Product Pricing Analysis (R Studio/RPubs)

The aim of this project is to perform regression and predictive analysis on a dataset that includes information on the quantity of products sold, the price of each product, and whether or not the sale occurred during a holiday period.

The data will be imported into R Studio and preprocessed as necessary. The analysis will involve fitting linear regression models to the data to explore the relationship between the predictors (quantity and holiday status) and the response variable (price). Additionally, the analysis will involve creating visualizations of the data and model results to better understand the relationships in the data.

Possible research questions that can be addressed by this data include:

• Is there a statistically significant relationship between the quantity of products sold and the price of each product?
• Does the relationship between quantity and price differ depending on whether the sale occurred during a holiday period?
• Can we use the holiday status and quantity information to accurately predict the price of a product?

The final deliverables of the project may include a report summarizing the findings, as well as interactive visualizations and regression analysis outputs published to RPubs (https://rpubs.com/smonica498/coke_canada_analysis). The insights gained from this analysis can be used to inform pricing strategies and sales forecasting. Additionally, this analysis can serve as a foundation for further analysis of the relationship between sales volume, price, and holiday periods.