Crusty Pizza Restaurant: Forecasting using Regressions
Group One: Jenna Baseler and Zachary Kain
The purpose of this case is to determine which key variables drive Crusty Pizza Restaurant’s monthly profit and then forecast what the monthly profit would be for potential stores. Based off of this information we will be able to make a recommendation to Crusty Dough Pizza Restaurant on which stores they should open and which they avoid. The group was provided 60 restaurants’ data that included monthly profit, student population, advertising expenditures, parking spots, population within 20 miles, pizza varieties, and competitors within 15 miles. For the potential stores we were given all of this…show more content… In a regression analysis we found that the correlation coefficient is .5862 with a very high level of confidence (see Appendix A for detailed Regression Analysis Data). We also found that the coefficient of determination was .3437. This shows that since there is a correlation between student population and profits, the impact student population has on the profits is approximately 34%. We also found from our regression equation that an increase in the student population by 1 student can increase profit by $1.07. You can see the positive trend in the scatter plot below.
Competitors within 15 Miles Competition is a strong driver in the free enterprise of America. Crusty Dough Pizza Company is no exception. While Crusty Dough Pizza Company varies in its number of competitors within 15 miles by store, this variance does not seem to correlate to higher or lower profits. When looking at the regression analysis completed using competitors within 15 miles as the independent variable and monthly profit as the dependent variable, we find that the correlation coefficient is a very low .133. It’s safe to say there is it not a correlation between these two figures (see Appendix A for detailed Regression Analysis Data).
As you can see from our findings, two main correlations we found that impacted monthly profit are monthly advertising expenditures and student population. We can use