The Matanuska-Susitna Borough Community Survey, 2006: A Sourcebook of Community Attitudes

The Matanuska-Susitna Borough Community Survey (Mat-Su Survey) was a cooperative effort on the part of Mat-Su College, the University of Alaska-Anchorage (UAA) and the Matanuska-Susitna Borough which asked Mat-Su Borough residents to evaluate the quality of Borough services, provide opinions about Borough decision-making, and sum up their perceptions about a range of issues relevant to the present and future of the Mat-Su community. The survey was distributed to every Borough household in the spring of 2006; a total of 2,600 were received, coded, and analyzed for the report. The Sourcebook provides detailed tabular results in six major areas: (1) evaluation of current borough services; (2) use of borough facilities; (3) life in Mat-Su neighborhoods; (4) local government access, policies, and practices; (5) higher education; and (6) respondent background information.Matanuska Susitna BoroughIntroduction / SECTION 1 DETAILED BOROUGH-WIDE RESULTS / Evaluation of Current Borough Services / Use of Borough Facilities / Life in Matanuska-Susitna Borough Neighborhoods / Local Government: Access, Policies and Practices / Higher Education / Respondent Background Information / SECTION 2: RESULTS FOR GEOGRAPHIC AREAS WITHIN THE BOROUGH / Evaluation of Current Borough Services / Use of Borough Facilities / Life in Matanuska-Susitna Borough Neighborhoods / Local Government: Access, Policies and Practices / Higher Education / Respondent Background Information / APPENDIX A: Questionnair

Advanced space system analysis software. Technical, user, and programmer guide

The LASS computer program provides a tool for interactive preliminary and conceptual design of LSS. Eight program modules were developed, including four automated model geometry generators, an associated mass properties module, an appendage synthesizer module, an rf analysis module, and an orbital transfer analysis module. The existing rigid body controls analysis module was modified to permit analysis of effects of solar pressure on orbital performance. A description of each module, user instructions, and programmer information are included

On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference

Nonparametric methods play a central role in modern empirical work. While they provide inference procedures that are more robust to parametric misspecification bias, they may be quite sensitive to tuning parameter choices. We study the effects of bias correction on confidence interval coverage in the context of kernel density and local polynomial regression estimation, and prove that bias correction can be preferred to undersmoothing for minimizing coverage error and increasing robustness to tuning parameter choice. This is achieved using a novel, yet simple, Studentization, which leads to a new way of constructing kernel-based bias-corrected confidence intervals. In addition, for practical cases, we derive coverage error optimal bandwidths and discuss easy-to-implement bandwidth selectors. For interior points, we show that the MSE-optimal bandwidth for the original point estimator (before bias correction) delivers the fastest coverage error decay rate after bias correction when second-order (equivalent) kernels are employed, but is otherwise suboptimal because it is too "large". Finally, for odd-degree local polynomial regression, we show that, as with point estimation, coverage error adapts to boundary points automatically when appropriate Studentization is used; however, the MSE-optimal bandwidth for the original point estimator is suboptimal. All the results are established using valid Edgeworth expansions and illustrated with simulated data. Our findings have important consequences for empirical work as they indicate that bias-corrected confidence intervals, coupled with appropriate standard errors, have smaller coverage error and are less sensitive to tuning parameter choices in practically relevant cases where additional smoothness is available

On Binscatter

Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression settings, and it is often used for informal evaluation of substantive hypotheses such as linearity or monotonicity of the regression function. This paper presents a foundational, thorough analysis of binscatter: we give an array of theoretical and practical results that aid both in understanding current practices (i.e., their validity or lack thereof) and in offering theory-based guidance for future applications. Our main results include principled number of bins selection, confidence intervals and bands, hypothesis tests for parametric and shape restrictions of the regression function, and several other new methods, applicable to canonical binscatter as well as higher-order polynomial, covariate-adjusted and smoothness-restricted extensions thereof. In particular, we highlight important methodological problems related to covariate adjustment methods used in current practice. We also discuss extensions to clustered data. Our results are illustrated with simulated and real data throughout. Companion general-purpose software packages for \texttt{Stata} and \texttt{R} are provided. Finally, from a technical perspective, new theoretical results for partitioning-based series estimation are obtained that may be of independent interest

Survey of multi-function display and control technology

The NASA orbiter spacecraft incorporates a complex array of systems, displays and controls. The incorporation of discrete dedicated controls into a multi-function display and control system (MFDCS) offers the potential for savings in weight, power, panel space and crew training time. The technology applicable to the development of a MFDCS for orbiter application is surveyed. Technology thought to be applicable presently or in the next five years is highlighted. Areas discussed include display media, data handling and processing, controls and operator interactions and the human factors considerations which are involved in a MFDCS design. Several examples of applicable MFDCS technology are described