Royal Pizza is a pizzeria that started in Brighton Beach, Brooklyn back in the early 1960s. Since then, it has grown into a major regional pizzeria chain with locaAons throughout the northeastern US, all owned and operated by Royal Pizza Inc.

Your team at Royal Pizza has been tasked by the CEO to assemble a detailed report with analysis to explain what drives sales at the companys pizza restaurants. The CEO has provided you a summary of last years data for thirty of the Royal Pizza locaAons; see the accompanying spreadsheet Pizzeria

Data.xlsx

PART A - SLR Analysis to Understand Pizzeria Performance

Examine the bivariate relaAonships using a .05 level of signi?cance:

• Use the data to determine the relaAonship between SALES and SIZE (the square footage of each pizzeria).
• Use the data to determine the relaAonship between SALES and DELIVERY (the annual $ amount each pizzeria spends on delivery service).
• Use the data to determine the relaAonship between SALES and ADVERTISING (the annual $ amount each pizzeria spends on adverAsing).
• Use the data to determine the relaAonship between SALES and FAMILIES (the number of families in the neighborhood of each pizzeria).
• Use the data to determine the relaAonship between SALES and COMPETITION (the number of compeAng pizza restaurants in the same neighborhood).

For each of the 5 SLR analyses listed above, you should create exactly one Excel worksheet (tab) that includes:

• A sca_er plot with SALES on the Y-axis and the corresponding independent variable on the X-axis. The sca_er plot should also have the trend line (the regression line) and its equaAon on the sca_er plot.
• The regression analysis from Excels Data Analysis Toolpak for each corresponding SLR analysis placed to the right of each sca_er plot.
• IdenA?caAon of the unusual and/or outliers in the Standard Residuals output (highlight the rows in yellow and idenAfy each as Unusual or Outlier by wriAng the correct quali?er in the cell to the right of the corresponding residual).

PART B - MLR Analysis to Understand Pizzeria Performance

Examine the mulAvariate relaAonships using a .05 level of signi?cance:

• Create an Excel worksheet (tab) with the MLR analysis from Excels Data Analysis Toolpak. IdenAfy any unusual and/or outliers in the Standard Residuals output (highlight the rows in yellow and idenAfy each as Unusual or Outlier by wriAng the correct quali?er in the cell to the right of the corresponding residual).
• On another Excel worksheet (tab), generate the correlaAon matrix to test each pair of predictor variables for collinearity.
• For each predictor variable, calculate the Variance In?aAon Factor (VIF) to test for mulAcollinearity. You should have 5 disAnct Excel worksheets (tabs), one for each predictor.

PART C - Predic

Once you have ?nalized your predicAon models, answer the following quesAon:

Royal Pizza has just opened a pizzeria in a neighborhood with 10,400 families and 7 compeAng pizza restaurants. The area of the new pizzeria is 4,900 square feet and is planning to spend $46,500 per year in delivery services and $12,500 per year in adverAsing.

What are the projected sales of this new pizzeria? You should have 6 answers: 1 from each of the 5 SLR equaAons, and 1 from the MLR equaAon. Be sure to include both a quick 95% con?dence interval and quick 95% predicAon interval for each of the 6 answers.

PART D - Project Report

Please use the following format for your project report:

• Introduc*on: Explain the background of the organizaAon and the goals you are a_empAng to accomplish on behalf of the organizaAon.
• Methodology: Describe what methodology your team will use to address the project.
• SLR Models and Analyses: Include a separate secAon for each of the 5 bivariate analyses. For each analysis, list your assumpAons regarding the data provided. Explain how you assembled each model. IdenAfy the dependent and independent variables for each model. Explain how well the variables ?t the model. Explain the outcome of your analysis for each model.
• MLR Model and Analysis: Include a separate secAon for the mulAvariate analysis. For the analysis, list your assumpAons regarding the data provided. Explain how you assembled the model. IdenAfy the dependent and independent variables for each model. Explain how well the variables ?t the model.
• Predic*on Analysis: Include a separate secAon to answer the predicAon analysis quesAon using the 6 di?erent regression equaAons. Include the quick 95% con?dence interval and quick 95% predicAon interval for each answer and explain your ?ndings.
• Conclusion: Summarize any insights and recommendaAons you have.

Be sure Include summary tables (i.e., summary of perAnent data from your Excel worksheets) and visuals/graphs in the report.

Please help with Part C.