Test selection diagram – two groups - PQStat

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SYSTEM REQUIREMENTS
INSTALLATION
SETTINGS

AI TOOLS

TEST POWER AND SAMPLE SIZE

Sample size determination
Power and sample size for test
Single-sample t-test
T-test for dependent groups
T-test for independent groups
Chi-square test for single sample variance
Chi-square test of two variances, Fisher-Snedecor
Chi-square test of goodness of fit
Chi-square test (RxC)
Test for two independent proportions, chi-square (2x2)
One-way ANOVA for independent groups
Test for one proportion

DESCRIPTIVE ANALYSES

Measurement scales
Tables
Frequency tables and empirical distributions of data
Table report
Analyses for contingency tables
Descriptive statistics
Measures of central tendency
Other measures of location
Measures of dispersion
Other distribution attributes
Descriptive summaries
PROBABILITY DISTRIBUTIONS
CONTINUOUS PROBABILITY DISTRIBUTIONS
PROBABILITY DISTRIBUTION CALCULATOR
Hypothesis testing

NORMALITY DISTRIBUTION TESTS

One-dimensional normality tests
Multivariate normality tests

COMPARISON - 1 GROUP

Test selection diagram – one group

PARAMETRIC TESTS

The single-sample t-test
The single-sample chi-square test for a population variance

NON-PARAMETRIC TESTS

The Wilcoxon test (signed-ranks)
The chi-square goodness-of-fit test
Tests for one proportion

COMPARISON - TWO GROUPS

Test selection diagram – two groups

PARAMETRIC TESTS

The Fisher-Snedecor test
The t-test for independent groups
The t-test with the Cochran-Cox adjustment
The t-test for dependent groups

NON-PARAMETRIC TESTS

The Mann-Whitney U test
The Wilcoxon test (matched-pairs)
The Chi-square tests
The Chi-square test for large tables
The Fisher test for large tables
The Chi-square test for small tables
The Chi-square test corrections for small tables
The Chi-square test for trend
The Relative Risk and the Odds Ratio
The Z test for two independent proportions
The McNemar test, the Bowker test of internal symmetry
The Z test for two dependent proportions

COMPARISON - MORE THAN TWO GROUPS

Test selection diagram – more than two groups

PARAMETRIC TESTS

The ANOVA for independent groups
Contrasts and POST-HOC tests
The ANOVA for independent groups with F* and F″ corrections
The Brown-Forsythe test and the Levene test
The ANOVA for dependent groups
Mauchly's sphericity
The ANOVA for dependent groups with Epsilon correction and MANOVA

NON-PARAMETRIC TESTS

The Kruskal-Wallis ANOVA
The Jonckheere-Terpstra test for trend
The Conover ranks test of variance
The Friedman ANOVA
The Page test for trend
The Durbin's ANOVA (missing data)
The Skillings-Mack ANOVA (missing data)
The Chi-square test for multidimensional contingency tables
The Q-Cochran ANOVA
MULTIPLE COMPARISONS

HOTELLING T-SQUARE

Hotelling's T-square test for a single sample
Hotelling's T-square test for dependent groups
Hotelling's T-square test for independent groups
Hotelling's T-square test for independent groups with correction for unequal variances
Box's test for equality of covariance matrices

UNIVARIATE MANOVA

One-way MANOVA for independent groups

ANALYSIS FOR STRATAS

The Mantel-Haenszel method for several 2x2 tables
The Mantel-Haenszel odds ratio
The Mantel-Haenszel relative risk

CORRELATION

Test selection diagram – correlation
Introduction to correlation

PARAMETRIC TESTS

The linear correlation coefficients
The Pearson correlation coefficient significance
The slope coefficient significance
Prediction
Comparison of correlation coefficients
Comparison of the slope of regression lines

NON-PARAMETRIC TESTS

The monotonic correlation coefficients
The t-test for the significance of the Spearman rank-order correlation coefficient
The test for the significance of the Kendall's tau correlation coefficient
Contingency table coefficients and their statistical significance

AGREEMENT ANALYSIS

Test selection diagram – agreement

PARAMETRIC TESTS

The intraclass correlation coefficient and a test to examine its significance

NON-PARAMETRIC TESTS

The Kendall's concordance coefficient and a test to examine its significance
The Cohen's Kappa coefficient and a test to examine its significance
The Fleiss' Kappa coefficient and a test to examine its significance

DIAGNOSTIC TESTS

Evaluation of a diagnostic test
The ROC curve
Selection of the optimal cut-off
ROC curves comparison

MULTIDIMENSIONAL MODELS

Matching groups
Preparation of variables for analysis in multivariate models

MULTIPLE LINEAR REGRESSION

Multiple linear regression
Validation and prediction for multiple regression

COMPARISON OF MULTIPLE LINEAR REGRESSION MODELS

Comparing multiple regression models

LOGISTIC REGRESSION

Logistic regression
Validation and prediction for logistic regression

COMPARISON OF LOGISTIC REGRESSION MODELS

Comparing logistic regression models
Factorial ANOVA – GLM
ANCOVA
Mediation effect

MIXED MODELS

Introduction: dependent data, fixed effects and random effects

MIXED MULTIPLE REGRESSION

Mixed multiple regression (linear mixed model)
Validation and prediction for mixed multiple regression
Comparing mixed multiple regression models

MIXED LOGISTIC REGRESSION

Mixed logistic regression (generalized linear mixed model)
Validation and prediction for mixed logistic regression
Comparing mixed logistic regression models

COX PH REGRESSION WITH RANDOM EFFECT

Cox PH regression with random effect (frailty)
Comparing Cox PH regression models with random effect

DIMENSION REDUCTION AND GROUPING

Principal component analysis
CLUSTER ANALYSIS
Hierarchical methods
The k-means method

SURVIVAL ANALYSIS

Life tables
Kaplan-Meier curves
Comparison of survival curves
Cox proportional hazards regression
Comparison of Cox PH regression models
Cox regression with time-dependent variables
Competing risks analysis – the Fine-Gray model

META-ANALYSIS

Introduction to meta-analysis and data preparation
Summary effect – fixed effect and random effect
Heterogeneity testing
Sensitivity testing
Asymmetry testing (publication bias)
Cumulative meta-analysis
Group comparison
Meta-regression

RELIABILITY ANALYSIS

Scale reliability
TEST SUMMARIES
THE WIZARD
OTHER NOTES
FILE FORMATS
References

SPATIAL ANALYSIS

SPATIAL ANALYSIS
BASIC DEFINITIONS
MAP OPENING
MAP MANAGER
Map Viewing Tools
Selection Area Tools
Layers
Map Style Edition
HOW TO REDUCE A WORKSPACE).
GEOMETRIC CALCULATIONS
SPATIAL WEIGHTS MATRIX
Weights matrix according to distance
Weights matrix according to contiguity
SPATIAL SMOOTHING
TESTING HYPOTHESES
DESCRIPTIVE STATISTICS
DENSITY ANALYSIS
Quadrat Count Methods
Kernel density estimator
Two-dimensional kernel estimator
Three-dimensional kernel estimator
RANDOMNESS OF POINT DISTRIBUTION
Nearest Neighbor Analysis
SPATIAL AUTOCORRELATION
Global Moran's I statistic
Global Geary's C statistic
LOCAL ESTIMATE OF SPATIAL CLUSTERING
Local Moran's I statistic
Local Getis-Ord’s G statistic
CutL

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Test selection diagram – two groups

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