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Geometrically, column standardization means squishing the data points such that the mean vector comes at origin and the variance(by either squishing or expanding) on any axes would be 1 in the transformed space. column standardization is often called mean centering and variance scaling(squishing/expanding).
Standardization is the process of developing, promoting and possibly mandating standards-based and compatible technologies and processes within a given industry. Standards for technologies can mandate the quality and consistency of technologies and ensure their compatibility, interoperability and safety.
From a business standpoint, standardization refers to the practice of maintaining some level of consistency in the product or service that a company provides. A company wants to produce a consistent product so consumers will know what they're buying. Say, for instance, a bakery produces loaves of wheat bread.
Standardization is the process of determining the exact concentration (molarity) of a solution. Titration is one type of analytical procedure often used in standardization. In a titration, an exact volume of one substance is reacted with a known amount of another substance.
Standardization refers to methods used in gathering and treating subjects for a specific study. In order to compare the results of one group to the results of a second group, we must assure that each group receives the same opportunities to succeed. Standardization of the research methods is often a lengthy process.
The terms normalization and standardization are sometimes used interchangeably, but they usually refer to different things. Normalization usually means to scale a variable to have a values between 0 and 1, while standardization transforms data to have a mean of zero and a standard deviation of 1.
Standardization: Standardizing the features around the center and 0 with a standard deviation of 1 is important when we compare measurements that have different units. Variables that are measured at different scales do not contribute equally to the analysis and might end up creating a bais.
In statistics, Standardization is the subtraction of the mean and then dividing by its standard deviation. In Algebra, Normalization is the process of dividing of a vector by its length and it transforms your data into a range between 0 and 1. Originally published at
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