Term |
Description |
Question | Displays the question name. |
Sample Size | Displays the valid number of respondents who answered the specific question. |
Missing | Displays the number of responses that are missing (blank). |
Mean | The average of the values in the population. |
Variance | A measure of how spread out a distribution is. It is computed as the average squared deviation of each number from its mean. |
Standard Deviation | A Measure of the dispersion of a set of data from its mean. The more spread apart the data is, the higher the deviation. It is calculated by taking the square root of the variance. |
Standard Error | The standard deviation of the sampling distribution of that statistic. Standard error reflect how much sampling fluctuation a statistic will show. |
Minimum | The minimum value in a range. |
Maximum | The maximum value in a range. |
Range | The distance between the maximum and minimum value. |
Skewness | A measure of the symmetry or lack of it in a set of data as evident from the shape of the distribution. A distribution is symmetric if the left half of the graph of the distribution is the mirror image of the right half. If a distribution is skewed to the right (positive skewness) the mean is greater than zero. If a distribution is skewed to the left (negative skewness) then the relationship is reversed; in which case the coefficient is less than zero. If there is no skewness or the distribution is symmetric like the bell-shaped normal curve then the mean=median=mode. |
Kurtosis | Kurtosis is based on the size of a distributions’s tails. Distributions with relatively larfe tails are called “leptokurtic”; those with small tails are called “platykurtic”. A distribution with the same kurtosis as the normal distribution is called “mesokurtic”. |
T-Value | A measure on a random sample (or pair of samples) in which a mean (or pair of means) appears in the numerator and an estimate of the numerator’s standard deviation appears in the denominator. The latter estimate is based on the calculated s square or s squares of the samples. If these calculations yield a balue of (t) that is sufficiently different from zero, the test is considered to be statiscally significant. |
Mean Absolute Vebiation | A measure of variation, which calculates the average distance a data balue is from the mean. |
Percentile (25 and 75) | Percentiles are values that divide a sample of data into one hundred groups containing (as far as possible) equal numbers of observations. For example, 25% of the data values lie below the 25th percentile. |
Median | The middle of a distribution: half the values are above the median and half are below the median. |
Inter Quartile Range | The difference between the 75th percentile and the 25th percentile. |
Confidence Interval (1, 5, 95, and 99%) |
A confidence interval gives an estimated range of values that is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data. If independent samples are taken repeatedly from the same population, and a confidence interval calculated for each sample, then a certain percentage (confidence level) of the intervals will include the unknown population parameters. Remark Quick Stats calculates Confidence Intervals of 1%, 5%, 95%, and 99%. |