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Engineering Statistics Handbook Episode 2 Part 10

Tham khảo tài liệu 'engineering statistics handbook episode 2 part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | I.4.2.2.2. Graphical Output and Interpretation and comparing confidence intervals in these cases. NIST rTT TTZ SEMATECH H WE TOOLS AIDS SEAftCH BACK NỄXĨ http www.itl.nist.gov div898 handbook eda section4 eda4222.htm 7 of 7 5 1 2006 9 58 33 AM I.4.2.2.3. Quantitative Output and Interpretation ikIO i iE ENGINEERING STATISTICS HANDBOOK TOOLS a AIDS I SEARCH back NỄXĨ1 1. Exploratory Data Analysis 1.4. EDA Case Studies 1.4.2. Case Studies 1.4.2.2. Uniform Random Numbers I.4.2.2.3. Quantitative Output and Interpretation Summary Statistics As a first step in the analysis a table of summary statistics is computed from the data. The following table generated by Dataplot shows a typical set of statistics. SUMMARY NUMBER OF OBSERVATIONS 500 LOCATION MEASURES DISPERSION MEASURES MIDRANGE 0.4997850E 00 RANGE 0.9945900E 00 MEAN 0.5078304E 00 STAND. DEV. 0.2943252E 00 MIDMEAN 0.5045621E 00 AV. AB. DEV. 0.2526468E 00 MEDIAN 0.5183650E 00 MINIMUM 0.2490000E-02 LOWER QUART. 0.2508093E 00 LOWER HINGE 0.2505935E 00 UPPER HINGE 0.7594775E 00 UPPER QUART. 0.7591152E 00 MAXIMUM 0.9970800E 00 RANDOMNESS MEASURES DISTRIBUTIONAL MEASURES AUTOCO COEF -0.3098569E-01 ST. 3RD MOM. -0.3443941E-01 0.0000000E 00 ST. 4TH MOM. 0.1796969E 01 0.0000000E 00 ST. WILK-SHA -0.2004886E 02 UNIFORM PPCC 0.9995682E 00 NORMAL PPCC 0.9771602E 00 http www.itl.nist.gov div898 handbook eda section4 eda4223.htm 1 of 7 5 1 2006 9 58 34 AM I.4.2.2.3. Quantitative Output and Interpretation TUK -.5 PPCC 0.7229201E 00 CAUCHY PPCC 0.3591767E 00 Note that under the distributional measures the uniform probability plot correlation coefficient PPCC value is significantly larger than the normal PPCC value. This is evidence that the uniform distribution fits these data better than does a normal distribution. Location One way to quantify a change in location over time is to fit a straight line to the data set using the index variable X 1 2 . N with N denoting the number of observations. If there is no significant drift in the