The scatterplot above plots number of unemployed Americans (in thousands) against gross national product (in billions of U.S. dollars). The data were gathered in an economics study from 1947 to 1962 by J.W. Longley.

(Reference: J. W. Longley (1967) An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819-841.)

The correlation is 0.60

What might we conclude from this data?

	a.	The weak positive correlation shows that higher unemployment numbers cause lower gross national product.
	b.	The weak positive correlation shows that there is no relationship between unemployment numbers and gross national product.
	c.	Common sense suggests that unemployment numbers and gross national product are negatively correlated. There is likely a lurking variable .

2.Divorce and prison rates: This scatterplot shows U.S. divorce rates (per 1,000) and prison rates for drug offenses (percentage admitted to state prison for drug offenses) for 7 years during 1960-1986. The correlation is 0.67.

What can we conclude from this data?

	a.	Higher divorce rates are associated with higher prison rates for drug offenses, but this data does not mean that higher divorce rates cause higher prison rates.
	b.	The strong positive correlation between divorce rates and prison rates supports the advocates of traditional family values who argue that keeping divorce rates low will improve society.
	c.	To lower prison rates for drug offenses, a state should provide more marriage counseling.
	d.	The strong correlation suggests that divorce rates contributed to higher prison rates for drug offenses.