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Mean Deviation - Measures of Dispersion, Business Mathematics & Statistics Video Lecture - SSC CGL

FAQs on Mean Deviation - Measures of Dispersion, Business Mathematics & Statistics Video Lecture - SSC CGL

1. What is mean deviation in statistics?

Ans. Mean deviation is a measure of dispersion that quantifies the average amount by which each data point in a dataset deviates from the mean. It is calculated by finding the absolute difference between each data point and the mean, summing up these differences, and dividing the sum by the total number of data points.

2. How is mean deviation different from standard deviation?

Ans. Mean deviation and standard deviation are both measures of dispersion, but they differ in the way they calculate the spread of data. Mean deviation calculates the average difference between each data point and the mean, while standard deviation calculates the average squared difference between each data point and the mean. Standard deviation is more widely used as it gives more weight to extreme values and provides a better representation of the overall variation in the dataset.

3. How do you interpret mean deviation?

Ans. Mean deviation provides a measure of the average amount of variability or dispersion in a dataset. A smaller mean deviation indicates that the data points are closer to the mean, suggesting less variability. Conversely, a larger mean deviation indicates that the data points are more spread out from the mean, suggesting greater variability or dispersion in the dataset.

4. Can mean deviation be negative?

Ans. No, mean deviation cannot be negative. Since mean deviation involves taking the absolute difference between each data point and the mean, it will always result in positive values. The sum of these positive values is divided by the total number of data points to calculate the mean deviation.

5. How is mean deviation useful in business and statistics?

Ans. Mean deviation is useful in business and statistics as it provides a measure of how spread out the data points are from the mean. It helps in understanding the variability and dispersion within a dataset, which is important for decision-making, risk assessment, and forecasting. Mean deviation can be used to compare the variability of different datasets, identify outliers, and assess the consistency of data.

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