Use the excel file attached below to answer those questions questions.Methods: discuss your modelling approachWhat are the independent variables (co-variates)?What is the dependent...

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Use the excel file attached below to answer those questions questions.













Methods: discuss your modelling approach









  1. What are the independent variables (co-variates)?















  1. What is the dependent variable?












  1. Provide as much detail about the various models you have in your study.















  1. Did you need to transform any variables?















  1. How well do the variables fit the model?















  1. How well do the variables describe the model?


























































Important : I added the whole project description file as well that might help you to understand more what the project is about








QNT2020 FOUNDATIONS OF PREDICTIVE ANALYTICS & DECISION MODELING Project on Predictive Models Understanding Franchise Performance u Your boss wants your help to understand what drives the performance of stores of the Subway franchise. u Your boss helps you acquire data from last year’s sales and the demographic factors of each store: u SALES u SQFT u INVENTORY u ADVERTISING u FAMILIES u STORES u SCHOOL u The EXCEL data file (Franchises.xlxs) has a data dictionary describing the meaning of the variables. Using Simple Linear Regression Models to Understand Franchise Performance u Use the dataset in Franchises.xlxs. u Build a model to establish which individual factors affect sales in our franchises and how? u Can we create a model to help design a store and achieve a certain level of sales? u Which factors are critical to the design of the store: area (sq ft), inventory, advertising spend, size of the sales district, number of competing stores, schools near by? Using SLR Analysis to Understand Franchise Performance u Examine the Bivariate Relationships. Use linear regression to build models to answer these questions. u Use the data to determine the relationship between SALES and FAMILIES (the size of the customer base around each store). u How is the size of the customer base around each store related to our expected sales? u Use the data to determine the relationship between SALES and SQFT (the area each store occupies). u Use the data to determine the relationship between SALES and INVENTORY (the number of items in each store). u Use the data to determine the relationship between SALES and ADVERTISING (the amount of money each store spends on promotion). u Use the data to determine the relationship between SALES and STORES (the number of competing stores near by each store in our franchise). u Use the data to determine the relationship between SALES and SCHOOL (whether there is school near by). Using SLR Analysis to Understand Franchise Performance u Perform the analysis and then give your answer to the questions posted above, with you reason for the answers. u The EXCEL data file has a data dictionary describing the meaning of the variables. Using Multivariate Regression Models to Understand Franchise Performance u Use the same dataset in Franchises.xlxs. u Build a model to answer the questions: u Which collection of factors affect sales in our franchises and how? u Can we create a model to design a store given certain demographics so we can achieve a certain level of sales? u Examine each of the variables and see if any variables need to be transformed to improve the multi-variate model. u Build a multivariate regression model that can be used to predict store sales Prediction Analysis u Use the model to do some analysis. u We just opened a store in a neighborhood with 5,000 families, the store is 5,000 sq ft, we are planning to spend $5,000 a month in advertising, carry $250,000 in inventory, there are 5 competing stores in the neighborhood and there are schools near by. u What are the projected sales? Project Report Guidelines u Include tables and visuals to support your analysis. u Your project report should be 5-7 pages. u You can use the following outline for your project report: 1. Introduction: introduce the problem, provide some background 2. Problem Statement: describe the problem you are proposing to solve u State the assumptions (if any) u Data Sources: list the sources of data you expect to extract data for developing the analysis. 3. Data Description: describe the data (explain any terms that are specific to your dataset Project Report Guidelines (cont.) u Outline (cont.) 4. Methods: discuss your modelling approach u What are the independent variables (co-variates)? u What is the dependent variable? u Provide as much detail about the various models you have in your study. u Did you need to transform any variables? u How well do the variables fit the model? u How well do the variables describe the model? 5. Results: what is the outcome of your analysis across the different models? 6. Conclusion: Do you have a recommendation? Project Submission Details u Please submit only one report (in PDF format) for each group u Your group report is due by Monday Apr 17. u Please submit your report to Blackboard -> Project 1 -> Submission u Also submit your EXCEL file showing all the models you have developed (one model per worksheet tab)
Answered 3 days AfterApr 13, 2023

Answer To: Use the excel file attached below to answer those questions questions.Methods: discuss your...

Banasree answered on Apr 16 2023
17 Votes
1.Ans.
1. Regression: The goal of regression would be to predict sales based on the other variables. In this case, the independent variables would be
SQFT, INVENTORY, ADVERTISING, FAMILIES, STORES, and SCHOOL (which would need to be converted to a dummy variable).
2. Classification: The goal of classification would be to predict whether a store is successful (i.e. has high sales) or not based on the other variables. In this case, the independent variables would be SQFT, INVENTORY, ADVERTISING, FAMILIES, STORES, and SCHOOL (which would need to be converted to a dummy variable).
3. Clustering: The goal of clustering would be to group stores based on their characteristics. In this case, all the variables except for SALES would be considered independent variables.
2.Ans.
The dependent variable is not explicitly given in the data. However, based on the context and information provided, it can be inferred that the dependent variable is likely to be the SALES column, which represents the sales figures of various stores.
3.Ans.
1. Regression Models: Regression models can be used to analyze the relationship between the independent variables (sales, square footage, inventory, advertising, families, stores, school code) and the dependent variable (school). Linear regression models, logistic regression models, and Poisson regression models can be used based on the nature of the dependent variable.
2. Decision Trees: Decision trees can be used to create a decision-making model based on the...
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