This worksheet allows you to see how IRT estimates the ML estimate of theta. Plus in the a and b for an item, as well as the response to each item (correct/incorrect).

"The Statistical Abstract of the United States, published since 1878, is the standard summary of statistics on the social, political, and economic organization of the United States. It is designed to serve as a convenient volume for statistical reference and as a guide to other statistical publications and sources. The latter function is served by the introductory text to each section, the source note appearing below each table, and Appendix I, which comprises the Guide to Sources of Statisti cs, the Guide to State Statistical Abstracts, and the Guide to Foreign Statistical Abstracts. The Statistical Abstract sections and tables are compiled into one Adobe PDF named StatAbstract2009.pdf. This PDF is bookmarked by section and by table and can be searched using the Acrobat Search feature. The Statistical Abstract on CD-ROM is best viewed using Adobe Acrobat 5, or any subsequent version of Acrobat or Acrobat Reader. The Statistical Abstract tables and the metropolitan areas tables from Appendix II are available as Excel(.xls or .xlw) spreadsheets. In most cases, these spreadsheet files offer the user direct access to more data than are shown either in the publication or Adobe Acrobat. These files usually contain more years of data, more geographic areas, and/or more categories of subjects than those shown in the Acrobat version. The extensive selection of statistics is provided for the United States, with selected data for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. Except as indicated, figures are for the United States as presently constituted. Although emphasis in the Statistical Abstract is primarily given to national data, many tables present data for regions and individual states and a smaller number for metropolitan areas and cities.Statistics for the Commonwealth of Puerto Rico and for island areas of the United States are included in many state tables and are supplemented by information in Section 29. Additional information for states, cities, counties, metropolitan areas, and other small units, as well as more historical data are available in various supplements to the Abstract. Statistics in this edition are generally for the most recent year or period available by summer 2006. Each year over 1,400 tables and charts are reviewed and evaluated; new tables and charts of current interest are added, continuing series are updated, and less timely data are condensed or eliminated. Text notes and appendices are revised as appropriate. This year we have introduced 72 new tables covering a wide range of subject areas. These cover a variety of topics including: learning disability for children, people impacted by the hurricanes in the Gulf Coast area, employees with alternative work arrangements, adult computer and Internet users by selected characteristics, North America cruise industry, women- and minority-owned businesses, and the percentage of the adult population considered to be obese. Some of the annually surveyed topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Demonstration of basic applications of instrumental variable estimation (IVE), with applications of the Durbin-Wu-Hausman (DWH) test as an augmented regression.

In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA in ARF/RRF 231 International Exposures. This return is the basis of the data provided by Australia to the Bank for International Settlements (BIS) for its International Banking Statistics (IBS) data collection. APRA ceased the RFC data collection after September 2010.

The IBS data are based on the methodology described in the BIS Guide on International Financial Statistics (see http://www.bis.org/statistics/intfinstatsguide.pdf; Part II International banking statistics). Data reported for Australia, and other countries, on the BIS website are expressed in United States dollars (USD).

Data are recorded on an end-quarter basis.

There are two sets of IBS data: locational data, which are used to gauge the role of banks and financial centres in the intermediation of international capital flows; and consolidated data, which can be used to monitor the country risk exposure of national banking systems. Only consolidated data are reported in this statistical table.

Data are shown for a range of countries and regions. Similar data for a selected group of countries are also available in statistical table B13.1.

Country and regional groupings are based on the classification used in the IBS.

Data presented in this statistical table are immediate risk claims (expressed by the BIS as claims on an immediate borrower basis), which cover exposures on an immediate counterparty location basis. Ultimate risk claims are presented in complementary statistical tables B13.2 and B13.2.1, which cover immediate exposures adjusted (via guarantees and other risk transfers) to reflect the location of the ultimate counterparty/risk.

International claims represent cross-border claims in all currencies and foreign offices’ local claims in non-local currencies (which would include, for example, USD claims on New Zealand residents by the New Zealand subsidiary of an Australian-owned bank).

This statistical table contains seven data worksheets. Six present data for countries within each specified region, while the 'Summary' worksheet shows total international claims of the globally consolidated operations of Australian-owned banks for each region, international organisations and unallocated. In each of these worksheets, the data in the last column measures total international claims for the region. Total international claims for each country add to total international claims for the region. However, in some quarters, this cannot be directly verified because data for individual countries and regions have blank entries in order to avoid disclosing confidential bank exposures.

In the 'Summary' worksheet, the positions by region and international organisation, and unallocated are summed to produce a ‘Total’ figure that represents reporting entities’ total international exposures.

"The Statistical Abstract is the nation's best known and most popular single source of statistics on the social, political, and economic organization of the country. The print version has been published since 1878, and a compact disc version has been available since 1993. Both are designed to serve as a convenient, easy-to-use statistical reference source and guide to statistical publications and sources. The extensive selection of statistics is provided for the United States, with selected d ata for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. The disc contains over 1,500 tables from over 250 different governmental, private, and international organizations. Some of the topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics. Significant changes in the 2002 data include new data from the 2000 census and new tables that include data covering resident population's migration status, educational attainment, disability status, ancestry, place of birth, and language spoken at home as well as househol d income, poverty, and selected housing characteristics from the sample portion of the 2000 census. New tables cover topics such as unmarried households, state children's health insurance programs, limitation of activity level caused by chronic conditions, characteristics of homeschooled children, firearm-use offenders, home- based work and flexible work by workers, computer use in the workplace, employee benefits, and computer and Internet use." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA in ARF/RRF 231 International …Show full descriptionIn March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA in ARF/RRF 231 International Exposures. This return is the basis of the data provided by Australia to the Bank for International Settlements (BIS) for its International Banking Statistics (IBS) data collection. APRA ceased the RFC data collection after September 2010. The IBS data are based on the methodology described in the BIS Guide on International Financial Statistics (see http://www.bis.org/statistics/intfinstatsguide.pdf; Part II International banking statistics). Data reported for Australia, and other countries, on the BIS website are expressed in United States dollars. Data are recorded on an end-quarter basis. There are two sets of IBS data: locational data, which are used to gauge the role of banks and financial centres in the intermediation of international capital flows; and consolidated data, which can be used to monitor the country risk exposure of national banking systems. Only locational data are reported in this statistical table. Data are shown for a range of countries and regions. Similar data for a selected group of countries are also available in B12.2. Country and regional groupings are based on the classification used in the IBS. Some liabilities are reported at market value, but contractual or nominal values are used where market values are not appropriate. This statistical table contains seven data worksheets. Six present data for countries within each specified region, while the 'Summary' worksheet shows total international liabilities of Australian-located banks (and RFCs between March 2003 and September 2010) for each region, and Australia. In each of these worksheets, the data in the last column measures total international liabilities for the region. Total international liabilities for each country add to total international liabilities for the region. However, in some quarters, this cannot be directly verified because data for individual countries and regions have blank entries in order to avoid disclosing confidential bank exposures. In the 'Summary' worksheet, the positions by region are summed to produce a ‘Total non-residents’ figure that represents reporting entities’ total positions with offshore counterparties in all currencies. The positions shown for 'Australia' are positions with residents in foreign currency.

In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA in ARF/RRF 231 International Exposures. This return is the basis of the data provided by Australia to the Bank for International Settlements (BIS) for its International Banking Statistics (IBS) data collection. APRA ceased the RFC data collection after September 2010.\r \r The IBS data are based on the methodology described in the BIS Guide on International Financial Statistics (see http://www.bis.org/statistics/intfinstatsguide.pdf; Part II International banking statistics). Data reported for Australia, and other countries, on the BIS website are expressed in United States dollars.\r \r Data are recorded on an end-quarter basis.\r \r There are two sets of IBS data: locational data, which are used to gauge the role of banks and financial centres in the intermediation of international capital flows; and consolidated data, which can be used to monitor the country risk exposure of national banking systems. Only locational data are reported in this statistical table and all assets are reported at market value. \r \r Data are shown for a range of countries and regions. Similar data for a selected group of countries are also available in B12.1.\r \r Country and regional groupings are based on the classification used in the IBS.\r \r This statistical table contains seven data worksheets. Six present data for countries within each specified region, while the 'Summary' worksheet shows total international assets of Australian-located banks (and RFCs between March 2003 and September 2010) for each region, and Australia. In each of these worksheets, the data in the last column measures total international assets for the region. Total international assets for each country add to total international assets for the region. However, in some quarters, this cannot be directly verified because data for individual countries and regions have blank entries in order to avoid disclosing confidential bank exposures.\r \r In the 'Summary' worksheet, the positions by region are summed to produce a ‘Total non-residents’ figure that represents reporting entities’ total positions with offshore counterparties in all currencies. The positions shown for 'Australia' are positions with residents in foreign currency.\r

Excel file containing diurnal ensemble statistics of 2-m temperature, 2-m mixing ratio and 10-m wind speed. This Excel file contains figures for Figure 2 in the paper and worksheets containing all statistics for the 14 members of the ensemble and a base simulation. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

This file consists of the four original sheets included in the District Profile Report download, as described on the About FY21 Report tab, an additional five sheets created for GIS use and documentation, and an About File tab.The original four sheets are as follows:1. The “District Profile Report” provides a list of statistics for a given school district under seven groupings listed above (A-G). There are six columns of data in this worksheet. The first three columns provide statistics for each data element for the selected district, similar districts average and statewide average. There are three additional columns that enable the user to look at the statistics of three other school districts for comparison. By clicking in the cell below the Comparison Districts 1, 2 and 3 labels, the user can choose three additional school districts to review simultaneously.2. The worksheet labeled “District Data” provides a downloadable file of school district data.3. The worksheet labeled “Similar Districts Data” provides a downloadable file of similar districts averages.4. The worksheet labeled “Statewide Data” provides statewide averages for every statistic in the report.The five new sheets are as follows:1. FY21_Data_Dictionary - contains the abbreviated column names for GIS use and their definitions as found on the original download page. (The link is on the About FY21 Report sheet)2. FY21_Join - contains the District Data formatted for use in a GIS in the following way: the attribute/column titles were abbreviated for use in GIS, column C - UNSDLEA21 was added to facilitate joining to the Census School District geography file, column D - COUNTY was added for geographical filtering purposes, column E - COUNTY_GEOID was added for geographical filtering purposes. All columns from F to BM remain in the same order as the original dataset3. About FY21 Report - contains the report description as found on the original download page. The link to that original download page is also on this sheet.4. Mismatches - contains the school districts where the Census UNSDLEA cannot be derived from the Ohio IRN for quality control purposes5. No Data - contains the school districts that are not in the Profile Report but are in the Census geography data, for a quality control check when performing a join

These datatasets relate to the computational study presented in the paper "The Berth Allocation Problem with Channel Restrictions", authored by Paul Corry and Christian Bierwirth. They consist of all the randomly generated problem instances along with the computational results presented in the paper.

Results across all problem instances assume ship separation parameters of [delta_1, delta_2, delta_3] = [0.25, 0, 0.5].

Excel Workbook Organisation:

The data is organised into separate Excel files for each table in the paper, as indicated by the file description. Within each file, each row of data presented (aggregating 10 replications) in the corrsponding table is captured in two worksheets, one with the problem instance data, and the other with generated solution data obtained from several solution methods (described in the paper). For example, row 3 of Tab. 2, will have data for 10 problem instances on worksheet T2R3, and corresponding solution data on T2R3X.

Problem Instance Data Format:

On each problem instance worksheet (e.g. T2R3), each row of data corresponds to a different problem instance, and there are 10 replications on each worksheet.

The first column provides a replication identifier which is referenced on the corresponding solution worksheet (e.g. T2R3X).

Following this, there are n*(2c+1) columns (n = number of ships, c = number of channel segmenets) with headers p(i)_(j).(k)., where i references the operation (channel transit/berth visit) id, j references the ship id, and k references the index of the operation within the ship. All indexing starts at 0. These columns define the transit or dwell times on each segment. A value of -1 indicates a segment on which a berth allocation must be applied, and hence the dwell time is unkown.

There are then a further n columns with headers r(j), defining the release times of each ship.

For ChSP problems, there are a final n colums with headers b(j), defining the berth to be visited by each ship. ChSP problems with fixed berth sequencing enforced have an additional n columns with headers toa(j), indicating the order in which ship j sits within its berth sequence. For BAP-CR problems, these columnns are not present, but replaced by n*m columns (m = number of berths) with headers p(j).(b) defining the berth processing time of ship j if allocated to berth b.

Solution Data Format:

Each row of data corresponds to a different solution.

Column A references the replication identifier (from the corresponding instance worksheet) that the soluion refers to.

Column B defines the algorithm that was used to generate the solution.

Column C shows the objective function value (total waiting and excess handling time) obtained.

Column D shows the CPU time consumed in generating the solution, rounded to the nearest second.

Column E shows the optimality gap as a proportion. A value of -1 or an empty value indicates that optimality gap is unknown.

From column F onwards, there are are n*(2c+1) columns with the previously described p(i)_(j).(k). headers. The values in these columns define the entry times at each segment.

For BAP-CR problems only, following this there are a further 2n columns. For each ship j, there will be columns titled b(j) and p.b(j) defining the berth that was allocated to ship j, and the processing time on that berth respectively.

This is a supplementary data sets for a manuscript submitted to JGR: Solid Earth entitled "A Fractal Reference Model Based on 3D Process-Based Digital Core for Evaluating the Complexity of Rock Pore Structure". This data sets present the digital core construction and fractal dimension calculation in detail, including process-based modeling program, CT digital core data, fractal dimension calculation program and analysis statistics worksheet.

This Excel file, entitled “raw data for metal analyses revised stats”, contains three worksheets. The first and second worksheets contain raw data and statistical analyses for data presented in Figs 1 and 2. The third worksheet contains raw data on the comparison of metal measurements in a subset of zebrafish samples by GFAAS and ICPMS. (XLSX)

This file consists of the four original sheets included in the District Profile Report download, as described on the About FY22 Report tab, an additional five sheets created for GIS use and documentation, and an About File tab.The original four sheets are as follows:1. The “District Profile Report” provides a list of statistics for a given school district under seven groupings listed above (A-G). There are six columns of data in this worksheet. The first three columns provide statistics for each data element for the selected district, similar districts average and statewide average. There are three additional columns that enable the user to look at the statistics of three other school districts for comparison. By clicking in the cell below the Comparison Districts 1, 2 and 3 labels, the user can choose three additional school districts to review simultaneously.2. The worksheet labeled “District Data” provides a downloadable file of school district data.3. The worksheet labeled “Similar Districts Data” provides a downloadable file of similar districts averages.4. The worksheet labeled “Statewide Data” provides statewide averages for every statistic in the report.The five new sheets are as follows:1. FY22_Data_Dictionary - contains the abbreviated column names for GIS use and their definitions as found on the original download page. (The link is on the About FY22 Report sheet)2. FY22_Join - contains the District Data formatted for use in a GIS in the following way: the attribute/column titles were abbreviated for use in GIS, column C - UNSDLEA22 was added to facilitate joining to the Census School District geography file, column D - COUNTY was added for geographical filtering purposes, column E - COUNTY_GEOID was added for geographical filtering purposes. All columns from F to BM remain in the same order as the original dataset3. About FY22 Report - contains the report description as found on the original download page. The link to that original download page is also on this sheet.4. Mismatches - contains the school districts where the Census UNSDLEA cannot be derived from the Ohio IRN for quality control purposes5. No Data - contains the school districts that are not in the Profile Report but are in the Census geography data, for a quality control check when performing a join

Abstract: Dieser Datensatz enthält Ergebnisse physikalisch-chemischer, faunistischer and statistischer Analysen der Studie "Temporal shift of groundwater fauna in South-West Germany" von Fabien Koch, Philipp Blum, Heide Stein, Andreas Fuchs, Hans Jürgen Hahn, Kathrin Menberg in der Zeitschrift "Hydrology and Earth System Sciences" (HESS) (https://doi.org/10.5194/hess-2024-29). Abstract: This data set contains results of the physical-chemical, faunistic and statistical analysis of the study "Temporal shift of groundwater fauna in South-West Germany" by Fabien Koch, Philipp Blum, Heide Stein, Andreas Fuchs, Hans Jürgen Hahn, Kathrin Menberg in the journal "Hydrology and Earth System Sciences" (HESS) (https://doi.org/10.5194/hess-2024-29). TechnicalRemarks: General note: The fauna sampling on which the dataset is based on was carried out by IGÖ (GmbH) as part of the long-term ecological monitoring commissioned by the LANDESANSTALT FÜR UMWELT, MESSUNGEN UND NATURSCHUTZ BADEN-WÜRTTEMBERG (LUBW). The sampling in 2001 and 2002 was carried out as part of the doctoral thesis of Dr. Andreas Fuch: Erhebung und Beschreibung der Grundwasserfauna in Baden-Württemberg, 2007, pp.1-109, URL: https://kola.opus.hbz-nrw.de/frontdoor/index/index/docId/175. The data on groundwater quality originates from the LUBW 4.0 data and map service, more precisely from the annual groundwater quality data catalogue (Link: https://umweltdaten.lubw.baden-wuerttemberg.de/workbooks/ElW90OFKM4Yp6SuaXMUC,hash=gqGNSioRDeBveDwfKlVLq7MkNdbkpGvI9WWuqz3AZz2zwkyofMtJQeByuHQ=/worksheets/EcsMAJuFpqt01exTJsUl

Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance. Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071. Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/. Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067. Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322. Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013. Please see README.txt for further information HMD international_updated Jan 2019.xlsx Comprises 20 worksheets, of which 14 contain data. These data are arranged by country and by year. Missing data codes: "" The tab 'contents and sources' provides descriptions of the data source and contents of each sheet. HMD Scotland time trend analysis.xlsx Comprises 5 worksheets, including a combination of data and charts. The sheet 'contents' describes the data source and contents of other sheets. The variables include year, life expectancy, and various measures of change in life expectancy Missing data codes: "" Segmented regression chart.xlsx Comprises 2 worksheets, 'Data' and 'Chart'. Variables within the 'data' worksheet include: Year 4 quarter rolling period ending Female observed mortality rate Female predicted by one-break model Female predicted by two-break model Male observed mortality rate Male predicted by one-break model Male predicted by two-break model Chart breakpoint indicator Missing data codes: (blank space) Summary findings from segmented regression.xlsx Excel workbook containing table 1 of paper 'summary of results of segmented regression by population group and model/test'