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Sheet1 Tues Wed Thu Fri Mon Tues Wed Thu Fri Mon Tues Wed Thu Fri Mon 199............................................... (Data for A) Total Sales Dollars by Day of Week (Data for B) Number of Sodas Sold by Day of Week (Data for C) Number of Coffees Sold by Day of Week Bonus Project 3: The Executive Express managers wish to know if there is a difference in sales between various days of the week. In particular, they would like to know: A) If the (population) mean Total Sales is the same for all five days of the week, or if at least one day has a significantly different mean, and if so, which are higher or lower than others and by how much B) If the (population) mean Number of Sodas Sold is the same for all five days of the week, or if at least one day has a significantly different mean, and if so, which are higher or lower than others and by how much C) If the (population) mean Number of Coffees Sold is the same for all five days of the week, or if at least one day has a significantly different mean, and if so, which are higher or lower than others and by how much Instructions: To Complete this project, paste the results of your analyses (e.g. tables from StatCrunch) and answers to these questions in a Word document. 1) State which statistical procedure studied in this course allows you to compare the means of five different groups 2) Address the manager's question regarding Total Sales (A). Copy the data from Excel to StatCrunch. Run the appropriate analysis. Answer the questions: Is there evidence that the mean Total Sales varies among the 5 weekdays? If so, which means appear to be significantly higher or lower than which others? Estimate the difference in Total Sales between two days that show statistically significant differences in means, or state why an estimated difference is not meaningful and therefore not reported 3) Address the manager's question regarding Number of Sodas Sold (B). Copy the data from Excel to StatCrunch. Run the appropriate analysis. Answer the questions: Is there evidence that the mean Number of Sodas sold varies among the 5 weekdays? If so, which means appear to be significantly higher or lower than which others? Estimate the difference in Number of Sodas Sold between two days that show statistically significant differences in means, or state why an estimated difference is not meaningful and therefore not reported 4) Address the manager's question regarding Number of Coffees Sold (C). Copy the data from Excel to StatCrunch. Run the appropriate analysis. Answer the questions: Is there evidence that the mean Number of Coffees sold varies among the 5 weekdays? If so, which means appear to be significantly higher or lower than which others? Estimate the difference in Number of Coffees Sold between two days that show statistically significant differences in means, or state why an estimated difference is not meaningful and therefore not reported 5) Write one or two sentences that make recommendations to the manager based on the results of your analyses.

Paper For Above instruction

To analyze whether there are significant differences in sales metrics across the days of the week at Executive Express, one of the most appropriate statistical procedures is the One-Way Analysis of Variance (ANOVA). This method is suitable because it compares the means across multiple groups—in this case, five days of the week—allowing us to determine if at least one day differs significantly from the others. Implementing ANOVA involves calculating the variation within groups and between groups, and then assessing whether the observed differences are statistically significant, typically at a chosen alpha level such as 0.05.

Regarding the Total Sales Dollars—Question A—data should be transferred from Excel into a statistical software such as StatCrunch. After running a one-way ANOVA, analysis of variance results indicate whether the mean total sales percentage varies across the five weekdays. If the F-test yields a p-value less than 0.05, it suggests at least one day’s mean sales significantly differ from others. Subsequently, a post-hoc comparison—such as Tukey’s Honestly Significant Difference (HSD) test—can identify specific days with higher or lower means. For example, if sales on Friday are significantly higher than Monday, this implies promotional or operational factors might impact peak sales days. The estimated differences provide actionable insights for management to strategize resource allocation or marketing efforts.

Similarly, for Question B, the analysis focuses on the number of Sodas Sold per day. After data input into StatCrunch, performing ANOVA will reveal if the means vary significantly among different days. If significant differences are detected, post-hoc tests clarify which days outperform or underperform relative to others. For example, a higher mean number of sodas sold on Saturday compared to Wednesday could reflect consumer demand patterns or promotional effectiveness. Calculating the mean differences offers quantifiable evidence to support managerial decisions.

For Question C, the same process applies to the number of Coffees Sold by day. Analysis indicates whether mean coffee sales differ significantly across weekdays. If so, identifying specific days with higher or lower sales can help inform staffing and supply chain decisions. For instance, increased coffee sales on Monday might result from commuters’ habits, requiring adjusted staffing levels or inventory planning. The statistical significance and estimated differences underpin evidence-based operational planning.

Based on the analysis of all three metrics, if significant variations are found, recommendations for the management could include targeting specific days for special promotions, adjusting staffing schedules, or optimizing inventory levels. For instance, if Fridays consistently show higher sales across categories, promotional events could be concentrated on that day to maximize revenue. Conversely, low-sales days might benefit from targeted marketing campaigns to boost customer engagement and sales. By understanding the specific days with higher or lower sales, management can allocate resources efficiently, improve customer service, and increase overall profitability.

References

  • Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). Sage Publications.
  • Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences (10th ed.). Cengage Learning.
  • Rumsey, D. J. (2016). Statistics For Dummies (2nd ed.). Wiley.
  • Moore, D. S., Notz, W., & Flinger, M. (2013). The Basic Practice of Statistics (6th ed.). W. H. Freeman.
  • McHugh, M. L. (2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143-149.
  • Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
  • Stevens, J. P. (2012). Applied Multivariate Statistics for the Social Sciences (5th ed.). Routledge.
  • Yuan, Y., & Bentler, P. M. (2016). Structural Equation Modeling. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling. Guilford Publications.
  • Abdi, H. (2010). The Tukey Honestly Significant Difference (HSD) test. Encyclopedia of Research Design.
  • Bartholomew, D. J., Knott, M., & Moustaki, I. (2011). Latent Variable Models and Factor Analysis. Wiley.

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