Chapter 1. Introduction to SASLE and Statistics

Transcription

1 Chapter 1 Introduction to SASLE and Statistics 1-1 Overview 1-2 Statistical Thinking 1-3 Types of Data 1-4 Critical Thinking 1-5 Collecting Sample Data

2 2 Chapter 1: Introduction to SASLE and Statistics 1-1 Overview This manual is intended to accompany Elementary Statistics, 11 th edition, by Mario F. Triola and follows the organization of that text. It has been written for readers who intend to use the SAS Learning Edition 4.1 software product. It is not intended for readers who would like to be proficient SAS programmers. However, Appendix A provides an overview of the SAS programming language, as well as the basic principles of SAS programming, and so this manual may also be useful to readers who would like a basic introduction to SAS programming. This chapter focuses on providing an overview of SASLE. In the process, it will touch on several of the topics discussed in Chapter 1 of Elementary Statistics. A more comprehensive introduction to the SASLE product is available as a tutorial by launching SASLE and then choosing the Help > Getting Started Tutorial menu option SAS Learning Edition SAS Learning Edition (SASLE) is one of several statistical analysis software products offered by the SAS Institute. The SAS Institute is primarily known for a suite of software products that are usually collectively referred to as the SAS System. The nucleus of this software suite is a product known as base SAS. It provides most of the functionality that users need to do non-trivial statistical analyses. Several more specialized products may be incorporated with base SAS if needed. However, the SAS System is intended for users that are willing to invest the time needed to write programs using the SAS programming language. SASLE, on the other hand, is intended for users that have a need for the powerful capabilities available in the SAS System but would prefer not to write SAS programs. SASLE seamlessly integrates an easy to use graphical user interface, known as SAS Enterprise Guide 4.1, with base SAS as well as several of the specialized SAS products mentioned above. As of this writing, SASLE Release 4.1 is the latest available version. It is currently only available for workstations using the Microsoft Windows operating system (Windows 2000 and XP only). As a result, it uses the desktop metaphor that Microsoft Windows users are familiar with. In summary, SASLE provides: Access to all the functionality of base SAS. Access to the functionality of several specialized products in the SAS System (i.e. SAS/STAT, SAS/QC, SAS/GRAPH and SAS/ETS). An intuitive, customizable, graphical user interface. Easy access to data from a variety of sources. A comprehensive selection of predefined tasks for statistical analysis and reporting. Facilities that allow easy exporting of data and results to other applications. Facilities for users to write and execute SAS programs. Note: SASLE does not limit access to the functionality of the SAS System but imposes a limit on the amount of data that can be analyzed. This limit is currently 1500 observations Starting SASLE Standard SASLE installations will result in the following SASLE icon somewhere on the desktop. Figure 1-1

3 Chapter 1: Introduction to SASLE and Statistics 3 As with any other Microsoft Windows application icon, double clicking the icon will launch the application. Alternatively, you should be able to launch the application by navigating to the SAS Learning Edition 4.1 menu item after clicking the Microsoft Windows Start button. On launching SASLE you will be presented with two windows. The window in the foreground (see below) is known as the SASLE Welcome dialog. It is a modal dialog which means that you must either dismiss it, by clicking on the icon, or by making a selection from one of options presented. Figure 1-2: Welcome dialog Let us say you decide to create a New Project, the main SASLE window will then be revealed. Figure 1-3: SASLE SAS Enterprise Guide

4 4 Chapter 1: Introduction to SASLE and Statistics Notice that this window offers the standard menu and toolbar options that you would expect from a Microsoft Windows application. It is known as Enterprise Guide and uses a project oriented workspace metaphor. In addition to menus, and toolbars, the window is divided into four areas. These areas are known as the Project Explorer, Task List, Task Status, and Workspace windows. Project Explorer The Project Explorer window is the window on the left of the Enterprise Guide window. Enterprise Guide organizes related data, tasks, code, and results into projects. Only one project can be open at a time. The Project Explorer window displays the active project as well as icons that represent its associated data, SAS code, tasks, notes, and results in a tree hierarchy. Task List The Task List window is initially hidden. It may be revealed by moving the cursor over the Task List tab which is located on the right edge of the Enterprise Guide window. Notice that as soon as you move the cursor away from the window, or its tab, it becomes hidden. You may change this behavior by clicking on the icon in the upper right corner of the window. Think of this action as pinning the contents of the Task List window to the Workspace window. You may toggle between this pinned and unpinned state by repeatedly clicking on the icon (Note: The Project Explorer window also exhibits this behavior but its default state is pinned). The Task List is a set of predefined tasks that may be used to do a variety of statistical analyses as well as several tasks that provide utility functions. The tasks are organized by type, under the Tasks by Category tab, and by name, under the Tasks by Name tab. Figure 1-4: Task List Tasks may be selected by either clicking on the required task in the Task List or by navigating to the required task through the Data, Describe, Graph, or Analyze menu options on the Enterprise Guide menu bar: Figure 1-5

5 Chapter 1: Introduction to SASLE and Statistics 5 Task Status The Task Status window is the window at the bottom of the Enterprise Guide window. It is used to report the status of tasks as they are being executed. Workspace The Workspace window is a container for output created by tasks that have been executed, data, SAS code, and notes. It also contains the Project Designer which is a tool that allows you to manage and organize your project as a process. The workspace is organized as a set of tabs where each tab corresponds to an item in the workspace SASLE Projects A project is a collection of related data, SAS code, tasks, results, notes, and logs. It is a convenient mechanism for keeping track of the items involved in an analysis. In addition, projects may be saved for later retrieval. Note that projects do not contain copies of permanent items (e.g. SAS datasets) or the items themselves. Instead, they contain links to these items. As a result, you may safely delete a project file without concern about losing the permanent items that may be associated with the project Consider the following snapshot of the analysis for Chapter 3 of this manual. At this point in the analysis, you can see that the project consists of a permanent SAS dataset (actress), plus three tasks, along with the items generated by these tasks. Notice that the Project Explorer presents a hierarchical tree view of the project whereas the Project Designer presents a process flow view. Figure 1-6: Project Explorer vs. Project Designer Note: A detailed treatment of projects is outside the scope of this manual. For further details, see the references in Appendix C. We recommend The Little SAS Book for Enterprise Guide 4.1 by Slaughter and Delwiche Adding Data and Tasks to SASLE Projects Let us say you wish to add data from an existing SAS dataset to a project. Choose the File > Open > Data menu option. The following dialog box will appear. Figure 1-7

6 6 Chapter 1: Introduction to SASLE and Statistics If you select either option an Open dialog like the following will appear. Figure 1-8 If you double-click on the TourDates SAS dataset then the contents of the dataset will be displayed in the Workspace window and the project window will be updated with the dataset: Figure 1-9 If necessary, you may add several datasets to a project. Also, these datasets may be in different formats. For example, you could now add a Microsoft Excel file to the project. You would proceed as for the SAS dataset above. Let us say you selected the file Bookings, the project window will be updated as follows: Figure 1-10 The contents of the Bookings file will also be displayed in the Workspace window and so it would now appear thus:

7 Chapter 1: Introduction to SASLE and Statistics 7 Figure 1-11 Tasks may now be executed. For example, you may want to produce a formatted report that lists the contents of Bookings. The SASLE List Data task may be used. As indicated above, you may invoke List Data by either clicking on List Data in the Task List window, or by selecting the Describe > List Data menu option. Note that the dataset to be processed should be the active dataset. You may make a dataset active by clicking on it in the Project Explorer window or selecting its tab in the Workspace window. In this case Bookings is already active and so the following dialog will appear when List Data is invoked. Figure 1-12 Now, select the Office variable, click on the button, and then choose the List variables option. The Office variable now appears in both the Variables to assign and Task roles windows. Repeat this process for CustomerID and Deposit. The dialog box should now appear as follows:

8 8 Chapter 1: Introduction to SASLE and Statistics Figure 1-13 Before proceeding, notice the layout of this dialog box. The various options of the List Data task (i.e. Task Roles, Options, and Titles) are organized in a window on the left of the dialog. This window is known as the selection pane. You may select the desired option directly. Most tasks will present dialog boxes that have this general layout. It is usually not necessary to go through all the options. Default settings are often adequate for most analyses and so, as you will see in subsequent chapters, the Run button can sometimes be clicked without changing many default settings. Now, select the Options option. This option allows you to customize your report. Figure 1-14

9 Chapter 1: Introduction to SASLE and Statistics 9 In this case we will change a few of the default settings. Let us list the first four rows and we will not print row numbers. Now, select the Titles option. This option allows you to further customize your report. Figure 1-15 If you now click the Run button then the Project Explorer window will be updated and the Workspace window will contain the formatted report. Notice how the Project Explorer window has been updated. Figure 1-16 Several entries have been added to the Project Explorer window. They were generated when the List Data task was executed. Note that they are nested under the Bookings node indicating that they pertain to this file only. The HTML node contains the formatted report presented below.

10 10 Chapter 1: Introduction to SASLE and Statistics Figure Printing Hardcopy of any item in the Workspace window may be obtained by selecting the File > Print menu option. Remember that the item to be printed should be the active item in the Output window. If it is not, then selecting it, by clicking on its icon or its tab, will make it active Saving Projects and Data If you want to save your project or data then you may use the icon on the toolbar, or go to the File menu. Let us say we want to save our project but we want to rename it FirstAssignment instead of Project. We would select the File > Save Project As menu option and specify the new project name in the resulting dialog box. Figure 1-18 Also, after saving your project you may wish to exit SASLE. This may be done in the usual way. That is, select the File > Exit menu option.

11 Chapter 1: Introduction to SASLE and Statistics 11 Notice that you may safely rely on your understanding of how to interact with a Microsoft Windows application to do basic SASLE activities. The preceding discussion provided an overview of the basic process one would follow in using SASLE to do an analysis. In summary, the process involves three steps. You first create a project, or open an existing one, then add data to the project, and then invoke the required task. Subsequent chapters focus on specific analyses and the SASLE tasks needed to accomplish them. However, in some instances, you may find that your data needs to be pre-processed in some way before the required task can be invoked to do the analysis. It may be that you have to transform existing data values. That is, you may need to create new data values from existing data. It is also possible that you may need to filter your data before conducting the analysis. Another possibility is that your data may not be in electronic form and so you may need to create a dataset in order to add it to your project. The following section provides an overview of how these data processing issues may be addressed. 1-2 Statistical Thinking In Section 1-2 of Elementary Statistics the author points out that analysis should be preceded by questioning the context and source of the data to be analyzed. One of the nice features that SASLE provides is the ability to add notes to a project as well as to the items in the project. Consider the project discussed in the preceding section. Let us say that you wanted to add a note to the TourDates dataset to record details about the context and origin of this data. To do this, you would start by selecting the File > New > Note menu option. This opens a basic editor in the Workspace window that allows such details to be captured. The Project Explorer window below shows a note attached to the TourDates dataset. Figure 1-19 The remainder of this section provides an overview of how SASLE may be used to capture data needed for analysis. We will create a dataset about movies. We will record the name of the movie, the studio, its length in minutes, the time in seconds of tobacco use, and the time in seconds of alcohol use. Our dataset is based on the Alcohol and Tobacco Use in Animated Children s Movies dataset (see dataset 5) in Appendix B of Elementary Statistics Entering Data Let us start by creating a new dataset. To do so, choose the File> New > Data menu option. The following dialog box will appear.

12 12 Chapter 1: Introduction to SASLE and Statistics Figure 1-20 Let us name the dataset mymovies then click the Next button. The following dialog allows us to define the columns for our dataset. Figure 1-21 Notice that we have already provided names for three of our five columns. Let us refer to named columns as variables. Variables may have different data types. For example, you would use the Character data type for qualitative data but you have four choices (i.e. Numeric, Date, Time, Currency) for quantitative data. Notice that the icons in the Name column change as the data type changes. When we click the Finish button a blank data grid appears. Figure 1-22

13 Chapter 1: Introduction to SASLE and Statistics 13 The resulting data grid now has appropriately named columns with appropriate data types. We may now enter data. The populated dataset is shown below. Figure 1-23 The dataset may now be saved by clicking the save icon or selecting the File > Save mymovies menu option. We will use this dataset for all illustrations in subsequent sections Transforming Data Let us say we wanted to generate a new variable named tobaccoyn that has a value of N if tobacco use is zero seconds and a value of Y if tobacco use is more than zero seconds. SASLE provides the Filter and Query task for such data transformation activities. Choose the Data > Filter and Query menu option. The following dialog box will appear. Figure 1-24 Click the Computed Columns button. The following dialog box will appear.

14 14 Chapter 1: Introduction to SASLE and Statistics Figure 1-25 If you click the New button the following menu will appear. Figure 1-26 Select the Build Expression option. The following dialog appears. It allows you to define an expression that will generate a Y or N depending on the corresponding value of tobacco. Figure 1-27 This dialog will allow you to specify an expression that will ensure that the values of the new variable tobaccoyn are as specified above. You may type this expression directly into the Expression text window. Following is the completed expression.

15 Chapter 1: Introduction to SASLE and Statistics 15 Figure 1-28 Notice how the expression is written. That is, when the numeric variable tobacco contains the value 0, then the new character variable tobaccoyn will be assigned the value N. In all other cases the variable tobaccoyn will be assigned the value Y. This is known as a conditional expression. The clause, tobacco=0, is known as a Boolean expression and may either be true or false. Note that conditional expressions may contain several WHEN clauses but should contain only one ELSE clause. Also, Boolean expressions may contain several basic clauses connected by the Boolean OR or AND operators and may be negated by the Boolean NOT operator. These operators (see Figure 1-29) are available from the button bar on the dialog box above. Figure 1-29 Instead of typing the expression directly you could have searched for a template and then filled in the relevant sections. For example, if you select the Functions tab and click on the Conditional entry in the Categories window you will notice a list of conditional expression templates in the Functions window. Selecting the CASE{else} template and clicking the Add to Expression button will add the template to the Expression text window. Once it is in the Expression text window you may edit it. If you click the OK button, the following dialog box will reappear. Figure 1-30 Notice the default name Calculation1. We will change this name to tobaccoyn. You may do this by either double-clicking the name thus switching to edit mode or you may select the name and switch to edit mode by clicking the Rename button. If you change the name and then click the Close button, the following dialog box will appear.

16 16 Chapter 1: Introduction to SASLE and Statistics Figure 1-31 Notice that tobaccoyn is in the Column Name list. If you click the Run button at this point then the generated dataset will contain tobaccoyn only. So, before we click the Run button we will add the other variables to the Column Name list. To do this, select each variable and then by clicking the right mouse button the following context menu will appear. Figure 1-32 Notice the selected variables in the background. Let us select the Add to Selection menu option. The Column Name list now contains all variables needed for our new datatset.

17 Chapter 1: Introduction to SASLE and Statistics 17 Figure 1-33 Let us make one more change before clicking the Run button. Notice that our computed column, tobaccoyn, is at the top of the list. We will move it to the bottom of the list. To do this, select it and then click on the icon until it is at the bottom. If you click on the Run button the query will be executed and the data grid for the desired dataset will be produced. Figure 1-34 We will briefly illustrate another type of data transformation activity. Let us say you wanted to generate a new variable named lengthsecs that contains the length in seconds of movies, minus the time in seconds of tobacco use and alcohol use. We would proceed in exactly the same way as we did above except for the expression needed to compute the values of lengthsecs. Figure 1-35

18 18 Chapter 1: Introduction to SASLE and Statistics This is an arithmetic expression. Arithmetic operators are the leftmost five buttons on the button bar. The data grid for the resulting query is presented below. Figure 1-36 These examples merely hint at the tremendous power of the Filter and Query task. For example, the Filter and Query task may also be used to filter the rows of a dataset using a Boolean expression. That is, you may want to retrieve only rows that meet certain criteria, based on the values of other columns in the dataset. We will not illustrate that activity. The preceding discussion addressed two issues. First, how do you create a dataset using SASLE? Second, how do you create new data values in an existing dataset from other values in the dataset? We will refer to these sections throughout subsequent chapters of this manual as the need arises. 1-3 Types of Data SASLE allows you to add data from files, saved in a variety of formats, to a project. In most instances, you will not need to convert the data to the SAS dataset format before adding it to your project. The primary requirement is that the data is in tabular format. This means that data residing in a variety of spreadsheet formats may be directly added to a project. Also, data residing in relational database tables can usually be directly added to a project. For those cases where the native file format is not accessible, SASLE provides an Import Wizard that may be used to automatically convert these file formats to the SAS dataset format. Therefore, you should expect the following file formats to be directly accommodated in their native form. DB2 and Oracle database tables. Microsoft Excel files. Microsoft Access tables. Lotus files. Note that when you add data to a project, you are merely creating a reference to the physical file. SASLE does not make a new copy of the file. 1-4 Critical Thinking In Section 1-3 of Elementary Statistics the author mentions that common sense should be used when thinking critically about data and statistics. The author also mentions that sample data should only be used to make inferences about populations if the sample is appropriately selected. As the author points out, GIGO - Garbage In Garbage Out. 1-5 Collecting Sample Data In Section 1-3 of Elementary Statistics the author presents the following definitions for random samples and simple random samples.

19 Chapter 1: Introduction to SASLE and Statistics 19 In a random sample members from the population are selected in such a way that each individual member has an equal chance of being selected. A simple random sample of size n subjects is selected in such a way that every possible sample of size n has the same chance of being chosen. The SASLE Random Sample task may be used to select such samples. To illustrate, we will use the CHMOVIES dataset introduced in Section 1-1 above. Choose the Data > Random Sample menu option or click on the Random Sample task in the Task List window. The following dialog box will appear. Figure 1-37 Assign the Tobacco_Use sec_ and Alcohol_Use sec_ variables to the Output variables role. This means that our sample will consist of these variables only. Note that the Strata variables role would be used to select a stratified sample. Now select the Options option from the selection pane. This option allows you to specify the sampling method and sample size.

20 20 Chapter 1: Introduction to SASLE and Statistics Figure 1-38 Notice that the drop down list box next to the Sample size box allows for either a predetermined sample size (i.e. rows) to be specified, or a sample based on a percentage of the available data to be specified. We will illustrate by selecting a simple random sample and so we will specify a sample size of ten rows. If you now click the Run button then a dataset with the sampled items will be created. The data grid for this dataset appears below. Figure 1-39 Note that you are unlikely to be able to reproduce this exact sample if you followed these steps. This is because there are several million different samples of size ten items that may be selected from our population of fifty items. However, whenever SASLE is used to generate a random sample a report is also produced that contains a seed that may be used to generate the exact sample if the original data is available. The seed is the number next to the Random Number Seed in the report below. You can use this seed to generate this sample at another time by selecting the Specify random seed number checkbox in Figure 1-38 above and then entering the seed into the edit box next to it.

21 Chapter 1: Introduction to SASLE and Statistics 21 Figure 1-40 Exercises Use the appropriate SASLE tasks for the following problems. A detailed description of the datasets mentioned, including the names of the associated data files on the supplied CD-ROM, is available in Appendix B of Elementary Statistics. 1. Create a new project with a name of your choosing (if necessary, use the name Chap1Q1). Add dataset 2 from Appendix B to the project. Use the List Data task to create a report that lists the contents of the dataset. Produce hardcopy of this report. 2. Create a new project with a name of your choosing (if necessary, use the name Chap1Q2). Add dataset 9 from Appendix B to the project. Use the List Data task to create a report that lists the first ten observations. Also, print the Title, Gross, and Viewer Rating variables only. Save the project before exiting. Hint: The Options option will allow you to specify the number of rows to be printed. 3. Create a new dataset with the following values: 100.1, 98.6, 98.8, 98.0, 97.5, 99.0, 100.9, 97.9, 100.3, 99.1, 97.2 Use an appropriate name for the dataset (if necessary, use the name Chap1temp). Let us say these values are temperatures in degrees Fahrenheit. Use the Filter and Query task to create a new variable that is the temperature in degrees Celsius. Hint: Use the expression C=5/9(F 32) to convert Fahrenheit to Celsius. 4. Dataset 7 in Appendix B contains tobacco and alcohol use data for several animated children s movies. Add this dataset to a project. Create a new variable alctob, which is set to N if alcohol and tobacco use is zero seconds and to Y otherwise. 5. Dataset 6 contains data on anesthetized bears. The dataset contains a variable named weight, which is the weight in pounds of each bear. Add this dataset to a project. Create a new variable wgtkg, which is set to the weight in kilograms of each bear. Hint: Your expression should multiply the weight variable by to obtain the weight in kilograms. 6. Dataset 1 in Appendix B contains health exam data. Use the Random Sample task to select a simple random sample of 25 items from this dataset. Your sample should contain the variables Age, HT, and WT only.