Case Study On The Price Of East Tuna

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(a) The price of other tuna brands affecting the sales of East Tuna Based on the graph above, we can see that when the price of East Tuna increases, the sales of East Tuna decreases. The correlation (Refer to correlation) can prove that the relationship between the price of East Tuna and the sales of East Tuna are negatively related and it is fairly strong (-0.6159). The price of Kings Tuna is negatively related to the sales but with a weak relationship (-0.0247). Furthermore, the sales of East Tuna and the price of Lescos Tuna has a positive but weak relationship (0.1852). This means that Lescos Tuna is a competitor for East Tuna because when the price of Lescos Tuna increases, the sales of East Tuna increases as well. A software …show more content…

The coefficient for eastsp is when an increase of £1 in the price of East Tuna, the ln sales of East Tuna is assumed to decrease by 6.217632 untis, by holding all other constant. For the coefficient of kingsp, when the price of Kings Tuna increases by £1, the ln sales of East Tuna is assumed to increase by 1.41743 units, by holding all other constant. Moreover, the coefficient for lescop means when the price of Lescos Tuna increases by £1, the ln sales of East Tuna is assumed to increase by 2.147209 units, by holding all other constant. R2 in the log linear regression equals to 0.5557 or 55.7%, when 55.57% of changes in the ln sales of East Tuna can be described by the explanatory variables. In linear regression, R2 is lower by 11.28% compare to the R2 in log linear regression. By just comparing the R2 in both the regression models is not suitable as their dependent variables are not the same. The number of significant variables in log linear regression is more than those in linear regression. At 0.05 α level in the log linear regression, the coefficient for eastsp, lescop and the intercept are statistically significant. This explains why the log linear regression is better compare to the linear regression. (b) The type of advertising that affects the sales of East …show more content…

While the interpretation of D1 shows that the sales of East Tuna is 52.77% higher when advertising with the store display, by holding all other constant. The coefficient of D1 shows that if advertising with the store display is used, the sales of East Tuna would be 12,192.254 units. Correspondingly, by interpreting D2, East Tuna sales will increase by 318.39% when advertising with store displays and leaflets are used, by holding all other constant. When East Tuna uses both ways to advertise themselves, the sales of East Tuna would be 33,391.779 units. The R2 is 0.8248, which is higher than the R2 in the log linear regression model. This shows that a 82.48% of change in the sales of East Tuna can be described by the explanatory variables. The dummy variable regression model is better log linear regression model. The coefficient of D1 and D2 are statistically significant at 0.01 α level as their p values are 0.000. According to the information provided, both advertising tools are encouraged to be used in order to increase the sales of East Tuna. According to the 2 way scatter plot diagram, red dots are advertising with store display, green dots are advertising with both store display and leaflets, and blue dots are without any advertising at all. Obviously, the average sales of East Tuna is at highest with a figure of £19,025.67 when both store display

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