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IBM SPSS Statistics 19 Statistical Procedures Companion [Multiple-component retail product]

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  • Formaat: Multiple-component retail product, 672 pages, kõrgus x laius x paksus: 196x232x22 mm, kaal: 904 g, Contains 1 Paperback / softback and 1 CD-ROM
  • Ilmumisaeg: 30-Jun-2011
  • Kirjastus: Pearson
  • ISBN-10: 0321748425
  • ISBN-13: 9780321748423
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IBM SPSS Statistics 19 Statistical Procedures CompanionSuurem pilt
  • Formaat: Multiple-component retail product, 672 pages, kõrgus x laius x paksus: 196x232x22 mm, kaal: 904 g, Contains 1 Paperback / softback and 1 CD-ROM
  • Ilmumisaeg: 30-Jun-2011
  • Kirjastus: Pearson
  • ISBN-10: 0321748425
  • ISBN-13: 9780321748423
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780321748423.html

IBM SPSS Statistics 19 Statistical Procedures Companion contains tips, warnings, and examples that will help you take advantage of IBM SPSS Statistics 19 to better analyze data. This book contains a basic review of the underlying statistical concepts, with an emphasis on the practice of analyzing data. Ideal for both new and experienced users, this companion offers suggestions and strategies for handling the issues that arise when analyzing data.

 

IBM SPSS Statistics 19 Statistical Procedures Companion covers various statistical procedures in IBM SPSS Statistics. This book also contains introductory chapters on using the software, creating and cleaning data files, testing hypotheses, and describing data.

Part 1
1 Introduction
1(14)
Statistical Procedures Described in the Book
1(14)
Descriptive Statistics
2(2)
Compare Means
4(1)
General Linear Model
5(2)
General Loglinear Analysis
7(1)
Correlate
8(1)
Regression
9(1)
Classify
10(2)
Dimension Reduction
12(1)
Scale
13(1)
Nonparametric Tests
14(1)
2 Getting to Know IBM SPSS Statistics
15(22)
In a Nutshell
16(1)
Tutorials
16(3)
Windows of IBM SPSS Statistics
19(1)
Inside a Dialog Box
20(5)
IBM SPSS Statistics Viewer
25(9)
Getting More Help
27(1)
Dressing Up Your Output
28(5)
Editing Your Charts
33(1)
Using Syntax
34(3)
3 Introducing Data
37(16)
Vocabulary
37(2)
Planning the Data File
39(1)
Getting Data into IBM SPSS Statistics
39(11)
Using IBM SPSS Statistics Data Files
39(2)
Using Spreadsheet and Database Files
41(1)
Feeding Text Files to the Text Wizard
42(1)
Typing Your Own Data
42(7)
Entering the Data
49(1)
Saving the Data File
50(1)
Entering Tabulated Data
50(1)
Creating New Variables
50(1)
Saving Time When Dealing with Your Data
50(3)
Selecting Cases for Analyses
51(1)
Repeating the Analysis for Different Groups of Cases
51(2)
4 Preparing Your Data
53(14)
Checking Variable Definitions
53(4)
Using the Utilities Menu
54(1)
Using Define Variable Properties
55(2)
Checking Your Case Count
57(2)
Eliminating Duplicate Cases
57(2)
Adding Missing Cases
59(1)
Checking Data Values
59(7)
Listing the Values
59(1)
Making Frequency Tables
60(1)
Looking At the Distribution of Values
61(2)
Looking At Combinations of Variables
63(3)
Caution
66(1)
5 Transforming Your Data
67(16)
Computing a New Variable
67(8)
One Size Fits All: Unconditional Transformation
68(3)
If and Then: Conditional Transformations
71(1)
Visual Binning
72(3)
Changing the Coding Scheme
75(8)
Checking the Recode
77(1)
Changing a String Variable to a Numeric Variable
78(1)
Ranking Variables
79(1)
Counting Occurrences
80(3)
6 Describing Your Data
83(28)
Examples
83(1)
In a Nutshell
84(1)
Newspaper Reading: The Example
84(8)
Examining Tables and Charts of Counts
84(4)
Examining Two-Way Tables of Counts
88(4)
Summarize Scale Variables
92(12)
Histograms
93(3)
Variability and Central Tendency
96(4)
Plotting Pairs of Variables
100(1)
Creating OLAP Cubes
101(1)
Percentiles
102(2)
Normal Distribution
104(2)
Standard Scores
105(1)
Navigating the Menus
106(1)
Obtaining the Output
107(4)
7 Testing Hypotheses
111(16)
In a Nutshell
111(1)
Setting the Stage
112(3)
Defining Samples and Populations
112(1)
Using Random Samples
112(1)
Creating a Good Experimental Design
112(1)
Dealing with Missing Data
113(2)
Testing a Hypothesis
115(7)
Step 1 Specifying the Null Hypothesis and the Alternative Hypothesis
115(1)
Prelude to the Remaining Steps
116(1)
Step 2 Selecting the Appropriate Statistical Procedure
117(1)
Step 3 Checking Whether Your Data Meet the Required Assumptions
117(2)
Step 4 Assuming That the Null Hypothesis Is True
119(1)
Step 5 Calculating the Observed Significance Level
119(2)
Step 6 Deciding Whether to Reject the Null Hypothesis
121(1)
Calculating Confidence Intervals
122(4)
Reporting Your Results Correctly
123(2)
To Err Is Statistical
125(1)
Commonly Used Tests for Popular Hypotheses
126(1)
Obtaining the Output
126(1)
8 T Tests
127(16)
In a Nutshell
127(1)
Deciding Which T Test to Use
128(5)
One-Sample T Test
128(2)
Paired-Samples T Test
130(1)
Two-Independent-Samples T Test
131(2)
Analyzing Truancy Data: The Example
133(9)
One-Sample T Test
134(3)
Paired-Samples T Test
137(3)
Two-Independent-Samples T Test
140(2)
Obtaining the Output
142(1)
9 One-Way Analysis of Variance
143(20)
Examples
143(1)
In a Nutshell
144(1)
Anorexia: The Example
144(9)
Arranging the Data
144(1)
Examining the Data
145(2)
Checking the Assumptions
147(4)
Testing the Hypothesis
151(1)
Analyzing Change
152(1)
Pinpointing the Differences
153(8)
Atoning for Many Comparisons
154(2)
Contrasts: Testing Linear Combinations of Means
156(5)
Obtaining the Output
161(2)
10 Crosstabulation
163(34)
Examples
163(1)
In a Nutshell
164(1)
Chi-Square Test: Are Two Variables Independent?
164(8)
Is It in the Stars?
165(3)
Are Proportions Equal?
168(3)
Measuring Change: McNemar Test
171(1)
How Strongly Are Two Variables Related?
172(14)
Measures of Association for Nominal Variables
173(3)
Proportional Reduction in Error Measures
176(3)
Ordinal Measures
179(4)
Eta Coefficient
183(1)
Measures Based on Correlation Coefficients
183(1)
Measuring Agreement
183(3)
Measuring Risk in 2-by-2 Tables
186(7)
Measuring the Relative Risk
186(2)
Calculating the Odds Ratio
188(2)
Stratifying the Cases
190(1)
Testing Hypotheses about the Odds Ratios
191(2)
Megatip: Entering Tables Directly
193(1)
Obtaining the Output
194(3)
11 Correlation
197(20)
Examples
197(1)
In a Nutshell
198(1)
Body Fat: The Example
198(8)
Plotting the Data
199(2)
Examining the Scatterplot Matrix
201(1)
Using the Pearson Correlation Coefficient
202(2)
Testing Hypotheses about Correlation Coefficients
204(2)
Comparing Two Indexes
206(2)
Basing Correlation Coefficients on Ranks
208(1)
Using Partial Correlation Coefficients
208(6)
Calculating Partial Correlation Coefficients
209(2)
Testing Hypotheses about Partial Correlation Coefficients
211(1)
Steps for Calculating the Partial Correlation Coefficient
212(1)
Missing Values
213(1)
Megatip: Identifying Points in a Scatterplot
214(2)
Labeling Individual Points with the Default Variable Value
215(1)
Labeling All Points with the Default Variable Value
215(1)
Changing the Variables Used to Identify Points
215(1)
Obtaining the Output
216(1)
12 Bivariate Linear Regression
217(20)
Examples
217(1)
In a Nutshell
218(1)
Predicting Body Fat: The Example
218(7)
Plotting the Values
218(1)
Calculating the Least-Squares Regression Line
219(1)
Predicting Body Fat
220(1)
Determining How Well the Line Fits
221(1)
Testing Hypotheses about the Population Regression Line
221(4)
Searching for Violations of Assumptions
225(5)
Looking for Unusual Points
230(2)
Dealing with Violations of the Assumptions
232(2)
Coaxing a Nonlinear Relationship to Linearity
232(1)
Coping with Non-Normality
233(1)
Stabilizing the Variance
234(1)
Adding More Circumferences: Multiple Linear Regression
234(1)
Obtaining the Output
235(2)
13 Multiple Linear Regression
237(38)
Examples
238(1)
In a Nutshell
238(1)
Reading Scores: The Example
238(2)
Formulating the Problem
239(1)
Multiple Linear Regression Model
240(14)
Before You Start
241(2)
Estimating the Coefficients of the Model
243(3)
Assumptions for Testing Hypotheses
246(1)
Regression Hypotheses
246(5)
Including Categorical Variables
251(1)
Comparing Two Models: Change in R-Square
252(1)
Many Paths Lead to the Same Hypothesis
253(1)
Using an Automated Method for Building a Model
254(6)
Examining the Methods
255(1)
Stepwise Selection: An Example
256(3)
Calculating Predicted Values
259(1)
After the Model Is Selected
260(1)
Checking for Violations of Regression Assumptions
261(4)
Calculating Residuals
261(4)
Looking for Unusual Observations
265(8)
Identifying Large Residuals
265(1)
Identifying Unusual Values of Independent Variables
266(1)
Looking for Influential Points
267(3)
Partial Regression Plots
270(1)
Looking for Collinearity
271(1)
A Final Comment
272(1)
Obtaining the Output
273(2)
14 Automatic Linear Modeling
275(16)
All-Possible-Subsets Regression Models
276(8)
Specifying the Model
277(1)
Model Building Summary
277(2)
Comparing Observed and Expected Values
279(1)
Examining the Coefficients
280(2)
Examining Assumptions
282(1)
Looking for Influential Points
283(1)
Ensemble Models
284(7)
Automatic Data Preparation
285(1)
Model Summary
285(2)
Evaluating the Predictors
287(4)
15 Discriminant Analysis
291
Examples
292(1)
In a Nutshell
292(1)
Predicting Internet Use: The Example
293(6)
Before You Start
293(3)
Examining Descriptive Statistics
296(3)
Calculating the Discriminant Function
299(7)
Discriminant Function Coefficients
300(6)
Testing Hypotheses about the Discriminant Function
306(3)
Testing Assumptions
307(1)
Testing Equality of Discriminant Function Means
308(1)
Classifying Cases into Groups
309(5)
Classification Summary
309(3)
Examining the Probability of Group Membership
312(2)
Automated Model Building
314(6)
Setting Criteria
314(3)
Following the Steps
317(3)
Classification Results
320(1)
Discriminant Analysis with Four Groups
320(6)
Discriminant Function Coefficients
321(3)
Testing Equality of Discriminant Function Means
324(1)
Pairwise Differences between Groups
325(1)
Classification
326(5)
Classification Function Coefficients
327(4)
Obtaining the Output
331
Marija Noruis earned a PhD in biostatistics from the University of Michigan. She was SPSS's first professional statistician. During this time, she wrote her first book, The SPSS Introductory Guide. Since then she has written numerous volumes of highly acclaimed SPSS documentation, and textbooks that demystify statistics and SPSS. Dr. Noruis has been on the faculties of the University of Chicago and Rush Medical College, teaching statistics to diverse audiences. When not working on SPSS guides, Marija analyzes real data as a statistical consultant.

For more detailed information about Dr. Noruis and her SPSS guides, visit her website at www.norusis.com.