Form Of Regression Analysis Research Articles

Digital Libraries and Personalised Learning Experiences of Final-year Economics Education Students in Public Universities in Lagos State, Nigeria

This study examined the influence of educational tools in digital libraries on the personalised learning experiences of final-year economics education students in public universities in Lagos State, Nigeria. A descriptive research design was employed, with the use of a questionnaire tagged Digital Libraries and Personalised Learning Experiences of Students Questionnaire (DLPLESQ) as an instrument of data collection. A total of 212 final-year economics education students were selected for the study using the snowball sampling technique. Descriptive statistics such as frequency distribution, percentage, mean, and standard deviation were used to provide answers to research questions. Additionally, inferential statistics in the form of regression analysis was used to test the relevant hypothesis. The findings revealed that digital library tools have a significant positive impact on students’ personalised learning experiences of students ( = 2.002; P<0.05). The study also demonstrated that final-year Economics education students are “Not Aware” of the resources in digital libraries in public universities in Lagos State, Nigeria. Out of the 13 digital library resources listed, sampled students seem to be “Aware” of only three resources which are “Google Scholar,” “Oxford Academic,” and “Research4life”. Furthermore, the study showed that final-year Economics education students make use of the digital library resources “Monthly”. Based on these findings, the study recommended strategies to enhance the utilisation of digital libraries, such as improving access to devices and internet connectivity, providing training programs to enhance digital literacy skills, and integrating digital library resources into the economics education curriculum

Path Students During the COVID-19 Pandemic Analysis of Factors Influencing the GPA of UNIMED FMIPA Mathematics

At the lecture level, learning achievement is measured by IPS (Semester Achievement Inkdes). The influencing factors are the long duration of online learning, attendance, network and study time. In this study, path analysis was used which is a particular form of regression analysis in which the independent variables studied not only directly affect the dependent variable but also indirectly. Based on the results of the discussion, the equation model Y=-0.548X_1+0.042X_2-5.66X_3+11.4 is thus the influence of exogenous variables (X), namely the duration of online learning, presence and networking on the endogenous variable (Y), namely the amount of study time per week of 88.58% and 11.42% influenced by other variables outside the path model. Z=-0.005X_1+0.317X_3+0.93Y+44.3. Thus the influence of the exogenous variable (X), which is many (????), namely IPS, is 11.4% and 44.3% is influenced by other variables outside the path model. And the factor that most influences the IPS score is the network because it has the greatest total influence among the other variables, namely 1.742.

THE IMPACT OF COVID-19 ON MARITIME TRADE AND TRANSPORTATION: AN ESTIMATION OF THE MARITIME TRADE POST-COVID-19

The coronavirus which emerged in December 2019 has affected the health of people and the whole world’s social and working life. This pandemic has impressed not only people’s health and life but also the world economy and especially maritime trade. International organizations and maritime authorities such as World Health Organization (WHO) and International Maritime Organization (IMO) have taken several measures and responses to mitigate the impact the Covid-19 on the maritime industry. Taking precautions, implementing protocols, and complying with guidelines at ships and ports which helps to protect the health of seafarers, passengers, port workers, and the general public have adversely affected the entire world economy and international trade. The study aims to identify the impacts of Covid-19 on maritime trade and transportation and to estimate world maritime trade for the years 2023, 2025, and 2030. In the study, a literature review was conducted, the impact of the Covid-19 on world maritime trade, maritime transportation, port calls, and shipping companies have been examined and the least squares method which is a form of regression analysis was used to estimate maritime trade and container throughput for these years. According to the result of the analysis, it is estimated that the world maritime trade will be 24,100.8 million tons in 2023, 25,163.2 million tons in 2025, and 27,819.2 million tons in 2030, and the estimated total container throughput at container ports in the world will be 906,104.1 thousand TEU in 2023, 959,701.5 thousand TEU in 2025 and 1,093,695 thousand TEU in 2030. It has been concluded that the total global maritime trade and amount of containers handled will increase gradually.

Social Support and Network Formation in a Small-Scale Horticulturalist Population

Evolutionary studies of cooperation in traditional human societies suggest that helping family and responding in kind when helped are the primary mechanisms for informally distributing resources vital to day-to-day survival (e.g., food, knowledge, money, childcare). However, these studies generally rely on forms of regression analysis that disregard complex interdependences between aid, resulting in the implicit assumption that kinship and reciprocity drive the emergence of entire networks of supportive social bonds. Here I evaluate this assumption using individual-oriented simulations of network formation (i.e., Stochastic Actor-Oriented Models). Specifically, I test standard predictions of cooperation derived from the evolutionary theories of kin selection and reciprocal altruism alongside well-established sociological predictions around the self-organisation of asymmetric relationships. Simulations are calibrated to exceptional public data on genetic relatedness and the provision of tangible aid amongst all 108 adult residents of a village of indigenous horticulturalists in Nicaragua (11,556 ordered dyads). Results indicate that relatedness and reciprocity are markedly less important to whom one helps compared to the supra-dyadic arrangement of the tangible aid network itself.

POLA DISTRIBUSI RUANG TERBUKA HIJAU TERHADAP TEMPERATUR WILAYAH KOTA SAMARINDA

The physical development of the city of Samarinda that is not in harmony between the existence of Built Space and the distribution of Green Open Space has an impact on changes in the urban microclimate. These changes occur in microclimate elements such as temperature, humidity, intensity of sunlight and wind. If the microclimate element changes, it is feared that a change will occur in a direction that is not in accordance with the comfort of the human body condition. Then it becomes a question in this study, namely how is the relationship between regional temperature and the distribution of green open space in Samarinda City. The targets carried out to achieve the research objectives are to analyze the temperature distribution of the Samarinda City area, analyze the distribution of Samarinda City Green open space, analyze the characteristics of Samarinda City Green open space, and analyze the relationship between regional temperature and the distribution of Samarinda City Green open space. To answer this goal, this research uses interpretation analysis of Landsat 8, Sentinel 2 imagery and statistical analysis in the form of regression analysis. In the sentinel image analysis, the data obtained are the distribution of temperature and the distribution of green open space. Where the minimum temperature is 29.8oC, maximum temperature is 38.8oC, and the distribution of green open space is obtained. The results of the ANN analysis show that there are 4 categories of green open space distribution data, namely clustered, spread out, uniform, and non-green open space. After the data obtained from the two analyzes, a quantitative analysis was carried out using SPSS programming to obtain the relationship between the Y variable and the X variable. In this analysis, the calculated F value was 1.930 with a significance level of 0.009 where the regression model can be used to predict the independent variables in this data. RTH with temperature showed a significant closeness. In the research area, it is stated that green open space has a significant relationship to the regional temperature distribution. In addition, the research findings also show that the ratio of green open space, the distribution of green open space, and the percentage of dense vegetation have an inverse relationship, which means that if green open space increases then the temperature decreases and vice versa.

Forecasting of dengue hemorrhages fever cases with autoregression distributed lag model using Poisson regression approach

Dengue hemorrhages fever (DHF) is an infectious disease epidemic in the community. DHF is caused by a virus that is transmitted through the bite of mosquitoes Aedes sp. The aim of this research was to develop and validate a forecasting model that could predict dengue fever (DHF) cases in Bandung, West Java, Indonesia. Model of dengue fever is developed using time series Poisson multivariate regression. Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable has a Poisson distribution and the logarithm of its expected value can be modeled by a linear combination of unknown parameters. Here, a time series Poisson multivariate regression model was developed using monthly mean temperature and cumulative rainfall over the period 2001–2016. Cross-correlation function between response and predictor is used to decide the lag parameter optimum in autoregression distributed lag model. Model selection and validation were based on the Akaike’s information criterion, mean square error, mean absolute percentage error, and residuals diagnostic test. The R-square obtained is 0.78, means that the temperature, rainfall, past dengue fever cases, season, and trend could explain 78% of the variance of monthly dengue distribution.

A New Method for the Calibration of Site‐Related Response Spectra

The calibration of response spectra is an important issue that needs further research in engineering earthquake resistance. This paper proposes an improved calibration method for site‐related response spectra. The seismic acceleration response spectra are statistically analyzed in the random period bands in the form of regression analysis, and the fitting indices in each frequency band under 11 different functions are given. Accordingly, the best fitting function for each period band is determined. Combined with a genetic algorithm, the control parameters of the seismic acceleration response spectrum are calibrated according to the determined new design spectral shape. After comparing the calibration results with the proposed piecewise results, a new calibration model of the three‐section curve expression is proposed by improving the piecewise standard until the calibration results are identical to the proposed period point. The accuracy of the proposed calibration method is validated against the other four available methods using Qian’a earthquake records and actual engineering examples. The research results show that the site‐related response spectrum calibration method given in this paper objectively reflects the spectrum characteristics of the site‐related response spectrum. The proposed method may have a certain reference value for the calibration of the site‐related response spectrum.

Pengembangan Aplikasi Regresi Parameter Cuaca Berbasis Visual Basic For Application

Abstract —
 This research is the development of the Microsoft Excel VBA application to perform regression analysis between weather parameters in terms of data centering measures (PAMER11). The combination of parameters and data centering measures obtained 55 parameters that can be combined into 1,677,255,139,725 annual regression analysis outputs. The case study used in this study only calculates air humidity with temperature and air pressure observation variables, then the analysis results are compared with the results of the Excel Toolpak analysis data with the Anova. The development of this application method is carried out in 4 stages, namely: define, design, develop, adn dissemination. The defined stage includes analysis of the needs of researchers in the BMKG field, analysis coverage of the needs of BMKG in the form of regression analysis. The design stage includes format selection, application program selection, and initial design (prototype 1). The development stage includes validation (prototype 2) obtained a programming aspect value of 4.1325 valid criteria, comparison of Anova results from the output of the PAMER11 application and analysis of the Excel toolpak, the f-value is with a p-value of 99% which results in the two calculations are significantly equal, for the display aspect get a value of 4,8175 valid criteria, limited and field product trials (prototype 3), where a limited trial was carried out on all faculty lecturers getting a score of 4.2440 very good criteria. Field trials conducted on the BMKG Cilacap got a score of 4.8333 very good criteria and data analysis at each stage of development has been improved towards the application program.
 Keywords — Development; Microsoft Excel; Visual Basic for Application (VBA); Regression Analysis; Weather Parameters

PENGARUH PENDAPATAN TERHADAP KESEJAHTERAAN KELUARGA PADA INDUSTRI KONVEKSI DI KAMPUNG BULAK TIMUR DEPOK JAWA BARAT

The purpose of this study is to determine how far the level of income affects the welfare of the family in the convection industry in Depok. This research was conducted using a survey method with the method of library (library research) and the field method (Field research) while the data analysis test technique in this study used a correlational approach in the form of regression analysis, product moment correlation analysis, coefficient of determination, and hypothesis testing. The results of this study can be seen that the influence of income on welfare. This is evidenced through the calculation results obtained from the correlation coefficient of 0.891. This shows the effect of income on welfare in the high impact category, the variable family welfare is influenced by 79.4% by the income variable. This is evidenced through the coefficient of determination of 0.794. There is a significant effect between the income variable on the welfare variable. This can be seen through the results of t test analysis in which the value of t is 10.773 and ttable is 2.042. Due to the tcount of 10.773> ttable of 2.042 it can be said that there is a significant influence between income on family welfare.

Comparison of Rice Price Forecasting Using the ARIMA Method on Amazon Forecast and Sagemaker

Rice is one of the main commodities of trade in Indonesia. PT Food Station as the management company of Cipinang Rice Main Market every day publishes data on price, type of rice and the amount of rice that enters and exits Jakarta area. This study aims to forecast rice prices in the Jakarta area using data held by PT FoodStation during the 2016-2018 data period. Rice price prediction is carried out for the next 30 days using the Auto Regressive Integrated Moving Average (ARIMA) method on the Amazon Forecast and Amazon Sagemaker platforms. The ARIMA model is a form of regression analysis that measures the strength of one dependent variable that is relatively influential on other change variables. The ARIMA model is a special type of regression model in which the dependent variable is considered stationary and the independent variable is the lag or previous value of the dependent variable itself and the error lag. ARIMA is a combination of auto-regressive and moving average processes. The final result obtained in this experiment is that the ARIMA model on Amazon Sagemaker cloud computing is superior when compared to Amazon Forecast. From the experimental results obtained the results of Amazon Sagemaker RMSE (313.379941) are smaller than Amazon Forecast (322.4118029). So it can be concluded that the ARIMA model run at Amazon Sagemaker is more accurate than Amazon Forecast for forecasting the price of rice for 30 days at the Cipinang Rice Main Market

A back-end view to climatic adaptation

PurposeThis study investigates the level of variance in the real time demand for bagged cement, induced in response to the climatic sequence of the humid tropics, to support best practice calls for a weather-responsive supply chain strategy.Design/methodology/approachData on the consumption of cement and site works for 100 ongoing building construction sites were gathered for a period of 12 months. The variance partitioning capabilities of the Ordinary Least Squares and Hierarchical Linear Modelling forms of regression analysis are comparatively used to evaluate the sensitivity of cement demand to the meteorological profile of wet-humid climateFindingsThe study outcome provides statistical evidence demonstrating that the meteorological profile of wet-humid climate induces a significantly high percentage of the variance in the real-time demand for bagged cement on construction sites. However, nested within this variance, are the fixed effects of the cement footprint of the building architecture inherent in the locality. Particularly, positive changes to reduce the wet trade composition of buildings or compensating changes in technological bias, are necessary to combat weather interference in the humid tropics.Research limitations/implicationsThe findings are exploratory, and not for the purposes of holistically forecasting cement demand, and can therefore only form part of a more comprehensive decision support system, bespoke to the study area.Practical implicationsThe study outcome provides a back-end view to climatic adaptation in wet humid settings, making a compelling case for localized climate-risk adaptive supply chain strategies and policies geared towards sustainability in cement usage.Originality/valueThe study delineates the confounding impact of weather, distinct from local building architecture and technological bias, thus creating a methodological platform for replication and comparative productivity studies in diverse geographical areas.

Lurking and participation in the virtual classroom: The effects of gender, race, and age among graduate students in computer science

Fully-online graduate degree programs are on the rise, generating new questions about how we conceptualize and measure student participation in the virtual classroom. To date, most studies examine participation patterns in single online and/or hybrid courses and do not take into account the demographic characteristics of online students. We develop and test hypotheses that address demographic differences in the nature and intensity of graduate student participation in online-only classrooms for a large degree program in computer science. This work is distinctive because it addresses participation across several classes and across multiple semesters. We select the Piazza forum because it is the required communication mechanism in the program that is the subject of our study. We extract Piazza discussion board activity logs from a sample of 1914 online computer science graduate students, specifically the numerical data indicating the type of access to Piazza students used. We distinguish between active (contributions), passive (viewership) participation and lurking behavior. Given the nature of the dependent variables of interest, we employ different forms of regression analysis. We use logistic regression to address the likelihood of non-participation in the online forum. We then use negative binomial regression to examine the intensity of passive and active engagement, and ordinary least squares regression to examine lurking behavior. We find that the intensity of participation varies by different demographic characteristics, including by age and by race/ethnicity, but not by gender. Our study also shows a notable impact of class size, where increasing class size is associated with decreasing levels of active participation and increasing lurking behavior.

Semiparametric regression based on three forms of trigonometric function in fourier series estimator

The semiparametric regression is one of the three forms of regression analysis which is made up of parametric and nonparametric. While the parametric is based on linear estimator, this nonparametric component is an innovation. This research proposes all the possible trigonometric basis usually used in Fourier series as nonparametric component estimator, its advantage, which includes its ability to overcome data with oscillation patterns. This study discusses nonparametric regression based on complete and sine Fourier series. Both estimators are developed using the cosine Fourier series concept. The outputs are two estimators which are used for parametric and nonparametric components with the corresponding form in semiparametric regression. In addition, all of these can be applied in real problems, and the best estimator is determined based on the smallest GCV and MSE for an oscillation parameter which gives the highest coefficient of determination for the selected one.

Modeling Continuous Non-Linear Data with Lagged Fractional Polynomial Regression

Fractional Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable is modelled as a 1/nth degree polynomial. Thus, this work is used to propose an extension of Fractional Polynomial Regression (FPR) term Lagged Fractional Polynomial Regression (LFPR) which is an alternative method to traditional techniques of analysing the pattern and degree of relationship between two or more continuous non-linear data. The coefficients of the proposed method were estimate using Maximum Likelihood Estimation method. From the results, the LFPR model indicated that for a unit increase in Evaporation, Humidity and Temperature there will be an increase in the millimeter of rainfall series on yearly basis. The value of coefficient of variation (R2) for the LFPR and FPR were 99% and 77%. While the value of adjusted Coefficient of Variation (R2) for LFPR and FPR were 96% and 75% respectively. Hence, the proposed method outperformed and adequately explained the variation in the dependent variable better than Fractional Polynomial Regression based on the values (R2) and adjusted (R2).

Day, duration, and start time: are the arts providing what their audiences require?

PurposeThe purpose of this paper is to explore whether variations in factors such as start time, duration of event, and day of presentation might lead to improved attendance at performing arts events in the USA.Design/methodology/approachThe research was conducted using a quantitative strategy. The approach consisted of a survey design that explored preference data for respondents. The data were examined to look for emergent themes in both raw data and cross-tabulations. The data collection also included survey questions for conjoint analysis, a form of regression analysis that compares consumer trade-offs for various levels of different attributes.FindingsThe researchers found that while anecdotal evidence regarding performing arts attendance largely holds up, there is room to explore the possibility that modern-day consumers require events of shorter duration, and that the utility of Saturday attendance is so overwhelming that start time preference can be manipulated within the Saturday level of that attribute with minimal effect on overall utility.Originality/valueThe study begins to fill a gap in the literature regarding attendance preference for the performing arts regarding several attributes. The study opens a discussion on how to address declining attendance figures by considering some of the factors that are likely contributing to the decline.

THE ROLE OF ORGANIZATIONAL COMMITMENT AND PERCEIVED ORGANIZATIONAL SUPPORT IN PROMOTING ORGANIZATIONAL CITIZENSHIP BEHAVIOR

This paper examined the impact organizational commitment and perceived organizational support in promoting organizational citizenship behavior of teacher in private sector universities of Pakistan. The data were collected from 140 teachers working with private sector universities in Pakistan. Multiple methods in the form of regression analysis were used to check the hypothesis. The consequences of the study indicate that organizational citizenship behavior is a factor that fairly depends upon organizational commitment and also on perceived organizational support; furthermore, perceived organizational support and organizational commitment have considerable affirmative impact means it positively influence the organizational citizenship behavior of teaches working in private universities of Pakistan. This study is primarily driven tentatively by the social exchange theory that deliberately lends support to this paper for explaining the idea of perceived organizational support (POS), the norm of reciprocity, and also organizational support theory. This article adds value to the restricted literature regarding the psychological concept that causes the creation of organizational citizenship behavior through the perceptions of organizational support theory concept (Eisenberger et al., 2001; Eisenberger, et al., 1986) and the social exchange theory (Blau, 1964).The paper suggests that if private sector universities in Pakistan desire to cultivate organizational citizenship behavior among its teachers then they must chalk out and implement such strategies that increase employee commitment as well as perceived organizational support (POS). Keywords: Organizational citizenship behavior (OCB), Organizational commitment (OC), Perceived organizational support (POS), Pakistan

Machine Learning Forecasts of Risk to Inform Sentencing Decisions

When threats to public safety are a factor in sentencingdecisions, forecasts of ‘‘future dangerousness’’ are neces-sarily being made. Sometimes the forecasts are effectivelymandatory. Federal judges, for example, are required toassess risk in every case. Under 18 U.S.C. § 3553(a)(2)(C),‘‘[t]he court, in determining the particular sentence to beimposed, shall consider...(2) the need for the sentenceimposed...(C) to protect the public from further crimes ofthe defendant...’’ A judge must look into the future,determine the likelihood and seriousness of criminalbehavior, and within certain bounds, sentence to minimizethe harm that could result.Ideally, the forecasts should be highly accurate. Theyalso should be derived from procedures that are practical,transparent, and sensitive to the consequences of forecast-ing errors. However, there is usually no compelling guid-ance on precisely how these goals can best be achieved.Subjective judgment, sometimes called ‘‘clinical judg-ment,’’ is an approach that relies on intuition guided byexperience. As discussed below, the resulting risk assess-ments are often wildly inaccurate and their rationale opa-que. ‘‘Actuarial’’ methods depend on data that allow one tolink ‘‘risk factors’’ to various outcomes of interest. Theassociations found can then be used to forecast those out-comes when they are not known. Over the past severaldecades, regression statistical procedures have dominatedthe actuarial determination of empirically based risk fac-tors. By and large, this enterprise has been a success. Butthe increasing availability of very large datasets coupledwith new data analysis tools promise dramatically bettersuccess in the future. Machine learning will be a dominantstatistical driver.There is now a substantial and compelling literature instatistics and computer science showing that machinelearning statistical procedures will forecast at least asaccurately, and typically more accurately, than olderapproaches commonly derived from various forms ofregression analysis.

A Systemic Analysis of Professional Certification Rates: The Case of a Six Sigma Training Certificate Program

:Professional certificate training is a challenge for trainees and trainers. A certification program's financial success depends not only on its quality, but also on certification rates. In this study, the authors theorize that trainee performance in the field project component of a training certificate program is the key variable to success; therefore, identifying bottlenecks in a trainee certification process is vital to increasing the probability of certification. In order to test this hypothesis, the authors use a hybrid methodology to identify bottlenecks through the analysis of a Six Sigma certification program. The goal is to evaluate the impact that the academic components have on the successful implementation of field projects and, therefore, on certification rates. The latter is a requirement to be certified in the program studied. As a result, managers in charge of training certificate programs can assess, elaborate, and corroborate hypotheses about causes and effects of training practices and their efficiency within their program. The proposed solution combines statistical methods in the form of regression analysis and systemic tools like system dynamics (SD) to provide further understanding when early detection and intervention on trainees' deviations from expected performance is required. Findings from this research can be used as the basis for a future confirmatory analysis.

Surflex-QMOD: physically meaningful QSAR

Computational methods for predicting ligand affinity where no protein structure is known generally take the form of regression analysis based on molecular features that have only a tangential relationship to a protein/ligand binding event. Such methods have utility in retrospective rationalization of activity patterns of substituents on a common scaffold, but are limited when either multiple scaffolds are present or when ligand alignment varies significantly based on structural changes. In addition, such methods generally assume independence and additivity of effect from scaffold substituents. Collectively, these non-physical modeling assumptions sharply limit the utility of widely used QSAR approaches for prospective prediction of ligand activity. The approach we report builds upon the Compass approach by constructing physical models of a protein binding site based upon ligand binding data. The result is a binding site composed of molecular fragments that can be treated as a target for molecular docking. The binding site model consists of molecular fragments that can account for multiple positions of protein residues. It is not a literal reconstruction of a single configuration of protein residues. New molecules are docked directly into the binding site, with their highest scoring poses serving as the prediction of binding geometry and the corresponding score being the predicted affinity. By deriving a virtual binding pocket at the same time as the relative poses of ligands are identified, the key analogy is that one can treat a computational model of a binding site as one treats a protein binding pocket. We seek the optimal fit of ligands into the binding site. One begins with a guess as to the initial alignment of ligands, then constructs a model of activity that depends on the ligands’ poses. The model can be thought of as a virtual receptor. Next, one seeks poses for each ligand that optimize their interaction with the virtual receptor. Then, the virtual receptor is refined, making use of the new ligand poses, and the process iterates between pose refinement and virtual receptor refinement. As the virtual receptor evolves, the changes in ligand scores due to pose optimization decrease. When the iterative process converges, the final poses of the ligands are optimal with respect to the final virtual receptor. The software implementing the algorithms for pocket construction and ligand activity prediction constitute a new module within the Surflex platform, called Surflex-QMOD (Quantitative Modeling). The Surflex-QMOD approach addresses the physical linkage between activity model and molecular binding mode with pockets having detailed structure comparable to true protein binding sites. Because the model building process results in a model that selects ligand alignments based on mutual interaction, there is a direct correspondence between the physical process of protein/ligand binding and the act of prediction.

Correlations between climate factors and incidence-a contributor to RSV seasonality

Respiratory syncytial virus is the most common respiratory virus infection in early childhood, causing a wide range of illness from mild colds to life-threatening croup, bronchiolitis and pneumonia that may require intensive care. Exactly which parameters contribute to the seasonality of RSV (and other respiratory viruses, such as influenza) and their comparative significance are the subject of ongoing intensive debate. This review article summarises the specific contributions and correlations between the incidence of RSV and various climate parameters. This systematic review of the literature specifically focuses on these climate associations and have been stratified by study site latitudes: tropical (0-23.5°N or S), subtropical (23.5-40°N or S) and temperate latitudes (>40°N or S). Correlations between RSV incidence and temperature and relative humidity are particularly variable and inconsistent amongst the tropical regions. In subtropical and temperate regions, RSV incidence is more consistently positively correlated with lower temperatures and higher relative humidity. Although there is some variation with the diagnostic methods used in these studies, most used immunofluorescent viral antigen testing to diagnose RSV infection. Statistically, most studies used some form of regression analysis, which assumes no dependence between data taken at different time points. A few used the autoregressive integrated moving average approach, which may be more realistic for an infectious agent as the total number of cases usually evolves in a time-dependent manner during a typical seasonal epidemic.