Asheville Buncombe Technical Data Analysis with SPSS Session 15 Follow instructions from Doing Data Analysis with SPSS, Session15 and practiceLinear Relati

Asheville Buncombe Technical Data Analysis with SPSS Session 15 Follow instructions from Doing Data Analysis with SPSS, Session15 and practiceLinear RelationshipsStatistical Inferences in Linear RegressionPlease replicate the exercises and answer the questions the book presented, based on your analysis of the output. Please include the output, result analysis, and answers for the questions in your assignment. Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
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Doing Data Analysis
with SPSS®
Version 18
Robert H. Carver
Stonehill College
Jane Gradwohl Nash
Stonehill College
Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States
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Doing Data Analysis with SPSS®
Version 18
Robert H. Carver, Jane Gradwohl Nash
Publisher: Richard Stratton
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© 2012, 2009, 2006, Brooks/Cole Cengage Learning
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Student Edition:
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In loving memory of my brother and teacher Barry,
and for Donna, Sam, and Ben, who teach me daily.
RHC
For Justin, Hanna and Sara—you are my world.
JGN
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Contents
Session 1. A First Look at SPSS Statisitcs 18 1
Objectives 1
Launching SPSS/PASW Statistics 18 1
Entering Data into the Data Editor 3
Saving a Data File 6
Creating a Bar Chart 7
Saving an Output File 11
Getting Help 12
Printing in SPSS 12
Quitting SPSS 12
Session 2. Tables and Graphs for One Variable 13
Objectives 13
Opening a Data File 13
Exploring the Data 14
Creating a Histogram 16
Frequency Distributions 20
Another Bar Chart 22
Printing Session Output 22
Moving On… 23
Session 3. Tables and Graphs for Two Variables 27
Objectives 27
Cross-Tabulating Data 27
Editing a Recent Dialog 29
More on Bar Charts 29
Comparing Two Distributions 32
v
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vi
Contents
Scatterplots to Detect Relationships 33
Moving On… 34
Session 4. One-Variable Descriptive Statistics 39
Objectives 39
Computing One Summary Measure for a Variable 39
Computing Additional Summary Measures 43
A Box-and-Whiskers Plot 46
Standardizing a Variable 47
Moving On… 48
Session 5. Two-Variable Descriptive Statistics 51
Objectives 51
Comparing Dispersion with the Coefficient of Variation 51
Descriptive Measures for Subsamples 53
Measures of Association: Covariance and Correlation 54
Moving On… 57
Session 6. Elementary Probability 61
Objectives 61
Simulation 61
A Classical Example 61
Observed Relative Frequency as Probability 63
Handling Alphanumeric Data 65
Moving On… 68
Session 7. Discrete Probability Distributions 71
Objectives 71
An Empirical Discrete Distribution 71
Graphing a Distribution 73
A Theoretical Distribution: The Binomial 74
Another Theoretical Distribution: The Poisson 76
Moving On… 77
Session 8. Normal Density Functions 81
Objectives 81
Continuous Random Variables 81
Generating Normal Distributions 82
Finding Areas under a Normal Curve 85
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Contents
vii
Normal Curves as Models 87
Moving On… 89
Session 9. Sampling Distributions 93
Objectives 93
What Is a Sampling Distribution? 93
Sampling from a Normal Population 94
Central Limit Theorem 97
Sampling Distribution of the Proportion 99
Moving On… 100
Session 10. Confidence Intervals 103
Objectives 103
The Concept of a Confidence Interval 103
Effect of Confidence Coefficient 106
Large Samples from a Non-normal (Known) Population 106
Dealing with Real Data 107
Small Samples from a Normal Population 108
Moving On… 110
Session 11. One-Sample Hypothesis Tests 113
Objectives 113
The Logic of Hypothesis Testing 113
An Artificial Example 114
A More Realistic Case: We Don’t Know Mu or Sigma 117
A Small-Sample Example 119
Moving On… 121
Session 12. Two-Sample Hypothesis Tests 125
Objectives 125
Working with Two Samples 125
Paired vs. Independent Samples 130
Moving On… 132
Session 13. Analysis of Variance (I) 137
Objectives 137
Comparing Three or More Means 137
One-Factor Independent Measures ANOVA 138
Where Are the Differences? 142
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viii
Contents
One-Factor Repeated Measures ANOVA 144
Where Are the Differences? 149
Moving On… 149
Session 14. Analysis of Variance (II) 153
Objectives 153
Two-Factor Independent Measures ANOVA 153
Another Example 159
One Last Note 161
Moving On… 162
Session 15. Linear Regression (I) 165
Objectives 165
Linear Relationships 165
Another Example 170
Statistical Inferences in Linear Regression 171
An Example of a Questionable Relationship 172
An Estimation Application 173
A Classic Example 174
Moving On… 175
Session 16. Linear Regression (II) 179
Objectives 179
Assumptions for Least Squares Regression 179
Examining Residuals to Check Assumptions 180
A Time Series Example 185
Issues in Forecasting and Prediction 187
A Caveat about “Mindless” Regression 190
Moving On… 191
Session 17. Multiple Regression 195
Objectives 195
Going Beyond a Single Explanatory Variable 195
Significance Testing and Goodness of Fit 201
Residual Analysis 202
Adding More Variables 202
Another Example 203
Working with Qualitative Variables 204
A New Concern 206
Moving On… 207
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Contents
ix
Session 18. Nonlinear Models 211
Objectives 211
When Relationships Are Not Linear 211
A Simple Example 212
Some Common Transformations 213
Another Quadratic Model 215
A Log-Linear Model 220
Adding More Variables 221
Moving On… 221
Session 19. Basic Forecasting Techniques 225
Objectives 225
Detecting Patterns over Time 225
Some Illustrative Examples 226
Forecasting Using Moving Averages 228
Forecasting Using Trend Analysis 231
Another Example 234
Moving On… 234
Session 20. Chi-Square Tests 237
Objectives 237
Qualitative vs. Quantitative Data 237
Chi-Square Goodness-of-Fit Test 237
Chi-Square Test of Independence 241
Another Example 244
Moving On… 245
Session 21. Nonparametric Tests 249
Objectives 249
Nonparametric Methods 249
Mann-Whitney U Test 250
Wilcoxon Signed Ranks Test 252
Kruskal-Wallis H Test 254
Spearman’s Rank Order Correlation 257
Moving On… 258
Session 22. Tools for Quality 261
Objectives 261
Processes and Variation 261
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x
Contents
Charting a Process Mean 262
Charting a Process Range 265
Another Way to Organize Data 266
Charting a Process Proportion 268
Pareto Charts 270
Moving On… 272
Appendix A. Dataset Descriptions 275
Appendix B. Working with Files 309
Objectives 309
Data Files 309
Viewer Document Files 310
Converting Other Data Files into SPSS Data Files 311
Index 315
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Preface
Quantitative Reasoning, Real Data, and Active Learning
Most undergraduate students in the U.S. now take an
introductory course in statistics, and many of us who teach statistics
strive to engage students in the practice of data analysis and quantitative
thinking about real problems. With the widespread availability of
personal computers and statistical software, and the near-universal
application of quantitative methods in many professions, introductory
statistics courses now emphasize statistical reasoning more than
computational skill development. Questions of how have given way to
more challenging questions of why, when, and what?
The goal of this book is to supplement an introductory
undergraduate statistics course with a comprehensive set of self-paced
exercises. Students can work independently, learning the software skills
outside of class, while coming to understand the underlying statistical
concepts and techniques. Instructors can teach statistics and statistical
reasoning, rather than teaching algebra or software. Both students and
teachers can devote their energies to using data analysis in ways that
inform their understanding of the world and investigate problems that
really matter.
The Approach of This Book
The book reflects the changes described above in several ways.
First and most obviously it provides some training in the use of a
powerful software package to relieve students of computational drudgery.
Second, each session is designed to address a statistical issue or need,
rather than to feature a particular command or menu in the software.
xi
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
xii
Preface
Third, nearly all of the datasets in the book are real, reflecting a variety
of disciplines and underscoring the wide applicability of statistical
reasoning. Fourth, the sessions follow a traditional sequence, making the
book compatible with many texts. Finally, as each session leads students
through the techniques, it also includes thought-provoking questions
and challenges, engaging the student in the processes of statistical
reasoning. In designing the sessions, we kept four ideas in mind:
•
Statistical reasoning, not computation, is the goal of the course.
This book asks students questions throughout, balancing
software instruction with reflection on the meaning of results.
•
Students arrive in the course ready to engage in statistical
reasoning. They need not slog all the way through descriptive
techniques before encountering the concept of inference. The
exercises invite students to think about inferences from the
start, and the questions grow in sophistication as students
master new material.
•
Exploration of real data is preferable to artificial datasets. With
the exception of the famous Anscombe regression dataset and
a few simulations, all of the datasets are real. Some are very
old and some are quite current, and they cover a wide range
of substantive areas.
•
Statistical topics, rather than software features, should drive
the design of each session. Each session features several SPSS
functions selected for their relevance to the statistical concept
under consideration.
This book provides a rigorous but limited introduction to the
software produced by SPSS, an IBM company.1 The SPSS/PASW2
Statistics 18 system is rich in features and options; this book makes no
attempt to “cover” the entire package. Instead, the level of coverage is
commensurate with an introductory course. There may be many ways to
perform a given task in SPSS; generally, we show one way. This book
provides a “foot in the door.” Interested students and other users can
explore the software possibilities via the extensive Help system or other
standard SPSS documentation.
SPSS was acquired by IBM in October 2009.
SPSS Statistics 18 was formerly known as PASW Statistics 18, and the
PASW name appears on several screens in the software. The book will reference
the SPSS name only, but note that SPSS and PASW are interchangeable terms.
1
2
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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Preface
xiii
Using This Book
We presume that this book is being used as a supplementary text
in an introductory-level statistics course. If your courses are like ours
(one in a psychology department, the other in a business department),
class time is a scarce resource. Adding new material is always a
balancing act. As such, supplementary readings and assignments must
be carefully integrated. We suggest that instructors use the sessions in
this book in four different ways, tailoring the approach throughout the
term to meet the needs of the students and course.
•
•
•
•
In-class activity: Part or all of some sessions might best be
done together in class, with each student at a computer. The
instructor can comment on particular points and can roam to
offer assistance. This may be especially effective in the earliest
sessions.
Stand-alone assignments: In conjunction with a topic covered
in the principal text, sessions can be assigned as independent
out-of-class work, along with selected Moving On… questions.
This is our most frequently-used approach. Students
independently learn the software, re-enforce the statistical
concepts, and come to class with questions about any
difficulties they encountered in the lab session.
Preparation for text-based case or problem: An instructor may
wish to use a textbook case for a major assignment. The
relevant session may prepare the class with the software skills
needed to complete the case.
Independent projects: Sessions may be assigned to prepare
students to undertake an independent analysis project
designed by the instructor. Many of the data files provided
with the book contain additional variables that are never used
within sessions. These variables may form the basis for
original analyses or explorations.
Solutions are available to instructors for all Moving On… and
bold-faced questions. Instructors should consult their Cengage Learning
sales representatives for details. A companion website is available to both
instructors and students at www.cengage.com/statistics/carver.
The Data Files
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