The problem did not occur when the effect size was specified in terms of Two probabilities. Renamed the Repetitions parameter in repeated measures procedures to Number of measurements (Repetitions was misleading because it incorrectly suggested that the first measurement would not be counted).įixed a problem in the sensitivity analysis of the logistic regression procedure: There was an error if Odds ratio was chosen as the effect size. 5 January 2010 - Release 3.1.2 Mac and Windows Changing the number of covariates now correctly leads to the appropriate change in the denominator degrees of freedom. 22 June 2011 - Release 3.1.3 Mac and Windowsįixed a bug in the ANCOVA module. The problem did not occur with post hoc analyses. 3 July 2012 - Release 3.1.4 Mac and WindowsĪdded an options dialog to the repeated-measures ANOVA which allows a more flexible specification of effect sizes.įixed a problem in calculating the sample size for Fisher's exact test. The problem did not occur when both sample sizes were identical.įixed a problem in calculating the effect size from variances in the repeated measures ANOVA. 20 August 2012 - Release 3.1.5 Mac and Windowsįixed a problem in the test of equality of two variances. The drawers now appear correctly after clicking on the Determine button. 12 September 2012 - Release 3.1.5.1 Macįixed a problem with the effect size drawers of ANOVA: Fixed effects. Macįixed a problem in Fisher’s exact test. 18 Frebruary 2013 - Release 3.1.6 Mac and Windowsįixed a problem in the sensitivity analysis of the logistic regression. 19 April 2013 - Release 3.1.7 Mac and Windowsįixed a problem in the exact test of Proportions: Inequality, two independent groups (uncontional). Improvements in the logistic regression module: (1) improved numerical stability (in particular for lognormal distributed covariates) (2) additional validity checks for input parameters (this applies also to the poisson regression module) (3) in sensitivity analyses the handling of cases in which the power does not increase monotonically with effect size is improved: an additional Actual power output field has been added a deviation of this actual power value from the one requested on the input side indicates such cases it is recommended that you check how the power depends on the effect size in the plot window. 31 January 2014 - Release 3.1.8 Mac and Windows Note, however, that the change affects the results only when N is very small. Negative effect directions, that is, slope|H1 = upper limit. ![]() 6 February 2019 - Release 3.1.9.4 Mac and Windowsįixed a bug in t tests: Linear bivariate regression: One group, size of slope. 14 January 2020 - Release 3.1.9.5 Macįixed a bug that caused the “Options” button (which is available for some tests in the main window) to disappear when “Hide distributions & control” was selected. 21 February 2020 - Release 3.1.9.6 Mac and Windowsįixed a bug in z tests: Generic z test: Analysis: Criterion: Compute alpha: The critical z was calculated incorrectly.įixed a bug in t tests: Linear bivariate regression: One group, size of slope: |sy/sx| was sometimes calculated inccorrecty. However the Multiple R and R Square are the two most important.Changed the behavior of the “X-Y plot for a range of values” which allowed plotting graphs after changing input parameters in the main window without hitting the “Calculate” button which, however, is required to update the “X-Y plot for a range of values” with the new input parameters from the main dialog. Unless you understand statistics and calculating regression models, the values at the bottom of the summary won't have a lot of meaning. Significance F: Statistical value known as P-value of F.This provides the significance of the regression model. F: The F statistic (F-test) for null hypothesis.MS: Mean square of the regression data.The ratio of the residual sum of squares versus the total SS should be smaller if most of your data fits the regression line. df: Statistical value known as degrees of freedom related to the sources of variance.The remaining values in the regression output give you details about smaller components in the regression analysis. Observations: The number of observations in your regression model. ![]() If this error is small then your regression results are more accurate. ![]()
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