Data standardization processing method

What is data normalization (Normalization)

Scale the data proportionally so that fall into a small specific range. It is often used in some comparison and evaluation index processing to remove the unit restriction of the data and convert it into a dimensionless pure value, so that indicators of different units or magnitudes can be compared and weighted. (Recommended learning: web front-end video tutorial)

What are the common methods?

Method 1: Normalization method

#Also called deviation standardization, it is a linear transformation of the original data so that the result is mapped to [0, 1] interval.

Method 2: Regularization method

This method is based on the mean and standard deviation of the original data ) to standardize the data. Normalize the original value x of A to x’ using z-score.

The z-score standardization method is suitable for situations where the maximum and minimum values ​​of attribute A are unknown, or when there are outlier data that exceed the value range.

The default standardization method of spss is z-score standardization.

How to use Excel to standardize z-score: There is no ready-made function in Excel, so you need to calculate it step by step. In fact, the standardization formula is very simple.

The steps are as follows:

1. Find the arithmetic mean (mathematical expectation) xi and standard deviation si of each variable (indicator);

2. Perform standardization processing:

zij=（xij－xi）/si

where: zij is the standardized variable value; xij is the actual variable value.

3. Reverse the positive and negative signs before the inverse indicator.

The standardized variable value fluctuates around 0. If it is greater than 0, it means it is above the average level, and if it is less than 0, it means it is below the average level.

Method 3: Normalization method

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