Linear regression is a scientific research concept used in predictive analysis. The statistical method is instrumental in summarizing and studying the correlation between two quantitative variables in research. Health care is a sector that exceedingly relies on scientific research for a wide range of reasons, including providing insights into diseases, clinical interventions, and health. A healthcare professional, therefore, requires vast knowledge of the technique to ascertain the degree to which a relationship exists between variables in healthcare research.
In regards to variables, the overall idea of linear regression is to determine two aspects: one being, whether or not the technique does a reliable function estimating a dependent variable and two, what variables (independent) predict the outcome (dependent) variable and to what extent (Malehi, Pourmotahari, & Angali, 2015). In public health, linear regression analysis is crucial to examining medical data; as it allows a healthcare researcher to identify and characterize multiple factors in a phenomenon. For example, regression analysis can be useful in determining the most effective interventions for a problem like malaria in quantitative research.
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The principle purpose of statistical evaluation in medical practice is to define and describe associations of two or more variables. This makes the concept of linear regression a valuable analytical tool in the field. In a more practical sense, a physician interested in determining the status of blood pressure in a patient and the extent to which factors like weight and age contribute to the problem, does so by assessing the factors (age and weight) and the manner and degree they are related to high blood pressure (Austin & Steyerberg, 2015). Simply put, the measure of relationship that the technique provides helps create an impression of statistical dependence between variables in research or practice.
References
Austin, P. C., & Steyerberg, E. W. (2015). The number of subjects per variable required in linear regression analyses. Journal of clinical epidemiology , 68 (6), 627-636.
Malehi, A. S., Pourmotahari, F., & Angali, K. A. (2015). Statistical models for the analysis of skewed healthcare cost data: a simulation study. Health economics review , 5 (1), 11.
