1. Let's start by understanding what measures of dispersion are. Measures of dispersion describe the spread of data points in a dataset, indicating how much variability exists. 2. Common measures include range, variance, and standard deviation. - Range is the difference between the maximum and minimum data points. - Variance measures the average squared deviations from the mean. - Standard deviation is the square root of variance, showing dispersion in original units. 3. To solve problems involving measures of dispersion, first calculate the mean $\mu$ using: $$\mu = \frac{\sum x_i}{n}$$ where $x_i$ are data points and $n$ is the number of points. 4. Then calculate variance $\sigma^2$: $$\sigma^2 = \frac{\sum (x_i - \mu)^2}{n}$$ 5. Finally, find standard deviation $\sigma$: $$\sigma = \sqrt{\sigma^2}$$ 6. These steps allow you to quantify how spread out your data is, helping to interpret and compare datasets.