Eco 550 Pizza Company

SUMMARY OUTPUTRegression StatisticsMultiple R0.912678R Square0.832981Adjusted R Square0.806258Standard Error14875.95Observations30ANOVAdfSSMSFSignificance FRegression42.76E106.9E0931.170812.2E-09Residual255.53E092.21E08Total293.31E10CoefficientsStandard Errort StatP-valueLower 95Upper 95Lower 95.0Upper 95.0Intercept128832.269974.821.8411230.077501-15283.6272948.1-15283.6272948.1Price (P)-198764100.856-4.846785.54E-05-28321.8-11430.1-28321.8-11430.1Competitor price (Px)15467.943459.284.4714320.0001478343.41622592.468343.41622592.46Advertisement0.2607010.094052.7719350.0103690.0670010.4544020.0670010.454402Income8.7804031.0172688.6313615.72E-096.68530210.875516.68530210.87551 Using this output, we can derive estimated regression line for demand for pizza as Q 128832.2 -19876Price 15467.94competitor price 0.260701advertisment 8.780403Income Coefficient of determination Here coefficient of determination (or R- square) 0.832981. This means 83.29 of the variation in demand for pizza is explained within the model. In other words, 83.29 of the variation in demand for pizza is explained by its price, competitor price, advertisement and income together. Given this result, the pizza company would enter the community when competitor price is high, there is large expenditure on advertisement, or people have higher level of income. So this pizza company would look to enter in those communities where there is large expenditure on advertisement or peoples are having higher levels of income. The other variable which we can include in this model consumers taste and preferences. This is not only an important factor determining demand for pizza in the pizza market but also would improve coefficient of determination. Test of significance of the independent variables and the regression equation From the regression table, we note that p-value of all independent variables