User manual SAS IML STUDIO 3.3

[. . . ] SAS/IML Studio 3. 3 User's Guide ® SAS® Documentation The correct bibliographic citation for this manual is as follows: SAS Institute Inc. SAS/IML® Studio 3. 3 User's Guide Copyright © 2010, SAS Institute Inc. , Cary, NC, USA ISBN 978-1-60764-676-1 All rights reserved. For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. [. . . ] Raw residuals adds residuals, which are calculated as observed values minus predicted values. The variable is named PolyR_Y . Figure 20. 8 The Output Variables Tab 314 !Chapter 20: Data Smoothing: Polynomial Regression Analysis of Selected Variables If one or more interval variables are selected in a data table when you run the analysis, then the following occurs: The first selected interval variable is automatically entered in the Y Variable field of the Variables tab. The second selected interval variable is automatically entered in the X Variable field. No role variables are used for this analysis. Chapter 21 Model Fitting: Linear Regression Contents Overview of the Linear Regression Analysis . 315 316 316 319 319 323 329 329 330 331 332 334 335 335 Overview of the Linear Regression Analysis The Linear Regression analysis fits a linear regression model by using ordinary least squares. You can write the multiple linear regression equation for a model with p explanatory variables as Y D b0 C b1 X1 C b2 X2 C : : : C bp Xp where Y is the response variable, the Xi 's are explanatory variables, and the bi 's are regression coefficients. You can run a Linear Regression analysis by selecting Analysis IModel Fitting ILinear Regression from the main menu. The computation of the regression function, confidence limits, and related statistics is implemented by calling the REG procedure in SAS/STAT software. See the documentation for the REG procedure in the SAS/STAT User's Guide for additional details. 316 !Chapter 21: Model Fitting: Linear Regression Example: Fit a Linear Regression Model In this example you fit a linear regression model to predict the 1987 salaries of Major League Baseball players as a function of several explanatory variables in the Baseball data set. The example examines three explanatory variables: two measures of hitting performance and one measure of longevity. The explanatory variables are described in the following list: no_hits, the number of hits in 1986 no_home, the number of home runs in 1986 yr_major, the number of years that the player had been in the major leagues as of 1987 The example has four major steps: 1. Set name to be the variables whose values are used to label observations. Interpret the various plots that the analysis can produce. Part 1: Transform the Response Variable The salary variable ranges from 67. 5 to 2, 460 (measured in thousands of dollars). Since the variation of salaries is much greater for the higher salaries, it is appropriate to apply a logarithmic transformation to the salaries before fitting the model. The following steps use the Variable Transformation wizard to transform the salary variable. (This wizard is described in further detail in Chapter 32, "Variable Transformations. ") 1 Open the Baseball data set. 2 Select Analysis IVariable Transformation from the main menu. The Variable Transformation wizard in Figure 21. 1 appears. 3 Select the log10(Y+a) transformation from the Transformations list. Part 1: Transform the Response Variable !317 Figure 21. 1 Selecting a Log10 Transformation 4 Click Next. The wizard displays the page shown in Figure 21. 2. 318 !Chapter 21: Model Fitting: Linear Regression Figure 21. 2 Selecting a Variable and Parameters 5 Scroll to the end of the variable list. Select the salary variable, and click Set Y. The parameter a is an offset that is useful if your variable contains nonpositive values. [. . . ] SAS/INSIGHT automatically recomputes analyses (including curves on graphs) and statistics if data are changed. SAS/INSIGHT supports recording an interactive session for later playback. The following list presents features of SAS/INSIGHT data views (tables and plots) that are not included in SAS/IML Studio. multiple plots in a single window "renewing" a plot or analysis GUI support for animation changing the orientation of plots changing the formats of table cells after the table is created saving tables to data sets after they are created changing the attributes of a curve after it is created user-defined formats a "Tools window" for rapidly changing attributes of markers and curves a mechanism to set a common view range for all plots that display a given variable multiple plots (for example, BY-group plots and scatter plot matrices) in a single window 586 ! [. . . ]