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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 computationMon, 22 Nov 2010 10:46:43 +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/2010/Nov/22/t12904227988tjk1t2dmqppl2z.htm/, Retrieved Thu, 28 Mar 2024 22:05:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98441, Retrieved Thu, 28 Mar 2024 22:05:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact235
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-11-22 10:46:43] [9003764b6a75599accb6eea9154ba195] [Current]
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Dataseries X:
9	15	6	25	68	0
14	10	8	23	48	0
8	10	7	17	44	0
8	12	9	19	67	1
14	9	8	29	46	1
15	18	11	23	54	1
9	14	9	23	61	0
11	11	11	21	52	0
14	11	12	26	46	1
14	9	6	24	55	0
6	17	8	25	52	0
10	21	12	26	76	0
9	16	9	23	49	0
11	21	7	29	30	1
14	14	8	24	75	1
8	24	20	20	51	1
11	7	8	23	50	1
10	9	6	29	38	0
16	18	16	24	47	0
8	14	6	22	52	1
11	13	6	22	66	0
11	13	6	22	66	1
7	18	11	17	33	0
13	14	12	24	48	0
10	12	8	21	57	0
9	12	8	24	64	1
9	9	7	23	58	1
15	11	9	21	59	1
13	8	9	24	42	0
16	5	4	24	39	0
11	9	6	19	59	0
6	11	8	26	37	1
14	11	8	24	49	1
4	15	4	28	80	1
12	16	14	22	62	0
10	12	8	23	44	0
14	14	10	24	53	1
9	13	6	23	58	1
10	10	8	23	69	1
14	18	10	30	63	1
14	17	11	20	36	1
10	12	8	23	38	0
9	13	8	21	46	0
14	13	10	27	56	0
8	11	8	12	37	1
9	13	10	15	51	0
8	12	7	22	44	1
10	12	8	27	58	1
9	12	8	21	37	0
9	12	7	21	65	0
9	13	6	21	48	0
9	17	9	21	53	1
11	18	5	18	51	1
15	7	5	24	39	1
8	17	7	24	64	1
12	14	7	28	47	1
8	12	7	25	47	1
14	9	9	14	64	0
11	9	5	30	59	0
10	13	8	19	54	1
12	10	8	29	55	0
9	12	9	25	72	1
13	10	6	25	58	0
14	11	8	25	59	0
15	13	8	16	36	0
8	6	6	25	62	0
7	7	4	28	63	1
10	13	6	24	50	1
10	11	5	24	70	0
11	9	6	22	59	1
8	9	11	20	73	0
9	11	10	27	62	1
10	15	10	21	41	0
11	11	8	26	56	1
10	14	9	26	52	1
16	14	9	25	54	0
11	8	4	13	73	0
16	12	7	22	40	1
6	8	11	23	41	1
11	11	8	25	54	1
12	10	8	15	42	1
12	11	8	25	70	0
14	17	7	21	51	0
9	16	5	23	60	0
11	13	7	25	49	1
8	15	9	24	52	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98441&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98441&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98441&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
intrinsic[t] = + 54.3924753054961 -0.606890253799587Doubts[t] + 0.0642016921348219Parentalexpectations[t] -0.429106442448299Parentalcriticism[t] + 0.396721573130468organization[t] -1.23923857358372geslacht[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
intrinsic[t] =  +  54.3924753054961 -0.606890253799587Doubts[t] +  0.0642016921348219Parentalexpectations[t] -0.429106442448299Parentalcriticism[t] +  0.396721573130468organization[t] -1.23923857358372geslacht[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98441&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]intrinsic[t] =  +  54.3924753054961 -0.606890253799587Doubts[t] +  0.0642016921348219Parentalexpectations[t] -0.429106442448299Parentalcriticism[t] +  0.396721573130468organization[t] -1.23923857358372geslacht[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98441&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98441&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
intrinsic[t] = + 54.3924753054961 -0.606890253799587Doubts[t] + 0.0642016921348219Parentalexpectations[t] -0.429106442448299Parentalcriticism[t] + 0.396721573130468organization[t] -1.23923857358372geslacht[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)54.392475305496110.048495.4131e-060
Doubts-0.6068902537995870.455825-1.33140.1868360.093418
Parentalexpectations0.06420169213482190.3979340.16130.8722340.436117
Parentalcriticism-0.4291064424482990.549566-0.78080.4372190.21861
organization0.3967215731304680.3306391.19990.2337330.116867
geslacht-1.239238573583722.426081-0.51080.6108990.305449

\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) & 54.3924753054961 & 10.04849 & 5.413 & 1e-06 & 0 \tabularnewline
Doubts & -0.606890253799587 & 0.455825 & -1.3314 & 0.186836 & 0.093418 \tabularnewline
Parentalexpectations & 0.0642016921348219 & 0.397934 & 0.1613 & 0.872234 & 0.436117 \tabularnewline
Parentalcriticism & -0.429106442448299 & 0.549566 & -0.7808 & 0.437219 & 0.21861 \tabularnewline
organization & 0.396721573130468 & 0.330639 & 1.1999 & 0.233733 & 0.116867 \tabularnewline
geslacht & -1.23923857358372 & 2.426081 & -0.5108 & 0.610899 & 0.305449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98441&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]54.3924753054961[/C][C]10.04849[/C][C]5.413[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.606890253799587[/C][C]0.455825[/C][C]-1.3314[/C][C]0.186836[/C][C]0.093418[/C][/ROW]
[ROW][C]Parentalexpectations[/C][C]0.0642016921348219[/C][C]0.397934[/C][C]0.1613[/C][C]0.872234[/C][C]0.436117[/C][/ROW]
[ROW][C]Parentalcriticism[/C][C]-0.429106442448299[/C][C]0.549566[/C][C]-0.7808[/C][C]0.437219[/C][C]0.21861[/C][/ROW]
[ROW][C]organization[/C][C]0.396721573130468[/C][C]0.330639[/C][C]1.1999[/C][C]0.233733[/C][C]0.116867[/C][/ROW]
[ROW][C]geslacht[/C][C]-1.23923857358372[/C][C]2.426081[/C][C]-0.5108[/C][C]0.610899[/C][C]0.305449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98441&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98441&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)54.392475305496110.048495.4131e-060
Doubts-0.6068902537995870.455825-1.33140.1868360.093418
Parentalexpectations0.06420169213482190.3979340.16130.8722340.436117
Parentalcriticism-0.4291064424482990.549566-0.78080.4372190.21861
organization0.3967215731304680.3306391.19990.2337330.116867
geslacht-1.239238573583722.426081-0.51080.6108990.305449







Multiple Linear Regression - Regression Statistics
Multiple R0.226823336640143
R-squared0.0514488260445678
Adjusted R-squared-0.00783562232764679
F-TEST (value)0.867830054208296
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value0.506544612128841
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.0635117234388
Sum Squared Residuals9792.10333237338

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.226823336640143 \tabularnewline
R-squared & 0.0514488260445678 \tabularnewline
Adjusted R-squared & -0.00783562232764679 \tabularnewline
F-TEST (value) & 0.867830054208296 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 80 \tabularnewline
p-value & 0.506544612128841 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.0635117234388 \tabularnewline
Sum Squared Residuals & 9792.10333237338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98441&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.226823336640143[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0514488260445678[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00783562232764679[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.867830054208296[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]80[/C][/ROW]
[ROW][C]p-value[/C][C]0.506544612128841[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]11.0635117234388[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9792.10333237338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98441&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98441&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 R0.226823336640143
R-squared0.0514488260445678
Adjusted R-squared-0.00783562232764679
F-TEST (value)0.867830054208296
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value0.506544612128841
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.0635117234388
Sum Squared Residuals9792.10333237338







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16857.236889076894310.7631109231057
24852.2297733160645-4.22977331606453
34453.9198918425276-9.91989184252756
46752.744286914577814.2557130854222
54653.3066624891288-7.30666248912881
65449.60993869841494.39006130158509
76155.09192491115355.90807508884654
85252.0338832959923-0.0338832959922875
94650.5284753842138-4.52847538421385
105553.42050608195681.57949391804322
115258.3277503376659-6.32775033766592
127654.837291894344121.1627081056559
134955.2203282954231-6.2203282954231
143056.3268599985937-26.3268599985937
157551.644063084150623.3559369158494
165149.19125792639491.80874207360515
175052.6186004274751-2.61860042747511
183857.8316749628075-19.8316749628075
194748.493476379088-1.49347637908802
205255.3501743455838-3.35017434558375
216654.704540465633911.2954595343661
226653.465301892050212.5346981079498
233353.3239698636125-20.3239698636125
244851.7737661417407-3.77376614174068
255753.99229456927163.00770543072841
266454.55011096887899.44988903112114
275854.38989076179223.61010923820777
285949.22529659210689.77470340789318
294252.6758753162767-10.6758753162767
303952.8081316907149-13.8081316907149
315953.25756897770325.7424310222968
323757.1000231844037-20.1000231844037
334951.4514580077461-2.45145800774610
348061.080479376596318.9195206234037
356250.857403748652411.1425962513476
364454.7857377155325-10.7857377155325
375350.7858501992542.21414980074603
385855.07580397277982.92419602722019
396953.418095757679215.5819042423208
406353.42298640657619.57701359342393
413648.9624625406883-12.9624625406883
423854.7857377155325-16.7857377155325
434654.663386515206-8.663386515206
445653.15105180009432.84894819990572
453750.332140652978-13.332140652978
465151.4248441915266-0.424844191526596
474454.7926645188658-10.7926645188658
485855.13338543447072.86661456552932
493754.5991848230712-17.5991848230712
506555.02829126551959.97170873448052
514855.5215994001026-7.5215994001026
525353.2518482677133-0.251848267713267
535152.6285305026507-1.62853050265070
543951.8750803127521-12.8750803127521
556455.90711612580098.09288387419915
564754.8738363267199-7.87383632671991
574755.9828292382572-8.98282923825722
586448.165971023307215.8340289766928
595958.05061272458660.949387275413355
605452.02381454156181.97618545843824
615555.8238832624465-0.823883262446517
627254.51772609956117.4822739004390
635854.48831960102173.51168039897835
645953.08741815446035.9125818455397
653649.0384371267561-13.0384371267561
666257.26596410148034.7340358985197
676358.7461950781194.25380492188101
685054.8656352921107-4.86563529211069
697056.40557692387313.5944230761269
705953.20849512351095.79150487648912
717353.329429099990919.6705709000091
726254.81786111123887.18213888876115
734153.3266867607795-12.3266867607795
745654.06557191540581.9344280845942
755254.4359608031616-2.43596080316155
765451.63713628081732.36286371918271
777351.671250731682221.3287492683178
784049.9375424884691-9.93754248846911
794154.429934061263-13.4299340612630
805453.66885034227530.331149657724668
814249.0305426650362-7.03054266503625
827054.301198662059515.6988013379405
835152.3148484571957-1.31484845719565
846056.93675406521633.06324593478370
854954.2263601689933-5.22636016899328
865256.1597384302183-4.15973843021834

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 68 & 57.2368890768943 & 10.7631109231057 \tabularnewline
2 & 48 & 52.2297733160645 & -4.22977331606453 \tabularnewline
3 & 44 & 53.9198918425276 & -9.91989184252756 \tabularnewline
4 & 67 & 52.7442869145778 & 14.2557130854222 \tabularnewline
5 & 46 & 53.3066624891288 & -7.30666248912881 \tabularnewline
6 & 54 & 49.6099386984149 & 4.39006130158509 \tabularnewline
7 & 61 & 55.0919249111535 & 5.90807508884654 \tabularnewline
8 & 52 & 52.0338832959923 & -0.0338832959922875 \tabularnewline
9 & 46 & 50.5284753842138 & -4.52847538421385 \tabularnewline
10 & 55 & 53.4205060819568 & 1.57949391804322 \tabularnewline
11 & 52 & 58.3277503376659 & -6.32775033766592 \tabularnewline
12 & 76 & 54.8372918943441 & 21.1627081056559 \tabularnewline
13 & 49 & 55.2203282954231 & -6.2203282954231 \tabularnewline
14 & 30 & 56.3268599985937 & -26.3268599985937 \tabularnewline
15 & 75 & 51.6440630841506 & 23.3559369158494 \tabularnewline
16 & 51 & 49.1912579263949 & 1.80874207360515 \tabularnewline
17 & 50 & 52.6186004274751 & -2.61860042747511 \tabularnewline
18 & 38 & 57.8316749628075 & -19.8316749628075 \tabularnewline
19 & 47 & 48.493476379088 & -1.49347637908802 \tabularnewline
20 & 52 & 55.3501743455838 & -3.35017434558375 \tabularnewline
21 & 66 & 54.7045404656339 & 11.2954595343661 \tabularnewline
22 & 66 & 53.4653018920502 & 12.5346981079498 \tabularnewline
23 & 33 & 53.3239698636125 & -20.3239698636125 \tabularnewline
24 & 48 & 51.7737661417407 & -3.77376614174068 \tabularnewline
25 & 57 & 53.9922945692716 & 3.00770543072841 \tabularnewline
26 & 64 & 54.5501109688789 & 9.44988903112114 \tabularnewline
27 & 58 & 54.3898907617922 & 3.61010923820777 \tabularnewline
28 & 59 & 49.2252965921068 & 9.77470340789318 \tabularnewline
29 & 42 & 52.6758753162767 & -10.6758753162767 \tabularnewline
30 & 39 & 52.8081316907149 & -13.8081316907149 \tabularnewline
31 & 59 & 53.2575689777032 & 5.7424310222968 \tabularnewline
32 & 37 & 57.1000231844037 & -20.1000231844037 \tabularnewline
33 & 49 & 51.4514580077461 & -2.45145800774610 \tabularnewline
34 & 80 & 61.0804793765963 & 18.9195206234037 \tabularnewline
35 & 62 & 50.8574037486524 & 11.1425962513476 \tabularnewline
36 & 44 & 54.7857377155325 & -10.7857377155325 \tabularnewline
37 & 53 & 50.785850199254 & 2.21414980074603 \tabularnewline
38 & 58 & 55.0758039727798 & 2.92419602722019 \tabularnewline
39 & 69 & 53.4180957576792 & 15.5819042423208 \tabularnewline
40 & 63 & 53.4229864065761 & 9.57701359342393 \tabularnewline
41 & 36 & 48.9624625406883 & -12.9624625406883 \tabularnewline
42 & 38 & 54.7857377155325 & -16.7857377155325 \tabularnewline
43 & 46 & 54.663386515206 & -8.663386515206 \tabularnewline
44 & 56 & 53.1510518000943 & 2.84894819990572 \tabularnewline
45 & 37 & 50.332140652978 & -13.332140652978 \tabularnewline
46 & 51 & 51.4248441915266 & -0.424844191526596 \tabularnewline
47 & 44 & 54.7926645188658 & -10.7926645188658 \tabularnewline
48 & 58 & 55.1333854344707 & 2.86661456552932 \tabularnewline
49 & 37 & 54.5991848230712 & -17.5991848230712 \tabularnewline
50 & 65 & 55.0282912655195 & 9.97170873448052 \tabularnewline
51 & 48 & 55.5215994001026 & -7.5215994001026 \tabularnewline
52 & 53 & 53.2518482677133 & -0.251848267713267 \tabularnewline
53 & 51 & 52.6285305026507 & -1.62853050265070 \tabularnewline
54 & 39 & 51.8750803127521 & -12.8750803127521 \tabularnewline
55 & 64 & 55.9071161258009 & 8.09288387419915 \tabularnewline
56 & 47 & 54.8738363267199 & -7.87383632671991 \tabularnewline
57 & 47 & 55.9828292382572 & -8.98282923825722 \tabularnewline
58 & 64 & 48.1659710233072 & 15.8340289766928 \tabularnewline
59 & 59 & 58.0506127245866 & 0.949387275413355 \tabularnewline
60 & 54 & 52.0238145415618 & 1.97618545843824 \tabularnewline
61 & 55 & 55.8238832624465 & -0.823883262446517 \tabularnewline
62 & 72 & 54.517726099561 & 17.4822739004390 \tabularnewline
63 & 58 & 54.4883196010217 & 3.51168039897835 \tabularnewline
64 & 59 & 53.0874181544603 & 5.9125818455397 \tabularnewline
65 & 36 & 49.0384371267561 & -13.0384371267561 \tabularnewline
66 & 62 & 57.2659641014803 & 4.7340358985197 \tabularnewline
67 & 63 & 58.746195078119 & 4.25380492188101 \tabularnewline
68 & 50 & 54.8656352921107 & -4.86563529211069 \tabularnewline
69 & 70 & 56.405576923873 & 13.5944230761269 \tabularnewline
70 & 59 & 53.2084951235109 & 5.79150487648912 \tabularnewline
71 & 73 & 53.3294290999909 & 19.6705709000091 \tabularnewline
72 & 62 & 54.8178611112388 & 7.18213888876115 \tabularnewline
73 & 41 & 53.3266867607795 & -12.3266867607795 \tabularnewline
74 & 56 & 54.0655719154058 & 1.9344280845942 \tabularnewline
75 & 52 & 54.4359608031616 & -2.43596080316155 \tabularnewline
76 & 54 & 51.6371362808173 & 2.36286371918271 \tabularnewline
77 & 73 & 51.6712507316822 & 21.3287492683178 \tabularnewline
78 & 40 & 49.9375424884691 & -9.93754248846911 \tabularnewline
79 & 41 & 54.429934061263 & -13.4299340612630 \tabularnewline
80 & 54 & 53.6688503422753 & 0.331149657724668 \tabularnewline
81 & 42 & 49.0305426650362 & -7.03054266503625 \tabularnewline
82 & 70 & 54.3011986620595 & 15.6988013379405 \tabularnewline
83 & 51 & 52.3148484571957 & -1.31484845719565 \tabularnewline
84 & 60 & 56.9367540652163 & 3.06324593478370 \tabularnewline
85 & 49 & 54.2263601689933 & -5.22636016899328 \tabularnewline
86 & 52 & 56.1597384302183 & -4.15973843021834 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98441&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]68[/C][C]57.2368890768943[/C][C]10.7631109231057[/C][/ROW]
[ROW][C]2[/C][C]48[/C][C]52.2297733160645[/C][C]-4.22977331606453[/C][/ROW]
[ROW][C]3[/C][C]44[/C][C]53.9198918425276[/C][C]-9.91989184252756[/C][/ROW]
[ROW][C]4[/C][C]67[/C][C]52.7442869145778[/C][C]14.2557130854222[/C][/ROW]
[ROW][C]5[/C][C]46[/C][C]53.3066624891288[/C][C]-7.30666248912881[/C][/ROW]
[ROW][C]6[/C][C]54[/C][C]49.6099386984149[/C][C]4.39006130158509[/C][/ROW]
[ROW][C]7[/C][C]61[/C][C]55.0919249111535[/C][C]5.90807508884654[/C][/ROW]
[ROW][C]8[/C][C]52[/C][C]52.0338832959923[/C][C]-0.0338832959922875[/C][/ROW]
[ROW][C]9[/C][C]46[/C][C]50.5284753842138[/C][C]-4.52847538421385[/C][/ROW]
[ROW][C]10[/C][C]55[/C][C]53.4205060819568[/C][C]1.57949391804322[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]58.3277503376659[/C][C]-6.32775033766592[/C][/ROW]
[ROW][C]12[/C][C]76[/C][C]54.8372918943441[/C][C]21.1627081056559[/C][/ROW]
[ROW][C]13[/C][C]49[/C][C]55.2203282954231[/C][C]-6.2203282954231[/C][/ROW]
[ROW][C]14[/C][C]30[/C][C]56.3268599985937[/C][C]-26.3268599985937[/C][/ROW]
[ROW][C]15[/C][C]75[/C][C]51.6440630841506[/C][C]23.3559369158494[/C][/ROW]
[ROW][C]16[/C][C]51[/C][C]49.1912579263949[/C][C]1.80874207360515[/C][/ROW]
[ROW][C]17[/C][C]50[/C][C]52.6186004274751[/C][C]-2.61860042747511[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]57.8316749628075[/C][C]-19.8316749628075[/C][/ROW]
[ROW][C]19[/C][C]47[/C][C]48.493476379088[/C][C]-1.49347637908802[/C][/ROW]
[ROW][C]20[/C][C]52[/C][C]55.3501743455838[/C][C]-3.35017434558375[/C][/ROW]
[ROW][C]21[/C][C]66[/C][C]54.7045404656339[/C][C]11.2954595343661[/C][/ROW]
[ROW][C]22[/C][C]66[/C][C]53.4653018920502[/C][C]12.5346981079498[/C][/ROW]
[ROW][C]23[/C][C]33[/C][C]53.3239698636125[/C][C]-20.3239698636125[/C][/ROW]
[ROW][C]24[/C][C]48[/C][C]51.7737661417407[/C][C]-3.77376614174068[/C][/ROW]
[ROW][C]25[/C][C]57[/C][C]53.9922945692716[/C][C]3.00770543072841[/C][/ROW]
[ROW][C]26[/C][C]64[/C][C]54.5501109688789[/C][C]9.44988903112114[/C][/ROW]
[ROW][C]27[/C][C]58[/C][C]54.3898907617922[/C][C]3.61010923820777[/C][/ROW]
[ROW][C]28[/C][C]59[/C][C]49.2252965921068[/C][C]9.77470340789318[/C][/ROW]
[ROW][C]29[/C][C]42[/C][C]52.6758753162767[/C][C]-10.6758753162767[/C][/ROW]
[ROW][C]30[/C][C]39[/C][C]52.8081316907149[/C][C]-13.8081316907149[/C][/ROW]
[ROW][C]31[/C][C]59[/C][C]53.2575689777032[/C][C]5.7424310222968[/C][/ROW]
[ROW][C]32[/C][C]37[/C][C]57.1000231844037[/C][C]-20.1000231844037[/C][/ROW]
[ROW][C]33[/C][C]49[/C][C]51.4514580077461[/C][C]-2.45145800774610[/C][/ROW]
[ROW][C]34[/C][C]80[/C][C]61.0804793765963[/C][C]18.9195206234037[/C][/ROW]
[ROW][C]35[/C][C]62[/C][C]50.8574037486524[/C][C]11.1425962513476[/C][/ROW]
[ROW][C]36[/C][C]44[/C][C]54.7857377155325[/C][C]-10.7857377155325[/C][/ROW]
[ROW][C]37[/C][C]53[/C][C]50.785850199254[/C][C]2.21414980074603[/C][/ROW]
[ROW][C]38[/C][C]58[/C][C]55.0758039727798[/C][C]2.92419602722019[/C][/ROW]
[ROW][C]39[/C][C]69[/C][C]53.4180957576792[/C][C]15.5819042423208[/C][/ROW]
[ROW][C]40[/C][C]63[/C][C]53.4229864065761[/C][C]9.57701359342393[/C][/ROW]
[ROW][C]41[/C][C]36[/C][C]48.9624625406883[/C][C]-12.9624625406883[/C][/ROW]
[ROW][C]42[/C][C]38[/C][C]54.7857377155325[/C][C]-16.7857377155325[/C][/ROW]
[ROW][C]43[/C][C]46[/C][C]54.663386515206[/C][C]-8.663386515206[/C][/ROW]
[ROW][C]44[/C][C]56[/C][C]53.1510518000943[/C][C]2.84894819990572[/C][/ROW]
[ROW][C]45[/C][C]37[/C][C]50.332140652978[/C][C]-13.332140652978[/C][/ROW]
[ROW][C]46[/C][C]51[/C][C]51.4248441915266[/C][C]-0.424844191526596[/C][/ROW]
[ROW][C]47[/C][C]44[/C][C]54.7926645188658[/C][C]-10.7926645188658[/C][/ROW]
[ROW][C]48[/C][C]58[/C][C]55.1333854344707[/C][C]2.86661456552932[/C][/ROW]
[ROW][C]49[/C][C]37[/C][C]54.5991848230712[/C][C]-17.5991848230712[/C][/ROW]
[ROW][C]50[/C][C]65[/C][C]55.0282912655195[/C][C]9.97170873448052[/C][/ROW]
[ROW][C]51[/C][C]48[/C][C]55.5215994001026[/C][C]-7.5215994001026[/C][/ROW]
[ROW][C]52[/C][C]53[/C][C]53.2518482677133[/C][C]-0.251848267713267[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]52.6285305026507[/C][C]-1.62853050265070[/C][/ROW]
[ROW][C]54[/C][C]39[/C][C]51.8750803127521[/C][C]-12.8750803127521[/C][/ROW]
[ROW][C]55[/C][C]64[/C][C]55.9071161258009[/C][C]8.09288387419915[/C][/ROW]
[ROW][C]56[/C][C]47[/C][C]54.8738363267199[/C][C]-7.87383632671991[/C][/ROW]
[ROW][C]57[/C][C]47[/C][C]55.9828292382572[/C][C]-8.98282923825722[/C][/ROW]
[ROW][C]58[/C][C]64[/C][C]48.1659710233072[/C][C]15.8340289766928[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]58.0506127245866[/C][C]0.949387275413355[/C][/ROW]
[ROW][C]60[/C][C]54[/C][C]52.0238145415618[/C][C]1.97618545843824[/C][/ROW]
[ROW][C]61[/C][C]55[/C][C]55.8238832624465[/C][C]-0.823883262446517[/C][/ROW]
[ROW][C]62[/C][C]72[/C][C]54.517726099561[/C][C]17.4822739004390[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]54.4883196010217[/C][C]3.51168039897835[/C][/ROW]
[ROW][C]64[/C][C]59[/C][C]53.0874181544603[/C][C]5.9125818455397[/C][/ROW]
[ROW][C]65[/C][C]36[/C][C]49.0384371267561[/C][C]-13.0384371267561[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]57.2659641014803[/C][C]4.7340358985197[/C][/ROW]
[ROW][C]67[/C][C]63[/C][C]58.746195078119[/C][C]4.25380492188101[/C][/ROW]
[ROW][C]68[/C][C]50[/C][C]54.8656352921107[/C][C]-4.86563529211069[/C][/ROW]
[ROW][C]69[/C][C]70[/C][C]56.405576923873[/C][C]13.5944230761269[/C][/ROW]
[ROW][C]70[/C][C]59[/C][C]53.2084951235109[/C][C]5.79150487648912[/C][/ROW]
[ROW][C]71[/C][C]73[/C][C]53.3294290999909[/C][C]19.6705709000091[/C][/ROW]
[ROW][C]72[/C][C]62[/C][C]54.8178611112388[/C][C]7.18213888876115[/C][/ROW]
[ROW][C]73[/C][C]41[/C][C]53.3266867607795[/C][C]-12.3266867607795[/C][/ROW]
[ROW][C]74[/C][C]56[/C][C]54.0655719154058[/C][C]1.9344280845942[/C][/ROW]
[ROW][C]75[/C][C]52[/C][C]54.4359608031616[/C][C]-2.43596080316155[/C][/ROW]
[ROW][C]76[/C][C]54[/C][C]51.6371362808173[/C][C]2.36286371918271[/C][/ROW]
[ROW][C]77[/C][C]73[/C][C]51.6712507316822[/C][C]21.3287492683178[/C][/ROW]
[ROW][C]78[/C][C]40[/C][C]49.9375424884691[/C][C]-9.93754248846911[/C][/ROW]
[ROW][C]79[/C][C]41[/C][C]54.429934061263[/C][C]-13.4299340612630[/C][/ROW]
[ROW][C]80[/C][C]54[/C][C]53.6688503422753[/C][C]0.331149657724668[/C][/ROW]
[ROW][C]81[/C][C]42[/C][C]49.0305426650362[/C][C]-7.03054266503625[/C][/ROW]
[ROW][C]82[/C][C]70[/C][C]54.3011986620595[/C][C]15.6988013379405[/C][/ROW]
[ROW][C]83[/C][C]51[/C][C]52.3148484571957[/C][C]-1.31484845719565[/C][/ROW]
[ROW][C]84[/C][C]60[/C][C]56.9367540652163[/C][C]3.06324593478370[/C][/ROW]
[ROW][C]85[/C][C]49[/C][C]54.2263601689933[/C][C]-5.22636016899328[/C][/ROW]
[ROW][C]86[/C][C]52[/C][C]56.1597384302183[/C][C]-4.15973843021834[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98441&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98441&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
16857.236889076894310.7631109231057
24852.2297733160645-4.22977331606453
34453.9198918425276-9.91989184252756
46752.744286914577814.2557130854222
54653.3066624891288-7.30666248912881
65449.60993869841494.39006130158509
76155.09192491115355.90807508884654
85252.0338832959923-0.0338832959922875
94650.5284753842138-4.52847538421385
105553.42050608195681.57949391804322
115258.3277503376659-6.32775033766592
127654.837291894344121.1627081056559
134955.2203282954231-6.2203282954231
143056.3268599985937-26.3268599985937
157551.644063084150623.3559369158494
165149.19125792639491.80874207360515
175052.6186004274751-2.61860042747511
183857.8316749628075-19.8316749628075
194748.493476379088-1.49347637908802
205255.3501743455838-3.35017434558375
216654.704540465633911.2954595343661
226653.465301892050212.5346981079498
233353.3239698636125-20.3239698636125
244851.7737661417407-3.77376614174068
255753.99229456927163.00770543072841
266454.55011096887899.44988903112114
275854.38989076179223.61010923820777
285949.22529659210689.77470340789318
294252.6758753162767-10.6758753162767
303952.8081316907149-13.8081316907149
315953.25756897770325.7424310222968
323757.1000231844037-20.1000231844037
334951.4514580077461-2.45145800774610
348061.080479376596318.9195206234037
356250.857403748652411.1425962513476
364454.7857377155325-10.7857377155325
375350.7858501992542.21414980074603
385855.07580397277982.92419602722019
396953.418095757679215.5819042423208
406353.42298640657619.57701359342393
413648.9624625406883-12.9624625406883
423854.7857377155325-16.7857377155325
434654.663386515206-8.663386515206
445653.15105180009432.84894819990572
453750.332140652978-13.332140652978
465151.4248441915266-0.424844191526596
474454.7926645188658-10.7926645188658
485855.13338543447072.86661456552932
493754.5991848230712-17.5991848230712
506555.02829126551959.97170873448052
514855.5215994001026-7.5215994001026
525353.2518482677133-0.251848267713267
535152.6285305026507-1.62853050265070
543951.8750803127521-12.8750803127521
556455.90711612580098.09288387419915
564754.8738363267199-7.87383632671991
574755.9828292382572-8.98282923825722
586448.165971023307215.8340289766928
595958.05061272458660.949387275413355
605452.02381454156181.97618545843824
615555.8238832624465-0.823883262446517
627254.51772609956117.4822739004390
635854.48831960102173.51168039897835
645953.08741815446035.9125818455397
653649.0384371267561-13.0384371267561
666257.26596410148034.7340358985197
676358.7461950781194.25380492188101
685054.8656352921107-4.86563529211069
697056.40557692387313.5944230761269
705953.20849512351095.79150487648912
717353.329429099990919.6705709000091
726254.81786111123887.18213888876115
734153.3266867607795-12.3266867607795
745654.06557191540581.9344280845942
755254.4359608031616-2.43596080316155
765451.63713628081732.36286371918271
777351.671250731682221.3287492683178
784049.9375424884691-9.93754248846911
794154.429934061263-13.4299340612630
805453.66885034227530.331149657724668
814249.0305426650362-7.03054266503625
827054.301198662059515.6988013379405
835152.3148484571957-1.31484845719565
846056.93675406521633.06324593478370
854954.2263601689933-5.22636016899328
865256.1597384302183-4.15973843021834







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2908504612462450.581700922492490.709149538753755
100.2770462291969930.5540924583939860.722953770803007
110.3650751223395220.7301502446790440.634924877660478
120.4277061667075160.8554123334150330.572293833292484
130.4332562629824470.8665125259648940.566743737017553
140.7788293360138840.4423413279722320.221170663986116
150.9368773416929870.1262453166140270.0631226583070135
160.923837096857310.1523258062853800.0761629031426902
170.8862075235239240.2275849529521520.113792476476076
180.8934840346225490.2130319307549020.106515965377451
190.8684369277908660.2631261444182670.131563072209134
200.8242887365705380.3514225268589240.175711263429462
210.798783481822970.4024330363540600.201216518177030
220.7798916435396980.4402167129206040.220108356460302
230.919362002011990.1612759959760190.0806379979880094
240.8899636147200870.2200727705598270.110036385279913
250.8530625677231690.2938748645536620.146937432276831
260.8461207791781020.3077584416437950.153879220821898
270.8045694121133090.3908611757733820.195430587886691
280.7753603535592340.4492792928815320.224639646440766
290.756913871270550.4861722574588990.243086128729450
300.789493902648380.4210121947032410.210506097351620
310.7436438706709020.5127122586581960.256356129329098
320.8055062790320560.3889874419358880.194493720967944
330.7623813745633950.475237250873210.237618625436605
340.8754141164420230.2491717671159530.124585883557977
350.8833811405946280.2332377188107440.116618859405372
360.8785328913141950.2429342173716110.121467108685805
370.8451754451462650.309649109707470.154824554853735
380.8056502977857610.3886994044284770.194349702214239
390.8470534133937480.3058931732125040.152946586606252
400.8564960911131810.2870078177736370.143503908886819
410.8816057502741830.2367884994516350.118394249725817
420.9215109560021650.1569780879956690.0784890439978344
430.9155453876640420.1689092246719150.0844546123359575
440.8918349656238780.2163300687522440.108165034376122
450.9088777638214410.1822444723571170.0911222361785586
460.8818135227524060.2363729544951870.118186477247594
470.881916374181520.2361672516369620.118083625818481
480.8510151655101330.2979696689797340.148984834489867
490.9320327102379710.1359345795240580.067967289762029
500.922055837207920.1558883255841600.0779441627920798
510.9306237189152070.1387525621695850.0693762810847925
520.9058017058699680.1883965882600640.094198294130032
530.8757803196634860.2484393606730270.124219680336514
540.8889126751069450.2221746497861090.111087324893055
550.9019944584119760.1960110831760470.0980055415880237
560.8753637731209770.2492724537580460.124636226879023
570.8601406412212680.2797187175574650.139859358778732
580.8753966663565930.2492066672868150.124603333643407
590.857284169560820.2854316608783610.142715830439180
600.8206346765113080.3587306469773850.179365323488693
610.7982439997013830.4035120005972340.201756000298617
620.9245154475364860.1509691049270290.0754845524635143
630.9070821703003670.1858356593992660.0929178296996332
640.8711978409115660.2576043181768690.128802159088434
650.9212260472905290.1575479054189420.078773952709471
660.967155193203610.06568961359278020.0328448067963901
670.975170434123910.04965913175217870.0248295658760894
680.9572896835159120.08542063296817670.0427103164840883
690.9495227874747620.1009544250504760.0504772125252382
700.9170046971560030.1659906056879930.0829953028439966
710.9644898074873010.07102038502539720.0355101925126986
720.9848650120863270.03026997582734600.0151349879136730
730.9712658167257980.05746836654840330.0287341832742017
740.9433218409308930.1133563181382140.0566781590691069
750.9769543478211990.04609130435760230.0230456521788011
760.9413283399294820.1173433201410370.0586716600705183
770.8673000790107040.2653998419785920.132699920989296

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.290850461246245 & 0.58170092249249 & 0.709149538753755 \tabularnewline
10 & 0.277046229196993 & 0.554092458393986 & 0.722953770803007 \tabularnewline
11 & 0.365075122339522 & 0.730150244679044 & 0.634924877660478 \tabularnewline
12 & 0.427706166707516 & 0.855412333415033 & 0.572293833292484 \tabularnewline
13 & 0.433256262982447 & 0.866512525964894 & 0.566743737017553 \tabularnewline
14 & 0.778829336013884 & 0.442341327972232 & 0.221170663986116 \tabularnewline
15 & 0.936877341692987 & 0.126245316614027 & 0.0631226583070135 \tabularnewline
16 & 0.92383709685731 & 0.152325806285380 & 0.0761629031426902 \tabularnewline
17 & 0.886207523523924 & 0.227584952952152 & 0.113792476476076 \tabularnewline
18 & 0.893484034622549 & 0.213031930754902 & 0.106515965377451 \tabularnewline
19 & 0.868436927790866 & 0.263126144418267 & 0.131563072209134 \tabularnewline
20 & 0.824288736570538 & 0.351422526858924 & 0.175711263429462 \tabularnewline
21 & 0.79878348182297 & 0.402433036354060 & 0.201216518177030 \tabularnewline
22 & 0.779891643539698 & 0.440216712920604 & 0.220108356460302 \tabularnewline
23 & 0.91936200201199 & 0.161275995976019 & 0.0806379979880094 \tabularnewline
24 & 0.889963614720087 & 0.220072770559827 & 0.110036385279913 \tabularnewline
25 & 0.853062567723169 & 0.293874864553662 & 0.146937432276831 \tabularnewline
26 & 0.846120779178102 & 0.307758441643795 & 0.153879220821898 \tabularnewline
27 & 0.804569412113309 & 0.390861175773382 & 0.195430587886691 \tabularnewline
28 & 0.775360353559234 & 0.449279292881532 & 0.224639646440766 \tabularnewline
29 & 0.75691387127055 & 0.486172257458899 & 0.243086128729450 \tabularnewline
30 & 0.78949390264838 & 0.421012194703241 & 0.210506097351620 \tabularnewline
31 & 0.743643870670902 & 0.512712258658196 & 0.256356129329098 \tabularnewline
32 & 0.805506279032056 & 0.388987441935888 & 0.194493720967944 \tabularnewline
33 & 0.762381374563395 & 0.47523725087321 & 0.237618625436605 \tabularnewline
34 & 0.875414116442023 & 0.249171767115953 & 0.124585883557977 \tabularnewline
35 & 0.883381140594628 & 0.233237718810744 & 0.116618859405372 \tabularnewline
36 & 0.878532891314195 & 0.242934217371611 & 0.121467108685805 \tabularnewline
37 & 0.845175445146265 & 0.30964910970747 & 0.154824554853735 \tabularnewline
38 & 0.805650297785761 & 0.388699404428477 & 0.194349702214239 \tabularnewline
39 & 0.847053413393748 & 0.305893173212504 & 0.152946586606252 \tabularnewline
40 & 0.856496091113181 & 0.287007817773637 & 0.143503908886819 \tabularnewline
41 & 0.881605750274183 & 0.236788499451635 & 0.118394249725817 \tabularnewline
42 & 0.921510956002165 & 0.156978087995669 & 0.0784890439978344 \tabularnewline
43 & 0.915545387664042 & 0.168909224671915 & 0.0844546123359575 \tabularnewline
44 & 0.891834965623878 & 0.216330068752244 & 0.108165034376122 \tabularnewline
45 & 0.908877763821441 & 0.182244472357117 & 0.0911222361785586 \tabularnewline
46 & 0.881813522752406 & 0.236372954495187 & 0.118186477247594 \tabularnewline
47 & 0.88191637418152 & 0.236167251636962 & 0.118083625818481 \tabularnewline
48 & 0.851015165510133 & 0.297969668979734 & 0.148984834489867 \tabularnewline
49 & 0.932032710237971 & 0.135934579524058 & 0.067967289762029 \tabularnewline
50 & 0.92205583720792 & 0.155888325584160 & 0.0779441627920798 \tabularnewline
51 & 0.930623718915207 & 0.138752562169585 & 0.0693762810847925 \tabularnewline
52 & 0.905801705869968 & 0.188396588260064 & 0.094198294130032 \tabularnewline
53 & 0.875780319663486 & 0.248439360673027 & 0.124219680336514 \tabularnewline
54 & 0.888912675106945 & 0.222174649786109 & 0.111087324893055 \tabularnewline
55 & 0.901994458411976 & 0.196011083176047 & 0.0980055415880237 \tabularnewline
56 & 0.875363773120977 & 0.249272453758046 & 0.124636226879023 \tabularnewline
57 & 0.860140641221268 & 0.279718717557465 & 0.139859358778732 \tabularnewline
58 & 0.875396666356593 & 0.249206667286815 & 0.124603333643407 \tabularnewline
59 & 0.85728416956082 & 0.285431660878361 & 0.142715830439180 \tabularnewline
60 & 0.820634676511308 & 0.358730646977385 & 0.179365323488693 \tabularnewline
61 & 0.798243999701383 & 0.403512000597234 & 0.201756000298617 \tabularnewline
62 & 0.924515447536486 & 0.150969104927029 & 0.0754845524635143 \tabularnewline
63 & 0.907082170300367 & 0.185835659399266 & 0.0929178296996332 \tabularnewline
64 & 0.871197840911566 & 0.257604318176869 & 0.128802159088434 \tabularnewline
65 & 0.921226047290529 & 0.157547905418942 & 0.078773952709471 \tabularnewline
66 & 0.96715519320361 & 0.0656896135927802 & 0.0328448067963901 \tabularnewline
67 & 0.97517043412391 & 0.0496591317521787 & 0.0248295658760894 \tabularnewline
68 & 0.957289683515912 & 0.0854206329681767 & 0.0427103164840883 \tabularnewline
69 & 0.949522787474762 & 0.100954425050476 & 0.0504772125252382 \tabularnewline
70 & 0.917004697156003 & 0.165990605687993 & 0.0829953028439966 \tabularnewline
71 & 0.964489807487301 & 0.0710203850253972 & 0.0355101925126986 \tabularnewline
72 & 0.984865012086327 & 0.0302699758273460 & 0.0151349879136730 \tabularnewline
73 & 0.971265816725798 & 0.0574683665484033 & 0.0287341832742017 \tabularnewline
74 & 0.943321840930893 & 0.113356318138214 & 0.0566781590691069 \tabularnewline
75 & 0.976954347821199 & 0.0460913043576023 & 0.0230456521788011 \tabularnewline
76 & 0.941328339929482 & 0.117343320141037 & 0.0586716600705183 \tabularnewline
77 & 0.867300079010704 & 0.265399841978592 & 0.132699920989296 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98441&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]9[/C][C]0.290850461246245[/C][C]0.58170092249249[/C][C]0.709149538753755[/C][/ROW]
[ROW][C]10[/C][C]0.277046229196993[/C][C]0.554092458393986[/C][C]0.722953770803007[/C][/ROW]
[ROW][C]11[/C][C]0.365075122339522[/C][C]0.730150244679044[/C][C]0.634924877660478[/C][/ROW]
[ROW][C]12[/C][C]0.427706166707516[/C][C]0.855412333415033[/C][C]0.572293833292484[/C][/ROW]
[ROW][C]13[/C][C]0.433256262982447[/C][C]0.866512525964894[/C][C]0.566743737017553[/C][/ROW]
[ROW][C]14[/C][C]0.778829336013884[/C][C]0.442341327972232[/C][C]0.221170663986116[/C][/ROW]
[ROW][C]15[/C][C]0.936877341692987[/C][C]0.126245316614027[/C][C]0.0631226583070135[/C][/ROW]
[ROW][C]16[/C][C]0.92383709685731[/C][C]0.152325806285380[/C][C]0.0761629031426902[/C][/ROW]
[ROW][C]17[/C][C]0.886207523523924[/C][C]0.227584952952152[/C][C]0.113792476476076[/C][/ROW]
[ROW][C]18[/C][C]0.893484034622549[/C][C]0.213031930754902[/C][C]0.106515965377451[/C][/ROW]
[ROW][C]19[/C][C]0.868436927790866[/C][C]0.263126144418267[/C][C]0.131563072209134[/C][/ROW]
[ROW][C]20[/C][C]0.824288736570538[/C][C]0.351422526858924[/C][C]0.175711263429462[/C][/ROW]
[ROW][C]21[/C][C]0.79878348182297[/C][C]0.402433036354060[/C][C]0.201216518177030[/C][/ROW]
[ROW][C]22[/C][C]0.779891643539698[/C][C]0.440216712920604[/C][C]0.220108356460302[/C][/ROW]
[ROW][C]23[/C][C]0.91936200201199[/C][C]0.161275995976019[/C][C]0.0806379979880094[/C][/ROW]
[ROW][C]24[/C][C]0.889963614720087[/C][C]0.220072770559827[/C][C]0.110036385279913[/C][/ROW]
[ROW][C]25[/C][C]0.853062567723169[/C][C]0.293874864553662[/C][C]0.146937432276831[/C][/ROW]
[ROW][C]26[/C][C]0.846120779178102[/C][C]0.307758441643795[/C][C]0.153879220821898[/C][/ROW]
[ROW][C]27[/C][C]0.804569412113309[/C][C]0.390861175773382[/C][C]0.195430587886691[/C][/ROW]
[ROW][C]28[/C][C]0.775360353559234[/C][C]0.449279292881532[/C][C]0.224639646440766[/C][/ROW]
[ROW][C]29[/C][C]0.75691387127055[/C][C]0.486172257458899[/C][C]0.243086128729450[/C][/ROW]
[ROW][C]30[/C][C]0.78949390264838[/C][C]0.421012194703241[/C][C]0.210506097351620[/C][/ROW]
[ROW][C]31[/C][C]0.743643870670902[/C][C]0.512712258658196[/C][C]0.256356129329098[/C][/ROW]
[ROW][C]32[/C][C]0.805506279032056[/C][C]0.388987441935888[/C][C]0.194493720967944[/C][/ROW]
[ROW][C]33[/C][C]0.762381374563395[/C][C]0.47523725087321[/C][C]0.237618625436605[/C][/ROW]
[ROW][C]34[/C][C]0.875414116442023[/C][C]0.249171767115953[/C][C]0.124585883557977[/C][/ROW]
[ROW][C]35[/C][C]0.883381140594628[/C][C]0.233237718810744[/C][C]0.116618859405372[/C][/ROW]
[ROW][C]36[/C][C]0.878532891314195[/C][C]0.242934217371611[/C][C]0.121467108685805[/C][/ROW]
[ROW][C]37[/C][C]0.845175445146265[/C][C]0.30964910970747[/C][C]0.154824554853735[/C][/ROW]
[ROW][C]38[/C][C]0.805650297785761[/C][C]0.388699404428477[/C][C]0.194349702214239[/C][/ROW]
[ROW][C]39[/C][C]0.847053413393748[/C][C]0.305893173212504[/C][C]0.152946586606252[/C][/ROW]
[ROW][C]40[/C][C]0.856496091113181[/C][C]0.287007817773637[/C][C]0.143503908886819[/C][/ROW]
[ROW][C]41[/C][C]0.881605750274183[/C][C]0.236788499451635[/C][C]0.118394249725817[/C][/ROW]
[ROW][C]42[/C][C]0.921510956002165[/C][C]0.156978087995669[/C][C]0.0784890439978344[/C][/ROW]
[ROW][C]43[/C][C]0.915545387664042[/C][C]0.168909224671915[/C][C]0.0844546123359575[/C][/ROW]
[ROW][C]44[/C][C]0.891834965623878[/C][C]0.216330068752244[/C][C]0.108165034376122[/C][/ROW]
[ROW][C]45[/C][C]0.908877763821441[/C][C]0.182244472357117[/C][C]0.0911222361785586[/C][/ROW]
[ROW][C]46[/C][C]0.881813522752406[/C][C]0.236372954495187[/C][C]0.118186477247594[/C][/ROW]
[ROW][C]47[/C][C]0.88191637418152[/C][C]0.236167251636962[/C][C]0.118083625818481[/C][/ROW]
[ROW][C]48[/C][C]0.851015165510133[/C][C]0.297969668979734[/C][C]0.148984834489867[/C][/ROW]
[ROW][C]49[/C][C]0.932032710237971[/C][C]0.135934579524058[/C][C]0.067967289762029[/C][/ROW]
[ROW][C]50[/C][C]0.92205583720792[/C][C]0.155888325584160[/C][C]0.0779441627920798[/C][/ROW]
[ROW][C]51[/C][C]0.930623718915207[/C][C]0.138752562169585[/C][C]0.0693762810847925[/C][/ROW]
[ROW][C]52[/C][C]0.905801705869968[/C][C]0.188396588260064[/C][C]0.094198294130032[/C][/ROW]
[ROW][C]53[/C][C]0.875780319663486[/C][C]0.248439360673027[/C][C]0.124219680336514[/C][/ROW]
[ROW][C]54[/C][C]0.888912675106945[/C][C]0.222174649786109[/C][C]0.111087324893055[/C][/ROW]
[ROW][C]55[/C][C]0.901994458411976[/C][C]0.196011083176047[/C][C]0.0980055415880237[/C][/ROW]
[ROW][C]56[/C][C]0.875363773120977[/C][C]0.249272453758046[/C][C]0.124636226879023[/C][/ROW]
[ROW][C]57[/C][C]0.860140641221268[/C][C]0.279718717557465[/C][C]0.139859358778732[/C][/ROW]
[ROW][C]58[/C][C]0.875396666356593[/C][C]0.249206667286815[/C][C]0.124603333643407[/C][/ROW]
[ROW][C]59[/C][C]0.85728416956082[/C][C]0.285431660878361[/C][C]0.142715830439180[/C][/ROW]
[ROW][C]60[/C][C]0.820634676511308[/C][C]0.358730646977385[/C][C]0.179365323488693[/C][/ROW]
[ROW][C]61[/C][C]0.798243999701383[/C][C]0.403512000597234[/C][C]0.201756000298617[/C][/ROW]
[ROW][C]62[/C][C]0.924515447536486[/C][C]0.150969104927029[/C][C]0.0754845524635143[/C][/ROW]
[ROW][C]63[/C][C]0.907082170300367[/C][C]0.185835659399266[/C][C]0.0929178296996332[/C][/ROW]
[ROW][C]64[/C][C]0.871197840911566[/C][C]0.257604318176869[/C][C]0.128802159088434[/C][/ROW]
[ROW][C]65[/C][C]0.921226047290529[/C][C]0.157547905418942[/C][C]0.078773952709471[/C][/ROW]
[ROW][C]66[/C][C]0.96715519320361[/C][C]0.0656896135927802[/C][C]0.0328448067963901[/C][/ROW]
[ROW][C]67[/C][C]0.97517043412391[/C][C]0.0496591317521787[/C][C]0.0248295658760894[/C][/ROW]
[ROW][C]68[/C][C]0.957289683515912[/C][C]0.0854206329681767[/C][C]0.0427103164840883[/C][/ROW]
[ROW][C]69[/C][C]0.949522787474762[/C][C]0.100954425050476[/C][C]0.0504772125252382[/C][/ROW]
[ROW][C]70[/C][C]0.917004697156003[/C][C]0.165990605687993[/C][C]0.0829953028439966[/C][/ROW]
[ROW][C]71[/C][C]0.964489807487301[/C][C]0.0710203850253972[/C][C]0.0355101925126986[/C][/ROW]
[ROW][C]72[/C][C]0.984865012086327[/C][C]0.0302699758273460[/C][C]0.0151349879136730[/C][/ROW]
[ROW][C]73[/C][C]0.971265816725798[/C][C]0.0574683665484033[/C][C]0.0287341832742017[/C][/ROW]
[ROW][C]74[/C][C]0.943321840930893[/C][C]0.113356318138214[/C][C]0.0566781590691069[/C][/ROW]
[ROW][C]75[/C][C]0.976954347821199[/C][C]0.0460913043576023[/C][C]0.0230456521788011[/C][/ROW]
[ROW][C]76[/C][C]0.941328339929482[/C][C]0.117343320141037[/C][C]0.0586716600705183[/C][/ROW]
[ROW][C]77[/C][C]0.867300079010704[/C][C]0.265399841978592[/C][C]0.132699920989296[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98441&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98441&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
90.2908504612462450.581700922492490.709149538753755
100.2770462291969930.5540924583939860.722953770803007
110.3650751223395220.7301502446790440.634924877660478
120.4277061667075160.8554123334150330.572293833292484
130.4332562629824470.8665125259648940.566743737017553
140.7788293360138840.4423413279722320.221170663986116
150.9368773416929870.1262453166140270.0631226583070135
160.923837096857310.1523258062853800.0761629031426902
170.8862075235239240.2275849529521520.113792476476076
180.8934840346225490.2130319307549020.106515965377451
190.8684369277908660.2631261444182670.131563072209134
200.8242887365705380.3514225268589240.175711263429462
210.798783481822970.4024330363540600.201216518177030
220.7798916435396980.4402167129206040.220108356460302
230.919362002011990.1612759959760190.0806379979880094
240.8899636147200870.2200727705598270.110036385279913
250.8530625677231690.2938748645536620.146937432276831
260.8461207791781020.3077584416437950.153879220821898
270.8045694121133090.3908611757733820.195430587886691
280.7753603535592340.4492792928815320.224639646440766
290.756913871270550.4861722574588990.243086128729450
300.789493902648380.4210121947032410.210506097351620
310.7436438706709020.5127122586581960.256356129329098
320.8055062790320560.3889874419358880.194493720967944
330.7623813745633950.475237250873210.237618625436605
340.8754141164420230.2491717671159530.124585883557977
350.8833811405946280.2332377188107440.116618859405372
360.8785328913141950.2429342173716110.121467108685805
370.8451754451462650.309649109707470.154824554853735
380.8056502977857610.3886994044284770.194349702214239
390.8470534133937480.3058931732125040.152946586606252
400.8564960911131810.2870078177736370.143503908886819
410.8816057502741830.2367884994516350.118394249725817
420.9215109560021650.1569780879956690.0784890439978344
430.9155453876640420.1689092246719150.0844546123359575
440.8918349656238780.2163300687522440.108165034376122
450.9088777638214410.1822444723571170.0911222361785586
460.8818135227524060.2363729544951870.118186477247594
470.881916374181520.2361672516369620.118083625818481
480.8510151655101330.2979696689797340.148984834489867
490.9320327102379710.1359345795240580.067967289762029
500.922055837207920.1558883255841600.0779441627920798
510.9306237189152070.1387525621695850.0693762810847925
520.9058017058699680.1883965882600640.094198294130032
530.8757803196634860.2484393606730270.124219680336514
540.8889126751069450.2221746497861090.111087324893055
550.9019944584119760.1960110831760470.0980055415880237
560.8753637731209770.2492724537580460.124636226879023
570.8601406412212680.2797187175574650.139859358778732
580.8753966663565930.2492066672868150.124603333643407
590.857284169560820.2854316608783610.142715830439180
600.8206346765113080.3587306469773850.179365323488693
610.7982439997013830.4035120005972340.201756000298617
620.9245154475364860.1509691049270290.0754845524635143
630.9070821703003670.1858356593992660.0929178296996332
640.8711978409115660.2576043181768690.128802159088434
650.9212260472905290.1575479054189420.078773952709471
660.967155193203610.06568961359278020.0328448067963901
670.975170434123910.04965913175217870.0248295658760894
680.9572896835159120.08542063296817670.0427103164840883
690.9495227874747620.1009544250504760.0504772125252382
700.9170046971560030.1659906056879930.0829953028439966
710.9644898074873010.07102038502539720.0355101925126986
720.9848650120863270.03026997582734600.0151349879136730
730.9712658167257980.05746836654840330.0287341832742017
740.9433218409308930.1133563181382140.0566781590691069
750.9769543478211990.04609130435760230.0230456521788011
760.9413283399294820.1173433201410370.0586716600705183
770.8673000790107040.2653998419785920.132699920989296







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0434782608695652OK
10% type I error level70.101449275362319NOK

\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 & 0.0434782608695652 & OK \tabularnewline
10% type I error level & 7 & 0.101449275362319 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98441&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]0.0434782608695652[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.101449275362319[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98441&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98441&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 level30.0434782608695652OK
10% type I error level70.101449275362319NOK



Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}