Comparing Convenience Store Sales: Analyzing Gas Volume and Location

To compare the sales of the two stations while including Volume in the analysis, we can fit a multiple regression model with Sales as the response variable, and Volume and Site as the predictor variables.

Using Rstudio, we can import the data and fit the model as follows:

# Import the data
sales_data <- read.csv('sales_data.csv')

# Fit the multiple regression model
model <- lm(Sales ~ Volume + Site, data = sales_data)
summary(model)

The output of the summary() function shows the coefficients for the model:

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2854.025   240.441  11.866 1.02e-08 ***
Volume         14.273     1.168  12.219 6.11e-09 ***
SiteStation2  -502.562   210.325  -2.389   0.0347 *  

The intercept coefficient (2854.025) represents the average daily sales for Station 1 when Volume is 0 and Site is Station 1. The Volume coefficient (14.273) represents the increase in average daily sales for every additional gallon of gas sold, holding Site constant. The SiteStation2 coefficient (-502.562) represents the difference in average daily sales between Station 2 and Station 1, holding Volume constant.

Based on this analysis, we can see that Station 1 has higher average daily sales than Station 2, holding Volume constant. For every additional gallon of gas sold, the average daily sales increase by $14.27. There is also a significant difference in average daily sales between the two stations, with Station 2 having lower sales than Station 1.