Types of Dispersion|Range Variance|Biostatistics|Statistical Techniques|Lecture 13

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Dear Viewer, In this video A measure of dispersion, also known as variability or spread, quantifies the extent to which individual data points in a dataset deviate from the central tendency, providing valuable insights into the distribution of the data. Here are some common measures of dispersion:
1. Range:
The range is the simplest measure of dispersion and is calculated as the difference between the maximum and minimum values in the dataset.
While easy to calculate, the range can be heavily influenced by outliers and may not provide a complete picture of variability.
2. Variance:
Variance measures the average squared deviation of each data point from the mean. It gives a comprehensive view of the spread of the data.
It's calculated by taking the average of the squared differences between each data point and the mean.
However, the variance is not directly interpretable in the same units as the original data, as it's squared.
3. Standard Deviation:
The standard deviation is the square root of the variance and provides a measure of dispersion in the same units as the original data.
It indicates the average deviation of data points from the mean.
Standard deviation is widely used due to its ease of interpretation and its property of giving more weight to larger deviations from the mean.
4. Mean Absolute Deviation (MAD):
MAD measures the average absolute deviation of each data point from the mean.
Unlike variance and standard deviation, MAD considers deviations without squaring them, making it less sensitive to outliers.
However, MAD may be less mathematically convenient to work with compared to variance and standard deviation.
5. Percentiles and Quartiles:
Percentiles divide a dataset into hundredths, with each percentile representing the percentage of data points that fall below it.
Quartiles divide a dataset into four equal parts, with each quartile representing 25% of the data.
Percentiles and quartiles provide insight into the spread of data and the location of specific values within the dataset.
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