hat is the major problem with R 2 and why Adj R 2 is pre
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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What is the major problem with R 2 and why Adj R 2 is preferred?
![The data set AIRQ contains observations for 30 standard metropolitan statistical areas
(SMSAS) in California for 1972 on the following variables.
indicator of air quality (The lower the better)
value added of companies (in 1000 US$)
amount of rain (in inches)
dummy variable, 1 for (SMSAS) at the coast, 0 for others
population density (per square mile)
average income per head (in US$)
airq
vala
rain
сoas
dens
тedi
reg airq vala rain coas dens medi
df
Number of obs
30
2.98
Source
MS
F( 5,
Prob > F
24) =
Model
Residual
8723. 84625
14058.4538
1744.76925
0.0313
R-squared
Adj R-squared =
0. 3829
0.2544
24.203
24
585.768906
Total
22782.3
29
785. 596552
Root MSE
airq
Coef.
std. Err.
P>|t|
[95% Conf. Interval]
vala
rain
0. 699
.0008834
.2506988
-33. 3983
-.0010734
.0005545
0. 39
0.73
-3.19
-0. 66
0.65
.0055399
.9596857
-11. 81504
.0022562
-.0037731
-.458288
-54.98156
-.0044237
-.0012003
80. 29141
3435183
0.473
0. 004
0. 515
0. 521
сoas
10.45752
dens
medi
.0016233
.0022769
.0008503
.0023093
_cons
111.9347
15. 33179
7.30
0.000
143. 5779](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb8a4644e-c0f7-45ab-8ca0-b43744e710eb%2Fc9e389b8-d785-4fec-a157-69abba2549fb%2Fz0mpt9s_processed.jpeg&w=3840&q=75)
Transcribed Image Text:The data set AIRQ contains observations for 30 standard metropolitan statistical areas
(SMSAS) in California for 1972 on the following variables.
indicator of air quality (The lower the better)
value added of companies (in 1000 US$)
amount of rain (in inches)
dummy variable, 1 for (SMSAS) at the coast, 0 for others
population density (per square mile)
average income per head (in US$)
airq
vala
rain
сoas
dens
тedi
reg airq vala rain coas dens medi
df
Number of obs
30
2.98
Source
MS
F( 5,
Prob > F
24) =
Model
Residual
8723. 84625
14058.4538
1744.76925
0.0313
R-squared
Adj R-squared =
0. 3829
0.2544
24.203
24
585.768906
Total
22782.3
29
785. 596552
Root MSE
airq
Coef.
std. Err.
P>|t|
[95% Conf. Interval]
vala
rain
0. 699
.0008834
.2506988
-33. 3983
-.0010734
.0005545
0. 39
0.73
-3.19
-0. 66
0.65
.0055399
.9596857
-11. 81504
.0022562
-.0037731
-.458288
-54.98156
-.0044237
-.0012003
80. 29141
3435183
0.473
0. 004
0. 515
0. 521
сoas
10.45752
dens
medi
.0016233
.0022769
.0008503
.0023093
_cons
111.9347
15. 33179
7.30
0.000
143. 5779
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