Factor analysis is a way to look at many pieces of information and find groups among them. These groups are called "factors".[1]
Factor analysis is used to see if many things are related or similar. For example, if a person is asked many questions, factor analysis could be used to see if some of the answers are similar.[2]
This method is used in many fields. For example, in psychology, it is used to find out if certain behaviors or feelings are related. In business, it can be used to see if different products are liked by the same group of people.
Factor analysis makes it easier to understand large amounts of data. It can help to show important patterns and relationships in the data. It can also help to simplify complex information.[3]
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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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