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Q: How do you calculate average deviation from the average value?

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The mean average deviation is the same as the mean deviation (or the average deviation) and they are, by definition, 0.

You don't need to. Average deviation (about the mean) is always zero!

Simple! The average deviation for any data set is zero - by definition.

There is no single function in Excel.You calculate the mean (average).For each observation, you calculate its deviation from the mean.Convert the deviation to absolute deviation.Calculate the mean (average) of these absolute deviations.

To Find Average Deviation 1. Find the average value of your measurements. 2. Find the difference between your first value and the average value. This is called the deviation. 3. Take the absolute value of this deviation. 4. Repeat steps 2 and 3 for your other values. 5. Find the average of the deviations. This is the average deviation The average deviation is an estimate of how far off the actual values are from the average value, assuming that your measuring device is accurate. You can use this as the estimated error. Sometimes it is given as a number (numerical form) or as a percentage. To Find Percent Error 1. Divide the average deviation by the average value. 2. Multiply this value by 100. 3. Add the % symbol.

to find percent deviation you divide the average deviation into the mean then multiply by 100% . to get the average deviation you must subtract the mean from a measured value.

The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:Find the mean (average) value for the set of data. Call it M.For each observation, O, calculate the deviation, which is O - M.The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.Calculate the average of all the absolute deviations.One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data. The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:Find the mean (average) value for the set of data. Call it M.For each observation, O, calculate the deviation, which is O - M.The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.Calculate the average of all the absolute deviations.One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data. The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:Find the mean (average) value for the set of data. Call it M.For each observation, O, calculate the deviation, which is O - M.The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.Calculate the average of all the absolute deviations.One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data. The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:Find the mean (average) value for the set of data. Call it M.For each observation, O, calculate the deviation, which is O - M.The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.Calculate the average of all the absolute deviations.One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data.

The mean is the average value and the standard deviation is the variation from the mean value.

The first step is to find out what the deviation is from: the mean, median, some other fixed value. Whatever it is, call it m.For each observation x, calculate the absolute deviation, which is x - m or m - x, whichever is positive or zero. Finally, calculate the mean value (arithmetic average) of this set.

The standard deviation (?, pronounced sigma) of a set of values is a measure of how much the set of values deviates from the average of the values. To calculate ? of a complete set of values (as opposed to a sampling),...Calculate the average of the set (the sum of the values divided by the quantity of the values).Calculate the difference between each value and the average calculated in step 1, then square the difference.Calculate the average of all the squares calculated in step 2.The standard deviation is the square root of the average calculated in step 3.

You cannot; there is insufficient information.

The standard deviation.

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