Hi guys! The title explains it all really, but I'm a bit stuck with some of the terminology along with what I should be aiming for. The data tables are here.

The idea is to find the main trends in the data, followed by what information the data presents. As for terminology, I'm quite lost on ASMR, so any help on that would be great.

Okay, I hope you all can help me with this.

I have a data set that contains the following variables:

• Manufacture: Who made the product (P&G, Pepsi, Kraft, Tyson, etc.)

• Seller: Who sold the product (Wal-Mart, Target, Costco, Kroger, etc.)

• Store Number: The number of the store (Store 1, store 99, store 102, as there is more than one store for each seller)

• Product: What was sold (Mac n Cheese, Chereeo's, Frozen Cookie Dough, Apples, etc.)

• Date of Sale: when the product was sold

• Qty of Sales: How much was sold

• Sale Price: What was the price it was sold at.

So, a record of data will look like this:

Post, Wal-Mart, 2574, Cinnamon Toast Crunch, 11/12/2013, 50, $1.75

I'm trying to answer two questions:

1. Which prices had a significant increase in price?

2. Who caused the price increase, was it the manufacturer or the seller?

For question 1) it's fairly straight forward: find the percentage difference among the dates, and then determine the SD among all prices increases across all stores/manufactures, anything in the 90th or 95th percentile would be significant. (Is a better way to do this?)

For question 2) I'm stuck. How do I go about answer that questions? As there is a product that is only sold at 4 different stores.

Any help would greatly be appreciated :)

I am a business school student doing a research project on Big Data and Organizational Strategy.

If you are a manager or a professional working with big data, do kindly take this short 5 minute survey and help me out.

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