In addition to the numeric reports, several graphs for assessing differences or test assumptions are also available. One-way analysis of variance is the typical method for comparing three or more group means. The usual goal is to determine if at least one group mean or median is different from the others.
Often follow-up multiple comparison tests are used to determine where the differences occur. The data for this procedure may be contained in either two or more columns or in one column indexed by a second grouping column. The procedure also provides an analysis of the typical assumptions associated with one-way ANOVA, as well as various multiple comparison options. Means plots and box plots are available in this procedure. This procedure performs an analysis of variance or analysis of covariance on up to ten factors using the general linear models approach.
The experimental design may include up to two nested terms, making possible various repeated measures and split-plot analyses. Because the program allows you to control which interactions are included and which are omitted, it can analyze designs with confounding such as Latin squares and fractional factorials. This procedure performs an analysis of variance on repeated measures within-subject designs using the general linear models approach. The experimental design may include up to three between-subject terms as well as three within-subject terms.
Geisser-Greenhouse, Box, and Huynh-Feldt corrected probability levels on the within-subject F tests are given along with the associated test power. Repeated measures designs are popular because they allow a subject to serve as their own control. This improves the precision of the experiment by reducing the size of the error variance on many of the F-tests, but additional assumptions concerning the structure of the error variance must be made. This procedure uses the general linear model GLM framework to perform its calculations.
This procedure performs an analysis of variance on up to ten factors. The experimental design must be of the factorial type no nested or repeated-measures factors with no missing cells. If the data are balanced equal-cell frequency , this procedure yields exact F-tests. If the data are not balanced, approximate F-tests are generated using the method of unweighted means UWM. The F-ratio is used to determine statistical significance. The tests are nondirectional in that the null hypothesis specifies that all means for a specified main effect or interaction are equal and the alternative hypothesis simply states that at least one is different.
Studies have shown that the properties of UWM F-tests are very good if the amount of unbalance in the cell frequencies is small. When there are several factors each with many levels, the GLM solution may not be obtainable. In these cases, UWM provides a very useful approximation. When the design is balanced, both procedures yield the same results, but the UWM method is much faster.
This test is the nonparametric analog of the F-test in a randomized block design. The multiple comparison options available in this procedure are the same as those given in the General Linear Models procedure. It is also capable of educating your employees in basic and advanced statistical concepts. The free statistical software for data analysis can connect and display data frames, frequency tables, matrices, etc.
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Singularity XDR. Singularity Core. Microsoft Office Business Basic. Filmora X. Hompath Firefly. Software Version : 1. All rights reserved. The non-commercial academic use of this software is free of charge. The only thing that is asked in return is to cite this software when results are used in publications. This free online software calculator computes the one- and two-sided T-Tests about the difference of means. Cite this software as: Ian E. Holliday , T-Tests v1.
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