Question

This technique uses mean and standard deviation scores to transform real-valued attributes.

a) decimal scaling
b) min-max normalization
c) z-score normalization
d) logarithmic normalization

Answer 1

Z-score normalization is the technique that uses mean and standard deviation to transform attributes, calculated as z = (x - μ) / σ. It is utilized to standardize different datasets.

Step-by-step explanation:

The technique that uses mean and standard deviation scores to transform real-valued attributes is called z-score normalization (also known as standardization). This method re-scales data to have a mean of 0 and a standard deviation of 1. The formula for calculating a z-score is z = (x - μ) / σ, where x is the raw score, μ is the mean of the distribution, and σ is the standard deviation. If we apply this formula to Susan's final exam score in a biology class (with a mean of 85 and a standard deviation of 5), her z-score would be (95 - 85) / 5, which equals 2. This indicates that Susan's score is 2 standard deviations above the mean.