Two variables in the dataset AllCountries are the size of the country (in 1000 sq km) and the percent of the population living in rural areas. We are interested in using the size of the country (LandArea) to predict the percent rural (Rural). The values of these variables for a random sample of ten countries is shown, with the 3-letter country codes, in Table 2.37, and is also available in TenCountries. Figure 2.74 shows a scatterplot of the data.

Table 2.37

Figure 2.74

(a) What is the explanatory variable? What is the response variable?
(b) Without doing any calculations, which do you think is the most likely correlation between the two variables?

(c) Use technology to find the regression line to predict percent rural from land area, and interpret the slope.
(d) Does the intercept make sense in this situation?

(e) Which country is the most influential on this regression line (use the 3 letter code)?
(f) Use the regression line to predict the percent of the US population living in rural areas given that the area of the US is 9147.4 thousand sq km. Does the prediction seem reasonable? Explain why it is not appropriate to use this regression line to predict the percent rural for the US.

Transcribed Image Text:

Country Land Area Rural ROU 229.9 45.8 LBN 10.2 13 SRB 88.3 BHS SVN 20.1 UZB 425.4 63.2 TUN ARM 28.5 MKD PRK 155.4 33.5 120.4 10.0 25.2 16.3 36.1 48.0 51.4 33.1 37.3 60 40 20 100 200 300 400 Land Area Rural