The other day, I created a data analysis toolbox for my project. The toolbox outlines how I am going to collect the data and what I am going to do with the data once it has been collected. First, the data must be organized so that its background can be examined. It questions who are the individuals that are described in the data, what are the variables, why was the data gathered, and where, when, how, and by whom was the data produced. Second, graphical displays must be shown to display the data correctly. Third, summary statistics are calculated so that the statistics used are relevant to the problem. Finally, an interpretation of the toolbox gives you details on what all of these parts mean in context of the problem.
To start out my toolbox, the data represents housing prices in the New Orleans area, as well as averages of housing prices in Louisiana and the United States. The variable in which the data is recorded is in U.S. dollars. The data is gathered to find, if any, a difference between housing price trends in New Orleans and the trends of the state of Louisiana and the United States. The data would be comprised from a New Orleans real estate agency during March 2009 by me. The graphs used to display the data would be a box plot, a histogram, and a line graph to display the price trends over time. The numerical summaries relevant to my problem are the five number summary, the mean, the median, and the standard deviation of the housing prices. The interpretation of the data would show how the data, graphs, and numerical summaries display the difference between the prices in New Orleans and the Louisiana and United States prices, if any, and explain the possibilities of why the trends are that way.
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