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Step-by-step interactive example for calculating standard deviation First, we need a data set to work with. Let's pick something small so we don't get overwhelmed by the number of data points.
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The range is easy to calculate—it's the difference between the largest and smallest data points in a set. Standard deviation is the square root of the variance. Standard deviation is a measure of how spread out the data is from its mean.
Review of population and sample standard deviation, including the formula and interpretation.
Standard deviation measures the spread of a data distribution. The more spread out a data distribution is, the greater its standard deviation. For example, the blue distribution on bottom has a greater standard deviation (SD) than the green distribution on top:
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The difference between standard error and standard deviation lies in their application and interpretation. Standard deviation (SD) measures the dispersion or spread of a set of values in a population or sample. It tells you how much individual values typically differ from the mean.
His sample mean was four years and his sample standard deviation was two years. Rory wants to use these sample data to conduct a t test on the mean. Assume that all conditions for inference have been met. Calculate the test statistic for Rory's test.
Practice calculating and interpreting the mean and standard deviation of a discrete random variable.
This unit covers common measures of center like mean and median. We'll also learn to measure spread or variability with standard deviation and interquartile range, and use these ideas to determine what data can be considered an outlier.