2

I would really appreciate it if someone could tell me where I would start in tackling the following tasks in R.

I'm not a data-science expert and am trying to teach myself data-analysis from a background in physics. I have introductory statistics and have just finished Udacity's course in R programming and exploratory data analysis.

Tasks:

1 )Simulate customer walk-ins for a given period. The simulations should take into account peak volumes at different times of the days and differences in week days and weekends.

2) Simulate customer purchases based on the customer walk-ins. Make assumptions on the average dollar price and variations for different times of the day.

Thank you

mayo naise
  • 21
  • 1

1 Answers1

0

You need either an initial data set or a large set of constraints to "fake" a data set. if you have an initial data set you could make somewhat valid assumptions regarding traffic flow, purchasing behaviors and the likes.

If not, you are going to need to research these different metrics from existing companies that are like what you want data for and then I assume put together a little program with those metrics as constraints and simulate your data. Once you have those constraints you could use simple averaging or weighted options for developing a constraint per variable.

Make sure they are semi accurate. For example, if you were modeling company growth for a "really good idea" research growth trends for like companies and for companies you want to emulate and then scale BACK from there.

You can also always purchase a dataset: http://datamarket.azure.com/browse/data

Jabberwockey
  • 131
  • 3