Modeling and Multivariate Methods - SAS

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354 Performing Time Series Analysis Chapter 14 Launch the Platform Figure 14.1 The Time Series Launch Dialog The Time Series Graph The Time Series platform begins with a plot of each times series by the time ID, or row number if no time ID is specified (Figure 14.2). The plot, like others in JMP, has features to resize the graph, highlight points with the cursor or brush tool, and label points. See the Using JMP for a discussion of these features. Figure 14.2 Time Series Plot of Seriesg (Airline Passenger) Data If you open Time Series Basic Diagnostic Tables, graphs of the autocorrelation and partial autocorrelation (Figure 14.3) of the time series are shown. The platform popup menu, discussed next, also has fitting commands and options for displaying additional graphs and statistical tables.
Chapter 14 Performing Time Series Analysis 355 Time Series Commands Time Series Commands Graph The platform red triangle menu has the options described in the following sections. The Time Series platform begins by showing a time series plot, like the one shown previously in Figure 14.2. The Graph command on the platform popup menu has a submenu of controls for the time series plot with the following commands. Time Series Graph hides or displays the time series graph. Show Points hides or displays the points in the time series graph. Connecting Lines hides or displays the lines connecting the points in the time series graph. Mean Line series. hides or displays a horizontal line in the time series graph that depicts the mean of the time Autocorrelation The Autocorrelation command alternately hides or displays the autocorrelation graph of the sample, often called the sample autocorrelation function. This graph describes the correlation between all the pairs of points in the time series with a given separation in time or lag. The autocorrelation for the kth lag is N c k 1 r k = ---- wherec k = --- c N ( y t – y) ( y t– k – y) 0 t = k + 1 where y is the mean of the N nonmissing points in the time series. The bars graphically depict the autocorrelations. By definition, the first autocorrelation (lag 0) always has length 1. The curves show twice the large-lag standard error (± 2 standard errors), computed as k – 1 1 SE --- k N 1 2 r 2 = + i i = 1 The autocorrelation plot for the Seriesg data is shown on the left in Figure 14.3. You can examine the autocorrelation and partial autocorrelations plots to determine whether the time series is stationary (meaning it has a fixed mean and standard deviation over time) and what model might be appropriate to fit the time series. In addition, the Ljung-Box Q and p-values are shown for each lag. The Q-statistic is used to test whether a group of autocorrelations is significantly different from zero or to test that the residuals from a model can be distinguished from white-noise.
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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10 8 Analyzing Screening Designs Us
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12 Examining the Residuals . . . .
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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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Contents Example of a Multiple Resp
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158 Fitting Multiple Response Model
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Contents Overview of Generalized Li
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180 Fitting Generalized Linear Mode
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Contents Introduction to Logistic M
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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404 Performing Choice Modeling Chap
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Contents Launch the Multivariate Pl
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444 Correlations and Multivariate T
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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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470 Clustering Data Chapter 18 K-Me
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472 Clustering Data Chapter 18 K-Me
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474 Clustering Data Chapter 18 Norm
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476 Clustering Data Chapter 18 Norm
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478 Clustering Data Chapter 18 Self
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480 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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484 Analyzing Principal Components
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Contents Introduction . . . . . . .
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494 Performing Discriminant Analysi
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Contents Introduction to Item Respo
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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642 References Draper, N. and Smith
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644 References Hooper, J. H. and Am
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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650 References Umetrics (1995), Mul
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Contents The Response Models . . .
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654 Statistical Details Appendix A
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690 Index Birth Death Subset.jmp 46
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692 Index Ellipses Transparency 451
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694 Index test for curvature 46 Kru
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696 Index Customizing 267 Nonlinear
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698 Index reliability analysis 453-
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700 Index Stable 363 Stable Inverti
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702 Index
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Modeling and Multivariate Methods - SAS

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