To: The Regional Food Marketer Date: November 4, 2012
Ye Olde FoodKing Company
Re: Big Suzy’s Snack Cakes Regression Analysis
The Regional Food Manager for Ye Olde FoodKing Company has retained Mark Craig of Blue Steel Consulting to perform a regression analysis to forecast demand of your product. The four characteristics readily available included price, competitors’ price, average income, and market population. The results of each regression analysis are presented at the end of this memo. The remainder of this memo describes the regression analysis used and limitations to the data available. Running a regression provides a statistical procedure to estimate the liner dependency of one or more…show more content… Key Regression Analysis Factors
The following factors are the most commonly reviewed results of a regression analysis:
* Correlation coefficient (R-squared) – This represents how well the independent variables (X) explain the response variable (Y). * Independent variable coefficient – This is the measured effect the independent variables have on the dependent variable. This is the main output of the regression analysis. * Statistical significance of the coefficient – This is a statistical test that confirms if the coefficient regardless of its value is robust and different from zero. Also referred to as the P-value.
Statistical significance of the coefficient
The statistical significance of a coefficient tests determines coefficients potential of being zero. The zero potential increases when there is significant variance in the independent variables. A large variance also suggests that the variable used have no effect on the dependent variable.
As you can see from the previous explanation, it shows that our regression model is highly effective and explains the variation in the number of pies you sold from market to market. The limitation of regression analysis can be described in terms of regression, correlation, and causation. Regression and correlation are related but describe