Decentralization of interaction variables

1. According to Hou Jietai: the so-called centralization refers to subtracting the mean value of a variable from its expected value. For sample data, each observation value of a variable is subtracted from the sample average value of the variable, and the transformed variable is centralized
for your question, subtract the mean from each measurement.

2. Centralization is to subtract the mean and Z-score is to divide it by the standard deviation. Both of them are centralization methods.

3. Not necessarily, centralization is only for the convenience of explanation, and does not affect the regression coefficients Central treatment of regulatory effect of South Heart Network

4. Suppose that CA is obtained by centralizing A. when CA is negative, it means that the value is less than the mean value. When CA is positive, it means that the value is greater than the mean value. In short, negative values also make sense.

5. 1. The dependent variable does not need to be centralized; 2. The first step is that the independent variable enters the regression equation; The second step is that independent variables and regulatory variables enter together; The third step is that the independent variable, regulatory variable and interaction item enter together;

6. After centralizing the independent variable and the adjusting variable, a new variable (that is, the proct of interaction terms) is obtained by multiplying the centralized values, and then put into regression
there are several methods of centralization. Here are the two most commonly used, one is to subtract the average value, and the other is Z-score
subtract the average value: first perform a description statistics to get the descriptive statistical results, with the average and standard deviation. Then use the compute command to create a new variable = original variable average
the Z-score is similar to the above result, except that the new variable is divided by the standard deviation to get a score.