Notes: Three population structures are considered. For the binary trait, the OR value ranges from 12 to 1.5. For the continuous trait, the contribution of the causal site ranges from 0.0025 to 0.01. Powers are estimated on 1,000 replicates. See notes in Table 1 for sample sizes.Abbreviations: T12, the proposed test for bivariate analysis; T1, the proposed test for only the first trait; T2, the proposed test for only the second trait.

Background: Clean water is an essential part of human healthy life and wellbeing. More recently, rapid population growth, high illiteracy rate, lack of sustainable development, and climate change; faces a global challenge in developing countries. The discontinuity of drinking water supply forces households either to use unsafe water storage materials or to use water from unsafe sources. The present study aimed to identify the determinants of water source types, use, quality of water, and sanitation perception of physical parameters among urban households in North-West Ethiopia.

Methods: A community-based cross-sectional study was conducted among households from February to March 2019. An interview-based a pretested and structured questionnaire was used to collect the data. Data collection samples were selected randomly and proportional to each of the kebeles' households. MS Excel and R Version 3.6.2 were used to enter and analyze the data; respectively. Descriptive statistics using frequencies and percentages were used to explain the sample data concerning the predictor variable. Both bivariate and multivariate logistic regressions were used to assess the association between independent and response variables.

Results: Four hundred eighteen (418) households have participated. Based on the study undertaken,78.95% of households used improved and 21.05% of households used unimproved drinking water sources. Households drinking water sources were significantly associated with the age of the participant (x2 = 20.392, df=3), educational status(x2 = 19.358, df=4), source of income (x2 = 21.777, df=3), monthly income (x2 = 13.322, df=3), availability of additional facilities (x2 = 98.144, df=7), cleanness status (x2 =42.979, df=4), scarcity of water (x2 = 5.1388, df=1) and family size (x2 = 9.934, df=2). The logistic regression analysis also indicated that those factors are significantly determining the water source types used by the households. Factors such as availability of toilet facility, household member type, and sex of the head of the household were not significantly associated with drinking water sources.

Conclusion: The uses of drinking water from improved sources were determined by different demographic, socio-economic, sanitation, and hygiene-related factors. Therefore, ; the local, regional, and national governments and other supporting organizations shall improve the accessibility and adequacy of drinking water from improved sources in the area.

An example of combining ANOVA terms for bivariate principle component data to create the ANODIS F-statistic where N is the total number of samples drawn and K, the number of assemblages compared.

This dataset supports a meta-analytic structural equation modelling (MASEM) study investigating the factors influencing students’ behavioural intention to use educational AI (EAI) technologies. The research integrates constructs from the Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB), and Artificial Intelligence Literacy (AIL), aiming to resolve inconsistencies in previous studies and improve theoretical understanding of EAI technology adoption.

Research Hypotheses The study hypothesized that: Students’ behavioural intention (INT) to use EAI technologies is influenced by perceived usefulness (PU), perceived ease of use (PEU), attitude (ATT), subjective norm (SN), and perceived behavioural control (PBC), as described in TAM and TPB. AI literacy (AIL) directly and indirectly predicts PU, PEU, ATT, and INT. These relationships are moderated by contextual factors such as academic level (K–12 vs. higher education) and regional economic development (developed vs. developing countries).

What the Data Shows The meta-analytic dataset comprises 166 empirical studies involving over 69,000 participants. It includes pairwise Pearson correlations among seven constructs (PU, PEU, ATT, SN, PBC, INT, AIL) and is used to compute a pooled correlation matrix. This matrix was then used to test three models via MASEM: A baseline TAM-TPB model, An internal-extended model with additional TPB internal paths, An AIL-integrated extended model. The AIL-integrated model achieved the best fit (CFI = 0.997, RMSEA = 0.053) and explained 62.3% of the variance in behavioural intention.

Notable Findings AI literacy (AIL) is the strongest predictor of intention to use EAI technologies (Total Effect = 0.408). PU, ATT, and SN also significantly influence intention. The effect of PEU on intention is fully mediated by PU and ATT. Moderation analysis showed that the relationships differ between developed and developing countries and between K–12 and higher education populations.

How the Data Can Be Interpreted and Used The dataset includes bivariate correlations between variables, publication metadata, sample sizes, coding information, and reliability values (e.g., CR scores). Suitable for replication of MASEM procedures, moderation analysis, and meta-regression. Researchers may use it to test additional theoretical models or assess the influence of new moderators (e.g., AI tool type). Educators and policymakers can leverage insights from the meta-analytic results to inform AI literacy training and technology adoption strategies.

Distance covariance (Székely, Rizzo, and Bakirov) is a fascinating recent notion, which is popular as a test for dependence of any type between random variables X and Y. This approach deserves to be touched upon in modern courses on mathematical statistics. It makes use of distances of the type |X−X′| and |Y−Y′|, where (X′,Y′) is an independent copy of (X, Y). This raises natural questions about independence of variables like X−X′ and Y−Y′, about the connection between cov(|X−X′|,|Y−Y′|) and the covariance between doubly centered distances, and about necessary and sufficient conditions for independence. We show some basic results and present a new and nontechnical counterexample to a common fallacy, which provides more insight. We also show some motivating examples involving bivariate distributions and contingency tables, which can be used as didactic material for introducing distance correlation.

Dataset for: Leipold, B. & Loepthien, T. (2021). Attentive and emotional listening to music: The role of positive and negative affect. Jahrbuch Musikpsychologie, 30. https://doi.org/10.5964/jbdgm.78 In a cross-sectional study associations of global affect with two ways of listening to music – attentive–analytical listening (AL) and emotional listening (EL) were examined. More specifically, the degrees to which AL and EL are differentially correlated with positive and negative affect were examined. In Study 1, a sample of 1,291 individuals responded to questionnaires on listening to music, positive affect (PA), and negative affect (NA). We used the PANAS that measures PA and NA as high arousal dimensions. AL was positively correlated with PA, EL with NA. Moderation analyses showed stronger associations between PA and AL when NA was low. Study 2 (499 participants) differentiated between three facets of affect and focused, in addition to PA and NA, on the role of relaxation. Similar to the findings of Study 1, AL was correlated with PA, EL with NA and PA. Moderation analyses indicated that the degree to which PA is associated with an individual´s tendency to listen to music attentively depends on their degree of relaxation. In addition, the correlation between pleasant activation and EL was stronger for individuals who were more relaxed; for individuals who were less relaxed the correlation between unpleasant activation and EL was stronger. In sum, the results demonstrate not only simple bivariate correlations, but also that the expected associations vary, depending on the different affective states. We argue that the results reflect a dual function of listening to music, which includes emotional regulation and information processing.: Dataset Study 2

SNP: Single nucleotide polymorphism.*Four patients without enough samples for IL28b rs8099917 assay.**Six patients without enough samples for IL28b rs12979860 assay.Bivariate analysis of IL28B polymorphisms and HTLV-1-associated myelopathy.

This folder contains four examples of merging crystallographic intensities with a bivariate prior:

• time-resolved Laue crystallography of the photoactive yellow protein
• anomalous diffraction from serial XFEL crystallography of thermolysin
• anomalous diffraction from Laue crystallography of NaI-soaked lysozyme
• fragment screening monochromatic data of Nsp3 Mac1

Additionally, we provide several auxilliary examples:

• For PYP, an example where we set aside a test fraction to semi-independently optimize the double-Wilson r
• for lysozyme, two examples, one where we use Laue-DIALS instead of precognition, and another where we set aside the first 90 images to semi-independently optimize the double-Wilson r
• For thermolysin, an example where we use a bivariate versus a univariate prior as the number of scaled images grows, and another where we set aside the first 395 images to semi-independently optimize the double-Wilson r

Every example includes scripts to run Careless as well as to analyze the outputs in order to reproduce the figures in the double-Wilson manuscript. For every example, there is a `README.md` that describes the contents of each example folder.

Pooling individual samples prior to DNA extraction can mitigate the cost of DNA extraction and genotyping; however, these methods need to accurately generate equal representation of individuals within pools. This data set was generated to determine accuracy of pool construction based on white blood cell counts compared to two common DNA quantification methods. Fifty individual bovine blood samples were collected, and then pooled with all individuals represented in each pool. Pools were constructed with the target of equal representation of each individual animal based on number of white blood cells, spectrophotometric readings, spectrofluorometric readings and whole blood volume with 9 pools per method and a total of 36 pools. Pools and individual samples that comprised the pools were genotyped using a commercially available genotyping array. ASReml was used to estimate variance components for individual animal contribution to pools. The correlation between animal contributions between two pools was estimated using bivariate analysis with starting values set to the result of a univariate analysis. The dataset includes: 1) pooling allele frequencies (PAF) for all pools and individual animals computed from normalized intensities for red (X) and green (Y); PAF = X/(X+Y). 2) Genotypes or number of copies of B(green) allele (0,1,2). 3) Definitions for each sample. Resources in this dataset:Resource Title: Pooling Allele Frequencies (paf) for all pools and individual animals. File Name: pafAnimal.csv.gzResource Description: Pooling Allele Frequencies (paf) for all pools and individual animals computed from normalized intensities for red (X) and green (Y); paf = X / (X + Y)Resource Title: Genotypes for individuals within pools. File Name: g.csv.gzResource Description: Genotypes (number of copies of the B (green) allele (0,1,2)) for individual bovine animals within pools.Resource Title: Sample Definitions . File Name: XY Data Key.xlsxResource Description: Definitions for each sample (both pools and individual animals).

This dataset provides foundational factor and portfolio return data used in empirical finance and asset pricing research. It contains: - Fama–French 3-Factor and 5-Factor models - Size (ME), Book-to-Market (B/M), Operating Profitability (OP), and Investment (Inv) portfolios - Bivariate portfolios (e.g., 2x3 Size-B/M sorts) - Industry portfolio returns All data originate from the Kenneth R. French Data Library and are based on CRSP and Compustat databases. Data are value-weighted and expressed in percentages.

Some files in this dataset contain header comments describing data sources and methodology (as shown below):

This file was created using the 202508 CRSP database.
The 1-month TBill rate data until 202405 are from Ibbotson Associates. 
Starting from 202406, the 1-month TBill rate is from ICE BofA US 1-Month Treasury Bill Index.

To correctly read such files in Python (pandas), use the comment parameter — it automatically ignores all lines starting with a specific symbol (e.g., none here, so you can skip manually):

Example 1 — Automatically detect header rows:

import pandas as pd

# Detect the first numeric line to find where data starts
file_path = "F-F_Research_Data_5_Factors_2x3.csv"

with open(file_path) as f:
  lines = f.readlines()

# Find where the header line (column names) appears
for i, line in enumerate(lines):
  if "Mkt-RF" in line:
    skip_rows = i
    break

df = pd.read_csv(file_path, skiprows=skip_rows, sep=r"\s+")
print(df.head())

Example 2 — Skip a known number of comment lines manually:

df = pd.read_csv("F-F_Research_Data_5_Factors_2x3.csv", skiprows=3, sep=r"\s+")

Example 3 — If comments are prefixed (e.g., with #):

df = pd.read_csv("F-F_Research_Data_5_Factors_2x3.csv", comment="#", sep=",")

File Structure Example

Average ultimate tensile strength (UTS) and tensile strain for the 45° and 90° specimens.

We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.

We consider the implications for forecast accuracy of imposing unit roots and cointegrating restrictions in linear systems of I(1) variables in levels, differences, and cointegrated combinations. Asymptotic formulae are obtained for multi-step forecast error variances for each representation. Alternative measures of forecast accuracy are discussed. Finite sample behaviour in a bivariate model is studied by Monte Carlo using control variables. We also analyse the interaction between unit roots and cointegrating restrictions and intercepts in the DGP. Some of the issues are illustrated with an empirical example of forecasting the demand for M1 in the UK.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. CPER Paleopedology Study – Particle and Grain Size - Grain size data from 39 pedons were compared with modal fluvial (7) and eolian (3) samples in order to characterize the origin of CPER parent materials and distinguish the origin of CPER geomorphic features. The seven fluvial sites were located along Owl and Eastman Creeks. The three eolian sites were located on the nearest undisputed dune fields located approximately 5 km north of Roggen, CO (Muhs, 1985). For statistical analysis, the sand and coarse silt fractions were shaken in a nest of half phi(0) interval sieves ranging from -1.0 0 (10 mesh) to 4.5 0 (325 mesh) for 3 minutes. Phi intervals (-log2) were utilized to normalize the particle size data for use in conventional statistics (Krumbein, 1934). The silt and clay fractions were separated by sedimentation using the pipette method. Statistical methods adopted from Folk and Ward (1957) were applied to the -1.0 0 to 7.0 0 fractions using the Sedimentary Petrology Computer Program SEDPET (Warner, 1970) to determine mean grain size (Mz), sorting (Iz), skewness (Skz), and kurtosis (Kz). These parameters were then subjected to univariate and bivariate analysis. The clay fraction was not included in the statistical computations to avoid excessively fine skewing the sample. Additional information and referenced materials can be found: http://hdl.handle.net/10217/85625. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=168 Webpage with information and links to data files for download

This repository contains example scripts for estimating genetic parameters using the BLUPF90 software suite. The scripts handle up to four traits simultaneously (from the 15 available in the dataset data.txt found at https://doi.org/10.57745/4MI9JN ). script1.sh runs renumf90 using the parameter file renum_ex1.par. This file processes the traits LW, AFW, CW, and BMW. The model includes the effects of animal, sex, and slaughter date. Optional instructions allow blupf90+ to compute variance ratios and their standard errors. script2.sh follows a similar structure but analyzes the traits LW, BW14r, and BW26. In this case, the fixed effects used in the model are different. script3.sh runs a bivariate analysis, using a categorical data (LCAT) to describe the liver. Hence, it calls gibbsf90+ instead of blupf90+. Pay attention to the missing value code, which must be 0. gibbs_samples.R is a R program to read the output from gibbsf90+. One must provide the number of estimated components (here NCOMP = 6) and the program computes the variance ratios and their posterior distributions.

BiChroM Raw R Files1. Dataset and tree 2. Raw R files for optimizations 3. Full model optimizations 4. Reduced model optimizations 5. Profile rhoH and Profile rhoW 6. Bivariate profile rhoqH and Bivariate profile rhoqW 7. Raw R files for simulations 8. Simulations number of taxa 9. Simulations for tree heightBiChroMRawRfiles.zip

IntroductionAlcohol-related problems increase the probability of frequent emergency department (ED) use. In this study, we compared the direct healthcare expenses incurred during a single visit among frequent and non-frequent ED users and analyzed the impact of alcohol-related issues in healthcare costs arising from ED usage.MethodsThe study relied on secondary analyses of economic data from a 1:1 matched case–control study with the primary aim of identifying the clinical characteristics of hospital ED frequent users in a Mediterranean European environment with a public, universal, and tax-funded health system. The participants ranged in age from 18 to 65 years and underwent ED visits at a high-complexity Spanish hospital (cases ≥5 times, controls <5) from December 2018 to November 2019. Each case was matched to a control with the same age, gender, and date of attendance at the ED. Clinical data and direct healthcare costs for a single ED visit were obtained by a retrospective review of the first electronic medical register. Costs and duration of stay were compared between cases and controls using paired-samples t-tests, and ED users with and without alcohol-related problems were compared using bivariate (independent-samples t-tests, one-way analysis of variance, Chi square tests, and multiple linear regression) and multivariate analyses (multiple linear regression models with backward stepwise selection algorithm, and dependent variable: total mean direct costs).ResultsAmong 609 case–control pairs (total n = 1218), mean total healthcare direct costs per ED visit were 22.2% higher among frequent compared with non-frequent users [mean difference 44.44 euros; 95% confidence interval (CI) 13.4–75.5; t(608) = 2.811; p = 0.005]. Multiple linear regression identified length of stay, triage level, ambulance arrival, and the specialty discharging the patient as associated with total healthcare costs for frequent users. In bivariate analyses, a history of alcohol-related problems was associated with a 32.5% higher mean total healthcare costs among frequent users [mean difference 72.61 euros; 95% confidence interval 25.24–119.97; t(320.016) = 3.015; p = 0.003].ConclusionThe findings confirm the high cost of frequent ED use among people with alcohol-related problems, suggesting that costs could be reduced through implementation of intervention protocols.

The maximum association between two multivariate variables X and Y is defined as the maximal value that a bivariate association measure between one-dimensional projections αtX and βtY can attain. Taking the Pearson correlation as projection index results in the first canonical correlation coefficient. We propose to use more robust association measures, such as Spearman’s or Kendall’s rank correlation, or association measures derived from bivariate scatter matrices. We study the robustness of the proposed maximum association measures and the corresponding estimators of the coefficients yielding the maximum association. In the important special case of Y being univariate, maximum rank correlation estimators yield regression estimators that are invariant against monotonic transformations of the response. We obtain asymptotic variances for this special case. It turns out that maximum rank correlation estimators combine good efficiency and robustness properties. Simulations and a real data example illustrate the robustness and the power for handling nonlinear relationships of these estimators. Supplementary materials for this article are available online.

This paper outlines a class of statistical procedures that permit testing of a broad range of multidimensional stochastic dominance hypotheses and, more generally, welfare hypotheses that rely upon multiple stochastic dominance conditions. We apply the procedures to data on income and leisure hours for individuals in Germany, the UK, and the USA. We find that no country first-order stochastically dominates the others in both dimensions for all years of comparison. Furthermore, while in general the USA stochastically dominates Germany and the UK with respect to income, in most periods Germany stochastically dominates with respect to leisure hours. Finally, we find evidence that bivariate poverty (which refers, for example, to the working poor, that is, those who have little leisure and low income) is lower in Germany than in either the UK or the USA. On the other hand, poverty comparisons between the UK and the USA are sensitive to the subpopulation of individuals considered.

1. Recent advances in bio-logging open promising perspectives in the study of animal movements at numerous scales. It is now possible to record time-series of animal locations and ancillary data (e.g. activity level derived from on-board accelerometers) over extended areas and long durations with a high spatial and temporal resolution. Such time-series are often piecewise stationary, as the animal may alternate between different stationary phases (i.e. characterised by a specific mean and variance of some key parameter for limited periods). Identifying when these phases start and end is a critical first step to understand the dynamics of the underlying movement processes.
2. We introduce a new segmentation-clustering method we called segclust2d (available as a R package at cran.r-project.org/package=segclust2d). It can segment bi- (or more generally multi-) variate time-series and possibly cluster the various segments obtained, corresponding to different phases assumed to be stationary. This method is easy to use, as it only requires specifying a minimum segment length (to prevent over-segmentation), based on biological rather than statistical considerations.
3. This method can be applied to bivariate piecewise time-series of any nature. We focus here on two types of time-series related to animal movement, corresponding to (i) at large scale, series of bivariate coordinates of relocations, to highlight temporary home ranges, and (ii) at smaller scale, bivariate series derived from relocations data, such as speed and turning angle, to highlight different behavioural modes such as transit, feeding and resting.
4. Using computer simulations, we show that segclust2d can rival and even outperform previous, more complex methods, which were specifically developed to highlight changes in movement modes or home range shifts (based on Hidden Markov and Ornstein-Uhlenbeck modelling), which, contrary to our method, usually require the user to provide relevant initial guesses to be efficient. Furthermore we demonstrate it on actual examples involving a zebra's small scale movements and an elephant's large scale movements, to illustrate how various movement modes and home range shifts, respectively, can be identified. 15-Aug-2019