1. The birth lengths in cm (x) and birth weights in kg (y) of a sample of 50 newborn female babies are compared, yielding a correlation coefficient of r=0.578=0.578 and a linear regression equation of =8.89+0.243x=8.89+0.243x. The babies all had lengths between 46.5 and 53.0 cm, and weights between 2.50 and 4.05 kg.

Based on this, predict the birth weight of a newborn female baby with a birth length of 48.5 cm.

i. Is it appropriate to use the linear regression equation to make the requested prediction for the value of the unknown variable?

(click to select)

Yes

No

ii. Compute the predicted birth weight. (Input 0 if your answer to part (i) is no.)

kg

Round to two decimal places.

2. Complete the following instructions: i. Identify the independent and dependent variables. ii. Calculate and interpret the Pearson correlation coefficient r for the paired data. Be sure to indicate if the correlation is positive or negative, and whether it is strong, moderate, or weak, or if there does not appear to be any significant correlation.

An LCBO employee compares the relationship between the price of 6 different vintage bottles of wine, and the number of those bottles that have been purchased in the last 3 months to see if there is a correlation between the number of bottles purchased and the price of the bottle.

Number of Bottles of Wine Purchased in the Last 3 Months	Price of Wine Bottle ($)
44	25
18	60
93	15
65	30
82	20
56	40

i.	Independent Variable:
	(click to select) Number of Bottles of WinePurchased in the Last 3 Months Price of Wine Bottle ($)
	Dependent Variable:
	(click to select) Number of Bottles of Wine Purchased in the Last 3 Months Price of Wine Bottle ($)

ii.	Pearson Correlation Coefficient (r):
	Round to 3 decimal places.
	Correlation:
	(click to select) Positive Negative Neither
	Correlation Strength:
	(click to select) Strong Moderate Weak No Significant Correlation