Jump to a section on this page:
- Descriptive statistics
- testing distributions
- comparison tests
- nonparametric comparison tests
- comparing regression lines
- frequency and contingency tables
- variance tests
- survival analysis
- exploratory data analysis
- fitting models to data
Descriptive statistics (sample size, number missing, mean, median, standard deviation, etc.)
Testing distributional goodness of fit
Testing equality of means or location
- t test
- analysis of variance [ANOVA]
- sign test [one-sample and two-sample paired
- signed rank test [one-sample and two-sample paired
- rank sum test
- Kruskal-Wallis test
- Friedman's test
- Compare Samples
- Cochran's Q test
- McNemar's Q test
Nonparametric comparison tests:
Nonparametric tests of location or spread
- (sign test [one-sample and two-sample paired
- signed rank test [one-sample and two-sample paired
- rank sum test
- Kruskal-Wallis test
- Friedman's test
- Compare Samples
- Cochran's Q test
- McNemar's Q test
- Ansari-Bradley test
Comparing simple linear (straight-line) regression lines (analysis of covariance [ANCOVA])
Frequency and contingency tables:
Frequency and contingency tables
- (categorical analysis) (creating frequency tables, cross-tabulation
- [creating contingency tables, measures of association], contingency table analysis
- [chi-square test for independence, Fisher's exact test]
Testing equality of variances or dispersions
Survival analysis
- (analyzing and comparing survival functions) (life tables
- Kaplan-Meier plot
- comparing survival curves
- [Mantel-Cox test, Gehan-Breslow test, Tarone-Ware test]
Exploratory data analysis
- (EDA) (discovering patterns in data) coded tables
- median polish
- letter values
- repeated-median regression
Model fitting
- simple linear (fitting a line: b0 + b1*x)
- multiple linear (fitting a linear combination of x terms: b0 + b1*x1 + b2*x2 + ... + bn*xn)
- polynomial (fitting a polynomial, b0 + b1*x + b2*x**2 + ... + bn*x**n)
- non-linear (fitting an arbitrary function f(x), e.g., (vmax*x) / (km+x), dose*exp(-k*t)
- logistic (fitting the logit of y by a linear combination of x terms: b0 + b1*x1 + b2*x2 + ... + bn*xn)
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