Z-score Conversion 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:
Option A. z = (x - mean(x))/stdev(x) is the correct formula to convert all values to a z-score.
In statistics, a z-score (also known as a standard score) measures how many standard deviations a data point is from the mean of the distribution. It is a way of standardizing the data and making comparisons between different sets of data.
The formula for calculating the z-score involves subtracting the mean of the data from the individual data point and then dividing by the standard deviation of the data. This gives a value that represents how many standard deviations the data point is from the mean.
In option A, (x - mean(x))/stdev(x), x is the value of the data point, mean(x) is the mean of the distribution, and stdev(x) is the standard deviation of the distribution.
For example, suppose we have a dataset of exam scores with a mean of 70 and a standard deviation of 10. If a student scored 80 on the exam, the z-score would be (80-70)/10 = 1, indicating that the score is one standard deviation above the mean.
Option B. z = (x-min(x))/(max(x) - min(x)) is a formula for scaling data to a range of 0 to 1, commonly known as min-max normalization. This formula does not calculate z-scores.
Option C. z = 1/(1 + exp(-x)) is a sigmoid function used in logistic regression to predict binary outcomes. This formula does not calculate z-scores.
Option D. z = Lognormal.CDF(x;μ,σ) is the cumulative distribution function for the log-normal distribution, which is used to model positive continuous variables that are skewed to the right. This formula does not calculate z-scores.