Regression Analysis Questions - Assignment Example

Regression Analysis Example of use of Regression Analysis, and three things to consider With nominal data, an analysis can only give insight into the data because of broad categorizations. However, to understand the intricacies that pertain to each category, for instance, the use of more robust methods of analysis is necessary, such as the regression analysis. For instance, regression analysis can establish what percentage of sales given other factors is necessary to attain a certain profit level.

When making use of the regression analysis tool, the three key things worth consideration are the response variable, the explanatory variable, and the constant. The explanatory variable is the scale upon which the dependent or the response variable changes, for instance time. The constant represent the unwavering value in the regression equation, for instance, fixed costs. #2 – An example of a three variable regression model and the significance of their slopesAn example of a multiple regression would be = mX + nZ, which is an equation to calculate profit for a company.

The variable Y would be for profit margin, X for fixed costs and Z for variable costs. The values of m and n are the coefficients of X and Z respectively. The value of m would tell of the rate of change in slope attributable to X while n would show the rate of change of slope attributable to Z. #3 – Why using only one independent variable would be bad use of regression analysisBy assuming that the impacts of the other two components, which make 20% of the total sales are negligible, the error value increases significantly.

In addition, since items B and C form part of the error, and will change with varying values of A, the model with be heteroscedastic, hence more difficult to use for the underlying trend in the data. Consequently, a linear model would make a poor fit for the data. Therefore, this use of regression analysis would be inappropriate. ReferenceDeanne-Seward. (2007). Applied Statistics in Business and Economics. McGraw-Hill Companies. Pp. 488-603.