Twо vаriаbles in the dаtaset AllCоuntries are the size оf the country (in 1000 sq km) and the percent of the population living in the rural areas. We are interested in using the size of the country (Land Area) 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 the table. A scatterplot is shown for the data. 12 pts Country SRB BHS SVN UZB TUN ARM ROU MKD LBN PRK Land Area 87.46 10.01 20.14 425.40 155.36 28.47 230.02 25.22 10.23 120.41 Rural 44.6 17.3 50.2 63.8 33.5 37.0 45.8 43.0 12.5 39.4 all countries(1).png a. What is the response variable? a. Rural b. Land Area Answer: ___________ b. Without doing any calculations, which do you think is the most likely correlation between the two variables? a. -0.60 b. 60 c. 0.00 d. 0.60 Answer: ___________ c. Use technology to find the regression line to predict percent rural from land area. Round your answer for the intercept to two decimal places and your answer for the slope to three decimal places. rural = __________ + __________(land area) d. Does the intercept make sense in this situation? a. Yes b. No Answer: ________ 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 9145.4 thousand sq km. Round your answer to two decimal places. Predicted percent of the US population living in rural areas = ________________% Does the prediction seem reasonable? Answer: _____________(Yes or No)
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