# Factorial (using addition operation) in c

The following C project contains the C source code and C examples used for factorial (using addition operation). to show simple factorial algorithm and code using addition operation only

The following Matlab project contains the source code and Matlab examples used for power and hypothesis testing for difference between two variances. .
Estimates the power and the hypothesis testing for difference between two variances.

The following Matlab project contains the source code and Matlab examples used for tukey's test of additivity for a two-way classification analysis of variance. .
Tukey's test of additivity for a two-way analysis of variance design with replication (can be equal or unequal sample sizes) or without replication.

The following Matlab project contains the source code and Matlab examples used for power estimation of a performed z test about mean(s). .
Estimates the statistical power of a performed Z test about mean(s). It recalls you the statistical result of the test you should have arrived. It only needs the Z statistic, specified direction and significance level (default = 0.05).

The following Matlab project contains the source code and Matlab examples used for power1var.
Besides the power estimation it makes the hypothesis testing concerning the one-sample variance. The function only needs the sample variance, the sample size, the hypothesized value and the significance level.

The following Matlab project contains the source code and Matlab examples used for sample size in tests for difference between two variances. .
Sample size required to achieve a specified test for difference between two variances, using the normal approximation.

The following Matlab project contains the source code and Matlab examples used for single-factor analysis of variance test. .
Computes the single-factor Analysis of Variance Model I or Model II for equal or unequal sample sizes.

The following Matlab project contains the source code and Matlab examples used for o'brien's test for homogeneity of variances. .
In the Obrien's test to check out the homogeneity of variances the data are transforming to
yij = ((nj-1.5)*nj*((xij-mean(xj))**2)-((0.5)*(var(xj))*(nj-1)))/((nj-1)*(nj-2))
and uses the F distribution performing an one-way ANOVA using y as the dependent variable.

The following Matlab project contains the source code and Matlab examples used for levene's test for homogeneity of variances. .
Levene's F test is used to test the null hypothesis that multiple population variances corresponding to multiplesamples are equal.

The following Matlab project contains the source code and Matlab examples used for calulation of the a priori statistical power of a one-way manova test. .
This file estimates the a priori statistical power of a single-factor Multivariate Analysis of Variance (MANOVA).

The following Matlab project contains the source code and Matlab examples used for computing of a standard (simple) split-plot design analysis of variance. .
This file computes the standard (simple) split-plot design analysis of variance, taking into account the linear model:
x_jkl = µ + a_j + ß_k + d_jk + Þ_l + (ßÞ)_kl + e_jkl
where j = 1,.

The following Matlab project contains the source code and Matlab examples used for computes a balanced incomplete block design [bibd] analysis of variance. .
This file computes a balanced incomplete block design [BIBD] analysis of variance.

The following Matlab project contains the source code and Matlab examples used for computes a multivariate analysis of variance for equal or unequal sample sizes. .
Computes a Multivariate Analysis of Variance for equal or unequal sample sizes.

The following Matlab project contains the source code and Matlab examples used for computes a two-way multivariate analysis of variance for equal or unequal sample sizes. .
Computing of a two-way multivariate analysis of variance for equal or unequal sample sizes by the testing of the mean differences in several variables among several samples with two factors.

The following Matlab project contains the source code and Matlab examples used for repeated measures two-way analysis of variance test. .
ANOVA with two within-subject variables is used to analyze the relationship between two independent variables and the dependent variable.

The following Matlab project contains the source code and Matlab examples used for repeated measures mixed two-way analysis of variance test. .
ANOVA with between- and within- subject variables (mixed) is used to analyze the relationship for designs that have a combination of between and within-subject variables.

The following Matlab project contains the source code and Matlab examples used for latin hypercube sampling.
This is sampling utility implementing Latin hypercube sampling from multivariate normal, uniform & empirical distribution. Correlation among variables can be sprecified.

The following Matlab project contains the source code and Matlab examples used for anova and variance components for genetic data.
These files are an adaptation of the ANOVA variance component estimators to be used in the analysis of genetic data, specifically Cockerham's 1969 Theta.

The following Matlab project contains the source code and Matlab examples used for statistical power of a performed (a posteriori) single-factor manova. .
Estimating of the statistical power of a performed (a posteriori) single factor Multivariate Analysis of Variance (MANOVA).

The following Matlab project contains the source code and Matlab examples used for identification of outliers in a one-way analysis of variance model ii (random effects). .
In a one-way analysis of variance random effects model one would expect that the measurements lie close together because the same quantity was measured and states that the class effects stem from a common source and therefore should not difer too much.

The following Matlab project contains the source code and Matlab examples used for 2^3 factorial design analysis. .
This m-file is used in experiments involving several factors where it is necessary to investigate the joint effects (main and interactions) of the factors on a response variable and determine by a formal analysis of variance which factor effects are nonzero.

The following Matlab project contains the source code and Matlab examples used for 2^2 factorial design analysis. .
This m-file is used in experiments involving several factors where it is necessary to investigate the joint effects (main and interactions) of the factors on a response variable and determine by a formal analysis of variance which factor effects are nonzero.

The following Matlab project contains the source code and Matlab examples used for three-way analysis of variance with repeated measures on two factors test. .
This is a three-factor analysis of variance design in which there are repeated measures on two of the factors.

The following Matlab project contains the source code and Matlab examples used for robust experimental designs for generalized linear models.
Optimal experimental designs for generalized linear models (GLM) depend on the unknown coefficients, and two experiments having the same model but different coefficient values will typically have different optimal designs.

The following Matlab project contains the source code and Matlab examples used for sequential experimental designs for glm.
Sequential Experimental Designs for Generalized Linear Models
Experimental Design is about choosing locations in which to take measurements.

The following Matlab project contains the source code and Matlab examples used for testing for curvature of a 2^2 factorial design analysis. .
This m-file is used in experiments where there is uncertain about the assumption of linearity over the region of exploration, and when the experimenter decides to conduct a 2^2 factorial design with a single or several replicates of each factorial run, augmented with some center points.

The following Matlab project contains the source code and Matlab examples used for adjustment of the f statistic by epsilon on repeated measures anova. .
Sphericity is an assumption of repeated measure ANOVA.

The following Matlab project contains the source code and Matlab examples used for profile analysis of multivariate repeated measures. .
Profile analysis is a special application of multivariate analysis of variance (MANOVA) in which several dependent variables are measured and they are all measured on the same scale.

The following Matlab project contains the source code and Matlab examples used for rma1multap.
One-sample repeated measures is used to analyze the relationship between the independent variable and dependent variable when:(1) the dependent variable is quantitative in nature and is measured on a level that at least approximates interval characteristics, (2) the independent variable is within-subjects in nature, and (3) the independent variable has three or more levels.

The following Matlab project contains the source code and Matlab examples used for determines the post-hoc statistical power of a performed analysis of variance model ii (random-effec .
Estimates the statistical power of an analysis of variance Model II (random-effects) after it has been performed. It requires the observed F-statistic value, numerator degrees of freedom, denominator degrees of freedom and significance level.