 75.
- 01Z = (75 - 60) / 10 = 15 / 10 = 1.5
- 0275 is 1.5 standard deviations above the mean
- 03After standardization, the value is 1.5
Domande Frequenti
What is the difference between standardization and normalization?
Standardization (z-score) centers data at 0 with SD 1; values are unbounded. Normalization (min-max) scales to [0,1]. Standardization is preferred when data is normally distributed; normalization when bounded values are needed.
Does standardization make data normal?
No. Standardization shifts and scales data but does not change the distribution shape. Skewed data remains skewed after standardization. For normality, you need transformations like log or Box-Cox.
Why is standardization used in machine learning?
Many algorithms (SVM, k-NN, gradient descent) are sensitive to feature scales. Standardization ensures all features contribute equally, preventing features with larger ranges from dominating the model.
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