Posts: 607
Threads: 553
Joined: Dec 2013
MTH302 GDB No.1 Ideal Solution and Discussion Spring 2014 Due Date August 12, 2014
Topic for Graded Discussion Board
“Since variance and standard deviation both are used to check variability in the data set. Discuss which one is more reliable to apply. Justify your answer at least with two appropriate examples.
Instructions:
(1) Post your comments on Graded MDB and not on Regular Lecture MDB.
(2) Zero marks will be given to copied or irrelevant comments.
(3) The comments should be clear and to the point.
Posts: 607
Threads: 553
Joined: Dec 2013
They can be factors used to determine if there is a statistically important difference between mean values (the standard deviation is used in the computation of a confidence interval) such as Student's t test. Or if variances can be assumed or not assumed to be equal in doing Student's t test: you can evaluate this assumption by using the F test, where you compare variances of samples. So the values come in handy.
Posts: 9,583
Threads: 8,089
Joined: Apr 2011
Standard deviation and variance are statistical measures of dispersion used to assess how far away individual data points are from the mean, or average, within a set of data. Knowing how far each data point lies from the average allows you to make more accurate conclusions about a data set, population or sample, because you can pick out outliers and extreme values and note how they affect your results.
Calculating Variance
Variance is defined as the mean of the squares of the individual differences from the mean. To calculate the variance within a set of data, you would first calculate the mean by adding up all of your data points and then dividing the total by the number of data points being used. Then, the mean is subtracted from each data point individually, the result is squared, and the mean is taken again from these values.
Posts: 121
Threads: 117
Joined: Dec 2013
Conceptual Difference Between Standard Deviation and Variance
The variance of a data set measures the mathematical dispersion of the data relative to the mean. However, though this value is theoretically correct, it is difficult to apply in a real-world sense because the values used to calculate it were squared. The standard deviation, as the square root of the variance gives a value that is in the same units as the original values, which makes it much easier to work with and easier to interpret in conjunction with the concept of the normal curve
mileys81: