Probability+Plots

Probability Plots ({Topic Category TBD}) (GWU EMSE-271)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | Nomenclature | Concepts

" In statistics, a **probability plot** is a graphical technique for comparing two data sets, either two sets of empirical observations, one empirical set against a theoretical set, or (more rarely) two theoretical sets against each other
 * "It commonly means one of P-P plot, Probability-Probability or Percent-Percent plot" or "Q-Q plot, Quantil-Quantile plot." - [|Wikipedia]

Big use in EMSE 271 are normality probability plots (in Mintab when we can) to validate the data is normal and statistical significance test can be used.

"The **normal probability plot** is a graphical technique for normality testing: assessing whether or not a data set is approximately normally distributed.

"The data are plotted against a theoretical normal distribution in such a way that the points should form an approximate straight line. Departures from this straight line indicate departures from normality." - [|Wikipedia]

//EMSE 271 Notes:// · Probability plots in MINITAB are a powerful visual tool for testing goodness of fit. · The AD value is the statistic value of the Anderson-Darling goodness-of-fit test (similar in spirit as the χ2-test). Large values of the AD-statistic indicate a larger deviation from the fitted theoretical distribution. · The larger the :-value the larger the support for the theoretical distribution. · If the theoretical distribution is a perfect fit of the data all data point should form a straight line. · Deviations from the straight line show deviations from the fitted theoretical distribution. · When can a data point be considered an outlier? Answer: when a data point is outside the boundaries that are drawn. The boundaries in the above figure are 95% confidence intervals for the cumulative distribution function F(x|ϴ^).


 * Sources:**
 * Probability plot. (2009, October 19). In //Wikipedia, The Free Encyclopedia//. Retrieved 16:15, March 6, 2010, from []
 * Normal probability plot. (2009, September 26). In //Wikipedia, The Free Encyclopedia//. Retrieved 16:20, March 6, 2010, from []
 * EMSE 271, Fall 2009