The mode is the number that occurs most often in the data set. In this case the mode is:

Az-score represents the distance away from the mean for a particular score, expressed in terms of standard deviations. Thus, a z-score of 0 is equal to the mean, and a z-score of 1.4 is equal to 1.4 standard deviations above a given mean.

When calculating the sum of squares (SS) as part of an ANOVA, each individual difference from the mean must be squared before summing. This makes intuitive sense when one realizes that the average of all scores which came together to make a mean in the first place must be that mean!

A two-way analysis of variance is the most appropriate tool for determining simultaneously the effects of two (or more) independent variables on a dependent variableand(如果强度的交互any) between the independent variables.

Thedffor each independent variable orfactoris determined by how many different levels of that factor are included in the analysis. This can create a problem for calculating sums of squares and ultimately for calculating post hoc analyses, as the number of levels in a factor can be very large.

A regression analysis, so long as all but one independent variable at a time can be held fixed, is able to produce a predictive window called aconditional expectationthat allows for prediction of dependent variable values given set independent variable levels.

调整后的平方和是一个不同的模型nes the amount of remaining variance in a model is explained by a factorgiven thatall the other factors are already in the model. For a sequential sum of squares, the amount of variance in a model is explainedonly giventhe factors which are already in the model. Thus, the first factor inserted in a sequential sum of squares carries the most weight.

The general linear model, expressed as, works by comparing a model matrix (sometimes called a design matrix) to a parametric estimate, while also factoring for generalized error terms within the data. It works best when errors are uncorrelated across measurements.

有偏见的EMS时最常用的随机的怪兽ms are part of the factors in the model (and thus, when the population means cannot be reasonably assumed to be equal irrespective of treatment effect).

Constructing a confidence interval for Pearson'srcan be done through a process called "bootstrapping", which involves re-sampling with replacement a certain number of values in each correlation a large number of times.