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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 11 Dec 2014 20:36:31 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/11/t1418330223uzlsfkdecloh4bc.htm/, Retrieved Thu, 31 Oct 2024 23:33:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266325, Retrieved Thu, 31 Oct 2024 23:33:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [paper16] [2014-12-11 20:36:31] [0015a2406d94cac8c1a56a29b9122359] [Current]
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Dataseries X:
11	8	7	13
19	18	20	13
16	12	9	11
24	24	19	14
15	16	12	15
17	19	16	14
19	16	17	11
19	15	9	13
28	28	28	16
26	21	20	14
15	18	16	14
26	22	22	15
16	19	17	15
24	22	12	13
25	25	18	14
22	20	20	11
15	16	12	12
21	19	16	14
22	18	16	13
27	26	21	12
26	24	15	15
26	20	17	15
22	19	17	14
21	19	17	14
22	23	18	12
20	18	15	12
21	16	20	12
20	18	13	15
22	21	21	14
21	20	12	16
8	15	6	12
22	19	13	12
18	27	6	NA
20	19	19	14
24	7	12	16
17	20	14	15
20	20	13	12
23	19	12	14
20	19	17	13
22	20	19	14
19	18	10	16
15	14	10	12
20	17	11	14
22	17	11	15
17	8	10	13
14	9	7	16
24	22	22	16
17	20	12	12
23	20	18	12
25	22	20	16
16	22	9	12
18	22	16	15
20	16	14	12
18	14	11	13
23	24	20	12
24	21	17	14
23	20	14	14
13	20	8	11
20	18	16	10
20	14	11	12
19	19	10	11
22	24	15	16
22	19	15	14
15	16	10	14
17	16	10	15
19	16	18	15
20	14	10	14
22	22	22	13
21	21	16	11
21	15	10	16
16	14	7	12
20	15	16	15
21	14	16	14
20	20	16	15
23	21	22	14
15	17	13	NA
18	14	5	13
22	19	18	6
16	16	10	12
17	13	8	12
24	26	16	14
13	13	8	14
19	18	16	15
20	15	14	11
22	18	15	13
19	21	9	14
21	17	21	16
15	18	7	13
21	20	17	14
24	18	18	16
22	25	16	11
20	20	16	13
21	19	14	13
19	18	15	15
14	12	8	12
25	22	22	13
11	16	5	12
17	18	13	14
22	23	22	14
20	20	18	16
22	20	15	15
15	16	11	14
23	22	19	13
20	19	19	14
22	23	21	15
16	6	4	14
25	19	17	12
18	24	10	7
19	19	13	12
25	15	15	15
21	18	11	12
22	18	20	13
21	22	13	11
22	23	18	14
23	18	20	13




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266325&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266325&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266325&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
S[t] = + 12.3568 + 0.0954132I1[t] -0.104224I2[t] + 0.0715122I3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
S[t] =  +  12.3568 +  0.0954132I1[t] -0.104224I2[t] +  0.0715122I3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266325&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]S[t] =  +  12.3568 +  0.0954132I1[t] -0.104224I2[t] +  0.0715122I3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266325&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266325&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
S[t] = + 12.3568 + 0.0954132I1[t] -0.104224I2[t] + 0.0715122I3[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.35681.0079612.263.06359e-221.53179e-22
I10.09541320.06570611.4520.149340.0746701
I2-0.1042240.0537269-1.940.05497860.0274893
I30.07151220.05382381.3290.1867430.0933716

\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) & 12.3568 & 1.00796 & 12.26 & 3.06359e-22 & 1.53179e-22 \tabularnewline
I1 & 0.0954132 & 0.0657061 & 1.452 & 0.14934 & 0.0746701 \tabularnewline
I2 & -0.104224 & 0.0537269 & -1.94 & 0.0549786 & 0.0274893 \tabularnewline
I3 & 0.0715122 & 0.0538238 & 1.329 & 0.186743 & 0.0933716 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266325&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]12.3568[/C][C]1.00796[/C][C]12.26[/C][C]3.06359e-22[/C][C]1.53179e-22[/C][/ROW]
[ROW][C]I1[/C][C]0.0954132[/C][C]0.0657061[/C][C]1.452[/C][C]0.14934[/C][C]0.0746701[/C][/ROW]
[ROW][C]I2[/C][C]-0.104224[/C][C]0.0537269[/C][C]-1.94[/C][C]0.0549786[/C][C]0.0274893[/C][/ROW]
[ROW][C]I3[/C][C]0.0715122[/C][C]0.0538238[/C][C]1.329[/C][C]0.186743[/C][C]0.0933716[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266325&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266325&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.35681.0079612.263.06359e-221.53179e-22
I10.09541320.06570611.4520.149340.0746701
I2-0.1042240.0537269-1.940.05497860.0274893
I30.07151220.05382381.3290.1867430.0933716







Multiple Linear Regression - Regression Statistics
Multiple R0.274275
R-squared0.0752269
Adjusted R-squared0.0497744
F-TEST (value)2.95558
F-TEST (DF numerator)3
F-TEST (DF denominator)109
p-value0.0356982
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.71106
Sum Squared Residuals319.12

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.274275 \tabularnewline
R-squared & 0.0752269 \tabularnewline
Adjusted R-squared & 0.0497744 \tabularnewline
F-TEST (value) & 2.95558 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 109 \tabularnewline
p-value & 0.0356982 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.71106 \tabularnewline
Sum Squared Residuals & 319.12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266325&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.274275[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0752269[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0497744[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.95558[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]109[/C][/ROW]
[ROW][C]p-value[/C][C]0.0356982[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.71106[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]319.12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266325&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266325&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 R0.274275
R-squared0.0752269
Adjusted R-squared0.0497744
F-TEST (value)2.95558
F-TEST (DF numerator)3
F-TEST (DF denominator)109
p-value0.0356982
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.71106
Sum Squared Residuals319.12







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11313.0732-0.0731915
21313.7239-0.72392
31113.2764-2.27639
41413.50410.495868
51512.97862.02138
61413.14280.857179
71113.7178-2.71783
81313.25-0.249956
91614.11251.8875
101414.0791-0.0791414
111413.05620.943782
121514.11790.882058
131513.11891.88108
141313.212-0.211994
151413.42380.57619
161113.8017-2.80171
171212.9786-0.978617
181413.52450.475526
191313.7241-0.724111
201213.7249-1.72495
211513.40891.59109
221513.96881.03117
231413.69140.308601
241413.5960.404014
251213.346-1.34602
261213.4618-1.46177
271214.1232-2.12319
281513.31871.68125
291413.7690.230999
301613.13422.8658
311211.98590.0141254
321213.4054-1.40535
33NANA0.356403
341412.77531.22465
351613.89562.10443
361516.1103-1.1103
371211.42930.570749
381414.5006-0.500573
391312.73020.2698
401411.00882.9912
411617.044-1.04404
421211.27990.720053
431412.47081.52923
441515.8602-0.860207
451310.25522.74479
461613.92712.07288
471616.7525-0.752549
481213.7541-1.7541
49129.87952.1205
501616.2342-0.234152
51129.925562.07444
521516.5987-1.59871
531212.4018-0.401791
541314.4802-1.48023
551211.67380.326222
561413.46810.531948
571415.0848-1.08485
581114.5333-3.53328
591011.5926-1.59262
601213.9046-1.90457
61118.027262.97274
621615.54840.451625
631412.83561.16441
641412.02641.97358
651513.78931.21066
661514.52110.478895
671414.7363-0.736289
681315.316-2.31603
69118.512292.48771
701616.9249-0.924916
711210.8461.15405
721515.0456-0.0455917
731412.32481.67516
741514.93590.0640738
751413.97270.027282
76NANA-7.76291
771313.931-0.931005
7867.19606-1.19606
791211.08110.918852
801210.81441.18559
811412.43791.56213
821416.7029-2.70293
831515.6526-0.652598
84119.624611.37539
851311.09051.90952
861413.41260.587392
871615.49180.508238
881311.0581.94204
891415.9945-1.99455
901616.3248-0.324837
911111.3814-0.381449
921311.36641.63364
931314.014-1.01405
941516.0225-1.02253
951212.0964-0.0963784
961312.03250.967492
971211.63210.367934
981411.46792.53214
991412.44421.55585
1001614.90711.0929
1011515.6172-0.617166
1021413.64360.356403
1031311.56061.43945
1041413.54420.455832
1051516.9776-1.97764
1061419.288-5.28804
1071213.1191-1.11911
10876.251510.748491
1091213.2711-1.27114
1101516.0102-1.01016
1111213.9973-1.99727
1121312.3460.653983
1131112.1056-1.10557
11414NANA
11513NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 13.0732 & -0.0731915 \tabularnewline
2 & 13 & 13.7239 & -0.72392 \tabularnewline
3 & 11 & 13.2764 & -2.27639 \tabularnewline
4 & 14 & 13.5041 & 0.495868 \tabularnewline
5 & 15 & 12.9786 & 2.02138 \tabularnewline
6 & 14 & 13.1428 & 0.857179 \tabularnewline
7 & 11 & 13.7178 & -2.71783 \tabularnewline
8 & 13 & 13.25 & -0.249956 \tabularnewline
9 & 16 & 14.1125 & 1.8875 \tabularnewline
10 & 14 & 14.0791 & -0.0791414 \tabularnewline
11 & 14 & 13.0562 & 0.943782 \tabularnewline
12 & 15 & 14.1179 & 0.882058 \tabularnewline
13 & 15 & 13.1189 & 1.88108 \tabularnewline
14 & 13 & 13.212 & -0.211994 \tabularnewline
15 & 14 & 13.4238 & 0.57619 \tabularnewline
16 & 11 & 13.8017 & -2.80171 \tabularnewline
17 & 12 & 12.9786 & -0.978617 \tabularnewline
18 & 14 & 13.5245 & 0.475526 \tabularnewline
19 & 13 & 13.7241 & -0.724111 \tabularnewline
20 & 12 & 13.7249 & -1.72495 \tabularnewline
21 & 15 & 13.4089 & 1.59109 \tabularnewline
22 & 15 & 13.9688 & 1.03117 \tabularnewline
23 & 14 & 13.6914 & 0.308601 \tabularnewline
24 & 14 & 13.596 & 0.404014 \tabularnewline
25 & 12 & 13.346 & -1.34602 \tabularnewline
26 & 12 & 13.4618 & -1.46177 \tabularnewline
27 & 12 & 14.1232 & -2.12319 \tabularnewline
28 & 15 & 13.3187 & 1.68125 \tabularnewline
29 & 14 & 13.769 & 0.230999 \tabularnewline
30 & 16 & 13.1342 & 2.8658 \tabularnewline
31 & 12 & 11.9859 & 0.0141254 \tabularnewline
32 & 12 & 13.4054 & -1.40535 \tabularnewline
33 & NA & NA & 0.356403 \tabularnewline
34 & 14 & 12.7753 & 1.22465 \tabularnewline
35 & 16 & 13.8956 & 2.10443 \tabularnewline
36 & 15 & 16.1103 & -1.1103 \tabularnewline
37 & 12 & 11.4293 & 0.570749 \tabularnewline
38 & 14 & 14.5006 & -0.500573 \tabularnewline
39 & 13 & 12.7302 & 0.2698 \tabularnewline
40 & 14 & 11.0088 & 2.9912 \tabularnewline
41 & 16 & 17.044 & -1.04404 \tabularnewline
42 & 12 & 11.2799 & 0.720053 \tabularnewline
43 & 14 & 12.4708 & 1.52923 \tabularnewline
44 & 15 & 15.8602 & -0.860207 \tabularnewline
45 & 13 & 10.2552 & 2.74479 \tabularnewline
46 & 16 & 13.9271 & 2.07288 \tabularnewline
47 & 16 & 16.7525 & -0.752549 \tabularnewline
48 & 12 & 13.7541 & -1.7541 \tabularnewline
49 & 12 & 9.8795 & 2.1205 \tabularnewline
50 & 16 & 16.2342 & -0.234152 \tabularnewline
51 & 12 & 9.92556 & 2.07444 \tabularnewline
52 & 15 & 16.5987 & -1.59871 \tabularnewline
53 & 12 & 12.4018 & -0.401791 \tabularnewline
54 & 13 & 14.4802 & -1.48023 \tabularnewline
55 & 12 & 11.6738 & 0.326222 \tabularnewline
56 & 14 & 13.4681 & 0.531948 \tabularnewline
57 & 14 & 15.0848 & -1.08485 \tabularnewline
58 & 11 & 14.5333 & -3.53328 \tabularnewline
59 & 10 & 11.5926 & -1.59262 \tabularnewline
60 & 12 & 13.9046 & -1.90457 \tabularnewline
61 & 11 & 8.02726 & 2.97274 \tabularnewline
62 & 16 & 15.5484 & 0.451625 \tabularnewline
63 & 14 & 12.8356 & 1.16441 \tabularnewline
64 & 14 & 12.0264 & 1.97358 \tabularnewline
65 & 15 & 13.7893 & 1.21066 \tabularnewline
66 & 15 & 14.5211 & 0.478895 \tabularnewline
67 & 14 & 14.7363 & -0.736289 \tabularnewline
68 & 13 & 15.316 & -2.31603 \tabularnewline
69 & 11 & 8.51229 & 2.48771 \tabularnewline
70 & 16 & 16.9249 & -0.924916 \tabularnewline
71 & 12 & 10.846 & 1.15405 \tabularnewline
72 & 15 & 15.0456 & -0.0455917 \tabularnewline
73 & 14 & 12.3248 & 1.67516 \tabularnewline
74 & 15 & 14.9359 & 0.0640738 \tabularnewline
75 & 14 & 13.9727 & 0.027282 \tabularnewline
76 & NA & NA & -7.76291 \tabularnewline
77 & 13 & 13.931 & -0.931005 \tabularnewline
78 & 6 & 7.19606 & -1.19606 \tabularnewline
79 & 12 & 11.0811 & 0.918852 \tabularnewline
80 & 12 & 10.8144 & 1.18559 \tabularnewline
81 & 14 & 12.4379 & 1.56213 \tabularnewline
82 & 14 & 16.7029 & -2.70293 \tabularnewline
83 & 15 & 15.6526 & -0.652598 \tabularnewline
84 & 11 & 9.62461 & 1.37539 \tabularnewline
85 & 13 & 11.0905 & 1.90952 \tabularnewline
86 & 14 & 13.4126 & 0.587392 \tabularnewline
87 & 16 & 15.4918 & 0.508238 \tabularnewline
88 & 13 & 11.058 & 1.94204 \tabularnewline
89 & 14 & 15.9945 & -1.99455 \tabularnewline
90 & 16 & 16.3248 & -0.324837 \tabularnewline
91 & 11 & 11.3814 & -0.381449 \tabularnewline
92 & 13 & 11.3664 & 1.63364 \tabularnewline
93 & 13 & 14.014 & -1.01405 \tabularnewline
94 & 15 & 16.0225 & -1.02253 \tabularnewline
95 & 12 & 12.0964 & -0.0963784 \tabularnewline
96 & 13 & 12.0325 & 0.967492 \tabularnewline
97 & 12 & 11.6321 & 0.367934 \tabularnewline
98 & 14 & 11.4679 & 2.53214 \tabularnewline
99 & 14 & 12.4442 & 1.55585 \tabularnewline
100 & 16 & 14.9071 & 1.0929 \tabularnewline
101 & 15 & 15.6172 & -0.617166 \tabularnewline
102 & 14 & 13.6436 & 0.356403 \tabularnewline
103 & 13 & 11.5606 & 1.43945 \tabularnewline
104 & 14 & 13.5442 & 0.455832 \tabularnewline
105 & 15 & 16.9776 & -1.97764 \tabularnewline
106 & 14 & 19.288 & -5.28804 \tabularnewline
107 & 12 & 13.1191 & -1.11911 \tabularnewline
108 & 7 & 6.25151 & 0.748491 \tabularnewline
109 & 12 & 13.2711 & -1.27114 \tabularnewline
110 & 15 & 16.0102 & -1.01016 \tabularnewline
111 & 12 & 13.9973 & -1.99727 \tabularnewline
112 & 13 & 12.346 & 0.653983 \tabularnewline
113 & 11 & 12.1056 & -1.10557 \tabularnewline
114 & 14 & NA & NA \tabularnewline
115 & 13 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266325&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]13.0732[/C][C]-0.0731915[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]13.7239[/C][C]-0.72392[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.2764[/C][C]-2.27639[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]13.5041[/C][C]0.495868[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]12.9786[/C][C]2.02138[/C][/ROW]
[ROW][C]6[/C][C]14[/C][C]13.1428[/C][C]0.857179[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]13.7178[/C][C]-2.71783[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]13.25[/C][C]-0.249956[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]14.1125[/C][C]1.8875[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]14.0791[/C][C]-0.0791414[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]13.0562[/C][C]0.943782[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]14.1179[/C][C]0.882058[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]13.1189[/C][C]1.88108[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]13.212[/C][C]-0.211994[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]13.4238[/C][C]0.57619[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]13.8017[/C][C]-2.80171[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]12.9786[/C][C]-0.978617[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]13.5245[/C][C]0.475526[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]13.7241[/C][C]-0.724111[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]13.7249[/C][C]-1.72495[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]13.4089[/C][C]1.59109[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]13.9688[/C][C]1.03117[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.6914[/C][C]0.308601[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]13.596[/C][C]0.404014[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]13.346[/C][C]-1.34602[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]13.4618[/C][C]-1.46177[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]14.1232[/C][C]-2.12319[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.3187[/C][C]1.68125[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]13.769[/C][C]0.230999[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]13.1342[/C][C]2.8658[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.9859[/C][C]0.0141254[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.4054[/C][C]-1.40535[/C][/ROW]
[ROW][C]33[/C][C]NA[/C][C]NA[/C][C]0.356403[/C][/ROW]
[ROW][C]34[/C][C]14[/C][C]12.7753[/C][C]1.22465[/C][/ROW]
[ROW][C]35[/C][C]16[/C][C]13.8956[/C][C]2.10443[/C][/ROW]
[ROW][C]36[/C][C]15[/C][C]16.1103[/C][C]-1.1103[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]11.4293[/C][C]0.570749[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]14.5006[/C][C]-0.500573[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.7302[/C][C]0.2698[/C][/ROW]
[ROW][C]40[/C][C]14[/C][C]11.0088[/C][C]2.9912[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]17.044[/C][C]-1.04404[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]11.2799[/C][C]0.720053[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]12.4708[/C][C]1.52923[/C][/ROW]
[ROW][C]44[/C][C]15[/C][C]15.8602[/C][C]-0.860207[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]10.2552[/C][C]2.74479[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.9271[/C][C]2.07288[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]16.7525[/C][C]-0.752549[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]13.7541[/C][C]-1.7541[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]9.8795[/C][C]2.1205[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]16.2342[/C][C]-0.234152[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]9.92556[/C][C]2.07444[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]16.5987[/C][C]-1.59871[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.4018[/C][C]-0.401791[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]14.4802[/C][C]-1.48023[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]11.6738[/C][C]0.326222[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]13.4681[/C][C]0.531948[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]15.0848[/C][C]-1.08485[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]14.5333[/C][C]-3.53328[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]11.5926[/C][C]-1.59262[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.9046[/C][C]-1.90457[/C][/ROW]
[ROW][C]61[/C][C]11[/C][C]8.02726[/C][C]2.97274[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.5484[/C][C]0.451625[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.8356[/C][C]1.16441[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.0264[/C][C]1.97358[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.7893[/C][C]1.21066[/C][/ROW]
[ROW][C]66[/C][C]15[/C][C]14.5211[/C][C]0.478895[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]14.7363[/C][C]-0.736289[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]15.316[/C][C]-2.31603[/C][/ROW]
[ROW][C]69[/C][C]11[/C][C]8.51229[/C][C]2.48771[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]16.9249[/C][C]-0.924916[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]10.846[/C][C]1.15405[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]15.0456[/C][C]-0.0455917[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.3248[/C][C]1.67516[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.9359[/C][C]0.0640738[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]13.9727[/C][C]0.027282[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]-7.76291[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]13.931[/C][C]-0.931005[/C][/ROW]
[ROW][C]78[/C][C]6[/C][C]7.19606[/C][C]-1.19606[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.0811[/C][C]0.918852[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]10.8144[/C][C]1.18559[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]12.4379[/C][C]1.56213[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]16.7029[/C][C]-2.70293[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6526[/C][C]-0.652598[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]9.62461[/C][C]1.37539[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]11.0905[/C][C]1.90952[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.4126[/C][C]0.587392[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.4918[/C][C]0.508238[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]11.058[/C][C]1.94204[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]15.9945[/C][C]-1.99455[/C][/ROW]
[ROW][C]90[/C][C]16[/C][C]16.3248[/C][C]-0.324837[/C][/ROW]
[ROW][C]91[/C][C]11[/C][C]11.3814[/C][C]-0.381449[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]11.3664[/C][C]1.63364[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]14.014[/C][C]-1.01405[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]16.0225[/C][C]-1.02253[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]12.0964[/C][C]-0.0963784[/C][/ROW]
[ROW][C]96[/C][C]13[/C][C]12.0325[/C][C]0.967492[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.6321[/C][C]0.367934[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]11.4679[/C][C]2.53214[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]12.4442[/C][C]1.55585[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]14.9071[/C][C]1.0929[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]15.6172[/C][C]-0.617166[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]13.6436[/C][C]0.356403[/C][/ROW]
[ROW][C]103[/C][C]13[/C][C]11.5606[/C][C]1.43945[/C][/ROW]
[ROW][C]104[/C][C]14[/C][C]13.5442[/C][C]0.455832[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]16.9776[/C][C]-1.97764[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]19.288[/C][C]-5.28804[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]13.1191[/C][C]-1.11911[/C][/ROW]
[ROW][C]108[/C][C]7[/C][C]6.25151[/C][C]0.748491[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]13.2711[/C][C]-1.27114[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]16.0102[/C][C]-1.01016[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]13.9973[/C][C]-1.99727[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]12.346[/C][C]0.653983[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.1056[/C][C]-1.10557[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]115[/C][C]13[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266325&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266325&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 IndexActualsInterpolationForecastResidualsPrediction Error
11313.0732-0.0731915
21313.7239-0.72392
31113.2764-2.27639
41413.50410.495868
51512.97862.02138
61413.14280.857179
71113.7178-2.71783
81313.25-0.249956
91614.11251.8875
101414.0791-0.0791414
111413.05620.943782
121514.11790.882058
131513.11891.88108
141313.212-0.211994
151413.42380.57619
161113.8017-2.80171
171212.9786-0.978617
181413.52450.475526
191313.7241-0.724111
201213.7249-1.72495
211513.40891.59109
221513.96881.03117
231413.69140.308601
241413.5960.404014
251213.346-1.34602
261213.4618-1.46177
271214.1232-2.12319
281513.31871.68125
291413.7690.230999
301613.13422.8658
311211.98590.0141254
321213.4054-1.40535
33NANA0.356403
341412.77531.22465
351613.89562.10443
361516.1103-1.1103
371211.42930.570749
381414.5006-0.500573
391312.73020.2698
401411.00882.9912
411617.044-1.04404
421211.27990.720053
431412.47081.52923
441515.8602-0.860207
451310.25522.74479
461613.92712.07288
471616.7525-0.752549
481213.7541-1.7541
49129.87952.1205
501616.2342-0.234152
51129.925562.07444
521516.5987-1.59871
531212.4018-0.401791
541314.4802-1.48023
551211.67380.326222
561413.46810.531948
571415.0848-1.08485
581114.5333-3.53328
591011.5926-1.59262
601213.9046-1.90457
61118.027262.97274
621615.54840.451625
631412.83561.16441
641412.02641.97358
651513.78931.21066
661514.52110.478895
671414.7363-0.736289
681315.316-2.31603
69118.512292.48771
701616.9249-0.924916
711210.8461.15405
721515.0456-0.0455917
731412.32481.67516
741514.93590.0640738
751413.97270.027282
76NANA-7.76291
771313.931-0.931005
7867.19606-1.19606
791211.08110.918852
801210.81441.18559
811412.43791.56213
821416.7029-2.70293
831515.6526-0.652598
84119.624611.37539
851311.09051.90952
861413.41260.587392
871615.49180.508238
881311.0581.94204
891415.9945-1.99455
901616.3248-0.324837
911111.3814-0.381449
921311.36641.63364
931314.014-1.01405
941516.0225-1.02253
951212.0964-0.0963784
961312.03250.967492
971211.63210.367934
981411.46792.53214
991412.44421.55585
1001614.90711.0929
1011515.6172-0.617166
1021413.64360.356403
1031311.56061.43945
1041413.54420.455832
1051516.9776-1.97764
1061419.288-5.28804
1071213.1191-1.11911
10876.251510.748491
1091213.2711-1.27114
1101516.0102-1.01016
1111213.9973-1.99727
1121312.3460.653983
1131112.1056-1.10557
11414NANA
11513NANA







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1668340.3336690.833166
80.1089490.2178980.891051
90.2550510.5101010.744949
100.2318450.463690.768155
110.150980.301960.84902
120.1418340.2836690.858166
130.09705660.1941130.902943
140.07794680.1558940.922053
150.05085930.1017190.949141
160.1417140.2834290.858286
170.1345820.2691650.865418
180.09938730.1987750.900613
190.06682530.1336510.933175
200.1021540.2043080.897846
210.0947270.1894540.905273
220.1140780.2281560.885922
230.08539030.1707810.91461
240.06183650.1236730.938164
250.07731410.1546280.922686
260.0688840.1377680.931116
270.05917090.1183420.940829
280.06412440.1282490.935876
290.04586420.09172830.954136
300.07076460.1415290.929235
310.06414050.1282810.935859
320.0611530.1223060.938847
330.04570330.09140660.954297
340.08231740.1646350.917683
350.08984210.1796840.910158
360.08346390.1669280.916536
370.063360.126720.93664
380.0476820.09536410.952318
390.03473160.06946310.965268
400.05975710.1195140.940243
410.05102950.1020590.94897
420.03863510.07727020.961365
430.03502970.07005940.96497
440.02669940.05339880.973301
450.0469290.0938580.953071
460.05869230.1173850.941308
470.0506290.1012580.949371
480.05301770.1060350.946982
490.06344910.1268980.936551
500.05159640.1031930.948404
510.05663750.1132750.943363
520.05614340.1122870.943857
530.04293940.08587890.957061
540.04132660.08265320.958673
550.03099560.06199120.969004
560.02353310.04706630.976467
570.02135010.04270030.97865
580.06000680.1200140.939993
590.05804460.1160890.941955
600.0635650.127130.936435
610.1132060.2264110.886794
620.0914030.1828060.908597
630.0778150.155630.922185
640.08322010.166440.91678
650.07290550.1458110.927094
660.05691470.1138290.943085
670.04488940.08977870.955111
680.05535650.1107130.944643
690.08161410.1632280.918386
700.06777850.1355570.932221
710.05748720.1149740.942513
720.0429230.0858460.957077
730.04231640.08463290.957684
740.0308980.0617960.969102
750.02372730.04745450.976273
760.8167570.3664860.183243
770.7887620.4224760.211238
780.7628790.4742420.237121
790.7925510.4148980.207449
800.7538650.492270.246135
810.7316380.5367230.268362
820.841420.317160.15858
830.8030510.3938990.196949
840.8906360.2187280.109364
850.8701980.2596050.129802
860.8744580.2510840.125542
870.8387640.3224720.161236
880.8580510.2838980.141949
890.8288690.3422620.171131
900.7781470.4437050.221853
910.7223750.5552490.277625
920.7108540.5782920.289146
930.7185040.5629930.281496
940.6718220.6563570.328178
950.5957090.8085820.404291
960.5358990.9282010.464101
970.4498160.8996320.550184
980.5247920.9504160.475208
990.6711790.6576420.328821
1000.6505070.6989870.349493
1010.5495670.9008670.450433
1020.4392640.8785290.560736
1030.5600920.8798150.439908
1040.4402760.8805510.559724
1050.5030780.9938440.496922
1060.7351360.5297270.264864
1070.6751330.6497340.324867
1080.3883650.7767310.611635

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.166834 & 0.333669 & 0.833166 \tabularnewline
8 & 0.108949 & 0.217898 & 0.891051 \tabularnewline
9 & 0.255051 & 0.510101 & 0.744949 \tabularnewline
10 & 0.231845 & 0.46369 & 0.768155 \tabularnewline
11 & 0.15098 & 0.30196 & 0.84902 \tabularnewline
12 & 0.141834 & 0.283669 & 0.858166 \tabularnewline
13 & 0.0970566 & 0.194113 & 0.902943 \tabularnewline
14 & 0.0779468 & 0.155894 & 0.922053 \tabularnewline
15 & 0.0508593 & 0.101719 & 0.949141 \tabularnewline
16 & 0.141714 & 0.283429 & 0.858286 \tabularnewline
17 & 0.134582 & 0.269165 & 0.865418 \tabularnewline
18 & 0.0993873 & 0.198775 & 0.900613 \tabularnewline
19 & 0.0668253 & 0.133651 & 0.933175 \tabularnewline
20 & 0.102154 & 0.204308 & 0.897846 \tabularnewline
21 & 0.094727 & 0.189454 & 0.905273 \tabularnewline
22 & 0.114078 & 0.228156 & 0.885922 \tabularnewline
23 & 0.0853903 & 0.170781 & 0.91461 \tabularnewline
24 & 0.0618365 & 0.123673 & 0.938164 \tabularnewline
25 & 0.0773141 & 0.154628 & 0.922686 \tabularnewline
26 & 0.068884 & 0.137768 & 0.931116 \tabularnewline
27 & 0.0591709 & 0.118342 & 0.940829 \tabularnewline
28 & 0.0641244 & 0.128249 & 0.935876 \tabularnewline
29 & 0.0458642 & 0.0917283 & 0.954136 \tabularnewline
30 & 0.0707646 & 0.141529 & 0.929235 \tabularnewline
31 & 0.0641405 & 0.128281 & 0.935859 \tabularnewline
32 & 0.061153 & 0.122306 & 0.938847 \tabularnewline
33 & 0.0457033 & 0.0914066 & 0.954297 \tabularnewline
34 & 0.0823174 & 0.164635 & 0.917683 \tabularnewline
35 & 0.0898421 & 0.179684 & 0.910158 \tabularnewline
36 & 0.0834639 & 0.166928 & 0.916536 \tabularnewline
37 & 0.06336 & 0.12672 & 0.93664 \tabularnewline
38 & 0.047682 & 0.0953641 & 0.952318 \tabularnewline
39 & 0.0347316 & 0.0694631 & 0.965268 \tabularnewline
40 & 0.0597571 & 0.119514 & 0.940243 \tabularnewline
41 & 0.0510295 & 0.102059 & 0.94897 \tabularnewline
42 & 0.0386351 & 0.0772702 & 0.961365 \tabularnewline
43 & 0.0350297 & 0.0700594 & 0.96497 \tabularnewline
44 & 0.0266994 & 0.0533988 & 0.973301 \tabularnewline
45 & 0.046929 & 0.093858 & 0.953071 \tabularnewline
46 & 0.0586923 & 0.117385 & 0.941308 \tabularnewline
47 & 0.050629 & 0.101258 & 0.949371 \tabularnewline
48 & 0.0530177 & 0.106035 & 0.946982 \tabularnewline
49 & 0.0634491 & 0.126898 & 0.936551 \tabularnewline
50 & 0.0515964 & 0.103193 & 0.948404 \tabularnewline
51 & 0.0566375 & 0.113275 & 0.943363 \tabularnewline
52 & 0.0561434 & 0.112287 & 0.943857 \tabularnewline
53 & 0.0429394 & 0.0858789 & 0.957061 \tabularnewline
54 & 0.0413266 & 0.0826532 & 0.958673 \tabularnewline
55 & 0.0309956 & 0.0619912 & 0.969004 \tabularnewline
56 & 0.0235331 & 0.0470663 & 0.976467 \tabularnewline
57 & 0.0213501 & 0.0427003 & 0.97865 \tabularnewline
58 & 0.0600068 & 0.120014 & 0.939993 \tabularnewline
59 & 0.0580446 & 0.116089 & 0.941955 \tabularnewline
60 & 0.063565 & 0.12713 & 0.936435 \tabularnewline
61 & 0.113206 & 0.226411 & 0.886794 \tabularnewline
62 & 0.091403 & 0.182806 & 0.908597 \tabularnewline
63 & 0.077815 & 0.15563 & 0.922185 \tabularnewline
64 & 0.0832201 & 0.16644 & 0.91678 \tabularnewline
65 & 0.0729055 & 0.145811 & 0.927094 \tabularnewline
66 & 0.0569147 & 0.113829 & 0.943085 \tabularnewline
67 & 0.0448894 & 0.0897787 & 0.955111 \tabularnewline
68 & 0.0553565 & 0.110713 & 0.944643 \tabularnewline
69 & 0.0816141 & 0.163228 & 0.918386 \tabularnewline
70 & 0.0677785 & 0.135557 & 0.932221 \tabularnewline
71 & 0.0574872 & 0.114974 & 0.942513 \tabularnewline
72 & 0.042923 & 0.085846 & 0.957077 \tabularnewline
73 & 0.0423164 & 0.0846329 & 0.957684 \tabularnewline
74 & 0.030898 & 0.061796 & 0.969102 \tabularnewline
75 & 0.0237273 & 0.0474545 & 0.976273 \tabularnewline
76 & 0.816757 & 0.366486 & 0.183243 \tabularnewline
77 & 0.788762 & 0.422476 & 0.211238 \tabularnewline
78 & 0.762879 & 0.474242 & 0.237121 \tabularnewline
79 & 0.792551 & 0.414898 & 0.207449 \tabularnewline
80 & 0.753865 & 0.49227 & 0.246135 \tabularnewline
81 & 0.731638 & 0.536723 & 0.268362 \tabularnewline
82 & 0.84142 & 0.31716 & 0.15858 \tabularnewline
83 & 0.803051 & 0.393899 & 0.196949 \tabularnewline
84 & 0.890636 & 0.218728 & 0.109364 \tabularnewline
85 & 0.870198 & 0.259605 & 0.129802 \tabularnewline
86 & 0.874458 & 0.251084 & 0.125542 \tabularnewline
87 & 0.838764 & 0.322472 & 0.161236 \tabularnewline
88 & 0.858051 & 0.283898 & 0.141949 \tabularnewline
89 & 0.828869 & 0.342262 & 0.171131 \tabularnewline
90 & 0.778147 & 0.443705 & 0.221853 \tabularnewline
91 & 0.722375 & 0.555249 & 0.277625 \tabularnewline
92 & 0.710854 & 0.578292 & 0.289146 \tabularnewline
93 & 0.718504 & 0.562993 & 0.281496 \tabularnewline
94 & 0.671822 & 0.656357 & 0.328178 \tabularnewline
95 & 0.595709 & 0.808582 & 0.404291 \tabularnewline
96 & 0.535899 & 0.928201 & 0.464101 \tabularnewline
97 & 0.449816 & 0.899632 & 0.550184 \tabularnewline
98 & 0.524792 & 0.950416 & 0.475208 \tabularnewline
99 & 0.671179 & 0.657642 & 0.328821 \tabularnewline
100 & 0.650507 & 0.698987 & 0.349493 \tabularnewline
101 & 0.549567 & 0.900867 & 0.450433 \tabularnewline
102 & 0.439264 & 0.878529 & 0.560736 \tabularnewline
103 & 0.560092 & 0.879815 & 0.439908 \tabularnewline
104 & 0.440276 & 0.880551 & 0.559724 \tabularnewline
105 & 0.503078 & 0.993844 & 0.496922 \tabularnewline
106 & 0.735136 & 0.529727 & 0.264864 \tabularnewline
107 & 0.675133 & 0.649734 & 0.324867 \tabularnewline
108 & 0.388365 & 0.776731 & 0.611635 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266325&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]7[/C][C]0.166834[/C][C]0.333669[/C][C]0.833166[/C][/ROW]
[ROW][C]8[/C][C]0.108949[/C][C]0.217898[/C][C]0.891051[/C][/ROW]
[ROW][C]9[/C][C]0.255051[/C][C]0.510101[/C][C]0.744949[/C][/ROW]
[ROW][C]10[/C][C]0.231845[/C][C]0.46369[/C][C]0.768155[/C][/ROW]
[ROW][C]11[/C][C]0.15098[/C][C]0.30196[/C][C]0.84902[/C][/ROW]
[ROW][C]12[/C][C]0.141834[/C][C]0.283669[/C][C]0.858166[/C][/ROW]
[ROW][C]13[/C][C]0.0970566[/C][C]0.194113[/C][C]0.902943[/C][/ROW]
[ROW][C]14[/C][C]0.0779468[/C][C]0.155894[/C][C]0.922053[/C][/ROW]
[ROW][C]15[/C][C]0.0508593[/C][C]0.101719[/C][C]0.949141[/C][/ROW]
[ROW][C]16[/C][C]0.141714[/C][C]0.283429[/C][C]0.858286[/C][/ROW]
[ROW][C]17[/C][C]0.134582[/C][C]0.269165[/C][C]0.865418[/C][/ROW]
[ROW][C]18[/C][C]0.0993873[/C][C]0.198775[/C][C]0.900613[/C][/ROW]
[ROW][C]19[/C][C]0.0668253[/C][C]0.133651[/C][C]0.933175[/C][/ROW]
[ROW][C]20[/C][C]0.102154[/C][C]0.204308[/C][C]0.897846[/C][/ROW]
[ROW][C]21[/C][C]0.094727[/C][C]0.189454[/C][C]0.905273[/C][/ROW]
[ROW][C]22[/C][C]0.114078[/C][C]0.228156[/C][C]0.885922[/C][/ROW]
[ROW][C]23[/C][C]0.0853903[/C][C]0.170781[/C][C]0.91461[/C][/ROW]
[ROW][C]24[/C][C]0.0618365[/C][C]0.123673[/C][C]0.938164[/C][/ROW]
[ROW][C]25[/C][C]0.0773141[/C][C]0.154628[/C][C]0.922686[/C][/ROW]
[ROW][C]26[/C][C]0.068884[/C][C]0.137768[/C][C]0.931116[/C][/ROW]
[ROW][C]27[/C][C]0.0591709[/C][C]0.118342[/C][C]0.940829[/C][/ROW]
[ROW][C]28[/C][C]0.0641244[/C][C]0.128249[/C][C]0.935876[/C][/ROW]
[ROW][C]29[/C][C]0.0458642[/C][C]0.0917283[/C][C]0.954136[/C][/ROW]
[ROW][C]30[/C][C]0.0707646[/C][C]0.141529[/C][C]0.929235[/C][/ROW]
[ROW][C]31[/C][C]0.0641405[/C][C]0.128281[/C][C]0.935859[/C][/ROW]
[ROW][C]32[/C][C]0.061153[/C][C]0.122306[/C][C]0.938847[/C][/ROW]
[ROW][C]33[/C][C]0.0457033[/C][C]0.0914066[/C][C]0.954297[/C][/ROW]
[ROW][C]34[/C][C]0.0823174[/C][C]0.164635[/C][C]0.917683[/C][/ROW]
[ROW][C]35[/C][C]0.0898421[/C][C]0.179684[/C][C]0.910158[/C][/ROW]
[ROW][C]36[/C][C]0.0834639[/C][C]0.166928[/C][C]0.916536[/C][/ROW]
[ROW][C]37[/C][C]0.06336[/C][C]0.12672[/C][C]0.93664[/C][/ROW]
[ROW][C]38[/C][C]0.047682[/C][C]0.0953641[/C][C]0.952318[/C][/ROW]
[ROW][C]39[/C][C]0.0347316[/C][C]0.0694631[/C][C]0.965268[/C][/ROW]
[ROW][C]40[/C][C]0.0597571[/C][C]0.119514[/C][C]0.940243[/C][/ROW]
[ROW][C]41[/C][C]0.0510295[/C][C]0.102059[/C][C]0.94897[/C][/ROW]
[ROW][C]42[/C][C]0.0386351[/C][C]0.0772702[/C][C]0.961365[/C][/ROW]
[ROW][C]43[/C][C]0.0350297[/C][C]0.0700594[/C][C]0.96497[/C][/ROW]
[ROW][C]44[/C][C]0.0266994[/C][C]0.0533988[/C][C]0.973301[/C][/ROW]
[ROW][C]45[/C][C]0.046929[/C][C]0.093858[/C][C]0.953071[/C][/ROW]
[ROW][C]46[/C][C]0.0586923[/C][C]0.117385[/C][C]0.941308[/C][/ROW]
[ROW][C]47[/C][C]0.050629[/C][C]0.101258[/C][C]0.949371[/C][/ROW]
[ROW][C]48[/C][C]0.0530177[/C][C]0.106035[/C][C]0.946982[/C][/ROW]
[ROW][C]49[/C][C]0.0634491[/C][C]0.126898[/C][C]0.936551[/C][/ROW]
[ROW][C]50[/C][C]0.0515964[/C][C]0.103193[/C][C]0.948404[/C][/ROW]
[ROW][C]51[/C][C]0.0566375[/C][C]0.113275[/C][C]0.943363[/C][/ROW]
[ROW][C]52[/C][C]0.0561434[/C][C]0.112287[/C][C]0.943857[/C][/ROW]
[ROW][C]53[/C][C]0.0429394[/C][C]0.0858789[/C][C]0.957061[/C][/ROW]
[ROW][C]54[/C][C]0.0413266[/C][C]0.0826532[/C][C]0.958673[/C][/ROW]
[ROW][C]55[/C][C]0.0309956[/C][C]0.0619912[/C][C]0.969004[/C][/ROW]
[ROW][C]56[/C][C]0.0235331[/C][C]0.0470663[/C][C]0.976467[/C][/ROW]
[ROW][C]57[/C][C]0.0213501[/C][C]0.0427003[/C][C]0.97865[/C][/ROW]
[ROW][C]58[/C][C]0.0600068[/C][C]0.120014[/C][C]0.939993[/C][/ROW]
[ROW][C]59[/C][C]0.0580446[/C][C]0.116089[/C][C]0.941955[/C][/ROW]
[ROW][C]60[/C][C]0.063565[/C][C]0.12713[/C][C]0.936435[/C][/ROW]
[ROW][C]61[/C][C]0.113206[/C][C]0.226411[/C][C]0.886794[/C][/ROW]
[ROW][C]62[/C][C]0.091403[/C][C]0.182806[/C][C]0.908597[/C][/ROW]
[ROW][C]63[/C][C]0.077815[/C][C]0.15563[/C][C]0.922185[/C][/ROW]
[ROW][C]64[/C][C]0.0832201[/C][C]0.16644[/C][C]0.91678[/C][/ROW]
[ROW][C]65[/C][C]0.0729055[/C][C]0.145811[/C][C]0.927094[/C][/ROW]
[ROW][C]66[/C][C]0.0569147[/C][C]0.113829[/C][C]0.943085[/C][/ROW]
[ROW][C]67[/C][C]0.0448894[/C][C]0.0897787[/C][C]0.955111[/C][/ROW]
[ROW][C]68[/C][C]0.0553565[/C][C]0.110713[/C][C]0.944643[/C][/ROW]
[ROW][C]69[/C][C]0.0816141[/C][C]0.163228[/C][C]0.918386[/C][/ROW]
[ROW][C]70[/C][C]0.0677785[/C][C]0.135557[/C][C]0.932221[/C][/ROW]
[ROW][C]71[/C][C]0.0574872[/C][C]0.114974[/C][C]0.942513[/C][/ROW]
[ROW][C]72[/C][C]0.042923[/C][C]0.085846[/C][C]0.957077[/C][/ROW]
[ROW][C]73[/C][C]0.0423164[/C][C]0.0846329[/C][C]0.957684[/C][/ROW]
[ROW][C]74[/C][C]0.030898[/C][C]0.061796[/C][C]0.969102[/C][/ROW]
[ROW][C]75[/C][C]0.0237273[/C][C]0.0474545[/C][C]0.976273[/C][/ROW]
[ROW][C]76[/C][C]0.816757[/C][C]0.366486[/C][C]0.183243[/C][/ROW]
[ROW][C]77[/C][C]0.788762[/C][C]0.422476[/C][C]0.211238[/C][/ROW]
[ROW][C]78[/C][C]0.762879[/C][C]0.474242[/C][C]0.237121[/C][/ROW]
[ROW][C]79[/C][C]0.792551[/C][C]0.414898[/C][C]0.207449[/C][/ROW]
[ROW][C]80[/C][C]0.753865[/C][C]0.49227[/C][C]0.246135[/C][/ROW]
[ROW][C]81[/C][C]0.731638[/C][C]0.536723[/C][C]0.268362[/C][/ROW]
[ROW][C]82[/C][C]0.84142[/C][C]0.31716[/C][C]0.15858[/C][/ROW]
[ROW][C]83[/C][C]0.803051[/C][C]0.393899[/C][C]0.196949[/C][/ROW]
[ROW][C]84[/C][C]0.890636[/C][C]0.218728[/C][C]0.109364[/C][/ROW]
[ROW][C]85[/C][C]0.870198[/C][C]0.259605[/C][C]0.129802[/C][/ROW]
[ROW][C]86[/C][C]0.874458[/C][C]0.251084[/C][C]0.125542[/C][/ROW]
[ROW][C]87[/C][C]0.838764[/C][C]0.322472[/C][C]0.161236[/C][/ROW]
[ROW][C]88[/C][C]0.858051[/C][C]0.283898[/C][C]0.141949[/C][/ROW]
[ROW][C]89[/C][C]0.828869[/C][C]0.342262[/C][C]0.171131[/C][/ROW]
[ROW][C]90[/C][C]0.778147[/C][C]0.443705[/C][C]0.221853[/C][/ROW]
[ROW][C]91[/C][C]0.722375[/C][C]0.555249[/C][C]0.277625[/C][/ROW]
[ROW][C]92[/C][C]0.710854[/C][C]0.578292[/C][C]0.289146[/C][/ROW]
[ROW][C]93[/C][C]0.718504[/C][C]0.562993[/C][C]0.281496[/C][/ROW]
[ROW][C]94[/C][C]0.671822[/C][C]0.656357[/C][C]0.328178[/C][/ROW]
[ROW][C]95[/C][C]0.595709[/C][C]0.808582[/C][C]0.404291[/C][/ROW]
[ROW][C]96[/C][C]0.535899[/C][C]0.928201[/C][C]0.464101[/C][/ROW]
[ROW][C]97[/C][C]0.449816[/C][C]0.899632[/C][C]0.550184[/C][/ROW]
[ROW][C]98[/C][C]0.524792[/C][C]0.950416[/C][C]0.475208[/C][/ROW]
[ROW][C]99[/C][C]0.671179[/C][C]0.657642[/C][C]0.328821[/C][/ROW]
[ROW][C]100[/C][C]0.650507[/C][C]0.698987[/C][C]0.349493[/C][/ROW]
[ROW][C]101[/C][C]0.549567[/C][C]0.900867[/C][C]0.450433[/C][/ROW]
[ROW][C]102[/C][C]0.439264[/C][C]0.878529[/C][C]0.560736[/C][/ROW]
[ROW][C]103[/C][C]0.560092[/C][C]0.879815[/C][C]0.439908[/C][/ROW]
[ROW][C]104[/C][C]0.440276[/C][C]0.880551[/C][C]0.559724[/C][/ROW]
[ROW][C]105[/C][C]0.503078[/C][C]0.993844[/C][C]0.496922[/C][/ROW]
[ROW][C]106[/C][C]0.735136[/C][C]0.529727[/C][C]0.264864[/C][/ROW]
[ROW][C]107[/C][C]0.675133[/C][C]0.649734[/C][C]0.324867[/C][/ROW]
[ROW][C]108[/C][C]0.388365[/C][C]0.776731[/C][C]0.611635[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266325&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266325&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1668340.3336690.833166
80.1089490.2178980.891051
90.2550510.5101010.744949
100.2318450.463690.768155
110.150980.301960.84902
120.1418340.2836690.858166
130.09705660.1941130.902943
140.07794680.1558940.922053
150.05085930.1017190.949141
160.1417140.2834290.858286
170.1345820.2691650.865418
180.09938730.1987750.900613
190.06682530.1336510.933175
200.1021540.2043080.897846
210.0947270.1894540.905273
220.1140780.2281560.885922
230.08539030.1707810.91461
240.06183650.1236730.938164
250.07731410.1546280.922686
260.0688840.1377680.931116
270.05917090.1183420.940829
280.06412440.1282490.935876
290.04586420.09172830.954136
300.07076460.1415290.929235
310.06414050.1282810.935859
320.0611530.1223060.938847
330.04570330.09140660.954297
340.08231740.1646350.917683
350.08984210.1796840.910158
360.08346390.1669280.916536
370.063360.126720.93664
380.0476820.09536410.952318
390.03473160.06946310.965268
400.05975710.1195140.940243
410.05102950.1020590.94897
420.03863510.07727020.961365
430.03502970.07005940.96497
440.02669940.05339880.973301
450.0469290.0938580.953071
460.05869230.1173850.941308
470.0506290.1012580.949371
480.05301770.1060350.946982
490.06344910.1268980.936551
500.05159640.1031930.948404
510.05663750.1132750.943363
520.05614340.1122870.943857
530.04293940.08587890.957061
540.04132660.08265320.958673
550.03099560.06199120.969004
560.02353310.04706630.976467
570.02135010.04270030.97865
580.06000680.1200140.939993
590.05804460.1160890.941955
600.0635650.127130.936435
610.1132060.2264110.886794
620.0914030.1828060.908597
630.0778150.155630.922185
640.08322010.166440.91678
650.07290550.1458110.927094
660.05691470.1138290.943085
670.04488940.08977870.955111
680.05535650.1107130.944643
690.08161410.1632280.918386
700.06777850.1355570.932221
710.05748720.1149740.942513
720.0429230.0858460.957077
730.04231640.08463290.957684
740.0308980.0617960.969102
750.02372730.04745450.976273
760.8167570.3664860.183243
770.7887620.4224760.211238
780.7628790.4742420.237121
790.7925510.4148980.207449
800.7538650.492270.246135
810.7316380.5367230.268362
820.841420.317160.15858
830.8030510.3938990.196949
840.8906360.2187280.109364
850.8701980.2596050.129802
860.8744580.2510840.125542
870.8387640.3224720.161236
880.8580510.2838980.141949
890.8288690.3422620.171131
900.7781470.4437050.221853
910.7223750.5552490.277625
920.7108540.5782920.289146
930.7185040.5629930.281496
940.6718220.6563570.328178
950.5957090.8085820.404291
960.5358990.9282010.464101
970.4498160.8996320.550184
980.5247920.9504160.475208
990.6711790.6576420.328821
1000.6505070.6989870.349493
1010.5495670.9008670.450433
1020.4392640.8785290.560736
1030.5600920.8798150.439908
1040.4402760.8805510.559724
1050.5030780.9938440.496922
1060.7351360.5297270.264864
1070.6751330.6497340.324867
1080.3883650.7767310.611635







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0294118OK
10% type I error level180.176471NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0294118 & OK \tabularnewline
10% type I error level & 18 & 0.176471 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266325&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0294118[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]18[/C][C]0.176471[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266325&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266325&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 testsOK/NOK
1% type I error level00OK
5% type I error level30.0294118OK
10% type I error level180.176471NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '4'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}