Hershey Company - Applications of Inference

The Hershey Company is the second largest candy maker in the nation (Levy, 2011). Known for their chocolate bars and Reese Peanut Butter Cups, the company wants to stay ahead of their competitors. The Mars Corporation is one of the leading competitors of The Hershey Company, as the Snickers Bar has been deemed the number one selling candy bar in the United States (“Snickers Celebrates 80 years,” 2010). In order to remain competitive in the marketplace, The Hershey Company has hired the JTL Research Company in order to determine their customer base and actual and expected sales among those customers.

JTL Research Company has collected sales data from 30 convenience stores in the United States by accessing approximately 3000 paid data bases that randomly selected the 30 convenience stores sales information used in this report. Linear regression and hypothesis testing for the difference of two independent means, as well as the difference of two means using software tools are the methods used to analyze the figures provided in this report.

The data contained in Appendix “A” shows that the independent variable is the store sales of Reese Cups from women, and the dependent variable is the store sales of Reese Cups from men. The Reese Cup Scatter Plot, as seen below, allows JTL to explore the sales data visually. The scatter plot shows no distinct pattern and suggests that there is no correlation between store sales of Reese Cups from women and store sales of Reese Cups from men.

Although the scatter plot provides an indication that the independent and dependent variable have no correlation, it is necessary to utilize a more objective measurement. Utilizing a linear correlation coefficient is useful in detecting straight-line patterns, indicating correlation between the variables (Triola, 2011).

Using the data in Appendix “A” Table 2: Reese Cup Sales Among Men and Women, the value of the linear correlation coefficient was found utilizing computer software. Table 3: Linear Regression and Correlation Coefficient Calculation, shows the results of the calculations where the linear correlation coefficient is .2362. In order to interpret the results, it is necessary to obtain the computed P-value, Table 3: Linear Regression and Correlation Coefficient Calculation, indicates that the P-value is .2089. A significance level of .05 was utilized; therefore, there is not sufficient evidence to support the conclusion of a linear correlation.

A regression equation expresses a relationship between the indicated independent variable and the dependent variable. In this case, the regression equation would show a relationship between sales of Reese Cups from women and sales of Reese Cups from men. Table 3: Linear Regression and Correlation Coefficient Calculation, shows that the shows that the y-intercept was calculated at 102.124 and the slope was calculated at