SciStatCalc version 1.3 released

SciStatCalc v 1.3 now available on the iTunes App Store. The new version includes the following updates:-

Mean and variance estimates for each of the 19 probability distributions added to the blue CDF/inverse CDF panel in landscape mode.

Eleven extra statistical tests/capabilities added to the pink panel - all accessible with a left/right swipe, making the switch between tests so much the easier!  Note: the terms datasets and populations are used interchangeably below

• Mann-Whitney U-test : tests if two datasets/populations are the same - can be regarded as the      non-parametric analogue to the Unpaired Student's t-test.
• Wilcoxon Signed Rank test: tests if two datasets/populations have the same rank - can be regarded as the non-parametric analogue to the Paired Student's t-test
• Linear Regression: Calculates values of $a$ and $b$ for model $y[n]=ax[n] + b[n]$ - the entries for $x[n]$ and $y[n]$ need to populate the left and right boxes of the test panel respectively. Also calculates various other parameters, including the coefficient of determination.
• Spearman's Rank Correlation test: A non-parametric test to test if two populations have a monotonic relationship
• Pearson Correlation test: Parametric test for linearity relationship between two populations 
• Shapiro-Wilk test: Calculates the W statistic to test for Gaussianity of two datasets - the higher the value of this statistic, the more confidence we have that the data comes from a Gaussian distribution
• Bartlett's test: tests for equality of variance amongst (more than two) datasets - multiple populations can be entered in the left hand text box as semi-colon separated datasets - each dataset consists of comma separated numbers  
• Kruskall-Wallis test: the non-paramteric Analysis of Variance (ANOVA) test
• One-Way ANOVA test: tests equality of means for three or more populations - assumes population variances are the same - a full breakdown of the results is displayed in the right text box. Also, post-hoc analysis using Tukey's Honestly Significant Difference (HSD) test is carried out, should the results be significant.
• Two-factor ANOVA test: tests the effect of two independent factors on multiple datasets and their interaction - data needs to be entered as bar (|) separated groups of semi-colon separated datasets, each dataset comprising comma separated numbers, in the left text box. More on this in an upcoming posting..
• Single dataset analysis: Simply enter comma separated numbers in the left text box - results such as number of samples, minimum,maximum, mean, variance, standard deviation, kurtosis, median and mode (for integer values) are displayed in the right test box.