the estimated b1 is significant.
(3) R Square=0.376, so the fit of the model is not bad.
B)
(1) In this example, using the data, linear regression would give us the
following estimate for the intercept and slope:
b0 = 24770.75, b1 = 94014.60,
so we donate
B)
(1) In this example, using the data, linear regression would give us the
following estimate for the intercept and slope:
b0 = 12.43, b1 = -0.18,
so we donate this line by:
#beeri = 12.43 - 0.18agei,
which means an additional year of age, other variable
D)
By applying the data into the model estimated in question 2.A),
we get the table.
E) The data I would collect is the average price in the same neighborhood,
annual salary of the people who live in this area, the crime rate, and the
distance to harbor e
C)
(1) In this example, using the data, linear regression would give us the
following estimate for the intercept and slope:
b0 = 208189.83, b1 = 4738.58,
so we donate this line by:
pricei = 208189.83 + 4738.58#bedroomi,
which means an additional bedroom,
Question 2
A)
(1) In this example, using the data, linear regression would give us the
following estimate for the intercept and slope:
b0 = -12857.23, b1 = 159.22,
so we donate this line by:
pricei = -12857.23 + 159.22 sqfti,
which means an additional squ
Question 1
(3) 10 different i.i.d samples of size n=100, from the standard normal distribution
(0,1):
Histogram
Frequency
6
4
2
Frequency
0
-0.2 -0.15 -0.1 -0.05
0
0.05
0.1
0.15
Bin
(4) 100 different i.i.d. samples of size n=100, from the standard normal
The P-value of this test is 1.04E-57 <= 0.05, we reject H0: b1=0, so
the estimated b1 is significant.
(3) R Square=0.3098, so the fit of the model is not bad.
Question 1
A)
(1) In this example, using the data, linear regression would give us the
following estimate for the intercept and slope:
b0 = -7.02, b1 = 0.09,
so we donate this line by:
#beeri = -7.02 + 0.09 weighti,
which means an additional weight, other