Explain why your predictions differ from these values

You are working in sales for Automobile Inc., a firm of car distributors, who are thinking of expanding into a new market. You have been asked to investigate the factors which determine vehicle ownership in various Asian countries; the most recent data you can find is for 2019 and is as shown in Cars3.xls.

To prepare for a presentation to management about opportunities for sales in Cyprus, carry out the following steps

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a. Plot scatter graphs of vehicles per thousand population against income, population, population density, and percentage of the population in urban areas in the Excel file with which you believe there might be a correlation. What do your results suggest?

b. For the variable which is more closely correlated to vehicles per thousand population, calculate the equation of the regression line, and interpret the results.

c. Plot scatter graphs of total vehicle ownership against income, population, population density per km^2, and population in urban areas. What do these graphs suggest?

d. For the variable which is more closely correlated to total vehicle ownership, calculate the equation of the regression line and interpret the results.

e. Which of the two regression equations do you think will be more useful to the company?

  • Data for Cyprus is as follows:
Income Population Population density % urban
6.71 73.7 99 73.3

Use this data and the regression equations calculated in b.) and d.) above to predict the total number of vehicles and number of vehicles per 1000 population for Cyprus. The actual figures are 7.04 million and 105.6 per 1000 population. Explain why your predictions differ from these values

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