::Free Statistics and Forecasting Software::

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.

Free Software for Statistical Hypothesis Tests (Calculators):

Main Menu	return to Main Menu
Univariate Statistical Hypothesis Testing - Ungrouped Data
Skewness/Kurtosis (old)	Normality tests for small and large samples.
Skewness/Kurtosis (new)	D'Agostino skewness test, Anscombe-Glynn kurtosis test, Jarque-Bera Normality Test (against normality).
Quasi Random-Walk Identification	Computes the logistic regression probability of a Quasi Random-Walk based on the small-sample Kurtosis p-value. If the probability is close to 1 then the (financial) time series under investigation is not consistent with the Efficient Market Hypothesis (c.q. Random-Walk).
Testing Mean (unknown Variance) - p-value (old)	computes the 2-sided p-value for the statistical hypothesis test about the mean when the population variance is unknown. This test can be applied to any univariate dataset.
Testing Mean (unknown Variance) - p-value (new)	computes the 2-sided p-value for the statistical hypothesis test about the mean when the population variance is unknown. This test can be applied to any univariate dataset.
Testing Mean (known Variance) - Critical Value	computes the critical value for one- and two-sided hypothesis tests about the mean. In this test it is assumed that the population variance is known.
Testing Mean (known Variance) - p-value	computes the p-value for one- and two-sided hypothesis tests about the mean. In this test it is assumed that the population variance is known.
Testing Mean (known Variance) - Type II Error	computes the Type II Error for the one-sided hypothesis test about the mean. In this test it is assumed that the population variance is known.
Testing Mean (known Variance) - sample size	computes the sample size for the one-sided hypothesis test about the mean, given a user-defined type I and II error. In this test it is assumed that the population variance is known.
Testing Population Mean with known Variance - Confidence Interval	computes the confidence intervals for the one-sided and two-sided hypothesis test about the population mean
Testing Sample Mean with known Variance - Confidence Interval	computes the confidence intervals for the one-sided and two-sided hypothesis test about the sample mean
Testing Variance - Critical Value (Region)	computes the critical value (region) for the hypothesis test about the variance
Testing Variance - p-value (probability)	computes the p-value (probability) for the hypothesis test about the variance
Testing Variance - Confidence Intervals for Sample Variance	computes the confidence intervals for the one-sided and two-sided hypothesis test about the sample variance
Testing Variance - Confidence Intervals for Population Variance	computes the confidence intervals for the one-sided and two-sided hypothesis test about the population variance
Testing Mean with unknown Variance - Critical Value	computes the 2-sided and 1-sided confidence intervals for the statistical hypothesis test about the mean when the population variance is unknown. This test can be applied to any univariate dataset.
Testing Population Proportion - Critical Value	computes the critical values for one- and two-sided hypothesis tests about the population proportion.
Testing Population Proportion - P-Value	computes the p-value of the population proportion test.
Bivariate and Multivariate Statistical Hypothesis Testing - Ungrouped Data
Two Sample Tests about the Mean	Paired and Unpaired Two Sample Tests about the Mean (paired t-test, unpaired t-test, Welch t-test, and Wilcoxon rank sum test with continuity correction).
Kendall tau Correlation Matrix	Multivariate correlation plot based on Kendall tau rank correlations and their respective p-values.
Notched Boxplots	Notched Boxplots for a multivariate dataset.
1-way ANOVA	Single Factor Analysis of Variance.
2-way ANOVA	Two Factor Analysis of Variance.
Chi-Squared Tests	This module computes the Pearson Chi-Squared test, Exact Pearson Chi-Squared by Simulation test, McNemar test, and the Association Plot.

To cite Wessa.net in publications use:
Wessa, P. (2013), Free Statistics Software, Office for Research Development and Education,
version 1.1.23-r7, URL http://www.wessa.net/

© All rights reserved. Academic license for non-commercial use only.
The free use of the scientific content, services, and applications in this website is granted for non commercial use only. In any case, the source (url) should always be clearly displayed. Under no circumstances are you allowed to reproduce, copy or redistribute the design, layout, or any content of this website (for commercial use) including any materials contained herein without the express written permission.

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. We make no warranties or representations as to the accuracy or completeness of such information (or software), and it assumes no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Software Version : 1.1.23-r7
Algorithms & Software : Patrick Wessa, PhD
Server : www.wessa.net

Comments, Feedback & Errors | Privacy Policy | Statistics Resources | Wessa.net Home