Significant predictors of mathematical literacy for top‐tiered countries/economies, Canada, and the United States on PISA 2012: Case for the sparse regression model

2018 ◽  
Vol 89 (4) ◽  
pp. 726-749
Author(s):  
Mark V. Brow
Keyword(s):  
United States ◽  
Regression Model ◽  
The United States ◽  
Mathematical Literacy ◽  
Sparse Regression ◽  
Pisa 2012

scholarly journals COVID-19 Tweets of Governors and Health Experts

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tim Hua ◽  
Chris Chankyo Kim ◽  
Zihan Zhang ◽  
Alex Lyford
Keyword(s):  
United States ◽  
Regression Model ◽  
State Level ◽  
The United States ◽  
Similar Rate ◽  
Significant Finding ◽  
Party Affiliation ◽  
Substantial Evidence ◽  
Python Package ◽  
Health Experts

As COVID-19 spread throughout the United States, governors and health experts (HEs) received a surge in followers on Twitter. This paper seeks to investigate how HEs, Democratic governors, and Republican governors discuss COVID-19 on Twitter. Tweets dating from January 1st, 2020 to October 18th, 2020 from official accounts of all fifty governors and 46 prominent U.S.-based HEs were scraped using python package Twint (N = 192,403) and analyzed using a custom-built wordcount program (Twintproject, 2020). The most significant finding is that in 2020, Democratic governors mentioned death at 4.03 times the rate of Republican governors in their COVID-19 tweets. In 2019, Democratic governors still mentioned death at twice the rate of Republicans. We believe we have substantial evidence that Republican governors are less comfortable talking about death than their Democratic counterparts. We also found that Democratic governors tweet about masks, stay-at-home measures, and solutions more often than Republicans. After controlling for state-level variations in COVID-19 data, our regression model confirms that party affiliation is still correlated with the prevalence of tweets in these three categories. However, there isn’t a large difference between the proportion of COVID-19 tweets, tweets about the economy, tweets about vaccines, and tweets containing “science-like” words between governors of the two parties. HEs tweeted about death and vaccines more than the governors. They also tweeted about solutions and testing at a similar rate compared to governors and mentioned lockdowns, the economy, and masks less frequently.

scholarly journals Simulating Hydrological Drought Properties at Different Spatial Units in the United States Based on Wavelet–Bayesian Regression Approach

2012 ◽  
Vol 16 (17) ◽  
pp. 1-23 ◽  
Author(s):  
Ashok K. Mishra ◽  
Vijay P. Singh
Keyword(s):  
United States ◽  
Regression Model ◽  
Climate Models ◽  
Global Climate ◽  
The United States ◽  
Hydrological Drought ◽  
Bayesian Regression ◽  
Global Climate Models ◽  
Meteorological Variables ◽  
The Relationship

Abstract Because of their stochastic nature, droughts vary in space and time, and therefore quantifying droughts at different time units is important for water resources planning. The authors investigated the relationship between meteorological variables and hydrological drought properties using the Palmer hydrological drought index (PHDI). Twenty different spatial units were chosen from the unit of a climatic division to a regional unit across the United States. The relationship between meteorological variables and PHDI was investigated using a wavelet–Bayesian regression model, which enhances the modeling strength of a simple Bayesian regression model. Further, the wavelet–Bayesian regression model was tested for the predictability of global climate models (GCMs) to simulate PHDI, which will also help understand their role for downscaling purposes.

scholarly journals Do Performance Goals and Development, Feedback and Recognition, and a Climate of Trust Improve Employee Engagement in Small Businesses in the United States?

2021 ◽  
Vol 14 (6) ◽  
pp. 1
Author(s):  
Tywanda D. Tate ◽  
Franklin M. Lartey ◽  
Phillip M. Randall
Keyword(s):  
United States ◽  
Small Business ◽  
Regression Model ◽  
Performance Management ◽  
Employee Engagement ◽  
Small Businesses ◽  
Pearson Correlation ◽  
The United States ◽  
Performance Goals ◽  
Overall Performance

Small businesses are the predominant contributors to the U.S. economy, yet they face many challenges to remain competitive and sustainable. There are several reasons a small business could fail, including a lack of human resources, limited financial resources, competition, technological advancements, disaster, and globalization. Improving employee performance by getting them engaged and productive in their work is an issue that cannot be overlooked for small businesses to function and remain competitive. There is limited empirical evidence that explains the dimensions of performance management and employee engagement in small businesses. However, how small businesses sustain their long-term performance remains uncertain. This study sought to bring together two previously distinct constructs: overall employee engagement and overall performance management, characterized by performance goals and development, a climate of trust, and feedback and recognition. The research was correlational in nature. A survey was conducted to generate and analyze data gathered from 121 employees of small businesses located in the United States. A series of Pearson correlation analyses confirmed the existence of statistically significant positive relationships between employee engagement and each variable of performance management, namely performance goals and development, feedback and recognition, and climate of trust. Notwithstanding these positive correlations, a multiple regression model with the three performance management variables as independent variables and employee engagement as the dependent variable suggested that there was a statistically significant regression model F(3, 117) = 32.34, p < .001, R2 = .453, explaining 45.3% of the variability in employee engagement. Nonetheless, this model confirmed that the variables performance goals and development and climate of trust were not statistically significant in the model (p > .05). In other words, only the feedback and recognition variable was statistically significant in the regression model, suggesting that it explained most of the variability in engagement, including that already explained by the other two variables. Overall, the outcome of this study suggests that small businesses implementing performance management processes have more engaged employees. The conclusions drawn from these findings suggest that overall performance management and overall employee engagement contribute to small business productivity and organizational success.

scholarly journals Deported Mexican migrants: health status and access to care

2014 ◽  
Vol 48 (3) ◽  
pp. 478-485 ◽  
Author(s):  
Julián Alfredo Fernández-Niño ◽  
Carlos Jacobo Ramírez-Valdés ◽  
Diego Cerecero-Garcia ◽  
Ietza Bojorquez-Chapela
Keyword(s):  
United States ◽  
Health Status ◽  
Regression Model ◽  
Access To Care ◽  
Secondary Data ◽  
Perceived Health ◽  
The United States ◽  
Return Migrants ◽  
Mexican Migrants ◽  
Northern Border

OBJECTIVE To describe the health status and access to care of forced-return Mexican migrants deported through the Mexico-United States border and to compare it with the situation of voluntary-return migrants. METHODS Secondary data analysis from the Survey on Migration in Mexico’s Northern Border from 2012. This is a continuous survey, designed to describe migration flows between Mexico and the United States, with a mobile-population sampling design. We analyzed indicators of health and access to care among deported migrants, and compare them with voluntary-return migrants. Our analysis sample included 2,680 voluntary-return migrants, and 6,862 deportees. We employ an ordinal multiple logistic regression model, to compare the adjusted odds of having worst self-reported health between the studied groups. RESULTS As compared to voluntary-return migrants, deportees were less likely to have medical insurance in the United States (OR = 0.05; 95%CI 0.04;0.06). In the regression model a poorer self-perceived health was found to be associated with having been deported (OR = 1.71, 95%CI 1.52;1.92), as well as age (OR = 1.03, 95%CI 1.02;1.03) and years of education (OR = 0.94 95%CI 0.93;0.95). CONCLUSIONS According to our results, deportees had less access to care while in the United States, as compared with voluntary-return migrants. Our results also showed an independent and statistically significant association between deportation and having poorer self-perceived health. To promote the health and access to care of deported Mexican migrants coming back from the United States, new health and social policies are required.

Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors

2015 ◽  
Author(s):  
Angshuman Deka ◽  
Nima Hamta ◽  
Behzad Esmaeilian ◽  
Sara Behdad
Keyword(s):  
United States ◽  
Regression Model ◽  
Energy Demand ◽  
Arima Model ◽  
Demographic Factors ◽  
The United States ◽  
Energy Information Administration ◽  
Ann Model ◽  
Ann Models ◽  
The U.S

Effective energy planning and governmental decision making policies heavily rely on accurate forecast of energy demand. This paper discusses and compares five different forecasting techniques to model energy demand in the United States using economic and demographic factors. Two Artificial Neural Network (ANN) models, two regression analysis models and one autoregressive integrated moving average (ARIMA) model are developed based on historical data from 1950–2013. While ANN model 1 and regression model 1 use Gross Domestic Product (GDP), Gross National Product (GNP) and per capita personal income as independent input factors, ANN model 2 and regression model 2 employ GDP, GNP and population (POP) as the predictive factors. The forecasted values resulted from these models are compared with the forecast made by the U.S. Energy Information Administration (EIA) for the period of 2014–2019. The forecasted results of ANN models and regression model 1 are close to those of the U.S. EIA, however the results of regression model 2 and ARIMA model are significantly different from the forecast made by the U.S. EIA. Finally, a comparison of the forecasted values resulted from three efficient models showed the energy demand would vary between 95.51 and 100.08 quadrillion British thermal unit for the period of 2014–2019.

scholarly journals Local Outcomes of Federal and State Urban & Community Forestry Programs

2011 ◽  
Vol 37 (4) ◽  
pp. 152-159
Author(s):  
Richard Hauer ◽  
Gary Johnson ◽  
Michael Kilgore
Keyword(s):  
United States ◽  
Regression Model ◽  
Multiple Regression ◽  
Community Forestry ◽  
Urban Community ◽  
The United States ◽  
Multiple Regression Model ◽  
Cost Share ◽  
Rate Of Increase ◽  
Forestry Programs

Increasing local urban and community forestry (U&CF) programs and activities in the United States is a goal of state and federal U&CF programs. This study found local U&CF programs within the 50 United States increased in activity between 1997 and 2002 at a 2.1% annual rate of increase. Several attributes of state U&CF forestry programs from a multiple regression model and correla-tion analysis partially explain the increase in local U&CF program activity. The number of technical assists in a state were a strong pre-dictor for increased local activity. Less certainty was found with state money used to fund the state U&CF program or the use of cost-share assistance (Federal Cooperative Forestry Assistance Challenge Cost-share Grants) and this increase. Study findings provide evidence that state and federal U&CF programs within the United States are furthering the building of capacity and development of local U&CF programs.

scholarly journals Fatigue in Incident Peritoneal Dialysis and Mortality: A Real-World Side-by-Side Study in Brazil and the United States

2021 ◽  
Author(s):  
Murilo Henrique Guedes ◽  
Liz Wallim ◽  
Camila R Guetter ◽  
Yue Jiao ◽  
Vladimir Rigodon ◽  
...  
Keyword(s):  
United States ◽  
Peritoneal Dialysis ◽  
Regression Model ◽  
Smoothing Splines ◽  
The United States ◽  
Survival Model ◽  
Continuous Measure ◽  
Increased Risk ◽  
All Cause Mortality ◽  
Unit Increase

Background: We tested if fatigue in incident Peritoneal Dialysis associated with an increased risk for mortality, independently from main confounders. Methods: We conducted a side-by-side study from two of incident PD patients in Brazil and the United States. We used the same code to independently analyze data in both countries during 2004 to 2011. We included data from adults who completed KDQOL-SF vitality subscale within 90 days after starting PD. Vitality score was categorized in four groups: >50 (high vitality), >=40 to <=50 (moderate vitality), >35 to <40 (moderate fatigue), <=35 (high fatigue; reference group). In each country's cohort, we built four distinct models to estimate the associations between vitality (exposure) and all-cause mortality (outcome): (i) Cox regression model; (ii) competitive risk model accounting for technique failure events; (iii) multilevel survival model of clinic-level clusters; (iv) multivariate regression model with smoothing splines treating vitality as a continuous measure. Analyses were adjusted for age, comorbidities, PD modality, hemoglobin, and albumin. A mixed-effects meta-analysis was used to pool hazard ratios (HRs) from both cohorts to model mortality risk for each 10-unit increase in vitality. Results: We used data from 4,285 PD patients (Brazil n=1,388 and United States n=2,897). Model estimates showed lower vitality levels within 90 days of starting PD were associated with a higher risk of mortality, which was consistent in Brazil and the United States cohorts. In the multivariate survival model, each 10-unit increase in vitality score was associated with lower risk of all-cause mortality in both cohorts (Brazil HR=0.79 [95%CI 0.70 to 0.90] and United States HR=0.90 [95%CI 0.88 to 0.93], pooled HR=0.86 [95%CI 0.75 to 0.98]). Results for all models provided consistent effect estimates. Conclusions: Among patients in Brazil and the United States, lower vitality score in the initial months of PD was independently associated with all-cause mortality.

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