STA 6207 – Exam 3 – Fall 2012
PRINT Name _____________________
All Questions are based on the following 2 regression models, where SIMPLE REGRESSION refers to
the case where p=1, and X is of full column rank (no linear dependencies among predictor variables).
Model 1 : Yi   0  1 X i1     p X ip   i

i  1,..., n  i ~ NID 0,  2



Model 2 : Y  Xβ  ε X  n  p' β  p'1 ε ~ N 0,  2 I
_______________________________________________________________________________________
CONDUCT ALL TESTS AT  = 0.05 SIGNIFICANCE LEVEL.
Q.1. The mean and variance of the Poisson distribution are equal. That is: . Use Bartlett’s method to obtain a
transformation to make the variance approximately constant.
Q.2. Random Coefficient Regression:
Yij   0i  1i X ij   ij
Derive V{Yij}.
   0i    02  01  
  0i 
~
NID
   , 
 
2 

 1i 
  1i   01  1  
 ij ~ NID  0,  2 
   0i  
      ij 
  1i  
Q.3. A regression through the origin is to be fit, relating Y to X for n=5 observations. The X levels are (1,2,3,4,10).
p.3.a. Obtain X’X, (X’X)-1, the P matrix, and the diagonal elements of P (vii).
p.3.b. What do the vii elements sum to? Which, if any, elements are potentially influential?
n
^
p.3.c. Below is the fit for all cases (top), and with observation 5 dropped (bottom) b1   1 
XY
i 1
n
X
i 1
X
Y
1
2
3
4
10
Y-hat
2
5
6
7
30
Observation 5 dropped
X
Y
Y-hat
1
2
2
5
3
6
4
7
^
e
2.75
5.51
8.26
11.02
27.54
2
i
b1
-0.75
-0.51
-2.26
-4.02
2.46
e
1.93
3.87
5.80
7.73
i i
2.75
SS(Res)
SS(Res) =
s=
b1
0.07
1.13
0.20
-0.73
All Cases:
1.93
SS(Res)
Observation 5 dropped:
SS(Res(5)) =
s(5)
p.3.d. Compute Y 5(5) , and using s(5)as estimate of  : DFFITS5 , and DFBETAS1(5)
n
Note:  ei2  0
i 1
 
^
^
Q.4..For model 1, a simple linear regression model (p=1), derive Cov  0 ,  1 completing the following parts:
^
p.4.a. Write  1 
n
^
n
 aiYi and  0   bYi i stating explicitly what the ai and bi values (functions) are
i 1
i 1
 
^
^
p.4.b. Using rules of Covariances of linear functions of random variables to derive Cov  0 ,  1
p.4.c. Researchers in the U.S. fit regressions of relationship between weight (Y) in pounds, and height (X) in inches, while
foreign researchers work with weight (Y) in kilograms, and height (X) in centimeters. Give the
Conversion Rates are as follow: 1 kilogram = 2 .2 pounds
1 cm = 2.54 inches
Suppose that in a population, the standard deviation of weights is 22.0 pounds, and in a sample, the heights are given
below:
Run#
X(Inches)
X(Cms)
1
100
254
 
^
2
200
508
3
300
762
4
400
1016
^
Cov  0 ,  1
U.S. = ________________________
Foreign = __________________________
Q.5. A regression model is fit, relating January mean temperature (Y) to ELEVation, LATitude, and LONGitude for
n=369 weather stations in Texas (the data are aggregated over a period of years). The following table gives the Regression
and Residual sums of squares for each model. All models contain an intercept.
Model
ELEV
LAT
LONG
ELEV,LAT
ELEV,LONG
LAT,LONG
ELEV,LAT,LONG
SS(REG) SS(RES)
5785.3
7986.3
12603.3
1168.3
2239.5 11532.1
13155.1
616.5
7669.7
6101.9
13087.0
684.6
13156.8
614.8
p.5.a. Compute R(LONG), R(LONG|ELEV), R(LONG|LAT), R(LONG|ELEV,LAT)
p.5.b. Based on the simple linear regression, relating Y to LONG: E{Y} = 0 +  LONGLONG, test
H0:  LONG = 0
HA:  LONG ≠ 0
Test Statistic: ____________________________ Rejection Region: ________________________
p.5.c. Based on the simple linear regression, relating Y to ELEV, LAT, LONG:
E{Y} = 0 + ELEVELEV+ LATLAT +  LONGLONG, test H0:  LONG = 0 HA:  LONG ≠ 0
Test Statistic: ____________________________ Rejection Region: ________________________
p.5.d. The Coefficient of Determination, when LONG is regressed on ELEV and LAT is 0.843. Compute the
Variance Inflation Factor for LONG.