Q1) Store records illustrate that people who live closer to store tend to buy more. Relationship between distance to store i miles (X) and amount customer spends over year in dollars (Y) fits following regression line Y = -100 X * 1000. If customer lives 7 miles from store, determine predicted amount that customer spends at the store annually?

a) $700

b) $300

c) $1700

d) $1000

Q2) U.S. News & World Report publishes average starting salary and average GMAT score for MBA graduates at each of top 80 ranked schools in country. Data fit given regression line Y - 288X - 111,408 where X is average GMAT score for students at school and Y is average starting salary for graduates from that same school. Determine estimated average starting salary for school with average GMAT of 600 points?

a) $58,390

b) $62,408

c)161.392

d) $71,408

Q3) U.S. News & World Report publishes the average starting salary and average given regression line: Y = 288X - 111,408 where X is average GMAT score for students at school and Y is average starting salary for graduates from that same school. What is a proper interpretation of coefficient 288 in regression equation?

a) For each additional point in average GMAT for school, average salary of he graduates from that school tends to be $288 higher

b) If graduate of school improves her/his GMAT by additional point, he/she will make $288 more in salary

c) Average salary will be within plus or minus $288 from $111,408

Q4) Consider following two variables"

X= Length of hospital stay by any given patients (days)

Y- Cost of the patients hospital stay(dollars)

What do you expect about relationship between these variables

a) Strong positive correlation  hut no causation between them

b) Strong positive correlation, and we expect longer stays to cost more for patients with alike illnesses.

c) Strong positive correlation, and we expect higher costs to cause longer stays for patients with similar illnesses

d) None of the above