Contents 1 Nature of the work 2 See also 3 References 4 External links

Nature of the work[edit] According to the United States Bureau of Labor Statistics, as of 2014, 26,970 jobs were classified as statistician in the United States. Of these people, approximately 30 percent worked for governments (federal, state, or local).[3] As of May 2016, the median pay for statisticians in the United States was $80,500.[3] Additionally, there is a substantial number of people who use statistics and data analysis in their work but have job titles other than statistician,[4] such as actuaries, applied mathematicians, economists, financial analysts, psychometricians, sociologists, epidemiologists, and quantitative psychologists.[5] Statisticians are included with the professions in various national and international occupational classifications.[6][7] In the United States most employment in the field requires either a masters degree in statistics or a related field or a PhD.[1] "Typical work includes collaborating with scientists, providing mathematical modeling, simulations, designing randomized experiments and randomized sampling plans, analyzing experimental or survey results, and forecasting future events (such as sales of a product)."[8]

See also[edit] List of statisticians History of statistics

References[edit] ^ a b "O*NET OnLine: 15-2041.00 - Statisticians". Retrieved 29 January 2017.  ^ "Royal Statistical Society StatsLife Types of Job". Retrieved 29 January 2017.  ^ a b Bureau of Labor Statistics, US Department of Labor. "Statisticians". Occupational Outlook Handbook (2016-17 ed.). Retrieved 30 May 2017.  ^ Bureau of Labor Statistics, US Department of Labor. "Statisticians". Occupational Outlook Handbook (2010-11 ed.). Archived from the original on 14 May 2011.  ^ Bureau of Labor Statistics, US Department of Labor. "Statisticians". Occupational Outlook Handbook (2006-07 ed.). Archived from the original on 1 October 2007. Retrieved 3 October 2007.  ^ "International Labour Organisation (ILO) International Standard Classification of Occupations (ISCO) ISCO-08 classification structure" (PDF). Retrieved 29 January 2017.  ^ "Canadian National Occupational Classification (NOC) 2011 21 - Professional occupations in natural and applied sciences". Retrieved 29 January 2017.  ^ "IMS Presidential Address: Let us own Data Science, 1 October 2014, News of the Institute of Mathematical Statistics". Retrieved 29 January 2017. 

External links[edit] Wikimedia Commons has media related to Statisticians. Statistician entry, Occupational Outlook Handbook, U.S. Bureau of Labor Statistics Careers Center, American Statistical Association Careers information, Royal Statistical Society (UK) Listing of tasks and duties - The International Standard Classification of Occupations (ISCO) Listings of nature of work etc - O*NET v t e Statistics Outline Index Descriptive statistics Continuous data Center Mean arithmetic geometric harmonic Median Mode Dispersion Variance Standard deviation Coefficient of variation Percentile Range Interquartile range Shape Central limit theorem Moments Skewness Kurtosis L-moments Count data Index of dispersion Summary tables Grouped data Frequency distribution Contingency table Dependence Pearson product-moment correlation Rank correlation Spearman's rho Kendall's tau Partial correlation Scatter plot 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 Data collection Study design Population Statistic Effect size Statistical power Sample size determination Missing data Survey methodology Sampling stratified cluster Standard error 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 Population Statistic Probability distribution Sampling distribution Order statistic Empirical distribution Density estimation Statistical model Lp space Parameter location scale shape Parametric family Likelihood (monotone) Location–scale family Exponential family Completeness Sufficiency Statistical functional Bootstrap U V Optimal decision loss function Efficiency Statistical distance divergence Asymptotics Robustness 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 Lilliefors Jarque–Bera 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 Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator Correlation Regression analysis 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 Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Multivariate Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model Factor analysis Multivariate distributions Elliptical distributions Normal 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 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