Introduction to SAS OnDemand for Academics: Enterprise Guide. Handout

Transcription

1 Introduction to SAS OnDemand for Academics: Enterprise Guide Handout

2 SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. Introduction to SAS OnDemand for Academics: Enterprise Guide Handout Copyright 2008 by SAS Institute Inc., Cary, NC 27513, USA. All rights reserved. Printed in the United States of America. 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. Book code E1425, handout code LWSOD, prepared date 28Aug08. LWSOD_002

3 For Your Information iii Table of Contents General Conventions... v Chapter 1 Introduction to SAS Enterprise Guide SAS Enterprise Guide and the SAS System SAS OnDemand for Academics Chapter 2 Analysis Capabilities An Overview of Tasks Data Exploration, Analysis, and Reporting by Example Chapter 3 Other Sources of Information Other Sources of Information

4 iv For Your Information To learn more A full curriculum of general and statistical instructor-based training is available at any of the Institute s training facilities. Institute instructors can also provide on-site training. For information on other courses in the curriculum, contact the SAS Education Division at , or send to training@sas.com. You can also find this information on the Web at support.sas.com/training/ as well as in the Training Course Catalog. For a list of other SAS books that relate to the topics covered in this Course Notes, USA customers can contact our SAS Publishing Department at or send to sasbook@sas.com. Customers outside the USA, please contact your local SAS office. Also, see the Publications Catalog on the Web at support.sas.com/pubs for a complete list of books and a convenient order form.

5 For Your Information v General Conventions This section explains the various conventions used in presenting text, SAS language syntax, and examples in this book. Typographical Conventions You will see several type styles in this book. This list explains the meaning of each style: UPPERCASE ROMAN italic bold monospace select is used for SAS statements and other SAS language elements when they appear in the text. identifies terms or concepts that are defined in text. Italic is also used for book titles when they are referenced in text, as well as for various syntax and mathematical elements. is used for emphasis within text. is used for examples of SAS programming statements and for SAS character strings. Monospace is also used to refer to variable and data set names, field names in windows, information in fields, and user-supplied information. indicates selectable items in windows and menus. This book also uses icons to represent selectable items. Syntax Conventions The general forms of SAS statements and commands shown in this book include only that part of the syntax actually taught in the course. For complete syntax, see the appropriate SAS reference guide. PROC CHART DATA= SAS-data-set; HBAR VBAR chart-variables </ options>; RUN; This is an example of how SAS syntax is shown in text: PROC and CHART are in uppercase bold because they are SAS keywords. DATA= is in uppercase to indicate that it must be spelled as shown. SAS-data-set is in italic because it represents a value that you supply. In this case, the value must be the name of a SAS data set. HBAR and VBAR are in uppercase bold because they are SAS keywords. They are separated by a vertical bar to indicate they are mutually exclusive; you can choose one or the other. chart-variables is in italic because it represents a value or values that you supply. </ options> represents optional syntax specific to the HBAR and VBAR statements. The angle brackets enclose the slash as well as options because if no options are specified you do not include the slash. RUN is in uppercase bold because it is a SAS keyword.

6 vi For Your Information

7 Chapter 1 Introduction to SAS Enterprise Guide 1.1 SAS Enterprise Guide and the SAS System SAS OnDemand for Academics

8 1-2 Chapter 1 Introduction to SAS Enterprise Guide

9 1.1 SAS Enterprise Guide and the SAS System SAS Enterprise Guide and the SAS System Objectives Define the relationship between SAS Enterprise Guide and SAS software. Review SAS OnDemand as it relates to the use of SAS Enterprise Guide. 3

10 1-4 Chapter 1 Introduction to SAS Enterprise Guide Introduction to SAS Enterprise Guide 6 SAS Enterprise Guide software is an easy-to-use Windows application that provides the following: an intuitive, visual interface access to the power of SAS transparent access to data ready-to-use tasks for analysis and reporting easy exporting of data and results to other applications scripting and automation Users of all experience levels, from novice to expert, can use SAS Enterprise Guide to produce meaningful results quickly.

11 1.1 SAS Enterprise Guide and the SAS System 1-5 Introduction to SAS Enterprise Guide 7 SAS Enterprise Guide provides a state-of-the-art Windows interface with these features: drag-and-drop functionality dialog boxes wizards a color-coded syntax editor a full Online Help facility, embedded context-sensitive help, and a Getting Started tutorial

12 1-6 Chapter 1 Introduction to SAS Enterprise Guide SAS Enterprise Guide Interface Programming Interface 8 If you are a SAS programmer, you can use the SAS Enterprise Guide Code Editor to create new code or to modify existing SAS programs If you are not a programmer, you can use SAS Enterprise Guide to access the power of SAS without learning the SAS programming language.

13 1.1 SAS Enterprise Guide and the SAS System 1-7 Behind the Scenes SAS Enterprise Guide can use the execution power of the server to access data and run SAS processes. 9 SAS Enterprise Guide... SAS Enterprise Guide provides a visual interface to SAS and is installed on a client machine. Behind the Scenes SAS Enterprise Guide can use the execution power of the server to access data and run SAS processes. SAS on Windows SAS on Mainframe 10 SAS Enterprise Guide SAS on UNIX... Using SAS Enterprise Guide, you build a project and SAS code is generated when you run the process flow. The SAS code is then executed using SAS and SAS can be installed on a server or on the client machine.

14 1-8 Chapter 1 Introduction to SAS Enterprise Guide Behind the Scenes Then SAS Enterprise Guide returns the results to your client PC. SAS on Windows SAS on Mainframe 11 SAS Enterprise Guide SAS on UNIX Once the code is executed, the results are sent back and displayed in the SAS Enterprise Guide interface. SAS OnDemand for Academics: Enterprise Guide uses SAS Enterprise Guide as a thin client installed on your personal computer and SAS installed on a server at SAS Headquarters in Cary, NC.

15 1.2 SAS OnDemand for Academics SAS OnDemand for Academics Objectives Review the steps necessary to register for SAS OnDemand for Academics. Review the steps to install SAS OnDemand for Academics: Enterprise Guide. 13 Registering for SAS OnDemand for Academics Create a SAS profile Register as an instructor 14 In order to register to use SAS OnDemand for Academics you must first create a SAS profile on our website. Once you have an active profile, you can register as an instructor. These two steps will only need to be accomplished once.

16 1-10 Chapter 1 Introduction to SAS Enterprise Guide Your Instructor Home Page 15 Your home page is part of the SAS OnDemand for Academics Control Center. As an instructor, you can use your home page to: obtain your user name for signing into the SAS cloud register courses (You can register any number of courses that you teach. This step will be done once for each course you will be teaching.) view and edit course information delete courses download software. Your students can use their home page to: purchase a license for a course view course information obtain your user name for signing into the SAS cloud. In addition to your home page, the Control Center also has directions for uploading files to the server and answers to frequently asked questions. Information to share with students about how they access SAS OnDemand for Academics: Enterprise Guide is included on the course information page for each course you register.

17 1.2 SAS OnDemand for Academics 1-11 Installing SAS OnDemand for Academics: Enterprise Guide Download the SAS download manager Download the software installation depot Install SAS Enterprise Guide Verify system requirements Install the software 16 The first step in installing the software is to download the SAS download manager. This will then allow you to download the files necessary to install SAS Enterprise Guide for Academics on your computer. The download manager allows you to download a number of different SAS products via the web. SAS Enterprise Guide is only one of these. At the conclusion of the download of the software installation depot, the actual installation of Enterprise Guide will automatically begin. You should first use the systems requirements wizard to check your system for minimum requirements. Once this has been done, you can install Enterprise Guide and you will be ready to begin. While downloading the software, it is essential that you watch for s that will be sent to you with important information. These s will include information such as user names and passwords that you will need during the download process. In particular, to open the download manager, you will need your order number and key that will be sent in an to you.

18 1-12 Chapter 1 Introduction to SAS Enterprise Guide

19 Chapter 2 Analysis Capabilities 2.1 An Overview of Tasks Data Exploration, Analysis, and Reporting by Example

20 2-2 Chapter 2 Analysis Capabilities

21 2.1 An Overview of Tasks An Overview of Tasks Objectives Describe the tasks incorporated into SAS Enterprise Guide. 3 Tasks Tasks represent commonly used procedures from the following SAS products: Base SAS SAS/STAT SAS/QC SAS/ETS SAS/GRAPH 4

22 2-4 Chapter 2 Analysis Capabilities Data Menu Commonly Used Base SAS Procedures 5 Most of the tasks in the Data menu call base SAS procedures such as SQL, SORT, FORMAT, COMPARE, DATASETS, and RANK. The Random Sample tasks calls the SURVEYSELECT procedure from SAS/STAT. In general, you use this menu whenever you want to make changes to a data table. The Read-only task in the Data menu toggles the data set between edit mode and read-only mode. There are two tasks in the Data menu that are unique to SAS OnDemand for Academics: Enterprise Guide. These are the tasks to upload data files to the server and download data files to the PC. These are to accommodate the need to upload files for use on the SAS server and to download files created in a project to your local computer.

23 2.1 An Overview of Tasks 2-5 Describe Menu Commonly Used Base SAS Procedures 6 The tasks in the Describe menu call Base SAS procedures such as the PRINT, MEANS, UNIVARIATE, FREQ, and TABULATE procedures. These tasks are useful for basic statistical reports with descriptive statistics and simple plots of data. The Table Analysis task performs two-way table analysis using the FREQ procedure. Various tests of association and crosstabulation statistics are available in the Table Analysis task.

24 2-6 Chapter 2 Analysis Capabilities The Analyze Menu 7 The Analyze menu contains tasks that use procedures from SAS/STAT, SAS/QC, and SAS/ETS, all for in-depth analysis of your data. The Model Scoring task enables you to score a data set against an existing SAS Enterprise Miner predictive model. The model that creates the scoring code must first be created and validated in SAS Enterprise Miner. The scoring code that was generated by the model must also be saved in the SAS Metadata Repository.

25 2.1 An Overview of Tasks 2-7 The ANOVA Menu SAS/STAT Procedures for Group Comparisons and Linear Models 8 The t Test task performs one-sample, paired, and two-sample t-tests by calling the TTEST procedure and produces plots of the means with SAS/GRAPH software. Use the one-sample t-test to compare a group mean to a known (or hypothesized) value (one-sample t-test). Use the paired t-test to compare the means of two related samples, or the same sample at two points in time. Use the two-sample t-test to compare the means of two independent groups. The output from the two-sample t-test includes a test for equality of variances, the t-test assuming equal variances, and the Satterthwaite adjusted degrees of freedom t-test. The One-Way ANOVA task compares the means of two or more groups that are defined by a single independent (or classification) variable by calling the ANOVA procedure. The ANOVA procedure is generally only appropriate for completely balanced data or one-way models. Multiple comparisons can be performed using a variety of methods to adjust for experimentwise and comparisonwise Type-I error. This task offers Bartlett s, Levene s, and Brown and Forsythe s tests for homogeneity of variances, and offers Welch s ANOVA for comparing group means with unequal variances. The task also produces plots of the means using SAS/GRAPH. The Nonparametric One-Way ANOVA task performs nonparametric group comparisons. (An underlying distribution is not assumed for the data.) Group comparisons using a wide range of tests are available, using asymptotic or exact p-values. The analysis also produces an empirical distribution function from the data. This task calls the NPAR1WAY procedure. The Linear Models task uses the GLM procedure to perform a variety of linear models including factorial ANOVA, ANCOVA, simple linear regression, multiple regression, polynomial regression, and many customized models. A variety of least-squares means comparisons are available to control for Type-I error, and the task offers many diagnostic plots for exploring patterns in your data. The Mixed Models task uses the MIXED procedure to analyze models with a variety of nested and factorial effects. The options available in the Mixed Models task are similar to the options available in the Linear Models task. As with other tasks, you can easily specify additional options and statements for the MIXED procedure with code.

26 2-8 Chapter 2 Analysis Capabilities The Regression Menu SAS/STAT Procedures for Regression Analyses 9 The first task under the Regression menu is the Linear Regression task, which uses the REG procedure to perform linear regression analysis with a variety of useful options for model selection, diagnostic statistics, and output data sets. The task also uses SAS/GRAPH to produce a variety of diagnostic, predictive, and influence plots. You can create plots for residuals, influential points, the least-squares regression line and confidence intervals, and many other graphics to help you understand the relationships in your data. You can perform analysis of variance by using indicator variables to represent coded categorical variables. The primary distinguishing feature of the Linear Regression task is the availability of several stepwise and all-regressions options for model selection. The Nonlinear Regression task performs nonlinear regression analysis by fitting a variety of power, inverse, log base e, and exponential models. These are models in which the response is not adequately predicted by a simple linear combination of predictors and constants, but by a nonlinear combination of predictors and constants. The Nonlinear Regression task uses the NLIN procedure to specify a variety of iteration and step-size search methods, and uses SAS/GRAPH to create plots for prediction, residuals, and influential observations. You can save many of the statistics from the nonlinear regression analysis. The Logistic Regression task calls the LOGISTIC procedure to perform logistic regression on dichotomous or multi-level response variables. The task allows for continuous or categorical predictor variables and offers the same model-building options as the Linear Models task, as well as automatic model-selection options. You can specify the logit, the probit, or the complementary log-log link functions for logistic regression, a variety of diagnostic statistics, and odds ratios based on profile likelihoods and/or Wald tests. The task uses SAS/GRAPH to create plots for prediction, influential points, and receiver operator characteristic (ROC) curves. The Generalized Linear Models task uses the GENMOD procedure to fit linear models with continuous or discrete responses with a variety of distributions. You can perform linear regression, ANOVA, and logistic regression with the Generalized Linear Models task, as well as many other models, such as log-linear models and Poisson regression. The task provides the same familiar model-building interface that you saw in the Linear Models task, as well as several others. As with other modeling tasks, the Generalized Linear Models task uses SAS/GRAPH to create graphs of observed, predicted, and residual values.

27 2.1 An Overview of Tasks 2-9 The Multivariate Menu SAS/STAT Procedures for Multivariate Analyses 10 The Multivariate menu offers a variety of commonly used multivariate statistics. The Correlations task calls the Base SAS procedure CORR and provides basic descriptive statistics, plots of data, and correlation analysis. The Canonical Correlation task enables you to investigate relationships (correlation) between two sets of numeric variables. Canonical correlation analysis is a multivariate extension of multiple correlation analysis, which is an extension of correlation analysis. Canonical correlation analysis creates canonical variates that are linear combinations of the variables within each set of variables and are maximally correlated with one another. The Canonical Correlation task calls the CANCORR procedure. The Principal Components task creates a set of uncorrelated variables (principal components) from a set of correlated, continuous (or dummy-coded discrete) variables. In principal components analysis, the first component accounts for the maximum shared variability among the input variables, the second accounts for the next most variability, and so on. Principal components analysis is a popular method for reducing a large number of correlated variables to a smaller number of uncorrelated variables. The Factor Analysis task performs common and canonical factor analysis, as well as several methods of components analysis, including principal components analysis. The task creates factor scores and saves useful statistics, and creates plots helpful for interpreting latent factors. The Factor Analysis task uses the FACTOR procedure. The Cluster Analysis task uses the CLUSTER and FASTCLUS procedures to perform hierarchical cluster analysis. Cluster analysis enables you to find groups of observations that are similar to one another on a set of input variables. The task also uses the TREE procedure to create tree diagrams using output from the cluster analysis. The Discriminant Analysis task uses the DISCRIM procedure to perform several types of parametric and nonparametric discriminant function analysis. The task enables you to specify options for prior probability estimates and to produce error tables for classification. You can also use leave-one-out crossvalidation or specify a second data set to perform empirical validation of the discriminant functions.

28 2-10 Chapter 2 Analysis Capabilities The Survival Analysis Menu SAS/STAT Procedures for Time-to-Event Analyses with Censoring 11 The two tasks on the Survival Analysis menu perform analyses on time-to-event data, which can include survival data, relapse data, recidivism data, failure data, warranty repair data, or any of a variety of other time-to-event data types. Time-to-event data often has censoring, which means that the event occurred before (left-censored), after (right-censored), or between (interval-censored) measurement periods. The Life Tables task uses the LIFETEST procedure to perform one of two nonparametric forms of survival analysis: the Kaplan-Meier method, which models the empirical distribution of the data the Life Tables method, which evaluates events per unit time where time is divided into equally spaced units The Life Tables task uses SAS/GRAPH to create useful plots for evaluating the distribution of time. Linearized plots for evaluating the exponential, Weibull, and lognormal distributions are available. Only uncensored or right-censored data should be used with this task. The Proportional Hazards task uses the PHREG procedure to perform semi-parametric survival analysis using the Cox Proportional Hazards model. The task offers a variety of options including stepwise model selection and methods for handling ties in event times.

29 2.1 An Overview of Tasks 2-11 The Capability Analysis Menu SAS/QC Procedure for Distribution and Capability Analysis 12 Capability analysis in SAS Enterprise Guide enables you to evaluate the distribution of a variable, enter target values and specification limits, and evaluate process capability using statistical and graphical methods. There are five tasks for capability analysis, each of which calls the CAPABILITY procedure and is named for the type of graph used to evaluate the specified distribution. There are many options for customizing the graph in capability analysis. By default, the plots display the target, upper specification limit, and lower specification limit when specified, as well as a reference line for the specified distribution.

30 2-12 Chapter 2 Analysis Capabilities The Control Charts Menu SAS/QC Procedure for Shewhart Control charts 13 The Control Charts tasks use the SHEWHART procedure to create eight different types of control charts appropriate for individual or subgrouped, measurement or attribute data. The Control Chart tasks share many features in common, although each creates a different control chart. You can specify methods for calculating control limits. You can specify a multiple of sigma (the within-group standard error of the estimate), an input data set with limits, or specific user-entered control limits. Block variables can identify groups of observations in the process without affecting control limit calculations, and you can apply standard tests for special causes to find nonrandom run patterns in your process. Many graphical options for customizing the appearance and usefulness of your control charts are available in the tasks. The Pareto Chart task uses the PARETO procedure to create a horizontal or vertical chart (similar to a bar chart) to enable you to identify commonly occurring categories in your data. Typically, these categories include causes of process or product failure, although they can include nearly any type of categorical variable that is counted. For example, you could identify offices with the most frequent repair calls, sales territories with the most customer inquiries, or retail locations with the greatest number of sales. The Pareto Chart task enables you to customize the chart in many ways. You can create two-way charts, label bars with frequencies or percentages, show cumulative percentages on the chart, use special colors to identify the largest or smallest n groups, and so on.

31 2.1 An Overview of Tasks 2-13 The Time Series Menu SAS/ETS Procedures for Time Series Analysis and Forecasting 14 The Time Series menu offers several tasks for time series analysis. Time series analysis enables you to identify autoregressive, seasonal, or cyclical patterns in a time series. The Prepare Time Series Data task prepares time series data for use by other Time Series tasks. It is also very useful for performing transformations on data for use in other SA Enterprise Guide tasks. For example, you can apply mathematical operations, functions, and other transformations to variables in a data set. The Prepare Time Series Data task uses the EXPAND procedure. The Basic Forecasting task uses the FORECAST procedure to generate forecasts for a time series in a single step using a variety of forecasting methods. The task also uses SAS/GRAPH to create plots of the observed, forecast, and residual values. The ARIMA Modeling and Forecasting task analyzes and forecasts equally spaced univariate time series data, transfer function data, and intervention data using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model. An ARIMA model predicts a value in a response time series as a linear combination of its own past values, past errors (also called shocks or innovations), and current and past values of other time series. The task uses the ARIMA procedure. The Regression Analysis with Autoregressive Errors task uses the AUTOREG procedure to perform linear regression analysis on time series data or data with autoregressive errors. The Regression Analysis of Panel Data task uses the TSCSREG procedure to fit linear econometric models for data in which time series and cross-sectional measurements are combined. This task deals with panel data sets that consist of time series observations on each of several cross-sectional units. The Create Time Series Data task helps you to convert transactional data into fixed interval time series data. Transactional data is time-stamped data that is collected over time with irregular or varied frequency.

32 2-14 Chapter 2 Analysis Capabilities The Graph Menu SAS/GRAPH Procedures 15 There is a gallery of graphs that can be created in SAS Enterprise Guide, including the following: Bar charts Pie charts Line plots vertical, horizontal, or three-dimensional block charts that compare numeric values or statistics between different values of a chart variable simple, group, or stacked charts that represent the relative contribution of the parts to the whole by displaying data as wedge-shaped slices of a circle line, spline, needle, step, regression, smooth, STD, Lagrange interpolation, or overlay charts that show the mathematical relationships between numeric variables by revealing trends or patterns of data points Scatter plots two-dimensional and three-dimensional scatter charts or three-dimensional needle charts that show the relationships between two or three variables by revealing patterns or a concentration of data points For a description of charts not listed here, select Help in any graph task dialog box.

33 2.2 Data Exploration, Analysis, and Reporting by Example Data Exploration, Analysis, and Reporting by Example Objectives Explain the concept of a SAS data library. Explore the SAS OnDemand for Academics: Enterprise Guide environment. Conduct data exploration and analysis. Generate a simple report. 17 SAS Data Libraries A SAS data library is a collection of SAS files that are recognized as a unit by SAS. Files Libraries 18 You can think of a SAS data library as a drawer in a filing cabinet and a SAS data set as one of the file folders in the drawer.

34 2-16 Chapter 2 Analysis Capabilities SAS Data Libraries Identify a SAS data library by assigning it a library reference. Libref 19 The library reference (libref) is a nickname for the physical location of the SAS data library. SAS Data Libraries When you invoke SAS, you automatically have access to a temporary and a permanent SAS data library. WORK - temporary library WORK SASUSER SASUSER - permanent library The SAS data sets stored in the temporary SAS data library WORK are deleted at the end of each SAS session.

35 2.2 Data Exploration, Analysis, and Reporting by Example 2-17 SAS Data Libraries Additional permanent libraries can also be accessed. UNIV - permanent library WORK SASUSER UNIV 21...

36 2-18 Chapter 2 Analysis Capabilities Libraries in SAS OnDemand for Academics: Enterprise Guide When using SAS OnDemand for Academics: Enterprise Guide, the only libraries that you can use are the read only library assigned to your class the WORK library. 22 When you register a course, you will be provided with a course data directory, which is persistent storage space on the SAS server (known as the SAS Cloud). As an instructor, you will be able to ftp files to this directory. Your students will be able to access the files that you have placed in the directory. Information on the location of your course directory can be found on the course information page. It is important to remember that the data tables in the WORK library will be deleted when the SAS OnDemand for Academics: Enterprise Guide session is closed. Therefore, you will either need to download the data tables to your local hard drive or rerun your project to recreate the data tables. Example Nutritional information was collected on a sample of candy bars. You are interested in what properties of the candy bars are related to the number of calories in the bar. 23

37 2.2 Data Exploration, Analysis, and Reporting by Example 2-19 Seventeen pieces of information about 75 different candy bars were collected in two different data tables. The data includes a few non-bar candies. Fields in Excel File: Food.xls (candy worksheet) Name Name Servings Weight Calories TotalFat SatFat Cholesterol Sodium Carbohydrate Sugars Description name of candy number of servings per package weight of candy in package number of calories number of fat grams saturated fat content cholesterol content sodium content carbohydrate content sugar content 24 The Food.xls file contains three worksheets. The worksheet you will use for this course is the Candy worksheet. Fields in SAS Data Table: Candyinfo.sas7bdat Name Brand Name Fiber Protein VitaminA VitaminC Calcium Iron Description manufacturer name of candy fiber content protein content vitamin A content vitamin C content calcium content iron content 25

38 2-20 Chapter 2 Analysis Capabilities Exploring the SAS OnDemand for Academics: Enterprise Guide Environment As mentioned earlier, some of the information on the candy was collected and is in a Microsoft Excel file. You want your students to do an initial data exploration and build a regression model to predict the number of calories in the candy bars. Begin by opening SAS Enterprise Guide, exploring the environment, and importing the data. 1. To open SAS Enterprise Guide, double-click the icon on the desktop or select Start Programs SAS Enterprise Guide 4 Academics. 2. When you open the software, it will prompt you for your user name and password which were provided to you when you registered your course. 3. After entering your user name and password, select.

39 2.2 Data Exploration, Analysis, and Reporting by Example You then see a window that prompts you to open an existing project or to create a new project, SAS program, or data. Select New Project. The SAS Enterprise Guide environment is shown below.

40 2-22 Chapter 2 Analysis Capabilities 5. The workspace can be customized to suit your working style. To examine the options available, select Tools Options. 6. After you examine the different available options, close the Options window by selecting Cancel.

41 2.2 Data Exploration, Analysis, and Reporting by Example 2-23 Inserting Data and Notes 1. Import the Excel data and a description of the data. In addition, you might include a file that contains directions for the assignment. To open the Microsoft Excel file, select File Open Data. 2. Select Local Computer. 3. Select the Food.xls file, and then select Open. 4. In this case, select the Candy$ worksheet in the Open Tables dialog box. You can select any or all of the worksheets from the Excel file. 5. Select Open.

42 2-24 Chapter 2 Analysis Capabilities 6. When opening this type of data, you have two options in the Open Data window. If you are relatively certain that you have a clean Excel file with no need to add labels or change formats, you can choose the first option,. If you want the option to change data formats and labels, choose the second option,. For illustrative purposes, select. 7. Be sure Region to Import is selected in the Selection pane and the field Specify line to use as column headings is specified as 1.

43 2.2 Data Exploration, Analysis, and Reporting by Example Select Column Options in the Selection pane. This is where you can change the variable label, type, length, and display or read-in format. You can also choose to exclude columns from the final data set. 9. To avoid truncation of some of the candy names, verify that the length of this variable is at least 35. In the Columns pane, select Name. In the Column Properties pane, check the Length. 10. Select Results in the Selection pane. (This is where you can specify the name of the resulting SAS data set.) 11. Select. 12. Change the file name to Candies1, and then select Save. 13. Select Run.

44 2-26 Chapter 2 Analysis Capabilities A new SAS data table is created from the Excel worksheet. A portion of the data table is visible in the workspace. 14. You have a text file that contains a description of the data and you want to include it in the SAS Enterprise Guide project. Select File Open Data. 15. Select Local Computer. 16. Select the file candy.txt and then select Open. 17. To be sure that the file is opened as-is, select.

45 2.2 Data Exploration, Analysis, and Reporting by Example 2-27 The text file is open and a portion of it is shown below. The file is also added to the Process Flow, but as a separate entity. Select the Project Designer tab to view the process flow and the placement of the text file in the flow. If you want this file clearly associated with the candy data set, you can add a link to the Process Flow. 18. Right-click on the WORK.CANDIES1 data set in the Process Flow. 19. Select Link WORK.CANDIES1 to...

46 2-28 Chapter 2 Analysis Capabilities 20. Select candy (Process Flow), and then select OK. Notice that the text file is now connected to the data table. However, the arrow is dashed which indicates that it was manually connected. If you want to add a blank note to the Process Flow, you can do so by selecting File New Note. This will open a blank now that is connected to the flow and to which you can add comments as you explore and analyze the data.

47 2.2 Data Exploration, Analysis, and Reporting by Example 2-29 Data Exploration As an initial step, examine the distributions of the variables. This can be done with tasks under the Describe menu. For illustrative purposes, analyze the distribution of the variables Calories and Weight. 1. Select Describe Summary Statistics. 2. With Task Roles selected in the Selection pane, hold down the CTRL key and click on the Variables to assign pane to select the variables Calories and Weight. 3. Drag these variables from the Variables to assign pane to the Analysis variables role in the Task roles pane as shown below:

48 2-30 Chapter 2 Analysis Capabilities 4. Select Basic in the Selection pane. 5. In the Basic Statistics pane, change the Maximum decimal places from Best fit to Select Plots in the Selection pane. 7. Select Histogram. 8. Select Titles in the Selection pane. 9. Click the box next to Use default text to deselect it.

49 2.2 Data Exploration, Analysis, and Reporting by Example Type in a new title for the output. 11. Select Run to generate the descriptive statistics and histograms. On average, candy in the sample weighs close to 2.2 ounces and has approximately 243 calories.

50 2-32 Chapter 2 Analysis Capabilities The variable Weight is skewed to the right with some unusually high values. The variable Calories is also skewed to the right. Perhaps after examining this data you decide that you also want to look at the same statistics for the variable Carbohydrate. You can create a new descriptive statistics task to do this, but you can also choose to include the third variable in the same output.

51 2.2 Data Exploration, Analysis, and Reporting by Example Click on the Project Designer tab or select View Project Designer to return to the Process Flow window. 13. Right-click on the Summary Statistics task in the Process Flow window and select Open. 14. With Task Roles selected in the Selection pane, select the variable Carbohydrate. 15. Drag this variable from the Variables to assign pane to the Analysis variables role in the Task roles pane. 16. Select Titles in the Selection pane and change the title to Summary Statistics for Candy Nutrition Data. 17. Select Run. 18. When prompted about replacing the results from the previous run, select Yes.

52 2-34 Chapter 2 Analysis Capabilities The new output includes the third variable of interest. Another task that generates descriptive statistics is the Distribution Analysis task, which is examined later. Examine the values for the variable Servings using a one-way frequency table. 1. Select Describe One-Way Frequencies. 2. With Task Roles selected in the Selection pane, select Servings and drag it from the Variables to assign pane to the Analysis variables role in the Task roles pane. 3. Select Run.

53 2.2 Data Exploration, Analysis, and Reporting by Example 2-35 While almost 87% of the candies in the data set are single serving packages, there are some multiple serving packages included. When you examine the data further, you discover that the candy weight is based on the entire package, while the other data is based on a single serving. You might choose to change the weight and convert it to a per-serving value. This can be done using the Filter and Query task. Create a new weight variable that is the package weight divided by the number of servings. 4. Select Data Filter and Query. 5. Select. 6. Select. 7. Select Build Expression To add Weight to the expression, double-click Weight in the Available Variables pane. 9. Type a / sign after CANDIES1.Weight or select.

54 2-36 Chapter 2 Analysis Capabilities 10. To add Servings to the expression, double-click Servings in the Available Variables pane. 11. Select OK to exit the Advanced Expression Editor. 12. In the Computed Columns window, select Calculation1 and then select. 13. Type a new name for the newly calculated variable Weight Select Close.

55 2.2 Data Exploration, Analysis, and Reporting by Example 2-37 The new Weight1 variable is shown on the Select Data tab. 15. Hold down the CTRL key and click to select the other variables from the data table that you want to include in the resulting table.

56 2-38 Chapter 2 Analysis Capabilities 16. Drag those variables to the Select Data tab. Other computed columns could be created in the data table within the same query if desired. 17. Select Run. A portion of the data table is shown below. Examine the distribution of Weight1 in the new data set. (For illustrative purposes, use the Distribution Analysis task instead of the Summary Statistics task.) 1. Select Describe Distribution Analysis. 2. With Task Roles selected in the Selection pane, select Weight1.

57 2.2 Data Exploration, Analysis, and Reporting by Example Drag the variable from the Variables to assign pane to the Analysis variables role in the Task roles pane as shown below. 4. Select Appearance in the Selection pane. 5. Select Histogram Plot. 6. Select Tables in the Selection pane. 7. Select the Tests for location table. To remove this table from the output, click on the check mark in the square to the left of the table name. This removes the check mark.

58 2-40 Chapter 2 Analysis Capabilities 8. To add the Moments table to the output, select the Moments table. Click in the square to the left of the table name. This places a check mark in the square. 9. Repeat the process to add the Quantiles table to the output. 10. Select Run. The average weight is somewhat smaller than in the original data and the standard deviation also decreased. The data is still skewed to the right as indicated by the skewness statistic.

59 2.2 Data Exploration, Analysis, and Reporting by Example 2-41 The median for the variable Weight1 is smaller than the mean, which is also an indication of a distribution that is skewed to the right.

60 2-42 Chapter 2 Analysis Capabilities 75% of the single serving weights are less than or equal to 2 ounces.

61 2.2 Data Exploration, Analysis, and Reporting by Example 2-43 Although the histogram still shows that the data is skewed to the right, the values are less extreme than with the original data.

62 2-44 Chapter 2 Analysis Capabilities Recoding and Formatting Data (Self-Study) You are interested in comparing the number of calories in the candy bars that are so called low fat bars versus those with higher fat content. You have decided the divide the candy bars into three groups based on their fat content. Low fat candy bars contain fewer than 4 grams of fat per serving, while high fat candy bars contain more than 10 grams of fat per serving. This data separation can be accomplished by recoding the variable TotalFat. 1. Select Data Filter and Query. 2. Select. 3. Select, and then select Recode a Column Select TotalFat from the variable list. 5. Select.

63 2.2 Data Exploration, Analysis, and Reporting by Example In the Recode Column window, change the New column name to FatGroup. 7. Select. 8. In the Specify a Replacement window select the Replace a Range tab. 9. Click on Set an upper limit to activate the field. 10. Use the drop down arrow to select the value In the With this value: field, enter A.

64 2-46 Chapter 2 Analysis Capabilities 12. Select. 13. Select. 14. In the Specify a Replacement window select the Replace a Range tab. 15. Click on Set a lower limit to activate the field. 16. Use the drop down arrow to select Click on Set an upper limit to activate the field. 18. Use the drop down arrow to select the value In the With this value: field, enter B.

65 2.2 Data Exploration, Analysis, and Reporting by Example Select. 21. Select. 22. In the Specify a Replacement window select the Replace a Range tab. 23. Click on Set a lower limit to activate the field. 24. Use the drop down arrow to select In the With this value: field, enter C. 26. Select.

66 2-48 Chapter 2 Analysis Capabilities 27. In the Other values panel, select A missing value for the Replace all other values with: field. 28. In the New column type panel, select Character. 29. Select. 30. Select. 31. Select the variables from the original data table and drag them into the pane to be included in the new data table. 32. Select to change the name of the resulting data table. 33. Name the new data table FatData. 34. Select.

67 2.2 Data Exploration, Analysis, and Reporting by Example Select Run. A portion of the data table is shown below. With the recoding completed, you now want to use a format for the new variable FatGroup. Although the data is coded A, B, and C, you would prefer that it display as low fat, average fat, and high fat. This can be done by creating a user defined format and the applying it to the data column. 1. To create the new format, select Data Create Format. 2. In the Create Format window, type a name for the format, such as FatContent. 3. Select Define formats in the Selection pane. 4. Select, then type Low Fat in the Label field. 5. Select, then type A in the Values field.

68 2-50 Chapter 2 Analysis Capabilities 6. Repeat steps 4 and 5 to assign the labels Average Fat and High Fat to the values B and C respectively. 7. Select Run. At this point, you have created the format that is available for use, but there is no visible output other than the icon for the Create Format task that appears on the Project Designer. Now that the format has been created, you want to assign the format to be used with the FatData data table. 1. Select Data Read-only to switch to edit mode. 2. To confirm the switch to edit mode, select. 3. Right-click on the FatGroup column in the data table, then select Properties.

69 2.2 Data Exploration, Analysis, and Reporting by Example In the Selection pane, select Formats. 5. In the Categories panel, select User Defined. Select the new format, $FATCONTENT. which should be available in the Formats panel. 6. Select.

70 2-52 Chapter 2 Analysis Capabilities The column in the data table now displays the formatted data values. A portion of the data table is shown below. For many tasks, the formatted or unformatted data values can be used when ordering the groups in SAS output. Use the Summary Statistics task to determine the average number of calories for each of the levels of fat content. 1. Select Describe Summary Statistics. 2. Select, to protect the data before proceeding. 3. With Task Roles selected in the Selection pane, assign Calories as the analysis variable and FatGroup as the classification variable.

71 2.2 Data Exploration, Analysis, and Reporting by Example 2-53 The default sort order will be the unformatted values as shown in the Class level FatGroup panel on the right of the window. 4. Select Run. There is not much difference in the mean calories between the Low Fat and Average Fat candy bars, but the High Fat candy bars have a higher mean calorie count.

72 2-54 Chapter 2 Analysis Capabilities Joining Data Tables Recall that additional information about the candy was available in the SAS data table CANDYINFO. In order to use all available information for analysis, join the two data tables. Because both data tables contain the column Name, which contains the name of each of the candy bars, you can join on that variable. Assume you, as the professor, have place the data table CANDYINFO in the course folder. In order to access the data table, you will first need to run the LIBNAME statement found on the course page. 1. To write and run the LIBNAME statement, open a new code window by selecting File New Code. 2. In the empty code window that opens, type (or copy and paste) the LIBNAME statement found on the course information page. For example: libname mydata "/courses/u_0/i_209868/c_289/saslib" access=readonly; 3. To submit the code, select Code Run Code on SASApp. 4. To view the log and be sure the LIBNAME was successfully assigned, return to the Process Flow diagram, right click on the Code icon and select Open Log. 5. Look for a message that tells you that the LIBNAME was successfully assigned. NOTE: Libref MYDATA was successfully assigned as follows: Engine: V9 Physical Name: /courses/u_0/i_209868/c_289/saslib 6. Change the active data table to be the table created when you queried the WORK.CANDIES1 data table. It will most likely be named QueryXXXX (where XXXX represents 4 random digits), unless you changed it before you ran the query. You can make this the active data table by selecting the data table in the Process Flow or by using the drop down menu at the Active Data window in the toolbar as shown below. 7. Select Data Filter and Query Select.

73 2.2 Data Exploration, Analysis, and Reporting by Example To include the table that you want to join, select. 10. Select SAS Servers. 11. Double-click on SASApp and then double-click on Libraries to see the libraries that are defined for this session. 12. Double-click on the MYDATA library to see the data tables available in the library. 13. There is only one data table in this library. Select CANDYINFO, and then select Open.

74 2-56 Chapter 2 Analysis Capabilities The software automatically chose to join the two variables on the column Name because this is the first matching column that it located. Also, the default join is an inner join. Both of these defaults are correct in this case, but might not always be correct. Therefore, you might have to change the join by right-clicking on the join symbol shown in the diagram. 14. Select. 15. Hold down the CTRL key and click to select, from the data tables, the variables that you want to include in the resulting table. 16. Drag those variables to the Select Data pane. 17. To change the name of the resulting data table, select. 18. Type the desired name for the new data table, such as joined_candy.

75 2.2 Data Exploration, Analysis, and Reporting by Example Select Save. 20. Select Run. A portion of the new data table is shown below.

76 2-58 Chapter 2 Analysis Capabilities Saving Your Project You will need to save your project on your local machine. If you share the machine with others or might want to access the project on a different machine, save the project to a CD or memory stick. The only persistent storage on the server is the course data directory to which the instructor can ftp files. The data files created in the project are in the WORK library. None of them will be saved once you close the project, unless you download them to your local machine. If you choose not to download the work files, you can recreate them again when you return to the project by running the project. 27 Students cannot save any files to the persistent storage on the server.

77 2.2 Data Exploration, Analysis, and Reporting by Example 2-59 Saving Data and the Project Presume that the joined_candy data set is one that you would like to save for future use. One way to do that is to download it from the server to your machine. 1. Select Data Download Data Files to PC. 2. Select. 3. Choose the data tables that you want to save from the list. You can choose more than one table by control-clicking. In this case select the data table JOINED_CANDY. 4. Select.

78 2-60 Chapter 2 Analysis Capabilities The data table to be saved appears in the list. 5. Select. 6. Select to choose the folder where the data table will be saved. 7. Select. The data table is now saved to your local computer for future use. To save the project for future use, you will also need to save it to your local computer. 8. Select File Save Project. 9. Select Local Computer. 10. Navigate to the folder where you would like to save your project and give it a file name, for example, Candy Analysis. Note that the file will be saved with a.egp extension.

79 2.2 Data Exploration, Analysis, and Reporting by Example Select. The file is now saved on your local computer as Candy Analysis.egp. You can now safely exit from the software. When you exit from the software, you will see a window about saving a project with temporary data. 12. Select OK.

80 2-62 Chapter 2 Analysis Capabilities More Data Exploration Assume that you want to return to the project saved and closed earlier to conduct more data exploration and analysis. 1. Open SAS OnDemand for Academics: Enterprise Guide and log in when prompted. 2. When the software opens, your previously saved projects will be available for selection in the Welcome to Enterprise Guide window.

81 2.2 Data Exploration, Analysis, and Reporting by Example Select Candy Analysis under Open a project. The project opens and appears as it did when you saved and closed it. Although the project appears to be identical, the data sets created during the project in the WORK library have been deleted and will need to be recreated. 4. First, run the SAS Code to redefine the library. Right-click on the Code icon and select Run Code on SASApp. 5. Select Yes to replace the previous results. 6. To rerun the remainder of the process flow, right-click in an open part of the Process Flow window and select Run Process Flow. In order to avoid the step to run the code to create the library, you can choose to connect the library code to the CANDYINFO data table in the process flow. This will ensure that the code is run before the software tries to access the data table.

82 2-64 Chapter 2 Analysis Capabilities The SAS data set Final_Candy has all of the variables from the two original data tables as well as the weight per serving variable. For the sake of time, continue the demonstration with the data set Final_Candy. Upload the data set Final_Candy to the project. 1. Select Data Upload Data Files to Server. 2. Select. 3. Navigate to and select the Final_Candy.sas7bdat data table, and then select. Note that you can upload more than one file at a time. 4. The data file should appear in the upload window. 5. Select.

83 2.2 Data Exploration, Analysis, and Reporting by Example 2-65 The default behavior is the add the data files uploaded to the current project. 6. Select. The data file has been uploaded and included in the project. Continue to explore the data by examining the correlations between Calories and some of the other variables. 1. Make sure the Final_Candy data table is shown in the Active Data window. 2. Select Analyze Multivariate Correlations. 3. With Task Roles selected in the Selection pane, control-click to select the variables Weight1, TotalFat, and Carbohydrate in the Variables to assign pane. 4. Drag the selected variables to the Task Roles pane and assign them as Analysis variables. 5. Select the variable Calories. 6. Assign Calories to the Correlate with role by dragging and dropping. 7. Select Options in the Selection pane. Examine the options available. 8. Select Results in the Selection pane. 9. Select Create a scatter plot for each correlation pair. Note the information about how many plots will be created. 10. Select Run.

84 2-66 Chapter 2 Analysis Capabilities The first part of the results shows a summary of which correlations will be calculated. The second part of the output shows simple descriptive statistics for the variables to be correlated. The final output table shows the correlations between the variables, as well as p-values testing the null hypothesis that the population correlation coefficient is equal to zero. Weight1 and TotalFat have a stronger correlation with Calories than Carbohydrate.

85 2.2 Data Exploration, Analysis, and Reporting by Example 2-67 The scatter plots are shown below. These two scatter plots confirm the strong correlations between Calories and the variables Weight1 and TotalFat.

86 2-68 Chapter 2 Analysis Capabilities

87 2.2 Data Exploration, Analysis, and Reporting by Example 2-69 In addition to two-dimensional scatter plots, you can also produce three-dimensional scatter plots. To illustrate this, generate a three-dimensional scatter plot with the variables Weight1, TotalFat, and Calories. 1. Select Graph Scatter Plot. 2. With Scatter Plot selected in the Selection pane, select 3D Scatter Plot. 3. Select Task Roles in the Selection pane. 4. Assign Weight1 as the Horizontal variable, Calories as the Vertical variable, and TotalFat as the Depth variable. 5. Select 3D Scatter in the Selection pane. 6. Change the Symbol type to Sphere. Observe that you do have the capability to control most aspects of the graph including such things as the axes and titles. 7. Choose a Symbol color. 8. Select Run.

88 2-70 Chapter 2 Analysis Capabilities The graph can still be edited.

89 2.2 Data Exploration, Analysis, and Reporting by Example To change graph features, right-click on the chart and select Graph Properties. 10. After examining the graph properties, make any changes that you want and select OK. You can rotate the graph by holding down the ALT key on your keyboard and moving the graph with your mouse. You can also observe the values graphed for any one point by pointing at the symbol with your cursor. There are many times when you might want to include a graph such as this in a slide presentation. For example, you can add this graph to a Microsoft PowerPoint slide. 1. Right-click on the graph and select Copy. 2. Open a PowerPoint document with a new slide. 3. Right-click on the new slide and select Paste.

90 2-72 Chapter 2 Analysis Capabilities Regression Analysis (Self-Study) After the initial data exploration is complete, you might be interested in using some of the other variables in the data set to predict the number of calories per serving. To that end, use the Final_Candy data set and do a stepwise regression to predict calories. 1. Be sure that Final_Candy is the active data set. 2. Select Analyze Regression Linear. 3. With Task Roles selected in the Selection pane, assign Calories as the dependent variable. 4. Assign all other numeric variables (except Servings and Weight) as explanatory variables. 5. Select Model in the Selection pane. 6. Change the Model selection method to Stepwise selection. 7. Select Run. 8. Scroll to the bottom of the output to see a summary of the stepwise selection. One easy way to scroll to the bottom of the output is to press CTRL+END while the window is active. Six variables were entered into the model. One variable, Weight1, was removed from the model. The resultant model has an R-square of

91 2.2 Data Exploration, Analysis, and Reporting by Example 2-73 To examine the parameter estimates and final p-values for the six-variable model, scroll up in the output. You can use these estimates to write the regression equation and attempt to understand the relationships between the variables. Examine this model further by looking at collinearity diagnostics, residual plots, and influential observation statistics. 1. Select Analyze Regression Linear. 2. With Task Roles selected in the Selection pane, assign Calories as the dependent variable. 3. Hold down the CTRL key and click to select the five variables chosen in the stepwise regression model in the previous run. The variables are TotalFat, SatFat, Sodium, Carbohydrate, and Protein. 4. Assign the selected variables as explanatory variables. 5. Select Statistics in the Selection pane. 6. Select Collinearity analysis and Variance inflation values. 7. Select Predicted in the Selection pane. 8. Select the Observed vs predicted plot. 9. Select Residual in the Selection pane, and then select Standardized vs predicted Y.

92 2-74 Chapter 2 Analysis Capabilities 10. Select Influence in the Selection pane, and then select DFFITS vs predicted Y. 11. Select Predictions in the Selection pane. 12. In the Data to predict pane, select Original sample. This creates a new data table that includes the predicted values along with the original data. 13. In the Additional statistics pane, select Residuals. This also adds the residuals to the new data table. 14. Select Run.

93 2.2 Data Exploration, Analysis, and Reporting by Example 2-75 The Analysis of Variance table is the same as the one seen earlier. The adjusted R-square is high and close to the R-squared value. The parameter estimates are the same as those shown earlier. It is suggested that a variance inflation factor greater than 10 indicates the presence of strong collinearity in the model. None of the variance inflation factors in this model is higher than 10, which indicates that there is no severe collinearity problem.

94 2-76 Chapter 2 Analysis Capabilities The collinearity analysis that you selected generates the collinearity diagnostics shown above. Condition index values between 10 and 30 suggest weak dependencies, between 30 and 100 indicate moderate dependencies, and greater than 100 indicates strong collinearity. The highest condition index for this model is less than 30, which suggests that there is no severe collinearity problem. The graph of Calories versus Predicted Calories does not reveal any unusual circumstances.

95 2.2 Data Exploration, Analysis, and Reporting by Example 2-77 There are no standardized residuals that are greater than 3. There is one standardized residual that is less than -3, which indicates that the observation is unusual and potentially an influential observation.

96 2-78 Chapter 2 Analysis Capabilities There is one observation that stands apart from the others on the plot of the DFFITS versus the predicted calories. You can see the specific values for any point on the graph by holding your cursor over the point. The graphed values appear in a pop-up box. To determine which observation(s) is the unusual one, look at the prediction data set that was created. To do this, look at the most recent task in the Project Explorer or Process Flow.

97 2.2 Data Exploration, Analysis, and Reporting by Example 2-79 The data set highlighted is the data set that contains the predictions. View that data set by double-clicking on it. Scroll to the right to see the new columns in this data set. The predicted values, residuals, student residuals, and rstudent residuals were added to the data table. Scroll through the data table to find the observation with the unusually large (in absolute value) residual (or sort it using the Sort Data task in the Data menu). It is the Milky Way Lite candy bar.

98 2-80 Chapter 2 Analysis Capabilities Subsetting Data and Analysis of Variance Either M&M/MARS or Hershey makes many of the candy items in the data set. You were asked to determine if there is a difference between the two companies in the average number of calories per serving of candy. Begin by creating a subset of the data that includes only candy items from these two companies. 1. Make sure Final_Candy is the active data table, and select Data Filter and Query. 2. To include all of the columns in the resulting data table, select Name and hold down the SHIFT key and click to select Iron and all of the columns between Name and Iron. 3. Drag the selected columns to the Select Data tab on the right. 4. Select the Filter Data tab. 5. Select Brand in the Selection pane and drag and drop it onto the Filter Data tab in right pane. 6. In the Edit Filter window that opens, change the Operator to In a list of values. 7. Select.

99 2.2 Data Exploration, Analysis, and Reporting by Example Select. A list of values in the column Brand is now shown. 9. Hold down the CTRL key and click to select Hershey and M&M/Mars. 10. Select OK. 11. Select OK. 12. To change the name of the resulting data table, select. 13. Change the data table name to Two_Brands.

100 2-82 Chapter 2 Analysis Capabilities 14. Select Save. 15. Select Run. The data table WORK.Two_Brands is created and opened. It contains 45 candy items made by either Hershey or M&M/Mars. To compare the average calories per serving for the two companies, generate an analysis of variance. 16. Select Analyze ANOVA One-Way ANOVA. 17. With Task Roles selected in the Selection pane, assign Calories as the Dependent variable. 18. Assign Brand as the Independent variable. 19. Select Plots in the Selection pane. 20. In the Types pane, select Means. 21. Select Run. The first part of the output gives information about the classification variable and the number of observations.

101 2.2 Data Exploration, Analysis, and Reporting by Example 2-83 Presuming a significance level of 0.05, the analysis of variance table shows that there is no statistically significant difference between the number of calories in the two brands of candy. The means plot confirms the statistical results of the ANOVA.

102 2-84 Chapter 2 Analysis Capabilities Reporting Using the Document Builder At this point, you are ready to write a report about the results of your analysis that includes relevant graphs and statistical tables. This is particularly easy with the Document Builder in SAS Enterprise Guide. 1. Close all windows in the project by selecting Window Close All. 2. Select Tools Create HTML Document. 3. Change the HTML title to Candy Report. 4. Select Add Task Result. 5. Hold down the CTRL key and click to select those items that you want to include in your document. If you select a task, then all items in that task are included in the document. If you select a part of the task result, only that table or graph is included in the document. For this example, scroll down to the Correlations task and select The Corr Procedure. 6. Hold down the CTRL key and click to select the Scatter Plot. 7. Scroll down to the One-Way ANOVA. Hold down the CTRL key and click to select Overall ANOVA, Fit Statistics, and Anova Model ANOVA. 8. Select OK.

103 2.2 Data Exploration, Analysis, and Reporting by Example xamine the list of items to be sure that you selected what you want to see in the document, and then select OK. You can look at a preview of the document by selecting Preview before actually creating it by selecting OK. The Document was added to the Project Designer. Save the document outside of SAS Enterprise Guide to your local computer.