Multiple Regression - Coursework Example

Multiple Regression Below is the data that I have been collecting to There is a difference between simple regression and multiple regression. Simple regression analysis is used to establish the relationship between one variable and the other. One set of the variables is the dependent variable and the other set is the independent variable. The independent variable changes and in turn affects the dependent variable. Simple regression is used to establish whether or not indeed the independent variable determines the change that takes place in the dependent variable.

It is also used to establish the way in which the two variables are related, such that on having one of the variables, it is possible to predict its corresponding value using the determined regression formula. On the other hand, multiple regressions is used to establish the relationship between one dependent variable and more than one independent variables. In this case, the dependent variable is affected directly by more than one independent variable and the manner in which it is affected is established by the multiple regression analysis.

This analysis comes up with a formula which can be used to predict the value of the dependent variable given a set of corresponding independent variables. It is not possible to conduct multiple regression analysis using only the data that I have been collecting as shown above. Multiple regression analysis is used in the case where the dependent variable is affected directly by the more than one independent variables. In the case where it is affected just by one of the variables it is not effective to use multiple regression analysis because it shall bring up a wrong analysis.

It shall involve using one or a number of the independent variables which do not directly affect the dependent variable. In this case, it shall be a wrong implication made that shall compel the figures of the dependent variables to be changed by an independent variable that does not contribute in changing the figures at all. It shall be making a wrong assumption that the dependent variable depends on an independent variable which in fact it does not depend on at all. Taking for instance the case of milk cattle rearing, the most celebrated commodity is the amount of milk yield in say a month.

There are very many factors that directly affect the milk yield per month. These include the amount of food fed to the cattle, the amount of water fed to them, the level of hygiene maintained, the healthcare level advanced to the cattle among others. These are independent variables which contribute in the amount of milk yield directly and hence can be analyzed in precisely predicting the amount of milk yield in a future date given a certain combination of the variables. They all can be used in a multiple regression analysis that involves predicting the amount of milk yield in a given month.

However, there are many other factors which do not affect the amount of milk yield at all. These include variable such as the population of chicken in the area, the number of children the farmer has, the size of the house of the farmer and the like. Making an inclusion of such independent variables in a multiple regression that involves determining the mothly milk yield of the farmer would be making a wrong analysis. Only the independent variables that directly affect the dependent variables can be included in the multiple regression analysis.

In the case of the collected data, the variables are the amount of sale, the sex, the age and the EDU. The most pertinent issue here is the sale and hence it is the dependent variable. All the other variables can be considered as being independent. The dependent variable sale is not directly affected by the independent variables sex and age. It is only affected directly by the independent variable EDU. In this case, a regression with the quest of establishing the sales is best done using simple regression.

It is impossible to conduct multiple regression unless another set of independent variables that directly affect the sales is added. REFERENCES Rensvold, R. (2011b).  Descriptive statistics using Excel.  Retrieved on  fromhttps://cdad.trident.edu/Uploads/Presentations/59792Descriptive%20Statistics%20using%20Excel.pdf