Reasons and situations in which researchers would want to use linear regression
Linear regression is a statistical method used to predict a dependent variable (Y) based on the values of an independent variable (X). Research is the methodical study of information done to describe, predict and explain a set of data to come up with an accurate conclusion (Nakoinz, 2018). Researchers prefer to use linear regression to examine data in their research design. This is because the arrangement of conditions of their research topic depends heavily on linear regression to accurately collect and analyse data. A particular research may require one to quantify the relative impacts of a predictor variable on that of an outcome variable (Pal & Bharati, 2019). In such a case, linear regression is the most appropriate predictive analysis tool.
How a researcher knows linear regression was the appropriate statistical technique to use
In the case of two variables; one dependent and the other independent, linear regression is the most suitable predictive tool a researcher would use. Linear regression helps to determine how much a dependent variable changes with a change in one or more independent variables and also helps to get the point estimates.
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Benefits of fitting the relationship between two variables to an equation for a straight line
It helps to remove any possible errors since a definite distinction is made between the linear regression equation and the linear functional relation of two variables (Hazra & Gogtay, 2016). Besides, since a straight line can be fitted to any set of data, fitting the relationship between two variables helps researchers to generalise from the sample in hand to the population from which a sample is drawn.
How to know if a certain correlation is a strong correlation
Correlation is the determination of whether a change in one variable is accompanied by a change in the other. For example, when one considers variables such as family income and expenditure, increase in expenditure leads to a direct decrease in family income. In correlation, a > 0(a = +1.0) indicates a positive relation while a < 0(a = -1.0) indicates a negative relation. Also, a = 0 indicates that the variables are independent of each other and are not related. The closer the coefficients are to +1.0 and -1.0, the greater the strength of the correlation; the relationship between the variables (Pal & Bharati, 2019).
Reference
Hazra, A., & Gogtay, N. (2016). Biostatistics series module 6: correlation and linear regression. Indian journal of dermatology , 61 (6), 593.
Nakoinz, O. (2018). Regression and Correlation Analysis. The Encyclopedia of Archaeological Sciences , 1-4.
Pal, M., & Bharati, P. (2019). Introduction to Correlation and Linear Regression Analysis. In Applications of Regression Techniques (pp. 1-18). Springer, Singapore.
