Page 1 of 17
EXAMPLE: Can we predict your final grade in the class from your 1st exam score?
Regression Analysis: Grade versus Exam1
Scatterplot of Grade vs Exam1
110
The regression equation is
Grade = 36.8 + 0.614 Exam1
100
90
80
Coef
36.832
0.61352
SE Coef
1.655
0.02060
T
22.26
29.78
P
0.000
0.000
Grade
Predictor
Constant
Exam1
70
60
50
S = 5.76575
R-Sq=67.1%
40
R-sq(adj)=67.1%
30
30
40
50
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
434
435
SS
29480
14428
43908
MS
29480
33
F
886.77
P
0.000
Unusual Observations
Obs Exam1
Grade
Fit SE Fit Residual St Resid
1
72 57.250 81.005
0.313
-23.755
-4.13R
2
42 58.000 62.600
0.814
-4.600
-0.81 X
4
72 60.313 81.005
0.313
-20.693
-3.59R
5
51 34.813 68.121
0.643
-33.309
-5.81R
6
36 53.720 58.919
0.932
-5.199
-0.91 X
7
63 60.000 75.484
0.433
-15.484
-2.69R
10
54 57.750 69.962
0.588
-12.212
-2.13R
13
48 57.500 66.281
0.699
-8.781
-1.53 X
15
75 67.500 82.846
0.289
-15.346
-2.66R
24
81 69.000 86.527
0.279
-17.527
-3.04R
33
45 65.250 64.440
0.756
0.810
0.14 X
39
78 72.750 84.686
0.277
-11.936
-2.07R
Etc….
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
Predicted Values for New Observations
New
Obs Exam1
Fit SE Fit
95% CI
1
80.0 85.913
0.277 (85.370, 86.457)
95% PI
(74.568, 97.259)
Residual Plots for Grade
Normal Probability Plot
Versus Fits
99.9
20
90
Residual
Percent
99
50
10
0
-20
1
0.1
-40
-20
0
Residual
-40
20
60
70
Histogram
100
20
75
Residual
Frequency
90
Versus Order
100
50
25
0
80
Fitted Value
-30.0 -22.5 -15.0
-7.5
0.0
Residual
7.5
15.0
22.5
0
-20
-40
1
50
100 150 200 250 300
Observation Order
350 400
60
70
Exam1
80
90
100
110
Page 2 of 17
EXAMPLE – What is the relationship between height and weight for UF students?
Data on UF students’ heights and weights collected by STA3024 students. N=1309
Questions about some data – are these heights correct?
HT
50.0
51.0
51.0
52.0
53.0
53.0
53.0
53.0
54.0
54.0
55.0
55.0
56.0
56.0
56.0
57.0
57.0
57.0
57.0
58.0
58.0
58.0
58.0
58.0
59.0
59.0
59.0
59.0
59.0
59.0
59.5
WT
111
115
95
113
118
120
120
130
117
130
121
128
120
122
128
103
116
140
165
104
130
90
92
95
104
110
115
125
96
97
145
M
M
M
M
M
80
83
83
84
89
160
227
227
255
296
M
M
F
72
73
64
60
105
270
Scatterplot of WT vs HT
300
250
200
WT
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
F
M
F
F
F
F
F
F
F
F
F
F
F
F
150
100
50
50
60
70
HT
80
90
Page 3 of 17
Fitted Line Plot
Regression Analysis: WT versus HT
WT = - 279.0 + 6.409 HT
S
R-Sq
R-Sq(adj)
300
The regression equation is
WT = - 279 + 6.41 HT
250
Coef
-279.01
6.4088
S = 24.2205
SE Coef
11.19
0.1649
T
-24.92
38.86
R-Sq = 54.2%
P
0.000
0.000
WT
Predictor
Constant
HT
200
150
R-Sq(adj) = 54.2%
100
60
65
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
1276
1277
70
75
80
HT
SS
885986
748543
1634529
MS
885986
587
F
1510.29
P
0.000
Predicted Values for New Observations
New
Obs
1
2
3
HT
65
60
76
Fit
137.562
105.518
208.059
SE Fit
0.816
1.448
1.519
95%
(135.961,
(102.678,
(205.080,
CI
139.163)
108.359)
211.038)
95% PI
(90.019, 185.106)
(57.917, 153.120)
(160.449, 255.669)
Residual Plots for WT
Normal Probability Plot
Versus Fits
99.99
150
100
90
Residual
Percent
99
50
10
1
50
0
-50
0.01
-100
-50
0
50
Residual
100
80
Histogram
200
Versus Order
90
Residual
Frequency
160
Fitted Value
150
120
60
30
0
120
100
50
0
-50
-50
-25
0
25
50
Residual
75
100
125
1 00 00 00 00 00 00 00 00 00 00 00 00
1 2 3 4 5 6 7 8 9 10 11 12
Observation Order
240
24.2205
54.2%
54.2%
Page 4 of 17
Regression Analysis: WT_F versus HT_F
The regression equation is
WT_F = - 125 + 3.96 HT_F
Predictor
Constant
HT_F
Coef
-125.21
3.9614
S = 19.1292
SE Coef
17.53
0.2700
R-Sq = 24.9%
T
-7.14
14.67
P
0.000
0.000
R-Sq(adj) = 24.8%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
650
651
SS
78781
237852
316633
MS
78781
366
F
215.29
P
0.000
Regression Analysis: WT_M versus HT_M
The regression equation is
WT_M = - 184 + 5.14 HT_M
Predictor
Constant
HT_M
Coef
-184.21
5.1421
S = 26.5446
SE Coef
25.73
0.3633
R-Sq = 24.3%
T
-7.16
14.16
P
0.000
0.000
R-Sq(adj) = 24.2%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
624
625
SS
141187
439681
580868
MS
141187
705
F
200.37
P
0.000
Regression Analysis: WT versus HT, GENDER_M_1
The regression equation is
WT = - 165 + 4.57 HT + 21.0 GENDER_M_1
Predictor
Constant
HT
GENDER_M_1
Coef
-164.68
4.5699
20.963
S = 23.1134
SE Coef
14.76
0.2271
1.866
R-Sq = 58.3%
T
-11.16
20.12
11.23
P
0.000
0.000
0.000
R-Sq(adj) = 58.3%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
2
1275
1277
SS
953389
681140
1634529
MS
476695
534
F
892.31
P
0.000
Page 5 of 17
Regression Analysis: WT versus HT, GENDER_M_1, HT*GENDER_M_1
The regression equation is
WT = - 128 + 4.00 HT - 56.2 GENDER_M_1 + 1.14 HT*GENDER_M_1
Predictor
Constant
HT
GENDER_M_1
HT*GENDER_M_1
S = 23.0840
Coef
-128.05
4.0039
-56.16
1.1382
SE Coef
21.21
0.3266
30.83
0.4544
R-Sq = 58.6%
T
-6.04
12.26
-1.82
2.50
P
0.000
0.000
0.069
0.012
R-Sq(adj) = 58.5%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
3
1274
1277
SS
960396
678879
1639274
MS
320132
533
F
600.77
P
0.000
Question: What if we coded gender the other way?
Regression Analysis: WT versus HT, GENDER_F_1
The regression equation is
WT = - 144 + 4.57 HT - 21.0 GENDER_F_1
Regression Analysis: WT versus HT, GENDER_F_1, HT*GENDER_F_1
The regression equation is
WT = - 184 + 5.14 HT + 56.2 GENDER_F_1 - 1.14 HT*GENDER_F_1
Page 6 of 17
EXAMPLE: Quadratic Regression
Fitted Line Plot
The regression equation is
y = - 14.0 + 3.24 x - 0.0283 x**2
y = - 14.00 + 3.236 x
- 0.02829 x**2
70
Coef
-14.00
3.236
-0.02829
SE Coef
30.18
2.763
0.05882
T
-0.46
1.17
-0.48
60
P
0.659
0.286
0.648
8.80513
79.2%
72.2%
50
y
Predictor
Constant
x
x**2
S
R-Sq
R-Sq(adj)
40
30
S = 8.80513
R-Sq = 79.2%
R-Sq(adj) = 72.2%
20
10
15
20
25
Analysis of Variance
Source
Regression
Residual Error
Total
30
35
x
DF
2
6
8
SS
1769.71
465.18
2234.89
MS
884.85
77.53
F
11.41
P
0.009
Fitted Line Plot
The regression equation is
y = - 0.24 + 1.92 x
y = - 0.240 + 1.922 x
70
S
R-Sq
R-Sq(adj)
60
Coef
-0.240
1.9220
SE Coef
9.039
0.3815
T
-0.03
5.04
P
0.980
0.001
50
y
Predictor
Constant
x
40
30
S = 8.30760
R-Sq = 78.4%
R-Sq(adj) = 75.3%
20
10
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
7
8
15
20
25
x
SS
1751.8
483.1
2234.9
MS
1751.8
69.0
F
25.38
P
0.001
30
35
8.30760
78.4%
75.3%
Page 7 of 17
EXAMPLE:
Electrical Consumption vs. Temperature
Linear Regression
Regression Analysis: electric versus temp
The regression equation is
electric = 24.4 + 0.514 temp
Predictor
Constant
temp
S = 13.5273
Coef
24.42
0.5139
SE Coef
10.57
0.1603
R-Sq = 29.1%
Analysis of Variance
Source
DF
SS
Regression
1 1880.7
Residual Error 25 4574.7
Total
26 6455.5
T
2.31
3.21
P
0.029
0.004
R-Sq(adj) = 26.3%
MS
1880.7
183.0
F
10.28
P
0.004
Unusual Observations
Obs temp electric
Fit SE Fit Residual St Resid
1 35.0
72.16 42.40
5.32
29.76
2.39R
R denotes an observation with a large standardized residual.
Page 8 of 17
Quadratic Regression
Regression Analysis: electric versus temp, temp2
The regression equation is
electric = 213 - 5.83 temp + 0.0499 temp**2
Predictor
Constant
temp
temp**2
Coef
212.93
-5.8278
0.049854
S = 4.42475
SE Coef
13.47
0.4411
0.003443
R-Sq = 92.7%
T
15.81
-13.21
14.48
P
0.000
0.000
0.000
R-Sq(adj) = 92.1%
Analysis of Variance
Source
Regression
Residual Error
Total
Source
temp
temp2
DF
1
1
DF
2
24
26
SS
5985.6
469.9
6455.5
MS
2992.8
19.6
F
152.86
P
0.000
Seq SS
1880.7
4104.8
Unusual Observations
Obs temp electric
Fit SE Fit Residual St Resid
1 35.0
72.164 70.032
2.582
2.132
0.59 X
22 81.0
79.468 67.974
1.243
11.494
2.71R
23 83.0
82.469 72.671
1.369
9.798
2.33R
27 91.0
87.265 95.445
2.356
-8.180
-2.18R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.
Page 9 of 17
EXAMPLE:
Wages vs Length of Service and Size of Company
Wages = yearly salary, in thousands of dollars
Length of Service measured in months
Coding of size of company: small = 0 large = 1
Regression Analysis: Wages versus LOS, size, LOS*size
The regression equation is
Wages = 35.9 + 0.104 LOS + 13.6 size - 0.0483 LOS*size
Predictor
Constant
LOS
size
LOS*size
Coef
35.914
0.10424
13.631
-0.04828
S = 10.9612
SE Coef
3.562
0.03632
4.910
0.05634
R-Sq = 26.6%
Analysis of Variance
Source
DF
SS
Regression
3 2438.1
Residual Error 56 6728.3
Total
59 9166.4
Source
LOS
size
LOS*size
DF
1
1
1
Seq SS
843.5
1506.3
88.2
T
10.08
2.87
2.78
-0.86
P
0.000
0.006
0.007
0.395
R-Sq(adj) = 22.7%
MS
812.7
120.1
F
6.76
P
0.001
Page 10 of 17
Regression Analysis: Wages versus LOS, size
The regression equation is
Wages = 37.5 + 0.0842 LOS + 10.2 size
Predictor
Constant
LOS
size
S = 10.9357
Coef
37.466
0.08417
10.228
SE Coef
3.061
0.02770
2.882
R-Sq = 25.6%
Analysis of Variance
Source
DF
SS
Regression
2 2349.9
Residual Error 57 6816.6
Total
59 9166.4
Source
LOS
size
DF
1
1
T
12.24
3.04
3.55
P
0.000
0.004
0.001
R-Sq(adj) = 23.0%
MS
1174.9
119.6
F
9.82
P
0.000
Seq SS
843.5
1506.3
Unusual Observations
Obs LOS Wages
Fit
15
70 97.68 53.59
22 222 54.95 56.15
29
98 34.34 55.94
42 228 67.91 56.66
47 204 50.17 64.87
SE Fit
1.85
4.57
2.05
4.71
4.26
Residual
44.09
-1.21
-21.60
11.25
-14.69
St Resid
4.09R
-0.12 X
-2.01R
1.14 X
-1.46 X
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.
Page 11 of 17
EXAMPLE:
Reaction Time in a Computer Game vs Distance to move mouse
and Hand used.
Reaction time measured in milliseconds to click on center of new circle
Distance from cursor location to center of new circle in untils that depend on the
screen size.
Coding of hand: right = 0 left = 1
Regression Analysis: time versus distance, hand, dist*hand
The regression equation is
time = 99.4 + 0.028 distance + 72.2 hand + 0.234 dist*hand
Predictor
Constant
distance
hand
dist*hand
Coef
99.36
0.0283
72.18
0.2336
S = 50.6067
SE Coef
25.25
0.1308
35.71
0.1850
R-Sq = 59.8%
Analysis of Variance
Source
DF
SS
Regression
3 136948
Residual Error 36
92198
Total
39 229146
Source
distance
hand
dist*hand
DF
1
1
1
T
3.93
0.22
2.02
1.26
P
0.000
0.830
0.051
0.215
R-Sq(adj) = 56.4%
MS
45649
2561
F
17.82
P
0.000
Seq SS
6303
126562
4083
Unusual Observations
Obs distance
time
Fit SE Fit Residual St Resid
25
163 315.00 214.29
11.38
100.71
2.04R
30
271 401.00 242.65
17.19
158.35
3.33R
31
40 320.00 182.09
20.68
137.91
2.99R
R denotes an observation with a large standardized residual.
Page 12 of 17
Regression Analysis: time versus distance, hand
The regression equation is
time = 79.2 + 0.145 distance + 112 hand
Predictor
Constant
distance
hand
S = 51.0116
Coef
79.21
0.14512
112.50
SE Coef
19.72
0.09324
16.13
R-Sq = 58.0%
Analysis of Variance
Source
DF
SS
Regression
2 132865
Residual Error 37
96281
Total
39 229146
T
4.02
1.56
6.97
P
0.000
0.128
0.000
R-Sq(adj) = 55.7%
MS
66433
2602
F
25.53
P
0.000
Unusual Observations
Obs distance
time
Fit SE Fit Residual St Resid
25
163 315.00 215.39
11.44
99.61
2.00R
30
271 401.00 231.10
14.67
169.90
3.48R
31
40 320.00 197.55
16.80
122.45
2.54R
R denotes an observation with a large standardized residual.
Regression Analysis: time versus hand
The regression equation is
time = 104 + 112 hand
Predictor
Constant
hand
S = 51.9573
Coef
104.25
112.50
SE Coef
11.62
16.43
R-Sq = 55.2%
Analysis of Variance
Source
DF
SS
Regression
1 126562
Residual Error 38 102583
Total
39 229146
T
8.97
6.85
P
0.000
0.000
R-Sq(adj) = 54.1%
MS
126562
2700
F
46.88
P
0.000
Unusual Observations
Obs hand
time
Fit SE Fit Residual St Resid
30 1.00 401.00 216.75
11.62
184.25
3.64R
31 1.00 320.00 216.75
11.62
103.25
2.04R
32 1.00 113.00 216.75
11.62
-103.75
-2.05R
R denotes an observation with a large standardized residual.
Page 13 of 17
One-way ANOVA: time versus hand
Source
hand
Error
Total
DF
1
38
39
S = 51.96
Level
0
1
N
20
20
SS
126563
102584
229146
MS
126563
2700
R-Sq = 55.23%
Mean
104.25
216.75
StDev
8.25
73.01
F
46.88
P
0.000
R-Sq(adj) = 54.05%
Individual 95% CIs For Mean Based on
Pooled StDev
+---------+---------+---------+--------(-----*-----)
(-----*-----)
+---------+---------+---------+--------80
120
160
200
Pooled StDev = 51.96
Two-Sample T-Test and CI: time, hand
Two-sample T for time
hand
0
1
N
20
20
Mean
104.25
216.8
StDev
8.25
73.0
SE Mean
1.8
16
Difference = mu (0) - mu (1)
Estimate for difference: -112.500
95% CI for difference: (-146.889, -78.111)
T-Test of difference = 0 (vs not =): T-Value = -6.85
P-Value = 0.000
DF = 19
Page 14 of 17
EXAMPLE: Predicting College GPA – data from book
Regression Analysis: CGPA versus Height, Gender, etc
The regression equation is
CGPA = 0.53 + 0.0194 Height + 0.047 Gender - 0.00163 Haircut - 0.042 Job
+ 0.0004 Studytime - 0.375 Smokecig + 0.0488 Dated + 0.546 HSGPA
+ 0.00315 HomeDist + 0.00069 BrowseInternet - 0.00128 WatchTV
- 0.0117 Exercise + 0.0140 ReadNewsP + 0.039 Vegan
- 0.0139 PoliticalDegree - 0.0801 PoliticalAff
Predictor
Constant
Height
Gender
Haircut
Job
Studytime
Smokecig
Dated
HSGPA
HomeDist
BrowseInternet
WatchTV
Exercise
ReadNewsP
Vegan
PoliticalDegree
PoliticalAff
S = 0.322198
Coef
0.532
0.01942
0.0468
-0.001633
-0.0418
0.00043
-0.3746
0.04881
0.5457
0.003147
0.000689
-0.0012840
-0.011657
0.01395
0.0392
-0.01390
-0.08006
R-Sq = 43.2%
SE Coef
1.496
0.01637
0.1429
0.001697
0.1024
0.01921
0.2249
0.07111
0.1776
0.003400
0.001163
0.0009710
0.005934
0.02272
0.1578
0.03185
0.07741
T
0.36
1.19
0.33
-0.96
-0.41
0.02
-1.67
0.69
3.07
0.93
0.59
-1.32
-1.96
0.61
0.25
-0.44
-1.03
P
0.724
0.242
0.745
0.341
0.685
0.982
0.103
0.496
0.004
0.360
0.557
0.193
0.056
0.543
0.805
0.665
0.307
R-Sq(adj) = 21.5%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
16
42
58
SS
3.3135
4.3601
7.6736
MS
0.2071
0.1038
F
1.99
P
0.037
Unusual Observations
Obs
28
40
59
Height
67.0
65.0
62.0
CGPA
2.9800
3.9300
2.5000
Fit
3.5898
3.3458
3.4718
SE Fit
0.2442
0.2176
0.1352
Residual
-0.6098
0.5842
-0.9718
St Resid
-2.90R
2.46R
-3.32R
R denotes an observation with a large standardized residual.
Page 15 of 17
Best Subsets Regression: CGPA versus Height, Gender, ...
Response is CGPA
Vars
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
R-Sq
25.5
13.0
31.6
29.4
33.8
33.7
35.7
35.3
37.3
37.0
38.3
38.3
39.6
39.3
40.4
40.4
41.5
41.0
41.9
41.8
42.2
42.2
42.6
42.6
42.9
42.8
43.1
43.0
43.2
43.1
43.2
R-Sq(adj)
24.2
11.5
29.2
26.9
30.2
30.0
31.0
30.5
31.4
31.1
31.2
31.2
31.3
30.9
30.8
30.8
30.8
30.2
29.8
29.7
28.7
28.7
27.6
27.6
26.4
26.3
25.0
24.9
23.4
23.2
21.5
Mallows
C-p
0.1
9.3
-2.4
-0.8
-2.1
-2.0
-1.5
-1.2
-0.6
-0.4
0.6
0.6
1.7
1.9
3.1
3.1
4.2
4.6
6.0
6.0
7.7
7.7
9.4
9.5
11.2
11.3
13.1
13.1
15.0
15.1
17.0
S
0.31667
0.34217
0.30613
0.31109
0.30389
0.30423
0.30223
0.30320
0.30132
0.30198
0.30163
0.30164
0.30150
0.30231
0.30249
0.30256
0.30266
0.30395
0.30478
0.30492
0.30712
0.30715
0.30945
0.30954
0.31205
0.31229
0.31502
0.31526
0.31843
0.31866
0.32220
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Page 16 of 17
Regression Analysis: CGPA versus HSGPA, Exercise
The regression equation is
CGPA = 1.55 + 0.560 HSGPA - 0.0111 Exercise
Predictor
Constant
HSGPA
Exercise
Coef
1.5489
0.5599
-0.011138
S = 0.306126
SE Coef
0.5551
0.1436
0.004985
R-Sq = 31.6%
Analysis of Variance
Source
DF
SS
Regression
2 2.4256
Residual Error 56 5.2479
Total
58 7.6736
Unusual Observations
Obs HSGPA
CGPA
Fit
3
3.00 3.6000 3.2176
9
3.50 2.8800 3.4808
14
3.30 2.6000 2.7284
27
2.55 3.1400 2.9099
28
3.80 2.9800 3.6544
59
3.60 2.5000 3.5424
T
2.79
3.90
-2.23
P
0.007
0.000
0.029
R-Sq(adj) = 29.2%
MS
1.2128
0.0937
F
12.94
P
0.000
SE Fit
0.1297
0.0642
0.2647
0.1840
0.0445
0.0556
Residual
0.3824
-0.6008
-0.1284
0.2301
-0.6744
-1.0424
St Resid
1.38 X
-2.01R
-0.83 X
0.94 X
-2.23R
-3.46R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.
Residual Plots for CGPA
Residuals Versus HSGPA
Residuals Versus Exercise
(response is CGPA)
2
1
1
Standardized Residual
Standardized Residual
(response is CGPA)
2
0
-1
-2
-3
0
-1
-2
-3
-4
-4
2.50
2.75
3.00
3.25
HSGPA
3.50
3.75
4.00
0
10
Residual Plots for CGPA
Normal Probability Plot of the Residuals
Percent
99
90
50
10
1
0.1
Residuals Versus the Fitted Values
Standardized Residual
99.9
-4
-2
0
2
Standardized Residual
1
0
-1
-2
-3
4
2.7
Histogram of the Residuals
12
8
4
0
-3
-2
-1
0
Standardized Residual
1
3.3
Fitted Value
3.6
3.9
Residuals Versus the Order of the Data
Standardized Residual
Frequency
16
3.0
1
0
-1
-2
-3
1
5
10 15 20 25 30 35 40 45 50 55
Observation Order
20
30
Exercise
40
50
60
Page 17 of 17
Regression Analysis: CGPA versus HSGPA, Exercise
The regression equation is
CGPA = 1.54 + 0.554 HSGPA - 0.00432 Exercise
Predictor
Constant
HSGPA
Exercise
Coef
1.5388
0.5542
-0.004320
S = 0.306969
SE Coef
0.5568
0.1441
0.009596
R-Sq = 21.9%
T
2.76
3.85
-0.45
P
0.008
0.000
0.654
R-Sq(adj) = 19.0%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
2
55
57
SS
1.45009
5.18265
6.63274
Unusual Observations
Obs HSGPA
CGPA
Fit
3
3.00 3.6000 3.1970
25
3.50 3.3100 3.3705
26
2.55 3.1400 2.9261
27
3.80 2.9800 3.6361
58
3.60 2.5000 3.5252
MS
0.72504
0.09423
SE Fit
0.1324
0.1974
0.1856
0.0497
0.0594
F
7.69
P
0.001
Residual
0.4030
-0.0605
0.2139
-0.6561
-1.0252
St Resid
1.45 X
-0.26 X
0.87 X
-2.17R
-3.40R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.
Regression Analysis: CGPA versus HSGPA
The regression equation is
CGPA = 1.50 + 0.560 HSGPA
Predictor
Constant
HSGPA
Coef
1.4964
0.5596
S = 0.304776
SE Coef
0.5448
0.1426
R-Sq = 21.6%
T
2.75
3.92
P
0.008
0.000
R-Sq(adj) = 20.2%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
56
57
SS
1.4310
5.2017
6.6327
MS
1.4310
0.0929
F
15.41
P
0.000
SE Fit
0.1223
0.1842
0.0400
0.0500
Residual
0.4247
0.2166
-0.6430
-1.0111
Unusual Observations
Obs
3
26
27
58
HSGPA
3.00
2.55
3.80
3.60
CGPA
3.6000
3.1400
2.9800
2.5000
Fit
3.1753
2.9234
3.6230
3.5111
St Resid
1.52 X
0.89 X
-2.13R
-3.36R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.