Graph and download economic data for Statistical discrepancy (IEASD) from Q1 1999 to Q2 2025 about residual and USA.

November's Institute of Medicine (IOM) report on medical errors has sparked debate among US health policy makers as to the appropriate response to the problem. Proposals range from the implementation of nationwide mandatory reporting with public release of performance data to voluntary reporting and quality-assurance efforts that protect the confidentiality of error-related data. Any successful safety program will require a national effort to make significant investments in information technology infrastructure, and to provide an environment and education that enables providers to contribute to an active quality-improvement process.

Engineering model development involves several simplifying assumptions for the purpose of mathematical tractability, which are often not realistic in practice. This leads to discrepancies in the model predictions. A commonly used statistical approach to overcome this problem is to build a statistical model for the discrepancies between the engineering model and observed data. In contrast, an engineering approach would be to find the causes of discrepancy and fix the engineering model using first principles. However, the engineering approach is time consuming, whereas the statistical approach is fast. The drawback of the statistical approach is that it treats the engineering model as a black box and therefore, the statistically adjusted models lack physical interpretability. This article proposes a new framework for model calibration and statistical adjustment. It tries to open up the black box using simple main effects analysis and graphical plots and introduces statistical models inside the engineering model. This approach leads to simpler adjustment models that are physically more interpretable. The approach is illustrated using a model for predicting the cutting forces in a laser-assisted mechanical micro-machining process. This article has supplementary material online.

Source Time Coverage Basis Definition of FTHB FTHB Reported Share Median Age of Buyer NAR Survey July 2024-June 2025 ~6,100 responses Member Survey Self-identified 21% 59 Ginnie Mae +GSE RMBS Loan-Level July 2024-June 2025 ~2.4 Million Actual lending data 3-year no-ownership rule 62% NA Primary Residence Primary Residence Home Purchase Loans Home Purchase HMDA LAR Loan-Level 2024 ~3.1 Million Actual lending data No clear definition - by age cohort 40% 65% 1-4 Primary Residence Under 35 Under 45 Home Purchase Loans American Community Survey 1YR PUMS Latest in CensusVision ~3.54 Million addresses Annual national survey - focus on movers who own No clear definition - by age Age-based share 43 CPS ASEC PUMS 2025 ~95,000 Annual national survey No clear definition - by age Age-based share 42 Households - focus on movers who own

The list of discrepancies between clinicaltrials.dec.gov.ua and clinicaltrials.gov registers.

Yearly citation counts for the publication titled "Discrepancies in Hospital Financial Information: Comparison of Financial Data in State Data Repositories and the Healthcare Cost Reporting Information System".

Online social media such as Twitter are widely used for mining public opinions and sentiments on various issues and topics. The sheer volume of the data generated and the eager adoption by the online-savvy public are helping to raise the profile of online media as a convenient source of news and public opinions on social and political issues as well. Due to the uncontrollable biases in the population who heavily use the media, however, it is often difficult to measure how accurately the online sphere reflects the offline world at large, undermining the usefulness of online media. One way of identifying and overcoming the online–offline discrepancies is to apply a common analytical and modeling framework to comparable data sets from online and offline sources and cross-analyzing the patterns found therein. In this paper we study the political spectra constructed from Twitter and from legislators' voting records as an example to demonstrate the potential limits of online media as the source for accurate public opinion mining, and how to overcome the limits by using offline data simultaneously.

The United States has two measures of economic output: gross domestic product (GDP) and gross domestic income (GDI). While these are conceptually equivalent, their initial estimates differ because these initial estimates are computed from different and incomplete data sources. I study the difference, or “statistical discrepancy,” between GDP and GDI in percent and document three features. First, its size does not materially shrink on average as more data become available. Second, the size of the initial discrepancy in absolute value does not predict the size of the discrepancy in absolute value after revisions. Third, the initial discrepancy has some predictive information about revisions to lagged GDP growth but no predictive information about revisions to lagged GDI growth.

This dataset is designed specifically for beginners and intermediate learners to practice data cleaning techniques using Python and Pandas.

It includes 500 rows of simulated employee data with intentional errors such as:

Missing values in Age and Salary

Typos in email addresses (@gamil.com)

Inconsistent city name casing (e.g., lahore, Karachi)

Extra spaces in department names (e.g., " HR ")

✅ Skills You Can Practice:

Detecting and handling missing data

String cleaning and formatting

Removing duplicates

Validating email formats

Standardizing categorical data

You can use this dataset to build your own data cleaning notebook, or use it in interviews, assessments, and tutorials.

CVS_DATAERRORS_TBL:

The data errors table was where any validation errors or height recalculation utility errors were sent. (Errors in terms of deviations from the established rules of the data collection procedures or project-specific data limitations). Error records corrected were moved to the ErrorHistory table and any remaining records were removed, including warnings, leaving this table blank.

Graph and download economic data for Instrument Discrepancies; Treasury Securities (Level), Market Value Levels (BOGZ1LM903061103Q) from Q4 1945 to Q2 2025 about instruments, market value, Treasury, securities, and USA.

Relevant number of data points, discrepancy type, number of discrepancies and discrepancy rate.

Australia GDP: Real: Discrepancy data was reported at 1.000 AUD mn in 2023. Australia GDP: Real: Discrepancy data is updated yearly, averaging 1.000 AUD mn from Dec 2023 (Median) to 2023, with 1 observations. The data reached an all-time high of 1.000 AUD mn in 2023 and a record low of 1.000 AUD mn in 2023. Australia GDP: Real: Discrepancy data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Gross Domestic Product: Real. A statistical discrepancy usually arises when the GDP components are estimated independently by industrial origin and by expenditure categories. This item represents the discrepancy in the use of resources (i.e., the estimate of GDP by expenditure categories). Data are in constant local currency.;World Bank national accounts data, and OECD National Accounts data files.;;

Errors under different data sizes.

The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.The EMIT Level 2A Estimated Surface Reflectance and Uncertainty and Masks (EMITL2ARFL) Version 1 data product provides surface reflectance data in a spatially raw, non-orthocorrected format. Each EMITL2ARFL granule consists of three Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Reflectance (EMIT_L2A_RFL), Reflectance Uncertainty (EMIT_L2A_RFLUNCERT), and Reflectance Mask (EMIT_L2A_MASK). The Reflectance file contains surface reflectance maps of 285 bands with a spectral range of 381-2493 nanometers (nm) at a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Reflectance Uncertainty file contains uncertainty estimates about the reflectance captured as per-pixel, per-band, posterior standard deviations. The Reflectance Mask file contains six binary flag bands and two data bands. The binary flag bands identify the presence of features including clouds, water, and spacecraft which indicate if a pixel should be excluded from analysis. The data bands contain estimates of aerosol optical depth (AOD) and water vapor.Each NetCDF4 file holds a location group containing a geometric lookup table (GLT) which is an orthorectified image that provides relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files will also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT’s spatial resolution.Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules at the end of an orbit segment reaching 150 km in length.Known Issues: Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe. Possible Reflectance Discrepancies: Due to changes in computational architecture, EMITL2ARFL reflectance data produced after December 4, 2024, with Software Build 010621 and onward may show discrepancies in reflectance of up to 0.8% in extreme cases in some wavelengths as compared to values in previously processed data. These discrepancies are generally lower than 0.8% and well within estimated uncertainties. Between earlier builds and Build 010621, neither resulting output should be interpreted as more ‘correct’ than the other, as their results are simply convergence differences from an optimization search. Most users are unlikely to observe the impact.

Graph and download economic data for Instrument Discrepancies; Total Miscellaneous Assets, Level (BOGZ1FL903090005A) from 1945 to 2024 about instruments, miscellaneous, assets, and USA.

Date of death discrepancies between SDV and original data.

United States Flow: saar: Discrepancies data was reported at -41.336 USD bn in Mar 2018. This records a decrease from the previous number of 26.396 USD bn for Dec 2017. United States Flow: saar: Discrepancies data is updated quarterly, averaging 0.657 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 109.197 USD bn in Mar 2002 and a record low of -130.091 USD bn in Mar 2007. United States Flow: saar: Discrepancies data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB063: Funds by Instruments: Flows and Outstanding: Taxes Payable by Businesses.

United States Outs: Discrepancies data was reported at -53.874 USD bn in Mar 2018. This records an increase from the previous number of -75.415 USD bn for Dec 2017. United States Outs: Discrepancies data is updated quarterly, averaging 14.972 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 154.666 USD bn in Sep 2001 and a record low of -89.721 USD bn in Jun 2017. United States Outs: Discrepancies data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB063: Funds by Instruments: Flows and Outstanding: Taxes Payable by Businesses.

Water buffalo (Bubalus bubalis L.) is an important livestock species worldwide. Like many other livestock species, water buffalo lacks high quality and continuous reference genome assembly, required for fine-scale comparative genomics studies. In this work, we present a dataset, which characterizes genomic differences between water buffalo genome and the extensively studied cattle (Bos taurus Taurus) reference genome. This data set is obtained after alignment of 14 river buffalo whole genome sequencing datasets to the cattle reference. This data set consisted of 13, 444 deletion CNV regions, and 11,050 merged mobile element insertion (MEI) events within the upstream regions of annotated cattle genes. Gene expression data from cattle and buffalo were also presented for genes impacted by these regions. This study sought to characterize differences in gene content, regulation and structure between taurine cattle and river buffalo (2n=50) (one extant type of water buffalo) using the extensively annotated UMD3.1 cattle reference genome as a basis for comparisons. Using 14 WGS datasets from river buffalo, we identified 13,444 deletion CNV regions (Supplemental Table 1) in river buffalo, but not identified in cattle. We also presented 11,050 merged mobile element insertion (MEI) events (Supplemental Table 2) in river buffalo, out of which, 568 of them are within the upstream regions of annotated cattle genes. Furthermore, our tissue transcriptomics analysis provided expression profiles of genes impacted by MEI (Supplemental Tables 3–6) and CNV (Supplemental Table 7) events identified in this study. This data provides the genomic coordinates of identified CNV-deletions and MEI events. Additionally, normalized read count of impacted genes, along with their adjusted p-values of statistical analysis were presented (Supplemental Tables 3–6). Genomic coordinates of identified CNV-deletion and MEI events, and Ensemble gene names of impacted genes (Supplemental Tables 1 and 2) Gene expression profiles and statistical significance (adjusted p-values) of genes impacted by MEI in liver (Supplemental Tables 3 and 4) Gene expression profiles and statistical significance (adjusted p-values) of genes impacted by MEI in muscle (Supplemental Tables 5 and 6) Gene expression profiles and statistical significance (adjusted p-values) of genes impacted by CNV deletions in river buffalo (Supplemental Table 7) Public assessment of this dataset will allow for further analyses and functional annotation of genes that are potentially associated with phenotypic difference between cattle and water buffalo. Raw read data of whole genome and transcriptome sequencing were deposited to NCBI Bioprojects. Resources in this dataset:Resource Title: Genomic structural differences between cattle and River Buffalo identified through comparative genomic and transcriptomic analysis. File Name: Web Page, url: https://www.sciencedirect.com/science/article/pii/S2352340918305183 Data in Brief presenting a dataset which characterizes genomic differences between water buffalo genome and the extensively studied cattle (Bos taurus Taurus) reference genome. This data set is obtained after alignment of 14 river buffalo whole genome sequencing datasets to the cattle reference. This data set consisted of 13, 444 deletion CNV regions, and 11,050 merged mobile element insertion (MEI) events within the upstream regions of annotated cattle genes. Gene expression data from cattle and buffalo were also presented for genes impacted by these regions. Tables are with this article. Raw read data of whole genome and transcriptome sequencing were deposited to NCBI Bioprojects as the following: PRJNA350833 (https://www.ncbi.nlm.nih.gov/bioproject/?term=350833) PRJNA277147 (https://www.ncbi.nlm.nih.gov/bioproject/?term=277147) PRJEB4351 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB4351)