Salary Variance Analysis

intermediate

HR analyzes salary spread to identify pay equity issues

SELECT

department,

COUNT(*) AS employee_count,

AVG(salary) AS avg_salary,

STDDEV(salary) AS salary_std_dev,

VARIANCE(salary) AS salary_variance

FROM employees

GROUP BY department

ORDER BY salary_std_dev DESC;

department | employee_count | avg_salary | salary_std_dev | salary_variance

Engineering | 45 | 95000 | 18500 | 342250000

Sales | 30 | 75000 | 8200 | 67240000

High standard deviation indicates wide salary spread, potential pay equity issues. Engineering has more variance than Sales.

PostgreSQL

Percentile Analysis

advanced

Calculate salary quartiles for compensation benchmarking

SELECT

department,

PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY salary) AS p25_salary,

PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY salary) AS median_salary,

PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY salary) AS p75_salary,

AVG(salary) AS avg_salary

FROM employees

GROUP BY department;

department | p25_salary | median_salary | p75_salary | avg_salary

Engineering | 82000 | 95000 | 110000 | 95000

Sales | 68000 | 75000 | 82000 | 75000

Percentiles show salary distribution. Median less affected by outliers than average. 25th-75th percentile shows middle 50% range.

PostgreSQL

Quality Control with Standard Deviation

intermediate

Manufacturing analyzes product weight variance for quality control

SELECT

product_line,

COUNT(*) AS units_produced,

AVG(weight_grams) AS avg_weight,