Dr. Larry Schroeder, Professor of Public Administration Maxwell Professor of Teaching Excellence, and Senior Research Associate in the Moynihan Institute of Global Affairs at the Maxwell School, Syracuse University is a public finance economist with primary interest in state and local public finance and financial management. He has conducted research on a variety of state and local government fiscal issues both in the U.S. and abroad. Much of his current research is directed at public finance issues arising in developing and transition economies. He is particularly interested in problems associated with financing the construction and maintenance of public infrastructure in these environments as well as the broader issues of decentralization, intergovernmental fiscal relations, and the effects of institutional arrangements on the provision of public services. He is the co-author of several books and has written a large number of articles addressing these subjects. Professor Schroeder has consulted with and led policy research projects sponsored by the United States Agency for International Development, the World Bank, and the United Nations Capital Development Fund in numerous countries, especially in South and Southeast Asia, but also in Africa and Eastern Europe. In 1998-99 he was a Visiting Professor at Universiti Sains Malaysia in Penang Malaysia and was a Visiting Research Fellow at the National College of Public Administration and Governance at the University of the Philippines (Diliman Campus) in Quezon City, Philippines in 2005-06. Professor Schroeder earned his Ph.D. in Economics from the University of Wisconsin in 1971. During the early 1970s he was on the economics faculty at Georgia State University. In 1977 he joined the Departments of Public Administration and Economics at the Maxwell School, Syracuse University and served here until 1994 when he was a Professor of Public and Environmental Affairs at Indiana University. He rejoined The Maxwell School faculty in 1998.
Inhaltsangabe
Series Editor's Introduction Preface Acknowledgments About the Authors 1. Linear Regression Introduction Hypothesized Relationships A Numerical Example Estimating a Linear Relationship Least Squares Regression Examples The Linear Correlation Coefficient The Coefficient of Determination Regression and Correlation Summary 2. Multiple Linear Regression Introduction Estimating Regression Coefficients Standardized Coefficients Associated Statistics Examples Summary 3. Hypothesis Testing Introduction Concepts Underlying Hypothesis Testing The Standard Error of the Regression Coefficient The Student's t Distribution Left-Tail Tests Two-Tail Tests Confidence Intervals F Statistic What Tests of Significance Can and Cannot Do Summary 4. Extensions to the Multiple Regression Model Introduction Types of Data Dummy Variables Interaction Variables Transformations Prediction Examples Summary 5. Problems and Issues Associated With Regression Introduction Specification of the Model Variables Used in Regression Equations and Measurement of Variables Violations of Assumptions Regarding Residual Errors Additional Topics Conclusions Appendix A: Derivation of a and b Appendix B: Critical Values for Student's t Distribution Appendix C: Regression Output From SAS, Stata, SPSS, R, and EXCEL Appendix D: Suggested Textbooks References Index
Series Editor's Introduction Preface Acknowledgments About the Authors 1. Linear Regression Introduction Hypothesized Relationships A Numerical Example Estimating a Linear Relationship Least Squares Regression Examples The Linear Correlation Coefficient The Coefficient of Determination Regression and Correlation Summary 2. Multiple Linear Regression Introduction Estimating Regression Coefficients Standardized Coefficients Associated Statistics Examples Summary 3. Hypothesis Testing Introduction Concepts Underlying Hypothesis Testing The Standard Error of the Regression Coefficient The Student's t Distribution Left-Tail Tests Two-Tail Tests Confidence Intervals F Statistic What Tests of Significance Can and Cannot Do Summary 4. Extensions to the Multiple Regression Model Introduction Types of Data Dummy Variables Interaction Variables Transformations Prediction Examples Summary 5. Problems and Issues Associated With Regression Introduction Specification of the Model Variables Used in Regression Equations and Measurement of Variables Violations of Assumptions Regarding Residual Errors Additional Topics Conclusions Appendix A: Derivation of a and b Appendix B: Critical Values for Student's t Distribution Appendix C: Regression Output From SAS, Stata, SPSS, R, and EXCEL Appendix D: Suggested Textbooks References Index
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