Why is Simple Linear Regression important?
Linear regression is common term used in the field of statistics. Statistics is one science that involves the collection, organization and interpretation of data that is more frequently associated in conducting surveys, studies, researches and experiments. Simple linear regression is one statistical approach that involves modeling of relationships between two variables, denoted by X and Y. this type of approach focuses on the conditional probability distribution of y in the presence of variable X. as the first type of regression analysis being thoroughly studied and analyzed, simple linear regression is found to be extensively useful in various practical applications and methodologies. Simple linear regression functions by assuming that the variables x and y have a linear relationship within the given set of data. As assumptions are and results are interpreted, persons responsible for analyzing such data should be more critical because it has been studied that there are some variables which inhibit marginal changes to occur while others will not consider being held at a fixed point.
Although the concept of linear regression is one complex subject, it remains to be one of the most important statistical approaches being used until this time. Simple linear regression is important because it has been applied in many biological, behavioral, environmental and social sciences. With its ability to describe possible relationships between identified variables, it has aided the fields of epidemiology, finance, economics and trend line in analyzing significant data that proves to be of essence in the identified fields. More so, simple linear regression is important because it provides an idea of what needs to anticipated, especially in controlling and regulating functions involved on some disciplines.
Despite the complexity of simple linear aggression, it has proven to be adequately useful in many daily applications of life.