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Essay / Research Paper Abstract
This 5 page paper uses the monthly share price of the Ford Motor company and the new car retail sales in the United States to demonstrate how regression analysis may be conducted in order to ascertain if there is a correlation between two sets of data. The bibliography cites 2 sources.
Page Count:
5 pages (~225 words per page)
File: TS14_TEregana.rtf
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Unformatted sample text from the term paper:
between the two data sets. In many instances the data set is time related, for example using regression analysis to forecast the growth in the following year when there is
a history that can be used to project forward from. In this paper we will use two data sets for a period
of two years. The monthly figures could be extended to five years, but this would take up a great deal of space in a paper. The theory and applications are
the same is the same. In seeking to see if there is a correlation we can make forecasts regarding what has not yet occurred, as this will take time to
be proven. However, we can look at the existing data and then make projects for periods where the results are already known, and compare these with the actual results.
For ease of demonstration, as this takes place of three years we will label the months 1 - 24, these can be replaced
by dates easily, however, this will make the theories more transferable. The data we use will be in relation to the automotive industry, with the share price for Ford and
then the US retail sales for new car dealers (in millions). We will use this second set of data because the US is the largest single market for this American
car company, and we would expect to seethe share price react in line with the retail sales index. It may be expected that if car sales fall, so would the
share price of a company in that market, partly due to potential fewer sales, and partly due to market expectation. The converse would also appear to be a valid hypothesis.
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