Standardization is a technique in statistics that uses the same scale to compare different variables (“Data Standardization,” n.d.). The variables are standardized by calculating the mean and standard deviation for the variables. Standardization of variables involves calculating the mean and the standard deviation for the variables. Every value of the variables observed is then subtracted from the mean and divided by the standard deviation. The result of this calculation produces the standard value or what is also referred to as the Z-score.
The value of data is determined by comparing it to some useful information or data. In regression analysis, Independent variables need to be standardized when models have interaction terms. The interaction terms produces high amounts of Independent variables that are connected and also generates important information about the relationship between the dependent and Independent variables. Therefore, standardizing independent variables can help determine which variable is the most important and also reduces multicollinearity that is produced by terms that are of high order (Gal & Rubinfeld, 2019).
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Z- Scores are also referred to as standard scores. This is very important in statistics because it helps in comparing two values of different normal distributions and also enables the calculation of probabilities of values that occur within normal distributions (McLeod, 2019). A Z-value is used in statistical standardization where, the standard deviation is divided by the value deviation. It also shows the standard deviation numbers between the provided data point and the mean. Standard scores are either positive or negative. A negative value shows that the mean is higher than the score while a positive value describes a mean that is lower than the score. Zero is the average score of every standard score data set.
References
Data Standardization. (n.d.). Retrieved October 10, 2020, from https://ohdsi.org/data-standardization/
Gal, M. S., & Rubinfeld, D. L. (2019). Data standardization. NYUL Rev., 94, 737. https://www.nyulawreview.org/wp-content/uploads/2019/10/NYULAWREVIEW-94-4-GalRubinfeld-1.pdf
McLeod, S. (2019, May 17). Z-Score: Definition, Calculation and Interpretation. Retrieved October 10, 2020, from https://www.simplypsychology.org/z-score.html
