In a simple linear regression problem, the correlation coefficient r and the slope b₁:

	must be equal to each other.
	must have the same sign.

	must have opposite signs.
	are not related.

Linear regression can achieve which of the following?

	Identify how much variable y will change if variable x changes.
	Predict someone's Biostatistics grade from her mathematical ability.

	If the weather gets 30% colder, then the sales of scarves will increase by 50%.
	All of the above.

A biostatistician finds the relationship between the number of weeks (x) spent in a hospital and number of seizures per week (y) and is described by the following equation: .9 -.91x This is based on a sample size of 50 patients and the associated coefficient of correlation is, r = -.93.

Using this information answer the following question: How many seizures per week would you predict for a person who has been at the hospital for ten weeks?

	24.0
	5.8

	14.9
	-.91

A biostatistician finds the relationship between the number of weeks (X) spent in a hospital and number of seizures per week (Y) and is described by the following equation: .9 (-.91X) This is based on a sample size of 50 patients and the associated coefficient of correlation is , r = -.93.

Using this information above, answer the following question: How do you interpret r = -.93?

	There is a perfect positive relationship between the number of weeks spent in a hospital and the number of seizures recorded.
	There is a perfect negative relationship between the number of weeks spent in a hospital and the number of seizures recorded.

	There is a strong negative relationship between the number of weeks spent in a hospital and the number of seizures recorded.
	There is a strong positive relationship between the number of weeks spent in a hospital and the number of seizures recorded.

The range of the correlation coefficient is?

	-1 to 0.
	0 to 1

-1 to 1

None of the above

Two variables have a positive association when

	the values of one variable tend to increase as the values of the other variable increase.
	the values of one variable tend to decrease as the values of the other variable increase.

	the values of one variable tend to increase regardless of how the values of the other variable change.
	the values of both variables are always positive