Linear regression refers to an analysis technique which involves modelling a relationship between two variables (one being an independent variable and the other a dependent variable) and integrating a linear equation to the data. Through linear regression analysis, we can make predictions of a variable using the independent variable. To qualify for regression analysis the variables involved must have a linear relationship and can be measured at a continuous level. In environmental data analysis, linear regression is used in forecasting. It, therefore, allows for analysis of trends in environmental variables and in making predictions. For instance, an analysis can be done on two variables; global warming levels and sea rise level or land use and pollution. Global warming is the independent variable, while the sea rise level is the dependent variable. Another example is measuring the impact of temperature on plant height. Generally, linear regression has numerous environmental applications where variables are being compared.
Linear regression is useful in data analysis as it identifies relationships among variables, which can be positive, negative or no relationships. Consequently, linear regression can be used in modelling multiple independent variables. In environmental research, linear regression is vital in responding to research questions since it allows for a better understanding of relationships among variables. It is worth noting that a researcher has control on the independent variable, and can therefore explore how a change or changes impacts the other variables. In doing so, deep insights are gained on how environmental variables interact. Environmentalists can utilize established relationships to inform the decision-making process regarding environmental issues. It helps in the development of appropriate policies regarding environmental concerns. Additionally, linear regression allows for a better understanding of the subject matter as it presents a range of approaches to the research questions.
Data trends are quickly produced with ESdat environmental data management software. Mann Kendall and Linear Regression trend graphs are created for any time-series data. Features of data graphs include trends (Mann Kendal or Linear Regression), environmental guidelines/trigger levels, and can be produced in Excel.
References
Letten, A. (2016). Linear regression. Retrieved from http://environmentalcomputing.net/linear-regression/
Rosenlund, M., Forastiere, F., Stafoggia, M. Porta, D., Perucci, M., Ranzi, A., Nussio, F. and Perucci, C.A. (2008). Comparison of regression models with land-use and emissions data to predict the spatial distribution of traffic-related air pollution in Rome. Journal of Exposure Science & Environmental Epidemiology, 18, 192–199.
