For example, the depreciation in the value of a car as time passes and the distance it is driven is done by simple regression. But this type of situation rarely exists since there are many complex factors or variables that exist in the real world. If we want to calculate the future earning of a person taking only his years in school is simple regression, but it is not accurate since other factors like age, qualification, industry, experience are not taken into account. If we take all these factors into account the situation becomes more complex and hence multiple regressions would have to be used.
Linear regression is used in similar circumstances as a simple regression. A relationship between height and weight of adults can be presented in a linear regression. The heights and weights are marked on a graph and a straight line is drawn through the middle.
On the other hand nonlinear regression (NLREG) is used in more complex situations. "NLREG is a powerful statistical analysis program that performs linear and nonlinear regression analysis, surface and curve fitting." (NLREG).
After the taking over of Company W, WidgeCorp is supposed to be one of the leading industries under marketing of snack foods and beverages. Their style of management and business decision-making was different from the company W.
The WidgeCo ...