/* NIST StRD benchmark from http://www.nist.gov/itl/div898/strd/ Univariate Summary Statistics Difficulty=Average N=1001 Generated File Name: numacc3.dat Dataset Name: Numerical-Accuracy-3 Description: This is a constructed/fabricated data set to test accuracy in summary statistic calculations. The numbers are 8-digit floating point values and differ only in the last decimal place. Note--by construction, this data set has sample mean = 1000000.2 (exact) sample standard dev. = 0.1 (exact) sample autocorr. coef. = -0.999 (exact) Stat Category: Univariate: Summary Statistics Reference: Simon, Stephen D. and Lesage, James P. (1989). Assessing the Accuracy of ANOVA Caluclations in Statistical Software", Computational Statistics & data Analysis, 8, pp. 325-332. Data: Constructed 1 Response : y 0 Predictors 1001 Observations Model: Average Level of Difficulty 2 Parameters : mu, sigma 1 Response Variable : y 0 Predictor Variables y = mu + e Certified Values Sample Mean ybar: 1000000.2 (exact) Sample Standard Deviation (denom. = n-1) s: 0.1 (exact) Sample Autocorrelation Coefficient (lag 1) r(1): -0.999 (exact) Number of Observations: 1001 */ clear scalar N = 1001 scalar mean = 1000000.2 scalar sd = 0.1 scalar rho1 = -0.999 qui input double y 1000000.2 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 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1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 1000000.1 1000000.3 end summarize y assert scalar(N) == r(N) lrecomp r(mean) mean sqrt(r(Var)) sd gen time = _n tsset time corrgram y, lag(1) lrecomp r(ac1) rho1