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Continuous Frequency Distribution, Mean deviation about mean - Statistics Video Lecture - Class 11

FAQs on Continuous Frequency Distribution, Mean deviation about mean - Statistics Video Lecture - Class 11

1. What is a continuous frequency distribution?
Ans. A continuous frequency distribution is a statistical representation of data that groups continuous numerical values into intervals or classes and shows the frequency of occurrence of each interval. It is used when data falls within a range of values rather than specific individual values.
2. What is mean deviation about mean?
Ans. Mean deviation about mean is a measure of the average distance between each data point and the mean of the data set. It is calculated by finding the difference between each data point and the mean, taking the absolute value of each difference, summing them up, and dividing by the total number of data points.
3. How is the mean deviation about mean calculated for a continuous frequency distribution?
Ans. To calculate the mean deviation about mean for a continuous frequency distribution, you first calculate the mean of the data set using the formula: Mean = (Sum of (Midpoint × Frequency)) / (Sum of Frequencies) Then, for each interval, subtract the mean from the midpoint of the interval, take the absolute value of the difference, and multiply it by the frequency of that interval. Sum up these products for all intervals and divide by the total number of data points.
4. What does the mean deviation about mean tell us about the data?
Ans. The mean deviation about mean provides an idea about the spread or dispersion of the data points from the mean. A smaller mean deviation indicates that the data points are closer to the mean, suggesting less variability. On the other hand, a larger mean deviation implies a greater dispersion of the data points from the mean, indicating more variability.
5. How is the mean deviation about mean useful in data analysis?
Ans. The mean deviation about mean is useful in data analysis as it helps in understanding the variability or dispersion of the data. It provides a measure of how spread out the data points are from the mean, which can be useful in comparing different data sets or understanding the consistency of data within a set. Additionally, it can be used to identify outliers or unusual data points that may significantly impact the overall mean deviation.
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