In the case study under review, there are two variables – annual revenue goal and the monthly ad deliveries. In this case the annual revenue goal is the dependent variable while the monthly ad delivery is the independent variable. Therefore, the most appropriate method for analysis of variance in this case study will be one-way ANOVA. A one-way ANOVA is used in cases where there is only one independent variable. It tests where there is a difference between the means of two or more groups of the independent variable to determine whether the means are statistically different or not.
The statistical results should demonstrate that there is a difference between the monthly number of ads delivered for the proposed plan to be approved as it is. This can be achieved by setting the null hypothesis (H 0 ) to imply that there is no difference between the monthly number of ads delivered and the alternative hypothesis (H 1 ) to imply that there is a difference between the monthly number of ads delivered.
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H 0 =µ 1 =µ 2 = … µ 12
H 1 = µ 1 =µ 2 = … µ 12
Where µ= group mean and 12= number of groups
If the calculated F is less than the critical value of F (degrees of freedom), the difference between the means are not significant and we fail to reject the null hypothesis. Since there is no difference between the monthly number of ads delivered, the proposed revenue plan should not be approved.
If the calculated F is greater than the critical value of F (degrees of freedom), the difference between the means are significant and we reject the null hypothesis. Since there is a difference between the monthly number of ads delivered, the proposed revenue plan should be approved as it is.
References
Anderson, A. (2013). Business statistics for dummies. For Dummies.
Mackenzie, R. J. (2018, July 20). One-way vs two-way ANOVA: Differences, assumptions and hypotheses. Technology Networks. https://www.technologynetworks.com/informatics/articles/one-way-vs-two-way-anova-definition-differences-assumptions-and-hypotheses-306553
