1. Suppose Isabella has developed the following estimated regression equation for the price of an Edmonton condo, based off square footage size (in square feet). Price = 144000 + 42*(Square Footage) (a) If someone wants to purchase an Edmonton condo with a size of 760 square feet, how much should they expect to pay? Answer: dollars (b) If they want to buy a condo that is 92 square feet bigger, then how much more money should they expect to pay? Answer: dollars (c) Would the intercept be meaningful if this regression represents the average Edmonton condo?

• Yes
• No

2. Brandon works as a statistician for the Toronto Blue Jays, and wants to analyze the relationship between a pitcher's age and how many strikeouts they accumulate in a season. He takes a sample of 8 Blue Jays pitchers with ages between 25 and 34 and finds there is a linear relationship between their ages and the number of strikeouts they had in the 2015 season. Here are the numerical summaries for age and the number of strikeouts: r=0.6,age=27.7,sage=3.67,strikeout=105.6,sstrikeout=7.44r=0.6,age=27.7,sage=3.67,strikeout=105.6,sstrikeout=7.44 (a) What is the value of b₁, the estimated slope? (Round your answer to 3 decimal places, if needed.) Answer: (b) What is the value of b₀, the estimated intercept? (Round your answer to 3 decimal places, if needed.) Answer: (c) What is the percent of variation in the number of strikeouts that is explained by age, using linear regression? (Round your answer to 2 decimal places, if needed.) Answer: _________% (d) Can we use this linear regression to predict the number of strikeouts for a player age at 35?

• No, because the correlation coefficient is not 1.
• Yes, because we know the slope and intercept values.
• No, because we are uncertain about the range of the number of strikeouts.
• Yes, because it is a linear relationship.
• No, because we cannot extrapolate.