confidence interval based parameter estimation—a new socr applet and activity基于置信区间参数estimation-a新的socr applet和活动.pdfVIP

Confidence Interval Based Parameter Estimation—A New SOCR Applet and Activity 1 2 Nicolas Christou , Ivo D. Dinov * 1 Department of Statistics, University of California Los Angeles, Los Angeles, California, United States of America, 2 Department of Statistics and Center for Computational Biology, University of California Los Angeles, Los Angeles, California, United States of America Abstract Many scientific investigations depend on obtaining data-driven, accurate, robust and computationally-tractable parameter estimates. In the face of unavoidable intrinsic variability, there are different algorithmic approaches, prior assumptions and fundamental principles for computing point and interval estimates. Efficient and reliable parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors. In this manuscript, we demonstrate simulation, construction, validation and interpretation of confidence intervals, under various assumptions, using the interactive web-based tools provided by the Statistics Online Computational Resource (http://www.SOCR.). Specifically, we present confidence interval examples for population means, with known or unknown population standard deviation; population variance; population proportion (exact and approximate), as well as confidence intervals based on bootstrapping or the asymptotic properties of the maximum likelihood estimates. Like all SOCR resources, these confidence interval resources may be openly accessed via an Internet-connected Java-enabled browser. The SOCR confidence interval applet enables the user to empirically explore and investigate the effects of the confidence-level, the sample-size and parameter of interes