Cross section and experimental data analysis using EViews:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Singapore
John Wiley & Sons
2011
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes index. |
| Physical Description: | xix, 564 p. ill. 26 cm |
| ISBN: | 9780470828427 0470828420 |
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MARC
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| 100 | 1 | |a Agung, I Gusti Ngurah |e Verfasser |0 (DE-588)171702786 |4 aut | |
| 245 | 1 | 0 | |a Cross section and experimental data analysis using EViews |c I Gusti Ngurah Agung |
| 264 | 1 | |a Singapore |b John Wiley & Sons |c 2011 | |
| 300 | |a xix, 564 p. |b ill. |c 26 cm | ||
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| adam_text | Contents
Preface
1
Misinterpretation of Selected Theoretical Concepts of Statistics
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
1.10
1.11
1.12
Introduction
What is a Population?
A Sample and Sample Space
1.3.1
What is a Sample?
1.3.2
What is the Sample Space?
1.3.3
What is a Representative Sample?
1.3.4
Relationship between the Sample Space, Population, and a Sample
Distribution of a Random Sample Space
What is a Random Variable?
Theoretical Concept of a Random Sample
1.6.1
What is a Random Sample in Statistics?
1.6.2
Central Limit Theorem
1.6.3
Unbiased Statistics based on Random Samples
1.6.4
Special Notes on Nonrandom Sample
Does a Representative Sample Really Exist?
Remarks on Statistical Powers and Sample Sizes
Hypothesis and Hypothesis Testing
Groups of Research Variables
1.10.1
Problem Indicators
.10.2
Controllable Cause Factors
.10.3
Uncontrollable Cause Factors
.10.4
Background or Classification Factors
.10.5
Environmental Factors
Causal Relationship between Variables
.11.1
Bivariate Correlation
. 11.2
Special Remarks
Misinterpretation of Selected Statistics
.12.1
Standard Error
.12.2
Significance Level and Power of a Test
.12.3
Reliability of a Test or Instrument
xv
1
1
2
2
2
3
6
7
8
9
9
9
10
16
19
19
21
24
25
26
26
26
27
27
27
27
30
31
31
31
32
Contents
1.12.4
Validity of a Test or Instrument
33
1.12.5
Reliability and Validity of Forecasting
34
1.12.6
Reliability and Validity of a Predicted Risk
35
2
Simple Statistical Analysis but Good for Strategic Decision Making
37
2.1
Introduction
37
2.2
A Single Input for Decision Making
39
2.2.1
A Single Sampled Unit
39
2.2.2
Descriptive Statistics Based on a Single Measurable Variable
39
2.2.3
Agung Six-Point Scale (ASPS) Problem Indicator
43
2.2.4
Latent Variables and Composite Indexes
45
2.2.5
Demographic and Social-Economic Factors
45
2.2.6
Garbage as a Data Source
46
2.2.7
Boxplot as an Input for Decision Making
46
2.2.8
A Series of Inputs for Strategic Decision Making
48
2.3
Data Transformation
48
2.3.1
To Generate Categorical Variables
49
2.3.2
To Generate Dummy Variables
51
2.4
Biserial Correlation Analysis
51
2.5
One-Way Tabulation of a Variable
53
2.6
Two-Way Tabulations
54
2.6.1
Measure of Associations for
Divariate
Categorical Variables
58
2.6.2
Other Measures of Association Based on a
2
χ
2
Table
58
2.6.3
Measures of Association Based on a I x
2
Table
64
2.7
Three-Way Tabulation
67
2.7.1
Conditional Measures of Association for a
2
χ
2
χ
2
Table
69
2.7.2
Conditional Odds Ratio for an
I x J x
2
Table
70
2.8
Special Notes and Comments
74
2.9
Special Cases of the N-Way Incomplete Tables
77
2.10
Partial Associations
80
2.11
Multiple Causal Associations Based on Categorical Variables
81
2.11.1
Theoretical and Empirical Concepts of Causal Associations
81
2.11.2
Multidimensional Frequency Table
85
2.12
Seemingly Causal Model Based on Categorical Variables
89
2.12.1
Causal Association Based on
(Xl,
X2, Yl) or
(Xl,
Yl, Y2)
90
2.12.2
Causal Association Based on
(Xl,
X2, Yl, Y2)
91
2.12.3
Causal Association Based on Multidimensional Variables
94
2.13
Alternative Descriptive Statistical Summaries
95
2.13.1
Application of the Object Descriptive Statistics and Test
95
2.13.2
Application of the Object Graph.
.. 102
2.14
How to Present Descriptive Statistical Summary?
107
2.14.1
DSS Based on a Set of Zero-One Indicators
107
2.14.2
Two-Dimensional DSS of Proportions
108
2.14.3
Multidimensional DSS of Proportions
108
2.14.4
DSS Based on a Set of Agung-Likert Scale Attributes
109
2.14.5
DSS Based on a Set of Numerical Problem Indicators
110
2.14.6
Additional Descriptive Statistical Summaries 111
Contents
2.15 General
Seemingly
Causal
Model
IH
2.16
Empirical Studies Presenting Descriptive Statistical Summaries
112
2.16.1
Studies in the Field of Nutrition
112
2.16.2
Studies in Public Health
114
2.16.3
Selected Experimental Studies
114
2.16.4
Studies in Public Relations
114
2.16.5
Studies on Other Population Problems
115
One-Way Proportion Models
117
3.1
Introduction
117
3.2
One-Way Proportion Models Based on a
2
χ
2
Table
117
3.2.1
Regression Functions
118
3.2.2
Binary Logit Functions
119
3.2.3
Odds Ratio Statistics
120
3.3
Binary Choice Models Based on
a K x
2
Table
121
3.3.1
Binary Logit Models
121
3.3.2
Binary Multiple Regressions
122
3.4
Binary Logit Models Based on N-Way Tabulation
122
3.4.1
Binary Logit Models Based on Three-Way Tabulation
122
3.4.2
Binary Choice Models Based on Higher Dimensional Tables
124
3.5
General Binary Choice Models
124
3.5.1
Binary Multiple Regression Model
125
3.5.2
The
Wald
Test
127
3.5.3
Binary Logit Models
134
3.5.4
Binary
Probit
Models
144
3.5.5
Binary Extreme-Value Models
147
3.6
Special Notes and Comments
151
3.6.1
The True Population Binary Choice Model
151
3.6.2
The Sampled Binary Choice Function
151
3.6.3
Alternative Equation Estimations
152
3.7
Association between Categorical Variables
152
3.7.1
Generating the Dummy Variables
153
3.7.2
Generating a Cell Factor
154
3.8
One-Way Binary Choice Models Based on JV-Way Tabulation
156
3.8.1
N-Way Tabulation without an Empty Cell
156
3.8.2
Л^-
Way Tabulation with Empty Cells
157
3.8.3
Testing Hypotheses
157
3.9
Special Notes and Comments on Binary Choice Models
160
iV-Way Cell-Proportion Models
165
4.1
Introduction
165
4.2
The
Λ -
Way Tabulation of Proportions
165
4.2.1
A
2
χ
2
Table of Proportions
165
4.2.2
AI x J
Table of Proportions
167
4.3
The
2x2
Factorial Model of Proportions
168
4.3.1
Pure Interaction Models
168
4.3.2
Interaction Models with a Main Factor
170
t
Contents
4.3.3
Interaction
Models
with Both Main Factors
174
4.3.4
Additive Binary Choice Models
175
4.4
I x J
Factorial Models of Proportions
176
4.4.1
Interaction Models
176
4.4.2
Special Notes and Comments
178
4.5
Multifactorial Cell-Proportion Model
180
4.6
Presenting the Statistical Summary
188
5
ЛГ
-Way
Cell-Mean Models
193
5.1
Introduction
193
5.2
One-Way Multivariate Cell-Mean Models
195
5.2.1
An MCMM without an Intercept
195
5.2.2
An MCMM with Intercepts
195
5.3
N-Way Multivariate Cell-Mean Models
197
5.3.1
Two-Way Multivariate Cell-Mean Models
197
5.3.2
Three-Way Multivariate Cell-Mean Model
201
5.3.3 Ar-
Way Multivariate Cell-Mean Model
202
5.4
Equality Test by Classification
202
5.5
Testing Weighted Means Differences
208
5.6
Descriptive Statistical Summary
212
6
Multinomial Choice Models with Categorical Exogenous Variables
213
6.1
Introduction
213
6.2
Multinomial Choice Models
213
6.2.1
Multinomial Logit Model as a Set of (M
- 1)
Binary Logit Models
213
6.2.2
Multinomial Logit Model as a Set of
M
Binary Choice Models
224
6.3
Ordered Choice Models
225
6.3.1
Simple Ordered Choice Models
225
6.4
Concordance-Discordance Measure of Association
231
6.5
Multifactorial Ordered Choice Models
234
6.6
Multilevel Choice Models
241
6.6.1
Two-Level Choice Models
241
6.6.2
Three-Level Choice Models
250
6.7
Special Notes on the Multinomial Logit Model
253
6.8
Selected Population Studies Using Multinomial Choice Models
256
6.8.1
Multinomial Problem Indicators and Gender Equity Indexes
256
6.8.2
Multinomial Problem and Poverty Indicators
259
7
General Choice Models
263
7.1
Introduction
263
7.2
Binary Choice Models with a Numerical Variable
263
7.2.1
The Simplest Binary Choice Model
263
7.2.2
Alternative Simple Binary Choice Models
269
7.2.3
Special Notes and Comments
276
7.3
Heterogeneous Binary Choice Models
276
7.3.1
The Simplest Heterogeneous Binary Choice Model
276
7.3.2
General Heterogeneous Binary Choice Model
282
Contents
7.4
Homogeneous Binary Choice Models
284
7.4.1
Binary Choice ANCOVA Model with a Numerical
Variable
284
7.4.2
Graphical Representation of an ANCOVA Model
287
7.5
General Binary Choice Models
288
7.5.1
Hierarchical Binary Logit Model
288
7.5.2
Nonhierarchical Binary Logit Model
289
7.5.3
Additive Binary Logit Model
290
7.5.4
GBCM with Two Numerical and a Dichotomous
Independent Variable
293
7.5.5
GBCM with Two Numerical and a Set of Categorical
Independent Variables
297
7.6
Advanced Binary Choice Models
298
7.6.1
Binary Choice Heterogeneous Regressions
298
7.6.2
Binary Choice ANCOVA Model
304
7.6.3
Descriptive Statistical Summaries
307
7.7
Multidimensional Binary Choice
Translog
Linear Model
307
7.8
Piecewise Binary Choice Models
309
7.9
Ordered Choice Models with Numerical Independent Variables
ЗІЗ
7.10
Studies Using General Choice Models
325
7.11
Two-Stage Binary Choice Model
326
Experimental Data Analysis
329
8.1
Introduction
329
8.2
Analysis Based on Cell-Mean Models
329
8.2.1
The Simplest Statistical Analysis
330
8.2.2
Special Remarks
331
8.2.3
Application of Multivariate Cell-Mean Models
332
8.3
Divariate
Correlation Analysis
333
8.4
Effects of the Experimental Factors
334
8.5
Effects of the Experimental Factors and Covariates
335
8.5.1
Effects of the Experimental Factors and a Covariate
336
8.5.2
Effects of the Experimental Factors and Two
Covariates
342
8.5.3
The Application of
Translog
Linear Models
346
8.6
Application of the Ordered Choice Models
356
8.7
Application of Seemingly Causal Models
360
8.7.1
The Simplest Seemingly Causal Model
361
8.7.2
Four Pairs of Causal Relationships
363
8.7.3
Five Pairs of Causal Relationships
364
8.7.4
All Pairs Have Causal Relationships
365
8.7.5
Alternative Seemingly Causal Models
368
8.7.6
Special Notes and Comments
369
8.8
Multivariate Analysis of Covariance
369
8.9
Tests for Equality of Medians
372
8.10
The Simplest Experimental Design
376
xii Contents
9
Seemingly Causal Models Based on Numerical Variables
381
9.1
Introduction
381
9.2
The Simplest Seemingly Causal Model
382
9.2.1
Bivanate Correlation and the Simplest Linear Regression
382
9.2.2
Scatter Graph with Regression Line
385
9.2.3
Residual Analysis
389
9.2.4
Special Notes and Comments
390
9.3
General Linear Models Based on Bivanate (X, Y)
391
9.3.1
Continuous Regression Models
391
9.3.2
Discontinuous Regressions
402
9.3.3
Regressions by a Classification Factor
405
9.4
Models Based on Numerical
Invariate
407
9.4.1
Continuous Regressions
407
9.4.2
Regressions by Classification Factors
416
9.5
Regression Analysis Using the Principal Components
417
9.6
Seemingly Causal Models Based on
(Xl,
X2,Y1,Y2)
420
9.7
Seemingly Causal Models Based on
(Xl,
X2, X3, Yl, Y2)
422
9.7.1
The Model with the Dependent Variable
Y
1 423
9.7.2
The Model with the Dependent Variable Y2
424
9.7.3
The Model with the Dependent Variable
Xl 424
9.7.4
The Model with the Dependent Variable X3
424
9.8
New Types of Interaction Model
426
9.8.1
Polynomial Interaction Model
426
9.8.2
General Polynomial Interaction Model
428
9.8.3
System Polynomial Interaction Model
430
9.9
Special Cases
431
9.9.1
Predicted Variables and Predictors
431
9.9.2
The Simplest and the Most Complex Seemingly
Causal Models
433
9.10
Special Notes and Comments
434
Appendix A.9.1 Hypothetical Data Set
435
10
Factor Analysis and Latent Variables Models
439
10.1
Introduction
439
10.2
The Basic Concept of Factor Analysis
440
10.3
The First-Level Latent Variables
441
10.3.1
Generating Additive Latent Variables
441
10.3.2
Interaction Latent Variables
447
10.3.3
Special Notes and Comments
449
10.4
Illustrations Based on Hamsal s
(2006)
Data Set
450
10.4.1
Generating Latent Variables
450
10.4.2
Latent Variable Regression Models
452
10.4.3
Alternative Latent Variable Models
453
10.5
Selected Cases Based on
Ary
Suta s
(2006)
Data Set
458
10.5.1
Multilevel Latent Variables
458
10.5.2
Problems with the Sample Sizes
459
Contents xiii
10.6 Evaluation
Analysis Based on Latent Variables
462
10.6.1
Ordinal Classification Based on a Latent Variable
462
10.6.2
Composite Index Based on a Latent Variable
463
10.6.3
/V-Way Tabulation Based on Latent Variables
464
11
Application of the Stepwise Selection Methods
467
11.1
Introduction
467
11.2
The Options for the Stepwise Selection Methods
467
11.3
Selection Method for the Numerical Variable Regression Models
469
11.3.1
Two-Way Interaction Stepwise Regressions
469
11.3.2
Three-Way Interaction Stepwise Regressions
473
11.3.3
Application of Multistage Stepwise Selections
476
11.3.4
Alternative Selection Methods
478
11.4
Multifactorial Stepwise Regression Models
480
11.4.1
Multifactorial Cell-Mean Models
480
11.4.2
Multifactorial Heterogeneous Regressions
482
11.4.3
Stepwise ANCOVA Models
491
11.5
Illustrative Stepwise Regressions Based on Mlogit.wfl
495
11.5.1
Classical ANCOVA Models
495
11.5.2
Interaction ANCOVA Models
497
11.6
Special Notes and Comments
503
12
Censored Multiple Regression Models
505
12.1
Introduction
505
12.2
Tobit Models
505
12.2.1
Tobit Cell-Mean Models
506
12.2.2
Tobit Regression Models with Numerical
Independent Variables
511
12.2.3
Selected Studies Using Tobit Regressions
517
12.3
General Tobit Model
517
12.4
Zero-One Indicator of Censoring
521
12.5
Illustrative Cases of Censored Observations
526
12.5.1
Outliers are Considered as Censored Observations
526
12.5.2
Both Tales of Observations are Considered as
Censored Observations
527
12.5.3
Waiting Time and Switching Status Variables
528
12.6
Series for a Censoring Variable
528
12.7
Switching Censored Regressions
531
12.8
Special Notes and Comments
542
Appendix A.
12.1
Hypothetical Censored Data, Modified from
Faad s
(2008)
Data Set
543
References
545
Index
551
|
| any_adam_object | 1 |
| author | Agung, I Gusti Ngurah |
| author_GND | (DE-588)171702786 |
| author_facet | Agung, I Gusti Ngurah |
| author_role | aut |
| author_sort | Agung, I Gusti Ngurah |
| author_variant | i g n a ign igna |
| building | Verbundindex |
| bvnumber | BV039919192 |
| callnumber-first | H - Social Science |
| callnumber-label | HA29 |
| callnumber-raw | HA29 |
| callnumber-search | HA29 |
| callnumber-sort | HA 229 |
| callnumber-subject | HA - Statistics |
| classification_rvk | QH 330 ST 304 ST 601 |
| ctrlnum | (OCoLC)667990346 (DE-599)BVBBV039919192 |
| dewey-full | 005.5/5 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.5/5 |
| dewey-search | 005.5/5 |
| dewey-sort | 15.5 15 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV039919192 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:14:08Z |
| institution | BVB |
| isbn | 9780470828427 0470828420 |
| language | English |
| lccn | 2010041053 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024777718 |
| oclc_num | 667990346 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-945 DE-1043 |
| owner_facet | DE-355 DE-BY-UBR DE-945 DE-1043 |
| physical | xix, 564 p. ill. 26 cm |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | John Wiley & Sons |
| record_format | marc |
| spelling | Agung, I Gusti Ngurah Verfasser (DE-588)171702786 aut Cross section and experimental data analysis using EViews I Gusti Ngurah Agung Singapore John Wiley & Sons 2011 xix, 564 p. ill. 26 cm txt rdacontent n rdamedia nc rdacarrier Includes index. EViews (Computer file) Statistik Statistics Automatische Programmierung (DE-588)4143702-0 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s Automatische Programmierung (DE-588)4143702-0 s b DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024777718&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Agung, I Gusti Ngurah Cross section and experimental data analysis using EViews EViews (Computer file) Statistik Statistics Automatische Programmierung (DE-588)4143702-0 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4143702-0 (DE-588)4056995-0 |
| title | Cross section and experimental data analysis using EViews |
| title_auth | Cross section and experimental data analysis using EViews |
| title_exact_search | Cross section and experimental data analysis using EViews |
| title_full | Cross section and experimental data analysis using EViews I Gusti Ngurah Agung |
| title_fullStr | Cross section and experimental data analysis using EViews I Gusti Ngurah Agung |
| title_full_unstemmed | Cross section and experimental data analysis using EViews I Gusti Ngurah Agung |
| title_short | Cross section and experimental data analysis using EViews |
| title_sort | cross section and experimental data analysis using eviews |
| topic | EViews (Computer file) Statistik Statistics Automatische Programmierung (DE-588)4143702-0 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | EViews (Computer file) Statistik Statistics Automatische Programmierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024777718&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT agungigustingurah crosssectionandexperimentaldataanalysisusingeviews |