Christopher Z. Mooney is a professor of political studies with a joint appointment in the Institute of Government and Public Affairs.Mooney studies U.S. state politics and policy, with special focus on legislative decision making, morality policy, and legislative term limits.He is the founding editor of State Politics and Policy Quarterly, the premier academic journal in its field and has published dozens of articles and books, including Lobbying Illinois - How You Can Make a Difference in Public Policy.Prior to arriving at UIS in 1999, he taught at West Virginia University and the University of Essex in the United Kingdom

• PART ONE: INTRODUCTIONTraditional Parametric Statistical InferenceBootstrap Statistical InferenceBootstrapping a Regression ModelTheoretical JustificationThe JackknifeMonte Carlo Evaluation of the BootstrapPART TWO: STATISTICAL INFERENCE USING THE BOOTSTRAPBias EstimationBootstrap Confidence IntervalsPART THREE: APPLICATIONS OF BOOTSTRAP CONFIDENCE INTERVALSConfidence Intervals for Statistics With Unknown Sampling DistributionsInference When Traditional Distributional Assumptions Are ViolatedPART FOUR: CONCLUSIONFuture WorkLimitations of the BootstrapConcluding Remarks

Christopher Z. Mooney is a professor of political studies with a joint appointment in the Institute of Government and Public Affairs.Mooney studies U.S. state politics and policy, with special focus on legislative decision making, morality policy, and legislative term limits.He is the founding editor of State Politics and Policy Quarterly, the premier academic journal in its field and has published dozens of articles and books, including Lobbying Illinois - How You Can Make a Difference in Public Policy.Prior to arriving at UIS in 1999, he taught at West Virginia University and the University of Essex in the United Kingdom

• PART ONE: INTRODUCTIONTraditional Parametric Statistical InferenceBootstrap Statistical InferenceBootstrapping a Regression ModelTheoretical JustificationThe JackknifeMonte Carlo Evaluation of the BootstrapPART TWO: STATISTICAL INFERENCE USING THE BOOTSTRAPBias EstimationBootstrap Confidence IntervalsPART THREE: APPLICATIONS OF BOOTSTRAP CONFIDENCE INTERVALSConfidence Intervals for Statistics With Unknown Sampling DistributionsInference When Traditional Distributional Assumptions Are ViolatedPART FOUR: CONCLUSIONFuture WorkLimitations of the BootstrapConcluding Remarks

Christopher Z. Mooney is a professor of political studies with a joint appointment in the Institute of Government and Public Affairs.Mooney studies U.S. state politics and policy, with special focus on legislative decision making, morality policy, and legislative term limits.He is the founding editor of State Politics and Policy Quarterly, the premier academic journal in its field and has published dozens of articles and books, including Lobbying Illinois - How You Can Make a Difference in Public Policy.Prior to arriving at UIS in 1999, he taught at West Virginia University and the University of Essex in the United Kingdom

• PART ONE: INTRODUCTIONTraditional Parametric Statistical InferenceBootstrap Statistical InferenceBootstrapping a Regression ModelTheoretical JustificationThe JackknifeMonte Carlo Evaluation of the BootstrapPART TWO: STATISTICAL INFERENCE USING THE BOOTSTRAPBias EstimationBootstrap Confidence IntervalsPART THREE: APPLICATIONS OF BOOTSTRAP CONFIDENCE INTERVALSConfidence Intervals for Statistics With Unknown Sampling DistributionsInference When Traditional Distributional Assumptions Are ViolatedPART FOUR: CONCLUSIONFuture WorkLimitations of the BootstrapConcluding Remarks