Regression Analysis

The dynamic nature of e-trading provides one of the best environments that can be used to determine the relationship between the number of buyers and expected auction revenue (Anderson, Friedman, Milam, & Singh, 2008). The expected auction revenue is the dependent variable while the number of buyers, reserve prices, unique bidders, duration of an auction in days, and bidding are part of the independent variables. The interactive pricing theory posits that the main reason that auction mechanisms are used to set prices is due to the expectation that auctions tend to result in higher revenue for the offered commodities (Schindler & Schindler, 2011). The development of the Internet has made it possible to have that enables traders to conduct electronic auctions for the commodities they wish to sell. This website applies the English auction mechanism – where the seller entertains ascending bids (Schindler & Schindler, 2011). For example, item 113742636399 (A3) has a reservation price (RP) of 99 – the minimum price that the seller was willing to get for the product. In this case, the bid prices exceeded the seller’s RP, meaning that the item went to the last remaining bidder. Item 1 was sold at 102.5 from three bid prices of 102.5, 100, and 99. Therefore, it is expected that the higher the number of buyers the higher the likelihood of getting a higher bid, thus more revenues. The eBay auction matches the model’s assumptions and my research approach. For example, the plotted graphs (see Figure 1) reveal that the more the number of bidders the higher the final price becomes. It is more of a positive linear relationship. We can compare two points on the graph below: (4, 180) and (7, 200). These are two points that lie along the plotted line…….