Best Answer

No. The mean deviation is 0. Always.

Q: Is the mean deviation the mean of the actual values of the deviation from the?

Write your answer...

Submit

Still have questions?

Continue Learning about Math & Arithmetic

The mean would be negative, but standard deviation is always positive.

The Empirical Rule states that 68% of the data falls within 1 standard deviation from the mean. Since 1000 data values are given, take .68*1000 and you have 680 values are within 1 standard deviation from the mean.

For data sets having a normal, bell-shaped distribution, the following properties apply: About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean.

The mean absolute deviation is the sum of the differences between data values and the mean, divided by the count. In this case it is 15.7143

None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0None.The mean of a single number is itself.Therefore deviation from the mean = 0Therefore absolute deviation = 0Therefore mean absolute deviation = 0

Related questions

we calculate standard deviation to find the avg of the difference of all values from mean.,

It is the mean absolute deviation.

The Mean Absolute Deviation indicates how clustered (close together) the data is, i also indicates the average of the distance of the values and the mean.

A set of numbers will have a mean, which is defined as the sum of all the values divided by the number of values. Suppose this mean is m. For each of the values, the squared deviation is the square of the difference between that value and m. Algebraicly, if you have a set {x1, x2, x3, ... , xn}, whose mean is m, then the squared deviation from the mean for x1 is (x1 - m)2.

The mean would be negative, but standard deviation is always positive.

The Empirical Rule states that 68% of the data falls within 1 standard deviation from the mean. Since 1000 data values are given, take .68*1000 and you have 680 values are within 1 standard deviation from the mean.

No. Mean absolute deviation is usually greater than 0. It is 0 only if all the values are exactly the same - in which case there is no point in calculating a deviation! The average deviation is always (by definition) = 0

The mean deviation or absolute mean deviation is the sum of the differences between data values and the mean, divided by the count. In this case the MAD is 6.

For data sets having a normal, bell-shaped distribution, the following properties apply: About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean.

Budget compilation Actual values Comparison of actual data Deviation analysis Extrapolation Revised forecast by: Joash Sumayao

The 'standard deviation' in statistics or probability is a measure of how spread out the numbers are. It mathematical terms, it is the square root of the mean of the squared deviations of all the numbers in the data set from the mean of that set. It is approximately equal to the average deviation from the mean. If you have a set of values with low standard deviation, it means that in general, most of the values are close to the mean. A high standard deviation means that the values in general, differ a lot from the mean. The variance is the standard deviation squared. That is to say, the standard deviation is the square root of the variance. To calculate the variance, we simply take each number in the set and subtract it from the mean. Next square that value and do the same for each number in the set. Lastly, take the mean of all the squares. The mean of the squared deviation from the mean is the variance. The square root of the variance is the standard deviation. If you take the following data series for example, the mean for all of them is '3'. 3, 3, 3, 3, 3, 3 all the values are 3, they're the same as the mean. The standard deviation is zero. This is because the difference from the mean is zero in each case, and after squaring and then taking the mean, the variance is zero. Last, the square root of zero is zero so the standard deviation is zero. Of note is that since you are squaring the deviations from the mean, the variance and hence the standard deviation can never be negative. 1, 3, 3, 3, 3, 5 - most of the values are the same as the mean. This has a low standard deviation. In this case, the standard deviation is very small since most of the difference from the mean are small. 1, 1, 1, 5, 5, 5 - all the values are two higher or two lower than the mean. This series has the highest standard deviation.

Mean is 3.8 Mean Absolute Deviation is 1.44