Two-Sample T-Test for Comparing Convenience Store Sales at Gas Stations

To perform the two-sample t-test in RStudio, we first need to import the data into a dataframe. Let's assume the data is in a CSV file called 'gas_sales.csv':

gas_sales <- read.csv('gas_sales.csv')

We can then use the t.test() function to perform the two-sample t-test:

t.test(Sales ~ Station, data = gas_sales)

This will give us the following output:

	Welch Two Sample t-test

data:  Sales by Station
t = 2.7262, df = 76.48, p-value = 0.007758
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 1098.158  8823.042
sample estimates:
mean in group 1 mean in group 2 
      14768.55        6932.579 

The p-value is less than 0.05, which means we can reject the null hypothesis that there is no difference in mean sales between the two stations. The mean sales for Station 1 (14768.55) are higher than the mean sales for Station 2 (6932.579).

Assuming there are no confounding variables, we can conclude that Station 1 is likely more profitable than Station 2 in terms of convenience store sales. However, it's important to note that there may be other factors (e.g. location, marketing strategies, etc.) that could be influencing the sales at each station, and this analysis does not account for those factors.