Exam
Name___________________________________

SHORT ANSWER.  Write the word or phrase that best completes each statement or answers the question. 

Provide an appropriate response. 

1) 

For a random sample of 100 American cities, the linear correlation coefficient between the number of robberies last year and the number of schools in the city was found to be r = 0.725.  What does this imply? Does this suggest that building more schools in a city could lead to more robberies?  Why or why not?  What is a likely lurking variable? 

1)  

_____________ 

2) 

For a random sample of 30 countries, the linear correlation coefficient between the infant mortality rate and the average number of cars per capita was found to be r = -0.717.  What does this imply?  Does this suggest that if people buy more cars, this could lower the infant mortality rate?  Why or why not?  What is a likely lurking variable? 

2)  

_____________ 

3) 

A random sample of 200 men aged between 20 and 60 was selected from a certain city.  The linear correlation coefficient between income and blood pressure was found to be r = 0.807.  What does this imply?  Does this suggest that if a man gets a salary raise his blood pressure is likely to rise?  Why or why not?  What are likely lurking variables? 

3)  

_____________ 

 

1) 

A positive correlation exists between the number of schools in a city and the number of robberies but this is an example of correlation not causation. Building more schools is unlikely to lead to an increase in robberies. A likely lurking variable is the population of the city and this lurking variable accounts for the positive correlation. Larger cities tend to have both more schools and more robberies.  

2) 

A negative correlation exists between the infant mortality rate and the number of cars per capita but this is an example of correlation not causation. If people buy more cars, this is unlikely to lead to a decrease in the infant mortality rate. A likely lurking variable is wealth and this lurking variable accounts for the negative correlation. More affluent countries tend to have both more cars per capita and lower infant mortality rates.  

3) 

A positive correlation exists between income and blood pressure but this is an example of correlation not causation. An increase in salary is unlikely to lead to an increase in blood pressure.  Age and level of job stress are possible lurking variables and these lurking variables account for the positive correlation. Older men tend to have both higher blood pressures and higher incomes. Also men in high stress jobs tend to have both higher blood pressures and higher incomes.