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Statistical models for causal analysis [[electronic resource] /] / Robert D. Retherford, Minja Kim Choe
| Statistical models for causal analysis [[electronic resource] /] / Robert D. Retherford, Minja Kim Choe |
| Autore | Retherford Robert D |
| Pubbl/distr/stampa | New York, : Wiley, 1993 |
| Descrizione fisica | 1 online resource (274 p.) |
| Disciplina |
519.5/35
519.535 |
| Altri autori (Persone) | ChoeMinja Kim <1941-> |
| Soggetto topico | Multivariate analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-25166-X
9786613813916 1-118-03313-2 1-118-03134-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
STATISTICAL MODELS FOR CAUSAL ANALYSIS; CONTENTS; 1 Bivariate Linear Regression; 1.1. Terminology; 1.2. Fitting a Least-Squares Line; 1.3. The Least-Squares Line as a Causal Model; 1.4. The Bivariate Linear Regression Model as a Statistical Model; 1.4.1. Simplifying Assumptions; 1.5. Statistical Inference: Generalizing from Sample to Underlying Population; 1.5.1. Hypothesis Testing; 1.5.2. Confidence Intervals; 1.5.3. t Values and Z Values; 1.5.4. p Value; 1.5.5. Importance of a Good Spread of Values of the Predictor Variable; 1.5.6. Beware of Outliers!
1.5.7. Beware of Selection on the Response Variable!1.5.8. Presentation of Results; 1.6. Goodness of Fit; 1.6.1. Standard Error of the Estimate, s; 1.6.2. Coefficient of Determination, r2 , and Correlation Coefficient, r; 1.7. Further Reading; 2 Multiple Regression; 2.1. The Problem of Bias in Bivariate Linear Regression; 2.2. Multiple Regression with Two Predictor Variables; 2.3. Multiple Regression with Three or More Predictor Variables; 2.4. Dummy Variables to Represent Categorical Variables; 2.4.1. Categorical Variables with Two Categories 2.4.2. Categorical Variables with More Than Two Categories2.5. Multicollinearity; 2.6. Interaction; 2.6.1. Model Specification; 2.6.2. More Complicated Interactions; 2.6.3. Correlation without Interaction; 2.6.4. Interaction without Correlation; 2.7. Nonlinearities; 2.7.1. Quadratic Specification; 2.7.2. Dummy Variable Specification; 2.8. Goodness of Fit; 2.8.1. Standard Error of the Estimate, s; 2.8.2. Coefficient of Determination, R2, and Multiple Correlation Coefficient, R; 2.8.3. Corrected R2 and Corrected R; 2.8.4. Partial Correlation Coefficient; 2.9. Statistical Inference 2.9.1. Hypothesis Testing, Confidence Intervals, and p Values for a Single Regression Coefficient2.9.2. Testing the Difference Between Two Regression Coefficients, βi and βj; 2.9.3. Testing Effects When There Is Interaction; 2.9.4. Testing Effects When There Is a Nonlinearity; 2.9.5. The ANOVA Table; 2.9.6. The Omnibus F Test of the Hypothesis β1 = β2 = ... = βk =0; 2.9.7. Test of the Hypothesis That Some of the βj Are Zero; 2.10. Stepwise Regression; 2.11. Illustrative Examples; 2.11.1. Example 1; 2.11.2. Example 2; 2.12. Further Reading; 3 Multiple Classification Analysis 3.1. The Basic MCA Table3.1.1. Unadjusted Values; 3.1.2. Adjusted Values; 3.1.3. Unadjusted and Adjusted, R; 3.1.4. A Numerical Example; 3.2. The MCA Table in Deviation Form; 3.2.1. First Approach to Table Set-up; 3.2.2. Second Approach to Table Set-up; 3.2.3. A Numerical Example; 3.3. MCA with Interactions; 3.3.1. Table Set-up; 3.3.2. A Numerical Example; 3.4. MCA with Additional Quantitative Control Variables; 3.4.1. Table Set-up; 3.4.2. A Numerical Example; 3.5. Expressing Results from Ordinary Multiple Regression in an MCA Format (all Variables Quantitative); 3.5.1. Table Set-up 3.5.2. A Numerical Example |
| Record Nr. | UNINA-9910139206203321 |
Retherford Robert D
|
||
| New York, : Wiley, 1993 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical models for causal analysis [[electronic resource] /] / Robert D. Retherford, Minja Kim Choe
| Statistical models for causal analysis [[electronic resource] /] / Robert D. Retherford, Minja Kim Choe |
| Autore | Retherford Robert D |
| Pubbl/distr/stampa | New York, : Wiley, 1993 |
| Descrizione fisica | 1 online resource (274 p.) |
| Disciplina |
519.5/35
519.535 |
| Altri autori (Persone) | ChoeMinja Kim <1941-> |
| Soggetto topico | Multivariate analysis |
| ISBN |
1-282-25166-X
9786613813916 1-118-03313-2 1-118-03134-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
STATISTICAL MODELS FOR CAUSAL ANALYSIS; CONTENTS; 1 Bivariate Linear Regression; 1.1. Terminology; 1.2. Fitting a Least-Squares Line; 1.3. The Least-Squares Line as a Causal Model; 1.4. The Bivariate Linear Regression Model as a Statistical Model; 1.4.1. Simplifying Assumptions; 1.5. Statistical Inference: Generalizing from Sample to Underlying Population; 1.5.1. Hypothesis Testing; 1.5.2. Confidence Intervals; 1.5.3. t Values and Z Values; 1.5.4. p Value; 1.5.5. Importance of a Good Spread of Values of the Predictor Variable; 1.5.6. Beware of Outliers!
1.5.7. Beware of Selection on the Response Variable!1.5.8. Presentation of Results; 1.6. Goodness of Fit; 1.6.1. Standard Error of the Estimate, s; 1.6.2. Coefficient of Determination, r2 , and Correlation Coefficient, r; 1.7. Further Reading; 2 Multiple Regression; 2.1. The Problem of Bias in Bivariate Linear Regression; 2.2. Multiple Regression with Two Predictor Variables; 2.3. Multiple Regression with Three or More Predictor Variables; 2.4. Dummy Variables to Represent Categorical Variables; 2.4.1. Categorical Variables with Two Categories 2.4.2. Categorical Variables with More Than Two Categories2.5. Multicollinearity; 2.6. Interaction; 2.6.1. Model Specification; 2.6.2. More Complicated Interactions; 2.6.3. Correlation without Interaction; 2.6.4. Interaction without Correlation; 2.7. Nonlinearities; 2.7.1. Quadratic Specification; 2.7.2. Dummy Variable Specification; 2.8. Goodness of Fit; 2.8.1. Standard Error of the Estimate, s; 2.8.2. Coefficient of Determination, R2, and Multiple Correlation Coefficient, R; 2.8.3. Corrected R2 and Corrected R; 2.8.4. Partial Correlation Coefficient; 2.9. Statistical Inference 2.9.1. Hypothesis Testing, Confidence Intervals, and p Values for a Single Regression Coefficient2.9.2. Testing the Difference Between Two Regression Coefficients, βi and βj; 2.9.3. Testing Effects When There Is Interaction; 2.9.4. Testing Effects When There Is a Nonlinearity; 2.9.5. The ANOVA Table; 2.9.6. The Omnibus F Test of the Hypothesis β1 = β2 = ... = βk =0; 2.9.7. Test of the Hypothesis That Some of the βj Are Zero; 2.10. Stepwise Regression; 2.11. Illustrative Examples; 2.11.1. Example 1; 2.11.2. Example 2; 2.12. Further Reading; 3 Multiple Classification Analysis 3.1. The Basic MCA Table3.1.1. Unadjusted Values; 3.1.2. Adjusted Values; 3.1.3. Unadjusted and Adjusted, R; 3.1.4. A Numerical Example; 3.2. The MCA Table in Deviation Form; 3.2.1. First Approach to Table Set-up; 3.2.2. Second Approach to Table Set-up; 3.2.3. A Numerical Example; 3.3. MCA with Interactions; 3.3.1. Table Set-up; 3.3.2. A Numerical Example; 3.4. MCA with Additional Quantitative Control Variables; 3.4.1. Table Set-up; 3.4.2. A Numerical Example; 3.5. Expressing Results from Ordinary Multiple Regression in an MCA Format (all Variables Quantitative); 3.5.1. Table Set-up 3.5.2. A Numerical Example |
| Record Nr. | UNINA-9910829830503321 |
|
Retherford Robert D
|
||
| New York, : Wiley, 1993 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical models for causal analysis / / Robert D. Retherford, Minja Kim Choe
| Statistical models for causal analysis / / Robert D. Retherford, Minja Kim Choe |
| Autore | Retherford Robert D |
| Pubbl/distr/stampa | New York, : Wiley, 1993 |
| Descrizione fisica | 1 online resource (274 p.) |
| Disciplina | 519.5/35 |
| Altri autori (Persone) | ChoeMinja Kim <1941-> |
| Soggetto topico | Multivariate analysis |
| ISBN |
1-282-25166-X
9786613813916 1-118-03313-2 1-118-03134-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
STATISTICAL MODELS FOR CAUSAL ANALYSIS; CONTENTS; 1 Bivariate Linear Regression; 1.1. Terminology; 1.2. Fitting a Least-Squares Line; 1.3. The Least-Squares Line as a Causal Model; 1.4. The Bivariate Linear Regression Model as a Statistical Model; 1.4.1. Simplifying Assumptions; 1.5. Statistical Inference: Generalizing from Sample to Underlying Population; 1.5.1. Hypothesis Testing; 1.5.2. Confidence Intervals; 1.5.3. t Values and Z Values; 1.5.4. p Value; 1.5.5. Importance of a Good Spread of Values of the Predictor Variable; 1.5.6. Beware of Outliers!
1.5.7. Beware of Selection on the Response Variable!1.5.8. Presentation of Results; 1.6. Goodness of Fit; 1.6.1. Standard Error of the Estimate, s; 1.6.2. Coefficient of Determination, r2 , and Correlation Coefficient, r; 1.7. Further Reading; 2 Multiple Regression; 2.1. The Problem of Bias in Bivariate Linear Regression; 2.2. Multiple Regression with Two Predictor Variables; 2.3. Multiple Regression with Three or More Predictor Variables; 2.4. Dummy Variables to Represent Categorical Variables; 2.4.1. Categorical Variables with Two Categories 2.4.2. Categorical Variables with More Than Two Categories2.5. Multicollinearity; 2.6. Interaction; 2.6.1. Model Specification; 2.6.2. More Complicated Interactions; 2.6.3. Correlation without Interaction; 2.6.4. Interaction without Correlation; 2.7. Nonlinearities; 2.7.1. Quadratic Specification; 2.7.2. Dummy Variable Specification; 2.8. Goodness of Fit; 2.8.1. Standard Error of the Estimate, s; 2.8.2. Coefficient of Determination, R2, and Multiple Correlation Coefficient, R; 2.8.3. Corrected R2 and Corrected R; 2.8.4. Partial Correlation Coefficient; 2.9. Statistical Inference 2.9.1. Hypothesis Testing, Confidence Intervals, and p Values for a Single Regression Coefficient2.9.2. Testing the Difference Between Two Regression Coefficients, βi and βj; 2.9.3. Testing Effects When There Is Interaction; 2.9.4. Testing Effects When There Is a Nonlinearity; 2.9.5. The ANOVA Table; 2.9.6. The Omnibus F Test of the Hypothesis β1 = β2 = ... = βk =0; 2.9.7. Test of the Hypothesis That Some of the βj Are Zero; 2.10. Stepwise Regression; 2.11. Illustrative Examples; 2.11.1. Example 1; 2.11.2. Example 2; 2.12. Further Reading; 3 Multiple Classification Analysis 3.1. The Basic MCA Table3.1.1. Unadjusted Values; 3.1.2. Adjusted Values; 3.1.3. Unadjusted and Adjusted, R; 3.1.4. A Numerical Example; 3.2. The MCA Table in Deviation Form; 3.2.1. First Approach to Table Set-up; 3.2.2. Second Approach to Table Set-up; 3.2.3. A Numerical Example; 3.3. MCA with Interactions; 3.3.1. Table Set-up; 3.3.2. A Numerical Example; 3.4. MCA with Additional Quantitative Control Variables; 3.4.1. Table Set-up; 3.4.2. A Numerical Example; 3.5. Expressing Results from Ordinary Multiple Regression in an MCA Format (all Variables Quantitative); 3.5.1. Table Set-up 3.5.2. A Numerical Example |
| Record Nr. | UNINA-9910876658803321 |
|
Retherford Robert D
|
||
| New York, : Wiley, 1993 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||