Moral statistics

Moral statistics most narrowly refers to numerical data generally considered to be indicative of social pathology in groups of people. Examples include statistics on crimes (against persons and property), illiteracy, suicide, illegitimacy, abortion, divorce, prostitution, and the economic situation sometimes called pauperism in the 19th century.

The gathering of anything that might be called social statistics is often dated from John Graunt’s (1662)^[1] analysis of the London Bills of Mortality, which tabulated birth and death data collected by London parishes. The beginnings of the systematic collection of population statistics (now called demography) occurred in the mid-18th century, often attributed to Johann Peter Süssmilch in 1741. Data on moral variables began to be collected and disseminated by various state agencies (most notably in France and Britain) in the early 19th century, and were widely used in debates about social reform.

The first major work on this topic was the Essay on moral statistics of France by André-Michel Guerry in 1833.^[2] In this book, Guerry presented thematic maps of the departments of France, shaded according to illiteracy, crimes against persons and against property, illegitimacy, donations to the poor and so forth, and used these to ask questions about how such moral variables were related.

In Britain this theme was taken up beginning in 1847 by Joseph Fletcher who published several articles on the topic Moral and educational statistics of England and Wales.^[3]^[4]

References

↑ Graunt, J. (1662) Natural and Political Observations on the Bills of Mortality., London: John Martyn and James Allestry.
↑ Guerry, A.-M. (1833) Essai sur la statistique morale de la France. Crochard, Paris.
↑ Fletcher, J. (1847). Moral and educational statistics of England and Wales. Journal of the Statistical Society of London, 10, 193–221. JSTOR 2338048
↑ Fletcher, J. (1849). Moral and educational statistics of England and Wales. Journal of the Statistical Society of London, 12, 151–176, 189–335. JSTOR 2338222

Study design	Population Statistic Effect size Statistical power Sample size determination Missing data

Survey methodology	Sampling Standard error stratified cluster Opinion poll Questionnaire

Controlled experiments	Design control optimal Controlled trial Randomized Random assignment Replication Blocking Interaction Factorial experiment

Uncontrolled studies	Observational study Natural experiment Quasi-experiment

Statistical inference

Statistical theory

Frequentist inference

Point estimation	Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization Lehmann–Scheffé theorem Median unbiased Plug-in

Interval estimation	Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling Bootstrap Jackknife

Testing hypotheses	1- & 2-tails Power Uniformly most powerful test Permutation test Randomization test Multiple comparisons

Parametric tests	Likelihood-ratio Wald Score

Specific tests

Z (normal) Student's t-test F

Goodness of fit	Chi-squared Kolmogorov–Smirnov Anderson–Darling Normality (Shapiro–Wilk) Likelihood-ratio test Model selection Cross validation AIC BIC

Rank statistics	Sign Sample median Signed rank (Wilcoxon) Hodges–Lehmann estimator Rank sum (Mann–Whitney) Nonparametric anova 1-way (Kruskal–Wallis) 2-way (Friedman) Ordered alternative (Jonckheere–Terpstra)

Bayesian inference

Correlation	Pearson product–moment Partial correlation Confounding variable Coefficient of determination

Regression analysis	Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines (MARS)

Linear regression	Simple linear regression Ordinary least squares General linear model Bayesian regression

Non-standard predictors	Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity

Generalized linear model	Exponential families Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance	Analysis of variance (ANOVA, anova) Analysis of covariance Multivariate ANOVA Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

Categorical

Multivariate

Time-series

General	Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality

Specific tests	Dickey–Fuller Johansen Q-statistic (Ljung–Box) Durbin–Watson Breusch–Godfrey

Time domain	Autocorrelation (ACF) partial (PACF) Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR)

Frequency domain	Spectral density estimation Fourier analysis Wavelet

Survival

Survival function	Kaplan–Meier estimator (product limit) Proportional hazards models Accelerated failure time (AFT) model First hitting time

Hazard function	Nelson–Aalen estimator

Test	Log-rank test

Applications

Biostatistics	Bioinformatics Clinical trials / studies Epidemiology Medical statistics

Engineering statistics	Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification

Social statistics	Actuarial science Census Crime statistics Demography Econometrics National accounts Official statistics Population statistics Psychometrics

Spatial statistics	Cartography Environmental statistics Geographic information system Geostatistics Kriging

Category
Portal
Commons
WikiProject

This article is issued from Wikipedia - version of the 8/7/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.

Moral statistics

References

Further reading