1. **Stating the problem:** We want to understand the concepts of variance and coefficient of variance, which are important statistical measures. 2. **Variance:** Variance measures how much the data points in a set differ from the mean (average) of the data. It tells us about the spread or dispersion of the data. The formula for variance $\sigma^2$ of a population is: $$\sigma^2 = \frac{1}{N} \sum_{i=1}^N (x_i - \mu)^2$$ where $N$ is the number of data points, $x_i$ are the data points, and $\mu$ is the mean. For a sample variance $s^2$, the formula is: $$s^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2$$ where $n$ is the sample size and $\bar{x}$ is the sample mean. 3. **Explanation:** Variance calculates the average of the squared differences from the mean. Squaring ensures all differences are positive and emphasizes larger deviations. 4. **Coefficient of Variance (CV):** CV is a normalized measure of dispersion, expressed as a percentage. It allows comparison of variability between datasets with different units or means. The formula for CV is: $$\text{CV} = \frac{\sigma}{\mu} \times 100\%$$ where $\sigma$ is the standard deviation (square root of variance) and $\mu$ is the mean. 5. **Interpretation:** A higher CV means more variability relative to the mean, while a lower CV means less variability. 6. **Summary:** Variance quantifies spread in squared units, while coefficient of variance expresses spread relative to the mean, making it unitless and comparable across datasets.