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Mean Deviation for Ungrouped Data Video Lecture | Mathematics (Maths) Class 11 - Commerce
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FAQs on Mean Deviation for Ungrouped Data Video Lecture - Mathematics (Maths) Class 11 - Commerce
| 1. What is the formula to calculate the mean deviation for ungrouped data? |  |
Ans. The formula to calculate the mean deviation for ungrouped data is:
Mean Deviation = Σ |xi - x̄| / n
Where:
- Σ represents the sum of all deviations,
- xi is each individual data point,
- x̄ is the mean of the data set, and
- n is the total number of data points.
| 2. How do you interpret the mean deviation for ungrouped data? | |
Ans. The mean deviation for ungrouped data measures the average distance of each data point from the mean. A smaller mean deviation indicates that the data points are closely clustered around the mean, while a larger mean deviation suggests greater dispersion or variability in the data set.
| 3. Can mean deviation for ungrouped data be negative? | |
Ans. No, mean deviation for ungrouped data cannot be negative. The absolute value of the deviation (|xi - x̄|) is always taken to ensure that the result is positive. This is because the mean deviation represents the average distance, and distance cannot be negative.
| 4. How does mean deviation for ungrouped data differ from standard deviation? | |
Ans. The mean deviation for ungrouped data and standard deviation are both measures of dispersion, but they differ in terms of the formula used and the way they account for deviations from the mean. The mean deviation is calculated by taking the absolute value of each deviation from the mean and averaging them, while the standard deviation squares each deviation, averages them, and then takes the square root.
| 5. What are the limitations of using mean deviation for ungrouped data? | |
Ans. Some limitations of mean deviation for ungrouped data are:
- It does not consider the direction of deviations, only their magnitude.
- It is more sensitive to outliers compared to standard deviation.
- It does not provide a clear-cut measure of variability due to the absolute value used in the formula.
- It may not be suitable for comparing data sets with different means or scales.
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Dec 25, 2024 Last updated |
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