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Only if the variance of y is much larger than its mean, then the right-most term is close to 0 (i.e., () = ¯), which reduces Spencer's design effect (for the estimated total) to be equal to Kish's design effect (for the ratio means): [32]: 5 (+) =. Otherwise, the two formulas will yield different results, which demonstrates the difference ...
PASS is a computer program for estimating sample size or determining the power of a statistical test or confidence interval. NCSS LLC is the company that produces PASS. NCSS LLC also produces NCSS (for statistical analysis). PASS includes over 920 documented sample size and power procedures.
Interactive help is available. The program provides methods that are appropriate for matched and independent t-tests, [2] survival analysis, [5] matched [6] and unmatched [7] [8] studies of dichotomous events, the Mantel-Haenszel test, [9] and linear regression. [3]
Compromise analyses find implied power based on the beta/alpha ratio, or q, and inputted values for effect size and sample size. Criterion analyses calculate the required alpha value based on the inputted power, effect size, and sample size. Post hoc analyses find actual power based on the inputted alpha, sample size, and effect size.
Post-hoc analysis of "observed power" is conducted after a study has been completed, and uses the obtained sample size and effect size to determine what the power was in the study, assuming the effect size in the sample is equal to the effect size in the population. Whereas the utility of prospective power analysis in experimental design is ...
There are a few reviews of free statistical software. There were two reviews in journals (but not peer reviewed), one by Zhu and Kuljaca [26] and another article by Grant that included mainly a brief review of R. [27] Zhu and Kuljaca outlined some useful characteristics of software, such as ease of use, having a number of statistical procedures and ability to develop new procedures.
Descriptive statistics Nonparametric statistics Quality control Survival analysis Data processing Base stat. [Note 2] Normality tests [Note 3] CTA [Note 4] Nonparametric comparison, ANOVA: Cluster analysis Discriminant analysis BDP [Note 5] Ext. [Note 6]
Due to the fact that the mixed-design ANOVA uses both between-subject variables and within-subject variables (a.k.a. repeated measures), it is necessary to partition out (or separate) the between-subject effects and the within-subject effects. [5]