Descriptive statistics is a branch of statistics. Its aim is to summarize a set of statistical data. The data are usually taken by sampling a population. To picture the way the data are distributed, a histogram may be drawn. The data may be summarized by computing some characterising values, like the "center" of the data, and the "spread". In some cases the different items of data will be grouped and the groups will be described, in some way.
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Continuous data | |
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Count data | |
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Summary tables | |
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Dependence | |
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Graphics |
- Bar chart
- Biplot
- Box plot
- Control chart
- Correlogram
- Fan chart
- Forest plot
- Histogram
- Pie chart
- Q–Q plot
- Run chart
- Scatter plot
- Stem-and-leaf display
- Radar chart
- Violin plot
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Study design |
- Population
- Statistic
- Effect size
- Statistical power
- Optimal design
- Sample size determination
- Replication
- Missing data
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Survey methodology | |
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Controlled experiments | |
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Adaptive Designs |
- Adaptive clinical trial
- Up-and-Down Designs
- Stochastic approximation
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Observational Studies |
- Cross-sectional study
- Cohort study
- Natural experiment
- Quasi-experiment
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Statistical theory | |
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Frequentist inference | Point estimation |
- Estimating equations
- Unbiased estimators
- Mean-unbiased minimum-variance
- Rao–Blackwellization
- Lehmann–Scheffé theorem
- Median unbiased
- Plug-in
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Interval estimation | |
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Testing hypotheses |
- 1- & 2-tails
- Power
- Uniformly most powerful test
- Permutation test
- Multiple comparisons
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Parametric tests |
- Likelihood-ratio
- Score/Lagrange multiplier
- Wald
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Specific tests | | Goodness of fit | |
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Rank statistics |
- Sign
- Signed rank (Wilcoxon)
- Rank sum (Mann–Whitney)
- Nonparametric anova
- 1-way (Kruskal–Wallis)
- 2-way (Friedman)
- Ordered alternative (Jonckheere–Terpstra)
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Bayesian inference | |
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Correlation | |
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Regression analysis |
- Errors and residuals
- Regression validation
- Mixed effects models
- Simultaneous equations models
- Multivariate adaptive regression splines (MARS)
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Linear regression | |
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Non-standard predictors |
- Nonlinear regression
- Nonparametric
- Semiparametric
- Isotonic
- Robust
- Heteroscedasticity
- Homoscedasticity
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Generalized linear model | |
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Partition of variance |
- Analysis of variance (ANOVA, anova)
- Analysis of covariance
- Multivariate ANOVA
- Degrees of freedom
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Categorical / Multivariate / Time-series / Survival analysis |
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Categorical |
- Cohen's kappa
- Contingency table
- Graphical model
- Log-linear model
- McNemar's test
- Cochran-Mantel-Haenszel statistics
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Multivariate |
- Regression
- Manova
- Principal components
- Canonical correlation
- Discriminant analysis
- Cluster analysis
- Classification
- Structural equation model
- Multivariate distributions
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Time-series | General |
- Decomposition
- Trend
- Stationarity
- Seasonal adjustment
- Exponential smoothing
- Cointegration
- Structural break
- Granger causality
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Specific tests |
- Dickey–Fuller
- Johansen
- Q-statistic (Ljung–Box)
- Durbin–Watson
- Breusch–Godfrey
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Time domain |
- Autocorrelation (ACF)
- Cross-correlation (XCF)
- ARMA model
- ARIMA model (Box–Jenkins)
- Autoregressive conditional heteroskedasticity (ARCH)
- Vector autoregression (VAR)
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Frequency domain | |
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Survival | Survival function |
- Kaplan–Meier estimator (product limit)
- Proportional hazards models
- Accelerated failure time (AFT) model
- First hitting time
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Hazard function | |
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Test | |
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Applications |
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Biostatistics | |
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Engineering statistics |
- Chemometrics
- Methods engineering
- Probabilistic design
- Process / quality control
- Reliability
- System identification
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Social statistics | |
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Spatial statistics |
- Cartography
- Environmental statistics
- Geographic information system
- Geostatistics
- Kriging
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