A z-score can be defined as how many standard deviations away a value X from the mean. If a value is exactly an average, z-score will be 0. Values above the mean have positive z-scores whereas values below the mean have negative z-scores.

The **Z score** is computed as:

$$ Z = \frac{(X-\mu)}{\sigma} $$

Here*μ*is the Population mean and

*σ*is the population standard deviation.

*X*is called as raw score.

Solution

$$ Z = \frac{(X-\mu)}{\sigma} $$

## Assumptions of Z Score

**Normal Distribution**: Z score assumes that the data is normally distributed.**Independence**: Z score assumes that the data points are independent of each other. If the data points are not independent, the Z-score may not accurately show the variation within the data.**Sample Size**: The sample size must be sufficiently large to provide a representative sample of the population.**Continuous Data**: The data must be measured on a continuous scale. If the data is measured on a categorical or ordinal scale, the Z-score may not be appropriate.**Outliers**: The Z-score assumes that the data does not contain outliers or extreme values.

## Examples of Z Score

**Example 1:** Suppose a student scored 75 on a test with a mean of 65 and a standard deviation of 5. The Z-score for this student would be:

Z = (75 - 65) / 5 = **2**

**Example 2:** Suppose the heights of a group of people are normally distributed with a mean of 68 inches and a standard deviation of 3 inches. A person who is 71 inches tall would have a Z-score of:

Z = (71 - 68) / 3 = **1**

**Example 3:** Suppose a company's sales are normally distributed with a mean of $50,000 and a standard deviation of $10,000. If the company had sales of $75,000 in a given month, the Z-score for that month's sales would be:

Z = (75,000 - 50,000) / 10,000 = **2.5**

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