2.2 (Data file: UBSprices) The international bank UBS regularly produces a repor
ID: 3275016 • Letter: 2
Question
2.2 (Data file: UBSprices) The international bank UBS regularly produces a report (UBS, 2009) on prices and earnings in major cities throughout the world. Three of the measures they include are prices of basic com modities, namely 1 kg of rice, a 1 kg loaf of bread, and the price of a Big Mac hamburger at McDonalds. An interesting feature of the prices they report is that prices are measured in the minutes of labor required for a 'typical" worker in that location to earn enough money to purchase the commodity. Using minutes of labor corrects at least in part for currency fluctuations, prevailing wage rates, and local prices. The data file includes measurements for rice, bread, and Big Mac prices from the 2003 and the 2009 reports. The year 2003 was before the major recession hit much of the world around 2006, and the year 2009 may reflect changes in prices due to the recession The figure below is the plot ofy-rice2009 versus x = r!ce2003, the price of rice in 2009 and 2003, respectively, with the cities correspond- ing to a few of the points marked.Explanation / Answer
2.2.1 For points above the y = x line, the rice price in 2009 was more than rice price in 2003. while for points below the line, price in 2009 was less than price in 2003.
2.2.2 Vilnius had the largesrt increase in rice price, while Mumbai had the largest decreae in rice price.
2.2.3 Though the slope of the regression line is < 1, there is an intercept. Hence by looking at the graph, we can conclude that for cities with prices on the lower side (<25), prices in 2009 are higher than in 2003, while for cities with cities with prices on the higher side (>25), the prices in 209 tend to be lower than in 2003. 25 is the point at which the regression line cuts the x = y line.
2.2.4
(i) Simple visual inspection of the data and the fitted regression line shows that deviations of the actual data points from the line are quite high. A few outliers like Mumbai, Nairobi and Seoul have distorted the fit quite a lot. If we eliminate even one outlier, we may get a much better fit.
(ii) Also the visual pattern doesn't look like a straight line, but more curvilinear, with higher increase for cities on the higher side.
Hence fitting a simple linear regression does not seem to be appropriate.
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