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

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  • Formaat: Multiple-component retail product, 464 pages, kõrgus x laius x paksus: 190x233x16 mm, kaal: 644 g, Contains 1 Paperback / softback and 1 CD-ROM
  • Ilmumisaeg: 28-Jul-2011
  • Kirjastus: Pearson
  • ISBN-10: 0321748433
  • ISBN-13: 9780321748430
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IBM SPSS Statistics 19 Advanced Statistical Procedures CompanionSuurem pilt
  • Formaat: Multiple-component retail product, 464 pages, kõrgus x laius x paksus: 190x233x16 mm, kaal: 644 g, Contains 1 Paperback / softback and 1 CD-ROM
  • Ilmumisaeg: 28-Jul-2011
  • Kirjastus: Pearson
  • ISBN-10: 0321748433
  • ISBN-13: 9780321748430
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780321748430.html

IBM SPSS Statistics 19 Advanced Statistical Procedures Companion contains valuable tips, warnings, and examples that will help you take advantage of IBM SPSS Statistics to better analyze data. This book offers clear and concise explanations and examples of advanced statistical procedures in the IBM SPSS Statistics Advanced and Regression modules.

1 Model Selection Loglinear Analysis
1(24)
Loglinear Modeling Basics
2(8)
A Two-Way Table
2(1)
The Saturated Model
3(1)
Main Effects
4(1)
Interactions
5(1)
Examining Parameters in a Saturated Model
6(2)
Calculating the Missing Parameter Estimates
8(1)
Testing Hypotheses about Parameters
9(1)
Fitting an Independence Model
10(4)
Specifying the Model
11(1)
Checking Convergence
11(2)
Chi-Square Goodness-of-Fit Tests
13(1)
Hierarchical Models
14(1)
Generating Classes
14(1)
Selecting a Model
14(6)
Evaluating Interactions
15(4)
Testing Individual Terms in the Model
19(1)
Model Selection Using Backward Elimination
20(5)
2 Logit Loglinear Analysis
25(18)
Dichotomous Logit Model
26(4)
Loglinear Representation
26(1)
Logit Model
27(1)
Specifying the Model
28(1)
Parameter Estimates for the Saturated Logit Model
28(2)
Unsaturated Logit Model
30(5)
Specifying the Analysis
30(1)
Goodness-of-Fit Statistics
31(1)
Observed and Expected Cell Counts
31(1)
Parameter Estimates
32(1)
Measures of Dispersion and Association
33(2)
Polychotomous Logit Model
35(7)
Specifying the Model
36(1)
Goodness of Fit of the Model
36(1)
Interpreting Parameter Estimates
37(4)
Examining Residuals
41(1)
Covariates
42(1)
Other Logit Models
42(1)
3 Multinomial Logistic Regression
43(26)
The Logit Model
44(1)
Baseline Logit Example
44(2)
Specifying the Model
46(8)
Parameter Estimates
46(5)
Likelihood-Ratio Test for Individual Effects
51(1)
Likelihood-Ratio Test for the Overall Model
52(2)
Evaluating the Model
54(5)
Calculating Predicted Probabilities and Expected Frequencies
54(1)
Classification Table
55(1)
Goodness-of-Fit Tests
56(2)
Examining the Residuals
58(1)
Pseudo-R-square Measures
58(1)
Correcting for Overdispersion
59(1)
Automated Variable Selection
59(4)
Hierarchical Variable Entry
60(1)
Specifying the Analysis
61(1)
Step Output
61(2)
Likelihood-Ratio Tests for Individual Effects
63(1)
Matched Case-Control Studies
63(6)
The Model
64(1)
Creating the Difference Variables
65(1)
The Data File
66(1)
Specifying the Analysis
66(1)
Examining the Results
67(2)
4 Ordinal Regression
69(22)
Fitting an Ordinal Logit Model
70(13)
Modeling Cumulative Counts
70(3)
Specifying the Analysis
73(1)
Parameter Estimates
73(1)
Testing Parallel Lines
74(1)
Does the Model Fit?
75(2)
Comparing Observed and Expected Counts
77(1)
Including Additional Predictor Variables
78(1)
Overall Model Test
79(2)
Measuring Strength of Association
81(1)
Classifying Cases
82(1)
Generalized Linear Models
83(2)
Link Function
83(2)
Fitting a Heteroscedastic Probit Model
85(6)
Modeling Signal Detection
85(1)
Fitting a Location-Only Model
86(2)
Fitting a Scale Parameter
88(1)
Parameter Estimates
88(1)
Model-Fitting Information
89(2)
5 Probit Regression
91(12)
Probit and Logit Response Models
92(8)
Evaluating Insecticides
93(3)
Confidence Intervals for Effective Dosages
96(1)
Comparing Several Groups
97(3)
Comparing Relative Potencies of the Agents
100(3)
Estimating the Natural Response Rate
101(1)
More than One Stimulus Variable
101(2)
6 Kaplan-Meier Survival Analysis
103(18)
IBM SPSS Statistics Procedures for Survival Data
103(1)
Background
104(3)
Calculating Length of Time
106(1)
Estimating the Survival Function
107(5)
Estimating the Conditional Probability of Survival
107(1)
Estimating the Cumulative Probability of Survival
108(1)
The IBM SPSS Statistics Kaplan-Meier Table
109(2)
Plotting Survival Functions
111(1)
Comparing Survival Functions
112(9)
Specifying the Analysis
112(1)
Comparing Groups
113(3)
Stratified Comparisons of Survival Functions
116(5)
7 Life Tables
121(12)
Background
121(12)
Studying Employment Longevity
122(1)
The Body of a Life Table
123(2)
Calculating Survival Probabilities
125(3)
Assumptions Needed to Use the Life Table
128(1)
Lost to Follow-up
129(1)
Plotting Survival Functions
129(2)
Comparing Survival Functions
131(2)
8 Cox Regression
133(38)
The Cox Regression Model
134(2)
The Hazard Function
135(1)
Proportional Hazards Assumption
136(1)
Modeling Survival Times
136(12)
Coding Categorical Variables
137(1)
Specifying the Analysis
138(1)
Testing Hypotheses about the Coefficient
139(1)
Interpreting the Regression Coefficient
140(1)
Baseline Hazard and Cumulative Survival Rates
141(1)
Including Multiple Covariates
142(1)
Model with Three Covariates
142(2)
Global Tests of the Model
144(2)
Plotting the Estimated Functions
146(2)
Checking the Proportional Hazards Assumption
148(2)
Stratification
148(1)
Log-Minus-Log Survival Plot
149(1)
Identifying Influential Cases
150(1)
Examining Residuals
151(4)
Partial (Schoenfeld) Residuals
152(1)
Martingale Residuals
152(3)
Selecting Predictor Variables
155(8)
Variable Selection Methods
155(1)
An Example of Forward Selection
156(5)
Omnibus Test of the Model At Each Step
161(2)
Time-Dependent Covariates
163(5)
Examining the Data
163(2)
Specifying a Time-Dependent Covariate
165(2)
Calculating Segmented Time-Dependent Covariates
167(1)
Testing the Proportional Hazard Assumption with a Time-Dependent Covariate
167(1)
Fitting a Conditional Logistic Regression Model
168(3)
Data File Structure
169(1)
Specifying the Analysis
170(1)
Parameter Estimates
170(1)
9 Variance Components
171(24)
Factors, Effects, and Models
171(2)
Types of Factors
171(1)
Types of Effects
172(1)
Types of Models
172(1)
Model for One-Way Classification
173(7)
Estimation Methods
174(5)
Negative Variance Estimates
179(1)
Nested Design Model for Two-Way Classification
180(4)
Univariate Repeated Measures Analysis Using a Mixed Model Approach
184(6)
Background Information
190(5)
Model
190(1)
Distribution Assumptions
191(1)
Estimation Methods
192(3)
10 Linear Mixed Models
195(50)
The Linear Mixed Model
195(50)
Background
197(2)
Example 1 Unconditional Random-Effects Models
199(7)
Example 2 Adding a Gender Fixed Effect
206(5)
Example 3 Hierarchical Models
211(4)
Example 4 Random-Coefficient Model
215(3)
Example 5 A Model with School-Level and Individual-Level Covariates
218(4)
Example 6 A Three-Level Hierarchical Model
222(5)
Example 7 Repeated Measurements
227(10)
Example 8 Selecting a Residual Covariance Structure
237(8)
11 Generalized Linear Models
245(34)
The Basics of Generalized Linear Models
246(1)
Selecting the Type of Model
247(3)
Count Data
248(1)
Continuous Data
249(1)
Mixture Distribution
249(1)
Selecting a Link Function
250(2)
Fitting a Logistic Regression
252(7)
Specifying the Analysis
253(1)
Likelihood-Ratio Tests
254(1)
How Well Does the Model Fit?
254(5)
Testing Hypotheses about Coefficients
259(3)
Testing Hypotheses about Effects
260(2)
Model Diagnostics
262(1)
Fitting a Poisson Rate Model
262(5)
Specifying the Analysis
264(1)
The Model
264(2)
Interpreting the Parameter Estimates
266(1)
Fitting a Probit Model
267(2)
Specifying the Model
268(1)
Fitting a Poisson Count Model
269(3)
Overdispersion and Underdispersion
270(2)
Fitting a Loglinear Model
272(2)
Specifying the Analysis
272(1)
Parameter Estimates
273(1)
Fitting a Gamma Distribution to Survival Time
274(5)
Parameter Estimates
274(2)
Estimated Means
276(3)
12 Generalized Estimating Equations
279(18)
The GEE Model
279(11)
Data Organization
280(1)
Specifying the Analysis
281(2)
Tests of Model Effects
283(2)
Correlation Structure
285(1)
Comparing Correlation Structures
286(3)
Comparing Models
289(1)
Variance Estimators
289(1)
Fitting a Poisson Model
290(7)
Selecting a Model
292(3)
Diagnostic Statistics
295(2)
13 Generalized Linear Mixed Models
297(26)
The Generalized Linear Mixed Model
298(6)
Using the IBM SPSS Generalized Linear Mixed Model Procedure
299(1)
Describing the Structure of the Data
299(2)
Identifying the Fields and Effects
301(3)
Examining Results in the Model Viewer
304(9)
Model Summary
304(1)
Data Structure
304(1)
Tests of Fixed Effects
305(3)
Random Effect Covariances
308(2)
Plot of Observed and Predicted Values of the Target Variable
310(1)
Estimated Means: Significant Effects
311(2)
Logistic Regression with Correlated Data
313(6)
Data Structure
314(1)
Fields and Effects
314(2)
Model Summary
316(1)
Classification
316(1)
Tests of Fixed Effects
317(2)
Estimated Means
319(1)
Analyzing Repeated Measures Data
319(1)
Fitting a Poisson Model
320(3)
14 Nonlinear Regression
323(18)
What Is a Nonlinear Model?
323(3)
Transforming Nonlinear Models
324(1)
Intrinsically Nonlinear Models
325(1)
Fitting a Logistic Population Growth Model
326(8)
Estimating a Nonlinear Model
326(1)
Finding Starting Values
327(1)
Specifying the Analysis
328(4)
Approximate Confidence Intervals for the Parameters
332(1)
Bootstrapped Estimates
332(2)
Estimating Starting Values
334(2)
Use Starting Values from Previous Analysis
334(1)
Look for a Linear Approximation
334(1)
Use Properties of the Nonlinear Model
335(1)
Solve a System of Equations
335(1)
Computational Issues
336(1)
Additional Nonlinear Regression Options
336(1)
Nonlinear Regression Common Models
337(1)
Specifying a Segmented Model
338(3)
15 Two-Stage Least-Squares Regression
341(10)
Artichoke Data
341(1)
Demand-Price-Income Economic Model
342(3)
Estimation with Ordinary Least Squares
343(1)
Feedback and Correlated Errors
343(2)
Two-Stage Least Squares
345(6)
Strategy
346(1)
Stage 1 Estimating Price
347(1)
Stage 2 Estimating the Model
348(1)
2-Stage Least Squares Procedure
349(2)
16 Weighted Least-Squares Regression
351(10)
Diagnosing the Problem
351(2)
Estimating the Weights
353(4)
Estimating Weights as Powers
354(1)
Specifying the Analysis
355(1)
Examining the Log-Likelihood Functions
355(1)
WLS Solutions
356(1)
Estimating Weights from Replicates
357(1)
Diagnostics from the Linear Regression Procedure
357(4)
17 Multidimensional Scaling
361(70)
Data, Models, and Analysis of Multidimensional Scaling
362(4)
Example: Flying Mileages
363(3)
Nature of Data Analyzed in MDS
366(3)
Measurement Level of Data
366(1)
Shape of Data
366(1)
Conditionality of Data
367(1)
Missing Data
368(1)
Multivariate Data
368(1)
Classical MDS
369(17)
Example: Flying Mileages Revisited
369(4)
Euclidean Model
373(3)
Details of CMDS
376(4)
Example: Ranked Flying Mileages
380(6)
Repeated CMDS
386(1)
Replicated MDS
387(12)
Details of RMDS
387(3)
Example: Perceived Body-Part Structure
390(9)
Weighted MDS
399(1)
Geometry of the Weighted Euclidean Model
400(6)
Algebra of the Weighted Euclidean Model
406(2)
Matrix Algebra of the Weighted Euclidean Model
408(2)
Details of WMDS
410(1)
Example: Perceived Body-Part Structure
411(13)
Weirdness Index
424(4)
Flattened Weights
428(3)
Bibliography 431(6)
Index 437
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.