SUMMARY
The discussion centers on determining a third number, \( y \), to be added to the set \{0, 2\} such that the variance remains unchanged when calculated using the formula for population variance, \( \sigma^2 = \frac{1}{n} \sum (x_i - \mu)^2 \). The initial variance is calculated as \( \sigma^2 = 1 \). The equations derived include \( \mu = \frac{1}{3}(0 + 2 + y) \) and \( \sigma^2 = \frac{1}{3}[(0 - \mu)^2 + (2 - \mu)^2 + (y - \mu)^2] = 1 \). This leads to a quadratic equation in \( y \), highlighting the need for the expected mean \( \mu \) to avoid losing a degree of freedom.
PREREQUISITES
- Understanding of population variance calculation
- Familiarity with quadratic equations
- Knowledge of mean and its significance in statistics
- Basic algebra skills for solving equations
NEXT STEPS
- Study the derivation of population variance formulas
- Learn how to solve quadratic equations effectively
- Explore the implications of degrees of freedom in statistical calculations
- Investigate the differences between population and sample variance
USEFUL FOR
Students, statisticians, and data analysts who are interested in understanding variance calculations and the effects of adding data points to a dataset.