Data Collection Experiment

Data Collection Experiment tài liệu, giáo án, bài giảng , luận văn, luận án, đồ án, bài tập lớn về tất cả các lĩnh vực k...

MỤC LỤC 1. Giới thiệu chung về phần mềm TEMS Investigation 2 1.1. Giới thiệu chung 2 1.2. Các thiết bị TEMS hỗ trợ .2 1.3 Một số thủ tục chuẩn bị trước và trong quá trình Drive test .3 1.4. Một số thao tác cơ bản khi sử dụng TEMS 4 2. Các yêu cầu cho hoạt động của TEMS Investigation .5 2.1. Kết nối đến các thiết bị bên ngoài 5 2.2. Thông tin về Cell .6 2.3. Chức năng điều khiển của TEMS 9 3. Phát hiện lỗi và tối ưu vùng phủ với TEMS Investigation .11 3.1 Các công cụ thường dùng trong quá trình Driving test .11 3.2 Phát hiện nhiễu .13 3.3 Phát hiện sai Fi-đơ 14 3.4 Phát hiện khai thiếu Neighbouring cell hoặc không phù hợp .15 3.6 Tối ưu vùng phủ thấp Data Collection Experiment Data Collection Experiment By: OpenStaxCollege Data Collection Experiment Class Time: Names: Student Learning Outcomes • The student will demonstrate the systematic sampling technique • The student will construct relative frequency tables • The student will interpret results and their differences from different data groupings Movie SurveyAsk five classmates from a different class how many movies they saw at the theater last month Do not include rented movies Record the data In class, randomly pick one person On the class list, mark that person’s name Move down four names on the class list Mark that person’s name Continue doing this until you have marked 12 names You may need to go back to the start of the list For each marked name record the five data values You now have a total of 60 data values For each name marked, record the data 1/2 Data Collection Experiment Order the DataComplete the two relative frequency tables below using your class data Frequency of Number of Movies Viewed Number of Movies Frequency Relative Frequency Cumulative Relative Frequency 7+ Frequency of Number of Movies Viewed Number of Movies Frequency Relative Frequency Cumulative Relative Frequency 0–1 2–3 4–5 6–7+ Using the tables, find the percent of data that is at most two Which table did you use and why? Using the tables, find the percent of data that is at most three Which table did you use and why? Using the tables, find the percent of data that is more than two Which table did you use and why? Using the tables, find the percent of data that is more than three Which table did you use and why? Discussion Questions Is one of the tables “more correct” than the other? Why or why not? In general, how could you group the data differently? Are there any advantages to either way of grouping the data? Why did you switch between tables, if you did, when answering the question above? 2/2 [...]... 316 PART III QUALITATIVE DATA 11 The Right (Soft) Stuff: Qualitative Methods and the Study of Welfare Reform 355 Katherine S Newman PART IV WELFARE LEAVERS AND WELFARE DYNAMICS 12 Studies of Welfare Leavers: Data, Methods, and Contributions to the Policy Process Gregory Acs and Pamela Loprest 387 13 Preexit Benefit Receipt and Employment Histories and Postexit Outcomes of Welfare Leavers Michele Ver... Michele Ver Ploeg 415 14 Experienced-Based Measures of Heterogeneity in the Welfare Caseload Robert A Moffitt 473 Appendix: Agenda of the Workshop on Data Collection for Low-Income and Welfare Populations 501 Index 507 STUDIES OF WELFARE POPULATIONS Introduction Robert A Moffitt, Constance F Citro, and Michele Ver Ploeg Academic and policy interest in the U.S welfare system has increased dramatically over... of administrative data; (2) issues of access and confidentiality; (3) problems in measuring employment and income with administrative data compared to survey data; and (4) the availability of administrative data on children Issues in the matching and cleaning of administrative data are discussed by Goerge and Lee The authors begin by noting the importance of “cleaning” administrative data in a comprehensive... INTRODUCTION The Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs of the National Research Council was formed in 1998 to review the evaluation methods and data that are needed to study the effects of welfare reform Sponsored by the Office of the Assistant Secretary for Planning and Evaluation (ASPE) of the U.S Department of Health and Human Services through... interim and final reports (National Research Council, 1999, 2001) Early in its deliberations, particularly after reviewing the large number of socalled welfare leaver” studies studies of how families who left the TANF rolls were faring off welfare the panel realized that the database for conducting studies of welfare reform had many deficiencies and required attention by policy makers and research. .. 8 Access and Confidentiality Issues with Administrative Data Henry E Brady, Susan A Grand, M Anne Powell, and Werner Schink 9 Measuring Employment and Income for Low-Income Populations with Administrative and Survey Data V Joseph Hotz and John Karl Scholz 10 Administrative Data on the Well-Being of Children On and Off Welfare Richard Barth, Eleanor RESEARCH Open Access Quality of data collection in a large HIV observational clinic database in sub-Saharan Africa: implications for clinical research and audit of care Agnes N Kiragga 1* , Barbara Castelnuovo 1 , Petra Schaefer 1,2 , Timothy Muwonge 1 , Philippa J Easterbrook 1 Abstract Background: Observational HIV clinic databases are now widely used to answer key questions related to HIV care and treatment, but there has been no systematic evaluation of their quality of data. Our objective was to evaluate the compl eteness and accuracy of recording of key data HIV items in a large routine observational HIV clinic database. Methods: We looked at the number and rate of opportunistic infections (OIs) per 100 person years at risk in the 24 months following antiretroviral therapy (ART) initiation in 559 patients who initiated ART in 2004-2005 and enrolled into a research cohort. We compared this with data in a routine clinic database for the same 559 patients, and a further 1233 patients who initiated ART in the same period. The Research Cohort database was considered as the reference “gold standard” for the assessment of data accuracy. A crude percentage of underreporting of OIs in the clinic database was calculated based on the difference between the OI rates reported in both databases. We reviewed 100 clinic patient medical records to assess the accuracy of recording of key data items of OIs, ART toxicities and ART regimen changes. Results: The overall incidence rate per 100 person years at risk for the initial OI in the 559 patients in the research cohort and clinic databases was 24.1 (95% CI: 20.5-28.2) and 13.2 (95% CI: 10.8-16.2) respectively, and 10.4 (95% CI: 9.1-11.9) for the 1233 clinic patients. This represents a 1.8- and 2.3-fold higher rate of events in the research cohort database compared with the same 599 patients and 1233 patients in the routine clinic database, or a 45.1% and 56.8% rate of underreporting, respectively. The c ombined error rate of missing and incorrect items from the medical records’ review was 67% for OIs, 52% for ART-related toxicities, and 83% and 58% for ART discontinuation and modification, respectively. Conclusions: There is a high rate of underreporting of OIs in a routine HIV clinic database. This has important implications for the use and interpretation of routine observational databases for research and audit, and highli ghts the need for regular data validation of these databases. Background Prospective research cohorts of HIV-infected persons have made a major contribution to an understanding of the transmission, natural history and pathogenesis of HIV infection [1-3], in addition to generating important information on the response to and long-term outcomes with antiretroviral therapy (ART). Distinctive features of these research cohorts are their voluntary enrolment of select ed eligi ble patients, prospective follow up and stan- dardized data collection at regular defined time points. Their principal disadvantages Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2011, Article ID 401802, 20 pages doi:10.1155/2011/401802 Research Article Opportunistic Data Collection in Sparse Wireless Sensor Networks Jorge M. Soares, 1 Mirko Franceschinis, 2 RuiM.Rocha, 1, 3 Wansheng Zhang, 4 and Maurizio A. Spirito 2 1 Instituto Superior T ´ ecnico, Technical University of Lisbon, Avenida. Prof. Dr. Cavaco Silva, 2744-016 Porto Salvo, Portugal 2 Pervasive Radio Technologies (PeRT) Lab, Istituto Superiore Mario Boella, Via Pier Carlo Boggio 61, 10138 Torino, Italy 3 Instituto de Telecomunicac¸ ˜ oes, Av. Rovisco Pais 1, 1049-011 Lisboa, Portugal 4 Dipartimento di Elettronica, Politecnico di Torino, Corso D uca degli Abruzzi 24, 10129 Torino, Italy Correspondence should be addressed to Rui M. Rocha, rui.rocha@lx.it.pt Received 30 April 2010; Accepted 4 September 2010 Academic Editor: Sergio Palazzo Copyright © 2011 Jorge M. Soares et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Opportunistic wireless sensor networks (WSNs) have recently been proposed as solutions for many remote monitoring problems. Many such problems, including environmental monitoring, involve large deployment scenarios with lower-than-average node density, as well as a long time scale and limited budgets. Traditional approaches designed for conventional situations, and thus not optimized for these scenarios, entail unnecessary complexity and larger costs. This paper discusses the issues related with the design and test of opportunistic architectures, and presents one possible solution—CHARON (Convergent Hybrid-replication Approach to Routing in Opportunistic Networks). Both algorithm-specific and comparative simulation results are presented, as well as real- world tests using a reference implementation. A comprehensive experimental setup was also used to seek a full characterization of the devised opportunistic approach including the derivation of a simple analytical model that is able to accurately predict the opportunistic message delivery performance in the used test bed. 1. Introduction Advances in miniaturized electronic systems and wireless communications have enabled their use for monitoring applications in scenarios which were previously very difficult or even impossible to monitor, giving birth to the field of wireless sensor networks (WSNs). These networks comprise a potentially large number of small nodes of limited capacity, communicating with each other using wireless links, also of limited range [1]. Many of the applications envisioned for WSNs, such as agricultural and habitat monitoring, require spreading the network over relatively large areas, causing the radio range to be insufficient to assure a fully and permanently connected network. The network will, therefore, be split into several partitions that are unable to directly transfer information to each other. For some networks, this is not a problem, as there can be individual base stations (sink nodes) that receive and use the information from their respective partitions. For others, however, such sink deployment may be impossible or impractical, or full connectivity may be an impor tant application requirement. In such cases, node mobility emerges as a possible solution. By making some nodes mobile and exploit- ing their mobility, new communication opportunities are created among otherwise isolated network elements. In some applications, such as wildlife monitoring, mobility may e ven be part of the problem specification, so taking advantage of it seems a logical choice. But exploiting node Data collection Guidelines for collecting and checking data Type of data z Quantitative - Height, diameter, density z Qualitative - Stem straightness Choosing traits for measurement and assessment • survival • dbh • height • stem volume • wood density, colour • timber strength, stiffness • timber defects • pulp yield • fibre length • stem straightness • axis persistence/forking • branch thickness • branch angle • pest and disease resistance • growth stress • tension wood • fodder production • fodder value • other traits? ? ? ? ? ? Choosing traits for measurement and assessment z Breeders aim to achieve genetic improvement in traits of economic importance z Breeders need to talk to the people (industry managers, farmers, etc.) who plant and use their tree species, to find out which traits are most important to the users z Examples : ¾ stem straightness is not very important for trees grown for pulpwood, but important for trees grown for sawlogs (bends in the stem reduce the recovery of sawn wood and therefore the value of the log) ¾ dry biomass/hectare, not volume/hectare is important for biomass energy users Selecting and breeding for a single trait, or for multiple traits z Breeding for a single trait is straightforward - we just rank the trees for the trait and choose the better trees for breeding and propagation z When breeding for two or more traits we must make “trade-offs” between traits. The tree with the largest stem volume may have very poor stem straightness - should we select this tree, if both traits are important to the user? Assessing traits z Objective or subjective scoring systems? z Objective - e.g. 1 = no flowering 2 = flowering z Subjective - e.g. stem straightness 1 = worst 2% of trees in trial 2 = next best 15% of trees in trial 3 = next best 33% of trees in trial 4 = next best 33% of trees in trial 5 = next best 15 % of trees in trial 6 = best 2 % of trees in trial Assessing stem straightness - subjective scoring system worst Prior to scoring, inspect trial and set proportions of scoring categories to approximate normal distribution - improves heritability of trait Stem straightness 1 2 43 5 6 best 33% 2% 15% Frequency Assessing traits z Best category gets highest score (gives consistency in constructing selection index) z An even number of categories (4, or 6) gives higher heritability than odd numbers of categories (3, 5, or 7) because we are forced to make decisions about the “average” trees - are they above or below the mean? 1 24 3 5 even odd 1 2 43 5 6 ⇐ ? ⇒ Axis persistence - objective scoring system 1 = stem axis forks at ground level 2 = stem forks in first quarter of tree height 3 = stem forks in second quarter of tree height 6 = axis persists to top of tree 4 = stem forks in third quarter of tree height 5 = stem forks in fourth quarter of tree height Forking defined as two or more leaders, stem diameter of smaller leader is more than 50% of diameter of larger leader just above fork Data collection z Indexing information on the field data sheets z Data sheets should be prepared with layout and treatment information included: replicate number, plot number, tree number, seedlot number, etc. [...]... 1 3 4 * * 1 1 4 4 * * 1 1 5 4 1.1 0 Check the data !!!!!!!!!!!!!!! There will always be mistakes in the data! Mistakes arise at different stages of the operation Read back the data from the computer screen, with somebody checking the field data sheet against the values which are being read out General tips for computer analysis of data Keep all the files for an experiment in one folder (directory) Check... replicates 5 rows and 12 columns spacing 3m .. .Data Collection Experiment Order the DataComplete the two relative frequency tables below using your class data Frequency of Number of Movies Viewed... 4–5 6–7+ Using the tables, find the percent of data that is at most two Which table did you use and why? Using the tables, find the percent of data that is at most three Which table did you use... and why? Using the tables, find the percent of data that is more than two Which table did you use and why? Using the tables, find the percent of data that is more than three Which table did you