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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, 24 Nov 2011 09:57:58 -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/Nov/24/t132214671068dj9mfbv03ori1.htm/, Retrieved Fri, 29 Mar 2024 05:34:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146902, Retrieved Fri, 29 Mar 2024 05:34:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact71
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple regression] [2011-11-24 14:57:58] [5363b79245edacd2d961915f77b3b63a] [Current]
- R  D    [Multiple Regression] [Multiple regression] [2011-11-24 15:12:52] [6bdab4f5b22620afa7d9dc512ad4e377]
- R  D    [Multiple Regression] [Multiple regression] [2011-11-24 15:12:52] [6bdab4f5b22620afa7d9dc512ad4e377]
- RMP       [Kendall tau Correlation Matrix] [Effects] [2011-11-24 16:08:17] [6bdab4f5b22620afa7d9dc512ad4e377]
- RMP       [Kendall tau Correlation Matrix] [Effects] [2011-11-24 16:08:17] [6bdab4f5b22620afa7d9dc512ad4e377]
Feedback Forum

Post a new message
Dataseries X:
68	13	5	20	0
17	26	7	28	0
1	0	0	0	0
114	37	12	40	0
95	47	15	60	0
148	80	16	60	1
56	21	12	44	0
26	36	13	52	0
63	35	15	60	0
96	40	13	52	1
74	35	6	24	1
65	46	16	64	0
40	20	7	26	0
173	24	12	48	4
28	19	9	36	3
55	15	10	40	3
58	48	16	64	0
25	0	5	20	4
103	38	20	79	0
29	12	7	16	0
31	10	13	52	0
43	51	13	52	0
74	4	11	44	0
99	24	9	29	1
25	39	10	40	1
69	19	7	28	0
62	23	13	49	0
25	39	15	60	0
38	37	13	52	0
57	20	7	28	0
52	20	14	56	0
91	41	11	35	2
48	26	3	12	4
52	0	8	32	0
35	31	12	48	1
0	0	0	0	0
31	8	12	48	0
107	35	8	31	3
242	3	20	64	4
41	47	18	72	0
57	42	9	36	1
32	11	14	56	0
17	10	7	28	2
36	26	13	52	1
29	27	11	44	1
22	0	11	44	2
21	15	14	55	1
41	32	9	36	0
64	13	12	48	1
71	24	11	44	0
28	10	17	66	0
36	14	10	40	0
45	24	11	44	0
22	29	12	48	0
27	40	17	68	1
38	22	6	24	3
26	27	8	32	0
41	8	12	44	0
21	27	13	52	0
28	0	14	56	4
36	0	17	68	0
58	17	8	32	0
65	7	9	34	4
29	18	9	36	3
21	7	9	34	0
19	24	15	56	0
55	18	16	64	4
119	39	13	52	2
34	17	12	48	0
25	0	10	40	0
113	39	9	36	2
46	20	3	10	0
28	29	12	48	1
63	27	8	25	0
52	23	17	68	0
35	0	9	36	1
32	31	8	32	0
45	19	9	36	0
42	12	12	43	0
28	23	5	17	0
32	33	14	52	0
32	21	14	56	0
27	17	10	40	0
69	27	12	48	0
30	14	10	40	0
48	12	12	48	0
57	21	17	68	0
36	14	11	44	0
20	14	10	40	2
54	22	11	40	0
26	25	7	28	0
58	36	10	40	1
35	10	11	44	0
28	16	5	20	0
8	12	6	22	0
96	20	14	56	0
50	38	13	52	0
15	13	1	2	0
65	12	13	52	0
33	11	9	30	0
7	8	1	3	0
17	22	6	20	0
55	14	12	48	0
32	7	9	32	1
22	14	9	36	0
41	2	12	45	0
50	35	10	40	0
7	5	2	8	0
0	0	0	0	0
26	34	8	32	0
22	12	7	28	0
26	34	11	44	0
37	30	14	56	0
29	21	4	13	0
0	0	0	0	0
0	0	0	0	0
42	28	13	52	0
51	16	17	51	0
77	12	13	52	1
32	14	12	48	0
63	7	1	3	0
50	41	12	48	1
18	21	6	24	0
37	28	11	37	0
23	1	8	32	3
19	10	2	8	1
39	31	12	44	2
38	7	12	48	0
55	26	14	56	0
22	1	2	8	0
7	0	0	0	0
21	12	9	25	0
5	0	1	4	0
21	17	3	12	0
1	5	0	0	0
22	4	2	6	1
0	0	0	0	0
31	6	12	48	0
25	0	14	52	0
0	0	0	0	1
4	0	0	0	0
20	15	4	12	1
29	0	7	28	0
33	12	10	40	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146902&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]5 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=146902&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146902&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CompendiumViews[t] = + 1.14044187533976 + 0.657469439225472BloggedComputations[t] + 13.7595952663329ReviewedCompendiums[t] -2.92441075041618submittedfeedback[t] + 9.70613089632181Sharedcompendiums[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CompendiumViews[t] =  +  1.14044187533976 +  0.657469439225472BloggedComputations[t] +  13.7595952663329ReviewedCompendiums[t] -2.92441075041618submittedfeedback[t] +  9.70613089632181Sharedcompendiums[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146902&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CompendiumViews[t] =  +  1.14044187533976 +  0.657469439225472BloggedComputations[t] +  13.7595952663329ReviewedCompendiums[t] -2.92441075041618submittedfeedback[t] +  9.70613089632181Sharedcompendiums[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146902&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146902&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
CompendiumViews[t] = + 1.14044187533976 + 0.657469439225472BloggedComputations[t] + 13.7595952663329ReviewedCompendiums[t] -2.92441075041618submittedfeedback[t] + 9.70613089632181Sharedcompendiums[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.140441875339764.9525670.23030.8182180.409109
BloggedComputations0.6574694392254720.1679893.91380.0001427.1e-05
ReviewedCompendiums13.75959526633292.9357824.68697e-063e-06
submittedfeedback-2.924410750416180.745027-3.92520.0001366.8e-05
Sharedcompendiums9.706130896321811.9425244.99672e-061e-06

\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) & 1.14044187533976 & 4.952567 & 0.2303 & 0.818218 & 0.409109 \tabularnewline
BloggedComputations & 0.657469439225472 & 0.167989 & 3.9138 & 0.000142 & 7.1e-05 \tabularnewline
ReviewedCompendiums & 13.7595952663329 & 2.935782 & 4.6869 & 7e-06 & 3e-06 \tabularnewline
submittedfeedback & -2.92441075041618 & 0.745027 & -3.9252 & 0.000136 & 6.8e-05 \tabularnewline
Sharedcompendiums & 9.70613089632181 & 1.942524 & 4.9967 & 2e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146902&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]1.14044187533976[/C][C]4.952567[/C][C]0.2303[/C][C]0.818218[/C][C]0.409109[/C][/ROW]
[ROW][C]BloggedComputations[/C][C]0.657469439225472[/C][C]0.167989[/C][C]3.9138[/C][C]0.000142[/C][C]7.1e-05[/C][/ROW]
[ROW][C]ReviewedCompendiums[/C][C]13.7595952663329[/C][C]2.935782[/C][C]4.6869[/C][C]7e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]submittedfeedback[/C][C]-2.92441075041618[/C][C]0.745027[/C][C]-3.9252[/C][C]0.000136[/C][C]6.8e-05[/C][/ROW]
[ROW][C]Sharedcompendiums[/C][C]9.70613089632181[/C][C]1.942524[/C][C]4.9967[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146902&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146902&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)1.140441875339764.9525670.23030.8182180.409109
BloggedComputations0.6574694392254720.1679893.91380.0001427.1e-05
ReviewedCompendiums13.75959526633292.9357824.68697e-063e-06
submittedfeedback-2.924410750416180.745027-3.92520.0001366.8e-05
Sharedcompendiums9.706130896321811.9425244.99672e-061e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.665476981780259
R-squared0.442859613279363
Adjusted R-squared0.426826796395315
F-TEST (value)27.6220714352457
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation25.2090325802105
Sum Squared Residuals88333.8499845862

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.665476981780259 \tabularnewline
R-squared & 0.442859613279363 \tabularnewline
Adjusted R-squared & 0.426826796395315 \tabularnewline
F-TEST (value) & 27.6220714352457 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 1.11022302462516e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 25.2090325802105 \tabularnewline
Sum Squared Residuals & 88333.8499845862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146902&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.665476981780259[/C][/ROW]
[ROW][C]R-squared[/C][C]0.442859613279363[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.426826796395315[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.6220714352457[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/C][/ROW]
[ROW][C]p-value[/C][C]1.11022302462516e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]25.2090325802105[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]88333.8499845862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146902&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146902&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 R0.665476981780259
R-squared0.442859613279363
Adjusted R-squared0.426826796395315
F-TEST (value)27.6220714352457
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation25.2090325802105
Sum Squared Residuals88333.8499845862







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16819.997305908611948.0026940913881
21732.6683131478794-15.6683131478794
311.14044187533979-0.140441875339792
411473.605524306030140.3944756939699
59562.970789488959932.0292105110401
6148108.13300714605539.8669928539447
75651.38837027675784.61162972324217
82651.6147211281434-25.6147211281434
96355.08115621825437.9188437817457
109663.950729781367132.0492702186329
117446.229716732562327.7702832674377
126564.37527231440270.624727685597311
134034.5723180133595.42768198664101
1417380.487659178056892.5123408219432
152861.308324291603-33.308324291603
165560.7403987993693-5.74039879936929
175865.6902111928536-7.69021119285363
182550.274726783968-25.274726783968
1910370.287736609687932.7122633903121
202958.5566700037171-29.5566700037171
213134.5205157082811-3.5205157082811
224361.4767627165254-18.4767627165254
237426.451794543591947.5482054564081
249965.65428494833.345715052
252557.107403548137-32.107403548137
266928.066027073301140.9339729266989
276251.840850669460810.1591493305392
282557.7110339751562-32.7110339751562
293852.2721905673688-14.2721905673688
305728.723496512526628.2765034874734
315243.1571623652048.84283763479597
329196.5101223413236-5.5101223413236
334863.2450276744939-15.2450276744939
345217.636059992685434.3639400073146
353555.9715525636696-20.9715525636696
3601.14044187533978-1.14044187533978
373131.143624565162-0.143624565161961
3810772.690293804958534.3097061950415
39242129.966991078326112.033008921674
404169.1566462829646-28.1566462829646
415757.0178596011452-0.0178596011452068
423237.2399374121748-5.23993741217478
431741.5610639129155-24.5610639129155
443654.7461576322105-18.7461576322105
452951.2797225420995-22.2797225420995
462243.2341785793336-21.2341785793336
472152.5003568158147-31.5003568158147
484140.73703431256870.262965687431316
496444.137102657611119.8628973423889
507139.601183328101331.3988166718987
512848.6171462677863-20.6171462677863
523630.96453667117845.03546332882161
534539.60118332810135.39881667189869
542244.9504827888969-22.9504827888969
552772.1985388400399-45.1985388400399
563857.0948758152748-19.0948758152748
572635.3877348517731-9.38773485177312
584142.8412675668267-1.8412675668267
592145.6974961751141-24.6974961751141
602868.8322971659818-40.8322971659818
613636.1936303746992-0.193630374699196
625828.813040459518429.1869595404816
636568.9736434180515-3.9736434180515
642960.6508548523775-31.6508548523775
652130.1491198327643-9.14911983276426
661959.5466353884388-40.5466353884388
675584.7906516013767-29.7906516013767
6811972.999391238463446.0006087615366
693437.0608495181912-3.0608495181912
702521.75996452202183.2400354779782
7111364.751582179790648.2484178202094
724626.324508954686219.6754910453138
732854.6566136852187-26.6566136852187
746355.85861010468647.14138989531359
755251.3154274768850.68457252311496
763529.40414315367545.5958568463246
773238.017612608675-6.01761260867501
784532.189931602637612.8100683973625
794248.3955560741448-6.39555607414476
802835.3452325521152-7.34523255211518
813263.4019080767999-31.4019080767999
823243.8146318044295-11.8146318044295
832732.9369449888548-5.93694498885481
846943.635543910445925.3644560895541
853030.9645366711784-0.964536671178393
864833.773502322063814.2264976779362
875750.00048859843416.99951140156591
883633.02648893584662.9735110641534
892050.376798463822-30.376798463822
905449.98388745131514.01611254868489
912632.010843708654-6.01084370865399
925855.13499523046062.86500476953942
933530.39661117894474.60338882105529
942821.96971422628836.03028577371167
95827.250610234887-19.250610234887
969643.15716236520452.842837634796
975052.9296600065943-2.92966000659431
981517.5983183507715-2.59831835077151
996535.83545458673229.164545413268
1003344.4766405913309-11.4766405913309
101711.3865604042279-4.38656040422795
1021739.6741261279741-22.6741261279741
1035535.088441200514819.9115587994852
1043245.7040722299184-13.7040722299184
1052228.9025844065102-6.90258440651019
1064135.97204018105775.02795981894231
1075044.77139489491335.2286051050867
10878.55169360080354-1.55169360080354
10901.14044187533978-1.14044187533978
1102639.9900209263514-13.9900209263514
1112223.4637409987229-1.46374099872285
1122646.175877720356-20.175877720356
1133749.7318567574587-12.7318567574587
1142931.968341408996-2.96834140899604
11501.14044187533978-1.14044187533978
11601.14044187533978-1.14044187533978
1174246.3549656143396-4.35496561433959
1185196.4281241593819-45.4281241593819
1197745.541585483053931.4584145169462
1203235.0884412005148-3.08844120051479
1216310.729090965002552.2709090349975
1225062.5462469559243-12.5462469559243
1231827.3190136870839-9.3190136870839
1243762.7019363379165-25.7019363379165
1252347.4119221208763-24.4119221208763
1261921.5451716932527-2.54517169325271
1273977.3753264616562-38.3753264616562
1283830.48615512593657.51384487406351
1295547.10197900055697.89802099944314
130225.9218158439016616.0781841560983
13171.140441875339785.85955812466022
1322159.7561637826373-38.7561637826373
13353.202394140007981.79760585999202
1342118.50327913617742.4967208638226
13514.42778907146715-3.42778907146715
1362223.4491765587322-1.44917655873225
13701.14044187533978-1.14044187533978
1383129.8286856867111.17131431328898
1392541.7054165823593-16.7054165823593
140010.8465727716616-10.8465727716616
14141.140441875339782.85955812466022
1422040.6540664203812-20.6540664203812
1432915.574107728017213.4258922719828
1443329.64959779272743.35040220727255

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 68 & 19.9973059086119 & 48.0026940913881 \tabularnewline
2 & 17 & 32.6683131478794 & -15.6683131478794 \tabularnewline
3 & 1 & 1.14044187533979 & -0.140441875339792 \tabularnewline
4 & 114 & 73.6055243060301 & 40.3944756939699 \tabularnewline
5 & 95 & 62.9707894889599 & 32.0292105110401 \tabularnewline
6 & 148 & 108.133007146055 & 39.8669928539447 \tabularnewline
7 & 56 & 51.3883702767578 & 4.61162972324217 \tabularnewline
8 & 26 & 51.6147211281434 & -25.6147211281434 \tabularnewline
9 & 63 & 55.0811562182543 & 7.9188437817457 \tabularnewline
10 & 96 & 63.9507297813671 & 32.0492702186329 \tabularnewline
11 & 74 & 46.2297167325623 & 27.7702832674377 \tabularnewline
12 & 65 & 64.3752723144027 & 0.624727685597311 \tabularnewline
13 & 40 & 34.572318013359 & 5.42768198664101 \tabularnewline
14 & 173 & 80.4876591780568 & 92.5123408219432 \tabularnewline
15 & 28 & 61.308324291603 & -33.308324291603 \tabularnewline
16 & 55 & 60.7403987993693 & -5.74039879936929 \tabularnewline
17 & 58 & 65.6902111928536 & -7.69021119285363 \tabularnewline
18 & 25 & 50.274726783968 & -25.274726783968 \tabularnewline
19 & 103 & 70.2877366096879 & 32.7122633903121 \tabularnewline
20 & 29 & 58.5566700037171 & -29.5566700037171 \tabularnewline
21 & 31 & 34.5205157082811 & -3.5205157082811 \tabularnewline
22 & 43 & 61.4767627165254 & -18.4767627165254 \tabularnewline
23 & 74 & 26.4517945435919 & 47.5482054564081 \tabularnewline
24 & 99 & 65.654284948 & 33.345715052 \tabularnewline
25 & 25 & 57.107403548137 & -32.107403548137 \tabularnewline
26 & 69 & 28.0660270733011 & 40.9339729266989 \tabularnewline
27 & 62 & 51.8408506694608 & 10.1591493305392 \tabularnewline
28 & 25 & 57.7110339751562 & -32.7110339751562 \tabularnewline
29 & 38 & 52.2721905673688 & -14.2721905673688 \tabularnewline
30 & 57 & 28.7234965125266 & 28.2765034874734 \tabularnewline
31 & 52 & 43.157162365204 & 8.84283763479597 \tabularnewline
32 & 91 & 96.5101223413236 & -5.5101223413236 \tabularnewline
33 & 48 & 63.2450276744939 & -15.2450276744939 \tabularnewline
34 & 52 & 17.6360599926854 & 34.3639400073146 \tabularnewline
35 & 35 & 55.9715525636696 & -20.9715525636696 \tabularnewline
36 & 0 & 1.14044187533978 & -1.14044187533978 \tabularnewline
37 & 31 & 31.143624565162 & -0.143624565161961 \tabularnewline
38 & 107 & 72.6902938049585 & 34.3097061950415 \tabularnewline
39 & 242 & 129.966991078326 & 112.033008921674 \tabularnewline
40 & 41 & 69.1566462829646 & -28.1566462829646 \tabularnewline
41 & 57 & 57.0178596011452 & -0.0178596011452068 \tabularnewline
42 & 32 & 37.2399374121748 & -5.23993741217478 \tabularnewline
43 & 17 & 41.5610639129155 & -24.5610639129155 \tabularnewline
44 & 36 & 54.7461576322105 & -18.7461576322105 \tabularnewline
45 & 29 & 51.2797225420995 & -22.2797225420995 \tabularnewline
46 & 22 & 43.2341785793336 & -21.2341785793336 \tabularnewline
47 & 21 & 52.5003568158147 & -31.5003568158147 \tabularnewline
48 & 41 & 40.7370343125687 & 0.262965687431316 \tabularnewline
49 & 64 & 44.1371026576111 & 19.8628973423889 \tabularnewline
50 & 71 & 39.6011833281013 & 31.3988166718987 \tabularnewline
51 & 28 & 48.6171462677863 & -20.6171462677863 \tabularnewline
52 & 36 & 30.9645366711784 & 5.03546332882161 \tabularnewline
53 & 45 & 39.6011833281013 & 5.39881667189869 \tabularnewline
54 & 22 & 44.9504827888969 & -22.9504827888969 \tabularnewline
55 & 27 & 72.1985388400399 & -45.1985388400399 \tabularnewline
56 & 38 & 57.0948758152748 & -19.0948758152748 \tabularnewline
57 & 26 & 35.3877348517731 & -9.38773485177312 \tabularnewline
58 & 41 & 42.8412675668267 & -1.8412675668267 \tabularnewline
59 & 21 & 45.6974961751141 & -24.6974961751141 \tabularnewline
60 & 28 & 68.8322971659818 & -40.8322971659818 \tabularnewline
61 & 36 & 36.1936303746992 & -0.193630374699196 \tabularnewline
62 & 58 & 28.8130404595184 & 29.1869595404816 \tabularnewline
63 & 65 & 68.9736434180515 & -3.9736434180515 \tabularnewline
64 & 29 & 60.6508548523775 & -31.6508548523775 \tabularnewline
65 & 21 & 30.1491198327643 & -9.14911983276426 \tabularnewline
66 & 19 & 59.5466353884388 & -40.5466353884388 \tabularnewline
67 & 55 & 84.7906516013767 & -29.7906516013767 \tabularnewline
68 & 119 & 72.9993912384634 & 46.0006087615366 \tabularnewline
69 & 34 & 37.0608495181912 & -3.0608495181912 \tabularnewline
70 & 25 & 21.7599645220218 & 3.2400354779782 \tabularnewline
71 & 113 & 64.7515821797906 & 48.2484178202094 \tabularnewline
72 & 46 & 26.3245089546862 & 19.6754910453138 \tabularnewline
73 & 28 & 54.6566136852187 & -26.6566136852187 \tabularnewline
74 & 63 & 55.8586101046864 & 7.14138989531359 \tabularnewline
75 & 52 & 51.315427476885 & 0.68457252311496 \tabularnewline
76 & 35 & 29.4041431536754 & 5.5958568463246 \tabularnewline
77 & 32 & 38.017612608675 & -6.01761260867501 \tabularnewline
78 & 45 & 32.1899316026376 & 12.8100683973625 \tabularnewline
79 & 42 & 48.3955560741448 & -6.39555607414476 \tabularnewline
80 & 28 & 35.3452325521152 & -7.34523255211518 \tabularnewline
81 & 32 & 63.4019080767999 & -31.4019080767999 \tabularnewline
82 & 32 & 43.8146318044295 & -11.8146318044295 \tabularnewline
83 & 27 & 32.9369449888548 & -5.93694498885481 \tabularnewline
84 & 69 & 43.6355439104459 & 25.3644560895541 \tabularnewline
85 & 30 & 30.9645366711784 & -0.964536671178393 \tabularnewline
86 & 48 & 33.7735023220638 & 14.2264976779362 \tabularnewline
87 & 57 & 50.0004885984341 & 6.99951140156591 \tabularnewline
88 & 36 & 33.0264889358466 & 2.9735110641534 \tabularnewline
89 & 20 & 50.376798463822 & -30.376798463822 \tabularnewline
90 & 54 & 49.9838874513151 & 4.01611254868489 \tabularnewline
91 & 26 & 32.010843708654 & -6.01084370865399 \tabularnewline
92 & 58 & 55.1349952304606 & 2.86500476953942 \tabularnewline
93 & 35 & 30.3966111789447 & 4.60338882105529 \tabularnewline
94 & 28 & 21.9697142262883 & 6.03028577371167 \tabularnewline
95 & 8 & 27.250610234887 & -19.250610234887 \tabularnewline
96 & 96 & 43.157162365204 & 52.842837634796 \tabularnewline
97 & 50 & 52.9296600065943 & -2.92966000659431 \tabularnewline
98 & 15 & 17.5983183507715 & -2.59831835077151 \tabularnewline
99 & 65 & 35.835454586732 & 29.164545413268 \tabularnewline
100 & 33 & 44.4766405913309 & -11.4766405913309 \tabularnewline
101 & 7 & 11.3865604042279 & -4.38656040422795 \tabularnewline
102 & 17 & 39.6741261279741 & -22.6741261279741 \tabularnewline
103 & 55 & 35.0884412005148 & 19.9115587994852 \tabularnewline
104 & 32 & 45.7040722299184 & -13.7040722299184 \tabularnewline
105 & 22 & 28.9025844065102 & -6.90258440651019 \tabularnewline
106 & 41 & 35.9720401810577 & 5.02795981894231 \tabularnewline
107 & 50 & 44.7713948949133 & 5.2286051050867 \tabularnewline
108 & 7 & 8.55169360080354 & -1.55169360080354 \tabularnewline
109 & 0 & 1.14044187533978 & -1.14044187533978 \tabularnewline
110 & 26 & 39.9900209263514 & -13.9900209263514 \tabularnewline
111 & 22 & 23.4637409987229 & -1.46374099872285 \tabularnewline
112 & 26 & 46.175877720356 & -20.175877720356 \tabularnewline
113 & 37 & 49.7318567574587 & -12.7318567574587 \tabularnewline
114 & 29 & 31.968341408996 & -2.96834140899604 \tabularnewline
115 & 0 & 1.14044187533978 & -1.14044187533978 \tabularnewline
116 & 0 & 1.14044187533978 & -1.14044187533978 \tabularnewline
117 & 42 & 46.3549656143396 & -4.35496561433959 \tabularnewline
118 & 51 & 96.4281241593819 & -45.4281241593819 \tabularnewline
119 & 77 & 45.5415854830539 & 31.4584145169462 \tabularnewline
120 & 32 & 35.0884412005148 & -3.08844120051479 \tabularnewline
121 & 63 & 10.7290909650025 & 52.2709090349975 \tabularnewline
122 & 50 & 62.5462469559243 & -12.5462469559243 \tabularnewline
123 & 18 & 27.3190136870839 & -9.3190136870839 \tabularnewline
124 & 37 & 62.7019363379165 & -25.7019363379165 \tabularnewline
125 & 23 & 47.4119221208763 & -24.4119221208763 \tabularnewline
126 & 19 & 21.5451716932527 & -2.54517169325271 \tabularnewline
127 & 39 & 77.3753264616562 & -38.3753264616562 \tabularnewline
128 & 38 & 30.4861551259365 & 7.51384487406351 \tabularnewline
129 & 55 & 47.1019790005569 & 7.89802099944314 \tabularnewline
130 & 22 & 5.92181584390166 & 16.0781841560983 \tabularnewline
131 & 7 & 1.14044187533978 & 5.85955812466022 \tabularnewline
132 & 21 & 59.7561637826373 & -38.7561637826373 \tabularnewline
133 & 5 & 3.20239414000798 & 1.79760585999202 \tabularnewline
134 & 21 & 18.5032791361774 & 2.4967208638226 \tabularnewline
135 & 1 & 4.42778907146715 & -3.42778907146715 \tabularnewline
136 & 22 & 23.4491765587322 & -1.44917655873225 \tabularnewline
137 & 0 & 1.14044187533978 & -1.14044187533978 \tabularnewline
138 & 31 & 29.828685686711 & 1.17131431328898 \tabularnewline
139 & 25 & 41.7054165823593 & -16.7054165823593 \tabularnewline
140 & 0 & 10.8465727716616 & -10.8465727716616 \tabularnewline
141 & 4 & 1.14044187533978 & 2.85955812466022 \tabularnewline
142 & 20 & 40.6540664203812 & -20.6540664203812 \tabularnewline
143 & 29 & 15.5741077280172 & 13.4258922719828 \tabularnewline
144 & 33 & 29.6495977927274 & 3.35040220727255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146902&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]19.9973059086119[/C][C]48.0026940913881[/C][/ROW]
[ROW][C]2[/C][C]17[/C][C]32.6683131478794[/C][C]-15.6683131478794[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.14044187533979[/C][C]-0.140441875339792[/C][/ROW]
[ROW][C]4[/C][C]114[/C][C]73.6055243060301[/C][C]40.3944756939699[/C][/ROW]
[ROW][C]5[/C][C]95[/C][C]62.9707894889599[/C][C]32.0292105110401[/C][/ROW]
[ROW][C]6[/C][C]148[/C][C]108.133007146055[/C][C]39.8669928539447[/C][/ROW]
[ROW][C]7[/C][C]56[/C][C]51.3883702767578[/C][C]4.61162972324217[/C][/ROW]
[ROW][C]8[/C][C]26[/C][C]51.6147211281434[/C][C]-25.6147211281434[/C][/ROW]
[ROW][C]9[/C][C]63[/C][C]55.0811562182543[/C][C]7.9188437817457[/C][/ROW]
[ROW][C]10[/C][C]96[/C][C]63.9507297813671[/C][C]32.0492702186329[/C][/ROW]
[ROW][C]11[/C][C]74[/C][C]46.2297167325623[/C][C]27.7702832674377[/C][/ROW]
[ROW][C]12[/C][C]65[/C][C]64.3752723144027[/C][C]0.624727685597311[/C][/ROW]
[ROW][C]13[/C][C]40[/C][C]34.572318013359[/C][C]5.42768198664101[/C][/ROW]
[ROW][C]14[/C][C]173[/C][C]80.4876591780568[/C][C]92.5123408219432[/C][/ROW]
[ROW][C]15[/C][C]28[/C][C]61.308324291603[/C][C]-33.308324291603[/C][/ROW]
[ROW][C]16[/C][C]55[/C][C]60.7403987993693[/C][C]-5.74039879936929[/C][/ROW]
[ROW][C]17[/C][C]58[/C][C]65.6902111928536[/C][C]-7.69021119285363[/C][/ROW]
[ROW][C]18[/C][C]25[/C][C]50.274726783968[/C][C]-25.274726783968[/C][/ROW]
[ROW][C]19[/C][C]103[/C][C]70.2877366096879[/C][C]32.7122633903121[/C][/ROW]
[ROW][C]20[/C][C]29[/C][C]58.5566700037171[/C][C]-29.5566700037171[/C][/ROW]
[ROW][C]21[/C][C]31[/C][C]34.5205157082811[/C][C]-3.5205157082811[/C][/ROW]
[ROW][C]22[/C][C]43[/C][C]61.4767627165254[/C][C]-18.4767627165254[/C][/ROW]
[ROW][C]23[/C][C]74[/C][C]26.4517945435919[/C][C]47.5482054564081[/C][/ROW]
[ROW][C]24[/C][C]99[/C][C]65.654284948[/C][C]33.345715052[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]57.107403548137[/C][C]-32.107403548137[/C][/ROW]
[ROW][C]26[/C][C]69[/C][C]28.0660270733011[/C][C]40.9339729266989[/C][/ROW]
[ROW][C]27[/C][C]62[/C][C]51.8408506694608[/C][C]10.1591493305392[/C][/ROW]
[ROW][C]28[/C][C]25[/C][C]57.7110339751562[/C][C]-32.7110339751562[/C][/ROW]
[ROW][C]29[/C][C]38[/C][C]52.2721905673688[/C][C]-14.2721905673688[/C][/ROW]
[ROW][C]30[/C][C]57[/C][C]28.7234965125266[/C][C]28.2765034874734[/C][/ROW]
[ROW][C]31[/C][C]52[/C][C]43.157162365204[/C][C]8.84283763479597[/C][/ROW]
[ROW][C]32[/C][C]91[/C][C]96.5101223413236[/C][C]-5.5101223413236[/C][/ROW]
[ROW][C]33[/C][C]48[/C][C]63.2450276744939[/C][C]-15.2450276744939[/C][/ROW]
[ROW][C]34[/C][C]52[/C][C]17.6360599926854[/C][C]34.3639400073146[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]55.9715525636696[/C][C]-20.9715525636696[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]1.14044187533978[/C][C]-1.14044187533978[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]31.143624565162[/C][C]-0.143624565161961[/C][/ROW]
[ROW][C]38[/C][C]107[/C][C]72.6902938049585[/C][C]34.3097061950415[/C][/ROW]
[ROW][C]39[/C][C]242[/C][C]129.966991078326[/C][C]112.033008921674[/C][/ROW]
[ROW][C]40[/C][C]41[/C][C]69.1566462829646[/C][C]-28.1566462829646[/C][/ROW]
[ROW][C]41[/C][C]57[/C][C]57.0178596011452[/C][C]-0.0178596011452068[/C][/ROW]
[ROW][C]42[/C][C]32[/C][C]37.2399374121748[/C][C]-5.23993741217478[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]41.5610639129155[/C][C]-24.5610639129155[/C][/ROW]
[ROW][C]44[/C][C]36[/C][C]54.7461576322105[/C][C]-18.7461576322105[/C][/ROW]
[ROW][C]45[/C][C]29[/C][C]51.2797225420995[/C][C]-22.2797225420995[/C][/ROW]
[ROW][C]46[/C][C]22[/C][C]43.2341785793336[/C][C]-21.2341785793336[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]52.5003568158147[/C][C]-31.5003568158147[/C][/ROW]
[ROW][C]48[/C][C]41[/C][C]40.7370343125687[/C][C]0.262965687431316[/C][/ROW]
[ROW][C]49[/C][C]64[/C][C]44.1371026576111[/C][C]19.8628973423889[/C][/ROW]
[ROW][C]50[/C][C]71[/C][C]39.6011833281013[/C][C]31.3988166718987[/C][/ROW]
[ROW][C]51[/C][C]28[/C][C]48.6171462677863[/C][C]-20.6171462677863[/C][/ROW]
[ROW][C]52[/C][C]36[/C][C]30.9645366711784[/C][C]5.03546332882161[/C][/ROW]
[ROW][C]53[/C][C]45[/C][C]39.6011833281013[/C][C]5.39881667189869[/C][/ROW]
[ROW][C]54[/C][C]22[/C][C]44.9504827888969[/C][C]-22.9504827888969[/C][/ROW]
[ROW][C]55[/C][C]27[/C][C]72.1985388400399[/C][C]-45.1985388400399[/C][/ROW]
[ROW][C]56[/C][C]38[/C][C]57.0948758152748[/C][C]-19.0948758152748[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]35.3877348517731[/C][C]-9.38773485177312[/C][/ROW]
[ROW][C]58[/C][C]41[/C][C]42.8412675668267[/C][C]-1.8412675668267[/C][/ROW]
[ROW][C]59[/C][C]21[/C][C]45.6974961751141[/C][C]-24.6974961751141[/C][/ROW]
[ROW][C]60[/C][C]28[/C][C]68.8322971659818[/C][C]-40.8322971659818[/C][/ROW]
[ROW][C]61[/C][C]36[/C][C]36.1936303746992[/C][C]-0.193630374699196[/C][/ROW]
[ROW][C]62[/C][C]58[/C][C]28.8130404595184[/C][C]29.1869595404816[/C][/ROW]
[ROW][C]63[/C][C]65[/C][C]68.9736434180515[/C][C]-3.9736434180515[/C][/ROW]
[ROW][C]64[/C][C]29[/C][C]60.6508548523775[/C][C]-31.6508548523775[/C][/ROW]
[ROW][C]65[/C][C]21[/C][C]30.1491198327643[/C][C]-9.14911983276426[/C][/ROW]
[ROW][C]66[/C][C]19[/C][C]59.5466353884388[/C][C]-40.5466353884388[/C][/ROW]
[ROW][C]67[/C][C]55[/C][C]84.7906516013767[/C][C]-29.7906516013767[/C][/ROW]
[ROW][C]68[/C][C]119[/C][C]72.9993912384634[/C][C]46.0006087615366[/C][/ROW]
[ROW][C]69[/C][C]34[/C][C]37.0608495181912[/C][C]-3.0608495181912[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]21.7599645220218[/C][C]3.2400354779782[/C][/ROW]
[ROW][C]71[/C][C]113[/C][C]64.7515821797906[/C][C]48.2484178202094[/C][/ROW]
[ROW][C]72[/C][C]46[/C][C]26.3245089546862[/C][C]19.6754910453138[/C][/ROW]
[ROW][C]73[/C][C]28[/C][C]54.6566136852187[/C][C]-26.6566136852187[/C][/ROW]
[ROW][C]74[/C][C]63[/C][C]55.8586101046864[/C][C]7.14138989531359[/C][/ROW]
[ROW][C]75[/C][C]52[/C][C]51.315427476885[/C][C]0.68457252311496[/C][/ROW]
[ROW][C]76[/C][C]35[/C][C]29.4041431536754[/C][C]5.5958568463246[/C][/ROW]
[ROW][C]77[/C][C]32[/C][C]38.017612608675[/C][C]-6.01761260867501[/C][/ROW]
[ROW][C]78[/C][C]45[/C][C]32.1899316026376[/C][C]12.8100683973625[/C][/ROW]
[ROW][C]79[/C][C]42[/C][C]48.3955560741448[/C][C]-6.39555607414476[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]35.3452325521152[/C][C]-7.34523255211518[/C][/ROW]
[ROW][C]81[/C][C]32[/C][C]63.4019080767999[/C][C]-31.4019080767999[/C][/ROW]
[ROW][C]82[/C][C]32[/C][C]43.8146318044295[/C][C]-11.8146318044295[/C][/ROW]
[ROW][C]83[/C][C]27[/C][C]32.9369449888548[/C][C]-5.93694498885481[/C][/ROW]
[ROW][C]84[/C][C]69[/C][C]43.6355439104459[/C][C]25.3644560895541[/C][/ROW]
[ROW][C]85[/C][C]30[/C][C]30.9645366711784[/C][C]-0.964536671178393[/C][/ROW]
[ROW][C]86[/C][C]48[/C][C]33.7735023220638[/C][C]14.2264976779362[/C][/ROW]
[ROW][C]87[/C][C]57[/C][C]50.0004885984341[/C][C]6.99951140156591[/C][/ROW]
[ROW][C]88[/C][C]36[/C][C]33.0264889358466[/C][C]2.9735110641534[/C][/ROW]
[ROW][C]89[/C][C]20[/C][C]50.376798463822[/C][C]-30.376798463822[/C][/ROW]
[ROW][C]90[/C][C]54[/C][C]49.9838874513151[/C][C]4.01611254868489[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]32.010843708654[/C][C]-6.01084370865399[/C][/ROW]
[ROW][C]92[/C][C]58[/C][C]55.1349952304606[/C][C]2.86500476953942[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]30.3966111789447[/C][C]4.60338882105529[/C][/ROW]
[ROW][C]94[/C][C]28[/C][C]21.9697142262883[/C][C]6.03028577371167[/C][/ROW]
[ROW][C]95[/C][C]8[/C][C]27.250610234887[/C][C]-19.250610234887[/C][/ROW]
[ROW][C]96[/C][C]96[/C][C]43.157162365204[/C][C]52.842837634796[/C][/ROW]
[ROW][C]97[/C][C]50[/C][C]52.9296600065943[/C][C]-2.92966000659431[/C][/ROW]
[ROW][C]98[/C][C]15[/C][C]17.5983183507715[/C][C]-2.59831835077151[/C][/ROW]
[ROW][C]99[/C][C]65[/C][C]35.835454586732[/C][C]29.164545413268[/C][/ROW]
[ROW][C]100[/C][C]33[/C][C]44.4766405913309[/C][C]-11.4766405913309[/C][/ROW]
[ROW][C]101[/C][C]7[/C][C]11.3865604042279[/C][C]-4.38656040422795[/C][/ROW]
[ROW][C]102[/C][C]17[/C][C]39.6741261279741[/C][C]-22.6741261279741[/C][/ROW]
[ROW][C]103[/C][C]55[/C][C]35.0884412005148[/C][C]19.9115587994852[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]45.7040722299184[/C][C]-13.7040722299184[/C][/ROW]
[ROW][C]105[/C][C]22[/C][C]28.9025844065102[/C][C]-6.90258440651019[/C][/ROW]
[ROW][C]106[/C][C]41[/C][C]35.9720401810577[/C][C]5.02795981894231[/C][/ROW]
[ROW][C]107[/C][C]50[/C][C]44.7713948949133[/C][C]5.2286051050867[/C][/ROW]
[ROW][C]108[/C][C]7[/C][C]8.55169360080354[/C][C]-1.55169360080354[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]1.14044187533978[/C][C]-1.14044187533978[/C][/ROW]
[ROW][C]110[/C][C]26[/C][C]39.9900209263514[/C][C]-13.9900209263514[/C][/ROW]
[ROW][C]111[/C][C]22[/C][C]23.4637409987229[/C][C]-1.46374099872285[/C][/ROW]
[ROW][C]112[/C][C]26[/C][C]46.175877720356[/C][C]-20.175877720356[/C][/ROW]
[ROW][C]113[/C][C]37[/C][C]49.7318567574587[/C][C]-12.7318567574587[/C][/ROW]
[ROW][C]114[/C][C]29[/C][C]31.968341408996[/C][C]-2.96834140899604[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]1.14044187533978[/C][C]-1.14044187533978[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]1.14044187533978[/C][C]-1.14044187533978[/C][/ROW]
[ROW][C]117[/C][C]42[/C][C]46.3549656143396[/C][C]-4.35496561433959[/C][/ROW]
[ROW][C]118[/C][C]51[/C][C]96.4281241593819[/C][C]-45.4281241593819[/C][/ROW]
[ROW][C]119[/C][C]77[/C][C]45.5415854830539[/C][C]31.4584145169462[/C][/ROW]
[ROW][C]120[/C][C]32[/C][C]35.0884412005148[/C][C]-3.08844120051479[/C][/ROW]
[ROW][C]121[/C][C]63[/C][C]10.7290909650025[/C][C]52.2709090349975[/C][/ROW]
[ROW][C]122[/C][C]50[/C][C]62.5462469559243[/C][C]-12.5462469559243[/C][/ROW]
[ROW][C]123[/C][C]18[/C][C]27.3190136870839[/C][C]-9.3190136870839[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]62.7019363379165[/C][C]-25.7019363379165[/C][/ROW]
[ROW][C]125[/C][C]23[/C][C]47.4119221208763[/C][C]-24.4119221208763[/C][/ROW]
[ROW][C]126[/C][C]19[/C][C]21.5451716932527[/C][C]-2.54517169325271[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]77.3753264616562[/C][C]-38.3753264616562[/C][/ROW]
[ROW][C]128[/C][C]38[/C][C]30.4861551259365[/C][C]7.51384487406351[/C][/ROW]
[ROW][C]129[/C][C]55[/C][C]47.1019790005569[/C][C]7.89802099944314[/C][/ROW]
[ROW][C]130[/C][C]22[/C][C]5.92181584390166[/C][C]16.0781841560983[/C][/ROW]
[ROW][C]131[/C][C]7[/C][C]1.14044187533978[/C][C]5.85955812466022[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]59.7561637826373[/C][C]-38.7561637826373[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]3.20239414000798[/C][C]1.79760585999202[/C][/ROW]
[ROW][C]134[/C][C]21[/C][C]18.5032791361774[/C][C]2.4967208638226[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]4.42778907146715[/C][C]-3.42778907146715[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]23.4491765587322[/C][C]-1.44917655873225[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]1.14044187533978[/C][C]-1.14044187533978[/C][/ROW]
[ROW][C]138[/C][C]31[/C][C]29.828685686711[/C][C]1.17131431328898[/C][/ROW]
[ROW][C]139[/C][C]25[/C][C]41.7054165823593[/C][C]-16.7054165823593[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]10.8465727716616[/C][C]-10.8465727716616[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]1.14044187533978[/C][C]2.85955812466022[/C][/ROW]
[ROW][C]142[/C][C]20[/C][C]40.6540664203812[/C][C]-20.6540664203812[/C][/ROW]
[ROW][C]143[/C][C]29[/C][C]15.5741077280172[/C][C]13.4258922719828[/C][/ROW]
[ROW][C]144[/C][C]33[/C][C]29.6495977927274[/C][C]3.35040220727255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146902&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146902&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
16819.997305908611948.0026940913881
21732.6683131478794-15.6683131478794
311.14044187533979-0.140441875339792
411473.605524306030140.3944756939699
59562.970789488959932.0292105110401
6148108.13300714605539.8669928539447
75651.38837027675784.61162972324217
82651.6147211281434-25.6147211281434
96355.08115621825437.9188437817457
109663.950729781367132.0492702186329
117446.229716732562327.7702832674377
126564.37527231440270.624727685597311
134034.5723180133595.42768198664101
1417380.487659178056892.5123408219432
152861.308324291603-33.308324291603
165560.7403987993693-5.74039879936929
175865.6902111928536-7.69021119285363
182550.274726783968-25.274726783968
1910370.287736609687932.7122633903121
202958.5566700037171-29.5566700037171
213134.5205157082811-3.5205157082811
224361.4767627165254-18.4767627165254
237426.451794543591947.5482054564081
249965.65428494833.345715052
252557.107403548137-32.107403548137
266928.066027073301140.9339729266989
276251.840850669460810.1591493305392
282557.7110339751562-32.7110339751562
293852.2721905673688-14.2721905673688
305728.723496512526628.2765034874734
315243.1571623652048.84283763479597
329196.5101223413236-5.5101223413236
334863.2450276744939-15.2450276744939
345217.636059992685434.3639400073146
353555.9715525636696-20.9715525636696
3601.14044187533978-1.14044187533978
373131.143624565162-0.143624565161961
3810772.690293804958534.3097061950415
39242129.966991078326112.033008921674
404169.1566462829646-28.1566462829646
415757.0178596011452-0.0178596011452068
423237.2399374121748-5.23993741217478
431741.5610639129155-24.5610639129155
443654.7461576322105-18.7461576322105
452951.2797225420995-22.2797225420995
462243.2341785793336-21.2341785793336
472152.5003568158147-31.5003568158147
484140.73703431256870.262965687431316
496444.137102657611119.8628973423889
507139.601183328101331.3988166718987
512848.6171462677863-20.6171462677863
523630.96453667117845.03546332882161
534539.60118332810135.39881667189869
542244.9504827888969-22.9504827888969
552772.1985388400399-45.1985388400399
563857.0948758152748-19.0948758152748
572635.3877348517731-9.38773485177312
584142.8412675668267-1.8412675668267
592145.6974961751141-24.6974961751141
602868.8322971659818-40.8322971659818
613636.1936303746992-0.193630374699196
625828.813040459518429.1869595404816
636568.9736434180515-3.9736434180515
642960.6508548523775-31.6508548523775
652130.1491198327643-9.14911983276426
661959.5466353884388-40.5466353884388
675584.7906516013767-29.7906516013767
6811972.999391238463446.0006087615366
693437.0608495181912-3.0608495181912
702521.75996452202183.2400354779782
7111364.751582179790648.2484178202094
724626.324508954686219.6754910453138
732854.6566136852187-26.6566136852187
746355.85861010468647.14138989531359
755251.3154274768850.68457252311496
763529.40414315367545.5958568463246
773238.017612608675-6.01761260867501
784532.189931602637612.8100683973625
794248.3955560741448-6.39555607414476
802835.3452325521152-7.34523255211518
813263.4019080767999-31.4019080767999
823243.8146318044295-11.8146318044295
832732.9369449888548-5.93694498885481
846943.635543910445925.3644560895541
853030.9645366711784-0.964536671178393
864833.773502322063814.2264976779362
875750.00048859843416.99951140156591
883633.02648893584662.9735110641534
892050.376798463822-30.376798463822
905449.98388745131514.01611254868489
912632.010843708654-6.01084370865399
925855.13499523046062.86500476953942
933530.39661117894474.60338882105529
942821.96971422628836.03028577371167
95827.250610234887-19.250610234887
969643.15716236520452.842837634796
975052.9296600065943-2.92966000659431
981517.5983183507715-2.59831835077151
996535.83545458673229.164545413268
1003344.4766405913309-11.4766405913309
101711.3865604042279-4.38656040422795
1021739.6741261279741-22.6741261279741
1035535.088441200514819.9115587994852
1043245.7040722299184-13.7040722299184
1052228.9025844065102-6.90258440651019
1064135.97204018105775.02795981894231
1075044.77139489491335.2286051050867
10878.55169360080354-1.55169360080354
10901.14044187533978-1.14044187533978
1102639.9900209263514-13.9900209263514
1112223.4637409987229-1.46374099872285
1122646.175877720356-20.175877720356
1133749.7318567574587-12.7318567574587
1142931.968341408996-2.96834140899604
11501.14044187533978-1.14044187533978
11601.14044187533978-1.14044187533978
1174246.3549656143396-4.35496561433959
1185196.4281241593819-45.4281241593819
1197745.541585483053931.4584145169462
1203235.0884412005148-3.08844120051479
1216310.729090965002552.2709090349975
1225062.5462469559243-12.5462469559243
1231827.3190136870839-9.3190136870839
1243762.7019363379165-25.7019363379165
1252347.4119221208763-24.4119221208763
1261921.5451716932527-2.54517169325271
1273977.3753264616562-38.3753264616562
1283830.48615512593657.51384487406351
1295547.10197900055697.89802099944314
130225.9218158439016616.0781841560983
13171.140441875339785.85955812466022
1322159.7561637826373-38.7561637826373
13353.202394140007981.79760585999202
1342118.50327913617742.4967208638226
13514.42778907146715-3.42778907146715
1362223.4491765587322-1.44917655873225
13701.14044187533978-1.14044187533978
1383129.8286856867111.17131431328898
1392541.7054165823593-16.7054165823593
140010.8465727716616-10.8465727716616
14141.140441875339782.85955812466022
1422040.6540664203812-20.6540664203812
1432915.574107728017213.4258922719828
1443329.64959779272743.35040220727255







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9106352086413630.1787295827172750.0893647913586373
90.8409102418523670.3181795162952670.159089758147633
100.7650387260049190.4699225479901630.234961273995081
110.669124786211120.661750427577760.33087521378888
120.5606150371879190.8787699256241610.439384962812081
130.4600757016501960.9201514033003920.539924298349804
140.4563327482487990.9126654964975980.543667251751201
150.9716409300695250.05671813986095080.0283590699304754
160.9729442813573610.05411143728527770.0270557186426388
170.9618800620314290.07623987593714290.0381199379685715
180.9764849318085990.04703013638280190.0235150681914009
190.9765459199532520.04690816009349560.0234540800467478
200.9827949846616230.03441003067675480.0172050153383774
210.9736937258127110.05261254837457840.0263062741872892
220.9785363016869830.04292739662603480.0214636983130174
230.9906604920238740.01867901595225260.00933950797612629
240.99083578479690.01832843040620050.00916421520310023
250.9940180901178970.01196381976420690.00598190988210347
260.9963016435666430.007396712866714280.00369835643335714
270.9943987747757730.01120245044845360.00560122522422682
280.9966315884449960.006736823110008330.00336841155500417
290.9957535435076460.0084929129847080.004246456492354
300.995664765837690.008670468324620360.00433523416231018
310.9935600938756020.01287981224879620.0064399061243981
320.9915822905348920.01683541893021560.0084177094651078
330.9895435471917340.02091290561653170.0104564528082658
340.9899057439455790.02018851210884140.0100942560544207
350.9896669193686870.02066616126262560.0103330806313128
360.9852261834585660.02954763308286880.0147738165414344
370.9800922592299690.03981548154006150.0199077407700307
380.9840033013072170.03199339738556520.0159966986927826
390.9999953641795349.27164093283227e-064.63582046641614e-06
400.9999973282212495.34355750123541e-062.67177875061771e-06
410.9999951656678759.6686642501223e-064.83433212506115e-06
420.9999930062383381.39875233241884e-056.99376166209421e-06
430.9999939611437331.20777125344418e-056.03885626722088e-06
440.9999933229552261.33540895470868e-056.67704477354338e-06
450.9999930643714591.38712570829939e-056.93562854149697e-06
460.9999936761207921.26477584168546e-056.32387920842732e-06
470.9999966138474456.77230510973992e-063.38615255486996e-06
480.9999939564413331.20871173347248e-056.04355866736239e-06
490.9999930707370051.38585259896582e-056.92926299482912e-06
500.9999954952906669.00941866758306e-064.50470933379153e-06
510.9999958422512858.31549742997881e-064.15774871498941e-06
520.9999927044415911.45911168184506e-057.29555840922532e-06
530.9999874961540492.50076919016613e-051.25038459508306e-05
540.9999880753080442.38493839115942e-051.19246919557971e-05
550.9999974902114325.01957713673195e-062.50978856836597e-06
560.9999964234318697.15313626143449e-063.57656813071724e-06
570.9999945036031041.09927937928851e-055.49639689644253e-06
580.9999915091862631.69816274745649e-058.49081373728245e-06
590.9999933532290651.32935418696052e-056.64677093480258e-06
600.9999965423170916.91536581804834e-063.45768290902417e-06
610.9999939521267351.20957465306759e-056.04787326533795e-06
620.9999954019996349.1960007323486e-064.5980003661743e-06
630.9999940336975891.19326048212133e-055.96630241060666e-06
640.9999944606632291.10786735430984e-055.5393367715492e-06
650.9999912532457961.7493508408414e-058.74675420420702e-06
660.9999973082180175.38356396523963e-062.69178198261981e-06
670.9999971368381055.72632378948276e-062.86316189474138e-06
680.9999998295843453.40831310276354e-071.70415655138177e-07
690.9999996921419456.15716109244932e-073.07858054622466e-07
700.9999994317819141.13643617275712e-065.68218086378561e-07
710.9999999981893673.62126569811825e-091.81063284905912e-09
720.9999999987482842.50343150932453e-091.25171575466226e-09
730.9999999987275622.54487597092198e-091.27243798546099e-09
740.9999999995477599.04482881520476e-104.52241440760238e-10
750.9999999990720271.85594582575148e-099.27972912875738e-10
760.9999999980454313.90913859158575e-091.95456929579287e-09
770.9999999958889478.22210632910617e-094.11105316455308e-09
780.999999992873831.42523406012255e-087.12617030061275e-09
790.9999999864814342.70371328125667e-081.35185664062833e-08
800.9999999747678415.04643183378049e-082.52321591689024e-08
810.9999999818865153.6226969704868e-081.8113484852434e-08
820.9999999795383924.09232166272499e-082.0461608313625e-08
830.9999999662524816.74950370775478e-083.37475185387739e-08
840.9999999762012094.75975830482832e-082.37987915241416e-08
850.9999999546622369.06755279244773e-084.53377639622386e-08
860.9999999162052071.67589585889433e-078.37947929447163e-08
870.9999998332698423.33460316586795e-071.66730158293397e-07
880.9999996740684636.51863073060909e-073.25931536530455e-07
890.999999710080045.79839920923901e-072.89919960461951e-07
900.9999995868299528.26340096277805e-074.13170048138902e-07
910.9999992089059981.58218800336624e-067.91094001683122e-07
920.9999989088917962.18221640763933e-061.09110820381966e-06
930.9999979582259514.08354809840501e-062.04177404920251e-06
940.9999961585963387.68280732375914e-063.84140366187957e-06
950.9999956860229588.62795408307358e-064.31397704153679e-06
960.9999999336804921.32639016607563e-076.63195083037817e-08
970.9999998539999142.92000172777666e-071.46000086388833e-07
980.9999996950158636.09968273004016e-073.04984136502008e-07
990.9999998268671313.46265738297626e-071.73132869148813e-07
1000.9999996672059616.65588078337187e-073.32794039168593e-07
1010.9999992919862411.41602751833341e-067.08013759166703e-07
1020.9999988775354112.2449291774456e-061.1224645887228e-06
1030.9999987718356772.45632864509219e-061.22816432254609e-06
1040.9999975321590594.93568188120598e-062.46784094060299e-06
1050.9999956350791498.72984170221102e-064.36492085110551e-06
1060.9999916002416431.67995167133773e-058.39975835668866e-06
1070.9999868456951792.6308609641962e-051.3154304820981e-05
1080.9999748051713335.03896573338682e-052.51948286669341e-05
1090.999954825289439.03494211395983e-054.51747105697991e-05
1100.9999208410730620.0001583178538752447.91589269376218e-05
1110.9998499424001180.0003001151997638680.000150057599881934
1120.9998271434608670.0003457130782651160.000172856539132558
1130.9997550152855220.0004899694289557910.000244984714477896
1140.9995521491008260.000895701798347570.000447850899173785
1150.9992427722133960.001514455573207150.000757227786603577
1160.998763791963560.002472416072879670.00123620803643984
1170.9978604556756160.004279088648768210.0021395443243841
1180.9979262377682840.004147524463432920.00207376223171646
1190.9997169590848880.0005660818302240860.000283040915112043
1200.9994415882489320.001116823502136270.000558411751068135
1210.999999991388181.72236404529182e-088.61182022645909e-09
1220.9999999643022827.13954363582444e-083.56977181791222e-08
1230.9999999673244496.53511020103706e-083.26755510051853e-08
1240.9999998678227142.64354572339659e-071.3217728616983e-07
1250.9999994667637251.06647255009605e-065.33236275048025e-07
1260.9999980444357783.91112844427639e-061.9555642221382e-06
1270.9999962179310327.56413793644881e-063.7820689682244e-06
1280.9999854009959212.91980081577637e-051.45990040788819e-05
1290.9999509006671149.81986657725857e-054.90993328862929e-05
1300.9999644227455157.11545089704929e-053.55772544852464e-05
1310.9998706712725460.0002586574549082560.000129328727454128
1320.9995252678938360.0009494642123276940.000474732106163847
1330.9980860250080940.003827949983811470.00191397499190573
1340.9929560423750340.01408791524993210.00704395762496605
1350.9786775000384990.0426449999230030.0213224999615015
1360.9721314729301120.05573705413977660.0278685270698883

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.910635208641363 & 0.178729582717275 & 0.0893647913586373 \tabularnewline
9 & 0.840910241852367 & 0.318179516295267 & 0.159089758147633 \tabularnewline
10 & 0.765038726004919 & 0.469922547990163 & 0.234961273995081 \tabularnewline
11 & 0.66912478621112 & 0.66175042757776 & 0.33087521378888 \tabularnewline
12 & 0.560615037187919 & 0.878769925624161 & 0.439384962812081 \tabularnewline
13 & 0.460075701650196 & 0.920151403300392 & 0.539924298349804 \tabularnewline
14 & 0.456332748248799 & 0.912665496497598 & 0.543667251751201 \tabularnewline
15 & 0.971640930069525 & 0.0567181398609508 & 0.0283590699304754 \tabularnewline
16 & 0.972944281357361 & 0.0541114372852777 & 0.0270557186426388 \tabularnewline
17 & 0.961880062031429 & 0.0762398759371429 & 0.0381199379685715 \tabularnewline
18 & 0.976484931808599 & 0.0470301363828019 & 0.0235150681914009 \tabularnewline
19 & 0.976545919953252 & 0.0469081600934956 & 0.0234540800467478 \tabularnewline
20 & 0.982794984661623 & 0.0344100306767548 & 0.0172050153383774 \tabularnewline
21 & 0.973693725812711 & 0.0526125483745784 & 0.0263062741872892 \tabularnewline
22 & 0.978536301686983 & 0.0429273966260348 & 0.0214636983130174 \tabularnewline
23 & 0.990660492023874 & 0.0186790159522526 & 0.00933950797612629 \tabularnewline
24 & 0.9908357847969 & 0.0183284304062005 & 0.00916421520310023 \tabularnewline
25 & 0.994018090117897 & 0.0119638197642069 & 0.00598190988210347 \tabularnewline
26 & 0.996301643566643 & 0.00739671286671428 & 0.00369835643335714 \tabularnewline
27 & 0.994398774775773 & 0.0112024504484536 & 0.00560122522422682 \tabularnewline
28 & 0.996631588444996 & 0.00673682311000833 & 0.00336841155500417 \tabularnewline
29 & 0.995753543507646 & 0.008492912984708 & 0.004246456492354 \tabularnewline
30 & 0.99566476583769 & 0.00867046832462036 & 0.00433523416231018 \tabularnewline
31 & 0.993560093875602 & 0.0128798122487962 & 0.0064399061243981 \tabularnewline
32 & 0.991582290534892 & 0.0168354189302156 & 0.0084177094651078 \tabularnewline
33 & 0.989543547191734 & 0.0209129056165317 & 0.0104564528082658 \tabularnewline
34 & 0.989905743945579 & 0.0201885121088414 & 0.0100942560544207 \tabularnewline
35 & 0.989666919368687 & 0.0206661612626256 & 0.0103330806313128 \tabularnewline
36 & 0.985226183458566 & 0.0295476330828688 & 0.0147738165414344 \tabularnewline
37 & 0.980092259229969 & 0.0398154815400615 & 0.0199077407700307 \tabularnewline
38 & 0.984003301307217 & 0.0319933973855652 & 0.0159966986927826 \tabularnewline
39 & 0.999995364179534 & 9.27164093283227e-06 & 4.63582046641614e-06 \tabularnewline
40 & 0.999997328221249 & 5.34355750123541e-06 & 2.67177875061771e-06 \tabularnewline
41 & 0.999995165667875 & 9.6686642501223e-06 & 4.83433212506115e-06 \tabularnewline
42 & 0.999993006238338 & 1.39875233241884e-05 & 6.99376166209421e-06 \tabularnewline
43 & 0.999993961143733 & 1.20777125344418e-05 & 6.03885626722088e-06 \tabularnewline
44 & 0.999993322955226 & 1.33540895470868e-05 & 6.67704477354338e-06 \tabularnewline
45 & 0.999993064371459 & 1.38712570829939e-05 & 6.93562854149697e-06 \tabularnewline
46 & 0.999993676120792 & 1.26477584168546e-05 & 6.32387920842732e-06 \tabularnewline
47 & 0.999996613847445 & 6.77230510973992e-06 & 3.38615255486996e-06 \tabularnewline
48 & 0.999993956441333 & 1.20871173347248e-05 & 6.04355866736239e-06 \tabularnewline
49 & 0.999993070737005 & 1.38585259896582e-05 & 6.92926299482912e-06 \tabularnewline
50 & 0.999995495290666 & 9.00941866758306e-06 & 4.50470933379153e-06 \tabularnewline
51 & 0.999995842251285 & 8.31549742997881e-06 & 4.15774871498941e-06 \tabularnewline
52 & 0.999992704441591 & 1.45911168184506e-05 & 7.29555840922532e-06 \tabularnewline
53 & 0.999987496154049 & 2.50076919016613e-05 & 1.25038459508306e-05 \tabularnewline
54 & 0.999988075308044 & 2.38493839115942e-05 & 1.19246919557971e-05 \tabularnewline
55 & 0.999997490211432 & 5.01957713673195e-06 & 2.50978856836597e-06 \tabularnewline
56 & 0.999996423431869 & 7.15313626143449e-06 & 3.57656813071724e-06 \tabularnewline
57 & 0.999994503603104 & 1.09927937928851e-05 & 5.49639689644253e-06 \tabularnewline
58 & 0.999991509186263 & 1.69816274745649e-05 & 8.49081373728245e-06 \tabularnewline
59 & 0.999993353229065 & 1.32935418696052e-05 & 6.64677093480258e-06 \tabularnewline
60 & 0.999996542317091 & 6.91536581804834e-06 & 3.45768290902417e-06 \tabularnewline
61 & 0.999993952126735 & 1.20957465306759e-05 & 6.04787326533795e-06 \tabularnewline
62 & 0.999995401999634 & 9.1960007323486e-06 & 4.5980003661743e-06 \tabularnewline
63 & 0.999994033697589 & 1.19326048212133e-05 & 5.96630241060666e-06 \tabularnewline
64 & 0.999994460663229 & 1.10786735430984e-05 & 5.5393367715492e-06 \tabularnewline
65 & 0.999991253245796 & 1.7493508408414e-05 & 8.74675420420702e-06 \tabularnewline
66 & 0.999997308218017 & 5.38356396523963e-06 & 2.69178198261981e-06 \tabularnewline
67 & 0.999997136838105 & 5.72632378948276e-06 & 2.86316189474138e-06 \tabularnewline
68 & 0.999999829584345 & 3.40831310276354e-07 & 1.70415655138177e-07 \tabularnewline
69 & 0.999999692141945 & 6.15716109244932e-07 & 3.07858054622466e-07 \tabularnewline
70 & 0.999999431781914 & 1.13643617275712e-06 & 5.68218086378561e-07 \tabularnewline
71 & 0.999999998189367 & 3.62126569811825e-09 & 1.81063284905912e-09 \tabularnewline
72 & 0.999999998748284 & 2.50343150932453e-09 & 1.25171575466226e-09 \tabularnewline
73 & 0.999999998727562 & 2.54487597092198e-09 & 1.27243798546099e-09 \tabularnewline
74 & 0.999999999547759 & 9.04482881520476e-10 & 4.52241440760238e-10 \tabularnewline
75 & 0.999999999072027 & 1.85594582575148e-09 & 9.27972912875738e-10 \tabularnewline
76 & 0.999999998045431 & 3.90913859158575e-09 & 1.95456929579287e-09 \tabularnewline
77 & 0.999999995888947 & 8.22210632910617e-09 & 4.11105316455308e-09 \tabularnewline
78 & 0.99999999287383 & 1.42523406012255e-08 & 7.12617030061275e-09 \tabularnewline
79 & 0.999999986481434 & 2.70371328125667e-08 & 1.35185664062833e-08 \tabularnewline
80 & 0.999999974767841 & 5.04643183378049e-08 & 2.52321591689024e-08 \tabularnewline
81 & 0.999999981886515 & 3.6226969704868e-08 & 1.8113484852434e-08 \tabularnewline
82 & 0.999999979538392 & 4.09232166272499e-08 & 2.0461608313625e-08 \tabularnewline
83 & 0.999999966252481 & 6.74950370775478e-08 & 3.37475185387739e-08 \tabularnewline
84 & 0.999999976201209 & 4.75975830482832e-08 & 2.37987915241416e-08 \tabularnewline
85 & 0.999999954662236 & 9.06755279244773e-08 & 4.53377639622386e-08 \tabularnewline
86 & 0.999999916205207 & 1.67589585889433e-07 & 8.37947929447163e-08 \tabularnewline
87 & 0.999999833269842 & 3.33460316586795e-07 & 1.66730158293397e-07 \tabularnewline
88 & 0.999999674068463 & 6.51863073060909e-07 & 3.25931536530455e-07 \tabularnewline
89 & 0.99999971008004 & 5.79839920923901e-07 & 2.89919960461951e-07 \tabularnewline
90 & 0.999999586829952 & 8.26340096277805e-07 & 4.13170048138902e-07 \tabularnewline
91 & 0.999999208905998 & 1.58218800336624e-06 & 7.91094001683122e-07 \tabularnewline
92 & 0.999998908891796 & 2.18221640763933e-06 & 1.09110820381966e-06 \tabularnewline
93 & 0.999997958225951 & 4.08354809840501e-06 & 2.04177404920251e-06 \tabularnewline
94 & 0.999996158596338 & 7.68280732375914e-06 & 3.84140366187957e-06 \tabularnewline
95 & 0.999995686022958 & 8.62795408307358e-06 & 4.31397704153679e-06 \tabularnewline
96 & 0.999999933680492 & 1.32639016607563e-07 & 6.63195083037817e-08 \tabularnewline
97 & 0.999999853999914 & 2.92000172777666e-07 & 1.46000086388833e-07 \tabularnewline
98 & 0.999999695015863 & 6.09968273004016e-07 & 3.04984136502008e-07 \tabularnewline
99 & 0.999999826867131 & 3.46265738297626e-07 & 1.73132869148813e-07 \tabularnewline
100 & 0.999999667205961 & 6.65588078337187e-07 & 3.32794039168593e-07 \tabularnewline
101 & 0.999999291986241 & 1.41602751833341e-06 & 7.08013759166703e-07 \tabularnewline
102 & 0.999998877535411 & 2.2449291774456e-06 & 1.1224645887228e-06 \tabularnewline
103 & 0.999998771835677 & 2.45632864509219e-06 & 1.22816432254609e-06 \tabularnewline
104 & 0.999997532159059 & 4.93568188120598e-06 & 2.46784094060299e-06 \tabularnewline
105 & 0.999995635079149 & 8.72984170221102e-06 & 4.36492085110551e-06 \tabularnewline
106 & 0.999991600241643 & 1.67995167133773e-05 & 8.39975835668866e-06 \tabularnewline
107 & 0.999986845695179 & 2.6308609641962e-05 & 1.3154304820981e-05 \tabularnewline
108 & 0.999974805171333 & 5.03896573338682e-05 & 2.51948286669341e-05 \tabularnewline
109 & 0.99995482528943 & 9.03494211395983e-05 & 4.51747105697991e-05 \tabularnewline
110 & 0.999920841073062 & 0.000158317853875244 & 7.91589269376218e-05 \tabularnewline
111 & 0.999849942400118 & 0.000300115199763868 & 0.000150057599881934 \tabularnewline
112 & 0.999827143460867 & 0.000345713078265116 & 0.000172856539132558 \tabularnewline
113 & 0.999755015285522 & 0.000489969428955791 & 0.000244984714477896 \tabularnewline
114 & 0.999552149100826 & 0.00089570179834757 & 0.000447850899173785 \tabularnewline
115 & 0.999242772213396 & 0.00151445557320715 & 0.000757227786603577 \tabularnewline
116 & 0.99876379196356 & 0.00247241607287967 & 0.00123620803643984 \tabularnewline
117 & 0.997860455675616 & 0.00427908864876821 & 0.0021395443243841 \tabularnewline
118 & 0.997926237768284 & 0.00414752446343292 & 0.00207376223171646 \tabularnewline
119 & 0.999716959084888 & 0.000566081830224086 & 0.000283040915112043 \tabularnewline
120 & 0.999441588248932 & 0.00111682350213627 & 0.000558411751068135 \tabularnewline
121 & 0.99999999138818 & 1.72236404529182e-08 & 8.61182022645909e-09 \tabularnewline
122 & 0.999999964302282 & 7.13954363582444e-08 & 3.56977181791222e-08 \tabularnewline
123 & 0.999999967324449 & 6.53511020103706e-08 & 3.26755510051853e-08 \tabularnewline
124 & 0.999999867822714 & 2.64354572339659e-07 & 1.3217728616983e-07 \tabularnewline
125 & 0.999999466763725 & 1.06647255009605e-06 & 5.33236275048025e-07 \tabularnewline
126 & 0.999998044435778 & 3.91112844427639e-06 & 1.9555642221382e-06 \tabularnewline
127 & 0.999996217931032 & 7.56413793644881e-06 & 3.7820689682244e-06 \tabularnewline
128 & 0.999985400995921 & 2.91980081577637e-05 & 1.45990040788819e-05 \tabularnewline
129 & 0.999950900667114 & 9.81986657725857e-05 & 4.90993328862929e-05 \tabularnewline
130 & 0.999964422745515 & 7.11545089704929e-05 & 3.55772544852464e-05 \tabularnewline
131 & 0.999870671272546 & 0.000258657454908256 & 0.000129328727454128 \tabularnewline
132 & 0.999525267893836 & 0.000949464212327694 & 0.000474732106163847 \tabularnewline
133 & 0.998086025008094 & 0.00382794998381147 & 0.00191397499190573 \tabularnewline
134 & 0.992956042375034 & 0.0140879152499321 & 0.00704395762496605 \tabularnewline
135 & 0.978677500038499 & 0.042644999923003 & 0.0213224999615015 \tabularnewline
136 & 0.972131472930112 & 0.0557370541397766 & 0.0278685270698883 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146902&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]8[/C][C]0.910635208641363[/C][C]0.178729582717275[/C][C]0.0893647913586373[/C][/ROW]
[ROW][C]9[/C][C]0.840910241852367[/C][C]0.318179516295267[/C][C]0.159089758147633[/C][/ROW]
[ROW][C]10[/C][C]0.765038726004919[/C][C]0.469922547990163[/C][C]0.234961273995081[/C][/ROW]
[ROW][C]11[/C][C]0.66912478621112[/C][C]0.66175042757776[/C][C]0.33087521378888[/C][/ROW]
[ROW][C]12[/C][C]0.560615037187919[/C][C]0.878769925624161[/C][C]0.439384962812081[/C][/ROW]
[ROW][C]13[/C][C]0.460075701650196[/C][C]0.920151403300392[/C][C]0.539924298349804[/C][/ROW]
[ROW][C]14[/C][C]0.456332748248799[/C][C]0.912665496497598[/C][C]0.543667251751201[/C][/ROW]
[ROW][C]15[/C][C]0.971640930069525[/C][C]0.0567181398609508[/C][C]0.0283590699304754[/C][/ROW]
[ROW][C]16[/C][C]0.972944281357361[/C][C]0.0541114372852777[/C][C]0.0270557186426388[/C][/ROW]
[ROW][C]17[/C][C]0.961880062031429[/C][C]0.0762398759371429[/C][C]0.0381199379685715[/C][/ROW]
[ROW][C]18[/C][C]0.976484931808599[/C][C]0.0470301363828019[/C][C]0.0235150681914009[/C][/ROW]
[ROW][C]19[/C][C]0.976545919953252[/C][C]0.0469081600934956[/C][C]0.0234540800467478[/C][/ROW]
[ROW][C]20[/C][C]0.982794984661623[/C][C]0.0344100306767548[/C][C]0.0172050153383774[/C][/ROW]
[ROW][C]21[/C][C]0.973693725812711[/C][C]0.0526125483745784[/C][C]0.0263062741872892[/C][/ROW]
[ROW][C]22[/C][C]0.978536301686983[/C][C]0.0429273966260348[/C][C]0.0214636983130174[/C][/ROW]
[ROW][C]23[/C][C]0.990660492023874[/C][C]0.0186790159522526[/C][C]0.00933950797612629[/C][/ROW]
[ROW][C]24[/C][C]0.9908357847969[/C][C]0.0183284304062005[/C][C]0.00916421520310023[/C][/ROW]
[ROW][C]25[/C][C]0.994018090117897[/C][C]0.0119638197642069[/C][C]0.00598190988210347[/C][/ROW]
[ROW][C]26[/C][C]0.996301643566643[/C][C]0.00739671286671428[/C][C]0.00369835643335714[/C][/ROW]
[ROW][C]27[/C][C]0.994398774775773[/C][C]0.0112024504484536[/C][C]0.00560122522422682[/C][/ROW]
[ROW][C]28[/C][C]0.996631588444996[/C][C]0.00673682311000833[/C][C]0.00336841155500417[/C][/ROW]
[ROW][C]29[/C][C]0.995753543507646[/C][C]0.008492912984708[/C][C]0.004246456492354[/C][/ROW]
[ROW][C]30[/C][C]0.99566476583769[/C][C]0.00867046832462036[/C][C]0.00433523416231018[/C][/ROW]
[ROW][C]31[/C][C]0.993560093875602[/C][C]0.0128798122487962[/C][C]0.0064399061243981[/C][/ROW]
[ROW][C]32[/C][C]0.991582290534892[/C][C]0.0168354189302156[/C][C]0.0084177094651078[/C][/ROW]
[ROW][C]33[/C][C]0.989543547191734[/C][C]0.0209129056165317[/C][C]0.0104564528082658[/C][/ROW]
[ROW][C]34[/C][C]0.989905743945579[/C][C]0.0201885121088414[/C][C]0.0100942560544207[/C][/ROW]
[ROW][C]35[/C][C]0.989666919368687[/C][C]0.0206661612626256[/C][C]0.0103330806313128[/C][/ROW]
[ROW][C]36[/C][C]0.985226183458566[/C][C]0.0295476330828688[/C][C]0.0147738165414344[/C][/ROW]
[ROW][C]37[/C][C]0.980092259229969[/C][C]0.0398154815400615[/C][C]0.0199077407700307[/C][/ROW]
[ROW][C]38[/C][C]0.984003301307217[/C][C]0.0319933973855652[/C][C]0.0159966986927826[/C][/ROW]
[ROW][C]39[/C][C]0.999995364179534[/C][C]9.27164093283227e-06[/C][C]4.63582046641614e-06[/C][/ROW]
[ROW][C]40[/C][C]0.999997328221249[/C][C]5.34355750123541e-06[/C][C]2.67177875061771e-06[/C][/ROW]
[ROW][C]41[/C][C]0.999995165667875[/C][C]9.6686642501223e-06[/C][C]4.83433212506115e-06[/C][/ROW]
[ROW][C]42[/C][C]0.999993006238338[/C][C]1.39875233241884e-05[/C][C]6.99376166209421e-06[/C][/ROW]
[ROW][C]43[/C][C]0.999993961143733[/C][C]1.20777125344418e-05[/C][C]6.03885626722088e-06[/C][/ROW]
[ROW][C]44[/C][C]0.999993322955226[/C][C]1.33540895470868e-05[/C][C]6.67704477354338e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999993064371459[/C][C]1.38712570829939e-05[/C][C]6.93562854149697e-06[/C][/ROW]
[ROW][C]46[/C][C]0.999993676120792[/C][C]1.26477584168546e-05[/C][C]6.32387920842732e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999996613847445[/C][C]6.77230510973992e-06[/C][C]3.38615255486996e-06[/C][/ROW]
[ROW][C]48[/C][C]0.999993956441333[/C][C]1.20871173347248e-05[/C][C]6.04355866736239e-06[/C][/ROW]
[ROW][C]49[/C][C]0.999993070737005[/C][C]1.38585259896582e-05[/C][C]6.92926299482912e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999995495290666[/C][C]9.00941866758306e-06[/C][C]4.50470933379153e-06[/C][/ROW]
[ROW][C]51[/C][C]0.999995842251285[/C][C]8.31549742997881e-06[/C][C]4.15774871498941e-06[/C][/ROW]
[ROW][C]52[/C][C]0.999992704441591[/C][C]1.45911168184506e-05[/C][C]7.29555840922532e-06[/C][/ROW]
[ROW][C]53[/C][C]0.999987496154049[/C][C]2.50076919016613e-05[/C][C]1.25038459508306e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999988075308044[/C][C]2.38493839115942e-05[/C][C]1.19246919557971e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999997490211432[/C][C]5.01957713673195e-06[/C][C]2.50978856836597e-06[/C][/ROW]
[ROW][C]56[/C][C]0.999996423431869[/C][C]7.15313626143449e-06[/C][C]3.57656813071724e-06[/C][/ROW]
[ROW][C]57[/C][C]0.999994503603104[/C][C]1.09927937928851e-05[/C][C]5.49639689644253e-06[/C][/ROW]
[ROW][C]58[/C][C]0.999991509186263[/C][C]1.69816274745649e-05[/C][C]8.49081373728245e-06[/C][/ROW]
[ROW][C]59[/C][C]0.999993353229065[/C][C]1.32935418696052e-05[/C][C]6.64677093480258e-06[/C][/ROW]
[ROW][C]60[/C][C]0.999996542317091[/C][C]6.91536581804834e-06[/C][C]3.45768290902417e-06[/C][/ROW]
[ROW][C]61[/C][C]0.999993952126735[/C][C]1.20957465306759e-05[/C][C]6.04787326533795e-06[/C][/ROW]
[ROW][C]62[/C][C]0.999995401999634[/C][C]9.1960007323486e-06[/C][C]4.5980003661743e-06[/C][/ROW]
[ROW][C]63[/C][C]0.999994033697589[/C][C]1.19326048212133e-05[/C][C]5.96630241060666e-06[/C][/ROW]
[ROW][C]64[/C][C]0.999994460663229[/C][C]1.10786735430984e-05[/C][C]5.5393367715492e-06[/C][/ROW]
[ROW][C]65[/C][C]0.999991253245796[/C][C]1.7493508408414e-05[/C][C]8.74675420420702e-06[/C][/ROW]
[ROW][C]66[/C][C]0.999997308218017[/C][C]5.38356396523963e-06[/C][C]2.69178198261981e-06[/C][/ROW]
[ROW][C]67[/C][C]0.999997136838105[/C][C]5.72632378948276e-06[/C][C]2.86316189474138e-06[/C][/ROW]
[ROW][C]68[/C][C]0.999999829584345[/C][C]3.40831310276354e-07[/C][C]1.70415655138177e-07[/C][/ROW]
[ROW][C]69[/C][C]0.999999692141945[/C][C]6.15716109244932e-07[/C][C]3.07858054622466e-07[/C][/ROW]
[ROW][C]70[/C][C]0.999999431781914[/C][C]1.13643617275712e-06[/C][C]5.68218086378561e-07[/C][/ROW]
[ROW][C]71[/C][C]0.999999998189367[/C][C]3.62126569811825e-09[/C][C]1.81063284905912e-09[/C][/ROW]
[ROW][C]72[/C][C]0.999999998748284[/C][C]2.50343150932453e-09[/C][C]1.25171575466226e-09[/C][/ROW]
[ROW][C]73[/C][C]0.999999998727562[/C][C]2.54487597092198e-09[/C][C]1.27243798546099e-09[/C][/ROW]
[ROW][C]74[/C][C]0.999999999547759[/C][C]9.04482881520476e-10[/C][C]4.52241440760238e-10[/C][/ROW]
[ROW][C]75[/C][C]0.999999999072027[/C][C]1.85594582575148e-09[/C][C]9.27972912875738e-10[/C][/ROW]
[ROW][C]76[/C][C]0.999999998045431[/C][C]3.90913859158575e-09[/C][C]1.95456929579287e-09[/C][/ROW]
[ROW][C]77[/C][C]0.999999995888947[/C][C]8.22210632910617e-09[/C][C]4.11105316455308e-09[/C][/ROW]
[ROW][C]78[/C][C]0.99999999287383[/C][C]1.42523406012255e-08[/C][C]7.12617030061275e-09[/C][/ROW]
[ROW][C]79[/C][C]0.999999986481434[/C][C]2.70371328125667e-08[/C][C]1.35185664062833e-08[/C][/ROW]
[ROW][C]80[/C][C]0.999999974767841[/C][C]5.04643183378049e-08[/C][C]2.52321591689024e-08[/C][/ROW]
[ROW][C]81[/C][C]0.999999981886515[/C][C]3.6226969704868e-08[/C][C]1.8113484852434e-08[/C][/ROW]
[ROW][C]82[/C][C]0.999999979538392[/C][C]4.09232166272499e-08[/C][C]2.0461608313625e-08[/C][/ROW]
[ROW][C]83[/C][C]0.999999966252481[/C][C]6.74950370775478e-08[/C][C]3.37475185387739e-08[/C][/ROW]
[ROW][C]84[/C][C]0.999999976201209[/C][C]4.75975830482832e-08[/C][C]2.37987915241416e-08[/C][/ROW]
[ROW][C]85[/C][C]0.999999954662236[/C][C]9.06755279244773e-08[/C][C]4.53377639622386e-08[/C][/ROW]
[ROW][C]86[/C][C]0.999999916205207[/C][C]1.67589585889433e-07[/C][C]8.37947929447163e-08[/C][/ROW]
[ROW][C]87[/C][C]0.999999833269842[/C][C]3.33460316586795e-07[/C][C]1.66730158293397e-07[/C][/ROW]
[ROW][C]88[/C][C]0.999999674068463[/C][C]6.51863073060909e-07[/C][C]3.25931536530455e-07[/C][/ROW]
[ROW][C]89[/C][C]0.99999971008004[/C][C]5.79839920923901e-07[/C][C]2.89919960461951e-07[/C][/ROW]
[ROW][C]90[/C][C]0.999999586829952[/C][C]8.26340096277805e-07[/C][C]4.13170048138902e-07[/C][/ROW]
[ROW][C]91[/C][C]0.999999208905998[/C][C]1.58218800336624e-06[/C][C]7.91094001683122e-07[/C][/ROW]
[ROW][C]92[/C][C]0.999998908891796[/C][C]2.18221640763933e-06[/C][C]1.09110820381966e-06[/C][/ROW]
[ROW][C]93[/C][C]0.999997958225951[/C][C]4.08354809840501e-06[/C][C]2.04177404920251e-06[/C][/ROW]
[ROW][C]94[/C][C]0.999996158596338[/C][C]7.68280732375914e-06[/C][C]3.84140366187957e-06[/C][/ROW]
[ROW][C]95[/C][C]0.999995686022958[/C][C]8.62795408307358e-06[/C][C]4.31397704153679e-06[/C][/ROW]
[ROW][C]96[/C][C]0.999999933680492[/C][C]1.32639016607563e-07[/C][C]6.63195083037817e-08[/C][/ROW]
[ROW][C]97[/C][C]0.999999853999914[/C][C]2.92000172777666e-07[/C][C]1.46000086388833e-07[/C][/ROW]
[ROW][C]98[/C][C]0.999999695015863[/C][C]6.09968273004016e-07[/C][C]3.04984136502008e-07[/C][/ROW]
[ROW][C]99[/C][C]0.999999826867131[/C][C]3.46265738297626e-07[/C][C]1.73132869148813e-07[/C][/ROW]
[ROW][C]100[/C][C]0.999999667205961[/C][C]6.65588078337187e-07[/C][C]3.32794039168593e-07[/C][/ROW]
[ROW][C]101[/C][C]0.999999291986241[/C][C]1.41602751833341e-06[/C][C]7.08013759166703e-07[/C][/ROW]
[ROW][C]102[/C][C]0.999998877535411[/C][C]2.2449291774456e-06[/C][C]1.1224645887228e-06[/C][/ROW]
[ROW][C]103[/C][C]0.999998771835677[/C][C]2.45632864509219e-06[/C][C]1.22816432254609e-06[/C][/ROW]
[ROW][C]104[/C][C]0.999997532159059[/C][C]4.93568188120598e-06[/C][C]2.46784094060299e-06[/C][/ROW]
[ROW][C]105[/C][C]0.999995635079149[/C][C]8.72984170221102e-06[/C][C]4.36492085110551e-06[/C][/ROW]
[ROW][C]106[/C][C]0.999991600241643[/C][C]1.67995167133773e-05[/C][C]8.39975835668866e-06[/C][/ROW]
[ROW][C]107[/C][C]0.999986845695179[/C][C]2.6308609641962e-05[/C][C]1.3154304820981e-05[/C][/ROW]
[ROW][C]108[/C][C]0.999974805171333[/C][C]5.03896573338682e-05[/C][C]2.51948286669341e-05[/C][/ROW]
[ROW][C]109[/C][C]0.99995482528943[/C][C]9.03494211395983e-05[/C][C]4.51747105697991e-05[/C][/ROW]
[ROW][C]110[/C][C]0.999920841073062[/C][C]0.000158317853875244[/C][C]7.91589269376218e-05[/C][/ROW]
[ROW][C]111[/C][C]0.999849942400118[/C][C]0.000300115199763868[/C][C]0.000150057599881934[/C][/ROW]
[ROW][C]112[/C][C]0.999827143460867[/C][C]0.000345713078265116[/C][C]0.000172856539132558[/C][/ROW]
[ROW][C]113[/C][C]0.999755015285522[/C][C]0.000489969428955791[/C][C]0.000244984714477896[/C][/ROW]
[ROW][C]114[/C][C]0.999552149100826[/C][C]0.00089570179834757[/C][C]0.000447850899173785[/C][/ROW]
[ROW][C]115[/C][C]0.999242772213396[/C][C]0.00151445557320715[/C][C]0.000757227786603577[/C][/ROW]
[ROW][C]116[/C][C]0.99876379196356[/C][C]0.00247241607287967[/C][C]0.00123620803643984[/C][/ROW]
[ROW][C]117[/C][C]0.997860455675616[/C][C]0.00427908864876821[/C][C]0.0021395443243841[/C][/ROW]
[ROW][C]118[/C][C]0.997926237768284[/C][C]0.00414752446343292[/C][C]0.00207376223171646[/C][/ROW]
[ROW][C]119[/C][C]0.999716959084888[/C][C]0.000566081830224086[/C][C]0.000283040915112043[/C][/ROW]
[ROW][C]120[/C][C]0.999441588248932[/C][C]0.00111682350213627[/C][C]0.000558411751068135[/C][/ROW]
[ROW][C]121[/C][C]0.99999999138818[/C][C]1.72236404529182e-08[/C][C]8.61182022645909e-09[/C][/ROW]
[ROW][C]122[/C][C]0.999999964302282[/C][C]7.13954363582444e-08[/C][C]3.56977181791222e-08[/C][/ROW]
[ROW][C]123[/C][C]0.999999967324449[/C][C]6.53511020103706e-08[/C][C]3.26755510051853e-08[/C][/ROW]
[ROW][C]124[/C][C]0.999999867822714[/C][C]2.64354572339659e-07[/C][C]1.3217728616983e-07[/C][/ROW]
[ROW][C]125[/C][C]0.999999466763725[/C][C]1.06647255009605e-06[/C][C]5.33236275048025e-07[/C][/ROW]
[ROW][C]126[/C][C]0.999998044435778[/C][C]3.91112844427639e-06[/C][C]1.9555642221382e-06[/C][/ROW]
[ROW][C]127[/C][C]0.999996217931032[/C][C]7.56413793644881e-06[/C][C]3.7820689682244e-06[/C][/ROW]
[ROW][C]128[/C][C]0.999985400995921[/C][C]2.91980081577637e-05[/C][C]1.45990040788819e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999950900667114[/C][C]9.81986657725857e-05[/C][C]4.90993328862929e-05[/C][/ROW]
[ROW][C]130[/C][C]0.999964422745515[/C][C]7.11545089704929e-05[/C][C]3.55772544852464e-05[/C][/ROW]
[ROW][C]131[/C][C]0.999870671272546[/C][C]0.000258657454908256[/C][C]0.000129328727454128[/C][/ROW]
[ROW][C]132[/C][C]0.999525267893836[/C][C]0.000949464212327694[/C][C]0.000474732106163847[/C][/ROW]
[ROW][C]133[/C][C]0.998086025008094[/C][C]0.00382794998381147[/C][C]0.00191397499190573[/C][/ROW]
[ROW][C]134[/C][C]0.992956042375034[/C][C]0.0140879152499321[/C][C]0.00704395762496605[/C][/ROW]
[ROW][C]135[/C][C]0.978677500038499[/C][C]0.042644999923003[/C][C]0.0213224999615015[/C][/ROW]
[ROW][C]136[/C][C]0.972131472930112[/C][C]0.0557370541397766[/C][C]0.0278685270698883[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146902&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146902&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
80.9106352086413630.1787295827172750.0893647913586373
90.8409102418523670.3181795162952670.159089758147633
100.7650387260049190.4699225479901630.234961273995081
110.669124786211120.661750427577760.33087521378888
120.5606150371879190.8787699256241610.439384962812081
130.4600757016501960.9201514033003920.539924298349804
140.4563327482487990.9126654964975980.543667251751201
150.9716409300695250.05671813986095080.0283590699304754
160.9729442813573610.05411143728527770.0270557186426388
170.9618800620314290.07623987593714290.0381199379685715
180.9764849318085990.04703013638280190.0235150681914009
190.9765459199532520.04690816009349560.0234540800467478
200.9827949846616230.03441003067675480.0172050153383774
210.9736937258127110.05261254837457840.0263062741872892
220.9785363016869830.04292739662603480.0214636983130174
230.9906604920238740.01867901595225260.00933950797612629
240.99083578479690.01832843040620050.00916421520310023
250.9940180901178970.01196381976420690.00598190988210347
260.9963016435666430.007396712866714280.00369835643335714
270.9943987747757730.01120245044845360.00560122522422682
280.9966315884449960.006736823110008330.00336841155500417
290.9957535435076460.0084929129847080.004246456492354
300.995664765837690.008670468324620360.00433523416231018
310.9935600938756020.01287981224879620.0064399061243981
320.9915822905348920.01683541893021560.0084177094651078
330.9895435471917340.02091290561653170.0104564528082658
340.9899057439455790.02018851210884140.0100942560544207
350.9896669193686870.02066616126262560.0103330806313128
360.9852261834585660.02954763308286880.0147738165414344
370.9800922592299690.03981548154006150.0199077407700307
380.9840033013072170.03199339738556520.0159966986927826
390.9999953641795349.27164093283227e-064.63582046641614e-06
400.9999973282212495.34355750123541e-062.67177875061771e-06
410.9999951656678759.6686642501223e-064.83433212506115e-06
420.9999930062383381.39875233241884e-056.99376166209421e-06
430.9999939611437331.20777125344418e-056.03885626722088e-06
440.9999933229552261.33540895470868e-056.67704477354338e-06
450.9999930643714591.38712570829939e-056.93562854149697e-06
460.9999936761207921.26477584168546e-056.32387920842732e-06
470.9999966138474456.77230510973992e-063.38615255486996e-06
480.9999939564413331.20871173347248e-056.04355866736239e-06
490.9999930707370051.38585259896582e-056.92926299482912e-06
500.9999954952906669.00941866758306e-064.50470933379153e-06
510.9999958422512858.31549742997881e-064.15774871498941e-06
520.9999927044415911.45911168184506e-057.29555840922532e-06
530.9999874961540492.50076919016613e-051.25038459508306e-05
540.9999880753080442.38493839115942e-051.19246919557971e-05
550.9999974902114325.01957713673195e-062.50978856836597e-06
560.9999964234318697.15313626143449e-063.57656813071724e-06
570.9999945036031041.09927937928851e-055.49639689644253e-06
580.9999915091862631.69816274745649e-058.49081373728245e-06
590.9999933532290651.32935418696052e-056.64677093480258e-06
600.9999965423170916.91536581804834e-063.45768290902417e-06
610.9999939521267351.20957465306759e-056.04787326533795e-06
620.9999954019996349.1960007323486e-064.5980003661743e-06
630.9999940336975891.19326048212133e-055.96630241060666e-06
640.9999944606632291.10786735430984e-055.5393367715492e-06
650.9999912532457961.7493508408414e-058.74675420420702e-06
660.9999973082180175.38356396523963e-062.69178198261981e-06
670.9999971368381055.72632378948276e-062.86316189474138e-06
680.9999998295843453.40831310276354e-071.70415655138177e-07
690.9999996921419456.15716109244932e-073.07858054622466e-07
700.9999994317819141.13643617275712e-065.68218086378561e-07
710.9999999981893673.62126569811825e-091.81063284905912e-09
720.9999999987482842.50343150932453e-091.25171575466226e-09
730.9999999987275622.54487597092198e-091.27243798546099e-09
740.9999999995477599.04482881520476e-104.52241440760238e-10
750.9999999990720271.85594582575148e-099.27972912875738e-10
760.9999999980454313.90913859158575e-091.95456929579287e-09
770.9999999958889478.22210632910617e-094.11105316455308e-09
780.999999992873831.42523406012255e-087.12617030061275e-09
790.9999999864814342.70371328125667e-081.35185664062833e-08
800.9999999747678415.04643183378049e-082.52321591689024e-08
810.9999999818865153.6226969704868e-081.8113484852434e-08
820.9999999795383924.09232166272499e-082.0461608313625e-08
830.9999999662524816.74950370775478e-083.37475185387739e-08
840.9999999762012094.75975830482832e-082.37987915241416e-08
850.9999999546622369.06755279244773e-084.53377639622386e-08
860.9999999162052071.67589585889433e-078.37947929447163e-08
870.9999998332698423.33460316586795e-071.66730158293397e-07
880.9999996740684636.51863073060909e-073.25931536530455e-07
890.999999710080045.79839920923901e-072.89919960461951e-07
900.9999995868299528.26340096277805e-074.13170048138902e-07
910.9999992089059981.58218800336624e-067.91094001683122e-07
920.9999989088917962.18221640763933e-061.09110820381966e-06
930.9999979582259514.08354809840501e-062.04177404920251e-06
940.9999961585963387.68280732375914e-063.84140366187957e-06
950.9999956860229588.62795408307358e-064.31397704153679e-06
960.9999999336804921.32639016607563e-076.63195083037817e-08
970.9999998539999142.92000172777666e-071.46000086388833e-07
980.9999996950158636.09968273004016e-073.04984136502008e-07
990.9999998268671313.46265738297626e-071.73132869148813e-07
1000.9999996672059616.65588078337187e-073.32794039168593e-07
1010.9999992919862411.41602751833341e-067.08013759166703e-07
1020.9999988775354112.2449291774456e-061.1224645887228e-06
1030.9999987718356772.45632864509219e-061.22816432254609e-06
1040.9999975321590594.93568188120598e-062.46784094060299e-06
1050.9999956350791498.72984170221102e-064.36492085110551e-06
1060.9999916002416431.67995167133773e-058.39975835668866e-06
1070.9999868456951792.6308609641962e-051.3154304820981e-05
1080.9999748051713335.03896573338682e-052.51948286669341e-05
1090.999954825289439.03494211395983e-054.51747105697991e-05
1100.9999208410730620.0001583178538752447.91589269376218e-05
1110.9998499424001180.0003001151997638680.000150057599881934
1120.9998271434608670.0003457130782651160.000172856539132558
1130.9997550152855220.0004899694289557910.000244984714477896
1140.9995521491008260.000895701798347570.000447850899173785
1150.9992427722133960.001514455573207150.000757227786603577
1160.998763791963560.002472416072879670.00123620803643984
1170.9978604556756160.004279088648768210.0021395443243841
1180.9979262377682840.004147524463432920.00207376223171646
1190.9997169590848880.0005660818302240860.000283040915112043
1200.9994415882489320.001116823502136270.000558411751068135
1210.999999991388181.72236404529182e-088.61182022645909e-09
1220.9999999643022827.13954363582444e-083.56977181791222e-08
1230.9999999673244496.53511020103706e-083.26755510051853e-08
1240.9999998678227142.64354572339659e-071.3217728616983e-07
1250.9999994667637251.06647255009605e-065.33236275048025e-07
1260.9999980444357783.91112844427639e-061.9555642221382e-06
1270.9999962179310327.56413793644881e-063.7820689682244e-06
1280.9999854009959212.91980081577637e-051.45990040788819e-05
1290.9999509006671149.81986657725857e-054.90993328862929e-05
1300.9999644227455157.11545089704929e-053.55772544852464e-05
1310.9998706712725460.0002586574549082560.000129328727454128
1320.9995252678938360.0009494642123276940.000474732106163847
1330.9980860250080940.003827949983811470.00191397499190573
1340.9929560423750340.01408791524993210.00704395762496605
1350.9786775000384990.0426449999230030.0213224999615015
1360.9721314729301120.05573705413977660.0278685270698883







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level990.767441860465116NOK
5% type I error level1170.906976744186046NOK
10% type I error level1220.945736434108527NOK

\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 & 99 & 0.767441860465116 & NOK \tabularnewline
5% type I error level & 117 & 0.906976744186046 & NOK \tabularnewline
10% type I error level & 122 & 0.945736434108527 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146902&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]99[/C][C]0.767441860465116[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]117[/C][C]0.906976744186046[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]122[/C][C]0.945736434108527[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146902&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146902&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 level990.767441860465116NOK
5% type I error level1170.906976744186046NOK
10% type I error level1220.945736434108527NOK



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')
}