Soft Drink Case Study 1 Essay Example

will change in the opposite direction.

A unit change in price will result in a change in the demand for soft drinks of 242. 97 in the opposite direction. Income is directly related to the quantity demanded soft drinks. As income rises, the quantity demanded soft drinks shall also increase. A unit change in income will result in a 1. 36 change in the demand for soft drinks in the same direction. Temperature is also directly related to the quantity demanded soft drinks. As the temperature rises, the quantity demanded of soft drinks increases. A unit change in temperature will result in a 2. 93 change in the quantity demanded soft drinks in the same direction.

The price elasticity of soft drink demand at the mean is as follows: Y: 514. 27 – 242. 97 (2. 20) + 1. 36 (16. 14) + 2. 93 (53. 60) = 158. 74 ~ 159 Price Elasticity = -242. 97; 2. 20/158. 74 = -3. 40

3. Omit price from the regression equation. Describe the signs of the estimated coefficients and the statistical significance of the coefficients. soft drink demand: = -56. 61 – 2. 28 income/capita + 4. 70 mean temp We see that both income/capita and mean temp have negative coefficients which indicate that a unit change in price will result in a change in the demand for soft drinks of both 56. 1 and 2. 28 in the opposite direction. T-Stat for coefficients: (-1. 131) Income/Capita and (5. 699) Mean Temp Using the “rule of 2” and the t-test as an approximation for the. 05 level of significance, we can say Income per Capita (-1. 131) is not

statistically significant because it’s lower than 2. On the other hand, Mean Temp (5. 699) is statistically significant because it’s greater than 2. Since the variable “mean temp” is statistically significant, we can conclude that the variable indeed has an impact on demand and that this sample parameter is truly reflective of the population.

4. Now omit both price and temperature from the regression equation. Should a marketing plan for soft drinks be designed that relocates most canned drink machines into low-income neighborhoods? Why or why not? Justify your answer. soft drink demand = 254. 56 - 5. 97  Income/Capita The regression result shows that the F-value for the overall significance of the regression equation is 5. 79 with a significance value of 0. 0202. As the significance value is less than 0. 05, the model is significant at a 5% level of significance. This means that the estimated regression equation is statistically significant.

But, the R- square value of the model is 0. 1118. This means that only 11. 18% variations in the annual demand can be explained by the variations in the independent variable, income per capita. The remaining 88. 82% of variations are left unexplained. Therefore, the regression coefficient of the independent variable income per capita was not statistically significant in the multiple regression models. Putting all of these facts into consideration, it would be a bad idea to change the marketing plan for soft drinks to relocate most canned drink machines into low-income neighborhoods.

In conclusion, we can conclude that the price of soft drinks has an inverse impact on the demand for soft drinks while income and temperature are directly related to soft

drink consumption. As the price of soft drinks increases, consumption decreases. On the other hand, as income and temperature increase, consumption also increases. We can also conclude that although income is statistically significant, income alone leaves too much unexplained variation that may be accounted for by other variables. Therefore, designing a marketing plan based solely on income would not be a good business decision.