Simple Explanation of Linear Regression Analysis

Linear Regression Analysis
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Linear Regression Analysis
Regression is a statistical method that can be used to describe the relationship between different variables under a study. In this case, a linear regression refers to the mathematical formula that can be used to create predictions. Since linear regression has been proven scientifically as a dependent and reliable way to make predictions, it has been widely applied across different sectors to aid in decision making processes (IBM, 2022). As long as data is available, linear regression analysis can be undertaken to provide a basis for evidence-based decisions through insights gained from such statistical techniques.
As its name suggests, linear regression relies on fitting the line of best fit to the data observed by a researcher. Therefore, this provides an opportunity to determine how the dependent and independent variables change in relation to each other. The dependent variable refers to the variable in the study which is under investigation and is being measured by the researcher. On the other hand, the independent variable refers to one that changes or is manipulated in order to initiate a change on the dependent variable (IBM, 2022). Consequently, by analyzing and observing the relationship between these two variables, a researcher can be able to make inferences.
The relationship between the dependent and independent variable can be measured using a simple linear regression analysis when only one independent variable is used in the research. In cases where several independent variables are used, multiple linear regression analysis is conducted. The formula for a simple linear regression is as shown below:
Y = β0 + β1X + ϵ
where Y represents the dependent variable, β0 represents the intercept, β1 represents the regression coefficient, X represents the independent variable, and ϵ represents the error estimate (IBM, 2022). The aim of the simple linear regression is to find the most suitable line of fit that can minimize the error estimate (ϵ) by using the regression coefficient (β1).
To construct the linear regression model, the data...