Basic Analysis and Graphing - SAS

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250 Bootstrapping Chapter 9 Example of Bootstrapping Note: The values in the results tables differ due to the random sampling of observations and assignment of frequencies. Row 1 is the estimate from the original data, and is marked with an X and has the excluded row state. Figure 9.3 Bootstrap Results Table 12. Select Analyze > Distribution. 13. Assign Displacement to the Y, Columns role. 14. Click OK. The Distribution report includes the Bootstrap Confidence Limits report (Figure 9.4). Figure 9.4 Bootstrap Report In this example, the estimate of the slope from the original data is 0.504. For 95% coverage of the bootstrap results, the company can estimate the slope to be between 0.398 and 0.605. When the displacement is changed by one unit, the horsepower changes by some amount between 0.398 and 0.605.
Chapter 9 Bootstrapping 251 Perform a Bootstrap Analysis Perform a Bootstrap Analysis To perform a bootstrap analysis, right-click on a report in a platform report window and select Bootstrap. Specify the number of bootstrap samples and choose whether to use fractional weights or to split the selected column. For more information, see “Bootstrap Window Options” on page 251. Every statistic in the report is bootstrapped. The bootstrapping process samples from the original data. An invisible data table is used to store the sampled data and perform the analysis. Bootstrap Window Options After you right-click a column in a report and select Bootstrap, the Bootstrapping window appears with the following options: Number of Bootstrap Samples The number of times that you want to re-sample the data and compute the statistics. A higher number results in more precise estimates of the statistics’ properties. Fractional Weights Uses fractional weights in the re-sampling process. If Fractional Weights is not selected, the following observations are true: – The sampling weights assigned to the original observations are integers. – The influence of an observation is determined by the relative weights. For example, if an observation is assigned a weight of 2, then that observation appears twice in the sample for the current bootstrap iteration. – The sampling weights can be zero. Observations with a weight of zero are not included in the estimation process for the current bootstrap iteration. If Fractional Weights is selected, the following observations are true: – The sampling weights can be non-integers. The influence of an observation is determined by the relative weights. – The sampling weights cannot be zero. All of the observations are included in the bootstrap process. In both cases, the sum of the weights is n (the number of observations in the original data). Split Selected Column Splits a stacked column of bootstrap results into separate columns, one for each statistic. For example, say that the column that you right-clicked before choosing Bootstrap has three statistics in it. In addition to the main results table with all the statistics stacked, another results table is created with the results for that column, unstacked into separate columns. For an example, see “Stacked Results Table” on page 251. Stacked Results Table The initial results of a bootstrap analysis appear in a stacked results table (Figure 9.5).
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Version 10 Basic Analysis and Graph
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USE, DATA OR PROFITS, WHETHER IN AN
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6 Options for Categorical Variables
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8 Nonparametric Bivariate Density R
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10 Example of the Power Option . .
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12 Statistical Details for the Logi
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14 Use Categorical Variables . . .
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16 Control Animation for Dynamic Bu
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18 Launch the Scatterplot Matrix Pl
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Contents Book Conventions. . . . .
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22 Learn about JMP Chapter 1 Book C
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Additi
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28 Learn about JMP Chapter 1 Additi
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Contents Overview of the Distributi
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32 Performing Univariate Analysis C
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Contents Overview of the Fit Y by X
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90 Introduction to the Fit Y by X P
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Contents Example of Bivariate Analy
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94 Performing Bivariate Analysis Ch
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100 Performing Bivariate Analysis C
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Contents Overview of Oneway Analysi
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130 Performing Oneway Analysis Chap
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Contents Example of Contingency Ana
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186 Performing Contingency Analysis
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Page 200 and 201: 200 Performing Contingency Analysis
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300 Interactive Data Visualization
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Contents Example of the Chart Platf
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314 Creating Summary Charts Chapter
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Contents Example of an Overlay Plot
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340 Creating Overlay Plots Chapter
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Contents Example of a 3D Scatterplo
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354 Creating Three-Dimensional Scat
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Contents Example of a Contour Plot
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374 Creating Contour Plots Chapter
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Contents Example of a Dynamic Bubbl
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384 Creating Bubble Plots Chapter 1
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Contents Overview of Mapping . . .
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406 Creating Maps Chapter 16 Exampl
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418 Creating Maps Chapter 16 Backgr
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Contents Example of a Parallel Plot
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450 Creating Parallel Plots Chapter
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Contents Example of a Cell Plot. .
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460 Creating Cell Plots Chapter 18
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Contents Example of Tree Maps . . .
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468 Creating Tree Maps Chapter 19 E
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470 Creating Tree Maps Chapter 19 E
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472 Creating Tree Maps Chapter 19 T
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474 Creating Tree Maps Chapter 19 A
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Contents Example of a Scatterplot M
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486 Creating Scatterplot Matrices C
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Contents Example of a Ternary Plot
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496 Creating Ternary Plots Chapter
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504 References Becker, R.A. and Cle
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506 References Dunnett, C.W. (1955)
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508 References Hosmer, D.W. and Lem
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510 References McLachlan, G.J. and
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512 References Robertson, T., Wrigh
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514 References Wadsworth, H. M., St
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516 Index examples 459, 463-464 lau
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518 Index Horizontal option 268, 32
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520 Index Proportion of Densities o
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522 Index examples 467-468, 473-482
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Basic Analysis and Graphing - SAS

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