Z-Score Transformation Formula
Question
While configuring Normalize data, you decide to apply the Zscore mathematical function from the Transformation method dropdown list to apply on the chosen columns.
From the given options, choose the right formula that is used to convert all values to a z-score.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: A
Zscore function converts all values for the chosen columns to Z-score.
The formula used to transform the values in a column is as given below:
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Option A is correct.
the given formula is the right formula for the Zscore function.
Option B is incorrect.
The given formula is for the MinMax function.
Option C is incorrect.
The given formula is for Logistic mathematical function.
Option D is incorrect.
The given formula is for the LogNormal function.
To know more about Normalizing Data Module, please visit the below-given link:
The correct formula to convert all values to a z-score is option A: z = (x - mean(x))/stdev(x).
Z-score is a statistical measure that represents the number of standard deviations an observation or data point is away from the mean of the population. It is a way of standardizing data to a common scale so that different data sets can be compared easily.
To calculate the z-score of a value, we first need to calculate the mean and standard deviation of the population. The mean is the average of all values in the population, and the standard deviation measures the amount of variation or spread of the data from the mean.
The formula for calculating the z-score is given by:
z = (x - mean(x))/stdev(x)
where x is the data point we want to standardize, mean(x) is the mean of the population, and stdev(x) is the standard deviation of the population.
In this formula, we subtract the mean from the value and then divide by the standard deviation. This gives us a standardized value that is expressed in terms of the number of standard deviations away from the mean. A positive z-score indicates that the value is above the mean, while a negative z-score indicates that the value is below the mean.
Option B is the formula for calculating the min-max normalization, which scales the data between 0 and 1 based on the minimum and maximum values in the population.
Option C is the formula for calculating the logistic function, which maps any input to a value between 0 and 1.
Option D is the formula for calculating the cumulative distribution function of a log-normal distribution, which is used to calculate the probability of a random variable being less than or equal to a given value.
