Estimation, inference, and data analysis for log-linear regression models in tagging studies

This study aims to analyze the effect of cocoa and rubber export imports on foreign exchange reserves in Indonesia. This study uses secondary data from 2005-2017 obtained from the Central Bureau of Statistics of Indonesia. The data analysis method used multiple linear regression models. The results partially show that cocoa and rubber export impors do not significantly influence the foreign exchange reserves in Indonesia. Simultaneous cocoa and rubber export imports have a positive and significant effect on foreign exchange reserves in Indonesia. The amount of influence is 0,9059 or 90,59% while the rest is influenced by other variables outside the model by 09,41%.

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PENGARUH CSR BERDASAR ISO 26000 TERHADAP NILAI PERUSAHAAN DENGAN KARAKTERSITIK PERUSAHAAN SEBAGAI VARIABEL MODERATING

Journal of Innovation in Business and Economics ◽

10.22219/jibe.vol6.no1.67-74 ◽

2015 ◽

Vol 6 (1) ◽

pp. 67

Author(s):

Sri Wahjuni Latifah

Keyword(s):

Data Analysis ◽

Linear Regression ◽

Regression Models ◽

Stock Exchange ◽

Firm Characteristics ◽

Linear Regression Models ◽

Firm Age ◽

Iso 26000

Research of the influence of ISO 26000 CSR toward the company's value as a moderating variable which is done on companies listed in Indonesia Stock Exchange. The Company’s characteristics are measured by firm age, size, leverage and profitability. The data analysis was done by using double linear regression models, the first is to see the effect based on the ISO 26000 CSR and firm characteristics on value, and the second is to see the effect of interaction with the ISO 26000 corporate characteristics. The results of the study showed that there was no influence of ISO 26000, the characteristics of the company toward the value of the company. However, moderated ISO 26000 by firm characteristics affect the value of the company.

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Analysis and Application of Log-Linear and Quantile Regression Models to Predict Bus Dwell Times

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119848701 ◽

2019 ◽

Vol 2673 (10) ◽

pp. 118-128 ◽

Cited By ~ 3

Author(s):

Travis B. Glick ◽

Miguel A. Figliozzi

Keyword(s):

Linear Regression ◽

Travel Time ◽

Regression Models ◽

Dwell Time ◽

Time Variability ◽

Linear Regression Models ◽

Ongoing Research ◽

Bus Stops ◽

Log Linear ◽

Travel Time Variability

Understanding the key factors that contribute to transit travel times and travel-time variability is an essential part of transit planning and research. Delay that occurs when buses service bus stops, dwell time, is one of the main sources of travel-time variability and has therefore been the subject of ongoing research to identify and quantify its determinants. Previous research has focused on testing new variables using linear regressions that may be added to models to improve predictions. An important assumption of linear regression models used in past research efforts is homoscedasticity or the equal distribution of the residuals across all values of the predicted dwell times. The homoscedasticity assumption is usually violated in linear regression models of dwell time and this can lead to inconsistent and inefficient estimations of the independent variable coefficients. Log-linear models can sometimes correct for the lack of homoscedasticity, that is, for heteroscedasticity in the residual distribution. Quantile regressions, which predict the conditional quantiles, rather than the conditional mean, are non-parametric and therefore more robust estimators in the presence of heteroscedasticity. This research furthers the understanding of established dwell determinants using these novel approaches to estimate dwell and provides a relatively simple approach to improve existing models at bus stops with low average dwell times.

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INTERACTION TERMS IN POISSON AND LOG LINEAR REGRESSION MODELS

Bulletin of Economic Research ◽

10.1111/boer.12120 ◽

2017 ◽

Vol 70 (1) ◽

pp. E89-E96 ◽

Cited By ~ 7

Author(s):

Shengwu Shang ◽

Erik Nesson ◽

Maoyong Fan

Keyword(s):

Linear Regression ◽

Regression Models ◽

Linear Regression Models ◽

Interaction Terms ◽

Log Linear

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Impacts of multimorbidity on medication treatment, primary healthcare and hospitalization among middle-aged and older adults in China: evidence from a nationwide longitudinal study

BMC Public Health ◽

10.1186/s12889-021-11456-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yang Zhao ◽

Siqi Zhao ◽

Lin Zhang ◽

Tilahun Nigatu Haregu ◽

Haipeng Wang

Keyword(s):

Logistic Regression ◽

Longitudinal Study ◽

Linear Regression ◽

Random Effects ◽

Primary Healthcare ◽

Outpatient Care ◽

Regression Models ◽

Financial Burden ◽

Linear Regression Models ◽

Log Linear

Abstract Background Multimorbidity is a significant contributor to inequalities in healthcare and has become a major unaddressed challenge for the health system in China. The aim of this study is to assess the socio-demographic distribution of multimorbidity and the relationships between multimorbidity, primary healthcare, hospitalization and healthcare spending. Methods We conducted this nationwide population-based panel data study in China. Study participants included 12,306 residents aged ≥45 years from the China Health and Retirement Longitudinal Study in 2011, 2013 and 2015. Random-effects logistic regression models were applied to estimate the association between multimorbidity and primary healthcare as well as admission to the hospital. We used log-linear regression models to investigate the association between multimorbidity and health spending. Results Overall, 46.2% of total interviewees reported multimorbidity. Random-effects logistic regression analyses showed that multimorbidity was associated with a higher likelihood of medication use (Adjusted odds ratio (AOR) =19.19, 95% CI = 17.60, 20.93), health check (AOR = 1.51, 95% CI = 1.43, 1.59), outpatient care (AOR = 2.39, 95% CI = 2.23, 2.56) and admission to hospital (AOR = 2.94, 95% CI = 2.68, 3.21). Log-linear regression models showed that multimorbidity was also positively associated with spending for outpatient care (coefficient = 0.64, 95% CI = 0.59, 0.68) and hospitalization (coefficient = 0.65, 95% CI = 0.60, 0.71). Conclusions Multimorbidity is associated with higher levels of primary care, hospitalization and greater financial burden to individuals in China. Health systems need to shift from single-disease models to new financing and service delivery models to more effectively manage multimorbidity.

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An ecological study to evaluate the association of Bacillus Calmette-Guerin (BCG) vaccination on cases of SARS-CoV2 infection and mortality from COVID-19

PLoS ONE ◽

10.1371/journal.pone.0243707 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243707

Author(s):

Lucy Chimoyi ◽

Kavindhran Velen ◽

Gavin J. Churchyard ◽

Robert Wallis ◽

James J. Lewis ◽

...

Keyword(s):

Linear Regression ◽

Regression Models ◽

Morbidity And Mortality ◽

World Health ◽

Linear Regression Models ◽

Vaccination Policy ◽

Geographical Differences ◽

Bcg Vaccination ◽

Country Level ◽

Log Linear

As the SARS-CoV2 pandemic has progressed, there have been marked geographical differences in the pace and extent of its spread. We evaluated the association of BCG vaccination on morbidity and mortality of SARS-CoV2, adjusted for country-specific responses to the epidemic, demographics and health. SARS-CoV2 cases and deaths as reported by 31 May 2020 in the World Health Organization situation reports were used. Countries with at least 28 days following the first 100 cases, and available information on BCG were included. We used log-linear regression models to explore associations of cases and deaths with the BCG vaccination policy in each country, adjusted for population size, gross domestic product, proportion aged over 65 years, stringency level measures, testing levels, smoking proportion, and the time difference from date of reporting the 100th case to 31 May 2020. We further looked at the association that might have been found if the analyses were done at earlier time points. The study included 97 countries with 73 having a policy of current BCG vaccination, 13 having previously had BCG vaccination, and 11 having never had BCG vaccination. In a log-linear regression model there was no effect of country-level BCG status on SARS-CoV2 cases or deaths. Univariable log-linear regression models showed a trend towards a weakening of the association over time. We found no statistical evidence for an association between BCG vaccination policy and either SARS-CoV2 morbidity or mortality. We urge countries to rather consider alternative tools with evidence supporting their effectiveness for controlling SARS-CoV2 morbidity and mortality.

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FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

Journal of Business Economics ◽

10.35760/eb.2018.v23i1.1812 ◽

2018 ◽

Vol 23 (1) ◽

pp. 60-71

Author(s):

Wigiyanti Masodah

Keyword(s):

Interest Rate ◽

Linear Regression ◽

Interest Rates ◽

Regression Models ◽

Linear Regression Models ◽

Negative Impacts ◽

Main Activity ◽

The Impact ◽

The Given ◽

Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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