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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 11 Dec 2016 16:17:59 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/11/t1481469530u7nml6wk2j6sr04.htm/, Retrieved Thu, 02 May 2024 11:27:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298812, Retrieved Thu, 02 May 2024 11:27:37 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [Multiple Regressi...] [2016-12-11 15:17:59] [f8e2c3c70b883e93ecb746821352be11] [Current]

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13	4	2	4	3	5	4
16	5	3	3	4	5	4
17	4	4	5	4	5	4
15	3	4	3	3	4	4
16	4	4	5	4	5	4
16	3	4	4	4	5	5
17	3	4	4	3	3	4
16	3	4	5	4	4	4
17	4	5	4	4	5	5
17	4	5	5	4	5	5
17	4	4	2	4	5	4
15	4	4	5	3	5	4
16	4	4	4	3	4	5
14	3	3	5	4	4	5
16	4	4	5	4	2	5
17	3	4	5	4	4	5
16	3	4	5	4	4	5
NA	5	5	5	5	5	5
15	5	5	4	3	4	4
17	4	4	4	4	5	4
16	3	4	5	3	4	5
15	4	4	4	4	5	5
16	4	4	5	4	4	5
15	4	4	5	4	4	4
17	4	4	5	4	4	5
15	3	4	4	4	4	4
16	3	4	4	3	5	5
15	4	4	4	4	4	4
16	2	4	5	4	5	5
16	5	4	4	4	4	4
13	4	3	5	4	4	4
15	4	5	5	4	5	5
17	5	4	5	4	4	5
15	4	3	5	4	4	5
13	2	3	5	4	5	4
17	4	5	2	4	4	4
15	3	4	5	4	4	4
14	4	3	5	3	4	5
14	4	3	3	4	4	4
18	4	4	5	4	4	4
15	5	4	4	4	4	4
17	4	5	5	4	5	5
13	3	3	4	4	4	4
16	5	5	5	3	5	5
15	5	4	5	3	4	4
15	4	4	4	3	4	5
16	4	4	4	4	4	4
15	3	5	5	3	3	4
13	4	4	4	4	5	4
NA	2	3	4	2	3	4
17	4	5	5	4	4	4
17	5	5	2	4	5	4
17	5	5	5	4	4	4
11	4	3	5	4	5	5
14	4	3	4	3	4	5
13	4	4	5	4	4	4
15	3	4	4	3	3	4
17	3	4	4	4	4	3
16	4	4	4	3	5	4
15	4	4	4	4	5	4
17	5	5	3	4	5	5
16	2	4	4	4	5	5
16	4	4	4	4	5	5
16	3	4	4	4	2	4
15	4	4	5	4	5	5
12	4	2	4	4	4	4
17	4	4	4	3	5	3
14	4	4	4	3	5	4
14	5	4	5	3	3	5
16	3	4	4	3	5	5
15	3	4	4	3	4	5
15	4	5	5	5	5	4
13	4	4	3	4	4	4
13	4	4	4	4	4	4
17	4	4	4	5	5	4
15	3	4	3	4	4	4
16	4	4	4	4	5	4
14	3	4	5	3	5	5
15	3	3	5	4	4	5
17	4	3	5	4	4	4
16	4	4	5	4	4	5
12	3	3	3	4	4	4
16	4	4	4	4	5	4
17	4	4	3	4	5	5
17	4	4	4	4	5	5
20	5	4	4	4	4	4
17	5	4	3	5	4	5
18	4	4	5	4	5	5
15	3	4	5	4	4	5
17	3	4	4	4	4	4
14	4	2	3	3	4	4
15	4	4	5	4	4	3
17	4	4	5	4	4	5
16	4	4	4	4	5	4
17	4	5	4	4	5	3
15	3	4	4	3	5	5
16	4	4	5	4	4	5
18	5	4	3	4	4	5
18	5	4	5	5	4	5
16	4	5	4	4	5	5
NA	3	4	5	4	4	5
17	5	3	4	4	5	5
15	4	4	5	4	4	5
13	5	4	4	4	4	5
15	3	4	4	3	4	4
17	5	4	4	5	5	5
16	4	4	5	3	4	5
16	4	4	3	3	4	3
15	4	4	5	4	4	4
16	4	4	5	4	4	4
16	3	4	5	4	5	3
13	4	4	4	4	4	4
15	4	4	4	3	4	5
12	3	3	4	3	5	5
19	4	4	4	3	4	4
16	3	4	5	4	4	4
16	4	4	5	4	3	4
17	5	4	5	5	5	5
16	5	4	5	4	5	5
14	4	4	4	4	4	3
15	4	4	5	3	4	4
14	3	4	4	3	4	5
16	4	4	4	4	4	4
15	4	4	4	4	5	4
17	4	5	3	4	4	4
15	3	4	4	4	4	4
16	4	4	4	3	4	4
16	4	4	4	4	4	5
15	3	4	3	3	4	4
15	4	4	4	3	4	3
11	3	2	4	2	4	4
16	4	4	4	3	5	4
18	5	4	4	3	5	4
13	2	4	4	3	3	5
11	3	3	4	4	4	4
16	4	4	4	3	4	4
18	5	5	4	4	5	4
NA	NA	NA	NA	NA	NA	NA
15	4	5	5	4	4	4
19	5	5	5	5	5	4
17	4	5	5	4	5	5
13	4	4	4	3	4	5
14	3	4	5	4	5	4
16	4	4	5	4	4	4
13	4	4	2	4	4	4
17	4	4	3	4	5	5
14	4	4	4	4	5	5
19	5	4	5	3	5	4
14	4	3	5	4	4	4
16	4	4	5	4	4	4
12	3	3	2	3	4	4
16	4	5	5	4	4	3
16	4	4	4	3	4	4
15	4	4	4	4	4	5
12	3	4	5	3	5	5
15	4	4	5	4	4	5
17	5	4	5	4	5	4
13	4	4	5	4	3	4
15	2	3	5	4	4	4
18	4	4	4	4	4	5
15	4	3	4	3	5	5
18	4	4	4	4	4	3
15	4	5	5	5	4	4
15	5	4	3	4	4	4
16	5	4	4	3	4	4
13	3	3	4	4	5	5
16	4	4	4	4	4	5
13	4	4	4	4	5	4
16	2	3	4	5	5	4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298812&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVSUM[t] = + 6.84393 + 0.499532SK1[t] + 1.09856SK2[t] + 0.0512869SK3[t] + 0.363232SK4[t] + 0.182546SK5[t] -0.00453751SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVSUM[t] =  +  6.84393 +  0.499532SK1[t] +  1.09856SK2[t] +  0.0512869SK3[t] +  0.363232SK4[t] +  0.182546SK5[t] -0.00453751SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVSUM[t] =  +  6.84393 +  0.499532SK1[t] +  1.09856SK2[t] +  0.0512869SK3[t] +  0.363232SK4[t] +  0.182546SK5[t] -0.00453751SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298812&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
TVSUM[t] = + 6.84393 + 0.499532SK1[t] + 1.09856SK2[t] + 0.0512869SK3[t] + 0.363232SK4[t] + 0.182546SK5[t] -0.00453751SK6[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.844	1.477	+4.6340e+00	7.449e-06	3.725e-06
SK1	+0.4995	0.1593	+3.1360e+00	0.002042	0.001021
SK2	+1.099	0.1915	+5.7360e+00	4.806e-08	2.403e-08
SK3	+0.05129	0.1496	+3.4280e-01	0.7322	0.3661
SK4	+0.3632	0.2067	+1.7580e+00	0.08076	0.04038
SK5	+0.1825	0.1833	+9.9570e-01	0.3209	0.1605
SK6	-0.004537	0.1896	-2.3940e-02	0.9809	0.4905

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +6.844 &  1.477 & +4.6340e+00 &  7.449e-06 &  3.725e-06 \tabularnewline
SK1 & +0.4995 &  0.1593 & +3.1360e+00 &  0.002042 &  0.001021 \tabularnewline
SK2 & +1.099 &  0.1915 & +5.7360e+00 &  4.806e-08 &  2.403e-08 \tabularnewline
SK3 & +0.05129 &  0.1496 & +3.4280e-01 &  0.7322 &  0.3661 \tabularnewline
SK4 & +0.3632 &  0.2067 & +1.7580e+00 &  0.08076 &  0.04038 \tabularnewline
SK5 & +0.1825 &  0.1833 & +9.9570e-01 &  0.3209 &  0.1605 \tabularnewline
SK6 & -0.004537 &  0.1896 & -2.3940e-02 &  0.9809 &  0.4905 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+6.844[/C][C] 1.477[/C][C]+4.6340e+00[/C][C] 7.449e-06[/C][C] 3.725e-06[/C][/ROW]
[ROW][C]SK1[/C][C]+0.4995[/C][C] 0.1593[/C][C]+3.1360e+00[/C][C] 0.002042[/C][C] 0.001021[/C][/ROW]
[ROW][C]SK2[/C][C]+1.099[/C][C] 0.1915[/C][C]+5.7360e+00[/C][C] 4.806e-08[/C][C] 2.403e-08[/C][/ROW]
[ROW][C]SK3[/C][C]+0.05129[/C][C] 0.1496[/C][C]+3.4280e-01[/C][C] 0.7322[/C][C] 0.3661[/C][/ROW]
[ROW][C]SK4[/C][C]+0.3632[/C][C] 0.2067[/C][C]+1.7580e+00[/C][C] 0.08076[/C][C] 0.04038[/C][/ROW]
[ROW][C]SK5[/C][C]+0.1825[/C][C] 0.1833[/C][C]+9.9570e-01[/C][C] 0.3209[/C][C] 0.1605[/C][/ROW]
[ROW][C]SK6[/C][C]-0.004537[/C][C] 0.1896[/C][C]-2.3940e-02[/C][C] 0.9809[/C][C] 0.4905[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298812&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.844	1.477	+4.6340e+00	7.449e-06	3.725e-06
SK1	+0.4995	0.1593	+3.1360e+00	0.002042	0.001021
SK2	+1.099	0.1915	+5.7360e+00	4.806e-08	2.403e-08
SK3	+0.05129	0.1496	+3.4280e-01	0.7322	0.3661
SK4	+0.3632	0.2067	+1.7580e+00	0.08076	0.04038
SK5	+0.1825	0.1833	+9.9570e-01	0.3209	0.1605
SK6	-0.004537	0.1896	-2.3940e-02	0.9809	0.4905

Multiple Linear Regression - Regression Statistics
Multiple R	0.5567
R-squared	0.3099
Adjusted R-squared	0.2837
F-TEST (value)	11.83
F-TEST (DF numerator)	6
F-TEST (DF denominator)	158
p-value	6.15e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.404
Sum Squared Residuals	311.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5567 \tabularnewline
R-squared &  0.3099 \tabularnewline
Adjusted R-squared &  0.2837 \tabularnewline
F-TEST (value) &  11.83 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value &  6.15e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.404 \tabularnewline
Sum Squared Residuals &  311.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5567[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3099[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2837[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 11.83[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C] 6.15e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.404[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 311.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298812&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R	0.5567
R-squared	0.3099
Adjusted R-squared	0.2837
F-TEST (value)	11.83
F-TEST (DF numerator)	6
F-TEST (DF denominator)	158
p-value	6.15e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.404
Sum Squared Residuals	311.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.23	-0.2286
2	16	15.14	0.8614
3	17	15.84	1.16
4	15	14.69	0.3077
5	16	15.84	0.1598
6	16	15.28	0.7151
7	17	14.56	2.439
8	16	15.16	0.8418
9	17	16.88	0.117
10	17	16.93	0.06575
11	17	15.69	1.314
12	15	15.48	-0.477
13	16	15.24	0.7614
14	14	14.06	-0.05506
15	16	15.29	0.7119
16	17	15.15	1.846
17	16	15.15	0.8464
18	15	16.84	-1.841
19	17	15.79	1.211
20	16	14.79	1.21
21	15	15.78	-0.7844
22	16	15.65	0.3469
23	15	15.66	-0.6577
24	17	15.65	1.347
25	15	15.11	-0.1069
26	16	14.92	1.078
27	15	15.61	-0.6064
28	16	14.84	1.163
29	16	16.11	-0.1059
30	13	14.56	-1.559
31	15	16.93	-1.934
32	17	16.15	0.8473
33	15	14.55	0.4454
34	13	13.74	-0.7426
35	17	16.6	0.3976
36	15	15.16	-0.1582
37	14	14.19	-0.1914
38	14	14.46	-0.4566
39	18	15.66	2.342
40	15	16.11	-1.106
41	17	16.93	0.06575
42	13	14.01	-1.008
43	16	17.07	-1.071
44	15	15.79	-0.794
45	15	15.24	-0.2386
46	16	15.61	0.3936
47	15	15.71	-0.7109
48	13	15.79	-2.789
49	17	16.76	0.2438
50	17	17.28	-0.2845
51	17	17.26	-0.2558
52	11	14.74	-3.737
53	14	14.14	-0.1401
54	13	15.66	-2.658
55	15	14.56	0.4389
56	17	15.11	1.889
57	16	15.43	0.5743
58	15	15.79	-0.7889
59	17	17.33	-0.3312
60	16	14.79	1.215
61	16	15.78	0.2156
62	16	14.74	1.258
63	15	15.84	-0.8357
64	12	13.41	-1.409
65	17	15.43	1.57
66	14	15.43	-1.426
67	14	15.61	-1.607
68	16	14.92	1.078
69	15	14.74	0.2609
70	15	17.3	-2.302
71	13	15.56	-2.555
72	13	15.61	-2.606
73	17	16.15	0.8478
74	15	15.06	-0.05558
75	16	15.79	0.2111
76	14	14.97	-0.9729
77	15	14.06	0.9449
78	17	14.56	2.441
79	16	15.65	0.3469
80	12	13.96	-1.957
81	16	15.79	0.2111
82	17	15.73	1.267
83	17	15.78	1.216
84	20	16.11	3.894
85	17	16.41	0.5867
86	18	15.84	2.164
87	15	15.15	-0.1536
88	17	15.11	1.893
89	14	12.99	1.005
90	15	15.66	-0.6622
91	17	15.65	1.347
92	16	15.79	0.2111
93	17	16.89	0.108
94	15	14.92	0.07836
95	16	15.65	0.3469
96	18	16.05	1.95
97	18	16.52	1.484
98	16	16.88	-0.883
99	17	15.19	1.815
100	15	15.65	-0.6531
101	13	16.1	-3.101
102	15	14.74	0.2564
103	17	16.65	0.3528
104	16	15.29	0.7101
105	16	15.2	0.8036
106	15	15.66	-0.6577
107	16	15.66	0.3423
108	16	15.35	0.6548
109	13	15.61	-2.606
110	15	15.24	-0.2386
111	12	13.82	-1.823
112	19	15.24	3.757
113	16	15.16	0.8418
114	16	15.48	0.5249
115	17	16.7	0.3015
116	16	16.34	-0.3352
117	14	15.61	-1.611
118	15	15.29	-0.2945
119	14	14.74	-0.7391
120	16	15.61	0.3936
121	15	15.79	-0.7889
122	17	16.65	0.3463
123	15	15.11	-0.1069
124	16	15.24	0.7568
125	16	15.6	0.3981
126	15	14.69	0.3077
127	15	15.25	-0.2477
128	11	12.18	-1.183
129	16	15.43	0.5743
130	18	15.93	2.075
131	13	14.06	-1.057
132	11	14.01	-3.008
133	16	15.24	0.7568
134	18	17.39	0.613
135	15	16.76	-1.756
136	19	17.8	1.198
137	17	16.93	0.06575
138	13	15.24	-2.239
139	14	15.34	-1.341
140	16	15.66	0.3423
141	13	15.5	-2.504
142	17	15.73	1.267
143	14	15.78	-1.784
144	19	15.98	3.023
145	14	14.56	-0.5591
146	16	15.66	0.3423
147	12	13.54	-1.543
148	16	16.76	-0.7608
149	16	15.24	0.7568
150	15	15.6	-0.6019
151	12	14.97	-2.973
152	15	15.65	-0.6531
153	17	16.34	0.6602
154	13	15.48	-2.475
155	15	13.56	1.44
156	18	15.6	2.398
157	15	14.32	0.6774
158	18	15.61	2.389
159	15	17.12	-2.119
160	15	16.05	-1.055
161	16	15.74	0.2573
162	13	14.19	-1.186
163	16	15.6	0.3981
164	13	15.79	-2.789
165	16	14.05	1.945

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.23 & -0.2286 \tabularnewline
2 &  16 &  15.14 &  0.8614 \tabularnewline
3 &  17 &  15.84 &  1.16 \tabularnewline
4 &  15 &  14.69 &  0.3077 \tabularnewline
5 &  16 &  15.84 &  0.1598 \tabularnewline
6 &  16 &  15.28 &  0.7151 \tabularnewline
7 &  17 &  14.56 &  2.439 \tabularnewline
8 &  16 &  15.16 &  0.8418 \tabularnewline
9 &  17 &  16.88 &  0.117 \tabularnewline
10 &  17 &  16.93 &  0.06575 \tabularnewline
11 &  17 &  15.69 &  1.314 \tabularnewline
12 &  15 &  15.48 & -0.477 \tabularnewline
13 &  16 &  15.24 &  0.7614 \tabularnewline
14 &  14 &  14.06 & -0.05506 \tabularnewline
15 &  16 &  15.29 &  0.7119 \tabularnewline
16 &  17 &  15.15 &  1.846 \tabularnewline
17 &  16 &  15.15 &  0.8464 \tabularnewline
18 &  15 &  16.84 & -1.841 \tabularnewline
19 &  17 &  15.79 &  1.211 \tabularnewline
20 &  16 &  14.79 &  1.21 \tabularnewline
21 &  15 &  15.78 & -0.7844 \tabularnewline
22 &  16 &  15.65 &  0.3469 \tabularnewline
23 &  15 &  15.66 & -0.6577 \tabularnewline
24 &  17 &  15.65 &  1.347 \tabularnewline
25 &  15 &  15.11 & -0.1069 \tabularnewline
26 &  16 &  14.92 &  1.078 \tabularnewline
27 &  15 &  15.61 & -0.6064 \tabularnewline
28 &  16 &  14.84 &  1.163 \tabularnewline
29 &  16 &  16.11 & -0.1059 \tabularnewline
30 &  13 &  14.56 & -1.559 \tabularnewline
31 &  15 &  16.93 & -1.934 \tabularnewline
32 &  17 &  16.15 &  0.8473 \tabularnewline
33 &  15 &  14.55 &  0.4454 \tabularnewline
34 &  13 &  13.74 & -0.7426 \tabularnewline
35 &  17 &  16.6 &  0.3976 \tabularnewline
36 &  15 &  15.16 & -0.1582 \tabularnewline
37 &  14 &  14.19 & -0.1914 \tabularnewline
38 &  14 &  14.46 & -0.4566 \tabularnewline
39 &  18 &  15.66 &  2.342 \tabularnewline
40 &  15 &  16.11 & -1.106 \tabularnewline
41 &  17 &  16.93 &  0.06575 \tabularnewline
42 &  13 &  14.01 & -1.008 \tabularnewline
43 &  16 &  17.07 & -1.071 \tabularnewline
44 &  15 &  15.79 & -0.794 \tabularnewline
45 &  15 &  15.24 & -0.2386 \tabularnewline
46 &  16 &  15.61 &  0.3936 \tabularnewline
47 &  15 &  15.71 & -0.7109 \tabularnewline
48 &  13 &  15.79 & -2.789 \tabularnewline
49 &  17 &  16.76 &  0.2438 \tabularnewline
50 &  17 &  17.28 & -0.2845 \tabularnewline
51 &  17 &  17.26 & -0.2558 \tabularnewline
52 &  11 &  14.74 & -3.737 \tabularnewline
53 &  14 &  14.14 & -0.1401 \tabularnewline
54 &  13 &  15.66 & -2.658 \tabularnewline
55 &  15 &  14.56 &  0.4389 \tabularnewline
56 &  17 &  15.11 &  1.889 \tabularnewline
57 &  16 &  15.43 &  0.5743 \tabularnewline
58 &  15 &  15.79 & -0.7889 \tabularnewline
59 &  17 &  17.33 & -0.3312 \tabularnewline
60 &  16 &  14.79 &  1.215 \tabularnewline
61 &  16 &  15.78 &  0.2156 \tabularnewline
62 &  16 &  14.74 &  1.258 \tabularnewline
63 &  15 &  15.84 & -0.8357 \tabularnewline
64 &  12 &  13.41 & -1.409 \tabularnewline
65 &  17 &  15.43 &  1.57 \tabularnewline
66 &  14 &  15.43 & -1.426 \tabularnewline
67 &  14 &  15.61 & -1.607 \tabularnewline
68 &  16 &  14.92 &  1.078 \tabularnewline
69 &  15 &  14.74 &  0.2609 \tabularnewline
70 &  15 &  17.3 & -2.302 \tabularnewline
71 &  13 &  15.56 & -2.555 \tabularnewline
72 &  13 &  15.61 & -2.606 \tabularnewline
73 &  17 &  16.15 &  0.8478 \tabularnewline
74 &  15 &  15.06 & -0.05558 \tabularnewline
75 &  16 &  15.79 &  0.2111 \tabularnewline
76 &  14 &  14.97 & -0.9729 \tabularnewline
77 &  15 &  14.06 &  0.9449 \tabularnewline
78 &  17 &  14.56 &  2.441 \tabularnewline
79 &  16 &  15.65 &  0.3469 \tabularnewline
80 &  12 &  13.96 & -1.957 \tabularnewline
81 &  16 &  15.79 &  0.2111 \tabularnewline
82 &  17 &  15.73 &  1.267 \tabularnewline
83 &  17 &  15.78 &  1.216 \tabularnewline
84 &  20 &  16.11 &  3.894 \tabularnewline
85 &  17 &  16.41 &  0.5867 \tabularnewline
86 &  18 &  15.84 &  2.164 \tabularnewline
87 &  15 &  15.15 & -0.1536 \tabularnewline
88 &  17 &  15.11 &  1.893 \tabularnewline
89 &  14 &  12.99 &  1.005 \tabularnewline
90 &  15 &  15.66 & -0.6622 \tabularnewline
91 &  17 &  15.65 &  1.347 \tabularnewline
92 &  16 &  15.79 &  0.2111 \tabularnewline
93 &  17 &  16.89 &  0.108 \tabularnewline
94 &  15 &  14.92 &  0.07836 \tabularnewline
95 &  16 &  15.65 &  0.3469 \tabularnewline
96 &  18 &  16.05 &  1.95 \tabularnewline
97 &  18 &  16.52 &  1.484 \tabularnewline
98 &  16 &  16.88 & -0.883 \tabularnewline
99 &  17 &  15.19 &  1.815 \tabularnewline
100 &  15 &  15.65 & -0.6531 \tabularnewline
101 &  13 &  16.1 & -3.101 \tabularnewline
102 &  15 &  14.74 &  0.2564 \tabularnewline
103 &  17 &  16.65 &  0.3528 \tabularnewline
104 &  16 &  15.29 &  0.7101 \tabularnewline
105 &  16 &  15.2 &  0.8036 \tabularnewline
106 &  15 &  15.66 & -0.6577 \tabularnewline
107 &  16 &  15.66 &  0.3423 \tabularnewline
108 &  16 &  15.35 &  0.6548 \tabularnewline
109 &  13 &  15.61 & -2.606 \tabularnewline
110 &  15 &  15.24 & -0.2386 \tabularnewline
111 &  12 &  13.82 & -1.823 \tabularnewline
112 &  19 &  15.24 &  3.757 \tabularnewline
113 &  16 &  15.16 &  0.8418 \tabularnewline
114 &  16 &  15.48 &  0.5249 \tabularnewline
115 &  17 &  16.7 &  0.3015 \tabularnewline
116 &  16 &  16.34 & -0.3352 \tabularnewline
117 &  14 &  15.61 & -1.611 \tabularnewline
118 &  15 &  15.29 & -0.2945 \tabularnewline
119 &  14 &  14.74 & -0.7391 \tabularnewline
120 &  16 &  15.61 &  0.3936 \tabularnewline
121 &  15 &  15.79 & -0.7889 \tabularnewline
122 &  17 &  16.65 &  0.3463 \tabularnewline
123 &  15 &  15.11 & -0.1069 \tabularnewline
124 &  16 &  15.24 &  0.7568 \tabularnewline
125 &  16 &  15.6 &  0.3981 \tabularnewline
126 &  15 &  14.69 &  0.3077 \tabularnewline
127 &  15 &  15.25 & -0.2477 \tabularnewline
128 &  11 &  12.18 & -1.183 \tabularnewline
129 &  16 &  15.43 &  0.5743 \tabularnewline
130 &  18 &  15.93 &  2.075 \tabularnewline
131 &  13 &  14.06 & -1.057 \tabularnewline
132 &  11 &  14.01 & -3.008 \tabularnewline
133 &  16 &  15.24 &  0.7568 \tabularnewline
134 &  18 &  17.39 &  0.613 \tabularnewline
135 &  15 &  16.76 & -1.756 \tabularnewline
136 &  19 &  17.8 &  1.198 \tabularnewline
137 &  17 &  16.93 &  0.06575 \tabularnewline
138 &  13 &  15.24 & -2.239 \tabularnewline
139 &  14 &  15.34 & -1.341 \tabularnewline
140 &  16 &  15.66 &  0.3423 \tabularnewline
141 &  13 &  15.5 & -2.504 \tabularnewline
142 &  17 &  15.73 &  1.267 \tabularnewline
143 &  14 &  15.78 & -1.784 \tabularnewline
144 &  19 &  15.98 &  3.023 \tabularnewline
145 &  14 &  14.56 & -0.5591 \tabularnewline
146 &  16 &  15.66 &  0.3423 \tabularnewline
147 &  12 &  13.54 & -1.543 \tabularnewline
148 &  16 &  16.76 & -0.7608 \tabularnewline
149 &  16 &  15.24 &  0.7568 \tabularnewline
150 &  15 &  15.6 & -0.6019 \tabularnewline
151 &  12 &  14.97 & -2.973 \tabularnewline
152 &  15 &  15.65 & -0.6531 \tabularnewline
153 &  17 &  16.34 &  0.6602 \tabularnewline
154 &  13 &  15.48 & -2.475 \tabularnewline
155 &  15 &  13.56 &  1.44 \tabularnewline
156 &  18 &  15.6 &  2.398 \tabularnewline
157 &  15 &  14.32 &  0.6774 \tabularnewline
158 &  18 &  15.61 &  2.389 \tabularnewline
159 &  15 &  17.12 & -2.119 \tabularnewline
160 &  15 &  16.05 & -1.055 \tabularnewline
161 &  16 &  15.74 &  0.2573 \tabularnewline
162 &  13 &  14.19 & -1.186 \tabularnewline
163 &  16 &  15.6 &  0.3981 \tabularnewline
164 &  13 &  15.79 & -2.789 \tabularnewline
165 &  16 &  14.05 &  1.945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.23[/C][C]-0.2286[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.14[/C][C] 0.8614[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.84[/C][C] 1.16[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.69[/C][C] 0.3077[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.84[/C][C] 0.1598[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.28[/C][C] 0.7151[/C][/ROW]
[ROW][C]7[/C][C] 17[/C][C] 14.56[/C][C] 2.439[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.16[/C][C] 0.8418[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.88[/C][C] 0.117[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 16.93[/C][C] 0.06575[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.69[/C][C] 1.314[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.48[/C][C]-0.477[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.24[/C][C] 0.7614[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.06[/C][C]-0.05506[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.29[/C][C] 0.7119[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.15[/C][C] 1.846[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.15[/C][C] 0.8464[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 16.84[/C][C]-1.841[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.79[/C][C] 1.211[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.79[/C][C] 1.21[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.78[/C][C]-0.7844[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.65[/C][C] 0.3469[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.66[/C][C]-0.6577[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.65[/C][C] 1.347[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 15.11[/C][C]-0.1069[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.92[/C][C] 1.078[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.61[/C][C]-0.6064[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.84[/C][C] 1.163[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.11[/C][C]-0.1059[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.56[/C][C]-1.559[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.93[/C][C]-1.934[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.15[/C][C] 0.8473[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.55[/C][C] 0.4454[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.74[/C][C]-0.7426[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.6[/C][C] 0.3976[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.16[/C][C]-0.1582[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.19[/C][C]-0.1914[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.46[/C][C]-0.4566[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.66[/C][C] 2.342[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.11[/C][C]-1.106[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 16.93[/C][C] 0.06575[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 14.01[/C][C]-1.008[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 17.07[/C][C]-1.071[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.79[/C][C]-0.794[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.24[/C][C]-0.2386[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.61[/C][C] 0.3936[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.71[/C][C]-0.7109[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.79[/C][C]-2.789[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 16.76[/C][C] 0.2438[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 17.28[/C][C]-0.2845[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 17.26[/C][C]-0.2558[/C][/ROW]
[ROW][C]52[/C][C] 11[/C][C] 14.74[/C][C]-3.737[/C][/ROW]
[ROW][C]53[/C][C] 14[/C][C] 14.14[/C][C]-0.1401[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 15.66[/C][C]-2.658[/C][/ROW]
[ROW][C]55[/C][C] 15[/C][C] 14.56[/C][C] 0.4389[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 15.11[/C][C] 1.889[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 15.43[/C][C] 0.5743[/C][/ROW]
[ROW][C]58[/C][C] 15[/C][C] 15.79[/C][C]-0.7889[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 17.33[/C][C]-0.3312[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 14.79[/C][C] 1.215[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.78[/C][C] 0.2156[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 14.74[/C][C] 1.258[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.84[/C][C]-0.8357[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.41[/C][C]-1.409[/C][/ROW]
[ROW][C]65[/C][C] 17[/C][C] 15.43[/C][C] 1.57[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.43[/C][C]-1.426[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.61[/C][C]-1.607[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 14.92[/C][C] 1.078[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.74[/C][C] 0.2609[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 17.3[/C][C]-2.302[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 15.56[/C][C]-2.555[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 15.61[/C][C]-2.606[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.15[/C][C] 0.8478[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.06[/C][C]-0.05558[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.79[/C][C] 0.2111[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 14.97[/C][C]-0.9729[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 14.06[/C][C] 0.9449[/C][/ROW]
[ROW][C]78[/C][C] 17[/C][C] 14.56[/C][C] 2.441[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.65[/C][C] 0.3469[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 13.96[/C][C]-1.957[/C][/ROW]
[ROW][C]81[/C][C] 16[/C][C] 15.79[/C][C] 0.2111[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 15.73[/C][C] 1.267[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.78[/C][C] 1.216[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 16.11[/C][C] 3.894[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.41[/C][C] 0.5867[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 15.84[/C][C] 2.164[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.15[/C][C]-0.1536[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.11[/C][C] 1.893[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 12.99[/C][C] 1.005[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 15.66[/C][C]-0.6622[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 15.65[/C][C] 1.347[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.79[/C][C] 0.2111[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.89[/C][C] 0.108[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.92[/C][C] 0.07836[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 15.65[/C][C] 0.3469[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 16.05[/C][C] 1.95[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.52[/C][C] 1.484[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.88[/C][C]-0.883[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 15.19[/C][C] 1.815[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 15.65[/C][C]-0.6531[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 16.1[/C][C]-3.101[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.74[/C][C] 0.2564[/C][/ROW]
[ROW][C]103[/C][C] 17[/C][C] 16.65[/C][C] 0.3528[/C][/ROW]
[ROW][C]104[/C][C] 16[/C][C] 15.29[/C][C] 0.7101[/C][/ROW]
[ROW][C]105[/C][C] 16[/C][C] 15.2[/C][C] 0.8036[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 15.66[/C][C]-0.6577[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.35[/C][C] 0.6548[/C][/ROW]
[ROW][C]109[/C][C] 13[/C][C] 15.61[/C][C]-2.606[/C][/ROW]
[ROW][C]110[/C][C] 15[/C][C] 15.24[/C][C]-0.2386[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 13.82[/C][C]-1.823[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 15.24[/C][C] 3.757[/C][/ROW]
[ROW][C]113[/C][C] 16[/C][C] 15.16[/C][C] 0.8418[/C][/ROW]
[ROW][C]114[/C][C] 16[/C][C] 15.48[/C][C] 0.5249[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 16.7[/C][C] 0.3015[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 16.34[/C][C]-0.3352[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 15.61[/C][C]-1.611[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.29[/C][C]-0.2945[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 14.74[/C][C]-0.7391[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.61[/C][C] 0.3936[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 15.79[/C][C]-0.7889[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.65[/C][C] 0.3463[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.11[/C][C]-0.1069[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.24[/C][C] 0.7568[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 15.6[/C][C] 0.3981[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 14.69[/C][C] 0.3077[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 15.25[/C][C]-0.2477[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 12.18[/C][C]-1.183[/C][/ROW]
[ROW][C]129[/C][C] 16[/C][C] 15.43[/C][C] 0.5743[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 15.93[/C][C] 2.075[/C][/ROW]
[ROW][C]131[/C][C] 13[/C][C] 14.06[/C][C]-1.057[/C][/ROW]
[ROW][C]132[/C][C] 11[/C][C] 14.01[/C][C]-3.008[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 15.24[/C][C] 0.7568[/C][/ROW]
[ROW][C]134[/C][C] 18[/C][C] 17.39[/C][C] 0.613[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 16.76[/C][C]-1.756[/C][/ROW]
[ROW][C]136[/C][C] 19[/C][C] 17.8[/C][C] 1.198[/C][/ROW]
[ROW][C]137[/C][C] 17[/C][C] 16.93[/C][C] 0.06575[/C][/ROW]
[ROW][C]138[/C][C] 13[/C][C] 15.24[/C][C]-2.239[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 15.34[/C][C]-1.341[/C][/ROW]
[ROW][C]140[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 15.5[/C][C]-2.504[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 15.73[/C][C] 1.267[/C][/ROW]
[ROW][C]143[/C][C] 14[/C][C] 15.78[/C][C]-1.784[/C][/ROW]
[ROW][C]144[/C][C] 19[/C][C] 15.98[/C][C] 3.023[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 14.56[/C][C]-0.5591[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 13.54[/C][C]-1.543[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 16.76[/C][C]-0.7608[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 15.24[/C][C] 0.7568[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 15.6[/C][C]-0.6019[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 14.97[/C][C]-2.973[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 15.65[/C][C]-0.6531[/C][/ROW]
[ROW][C]153[/C][C] 17[/C][C] 16.34[/C][C] 0.6602[/C][/ROW]
[ROW][C]154[/C][C] 13[/C][C] 15.48[/C][C]-2.475[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.56[/C][C] 1.44[/C][/ROW]
[ROW][C]156[/C][C] 18[/C][C] 15.6[/C][C] 2.398[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 14.32[/C][C] 0.6774[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 15.61[/C][C] 2.389[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 17.12[/C][C]-2.119[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 16.05[/C][C]-1.055[/C][/ROW]
[ROW][C]161[/C][C] 16[/C][C] 15.74[/C][C] 0.2573[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.19[/C][C]-1.186[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.6[/C][C] 0.3981[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 15.79[/C][C]-2.789[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 14.05[/C][C] 1.945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298812&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.23	-0.2286
2	16	15.14	0.8614
3	17	15.84	1.16
4	15	14.69	0.3077
5	16	15.84	0.1598
6	16	15.28	0.7151
7	17	14.56	2.439
8	16	15.16	0.8418
9	17	16.88	0.117
10	17	16.93	0.06575
11	17	15.69	1.314
12	15	15.48	-0.477
13	16	15.24	0.7614
14	14	14.06	-0.05506
15	16	15.29	0.7119
16	17	15.15	1.846
17	16	15.15	0.8464
18	15	16.84	-1.841
19	17	15.79	1.211
20	16	14.79	1.21
21	15	15.78	-0.7844
22	16	15.65	0.3469
23	15	15.66	-0.6577
24	17	15.65	1.347
25	15	15.11	-0.1069
26	16	14.92	1.078
27	15	15.61	-0.6064
28	16	14.84	1.163
29	16	16.11	-0.1059
30	13	14.56	-1.559
31	15	16.93	-1.934
32	17	16.15	0.8473
33	15	14.55	0.4454
34	13	13.74	-0.7426
35	17	16.6	0.3976
36	15	15.16	-0.1582
37	14	14.19	-0.1914
38	14	14.46	-0.4566
39	18	15.66	2.342
40	15	16.11	-1.106
41	17	16.93	0.06575
42	13	14.01	-1.008
43	16	17.07	-1.071
44	15	15.79	-0.794
45	15	15.24	-0.2386
46	16	15.61	0.3936
47	15	15.71	-0.7109
48	13	15.79	-2.789
49	17	16.76	0.2438
50	17	17.28	-0.2845
51	17	17.26	-0.2558
52	11	14.74	-3.737
53	14	14.14	-0.1401
54	13	15.66	-2.658
55	15	14.56	0.4389
56	17	15.11	1.889
57	16	15.43	0.5743
58	15	15.79	-0.7889
59	17	17.33	-0.3312
60	16	14.79	1.215
61	16	15.78	0.2156
62	16	14.74	1.258
63	15	15.84	-0.8357
64	12	13.41	-1.409
65	17	15.43	1.57
66	14	15.43	-1.426
67	14	15.61	-1.607
68	16	14.92	1.078
69	15	14.74	0.2609
70	15	17.3	-2.302
71	13	15.56	-2.555
72	13	15.61	-2.606
73	17	16.15	0.8478
74	15	15.06	-0.05558
75	16	15.79	0.2111
76	14	14.97	-0.9729
77	15	14.06	0.9449
78	17	14.56	2.441
79	16	15.65	0.3469
80	12	13.96	-1.957
81	16	15.79	0.2111
82	17	15.73	1.267
83	17	15.78	1.216
84	20	16.11	3.894
85	17	16.41	0.5867
86	18	15.84	2.164
87	15	15.15	-0.1536
88	17	15.11	1.893
89	14	12.99	1.005
90	15	15.66	-0.6622
91	17	15.65	1.347
92	16	15.79	0.2111
93	17	16.89	0.108
94	15	14.92	0.07836
95	16	15.65	0.3469
96	18	16.05	1.95
97	18	16.52	1.484
98	16	16.88	-0.883
99	17	15.19	1.815
100	15	15.65	-0.6531
101	13	16.1	-3.101
102	15	14.74	0.2564
103	17	16.65	0.3528
104	16	15.29	0.7101
105	16	15.2	0.8036
106	15	15.66	-0.6577
107	16	15.66	0.3423
108	16	15.35	0.6548
109	13	15.61	-2.606
110	15	15.24	-0.2386
111	12	13.82	-1.823
112	19	15.24	3.757
113	16	15.16	0.8418
114	16	15.48	0.5249
115	17	16.7	0.3015
116	16	16.34	-0.3352
117	14	15.61	-1.611
118	15	15.29	-0.2945
119	14	14.74	-0.7391
120	16	15.61	0.3936
121	15	15.79	-0.7889
122	17	16.65	0.3463
123	15	15.11	-0.1069
124	16	15.24	0.7568
125	16	15.6	0.3981
126	15	14.69	0.3077
127	15	15.25	-0.2477
128	11	12.18	-1.183
129	16	15.43	0.5743
130	18	15.93	2.075
131	13	14.06	-1.057
132	11	14.01	-3.008
133	16	15.24	0.7568
134	18	17.39	0.613
135	15	16.76	-1.756
136	19	17.8	1.198
137	17	16.93	0.06575
138	13	15.24	-2.239
139	14	15.34	-1.341
140	16	15.66	0.3423
141	13	15.5	-2.504
142	17	15.73	1.267
143	14	15.78	-1.784
144	19	15.98	3.023
145	14	14.56	-0.5591
146	16	15.66	0.3423
147	12	13.54	-1.543
148	16	16.76	-0.7608
149	16	15.24	0.7568
150	15	15.6	-0.6019
151	12	14.97	-2.973
152	15	15.65	-0.6531
153	17	16.34	0.6602
154	13	15.48	-2.475
155	15	13.56	1.44
156	18	15.6	2.398
157	15	14.32	0.6774
158	18	15.61	2.389
159	15	17.12	-2.119
160	15	16.05	-1.055
161	16	15.74	0.2573
162	13	14.19	-1.186
163	16	15.6	0.3981
164	13	15.79	-2.789
165	16	14.05	1.945

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1577	0.3155	0.8423
11	0.08825	0.1765	0.9117
12	0.03678	0.07356	0.9632
13	0.01406	0.02812	0.9859
14	0.01361	0.02723	0.9864
15	0.01454	0.02908	0.9855
16	0.02284	0.04569	0.9772
17	0.01123	0.02247	0.9888
18	0.0312	0.06241	0.9688
19	0.02037	0.04073	0.9796
20	0.01698	0.03396	0.983
21	0.01626	0.03251	0.9837
22	0.009048	0.0181	0.991
23	0.009609	0.01922	0.9904
24	0.009575	0.01915	0.9904
25	0.01225	0.02449	0.9878
26	0.007882	0.01576	0.9921
27	0.007062	0.01412	0.9929
28	0.004441	0.008881	0.9956
29	0.002525	0.005051	0.9975
30	0.004836	0.009673	0.9952
31	0.01038	0.02077	0.9896
32	0.01056	0.02112	0.9894
33	0.006699	0.0134	0.9933
34	0.007513	0.01503	0.9925
35	0.005079	0.01016	0.9949
36	0.003242	0.006484	0.9968
37	0.00223	0.004461	0.9978
38	0.001928	0.003856	0.9981
39	0.007848	0.0157	0.9922
40	0.006725	0.01345	0.9933
41	0.004374	0.008747	0.9956
42	0.005196	0.01039	0.9948
43	0.004108	0.008217	0.9959
44	0.002783	0.005566	0.9972
45	0.001978	0.003957	0.998
46	0.001274	0.002547	0.9987
47	0.001073	0.002147	0.9989
48	0.005069	0.01014	0.9949
49	0.003575	0.00715	0.9964
50	0.002376	0.004752	0.9976
51	0.001586	0.003172	0.9984
52	0.02193	0.04386	0.9781
53	0.01609	0.03217	0.9839
54	0.03523	0.07046	0.9648
55	0.02735	0.05471	0.9726
56	0.03524	0.07048	0.9648
57	0.02975	0.05949	0.9703
58	0.02376	0.04753	0.9762
59	0.01776	0.03552	0.9822
60	0.01512	0.03025	0.9849
61	0.0111	0.02219	0.9889
62	0.01016	0.02033	0.9898
63	0.00798	0.01596	0.992
64	0.008023	0.01605	0.992
65	0.01058	0.02116	0.9894
66	0.01124	0.02247	0.9888
67	0.01175	0.0235	0.9882
68	0.00992	0.01984	0.9901
69	0.007696	0.01539	0.9923
70	0.0122	0.0244	0.9878
71	0.02964	0.05928	0.9704
72	0.05709	0.1142	0.9429
73	0.05461	0.1092	0.9454
74	0.0456	0.0912	0.9544
75	0.03642	0.07284	0.9636
76	0.03369	0.06738	0.9663
77	0.02938	0.05876	0.9706
78	0.05816	0.1163	0.9418
79	0.04714	0.09428	0.9529
80	0.06555	0.1311	0.9344
81	0.0531	0.1062	0.9469
82	0.05201	0.104	0.948
83	0.05029	0.1006	0.9497
84	0.2138	0.4277	0.7862
85	0.1879	0.3758	0.8121
86	0.2361	0.4722	0.7639
87	0.2062	0.4125	0.7938
88	0.2444	0.4888	0.7556
89	0.2228	0.4457	0.7772
90	0.1973	0.3945	0.8027
91	0.1994	0.3988	0.8006
92	0.1695	0.3389	0.8305
93	0.1427	0.2853	0.8573
94	0.1203	0.2406	0.8797
95	0.1012	0.2024	0.8988
96	0.1247	0.2495	0.8753
97	0.1338	0.2675	0.8662
98	0.118	0.2359	0.882
99	0.1348	0.2696	0.8652
100	0.115	0.2299	0.885
101	0.2131	0.4262	0.7869
102	0.1836	0.3672	0.8164
103	0.1564	0.3128	0.8436
104	0.1383	0.2766	0.8617
105	0.1197	0.2395	0.8803
106	0.101	0.202	0.899
107	0.08286	0.1657	0.9171
108	0.06892	0.1378	0.9311
109	0.1101	0.2202	0.8899
110	0.08949	0.179	0.9105
111	0.09903	0.1981	0.901
112	0.2975	0.595	0.7025
113	0.2831	0.5663	0.7169
114	0.261	0.5221	0.739
115	0.2236	0.4471	0.7764
116	0.1937	0.3874	0.8063
117	0.2028	0.4055	0.7972
118	0.169	0.3379	0.831
119	0.1446	0.2891	0.8554
120	0.121	0.2419	0.879
121	0.1068	0.2136	0.8932
122	0.09099	0.182	0.909
123	0.07418	0.1484	0.9258
124	0.06409	0.1282	0.9359
125	0.05484	0.1097	0.9452
126	0.04929	0.09859	0.9507
127	0.03698	0.07397	0.963
128	0.03473	0.06946	0.9653
129	0.02612	0.05224	0.9739
130	0.02895	0.0579	0.9711
131	0.03091	0.06182	0.9691
132	0.07225	0.1445	0.9277
133	0.06733	0.1347	0.9327
134	0.05376	0.1075	0.9462
135	0.04543	0.09085	0.9546
136	0.03683	0.07365	0.9632
137	0.03307	0.06615	0.9669
138	0.03182	0.06364	0.9682
139	0.02693	0.05386	0.9731
140	0.01855	0.03709	0.9815
141	0.0231	0.04621	0.9769
142	0.02821	0.05643	0.9718
143	0.02394	0.04788	0.9761
144	0.05981	0.1196	0.9402
145	0.06834	0.1367	0.9317
146	0.0463	0.0926	0.9537
147	0.06702	0.134	0.933
148	0.04647	0.09295	0.9535
149	0.03674	0.07348	0.9633
150	0.02197	0.04393	0.978
151	0.01986	0.03971	0.9801
152	0.0106	0.0212	0.9894
153	0.09632	0.1926	0.9037
154	0.09215	0.1843	0.9079
155	0.3534	0.7069	0.6466

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1577 &  0.3155 &  0.8423 \tabularnewline
11 &  0.08825 &  0.1765 &  0.9117 \tabularnewline
12 &  0.03678 &  0.07356 &  0.9632 \tabularnewline
13 &  0.01406 &  0.02812 &  0.9859 \tabularnewline
14 &  0.01361 &  0.02723 &  0.9864 \tabularnewline
15 &  0.01454 &  0.02908 &  0.9855 \tabularnewline
16 &  0.02284 &  0.04569 &  0.9772 \tabularnewline
17 &  0.01123 &  0.02247 &  0.9888 \tabularnewline
18 &  0.0312 &  0.06241 &  0.9688 \tabularnewline
19 &  0.02037 &  0.04073 &  0.9796 \tabularnewline
20 &  0.01698 &  0.03396 &  0.983 \tabularnewline
21 &  0.01626 &  0.03251 &  0.9837 \tabularnewline
22 &  0.009048 &  0.0181 &  0.991 \tabularnewline
23 &  0.009609 &  0.01922 &  0.9904 \tabularnewline
24 &  0.009575 &  0.01915 &  0.9904 \tabularnewline
25 &  0.01225 &  0.02449 &  0.9878 \tabularnewline
26 &  0.007882 &  0.01576 &  0.9921 \tabularnewline
27 &  0.007062 &  0.01412 &  0.9929 \tabularnewline
28 &  0.004441 &  0.008881 &  0.9956 \tabularnewline
29 &  0.002525 &  0.005051 &  0.9975 \tabularnewline
30 &  0.004836 &  0.009673 &  0.9952 \tabularnewline
31 &  0.01038 &  0.02077 &  0.9896 \tabularnewline
32 &  0.01056 &  0.02112 &  0.9894 \tabularnewline
33 &  0.006699 &  0.0134 &  0.9933 \tabularnewline
34 &  0.007513 &  0.01503 &  0.9925 \tabularnewline
35 &  0.005079 &  0.01016 &  0.9949 \tabularnewline
36 &  0.003242 &  0.006484 &  0.9968 \tabularnewline
37 &  0.00223 &  0.004461 &  0.9978 \tabularnewline
38 &  0.001928 &  0.003856 &  0.9981 \tabularnewline
39 &  0.007848 &  0.0157 &  0.9922 \tabularnewline
40 &  0.006725 &  0.01345 &  0.9933 \tabularnewline
41 &  0.004374 &  0.008747 &  0.9956 \tabularnewline
42 &  0.005196 &  0.01039 &  0.9948 \tabularnewline
43 &  0.004108 &  0.008217 &  0.9959 \tabularnewline
44 &  0.002783 &  0.005566 &  0.9972 \tabularnewline
45 &  0.001978 &  0.003957 &  0.998 \tabularnewline
46 &  0.001274 &  0.002547 &  0.9987 \tabularnewline
47 &  0.001073 &  0.002147 &  0.9989 \tabularnewline
48 &  0.005069 &  0.01014 &  0.9949 \tabularnewline
49 &  0.003575 &  0.00715 &  0.9964 \tabularnewline
50 &  0.002376 &  0.004752 &  0.9976 \tabularnewline
51 &  0.001586 &  0.003172 &  0.9984 \tabularnewline
52 &  0.02193 &  0.04386 &  0.9781 \tabularnewline
53 &  0.01609 &  0.03217 &  0.9839 \tabularnewline
54 &  0.03523 &  0.07046 &  0.9648 \tabularnewline
55 &  0.02735 &  0.05471 &  0.9726 \tabularnewline
56 &  0.03524 &  0.07048 &  0.9648 \tabularnewline
57 &  0.02975 &  0.05949 &  0.9703 \tabularnewline
58 &  0.02376 &  0.04753 &  0.9762 \tabularnewline
59 &  0.01776 &  0.03552 &  0.9822 \tabularnewline
60 &  0.01512 &  0.03025 &  0.9849 \tabularnewline
61 &  0.0111 &  0.02219 &  0.9889 \tabularnewline
62 &  0.01016 &  0.02033 &  0.9898 \tabularnewline
63 &  0.00798 &  0.01596 &  0.992 \tabularnewline
64 &  0.008023 &  0.01605 &  0.992 \tabularnewline
65 &  0.01058 &  0.02116 &  0.9894 \tabularnewline
66 &  0.01124 &  0.02247 &  0.9888 \tabularnewline
67 &  0.01175 &  0.0235 &  0.9882 \tabularnewline
68 &  0.00992 &  0.01984 &  0.9901 \tabularnewline
69 &  0.007696 &  0.01539 &  0.9923 \tabularnewline
70 &  0.0122 &  0.0244 &  0.9878 \tabularnewline
71 &  0.02964 &  0.05928 &  0.9704 \tabularnewline
72 &  0.05709 &  0.1142 &  0.9429 \tabularnewline
73 &  0.05461 &  0.1092 &  0.9454 \tabularnewline
74 &  0.0456 &  0.0912 &  0.9544 \tabularnewline
75 &  0.03642 &  0.07284 &  0.9636 \tabularnewline
76 &  0.03369 &  0.06738 &  0.9663 \tabularnewline
77 &  0.02938 &  0.05876 &  0.9706 \tabularnewline
78 &  0.05816 &  0.1163 &  0.9418 \tabularnewline
79 &  0.04714 &  0.09428 &  0.9529 \tabularnewline
80 &  0.06555 &  0.1311 &  0.9344 \tabularnewline
81 &  0.0531 &  0.1062 &  0.9469 \tabularnewline
82 &  0.05201 &  0.104 &  0.948 \tabularnewline
83 &  0.05029 &  0.1006 &  0.9497 \tabularnewline
84 &  0.2138 &  0.4277 &  0.7862 \tabularnewline
85 &  0.1879 &  0.3758 &  0.8121 \tabularnewline
86 &  0.2361 &  0.4722 &  0.7639 \tabularnewline
87 &  0.2062 &  0.4125 &  0.7938 \tabularnewline
88 &  0.2444 &  0.4888 &  0.7556 \tabularnewline
89 &  0.2228 &  0.4457 &  0.7772 \tabularnewline
90 &  0.1973 &  0.3945 &  0.8027 \tabularnewline
91 &  0.1994 &  0.3988 &  0.8006 \tabularnewline
92 &  0.1695 &  0.3389 &  0.8305 \tabularnewline
93 &  0.1427 &  0.2853 &  0.8573 \tabularnewline
94 &  0.1203 &  0.2406 &  0.8797 \tabularnewline
95 &  0.1012 &  0.2024 &  0.8988 \tabularnewline
96 &  0.1247 &  0.2495 &  0.8753 \tabularnewline
97 &  0.1338 &  0.2675 &  0.8662 \tabularnewline
98 &  0.118 &  0.2359 &  0.882 \tabularnewline
99 &  0.1348 &  0.2696 &  0.8652 \tabularnewline
100 &  0.115 &  0.2299 &  0.885 \tabularnewline
101 &  0.2131 &  0.4262 &  0.7869 \tabularnewline
102 &  0.1836 &  0.3672 &  0.8164 \tabularnewline
103 &  0.1564 &  0.3128 &  0.8436 \tabularnewline
104 &  0.1383 &  0.2766 &  0.8617 \tabularnewline
105 &  0.1197 &  0.2395 &  0.8803 \tabularnewline
106 &  0.101 &  0.202 &  0.899 \tabularnewline
107 &  0.08286 &  0.1657 &  0.9171 \tabularnewline
108 &  0.06892 &  0.1378 &  0.9311 \tabularnewline
109 &  0.1101 &  0.2202 &  0.8899 \tabularnewline
110 &  0.08949 &  0.179 &  0.9105 \tabularnewline
111 &  0.09903 &  0.1981 &  0.901 \tabularnewline
112 &  0.2975 &  0.595 &  0.7025 \tabularnewline
113 &  0.2831 &  0.5663 &  0.7169 \tabularnewline
114 &  0.261 &  0.5221 &  0.739 \tabularnewline
115 &  0.2236 &  0.4471 &  0.7764 \tabularnewline
116 &  0.1937 &  0.3874 &  0.8063 \tabularnewline
117 &  0.2028 &  0.4055 &  0.7972 \tabularnewline
118 &  0.169 &  0.3379 &  0.831 \tabularnewline
119 &  0.1446 &  0.2891 &  0.8554 \tabularnewline
120 &  0.121 &  0.2419 &  0.879 \tabularnewline
121 &  0.1068 &  0.2136 &  0.8932 \tabularnewline
122 &  0.09099 &  0.182 &  0.909 \tabularnewline
123 &  0.07418 &  0.1484 &  0.9258 \tabularnewline
124 &  0.06409 &  0.1282 &  0.9359 \tabularnewline
125 &  0.05484 &  0.1097 &  0.9452 \tabularnewline
126 &  0.04929 &  0.09859 &  0.9507 \tabularnewline
127 &  0.03698 &  0.07397 &  0.963 \tabularnewline
128 &  0.03473 &  0.06946 &  0.9653 \tabularnewline
129 &  0.02612 &  0.05224 &  0.9739 \tabularnewline
130 &  0.02895 &  0.0579 &  0.9711 \tabularnewline
131 &  0.03091 &  0.06182 &  0.9691 \tabularnewline
132 &  0.07225 &  0.1445 &  0.9277 \tabularnewline
133 &  0.06733 &  0.1347 &  0.9327 \tabularnewline
134 &  0.05376 &  0.1075 &  0.9462 \tabularnewline
135 &  0.04543 &  0.09085 &  0.9546 \tabularnewline
136 &  0.03683 &  0.07365 &  0.9632 \tabularnewline
137 &  0.03307 &  0.06615 &  0.9669 \tabularnewline
138 &  0.03182 &  0.06364 &  0.9682 \tabularnewline
139 &  0.02693 &  0.05386 &  0.9731 \tabularnewline
140 &  0.01855 &  0.03709 &  0.9815 \tabularnewline
141 &  0.0231 &  0.04621 &  0.9769 \tabularnewline
142 &  0.02821 &  0.05643 &  0.9718 \tabularnewline
143 &  0.02394 &  0.04788 &  0.9761 \tabularnewline
144 &  0.05981 &  0.1196 &  0.9402 \tabularnewline
145 &  0.06834 &  0.1367 &  0.9317 \tabularnewline
146 &  0.0463 &  0.0926 &  0.9537 \tabularnewline
147 &  0.06702 &  0.134 &  0.933 \tabularnewline
148 &  0.04647 &  0.09295 &  0.9535 \tabularnewline
149 &  0.03674 &  0.07348 &  0.9633 \tabularnewline
150 &  0.02197 &  0.04393 &  0.978 \tabularnewline
151 &  0.01986 &  0.03971 &  0.9801 \tabularnewline
152 &  0.0106 &  0.0212 &  0.9894 \tabularnewline
153 &  0.09632 &  0.1926 &  0.9037 \tabularnewline
154 &  0.09215 &  0.1843 &  0.9079 \tabularnewline
155 &  0.3534 &  0.7069 &  0.6466 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C] 0.1577[/C][C] 0.3155[/C][C] 0.8423[/C][/ROW]
[ROW][C]11[/C][C] 0.08825[/C][C] 0.1765[/C][C] 0.9117[/C][/ROW]
[ROW][C]12[/C][C] 0.03678[/C][C] 0.07356[/C][C] 0.9632[/C][/ROW]
[ROW][C]13[/C][C] 0.01406[/C][C] 0.02812[/C][C] 0.9859[/C][/ROW]
[ROW][C]14[/C][C] 0.01361[/C][C] 0.02723[/C][C] 0.9864[/C][/ROW]
[ROW][C]15[/C][C] 0.01454[/C][C] 0.02908[/C][C] 0.9855[/C][/ROW]
[ROW][C]16[/C][C] 0.02284[/C][C] 0.04569[/C][C] 0.9772[/C][/ROW]
[ROW][C]17[/C][C] 0.01123[/C][C] 0.02247[/C][C] 0.9888[/C][/ROW]
[ROW][C]18[/C][C] 0.0312[/C][C] 0.06241[/C][C] 0.9688[/C][/ROW]
[ROW][C]19[/C][C] 0.02037[/C][C] 0.04073[/C][C] 0.9796[/C][/ROW]
[ROW][C]20[/C][C] 0.01698[/C][C] 0.03396[/C][C] 0.983[/C][/ROW]
[ROW][C]21[/C][C] 0.01626[/C][C] 0.03251[/C][C] 0.9837[/C][/ROW]
[ROW][C]22[/C][C] 0.009048[/C][C] 0.0181[/C][C] 0.991[/C][/ROW]
[ROW][C]23[/C][C] 0.009609[/C][C] 0.01922[/C][C] 0.9904[/C][/ROW]
[ROW][C]24[/C][C] 0.009575[/C][C] 0.01915[/C][C] 0.9904[/C][/ROW]
[ROW][C]25[/C][C] 0.01225[/C][C] 0.02449[/C][C] 0.9878[/C][/ROW]
[ROW][C]26[/C][C] 0.007882[/C][C] 0.01576[/C][C] 0.9921[/C][/ROW]
[ROW][C]27[/C][C] 0.007062[/C][C] 0.01412[/C][C] 0.9929[/C][/ROW]
[ROW][C]28[/C][C] 0.004441[/C][C] 0.008881[/C][C] 0.9956[/C][/ROW]
[ROW][C]29[/C][C] 0.002525[/C][C] 0.005051[/C][C] 0.9975[/C][/ROW]
[ROW][C]30[/C][C] 0.004836[/C][C] 0.009673[/C][C] 0.9952[/C][/ROW]
[ROW][C]31[/C][C] 0.01038[/C][C] 0.02077[/C][C] 0.9896[/C][/ROW]
[ROW][C]32[/C][C] 0.01056[/C][C] 0.02112[/C][C] 0.9894[/C][/ROW]
[ROW][C]33[/C][C] 0.006699[/C][C] 0.0134[/C][C] 0.9933[/C][/ROW]
[ROW][C]34[/C][C] 0.007513[/C][C] 0.01503[/C][C] 0.9925[/C][/ROW]
[ROW][C]35[/C][C] 0.005079[/C][C] 0.01016[/C][C] 0.9949[/C][/ROW]
[ROW][C]36[/C][C] 0.003242[/C][C] 0.006484[/C][C] 0.9968[/C][/ROW]
[ROW][C]37[/C][C] 0.00223[/C][C] 0.004461[/C][C] 0.9978[/C][/ROW]
[ROW][C]38[/C][C] 0.001928[/C][C] 0.003856[/C][C] 0.9981[/C][/ROW]
[ROW][C]39[/C][C] 0.007848[/C][C] 0.0157[/C][C] 0.9922[/C][/ROW]
[ROW][C]40[/C][C] 0.006725[/C][C] 0.01345[/C][C] 0.9933[/C][/ROW]
[ROW][C]41[/C][C] 0.004374[/C][C] 0.008747[/C][C] 0.9956[/C][/ROW]
[ROW][C]42[/C][C] 0.005196[/C][C] 0.01039[/C][C] 0.9948[/C][/ROW]
[ROW][C]43[/C][C] 0.004108[/C][C] 0.008217[/C][C] 0.9959[/C][/ROW]
[ROW][C]44[/C][C] 0.002783[/C][C] 0.005566[/C][C] 0.9972[/C][/ROW]
[ROW][C]45[/C][C] 0.001978[/C][C] 0.003957[/C][C] 0.998[/C][/ROW]
[ROW][C]46[/C][C] 0.001274[/C][C] 0.002547[/C][C] 0.9987[/C][/ROW]
[ROW][C]47[/C][C] 0.001073[/C][C] 0.002147[/C][C] 0.9989[/C][/ROW]
[ROW][C]48[/C][C] 0.005069[/C][C] 0.01014[/C][C] 0.9949[/C][/ROW]
[ROW][C]49[/C][C] 0.003575[/C][C] 0.00715[/C][C] 0.9964[/C][/ROW]
[ROW][C]50[/C][C] 0.002376[/C][C] 0.004752[/C][C] 0.9976[/C][/ROW]
[ROW][C]51[/C][C] 0.001586[/C][C] 0.003172[/C][C] 0.9984[/C][/ROW]
[ROW][C]52[/C][C] 0.02193[/C][C] 0.04386[/C][C] 0.9781[/C][/ROW]
[ROW][C]53[/C][C] 0.01609[/C][C] 0.03217[/C][C] 0.9839[/C][/ROW]
[ROW][C]54[/C][C] 0.03523[/C][C] 0.07046[/C][C] 0.9648[/C][/ROW]
[ROW][C]55[/C][C] 0.02735[/C][C] 0.05471[/C][C] 0.9726[/C][/ROW]
[ROW][C]56[/C][C] 0.03524[/C][C] 0.07048[/C][C] 0.9648[/C][/ROW]
[ROW][C]57[/C][C] 0.02975[/C][C] 0.05949[/C][C] 0.9703[/C][/ROW]
[ROW][C]58[/C][C] 0.02376[/C][C] 0.04753[/C][C] 0.9762[/C][/ROW]
[ROW][C]59[/C][C] 0.01776[/C][C] 0.03552[/C][C] 0.9822[/C][/ROW]
[ROW][C]60[/C][C] 0.01512[/C][C] 0.03025[/C][C] 0.9849[/C][/ROW]
[ROW][C]61[/C][C] 0.0111[/C][C] 0.02219[/C][C] 0.9889[/C][/ROW]
[ROW][C]62[/C][C] 0.01016[/C][C] 0.02033[/C][C] 0.9898[/C][/ROW]
[ROW][C]63[/C][C] 0.00798[/C][C] 0.01596[/C][C] 0.992[/C][/ROW]
[ROW][C]64[/C][C] 0.008023[/C][C] 0.01605[/C][C] 0.992[/C][/ROW]
[ROW][C]65[/C][C] 0.01058[/C][C] 0.02116[/C][C] 0.9894[/C][/ROW]
[ROW][C]66[/C][C] 0.01124[/C][C] 0.02247[/C][C] 0.9888[/C][/ROW]
[ROW][C]67[/C][C] 0.01175[/C][C] 0.0235[/C][C] 0.9882[/C][/ROW]
[ROW][C]68[/C][C] 0.00992[/C][C] 0.01984[/C][C] 0.9901[/C][/ROW]
[ROW][C]69[/C][C] 0.007696[/C][C] 0.01539[/C][C] 0.9923[/C][/ROW]
[ROW][C]70[/C][C] 0.0122[/C][C] 0.0244[/C][C] 0.9878[/C][/ROW]
[ROW][C]71[/C][C] 0.02964[/C][C] 0.05928[/C][C] 0.9704[/C][/ROW]
[ROW][C]72[/C][C] 0.05709[/C][C] 0.1142[/C][C] 0.9429[/C][/ROW]
[ROW][C]73[/C][C] 0.05461[/C][C] 0.1092[/C][C] 0.9454[/C][/ROW]
[ROW][C]74[/C][C] 0.0456[/C][C] 0.0912[/C][C] 0.9544[/C][/ROW]
[ROW][C]75[/C][C] 0.03642[/C][C] 0.07284[/C][C] 0.9636[/C][/ROW]
[ROW][C]76[/C][C] 0.03369[/C][C] 0.06738[/C][C] 0.9663[/C][/ROW]
[ROW][C]77[/C][C] 0.02938[/C][C] 0.05876[/C][C] 0.9706[/C][/ROW]
[ROW][C]78[/C][C] 0.05816[/C][C] 0.1163[/C][C] 0.9418[/C][/ROW]
[ROW][C]79[/C][C] 0.04714[/C][C] 0.09428[/C][C] 0.9529[/C][/ROW]
[ROW][C]80[/C][C] 0.06555[/C][C] 0.1311[/C][C] 0.9344[/C][/ROW]
[ROW][C]81[/C][C] 0.0531[/C][C] 0.1062[/C][C] 0.9469[/C][/ROW]
[ROW][C]82[/C][C] 0.05201[/C][C] 0.104[/C][C] 0.948[/C][/ROW]
[ROW][C]83[/C][C] 0.05029[/C][C] 0.1006[/C][C] 0.9497[/C][/ROW]
[ROW][C]84[/C][C] 0.2138[/C][C] 0.4277[/C][C] 0.7862[/C][/ROW]
[ROW][C]85[/C][C] 0.1879[/C][C] 0.3758[/C][C] 0.8121[/C][/ROW]
[ROW][C]86[/C][C] 0.2361[/C][C] 0.4722[/C][C] 0.7639[/C][/ROW]
[ROW][C]87[/C][C] 0.2062[/C][C] 0.4125[/C][C] 0.7938[/C][/ROW]
[ROW][C]88[/C][C] 0.2444[/C][C] 0.4888[/C][C] 0.7556[/C][/ROW]
[ROW][C]89[/C][C] 0.2228[/C][C] 0.4457[/C][C] 0.7772[/C][/ROW]
[ROW][C]90[/C][C] 0.1973[/C][C] 0.3945[/C][C] 0.8027[/C][/ROW]
[ROW][C]91[/C][C] 0.1994[/C][C] 0.3988[/C][C] 0.8006[/C][/ROW]
[ROW][C]92[/C][C] 0.1695[/C][C] 0.3389[/C][C] 0.8305[/C][/ROW]
[ROW][C]93[/C][C] 0.1427[/C][C] 0.2853[/C][C] 0.8573[/C][/ROW]
[ROW][C]94[/C][C] 0.1203[/C][C] 0.2406[/C][C] 0.8797[/C][/ROW]
[ROW][C]95[/C][C] 0.1012[/C][C] 0.2024[/C][C] 0.8988[/C][/ROW]
[ROW][C]96[/C][C] 0.1247[/C][C] 0.2495[/C][C] 0.8753[/C][/ROW]
[ROW][C]97[/C][C] 0.1338[/C][C] 0.2675[/C][C] 0.8662[/C][/ROW]
[ROW][C]98[/C][C] 0.118[/C][C] 0.2359[/C][C] 0.882[/C][/ROW]
[ROW][C]99[/C][C] 0.1348[/C][C] 0.2696[/C][C] 0.8652[/C][/ROW]
[ROW][C]100[/C][C] 0.115[/C][C] 0.2299[/C][C] 0.885[/C][/ROW]
[ROW][C]101[/C][C] 0.2131[/C][C] 0.4262[/C][C] 0.7869[/C][/ROW]
[ROW][C]102[/C][C] 0.1836[/C][C] 0.3672[/C][C] 0.8164[/C][/ROW]
[ROW][C]103[/C][C] 0.1564[/C][C] 0.3128[/C][C] 0.8436[/C][/ROW]
[ROW][C]104[/C][C] 0.1383[/C][C] 0.2766[/C][C] 0.8617[/C][/ROW]
[ROW][C]105[/C][C] 0.1197[/C][C] 0.2395[/C][C] 0.8803[/C][/ROW]
[ROW][C]106[/C][C] 0.101[/C][C] 0.202[/C][C] 0.899[/C][/ROW]
[ROW][C]107[/C][C] 0.08286[/C][C] 0.1657[/C][C] 0.9171[/C][/ROW]
[ROW][C]108[/C][C] 0.06892[/C][C] 0.1378[/C][C] 0.9311[/C][/ROW]
[ROW][C]109[/C][C] 0.1101[/C][C] 0.2202[/C][C] 0.8899[/C][/ROW]
[ROW][C]110[/C][C] 0.08949[/C][C] 0.179[/C][C] 0.9105[/C][/ROW]
[ROW][C]111[/C][C] 0.09903[/C][C] 0.1981[/C][C] 0.901[/C][/ROW]
[ROW][C]112[/C][C] 0.2975[/C][C] 0.595[/C][C] 0.7025[/C][/ROW]
[ROW][C]113[/C][C] 0.2831[/C][C] 0.5663[/C][C] 0.7169[/C][/ROW]
[ROW][C]114[/C][C] 0.261[/C][C] 0.5221[/C][C] 0.739[/C][/ROW]
[ROW][C]115[/C][C] 0.2236[/C][C] 0.4471[/C][C] 0.7764[/C][/ROW]
[ROW][C]116[/C][C] 0.1937[/C][C] 0.3874[/C][C] 0.8063[/C][/ROW]
[ROW][C]117[/C][C] 0.2028[/C][C] 0.4055[/C][C] 0.7972[/C][/ROW]
[ROW][C]118[/C][C] 0.169[/C][C] 0.3379[/C][C] 0.831[/C][/ROW]
[ROW][C]119[/C][C] 0.1446[/C][C] 0.2891[/C][C] 0.8554[/C][/ROW]
[ROW][C]120[/C][C] 0.121[/C][C] 0.2419[/C][C] 0.879[/C][/ROW]
[ROW][C]121[/C][C] 0.1068[/C][C] 0.2136[/C][C] 0.8932[/C][/ROW]
[ROW][C]122[/C][C] 0.09099[/C][C] 0.182[/C][C] 0.909[/C][/ROW]
[ROW][C]123[/C][C] 0.07418[/C][C] 0.1484[/C][C] 0.9258[/C][/ROW]
[ROW][C]124[/C][C] 0.06409[/C][C] 0.1282[/C][C] 0.9359[/C][/ROW]
[ROW][C]125[/C][C] 0.05484[/C][C] 0.1097[/C][C] 0.9452[/C][/ROW]
[ROW][C]126[/C][C] 0.04929[/C][C] 0.09859[/C][C] 0.9507[/C][/ROW]
[ROW][C]127[/C][C] 0.03698[/C][C] 0.07397[/C][C] 0.963[/C][/ROW]
[ROW][C]128[/C][C] 0.03473[/C][C] 0.06946[/C][C] 0.9653[/C][/ROW]
[ROW][C]129[/C][C] 0.02612[/C][C] 0.05224[/C][C] 0.9739[/C][/ROW]
[ROW][C]130[/C][C] 0.02895[/C][C] 0.0579[/C][C] 0.9711[/C][/ROW]
[ROW][C]131[/C][C] 0.03091[/C][C] 0.06182[/C][C] 0.9691[/C][/ROW]
[ROW][C]132[/C][C] 0.07225[/C][C] 0.1445[/C][C] 0.9277[/C][/ROW]
[ROW][C]133[/C][C] 0.06733[/C][C] 0.1347[/C][C] 0.9327[/C][/ROW]
[ROW][C]134[/C][C] 0.05376[/C][C] 0.1075[/C][C] 0.9462[/C][/ROW]
[ROW][C]135[/C][C] 0.04543[/C][C] 0.09085[/C][C] 0.9546[/C][/ROW]
[ROW][C]136[/C][C] 0.03683[/C][C] 0.07365[/C][C] 0.9632[/C][/ROW]
[ROW][C]137[/C][C] 0.03307[/C][C] 0.06615[/C][C] 0.9669[/C][/ROW]
[ROW][C]138[/C][C] 0.03182[/C][C] 0.06364[/C][C] 0.9682[/C][/ROW]
[ROW][C]139[/C][C] 0.02693[/C][C] 0.05386[/C][C] 0.9731[/C][/ROW]
[ROW][C]140[/C][C] 0.01855[/C][C] 0.03709[/C][C] 0.9815[/C][/ROW]
[ROW][C]141[/C][C] 0.0231[/C][C] 0.04621[/C][C] 0.9769[/C][/ROW]
[ROW][C]142[/C][C] 0.02821[/C][C] 0.05643[/C][C] 0.9718[/C][/ROW]
[ROW][C]143[/C][C] 0.02394[/C][C] 0.04788[/C][C] 0.9761[/C][/ROW]
[ROW][C]144[/C][C] 0.05981[/C][C] 0.1196[/C][C] 0.9402[/C][/ROW]
[ROW][C]145[/C][C] 0.06834[/C][C] 0.1367[/C][C] 0.9317[/C][/ROW]
[ROW][C]146[/C][C] 0.0463[/C][C] 0.0926[/C][C] 0.9537[/C][/ROW]
[ROW][C]147[/C][C] 0.06702[/C][C] 0.134[/C][C] 0.933[/C][/ROW]
[ROW][C]148[/C][C] 0.04647[/C][C] 0.09295[/C][C] 0.9535[/C][/ROW]
[ROW][C]149[/C][C] 0.03674[/C][C] 0.07348[/C][C] 0.9633[/C][/ROW]
[ROW][C]150[/C][C] 0.02197[/C][C] 0.04393[/C][C] 0.978[/C][/ROW]
[ROW][C]151[/C][C] 0.01986[/C][C] 0.03971[/C][C] 0.9801[/C][/ROW]
[ROW][C]152[/C][C] 0.0106[/C][C] 0.0212[/C][C] 0.9894[/C][/ROW]
[ROW][C]153[/C][C] 0.09632[/C][C] 0.1926[/C][C] 0.9037[/C][/ROW]
[ROW][C]154[/C][C] 0.09215[/C][C] 0.1843[/C][C] 0.9079[/C][/ROW]
[ROW][C]155[/C][C] 0.3534[/C][C] 0.7069[/C][C] 0.6466[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298812&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1577	0.3155	0.8423
11	0.08825	0.1765	0.9117
12	0.03678	0.07356	0.9632
13	0.01406	0.02812	0.9859
14	0.01361	0.02723	0.9864
15	0.01454	0.02908	0.9855
16	0.02284	0.04569	0.9772
17	0.01123	0.02247	0.9888
18	0.0312	0.06241	0.9688
19	0.02037	0.04073	0.9796
20	0.01698	0.03396	0.983
21	0.01626	0.03251	0.9837
22	0.009048	0.0181	0.991
23	0.009609	0.01922	0.9904
24	0.009575	0.01915	0.9904
25	0.01225	0.02449	0.9878
26	0.007882	0.01576	0.9921
27	0.007062	0.01412	0.9929
28	0.004441	0.008881	0.9956
29	0.002525	0.005051	0.9975
30	0.004836	0.009673	0.9952
31	0.01038	0.02077	0.9896
32	0.01056	0.02112	0.9894
33	0.006699	0.0134	0.9933
34	0.007513	0.01503	0.9925
35	0.005079	0.01016	0.9949
36	0.003242	0.006484	0.9968
37	0.00223	0.004461	0.9978
38	0.001928	0.003856	0.9981
39	0.007848	0.0157	0.9922
40	0.006725	0.01345	0.9933
41	0.004374	0.008747	0.9956
42	0.005196	0.01039	0.9948
43	0.004108	0.008217	0.9959
44	0.002783	0.005566	0.9972
45	0.001978	0.003957	0.998
46	0.001274	0.002547	0.9987
47	0.001073	0.002147	0.9989
48	0.005069	0.01014	0.9949
49	0.003575	0.00715	0.9964
50	0.002376	0.004752	0.9976
51	0.001586	0.003172	0.9984
52	0.02193	0.04386	0.9781
53	0.01609	0.03217	0.9839
54	0.03523	0.07046	0.9648
55	0.02735	0.05471	0.9726
56	0.03524	0.07048	0.9648
57	0.02975	0.05949	0.9703
58	0.02376	0.04753	0.9762
59	0.01776	0.03552	0.9822
60	0.01512	0.03025	0.9849
61	0.0111	0.02219	0.9889
62	0.01016	0.02033	0.9898
63	0.00798	0.01596	0.992
64	0.008023	0.01605	0.992
65	0.01058	0.02116	0.9894
66	0.01124	0.02247	0.9888
67	0.01175	0.0235	0.9882
68	0.00992	0.01984	0.9901
69	0.007696	0.01539	0.9923
70	0.0122	0.0244	0.9878
71	0.02964	0.05928	0.9704
72	0.05709	0.1142	0.9429
73	0.05461	0.1092	0.9454
74	0.0456	0.0912	0.9544
75	0.03642	0.07284	0.9636
76	0.03369	0.06738	0.9663
77	0.02938	0.05876	0.9706
78	0.05816	0.1163	0.9418
79	0.04714	0.09428	0.9529
80	0.06555	0.1311	0.9344
81	0.0531	0.1062	0.9469
82	0.05201	0.104	0.948
83	0.05029	0.1006	0.9497
84	0.2138	0.4277	0.7862
85	0.1879	0.3758	0.8121
86	0.2361	0.4722	0.7639
87	0.2062	0.4125	0.7938
88	0.2444	0.4888	0.7556
89	0.2228	0.4457	0.7772
90	0.1973	0.3945	0.8027
91	0.1994	0.3988	0.8006
92	0.1695	0.3389	0.8305
93	0.1427	0.2853	0.8573
94	0.1203	0.2406	0.8797
95	0.1012	0.2024	0.8988
96	0.1247	0.2495	0.8753
97	0.1338	0.2675	0.8662
98	0.118	0.2359	0.882
99	0.1348	0.2696	0.8652
100	0.115	0.2299	0.885
101	0.2131	0.4262	0.7869
102	0.1836	0.3672	0.8164
103	0.1564	0.3128	0.8436
104	0.1383	0.2766	0.8617
105	0.1197	0.2395	0.8803
106	0.101	0.202	0.899
107	0.08286	0.1657	0.9171
108	0.06892	0.1378	0.9311
109	0.1101	0.2202	0.8899
110	0.08949	0.179	0.9105
111	0.09903	0.1981	0.901
112	0.2975	0.595	0.7025
113	0.2831	0.5663	0.7169
114	0.261	0.5221	0.739
115	0.2236	0.4471	0.7764
116	0.1937	0.3874	0.8063
117	0.2028	0.4055	0.7972
118	0.169	0.3379	0.831
119	0.1446	0.2891	0.8554
120	0.121	0.2419	0.879
121	0.1068	0.2136	0.8932
122	0.09099	0.182	0.909
123	0.07418	0.1484	0.9258
124	0.06409	0.1282	0.9359
125	0.05484	0.1097	0.9452
126	0.04929	0.09859	0.9507
127	0.03698	0.07397	0.963
128	0.03473	0.06946	0.9653
129	0.02612	0.05224	0.9739
130	0.02895	0.0579	0.9711
131	0.03091	0.06182	0.9691
132	0.07225	0.1445	0.9277
133	0.06733	0.1347	0.9327
134	0.05376	0.1075	0.9462
135	0.04543	0.09085	0.9546
136	0.03683	0.07365	0.9632
137	0.03307	0.06615	0.9669
138	0.03182	0.06364	0.9682
139	0.02693	0.05386	0.9731
140	0.01855	0.03709	0.9815
141	0.0231	0.04621	0.9769
142	0.02821	0.05643	0.9718
143	0.02394	0.04788	0.9761
144	0.05981	0.1196	0.9402
145	0.06834	0.1367	0.9317
146	0.0463	0.0926	0.9537
147	0.06702	0.134	0.933
148	0.04647	0.09295	0.9535
149	0.03674	0.07348	0.9633
150	0.02197	0.04393	0.978
151	0.01986	0.03971	0.9801
152	0.0106	0.0212	0.9894
153	0.09632	0.1926	0.9037
154	0.09215	0.1843	0.9079
155	0.3534	0.7069	0.6466

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.1027	NOK
5% type I error level	59	0.40411	NOK
10% type I error level	86	0.589041	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 15 &  0.1027 & NOK \tabularnewline
5% type I error level & 59 & 0.40411 & NOK \tabularnewline
10% type I error level & 86 & 0.589041 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]15[/C][C] 0.1027[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]59[/C][C]0.40411[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]86[/C][C]0.589041[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298812&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.1027	NOK
5% type I error level	59	0.40411	NOK
10% type I error level	86	0.589041	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.6822, df1 = 2, df2 = 156, p-value = 0.1893
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2642, df1 = 12, df2 = 146, p-value = 0.2459
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.0937, df1 = 2, df2 = 156, p-value = 0.3375

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.6822, df1 = 2, df2 = 156, p-value = 0.1893
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2642, df1 = 12, df2 = 146, p-value = 0.2459
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0937, df1 = 2, df2 = 156, p-value = 0.3375
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.6822, df1 = 2, df2 = 156, p-value = 0.1893
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2642, df1 = 12, df2 = 146, p-value = 0.2459
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0937, df1 = 2, df2 = 156, p-value = 0.3375
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298812&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=7

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.6822, df1 = 2, df2 = 156, p-value = 0.1893
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2642, df1 = 12, df2 = 146, p-value = 0.2459
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.0937, df1 = 2, df2 = 156, p-value = 0.3375

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.104930 1.109996 1.060104 1.079364 1.051148 1.045297

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.104930 1.109996 1.060104 1.079364 1.051148 1.045297 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298812&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.104930 1.109996 1.060104 1.079364 1.051148 1.045297 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298812&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298812&T=8

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.104930 1.109996 1.060104 1.079364 1.051148 1.045297

Figure 1

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Figure 6

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Figure 8

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code