Statistics

Mean

Mean, also called the Average, is the most common measure of central tendency. It is equal to the sum of the case values divided by the number of cases.

Standard deviation

The standard deviation is a measure of the dispersion about the mean for the sample. This statistic helps measure how much the values cluster around the mean. The standard deviation is often denoted by the symbol s and is the square root of the variance. The advantage of using the standard deviation over the variance is that taking the square root of the variance puts the statistic back into the original units.

Standard error

The standard error is a measure that helps reveal the degree of difference between the sample mean and the population mean. The Central Limit Theory says that in repeated sampling of n observations from the population, the distribution of the sample means are approximately bell-shaped or normally distributed. This means that the larger the sample size the better the approximation to the mean.

1st Quartile

1st Quartile reports the value where 25% of the values fall below that value and 75% of the values fall above it.

Median

The median, like the mean, is a measure of central tendency. It is the middle case if all the cases are sorted in numeric order. It is also the value that would occur at the 50th percentile.

3rd Quartile

3rd Quartile reports the value where 25% of the values fall above that value and 75% of the values fall below it.

Minimum

The minimum is the minimum value of all the values in the distribution.

Maximum

The maximum is the maximum value of all the values in the distribution.

Mode

The mode is the value that occurs most often and is also a measure of central tendency. If more than one value is tied for the most cases, then the lowest value will be used as the mode.

Kurtosis

The kurtosis is a measure of peakedness. A negative kurtosis means the distribution is more flat and a positive kurtosis means the distribution is more peaked. A kurtosis of zero approximates a normal or bell-shaped distribution. Selecting the Kurtosis statistic will also display the Kurtosis Standard Error.

Skewness

The skewness measures the degree to which the sample approximates a normal, bell-shaped curve. If the value is zero, the sample is symmetrical about the mean. If the value is positive, the sample values are clustered more to the left. Conversely, if the value is negative, the sample values are clustered more to the right. Selecting the Skewness statistic will also display the Skewness Standard Error.

Values to exclude from statistics

Values entered here are excluded from the Frequency statistics.