Quality and Reliability Methods - SAS

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Contents Pareto Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Assigning Variable Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Pareto Plot Platform Commands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Options for Bars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Launch Dialog Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Threshold of Combined Causes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 One-Way Comparative Pareto Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Two-Way Comparative Pareto Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .190 Defect Per Unit Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Using Number of Defects as Sample Size. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Using a Constant Sample Size Across Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Using a Non-Constant Sample Size Across Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .194
Chapter 11 Pareto Plots 181 Pareto Plots Pareto Plots The Pareto Plot command produces charts to display the relative frequency or severity of problems in a quality-related process or operation. A Pareto plot is a bar chart that displays the classification of problems arranged in decreasing order. The column whose values are the cause of a problem is assigned as Y and is called the process variable. The column whose values hold the frequencies are assigned as Freq. You can also request a comparative Pareto plot, which is a graphical display that combines two or more Pareto Plots for the same process variable. JMP then produces a single graphical display with plots for each value in a column assigned the X role, or combination of levels from two X variables. Columns with the X role are called classification variables. The Pareto Plot command can chart a single Y (process) variable with no X classification variables, with a single X, or with two X variables. The Pareto facility does not distinguish between numeric and character variables or between modeling types. All values are treated as discrete, and bars represent either counts or percentages. The following list describes the arrangement of the Pareto graphical display: • A Y variable with no X classification variables produces a single chart with a bar for each value of the Y variable. • A Y variable with one X classification variable produces a row of Pareto plots. There is a plot for each level of the X variable with bars for each Y level. • A Y variable with two X variables produces rows and columns of Pareto plots. There is a row for each level of the first X variable and a column for each level of the second X variable. The rows have a Pareto Plot for each value of the first X variable, as described previously. The following sections illustrate each of these arrangements. Assigning Variable Roles The Failure.jmp table (Figure 11.2) from the Quality Control sample data folder lists causes of failure during the fabrication of integrated circuits. The N column in the table to the right lists the number of times each type of defect occurred. It is a Freq variable in the Pareto Launch dialog. For the raw data table, shown on the left (Figure 11.2), causes of failure are not grouped. The Pareto Plot command produces the same results from either of these tables. The following example uses the failure data with a frequency column.
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Version 10 Quality and Reliability
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Nelson Rules . . . . . . . . . .
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8 8 Assess Measurement Systems The
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10 Other Menu Options . . . . . . .
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12 Reliability Forecast Platform Op
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Chapter 1 Learn about JMP Documenta
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Chapter 1 Learn about JMP 17 Book C
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Chapter 1 Learn about JMP 19 Book C
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Chapter 1 Learn about JMP 21 Additi
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Chapter 1 Learn about JMP 23 Additi
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Chapter 2 Statistical Control Chart
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Chapter 3 Introduction to Control C
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Chapter 4 Interactive Control Chart
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Chapter 5 Shewhart Control Charts V
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Chapter 6 Cumulative Sum Control Ch
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Chapter 7 Multivariate Control Char
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Chapter 8 Assess Measurement System
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Chapter 13 Lifetime Distribution 23
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Chapter 14 Lifetime Distribution II
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Chapter 15 Recurrence Analysis The
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Chapter 15 Recurrence Analysis 263
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Chapter 16 Degradation Using the De
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Chapter 16 Degradation 279 Overview
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Chapter 16 Degradation 281 The Degr
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Chapter 16 Degradation 283 Model Sp
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Chapter 16 Degradation 291 Inverse
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Chapter 16 Degradation 293 Platform
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Chapter 16 Degradation 295 Platform
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Chapter 16 Degradation 297 Model Re
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Chapter 16 Degradation 299 Destruct
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Chapter 16 Degradation 301 Stabilit
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Chapter 17 Forecasting Product Reli
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Chapter 18 Reliability Growth Using
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Chapter 18 Reliability Growth 319 O
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Chapter 18 Reliability Growth 321 E
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Chapter 18 Reliability Growth 327 T
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Chapter 18 Reliability Growth 329 F
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Chapter 18 Reliability Growth 343 A
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Chapter 18 Reliability Growth 345 A
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Chapter 19 Reliability and Survival
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References Abernethy, Robert B. (19
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References 407 Carroll, R.J., Ruppe
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References 409 Haaland, P.D. (1989)
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References 411 Lawless, J.F. (1982)
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References 413 Nelson, W.B. (2003),
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References 415 Snee, R.D. and Marqu
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Index JMP Quality and Reliability M
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Index 419 Fit SEV 242 Fit Weibull 2
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Index 421 Q Quantiles table 356 R R
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Index 423 Weibull Plot 360 Westgard
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Quality and Reliability Methods - SAS

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