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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Nov 2010 13:13:58 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291122841cxhfjmqtndlra2i.htm/, Retrieved Mon, 29 Apr 2024 10:49:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103384, Retrieved Mon, 29 Apr 2024 10:49:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [ws4q1] [2010-11-30 13:13:58] [b7765ad69c3ab250b1ef04c2ab1247ec] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	14	13	3	25	55	147
12	8	13	5	158	7	71
10	12	16	6	0	0	0
9	7	12	6	143	10	0
10	10	11	5	67	74	43
12	7	12	3	0	0	0
13	16	18	8	148	138	8
12	11	11	4	28	0	0
12	14	14	4	114	113	34
6	6	9	4	0	0	0
5	16	14	6	123	115	103
12	11	12	6	145	9	0
11	16	11	5	113	114	73
14	12	12	4	152	59	159
14	7	13	6	0	0	0
12	13	11	4	36	114	113
12	11	12	6	0	0	0
11	15	16	6	8	102	44
11	7	9	4	108	0	0
7	9	11	4	112	86	0
9	7	13	2	51	17	41
11	14	15	7	43	45	74
11	15	10	5	120	123	0
12	7	11	4	13	24	0
12	15	13	6	55	5	0
11	17	16	6	103	123	32
11	15	15	7	127	136	126
8	14	14	5	14	4	154
9	14	14	6	135	76	129
12	8	14	4	38	99	98
10	8	8	4	11	98	82
10	14	13	7	43	67	45
12	14	15	7	141	92	8
8	8	13	4	62	13	0
12	11	11	4	62	24	129
11	16	15	6	135	129	31
12	10	15	6	117	117	117
7	8	9	5	82	11	99
11	14	13	6	145	20	55
11	16	16	7	87	91	132
12	13	13	6	76	111	58
9	5	11	3	124	0	0
15	8	12	3	151	58	0
11	10	12	4	131	0	0
11	8	12	6	127	146	101
11	13	14	7	76	129	31
11	15	14	5	25	48	147
15	6	8	4	0	0	0
11	12	13	5	58	111	132
12	16	16	6	115	32	123
12	5	13	6	130	112	39
9	15	11	6	17	51	136
12	12	14	5	102	53	141
12	8	13	4	21	131	0
13	13	13	5	0	0	0
11	14	13	5	14	76	135
9	12	12	4	110	106	118
9	16	16	6	133	26	154
11	10	15	2	83	44	
11	15	15	8	56	63	116
12	8	12	3	0	0	0
12	16	14	6	44	116	88
9	19	12	6	70	119	25
11	14	15	6	36	18	113
9	6	12	5	5	134	157
12	13	13	5	118	138	26
12	15	12	6	17	41	38
12	7	12	5	79	0	0
12	13	13	6	122	57	53
14	4	5	2	119	101	0
11	14	13	5	36	114	106
12	13	13	5	36	113	106
11	11	14	5	141	122	102
6	14	17	6	0	14	138
10	12	13	6	37	10	142
12	15	13	6	110	27	73
13	14	12	5	10	39	130
8	13	13	5	14	133	86
12	8	14	4	157	42	78
12	6	11	2	59	0	0
12	7	12	4	77	58	0
6	13	12	6	129	133	4
11	13	16	6	125	151	91
10	11	12	5	87	111	132
12	5	12	3	61	139	0
13	12	12	6	146	126	0
11	8	10	4	96	139	0
7	11	15	5	133	138	14
11	14	15	8	47	52	97
11	9	12	4	74	67	45
11	10	16	6	109	97	0
11	13	15	6	30	137	149
12	16	16	7	116	56	57
10	16	13	6	149	3	105
11	11	12	5	19	78	0
12	8	11	4	96	0	0
7	4	13	6	0	0	0
13	7	10	3	21	0	0
8	14	15	5	26	118	128
12	11	13	6	156	39	29
11	17	16	7	53	63	148
12	15	15	7	72	78	93
14	17	18	6	27	26	4
10	5	13	3	66	50	0
10	4	10	2	71	104	158
13	10	16	8	66	54	144
10	11	13	3	40	104	0
11	15	15	8	57	148	122
10	10	14	3	3	30	149
7	9	15	4	12	38	17
10	12	14	5	107	132	91
8	15	13	7	80	132	111
12	7	13	6	98	84	99
12	13	15	6	155	71	40
12	12	16	7	111	125	132
11	14	14	6	81	25	123
12	14	14	6	50	66	54
12	8	16	6	49	86	90
12	15	14	6	96	61	86
11	12	12	4	2	60	152
12	12	13	4	1	144	152
11	16	12	5	22	120	123
11	9	12	4	64	139	100
13	15	14	6	56	131	116
12	15	14	6	144	159	59
12	6	14	5	0	0	0
12	14	16	8	94	18	5
12	15	13	6	25	123	147
8	10	14	5	93	18	139
8	6	4	4	0	0	0
12	14	16	8	48	123	81
11	12	13	6	30	105	3
12	8	16	4	19	0	0
13	11	15	6	0	0	0
12	13	14	6	10	68	37
12	9	13	4	78	157	5
11	15	14	6	93	94	69
12	13	12	3	0	0	0
12	15	15	6	95	87	0
10	14	14	5	50	156	142
11	16	13	4	86	139	17
12	14	14	6	33	145	100
12	14	16	4	152	55	70
10	10	6	4	51	41	0
12	10	13	4	48	25	123
13	4	13	6	97	47	109
12	8	14	5	77	0	0
15	15	15	6	130	143	37
11	16	14	6	8	102	44
12	12	15	8	84	148	98
11	12	13	7	51	153	11
12	15	16	7	33	32	9
11	9	12	4	6	106	0
10	12	15	6	116	63	57
11	14	12	6	88	56	63
11	11	14	2	142	39	66

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103384&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103384&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103384&T=0

As an alternative you can also use a QR Code:  
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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Liked[t] = + 6.43355012221171 + 0.193380953793444FindingFriends[t] -0.0464345383611361KnowingPeople[t] -0.0364209175232498Celebrity[t] + 0.00735267314025024firstbestfriend[t] -0.00158359380850347secondbestfriend[t] + 0.0410246866096088thirdbestfriend[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Liked[t] =  +  6.43355012221171 +  0.193380953793444FindingFriends[t] -0.0464345383611361KnowingPeople[t] -0.0364209175232498Celebrity[t] +  0.00735267314025024firstbestfriend[t] -0.00158359380850347secondbestfriend[t] +  0.0410246866096088thirdbestfriend[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103384&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Liked[t] =  +  6.43355012221171 +  0.193380953793444FindingFriends[t] -0.0464345383611361KnowingPeople[t] -0.0364209175232498Celebrity[t] +  0.00735267314025024firstbestfriend[t] -0.00158359380850347secondbestfriend[t] +  0.0410246866096088thirdbestfriend[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103384&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103384&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Liked[t] = + 6.43355012221171 + 0.193380953793444FindingFriends[t] -0.0464345383611361KnowingPeople[t] -0.0364209175232498Celebrity[t] + 0.00735267314025024firstbestfriend[t] -0.00158359380850347secondbestfriend[t] + 0.0410246866096088thirdbestfriend[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.433550122211711.2905034.98532e-061e-06
FindingFriends0.1933809537934440.085542.26070.0252260.012613
KnowingPeople-0.04643453836113610.094326-0.49230.6232490.311625
Celebrity-0.03642091752324980.005816-6.262500
firstbestfriend0.007352673140250240.0048761.5080.1336760.066838
secondbestfriend-0.001583593808503470.004979-0.31810.7508910.375446
thirdbestfriend0.04102468660960880.0062746.538600

\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) & 6.43355012221171 & 1.290503 & 4.9853 & 2e-06 & 1e-06 \tabularnewline
FindingFriends & 0.193380953793444 & 0.08554 & 2.2607 & 0.025226 & 0.012613 \tabularnewline
KnowingPeople & -0.0464345383611361 & 0.094326 & -0.4923 & 0.623249 & 0.311625 \tabularnewline
Celebrity & -0.0364209175232498 & 0.005816 & -6.2625 & 0 & 0 \tabularnewline
firstbestfriend & 0.00735267314025024 & 0.004876 & 1.508 & 0.133676 & 0.066838 \tabularnewline
secondbestfriend & -0.00158359380850347 & 0.004979 & -0.3181 & 0.750891 & 0.375446 \tabularnewline
thirdbestfriend & 0.0410246866096088 & 0.006274 & 6.5386 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103384&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]6.43355012221171[/C][C]1.290503[/C][C]4.9853[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.193380953793444[/C][C]0.08554[/C][C]2.2607[/C][C]0.025226[/C][C]0.012613[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]-0.0464345383611361[/C][C]0.094326[/C][C]-0.4923[/C][C]0.623249[/C][C]0.311625[/C][/ROW]
[ROW][C]Celebrity[/C][C]-0.0364209175232498[/C][C]0.005816[/C][C]-6.2625[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]firstbestfriend[/C][C]0.00735267314025024[/C][C]0.004876[/C][C]1.508[/C][C]0.133676[/C][C]0.066838[/C][/ROW]
[ROW][C]secondbestfriend[/C][C]-0.00158359380850347[/C][C]0.004979[/C][C]-0.3181[/C][C]0.750891[/C][C]0.375446[/C][/ROW]
[ROW][C]thirdbestfriend[/C][C]0.0410246866096088[/C][C]0.006274[/C][C]6.5386[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103384&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103384&T=2

As an alternative you can also use a QR Code:  
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)6.433550122211711.2905034.98532e-061e-06
FindingFriends0.1933809537934440.085542.26070.0252260.012613
KnowingPeople-0.04643453836113610.094326-0.49230.6232490.311625
Celebrity-0.03642091752324980.005816-6.262500
firstbestfriend0.007352673140250240.0048761.5080.1336760.066838
secondbestfriend-0.001583593808503470.004979-0.31810.7508910.375446
thirdbestfriend0.04102468660960880.0062746.538600






Multiple Linear Regression - Regression Statistics
Multiple R0.692252665096913
R-squared0.479213752333779
Adjusted R-squared0.458242494038495
F-TEST (value)22.8509775420363
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98677930956923
Sum Squared Residuals1329.20674596655

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.692252665096913 \tabularnewline
R-squared & 0.479213752333779 \tabularnewline
Adjusted R-squared & 0.458242494038495 \tabularnewline
F-TEST (value) & 22.8509775420363 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.98677930956923 \tabularnewline
Sum Squared Residuals & 1329.20674596655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103384&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.692252665096913[/C][/ROW]
[ROW][C]R-squared[/C][C]0.479213752333779[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.458242494038495[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.8509775420363[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.98677930956923[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1329.20674596655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103384&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103384&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.692252665096913
R-squared0.479213752333779
Adjusted R-squared0.458242494038495
F-TEST (value)22.8509775420363
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98677930956923
Sum Squared Residuals1329.20674596655






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11314.3155043325520-1.31550433255197
21312.26393062200990.736069377990063
3167.591619694673048.40838030532696
4128.6660077536563.333992246344
5119.860414371699241.13958562830077
6128.319817046635353.68018295336465
7189.111039739622758.88896026037725
8118.303532823594552.69646717640544
91410.01245234293853.98754765706153
1097.169544944712571.83045505528743
111411.38678500132382.61321499867616
12129.07670140168082.92329859831920
131111.2808079057255-0.280807905725493
141215.9850847984374-3.98508479843742
15138.597316201652494.40268379834751
161112.7247450247107-1.72474502471068
17128.024816140621053.97518385937895
18169.34807806086056.65192193913951
1998.884103874465680.115896125534322
20117.910932607599333.08906739240067
21139.806172489180433.19382751081957
221510.93644068698014.0635593130199
23108.369656689290411.63034331070959
24118.340974628531272.65902537146873
25138.235557040847754.76444295915225
26169.428161223168116.57183877683189
271513.49680735952581.50319264047418
281413.56281441449640.437185585503645
291413.46981198128440.530188018715643
301412.38000667078031.61999332921972
31811.1399111964614-3.1399111964614
32139.518504757720923.48149524227908
33159.068325383284225.93167461671578
34137.898716790762155.10128320923785
351113.8077020315985-2.80770203159851
36159.659355052556635.34064494744337
371513.54612129412081.45387870587922
38911.8805795442187-2.88057954421874
391310.98295506443892.01704493556111
401613.47367573659502.52632426340501
411310.69440313317122.30559686682878
42118.744274731368322.25572526863168
43129.871930572939122.12806942706087
44128.913911741608023.08608825839198
451212.8168169422518-0.816816942251775
46149.328430034842034.67156996515797
471413.82055120821690.179448791783113
4888.90997352885356-0.909973528853565
491313.4873563278487-0.487356327848704
501613.63356231105392.36643768894609
511310.68187115024282.31812884975725
521112.8825246638535-1.88252466385354
531414.4653255201928-0.465325520192839
54138.183916937782264.81608306221774
55138.161748935215474.83825106478453
561313.2494694760819-0.249469476081869
571212.9529266975861-0.952926697586074
581614.46703441394701.53296558605302
59159.015418691054065.98458130894594
6087.369992737759020.630007262240984
6137.91567953154094-4.91567953154094
6268.35781748343513-2.35781748343513
6368.28775936850893-2.28775936850893
6467.45583656471599-1.45583656471599
6558.08344700919629-3.08344700919629
6655.511976948737-0.511976948736998
6768.89147364422693-2.89147364422693
6854.845046093410770.154953906589231
6964.810019094673351.18998090532665
7023.83470360018407-1.83470360018407
7158.38872147939064-3.38872147939064
7258.14696316584733-3.14696316584733
7353.756955380406311.24304461959369
7468.64614466766673-2.64614466766673
7567.14385127058835-1.14385127058835
7665.340555255552050.659444744447947
7758.62854438999498-3.62854438999498
7859.16797337694351-4.16797337694351
7942.29001835849571.7099816415043
8025.42651802844346-3.42651802844346
8145.09819485093413-1.09819485093413
8265.11483240581860.885167594181396
8365.028328691042150.97167130895785
8455.93431490424386-0.934314904243865
8536.17690695434677-3.17690695434677
8664.257161517382181.74283848261782
8745.32903865947798-1.32903865947798
8854.464010640671520.535989359328476
8987.412584232884020.587415767115976
9045.79425528101841-1.79425528101841
9164.819007883643731.18099211635627
9268.42201390247452-2.42201390247452
9375.291598051297071.70840194870293
9463.804331895481422.19566810451858
9558.37733346491905-3.37733346491905
9644.26058255462206-0.260582554622060
9767.13674586461564-1.13674586461564
9836.88622964004309-3.88622964004309
9958.65463320667478-3.65463320667478
10062.967530066346733.03246993365327
10177.7689078576824-0.768907857682404
10277.01602018530557-0.0160201853055708
10368.49692186558065-2.49692186558065
10435.17090585905829-2.17090585905829
10525.20463452039079-3.20463452039079
10685.79988019707862.20011980292140
10737.71620447360656-4.71620447360656
10887.86699810108680.133001898913202
10937.87981089352825-4.87981089352825
11047.70313697133771-3.70313697133771
11155.3616431665055-0.361643166505503
11277.10401220964308-0.104012209643079
11364.567462878847061.43253712115294
11463.556734509938672.44326549006133
11575.129768421388661.87023157861134
11666.02203664825643-0.0220366482564316
11767.56181266301417-1.56181266301417
11866.43512286675337-0.435122866753366
11965.951368356555160.048631643444842
12048.8168156411907-4.8168156411907
12149.38340187752423-5.38340187752423
12259.30798102815149-4.30798102815149
12346.68280863610045-2.68280863610045
12467.91640904965717-1.91640904965717
12565.00750800262290.992491997377104
12657.43604854723178-2.43604854723178
12785.591091001153572.40890899884643
12868.819880491615-2.819880491615
12954.570556863447420.429443136552582
13047.90039393084315-3.90039393084315
13187.877106070893460.122893929106540
13268.31742118095685-2.31742118095685
13347.07896863176426-3.07896863176426
13468.35651877783786-2.35651877783786
13568.86689485117578-2.86689485117578
13646.32722140752693-2.32722140752693
13766.37121510410733-0.371215104107333
13838.88258430050816-5.88258430050816
13966.22768861828547-0.227688618285467
14058.04317230387878-3.04317230387878
14147.2791941882781-3.2791941882781
14268.68898412379803-2.68898412379803
14343.56574372022240.434256279777599
14447.02504147435917-3.02504147435917
14546.53786233632064-2.53786233632064
14663.735856090715692.26414390928431
14755.14147386535727-0.141473865357265
14865.347137916520720.65286208347928
14969.7587852777116-3.75878527771159
15086.382521404592421.61747859540758
15177.8928414732326-0.892841473232592
15278.06172628598513-1.06172628598513
15348.5878689598422-4.58786895984219
15464.65700217307581.34299782692420
15566.14188311156431-0.141883111564314
15623.45444477561568-1.45444477561568

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 14.3155043325520 & -1.31550433255197 \tabularnewline
2 & 13 & 12.2639306220099 & 0.736069377990063 \tabularnewline
3 & 16 & 7.59161969467304 & 8.40838030532696 \tabularnewline
4 & 12 & 8.666007753656 & 3.333992246344 \tabularnewline
5 & 11 & 9.86041437169924 & 1.13958562830077 \tabularnewline
6 & 12 & 8.31981704663535 & 3.68018295336465 \tabularnewline
7 & 18 & 9.11103973962275 & 8.88896026037725 \tabularnewline
8 & 11 & 8.30353282359455 & 2.69646717640544 \tabularnewline
9 & 14 & 10.0124523429385 & 3.98754765706153 \tabularnewline
10 & 9 & 7.16954494471257 & 1.83045505528743 \tabularnewline
11 & 14 & 11.3867850013238 & 2.61321499867616 \tabularnewline
12 & 12 & 9.0767014016808 & 2.92329859831920 \tabularnewline
13 & 11 & 11.2808079057255 & -0.280807905725493 \tabularnewline
14 & 12 & 15.9850847984374 & -3.98508479843742 \tabularnewline
15 & 13 & 8.59731620165249 & 4.40268379834751 \tabularnewline
16 & 11 & 12.7247450247107 & -1.72474502471068 \tabularnewline
17 & 12 & 8.02481614062105 & 3.97518385937895 \tabularnewline
18 & 16 & 9.3480780608605 & 6.65192193913951 \tabularnewline
19 & 9 & 8.88410387446568 & 0.115896125534322 \tabularnewline
20 & 11 & 7.91093260759933 & 3.08906739240067 \tabularnewline
21 & 13 & 9.80617248918043 & 3.19382751081957 \tabularnewline
22 & 15 & 10.9364406869801 & 4.0635593130199 \tabularnewline
23 & 10 & 8.36965668929041 & 1.63034331070959 \tabularnewline
24 & 11 & 8.34097462853127 & 2.65902537146873 \tabularnewline
25 & 13 & 8.23555704084775 & 4.76444295915225 \tabularnewline
26 & 16 & 9.42816122316811 & 6.57183877683189 \tabularnewline
27 & 15 & 13.4968073595258 & 1.50319264047418 \tabularnewline
28 & 14 & 13.5628144144964 & 0.437185585503645 \tabularnewline
29 & 14 & 13.4698119812844 & 0.530188018715643 \tabularnewline
30 & 14 & 12.3800066707803 & 1.61999332921972 \tabularnewline
31 & 8 & 11.1399111964614 & -3.1399111964614 \tabularnewline
32 & 13 & 9.51850475772092 & 3.48149524227908 \tabularnewline
33 & 15 & 9.06832538328422 & 5.93167461671578 \tabularnewline
34 & 13 & 7.89871679076215 & 5.10128320923785 \tabularnewline
35 & 11 & 13.8077020315985 & -2.80770203159851 \tabularnewline
36 & 15 & 9.65935505255663 & 5.34064494744337 \tabularnewline
37 & 15 & 13.5461212941208 & 1.45387870587922 \tabularnewline
38 & 9 & 11.8805795442187 & -2.88057954421874 \tabularnewline
39 & 13 & 10.9829550644389 & 2.01704493556111 \tabularnewline
40 & 16 & 13.4736757365950 & 2.52632426340501 \tabularnewline
41 & 13 & 10.6944031331712 & 2.30559686682878 \tabularnewline
42 & 11 & 8.74427473136832 & 2.25572526863168 \tabularnewline
43 & 12 & 9.87193057293912 & 2.12806942706087 \tabularnewline
44 & 12 & 8.91391174160802 & 3.08608825839198 \tabularnewline
45 & 12 & 12.8168169422518 & -0.816816942251775 \tabularnewline
46 & 14 & 9.32843003484203 & 4.67156996515797 \tabularnewline
47 & 14 & 13.8205512082169 & 0.179448791783113 \tabularnewline
48 & 8 & 8.90997352885356 & -0.909973528853565 \tabularnewline
49 & 13 & 13.4873563278487 & -0.487356327848704 \tabularnewline
50 & 16 & 13.6335623110539 & 2.36643768894609 \tabularnewline
51 & 13 & 10.6818711502428 & 2.31812884975725 \tabularnewline
52 & 11 & 12.8825246638535 & -1.88252466385354 \tabularnewline
53 & 14 & 14.4653255201928 & -0.465325520192839 \tabularnewline
54 & 13 & 8.18391693778226 & 4.81608306221774 \tabularnewline
55 & 13 & 8.16174893521547 & 4.83825106478453 \tabularnewline
56 & 13 & 13.2494694760819 & -0.249469476081869 \tabularnewline
57 & 12 & 12.9529266975861 & -0.952926697586074 \tabularnewline
58 & 16 & 14.4670344139470 & 1.53296558605302 \tabularnewline
59 & 15 & 9.01541869105406 & 5.98458130894594 \tabularnewline
60 & 8 & 7.36999273775902 & 0.630007262240984 \tabularnewline
61 & 3 & 7.91567953154094 & -4.91567953154094 \tabularnewline
62 & 6 & 8.35781748343513 & -2.35781748343513 \tabularnewline
63 & 6 & 8.28775936850893 & -2.28775936850893 \tabularnewline
64 & 6 & 7.45583656471599 & -1.45583656471599 \tabularnewline
65 & 5 & 8.08344700919629 & -3.08344700919629 \tabularnewline
66 & 5 & 5.511976948737 & -0.511976948736998 \tabularnewline
67 & 6 & 8.89147364422693 & -2.89147364422693 \tabularnewline
68 & 5 & 4.84504609341077 & 0.154953906589231 \tabularnewline
69 & 6 & 4.81001909467335 & 1.18998090532665 \tabularnewline
70 & 2 & 3.83470360018407 & -1.83470360018407 \tabularnewline
71 & 5 & 8.38872147939064 & -3.38872147939064 \tabularnewline
72 & 5 & 8.14696316584733 & -3.14696316584733 \tabularnewline
73 & 5 & 3.75695538040631 & 1.24304461959369 \tabularnewline
74 & 6 & 8.64614466766673 & -2.64614466766673 \tabularnewline
75 & 6 & 7.14385127058835 & -1.14385127058835 \tabularnewline
76 & 6 & 5.34055525555205 & 0.659444744447947 \tabularnewline
77 & 5 & 8.62854438999498 & -3.62854438999498 \tabularnewline
78 & 5 & 9.16797337694351 & -4.16797337694351 \tabularnewline
79 & 4 & 2.2900183584957 & 1.7099816415043 \tabularnewline
80 & 2 & 5.42651802844346 & -3.42651802844346 \tabularnewline
81 & 4 & 5.09819485093413 & -1.09819485093413 \tabularnewline
82 & 6 & 5.1148324058186 & 0.885167594181396 \tabularnewline
83 & 6 & 5.02832869104215 & 0.97167130895785 \tabularnewline
84 & 5 & 5.93431490424386 & -0.934314904243865 \tabularnewline
85 & 3 & 6.17690695434677 & -3.17690695434677 \tabularnewline
86 & 6 & 4.25716151738218 & 1.74283848261782 \tabularnewline
87 & 4 & 5.32903865947798 & -1.32903865947798 \tabularnewline
88 & 5 & 4.46401064067152 & 0.535989359328476 \tabularnewline
89 & 8 & 7.41258423288402 & 0.587415767115976 \tabularnewline
90 & 4 & 5.79425528101841 & -1.79425528101841 \tabularnewline
91 & 6 & 4.81900788364373 & 1.18099211635627 \tabularnewline
92 & 6 & 8.42201390247452 & -2.42201390247452 \tabularnewline
93 & 7 & 5.29159805129707 & 1.70840194870293 \tabularnewline
94 & 6 & 3.80433189548142 & 2.19566810451858 \tabularnewline
95 & 5 & 8.37733346491905 & -3.37733346491905 \tabularnewline
96 & 4 & 4.26058255462206 & -0.260582554622060 \tabularnewline
97 & 6 & 7.13674586461564 & -1.13674586461564 \tabularnewline
98 & 3 & 6.88622964004309 & -3.88622964004309 \tabularnewline
99 & 5 & 8.65463320667478 & -3.65463320667478 \tabularnewline
100 & 6 & 2.96753006634673 & 3.03246993365327 \tabularnewline
101 & 7 & 7.7689078576824 & -0.768907857682404 \tabularnewline
102 & 7 & 7.01602018530557 & -0.0160201853055708 \tabularnewline
103 & 6 & 8.49692186558065 & -2.49692186558065 \tabularnewline
104 & 3 & 5.17090585905829 & -2.17090585905829 \tabularnewline
105 & 2 & 5.20463452039079 & -3.20463452039079 \tabularnewline
106 & 8 & 5.7998801970786 & 2.20011980292140 \tabularnewline
107 & 3 & 7.71620447360656 & -4.71620447360656 \tabularnewline
108 & 8 & 7.8669981010868 & 0.133001898913202 \tabularnewline
109 & 3 & 7.87981089352825 & -4.87981089352825 \tabularnewline
110 & 4 & 7.70313697133771 & -3.70313697133771 \tabularnewline
111 & 5 & 5.3616431665055 & -0.361643166505503 \tabularnewline
112 & 7 & 7.10401220964308 & -0.104012209643079 \tabularnewline
113 & 6 & 4.56746287884706 & 1.43253712115294 \tabularnewline
114 & 6 & 3.55673450993867 & 2.44326549006133 \tabularnewline
115 & 7 & 5.12976842138866 & 1.87023157861134 \tabularnewline
116 & 6 & 6.02203664825643 & -0.0220366482564316 \tabularnewline
117 & 6 & 7.56181266301417 & -1.56181266301417 \tabularnewline
118 & 6 & 6.43512286675337 & -0.435122866753366 \tabularnewline
119 & 6 & 5.95136835655516 & 0.048631643444842 \tabularnewline
120 & 4 & 8.8168156411907 & -4.8168156411907 \tabularnewline
121 & 4 & 9.38340187752423 & -5.38340187752423 \tabularnewline
122 & 5 & 9.30798102815149 & -4.30798102815149 \tabularnewline
123 & 4 & 6.68280863610045 & -2.68280863610045 \tabularnewline
124 & 6 & 7.91640904965717 & -1.91640904965717 \tabularnewline
125 & 6 & 5.0075080026229 & 0.992491997377104 \tabularnewline
126 & 5 & 7.43604854723178 & -2.43604854723178 \tabularnewline
127 & 8 & 5.59109100115357 & 2.40890899884643 \tabularnewline
128 & 6 & 8.819880491615 & -2.819880491615 \tabularnewline
129 & 5 & 4.57055686344742 & 0.429443136552582 \tabularnewline
130 & 4 & 7.90039393084315 & -3.90039393084315 \tabularnewline
131 & 8 & 7.87710607089346 & 0.122893929106540 \tabularnewline
132 & 6 & 8.31742118095685 & -2.31742118095685 \tabularnewline
133 & 4 & 7.07896863176426 & -3.07896863176426 \tabularnewline
134 & 6 & 8.35651877783786 & -2.35651877783786 \tabularnewline
135 & 6 & 8.86689485117578 & -2.86689485117578 \tabularnewline
136 & 4 & 6.32722140752693 & -2.32722140752693 \tabularnewline
137 & 6 & 6.37121510410733 & -0.371215104107333 \tabularnewline
138 & 3 & 8.88258430050816 & -5.88258430050816 \tabularnewline
139 & 6 & 6.22768861828547 & -0.227688618285467 \tabularnewline
140 & 5 & 8.04317230387878 & -3.04317230387878 \tabularnewline
141 & 4 & 7.2791941882781 & -3.2791941882781 \tabularnewline
142 & 6 & 8.68898412379803 & -2.68898412379803 \tabularnewline
143 & 4 & 3.5657437202224 & 0.434256279777599 \tabularnewline
144 & 4 & 7.02504147435917 & -3.02504147435917 \tabularnewline
145 & 4 & 6.53786233632064 & -2.53786233632064 \tabularnewline
146 & 6 & 3.73585609071569 & 2.26414390928431 \tabularnewline
147 & 5 & 5.14147386535727 & -0.141473865357265 \tabularnewline
148 & 6 & 5.34713791652072 & 0.65286208347928 \tabularnewline
149 & 6 & 9.7587852777116 & -3.75878527771159 \tabularnewline
150 & 8 & 6.38252140459242 & 1.61747859540758 \tabularnewline
151 & 7 & 7.8928414732326 & -0.892841473232592 \tabularnewline
152 & 7 & 8.06172628598513 & -1.06172628598513 \tabularnewline
153 & 4 & 8.5878689598422 & -4.58786895984219 \tabularnewline
154 & 6 & 4.6570021730758 & 1.34299782692420 \tabularnewline
155 & 6 & 6.14188311156431 & -0.141883111564314 \tabularnewline
156 & 2 & 3.45444477561568 & -1.45444477561568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103384&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]14.3155043325520[/C][C]-1.31550433255197[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]12.2639306220099[/C][C]0.736069377990063[/C][/ROW]
[ROW][C]3[/C][C]16[/C][C]7.59161969467304[/C][C]8.40838030532696[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]8.666007753656[/C][C]3.333992246344[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]9.86041437169924[/C][C]1.13958562830077[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]8.31981704663535[/C][C]3.68018295336465[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]9.11103973962275[/C][C]8.88896026037725[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]8.30353282359455[/C][C]2.69646717640544[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]10.0124523429385[/C][C]3.98754765706153[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]7.16954494471257[/C][C]1.83045505528743[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]11.3867850013238[/C][C]2.61321499867616[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]9.0767014016808[/C][C]2.92329859831920[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]11.2808079057255[/C][C]-0.280807905725493[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]15.9850847984374[/C][C]-3.98508479843742[/C][/ROW]
[ROW][C]15[/C][C]13[/C][C]8.59731620165249[/C][C]4.40268379834751[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]12.7247450247107[/C][C]-1.72474502471068[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]8.02481614062105[/C][C]3.97518385937895[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]9.3480780608605[/C][C]6.65192193913951[/C][/ROW]
[ROW][C]19[/C][C]9[/C][C]8.88410387446568[/C][C]0.115896125534322[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]7.91093260759933[/C][C]3.08906739240067[/C][/ROW]
[ROW][C]21[/C][C]13[/C][C]9.80617248918043[/C][C]3.19382751081957[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]10.9364406869801[/C][C]4.0635593130199[/C][/ROW]
[ROW][C]23[/C][C]10[/C][C]8.36965668929041[/C][C]1.63034331070959[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]8.34097462853127[/C][C]2.65902537146873[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]8.23555704084775[/C][C]4.76444295915225[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]9.42816122316811[/C][C]6.57183877683189[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]13.4968073595258[/C][C]1.50319264047418[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.5628144144964[/C][C]0.437185585503645[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]13.4698119812844[/C][C]0.530188018715643[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]12.3800066707803[/C][C]1.61999332921972[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]11.1399111964614[/C][C]-3.1399111964614[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]9.51850475772092[/C][C]3.48149524227908[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]9.06832538328422[/C][C]5.93167461671578[/C][/ROW]
[ROW][C]34[/C][C]13[/C][C]7.89871679076215[/C][C]5.10128320923785[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]13.8077020315985[/C][C]-2.80770203159851[/C][/ROW]
[ROW][C]36[/C][C]15[/C][C]9.65935505255663[/C][C]5.34064494744337[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]13.5461212941208[/C][C]1.45387870587922[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]11.8805795442187[/C][C]-2.88057954421874[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]10.9829550644389[/C][C]2.01704493556111[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.4736757365950[/C][C]2.52632426340501[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]10.6944031331712[/C][C]2.30559686682878[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]8.74427473136832[/C][C]2.25572526863168[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]9.87193057293912[/C][C]2.12806942706087[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]8.91391174160802[/C][C]3.08608825839198[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]12.8168169422518[/C][C]-0.816816942251775[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]9.32843003484203[/C][C]4.67156996515797[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]13.8205512082169[/C][C]0.179448791783113[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]8.90997352885356[/C][C]-0.909973528853565[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.4873563278487[/C][C]-0.487356327848704[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]13.6335623110539[/C][C]2.36643768894609[/C][/ROW]
[ROW][C]51[/C][C]13[/C][C]10.6818711502428[/C][C]2.31812884975725[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]12.8825246638535[/C][C]-1.88252466385354[/C][/ROW]
[ROW][C]53[/C][C]14[/C][C]14.4653255201928[/C][C]-0.465325520192839[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]8.18391693778226[/C][C]4.81608306221774[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]8.16174893521547[/C][C]4.83825106478453[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]13.2494694760819[/C][C]-0.249469476081869[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.9529266975861[/C][C]-0.952926697586074[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.4670344139470[/C][C]1.53296558605302[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]9.01541869105406[/C][C]5.98458130894594[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]7.36999273775902[/C][C]0.630007262240984[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]7.91567953154094[/C][C]-4.91567953154094[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]8.35781748343513[/C][C]-2.35781748343513[/C][/ROW]
[ROW][C]63[/C][C]6[/C][C]8.28775936850893[/C][C]-2.28775936850893[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]7.45583656471599[/C][C]-1.45583656471599[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]8.08344700919629[/C][C]-3.08344700919629[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.511976948737[/C][C]-0.511976948736998[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]8.89147364422693[/C][C]-2.89147364422693[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]4.84504609341077[/C][C]0.154953906589231[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]4.81001909467335[/C][C]1.18998090532665[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]3.83470360018407[/C][C]-1.83470360018407[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]8.38872147939064[/C][C]-3.38872147939064[/C][/ROW]
[ROW][C]72[/C][C]5[/C][C]8.14696316584733[/C][C]-3.14696316584733[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]3.75695538040631[/C][C]1.24304461959369[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]8.64614466766673[/C][C]-2.64614466766673[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]7.14385127058835[/C][C]-1.14385127058835[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]5.34055525555205[/C][C]0.659444744447947[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]8.62854438999498[/C][C]-3.62854438999498[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]9.16797337694351[/C][C]-4.16797337694351[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]2.2900183584957[/C][C]1.7099816415043[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]5.42651802844346[/C][C]-3.42651802844346[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]5.09819485093413[/C][C]-1.09819485093413[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]5.1148324058186[/C][C]0.885167594181396[/C][/ROW]
[ROW][C]83[/C][C]6[/C][C]5.02832869104215[/C][C]0.97167130895785[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]5.93431490424386[/C][C]-0.934314904243865[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]6.17690695434677[/C][C]-3.17690695434677[/C][/ROW]
[ROW][C]86[/C][C]6[/C][C]4.25716151738218[/C][C]1.74283848261782[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]5.32903865947798[/C][C]-1.32903865947798[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]4.46401064067152[/C][C]0.535989359328476[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]7.41258423288402[/C][C]0.587415767115976[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]5.79425528101841[/C][C]-1.79425528101841[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]4.81900788364373[/C][C]1.18099211635627[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]8.42201390247452[/C][C]-2.42201390247452[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]5.29159805129707[/C][C]1.70840194870293[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]3.80433189548142[/C][C]2.19566810451858[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]8.37733346491905[/C][C]-3.37733346491905[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]4.26058255462206[/C][C]-0.260582554622060[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]7.13674586461564[/C][C]-1.13674586461564[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]6.88622964004309[/C][C]-3.88622964004309[/C][/ROW]
[ROW][C]99[/C][C]5[/C][C]8.65463320667478[/C][C]-3.65463320667478[/C][/ROW]
[ROW][C]100[/C][C]6[/C][C]2.96753006634673[/C][C]3.03246993365327[/C][/ROW]
[ROW][C]101[/C][C]7[/C][C]7.7689078576824[/C][C]-0.768907857682404[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]7.01602018530557[/C][C]-0.0160201853055708[/C][/ROW]
[ROW][C]103[/C][C]6[/C][C]8.49692186558065[/C][C]-2.49692186558065[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]5.17090585905829[/C][C]-2.17090585905829[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]5.20463452039079[/C][C]-3.20463452039079[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]5.7998801970786[/C][C]2.20011980292140[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]7.71620447360656[/C][C]-4.71620447360656[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]7.8669981010868[/C][C]0.133001898913202[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]7.87981089352825[/C][C]-4.87981089352825[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]7.70313697133771[/C][C]-3.70313697133771[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]5.3616431665055[/C][C]-0.361643166505503[/C][/ROW]
[ROW][C]112[/C][C]7[/C][C]7.10401220964308[/C][C]-0.104012209643079[/C][/ROW]
[ROW][C]113[/C][C]6[/C][C]4.56746287884706[/C][C]1.43253712115294[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]3.55673450993867[/C][C]2.44326549006133[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]5.12976842138866[/C][C]1.87023157861134[/C][/ROW]
[ROW][C]116[/C][C]6[/C][C]6.02203664825643[/C][C]-0.0220366482564316[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]7.56181266301417[/C][C]-1.56181266301417[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]6.43512286675337[/C][C]-0.435122866753366[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]5.95136835655516[/C][C]0.048631643444842[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]8.8168156411907[/C][C]-4.8168156411907[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]9.38340187752423[/C][C]-5.38340187752423[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]9.30798102815149[/C][C]-4.30798102815149[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]6.68280863610045[/C][C]-2.68280863610045[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]7.91640904965717[/C][C]-1.91640904965717[/C][/ROW]
[ROW][C]125[/C][C]6[/C][C]5.0075080026229[/C][C]0.992491997377104[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]7.43604854723178[/C][C]-2.43604854723178[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]5.59109100115357[/C][C]2.40890899884643[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]8.819880491615[/C][C]-2.819880491615[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]4.57055686344742[/C][C]0.429443136552582[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]7.90039393084315[/C][C]-3.90039393084315[/C][/ROW]
[ROW][C]131[/C][C]8[/C][C]7.87710607089346[/C][C]0.122893929106540[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]8.31742118095685[/C][C]-2.31742118095685[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]7.07896863176426[/C][C]-3.07896863176426[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]8.35651877783786[/C][C]-2.35651877783786[/C][/ROW]
[ROW][C]135[/C][C]6[/C][C]8.86689485117578[/C][C]-2.86689485117578[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]6.32722140752693[/C][C]-2.32722140752693[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]6.37121510410733[/C][C]-0.371215104107333[/C][/ROW]
[ROW][C]138[/C][C]3[/C][C]8.88258430050816[/C][C]-5.88258430050816[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]6.22768861828547[/C][C]-0.227688618285467[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]8.04317230387878[/C][C]-3.04317230387878[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]7.2791941882781[/C][C]-3.2791941882781[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]8.68898412379803[/C][C]-2.68898412379803[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]3.5657437202224[/C][C]0.434256279777599[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]7.02504147435917[/C][C]-3.02504147435917[/C][/ROW]
[ROW][C]145[/C][C]4[/C][C]6.53786233632064[/C][C]-2.53786233632064[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]3.73585609071569[/C][C]2.26414390928431[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]5.14147386535727[/C][C]-0.141473865357265[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]5.34713791652072[/C][C]0.65286208347928[/C][/ROW]
[ROW][C]149[/C][C]6[/C][C]9.7587852777116[/C][C]-3.75878527771159[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]6.38252140459242[/C][C]1.61747859540758[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]7.8928414732326[/C][C]-0.892841473232592[/C][/ROW]
[ROW][C]152[/C][C]7[/C][C]8.06172628598513[/C][C]-1.06172628598513[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]8.5878689598422[/C][C]-4.58786895984219[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]4.6570021730758[/C][C]1.34299782692420[/C][/ROW]
[ROW][C]155[/C][C]6[/C][C]6.14188311156431[/C][C]-0.141883111564314[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]3.45444477561568[/C][C]-1.45444477561568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103384&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103384&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11314.3155043325520-1.31550433255197
21312.26393062200990.736069377990063
3167.591619694673048.40838030532696
4128.6660077536563.333992246344
5119.860414371699241.13958562830077
6128.319817046635353.68018295336465
7189.111039739622758.88896026037725
8118.303532823594552.69646717640544
91410.01245234293853.98754765706153
1097.169544944712571.83045505528743
111411.38678500132382.61321499867616
12129.07670140168082.92329859831920
131111.2808079057255-0.280807905725493
141215.9850847984374-3.98508479843742
15138.597316201652494.40268379834751
161112.7247450247107-1.72474502471068
17128.024816140621053.97518385937895
18169.34807806086056.65192193913951
1998.884103874465680.115896125534322
20117.910932607599333.08906739240067
21139.806172489180433.19382751081957
221510.93644068698014.0635593130199
23108.369656689290411.63034331070959
24118.340974628531272.65902537146873
25138.235557040847754.76444295915225
26169.428161223168116.57183877683189
271513.49680735952581.50319264047418
281413.56281441449640.437185585503645
291413.46981198128440.530188018715643
301412.38000667078031.61999332921972
31811.1399111964614-3.1399111964614
32139.518504757720923.48149524227908
33159.068325383284225.93167461671578
34137.898716790762155.10128320923785
351113.8077020315985-2.80770203159851
36159.659355052556635.34064494744337
371513.54612129412081.45387870587922
38911.8805795442187-2.88057954421874
391310.98295506443892.01704493556111
401613.47367573659502.52632426340501
411310.69440313317122.30559686682878
42118.744274731368322.25572526863168
43129.871930572939122.12806942706087
44128.913911741608023.08608825839198
451212.8168169422518-0.816816942251775
46149.328430034842034.67156996515797
471413.82055120821690.179448791783113
4888.90997352885356-0.909973528853565
491313.4873563278487-0.487356327848704
501613.63356231105392.36643768894609
511310.68187115024282.31812884975725
521112.8825246638535-1.88252466385354
531414.4653255201928-0.465325520192839
54138.183916937782264.81608306221774
55138.161748935215474.83825106478453
561313.2494694760819-0.249469476081869
571212.9529266975861-0.952926697586074
581614.46703441394701.53296558605302
59159.015418691054065.98458130894594
6087.369992737759020.630007262240984
6137.91567953154094-4.91567953154094
6268.35781748343513-2.35781748343513
6368.28775936850893-2.28775936850893
6467.45583656471599-1.45583656471599
6558.08344700919629-3.08344700919629
6655.511976948737-0.511976948736998
6768.89147364422693-2.89147364422693
6854.845046093410770.154953906589231
6964.810019094673351.18998090532665
7023.83470360018407-1.83470360018407
7158.38872147939064-3.38872147939064
7258.14696316584733-3.14696316584733
7353.756955380406311.24304461959369
7468.64614466766673-2.64614466766673
7567.14385127058835-1.14385127058835
7665.340555255552050.659444744447947
7758.62854438999498-3.62854438999498
7859.16797337694351-4.16797337694351
7942.29001835849571.7099816415043
8025.42651802844346-3.42651802844346
8145.09819485093413-1.09819485093413
8265.11483240581860.885167594181396
8365.028328691042150.97167130895785
8455.93431490424386-0.934314904243865
8536.17690695434677-3.17690695434677
8664.257161517382181.74283848261782
8745.32903865947798-1.32903865947798
8854.464010640671520.535989359328476
8987.412584232884020.587415767115976
9045.79425528101841-1.79425528101841
9164.819007883643731.18099211635627
9268.42201390247452-2.42201390247452
9375.291598051297071.70840194870293
9463.804331895481422.19566810451858
9558.37733346491905-3.37733346491905
9644.26058255462206-0.260582554622060
9767.13674586461564-1.13674586461564
9836.88622964004309-3.88622964004309
9958.65463320667478-3.65463320667478
10062.967530066346733.03246993365327
10177.7689078576824-0.768907857682404
10277.01602018530557-0.0160201853055708
10368.49692186558065-2.49692186558065
10435.17090585905829-2.17090585905829
10525.20463452039079-3.20463452039079
10685.79988019707862.20011980292140
10737.71620447360656-4.71620447360656
10887.86699810108680.133001898913202
10937.87981089352825-4.87981089352825
11047.70313697133771-3.70313697133771
11155.3616431665055-0.361643166505503
11277.10401220964308-0.104012209643079
11364.567462878847061.43253712115294
11463.556734509938672.44326549006133
11575.129768421388661.87023157861134
11666.02203664825643-0.0220366482564316
11767.56181266301417-1.56181266301417
11866.43512286675337-0.435122866753366
11965.951368356555160.048631643444842
12048.8168156411907-4.8168156411907
12149.38340187752423-5.38340187752423
12259.30798102815149-4.30798102815149
12346.68280863610045-2.68280863610045
12467.91640904965717-1.91640904965717
12565.00750800262290.992491997377104
12657.43604854723178-2.43604854723178
12785.591091001153572.40890899884643
12868.819880491615-2.819880491615
12954.570556863447420.429443136552582
13047.90039393084315-3.90039393084315
13187.877106070893460.122893929106540
13268.31742118095685-2.31742118095685
13347.07896863176426-3.07896863176426
13468.35651877783786-2.35651877783786
13568.86689485117578-2.86689485117578
13646.32722140752693-2.32722140752693
13766.37121510410733-0.371215104107333
13838.88258430050816-5.88258430050816
13966.22768861828547-0.227688618285467
14058.04317230387878-3.04317230387878
14147.2791941882781-3.2791941882781
14268.68898412379803-2.68898412379803
14343.56574372022240.434256279777599
14447.02504147435917-3.02504147435917
14546.53786233632064-2.53786233632064
14663.735856090715692.26414390928431
14755.14147386535727-0.141473865357265
14865.347137916520720.65286208347928
14969.7587852777116-3.75878527771159
15086.382521404592421.61747859540758
15177.8928414732326-0.892841473232592
15278.06172628598513-1.06172628598513
15348.5878689598422-4.58786895984219
15464.65700217307581.34299782692420
15566.14188311156431-0.141883111564314
15623.45444477561568-1.45444477561568






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2635823125255390.5271646250510780.736417687474461
110.1447141508770340.2894283017540670.855285849122966
120.1321052381692630.2642104763385260.867894761830737
130.1854107302658250.370821460531650.814589269734175
140.1190049604792860.2380099209585720.880995039520714
150.09544000992545710.1908800198509140.904559990074543
160.0703731868514870.1407463737029740.929626813148513
170.07065705195198850.1413141039039770.929342948048012
180.06176516618527470.1235303323705490.938234833814725
190.04463202917591890.08926405835183780.955367970824081
200.03074495567784910.06148991135569810.96925504432215
210.08585334361592630.1717066872318530.914146656384074
220.06259783393171050.1251956678634210.93740216606829
230.09053794255994480.1810758851198900.909462057440055
240.06873344877651560.1374668975530310.931266551223484
250.06077546973823170.1215509394764630.939224530261768
260.07475268431793430.1495053686358690.925247315682066
270.05216338954776670.1043267790955330.947836610452233
280.03556641170911350.0711328234182270.964433588290887
290.02351924647556220.04703849295112440.976480753524438
300.01970578333922920.03941156667845850.98029421666077
310.04725261940204650.0945052388040930.952747380597954
320.04664213203902000.09328426407804010.95335786796098
330.05510010993100590.1102002198620120.944899890068994
340.08465452156922220.1693090431384440.915345478430778
350.07565826912487180.1513165382497440.924341730875128
360.09678235970756020.1935647194151200.90321764029244
370.08635362532555320.1727072506511060.913646374674447
380.1015346097705830.2030692195411670.898465390229417
390.09092145077929020.1818429015585800.90907854922071
400.07755396169795050.1551079233959010.92244603830205
410.07213461451338220.1442692290267640.927865385486618
420.08123357011770740.1624671402354150.918766429882293
430.08496998582509330.1699399716501870.915030014174907
440.1159994067305580.2319988134611160.884000593269442
450.09431405281223580.1886281056244720.905685947187764
460.1415511666527090.2831023333054180.85844883334729
470.1144849094960620.2289698189921240.885515090503938
480.1602623543524610.3205247087049210.83973764564754
490.1309839188364510.2619678376729020.86901608116355
500.1336848813230560.2673697626461130.866315118676944
510.1752345775808670.3504691551617350.824765422419133
520.2164972763583350.4329945527166700.783502723641665
530.1869844607142580.3739689214285160.813015539285742
540.455228768664360.910457537328720.54477123133564
550.7790720120165260.4418559759669490.220927987983474
560.7461559420917140.5076881158165730.253844057908286
570.7074307032849610.5851385934300790.292569296715039
580.6977182053097920.6045635893804150.302281794690208
590.9998911117420830.0002177765158332460.000108888257916623
600.9999829053830573.41892338854004e-051.70946169427002e-05
610.9999999943866221.12267549963632e-085.61337749818159e-09
620.9999999971686235.6627538295087e-092.83137691475435e-09
630.99999999446181.10764001892749e-085.53820009463744e-09
640.9999999958654458.26910938794447e-094.13455469397223e-09
650.9999999998338783.32244216904573e-101.66122108452287e-10
660.9999999999484541.03091502986249e-105.15457514931245e-11
670.9999999999860052.79909869098885e-111.39954934549443e-11
680.999999999975734.85393561900936e-112.42696780950468e-11
690.9999999999786274.27460368261367e-112.13730184130684e-11
700.9999999999545969.08077927007784e-114.54038963503892e-11
710.9999999999829943.40116280416127e-111.70058140208064e-11
720.9999999999906981.8603695594998e-119.301847797499e-12
730.9999999999871322.57358424944906e-111.28679212472453e-11
740.9999999999962227.55568715449036e-123.77784357724518e-12
750.9999999999938231.23530247009649e-116.17651235048244e-12
760.99999999998872.26018830969202e-111.13009415484601e-11
770.9999999999924531.50937844902276e-117.5468922451138e-12
780.9999999999973185.36316233354578e-122.68158116677289e-12
790.9999999999962637.47324943916945e-123.73662471958473e-12
800.9999999999987442.51241692455181e-121.25620846227591e-12
810.9999999999975924.81603390464634e-122.40801695232317e-12
820.9999999999953719.25809267085742e-124.62904633542871e-12
830.9999999999892952.14107906756972e-111.07053953378486e-11
840.9999999999750874.98267680180559e-112.49133840090280e-11
850.9999999999843543.12927092675092e-111.56463546337546e-11
860.9999999999759044.81919179205085e-112.40959589602543e-11
870.999999999949981.00041482533270e-105.00207412666352e-11
880.99999999989582.08398922090946e-101.04199461045473e-10
890.9999999999108111.78377242550908e-108.9188621275454e-11
900.999999999840453.19098903952809e-101.59549451976405e-10
910.9999999996326427.34715958726463e-103.67357979363231e-10
920.9999999994903861.01922790240372e-095.09613951201858e-10
930.9999999990306981.93860390428540e-099.6930195214270e-10
940.9999999985765042.84699154829328e-091.42349577414664e-09
950.9999999985398252.92035062382435e-091.46017531191217e-09
960.9999999967879936.42401489702765e-093.21200744851383e-09
970.9999999976237044.75259186919359e-092.37629593459679e-09
980.99999999712975.74059842356264e-092.87029921178132e-09
990.9999999974753645.04927255294656e-092.52463627647328e-09
1000.9999999965889986.82200438806172e-093.41100219403086e-09
1010.9999999927982951.44034103947680e-087.20170519738401e-09
1020.9999999850181662.99636684108531e-081.49818342054265e-08
1030.999999979689714.06205810610318e-082.03102905305159e-08
1040.9999999697736126.04527753232706e-083.02263876616353e-08
1050.9999999748615795.02768427219898e-082.51384213609949e-08
1060.9999999848450443.03099114289308e-081.51549557144654e-08
1070.9999999942943161.14113674658132e-085.70568373290659e-09
1080.9999999944070631.11858740718162e-085.59293703590808e-09
1090.999999996426037.14793816832067e-093.57396908416033e-09
1100.9999999964374897.12502233172949e-093.56251116586474e-09
1110.999999992648641.47027182844689e-087.35135914223446e-09
1120.999999989526892.09462197209658e-081.04731098604829e-08
1130.9999999819872533.60254932229619e-081.80127466114810e-08
1140.9999999610912467.78175074080563e-083.89087537040281e-08
1150.9999999326978481.34604303444207e-076.73021517221035e-08
1160.9999998658108822.68378236135561e-071.34189118067781e-07
1170.9999997157514295.68497141843663e-072.84248570921831e-07
1180.9999993642950551.27140989035312e-066.3570494517656e-07
1190.999998619861972.76027606046535e-061.38013803023268e-06
1200.9999979657729124.06845417683544e-062.03422708841772e-06
1210.9999982160481973.56790360613776e-061.78395180306888e-06
1220.9999970168915425.9662169156031e-062.98310845780155e-06
1230.9999949311237531.01377524932722e-055.06887624663609e-06
1240.9999886836279122.26327441755489e-051.13163720877745e-05
1250.9999750549470654.98901058698661e-052.49450529349330e-05
1260.9999491944959170.0001016110081664185.08055040832088e-05
1270.9999771559365084.56881269843372e-052.28440634921686e-05
1280.9999528417746879.43164506252129e-054.71582253126064e-05
1290.9998958919990820.0002082160018359730.000104108000917986
1300.9998332562170180.0003334875659637560.000166743782981878
1310.9997595132171930.0004809735656143390.000240486782807170
1320.9995310871166240.0009378257667526340.000468912883376317
1330.9993306796561880.001338640687623430.000669320343811715
1340.998660729491670.002678541016661160.00133927050833058
1350.9973754725145680.005249054970863520.00262452748543176
1360.9971752674153760.005649465169248940.00282473258462447
1370.9948145152027720.01037096959445580.00518548479722792
1380.9938671042942030.01226579141159460.00613289570579732
1390.9871139548075890.02577209038482230.0128860451924111
1400.9794950733144880.04100985337102380.0205049266855119
1410.9713543285386260.05729134292274710.0286456714613735
1420.9485139894377640.1029720211244730.0514860105622363
1430.9165231262114820.1669537475770350.0834768737885176
1440.875848623986790.2483027520264180.124151376013209
1450.8120441039412030.3759117921175940.187955896058797
1460.6883346114526060.6233307770947890.311665388547394

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.263582312525539 & 0.527164625051078 & 0.736417687474461 \tabularnewline
11 & 0.144714150877034 & 0.289428301754067 & 0.855285849122966 \tabularnewline
12 & 0.132105238169263 & 0.264210476338526 & 0.867894761830737 \tabularnewline
13 & 0.185410730265825 & 0.37082146053165 & 0.814589269734175 \tabularnewline
14 & 0.119004960479286 & 0.238009920958572 & 0.880995039520714 \tabularnewline
15 & 0.0954400099254571 & 0.190880019850914 & 0.904559990074543 \tabularnewline
16 & 0.070373186851487 & 0.140746373702974 & 0.929626813148513 \tabularnewline
17 & 0.0706570519519885 & 0.141314103903977 & 0.929342948048012 \tabularnewline
18 & 0.0617651661852747 & 0.123530332370549 & 0.938234833814725 \tabularnewline
19 & 0.0446320291759189 & 0.0892640583518378 & 0.955367970824081 \tabularnewline
20 & 0.0307449556778491 & 0.0614899113556981 & 0.96925504432215 \tabularnewline
21 & 0.0858533436159263 & 0.171706687231853 & 0.914146656384074 \tabularnewline
22 & 0.0625978339317105 & 0.125195667863421 & 0.93740216606829 \tabularnewline
23 & 0.0905379425599448 & 0.181075885119890 & 0.909462057440055 \tabularnewline
24 & 0.0687334487765156 & 0.137466897553031 & 0.931266551223484 \tabularnewline
25 & 0.0607754697382317 & 0.121550939476463 & 0.939224530261768 \tabularnewline
26 & 0.0747526843179343 & 0.149505368635869 & 0.925247315682066 \tabularnewline
27 & 0.0521633895477667 & 0.104326779095533 & 0.947836610452233 \tabularnewline
28 & 0.0355664117091135 & 0.071132823418227 & 0.964433588290887 \tabularnewline
29 & 0.0235192464755622 & 0.0470384929511244 & 0.976480753524438 \tabularnewline
30 & 0.0197057833392292 & 0.0394115666784585 & 0.98029421666077 \tabularnewline
31 & 0.0472526194020465 & 0.094505238804093 & 0.952747380597954 \tabularnewline
32 & 0.0466421320390200 & 0.0932842640780401 & 0.95335786796098 \tabularnewline
33 & 0.0551001099310059 & 0.110200219862012 & 0.944899890068994 \tabularnewline
34 & 0.0846545215692222 & 0.169309043138444 & 0.915345478430778 \tabularnewline
35 & 0.0756582691248718 & 0.151316538249744 & 0.924341730875128 \tabularnewline
36 & 0.0967823597075602 & 0.193564719415120 & 0.90321764029244 \tabularnewline
37 & 0.0863536253255532 & 0.172707250651106 & 0.913646374674447 \tabularnewline
38 & 0.101534609770583 & 0.203069219541167 & 0.898465390229417 \tabularnewline
39 & 0.0909214507792902 & 0.181842901558580 & 0.90907854922071 \tabularnewline
40 & 0.0775539616979505 & 0.155107923395901 & 0.92244603830205 \tabularnewline
41 & 0.0721346145133822 & 0.144269229026764 & 0.927865385486618 \tabularnewline
42 & 0.0812335701177074 & 0.162467140235415 & 0.918766429882293 \tabularnewline
43 & 0.0849699858250933 & 0.169939971650187 & 0.915030014174907 \tabularnewline
44 & 0.115999406730558 & 0.231998813461116 & 0.884000593269442 \tabularnewline
45 & 0.0943140528122358 & 0.188628105624472 & 0.905685947187764 \tabularnewline
46 & 0.141551166652709 & 0.283102333305418 & 0.85844883334729 \tabularnewline
47 & 0.114484909496062 & 0.228969818992124 & 0.885515090503938 \tabularnewline
48 & 0.160262354352461 & 0.320524708704921 & 0.83973764564754 \tabularnewline
49 & 0.130983918836451 & 0.261967837672902 & 0.86901608116355 \tabularnewline
50 & 0.133684881323056 & 0.267369762646113 & 0.866315118676944 \tabularnewline
51 & 0.175234577580867 & 0.350469155161735 & 0.824765422419133 \tabularnewline
52 & 0.216497276358335 & 0.432994552716670 & 0.783502723641665 \tabularnewline
53 & 0.186984460714258 & 0.373968921428516 & 0.813015539285742 \tabularnewline
54 & 0.45522876866436 & 0.91045753732872 & 0.54477123133564 \tabularnewline
55 & 0.779072012016526 & 0.441855975966949 & 0.220927987983474 \tabularnewline
56 & 0.746155942091714 & 0.507688115816573 & 0.253844057908286 \tabularnewline
57 & 0.707430703284961 & 0.585138593430079 & 0.292569296715039 \tabularnewline
58 & 0.697718205309792 & 0.604563589380415 & 0.302281794690208 \tabularnewline
59 & 0.999891111742083 & 0.000217776515833246 & 0.000108888257916623 \tabularnewline
60 & 0.999982905383057 & 3.41892338854004e-05 & 1.70946169427002e-05 \tabularnewline
61 & 0.999999994386622 & 1.12267549963632e-08 & 5.61337749818159e-09 \tabularnewline
62 & 0.999999997168623 & 5.6627538295087e-09 & 2.83137691475435e-09 \tabularnewline
63 & 0.9999999944618 & 1.10764001892749e-08 & 5.53820009463744e-09 \tabularnewline
64 & 0.999999995865445 & 8.26910938794447e-09 & 4.13455469397223e-09 \tabularnewline
65 & 0.999999999833878 & 3.32244216904573e-10 & 1.66122108452287e-10 \tabularnewline
66 & 0.999999999948454 & 1.03091502986249e-10 & 5.15457514931245e-11 \tabularnewline
67 & 0.999999999986005 & 2.79909869098885e-11 & 1.39954934549443e-11 \tabularnewline
68 & 0.99999999997573 & 4.85393561900936e-11 & 2.42696780950468e-11 \tabularnewline
69 & 0.999999999978627 & 4.27460368261367e-11 & 2.13730184130684e-11 \tabularnewline
70 & 0.999999999954596 & 9.08077927007784e-11 & 4.54038963503892e-11 \tabularnewline
71 & 0.999999999982994 & 3.40116280416127e-11 & 1.70058140208064e-11 \tabularnewline
72 & 0.999999999990698 & 1.8603695594998e-11 & 9.301847797499e-12 \tabularnewline
73 & 0.999999999987132 & 2.57358424944906e-11 & 1.28679212472453e-11 \tabularnewline
74 & 0.999999999996222 & 7.55568715449036e-12 & 3.77784357724518e-12 \tabularnewline
75 & 0.999999999993823 & 1.23530247009649e-11 & 6.17651235048244e-12 \tabularnewline
76 & 0.9999999999887 & 2.26018830969202e-11 & 1.13009415484601e-11 \tabularnewline
77 & 0.999999999992453 & 1.50937844902276e-11 & 7.5468922451138e-12 \tabularnewline
78 & 0.999999999997318 & 5.36316233354578e-12 & 2.68158116677289e-12 \tabularnewline
79 & 0.999999999996263 & 7.47324943916945e-12 & 3.73662471958473e-12 \tabularnewline
80 & 0.999999999998744 & 2.51241692455181e-12 & 1.25620846227591e-12 \tabularnewline
81 & 0.999999999997592 & 4.81603390464634e-12 & 2.40801695232317e-12 \tabularnewline
82 & 0.999999999995371 & 9.25809267085742e-12 & 4.62904633542871e-12 \tabularnewline
83 & 0.999999999989295 & 2.14107906756972e-11 & 1.07053953378486e-11 \tabularnewline
84 & 0.999999999975087 & 4.98267680180559e-11 & 2.49133840090280e-11 \tabularnewline
85 & 0.999999999984354 & 3.12927092675092e-11 & 1.56463546337546e-11 \tabularnewline
86 & 0.999999999975904 & 4.81919179205085e-11 & 2.40959589602543e-11 \tabularnewline
87 & 0.99999999994998 & 1.00041482533270e-10 & 5.00207412666352e-11 \tabularnewline
88 & 0.9999999998958 & 2.08398922090946e-10 & 1.04199461045473e-10 \tabularnewline
89 & 0.999999999910811 & 1.78377242550908e-10 & 8.9188621275454e-11 \tabularnewline
90 & 0.99999999984045 & 3.19098903952809e-10 & 1.59549451976405e-10 \tabularnewline
91 & 0.999999999632642 & 7.34715958726463e-10 & 3.67357979363231e-10 \tabularnewline
92 & 0.999999999490386 & 1.01922790240372e-09 & 5.09613951201858e-10 \tabularnewline
93 & 0.999999999030698 & 1.93860390428540e-09 & 9.6930195214270e-10 \tabularnewline
94 & 0.999999998576504 & 2.84699154829328e-09 & 1.42349577414664e-09 \tabularnewline
95 & 0.999999998539825 & 2.92035062382435e-09 & 1.46017531191217e-09 \tabularnewline
96 & 0.999999996787993 & 6.42401489702765e-09 & 3.21200744851383e-09 \tabularnewline
97 & 0.999999997623704 & 4.75259186919359e-09 & 2.37629593459679e-09 \tabularnewline
98 & 0.9999999971297 & 5.74059842356264e-09 & 2.87029921178132e-09 \tabularnewline
99 & 0.999999997475364 & 5.04927255294656e-09 & 2.52463627647328e-09 \tabularnewline
100 & 0.999999996588998 & 6.82200438806172e-09 & 3.41100219403086e-09 \tabularnewline
101 & 0.999999992798295 & 1.44034103947680e-08 & 7.20170519738401e-09 \tabularnewline
102 & 0.999999985018166 & 2.99636684108531e-08 & 1.49818342054265e-08 \tabularnewline
103 & 0.99999997968971 & 4.06205810610318e-08 & 2.03102905305159e-08 \tabularnewline
104 & 0.999999969773612 & 6.04527753232706e-08 & 3.02263876616353e-08 \tabularnewline
105 & 0.999999974861579 & 5.02768427219898e-08 & 2.51384213609949e-08 \tabularnewline
106 & 0.999999984845044 & 3.03099114289308e-08 & 1.51549557144654e-08 \tabularnewline
107 & 0.999999994294316 & 1.14113674658132e-08 & 5.70568373290659e-09 \tabularnewline
108 & 0.999999994407063 & 1.11858740718162e-08 & 5.59293703590808e-09 \tabularnewline
109 & 0.99999999642603 & 7.14793816832067e-09 & 3.57396908416033e-09 \tabularnewline
110 & 0.999999996437489 & 7.12502233172949e-09 & 3.56251116586474e-09 \tabularnewline
111 & 0.99999999264864 & 1.47027182844689e-08 & 7.35135914223446e-09 \tabularnewline
112 & 0.99999998952689 & 2.09462197209658e-08 & 1.04731098604829e-08 \tabularnewline
113 & 0.999999981987253 & 3.60254932229619e-08 & 1.80127466114810e-08 \tabularnewline
114 & 0.999999961091246 & 7.78175074080563e-08 & 3.89087537040281e-08 \tabularnewline
115 & 0.999999932697848 & 1.34604303444207e-07 & 6.73021517221035e-08 \tabularnewline
116 & 0.999999865810882 & 2.68378236135561e-07 & 1.34189118067781e-07 \tabularnewline
117 & 0.999999715751429 & 5.68497141843663e-07 & 2.84248570921831e-07 \tabularnewline
118 & 0.999999364295055 & 1.27140989035312e-06 & 6.3570494517656e-07 \tabularnewline
119 & 0.99999861986197 & 2.76027606046535e-06 & 1.38013803023268e-06 \tabularnewline
120 & 0.999997965772912 & 4.06845417683544e-06 & 2.03422708841772e-06 \tabularnewline
121 & 0.999998216048197 & 3.56790360613776e-06 & 1.78395180306888e-06 \tabularnewline
122 & 0.999997016891542 & 5.9662169156031e-06 & 2.98310845780155e-06 \tabularnewline
123 & 0.999994931123753 & 1.01377524932722e-05 & 5.06887624663609e-06 \tabularnewline
124 & 0.999988683627912 & 2.26327441755489e-05 & 1.13163720877745e-05 \tabularnewline
125 & 0.999975054947065 & 4.98901058698661e-05 & 2.49450529349330e-05 \tabularnewline
126 & 0.999949194495917 & 0.000101611008166418 & 5.08055040832088e-05 \tabularnewline
127 & 0.999977155936508 & 4.56881269843372e-05 & 2.28440634921686e-05 \tabularnewline
128 & 0.999952841774687 & 9.43164506252129e-05 & 4.71582253126064e-05 \tabularnewline
129 & 0.999895891999082 & 0.000208216001835973 & 0.000104108000917986 \tabularnewline
130 & 0.999833256217018 & 0.000333487565963756 & 0.000166743782981878 \tabularnewline
131 & 0.999759513217193 & 0.000480973565614339 & 0.000240486782807170 \tabularnewline
132 & 0.999531087116624 & 0.000937825766752634 & 0.000468912883376317 \tabularnewline
133 & 0.999330679656188 & 0.00133864068762343 & 0.000669320343811715 \tabularnewline
134 & 0.99866072949167 & 0.00267854101666116 & 0.00133927050833058 \tabularnewline
135 & 0.997375472514568 & 0.00524905497086352 & 0.00262452748543176 \tabularnewline
136 & 0.997175267415376 & 0.00564946516924894 & 0.00282473258462447 \tabularnewline
137 & 0.994814515202772 & 0.0103709695944558 & 0.00518548479722792 \tabularnewline
138 & 0.993867104294203 & 0.0122657914115946 & 0.00613289570579732 \tabularnewline
139 & 0.987113954807589 & 0.0257720903848223 & 0.0128860451924111 \tabularnewline
140 & 0.979495073314488 & 0.0410098533710238 & 0.0205049266855119 \tabularnewline
141 & 0.971354328538626 & 0.0572913429227471 & 0.0286456714613735 \tabularnewline
142 & 0.948513989437764 & 0.102972021124473 & 0.0514860105622363 \tabularnewline
143 & 0.916523126211482 & 0.166953747577035 & 0.0834768737885176 \tabularnewline
144 & 0.87584862398679 & 0.248302752026418 & 0.124151376013209 \tabularnewline
145 & 0.812044103941203 & 0.375911792117594 & 0.187955896058797 \tabularnewline
146 & 0.688334611452606 & 0.623330777094789 & 0.311665388547394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103384&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]10[/C][C]0.263582312525539[/C][C]0.527164625051078[/C][C]0.736417687474461[/C][/ROW]
[ROW][C]11[/C][C]0.144714150877034[/C][C]0.289428301754067[/C][C]0.855285849122966[/C][/ROW]
[ROW][C]12[/C][C]0.132105238169263[/C][C]0.264210476338526[/C][C]0.867894761830737[/C][/ROW]
[ROW][C]13[/C][C]0.185410730265825[/C][C]0.37082146053165[/C][C]0.814589269734175[/C][/ROW]
[ROW][C]14[/C][C]0.119004960479286[/C][C]0.238009920958572[/C][C]0.880995039520714[/C][/ROW]
[ROW][C]15[/C][C]0.0954400099254571[/C][C]0.190880019850914[/C][C]0.904559990074543[/C][/ROW]
[ROW][C]16[/C][C]0.070373186851487[/C][C]0.140746373702974[/C][C]0.929626813148513[/C][/ROW]
[ROW][C]17[/C][C]0.0706570519519885[/C][C]0.141314103903977[/C][C]0.929342948048012[/C][/ROW]
[ROW][C]18[/C][C]0.0617651661852747[/C][C]0.123530332370549[/C][C]0.938234833814725[/C][/ROW]
[ROW][C]19[/C][C]0.0446320291759189[/C][C]0.0892640583518378[/C][C]0.955367970824081[/C][/ROW]
[ROW][C]20[/C][C]0.0307449556778491[/C][C]0.0614899113556981[/C][C]0.96925504432215[/C][/ROW]
[ROW][C]21[/C][C]0.0858533436159263[/C][C]0.171706687231853[/C][C]0.914146656384074[/C][/ROW]
[ROW][C]22[/C][C]0.0625978339317105[/C][C]0.125195667863421[/C][C]0.93740216606829[/C][/ROW]
[ROW][C]23[/C][C]0.0905379425599448[/C][C]0.181075885119890[/C][C]0.909462057440055[/C][/ROW]
[ROW][C]24[/C][C]0.0687334487765156[/C][C]0.137466897553031[/C][C]0.931266551223484[/C][/ROW]
[ROW][C]25[/C][C]0.0607754697382317[/C][C]0.121550939476463[/C][C]0.939224530261768[/C][/ROW]
[ROW][C]26[/C][C]0.0747526843179343[/C][C]0.149505368635869[/C][C]0.925247315682066[/C][/ROW]
[ROW][C]27[/C][C]0.0521633895477667[/C][C]0.104326779095533[/C][C]0.947836610452233[/C][/ROW]
[ROW][C]28[/C][C]0.0355664117091135[/C][C]0.071132823418227[/C][C]0.964433588290887[/C][/ROW]
[ROW][C]29[/C][C]0.0235192464755622[/C][C]0.0470384929511244[/C][C]0.976480753524438[/C][/ROW]
[ROW][C]30[/C][C]0.0197057833392292[/C][C]0.0394115666784585[/C][C]0.98029421666077[/C][/ROW]
[ROW][C]31[/C][C]0.0472526194020465[/C][C]0.094505238804093[/C][C]0.952747380597954[/C][/ROW]
[ROW][C]32[/C][C]0.0466421320390200[/C][C]0.0932842640780401[/C][C]0.95335786796098[/C][/ROW]
[ROW][C]33[/C][C]0.0551001099310059[/C][C]0.110200219862012[/C][C]0.944899890068994[/C][/ROW]
[ROW][C]34[/C][C]0.0846545215692222[/C][C]0.169309043138444[/C][C]0.915345478430778[/C][/ROW]
[ROW][C]35[/C][C]0.0756582691248718[/C][C]0.151316538249744[/C][C]0.924341730875128[/C][/ROW]
[ROW][C]36[/C][C]0.0967823597075602[/C][C]0.193564719415120[/C][C]0.90321764029244[/C][/ROW]
[ROW][C]37[/C][C]0.0863536253255532[/C][C]0.172707250651106[/C][C]0.913646374674447[/C][/ROW]
[ROW][C]38[/C][C]0.101534609770583[/C][C]0.203069219541167[/C][C]0.898465390229417[/C][/ROW]
[ROW][C]39[/C][C]0.0909214507792902[/C][C]0.181842901558580[/C][C]0.90907854922071[/C][/ROW]
[ROW][C]40[/C][C]0.0775539616979505[/C][C]0.155107923395901[/C][C]0.92244603830205[/C][/ROW]
[ROW][C]41[/C][C]0.0721346145133822[/C][C]0.144269229026764[/C][C]0.927865385486618[/C][/ROW]
[ROW][C]42[/C][C]0.0812335701177074[/C][C]0.162467140235415[/C][C]0.918766429882293[/C][/ROW]
[ROW][C]43[/C][C]0.0849699858250933[/C][C]0.169939971650187[/C][C]0.915030014174907[/C][/ROW]
[ROW][C]44[/C][C]0.115999406730558[/C][C]0.231998813461116[/C][C]0.884000593269442[/C][/ROW]
[ROW][C]45[/C][C]0.0943140528122358[/C][C]0.188628105624472[/C][C]0.905685947187764[/C][/ROW]
[ROW][C]46[/C][C]0.141551166652709[/C][C]0.283102333305418[/C][C]0.85844883334729[/C][/ROW]
[ROW][C]47[/C][C]0.114484909496062[/C][C]0.228969818992124[/C][C]0.885515090503938[/C][/ROW]
[ROW][C]48[/C][C]0.160262354352461[/C][C]0.320524708704921[/C][C]0.83973764564754[/C][/ROW]
[ROW][C]49[/C][C]0.130983918836451[/C][C]0.261967837672902[/C][C]0.86901608116355[/C][/ROW]
[ROW][C]50[/C][C]0.133684881323056[/C][C]0.267369762646113[/C][C]0.866315118676944[/C][/ROW]
[ROW][C]51[/C][C]0.175234577580867[/C][C]0.350469155161735[/C][C]0.824765422419133[/C][/ROW]
[ROW][C]52[/C][C]0.216497276358335[/C][C]0.432994552716670[/C][C]0.783502723641665[/C][/ROW]
[ROW][C]53[/C][C]0.186984460714258[/C][C]0.373968921428516[/C][C]0.813015539285742[/C][/ROW]
[ROW][C]54[/C][C]0.45522876866436[/C][C]0.91045753732872[/C][C]0.54477123133564[/C][/ROW]
[ROW][C]55[/C][C]0.779072012016526[/C][C]0.441855975966949[/C][C]0.220927987983474[/C][/ROW]
[ROW][C]56[/C][C]0.746155942091714[/C][C]0.507688115816573[/C][C]0.253844057908286[/C][/ROW]
[ROW][C]57[/C][C]0.707430703284961[/C][C]0.585138593430079[/C][C]0.292569296715039[/C][/ROW]
[ROW][C]58[/C][C]0.697718205309792[/C][C]0.604563589380415[/C][C]0.302281794690208[/C][/ROW]
[ROW][C]59[/C][C]0.999891111742083[/C][C]0.000217776515833246[/C][C]0.000108888257916623[/C][/ROW]
[ROW][C]60[/C][C]0.999982905383057[/C][C]3.41892338854004e-05[/C][C]1.70946169427002e-05[/C][/ROW]
[ROW][C]61[/C][C]0.999999994386622[/C][C]1.12267549963632e-08[/C][C]5.61337749818159e-09[/C][/ROW]
[ROW][C]62[/C][C]0.999999997168623[/C][C]5.6627538295087e-09[/C][C]2.83137691475435e-09[/C][/ROW]
[ROW][C]63[/C][C]0.9999999944618[/C][C]1.10764001892749e-08[/C][C]5.53820009463744e-09[/C][/ROW]
[ROW][C]64[/C][C]0.999999995865445[/C][C]8.26910938794447e-09[/C][C]4.13455469397223e-09[/C][/ROW]
[ROW][C]65[/C][C]0.999999999833878[/C][C]3.32244216904573e-10[/C][C]1.66122108452287e-10[/C][/ROW]
[ROW][C]66[/C][C]0.999999999948454[/C][C]1.03091502986249e-10[/C][C]5.15457514931245e-11[/C][/ROW]
[ROW][C]67[/C][C]0.999999999986005[/C][C]2.79909869098885e-11[/C][C]1.39954934549443e-11[/C][/ROW]
[ROW][C]68[/C][C]0.99999999997573[/C][C]4.85393561900936e-11[/C][C]2.42696780950468e-11[/C][/ROW]
[ROW][C]69[/C][C]0.999999999978627[/C][C]4.27460368261367e-11[/C][C]2.13730184130684e-11[/C][/ROW]
[ROW][C]70[/C][C]0.999999999954596[/C][C]9.08077927007784e-11[/C][C]4.54038963503892e-11[/C][/ROW]
[ROW][C]71[/C][C]0.999999999982994[/C][C]3.40116280416127e-11[/C][C]1.70058140208064e-11[/C][/ROW]
[ROW][C]72[/C][C]0.999999999990698[/C][C]1.8603695594998e-11[/C][C]9.301847797499e-12[/C][/ROW]
[ROW][C]73[/C][C]0.999999999987132[/C][C]2.57358424944906e-11[/C][C]1.28679212472453e-11[/C][/ROW]
[ROW][C]74[/C][C]0.999999999996222[/C][C]7.55568715449036e-12[/C][C]3.77784357724518e-12[/C][/ROW]
[ROW][C]75[/C][C]0.999999999993823[/C][C]1.23530247009649e-11[/C][C]6.17651235048244e-12[/C][/ROW]
[ROW][C]76[/C][C]0.9999999999887[/C][C]2.26018830969202e-11[/C][C]1.13009415484601e-11[/C][/ROW]
[ROW][C]77[/C][C]0.999999999992453[/C][C]1.50937844902276e-11[/C][C]7.5468922451138e-12[/C][/ROW]
[ROW][C]78[/C][C]0.999999999997318[/C][C]5.36316233354578e-12[/C][C]2.68158116677289e-12[/C][/ROW]
[ROW][C]79[/C][C]0.999999999996263[/C][C]7.47324943916945e-12[/C][C]3.73662471958473e-12[/C][/ROW]
[ROW][C]80[/C][C]0.999999999998744[/C][C]2.51241692455181e-12[/C][C]1.25620846227591e-12[/C][/ROW]
[ROW][C]81[/C][C]0.999999999997592[/C][C]4.81603390464634e-12[/C][C]2.40801695232317e-12[/C][/ROW]
[ROW][C]82[/C][C]0.999999999995371[/C][C]9.25809267085742e-12[/C][C]4.62904633542871e-12[/C][/ROW]
[ROW][C]83[/C][C]0.999999999989295[/C][C]2.14107906756972e-11[/C][C]1.07053953378486e-11[/C][/ROW]
[ROW][C]84[/C][C]0.999999999975087[/C][C]4.98267680180559e-11[/C][C]2.49133840090280e-11[/C][/ROW]
[ROW][C]85[/C][C]0.999999999984354[/C][C]3.12927092675092e-11[/C][C]1.56463546337546e-11[/C][/ROW]
[ROW][C]86[/C][C]0.999999999975904[/C][C]4.81919179205085e-11[/C][C]2.40959589602543e-11[/C][/ROW]
[ROW][C]87[/C][C]0.99999999994998[/C][C]1.00041482533270e-10[/C][C]5.00207412666352e-11[/C][/ROW]
[ROW][C]88[/C][C]0.9999999998958[/C][C]2.08398922090946e-10[/C][C]1.04199461045473e-10[/C][/ROW]
[ROW][C]89[/C][C]0.999999999910811[/C][C]1.78377242550908e-10[/C][C]8.9188621275454e-11[/C][/ROW]
[ROW][C]90[/C][C]0.99999999984045[/C][C]3.19098903952809e-10[/C][C]1.59549451976405e-10[/C][/ROW]
[ROW][C]91[/C][C]0.999999999632642[/C][C]7.34715958726463e-10[/C][C]3.67357979363231e-10[/C][/ROW]
[ROW][C]92[/C][C]0.999999999490386[/C][C]1.01922790240372e-09[/C][C]5.09613951201858e-10[/C][/ROW]
[ROW][C]93[/C][C]0.999999999030698[/C][C]1.93860390428540e-09[/C][C]9.6930195214270e-10[/C][/ROW]
[ROW][C]94[/C][C]0.999999998576504[/C][C]2.84699154829328e-09[/C][C]1.42349577414664e-09[/C][/ROW]
[ROW][C]95[/C][C]0.999999998539825[/C][C]2.92035062382435e-09[/C][C]1.46017531191217e-09[/C][/ROW]
[ROW][C]96[/C][C]0.999999996787993[/C][C]6.42401489702765e-09[/C][C]3.21200744851383e-09[/C][/ROW]
[ROW][C]97[/C][C]0.999999997623704[/C][C]4.75259186919359e-09[/C][C]2.37629593459679e-09[/C][/ROW]
[ROW][C]98[/C][C]0.9999999971297[/C][C]5.74059842356264e-09[/C][C]2.87029921178132e-09[/C][/ROW]
[ROW][C]99[/C][C]0.999999997475364[/C][C]5.04927255294656e-09[/C][C]2.52463627647328e-09[/C][/ROW]
[ROW][C]100[/C][C]0.999999996588998[/C][C]6.82200438806172e-09[/C][C]3.41100219403086e-09[/C][/ROW]
[ROW][C]101[/C][C]0.999999992798295[/C][C]1.44034103947680e-08[/C][C]7.20170519738401e-09[/C][/ROW]
[ROW][C]102[/C][C]0.999999985018166[/C][C]2.99636684108531e-08[/C][C]1.49818342054265e-08[/C][/ROW]
[ROW][C]103[/C][C]0.99999997968971[/C][C]4.06205810610318e-08[/C][C]2.03102905305159e-08[/C][/ROW]
[ROW][C]104[/C][C]0.999999969773612[/C][C]6.04527753232706e-08[/C][C]3.02263876616353e-08[/C][/ROW]
[ROW][C]105[/C][C]0.999999974861579[/C][C]5.02768427219898e-08[/C][C]2.51384213609949e-08[/C][/ROW]
[ROW][C]106[/C][C]0.999999984845044[/C][C]3.03099114289308e-08[/C][C]1.51549557144654e-08[/C][/ROW]
[ROW][C]107[/C][C]0.999999994294316[/C][C]1.14113674658132e-08[/C][C]5.70568373290659e-09[/C][/ROW]
[ROW][C]108[/C][C]0.999999994407063[/C][C]1.11858740718162e-08[/C][C]5.59293703590808e-09[/C][/ROW]
[ROW][C]109[/C][C]0.99999999642603[/C][C]7.14793816832067e-09[/C][C]3.57396908416033e-09[/C][/ROW]
[ROW][C]110[/C][C]0.999999996437489[/C][C]7.12502233172949e-09[/C][C]3.56251116586474e-09[/C][/ROW]
[ROW][C]111[/C][C]0.99999999264864[/C][C]1.47027182844689e-08[/C][C]7.35135914223446e-09[/C][/ROW]
[ROW][C]112[/C][C]0.99999998952689[/C][C]2.09462197209658e-08[/C][C]1.04731098604829e-08[/C][/ROW]
[ROW][C]113[/C][C]0.999999981987253[/C][C]3.60254932229619e-08[/C][C]1.80127466114810e-08[/C][/ROW]
[ROW][C]114[/C][C]0.999999961091246[/C][C]7.78175074080563e-08[/C][C]3.89087537040281e-08[/C][/ROW]
[ROW][C]115[/C][C]0.999999932697848[/C][C]1.34604303444207e-07[/C][C]6.73021517221035e-08[/C][/ROW]
[ROW][C]116[/C][C]0.999999865810882[/C][C]2.68378236135561e-07[/C][C]1.34189118067781e-07[/C][/ROW]
[ROW][C]117[/C][C]0.999999715751429[/C][C]5.68497141843663e-07[/C][C]2.84248570921831e-07[/C][/ROW]
[ROW][C]118[/C][C]0.999999364295055[/C][C]1.27140989035312e-06[/C][C]6.3570494517656e-07[/C][/ROW]
[ROW][C]119[/C][C]0.99999861986197[/C][C]2.76027606046535e-06[/C][C]1.38013803023268e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999997965772912[/C][C]4.06845417683544e-06[/C][C]2.03422708841772e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999998216048197[/C][C]3.56790360613776e-06[/C][C]1.78395180306888e-06[/C][/ROW]
[ROW][C]122[/C][C]0.999997016891542[/C][C]5.9662169156031e-06[/C][C]2.98310845780155e-06[/C][/ROW]
[ROW][C]123[/C][C]0.999994931123753[/C][C]1.01377524932722e-05[/C][C]5.06887624663609e-06[/C][/ROW]
[ROW][C]124[/C][C]0.999988683627912[/C][C]2.26327441755489e-05[/C][C]1.13163720877745e-05[/C][/ROW]
[ROW][C]125[/C][C]0.999975054947065[/C][C]4.98901058698661e-05[/C][C]2.49450529349330e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999949194495917[/C][C]0.000101611008166418[/C][C]5.08055040832088e-05[/C][/ROW]
[ROW][C]127[/C][C]0.999977155936508[/C][C]4.56881269843372e-05[/C][C]2.28440634921686e-05[/C][/ROW]
[ROW][C]128[/C][C]0.999952841774687[/C][C]9.43164506252129e-05[/C][C]4.71582253126064e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999895891999082[/C][C]0.000208216001835973[/C][C]0.000104108000917986[/C][/ROW]
[ROW][C]130[/C][C]0.999833256217018[/C][C]0.000333487565963756[/C][C]0.000166743782981878[/C][/ROW]
[ROW][C]131[/C][C]0.999759513217193[/C][C]0.000480973565614339[/C][C]0.000240486782807170[/C][/ROW]
[ROW][C]132[/C][C]0.999531087116624[/C][C]0.000937825766752634[/C][C]0.000468912883376317[/C][/ROW]
[ROW][C]133[/C][C]0.999330679656188[/C][C]0.00133864068762343[/C][C]0.000669320343811715[/C][/ROW]
[ROW][C]134[/C][C]0.99866072949167[/C][C]0.00267854101666116[/C][C]0.00133927050833058[/C][/ROW]
[ROW][C]135[/C][C]0.997375472514568[/C][C]0.00524905497086352[/C][C]0.00262452748543176[/C][/ROW]
[ROW][C]136[/C][C]0.997175267415376[/C][C]0.00564946516924894[/C][C]0.00282473258462447[/C][/ROW]
[ROW][C]137[/C][C]0.994814515202772[/C][C]0.0103709695944558[/C][C]0.00518548479722792[/C][/ROW]
[ROW][C]138[/C][C]0.993867104294203[/C][C]0.0122657914115946[/C][C]0.00613289570579732[/C][/ROW]
[ROW][C]139[/C][C]0.987113954807589[/C][C]0.0257720903848223[/C][C]0.0128860451924111[/C][/ROW]
[ROW][C]140[/C][C]0.979495073314488[/C][C]0.0410098533710238[/C][C]0.0205049266855119[/C][/ROW]
[ROW][C]141[/C][C]0.971354328538626[/C][C]0.0572913429227471[/C][C]0.0286456714613735[/C][/ROW]
[ROW][C]142[/C][C]0.948513989437764[/C][C]0.102972021124473[/C][C]0.0514860105622363[/C][/ROW]
[ROW][C]143[/C][C]0.916523126211482[/C][C]0.166953747577035[/C][C]0.0834768737885176[/C][/ROW]
[ROW][C]144[/C][C]0.87584862398679[/C][C]0.248302752026418[/C][C]0.124151376013209[/C][/ROW]
[ROW][C]145[/C][C]0.812044103941203[/C][C]0.375911792117594[/C][C]0.187955896058797[/C][/ROW]
[ROW][C]146[/C][C]0.688334611452606[/C][C]0.623330777094789[/C][C]0.311665388547394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103384&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103384&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2635823125255390.5271646250510780.736417687474461
110.1447141508770340.2894283017540670.855285849122966
120.1321052381692630.2642104763385260.867894761830737
130.1854107302658250.370821460531650.814589269734175
140.1190049604792860.2380099209585720.880995039520714
150.09544000992545710.1908800198509140.904559990074543
160.0703731868514870.1407463737029740.929626813148513
170.07065705195198850.1413141039039770.929342948048012
180.06176516618527470.1235303323705490.938234833814725
190.04463202917591890.08926405835183780.955367970824081
200.03074495567784910.06148991135569810.96925504432215
210.08585334361592630.1717066872318530.914146656384074
220.06259783393171050.1251956678634210.93740216606829
230.09053794255994480.1810758851198900.909462057440055
240.06873344877651560.1374668975530310.931266551223484
250.06077546973823170.1215509394764630.939224530261768
260.07475268431793430.1495053686358690.925247315682066
270.05216338954776670.1043267790955330.947836610452233
280.03556641170911350.0711328234182270.964433588290887
290.02351924647556220.04703849295112440.976480753524438
300.01970578333922920.03941156667845850.98029421666077
310.04725261940204650.0945052388040930.952747380597954
320.04664213203902000.09328426407804010.95335786796098
330.05510010993100590.1102002198620120.944899890068994
340.08465452156922220.1693090431384440.915345478430778
350.07565826912487180.1513165382497440.924341730875128
360.09678235970756020.1935647194151200.90321764029244
370.08635362532555320.1727072506511060.913646374674447
380.1015346097705830.2030692195411670.898465390229417
390.09092145077929020.1818429015585800.90907854922071
400.07755396169795050.1551079233959010.92244603830205
410.07213461451338220.1442692290267640.927865385486618
420.08123357011770740.1624671402354150.918766429882293
430.08496998582509330.1699399716501870.915030014174907
440.1159994067305580.2319988134611160.884000593269442
450.09431405281223580.1886281056244720.905685947187764
460.1415511666527090.2831023333054180.85844883334729
470.1144849094960620.2289698189921240.885515090503938
480.1602623543524610.3205247087049210.83973764564754
490.1309839188364510.2619678376729020.86901608116355
500.1336848813230560.2673697626461130.866315118676944
510.1752345775808670.3504691551617350.824765422419133
520.2164972763583350.4329945527166700.783502723641665
530.1869844607142580.3739689214285160.813015539285742
540.455228768664360.910457537328720.54477123133564
550.7790720120165260.4418559759669490.220927987983474
560.7461559420917140.5076881158165730.253844057908286
570.7074307032849610.5851385934300790.292569296715039
580.6977182053097920.6045635893804150.302281794690208
590.9998911117420830.0002177765158332460.000108888257916623
600.9999829053830573.41892338854004e-051.70946169427002e-05
610.9999999943866221.12267549963632e-085.61337749818159e-09
620.9999999971686235.6627538295087e-092.83137691475435e-09
630.99999999446181.10764001892749e-085.53820009463744e-09
640.9999999958654458.26910938794447e-094.13455469397223e-09
650.9999999998338783.32244216904573e-101.66122108452287e-10
660.9999999999484541.03091502986249e-105.15457514931245e-11
670.9999999999860052.79909869098885e-111.39954934549443e-11
680.999999999975734.85393561900936e-112.42696780950468e-11
690.9999999999786274.27460368261367e-112.13730184130684e-11
700.9999999999545969.08077927007784e-114.54038963503892e-11
710.9999999999829943.40116280416127e-111.70058140208064e-11
720.9999999999906981.8603695594998e-119.301847797499e-12
730.9999999999871322.57358424944906e-111.28679212472453e-11
740.9999999999962227.55568715449036e-123.77784357724518e-12
750.9999999999938231.23530247009649e-116.17651235048244e-12
760.99999999998872.26018830969202e-111.13009415484601e-11
770.9999999999924531.50937844902276e-117.5468922451138e-12
780.9999999999973185.36316233354578e-122.68158116677289e-12
790.9999999999962637.47324943916945e-123.73662471958473e-12
800.9999999999987442.51241692455181e-121.25620846227591e-12
810.9999999999975924.81603390464634e-122.40801695232317e-12
820.9999999999953719.25809267085742e-124.62904633542871e-12
830.9999999999892952.14107906756972e-111.07053953378486e-11
840.9999999999750874.98267680180559e-112.49133840090280e-11
850.9999999999843543.12927092675092e-111.56463546337546e-11
860.9999999999759044.81919179205085e-112.40959589602543e-11
870.999999999949981.00041482533270e-105.00207412666352e-11
880.99999999989582.08398922090946e-101.04199461045473e-10
890.9999999999108111.78377242550908e-108.9188621275454e-11
900.999999999840453.19098903952809e-101.59549451976405e-10
910.9999999996326427.34715958726463e-103.67357979363231e-10
920.9999999994903861.01922790240372e-095.09613951201858e-10
930.9999999990306981.93860390428540e-099.6930195214270e-10
940.9999999985765042.84699154829328e-091.42349577414664e-09
950.9999999985398252.92035062382435e-091.46017531191217e-09
960.9999999967879936.42401489702765e-093.21200744851383e-09
970.9999999976237044.75259186919359e-092.37629593459679e-09
980.99999999712975.74059842356264e-092.87029921178132e-09
990.9999999974753645.04927255294656e-092.52463627647328e-09
1000.9999999965889986.82200438806172e-093.41100219403086e-09
1010.9999999927982951.44034103947680e-087.20170519738401e-09
1020.9999999850181662.99636684108531e-081.49818342054265e-08
1030.999999979689714.06205810610318e-082.03102905305159e-08
1040.9999999697736126.04527753232706e-083.02263876616353e-08
1050.9999999748615795.02768427219898e-082.51384213609949e-08
1060.9999999848450443.03099114289308e-081.51549557144654e-08
1070.9999999942943161.14113674658132e-085.70568373290659e-09
1080.9999999944070631.11858740718162e-085.59293703590808e-09
1090.999999996426037.14793816832067e-093.57396908416033e-09
1100.9999999964374897.12502233172949e-093.56251116586474e-09
1110.999999992648641.47027182844689e-087.35135914223446e-09
1120.999999989526892.09462197209658e-081.04731098604829e-08
1130.9999999819872533.60254932229619e-081.80127466114810e-08
1140.9999999610912467.78175074080563e-083.89087537040281e-08
1150.9999999326978481.34604303444207e-076.73021517221035e-08
1160.9999998658108822.68378236135561e-071.34189118067781e-07
1170.9999997157514295.68497141843663e-072.84248570921831e-07
1180.9999993642950551.27140989035312e-066.3570494517656e-07
1190.999998619861972.76027606046535e-061.38013803023268e-06
1200.9999979657729124.06845417683544e-062.03422708841772e-06
1210.9999982160481973.56790360613776e-061.78395180306888e-06
1220.9999970168915425.9662169156031e-062.98310845780155e-06
1230.9999949311237531.01377524932722e-055.06887624663609e-06
1240.9999886836279122.26327441755489e-051.13163720877745e-05
1250.9999750549470654.98901058698661e-052.49450529349330e-05
1260.9999491944959170.0001016110081664185.08055040832088e-05
1270.9999771559365084.56881269843372e-052.28440634921686e-05
1280.9999528417746879.43164506252129e-054.71582253126064e-05
1290.9998958919990820.0002082160018359730.000104108000917986
1300.9998332562170180.0003334875659637560.000166743782981878
1310.9997595132171930.0004809735656143390.000240486782807170
1320.9995310871166240.0009378257667526340.000468912883376317
1330.9993306796561880.001338640687623430.000669320343811715
1340.998660729491670.002678541016661160.00133927050833058
1350.9973754725145680.005249054970863520.00262452748543176
1360.9971752674153760.005649465169248940.00282473258462447
1370.9948145152027720.01037096959445580.00518548479722792
1380.9938671042942030.01226579141159460.00613289570579732
1390.9871139548075890.02577209038482230.0128860451924111
1400.9794950733144880.04100985337102380.0205049266855119
1410.9713543285386260.05729134292274710.0286456714613735
1420.9485139894377640.1029720211244730.0514860105622363
1430.9165231262114820.1669537475770350.0834768737885176
1440.875848623986790.2483027520264180.124151376013209
1450.8120441039412030.3759117921175940.187955896058797
1460.6883346114526060.6233307770947890.311665388547394






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level780.569343065693431NOK
5% type I error level840.613138686131387NOK
10% type I error level900.656934306569343NOK

\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 & 78 & 0.569343065693431 & NOK \tabularnewline
5% type I error level & 84 & 0.613138686131387 & NOK \tabularnewline
10% type I error level & 90 & 0.656934306569343 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103384&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]78[/C][C]0.569343065693431[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]84[/C][C]0.613138686131387[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]90[/C][C]0.656934306569343[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103384&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103384&T=6

As an alternative you can also use a QR Code:  
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 level780.569343065693431NOK
5% type I error level840.613138686131387NOK
10% type I error level900.656934306569343NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}