Statistical analysis for public and nonprofit managers:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Praeger
1990
|
| Ausgabe: | 1. publ., 1. print. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIV, 209 S. |
| ISBN: | 0275933016 |
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Datensatz im Suchindex
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| adam_text | Contents
Figures and Tables xi
Preface xiii
1. Introduction 1
The Idea and Importance of Multivariate Models 1
More Detail on Multivariate Models 4
Classification of Data Used in Multivariate Models 5
Statistical Techniques and Their Data Requirements 6
Three Main Uses of Multiple Regression and Some Simplified
Examples 9
Conclusions 12
Summary of Chapter 1 12
2. The Bivariate or Simple Regression Model 13
The Meaning of the Term Bivariate Regression 13
Deterministic Versus Stochastic Relationships 17
Populations Versus Samples 18
Estimators for the Bivariate Regression Model 19
The Coefficient of Determination or R2 24
Conclusions 25
Summary of Chapter 2 26
3. The Multiple Regression Model, Part I: Estimators, Statistical
Properties, and Significance Tests 27
viii / Contents
An Example of the Need for Multivariate Relationships 27
The Three Variable Regression Model 29
Statistical Characteristics of the Least Squares Estimators 30
Significance Tests and Confidence Intervals 33
Using the Computer to Obtain Regression Statistics 42
Conclusions 46
Summary of Chapter 3 46
4. The Multiple Regression Model, Part II: Importance of
Variables, Model Building, and Forecasting 47
Measuring the Importance of Individual Variables 47
Techniques to Help with Model Building 51
Forecasting with a Regression Model 55
An Example of Model Building and Stepwise Regression Using the
Computer 60
Conclusions 62
Summary of Chapter 4 62
5. Dummy Variables and Nonlinear and Nonadditive Relationships 63
Dummy Variables 63
Testing for the Significance of Dummy Variables 68
Nonlinear Models 71
Nonadditive Models 81
Examples of Computer Output for Dummy Variables and for
Nonlinear Relationships 87
A Summary Example 90
Conclusions 92
Summary of Chapter 5 93
6. Basic Assumptions and Common Problems in Regression
Models 95
The Assumption of Constant Variance or Homoskedasticity 96
The Assumption of Zero Covariance Between Errors or Zero
Autocorrelation 102
The Assumption That the Error Term and the Independent Variables
are Uncorrelated 110
Multicollinearity 114
An Example of Computer Output Showing Autocorrelation 118
Conclusions 122
Summary of Chapter 6 123
7. Qualitative Dependent Variables 125
The Linear Probability Model 126
The Logit and Probit Forms for Dichotomous Qualitative Choice
Models 128
Contents / ix
Estimating the Logit Model 133
Estimating the Probit Model 137
Conclusions 140
Summary of Chapter 7 140
8. Some Advanced Topics: Pooled Time Series and Cross Section
Analysis, Lagged Variables, Missing Data, Time Series
Analysis, and Multiequation Systems 143
Using Pooled Cross Section and Time Series Data 144
Lagged Variables 148
Missing Data 149
Time Series Analysis 151
Multiequation Models 156
Conclusions 166
Summary of Chapter 8 168
9. An Overview 169
A Management Use of Multiple Regression: Adjusted Productivity
Measures 170
A Policy Use of Multiple Regression: Adjusted Hospital Mortality
Rates 171
Many Uses, Not So Many Distinctions 173
Appendices
A. Simple Summation Operators and Expected Values 175
Algebra of Summations 175
Expected Values 176
B. The Pearson Correlation Coefficient 179
C. Review of Hypothesis Testing for the Sample Mean 183
Forming the Null and Alternative Hypotheses 183
The Logic of Hypothesis Testing 184
The Test Statistic 184
Setting Significance Levels and Determining Cutoff Points 185
Confidence Intervals 186
The Alternative Hypothesis Again 186
D. Statistical Tables 187
Notes 191
Bibliographical Essay 203
Index 205
Figures and Tables
FIGURES
2.1 Scattergram for a bivariate regression 14
2.2 Regression line and errors 20
2.3 Explained, unexplained and total variation for one observation,
Z(l) 25
3.1 Biased and unbiased slope estimators 31
3.2 Best (minimum variance) and not best slope estimators 32
3.3 Cutoff points for a two sided hypothesis test 36
3.4 Descriptive statistics, correlation matrix, and regression results 44
4.1 Confidence intervals for known population coefficients 57
4.2 Confidence intervals for unknown population coefficients 59
4.3 Stepwise regression: first variable (UUNEMPR) and last variable
(SCPIUS) to be entered 61
5.1 Earnings equation with a sex dummy 67
5.2 Optimal city size example using a quadratic polynomial 80
5.3 Earnings equation with an interaction effect 82
5.4 CYTC regression results with dummy variable, DUMDEP 88
5.5 Descriptive statistics, correlation matrix, and regression results:
log sales tax revenue (LTXSALE) 89
6.1 Homoskedastic distribution of errors 96
6.2 Heteroskedastic distribution of errors 97
6.3 Extreme heteroskedasticity: zero variances for two observations 98
6.4 Heteroskedastic residuals 100
6.5 Positive autocorrelation 103
xii / Figures and Tables
6.6 CALLY equation before adjustment for autocorrelation 119
6.7 Plot of residuals and Durbin Watson statistic: CALLY equation
before adjustment for autocorrelation 120
7.1 The linear probability model 127
7.2 The probit versus logit cumulative distributions 131
8.1 Spending per pupil and dropout rates 163
8.2 Spending per pupil and dropout rates with exogenous variable 164
B.I Scattergrams for several possible Pearson correlation coefficients 180
TABLES
1.1 Example of Type of Data for Chi Square Analysis 7
1.2 Example of Type of Data for ANOVA 8
5.1 Regression Results for Preoperative Length of Stay (LOS) 72
5.2 Regression Results for Operating Expenditures per Participant in
Municipal Retirement Systems 78
5.3 Regression Results for Research Unit Productivity 86
5.4 Regression Results for Earnings Equations: American and
Japanese Male Workers 91
6.1 OLS Regression Results for Total City Income 108
6.2 Differenced Regression Results for Total City Income 109
6.3 Regression Results for Return on Assets for New York State
Voluntary Hospitals 117
7.1 OLS Regression Results for Probability of Physician Visit 130
7.2 Logit Regression Results for Probability of Hospital Having a
Mammography Unit 136
7.3 Probit Regression Results for Probability of Exceeding Mental
Health Insurance Coverage Limit 139
8.1 Random Effects and Fixed Effects Regression Results for Black
Children s Literacy Rates 146
8.2 Percentage Changes in Literacy Gap for Black Children 147
8.3 OLS Regression Results for per Pupil School Expenditures and
Average Annual Earnings 161
8.4 OLS and 2SLS Regression Results for Number of Visits to
Physician 167
D.I Percentage Points of Student s t Distribution 187
D.2 F Distribution, 5 Percent Significance 188
D.3 Critical Points of the Durbin Watson Test, 5 Percent Significance
(d, and da) 190
|
| adam_txt |
Contents
Figures and Tables xi
Preface xiii
1. Introduction 1
The Idea and Importance of Multivariate Models 1
More Detail on Multivariate Models 4
Classification of Data Used in Multivariate Models 5
Statistical Techniques and Their Data Requirements 6
Three Main Uses of Multiple Regression and Some Simplified
Examples 9
Conclusions 12
Summary of Chapter 1 12
2. The Bivariate or Simple Regression Model 13
The Meaning of the Term Bivariate Regression 13
Deterministic Versus Stochastic Relationships 17
Populations Versus Samples 18
Estimators for the Bivariate Regression Model 19
The Coefficient of Determination or R2 24
Conclusions 25
Summary of Chapter 2 26
3. The Multiple Regression Model, Part I: Estimators, Statistical
Properties, and Significance Tests 27
viii / Contents
An Example of the Need for Multivariate Relationships 27
The Three Variable Regression Model 29
Statistical Characteristics of the Least Squares Estimators 30
Significance Tests and Confidence Intervals 33
Using the Computer to Obtain Regression Statistics 42
Conclusions 46
Summary of Chapter 3 46
4. The Multiple Regression Model, Part II: Importance of
Variables, Model Building, and Forecasting 47
Measuring the Importance of Individual Variables 47
Techniques to Help with Model Building 51
Forecasting with a Regression Model 55
An Example of Model Building and Stepwise Regression Using the
Computer 60
Conclusions 62
Summary of Chapter 4 62
5. Dummy Variables and Nonlinear and Nonadditive Relationships 63
Dummy Variables 63
Testing for the Significance of Dummy Variables 68
Nonlinear Models 71
Nonadditive Models 81
Examples of Computer Output for Dummy Variables and for
Nonlinear Relationships 87
A Summary Example 90
Conclusions 92
Summary of Chapter 5 93
6. Basic Assumptions and Common Problems in Regression
Models 95
The Assumption of Constant Variance or Homoskedasticity 96
The Assumption of Zero Covariance Between Errors or Zero
Autocorrelation 102
The Assumption That the Error Term and the Independent Variables
are Uncorrelated 110
Multicollinearity 114
An Example of Computer Output Showing Autocorrelation 118
Conclusions 122
Summary of Chapter 6 123
7. Qualitative Dependent Variables 125
The Linear Probability Model 126
The Logit and Probit Forms for Dichotomous Qualitative Choice
Models 128
Contents / ix
Estimating the Logit Model 133
Estimating the Probit Model 137
Conclusions 140
Summary of Chapter 7 140
8. Some Advanced Topics: Pooled Time Series and Cross Section
Analysis, Lagged Variables, Missing Data, Time Series
Analysis, and Multiequation Systems 143
Using Pooled Cross Section and Time Series Data 144
Lagged Variables 148
Missing Data 149
Time Series Analysis 151
Multiequation Models 156
Conclusions 166
Summary of Chapter 8 168
9. An Overview 169
A Management Use of Multiple Regression: Adjusted Productivity
Measures 170
A Policy Use of Multiple Regression: Adjusted Hospital Mortality
Rates 171
Many Uses, Not So Many Distinctions 173
Appendices
A. Simple Summation Operators and Expected Values 175
Algebra of Summations 175
Expected Values 176
B. The Pearson Correlation Coefficient 179
C. Review of Hypothesis Testing for the Sample Mean 183
Forming the Null and Alternative Hypotheses 183
The Logic of Hypothesis Testing 184
The Test Statistic 184
Setting Significance Levels and Determining Cutoff Points 185
Confidence Intervals 186
The Alternative Hypothesis Again 186
D. Statistical Tables 187
Notes 191
Bibliographical Essay 203
Index 205
Figures and Tables
FIGURES
2.1 Scattergram for a bivariate regression 14
2.2 Regression line and errors 20
2.3 Explained, unexplained and total variation for one observation,
Z(l) 25
3.1 Biased and unbiased slope estimators 31
3.2 Best (minimum variance) and not best slope estimators 32
3.3 Cutoff points for a two sided hypothesis test 36
3.4 Descriptive statistics, correlation matrix, and regression results 44
4.1 Confidence intervals for known population coefficients 57
4.2 Confidence intervals for unknown population coefficients 59
4.3 Stepwise regression: first variable (UUNEMPR) and last variable
(SCPIUS) to be entered 61
5.1 Earnings equation with a sex dummy 67
5.2 Optimal city size example using a quadratic polynomial 80
5.3 Earnings equation with an interaction effect 82
5.4 CYTC regression results with dummy variable, DUMDEP 88
5.5 Descriptive statistics, correlation matrix, and regression results:
log sales tax revenue (LTXSALE) 89
6.1 Homoskedastic distribution of errors 96
6.2 Heteroskedastic distribution of errors 97
6.3 Extreme heteroskedasticity: zero variances for two observations 98
6.4 Heteroskedastic residuals 100
6.5 Positive autocorrelation 103
xii / Figures and Tables
6.6 CALLY equation before adjustment for autocorrelation 119
6.7 Plot of residuals and Durbin Watson statistic: CALLY equation
before adjustment for autocorrelation 120
7.1 The linear probability model 127
7.2 The probit versus logit cumulative distributions 131
8.1 Spending per pupil and dropout rates 163
8.2 Spending per pupil and dropout rates with exogenous variable 164
B.I Scattergrams for several possible Pearson correlation coefficients 180
TABLES
1.1 Example of Type of Data for Chi Square Analysis 7
1.2 Example of Type of Data for ANOVA 8
5.1 Regression Results for Preoperative Length of Stay (LOS) 72
5.2 Regression Results for Operating Expenditures per Participant in
Municipal Retirement Systems 78
5.3 Regression Results for Research Unit Productivity 86
5.4 Regression Results for Earnings Equations: American and
Japanese Male Workers 91
6.1 OLS Regression Results for Total City Income 108
6.2 Differenced Regression Results for Total City Income 109
6.3 Regression Results for Return on Assets for New York State
Voluntary Hospitals 117
7.1 OLS Regression Results for Probability of Physician Visit 130
7.2 Logit Regression Results for Probability of Hospital Having a
Mammography Unit 136
7.3 Probit Regression Results for Probability of Exceeding Mental
Health Insurance Coverage Limit 139
8.1 Random Effects and Fixed Effects Regression Results for Black
Children's Literacy Rates 146
8.2 Percentage Changes in Literacy Gap for Black Children 147
8.3 OLS Regression Results for per Pupil School Expenditures and
Average Annual Earnings 161
8.4 OLS and 2SLS Regression Results for Number of Visits to
Physician 167
D.I Percentage Points of Student's t Distribution 187
D.2 F Distribution, 5 Percent Significance 188
D.3 Critical Points of the Durbin Watson Test, 5 Percent Significance
(d, and da) 190 |
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| id | DE-604.BV021900623 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T16:04:39Z |
| indexdate | 2024-07-09T20:47:00Z |
| institution | BVB |
| isbn | 0275933016 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015115805 |
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| spelling | Stiefel, Leanna Verfasser aut Statistical analysis for public and nonprofit managers Leanna Stiefel 1. publ., 1. print. New York [u.a.] Praeger 1990 XIV, 209 S. txt rdacontent n rdamedia nc rdacarrier Administration publique - Méthodes statistiques ram Association sans but lucratif - Méthodes statistiques ram Gestion - Méthodes statistiques ram Management Statistical methods Nonprofit organizations Management Statistical methods Public administration Statistical methods Wirtschaft (DE-588)4066399-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 s DE-604 Statistik (DE-588)4056995-0 s Wirtschaft (DE-588)4066399-1 s HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015115805&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Stiefel, Leanna Statistical analysis for public and nonprofit managers Administration publique - Méthodes statistiques ram Association sans but lucratif - Méthodes statistiques ram Gestion - Méthodes statistiques ram Management Statistical methods Nonprofit organizations Management Statistical methods Public administration Statistical methods Wirtschaft (DE-588)4066399-1 gnd Statistik (DE-588)4056995-0 gnd Sozialwissenschaften (DE-588)4055916-6 gnd |
| subject_GND | (DE-588)4066399-1 (DE-588)4056995-0 (DE-588)4055916-6 |
| title | Statistical analysis for public and nonprofit managers |
| title_auth | Statistical analysis for public and nonprofit managers |
| title_exact_search | Statistical analysis for public and nonprofit managers |
| title_exact_search_txtP | Statistical analysis for public and nonprofit managers |
| title_full | Statistical analysis for public and nonprofit managers Leanna Stiefel |
| title_fullStr | Statistical analysis for public and nonprofit managers Leanna Stiefel |
| title_full_unstemmed | Statistical analysis for public and nonprofit managers Leanna Stiefel |
| title_short | Statistical analysis for public and nonprofit managers |
| title_sort | statistical analysis for public and nonprofit managers |
| topic | Administration publique - Méthodes statistiques ram Association sans but lucratif - Méthodes statistiques ram Gestion - Méthodes statistiques ram Management Statistical methods Nonprofit organizations Management Statistical methods Public administration Statistical methods Wirtschaft (DE-588)4066399-1 gnd Statistik (DE-588)4056995-0 gnd Sozialwissenschaften (DE-588)4055916-6 gnd |
| topic_facet | Administration publique - Méthodes statistiques Association sans but lucratif - Méthodes statistiques Gestion - Méthodes statistiques Management Statistical methods Nonprofit organizations Management Statistical methods Public administration Statistical methods Wirtschaft Statistik Sozialwissenschaften |
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