Generating variables is a data processing method used to calculate and preprocess a single variable or multiple variables. Generating variables can include simple mathematical operations such as addition, subtraction, multiplication, and division, as well as complex statistical methods and algorithms.

    This process aims to create new variables and provide richer and more useful information by manipulating and transforming the original data. By generating variables, we can extract deeper and more practical features from the original data, thereby better understanding and analyzing the data, and providing a reliable foundation for subsequent data analysis and modeling work.

    Whether in exploratory data analysis or machine learning model construction, generating variables is an important step that can greatly enhance the application value and interpretation ability of data.

In SPSSMAX, users can choose different generation variable methods to generate new data according to their own needs, name the newly generated data, and calculate it after clicking 'Analyze'. The new data will be stored in the original data.

    The following are various commonly used methods for generating variables:

    Sum: Sum is the operation of adding a set of numerical values to obtain the total. It can be used to calculate the sum of a column of data or to calculate the sum of multiple columns of data.

    Mean: The mean is the average of a set of values, which is the number of values divided by the sum of these values. It is used to measure the centralized trend of data.

    Multiply two columns: Multiply the numerical values at the corresponding positions of the two columns of data to obtain a new column of data. This operation is commonly used to calculate the interaction effect or derivative variable between two variables.

    Standardization: Standardization is the transformation of a set of data into a form with a mean of 0 and a standard deviation of 1. It can be done by subtracting the mean and dividing by the standard deviation.

    Centralization: Centralization is the process of subtracting the mean from a set of data, resulting in a mean of 0. It is commonly used to control variables or calculate the difference between variables and the mean.

    Reverse transformation: Reverse transformation refers to the transformation of a set of data in the opposite direction. For example, if the original data is a positive score (such as 5), the inverted data will become a negative score (such as 1/5).

    Virtual variables: Virtual variables are the process of converting a categorical variable into a set of binary variables. Each binary variable represents a value of the classification variable, with one variable being 1 indicating that the value exists and the other variables being 0 indicating that the value does not exist.

    Logarithmic: Logarithmic refers to the logarithmic operation based on a certain cardinality. The commonly used logarithms are natural logarithms (based on e) and common logarithms (based on 10). Logarithmic operations can convert exponential growth of data into linear growth, or be used to handle skewness or scaling issues of data.

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