Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1

Business' or organization's level of confidence in making its debt payments in full and on time, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2024.

The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF25) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Decennial Census Disclosure Avoidance System (DAS) into the Redistricting and DHC products. The PPMF0 was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential 2010 Census Edited File (CEF) as the initial input; the replicates were then created by executing the 2020 DAS TDA repeatedly with the PPMF0 as its initial input. Inspired by analogy to the use of bootstrap methods in non-private contexts, U.S. Census Bureau (USCB) researchers explored whether simple calculations based on comparing each PPMFi to the PPMF0 could be used to reliably estimate the scale of errors introduced by the 2020 DAS, and generally found this approach worked well.

The PPMF0 and PPMFi files contained here are provided so that external researchers can estimate properties of DAS-introduced error without privileged access to internal USCB-curated data sets; further information on the estimation methodology can be found in Ashmead et. al 2024.

The 2010 DHC AMC seed PPMF0 and PPMF replicates have been cleared for public dissemination by the USCB Disclosure Review Board (CBDRB-FY24-DSEP-0002). The 2010 PPMF0 included in these files was produced using the same parameters and settings as were used to produce the 2010 Demonstration Data Product Suite (2023-04-03) PPMF, but represents an independent execution of the TopDown Algorithm. The PPMF0 and PPMF replicates contain all Person and Units attributes necessary to produce the Redistricting and DHC publications for both the United States and Puerto Rico, and include geographic detail down to the Census Block level. They do not include attributes specific to either the Detailed DHC-A or Detailed DHC-B products; in particular, data on Major Race (e.g., White Alone) is included, but data on Detailed Race (e.g., Cambodian) is not included in the PPMF0 and replicates.

The 2020 AMC replicate files for estimating confidence intervals for the official 2020 Census statistics are available.

Confidence level of business or organization in its ability to make payments to suppliers and service providers in full and on time, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, first quarter of 2025.

The indicator measures confidence among EU citizens in a selection of EU institutions: the European Parliament, the European Commission, and the European Central Bank. It is expressed as the share of positive opinions (people who declare that they tend to trust) about the institutions. Citizens are asked to express their confidence levels by choosing the following alternatives: ‘tend to trust’, ‘tend not to trust’ and ‘don’t know’ or ‘no answer’. The indicator is based on the Eurobarometer, a survey which has been conducted twice a year since 1973 to monitor the evolution of public opinion in the Member States.

Expected and observed 95% CL lower limits on first- and second-generation leptoquark masses for different values of $\beta$.

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/Agvl4w4bhTLLpvo3GIVrw The level of citizens confidence in EU institutions (Council of the European Union, European Parliament and European Commission) is expressed as the share of positive opinions (people who declare that they tend to trust) about the institutions. The indicator is based on the Eurobarometer, a survey which has been conducted twice a year since 1973 to monitor the evolution of public opinion in the Member States. The indicator only displays the results of the autumn survey. Potential replies to the question on the level of confidence include 'tend to trust', 'tend not to trust' and 'don't know' or 'no answer'. Trust is not precisely defined and could leave some room for interpretation to the interviewees.

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student’s t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

The Scotland Heat Map provides estimates of heat demand for all properties in Scotland. To indicate reliability, each estimate is assigned a confidence level from 1 to 5. Level 1 is least reliable and level 5 most. This is mainly determined by the presence of information that would directly impact on heat demand in the estimate's source data. For example, estimates based on data that includes building type, age and floor area would be more reliable than estimates based solely on floor area derived from mapping data. This raster dataset gives the average (mean) confidence level of properties within 250m x 250m grid squares covering all of Scotland. The Scotland Heat Map is a tool to help plan for the reduction of carbon emissions from heat in buildings. Average confidence level is an indicator of reliability of the heat demand estimates within an area and allows planners to decide whether they meet their needs. The map is produced by the Scottish Government. The most recent version is the Scotland Heat Map 2022, which was released to local authorities in November 2023. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer depicting potential water level increase and decrease in the coastal areas of the Great Lakes. The lakes included are: Erie, Huron, Michigan, Ontario, St. Clair, and Superior. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at lake level change and potential coastal impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses to help users examine multiple scenarios and prioritize actions. The Lake Level Viewer may be accessed at: https://coast.noaa.gov/llv These data depict the mapping confidence of the associated lake water level data for the water level amounts of -6 feet through +6 feet. The mapping process is designed to give the most accurate picture of water extent possible, but inherent data errors introduce some uncertainty in the exact water extents. The presentation of data confidence only represents the known error in the elevation data and not uncertainty associated with the natural evolution of the coastal landforms (e.g., erosion or bluff failure) or future climate change impacts on lake levels. To access the associated data to be used with this data: NOAA Office for Coastal Management Lake Level Data: -6 Feet to +6 Feet Water Level Change data may be downloaded at: https://coast.noaa.gov/llv The NOAA Office for Coastal Management has tentatively adopted an 80 percent rank (as either inundated or not inundated) as the zone of relative confidence. The use of 80 percent has no special significance but is a commonly used rule of thumb measure to describe economic systems (Epstein and Axtell, 1996). The method used to determine the confidence data only includes the uncertainty in the lidar derived elevation data (root mean square error, or RMSE). This confidence data shows that the water level depicted in the -6 feet to +6 feet water level change data is not really a hard line, but rather a zone with greater and lesser chances of being wet or dry. Areas that have a high level of confidence that they will be wet, means that there is an 80 percent or greater likelihood that these areas will be covered with water. Conversely, there is a 20 percent or less likelihood that the area will be dry. Areas mapped as wet (inundation) with a high confidence (or low uncertainty) are coded as 2. Areas that have a high level of confidence that they will be dry, means that there is an 80 percent or greater likelihood that these areas will be dry. Conversely, there is a 20 percent or less likelihood that the area will be wet. Areas mapped as dry (no inundation) with a high confidence (or low uncertainty) are coded as 0. Areas that have a low level of confidence, means that there is a 21 - 79 percent likelihood of wet or dry conditions. Note that 60 percent of the time, the land-water interface will be within this zone. Areas mapped as dry or wet with a low confidence (or high uncertainty) are coded as 1. As with all remotely sensed data, all features should be verified with a site visit. The data are provided as is, without warranty to their performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of these data is assumed by the user. This data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes. For a detailed description of the confidence level and its computation, please see the Mapping Inundation Uncertainty document available at: http://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-13-00118.1

The MSFD commission staff working paper (CSWP) defines predominant habitat types required for use in MSFD habitats initial reporting article 8 - "Analysis of essential features and characteristics (Art 8.1a - Habitats)". These predominant habitat typologies broadly correspond to EUNIS level 2 habitat typologies. This supported the process of mapping existing habitat types (evidence based and predicted) to the CSWP PHT classification by means of data re-engineering. In areas where neither evidence nor predicted data was available, a depth proxy was created using INFOMAR bathymetry. When viewing the PHT habitats layer, the Predominant Habitat Confidence should be also be used as reference as this provides the user with a means to establish the data source underpinning the derived PHT habitat type per polygon.

ObjectivesThe visceral adiposity index (VAI), an indirect marker of visceral adipose tissue, serves as a model associated with cardiometabolic risk, but has limitations regarding the Asian population. We sought to develop a new VAI (NVAI) for the Korean population and compare it to VAI for prediction of atherosclerotic cardiovascular disease (ASCVD) risk and development of major cardiovascular diseases (CVD) and stroke.MethodsPatients (969) who underwent visceral fat area measurement were analyzed. After exclusion, 539 patients (142 men, 397 women) were randomly divided into internal (n = 374) and external validation (n = 165) data set. The NVAI was developed using univariate and multivariate logistic regression with backward selection of predictors. Receiver operating characteristic (ROC) curve analysis and comparison of the area under the curve (AUC) verified the better predictor of ASCVD risk score. Additionally, nationwide population-based cross-sectional survey data (Korean National Health and Nutrition Examination Survey [KNHANES] 2008–2010, n = 29,235) was used to validate the NVAI’s ability to predict ASCVD risk and major CVD and stroke.ResultsThe NVAI better reflected visceral fat area in internal and external data sets, with AUCs of 0.911 (95% confidence interval [CI]: 0.882–0.940) and 0.879 (95% CI: 0.828–0.931), respectively. NVAI better discriminated for ASCVD risk (AUC = 0.892, 95% CI: 0.846–0.938) compared to VAI (0.559, 95% CI: 0.439–0.679). The NVAI also better predicted MI or angina, and stroke with AUCs of 0.771 (95% CI: 0.752–0.789), and 0.812 (95% CI: 0.794–0.830), respectively, compared with waist circumference (WC), body mass index (BMI), TG to HDL ratio, and VAI via KNHANES, in a statistically significant manner.ConclusionsThe NVAI has advantages as a predictor of visceral obesity and is significantly associated with ASCVD risks and development of major CVD and stroke in the Korean population. The NVAI could be a screening tool for improved risk estimation related to visceral obesity.

Percentage of persons aged 15 years and over by level of confidence in selected types of institutions, by gender and other selected sociodemographic characteristics: age group; immigrant status; visible minority group; Indigenous identity; persons with a disability, difficulty or long-term condition; LGBTQ2+ people; highest certificate, diploma or degree; main activity; and urban and rural areas.

Consumer Confidence in China increased to 88 points in May from 87.80 points in April of 2025. This dataset provides - China Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This data shows the relative confidence on the EFH model prediction calculated by combining information on the statistical model predictive ability, confidence assigned to the input data layers used for model implementation and relative importance of environmental input data layers as determined by the statistical model. Confidence is assigned to each species life stage model (ConfScoreis the normalised value of confidence, ranging between 0 and 1; ConfCatis the relative confidence category used to identify relative confidence levels, from lower to higher).

Percentage of persons aged 15 years and over by level of confidence in selected types of institutions, by gender, for Canada, regions and provinces.

This data package contains the slides, Matlab m files and a dataset for an ERP workshop I gave in Washington DC, Glasgow, Fribourg, Frankfurt & Berlin. The goal of the workshop is to use hands-on exercises to introduce the basic principles and the Matlab implementation of robust estimation, using resampling methods (bootstrap & permutation) in conjunction with robust estimators. The workshop covers why classic t-tests and ANOVAs on means are not necessarily the best options, and how robust approaches can help. In particular, it demonstrates techniques to compare entire distributions, how to build confidence intervals about any quantity using the bootstrap, and how to effectively control for multiple comparisons. The methods are applied to single-subject and group analyses, and examples are provided to integrate both levels into informative figures.

In this paper we consider a special kind of semicontinous distribution. We try to concern with the situation where the probability of zero observation is associated with the location and scale parameters in lognormal distribution. We first propose a goodness-of-fit test to ensure that the data can be fit by the associated delta-lognormal distribution. Then we define the updated fiducial distributions of the parameters and establish the results that the confidence interval has asymtotically correct level while the significance level of the hypothesis testing is also asymtotically correct. We propose an exact sampling method to sample from the updated fiducial distribution. It can be seen in our simulation study that the inference on the parameters is largely improved. A real data example is also used to illustrate our method.

Originally published by Harte-Hanks, the CiTDS dataset is now produced by Aberdeen Group, a subsidiary of Spiceworks Ziff Davis (SWZD). It is also referred to as CiTDB (Computer Intelligence Technology Database). CiTDS provides data on digital investments of businesses across the globe. It includes two types of technology datasets: (i) hardware expenditures and (ii) product installs. Hardware expenditure data is constructed through a combination of surveys and modeling. A survey is administered to a number of companies and the data from surveys is used to develop a prediction model of expenditures as a function of firm characteristics. CiTDS uses this model to predict the expenditures of non-surveyed firms and reports them in the dataset. In contrast, CiTDS does not do any imputation for product install data, which comes entirely from web scraping and surveys. A confidence score between 1-3 is assigned to indicate how much the source of information can be trusted. A 3 corresponds to 90-100 percent install likelihood, 2 corresponds to 75-90 percent install likelihood and 1 corresponds to 65-75 percent install likelihood. CiTDS reports technology adoption at the site level with a unique DUNS identifier. One of these sites is identified as an “enterprise,” corresponding to the firm that owns the sites. Therefore, it is possible to analyze technology adoption both at the site (establishment) and enterprise (firm) levels. CiTDS sources the site population from Dun and Bradstreet every year and drops sites that are not relevant to their clients. Due to this sample selection, there is quite a bit of variation in the number of sites from year to year, where on average, 10-15 percent of sites enter and exit every year in the US data. This number is higher in the EU data. We observe similar turnover year-to-year in the products included in the dataset. Some products have become absolute, and some new products are added every year. There are two versions of the data: (i) version 3, which covers 2016-2020, and (ii) version 4, which covers 2020-2021. The quality of version 4 is significantly better regarding the information included about the technology products. In version 3, product categories have missing values, and they are abbreviated in a way that are sometimes difficult to interpret. Version 4 does not have any major issues. Since both versions of the data are available in 2020, CiTDS provides a crosswalk between the versions. This makes it possible to use information about products in Version 4 for the products in Version 3, with the caveats that there will be no crosswalk for the products that exist in 2016-2019 but not in 2020. Finally, special attention should be paid to data from 2016, where the coverage is significantly different from 2017. From 2017 onwards, coverage is more consistent. Years of Coverage: APac: 2019 - 2021 Canada: 2015 - 2021 EMEA: 2019 - 2021 Europe: 2015 - 2018 Latin America: 2015, 2019- 2021 United States: 2015 - 2021

This is an integration of 10 independent multi-country, multi-region, multi-cultural social surveys fielded by Gallup International between 2000 and 2013. The integrated data file contains responses from 535,159 adults living in 103 countries. In total, the harmonization project combined 571 social surveys.

These data have value in a number of longitudinal multi-country, multi-regional, and multi-cultural (L3M) research designs. Understood as independent, though non-random, L3M samples containing a number of multiple indicator ASQ (ask same questions) and ADQ (ask different questions) measures of human development, the environment, international relations, gender equality, security, international organizations, and democracy, to name a few [see full list below].

The data can be used for exploratory and descriptive analysis, with greatest utility at low levels of resolution (e.g. nation-states, supranational groupings). Level of resolution in analysis of these data should be sufficiently low to approximate confidence intervals.

These data can be used for teaching 3M methods, including data harmonization in L3M, 3M research design, survey design, 3M measurement invariance, analysis, and visualization, and reporting. Opportunities to teach about para data, meta data, and data management in L3M designs.

The country units are an unbalanced panel derived from non-probability samples of countries and respondents> Panels (countries) have left and right censorship and are thusly unbalanced. This design limitation can be overcome to the extent that VOTP panels are harmonized with public measurements from other 3M surveys to establish balance in terms of panels and occasions of measurement. Should L3M harmonization occur, these data can be assigned confidence weights to reflect the amount of error in these surveys.

Pooled public opinion surveys (country means), when combine with higher quality country measurements of the same concepts (ASQ, ADQ), can be leveraged to increase the statistical power of pooled publics opinion research designs (multiple L3M datasets)…that is, in studies of public, rather than personal, beliefs.

The Gallup Voice of the People survey data are based on uncertain sampling methods based on underspecified methods. Country sampling is non-random. The sampling method appears be primarily probability and quota sampling, with occasional oversample of urban populations in difficult to survey populations. The sampling units (countries and individuals) are poorly defined, suggesting these data have more value in research designs calling for independent samples replication and repeated-measures frameworks.

**The Voice of the People Survey Series is WIN/Gallup International Association's End of Year survey and is a global study that collects the public's view on the challenges that the world faces today. Ongoing since 1977, the purpose of WIN/Gallup International's End of Year survey is to provide a platform for respondents to speak out concerning government and corporate policies. The Voice of the People, End of Year Surveys for 2012, fielded June 2012 to February 2013, were conducted in 56 countries to solicit public opinion on social and political issues. Respondents were asked whether their country was governed by the will of the people, as well as their attitudes about their society. Additional questions addressed respondents' living conditions and feelings of safety around their living area, as well as personal happiness. Respondents' opinions were also gathered in relation to business development and their views on the effectiveness of the World Health Organization. Respondents were also surveyed on ownership and use of mobile devices. Demographic information includes sex, age, income, education level, employment status, and type of living area.