biased estimator
a formula for a sample’s variability that involves dividing by N that is biased toward underestimating the corresponding population variability
degrees of freedom
the number of scores in a sample that are free to vary, and thus the number that is used to calculate an estimate of the population variability; symbolized by df
estimated population standard deviation
the unbiased estimate of the population standard deviation, calculated from sample data using degrees of freedom (N-1)
estimated population variance
the unbiased estimate of the population variance, calculated from sample data using degrees of freedom (N-1)
population standard deviation
the square root of the population variance, or the square root of the average squared deviation of scores around the population mean
measures of variability
measures that summarize the extent to which scores in a distribution differ from one another
population variance
the average squared deviation of scores around the population mean
proportion of variance accounted for
the proportion of the error in predicting scores that is eliminated when, instead of using the mean of Y, we use the relationship with the X variable to predict Y scores; the proportional improvement in predicting Y scores thus achieved
range
the distance between the highest and lowest scores in a set of data
sample standard deviation
the square root of the same variance or the square root of the average squared deviation of sample scores around the sample mean
sample variance
the average squared deviation of a sample of scores around the sample mean
squared sum of X
a result calculated by adding all scores and the squaring their sum
sum of the squared Xs
a result calculated by squaring each score in a sample and adding the squared scores
unbiased estimator
a formula for a sample’s variability that involves dividing by N-1 that equally often under- and over-estimates the corresponding population variability
variance
a measure of the variability of the scores in a set of data, computed as the average of the squared deviations of the scores around the mean
