Explanation describes the statistical relationship between the dependent variable (Y) and the independent variable (X). Regression analysis provides the researcher with three essential purposes: 1) explanation, 2) control, and 3) prediction. The expression of the general tendency of a dependent variable (Y) and an independent variable (X) is potent in many different fields. 414), implying that there are random errors present, hence the non-perfect fit.
However, the approximate nature of the model must always be borne in mind” (p. Essentially, all models are wrong, but some are useful. Box (2007) states, “all models are approximations. The independent variable (X) is also referred to as the predictor variable. Ī regression model is a formal means of expressing the general tendency of a dependent variable (Y) to vary with the independent variable (X) systematically. It is commonly accepted that the term ‘regression’ describes the statistical, not the functional relationship between variables. For example, cholesterol levels and body weight have a non-perfect fit. Statistical relationships typically do not have perfect fits. For example, ice cream cones cost $1 each, so that one can buy 10 ice cream cones for $10 or 20 ice cream cones for $20.
Functional relationships have perfect fits. These relationships are either functional or statistical.
Regression analysis utilizes the relationship between quantitative variables to predict one variable from one or more other variables. Keywords: nonlinear regression, statistical relationship, Solver, ENIG Linear and nonlinear regression methods are reviewed, and a worked example using electroless nickel immersion gold (ENIG) is provided.
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Understanding how to choose the proper model and starting parameters is critical. Nonlinear regression is a powerful statistical tool, but it can be challenging to find the appropriate model and starting parameters.