Mean and Standard Deviation

In statistics, the mean and standard deviation are measures of central tendency and variability, respectively. The mean (also known as the average) represents the central value of a dataset, while the standard deviation measures the spread or dispersion of the data points from the mean.

Mean of x

Given that the mean of x is 16, it implies that the average value of the dataset x is 16. This means that if we were to take multiple measurements of x and calculate their average, it would be approximately equal to 16.

Standard Deviation of x

The standard deviation of x is given as 4. The standard deviation quantifies the amount of variation or dispersion in the dataset. In the case of x, a standard deviation of 4 suggests that the data points are, on average, 4 units away from the mean. It provides a measure of how spread out the data points are around the mean.

Mean of y

The mean of y is stated as 20. This means that the average value of the dataset y is 20. If we were to take multiple measurements of y and calculate their average, it would be approximately equal to 20.

Standard Deviation of y

The standard deviation of y is given as 9.6. This indicates that, on average, the data points in y are 9.6 units away from the mean. It provides a measure of the dispersion or spread of the data points around the mean.

Summary

To summarize, in the given information, the mean of x is 16 with a standard deviation of 4, while the mean of y is 20 with a standard deviation of 9.6. These measures provide insights into the central tendency and variability of the datasets. The mean gives an average value, while the standard deviation shows how much the data points deviate from the mean.