Graphing univariate distributions is central to both statistical graphics, in general, and Stata's graphics, in particular. Now that Stata 8 is out, a review of official and user-written commands is timely. The emphasis here is on going beyond what is obviously and readily available, with pointers to minor and major trickery and various user-written commands. For plotting histogram-like displays, kernel-density estimates and plots based on distribution functions or quantile functions, a large variety of choices is now available to the researcher.
AltmanD. G.1991. Practical Statistics for Medical Research.London: Chapman & Hall.
2.
BagnoldR. A.1937. The size-grading of sand by wind. Proceedings of the Royal Society Series A163: 250–264.
3.
BagnoldR. A.1941. The Physics of Blown Sand and Desert Dunes.London: Methuen.
4.
BagnoldR. A.1990. Sand, Wind, and War: Memoirs of a Desert Explorer.Tucson: University of Arizona Press.
5.
BardouF., BouchaudJ.-P., AspectA., and Cohen-TannoudjiC.2002. Lévy Statistics and Laser Cooling: How Rare Events Bring Atoms to Rest.Cambridge: Cambridge University Press.
6.
BenjaminiY., and KriegerA.M.1996. Concepts and measures for skewness with data-analytic implications. Canadian Journal of Statistics24: 131–140.
7.
BenjaminiY.1999. Skewness—concepts and measures. In Encyclopedia of Statistical Sciences Update, ed. KotzS., ReadC. B., and BanksD. L., vol. 3, 663–670. New York: John Wiley & Sons.
8.
BowleyA. L.1902. Elements of Statistics.2d ed. London: P. S. King.
9.
BowmanA. W., and AzzaliniA.1997. Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Applications.Oxford: Oxford University Press.
10.
BreimanL.1973. Statistics: With a View towards Applications.Boston: Houghton Mifflin.
11.
ChambersJ. M., ClevelandW. S., KleinerB., and TukeyP.A.1983. Graphical Methods for Data Analysis.Belmont, CA: Wadsworth.
CoxN. J.1999a. gr41: Distribution function plots. Stata Technical Bulletin51: 12–16. In Stata Technical Bulletin Reprints, vol. 9, 108–112. College Station, TX: Stata Press.
16.
CoxN. J.1999b. gr42: Quantile plots, generalized. Stata Technical Bulletin51: 16–18. In Stata Technical Bulletin Reprints, vol. 9, 113–116. College Station, TX: Stata Press.
17.
CoxN. J.2001. gr42.1: Quantile plots, generalized: update to Stata 7.0. Stata Technical Bulletin61: 10–11. In Stata Technical Bulletin Reprints, vol. 10, 55–56. College Station, TX: Stata Press.
18.
CoxN. J.2003a. Software update: gr41_1: Distribution function plots. Stata Journal3(2): 211.
19.
CoxN. J.2003b. Software update: gr41_2: Distribution function plots. Stata Journal3(4): 449.
20.
CoxN. J.2003c. Stata tip 2: Building with floors and ceilings. Stata Journal3(4): 446–447.
GnanadesikanR.1977. Methods for Statistical Data Analysis of Multivariate Observations.New York: John Wiley & Sons.
30.
GnanadesikanR.1997. Methods for Statistical Data Analysis of Multivariate Observations.2d ed. New York: John Wiley & Sons.
31.
GroeneveldR.1998. Skewness, Bowley's measure of. In Encyclopedia of Statistical Scienes Update, ed. KotzS., ReadC. B., and BanksD. L., vol. 2, 619–621. New York: John Wiley & Sons.
32.
GumbelE. J.1943. On the reliability of the classical chi-square test. Annals of Mathematical Statistics14: 253–263.
33.
HaldA.1990. A History of Probability and Statistics and their Applications before 1750.New York: John Wiley & Sons.
34.
HazeltonM. L.2003. A graphical tool for assessing normality. American Statistician57: 285–288.
35.
HoaglinD. C.1985. Using quantiles to study shape. In Exploring Data Tables, Trends, and Shapes, ed. HoaglinD. C., MostellerF., and TukeyJ. W., 417–460. New York: John Wiley & Sons.
36.
van LangrenM. F.1644. La Verdadera Longitud po Mar y Tierra.Antwerp.
37.
MannH. B., and WaldA.1942. On the choice of the number of class intervals in the application of the chi-square test. Annals of Mathematical Statistics13: 306–317.
38.
ParzenE.1979. Nonparametric statistical data modeling. Journal of the American Statistical Association74: 105–131.
39.
QueteletA.1827. Recherches sur la population, les naissances, les décès, les prisons, les dépôts de mendicité, etc., dans le Royaume des Pays-Bas. Nouveaux Mémoires de l'Académie Royale des Sciences et Belles-lettres de Bruxelles4: 117–192.
40.
Salgado-UgarteI. H., and Pérez-HernándezM. A.2003. Exploring the use of variable bandwidth kernel density estimators. Stata Journal3(2): 133–147.
41.
ScottD. W.1992. Multivariate Density Estimation: Theory, Practice, and Visualization.New York: John Wiley & Sons.
42.
SilvermanB. W.1986. Density Estimation for Statistics and Data Analysis.Monographs on Statistics and Applied Probability, London: Chapman & Hall.
43.
SimonoffJ. S.1996. Smoothing Methods in Statistics.New York: Springer.
44.
StiglerS. M.1986. The History of Statistics: The Measurement of Uncertainty before 1900.Cambridge, MA: Harvard University Press.
45.
ThorneC. R., MacArthurR. C., and BradleyJ. B., ed. 1988. The Physics of Sediment Transport by Wind and Water: A Collection of Hallmark Papers by R. A. Bagnold.New York: American Society of Civil Engineers.
46.
TufteE. R.1997. Visual Explanations: Images and Quantities, Evidence and Narrative.Cheshire, CT: Graphics Press.
47.
TukeyJ. W.1977. Exploratory Data Analysis.Reading, MA: Addison–Wesley.
48.
Van KermP.2003. Adaptive kernel density estimation. Stata Journal3(2): 148–156.
49.
WandM. P., and JonesM.C.1995. Kernel Smoothing.London: Chapman & Hall.
50.
WildC. J., and SeberG.2000. Chance Encounters: A First Course in Data Analysis and Inference.New York: John Wiley & Sons.
51.
WilkM. B., and GnanadesikanR.1968. Probability plotting methods for the analysis of data. Biometrika55: 1–17.
52.
WilkinsonL.1999. Dot plots. American Statistician53: 276–281.
