Learning data analysis with Data desk

著者

書誌事項

Learning data analysis with Data desk

Paul F. Velleman

W.H. Freeman, c1993

Rev. and updated

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注記

System requirements for computer disks: Macintosh; 1MB RAM; 800K disk drive and hard disk or second floppy drive; floating point unit and color monitor optional

Includes index

内容説明・目次

内容説明

Data Desk is a graphical and interactive data analysis, and statistics program which runs on the Macintosh and is based on the philosophy of Exploratory Data Analysis. It is a supplement for anyone taking a statistics course who has access to a Macintosh computer. Originally developed for teaching undergraduates at Cornell University, Data Desk Student Version introduces the world of modern data analysis. The linked graphics, help the user to visualize concepts and see the relationships between the concepts and data. "Learning Data Analysis with Data Disk" covers the statistics and graphics methods needed by students in undergraduate statistics courses. Many examples are worked out in detail, and more than 200 exercises are built around data sets that are included on the disk. This revised and updated edition of the student version is fully compatible with Data Desk 4. Data Desk excels in practical data analysis, and among the many new features and improvements are: more hyperviews, control over font and size of text, supports colour, enhanced interface to make it easier for students to use. A special feature of the student version is its compatibility with the professional version of Data Desk.

目次

  • 1. Introduction: How To Find Your Way In This Book
  • Data Desk and Your Statistics Course
  • Data Desk and Your Statistics Text
  • What Computing Background Do I Need?
  • A Brief History of Statistics Software
  • The Macintosh Revolution
  • Appendix IA Macintosh Terms and Operations. 2. Basic Concepts: The Data Desktop
  • Menus and Submenus
  • Datafiles
  • Windows
  • Variables
  • Bundles
  • The Results Bundle Selecting
  • Output Records and Plots. 3. Data Desk Concepts: Derived Variables
  • Information Records
  • Scratch Pads
  • HyperViews
  • Updating Windows
  • Dependencies
  • Leaving Data Desk
  • The Clipboard
  • Selecting Variables
  • Datafile Details
  • Memory Requirements. Appendices: 3A Hints and Shortcuts
  • 3B Differences between the Data Desktop and the Finder Desktop
  • 3C Cold, Hot, and Warm Objects
  • 3D SANE
  • 3E Data Desk Limits. 4. Data: Basic Concepts
  • Variables, Cases, and Data Structure
  • Missing Data
  • Infinities and NaN's
  • Outliers, Blunders, and Rogues
  • Numbers and Numerals. Appendix 4A Kinds of Data
  • Appendix 4B Matrices, Tables, and Bundles. 5. Entering and Editing Data: Entering Data for One Variable
  • Editing a Variable
  • Extended and Discontinuous Selection
  • Editing Several Variables
  • The Editing Sequence
  • Moving Around
  • Cutting, Copying, and Pasting
  • Undo
  • Unlinking Windows
  • Scrolling
  • Filling and Inserting
  • Managing Windows
  • Details of Editing
  • Shifting Cases
  • More on Linking
  • Finding and Replacing Cases
  • Store and Revert
  • Printing Variables
  • Example
  • Example: Editing Data. Appendix 5A Configuring Data Editing. 6. (Omitted in Student Version). 7. Simple Summaries: Measures of Centre
  • Example
  • Measures of Spread
  • Order Statistics
  • General Summaries
  • Moments
  • Using Summary Statistics
  • Hyper views. Appendix 7A: The Biweight, a Robust Center. 8 Displaying Data: Data Analysis Displays
  • Plotting Conventions
  • Histograms
  • Recentering and Rescaiing Histograms
  • Dotplots
  • Dotplots of Several Groups
  • Boxplots
  • Boxplots and Inference
  • Bar Charts
  • Pie Charts
  • Scatterplots
  • Lineplots Normal Probability Plots. Appendix 8A Boxplot Definitions
  • Appendix 8B Probability Plots. 9. Working with Displays: Chapter Organization
  • Organization of Features and Commands in Data Desk
  • Basic Plot Actions
  • Select
  • Link
  • Brush and Slice
  • Identity
  • Move
  • Isolate
  • Resize. Appendix 9A Plot Tool Shortcuts. 10. Brushing and Slicing: Brushing and Slicing
  • Principles of Brushing and Slicing. 11. Derived Variables: The Transform Submenus
  • General Derived Variable Expressions
  • Expression Conventions
  • Dependencies
  • Identifying Variables by Name
  • Working with Open Derived Variables
  • Re-expressing Data to Improve Analyses Example
  • Indicator Variables and Logical Expressions
  • Common Errors and How to avoid Them. Appendix 11A Derived Variable Expressions
  • Appendix 11B Casewise Functions
  • Appendix 11C Collapsing Functions. 12. Manipulating Variables Sorting
  • Ranking
  • Generating Patterned Variables
  • Appending and Splitting Variables
  • Duplicating Icons
  • Selectors
  • Copying and Printing Results.

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