The US economy is undergoing a difficult economic crisis period. The college students are facing the hardships of unemployment, rising tuition fees and lack of economic prosperity. Assume you are working for the department of education (DOE) in the Capital and the Director of Education asks you to run a regression model to estimate the relationship among percent of college graduates across states in US and various factors that determine education levels in various states. Run two regression models from a sub-set of the data set from 2010 and answer the questions (A, B &C):

Model 1: ColGrad%=0 + 1 opo + 2 p + 3 + 4 + 5 m +

Variable Names: ColGrad% = percent of state population with a college degree, Dropout = percent of high school students who do not graduate, EdSpend = per capita spending on K-12 education, Urban = percent of state population living in urban areas, Age = median age of state’s population, FemLab = percent of adult females who are in the labor force, Neast = 1 if state is in the northeast, 0 otherwise, Seast = 1 if state is in the southeast, 0 otherwise, West = 1 if state is in the west, 0 otherwise, Midwest = 1 if state is in the Midwest, 0 otherwise.

A.) Which variables are the most significant without including the state dummies in a regression model to explain the variability in percent of state population with college degree in US ColGrad% (Model 1)?

B.) Provide an interpretation of the slopes of: Dropout, EdSpend, Urban, Age and Femlab (Model 1)

C.) Run the following multiple regression model (Model 2) with ColGrad% as dependent variable and Urban, FemLab, Dropout, Dummy NorthEast, Southeast and Midwest as independent variables. How would you interpret the beta coefficient for the dummy variable Midwest?

(Model 2)

ColGrad%= 0 +1 + 2 FemLab+3 opo + 4 oh+5 oh + 6W +

data:

State CollegeGrad% Dropout (%) EdSpend ($) Urban (%) Age (Yrs) Femlab (%) NorthEast SoutthEast West MidWest
AL 15.6 35.3 3627 60.4 33 51.8 0 1 0 0
AK 23 31.6 8330 67.5 29.4 64.9 0 0 1 0
AZ 20.3 27.5 4309 87.5 32.2 55.6 0 0 1 0
AR 13.4 23.3 3700 53.5 33.8 53.6 0 1 0 0
CA 23.4 32.2 4491 92.6 31.5 56 0 0 1 0
CO 27 25.9 5064 82.4 32.5 63.3 0 0 1 0
CT 27.2 25.1 7602 79.1 34.4 63.9 1 0 0 0
DE 21.4 31.5 5865 73 32.9 60.7 1 0 0 0
FL 18.3 38.9 5276 84.8 36.4 54.6 0 1 0 0
GA 19.3 37.3 4466 63.2 31.6 57.4 0 1 0 0
HI 22.9 13.2 5166 89 32.6 61.7 0 0 1 0
ID 17.7 20.6 3386 57.4 31.5 57.8 0 0 1 0
IL 21.1 23.4 5520 84.6 32.8 58.3 0 0 0 1
IN 15.6 25 4930 64.9 32.8 57.4 0 0 0 1
IA 16.9 12.5 4679 60.6 34 62.2 0 0 0 1
KS 21.1 18 4874 69.1 32.9 60.4 0 0 0 1
KY 13.6 31 4354 51.8 33 52.9 0 1 0 0
LA 16.1 43.3 4146 68.1 31 52.7 0 1 0 0
ME 18.8 22.4 5458 44.6 33.9 61 1 0 0 0
MD 26.5 27.2 6566 81.3 33 62.8 1 0 0 0
MA 27.2 23 6366 84.3 33.6 59.9 1 0 0 0
MI 17.3 29.9 5883 70.5 32.6 55.7 0 0 0 1
MN 21.9 10.3 5239 69.9 32.5 66.6 0 0 0 1
MS 14.8 35.8 3187 47.1 31.2 54.1 0 1 0 0
MO 17.8 27.8 4754 68.7 33.5 60.2 0 0 0 1
MT 19.8 16.7 5204 52.5 33.8 60.8 0 0 1 0
NE 19 14.5 5038 66.1 33 63.4 0 0 0 1
NV 15.3 22.8 4653 88.3 33.3 61.6 0 0 1 0
NH 24.3 26 5672 51 32.8 65.3 1 0 0 0
NJ 24.8 20.2 8645 89.4 34.5 57.4 1 0 0 0
NM 20.4 31.8 3895 73 31.3 54.8 0 0 1 0
NY 23.1 34.9 8565 84.3 33.9 52.6 1 0 0 0
NC 17.4 32 4488 50.4 33.1 60.5 0 1 0 0
ND 18 11.9 4199 53.3 32.4 59.9 0 0 0 1
OH 17 26 5245 74.1 33.3 56.8 0 0 0 1
OK 17.8 22.5 3791 67.7 33.2 54.3 0 0 0 1
OR 20.6 28.4 5683 70.5 34.5 58.4 0 0 1 0
PA 17.9 20.9 6541 68.9 35 54.1 1 0 0 0
RI 21.3 30.4 6343 86 34 59.9 1 0 0 0
SC 16.6 41.5 4351 54.6 32 59.5 0 1 0 0
SD 17.2 14.3 3965 50 32.5 62.7 0 0 0 1
TN 15.9 32.1 3782 60.9 33.6 53.9 0 1 0 0
TX 20.4 35.9 4438 80.3 30.8 58.7 0 0 1 0
UT 22.2 16.9 2960 87 26.2 61.2 0 0 1 0
VT 24.3 18.4 6738 32.2 33 63.8 1 0 0 0
VA 24.5 26.4 4836 69.4 32.6 64 0 1 0 0
WA 22.9 22.8 5000 76.4 33.1 59.3 0 0 1 0
WV 12.3 22.7 4911 36.1 35.4 44.2 0 1 0 0
WI 17.7 15.8 5871 65.7 32.9 63.1 0 0 0 1
WY 18.8 21.4 5723 65 32 61.8 0 0 1 0