>>variance While it is true that, in many cases, variance does provide a good deal of information, particularly for statistical analysis, in many situations e.g. when the data is not normal, there are only a few points in a data set, or one wishes to examine only a single data set many other properties can be considered.
Specifically, it is often very useful to look at the median as well as the interquartile range. Quickly, just in case, the median is, after the data is sorted, the middle number. The inner quartile range is the difference between the value at the 75th percentile and the 25% i.e the range of the middle 50%. What is nice about these two values is that they eliminate outliers (numbers which are, for whatever reason, exceptionally large or small compared to the data set) and gives a better idea of where the data lies. The mean cannot account for large outliers and, for small data sets, can differ significantly from the median. While statistical analysis is more limited with the median, it can often be a more accurate representation of a population.
As an example, income reports very dramatically when looking at the difference between variance and inner quartile range. Because the median US income is far below the mean income (i.e. there are a small group of VERY wealthy people, thus the mean is pushed above the median) the inner quartile range is more informative that the variance. This is especially true on the micro level when e.g looking by county.
A frequency distribution plot.
It is one of the key measures of a data set: it shows the value around which the observations are spread out.
It is a measure of the spread of the data around its mean value.
I think the answer is variance
In statistics a Bell curve is the most common way that the distribution of results is plotted. If you know the mean and the standard deviation you can predict with that distribution with reasonable accuracy.
For a sample of data it is a measure of the spread of the observations about their mean value.
The standard deviation is a measure of the spread of data about the mean. Although it is essentially a measure of the spread, the fact that it is the spread ABOUT THE MEAN that is being measured means that it does depend on the value of the mean. However, the SD is not affected by a translation of the data. What that means is that if I add any fixed number to each data point, the mean will increase by that number, but the SD will be unchanged.
It is a measure of the spread or dispersion of the data.
The mean and standard deviation often go together because they both describe different but complementary things about a distribution of data. The mean can tell you where the center of the distribution is and the standard deviation can tell you how much the data is spread around the mean.
The standard deviation of a set of data is a measure of the spread of the observations. It is the square root of the mean squared deviations from the mean of the data.
Standard deviation shows how much variation there is from the "average" (mean). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values.
i dont know...... it means
Compute the variance (or its square root , standard deviation) of each of the data set. Set 1: standard deviation = 10.121 Set 2: standard deviation = 12.09 Set 2 shows more variation around the mean. Check the link below
Yes. Standard deviation depends entirely upon the distribution; it is a measure of how spread out it is (ie how far from the mean "on average" the data is): the larger it is the more spread out it is, the smaller the less spread out. If every data point was the mean, the standard deviation would be zero!
Then nothing. It simply means the spread is smaller.
The mean is used for evenly spread data, and median for skewed data. Not sure when the mode should be used.
It tells you how much variability there is in the data. A small standard deviation (SD) shows that the data are all very close to the mean whereas a large SD indicates a lot of variability around the mean. Of course, the variability, as measured by the SD, can be reduced simply by using a larger measurement scale!
distribution of data is the way that you show or "distribute" your data. It just means how you show your work.In some cases it means how you put out or spread out your work like in math.
It means that all of the ten numbers are 15!Standard deviation tells you how spread out the data is from the mean value. Or in other words, it tells you how far the numbers in your data are away from the mean value.If the standard deviation is a high number, it means the data is largely spread out and that there are big differences in the data. The numbers in the data would be quite far from each other. For example, if you had data like: 8, 35, 13, 47, 22, 64, this would probably mean that you'll get a high standard deviation because each of the numbers are very spread out.On the other hand, if the standard deviation is small, it tells you that the numbers in the data are quite close together and that there is only a small difference between the numbers in the data. For example, if you had data like: 19, 25, 20, 22, 23, 18, this would probably mean that you'll get a low standard deviation because each of the numbers aren't that spread outIn the scenario you've given, the standard deviation is ZERO. This means that there is no spread or variation AT ALL with the numbers in your data. This means every single number in the data is the same.Since your mean is 15 and every number in your data is the same, that means that all the ten numbers in your data have to be 15!Hope that makes sense.Jamz159
The mean alone does not provide any information about the higher order moments of the distribution of the data set. Most important amongst these is that it does not give information about the variance - or the spread of the distribution. The data set could be distributed in a narrow band about the mean or spread more evenly over a wider range. It does not tell you anything about the skewness, that is whether there are many values smaller than the mean balanced by a few large values (or the other way around). Moments of the fourth or higher order are difficult to comprehend and, in most cases, ignored.
The blue light on the side of the 3DS shows that the 3DS is on and the light on the hinge of the 3DS shows that SpotPass data can be downloaded
A variance is a measure of how far a set of numbers is spread out around its mean.