Regression analysis, a cornerstone in the world of statistical modeling, is a technique that discerns relationships between a dependent variable and one or multiple independent variables. In essence, it quantifies how changes in the independent variables, often termed predictors or features, influence the outcome or dependent variable. From simple linear regression, which considers one predictor, to multiple regression, logistic regression, and even more complex models like polynomial regression – the family of regression models provides a wide-ranging toolkit for understanding relationships, predicting future outcomes, and establishing cause-and-effect in controlled settings. This predictive power of regression is vital in many sectors, from finance and healthcare to social sciences. One significant area that has seen a surge in the utilization of regression models is the monitoring and evaluation of the United Nations' Sustainable Development Goals (SDGs).
The SDGs, a collection of 17 global goals, were established in 2015 with an agenda to tackle some of the world's most pressing challenges, including poverty, hunger, health, education, gender equality, and more. These goals are broad, ambitious, and interconnected, representing a global commitment to create a more sustainable, equitable future for all. Their success, however, relies not only on effective implementation but also on rigorous monitoring and evaluation. That's where regression analysis comes into play.
By employing regression models, policymakers and researchers can unpack the intricate dynamics that drive progress towards or away from these goals. For instance, a country might want to understand the factors influencing its maternal mortality rate – a key metric for SDG 3, which focuses on health and well-being. By setting the mortality rate as the dependent variable, and considering a range of predictors such as healthcare expenditure, literacy rate, urbanization, and access to prenatal care, regression analysis can tease out which factors have the most significant impact. Such insights allow policymakers to prioritize interventions and allocate resources more effectively.
Furthermore, regression analysis offers a mechanism to account for confounding variables – factors that could bias results if not controlled for. This ensures that the relationships being observed are genuine rather than spurious. In the context of the SDGs, where many factors interplay, this control is invaluable. Another application is in forecasting. Policymakers can use regression models to predict future trends based on current data. For instance, if a nation wishes to determine if they are on track to achieve SDG 4, quality education, they could employ regression to project literacy rates, enrollment figures, or other pertinent metrics into the future.
Regression analysis provides a powerful methodological toolset to decipher the complex web of factors that influence progress towards the SDGs. By understanding these relationships, stakeholders can make data-driven decisions, prioritize interventions, and optimize resource allocation to make the vision of a sustainable, equitable future a reality.
Background: Extreme heat exposure can lead to premature death. Climate change is expected to increase the frequency, intensity, and duration of extreme heat events, resulting in many additional heat-related deaths globally, as well as changing the nature of extreme cold events. At the same time, vulnerability to extreme heat has decreased over time, probably due to a combination of physiological, behavioural, infrastructural, and technological adaptations. We aimed to account for these changes in vulnerability and avoid overstated projections for temperature-related mortality.
Background Sex workers are disproportionately affected by HIV compared with the general population. Most studies of HIV risk among sex workers have focused on individual-level risk factors, with few studies assessing potential structural determinants of HIV risk. In this Article, we examine whether criminal laws around sex work are associated with HIV prevalence among female sex workers.
Shortages of freshwater have become a serious issue in many regions around the world, partly due to rapid urbanisation and climate change. Sustainable city development should consider minimising water use by people living in cities and urban areas. The purpose of this paper is to improve our understanding of water-use behaviour and to reliably predict water use. We collected appropriate data of daily water use, meteorological parameters, and socioeconomic factors for the City of Brossard in Quebec, Canada, and analysed these data using multiple regression techniques.
