Tham khảo tài liệu 'engineering statistics handbook episode 2 part 11', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | . Test Underlying Assumptions Spectral Plot Another useful plot for non-random data is the spectral plot. Quantitative Output Summary Statistics This spectral plot shows a single dominant low frequency peak. Although the 4-plot above clearly shows the violation of the assumptions we supplement the graphical output with some quantitative measures. 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 01 RANGE 01 MEAN 01 STAND . DEV. 01 MIDMEAN 01 AV. AB. DEV. 01 MEDIAN 01 MINIMUM 01 LOWER QUART. 01 LOWER HINGE 01 UPPER HINGE 01 UPPER QUART. 01 http div898 handbook eda section4 3 of 7 5 1 2006 9 58 36 AM . Test Underlying Assumptions MAXIMUM 01 RANDOMNESS MEASURES DISTRIBUTIONAL MEASURES AUTOCO COEF 00 ST. 3RD MOM. 00 00 ST. 4TH MOM. 01 00 ST. WILK-SHA 02 UNIFORM PPCC 00 NORMAL PPCC 00 TUK PPCC 00 CAUCHY PPCC 00 The value of the autocorrelation statistic is evidence of a very strong autocorrelation. 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 location the slope parameter should be zero. For this data set Dataplot generates the following output LEAST SQUARES MULTILINEAR FIT sAmple size N 500 NUMBER OF VARIABLES 1 NO REPLICATION CASE PARAMETER ESTIMATES APPROX. ST. DEV. T VALUE 1 A0 2 A1 X RESIDUAL STANDARD DEVIATION RESIDUAL DEGREES OF .
