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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 10:12:52 -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/t1322147623q5msb2yebnipy2w.htm/, Retrieved Thu, 28 Mar 2024 20:07:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146946, Retrieved Thu, 28 Mar 2024 20:07:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72
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] [6bdab4f5b22620afa7d9dc512ad4e377]
- R  D    [Multiple Regression] [Multiple regression] [2011-11-24 15:12:52] [5363b79245edacd2d961915f77b3b63a] [Current]
- 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	13	20
17	26	27	28
1	0	0	0
114	37	37	40
95	47	39	60
148	80	99	60
56	21	21	44
26	36	33	52
63	35	36	60
96	40	44	52
74	35	33	24
65	46	47	64
40	20	19	26
173	24	41	48
28	19	22	36
55	15	17	40
58	48	46	64
25	0	0	20
103	38	31	79
29	12	20	16
31	10	10	52
43	51	55	52
74	4	6	44
99	24	17	29
25	39	33	40
69	19	33	28
62	23	32	49
25	39	37	60
38	37	44	52
57	20	22	28
52	20	15	56
91	41	18	35
48	26	25	12
52	0	7	32
35	31	35	48
0	0	0	0
31	8	14	48
107	35	31	31
242	3	9	64
41	47	59	72
57	42	62	36
32	11	12	56
17	10	23	28
36	26	31	52
29	27	57	44
22	0	23	44
21	15	14	55
41	32	31	36
64	13	17	48
71	24	24	44
28	10	11	66
36	14	16	40
45	24	32	44
22	29	36	48
27	40	37	68
38	22	25	24
26	27	30	32
41	8	10	44
21	27	16	52
28	0	3	56
36	0	0	68
58	17	17	32
65	7	9	34
29	18	22	36
21	7	5	34
19	24	23	56
55	18	16	64
119	39	53	52
34	17	23	48
25	0	0	40
113	39	51	36
46	20	25	10
28	29	51	48
63	27	46	25
52	23	16	68
35	0	0	36
32	31	25	32
45	19	34	36
42	12	14	43
28	23	32	17
32	33	24	52
32	21	16	56
27	17	19	40
69	27	27	48
30	14	24	40
48	12	12	48
57	21	43	68
36	14	13	44
20	14	19	40
54	22	24	40
26	25	27	28
58	36	26	40
35	10	14	44
28	16	26	20
8	12	15	22
96	20	30	56
50	38	33	52
15	13	14	2
65	12	11	52
33	11	12	30
7	8	8	3
17	22	22	20
55	14	12	48
32	7	6	32
22	14	10	36
41	2	1	45
50	35	31	40
7	5	5	8
0	0	0	0
26	34	35	32
22	12	15	28
26	34	36	44
37	30	27	56
29	21	36	13
0	0	0	0
0	0	0	0
42	28	29	52
51	16	19	51
77	12	16	52
32	14	15	48
63	7	1	3
50	41	36	48
18	21	22	24
37	28	16	37
23	1	1	32
19	10	10	8
39	31	31	44
38	7	22	48
55	26	22	56
22	1	0	8
7	0	0	0
21	12	10	25
5	0	0	4
21	17	9	12
1	5	0	0
22	4	0	6
0	0	0	0
31	6	7	48
25	0	2	52
0	0	0	0
4	0	0	0
20	15	16	12
29	0	25	28
33	12	6	40




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146946&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146946&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146946&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
CompendiumViews[t] = + 9.63443536226323 + 0.159598779227482BloggedComputations[t] + 0.471884456993842Hyperlinks[t] + 0.539024502581444submittedfeedback[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CompendiumViews[t] =  +  9.63443536226323 +  0.159598779227482BloggedComputations[t] +  0.471884456993842Hyperlinks[t] +  0.539024502581444submittedfeedback[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146946&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CompendiumViews[t] =  +  9.63443536226323 +  0.159598779227482BloggedComputations[t] +  0.471884456993842Hyperlinks[t] +  0.539024502581444submittedfeedback[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146946&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146946&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] = + 9.63443536226323 + 0.159598779227482BloggedComputations[t] + 0.471884456993842Hyperlinks[t] + 0.539024502581444submittedfeedback[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.634435362263235.5125541.74770.0827040.041352
BloggedComputations0.1595987792274820.3699340.43140.6668230.333412
Hyperlinks0.4718844569938420.3225541.4630.145720.07286
submittedfeedback0.5390245025814440.1456783.70010.0003090.000154

\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) & 9.63443536226323 & 5.512554 & 1.7477 & 0.082704 & 0.041352 \tabularnewline
BloggedComputations & 0.159598779227482 & 0.369934 & 0.4314 & 0.666823 & 0.333412 \tabularnewline
Hyperlinks & 0.471884456993842 & 0.322554 & 1.463 & 0.14572 & 0.07286 \tabularnewline
submittedfeedback & 0.539024502581444 & 0.145678 & 3.7001 & 0.000309 & 0.000154 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146946&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]9.63443536226323[/C][C]5.512554[/C][C]1.7477[/C][C]0.082704[/C][C]0.041352[/C][/ROW]
[ROW][C]BloggedComputations[/C][C]0.159598779227482[/C][C]0.369934[/C][C]0.4314[/C][C]0.666823[/C][C]0.333412[/C][/ROW]
[ROW][C]Hyperlinks[/C][C]0.471884456993842[/C][C]0.322554[/C][C]1.463[/C][C]0.14572[/C][C]0.07286[/C][/ROW]
[ROW][C]submittedfeedback[/C][C]0.539024502581444[/C][C]0.145678[/C][C]3.7001[/C][C]0.000309[/C][C]0.000154[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146946&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146946&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)9.634435362263235.5125541.74770.0827040.041352
BloggedComputations0.1595987792274820.3699340.43140.6668230.333412
Hyperlinks0.4718844569938420.3225541.4630.145720.07286
submittedfeedback0.5390245025814440.1456783.70010.0003090.000154







Multiple Linear Regression - Regression Statistics
Multiple R0.507587870131413
R-squared0.257645445904544
Adjusted R-squared0.241737848316784
F-TEST (value)16.1963768874058
F-TEST (DF numerator)3
F-TEST (DF denominator)140
p-value4.30712798760169e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation28.9949877595622
Sum Squared Residuals117699.304124803

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.507587870131413 \tabularnewline
R-squared & 0.257645445904544 \tabularnewline
Adjusted R-squared & 0.241737848316784 \tabularnewline
F-TEST (value) & 16.1963768874058 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 140 \tabularnewline
p-value & 4.30712798760169e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 28.9949877595622 \tabularnewline
Sum Squared Residuals & 117699.304124803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146946&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.507587870131413[/C][/ROW]
[ROW][C]R-squared[/C][C]0.257645445904544[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.241737848316784[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.1963768874058[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]140[/C][/ROW]
[ROW][C]p-value[/C][C]4.30712798760169e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]28.9949877595622[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]117699.304124803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146946&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146946&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 R0.507587870131413
R-squared0.257645445904544
Adjusted R-squared0.241737848316784
F-TEST (value)16.1963768874058
F-TEST (DF numerator)3
F-TEST (DF denominator)140
p-value4.30712798760169e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation28.9949877595622
Sum Squared Residuals117699.304124803







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16828.624207484769339.3757925152307
21741.6175700332919-24.6175700332919
319.63443536226322-8.63443536226322
411454.5602952057159.43970479429
59567.880541963601427.1194580363986
6148101.46036909773946.5396309022612
75646.61266143649469.3873385635054
82658.9814526294845-32.9814526294845
96364.54970324189-1.54970324189008
109664.810576773326731.1894232266733
117443.729167777976630.2708322220234
126573.6521168506504-8.65211685065042
134035.80685269681344.19314730318658
1417358.6852449243797114.31475507562
152842.4531523143819-14.4531523143819
165541.611432922828513.3885670771715
175873.4994299521115-15.4994299521115
182520.41492541389214.58507458610788
1910372.910542843650830.0894571563492
202929.611701894173-0.611701894172959
213143.9785418587116-12.9785418587116
224371.7568923717612-28.7568923717612
237436.821215334719837.1787846652802
249937.1185524074861.88144759252
252552.9919549361896-27.9919549361896
266943.331685320662625.6683146793374
276254.8177105347897.18228946521097
282565.6599828157939-40.6599828157939
293864.3317804356442-26.3317804356442
305738.300555072957818.6994449270422
315250.09004994628141.90995005371861
329143.537763126829747.4622368731703
334832.049409078001115.9505909219989
345230.186410643826321.8135893561737
353556.971129637009-21.971129637009
3609.63443536226322-9.63443536226322
373143.3907841179062-12.3907841179062
3810746.55857038205960.441429617941
3924248.8577599781027193.142240021897
404183.7865251344556-42.7865251344556
415764.9993025163677-7.99930251636766
423247.2380075622525-15.2380075622525
431737.1764517376769-20.1764517376769
443656.441695923222-20.441695923222
452964.5580945636378-35.5580945636378
462244.2048559867051-22.2048559867051
472148.2811470905687-27.2811470905687
484148.7748965572838-7.77489655728375
496445.604431385025118.3955686149749
507148.507111145158622.4928888548414
512851.9967693518456-23.9967693518456
523640.9799496866072-4.97994968660723
534552.2821868011093-7.28218680110929
542257.1238165355479-35.1238165355479
552770.1317776156729-43.1317776156729
563837.87930799206850.120692007931454
572645.3489201938267-19.3489201938267
584139.34714827960511.65285172039494
592149.5230278475418-28.5230278475418
602841.2354608778056-13.2354608778056
613646.2881015378014-10.2881015378014
625837.61843446063220.381565539368
636533.325420017569331.6745799824307
642942.2935535351544-13.2935535351544
652131.4378821895939-10.4378821895939
661954.5035207191421-35.5035207191421
675554.55493286547180.445067134528177
6811968.897938107043850.1020618929563
693449.0741332438981-15.0741332438981
702531.195415465521-6.19541546552101
7111359.32977715175353.670222848247
724630.013767397473415.9862326025266
732864.2020833904555-36.2020833904555
746349.125899987658113.8741000123419
755257.509024771935-5.50902477193501
763529.03931745519525.96068254480477
773243.6278930257674-11.6278930257674
784548.115765798308-3.11576579830801
794241.33405672190890.665943278091089
802837.5689264521828-9.5689264521828
813254.2556961788575-22.2556961788575
823250.7215331825027-18.7215331825027
832742.8743993952712-15.8743993952712
846952.557658864148316.4423411358517
853044.755025342558-14.755025342558
864843.08541032082854.91458967917155
875769.9307075523138-12.9307075523138
883641.7203943259515-5.72039432595148
892042.3956030575888-22.3956030575888
905446.03181557637787.96818442362218
912641.4579712540645-15.4579712540645
925849.20996739955038.79003260044975
933541.5538836660354-6.55388366603539
942835.2375017633717-7.23750176337172
95830.4864266246924-22.4864266246924
969657.16831680118938.831683198811
975059.3006501879394-9.30065018793944
981519.3936508952972-4.39365089529717
996544.769623874160420.2303761258396
1003333.223370495135-0.223370495134966
101716.3033747597781-9.30337475977815
1021734.3075566107612-17.3075566107612
1035543.404607879283411.5953921207166
1043230.83171764142491.16828235857512
1052235.9925449343184-13.9925449343184
1064134.6816199938776.31838000612296
1075051.409790905292-1.40979090529198
108717.1040475640214-10.1040475640214
10909.63443536226322-9.63443536226322
1102648.8255339333883-22.8255339333883
1112233.7205736401811-11.7205736401811
1122655.7657124213595-29.7657124213595
1133757.3486512224823-20.3486512224823
1142936.9811687113774-7.98116871137743
11509.63443536226322-9.63443536226322
11609.63443536226322-9.63443536226322
1174255.8171245676893-13.8171245676893
1185148.64407014443962.3559298555604
1197747.129046159129629.8709538408704
1203244.8202612502649-12.8202612502649
1216312.840584781593850.1594152184062
1225059.0390018862776-9.03900188627764
1231836.3040558418595-18.3040558418595
1243741.5972590880476-4.59725908804765
1252327.5147026810908-4.51470268109078
1261920.261463745128-1.26146374512802
1273952.9274937987078-13.9274937987078
1283847.0062609946295-9.00626099462945
1295554.35083382060320.649166179396827
1302214.10623016214237.89376983785773
13179.63443536226322-2.63443536226322
1322129.7440778474675-8.74407784746755
133511.790533372589-6.790533372589
1342123.0628687530523-2.06286875305234
135110.4324292584006-9.43242925840064
1362213.50697749466188.49302250533817
13709.63443536226322-9.63443536226322
1383139.7683953604943-8.76839536049435
1392538.607478410486-13.607478410486
14009.63443536226322-9.63443536226322
14149.63443536226322-5.63443536226322
1422026.0468623935543-6.04686239355427
1432936.5242328593897-7.52423285938971
1443335.9419075582138-2.94190755821384

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 68 & 28.6242074847693 & 39.3757925152307 \tabularnewline
2 & 17 & 41.6175700332919 & -24.6175700332919 \tabularnewline
3 & 1 & 9.63443536226322 & -8.63443536226322 \tabularnewline
4 & 114 & 54.56029520571 & 59.43970479429 \tabularnewline
5 & 95 & 67.8805419636014 & 27.1194580363986 \tabularnewline
6 & 148 & 101.460369097739 & 46.5396309022612 \tabularnewline
7 & 56 & 46.6126614364946 & 9.3873385635054 \tabularnewline
8 & 26 & 58.9814526294845 & -32.9814526294845 \tabularnewline
9 & 63 & 64.54970324189 & -1.54970324189008 \tabularnewline
10 & 96 & 64.8105767733267 & 31.1894232266733 \tabularnewline
11 & 74 & 43.7291677779766 & 30.2708322220234 \tabularnewline
12 & 65 & 73.6521168506504 & -8.65211685065042 \tabularnewline
13 & 40 & 35.8068526968134 & 4.19314730318658 \tabularnewline
14 & 173 & 58.6852449243797 & 114.31475507562 \tabularnewline
15 & 28 & 42.4531523143819 & -14.4531523143819 \tabularnewline
16 & 55 & 41.6114329228285 & 13.3885670771715 \tabularnewline
17 & 58 & 73.4994299521115 & -15.4994299521115 \tabularnewline
18 & 25 & 20.4149254138921 & 4.58507458610788 \tabularnewline
19 & 103 & 72.9105428436508 & 30.0894571563492 \tabularnewline
20 & 29 & 29.611701894173 & -0.611701894172959 \tabularnewline
21 & 31 & 43.9785418587116 & -12.9785418587116 \tabularnewline
22 & 43 & 71.7568923717612 & -28.7568923717612 \tabularnewline
23 & 74 & 36.8212153347198 & 37.1787846652802 \tabularnewline
24 & 99 & 37.11855240748 & 61.88144759252 \tabularnewline
25 & 25 & 52.9919549361896 & -27.9919549361896 \tabularnewline
26 & 69 & 43.3316853206626 & 25.6683146793374 \tabularnewline
27 & 62 & 54.817710534789 & 7.18228946521097 \tabularnewline
28 & 25 & 65.6599828157939 & -40.6599828157939 \tabularnewline
29 & 38 & 64.3317804356442 & -26.3317804356442 \tabularnewline
30 & 57 & 38.3005550729578 & 18.6994449270422 \tabularnewline
31 & 52 & 50.0900499462814 & 1.90995005371861 \tabularnewline
32 & 91 & 43.5377631268297 & 47.4622368731703 \tabularnewline
33 & 48 & 32.0494090780011 & 15.9505909219989 \tabularnewline
34 & 52 & 30.1864106438263 & 21.8135893561737 \tabularnewline
35 & 35 & 56.971129637009 & -21.971129637009 \tabularnewline
36 & 0 & 9.63443536226322 & -9.63443536226322 \tabularnewline
37 & 31 & 43.3907841179062 & -12.3907841179062 \tabularnewline
38 & 107 & 46.558570382059 & 60.441429617941 \tabularnewline
39 & 242 & 48.8577599781027 & 193.142240021897 \tabularnewline
40 & 41 & 83.7865251344556 & -42.7865251344556 \tabularnewline
41 & 57 & 64.9993025163677 & -7.99930251636766 \tabularnewline
42 & 32 & 47.2380075622525 & -15.2380075622525 \tabularnewline
43 & 17 & 37.1764517376769 & -20.1764517376769 \tabularnewline
44 & 36 & 56.441695923222 & -20.441695923222 \tabularnewline
45 & 29 & 64.5580945636378 & -35.5580945636378 \tabularnewline
46 & 22 & 44.2048559867051 & -22.2048559867051 \tabularnewline
47 & 21 & 48.2811470905687 & -27.2811470905687 \tabularnewline
48 & 41 & 48.7748965572838 & -7.77489655728375 \tabularnewline
49 & 64 & 45.6044313850251 & 18.3955686149749 \tabularnewline
50 & 71 & 48.5071111451586 & 22.4928888548414 \tabularnewline
51 & 28 & 51.9967693518456 & -23.9967693518456 \tabularnewline
52 & 36 & 40.9799496866072 & -4.97994968660723 \tabularnewline
53 & 45 & 52.2821868011093 & -7.28218680110929 \tabularnewline
54 & 22 & 57.1238165355479 & -35.1238165355479 \tabularnewline
55 & 27 & 70.1317776156729 & -43.1317776156729 \tabularnewline
56 & 38 & 37.8793079920685 & 0.120692007931454 \tabularnewline
57 & 26 & 45.3489201938267 & -19.3489201938267 \tabularnewline
58 & 41 & 39.3471482796051 & 1.65285172039494 \tabularnewline
59 & 21 & 49.5230278475418 & -28.5230278475418 \tabularnewline
60 & 28 & 41.2354608778056 & -13.2354608778056 \tabularnewline
61 & 36 & 46.2881015378014 & -10.2881015378014 \tabularnewline
62 & 58 & 37.618434460632 & 20.381565539368 \tabularnewline
63 & 65 & 33.3254200175693 & 31.6745799824307 \tabularnewline
64 & 29 & 42.2935535351544 & -13.2935535351544 \tabularnewline
65 & 21 & 31.4378821895939 & -10.4378821895939 \tabularnewline
66 & 19 & 54.5035207191421 & -35.5035207191421 \tabularnewline
67 & 55 & 54.5549328654718 & 0.445067134528177 \tabularnewline
68 & 119 & 68.8979381070438 & 50.1020618929563 \tabularnewline
69 & 34 & 49.0741332438981 & -15.0741332438981 \tabularnewline
70 & 25 & 31.195415465521 & -6.19541546552101 \tabularnewline
71 & 113 & 59.329777151753 & 53.670222848247 \tabularnewline
72 & 46 & 30.0137673974734 & 15.9862326025266 \tabularnewline
73 & 28 & 64.2020833904555 & -36.2020833904555 \tabularnewline
74 & 63 & 49.1258999876581 & 13.8741000123419 \tabularnewline
75 & 52 & 57.509024771935 & -5.50902477193501 \tabularnewline
76 & 35 & 29.0393174551952 & 5.96068254480477 \tabularnewline
77 & 32 & 43.6278930257674 & -11.6278930257674 \tabularnewline
78 & 45 & 48.115765798308 & -3.11576579830801 \tabularnewline
79 & 42 & 41.3340567219089 & 0.665943278091089 \tabularnewline
80 & 28 & 37.5689264521828 & -9.5689264521828 \tabularnewline
81 & 32 & 54.2556961788575 & -22.2556961788575 \tabularnewline
82 & 32 & 50.7215331825027 & -18.7215331825027 \tabularnewline
83 & 27 & 42.8743993952712 & -15.8743993952712 \tabularnewline
84 & 69 & 52.5576588641483 & 16.4423411358517 \tabularnewline
85 & 30 & 44.755025342558 & -14.755025342558 \tabularnewline
86 & 48 & 43.0854103208285 & 4.91458967917155 \tabularnewline
87 & 57 & 69.9307075523138 & -12.9307075523138 \tabularnewline
88 & 36 & 41.7203943259515 & -5.72039432595148 \tabularnewline
89 & 20 & 42.3956030575888 & -22.3956030575888 \tabularnewline
90 & 54 & 46.0318155763778 & 7.96818442362218 \tabularnewline
91 & 26 & 41.4579712540645 & -15.4579712540645 \tabularnewline
92 & 58 & 49.2099673995503 & 8.79003260044975 \tabularnewline
93 & 35 & 41.5538836660354 & -6.55388366603539 \tabularnewline
94 & 28 & 35.2375017633717 & -7.23750176337172 \tabularnewline
95 & 8 & 30.4864266246924 & -22.4864266246924 \tabularnewline
96 & 96 & 57.168316801189 & 38.831683198811 \tabularnewline
97 & 50 & 59.3006501879394 & -9.30065018793944 \tabularnewline
98 & 15 & 19.3936508952972 & -4.39365089529717 \tabularnewline
99 & 65 & 44.7696238741604 & 20.2303761258396 \tabularnewline
100 & 33 & 33.223370495135 & -0.223370495134966 \tabularnewline
101 & 7 & 16.3033747597781 & -9.30337475977815 \tabularnewline
102 & 17 & 34.3075566107612 & -17.3075566107612 \tabularnewline
103 & 55 & 43.4046078792834 & 11.5953921207166 \tabularnewline
104 & 32 & 30.8317176414249 & 1.16828235857512 \tabularnewline
105 & 22 & 35.9925449343184 & -13.9925449343184 \tabularnewline
106 & 41 & 34.681619993877 & 6.31838000612296 \tabularnewline
107 & 50 & 51.409790905292 & -1.40979090529198 \tabularnewline
108 & 7 & 17.1040475640214 & -10.1040475640214 \tabularnewline
109 & 0 & 9.63443536226322 & -9.63443536226322 \tabularnewline
110 & 26 & 48.8255339333883 & -22.8255339333883 \tabularnewline
111 & 22 & 33.7205736401811 & -11.7205736401811 \tabularnewline
112 & 26 & 55.7657124213595 & -29.7657124213595 \tabularnewline
113 & 37 & 57.3486512224823 & -20.3486512224823 \tabularnewline
114 & 29 & 36.9811687113774 & -7.98116871137743 \tabularnewline
115 & 0 & 9.63443536226322 & -9.63443536226322 \tabularnewline
116 & 0 & 9.63443536226322 & -9.63443536226322 \tabularnewline
117 & 42 & 55.8171245676893 & -13.8171245676893 \tabularnewline
118 & 51 & 48.6440701444396 & 2.3559298555604 \tabularnewline
119 & 77 & 47.1290461591296 & 29.8709538408704 \tabularnewline
120 & 32 & 44.8202612502649 & -12.8202612502649 \tabularnewline
121 & 63 & 12.8405847815938 & 50.1594152184062 \tabularnewline
122 & 50 & 59.0390018862776 & -9.03900188627764 \tabularnewline
123 & 18 & 36.3040558418595 & -18.3040558418595 \tabularnewline
124 & 37 & 41.5972590880476 & -4.59725908804765 \tabularnewline
125 & 23 & 27.5147026810908 & -4.51470268109078 \tabularnewline
126 & 19 & 20.261463745128 & -1.26146374512802 \tabularnewline
127 & 39 & 52.9274937987078 & -13.9274937987078 \tabularnewline
128 & 38 & 47.0062609946295 & -9.00626099462945 \tabularnewline
129 & 55 & 54.3508338206032 & 0.649166179396827 \tabularnewline
130 & 22 & 14.1062301621423 & 7.89376983785773 \tabularnewline
131 & 7 & 9.63443536226322 & -2.63443536226322 \tabularnewline
132 & 21 & 29.7440778474675 & -8.74407784746755 \tabularnewline
133 & 5 & 11.790533372589 & -6.790533372589 \tabularnewline
134 & 21 & 23.0628687530523 & -2.06286875305234 \tabularnewline
135 & 1 & 10.4324292584006 & -9.43242925840064 \tabularnewline
136 & 22 & 13.5069774946618 & 8.49302250533817 \tabularnewline
137 & 0 & 9.63443536226322 & -9.63443536226322 \tabularnewline
138 & 31 & 39.7683953604943 & -8.76839536049435 \tabularnewline
139 & 25 & 38.607478410486 & -13.607478410486 \tabularnewline
140 & 0 & 9.63443536226322 & -9.63443536226322 \tabularnewline
141 & 4 & 9.63443536226322 & -5.63443536226322 \tabularnewline
142 & 20 & 26.0468623935543 & -6.04686239355427 \tabularnewline
143 & 29 & 36.5242328593897 & -7.52423285938971 \tabularnewline
144 & 33 & 35.9419075582138 & -2.94190755821384 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146946&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]28.6242074847693[/C][C]39.3757925152307[/C][/ROW]
[ROW][C]2[/C][C]17[/C][C]41.6175700332919[/C][C]-24.6175700332919[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]9.63443536226322[/C][C]-8.63443536226322[/C][/ROW]
[ROW][C]4[/C][C]114[/C][C]54.56029520571[/C][C]59.43970479429[/C][/ROW]
[ROW][C]5[/C][C]95[/C][C]67.8805419636014[/C][C]27.1194580363986[/C][/ROW]
[ROW][C]6[/C][C]148[/C][C]101.460369097739[/C][C]46.5396309022612[/C][/ROW]
[ROW][C]7[/C][C]56[/C][C]46.6126614364946[/C][C]9.3873385635054[/C][/ROW]
[ROW][C]8[/C][C]26[/C][C]58.9814526294845[/C][C]-32.9814526294845[/C][/ROW]
[ROW][C]9[/C][C]63[/C][C]64.54970324189[/C][C]-1.54970324189008[/C][/ROW]
[ROW][C]10[/C][C]96[/C][C]64.8105767733267[/C][C]31.1894232266733[/C][/ROW]
[ROW][C]11[/C][C]74[/C][C]43.7291677779766[/C][C]30.2708322220234[/C][/ROW]
[ROW][C]12[/C][C]65[/C][C]73.6521168506504[/C][C]-8.65211685065042[/C][/ROW]
[ROW][C]13[/C][C]40[/C][C]35.8068526968134[/C][C]4.19314730318658[/C][/ROW]
[ROW][C]14[/C][C]173[/C][C]58.6852449243797[/C][C]114.31475507562[/C][/ROW]
[ROW][C]15[/C][C]28[/C][C]42.4531523143819[/C][C]-14.4531523143819[/C][/ROW]
[ROW][C]16[/C][C]55[/C][C]41.6114329228285[/C][C]13.3885670771715[/C][/ROW]
[ROW][C]17[/C][C]58[/C][C]73.4994299521115[/C][C]-15.4994299521115[/C][/ROW]
[ROW][C]18[/C][C]25[/C][C]20.4149254138921[/C][C]4.58507458610788[/C][/ROW]
[ROW][C]19[/C][C]103[/C][C]72.9105428436508[/C][C]30.0894571563492[/C][/ROW]
[ROW][C]20[/C][C]29[/C][C]29.611701894173[/C][C]-0.611701894172959[/C][/ROW]
[ROW][C]21[/C][C]31[/C][C]43.9785418587116[/C][C]-12.9785418587116[/C][/ROW]
[ROW][C]22[/C][C]43[/C][C]71.7568923717612[/C][C]-28.7568923717612[/C][/ROW]
[ROW][C]23[/C][C]74[/C][C]36.8212153347198[/C][C]37.1787846652802[/C][/ROW]
[ROW][C]24[/C][C]99[/C][C]37.11855240748[/C][C]61.88144759252[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]52.9919549361896[/C][C]-27.9919549361896[/C][/ROW]
[ROW][C]26[/C][C]69[/C][C]43.3316853206626[/C][C]25.6683146793374[/C][/ROW]
[ROW][C]27[/C][C]62[/C][C]54.817710534789[/C][C]7.18228946521097[/C][/ROW]
[ROW][C]28[/C][C]25[/C][C]65.6599828157939[/C][C]-40.6599828157939[/C][/ROW]
[ROW][C]29[/C][C]38[/C][C]64.3317804356442[/C][C]-26.3317804356442[/C][/ROW]
[ROW][C]30[/C][C]57[/C][C]38.3005550729578[/C][C]18.6994449270422[/C][/ROW]
[ROW][C]31[/C][C]52[/C][C]50.0900499462814[/C][C]1.90995005371861[/C][/ROW]
[ROW][C]32[/C][C]91[/C][C]43.5377631268297[/C][C]47.4622368731703[/C][/ROW]
[ROW][C]33[/C][C]48[/C][C]32.0494090780011[/C][C]15.9505909219989[/C][/ROW]
[ROW][C]34[/C][C]52[/C][C]30.1864106438263[/C][C]21.8135893561737[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]56.971129637009[/C][C]-21.971129637009[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]9.63443536226322[/C][C]-9.63443536226322[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]43.3907841179062[/C][C]-12.3907841179062[/C][/ROW]
[ROW][C]38[/C][C]107[/C][C]46.558570382059[/C][C]60.441429617941[/C][/ROW]
[ROW][C]39[/C][C]242[/C][C]48.8577599781027[/C][C]193.142240021897[/C][/ROW]
[ROW][C]40[/C][C]41[/C][C]83.7865251344556[/C][C]-42.7865251344556[/C][/ROW]
[ROW][C]41[/C][C]57[/C][C]64.9993025163677[/C][C]-7.99930251636766[/C][/ROW]
[ROW][C]42[/C][C]32[/C][C]47.2380075622525[/C][C]-15.2380075622525[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]37.1764517376769[/C][C]-20.1764517376769[/C][/ROW]
[ROW][C]44[/C][C]36[/C][C]56.441695923222[/C][C]-20.441695923222[/C][/ROW]
[ROW][C]45[/C][C]29[/C][C]64.5580945636378[/C][C]-35.5580945636378[/C][/ROW]
[ROW][C]46[/C][C]22[/C][C]44.2048559867051[/C][C]-22.2048559867051[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]48.2811470905687[/C][C]-27.2811470905687[/C][/ROW]
[ROW][C]48[/C][C]41[/C][C]48.7748965572838[/C][C]-7.77489655728375[/C][/ROW]
[ROW][C]49[/C][C]64[/C][C]45.6044313850251[/C][C]18.3955686149749[/C][/ROW]
[ROW][C]50[/C][C]71[/C][C]48.5071111451586[/C][C]22.4928888548414[/C][/ROW]
[ROW][C]51[/C][C]28[/C][C]51.9967693518456[/C][C]-23.9967693518456[/C][/ROW]
[ROW][C]52[/C][C]36[/C][C]40.9799496866072[/C][C]-4.97994968660723[/C][/ROW]
[ROW][C]53[/C][C]45[/C][C]52.2821868011093[/C][C]-7.28218680110929[/C][/ROW]
[ROW][C]54[/C][C]22[/C][C]57.1238165355479[/C][C]-35.1238165355479[/C][/ROW]
[ROW][C]55[/C][C]27[/C][C]70.1317776156729[/C][C]-43.1317776156729[/C][/ROW]
[ROW][C]56[/C][C]38[/C][C]37.8793079920685[/C][C]0.120692007931454[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]45.3489201938267[/C][C]-19.3489201938267[/C][/ROW]
[ROW][C]58[/C][C]41[/C][C]39.3471482796051[/C][C]1.65285172039494[/C][/ROW]
[ROW][C]59[/C][C]21[/C][C]49.5230278475418[/C][C]-28.5230278475418[/C][/ROW]
[ROW][C]60[/C][C]28[/C][C]41.2354608778056[/C][C]-13.2354608778056[/C][/ROW]
[ROW][C]61[/C][C]36[/C][C]46.2881015378014[/C][C]-10.2881015378014[/C][/ROW]
[ROW][C]62[/C][C]58[/C][C]37.618434460632[/C][C]20.381565539368[/C][/ROW]
[ROW][C]63[/C][C]65[/C][C]33.3254200175693[/C][C]31.6745799824307[/C][/ROW]
[ROW][C]64[/C][C]29[/C][C]42.2935535351544[/C][C]-13.2935535351544[/C][/ROW]
[ROW][C]65[/C][C]21[/C][C]31.4378821895939[/C][C]-10.4378821895939[/C][/ROW]
[ROW][C]66[/C][C]19[/C][C]54.5035207191421[/C][C]-35.5035207191421[/C][/ROW]
[ROW][C]67[/C][C]55[/C][C]54.5549328654718[/C][C]0.445067134528177[/C][/ROW]
[ROW][C]68[/C][C]119[/C][C]68.8979381070438[/C][C]50.1020618929563[/C][/ROW]
[ROW][C]69[/C][C]34[/C][C]49.0741332438981[/C][C]-15.0741332438981[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]31.195415465521[/C][C]-6.19541546552101[/C][/ROW]
[ROW][C]71[/C][C]113[/C][C]59.329777151753[/C][C]53.670222848247[/C][/ROW]
[ROW][C]72[/C][C]46[/C][C]30.0137673974734[/C][C]15.9862326025266[/C][/ROW]
[ROW][C]73[/C][C]28[/C][C]64.2020833904555[/C][C]-36.2020833904555[/C][/ROW]
[ROW][C]74[/C][C]63[/C][C]49.1258999876581[/C][C]13.8741000123419[/C][/ROW]
[ROW][C]75[/C][C]52[/C][C]57.509024771935[/C][C]-5.50902477193501[/C][/ROW]
[ROW][C]76[/C][C]35[/C][C]29.0393174551952[/C][C]5.96068254480477[/C][/ROW]
[ROW][C]77[/C][C]32[/C][C]43.6278930257674[/C][C]-11.6278930257674[/C][/ROW]
[ROW][C]78[/C][C]45[/C][C]48.115765798308[/C][C]-3.11576579830801[/C][/ROW]
[ROW][C]79[/C][C]42[/C][C]41.3340567219089[/C][C]0.665943278091089[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]37.5689264521828[/C][C]-9.5689264521828[/C][/ROW]
[ROW][C]81[/C][C]32[/C][C]54.2556961788575[/C][C]-22.2556961788575[/C][/ROW]
[ROW][C]82[/C][C]32[/C][C]50.7215331825027[/C][C]-18.7215331825027[/C][/ROW]
[ROW][C]83[/C][C]27[/C][C]42.8743993952712[/C][C]-15.8743993952712[/C][/ROW]
[ROW][C]84[/C][C]69[/C][C]52.5576588641483[/C][C]16.4423411358517[/C][/ROW]
[ROW][C]85[/C][C]30[/C][C]44.755025342558[/C][C]-14.755025342558[/C][/ROW]
[ROW][C]86[/C][C]48[/C][C]43.0854103208285[/C][C]4.91458967917155[/C][/ROW]
[ROW][C]87[/C][C]57[/C][C]69.9307075523138[/C][C]-12.9307075523138[/C][/ROW]
[ROW][C]88[/C][C]36[/C][C]41.7203943259515[/C][C]-5.72039432595148[/C][/ROW]
[ROW][C]89[/C][C]20[/C][C]42.3956030575888[/C][C]-22.3956030575888[/C][/ROW]
[ROW][C]90[/C][C]54[/C][C]46.0318155763778[/C][C]7.96818442362218[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]41.4579712540645[/C][C]-15.4579712540645[/C][/ROW]
[ROW][C]92[/C][C]58[/C][C]49.2099673995503[/C][C]8.79003260044975[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]41.5538836660354[/C][C]-6.55388366603539[/C][/ROW]
[ROW][C]94[/C][C]28[/C][C]35.2375017633717[/C][C]-7.23750176337172[/C][/ROW]
[ROW][C]95[/C][C]8[/C][C]30.4864266246924[/C][C]-22.4864266246924[/C][/ROW]
[ROW][C]96[/C][C]96[/C][C]57.168316801189[/C][C]38.831683198811[/C][/ROW]
[ROW][C]97[/C][C]50[/C][C]59.3006501879394[/C][C]-9.30065018793944[/C][/ROW]
[ROW][C]98[/C][C]15[/C][C]19.3936508952972[/C][C]-4.39365089529717[/C][/ROW]
[ROW][C]99[/C][C]65[/C][C]44.7696238741604[/C][C]20.2303761258396[/C][/ROW]
[ROW][C]100[/C][C]33[/C][C]33.223370495135[/C][C]-0.223370495134966[/C][/ROW]
[ROW][C]101[/C][C]7[/C][C]16.3033747597781[/C][C]-9.30337475977815[/C][/ROW]
[ROW][C]102[/C][C]17[/C][C]34.3075566107612[/C][C]-17.3075566107612[/C][/ROW]
[ROW][C]103[/C][C]55[/C][C]43.4046078792834[/C][C]11.5953921207166[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]30.8317176414249[/C][C]1.16828235857512[/C][/ROW]
[ROW][C]105[/C][C]22[/C][C]35.9925449343184[/C][C]-13.9925449343184[/C][/ROW]
[ROW][C]106[/C][C]41[/C][C]34.681619993877[/C][C]6.31838000612296[/C][/ROW]
[ROW][C]107[/C][C]50[/C][C]51.409790905292[/C][C]-1.40979090529198[/C][/ROW]
[ROW][C]108[/C][C]7[/C][C]17.1040475640214[/C][C]-10.1040475640214[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]9.63443536226322[/C][C]-9.63443536226322[/C][/ROW]
[ROW][C]110[/C][C]26[/C][C]48.8255339333883[/C][C]-22.8255339333883[/C][/ROW]
[ROW][C]111[/C][C]22[/C][C]33.7205736401811[/C][C]-11.7205736401811[/C][/ROW]
[ROW][C]112[/C][C]26[/C][C]55.7657124213595[/C][C]-29.7657124213595[/C][/ROW]
[ROW][C]113[/C][C]37[/C][C]57.3486512224823[/C][C]-20.3486512224823[/C][/ROW]
[ROW][C]114[/C][C]29[/C][C]36.9811687113774[/C][C]-7.98116871137743[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]9.63443536226322[/C][C]-9.63443536226322[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]9.63443536226322[/C][C]-9.63443536226322[/C][/ROW]
[ROW][C]117[/C][C]42[/C][C]55.8171245676893[/C][C]-13.8171245676893[/C][/ROW]
[ROW][C]118[/C][C]51[/C][C]48.6440701444396[/C][C]2.3559298555604[/C][/ROW]
[ROW][C]119[/C][C]77[/C][C]47.1290461591296[/C][C]29.8709538408704[/C][/ROW]
[ROW][C]120[/C][C]32[/C][C]44.8202612502649[/C][C]-12.8202612502649[/C][/ROW]
[ROW][C]121[/C][C]63[/C][C]12.8405847815938[/C][C]50.1594152184062[/C][/ROW]
[ROW][C]122[/C][C]50[/C][C]59.0390018862776[/C][C]-9.03900188627764[/C][/ROW]
[ROW][C]123[/C][C]18[/C][C]36.3040558418595[/C][C]-18.3040558418595[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]41.5972590880476[/C][C]-4.59725908804765[/C][/ROW]
[ROW][C]125[/C][C]23[/C][C]27.5147026810908[/C][C]-4.51470268109078[/C][/ROW]
[ROW][C]126[/C][C]19[/C][C]20.261463745128[/C][C]-1.26146374512802[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]52.9274937987078[/C][C]-13.9274937987078[/C][/ROW]
[ROW][C]128[/C][C]38[/C][C]47.0062609946295[/C][C]-9.00626099462945[/C][/ROW]
[ROW][C]129[/C][C]55[/C][C]54.3508338206032[/C][C]0.649166179396827[/C][/ROW]
[ROW][C]130[/C][C]22[/C][C]14.1062301621423[/C][C]7.89376983785773[/C][/ROW]
[ROW][C]131[/C][C]7[/C][C]9.63443536226322[/C][C]-2.63443536226322[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]29.7440778474675[/C][C]-8.74407784746755[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]11.790533372589[/C][C]-6.790533372589[/C][/ROW]
[ROW][C]134[/C][C]21[/C][C]23.0628687530523[/C][C]-2.06286875305234[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]10.4324292584006[/C][C]-9.43242925840064[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]13.5069774946618[/C][C]8.49302250533817[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]9.63443536226322[/C][C]-9.63443536226322[/C][/ROW]
[ROW][C]138[/C][C]31[/C][C]39.7683953604943[/C][C]-8.76839536049435[/C][/ROW]
[ROW][C]139[/C][C]25[/C][C]38.607478410486[/C][C]-13.607478410486[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]9.63443536226322[/C][C]-9.63443536226322[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]9.63443536226322[/C][C]-5.63443536226322[/C][/ROW]
[ROW][C]142[/C][C]20[/C][C]26.0468623935543[/C][C]-6.04686239355427[/C][/ROW]
[ROW][C]143[/C][C]29[/C][C]36.5242328593897[/C][C]-7.52423285938971[/C][/ROW]
[ROW][C]144[/C][C]33[/C][C]35.9419075582138[/C][C]-2.94190755821384[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146946&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146946&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
16828.624207484769339.3757925152307
21741.6175700332919-24.6175700332919
319.63443536226322-8.63443536226322
411454.5602952057159.43970479429
59567.880541963601427.1194580363986
6148101.46036909773946.5396309022612
75646.61266143649469.3873385635054
82658.9814526294845-32.9814526294845
96364.54970324189-1.54970324189008
109664.810576773326731.1894232266733
117443.729167777976630.2708322220234
126573.6521168506504-8.65211685065042
134035.80685269681344.19314730318658
1417358.6852449243797114.31475507562
152842.4531523143819-14.4531523143819
165541.611432922828513.3885670771715
175873.4994299521115-15.4994299521115
182520.41492541389214.58507458610788
1910372.910542843650830.0894571563492
202929.611701894173-0.611701894172959
213143.9785418587116-12.9785418587116
224371.7568923717612-28.7568923717612
237436.821215334719837.1787846652802
249937.1185524074861.88144759252
252552.9919549361896-27.9919549361896
266943.331685320662625.6683146793374
276254.8177105347897.18228946521097
282565.6599828157939-40.6599828157939
293864.3317804356442-26.3317804356442
305738.300555072957818.6994449270422
315250.09004994628141.90995005371861
329143.537763126829747.4622368731703
334832.049409078001115.9505909219989
345230.186410643826321.8135893561737
353556.971129637009-21.971129637009
3609.63443536226322-9.63443536226322
373143.3907841179062-12.3907841179062
3810746.55857038205960.441429617941
3924248.8577599781027193.142240021897
404183.7865251344556-42.7865251344556
415764.9993025163677-7.99930251636766
423247.2380075622525-15.2380075622525
431737.1764517376769-20.1764517376769
443656.441695923222-20.441695923222
452964.5580945636378-35.5580945636378
462244.2048559867051-22.2048559867051
472148.2811470905687-27.2811470905687
484148.7748965572838-7.77489655728375
496445.604431385025118.3955686149749
507148.507111145158622.4928888548414
512851.9967693518456-23.9967693518456
523640.9799496866072-4.97994968660723
534552.2821868011093-7.28218680110929
542257.1238165355479-35.1238165355479
552770.1317776156729-43.1317776156729
563837.87930799206850.120692007931454
572645.3489201938267-19.3489201938267
584139.34714827960511.65285172039494
592149.5230278475418-28.5230278475418
602841.2354608778056-13.2354608778056
613646.2881015378014-10.2881015378014
625837.61843446063220.381565539368
636533.325420017569331.6745799824307
642942.2935535351544-13.2935535351544
652131.4378821895939-10.4378821895939
661954.5035207191421-35.5035207191421
675554.55493286547180.445067134528177
6811968.897938107043850.1020618929563
693449.0741332438981-15.0741332438981
702531.195415465521-6.19541546552101
7111359.32977715175353.670222848247
724630.013767397473415.9862326025266
732864.2020833904555-36.2020833904555
746349.125899987658113.8741000123419
755257.509024771935-5.50902477193501
763529.03931745519525.96068254480477
773243.6278930257674-11.6278930257674
784548.115765798308-3.11576579830801
794241.33405672190890.665943278091089
802837.5689264521828-9.5689264521828
813254.2556961788575-22.2556961788575
823250.7215331825027-18.7215331825027
832742.8743993952712-15.8743993952712
846952.557658864148316.4423411358517
853044.755025342558-14.755025342558
864843.08541032082854.91458967917155
875769.9307075523138-12.9307075523138
883641.7203943259515-5.72039432595148
892042.3956030575888-22.3956030575888
905446.03181557637787.96818442362218
912641.4579712540645-15.4579712540645
925849.20996739955038.79003260044975
933541.5538836660354-6.55388366603539
942835.2375017633717-7.23750176337172
95830.4864266246924-22.4864266246924
969657.16831680118938.831683198811
975059.3006501879394-9.30065018793944
981519.3936508952972-4.39365089529717
996544.769623874160420.2303761258396
1003333.223370495135-0.223370495134966
101716.3033747597781-9.30337475977815
1021734.3075566107612-17.3075566107612
1035543.404607879283411.5953921207166
1043230.83171764142491.16828235857512
1052235.9925449343184-13.9925449343184
1064134.6816199938776.31838000612296
1075051.409790905292-1.40979090529198
108717.1040475640214-10.1040475640214
10909.63443536226322-9.63443536226322
1102648.8255339333883-22.8255339333883
1112233.7205736401811-11.7205736401811
1122655.7657124213595-29.7657124213595
1133757.3486512224823-20.3486512224823
1142936.9811687113774-7.98116871137743
11509.63443536226322-9.63443536226322
11609.63443536226322-9.63443536226322
1174255.8171245676893-13.8171245676893
1185148.64407014443962.3559298555604
1197747.129046159129629.8709538408704
1203244.8202612502649-12.8202612502649
1216312.840584781593850.1594152184062
1225059.0390018862776-9.03900188627764
1231836.3040558418595-18.3040558418595
1243741.5972590880476-4.59725908804765
1252327.5147026810908-4.51470268109078
1261920.261463745128-1.26146374512802
1273952.9274937987078-13.9274937987078
1283847.0062609946295-9.00626099462945
1295554.35083382060320.649166179396827
1302214.10623016214237.89376983785773
13179.63443536226322-2.63443536226322
1322129.7440778474675-8.74407784746755
133511.790533372589-6.790533372589
1342123.0628687530523-2.06286875305234
135110.4324292584006-9.43242925840064
1362213.50697749466188.49302250533817
13709.63443536226322-9.63443536226322
1383139.7683953604943-8.76839536049435
1392538.607478410486-13.607478410486
14009.63443536226322-9.63443536226322
14149.63443536226322-5.63443536226322
1422026.0468623935543-6.04686239355427
1432936.5242328593897-7.52423285938971
1443335.9419075582138-2.94190755821384







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8197103968751950.3605792062496090.180289603124805
80.9055827331774420.1888345336451160.094417266822558
90.8382517197242040.3234965605515910.161748280275796
100.7861822127432090.4276355745135820.213817787256791
110.7031481013335110.5937037973329770.296851898666489
120.646113836686250.70777232662750.35388616331375
130.5505933494908920.8988133010182170.449406650509108
140.9142065922703060.1715868154593880.0857934077296942
150.9266207102403840.1467585795192330.0733792897596165
160.8952500230368630.2094999539262730.104749976963137
170.8685800953617360.2628398092765280.131419904638264
180.824636575628660.3507268487426810.17536342437134
190.843755918926160.3124881621476790.156244081073839
200.8334320555005050.333135888998990.166567944499495
210.8230218829574380.3539562340851240.176978117042562
220.8745342366801430.2509315266397140.125465763319857
230.8577086304008890.2845827391982220.142291369599111
240.9601449349980670.0797101300038660.039855065001933
250.9553441326074760.0893117347850490.0446558673925245
260.9453581384475320.1092837231049350.0546418615524676
270.9352075191781710.1295849616436580.0647924808218288
280.9545451701775090.09090965964498280.0454548298224914
290.9645604249777460.0708791500445090.0354395750222545
300.9539547959019040.09209040819619220.0460452040980961
310.9378676675436230.1242646649127540.0621323324563772
320.9739070607981790.05218587840364290.0260929392018214
330.9659963802590080.06800723948198440.0340036197409922
340.9563784798682920.08724304026341660.0436215201317083
350.9557596753003070.08848064939938640.0442403246996932
360.9487092954865450.1025814090269090.0512907045134547
370.9388503953725480.1222992092549050.0611496046274524
380.9716301247572350.05673975048553060.0283698752427653
390.9999999999996946.12810038861702e-133.06405019430851e-13
400.9999999999999441.12334536046859e-135.61672680234296e-14
410.999999999999872.59005396717537e-131.29502698358768e-13
420.9999999999998463.08244571166849e-131.54122285583425e-13
430.9999999999998283.44471981571082e-131.72235990785541e-13
440.9999999999997724.55110056019045e-132.27555028009523e-13
450.9999999999998872.26446405027554e-131.13223202513777e-13
460.999999999999882.38944809433327e-131.19472404716663e-13
470.9999999999999041.9265956656908e-139.632978328454e-14
480.9999999999997784.44709029947933e-132.22354514973967e-13
490.9999999999996846.32371810263495e-133.16185905131747e-13
500.999999999999686.38650223370183e-133.19325111685091e-13
510.999999999999666.81468607435504e-133.40734303717752e-13
520.9999999999992251.54978357452768e-127.7489178726384e-13
530.9999999999982373.52654115935765e-121.76327057967882e-12
540.9999999999990381.92416239309116e-129.6208119654558e-13
550.9999999999997375.26263656721002e-132.63131828360501e-13
560.9999999999993651.26939922870726e-126.34699614353629e-13
570.9999999999990461.90775861010983e-129.53879305054914e-13
580.9999999999978194.36245893422181e-122.1812294671109e-12
590.9999999999980123.97640253922006e-121.98820126961003e-12
600.9999999999961537.69386222672675e-123.84693111336337e-12
610.999999999991791.64225267762037e-118.21126338810186e-12
620.9999999999902381.95247074144902e-119.76235370724509e-12
630.9999999999954189.16409533340718e-124.58204766670359e-12
640.9999999999912361.75288269875744e-118.7644134937872e-12
650.9999999999820063.59877012450372e-111.79938506225186e-11
660.9999999999913131.73745480598724e-118.68727402993622e-12
670.999999999979944.01186301445115e-112.00593150722557e-11
680.9999999999994061.18805061385821e-125.94025306929107e-13
690.9999999999988542.2913077938302e-121.1456538969151e-12
700.9999999999973095.38246664468573e-122.69123332234287e-12
710.9999999999999959.49607818782027e-154.74803909391014e-15
720.9999999999999967.55066111116636e-153.77533055558318e-15
730.9999999999999984.06162694227334e-152.03081347113667e-15
740.9999999999999992.33939958398967e-151.16969979199484e-15
750.9999999999999976.33644230219858e-153.16822115109929e-15
760.9999999999999911.74341747290549e-148.71708736452745e-15
770.9999999999999784.39032322651172e-142.19516161325586e-14
780.9999999999999461.07127629120709e-135.35638145603543e-14
790.9999999999998562.87183297074307e-131.43591648537154e-13
800.999999999999657.01395554827374e-133.50697777413687e-13
810.9999999999995678.65406871178464e-134.32703435589232e-13
820.9999999999994871.02557663649323e-125.12788318246616e-13
830.9999999999990521.89566284465675e-129.47831422328373e-13
840.9999999999991341.73260167514486e-128.66300837572432e-13
850.9999999999980853.8307786706843e-121.91538933534215e-12
860.9999999999951469.7088848180304e-124.8544424090152e-12
870.9999999999876962.46081178750903e-111.23040589375452e-11
880.9999999999699376.01259637833978e-113.00629818916989e-11
890.9999999999657076.85864658402124e-113.42932329201062e-11
900.9999999999403981.19204976065e-105.96024880325e-11
910.9999999998693672.61266551516106e-101.30633275758053e-10
920.999999999810173.79661492645525e-101.89830746322763e-10
930.9999999995576178.84765511408297e-104.42382755704148e-10
940.9999999989506552.09869060862027e-091.04934530431014e-09
950.9999999987034032.59319488677954e-091.29659744338977e-09
960.999999999978984.20396229438994e-112.10198114719497e-11
970.9999999999428181.14365000820944e-105.7182500410472e-11
980.999999999854622.90758540943593e-101.45379270471796e-10
990.999999999881012.37978291399285e-101.18989145699642e-10
1000.9999999996895686.20864718597575e-103.10432359298787e-10
1010.999999999218451.56309933801383e-097.81549669006913e-10
1020.9999999983015083.39698446978591e-091.69849223489295e-09
1030.9999999974226995.15460242910988e-092.57730121455494e-09
1040.9999999934043721.31912566850295e-086.59562834251475e-09
1050.9999999874449762.51100484466273e-081.25550242233137e-08
1060.9999999703350445.93299128119553e-082.96649564059776e-08
1070.9999999408876041.18224792222059e-075.91123961110294e-08
1080.9999998697656062.60468788319896e-071.30234394159948e-07
1090.999999732230085.35539839799915e-072.67769919899958e-07
1100.9999994819173971.03616520695596e-065.18082603477979e-07
1110.9999988517140142.29657197257522e-061.14828598628761e-06
1120.999998718850882.56229823902054e-061.28114911951027e-06
1130.9999980202912953.95941740992522e-061.97970870496261e-06
1140.999995514330038.9713399410638e-064.4856699705319e-06
1150.9999909497628341.81004743317034e-059.05023716585168e-06
1160.9999823966587393.52066825223522e-051.76033412611761e-05
1170.9999633258525477.33482949056262e-053.66741474528131e-05
1180.9999262065840760.0001475868318487697.37934159243844e-05
1190.9999923249781451.53500437104386e-057.6750218552193e-06
1200.9999816160637883.67678724237007e-051.83839362118503e-05
1210.9999999999114381.77123861639059e-108.85619308195294e-11
1220.9999999995466759.06649422271242e-104.53324711135621e-10
1230.9999999995639188.72164236012617e-104.36082118006309e-10
1240.9999999976724044.65519274907878e-092.32759637453939e-09
1250.9999999881698362.36603271579509e-081.18301635789755e-08
1260.9999999472587171.0548256569933e-075.27412828496652e-08
1270.999999906527871.86944259909657e-079.34721299548284e-08
1280.9999995273559529.45288095830572e-074.72644047915286e-07
1290.9999982514509193.49709816197069e-061.74854908098535e-06
1300.9999990988870841.80222583237223e-069.01112916186114e-07
1310.9999956212843628.75743127633575e-064.37871563816788e-06
1320.999980391533263.92169334815279e-051.96084667407639e-05
1330.9998947042757680.0002105914484640020.000105295724232001
1340.9994705832034450.001058833593110540.00052941679655527
1350.9981637249970770.003672550005846020.00183627500292301
1360.9997821353999020.0004357292001959250.000217864600097963
1370.9978839326409540.004232134718092210.0021160673590461

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.819710396875195 & 0.360579206249609 & 0.180289603124805 \tabularnewline
8 & 0.905582733177442 & 0.188834533645116 & 0.094417266822558 \tabularnewline
9 & 0.838251719724204 & 0.323496560551591 & 0.161748280275796 \tabularnewline
10 & 0.786182212743209 & 0.427635574513582 & 0.213817787256791 \tabularnewline
11 & 0.703148101333511 & 0.593703797332977 & 0.296851898666489 \tabularnewline
12 & 0.64611383668625 & 0.7077723266275 & 0.35388616331375 \tabularnewline
13 & 0.550593349490892 & 0.898813301018217 & 0.449406650509108 \tabularnewline
14 & 0.914206592270306 & 0.171586815459388 & 0.0857934077296942 \tabularnewline
15 & 0.926620710240384 & 0.146758579519233 & 0.0733792897596165 \tabularnewline
16 & 0.895250023036863 & 0.209499953926273 & 0.104749976963137 \tabularnewline
17 & 0.868580095361736 & 0.262839809276528 & 0.131419904638264 \tabularnewline
18 & 0.82463657562866 & 0.350726848742681 & 0.17536342437134 \tabularnewline
19 & 0.84375591892616 & 0.312488162147679 & 0.156244081073839 \tabularnewline
20 & 0.833432055500505 & 0.33313588899899 & 0.166567944499495 \tabularnewline
21 & 0.823021882957438 & 0.353956234085124 & 0.176978117042562 \tabularnewline
22 & 0.874534236680143 & 0.250931526639714 & 0.125465763319857 \tabularnewline
23 & 0.857708630400889 & 0.284582739198222 & 0.142291369599111 \tabularnewline
24 & 0.960144934998067 & 0.079710130003866 & 0.039855065001933 \tabularnewline
25 & 0.955344132607476 & 0.089311734785049 & 0.0446558673925245 \tabularnewline
26 & 0.945358138447532 & 0.109283723104935 & 0.0546418615524676 \tabularnewline
27 & 0.935207519178171 & 0.129584961643658 & 0.0647924808218288 \tabularnewline
28 & 0.954545170177509 & 0.0909096596449828 & 0.0454548298224914 \tabularnewline
29 & 0.964560424977746 & 0.070879150044509 & 0.0354395750222545 \tabularnewline
30 & 0.953954795901904 & 0.0920904081961922 & 0.0460452040980961 \tabularnewline
31 & 0.937867667543623 & 0.124264664912754 & 0.0621323324563772 \tabularnewline
32 & 0.973907060798179 & 0.0521858784036429 & 0.0260929392018214 \tabularnewline
33 & 0.965996380259008 & 0.0680072394819844 & 0.0340036197409922 \tabularnewline
34 & 0.956378479868292 & 0.0872430402634166 & 0.0436215201317083 \tabularnewline
35 & 0.955759675300307 & 0.0884806493993864 & 0.0442403246996932 \tabularnewline
36 & 0.948709295486545 & 0.102581409026909 & 0.0512907045134547 \tabularnewline
37 & 0.938850395372548 & 0.122299209254905 & 0.0611496046274524 \tabularnewline
38 & 0.971630124757235 & 0.0567397504855306 & 0.0283698752427653 \tabularnewline
39 & 0.999999999999694 & 6.12810038861702e-13 & 3.06405019430851e-13 \tabularnewline
40 & 0.999999999999944 & 1.12334536046859e-13 & 5.61672680234296e-14 \tabularnewline
41 & 0.99999999999987 & 2.59005396717537e-13 & 1.29502698358768e-13 \tabularnewline
42 & 0.999999999999846 & 3.08244571166849e-13 & 1.54122285583425e-13 \tabularnewline
43 & 0.999999999999828 & 3.44471981571082e-13 & 1.72235990785541e-13 \tabularnewline
44 & 0.999999999999772 & 4.55110056019045e-13 & 2.27555028009523e-13 \tabularnewline
45 & 0.999999999999887 & 2.26446405027554e-13 & 1.13223202513777e-13 \tabularnewline
46 & 0.99999999999988 & 2.38944809433327e-13 & 1.19472404716663e-13 \tabularnewline
47 & 0.999999999999904 & 1.9265956656908e-13 & 9.632978328454e-14 \tabularnewline
48 & 0.999999999999778 & 4.44709029947933e-13 & 2.22354514973967e-13 \tabularnewline
49 & 0.999999999999684 & 6.32371810263495e-13 & 3.16185905131747e-13 \tabularnewline
50 & 0.99999999999968 & 6.38650223370183e-13 & 3.19325111685091e-13 \tabularnewline
51 & 0.99999999999966 & 6.81468607435504e-13 & 3.40734303717752e-13 \tabularnewline
52 & 0.999999999999225 & 1.54978357452768e-12 & 7.7489178726384e-13 \tabularnewline
53 & 0.999999999998237 & 3.52654115935765e-12 & 1.76327057967882e-12 \tabularnewline
54 & 0.999999999999038 & 1.92416239309116e-12 & 9.6208119654558e-13 \tabularnewline
55 & 0.999999999999737 & 5.26263656721002e-13 & 2.63131828360501e-13 \tabularnewline
56 & 0.999999999999365 & 1.26939922870726e-12 & 6.34699614353629e-13 \tabularnewline
57 & 0.999999999999046 & 1.90775861010983e-12 & 9.53879305054914e-13 \tabularnewline
58 & 0.999999999997819 & 4.36245893422181e-12 & 2.1812294671109e-12 \tabularnewline
59 & 0.999999999998012 & 3.97640253922006e-12 & 1.98820126961003e-12 \tabularnewline
60 & 0.999999999996153 & 7.69386222672675e-12 & 3.84693111336337e-12 \tabularnewline
61 & 0.99999999999179 & 1.64225267762037e-11 & 8.21126338810186e-12 \tabularnewline
62 & 0.999999999990238 & 1.95247074144902e-11 & 9.76235370724509e-12 \tabularnewline
63 & 0.999999999995418 & 9.16409533340718e-12 & 4.58204766670359e-12 \tabularnewline
64 & 0.999999999991236 & 1.75288269875744e-11 & 8.7644134937872e-12 \tabularnewline
65 & 0.999999999982006 & 3.59877012450372e-11 & 1.79938506225186e-11 \tabularnewline
66 & 0.999999999991313 & 1.73745480598724e-11 & 8.68727402993622e-12 \tabularnewline
67 & 0.99999999997994 & 4.01186301445115e-11 & 2.00593150722557e-11 \tabularnewline
68 & 0.999999999999406 & 1.18805061385821e-12 & 5.94025306929107e-13 \tabularnewline
69 & 0.999999999998854 & 2.2913077938302e-12 & 1.1456538969151e-12 \tabularnewline
70 & 0.999999999997309 & 5.38246664468573e-12 & 2.69123332234287e-12 \tabularnewline
71 & 0.999999999999995 & 9.49607818782027e-15 & 4.74803909391014e-15 \tabularnewline
72 & 0.999999999999996 & 7.55066111116636e-15 & 3.77533055558318e-15 \tabularnewline
73 & 0.999999999999998 & 4.06162694227334e-15 & 2.03081347113667e-15 \tabularnewline
74 & 0.999999999999999 & 2.33939958398967e-15 & 1.16969979199484e-15 \tabularnewline
75 & 0.999999999999997 & 6.33644230219858e-15 & 3.16822115109929e-15 \tabularnewline
76 & 0.999999999999991 & 1.74341747290549e-14 & 8.71708736452745e-15 \tabularnewline
77 & 0.999999999999978 & 4.39032322651172e-14 & 2.19516161325586e-14 \tabularnewline
78 & 0.999999999999946 & 1.07127629120709e-13 & 5.35638145603543e-14 \tabularnewline
79 & 0.999999999999856 & 2.87183297074307e-13 & 1.43591648537154e-13 \tabularnewline
80 & 0.99999999999965 & 7.01395554827374e-13 & 3.50697777413687e-13 \tabularnewline
81 & 0.999999999999567 & 8.65406871178464e-13 & 4.32703435589232e-13 \tabularnewline
82 & 0.999999999999487 & 1.02557663649323e-12 & 5.12788318246616e-13 \tabularnewline
83 & 0.999999999999052 & 1.89566284465675e-12 & 9.47831422328373e-13 \tabularnewline
84 & 0.999999999999134 & 1.73260167514486e-12 & 8.66300837572432e-13 \tabularnewline
85 & 0.999999999998085 & 3.8307786706843e-12 & 1.91538933534215e-12 \tabularnewline
86 & 0.999999999995146 & 9.7088848180304e-12 & 4.8544424090152e-12 \tabularnewline
87 & 0.999999999987696 & 2.46081178750903e-11 & 1.23040589375452e-11 \tabularnewline
88 & 0.999999999969937 & 6.01259637833978e-11 & 3.00629818916989e-11 \tabularnewline
89 & 0.999999999965707 & 6.85864658402124e-11 & 3.42932329201062e-11 \tabularnewline
90 & 0.999999999940398 & 1.19204976065e-10 & 5.96024880325e-11 \tabularnewline
91 & 0.999999999869367 & 2.61266551516106e-10 & 1.30633275758053e-10 \tabularnewline
92 & 0.99999999981017 & 3.79661492645525e-10 & 1.89830746322763e-10 \tabularnewline
93 & 0.999999999557617 & 8.84765511408297e-10 & 4.42382755704148e-10 \tabularnewline
94 & 0.999999998950655 & 2.09869060862027e-09 & 1.04934530431014e-09 \tabularnewline
95 & 0.999999998703403 & 2.59319488677954e-09 & 1.29659744338977e-09 \tabularnewline
96 & 0.99999999997898 & 4.20396229438994e-11 & 2.10198114719497e-11 \tabularnewline
97 & 0.999999999942818 & 1.14365000820944e-10 & 5.7182500410472e-11 \tabularnewline
98 & 0.99999999985462 & 2.90758540943593e-10 & 1.45379270471796e-10 \tabularnewline
99 & 0.99999999988101 & 2.37978291399285e-10 & 1.18989145699642e-10 \tabularnewline
100 & 0.999999999689568 & 6.20864718597575e-10 & 3.10432359298787e-10 \tabularnewline
101 & 0.99999999921845 & 1.56309933801383e-09 & 7.81549669006913e-10 \tabularnewline
102 & 0.999999998301508 & 3.39698446978591e-09 & 1.69849223489295e-09 \tabularnewline
103 & 0.999999997422699 & 5.15460242910988e-09 & 2.57730121455494e-09 \tabularnewline
104 & 0.999999993404372 & 1.31912566850295e-08 & 6.59562834251475e-09 \tabularnewline
105 & 0.999999987444976 & 2.51100484466273e-08 & 1.25550242233137e-08 \tabularnewline
106 & 0.999999970335044 & 5.93299128119553e-08 & 2.96649564059776e-08 \tabularnewline
107 & 0.999999940887604 & 1.18224792222059e-07 & 5.91123961110294e-08 \tabularnewline
108 & 0.999999869765606 & 2.60468788319896e-07 & 1.30234394159948e-07 \tabularnewline
109 & 0.99999973223008 & 5.35539839799915e-07 & 2.67769919899958e-07 \tabularnewline
110 & 0.999999481917397 & 1.03616520695596e-06 & 5.18082603477979e-07 \tabularnewline
111 & 0.999998851714014 & 2.29657197257522e-06 & 1.14828598628761e-06 \tabularnewline
112 & 0.99999871885088 & 2.56229823902054e-06 & 1.28114911951027e-06 \tabularnewline
113 & 0.999998020291295 & 3.95941740992522e-06 & 1.97970870496261e-06 \tabularnewline
114 & 0.99999551433003 & 8.9713399410638e-06 & 4.4856699705319e-06 \tabularnewline
115 & 0.999990949762834 & 1.81004743317034e-05 & 9.05023716585168e-06 \tabularnewline
116 & 0.999982396658739 & 3.52066825223522e-05 & 1.76033412611761e-05 \tabularnewline
117 & 0.999963325852547 & 7.33482949056262e-05 & 3.66741474528131e-05 \tabularnewline
118 & 0.999926206584076 & 0.000147586831848769 & 7.37934159243844e-05 \tabularnewline
119 & 0.999992324978145 & 1.53500437104386e-05 & 7.6750218552193e-06 \tabularnewline
120 & 0.999981616063788 & 3.67678724237007e-05 & 1.83839362118503e-05 \tabularnewline
121 & 0.999999999911438 & 1.77123861639059e-10 & 8.85619308195294e-11 \tabularnewline
122 & 0.999999999546675 & 9.06649422271242e-10 & 4.53324711135621e-10 \tabularnewline
123 & 0.999999999563918 & 8.72164236012617e-10 & 4.36082118006309e-10 \tabularnewline
124 & 0.999999997672404 & 4.65519274907878e-09 & 2.32759637453939e-09 \tabularnewline
125 & 0.999999988169836 & 2.36603271579509e-08 & 1.18301635789755e-08 \tabularnewline
126 & 0.999999947258717 & 1.0548256569933e-07 & 5.27412828496652e-08 \tabularnewline
127 & 0.99999990652787 & 1.86944259909657e-07 & 9.34721299548284e-08 \tabularnewline
128 & 0.999999527355952 & 9.45288095830572e-07 & 4.72644047915286e-07 \tabularnewline
129 & 0.999998251450919 & 3.49709816197069e-06 & 1.74854908098535e-06 \tabularnewline
130 & 0.999999098887084 & 1.80222583237223e-06 & 9.01112916186114e-07 \tabularnewline
131 & 0.999995621284362 & 8.75743127633575e-06 & 4.37871563816788e-06 \tabularnewline
132 & 0.99998039153326 & 3.92169334815279e-05 & 1.96084667407639e-05 \tabularnewline
133 & 0.999894704275768 & 0.000210591448464002 & 0.000105295724232001 \tabularnewline
134 & 0.999470583203445 & 0.00105883359311054 & 0.00052941679655527 \tabularnewline
135 & 0.998163724997077 & 0.00367255000584602 & 0.00183627500292301 \tabularnewline
136 & 0.999782135399902 & 0.000435729200195925 & 0.000217864600097963 \tabularnewline
137 & 0.997883932640954 & 0.00423213471809221 & 0.0021160673590461 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146946&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.819710396875195[/C][C]0.360579206249609[/C][C]0.180289603124805[/C][/ROW]
[ROW][C]8[/C][C]0.905582733177442[/C][C]0.188834533645116[/C][C]0.094417266822558[/C][/ROW]
[ROW][C]9[/C][C]0.838251719724204[/C][C]0.323496560551591[/C][C]0.161748280275796[/C][/ROW]
[ROW][C]10[/C][C]0.786182212743209[/C][C]0.427635574513582[/C][C]0.213817787256791[/C][/ROW]
[ROW][C]11[/C][C]0.703148101333511[/C][C]0.593703797332977[/C][C]0.296851898666489[/C][/ROW]
[ROW][C]12[/C][C]0.64611383668625[/C][C]0.7077723266275[/C][C]0.35388616331375[/C][/ROW]
[ROW][C]13[/C][C]0.550593349490892[/C][C]0.898813301018217[/C][C]0.449406650509108[/C][/ROW]
[ROW][C]14[/C][C]0.914206592270306[/C][C]0.171586815459388[/C][C]0.0857934077296942[/C][/ROW]
[ROW][C]15[/C][C]0.926620710240384[/C][C]0.146758579519233[/C][C]0.0733792897596165[/C][/ROW]
[ROW][C]16[/C][C]0.895250023036863[/C][C]0.209499953926273[/C][C]0.104749976963137[/C][/ROW]
[ROW][C]17[/C][C]0.868580095361736[/C][C]0.262839809276528[/C][C]0.131419904638264[/C][/ROW]
[ROW][C]18[/C][C]0.82463657562866[/C][C]0.350726848742681[/C][C]0.17536342437134[/C][/ROW]
[ROW][C]19[/C][C]0.84375591892616[/C][C]0.312488162147679[/C][C]0.156244081073839[/C][/ROW]
[ROW][C]20[/C][C]0.833432055500505[/C][C]0.33313588899899[/C][C]0.166567944499495[/C][/ROW]
[ROW][C]21[/C][C]0.823021882957438[/C][C]0.353956234085124[/C][C]0.176978117042562[/C][/ROW]
[ROW][C]22[/C][C]0.874534236680143[/C][C]0.250931526639714[/C][C]0.125465763319857[/C][/ROW]
[ROW][C]23[/C][C]0.857708630400889[/C][C]0.284582739198222[/C][C]0.142291369599111[/C][/ROW]
[ROW][C]24[/C][C]0.960144934998067[/C][C]0.079710130003866[/C][C]0.039855065001933[/C][/ROW]
[ROW][C]25[/C][C]0.955344132607476[/C][C]0.089311734785049[/C][C]0.0446558673925245[/C][/ROW]
[ROW][C]26[/C][C]0.945358138447532[/C][C]0.109283723104935[/C][C]0.0546418615524676[/C][/ROW]
[ROW][C]27[/C][C]0.935207519178171[/C][C]0.129584961643658[/C][C]0.0647924808218288[/C][/ROW]
[ROW][C]28[/C][C]0.954545170177509[/C][C]0.0909096596449828[/C][C]0.0454548298224914[/C][/ROW]
[ROW][C]29[/C][C]0.964560424977746[/C][C]0.070879150044509[/C][C]0.0354395750222545[/C][/ROW]
[ROW][C]30[/C][C]0.953954795901904[/C][C]0.0920904081961922[/C][C]0.0460452040980961[/C][/ROW]
[ROW][C]31[/C][C]0.937867667543623[/C][C]0.124264664912754[/C][C]0.0621323324563772[/C][/ROW]
[ROW][C]32[/C][C]0.973907060798179[/C][C]0.0521858784036429[/C][C]0.0260929392018214[/C][/ROW]
[ROW][C]33[/C][C]0.965996380259008[/C][C]0.0680072394819844[/C][C]0.0340036197409922[/C][/ROW]
[ROW][C]34[/C][C]0.956378479868292[/C][C]0.0872430402634166[/C][C]0.0436215201317083[/C][/ROW]
[ROW][C]35[/C][C]0.955759675300307[/C][C]0.0884806493993864[/C][C]0.0442403246996932[/C][/ROW]
[ROW][C]36[/C][C]0.948709295486545[/C][C]0.102581409026909[/C][C]0.0512907045134547[/C][/ROW]
[ROW][C]37[/C][C]0.938850395372548[/C][C]0.122299209254905[/C][C]0.0611496046274524[/C][/ROW]
[ROW][C]38[/C][C]0.971630124757235[/C][C]0.0567397504855306[/C][C]0.0283698752427653[/C][/ROW]
[ROW][C]39[/C][C]0.999999999999694[/C][C]6.12810038861702e-13[/C][C]3.06405019430851e-13[/C][/ROW]
[ROW][C]40[/C][C]0.999999999999944[/C][C]1.12334536046859e-13[/C][C]5.61672680234296e-14[/C][/ROW]
[ROW][C]41[/C][C]0.99999999999987[/C][C]2.59005396717537e-13[/C][C]1.29502698358768e-13[/C][/ROW]
[ROW][C]42[/C][C]0.999999999999846[/C][C]3.08244571166849e-13[/C][C]1.54122285583425e-13[/C][/ROW]
[ROW][C]43[/C][C]0.999999999999828[/C][C]3.44471981571082e-13[/C][C]1.72235990785541e-13[/C][/ROW]
[ROW][C]44[/C][C]0.999999999999772[/C][C]4.55110056019045e-13[/C][C]2.27555028009523e-13[/C][/ROW]
[ROW][C]45[/C][C]0.999999999999887[/C][C]2.26446405027554e-13[/C][C]1.13223202513777e-13[/C][/ROW]
[ROW][C]46[/C][C]0.99999999999988[/C][C]2.38944809433327e-13[/C][C]1.19472404716663e-13[/C][/ROW]
[ROW][C]47[/C][C]0.999999999999904[/C][C]1.9265956656908e-13[/C][C]9.632978328454e-14[/C][/ROW]
[ROW][C]48[/C][C]0.999999999999778[/C][C]4.44709029947933e-13[/C][C]2.22354514973967e-13[/C][/ROW]
[ROW][C]49[/C][C]0.999999999999684[/C][C]6.32371810263495e-13[/C][C]3.16185905131747e-13[/C][/ROW]
[ROW][C]50[/C][C]0.99999999999968[/C][C]6.38650223370183e-13[/C][C]3.19325111685091e-13[/C][/ROW]
[ROW][C]51[/C][C]0.99999999999966[/C][C]6.81468607435504e-13[/C][C]3.40734303717752e-13[/C][/ROW]
[ROW][C]52[/C][C]0.999999999999225[/C][C]1.54978357452768e-12[/C][C]7.7489178726384e-13[/C][/ROW]
[ROW][C]53[/C][C]0.999999999998237[/C][C]3.52654115935765e-12[/C][C]1.76327057967882e-12[/C][/ROW]
[ROW][C]54[/C][C]0.999999999999038[/C][C]1.92416239309116e-12[/C][C]9.6208119654558e-13[/C][/ROW]
[ROW][C]55[/C][C]0.999999999999737[/C][C]5.26263656721002e-13[/C][C]2.63131828360501e-13[/C][/ROW]
[ROW][C]56[/C][C]0.999999999999365[/C][C]1.26939922870726e-12[/C][C]6.34699614353629e-13[/C][/ROW]
[ROW][C]57[/C][C]0.999999999999046[/C][C]1.90775861010983e-12[/C][C]9.53879305054914e-13[/C][/ROW]
[ROW][C]58[/C][C]0.999999999997819[/C][C]4.36245893422181e-12[/C][C]2.1812294671109e-12[/C][/ROW]
[ROW][C]59[/C][C]0.999999999998012[/C][C]3.97640253922006e-12[/C][C]1.98820126961003e-12[/C][/ROW]
[ROW][C]60[/C][C]0.999999999996153[/C][C]7.69386222672675e-12[/C][C]3.84693111336337e-12[/C][/ROW]
[ROW][C]61[/C][C]0.99999999999179[/C][C]1.64225267762037e-11[/C][C]8.21126338810186e-12[/C][/ROW]
[ROW][C]62[/C][C]0.999999999990238[/C][C]1.95247074144902e-11[/C][C]9.76235370724509e-12[/C][/ROW]
[ROW][C]63[/C][C]0.999999999995418[/C][C]9.16409533340718e-12[/C][C]4.58204766670359e-12[/C][/ROW]
[ROW][C]64[/C][C]0.999999999991236[/C][C]1.75288269875744e-11[/C][C]8.7644134937872e-12[/C][/ROW]
[ROW][C]65[/C][C]0.999999999982006[/C][C]3.59877012450372e-11[/C][C]1.79938506225186e-11[/C][/ROW]
[ROW][C]66[/C][C]0.999999999991313[/C][C]1.73745480598724e-11[/C][C]8.68727402993622e-12[/C][/ROW]
[ROW][C]67[/C][C]0.99999999997994[/C][C]4.01186301445115e-11[/C][C]2.00593150722557e-11[/C][/ROW]
[ROW][C]68[/C][C]0.999999999999406[/C][C]1.18805061385821e-12[/C][C]5.94025306929107e-13[/C][/ROW]
[ROW][C]69[/C][C]0.999999999998854[/C][C]2.2913077938302e-12[/C][C]1.1456538969151e-12[/C][/ROW]
[ROW][C]70[/C][C]0.999999999997309[/C][C]5.38246664468573e-12[/C][C]2.69123332234287e-12[/C][/ROW]
[ROW][C]71[/C][C]0.999999999999995[/C][C]9.49607818782027e-15[/C][C]4.74803909391014e-15[/C][/ROW]
[ROW][C]72[/C][C]0.999999999999996[/C][C]7.55066111116636e-15[/C][C]3.77533055558318e-15[/C][/ROW]
[ROW][C]73[/C][C]0.999999999999998[/C][C]4.06162694227334e-15[/C][C]2.03081347113667e-15[/C][/ROW]
[ROW][C]74[/C][C]0.999999999999999[/C][C]2.33939958398967e-15[/C][C]1.16969979199484e-15[/C][/ROW]
[ROW][C]75[/C][C]0.999999999999997[/C][C]6.33644230219858e-15[/C][C]3.16822115109929e-15[/C][/ROW]
[ROW][C]76[/C][C]0.999999999999991[/C][C]1.74341747290549e-14[/C][C]8.71708736452745e-15[/C][/ROW]
[ROW][C]77[/C][C]0.999999999999978[/C][C]4.39032322651172e-14[/C][C]2.19516161325586e-14[/C][/ROW]
[ROW][C]78[/C][C]0.999999999999946[/C][C]1.07127629120709e-13[/C][C]5.35638145603543e-14[/C][/ROW]
[ROW][C]79[/C][C]0.999999999999856[/C][C]2.87183297074307e-13[/C][C]1.43591648537154e-13[/C][/ROW]
[ROW][C]80[/C][C]0.99999999999965[/C][C]7.01395554827374e-13[/C][C]3.50697777413687e-13[/C][/ROW]
[ROW][C]81[/C][C]0.999999999999567[/C][C]8.65406871178464e-13[/C][C]4.32703435589232e-13[/C][/ROW]
[ROW][C]82[/C][C]0.999999999999487[/C][C]1.02557663649323e-12[/C][C]5.12788318246616e-13[/C][/ROW]
[ROW][C]83[/C][C]0.999999999999052[/C][C]1.89566284465675e-12[/C][C]9.47831422328373e-13[/C][/ROW]
[ROW][C]84[/C][C]0.999999999999134[/C][C]1.73260167514486e-12[/C][C]8.66300837572432e-13[/C][/ROW]
[ROW][C]85[/C][C]0.999999999998085[/C][C]3.8307786706843e-12[/C][C]1.91538933534215e-12[/C][/ROW]
[ROW][C]86[/C][C]0.999999999995146[/C][C]9.7088848180304e-12[/C][C]4.8544424090152e-12[/C][/ROW]
[ROW][C]87[/C][C]0.999999999987696[/C][C]2.46081178750903e-11[/C][C]1.23040589375452e-11[/C][/ROW]
[ROW][C]88[/C][C]0.999999999969937[/C][C]6.01259637833978e-11[/C][C]3.00629818916989e-11[/C][/ROW]
[ROW][C]89[/C][C]0.999999999965707[/C][C]6.85864658402124e-11[/C][C]3.42932329201062e-11[/C][/ROW]
[ROW][C]90[/C][C]0.999999999940398[/C][C]1.19204976065e-10[/C][C]5.96024880325e-11[/C][/ROW]
[ROW][C]91[/C][C]0.999999999869367[/C][C]2.61266551516106e-10[/C][C]1.30633275758053e-10[/C][/ROW]
[ROW][C]92[/C][C]0.99999999981017[/C][C]3.79661492645525e-10[/C][C]1.89830746322763e-10[/C][/ROW]
[ROW][C]93[/C][C]0.999999999557617[/C][C]8.84765511408297e-10[/C][C]4.42382755704148e-10[/C][/ROW]
[ROW][C]94[/C][C]0.999999998950655[/C][C]2.09869060862027e-09[/C][C]1.04934530431014e-09[/C][/ROW]
[ROW][C]95[/C][C]0.999999998703403[/C][C]2.59319488677954e-09[/C][C]1.29659744338977e-09[/C][/ROW]
[ROW][C]96[/C][C]0.99999999997898[/C][C]4.20396229438994e-11[/C][C]2.10198114719497e-11[/C][/ROW]
[ROW][C]97[/C][C]0.999999999942818[/C][C]1.14365000820944e-10[/C][C]5.7182500410472e-11[/C][/ROW]
[ROW][C]98[/C][C]0.99999999985462[/C][C]2.90758540943593e-10[/C][C]1.45379270471796e-10[/C][/ROW]
[ROW][C]99[/C][C]0.99999999988101[/C][C]2.37978291399285e-10[/C][C]1.18989145699642e-10[/C][/ROW]
[ROW][C]100[/C][C]0.999999999689568[/C][C]6.20864718597575e-10[/C][C]3.10432359298787e-10[/C][/ROW]
[ROW][C]101[/C][C]0.99999999921845[/C][C]1.56309933801383e-09[/C][C]7.81549669006913e-10[/C][/ROW]
[ROW][C]102[/C][C]0.999999998301508[/C][C]3.39698446978591e-09[/C][C]1.69849223489295e-09[/C][/ROW]
[ROW][C]103[/C][C]0.999999997422699[/C][C]5.15460242910988e-09[/C][C]2.57730121455494e-09[/C][/ROW]
[ROW][C]104[/C][C]0.999999993404372[/C][C]1.31912566850295e-08[/C][C]6.59562834251475e-09[/C][/ROW]
[ROW][C]105[/C][C]0.999999987444976[/C][C]2.51100484466273e-08[/C][C]1.25550242233137e-08[/C][/ROW]
[ROW][C]106[/C][C]0.999999970335044[/C][C]5.93299128119553e-08[/C][C]2.96649564059776e-08[/C][/ROW]
[ROW][C]107[/C][C]0.999999940887604[/C][C]1.18224792222059e-07[/C][C]5.91123961110294e-08[/C][/ROW]
[ROW][C]108[/C][C]0.999999869765606[/C][C]2.60468788319896e-07[/C][C]1.30234394159948e-07[/C][/ROW]
[ROW][C]109[/C][C]0.99999973223008[/C][C]5.35539839799915e-07[/C][C]2.67769919899958e-07[/C][/ROW]
[ROW][C]110[/C][C]0.999999481917397[/C][C]1.03616520695596e-06[/C][C]5.18082603477979e-07[/C][/ROW]
[ROW][C]111[/C][C]0.999998851714014[/C][C]2.29657197257522e-06[/C][C]1.14828598628761e-06[/C][/ROW]
[ROW][C]112[/C][C]0.99999871885088[/C][C]2.56229823902054e-06[/C][C]1.28114911951027e-06[/C][/ROW]
[ROW][C]113[/C][C]0.999998020291295[/C][C]3.95941740992522e-06[/C][C]1.97970870496261e-06[/C][/ROW]
[ROW][C]114[/C][C]0.99999551433003[/C][C]8.9713399410638e-06[/C][C]4.4856699705319e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999990949762834[/C][C]1.81004743317034e-05[/C][C]9.05023716585168e-06[/C][/ROW]
[ROW][C]116[/C][C]0.999982396658739[/C][C]3.52066825223522e-05[/C][C]1.76033412611761e-05[/C][/ROW]
[ROW][C]117[/C][C]0.999963325852547[/C][C]7.33482949056262e-05[/C][C]3.66741474528131e-05[/C][/ROW]
[ROW][C]118[/C][C]0.999926206584076[/C][C]0.000147586831848769[/C][C]7.37934159243844e-05[/C][/ROW]
[ROW][C]119[/C][C]0.999992324978145[/C][C]1.53500437104386e-05[/C][C]7.6750218552193e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999981616063788[/C][C]3.67678724237007e-05[/C][C]1.83839362118503e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999999999911438[/C][C]1.77123861639059e-10[/C][C]8.85619308195294e-11[/C][/ROW]
[ROW][C]122[/C][C]0.999999999546675[/C][C]9.06649422271242e-10[/C][C]4.53324711135621e-10[/C][/ROW]
[ROW][C]123[/C][C]0.999999999563918[/C][C]8.72164236012617e-10[/C][C]4.36082118006309e-10[/C][/ROW]
[ROW][C]124[/C][C]0.999999997672404[/C][C]4.65519274907878e-09[/C][C]2.32759637453939e-09[/C][/ROW]
[ROW][C]125[/C][C]0.999999988169836[/C][C]2.36603271579509e-08[/C][C]1.18301635789755e-08[/C][/ROW]
[ROW][C]126[/C][C]0.999999947258717[/C][C]1.0548256569933e-07[/C][C]5.27412828496652e-08[/C][/ROW]
[ROW][C]127[/C][C]0.99999990652787[/C][C]1.86944259909657e-07[/C][C]9.34721299548284e-08[/C][/ROW]
[ROW][C]128[/C][C]0.999999527355952[/C][C]9.45288095830572e-07[/C][C]4.72644047915286e-07[/C][/ROW]
[ROW][C]129[/C][C]0.999998251450919[/C][C]3.49709816197069e-06[/C][C]1.74854908098535e-06[/C][/ROW]
[ROW][C]130[/C][C]0.999999098887084[/C][C]1.80222583237223e-06[/C][C]9.01112916186114e-07[/C][/ROW]
[ROW][C]131[/C][C]0.999995621284362[/C][C]8.75743127633575e-06[/C][C]4.37871563816788e-06[/C][/ROW]
[ROW][C]132[/C][C]0.99998039153326[/C][C]3.92169334815279e-05[/C][C]1.96084667407639e-05[/C][/ROW]
[ROW][C]133[/C][C]0.999894704275768[/C][C]0.000210591448464002[/C][C]0.000105295724232001[/C][/ROW]
[ROW][C]134[/C][C]0.999470583203445[/C][C]0.00105883359311054[/C][C]0.00052941679655527[/C][/ROW]
[ROW][C]135[/C][C]0.998163724997077[/C][C]0.00367255000584602[/C][C]0.00183627500292301[/C][/ROW]
[ROW][C]136[/C][C]0.999782135399902[/C][C]0.000435729200195925[/C][C]0.000217864600097963[/C][/ROW]
[ROW][C]137[/C][C]0.997883932640954[/C][C]0.00423213471809221[/C][C]0.0021160673590461[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146946&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146946&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8197103968751950.3605792062496090.180289603124805
80.9055827331774420.1888345336451160.094417266822558
90.8382517197242040.3234965605515910.161748280275796
100.7861822127432090.4276355745135820.213817787256791
110.7031481013335110.5937037973329770.296851898666489
120.646113836686250.70777232662750.35388616331375
130.5505933494908920.8988133010182170.449406650509108
140.9142065922703060.1715868154593880.0857934077296942
150.9266207102403840.1467585795192330.0733792897596165
160.8952500230368630.2094999539262730.104749976963137
170.8685800953617360.2628398092765280.131419904638264
180.824636575628660.3507268487426810.17536342437134
190.843755918926160.3124881621476790.156244081073839
200.8334320555005050.333135888998990.166567944499495
210.8230218829574380.3539562340851240.176978117042562
220.8745342366801430.2509315266397140.125465763319857
230.8577086304008890.2845827391982220.142291369599111
240.9601449349980670.0797101300038660.039855065001933
250.9553441326074760.0893117347850490.0446558673925245
260.9453581384475320.1092837231049350.0546418615524676
270.9352075191781710.1295849616436580.0647924808218288
280.9545451701775090.09090965964498280.0454548298224914
290.9645604249777460.0708791500445090.0354395750222545
300.9539547959019040.09209040819619220.0460452040980961
310.9378676675436230.1242646649127540.0621323324563772
320.9739070607981790.05218587840364290.0260929392018214
330.9659963802590080.06800723948198440.0340036197409922
340.9563784798682920.08724304026341660.0436215201317083
350.9557596753003070.08848064939938640.0442403246996932
360.9487092954865450.1025814090269090.0512907045134547
370.9388503953725480.1222992092549050.0611496046274524
380.9716301247572350.05673975048553060.0283698752427653
390.9999999999996946.12810038861702e-133.06405019430851e-13
400.9999999999999441.12334536046859e-135.61672680234296e-14
410.999999999999872.59005396717537e-131.29502698358768e-13
420.9999999999998463.08244571166849e-131.54122285583425e-13
430.9999999999998283.44471981571082e-131.72235990785541e-13
440.9999999999997724.55110056019045e-132.27555028009523e-13
450.9999999999998872.26446405027554e-131.13223202513777e-13
460.999999999999882.38944809433327e-131.19472404716663e-13
470.9999999999999041.9265956656908e-139.632978328454e-14
480.9999999999997784.44709029947933e-132.22354514973967e-13
490.9999999999996846.32371810263495e-133.16185905131747e-13
500.999999999999686.38650223370183e-133.19325111685091e-13
510.999999999999666.81468607435504e-133.40734303717752e-13
520.9999999999992251.54978357452768e-127.7489178726384e-13
530.9999999999982373.52654115935765e-121.76327057967882e-12
540.9999999999990381.92416239309116e-129.6208119654558e-13
550.9999999999997375.26263656721002e-132.63131828360501e-13
560.9999999999993651.26939922870726e-126.34699614353629e-13
570.9999999999990461.90775861010983e-129.53879305054914e-13
580.9999999999978194.36245893422181e-122.1812294671109e-12
590.9999999999980123.97640253922006e-121.98820126961003e-12
600.9999999999961537.69386222672675e-123.84693111336337e-12
610.999999999991791.64225267762037e-118.21126338810186e-12
620.9999999999902381.95247074144902e-119.76235370724509e-12
630.9999999999954189.16409533340718e-124.58204766670359e-12
640.9999999999912361.75288269875744e-118.7644134937872e-12
650.9999999999820063.59877012450372e-111.79938506225186e-11
660.9999999999913131.73745480598724e-118.68727402993622e-12
670.999999999979944.01186301445115e-112.00593150722557e-11
680.9999999999994061.18805061385821e-125.94025306929107e-13
690.9999999999988542.2913077938302e-121.1456538969151e-12
700.9999999999973095.38246664468573e-122.69123332234287e-12
710.9999999999999959.49607818782027e-154.74803909391014e-15
720.9999999999999967.55066111116636e-153.77533055558318e-15
730.9999999999999984.06162694227334e-152.03081347113667e-15
740.9999999999999992.33939958398967e-151.16969979199484e-15
750.9999999999999976.33644230219858e-153.16822115109929e-15
760.9999999999999911.74341747290549e-148.71708736452745e-15
770.9999999999999784.39032322651172e-142.19516161325586e-14
780.9999999999999461.07127629120709e-135.35638145603543e-14
790.9999999999998562.87183297074307e-131.43591648537154e-13
800.999999999999657.01395554827374e-133.50697777413687e-13
810.9999999999995678.65406871178464e-134.32703435589232e-13
820.9999999999994871.02557663649323e-125.12788318246616e-13
830.9999999999990521.89566284465675e-129.47831422328373e-13
840.9999999999991341.73260167514486e-128.66300837572432e-13
850.9999999999980853.8307786706843e-121.91538933534215e-12
860.9999999999951469.7088848180304e-124.8544424090152e-12
870.9999999999876962.46081178750903e-111.23040589375452e-11
880.9999999999699376.01259637833978e-113.00629818916989e-11
890.9999999999657076.85864658402124e-113.42932329201062e-11
900.9999999999403981.19204976065e-105.96024880325e-11
910.9999999998693672.61266551516106e-101.30633275758053e-10
920.999999999810173.79661492645525e-101.89830746322763e-10
930.9999999995576178.84765511408297e-104.42382755704148e-10
940.9999999989506552.09869060862027e-091.04934530431014e-09
950.9999999987034032.59319488677954e-091.29659744338977e-09
960.999999999978984.20396229438994e-112.10198114719497e-11
970.9999999999428181.14365000820944e-105.7182500410472e-11
980.999999999854622.90758540943593e-101.45379270471796e-10
990.999999999881012.37978291399285e-101.18989145699642e-10
1000.9999999996895686.20864718597575e-103.10432359298787e-10
1010.999999999218451.56309933801383e-097.81549669006913e-10
1020.9999999983015083.39698446978591e-091.69849223489295e-09
1030.9999999974226995.15460242910988e-092.57730121455494e-09
1040.9999999934043721.31912566850295e-086.59562834251475e-09
1050.9999999874449762.51100484466273e-081.25550242233137e-08
1060.9999999703350445.93299128119553e-082.96649564059776e-08
1070.9999999408876041.18224792222059e-075.91123961110294e-08
1080.9999998697656062.60468788319896e-071.30234394159948e-07
1090.999999732230085.35539839799915e-072.67769919899958e-07
1100.9999994819173971.03616520695596e-065.18082603477979e-07
1110.9999988517140142.29657197257522e-061.14828598628761e-06
1120.999998718850882.56229823902054e-061.28114911951027e-06
1130.9999980202912953.95941740992522e-061.97970870496261e-06
1140.999995514330038.9713399410638e-064.4856699705319e-06
1150.9999909497628341.81004743317034e-059.05023716585168e-06
1160.9999823966587393.52066825223522e-051.76033412611761e-05
1170.9999633258525477.33482949056262e-053.66741474528131e-05
1180.9999262065840760.0001475868318487697.37934159243844e-05
1190.9999923249781451.53500437104386e-057.6750218552193e-06
1200.9999816160637883.67678724237007e-051.83839362118503e-05
1210.9999999999114381.77123861639059e-108.85619308195294e-11
1220.9999999995466759.06649422271242e-104.53324711135621e-10
1230.9999999995639188.72164236012617e-104.36082118006309e-10
1240.9999999976724044.65519274907878e-092.32759637453939e-09
1250.9999999881698362.36603271579509e-081.18301635789755e-08
1260.9999999472587171.0548256569933e-075.27412828496652e-08
1270.999999906527871.86944259909657e-079.34721299548284e-08
1280.9999995273559529.45288095830572e-074.72644047915286e-07
1290.9999982514509193.49709816197069e-061.74854908098535e-06
1300.9999990988870841.80222583237223e-069.01112916186114e-07
1310.9999956212843628.75743127633575e-064.37871563816788e-06
1320.999980391533263.92169334815279e-051.96084667407639e-05
1330.9998947042757680.0002105914484640020.000105295724232001
1340.9994705832034450.001058833593110540.00052941679655527
1350.9981637249970770.003672550005846020.00183627500292301
1360.9997821353999020.0004357292001959250.000217864600097963
1370.9978839326409540.004232134718092210.0021160673590461







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level990.755725190839695NOK
5% type I error level990.755725190839695NOK
10% type I error level1090.83206106870229NOK

\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 & 0.755725190839695 & NOK \tabularnewline
5% type I error level & 99 & 0.755725190839695 & NOK \tabularnewline
10% type I error level & 109 & 0.83206106870229 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146946&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]0.755725190839695[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]99[/C][C]0.755725190839695[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]109[/C][C]0.83206106870229[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146946&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146946&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 level990.755725190839695NOK
5% type I error level990.755725190839695NOK
10% type I error level1090.83206106870229NOK



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')
}