Standardized variables, also known as z-scores, are a way to transform data so that it has a mean of 0 and a standard deviation of 1. This process is called standardization and is particularly useful when comparing data that have different units or scales.
To standardize a variable, you subtract the mean of the data from each individual data point and then divide by the standard deviation. The formula for standardization is:
z = (X – μ) / σ
Where:
- z is the standardized score.
- X is the individual data point.
- μ is the mean of the data.
- σ is the standard deviation of the data.
Standardized variables are commonly used in statistical analyses, such as regression analysis, to ensure that the variables are on the same scale. This makes it easier to interpret the results and compare the effects of different variables.
For example, if you have two variables, one measured in kilograms and the other in meters, standardizing these variables allows you to compare their relative contributions to a model without being influenced by their original units.
In summary, standardized variables help in making data more comparable and interpretable, especially when dealing with different units or scales.