Statistics for the comparisons of the most commonly occurring melodic interval sizes in tone and non-tone language music databases; n1 and n2 refer to the sample sizes of tone and non-tone language music databases. (All comparisons were made with the two-tailed independent samples t-test, α-level adjusted using the Bonferroni method).

This folder contains the SPSS macro ‘norms.sps’ and the SPSS data file ‘norms.sav’, and the R script file ‘norms.R’ and the R data file ‘norms.Rdata’. It is assumed that the files have been saved in the directory ‘C:\mydir’. This can be changed to another directory if required. It may be noted that function NORMS in SPSS can have one argument only, and does not work well for variables whose values have been given a value label. The output contains for each norm statistic: the point estimates, the SEs, and the lower (LO) and upper (UP) bounds of the Wald-based confidence interval.

Data on the median length of time (in months) between the first and second born offspring of a family in the United Kingdom (UK) from 2005 to 2019 shows that throughout this period the average length of time in between births remained fairly stable with the exception of 2005, 2011, 2012 and 2016 whereby the median increased by one month.

Abstract A calculator program has been written to give confidence intervals on branching ratios for rare decay modes (or similar quantities) calculated from the number of events observed, the acceptance factor, the background estimate and the associated errors. Results from different experiments (or different channels from the same experiment) can be combined. The calculator is available in http://www.slac.stanford.edu/~barlow/limits.html.

Title of program: syslimit Catalogue Id: ADQN_v1_0

Nature of problem Calculating confidence intervals for a Poisson mean based on observed data, with uncertainties in efficiencies and backgrounds.

Versions of this program held in the CPC repository in Mendeley Data ADQN_v1_0; syslimit; 10.1016/S0010-4655(02)00588-X

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

Review of Economics and Statistics: Forthcoming.. Visit https://dataone.org/datasets/sha256%3Afe9a68ea4b55aab5810b99a3eb1021b4c72604b4885c2401172bde3892e952d2 for complete metadata about this dataset.

The 2020 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF50) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Census Disclosure Avoidance System (DAS) into the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile.

The PPMF0 was the source of the publicly released, official 2020 Census data products referenced above, and was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential 2020 Census Edited File (CEF) as the initial input; the official location for the PPMF0 is on the United States Census Bureau FTP server, but we also include a copy of it here for convenience. 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 2020 DHC AMC seed PPMF0 and PPMF replicates have been cleared for public dissemination by the USCB Disclosure Review Board (CBDRB-FY22-DSEP-004). The PPMF0 and PPMF replicates contain all Person and Units attributes necessary to produce the 2020 Census Redistricting Data Summary File (P.L. 94-171), the Demographic and Housing Characteristics File, and the Demographic Profile 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.

We present a Wilson interval for binomial proportions for use with multiple imputation for missing data. Using simulation studies, we show that it can have better repeated sampling properties than the usual confidence interval for binomial proportions based on Rubin’s combining rules. Further, in contrast to the usual multiple imputation confidence interval for proportions, the multiple imputation Wilson interval is always bounded by zero and one. Supplementary material is available online.

This table shows overall ATCEMS response interval performance for entire fiscal years. Data in the table is broken out by incident response priority and service area (City of Austin or Travis County).

Confidence intervals for the Travel trends estimates.

Video content for Research and Evidence and Practice

1. Researchers often want to place a confidence interval around estimated parameter values calculated from a sample. This is commonly implemented by bootstrapping. There are several different frequently used bootstrapping methods for this purpose. 2. Here we demonstrate that authors of recent papers frequently do not specify the method they have used and that different methods can produce markedly different confidence intervals for the same sample and parameter estimate. 3. We encourage authors to be more explicit about the method they use (and number of bootstrap resamples used). 4. We recommend the bias corrected and accelerated method as giving generally good performance; although researchers should be warned that coverage of bootstrap confidence intervals is characteristically less than the specified nominal level, and confidence interval evaluation by any method can be unreliable for small samples in some situations.

Labels for Linked Data time interval Universal Resource Identifiers (URIs).

Labels for Linked Data time interval Universal Resource Identifiers (URIs).

The URIs for time intervals used in datasets on the site are taken from http://reference.data.gov.uk. This dataset consists of a set of labels for a commonly used set of time intervals. This is to support a more readable presentation of these URIs.

Update frequency:

Review date:

Temporal coverage:

Geographical coverage:

Data lineage:

Maintainer contact:

A comment on Tressoldi et al's article on journals' impact factor and statistical quality (PLOS ONE 8(2), e56180, 2013, doi:10.1371/journal.pone.0056180) on the author's page at Frontiers in Psychology's Loop profiles.

reference interval

The scaled-uniform model has been lately considered to illustrate problems in frequentist point estimation arising when the minimal sufficient statistic is not complete. Here we consider the problem of interval estimation and derive pivotal quantities based on a series of point estimators proposed in the literature. We compare the resulting intervals of given confidence level in terms of expected lengths. Pivotal quantities, confidence intervals and expected lengths are all computed using simulations implemented with R (code is available). Numerical results suggest that the maximum likelihood estimator, regardless of its inefficiency, yields confidence intervals that outperform the other available sets of the same level.

Annual data on internet usage in Great Britain, including how households connect to the internet, internet activities and internet purchasing.

This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier.

The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided.

This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.

Population intervals by household position and households Forecast intervals 95% and 67% (lower and upper limit) Data available: from 2005 Frequency: 18 April 2007 discontinued Infoservice: http://www.cbs.nl/infoservice Copyright (c) Statistics Netherlands Reproduction is permitted, provided Statistics Netherlands is cited as the source.

Linked data for every time interval and instant into the past and future, from years down to seconds. This is an infinite set of linked data. It includes government years and properly handles the transition to the Gregorian calendar within the UK.

Part of package:reference-data-gov-uk