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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, 01 Dec 2011 10:22:31 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/01/t1322752958o9ma84znuyzg497.htm/, Retrieved Fri, 29 Mar 2024 07:19:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149801, Retrieved Fri, 29 Mar 2024 07:19:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Monthly US soldie...] [2010-11-02 12:07:39] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [Soldiers] [2010-11-30 14:13:36] [b98453cac15ba1066b407e146608df68]
- R P       [Multiple Regression] [] [2011-12-01 15:22:31] [aedc5b8e4f26bdca34b1a0cf88d6dfa2] [Current]
- RMPD        [Central Tendency] [] [2011-12-01 15:28:44] [2805bc4d0d3810b6cd96238758e5985d]
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Dataseries X:
37	1	0	0
30	2	0	0
47	3	0	0
35	4	0	0
30	5	0	0
43	6	0	0
82	7	0	0
40	8	0	0
47	9	0	0
19	10	0	0
52	11	0	0
136	12	0	0
80	13	0	0
42	14	0	0
54	15	0	0
66	16	0	0
81	17	0	0
63	18	0	0
137	19	0	0
72	20	0	0
107	21	0	0
58	22	0	0
36	23	0	0
52	24	0	0
79	25	0	0
77	26	0	0
54	27	0	0
84	28	0	0
48	29	0	0
96	30	0	0
83	31	0	0
66	32	0	0
61	33	0	0
53	34	0	0
30	35	0	0
74	36	0	0
69	37	0	0
59	38	0	0
42	39	0	0
65	40	0	0
70	41	0	0
100	42	0	0
63	43	0	0
105	44	0	0
82	45	0	0
81	46	0	0
75	47	0	0
102	48	0	0
121	49	0	0
98	50	0	0
76	51	0	0
77	52	0	0
63	53	0	0
37	54	1	54
35	55	1	55
23	56	1	56
40	57	1	57
29	58	1	58
37	59	1	59
51	60	1	60
20	61	1	61
28	62	1	62
13	63	1	63
22	64	1	64
25	65	1	65
13	66	1	66
16	67	1	67
13	68	1	68
16	69	1	69
17	70	1	70
9	71	1	71
17	72	1	72
25	73	1	73
14	74	1	74
8	75	1	75
7	76	1	76
10	77	1	77
7	78	1	78
10	79	1	79
3	80	1	80




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149801&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]4 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=149801&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149801&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 49.2677793904209 + 0.690291888405096t + 51.9473203246788D[t] -1.89969359780681tD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  49.2677793904209 +  0.690291888405096t +  51.9473203246788D[t] -1.89969359780681tD[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149801&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  49.2677793904209 +  0.690291888405096t +  51.9473203246788D[t] -1.89969359780681tD[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149801&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149801&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
Y[t] = + 49.2677793904209 + 0.690291888405096t + 51.9473203246788D[t] -1.89969359780681tD[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)49.26777939042095.6682818.691800
t0.6902918884050960.1826583.77920.0003110.000155
D51.947320324678834.3719471.51130.1348520.067426
tD-1.899693597806810.534767-3.55240.000660.00033

\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) & 49.2677793904209 & 5.668281 & 8.6918 & 0 & 0 \tabularnewline
t & 0.690291888405096 & 0.182658 & 3.7792 & 0.000311 & 0.000155 \tabularnewline
D & 51.9473203246788 & 34.371947 & 1.5113 & 0.134852 & 0.067426 \tabularnewline
tD & -1.89969359780681 & 0.534767 & -3.5524 & 0.00066 & 0.00033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149801&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]49.2677793904209[/C][C]5.668281[/C][C]8.6918[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.690291888405096[/C][C]0.182658[/C][C]3.7792[/C][C]0.000311[/C][C]0.000155[/C][/ROW]
[ROW][C]D[/C][C]51.9473203246788[/C][C]34.371947[/C][C]1.5113[/C][C]0.134852[/C][C]0.067426[/C][/ROW]
[ROW][C]tD[/C][C]-1.89969359780681[/C][C]0.534767[/C][C]-3.5524[/C][C]0.00066[/C][C]0.00033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149801&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149801&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)49.26777939042095.6682818.691800
t0.6902918884050960.1826583.77920.0003110.000155
D51.947320324678834.3719471.51130.1348520.067426
tD-1.899693597806810.534767-3.55240.000660.00033







Multiple Linear Regression - Regression Statistics
Multiple R0.780568783410858
R-squared0.609287625635507
Adjusted R-squared0.593864768752698
F-TEST (value)39.5054969559271
F-TEST (DF numerator)3
F-TEST (DF denominator)76
p-value1.66533453693773e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation20.34155350799
Sum Squared Residuals31447.1887330001

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.780568783410858 \tabularnewline
R-squared & 0.609287625635507 \tabularnewline
Adjusted R-squared & 0.593864768752698 \tabularnewline
F-TEST (value) & 39.5054969559271 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 76 \tabularnewline
p-value & 1.66533453693773e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 20.34155350799 \tabularnewline
Sum Squared Residuals & 31447.1887330001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149801&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.780568783410858[/C][/ROW]
[ROW][C]R-squared[/C][C]0.609287625635507[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.593864768752698[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]39.5054969559271[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]76[/C][/ROW]
[ROW][C]p-value[/C][C]1.66533453693773e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]20.34155350799[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]31447.1887330001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149801&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149801&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 R0.780568783410858
R-squared0.609287625635507
Adjusted R-squared0.593864768752698
F-TEST (value)39.5054969559271
F-TEST (DF numerator)3
F-TEST (DF denominator)76
p-value1.66533453693773e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation20.34155350799
Sum Squared Residuals31447.1887330001







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13749.9580712788261-12.9580712788261
23050.6483631672311-20.6483631672311
34751.3386550556362-4.33865505563619
43552.0289469440413-17.0289469440413
53052.7192388324464-22.7192388324464
64353.4095307208515-10.4095307208515
78254.099822609256627.9001773907434
84054.7901144976617-14.7901144976617
94755.4804063860668-8.48040638606676
101956.1706982744719-37.1706982744719
115256.860990162877-4.86099016287695
1213657.55128205128278.448717948718
138058.241573939687121.7584260603129
144258.9318658280922-16.9318658280922
155459.6221577164973-5.62215771649733
166660.31244960490245.68755039509757
178161.002741493307519.9972585066925
186361.69303338171261.30696661828738
1913762.383325270117774.6166747298823
207263.07361715852288.92638284147719
2110763.763909046927943.2360909530721
225864.454200935333-6.45420093533301
233665.1444928237381-29.1444928237381
245265.8347847121432-13.8347847121432
257966.525076600548312.4749233994517
267767.21536848895349.78463151104661
275467.9056603773585-13.9056603773585
288468.595952265763615.4040477342364
294869.2862441541687-21.2862441541687
309669.976536042573826.0234639574262
318370.666827930978912.3331720690211
326671.357119819384-5.35711981938397
336172.0474117077891-11.0474117077891
345372.7377035961942-19.7377035961942
353073.4279954845993-43.4279954845993
367474.1182873730043-0.118287373004353
376974.8085792614094-5.80857926140945
385975.4988711498145-16.4988711498145
394276.1891630382196-34.1891630382196
406576.8794549266247-11.8794549266247
417077.5697468150298-7.56974681502983
4210078.260038703434921.7399612965651
436378.95033059184-15.95033059184
4410579.640622480245125.3593775197549
458280.33091436865021.66908563134978
468181.0212062570553-0.0212062570553174
477581.7114981454604-6.71149814546042
4810282.401790033865519.5982099661345
4912183.092081922270637.9079180777294
509883.782373810675714.2176261893243
517684.4726656990808-8.47266569908079
527785.1629575874859-8.16295758748589
536385.853249475891-22.853249475891
543735.90740740740741.09259259259259
553534.69800569800570.301994301994303
562333.488603988604-10.488603988604
574032.27920227920237.72079772079772
582931.0698005698006-2.06980056980057
593729.86039886039897.13960113960113
605128.650997150997122.3490028490029
612027.4415954415954-7.44159544159544
622826.23219373219371.76780626780627
631325.022792022792-12.022792022792
642223.8133903133903-1.81339031339031
652522.60398860398862.3960113960114
661321.3945868945869-8.3945868945869
671620.1851851851852-4.18518518518519
681318.9757834757835-5.97578347578348
691617.7663817663818-1.76638176638177
701716.55698005698010.443019943019937
71915.3475783475783-6.34757834757835
721714.13817663817662.86182336182336
732512.928774928774912.0712250712251
741411.71937321937322.28062678062678
75810.5099715099715-2.50997150997151
7679.3005698005698-2.3005698005698
77108.091168091168091.90883190883191
7876.881766381766380.118233618233619
79105.672364672364674.32763532763533
8034.46296296296296-1.46296296296296

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 37 & 49.9580712788261 & -12.9580712788261 \tabularnewline
2 & 30 & 50.6483631672311 & -20.6483631672311 \tabularnewline
3 & 47 & 51.3386550556362 & -4.33865505563619 \tabularnewline
4 & 35 & 52.0289469440413 & -17.0289469440413 \tabularnewline
5 & 30 & 52.7192388324464 & -22.7192388324464 \tabularnewline
6 & 43 & 53.4095307208515 & -10.4095307208515 \tabularnewline
7 & 82 & 54.0998226092566 & 27.9001773907434 \tabularnewline
8 & 40 & 54.7901144976617 & -14.7901144976617 \tabularnewline
9 & 47 & 55.4804063860668 & -8.48040638606676 \tabularnewline
10 & 19 & 56.1706982744719 & -37.1706982744719 \tabularnewline
11 & 52 & 56.860990162877 & -4.86099016287695 \tabularnewline
12 & 136 & 57.551282051282 & 78.448717948718 \tabularnewline
13 & 80 & 58.2415739396871 & 21.7584260603129 \tabularnewline
14 & 42 & 58.9318658280922 & -16.9318658280922 \tabularnewline
15 & 54 & 59.6221577164973 & -5.62215771649733 \tabularnewline
16 & 66 & 60.3124496049024 & 5.68755039509757 \tabularnewline
17 & 81 & 61.0027414933075 & 19.9972585066925 \tabularnewline
18 & 63 & 61.6930333817126 & 1.30696661828738 \tabularnewline
19 & 137 & 62.3833252701177 & 74.6166747298823 \tabularnewline
20 & 72 & 63.0736171585228 & 8.92638284147719 \tabularnewline
21 & 107 & 63.7639090469279 & 43.2360909530721 \tabularnewline
22 & 58 & 64.454200935333 & -6.45420093533301 \tabularnewline
23 & 36 & 65.1444928237381 & -29.1444928237381 \tabularnewline
24 & 52 & 65.8347847121432 & -13.8347847121432 \tabularnewline
25 & 79 & 66.5250766005483 & 12.4749233994517 \tabularnewline
26 & 77 & 67.2153684889534 & 9.78463151104661 \tabularnewline
27 & 54 & 67.9056603773585 & -13.9056603773585 \tabularnewline
28 & 84 & 68.5959522657636 & 15.4040477342364 \tabularnewline
29 & 48 & 69.2862441541687 & -21.2862441541687 \tabularnewline
30 & 96 & 69.9765360425738 & 26.0234639574262 \tabularnewline
31 & 83 & 70.6668279309789 & 12.3331720690211 \tabularnewline
32 & 66 & 71.357119819384 & -5.35711981938397 \tabularnewline
33 & 61 & 72.0474117077891 & -11.0474117077891 \tabularnewline
34 & 53 & 72.7377035961942 & -19.7377035961942 \tabularnewline
35 & 30 & 73.4279954845993 & -43.4279954845993 \tabularnewline
36 & 74 & 74.1182873730043 & -0.118287373004353 \tabularnewline
37 & 69 & 74.8085792614094 & -5.80857926140945 \tabularnewline
38 & 59 & 75.4988711498145 & -16.4988711498145 \tabularnewline
39 & 42 & 76.1891630382196 & -34.1891630382196 \tabularnewline
40 & 65 & 76.8794549266247 & -11.8794549266247 \tabularnewline
41 & 70 & 77.5697468150298 & -7.56974681502983 \tabularnewline
42 & 100 & 78.2600387034349 & 21.7399612965651 \tabularnewline
43 & 63 & 78.95033059184 & -15.95033059184 \tabularnewline
44 & 105 & 79.6406224802451 & 25.3593775197549 \tabularnewline
45 & 82 & 80.3309143686502 & 1.66908563134978 \tabularnewline
46 & 81 & 81.0212062570553 & -0.0212062570553174 \tabularnewline
47 & 75 & 81.7114981454604 & -6.71149814546042 \tabularnewline
48 & 102 & 82.4017900338655 & 19.5982099661345 \tabularnewline
49 & 121 & 83.0920819222706 & 37.9079180777294 \tabularnewline
50 & 98 & 83.7823738106757 & 14.2176261893243 \tabularnewline
51 & 76 & 84.4726656990808 & -8.47266569908079 \tabularnewline
52 & 77 & 85.1629575874859 & -8.16295758748589 \tabularnewline
53 & 63 & 85.853249475891 & -22.853249475891 \tabularnewline
54 & 37 & 35.9074074074074 & 1.09259259259259 \tabularnewline
55 & 35 & 34.6980056980057 & 0.301994301994303 \tabularnewline
56 & 23 & 33.488603988604 & -10.488603988604 \tabularnewline
57 & 40 & 32.2792022792023 & 7.72079772079772 \tabularnewline
58 & 29 & 31.0698005698006 & -2.06980056980057 \tabularnewline
59 & 37 & 29.8603988603989 & 7.13960113960113 \tabularnewline
60 & 51 & 28.6509971509971 & 22.3490028490029 \tabularnewline
61 & 20 & 27.4415954415954 & -7.44159544159544 \tabularnewline
62 & 28 & 26.2321937321937 & 1.76780626780627 \tabularnewline
63 & 13 & 25.022792022792 & -12.022792022792 \tabularnewline
64 & 22 & 23.8133903133903 & -1.81339031339031 \tabularnewline
65 & 25 & 22.6039886039886 & 2.3960113960114 \tabularnewline
66 & 13 & 21.3945868945869 & -8.3945868945869 \tabularnewline
67 & 16 & 20.1851851851852 & -4.18518518518519 \tabularnewline
68 & 13 & 18.9757834757835 & -5.97578347578348 \tabularnewline
69 & 16 & 17.7663817663818 & -1.76638176638177 \tabularnewline
70 & 17 & 16.5569800569801 & 0.443019943019937 \tabularnewline
71 & 9 & 15.3475783475783 & -6.34757834757835 \tabularnewline
72 & 17 & 14.1381766381766 & 2.86182336182336 \tabularnewline
73 & 25 & 12.9287749287749 & 12.0712250712251 \tabularnewline
74 & 14 & 11.7193732193732 & 2.28062678062678 \tabularnewline
75 & 8 & 10.5099715099715 & -2.50997150997151 \tabularnewline
76 & 7 & 9.3005698005698 & -2.3005698005698 \tabularnewline
77 & 10 & 8.09116809116809 & 1.90883190883191 \tabularnewline
78 & 7 & 6.88176638176638 & 0.118233618233619 \tabularnewline
79 & 10 & 5.67236467236467 & 4.32763532763533 \tabularnewline
80 & 3 & 4.46296296296296 & -1.46296296296296 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149801&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]37[/C][C]49.9580712788261[/C][C]-12.9580712788261[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]50.6483631672311[/C][C]-20.6483631672311[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]51.3386550556362[/C][C]-4.33865505563619[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]52.0289469440413[/C][C]-17.0289469440413[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]52.7192388324464[/C][C]-22.7192388324464[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]53.4095307208515[/C][C]-10.4095307208515[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]54.0998226092566[/C][C]27.9001773907434[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]54.7901144976617[/C][C]-14.7901144976617[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]55.4804063860668[/C][C]-8.48040638606676[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]56.1706982744719[/C][C]-37.1706982744719[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]56.860990162877[/C][C]-4.86099016287695[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]57.551282051282[/C][C]78.448717948718[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]58.2415739396871[/C][C]21.7584260603129[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]58.9318658280922[/C][C]-16.9318658280922[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]59.6221577164973[/C][C]-5.62215771649733[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]60.3124496049024[/C][C]5.68755039509757[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]61.0027414933075[/C][C]19.9972585066925[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]61.6930333817126[/C][C]1.30696661828738[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]62.3833252701177[/C][C]74.6166747298823[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]63.0736171585228[/C][C]8.92638284147719[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]63.7639090469279[/C][C]43.2360909530721[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]64.454200935333[/C][C]-6.45420093533301[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]65.1444928237381[/C][C]-29.1444928237381[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]65.8347847121432[/C][C]-13.8347847121432[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]66.5250766005483[/C][C]12.4749233994517[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]67.2153684889534[/C][C]9.78463151104661[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]67.9056603773585[/C][C]-13.9056603773585[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]68.5959522657636[/C][C]15.4040477342364[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]69.2862441541687[/C][C]-21.2862441541687[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]69.9765360425738[/C][C]26.0234639574262[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]70.6668279309789[/C][C]12.3331720690211[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]71.357119819384[/C][C]-5.35711981938397[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]72.0474117077891[/C][C]-11.0474117077891[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]72.7377035961942[/C][C]-19.7377035961942[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]73.4279954845993[/C][C]-43.4279954845993[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]74.1182873730043[/C][C]-0.118287373004353[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]74.8085792614094[/C][C]-5.80857926140945[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]75.4988711498145[/C][C]-16.4988711498145[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]76.1891630382196[/C][C]-34.1891630382196[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]76.8794549266247[/C][C]-11.8794549266247[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]77.5697468150298[/C][C]-7.56974681502983[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]78.2600387034349[/C][C]21.7399612965651[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]78.95033059184[/C][C]-15.95033059184[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]79.6406224802451[/C][C]25.3593775197549[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]80.3309143686502[/C][C]1.66908563134978[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]81.0212062570553[/C][C]-0.0212062570553174[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]81.7114981454604[/C][C]-6.71149814546042[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]82.4017900338655[/C][C]19.5982099661345[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]83.0920819222706[/C][C]37.9079180777294[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]83.7823738106757[/C][C]14.2176261893243[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]84.4726656990808[/C][C]-8.47266569908079[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]85.1629575874859[/C][C]-8.16295758748589[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]85.853249475891[/C][C]-22.853249475891[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]35.9074074074074[/C][C]1.09259259259259[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]34.6980056980057[/C][C]0.301994301994303[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]33.488603988604[/C][C]-10.488603988604[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]32.2792022792023[/C][C]7.72079772079772[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]31.0698005698006[/C][C]-2.06980056980057[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]29.8603988603989[/C][C]7.13960113960113[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]28.6509971509971[/C][C]22.3490028490029[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]27.4415954415954[/C][C]-7.44159544159544[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]26.2321937321937[/C][C]1.76780626780627[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]25.022792022792[/C][C]-12.022792022792[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]23.8133903133903[/C][C]-1.81339031339031[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]22.6039886039886[/C][C]2.3960113960114[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]21.3945868945869[/C][C]-8.3945868945869[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]20.1851851851852[/C][C]-4.18518518518519[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]18.9757834757835[/C][C]-5.97578347578348[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]17.7663817663818[/C][C]-1.76638176638177[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]16.5569800569801[/C][C]0.443019943019937[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]15.3475783475783[/C][C]-6.34757834757835[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]14.1381766381766[/C][C]2.86182336182336[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]12.9287749287749[/C][C]12.0712250712251[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]11.7193732193732[/C][C]2.28062678062678[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]10.5099715099715[/C][C]-2.50997150997151[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]9.3005698005698[/C][C]-2.3005698005698[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]8.09116809116809[/C][C]1.90883190883191[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]6.88176638176638[/C][C]0.118233618233619[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]5.67236467236467[/C][C]4.32763532763533[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]4.46296296296296[/C][C]-1.46296296296296[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149801&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149801&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
13749.9580712788261-12.9580712788261
23050.6483631672311-20.6483631672311
34751.3386550556362-4.33865505563619
43552.0289469440413-17.0289469440413
53052.7192388324464-22.7192388324464
64353.4095307208515-10.4095307208515
78254.099822609256627.9001773907434
84054.7901144976617-14.7901144976617
94755.4804063860668-8.48040638606676
101956.1706982744719-37.1706982744719
115256.860990162877-4.86099016287695
1213657.55128205128278.448717948718
138058.241573939687121.7584260603129
144258.9318658280922-16.9318658280922
155459.6221577164973-5.62215771649733
166660.31244960490245.68755039509757
178161.002741493307519.9972585066925
186361.69303338171261.30696661828738
1913762.383325270117774.6166747298823
207263.07361715852288.92638284147719
2110763.763909046927943.2360909530721
225864.454200935333-6.45420093533301
233665.1444928237381-29.1444928237381
245265.8347847121432-13.8347847121432
257966.525076600548312.4749233994517
267767.21536848895349.78463151104661
275467.9056603773585-13.9056603773585
288468.595952265763615.4040477342364
294869.2862441541687-21.2862441541687
309669.976536042573826.0234639574262
318370.666827930978912.3331720690211
326671.357119819384-5.35711981938397
336172.0474117077891-11.0474117077891
345372.7377035961942-19.7377035961942
353073.4279954845993-43.4279954845993
367474.1182873730043-0.118287373004353
376974.8085792614094-5.80857926140945
385975.4988711498145-16.4988711498145
394276.1891630382196-34.1891630382196
406576.8794549266247-11.8794549266247
417077.5697468150298-7.56974681502983
4210078.260038703434921.7399612965651
436378.95033059184-15.95033059184
4410579.640622480245125.3593775197549
458280.33091436865021.66908563134978
468181.0212062570553-0.0212062570553174
477581.7114981454604-6.71149814546042
4810282.401790033865519.5982099661345
4912183.092081922270637.9079180777294
509883.782373810675714.2176261893243
517684.4726656990808-8.47266569908079
527785.1629575874859-8.16295758748589
536385.853249475891-22.853249475891
543735.90740740740741.09259259259259
553534.69800569800570.301994301994303
562333.488603988604-10.488603988604
574032.27920227920237.72079772079772
582931.0698005698006-2.06980056980057
593729.86039886039897.13960113960113
605128.650997150997122.3490028490029
612027.4415954415954-7.44159544159544
622826.23219373219371.76780626780627
631325.022792022792-12.022792022792
642223.8133903133903-1.81339031339031
652522.60398860398862.3960113960114
661321.3945868945869-8.3945868945869
671620.1851851851852-4.18518518518519
681318.9757834757835-5.97578347578348
691617.7663817663818-1.76638176638177
701716.55698005698010.443019943019937
71915.3475783475783-6.34757834757835
721714.13817663817662.86182336182336
732512.928774928774912.0712250712251
741411.71937321937322.28062678062678
75810.5099715099715-2.50997150997151
7679.3005698005698-2.3005698005698
77108.091168091168091.90883190883191
7876.881766381766380.118233618233619
79105.672364672364674.32763532763533
8034.46296296296296-1.46296296296296







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.5964863136999340.8070273726001330.403513686300066
80.5566463808437740.8867072383124520.443353619156226
90.4347791794225540.8695583588451080.565220820577446
100.6176158636721110.7647682726557790.382384136327889
110.5205424614002790.9589150771994420.479457538599721
120.9954225800521850.009154839895630630.00457741994781531
130.9918404865651870.01631902686962570.00815951343481287
140.9953390565001180.009321886999764850.00466094349988242
150.9938416241671360.01231675166572840.00615837583286419
160.9893671329067890.02126573418642150.0106328670932107
170.9833243267602720.03335134647945580.0166756732397279
180.9760684449039860.0478631101920280.023931555096014
190.999569401029630.0008611979407407320.000430598970370366
200.9994045602039440.001190879592112160.000595439796056079
210.9998539769333450.000292046133310250.000146023066655125
220.9998970553746990.0002058892506015270.000102944625300763
230.9999846823393113.06353213781565e-051.53176606890783e-05
240.9999853112091212.93775817585418e-051.46887908792709e-05
250.9999776377673994.47244652015716e-052.23622326007858e-05
260.9999666962743856.66074512308923e-053.33037256154462e-05
270.9999613318328427.73363343152837e-053.86681671576418e-05
280.9999586661713958.26676572097065e-054.13338286048533e-05
290.9999646438194257.07123611496598e-053.53561805748299e-05
300.9999886883997852.26232004293081e-051.13116002146541e-05
310.9999921589291011.56821417981765e-057.84107089908826e-06
320.9999888782947462.22434105087204e-051.11217052543602e-05
330.9999832159028473.3568194305032e-051.6784097152516e-05
340.9999773785568944.52428862113866e-052.26214431056933e-05
350.9999974127402715.17451945741674e-062.58725972870837e-06
360.9999948941221261.02117557471183e-055.10587787355914e-06
370.9999892128937092.15742125829074e-051.07871062914537e-05
380.999982630800163.4738399679779e-051.73691998398895e-05
390.9999975524390424.89512191560304e-062.44756095780152e-06
400.9999975470059724.90598805544757e-062.45299402772378e-06
410.999997751854854.4962903007209e-062.24814515036045e-06
420.9999970248336725.95033265686823e-062.97516632843412e-06
430.9999995227339389.54532123493381e-074.7726606174669e-07
440.9999993748226891.25035462143723e-066.25177310718613e-07
450.9999990883922351.82321553023408e-069.11607765117042e-07
460.9999993630192331.27396153463743e-066.36980767318715e-07
470.9999999972608765.47824765606904e-092.73912382803452e-09
480.9999999993207011.35859884760844e-096.79299423804218e-10
490.9999999994536911.09261711056506e-095.46308555282529e-10
500.9999999987748862.45022865256925e-091.22511432628462e-09
510.9999999969424636.11507354147898e-093.05753677073949e-09
520.9999999895729732.08540543373796e-081.04270271686898e-08
530.9999999703392775.93214459900001e-082.96607229950001e-08
540.9999999010516771.97896645225074e-079.89483226125368e-08
550.9999996772279556.45544089206169e-073.22772044603085e-07
560.9999995758758638.48248274005867e-074.24124137002934e-07
570.9999990604682551.87906349089992e-069.39531745449958e-07
580.9999971544810655.69103786936431e-062.84551893468216e-06
590.9999937660343111.246793137709e-056.23396568854499e-06
600.9999999746686385.0662723785883e-082.53313618929415e-08
610.9999999033676271.93264745870599e-079.66323729352997e-08
620.9999997910978394.17804321748743e-072.08902160874372e-07
630.9999996755728916.48854218794921e-073.24427109397461e-07
640.9999985240150042.95196999253361e-061.4759849962668e-06
650.9999965881938896.82361222095723e-063.41180611047862e-06
660.9999896660974772.06678050462706e-051.03339025231353e-05
670.9999560279733018.79440533989025e-054.39720266994512e-05
680.9998747768673840.0002504462652319620.000125223132615981
690.9995046107281940.0009907785436118790.00049538927180594
700.9980076071718290.003984785656341470.00199239282817074
710.9977265029492070.004546994101586360.00227349705079318
720.990678006182330.01864398763534090.00932199381767045
730.9932653169728020.0134693660543970.00673468302719851

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.596486313699934 & 0.807027372600133 & 0.403513686300066 \tabularnewline
8 & 0.556646380843774 & 0.886707238312452 & 0.443353619156226 \tabularnewline
9 & 0.434779179422554 & 0.869558358845108 & 0.565220820577446 \tabularnewline
10 & 0.617615863672111 & 0.764768272655779 & 0.382384136327889 \tabularnewline
11 & 0.520542461400279 & 0.958915077199442 & 0.479457538599721 \tabularnewline
12 & 0.995422580052185 & 0.00915483989563063 & 0.00457741994781531 \tabularnewline
13 & 0.991840486565187 & 0.0163190268696257 & 0.00815951343481287 \tabularnewline
14 & 0.995339056500118 & 0.00932188699976485 & 0.00466094349988242 \tabularnewline
15 & 0.993841624167136 & 0.0123167516657284 & 0.00615837583286419 \tabularnewline
16 & 0.989367132906789 & 0.0212657341864215 & 0.0106328670932107 \tabularnewline
17 & 0.983324326760272 & 0.0333513464794558 & 0.0166756732397279 \tabularnewline
18 & 0.976068444903986 & 0.047863110192028 & 0.023931555096014 \tabularnewline
19 & 0.99956940102963 & 0.000861197940740732 & 0.000430598970370366 \tabularnewline
20 & 0.999404560203944 & 0.00119087959211216 & 0.000595439796056079 \tabularnewline
21 & 0.999853976933345 & 0.00029204613331025 & 0.000146023066655125 \tabularnewline
22 & 0.999897055374699 & 0.000205889250601527 & 0.000102944625300763 \tabularnewline
23 & 0.999984682339311 & 3.06353213781565e-05 & 1.53176606890783e-05 \tabularnewline
24 & 0.999985311209121 & 2.93775817585418e-05 & 1.46887908792709e-05 \tabularnewline
25 & 0.999977637767399 & 4.47244652015716e-05 & 2.23622326007858e-05 \tabularnewline
26 & 0.999966696274385 & 6.66074512308923e-05 & 3.33037256154462e-05 \tabularnewline
27 & 0.999961331832842 & 7.73363343152837e-05 & 3.86681671576418e-05 \tabularnewline
28 & 0.999958666171395 & 8.26676572097065e-05 & 4.13338286048533e-05 \tabularnewline
29 & 0.999964643819425 & 7.07123611496598e-05 & 3.53561805748299e-05 \tabularnewline
30 & 0.999988688399785 & 2.26232004293081e-05 & 1.13116002146541e-05 \tabularnewline
31 & 0.999992158929101 & 1.56821417981765e-05 & 7.84107089908826e-06 \tabularnewline
32 & 0.999988878294746 & 2.22434105087204e-05 & 1.11217052543602e-05 \tabularnewline
33 & 0.999983215902847 & 3.3568194305032e-05 & 1.6784097152516e-05 \tabularnewline
34 & 0.999977378556894 & 4.52428862113866e-05 & 2.26214431056933e-05 \tabularnewline
35 & 0.999997412740271 & 5.17451945741674e-06 & 2.58725972870837e-06 \tabularnewline
36 & 0.999994894122126 & 1.02117557471183e-05 & 5.10587787355914e-06 \tabularnewline
37 & 0.999989212893709 & 2.15742125829074e-05 & 1.07871062914537e-05 \tabularnewline
38 & 0.99998263080016 & 3.4738399679779e-05 & 1.73691998398895e-05 \tabularnewline
39 & 0.999997552439042 & 4.89512191560304e-06 & 2.44756095780152e-06 \tabularnewline
40 & 0.999997547005972 & 4.90598805544757e-06 & 2.45299402772378e-06 \tabularnewline
41 & 0.99999775185485 & 4.4962903007209e-06 & 2.24814515036045e-06 \tabularnewline
42 & 0.999997024833672 & 5.95033265686823e-06 & 2.97516632843412e-06 \tabularnewline
43 & 0.999999522733938 & 9.54532123493381e-07 & 4.7726606174669e-07 \tabularnewline
44 & 0.999999374822689 & 1.25035462143723e-06 & 6.25177310718613e-07 \tabularnewline
45 & 0.999999088392235 & 1.82321553023408e-06 & 9.11607765117042e-07 \tabularnewline
46 & 0.999999363019233 & 1.27396153463743e-06 & 6.36980767318715e-07 \tabularnewline
47 & 0.999999997260876 & 5.47824765606904e-09 & 2.73912382803452e-09 \tabularnewline
48 & 0.999999999320701 & 1.35859884760844e-09 & 6.79299423804218e-10 \tabularnewline
49 & 0.999999999453691 & 1.09261711056506e-09 & 5.46308555282529e-10 \tabularnewline
50 & 0.999999998774886 & 2.45022865256925e-09 & 1.22511432628462e-09 \tabularnewline
51 & 0.999999996942463 & 6.11507354147898e-09 & 3.05753677073949e-09 \tabularnewline
52 & 0.999999989572973 & 2.08540543373796e-08 & 1.04270271686898e-08 \tabularnewline
53 & 0.999999970339277 & 5.93214459900001e-08 & 2.96607229950001e-08 \tabularnewline
54 & 0.999999901051677 & 1.97896645225074e-07 & 9.89483226125368e-08 \tabularnewline
55 & 0.999999677227955 & 6.45544089206169e-07 & 3.22772044603085e-07 \tabularnewline
56 & 0.999999575875863 & 8.48248274005867e-07 & 4.24124137002934e-07 \tabularnewline
57 & 0.999999060468255 & 1.87906349089992e-06 & 9.39531745449958e-07 \tabularnewline
58 & 0.999997154481065 & 5.69103786936431e-06 & 2.84551893468216e-06 \tabularnewline
59 & 0.999993766034311 & 1.246793137709e-05 & 6.23396568854499e-06 \tabularnewline
60 & 0.999999974668638 & 5.0662723785883e-08 & 2.53313618929415e-08 \tabularnewline
61 & 0.999999903367627 & 1.93264745870599e-07 & 9.66323729352997e-08 \tabularnewline
62 & 0.999999791097839 & 4.17804321748743e-07 & 2.08902160874372e-07 \tabularnewline
63 & 0.999999675572891 & 6.48854218794921e-07 & 3.24427109397461e-07 \tabularnewline
64 & 0.999998524015004 & 2.95196999253361e-06 & 1.4759849962668e-06 \tabularnewline
65 & 0.999996588193889 & 6.82361222095723e-06 & 3.41180611047862e-06 \tabularnewline
66 & 0.999989666097477 & 2.06678050462706e-05 & 1.03339025231353e-05 \tabularnewline
67 & 0.999956027973301 & 8.79440533989025e-05 & 4.39720266994512e-05 \tabularnewline
68 & 0.999874776867384 & 0.000250446265231962 & 0.000125223132615981 \tabularnewline
69 & 0.999504610728194 & 0.000990778543611879 & 0.00049538927180594 \tabularnewline
70 & 0.998007607171829 & 0.00398478565634147 & 0.00199239282817074 \tabularnewline
71 & 0.997726502949207 & 0.00454699410158636 & 0.00227349705079318 \tabularnewline
72 & 0.99067800618233 & 0.0186439876353409 & 0.00932199381767045 \tabularnewline
73 & 0.993265316972802 & 0.013469366054397 & 0.00673468302719851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149801&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]0.596486313699934[/C][C]0.807027372600133[/C][C]0.403513686300066[/C][/ROW]
[ROW][C]8[/C][C]0.556646380843774[/C][C]0.886707238312452[/C][C]0.443353619156226[/C][/ROW]
[ROW][C]9[/C][C]0.434779179422554[/C][C]0.869558358845108[/C][C]0.565220820577446[/C][/ROW]
[ROW][C]10[/C][C]0.617615863672111[/C][C]0.764768272655779[/C][C]0.382384136327889[/C][/ROW]
[ROW][C]11[/C][C]0.520542461400279[/C][C]0.958915077199442[/C][C]0.479457538599721[/C][/ROW]
[ROW][C]12[/C][C]0.995422580052185[/C][C]0.00915483989563063[/C][C]0.00457741994781531[/C][/ROW]
[ROW][C]13[/C][C]0.991840486565187[/C][C]0.0163190268696257[/C][C]0.00815951343481287[/C][/ROW]
[ROW][C]14[/C][C]0.995339056500118[/C][C]0.00932188699976485[/C][C]0.00466094349988242[/C][/ROW]
[ROW][C]15[/C][C]0.993841624167136[/C][C]0.0123167516657284[/C][C]0.00615837583286419[/C][/ROW]
[ROW][C]16[/C][C]0.989367132906789[/C][C]0.0212657341864215[/C][C]0.0106328670932107[/C][/ROW]
[ROW][C]17[/C][C]0.983324326760272[/C][C]0.0333513464794558[/C][C]0.0166756732397279[/C][/ROW]
[ROW][C]18[/C][C]0.976068444903986[/C][C]0.047863110192028[/C][C]0.023931555096014[/C][/ROW]
[ROW][C]19[/C][C]0.99956940102963[/C][C]0.000861197940740732[/C][C]0.000430598970370366[/C][/ROW]
[ROW][C]20[/C][C]0.999404560203944[/C][C]0.00119087959211216[/C][C]0.000595439796056079[/C][/ROW]
[ROW][C]21[/C][C]0.999853976933345[/C][C]0.00029204613331025[/C][C]0.000146023066655125[/C][/ROW]
[ROW][C]22[/C][C]0.999897055374699[/C][C]0.000205889250601527[/C][C]0.000102944625300763[/C][/ROW]
[ROW][C]23[/C][C]0.999984682339311[/C][C]3.06353213781565e-05[/C][C]1.53176606890783e-05[/C][/ROW]
[ROW][C]24[/C][C]0.999985311209121[/C][C]2.93775817585418e-05[/C][C]1.46887908792709e-05[/C][/ROW]
[ROW][C]25[/C][C]0.999977637767399[/C][C]4.47244652015716e-05[/C][C]2.23622326007858e-05[/C][/ROW]
[ROW][C]26[/C][C]0.999966696274385[/C][C]6.66074512308923e-05[/C][C]3.33037256154462e-05[/C][/ROW]
[ROW][C]27[/C][C]0.999961331832842[/C][C]7.73363343152837e-05[/C][C]3.86681671576418e-05[/C][/ROW]
[ROW][C]28[/C][C]0.999958666171395[/C][C]8.26676572097065e-05[/C][C]4.13338286048533e-05[/C][/ROW]
[ROW][C]29[/C][C]0.999964643819425[/C][C]7.07123611496598e-05[/C][C]3.53561805748299e-05[/C][/ROW]
[ROW][C]30[/C][C]0.999988688399785[/C][C]2.26232004293081e-05[/C][C]1.13116002146541e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999992158929101[/C][C]1.56821417981765e-05[/C][C]7.84107089908826e-06[/C][/ROW]
[ROW][C]32[/C][C]0.999988878294746[/C][C]2.22434105087204e-05[/C][C]1.11217052543602e-05[/C][/ROW]
[ROW][C]33[/C][C]0.999983215902847[/C][C]3.3568194305032e-05[/C][C]1.6784097152516e-05[/C][/ROW]
[ROW][C]34[/C][C]0.999977378556894[/C][C]4.52428862113866e-05[/C][C]2.26214431056933e-05[/C][/ROW]
[ROW][C]35[/C][C]0.999997412740271[/C][C]5.17451945741674e-06[/C][C]2.58725972870837e-06[/C][/ROW]
[ROW][C]36[/C][C]0.999994894122126[/C][C]1.02117557471183e-05[/C][C]5.10587787355914e-06[/C][/ROW]
[ROW][C]37[/C][C]0.999989212893709[/C][C]2.15742125829074e-05[/C][C]1.07871062914537e-05[/C][/ROW]
[ROW][C]38[/C][C]0.99998263080016[/C][C]3.4738399679779e-05[/C][C]1.73691998398895e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999997552439042[/C][C]4.89512191560304e-06[/C][C]2.44756095780152e-06[/C][/ROW]
[ROW][C]40[/C][C]0.999997547005972[/C][C]4.90598805544757e-06[/C][C]2.45299402772378e-06[/C][/ROW]
[ROW][C]41[/C][C]0.99999775185485[/C][C]4.4962903007209e-06[/C][C]2.24814515036045e-06[/C][/ROW]
[ROW][C]42[/C][C]0.999997024833672[/C][C]5.95033265686823e-06[/C][C]2.97516632843412e-06[/C][/ROW]
[ROW][C]43[/C][C]0.999999522733938[/C][C]9.54532123493381e-07[/C][C]4.7726606174669e-07[/C][/ROW]
[ROW][C]44[/C][C]0.999999374822689[/C][C]1.25035462143723e-06[/C][C]6.25177310718613e-07[/C][/ROW]
[ROW][C]45[/C][C]0.999999088392235[/C][C]1.82321553023408e-06[/C][C]9.11607765117042e-07[/C][/ROW]
[ROW][C]46[/C][C]0.999999363019233[/C][C]1.27396153463743e-06[/C][C]6.36980767318715e-07[/C][/ROW]
[ROW][C]47[/C][C]0.999999997260876[/C][C]5.47824765606904e-09[/C][C]2.73912382803452e-09[/C][/ROW]
[ROW][C]48[/C][C]0.999999999320701[/C][C]1.35859884760844e-09[/C][C]6.79299423804218e-10[/C][/ROW]
[ROW][C]49[/C][C]0.999999999453691[/C][C]1.09261711056506e-09[/C][C]5.46308555282529e-10[/C][/ROW]
[ROW][C]50[/C][C]0.999999998774886[/C][C]2.45022865256925e-09[/C][C]1.22511432628462e-09[/C][/ROW]
[ROW][C]51[/C][C]0.999999996942463[/C][C]6.11507354147898e-09[/C][C]3.05753677073949e-09[/C][/ROW]
[ROW][C]52[/C][C]0.999999989572973[/C][C]2.08540543373796e-08[/C][C]1.04270271686898e-08[/C][/ROW]
[ROW][C]53[/C][C]0.999999970339277[/C][C]5.93214459900001e-08[/C][C]2.96607229950001e-08[/C][/ROW]
[ROW][C]54[/C][C]0.999999901051677[/C][C]1.97896645225074e-07[/C][C]9.89483226125368e-08[/C][/ROW]
[ROW][C]55[/C][C]0.999999677227955[/C][C]6.45544089206169e-07[/C][C]3.22772044603085e-07[/C][/ROW]
[ROW][C]56[/C][C]0.999999575875863[/C][C]8.48248274005867e-07[/C][C]4.24124137002934e-07[/C][/ROW]
[ROW][C]57[/C][C]0.999999060468255[/C][C]1.87906349089992e-06[/C][C]9.39531745449958e-07[/C][/ROW]
[ROW][C]58[/C][C]0.999997154481065[/C][C]5.69103786936431e-06[/C][C]2.84551893468216e-06[/C][/ROW]
[ROW][C]59[/C][C]0.999993766034311[/C][C]1.246793137709e-05[/C][C]6.23396568854499e-06[/C][/ROW]
[ROW][C]60[/C][C]0.999999974668638[/C][C]5.0662723785883e-08[/C][C]2.53313618929415e-08[/C][/ROW]
[ROW][C]61[/C][C]0.999999903367627[/C][C]1.93264745870599e-07[/C][C]9.66323729352997e-08[/C][/ROW]
[ROW][C]62[/C][C]0.999999791097839[/C][C]4.17804321748743e-07[/C][C]2.08902160874372e-07[/C][/ROW]
[ROW][C]63[/C][C]0.999999675572891[/C][C]6.48854218794921e-07[/C][C]3.24427109397461e-07[/C][/ROW]
[ROW][C]64[/C][C]0.999998524015004[/C][C]2.95196999253361e-06[/C][C]1.4759849962668e-06[/C][/ROW]
[ROW][C]65[/C][C]0.999996588193889[/C][C]6.82361222095723e-06[/C][C]3.41180611047862e-06[/C][/ROW]
[ROW][C]66[/C][C]0.999989666097477[/C][C]2.06678050462706e-05[/C][C]1.03339025231353e-05[/C][/ROW]
[ROW][C]67[/C][C]0.999956027973301[/C][C]8.79440533989025e-05[/C][C]4.39720266994512e-05[/C][/ROW]
[ROW][C]68[/C][C]0.999874776867384[/C][C]0.000250446265231962[/C][C]0.000125223132615981[/C][/ROW]
[ROW][C]69[/C][C]0.999504610728194[/C][C]0.000990778543611879[/C][C]0.00049538927180594[/C][/ROW]
[ROW][C]70[/C][C]0.998007607171829[/C][C]0.00398478565634147[/C][C]0.00199239282817074[/C][/ROW]
[ROW][C]71[/C][C]0.997726502949207[/C][C]0.00454699410158636[/C][C]0.00227349705079318[/C][/ROW]
[ROW][C]72[/C][C]0.99067800618233[/C][C]0.0186439876353409[/C][C]0.00932199381767045[/C][/ROW]
[ROW][C]73[/C][C]0.993265316972802[/C][C]0.013469366054397[/C][C]0.00673468302719851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149801&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149801&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
70.5964863136999340.8070273726001330.403513686300066
80.5566463808437740.8867072383124520.443353619156226
90.4347791794225540.8695583588451080.565220820577446
100.6176158636721110.7647682726557790.382384136327889
110.5205424614002790.9589150771994420.479457538599721
120.9954225800521850.009154839895630630.00457741994781531
130.9918404865651870.01631902686962570.00815951343481287
140.9953390565001180.009321886999764850.00466094349988242
150.9938416241671360.01231675166572840.00615837583286419
160.9893671329067890.02126573418642150.0106328670932107
170.9833243267602720.03335134647945580.0166756732397279
180.9760684449039860.0478631101920280.023931555096014
190.999569401029630.0008611979407407320.000430598970370366
200.9994045602039440.001190879592112160.000595439796056079
210.9998539769333450.000292046133310250.000146023066655125
220.9998970553746990.0002058892506015270.000102944625300763
230.9999846823393113.06353213781565e-051.53176606890783e-05
240.9999853112091212.93775817585418e-051.46887908792709e-05
250.9999776377673994.47244652015716e-052.23622326007858e-05
260.9999666962743856.66074512308923e-053.33037256154462e-05
270.9999613318328427.73363343152837e-053.86681671576418e-05
280.9999586661713958.26676572097065e-054.13338286048533e-05
290.9999646438194257.07123611496598e-053.53561805748299e-05
300.9999886883997852.26232004293081e-051.13116002146541e-05
310.9999921589291011.56821417981765e-057.84107089908826e-06
320.9999888782947462.22434105087204e-051.11217052543602e-05
330.9999832159028473.3568194305032e-051.6784097152516e-05
340.9999773785568944.52428862113866e-052.26214431056933e-05
350.9999974127402715.17451945741674e-062.58725972870837e-06
360.9999948941221261.02117557471183e-055.10587787355914e-06
370.9999892128937092.15742125829074e-051.07871062914537e-05
380.999982630800163.4738399679779e-051.73691998398895e-05
390.9999975524390424.89512191560304e-062.44756095780152e-06
400.9999975470059724.90598805544757e-062.45299402772378e-06
410.999997751854854.4962903007209e-062.24814515036045e-06
420.9999970248336725.95033265686823e-062.97516632843412e-06
430.9999995227339389.54532123493381e-074.7726606174669e-07
440.9999993748226891.25035462143723e-066.25177310718613e-07
450.9999990883922351.82321553023408e-069.11607765117042e-07
460.9999993630192331.27396153463743e-066.36980767318715e-07
470.9999999972608765.47824765606904e-092.73912382803452e-09
480.9999999993207011.35859884760844e-096.79299423804218e-10
490.9999999994536911.09261711056506e-095.46308555282529e-10
500.9999999987748862.45022865256925e-091.22511432628462e-09
510.9999999969424636.11507354147898e-093.05753677073949e-09
520.9999999895729732.08540543373796e-081.04270271686898e-08
530.9999999703392775.93214459900001e-082.96607229950001e-08
540.9999999010516771.97896645225074e-079.89483226125368e-08
550.9999996772279556.45544089206169e-073.22772044603085e-07
560.9999995758758638.48248274005867e-074.24124137002934e-07
570.9999990604682551.87906349089992e-069.39531745449958e-07
580.9999971544810655.69103786936431e-062.84551893468216e-06
590.9999937660343111.246793137709e-056.23396568854499e-06
600.9999999746686385.0662723785883e-082.53313618929415e-08
610.9999999033676271.93264745870599e-079.66323729352997e-08
620.9999997910978394.17804321748743e-072.08902160874372e-07
630.9999996755728916.48854218794921e-073.24427109397461e-07
640.9999985240150042.95196999253361e-061.4759849962668e-06
650.9999965881938896.82361222095723e-063.41180611047862e-06
660.9999896660974772.06678050462706e-051.03339025231353e-05
670.9999560279733018.79440533989025e-054.39720266994512e-05
680.9998747768673840.0002504462652319620.000125223132615981
690.9995046107281940.0009907785436118790.00049538927180594
700.9980076071718290.003984785656341470.00199239282817074
710.9977265029492070.004546994101586360.00227349705079318
720.990678006182330.01864398763534090.00932199381767045
730.9932653169728020.0134693660543970.00673468302719851







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.82089552238806NOK
5% type I error level620.925373134328358NOK
10% type I error level620.925373134328358NOK

\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 & 55 & 0.82089552238806 & NOK \tabularnewline
5% type I error level & 62 & 0.925373134328358 & NOK \tabularnewline
10% type I error level & 62 & 0.925373134328358 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149801&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]55[/C][C]0.82089552238806[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]62[/C][C]0.925373134328358[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]62[/C][C]0.925373134328358[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149801&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.82089552238806NOK
5% type I error level620.925373134328358NOK
10% type I error level620.925373134328358NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}