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tumelo
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Can somebody explain to me why we have to square the variances when calculating the deviation and then finding the square root(whc is suppose to reverse the squaring) ,it doesn't make sense to me
tumelo said:Can somebody explain to me why we have to square the variances when calculating the deviation and then finding the square root(whc is suppose to reverse the squaring) ,it doesn't make sense to me
tumelo said:Can somebody explain to me why we have to square the variances when calculating the deviation and then finding the square root(whc is suppose to reverse the squaring) ,it doesn't make sense to me
Standard deviation is a measure of how much the data values deviate from the average or mean value. It is important in statistics because it helps us understand the spread or variability of data, which is crucial for making inferences and drawing conclusions from a sample.
Standard deviation is calculated by finding the average of the squared differences between each data value and the mean, and then taking the square root of this value. This gives us a measure of the spread of data around the mean.
Squaring the differences in standard deviation is necessary because it ensures that all deviations, both positive and negative, are taken into account. This prevents the deviations from cancelling each other out, giving us a more accurate measure of variability.
Variance is simply the squared value of standard deviation. It gives us a measure of the average squared distance from the mean. Standard deviation is the square root of variance and is a more commonly used measure as it is in the same units as the original data.
A large standard deviation indicates that the data values are spread out over a wider range, while a small standard deviation indicates that the data values are closer to the mean. In other words, a large standard deviation suggests that there is more variability in the data, while a small standard deviation suggests that the data is more consistent.