The mean absolute deviation is 2
5.142857143 is the mean.12.43956044 is the variance.3.526976104 is the standard deviation.
A large standard deviation indicates that the distribution is heavily weighted far from the mean. Take the following example: {1,1,1,1,1,19,19,19,19,19} Mean is 10 and StDev = 9.49 Now look at this data set: {5, 6, 7, 8, 9, 11, 12, 13, 14, 15} Mean is still 10, but StDev = 3.5
For 8 9 9 9 10 11 11 12: σ=1.3562
7.087547766 is the standard deviation for those figures.
The purpose of obtaining the standard deviation is to measure the dispersion data has from the mean. Data sets can be widely dispersed, or narrowly dispersed. The standard deviation measures the degree of dispersion. Each standard deviation has a percentage probability that a single datum will fall within that distance from the mean. One standard deviation of a normal distribution contains 66.67% of all data in a particular data set. Therefore, any single datum in the data has a 66.67% chance of falling within one standard deviation from the mean. 95% of all data in the data set will fall within two standard deviations of the mean. So, how does this help us in the real world? Well, I will use the world of finance/investments to illustrate real world application. In finance, we use the standard deviation and variance to measure risk of a particular investment. Assume the mean is 15%. That would indicate that we expect to earn a 15% return on an investment. However, we never earn what we expect, so we use the standard deviation to measure the likelihood the expected return will fall away from that expected return (or mean). If the standard deviation is 2%, we have a 66.67% chance the return will actually be between 13% and 17%. We expect a 95% chance that the return on the investment will yield an 11% to 19% return. The larger the standard deviation, the greater the risk involved with a particular investment. That is a real world example of how we use the standard deviation to measure risk, and expected return on an investment.
To calculate the mean absolute deviation (MAD), first, find the mean of the data set: (16 + 19 + 20 + 22 + 26 + 34 + 35 + 39) / 8 = 24. Next, calculate the absolute deviations from the mean: |16-24|, |19-24|, |20-24|, |22-24|, |26-24|, |34-24|, |35-24|, |39-24|, which results in 8, 5, 4, 2, 2, 10, 11, and 15. The average of these absolute deviations is (8 + 5 + 4 + 2 + 2 + 10 + 11 + 15) / 8 = 7.125. Thus, the mean absolute deviation is 7.125.
5.142857143 is the mean.12.43956044 is the variance.3.526976104 is the standard deviation.
A large standard deviation indicates that the distribution is heavily weighted far from the mean. Take the following example: {1,1,1,1,1,19,19,19,19,19} Mean is 10 and StDev = 9.49 Now look at this data set: {5, 6, 7, 8, 9, 11, 12, 13, 14, 15} Mean is still 10, but StDev = 3.5
For 8 9 9 9 10 11 11 12: σ=1.3562
If "standard" is meant to be standard deviation, the answer is the second.
Think of the absolute value of a number as it's distance from the origin or zero point on the number line. Since it is a distance it is always going to be positive. The absolute value of -11 is then 11.Absolute value of -11 is 11.
(85 - 58)/11 = 27/11 = 2.4545.. sd
3.898717738 is the standard deviation.
If you mean -3a = 10-11 then a = 1/3
If you mean: 8 9 10 11 and 12 then the average mean is 10
mean = 11ftstandard deviation = 1.6ftx=13z= 13-11 = 1.251.6P(0
11