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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 19:02:35 +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/t12911437040kfjuir3avzjwdb.htm/, Retrieved Mon, 29 Apr 2024 09:33:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103761, Retrieved Mon, 29 Apr 2024 09:33:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact177

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] [ws4] [2010-11-30 12:25:45] [a2638725f7f7c6bd63902ba17eba666b]
-           [Multiple Regression] [ws4] [2010-11-30 19:02:35] [b7765ad69c3ab250b1ef04c2ab1247ec] [Current]
-   PD        [Multiple Regression] [ws 7] [2010-12-01 13:24:40] [20c5a34fea7ed3b9b27ff444f2eb4dfe]
-             [Multiple Regression] [Ws4] [2010-12-02 16:14:40] [9c3137400ced3280b419f1e434c29e1d]
F             [Multiple Regression] [] [2010-12-02 18:24:04] [c91278f1cd2d8b4eeb874e50bb706c21]
-   P           [Multiple Regression] [] [2010-12-02 19:05:14] [c91278f1cd2d8b4eeb874e50bb706c21]
- R PD          [Multiple Regression] [] [2012-11-20 19:04:18] [a7bb96ad3a378f4e1515d1e46b6029a5]
-   P             [Multiple Regression] [] [2012-11-20 19:34:33] [a7bb96ad3a378f4e1515d1e46b6029a5]
- RMPD          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-20 12:07:10] [26ad7b3e1a1c0dbfc150b12ad04e69c7]
-   PD          [Multiple Regression] [] [2012-12-20 13:11:34] [26ad7b3e1a1c0dbfc150b12ad04e69c7]
-   PD        [Multiple Regression] [WS4: Lineaire reg...] [2010-12-03 16:58:53] [7f54ec67e5798cc59f49446b41e2f221]
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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	0
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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103761&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103761&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103761&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'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
liked[t] = + 6.89981578359029 + 0.097864463533243findingFriends[t] + 0.167368197229076knowingPeople[t] + 0.571325185382164celebrity[t] + 0.00243721157219217friend[t] -0.00131784757087074secondbestfriend[t] + 0.00336547427871851thirdbestfriend[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
liked[t] =  +  6.89981578359029 +  0.097864463533243findingFriends[t] +  0.167368197229076knowingPeople[t] +  0.571325185382164celebrity[t] +  0.00243721157219217friend[t] -0.00131784757087074secondbestfriend[t] +  0.00336547427871851thirdbestfriend[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103761&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]liked[t] =  +  6.89981578359029 +  0.097864463533243findingFriends[t] +  0.167368197229076knowingPeople[t] +  0.571325185382164celebrity[t] +  0.00243721157219217friend[t] -0.00131784757087074secondbestfriend[t] +  0.00336547427871851thirdbestfriend[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103761&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103761&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] = + 6.89981578359029 + 0.097864463533243findingFriends[t] + 0.167368197229076knowingPeople[t] + 0.571325185382164celebrity[t] + 0.00243721157219217friend[t] -0.00131784757087074secondbestfriend[t] + 0.00336547427871851thirdbestfriend[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.899815783590291.0641826.483700
findingFriends0.0978644635332430.0814631.20130.2315270.115764
knowingPeople0.1673681972290760.052263.20260.0016650.000833
celebrity0.5713251853821640.1236064.62218e-064e-06
friend0.002437211572192170.0030140.80870.4199680.209984
secondbestfriend-0.001317847570870740.003026-0.43550.6638590.33193
thirdbestfriend0.003365474278718510.0027831.20910.2285240.114262

\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) & 6.89981578359029 & 1.064182 & 6.4837 & 0 & 0 \tabularnewline
findingFriends & 0.097864463533243 & 0.081463 & 1.2013 & 0.231527 & 0.115764 \tabularnewline
knowingPeople & 0.167368197229076 & 0.05226 & 3.2026 & 0.001665 & 0.000833 \tabularnewline
celebrity & 0.571325185382164 & 0.123606 & 4.6221 & 8e-06 & 4e-06 \tabularnewline
friend & 0.00243721157219217 & 0.003014 & 0.8087 & 0.419968 & 0.209984 \tabularnewline
secondbestfriend & -0.00131784757087074 & 0.003026 & -0.4355 & 0.663859 & 0.33193 \tabularnewline
thirdbestfriend & 0.00336547427871851 & 0.002783 & 1.2091 & 0.228524 & 0.114262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103761&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]6.89981578359029[/C][C]1.064182[/C][C]6.4837[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]findingFriends[/C][C]0.097864463533243[/C][C]0.081463[/C][C]1.2013[/C][C]0.231527[/C][C]0.115764[/C][/ROW]
[ROW][C]knowingPeople[/C][C]0.167368197229076[/C][C]0.05226[/C][C]3.2026[/C][C]0.001665[/C][C]0.000833[/C][/ROW]
[ROW][C]celebrity[/C][C]0.571325185382164[/C][C]0.123606[/C][C]4.6221[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]friend[/C][C]0.00243721157219217[/C][C]0.003014[/C][C]0.8087[/C][C]0.419968[/C][C]0.209984[/C][/ROW]
[ROW][C]secondbestfriend[/C][C]-0.00131784757087074[/C][C]0.003026[/C][C]-0.4355[/C][C]0.663859[/C][C]0.33193[/C][/ROW]
[ROW][C]thirdbestfriend[/C][C]0.00336547427871851[/C][C]0.002783[/C][C]1.2091[/C][C]0.228524[/C][C]0.114262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103761&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103761&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)6.899815783590291.0641826.483700
findingFriends0.0978644635332430.0814631.20130.2315270.115764
knowingPeople0.1673681972290760.052263.20260.0016650.000833
celebrity0.5713251853821640.1236064.62218e-064e-06
friend0.002437211572192170.0030140.80870.4199680.209984
secondbestfriend-0.001317847570870740.003026-0.43550.6638590.33193
thirdbestfriend0.003365474278718510.0027831.20910.2285240.114262






Multiple Linear Regression - Regression Statistics
Multiple R0.599396383338163
R-squared0.35927602435887
Adjusted R-squared0.333475058896811
F-TEST (value)13.9249062166761
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value1.54498636106837e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.77612967854698
Sum Squared Residuals470.040858617294

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.599396383338163 \tabularnewline
R-squared & 0.35927602435887 \tabularnewline
Adjusted R-squared & 0.333475058896811 \tabularnewline
F-TEST (value) & 13.9249062166761 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 1.54498636106837e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.77612967854698 \tabularnewline
Sum Squared Residuals & 470.040858617294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103761&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.599396383338163[/C][/ROW]
[ROW][C]R-squared[/C][C]0.35927602435887[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.333475058896811[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.9249062166761[/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]1.54498636106837e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.77612967854698[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]470.040858617294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103761&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103761&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 R0.599396383338163
R-squared0.35927602435887
Adjusted R-squared0.333475058896811
F-TEST (value)13.9249062166761
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value1.54498636106837e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.77612967854698
Sum Squared Residuals470.040858617294






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.71235751875480.287642481245227
21312.88456401993190.115435980068071
31613.31482989796462.68517010203537
41212.7154672274008-0.715467227400786
51112.6192561672017-1.61925616720165
61210.95974228273921.04025771726075
71815.62631459037902.37368540962098
81112.2687821810591-1.26878218105909
91412.94599631792291.05400368207712
10910.7765124896929-1.77651248969288
111413.98984877265220.0101512273477707
121213.6847256776321-1.68472567763207
131113.8816918719569-2.88169187195692
141213.3914509439414-1.39145094394135
151312.86944676595220.130553234047775
161112.8530802385107-1.85308023851071
171213.3431906278020-1.34319062780204
181613.94795706179742.05204293820256
19911.6964218543849-2.69642185438492
201111.5361143499040-0.536114349903987
211310.33470253366182.66529746633819
221514.61329799487840.386702005121559
231013.4738439052489-3.4738439052489
241111.5311228768590-0.531122876859011
251314.1401208153346-1.14012081533456
261614.44616806528091.55383193471906
271515.0404724977158-0.040472497715783
281413.4296447906240.570355209376008
291414.214715157704-0.214715157703995
301411.9903992748922.009600725108
31811.6763358944877-3.6763358944877
321314.3888421307032-1.38884213070320
331514.66594905426020.334050945739831
341311.44095291027211.55904708972789
351112.7541652147674-1.75416521476742
361514.34551807865810.654481921341931
371513.70054850933761.29945149066236
38912.2989755122997-3.29897551229975
391314.259570567836-1.25957056783599
401615.18984821841870.810151781581333
411313.9120715295458-0.91207152954582
421110.63362673263320.366373267366805
431211.71228765885860.287712341141435
441212.2545823122326-0.254582312232569
451213.2002545990534-1.20025459905342
461414.2709431895937-0.270943189593668
471413.83587209267760.164127907322432
48811.6572926614921-3.65729266149206
491313.2806889717270-0.280688971727048
501614.83209315876401.16790684123597
511312.63947351774510.360526482254932
521114.1509968986372-3.15099689863719
531413.59251517205570.407484827944284
541311.57697907658251.42302092341754
551313.2044663004113-0.204466300411272
561313.5644091448254-0.564409144825376
571212.5998424589847-0.599842458984685
581614.69460636452931.30539363547074
591510.93696049288474.0630395071153
601515.5013037913587-0.501303791358683
611211.12711047996830.872889520031678
621414.4305603414301-0.4305603414301
631214.4864606211227-2.48646062112272
641514.19174770977450.808252290225537
651212.0054050107979-0.005405010797859
661313.2998321688632-0.299832168863224
671214.1279522856312-2.12795228563121
681212.2949323677068-0.294932367706756
691314.0785196593001-1.07851965930009
70510.2389670051693-5.23896700516926
711313.4703508376377-0.470350837637678
721313.4021649515127-0.402164951512716
731413.20014878334880.799851216651212
741713.70409402276083.29590597723922
751313.8697255980051-0.869725598005064
761314.4908544275924-1.49085442759242
771213.7823222143317-1.78232221433172
781312.86342200579970.136577994200268
791412.28823527794811.71176472205187
801110.36484438288730.635155617112655
811211.64229760006970.357702399930296
821213.2433287040626-1.24332870406259
831613.99197718141292.00802281858713
841213.0861354465583-1.08613544655830
851210.59049498183381.40950501816622
861213.7982073841747-1.79820738417470
871011.6513627003967-1.65136270039666
881512.47194593897712.52805406102288
891515.2625530019638-0.262553001963804
901212.0114436106825-0.0114436106825331
911613.21578281403422.78421718596579
921513.97408945621251.02591054378752
931615.15210591162210.84789408837793
941314.6968684676904-1.6968684676904
951212.6175158882304-0.617515888230369
961111.9324079762809-0.932407976280935
971311.68229092953231.31770907046770
981011.1087881892885-1.10878818928852
991513.22115437516441.77884562483565
1001313.7695983318829-0.769598331882898
1011615.36509854263710.634901457362914
1021514.96966483269120.0303351673087509
1031814.58813131096443.41186868903559
1041310.52424054643582.47575945356424
1051010.2583143888960-0.258314388895981
1061614.99065775594371.00934224405633
1071311.39391846010621.60608153989381
1081515.4119168050792-0.411916805079173
1091411.73534982247952.2646501775205
1101511.41286293882483.58713706117522
1111413.13658863081550.863411369184523
1121314.5871194393257-1.58711943932573
1131313.1350473306006-0.135047330600562
1141514.09674660956690.903253390433103
1151614.63192615335861.36807384664139
1161414.3258520403142-0.325852040314200
1171414.0619134694722-0.0619134694722086
1181613.15006719714202.84993280285799
1191414.4556778137955-0.45567781379547
1201212.7073996499909-0.7073996499909
1211312.69212770599880.307872294001191
1221213.8202722473981-1.82027224739813
1231212.0772875551874-0.0772875551874363
1241414.4647687128416-0.46476871284163
1251414.3526471017900-0.352647101789961
1261411.93502445627452.0649755437255
1271615.21014959315760.78985040684242
1281314.4062231737777-1.40622317377767
1291412.88377973573791.11622026426210
130410.9722414167594-6.97224141675937
1311615.21543991107790.78456008892208
1321313.3575331365584-0.357533136558368
1331611.74474268522214.25525731477786
1341513.44105509133531.55894490866471
1351413.73720805147550.26279194852451
1361311.86583166797741.13416833202256
1371414.2497996829687-0.249799682968701
1381211.96395146611370.0360485338862988
1391514.12954577741080.870454222589154
1401413.47241481217240.527585187827579
1411313.0231492252456-0.023149225245558
1421414.0711827314672-0.071182731467205
1431613.23620259081062.76379740918944
144611.9077091725183-5.90770917251825
1451312.53116536228450.46883463771553
1461312.81078509378380.189214906216212
1471412.45742614179141.54257385820855
1481514.55916465738110.440835342618876
1491414.1153252590265-0.115325259026515
1501514.99271000668510.00728999331486742
1511313.9437068757845-0.94370687578452
1521614.65253473022351.34746526977655
1531211.64287082596720.357129174032826
1541513.70635407726101.29364592273898
1551214.1001307898994-2.10013078989944
1561411.47683471312292.52316528687711

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 12.7123575187548 & 0.287642481245227 \tabularnewline
2 & 13 & 12.8845640199319 & 0.115435980068071 \tabularnewline
3 & 16 & 13.3148298979646 & 2.68517010203537 \tabularnewline
4 & 12 & 12.7154672274008 & -0.715467227400786 \tabularnewline
5 & 11 & 12.6192561672017 & -1.61925616720165 \tabularnewline
6 & 12 & 10.9597422827392 & 1.04025771726075 \tabularnewline
7 & 18 & 15.6263145903790 & 2.37368540962098 \tabularnewline
8 & 11 & 12.2687821810591 & -1.26878218105909 \tabularnewline
9 & 14 & 12.9459963179229 & 1.05400368207712 \tabularnewline
10 & 9 & 10.7765124896929 & -1.77651248969288 \tabularnewline
11 & 14 & 13.9898487726522 & 0.0101512273477707 \tabularnewline
12 & 12 & 13.6847256776321 & -1.68472567763207 \tabularnewline
13 & 11 & 13.8816918719569 & -2.88169187195692 \tabularnewline
14 & 12 & 13.3914509439414 & -1.39145094394135 \tabularnewline
15 & 13 & 12.8694467659522 & 0.130553234047775 \tabularnewline
16 & 11 & 12.8530802385107 & -1.85308023851071 \tabularnewline
17 & 12 & 13.3431906278020 & -1.34319062780204 \tabularnewline
18 & 16 & 13.9479570617974 & 2.05204293820256 \tabularnewline
19 & 9 & 11.6964218543849 & -2.69642185438492 \tabularnewline
20 & 11 & 11.5361143499040 & -0.536114349903987 \tabularnewline
21 & 13 & 10.3347025336618 & 2.66529746633819 \tabularnewline
22 & 15 & 14.6132979948784 & 0.386702005121559 \tabularnewline
23 & 10 & 13.4738439052489 & -3.4738439052489 \tabularnewline
24 & 11 & 11.5311228768590 & -0.531122876859011 \tabularnewline
25 & 13 & 14.1401208153346 & -1.14012081533456 \tabularnewline
26 & 16 & 14.4461680652809 & 1.55383193471906 \tabularnewline
27 & 15 & 15.0404724977158 & -0.040472497715783 \tabularnewline
28 & 14 & 13.429644790624 & 0.570355209376008 \tabularnewline
29 & 14 & 14.214715157704 & -0.214715157703995 \tabularnewline
30 & 14 & 11.990399274892 & 2.009600725108 \tabularnewline
31 & 8 & 11.6763358944877 & -3.6763358944877 \tabularnewline
32 & 13 & 14.3888421307032 & -1.38884213070320 \tabularnewline
33 & 15 & 14.6659490542602 & 0.334050945739831 \tabularnewline
34 & 13 & 11.4409529102721 & 1.55904708972789 \tabularnewline
35 & 11 & 12.7541652147674 & -1.75416521476742 \tabularnewline
36 & 15 & 14.3455180786581 & 0.654481921341931 \tabularnewline
37 & 15 & 13.7005485093376 & 1.29945149066236 \tabularnewline
38 & 9 & 12.2989755122997 & -3.29897551229975 \tabularnewline
39 & 13 & 14.259570567836 & -1.25957056783599 \tabularnewline
40 & 16 & 15.1898482184187 & 0.810151781581333 \tabularnewline
41 & 13 & 13.9120715295458 & -0.91207152954582 \tabularnewline
42 & 11 & 10.6336267326332 & 0.366373267366805 \tabularnewline
43 & 12 & 11.7122876588586 & 0.287712341141435 \tabularnewline
44 & 12 & 12.2545823122326 & -0.254582312232569 \tabularnewline
45 & 12 & 13.2002545990534 & -1.20025459905342 \tabularnewline
46 & 14 & 14.2709431895937 & -0.270943189593668 \tabularnewline
47 & 14 & 13.8358720926776 & 0.164127907322432 \tabularnewline
48 & 8 & 11.6572926614921 & -3.65729266149206 \tabularnewline
49 & 13 & 13.2806889717270 & -0.280688971727048 \tabularnewline
50 & 16 & 14.8320931587640 & 1.16790684123597 \tabularnewline
51 & 13 & 12.6394735177451 & 0.360526482254932 \tabularnewline
52 & 11 & 14.1509968986372 & -3.15099689863719 \tabularnewline
53 & 14 & 13.5925151720557 & 0.407484827944284 \tabularnewline
54 & 13 & 11.5769790765825 & 1.42302092341754 \tabularnewline
55 & 13 & 13.2044663004113 & -0.204466300411272 \tabularnewline
56 & 13 & 13.5644091448254 & -0.564409144825376 \tabularnewline
57 & 12 & 12.5998424589847 & -0.599842458984685 \tabularnewline
58 & 16 & 14.6946063645293 & 1.30539363547074 \tabularnewline
59 & 15 & 10.9369604928847 & 4.0630395071153 \tabularnewline
60 & 15 & 15.5013037913587 & -0.501303791358683 \tabularnewline
61 & 12 & 11.1271104799683 & 0.872889520031678 \tabularnewline
62 & 14 & 14.4305603414301 & -0.4305603414301 \tabularnewline
63 & 12 & 14.4864606211227 & -2.48646062112272 \tabularnewline
64 & 15 & 14.1917477097745 & 0.808252290225537 \tabularnewline
65 & 12 & 12.0054050107979 & -0.005405010797859 \tabularnewline
66 & 13 & 13.2998321688632 & -0.299832168863224 \tabularnewline
67 & 12 & 14.1279522856312 & -2.12795228563121 \tabularnewline
68 & 12 & 12.2949323677068 & -0.294932367706756 \tabularnewline
69 & 13 & 14.0785196593001 & -1.07851965930009 \tabularnewline
70 & 5 & 10.2389670051693 & -5.23896700516926 \tabularnewline
71 & 13 & 13.4703508376377 & -0.470350837637678 \tabularnewline
72 & 13 & 13.4021649515127 & -0.402164951512716 \tabularnewline
73 & 14 & 13.2001487833488 & 0.799851216651212 \tabularnewline
74 & 17 & 13.7040940227608 & 3.29590597723922 \tabularnewline
75 & 13 & 13.8697255980051 & -0.869725598005064 \tabularnewline
76 & 13 & 14.4908544275924 & -1.49085442759242 \tabularnewline
77 & 12 & 13.7823222143317 & -1.78232221433172 \tabularnewline
78 & 13 & 12.8634220057997 & 0.136577994200268 \tabularnewline
79 & 14 & 12.2882352779481 & 1.71176472205187 \tabularnewline
80 & 11 & 10.3648443828873 & 0.635155617112655 \tabularnewline
81 & 12 & 11.6422976000697 & 0.357702399930296 \tabularnewline
82 & 12 & 13.2433287040626 & -1.24332870406259 \tabularnewline
83 & 16 & 13.9919771814129 & 2.00802281858713 \tabularnewline
84 & 12 & 13.0861354465583 & -1.08613544655830 \tabularnewline
85 & 12 & 10.5904949818338 & 1.40950501816622 \tabularnewline
86 & 12 & 13.7982073841747 & -1.79820738417470 \tabularnewline
87 & 10 & 11.6513627003967 & -1.65136270039666 \tabularnewline
88 & 15 & 12.4719459389771 & 2.52805406102288 \tabularnewline
89 & 15 & 15.2625530019638 & -0.262553001963804 \tabularnewline
90 & 12 & 12.0114436106825 & -0.0114436106825331 \tabularnewline
91 & 16 & 13.2157828140342 & 2.78421718596579 \tabularnewline
92 & 15 & 13.9740894562125 & 1.02591054378752 \tabularnewline
93 & 16 & 15.1521059116221 & 0.84789408837793 \tabularnewline
94 & 13 & 14.6968684676904 & -1.6968684676904 \tabularnewline
95 & 12 & 12.6175158882304 & -0.617515888230369 \tabularnewline
96 & 11 & 11.9324079762809 & -0.932407976280935 \tabularnewline
97 & 13 & 11.6822909295323 & 1.31770907046770 \tabularnewline
98 & 10 & 11.1087881892885 & -1.10878818928852 \tabularnewline
99 & 15 & 13.2211543751644 & 1.77884562483565 \tabularnewline
100 & 13 & 13.7695983318829 & -0.769598331882898 \tabularnewline
101 & 16 & 15.3650985426371 & 0.634901457362914 \tabularnewline
102 & 15 & 14.9696648326912 & 0.0303351673087509 \tabularnewline
103 & 18 & 14.5881313109644 & 3.41186868903559 \tabularnewline
104 & 13 & 10.5242405464358 & 2.47575945356424 \tabularnewline
105 & 10 & 10.2583143888960 & -0.258314388895981 \tabularnewline
106 & 16 & 14.9906577559437 & 1.00934224405633 \tabularnewline
107 & 13 & 11.3939184601062 & 1.60608153989381 \tabularnewline
108 & 15 & 15.4119168050792 & -0.411916805079173 \tabularnewline
109 & 14 & 11.7353498224795 & 2.2646501775205 \tabularnewline
110 & 15 & 11.4128629388248 & 3.58713706117522 \tabularnewline
111 & 14 & 13.1365886308155 & 0.863411369184523 \tabularnewline
112 & 13 & 14.5871194393257 & -1.58711943932573 \tabularnewline
113 & 13 & 13.1350473306006 & -0.135047330600562 \tabularnewline
114 & 15 & 14.0967466095669 & 0.903253390433103 \tabularnewline
115 & 16 & 14.6319261533586 & 1.36807384664139 \tabularnewline
116 & 14 & 14.3258520403142 & -0.325852040314200 \tabularnewline
117 & 14 & 14.0619134694722 & -0.0619134694722086 \tabularnewline
118 & 16 & 13.1500671971420 & 2.84993280285799 \tabularnewline
119 & 14 & 14.4556778137955 & -0.45567781379547 \tabularnewline
120 & 12 & 12.7073996499909 & -0.7073996499909 \tabularnewline
121 & 13 & 12.6921277059988 & 0.307872294001191 \tabularnewline
122 & 12 & 13.8202722473981 & -1.82027224739813 \tabularnewline
123 & 12 & 12.0772875551874 & -0.0772875551874363 \tabularnewline
124 & 14 & 14.4647687128416 & -0.46476871284163 \tabularnewline
125 & 14 & 14.3526471017900 & -0.352647101789961 \tabularnewline
126 & 14 & 11.9350244562745 & 2.0649755437255 \tabularnewline
127 & 16 & 15.2101495931576 & 0.78985040684242 \tabularnewline
128 & 13 & 14.4062231737777 & -1.40622317377767 \tabularnewline
129 & 14 & 12.8837797357379 & 1.11622026426210 \tabularnewline
130 & 4 & 10.9722414167594 & -6.97224141675937 \tabularnewline
131 & 16 & 15.2154399110779 & 0.78456008892208 \tabularnewline
132 & 13 & 13.3575331365584 & -0.357533136558368 \tabularnewline
133 & 16 & 11.7447426852221 & 4.25525731477786 \tabularnewline
134 & 15 & 13.4410550913353 & 1.55894490866471 \tabularnewline
135 & 14 & 13.7372080514755 & 0.26279194852451 \tabularnewline
136 & 13 & 11.8658316679774 & 1.13416833202256 \tabularnewline
137 & 14 & 14.2497996829687 & -0.249799682968701 \tabularnewline
138 & 12 & 11.9639514661137 & 0.0360485338862988 \tabularnewline
139 & 15 & 14.1295457774108 & 0.870454222589154 \tabularnewline
140 & 14 & 13.4724148121724 & 0.527585187827579 \tabularnewline
141 & 13 & 13.0231492252456 & -0.023149225245558 \tabularnewline
142 & 14 & 14.0711827314672 & -0.071182731467205 \tabularnewline
143 & 16 & 13.2362025908106 & 2.76379740918944 \tabularnewline
144 & 6 & 11.9077091725183 & -5.90770917251825 \tabularnewline
145 & 13 & 12.5311653622845 & 0.46883463771553 \tabularnewline
146 & 13 & 12.8107850937838 & 0.189214906216212 \tabularnewline
147 & 14 & 12.4574261417914 & 1.54257385820855 \tabularnewline
148 & 15 & 14.5591646573811 & 0.440835342618876 \tabularnewline
149 & 14 & 14.1153252590265 & -0.115325259026515 \tabularnewline
150 & 15 & 14.9927100066851 & 0.00728999331486742 \tabularnewline
151 & 13 & 13.9437068757845 & -0.94370687578452 \tabularnewline
152 & 16 & 14.6525347302235 & 1.34746526977655 \tabularnewline
153 & 12 & 11.6428708259672 & 0.357129174032826 \tabularnewline
154 & 15 & 13.7063540772610 & 1.29364592273898 \tabularnewline
155 & 12 & 14.1001307898994 & -2.10013078989944 \tabularnewline
156 & 14 & 11.4768347131229 & 2.52316528687711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103761&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]12.7123575187548[/C][C]0.287642481245227[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]12.8845640199319[/C][C]0.115435980068071[/C][/ROW]
[ROW][C]3[/C][C]16[/C][C]13.3148298979646[/C][C]2.68517010203537[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.7154672274008[/C][C]-0.715467227400786[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]12.6192561672017[/C][C]-1.61925616720165[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.9597422827392[/C][C]1.04025771726075[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]15.6263145903790[/C][C]2.37368540962098[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.2687821810591[/C][C]-1.26878218105909[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]12.9459963179229[/C][C]1.05400368207712[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]10.7765124896929[/C][C]-1.77651248969288[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]13.9898487726522[/C][C]0.0101512273477707[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.6847256776321[/C][C]-1.68472567763207[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]13.8816918719569[/C][C]-2.88169187195692[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.3914509439414[/C][C]-1.39145094394135[/C][/ROW]
[ROW][C]15[/C][C]13[/C][C]12.8694467659522[/C][C]0.130553234047775[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]12.8530802385107[/C][C]-1.85308023851071[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]13.3431906278020[/C][C]-1.34319062780204[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]13.9479570617974[/C][C]2.05204293820256[/C][/ROW]
[ROW][C]19[/C][C]9[/C][C]11.6964218543849[/C][C]-2.69642185438492[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]11.5361143499040[/C][C]-0.536114349903987[/C][/ROW]
[ROW][C]21[/C][C]13[/C][C]10.3347025336618[/C][C]2.66529746633819[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.6132979948784[/C][C]0.386702005121559[/C][/ROW]
[ROW][C]23[/C][C]10[/C][C]13.4738439052489[/C][C]-3.4738439052489[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.5311228768590[/C][C]-0.531122876859011[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]14.1401208153346[/C][C]-1.14012081533456[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]14.4461680652809[/C][C]1.55383193471906[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]15.0404724977158[/C][C]-0.040472497715783[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.429644790624[/C][C]0.570355209376008[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]14.214715157704[/C][C]-0.214715157703995[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]11.990399274892[/C][C]2.009600725108[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]11.6763358944877[/C][C]-3.6763358944877[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]14.3888421307032[/C][C]-1.38884213070320[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]14.6659490542602[/C][C]0.334050945739831[/C][/ROW]
[ROW][C]34[/C][C]13[/C][C]11.4409529102721[/C][C]1.55904708972789[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]12.7541652147674[/C][C]-1.75416521476742[/C][/ROW]
[ROW][C]36[/C][C]15[/C][C]14.3455180786581[/C][C]0.654481921341931[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]13.7005485093376[/C][C]1.29945149066236[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]12.2989755122997[/C][C]-3.29897551229975[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]14.259570567836[/C][C]-1.25957056783599[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.1898482184187[/C][C]0.810151781581333[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.9120715295458[/C][C]-0.91207152954582[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]10.6336267326332[/C][C]0.366373267366805[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]11.7122876588586[/C][C]0.287712341141435[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.2545823122326[/C][C]-0.254582312232569[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]13.2002545990534[/C][C]-1.20025459905342[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]14.2709431895937[/C][C]-0.270943189593668[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]13.8358720926776[/C][C]0.164127907322432[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]11.6572926614921[/C][C]-3.65729266149206[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.2806889717270[/C][C]-0.280688971727048[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]14.8320931587640[/C][C]1.16790684123597[/C][/ROW]
[ROW][C]51[/C][C]13[/C][C]12.6394735177451[/C][C]0.360526482254932[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]14.1509968986372[/C][C]-3.15099689863719[/C][/ROW]
[ROW][C]53[/C][C]14[/C][C]13.5925151720557[/C][C]0.407484827944284[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.5769790765825[/C][C]1.42302092341754[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]13.2044663004113[/C][C]-0.204466300411272[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]13.5644091448254[/C][C]-0.564409144825376[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.5998424589847[/C][C]-0.599842458984685[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.6946063645293[/C][C]1.30539363547074[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]10.9369604928847[/C][C]4.0630395071153[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.5013037913587[/C][C]-0.501303791358683[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.1271104799683[/C][C]0.872889520031678[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]14.4305603414301[/C][C]-0.4305603414301[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.4864606211227[/C][C]-2.48646062112272[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]14.1917477097745[/C][C]0.808252290225537[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]12.0054050107979[/C][C]-0.005405010797859[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]13.2998321688632[/C][C]-0.299832168863224[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]14.1279522856312[/C][C]-2.12795228563121[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]12.2949323677068[/C][C]-0.294932367706756[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]14.0785196593001[/C][C]-1.07851965930009[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]10.2389670051693[/C][C]-5.23896700516926[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.4703508376377[/C][C]-0.470350837637678[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]13.4021649515127[/C][C]-0.402164951512716[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]13.2001487833488[/C][C]0.799851216651212[/C][/ROW]
[ROW][C]74[/C][C]17[/C][C]13.7040940227608[/C][C]3.29590597723922[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.8697255980051[/C][C]-0.869725598005064[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]14.4908544275924[/C][C]-1.49085442759242[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]13.7823222143317[/C][C]-1.78232221433172[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.8634220057997[/C][C]0.136577994200268[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]12.2882352779481[/C][C]1.71176472205187[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]10.3648443828873[/C][C]0.635155617112655[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.6422976000697[/C][C]0.357702399930296[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.2433287040626[/C][C]-1.24332870406259[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]13.9919771814129[/C][C]2.00802281858713[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]13.0861354465583[/C][C]-1.08613544655830[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]10.5904949818338[/C][C]1.40950501816622[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]13.7982073841747[/C][C]-1.79820738417470[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]11.6513627003967[/C][C]-1.65136270039666[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.4719459389771[/C][C]2.52805406102288[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]15.2625530019638[/C][C]-0.262553001963804[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]12.0114436106825[/C][C]-0.0114436106825331[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]13.2157828140342[/C][C]2.78421718596579[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.9740894562125[/C][C]1.02591054378752[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.1521059116221[/C][C]0.84789408837793[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.6968684676904[/C][C]-1.6968684676904[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]12.6175158882304[/C][C]-0.617515888230369[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.9324079762809[/C][C]-0.932407976280935[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.6822909295323[/C][C]1.31770907046770[/C][/ROW]
[ROW][C]98[/C][C]10[/C][C]11.1087881892885[/C][C]-1.10878818928852[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]13.2211543751644[/C][C]1.77884562483565[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]13.7695983318829[/C][C]-0.769598331882898[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.3650985426371[/C][C]0.634901457362914[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]14.9696648326912[/C][C]0.0303351673087509[/C][/ROW]
[ROW][C]103[/C][C]18[/C][C]14.5881313109644[/C][C]3.41186868903559[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]10.5242405464358[/C][C]2.47575945356424[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]10.2583143888960[/C][C]-0.258314388895981[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]14.9906577559437[/C][C]1.00934224405633[/C][/ROW]
[ROW][C]107[/C][C]13[/C][C]11.3939184601062[/C][C]1.60608153989381[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]15.4119168050792[/C][C]-0.411916805079173[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]11.7353498224795[/C][C]2.2646501775205[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]11.4128629388248[/C][C]3.58713706117522[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.1365886308155[/C][C]0.863411369184523[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]14.5871194393257[/C][C]-1.58711943932573[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.1350473306006[/C][C]-0.135047330600562[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]14.0967466095669[/C][C]0.903253390433103[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]14.6319261533586[/C][C]1.36807384664139[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.3258520403142[/C][C]-0.325852040314200[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.0619134694722[/C][C]-0.0619134694722086[/C][/ROW]
[ROW][C]118[/C][C]16[/C][C]13.1500671971420[/C][C]2.84993280285799[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.4556778137955[/C][C]-0.45567781379547[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]12.7073996499909[/C][C]-0.7073996499909[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]12.6921277059988[/C][C]0.307872294001191[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]13.8202722473981[/C][C]-1.82027224739813[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]12.0772875551874[/C][C]-0.0772875551874363[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]14.4647687128416[/C][C]-0.46476871284163[/C][/ROW]
[ROW][C]125[/C][C]14[/C][C]14.3526471017900[/C][C]-0.352647101789961[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]11.9350244562745[/C][C]2.0649755437255[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.2101495931576[/C][C]0.78985040684242[/C][/ROW]
[ROW][C]128[/C][C]13[/C][C]14.4062231737777[/C][C]-1.40622317377767[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]12.8837797357379[/C][C]1.11622026426210[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]10.9722414167594[/C][C]-6.97224141675937[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.2154399110779[/C][C]0.78456008892208[/C][/ROW]
[ROW][C]132[/C][C]13[/C][C]13.3575331365584[/C][C]-0.357533136558368[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]11.7447426852221[/C][C]4.25525731477786[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.4410550913353[/C][C]1.55894490866471[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]13.7372080514755[/C][C]0.26279194852451[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]11.8658316679774[/C][C]1.13416833202256[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]14.2497996829687[/C][C]-0.249799682968701[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]11.9639514661137[/C][C]0.0360485338862988[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]14.1295457774108[/C][C]0.870454222589154[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]13.4724148121724[/C][C]0.527585187827579[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.0231492252456[/C][C]-0.023149225245558[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]14.0711827314672[/C][C]-0.071182731467205[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.2362025908106[/C][C]2.76379740918944[/C][/ROW]
[ROW][C]144[/C][C]6[/C][C]11.9077091725183[/C][C]-5.90770917251825[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]12.5311653622845[/C][C]0.46883463771553[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]12.8107850937838[/C][C]0.189214906216212[/C][/ROW]
[ROW][C]147[/C][C]14[/C][C]12.4574261417914[/C][C]1.54257385820855[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]14.5591646573811[/C][C]0.440835342618876[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]14.1153252590265[/C][C]-0.115325259026515[/C][/ROW]
[ROW][C]150[/C][C]15[/C][C]14.9927100066851[/C][C]0.00728999331486742[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.9437068757845[/C][C]-0.94370687578452[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]14.6525347302235[/C][C]1.34746526977655[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]11.6428708259672[/C][C]0.357129174032826[/C][/ROW]
[ROW][C]154[/C][C]15[/C][C]13.7063540772610[/C][C]1.29364592273898[/C][/ROW]
[ROW][C]155[/C][C]12[/C][C]14.1001307898994[/C][C]-2.10013078989944[/C][/ROW]
[ROW][C]156[/C][C]14[/C][C]11.4768347131229[/C][C]2.52316528687711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103761&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103761&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
11312.71235751875480.287642481245227
21312.88456401993190.115435980068071
31613.31482989796462.68517010203537
41212.7154672274008-0.715467227400786
51112.6192561672017-1.61925616720165
61210.95974228273921.04025771726075
71815.62631459037902.37368540962098
81112.2687821810591-1.26878218105909
91412.94599631792291.05400368207712
10910.7765124896929-1.77651248969288
111413.98984877265220.0101512273477707
121213.6847256776321-1.68472567763207
131113.8816918719569-2.88169187195692
141213.3914509439414-1.39145094394135
151312.86944676595220.130553234047775
161112.8530802385107-1.85308023851071
171213.3431906278020-1.34319062780204
181613.94795706179742.05204293820256
19911.6964218543849-2.69642185438492
201111.5361143499040-0.536114349903987
211310.33470253366182.66529746633819
221514.61329799487840.386702005121559
231013.4738439052489-3.4738439052489
241111.5311228768590-0.531122876859011
251314.1401208153346-1.14012081533456
261614.44616806528091.55383193471906
271515.0404724977158-0.040472497715783
281413.4296447906240.570355209376008
291414.214715157704-0.214715157703995
301411.9903992748922.009600725108
31811.6763358944877-3.6763358944877
321314.3888421307032-1.38884213070320
331514.66594905426020.334050945739831
341311.44095291027211.55904708972789
351112.7541652147674-1.75416521476742
361514.34551807865810.654481921341931
371513.70054850933761.29945149066236
38912.2989755122997-3.29897551229975
391314.259570567836-1.25957056783599
401615.18984821841870.810151781581333
411313.9120715295458-0.91207152954582
421110.63362673263320.366373267366805
431211.71228765885860.287712341141435
441212.2545823122326-0.254582312232569
451213.2002545990534-1.20025459905342
461414.2709431895937-0.270943189593668
471413.83587209267760.164127907322432
48811.6572926614921-3.65729266149206
491313.2806889717270-0.280688971727048
501614.83209315876401.16790684123597
511312.63947351774510.360526482254932
521114.1509968986372-3.15099689863719
531413.59251517205570.407484827944284
541311.57697907658251.42302092341754
551313.2044663004113-0.204466300411272
561313.5644091448254-0.564409144825376
571212.5998424589847-0.599842458984685
581614.69460636452931.30539363547074
591510.93696049288474.0630395071153
601515.5013037913587-0.501303791358683
611211.12711047996830.872889520031678
621414.4305603414301-0.4305603414301
631214.4864606211227-2.48646062112272
641514.19174770977450.808252290225537
651212.0054050107979-0.005405010797859
661313.2998321688632-0.299832168863224
671214.1279522856312-2.12795228563121
681212.2949323677068-0.294932367706756
691314.0785196593001-1.07851965930009
70510.2389670051693-5.23896700516926
711313.4703508376377-0.470350837637678
721313.4021649515127-0.402164951512716
731413.20014878334880.799851216651212
741713.70409402276083.29590597723922
751313.8697255980051-0.869725598005064
761314.4908544275924-1.49085442759242
771213.7823222143317-1.78232221433172
781312.86342200579970.136577994200268
791412.28823527794811.71176472205187
801110.36484438288730.635155617112655
811211.64229760006970.357702399930296
821213.2433287040626-1.24332870406259
831613.99197718141292.00802281858713
841213.0861354465583-1.08613544655830
851210.59049498183381.40950501816622
861213.7982073841747-1.79820738417470
871011.6513627003967-1.65136270039666
881512.47194593897712.52805406102288
891515.2625530019638-0.262553001963804
901212.0114436106825-0.0114436106825331
911613.21578281403422.78421718596579
921513.97408945621251.02591054378752
931615.15210591162210.84789408837793
941314.6968684676904-1.6968684676904
951212.6175158882304-0.617515888230369
961111.9324079762809-0.932407976280935
971311.68229092953231.31770907046770
981011.1087881892885-1.10878818928852
991513.22115437516441.77884562483565
1001313.7695983318829-0.769598331882898
1011615.36509854263710.634901457362914
1021514.96966483269120.0303351673087509
1031814.58813131096443.41186868903559
1041310.52424054643582.47575945356424
1051010.2583143888960-0.258314388895981
1061614.99065775594371.00934224405633
1071311.39391846010621.60608153989381
1081515.4119168050792-0.411916805079173
1091411.73534982247952.2646501775205
1101511.41286293882483.58713706117522
1111413.13658863081550.863411369184523
1121314.5871194393257-1.58711943932573
1131313.1350473306006-0.135047330600562
1141514.09674660956690.903253390433103
1151614.63192615335861.36807384664139
1161414.3258520403142-0.325852040314200
1171414.0619134694722-0.0619134694722086
1181613.15006719714202.84993280285799
1191414.4556778137955-0.45567781379547
1201212.7073996499909-0.7073996499909
1211312.69212770599880.307872294001191
1221213.8202722473981-1.82027224739813
1231212.0772875551874-0.0772875551874363
1241414.4647687128416-0.46476871284163
1251414.3526471017900-0.352647101789961
1261411.93502445627452.0649755437255
1271615.21014959315760.78985040684242
1281314.4062231737777-1.40622317377767
1291412.88377973573791.11622026426210
130410.9722414167594-6.97224141675937
1311615.21543991107790.78456008892208
1321313.3575331365584-0.357533136558368
1331611.74474268522214.25525731477786
1341513.44105509133531.55894490866471
1351413.73720805147550.26279194852451
1361311.86583166797741.13416833202256
1371414.2497996829687-0.249799682968701
1381211.96395146611370.0360485338862988
1391514.12954577741080.870454222589154
1401413.47241481217240.527585187827579
1411313.0231492252456-0.023149225245558
1421414.0711827314672-0.071182731467205
1431613.23620259081062.76379740918944
144611.9077091725183-5.90770917251825
1451312.53116536228450.46883463771553
1461312.81078509378380.189214906216212
1471412.45742614179141.54257385820855
1481514.55916465738110.440835342618876
1491414.1153252590265-0.115325259026515
1501514.99271000668510.00728999331486742
1511313.9437068757845-0.94370687578452
1521614.65253473022351.34746526977655
1531211.64287082596720.357129174032826
1541513.70635407726101.29364592273898
1551214.1001307898994-2.10013078989944
1561411.47683471312292.52316528687711






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6266056607596030.7467886784807940.373394339240397
110.4846369116986970.9692738233973940.515363088301303
120.5203468486641110.9593063026717770.479653151335889
130.7000571067524540.5998857864950910.299942893247546
140.6071914207047110.7856171585905780.392808579295289
150.5738229107136070.8523541785727860.426177089286393
160.5394734032700870.9210531934598250.460526596729913
170.565293736271260.8694125274574810.434706263728740
180.5328731639281640.9342536721436720.467126836071836
190.505881639492990.988236721014020.49411836050701
200.4387407882569060.8774815765138110.561259211743094
210.7043064302206370.5913871395587270.295693569779363
220.6358703227001540.7282593545996920.364129677299846
230.7650675552021120.4698648895957760.234932444797888
240.7081473195160.5837053609680.291852680484
250.6584419857661520.6831160284676970.341558014233848
260.6554886540030960.6890226919938080.344511345996904
270.590212803594490.8195743928110190.409787196405510
280.5258684736272530.9482630527454940.474131526372747
290.4595444189082090.9190888378164180.540455581091791
300.4505586751994230.9011173503988450.549441324800577
310.6766829805600750.646634038879850.323317019439925
320.6610304671362660.6779390657274680.338969532863734
330.6097201046938530.7805597906122940.390279895306147
340.6215154508534560.7569690982930870.378484549146544
350.5995846536861050.8008306926277890.400415346313895
360.5534303196918680.8931393606162650.446569680308132
370.5369038181173440.9261923637653120.463096181882656
380.6205379357987910.7589241284024170.379462064201209
390.5792858843678460.8414282312643090.420714115632154
400.5345184786192030.9309630427615950.465481521380797
410.4954853452242640.9909706904485290.504514654775736
420.4703058688085280.9406117376170570.529694131191472
430.4229901758855720.8459803517711440.577009824114428
440.3739709565156330.7479419130312650.626029043484367
450.3382133647752150.676426729550430.661786635224785
460.2913354220013950.582670844002790.708664577998605
470.2486367311766970.4972734623533930.751363268823303
480.3750487061153420.7500974122306850.624951293884658
490.3266400399781920.6532800799563850.673359960021808
500.3036785653972210.6073571307944420.696321434602779
510.2726315565492560.5452631130985110.727368443450744
520.3573339286183860.7146678572367730.642666071381614
530.3194727865729870.6389455731459740.680527213427013
540.3082080386009010.6164160772018010.691791961399099
550.2654552537034860.5309105074069710.734544746296514
560.2277578258043470.4555156516086930.772242174195653
570.1945660840942920.3891321681885830.805433915905708
580.1874525396207620.3749050792415230.812547460379238
590.3510293867604140.7020587735208270.648970613239586
600.3094881227659530.6189762455319060.690511877234047
610.2787825007194430.5575650014388860.721217499280557
620.2409954616561160.4819909233122310.759004538343884
630.2899894787517170.5799789575034350.710010521248283
640.2640498676685840.5280997353371680.735950132331416
650.2300964813530800.4601929627061610.76990351864692
660.1959130930386690.3918261860773380.804086906961331
670.2062093659423710.4124187318847420.793790634057629
680.1749274926483560.3498549852967110.825072507351644
690.1552603459305770.3105206918611540.844739654069423
700.4578045630315560.9156091260631120.542195436968444
710.4130079960529740.8260159921059480.586992003947026
720.3691888411143090.7383776822286170.630811158885692
730.3375799234306740.6751598468613480.662420076569326
740.4548555623514890.9097111247029780.545144437648511
750.4176798069924680.8353596139849360.582320193007532
760.4064434955345850.8128869910691690.593556504465415
770.4086629693899430.8173259387798870.591337030610056
780.3642661153509390.7285322307018770.635733884649061
790.3621552505565270.7243105011130550.637844749443472
800.326767341613170.653534683226340.67323265838683
810.2902871815030880.5805743630061760.709712818496912
820.2658286839551700.5316573679103410.73417131604483
830.2800048416101180.5600096832202350.719995158389882
840.2554154858796300.5108309717592610.74458451412037
850.241821853095620.483643706191240.75817814690438
860.2480676193543580.4961352387087160.751932380645642
870.2520035392186870.5040070784373730.747996460781313
880.2928762763121840.5857525526243680.707123723687816
890.2539436447708610.5078872895417210.74605635522914
900.2189107479041280.4378214958082570.781089252095872
910.2708815401628960.5417630803257930.729118459837104
920.2460808566615280.4921617133230560.753919143338472
930.2173936454491750.4347872908983490.782606354550825
940.2182480432594230.4364960865188460.781751956740577
950.1883995884139890.3767991768279790.81160041158601
960.1788384201006610.3576768402013220.821161579899339
970.1717554521915830.3435109043831660.828244547808417
980.1774187593922090.3548375187844180.822581240607791
990.2022079174076720.4044158348153450.797792082592328
1000.1930373262140670.3860746524281340.806962673785933
1010.1671108394509870.3342216789019740.832889160549013
1020.1383625979434100.2767251958868200.86163740205659
1030.1931306431702190.3862612863404390.80686935682978
1040.2078884102448120.4157768204896240.792111589755188
1050.1808592873577510.3617185747155010.81914071264225
1060.1561860601525690.3123721203051380.84381393984743
1070.1504612866375430.3009225732750860.849538713362457
1080.1232187862267580.2464375724535160.876781213773242
1090.1361453125613810.2722906251227620.86385468743862
1100.4218007027463040.8436014054926080.578199297253696
1110.3994845158471860.7989690316943730.600515484152814
1120.3776735768226690.7553471536453380.622326423177331
1130.3460217108979820.6920434217959650.653978289102018
1140.3052110554030910.6104221108061830.694788944596909
1150.2731195704945670.5462391409891350.726880429505433
1160.2327185368889010.4654370737778020.767281463111099
1170.1936617425544690.3873234851089370.806338257445531
1180.2341616203637070.4683232407274130.765838379636293
1190.2109824019260020.4219648038520040.789017598073998
1200.1759129203148090.3518258406296170.824087079685191
1210.1421435418467210.2842870836934420.857856458153279
1220.1304079702828290.2608159405656590.86959202971717
1230.1021180122794230.2042360245588450.897881987720577
1240.0920577234578290.1841154469156580.907942276542171
1250.07489508281497510.1497901656299500.925104917185025
1260.0803289797234290.1606579594468580.919671020276571
1270.06035652509836040.1207130501967210.93964347490164
1280.06705445921007450.1341089184201490.932945540789926
1290.09306505724185160.1861301144837030.906934942758148
1300.3543831287866140.7087662575732280.645616871213386
1310.305144483138020.610288966276040.69485551686198
1320.246415197660010.492830395320020.75358480233999
1330.4686048755605270.9372097511210540.531395124439473
1340.4410754679320210.8821509358640420.558924532067979
1350.3720449620220810.7440899240441620.62795503797792
1360.3305585381713010.6611170763426020.669441461828699
1370.2661051281187740.5322102562375470.733894871881226
1380.2005834785555360.4011669571110720.799416521444464
1390.1495473868872670.2990947737745340.850452613112733
1400.1079300596938190.2158601193876370.892069940306181
1410.07006605129973360.1401321025994670.929933948700266
1420.0424656113307760.0849312226615520.957534388669224
1430.03187073597952030.06374147195904070.96812926402048
1440.7189902726935810.5620194546128380.281009727306419
1450.5773325549946870.8453348900106260.422667445005313
1460.4271041583578890.8542083167157770.572895841642111

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.626605660759603 & 0.746788678480794 & 0.373394339240397 \tabularnewline
11 & 0.484636911698697 & 0.969273823397394 & 0.515363088301303 \tabularnewline
12 & 0.520346848664111 & 0.959306302671777 & 0.479653151335889 \tabularnewline
13 & 0.700057106752454 & 0.599885786495091 & 0.299942893247546 \tabularnewline
14 & 0.607191420704711 & 0.785617158590578 & 0.392808579295289 \tabularnewline
15 & 0.573822910713607 & 0.852354178572786 & 0.426177089286393 \tabularnewline
16 & 0.539473403270087 & 0.921053193459825 & 0.460526596729913 \tabularnewline
17 & 0.56529373627126 & 0.869412527457481 & 0.434706263728740 \tabularnewline
18 & 0.532873163928164 & 0.934253672143672 & 0.467126836071836 \tabularnewline
19 & 0.50588163949299 & 0.98823672101402 & 0.49411836050701 \tabularnewline
20 & 0.438740788256906 & 0.877481576513811 & 0.561259211743094 \tabularnewline
21 & 0.704306430220637 & 0.591387139558727 & 0.295693569779363 \tabularnewline
22 & 0.635870322700154 & 0.728259354599692 & 0.364129677299846 \tabularnewline
23 & 0.765067555202112 & 0.469864889595776 & 0.234932444797888 \tabularnewline
24 & 0.708147319516 & 0.583705360968 & 0.291852680484 \tabularnewline
25 & 0.658441985766152 & 0.683116028467697 & 0.341558014233848 \tabularnewline
26 & 0.655488654003096 & 0.689022691993808 & 0.344511345996904 \tabularnewline
27 & 0.59021280359449 & 0.819574392811019 & 0.409787196405510 \tabularnewline
28 & 0.525868473627253 & 0.948263052745494 & 0.474131526372747 \tabularnewline
29 & 0.459544418908209 & 0.919088837816418 & 0.540455581091791 \tabularnewline
30 & 0.450558675199423 & 0.901117350398845 & 0.549441324800577 \tabularnewline
31 & 0.676682980560075 & 0.64663403887985 & 0.323317019439925 \tabularnewline
32 & 0.661030467136266 & 0.677939065727468 & 0.338969532863734 \tabularnewline
33 & 0.609720104693853 & 0.780559790612294 & 0.390279895306147 \tabularnewline
34 & 0.621515450853456 & 0.756969098293087 & 0.378484549146544 \tabularnewline
35 & 0.599584653686105 & 0.800830692627789 & 0.400415346313895 \tabularnewline
36 & 0.553430319691868 & 0.893139360616265 & 0.446569680308132 \tabularnewline
37 & 0.536903818117344 & 0.926192363765312 & 0.463096181882656 \tabularnewline
38 & 0.620537935798791 & 0.758924128402417 & 0.379462064201209 \tabularnewline
39 & 0.579285884367846 & 0.841428231264309 & 0.420714115632154 \tabularnewline
40 & 0.534518478619203 & 0.930963042761595 & 0.465481521380797 \tabularnewline
41 & 0.495485345224264 & 0.990970690448529 & 0.504514654775736 \tabularnewline
42 & 0.470305868808528 & 0.940611737617057 & 0.529694131191472 \tabularnewline
43 & 0.422990175885572 & 0.845980351771144 & 0.577009824114428 \tabularnewline
44 & 0.373970956515633 & 0.747941913031265 & 0.626029043484367 \tabularnewline
45 & 0.338213364775215 & 0.67642672955043 & 0.661786635224785 \tabularnewline
46 & 0.291335422001395 & 0.58267084400279 & 0.708664577998605 \tabularnewline
47 & 0.248636731176697 & 0.497273462353393 & 0.751363268823303 \tabularnewline
48 & 0.375048706115342 & 0.750097412230685 & 0.624951293884658 \tabularnewline
49 & 0.326640039978192 & 0.653280079956385 & 0.673359960021808 \tabularnewline
50 & 0.303678565397221 & 0.607357130794442 & 0.696321434602779 \tabularnewline
51 & 0.272631556549256 & 0.545263113098511 & 0.727368443450744 \tabularnewline
52 & 0.357333928618386 & 0.714667857236773 & 0.642666071381614 \tabularnewline
53 & 0.319472786572987 & 0.638945573145974 & 0.680527213427013 \tabularnewline
54 & 0.308208038600901 & 0.616416077201801 & 0.691791961399099 \tabularnewline
55 & 0.265455253703486 & 0.530910507406971 & 0.734544746296514 \tabularnewline
56 & 0.227757825804347 & 0.455515651608693 & 0.772242174195653 \tabularnewline
57 & 0.194566084094292 & 0.389132168188583 & 0.805433915905708 \tabularnewline
58 & 0.187452539620762 & 0.374905079241523 & 0.812547460379238 \tabularnewline
59 & 0.351029386760414 & 0.702058773520827 & 0.648970613239586 \tabularnewline
60 & 0.309488122765953 & 0.618976245531906 & 0.690511877234047 \tabularnewline
61 & 0.278782500719443 & 0.557565001438886 & 0.721217499280557 \tabularnewline
62 & 0.240995461656116 & 0.481990923312231 & 0.759004538343884 \tabularnewline
63 & 0.289989478751717 & 0.579978957503435 & 0.710010521248283 \tabularnewline
64 & 0.264049867668584 & 0.528099735337168 & 0.735950132331416 \tabularnewline
65 & 0.230096481353080 & 0.460192962706161 & 0.76990351864692 \tabularnewline
66 & 0.195913093038669 & 0.391826186077338 & 0.804086906961331 \tabularnewline
67 & 0.206209365942371 & 0.412418731884742 & 0.793790634057629 \tabularnewline
68 & 0.174927492648356 & 0.349854985296711 & 0.825072507351644 \tabularnewline
69 & 0.155260345930577 & 0.310520691861154 & 0.844739654069423 \tabularnewline
70 & 0.457804563031556 & 0.915609126063112 & 0.542195436968444 \tabularnewline
71 & 0.413007996052974 & 0.826015992105948 & 0.586992003947026 \tabularnewline
72 & 0.369188841114309 & 0.738377682228617 & 0.630811158885692 \tabularnewline
73 & 0.337579923430674 & 0.675159846861348 & 0.662420076569326 \tabularnewline
74 & 0.454855562351489 & 0.909711124702978 & 0.545144437648511 \tabularnewline
75 & 0.417679806992468 & 0.835359613984936 & 0.582320193007532 \tabularnewline
76 & 0.406443495534585 & 0.812886991069169 & 0.593556504465415 \tabularnewline
77 & 0.408662969389943 & 0.817325938779887 & 0.591337030610056 \tabularnewline
78 & 0.364266115350939 & 0.728532230701877 & 0.635733884649061 \tabularnewline
79 & 0.362155250556527 & 0.724310501113055 & 0.637844749443472 \tabularnewline
80 & 0.32676734161317 & 0.65353468322634 & 0.67323265838683 \tabularnewline
81 & 0.290287181503088 & 0.580574363006176 & 0.709712818496912 \tabularnewline
82 & 0.265828683955170 & 0.531657367910341 & 0.73417131604483 \tabularnewline
83 & 0.280004841610118 & 0.560009683220235 & 0.719995158389882 \tabularnewline
84 & 0.255415485879630 & 0.510830971759261 & 0.74458451412037 \tabularnewline
85 & 0.24182185309562 & 0.48364370619124 & 0.75817814690438 \tabularnewline
86 & 0.248067619354358 & 0.496135238708716 & 0.751932380645642 \tabularnewline
87 & 0.252003539218687 & 0.504007078437373 & 0.747996460781313 \tabularnewline
88 & 0.292876276312184 & 0.585752552624368 & 0.707123723687816 \tabularnewline
89 & 0.253943644770861 & 0.507887289541721 & 0.74605635522914 \tabularnewline
90 & 0.218910747904128 & 0.437821495808257 & 0.781089252095872 \tabularnewline
91 & 0.270881540162896 & 0.541763080325793 & 0.729118459837104 \tabularnewline
92 & 0.246080856661528 & 0.492161713323056 & 0.753919143338472 \tabularnewline
93 & 0.217393645449175 & 0.434787290898349 & 0.782606354550825 \tabularnewline
94 & 0.218248043259423 & 0.436496086518846 & 0.781751956740577 \tabularnewline
95 & 0.188399588413989 & 0.376799176827979 & 0.81160041158601 \tabularnewline
96 & 0.178838420100661 & 0.357676840201322 & 0.821161579899339 \tabularnewline
97 & 0.171755452191583 & 0.343510904383166 & 0.828244547808417 \tabularnewline
98 & 0.177418759392209 & 0.354837518784418 & 0.822581240607791 \tabularnewline
99 & 0.202207917407672 & 0.404415834815345 & 0.797792082592328 \tabularnewline
100 & 0.193037326214067 & 0.386074652428134 & 0.806962673785933 \tabularnewline
101 & 0.167110839450987 & 0.334221678901974 & 0.832889160549013 \tabularnewline
102 & 0.138362597943410 & 0.276725195886820 & 0.86163740205659 \tabularnewline
103 & 0.193130643170219 & 0.386261286340439 & 0.80686935682978 \tabularnewline
104 & 0.207888410244812 & 0.415776820489624 & 0.792111589755188 \tabularnewline
105 & 0.180859287357751 & 0.361718574715501 & 0.81914071264225 \tabularnewline
106 & 0.156186060152569 & 0.312372120305138 & 0.84381393984743 \tabularnewline
107 & 0.150461286637543 & 0.300922573275086 & 0.849538713362457 \tabularnewline
108 & 0.123218786226758 & 0.246437572453516 & 0.876781213773242 \tabularnewline
109 & 0.136145312561381 & 0.272290625122762 & 0.86385468743862 \tabularnewline
110 & 0.421800702746304 & 0.843601405492608 & 0.578199297253696 \tabularnewline
111 & 0.399484515847186 & 0.798969031694373 & 0.600515484152814 \tabularnewline
112 & 0.377673576822669 & 0.755347153645338 & 0.622326423177331 \tabularnewline
113 & 0.346021710897982 & 0.692043421795965 & 0.653978289102018 \tabularnewline
114 & 0.305211055403091 & 0.610422110806183 & 0.694788944596909 \tabularnewline
115 & 0.273119570494567 & 0.546239140989135 & 0.726880429505433 \tabularnewline
116 & 0.232718536888901 & 0.465437073777802 & 0.767281463111099 \tabularnewline
117 & 0.193661742554469 & 0.387323485108937 & 0.806338257445531 \tabularnewline
118 & 0.234161620363707 & 0.468323240727413 & 0.765838379636293 \tabularnewline
119 & 0.210982401926002 & 0.421964803852004 & 0.789017598073998 \tabularnewline
120 & 0.175912920314809 & 0.351825840629617 & 0.824087079685191 \tabularnewline
121 & 0.142143541846721 & 0.284287083693442 & 0.857856458153279 \tabularnewline
122 & 0.130407970282829 & 0.260815940565659 & 0.86959202971717 \tabularnewline
123 & 0.102118012279423 & 0.204236024558845 & 0.897881987720577 \tabularnewline
124 & 0.092057723457829 & 0.184115446915658 & 0.907942276542171 \tabularnewline
125 & 0.0748950828149751 & 0.149790165629950 & 0.925104917185025 \tabularnewline
126 & 0.080328979723429 & 0.160657959446858 & 0.919671020276571 \tabularnewline
127 & 0.0603565250983604 & 0.120713050196721 & 0.93964347490164 \tabularnewline
128 & 0.0670544592100745 & 0.134108918420149 & 0.932945540789926 \tabularnewline
129 & 0.0930650572418516 & 0.186130114483703 & 0.906934942758148 \tabularnewline
130 & 0.354383128786614 & 0.708766257573228 & 0.645616871213386 \tabularnewline
131 & 0.30514448313802 & 0.61028896627604 & 0.69485551686198 \tabularnewline
132 & 0.24641519766001 & 0.49283039532002 & 0.75358480233999 \tabularnewline
133 & 0.468604875560527 & 0.937209751121054 & 0.531395124439473 \tabularnewline
134 & 0.441075467932021 & 0.882150935864042 & 0.558924532067979 \tabularnewline
135 & 0.372044962022081 & 0.744089924044162 & 0.62795503797792 \tabularnewline
136 & 0.330558538171301 & 0.661117076342602 & 0.669441461828699 \tabularnewline
137 & 0.266105128118774 & 0.532210256237547 & 0.733894871881226 \tabularnewline
138 & 0.200583478555536 & 0.401166957111072 & 0.799416521444464 \tabularnewline
139 & 0.149547386887267 & 0.299094773774534 & 0.850452613112733 \tabularnewline
140 & 0.107930059693819 & 0.215860119387637 & 0.892069940306181 \tabularnewline
141 & 0.0700660512997336 & 0.140132102599467 & 0.929933948700266 \tabularnewline
142 & 0.042465611330776 & 0.084931222661552 & 0.957534388669224 \tabularnewline
143 & 0.0318707359795203 & 0.0637414719590407 & 0.96812926402048 \tabularnewline
144 & 0.718990272693581 & 0.562019454612838 & 0.281009727306419 \tabularnewline
145 & 0.577332554994687 & 0.845334890010626 & 0.422667445005313 \tabularnewline
146 & 0.427104158357889 & 0.854208316715777 & 0.572895841642111 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103761&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]0.626605660759603[/C][C]0.746788678480794[/C][C]0.373394339240397[/C][/ROW]
[ROW][C]11[/C][C]0.484636911698697[/C][C]0.969273823397394[/C][C]0.515363088301303[/C][/ROW]
[ROW][C]12[/C][C]0.520346848664111[/C][C]0.959306302671777[/C][C]0.479653151335889[/C][/ROW]
[ROW][C]13[/C][C]0.700057106752454[/C][C]0.599885786495091[/C][C]0.299942893247546[/C][/ROW]
[ROW][C]14[/C][C]0.607191420704711[/C][C]0.785617158590578[/C][C]0.392808579295289[/C][/ROW]
[ROW][C]15[/C][C]0.573822910713607[/C][C]0.852354178572786[/C][C]0.426177089286393[/C][/ROW]
[ROW][C]16[/C][C]0.539473403270087[/C][C]0.921053193459825[/C][C]0.460526596729913[/C][/ROW]
[ROW][C]17[/C][C]0.56529373627126[/C][C]0.869412527457481[/C][C]0.434706263728740[/C][/ROW]
[ROW][C]18[/C][C]0.532873163928164[/C][C]0.934253672143672[/C][C]0.467126836071836[/C][/ROW]
[ROW][C]19[/C][C]0.50588163949299[/C][C]0.98823672101402[/C][C]0.49411836050701[/C][/ROW]
[ROW][C]20[/C][C]0.438740788256906[/C][C]0.877481576513811[/C][C]0.561259211743094[/C][/ROW]
[ROW][C]21[/C][C]0.704306430220637[/C][C]0.591387139558727[/C][C]0.295693569779363[/C][/ROW]
[ROW][C]22[/C][C]0.635870322700154[/C][C]0.728259354599692[/C][C]0.364129677299846[/C][/ROW]
[ROW][C]23[/C][C]0.765067555202112[/C][C]0.469864889595776[/C][C]0.234932444797888[/C][/ROW]
[ROW][C]24[/C][C]0.708147319516[/C][C]0.583705360968[/C][C]0.291852680484[/C][/ROW]
[ROW][C]25[/C][C]0.658441985766152[/C][C]0.683116028467697[/C][C]0.341558014233848[/C][/ROW]
[ROW][C]26[/C][C]0.655488654003096[/C][C]0.689022691993808[/C][C]0.344511345996904[/C][/ROW]
[ROW][C]27[/C][C]0.59021280359449[/C][C]0.819574392811019[/C][C]0.409787196405510[/C][/ROW]
[ROW][C]28[/C][C]0.525868473627253[/C][C]0.948263052745494[/C][C]0.474131526372747[/C][/ROW]
[ROW][C]29[/C][C]0.459544418908209[/C][C]0.919088837816418[/C][C]0.540455581091791[/C][/ROW]
[ROW][C]30[/C][C]0.450558675199423[/C][C]0.901117350398845[/C][C]0.549441324800577[/C][/ROW]
[ROW][C]31[/C][C]0.676682980560075[/C][C]0.64663403887985[/C][C]0.323317019439925[/C][/ROW]
[ROW][C]32[/C][C]0.661030467136266[/C][C]0.677939065727468[/C][C]0.338969532863734[/C][/ROW]
[ROW][C]33[/C][C]0.609720104693853[/C][C]0.780559790612294[/C][C]0.390279895306147[/C][/ROW]
[ROW][C]34[/C][C]0.621515450853456[/C][C]0.756969098293087[/C][C]0.378484549146544[/C][/ROW]
[ROW][C]35[/C][C]0.599584653686105[/C][C]0.800830692627789[/C][C]0.400415346313895[/C][/ROW]
[ROW][C]36[/C][C]0.553430319691868[/C][C]0.893139360616265[/C][C]0.446569680308132[/C][/ROW]
[ROW][C]37[/C][C]0.536903818117344[/C][C]0.926192363765312[/C][C]0.463096181882656[/C][/ROW]
[ROW][C]38[/C][C]0.620537935798791[/C][C]0.758924128402417[/C][C]0.379462064201209[/C][/ROW]
[ROW][C]39[/C][C]0.579285884367846[/C][C]0.841428231264309[/C][C]0.420714115632154[/C][/ROW]
[ROW][C]40[/C][C]0.534518478619203[/C][C]0.930963042761595[/C][C]0.465481521380797[/C][/ROW]
[ROW][C]41[/C][C]0.495485345224264[/C][C]0.990970690448529[/C][C]0.504514654775736[/C][/ROW]
[ROW][C]42[/C][C]0.470305868808528[/C][C]0.940611737617057[/C][C]0.529694131191472[/C][/ROW]
[ROW][C]43[/C][C]0.422990175885572[/C][C]0.845980351771144[/C][C]0.577009824114428[/C][/ROW]
[ROW][C]44[/C][C]0.373970956515633[/C][C]0.747941913031265[/C][C]0.626029043484367[/C][/ROW]
[ROW][C]45[/C][C]0.338213364775215[/C][C]0.67642672955043[/C][C]0.661786635224785[/C][/ROW]
[ROW][C]46[/C][C]0.291335422001395[/C][C]0.58267084400279[/C][C]0.708664577998605[/C][/ROW]
[ROW][C]47[/C][C]0.248636731176697[/C][C]0.497273462353393[/C][C]0.751363268823303[/C][/ROW]
[ROW][C]48[/C][C]0.375048706115342[/C][C]0.750097412230685[/C][C]0.624951293884658[/C][/ROW]
[ROW][C]49[/C][C]0.326640039978192[/C][C]0.653280079956385[/C][C]0.673359960021808[/C][/ROW]
[ROW][C]50[/C][C]0.303678565397221[/C][C]0.607357130794442[/C][C]0.696321434602779[/C][/ROW]
[ROW][C]51[/C][C]0.272631556549256[/C][C]0.545263113098511[/C][C]0.727368443450744[/C][/ROW]
[ROW][C]52[/C][C]0.357333928618386[/C][C]0.714667857236773[/C][C]0.642666071381614[/C][/ROW]
[ROW][C]53[/C][C]0.319472786572987[/C][C]0.638945573145974[/C][C]0.680527213427013[/C][/ROW]
[ROW][C]54[/C][C]0.308208038600901[/C][C]0.616416077201801[/C][C]0.691791961399099[/C][/ROW]
[ROW][C]55[/C][C]0.265455253703486[/C][C]0.530910507406971[/C][C]0.734544746296514[/C][/ROW]
[ROW][C]56[/C][C]0.227757825804347[/C][C]0.455515651608693[/C][C]0.772242174195653[/C][/ROW]
[ROW][C]57[/C][C]0.194566084094292[/C][C]0.389132168188583[/C][C]0.805433915905708[/C][/ROW]
[ROW][C]58[/C][C]0.187452539620762[/C][C]0.374905079241523[/C][C]0.812547460379238[/C][/ROW]
[ROW][C]59[/C][C]0.351029386760414[/C][C]0.702058773520827[/C][C]0.648970613239586[/C][/ROW]
[ROW][C]60[/C][C]0.309488122765953[/C][C]0.618976245531906[/C][C]0.690511877234047[/C][/ROW]
[ROW][C]61[/C][C]0.278782500719443[/C][C]0.557565001438886[/C][C]0.721217499280557[/C][/ROW]
[ROW][C]62[/C][C]0.240995461656116[/C][C]0.481990923312231[/C][C]0.759004538343884[/C][/ROW]
[ROW][C]63[/C][C]0.289989478751717[/C][C]0.579978957503435[/C][C]0.710010521248283[/C][/ROW]
[ROW][C]64[/C][C]0.264049867668584[/C][C]0.528099735337168[/C][C]0.735950132331416[/C][/ROW]
[ROW][C]65[/C][C]0.230096481353080[/C][C]0.460192962706161[/C][C]0.76990351864692[/C][/ROW]
[ROW][C]66[/C][C]0.195913093038669[/C][C]0.391826186077338[/C][C]0.804086906961331[/C][/ROW]
[ROW][C]67[/C][C]0.206209365942371[/C][C]0.412418731884742[/C][C]0.793790634057629[/C][/ROW]
[ROW][C]68[/C][C]0.174927492648356[/C][C]0.349854985296711[/C][C]0.825072507351644[/C][/ROW]
[ROW][C]69[/C][C]0.155260345930577[/C][C]0.310520691861154[/C][C]0.844739654069423[/C][/ROW]
[ROW][C]70[/C][C]0.457804563031556[/C][C]0.915609126063112[/C][C]0.542195436968444[/C][/ROW]
[ROW][C]71[/C][C]0.413007996052974[/C][C]0.826015992105948[/C][C]0.586992003947026[/C][/ROW]
[ROW][C]72[/C][C]0.369188841114309[/C][C]0.738377682228617[/C][C]0.630811158885692[/C][/ROW]
[ROW][C]73[/C][C]0.337579923430674[/C][C]0.675159846861348[/C][C]0.662420076569326[/C][/ROW]
[ROW][C]74[/C][C]0.454855562351489[/C][C]0.909711124702978[/C][C]0.545144437648511[/C][/ROW]
[ROW][C]75[/C][C]0.417679806992468[/C][C]0.835359613984936[/C][C]0.582320193007532[/C][/ROW]
[ROW][C]76[/C][C]0.406443495534585[/C][C]0.812886991069169[/C][C]0.593556504465415[/C][/ROW]
[ROW][C]77[/C][C]0.408662969389943[/C][C]0.817325938779887[/C][C]0.591337030610056[/C][/ROW]
[ROW][C]78[/C][C]0.364266115350939[/C][C]0.728532230701877[/C][C]0.635733884649061[/C][/ROW]
[ROW][C]79[/C][C]0.362155250556527[/C][C]0.724310501113055[/C][C]0.637844749443472[/C][/ROW]
[ROW][C]80[/C][C]0.32676734161317[/C][C]0.65353468322634[/C][C]0.67323265838683[/C][/ROW]
[ROW][C]81[/C][C]0.290287181503088[/C][C]0.580574363006176[/C][C]0.709712818496912[/C][/ROW]
[ROW][C]82[/C][C]0.265828683955170[/C][C]0.531657367910341[/C][C]0.73417131604483[/C][/ROW]
[ROW][C]83[/C][C]0.280004841610118[/C][C]0.560009683220235[/C][C]0.719995158389882[/C][/ROW]
[ROW][C]84[/C][C]0.255415485879630[/C][C]0.510830971759261[/C][C]0.74458451412037[/C][/ROW]
[ROW][C]85[/C][C]0.24182185309562[/C][C]0.48364370619124[/C][C]0.75817814690438[/C][/ROW]
[ROW][C]86[/C][C]0.248067619354358[/C][C]0.496135238708716[/C][C]0.751932380645642[/C][/ROW]
[ROW][C]87[/C][C]0.252003539218687[/C][C]0.504007078437373[/C][C]0.747996460781313[/C][/ROW]
[ROW][C]88[/C][C]0.292876276312184[/C][C]0.585752552624368[/C][C]0.707123723687816[/C][/ROW]
[ROW][C]89[/C][C]0.253943644770861[/C][C]0.507887289541721[/C][C]0.74605635522914[/C][/ROW]
[ROW][C]90[/C][C]0.218910747904128[/C][C]0.437821495808257[/C][C]0.781089252095872[/C][/ROW]
[ROW][C]91[/C][C]0.270881540162896[/C][C]0.541763080325793[/C][C]0.729118459837104[/C][/ROW]
[ROW][C]92[/C][C]0.246080856661528[/C][C]0.492161713323056[/C][C]0.753919143338472[/C][/ROW]
[ROW][C]93[/C][C]0.217393645449175[/C][C]0.434787290898349[/C][C]0.782606354550825[/C][/ROW]
[ROW][C]94[/C][C]0.218248043259423[/C][C]0.436496086518846[/C][C]0.781751956740577[/C][/ROW]
[ROW][C]95[/C][C]0.188399588413989[/C][C]0.376799176827979[/C][C]0.81160041158601[/C][/ROW]
[ROW][C]96[/C][C]0.178838420100661[/C][C]0.357676840201322[/C][C]0.821161579899339[/C][/ROW]
[ROW][C]97[/C][C]0.171755452191583[/C][C]0.343510904383166[/C][C]0.828244547808417[/C][/ROW]
[ROW][C]98[/C][C]0.177418759392209[/C][C]0.354837518784418[/C][C]0.822581240607791[/C][/ROW]
[ROW][C]99[/C][C]0.202207917407672[/C][C]0.404415834815345[/C][C]0.797792082592328[/C][/ROW]
[ROW][C]100[/C][C]0.193037326214067[/C][C]0.386074652428134[/C][C]0.806962673785933[/C][/ROW]
[ROW][C]101[/C][C]0.167110839450987[/C][C]0.334221678901974[/C][C]0.832889160549013[/C][/ROW]
[ROW][C]102[/C][C]0.138362597943410[/C][C]0.276725195886820[/C][C]0.86163740205659[/C][/ROW]
[ROW][C]103[/C][C]0.193130643170219[/C][C]0.386261286340439[/C][C]0.80686935682978[/C][/ROW]
[ROW][C]104[/C][C]0.207888410244812[/C][C]0.415776820489624[/C][C]0.792111589755188[/C][/ROW]
[ROW][C]105[/C][C]0.180859287357751[/C][C]0.361718574715501[/C][C]0.81914071264225[/C][/ROW]
[ROW][C]106[/C][C]0.156186060152569[/C][C]0.312372120305138[/C][C]0.84381393984743[/C][/ROW]
[ROW][C]107[/C][C]0.150461286637543[/C][C]0.300922573275086[/C][C]0.849538713362457[/C][/ROW]
[ROW][C]108[/C][C]0.123218786226758[/C][C]0.246437572453516[/C][C]0.876781213773242[/C][/ROW]
[ROW][C]109[/C][C]0.136145312561381[/C][C]0.272290625122762[/C][C]0.86385468743862[/C][/ROW]
[ROW][C]110[/C][C]0.421800702746304[/C][C]0.843601405492608[/C][C]0.578199297253696[/C][/ROW]
[ROW][C]111[/C][C]0.399484515847186[/C][C]0.798969031694373[/C][C]0.600515484152814[/C][/ROW]
[ROW][C]112[/C][C]0.377673576822669[/C][C]0.755347153645338[/C][C]0.622326423177331[/C][/ROW]
[ROW][C]113[/C][C]0.346021710897982[/C][C]0.692043421795965[/C][C]0.653978289102018[/C][/ROW]
[ROW][C]114[/C][C]0.305211055403091[/C][C]0.610422110806183[/C][C]0.694788944596909[/C][/ROW]
[ROW][C]115[/C][C]0.273119570494567[/C][C]0.546239140989135[/C][C]0.726880429505433[/C][/ROW]
[ROW][C]116[/C][C]0.232718536888901[/C][C]0.465437073777802[/C][C]0.767281463111099[/C][/ROW]
[ROW][C]117[/C][C]0.193661742554469[/C][C]0.387323485108937[/C][C]0.806338257445531[/C][/ROW]
[ROW][C]118[/C][C]0.234161620363707[/C][C]0.468323240727413[/C][C]0.765838379636293[/C][/ROW]
[ROW][C]119[/C][C]0.210982401926002[/C][C]0.421964803852004[/C][C]0.789017598073998[/C][/ROW]
[ROW][C]120[/C][C]0.175912920314809[/C][C]0.351825840629617[/C][C]0.824087079685191[/C][/ROW]
[ROW][C]121[/C][C]0.142143541846721[/C][C]0.284287083693442[/C][C]0.857856458153279[/C][/ROW]
[ROW][C]122[/C][C]0.130407970282829[/C][C]0.260815940565659[/C][C]0.86959202971717[/C][/ROW]
[ROW][C]123[/C][C]0.102118012279423[/C][C]0.204236024558845[/C][C]0.897881987720577[/C][/ROW]
[ROW][C]124[/C][C]0.092057723457829[/C][C]0.184115446915658[/C][C]0.907942276542171[/C][/ROW]
[ROW][C]125[/C][C]0.0748950828149751[/C][C]0.149790165629950[/C][C]0.925104917185025[/C][/ROW]
[ROW][C]126[/C][C]0.080328979723429[/C][C]0.160657959446858[/C][C]0.919671020276571[/C][/ROW]
[ROW][C]127[/C][C]0.0603565250983604[/C][C]0.120713050196721[/C][C]0.93964347490164[/C][/ROW]
[ROW][C]128[/C][C]0.0670544592100745[/C][C]0.134108918420149[/C][C]0.932945540789926[/C][/ROW]
[ROW][C]129[/C][C]0.0930650572418516[/C][C]0.186130114483703[/C][C]0.906934942758148[/C][/ROW]
[ROW][C]130[/C][C]0.354383128786614[/C][C]0.708766257573228[/C][C]0.645616871213386[/C][/ROW]
[ROW][C]131[/C][C]0.30514448313802[/C][C]0.61028896627604[/C][C]0.69485551686198[/C][/ROW]
[ROW][C]132[/C][C]0.24641519766001[/C][C]0.49283039532002[/C][C]0.75358480233999[/C][/ROW]
[ROW][C]133[/C][C]0.468604875560527[/C][C]0.937209751121054[/C][C]0.531395124439473[/C][/ROW]
[ROW][C]134[/C][C]0.441075467932021[/C][C]0.882150935864042[/C][C]0.558924532067979[/C][/ROW]
[ROW][C]135[/C][C]0.372044962022081[/C][C]0.744089924044162[/C][C]0.62795503797792[/C][/ROW]
[ROW][C]136[/C][C]0.330558538171301[/C][C]0.661117076342602[/C][C]0.669441461828699[/C][/ROW]
[ROW][C]137[/C][C]0.266105128118774[/C][C]0.532210256237547[/C][C]0.733894871881226[/C][/ROW]
[ROW][C]138[/C][C]0.200583478555536[/C][C]0.401166957111072[/C][C]0.799416521444464[/C][/ROW]
[ROW][C]139[/C][C]0.149547386887267[/C][C]0.299094773774534[/C][C]0.850452613112733[/C][/ROW]
[ROW][C]140[/C][C]0.107930059693819[/C][C]0.215860119387637[/C][C]0.892069940306181[/C][/ROW]
[ROW][C]141[/C][C]0.0700660512997336[/C][C]0.140132102599467[/C][C]0.929933948700266[/C][/ROW]
[ROW][C]142[/C][C]0.042465611330776[/C][C]0.084931222661552[/C][C]0.957534388669224[/C][/ROW]
[ROW][C]143[/C][C]0.0318707359795203[/C][C]0.0637414719590407[/C][C]0.96812926402048[/C][/ROW]
[ROW][C]144[/C][C]0.718990272693581[/C][C]0.562019454612838[/C][C]0.281009727306419[/C][/ROW]
[ROW][C]145[/C][C]0.577332554994687[/C][C]0.845334890010626[/C][C]0.422667445005313[/C][/ROW]
[ROW][C]146[/C][C]0.427104158357889[/C][C]0.854208316715777[/C][C]0.572895841642111[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103761&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103761&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
100.6266056607596030.7467886784807940.373394339240397
110.4846369116986970.9692738233973940.515363088301303
120.5203468486641110.9593063026717770.479653151335889
130.7000571067524540.5998857864950910.299942893247546
140.6071914207047110.7856171585905780.392808579295289
150.5738229107136070.8523541785727860.426177089286393
160.5394734032700870.9210531934598250.460526596729913
170.565293736271260.8694125274574810.434706263728740
180.5328731639281640.9342536721436720.467126836071836
190.505881639492990.988236721014020.49411836050701
200.4387407882569060.8774815765138110.561259211743094
210.7043064302206370.5913871395587270.295693569779363
220.6358703227001540.7282593545996920.364129677299846
230.7650675552021120.4698648895957760.234932444797888
240.7081473195160.5837053609680.291852680484
250.6584419857661520.6831160284676970.341558014233848
260.6554886540030960.6890226919938080.344511345996904
270.590212803594490.8195743928110190.409787196405510
280.5258684736272530.9482630527454940.474131526372747
290.4595444189082090.9190888378164180.540455581091791
300.4505586751994230.9011173503988450.549441324800577
310.6766829805600750.646634038879850.323317019439925
320.6610304671362660.6779390657274680.338969532863734
330.6097201046938530.7805597906122940.390279895306147
340.6215154508534560.7569690982930870.378484549146544
350.5995846536861050.8008306926277890.400415346313895
360.5534303196918680.8931393606162650.446569680308132
370.5369038181173440.9261923637653120.463096181882656
380.6205379357987910.7589241284024170.379462064201209
390.5792858843678460.8414282312643090.420714115632154
400.5345184786192030.9309630427615950.465481521380797
410.4954853452242640.9909706904485290.504514654775736
420.4703058688085280.9406117376170570.529694131191472
430.4229901758855720.8459803517711440.577009824114428
440.3739709565156330.7479419130312650.626029043484367
450.3382133647752150.676426729550430.661786635224785
460.2913354220013950.582670844002790.708664577998605
470.2486367311766970.4972734623533930.751363268823303
480.3750487061153420.7500974122306850.624951293884658
490.3266400399781920.6532800799563850.673359960021808
500.3036785653972210.6073571307944420.696321434602779
510.2726315565492560.5452631130985110.727368443450744
520.3573339286183860.7146678572367730.642666071381614
530.3194727865729870.6389455731459740.680527213427013
540.3082080386009010.6164160772018010.691791961399099
550.2654552537034860.5309105074069710.734544746296514
560.2277578258043470.4555156516086930.772242174195653
570.1945660840942920.3891321681885830.805433915905708
580.1874525396207620.3749050792415230.812547460379238
590.3510293867604140.7020587735208270.648970613239586
600.3094881227659530.6189762455319060.690511877234047
610.2787825007194430.5575650014388860.721217499280557
620.2409954616561160.4819909233122310.759004538343884
630.2899894787517170.5799789575034350.710010521248283
640.2640498676685840.5280997353371680.735950132331416
650.2300964813530800.4601929627061610.76990351864692
660.1959130930386690.3918261860773380.804086906961331
670.2062093659423710.4124187318847420.793790634057629
680.1749274926483560.3498549852967110.825072507351644
690.1552603459305770.3105206918611540.844739654069423
700.4578045630315560.9156091260631120.542195436968444
710.4130079960529740.8260159921059480.586992003947026
720.3691888411143090.7383776822286170.630811158885692
730.3375799234306740.6751598468613480.662420076569326
740.4548555623514890.9097111247029780.545144437648511
750.4176798069924680.8353596139849360.582320193007532
760.4064434955345850.8128869910691690.593556504465415
770.4086629693899430.8173259387798870.591337030610056
780.3642661153509390.7285322307018770.635733884649061
790.3621552505565270.7243105011130550.637844749443472
800.326767341613170.653534683226340.67323265838683
810.2902871815030880.5805743630061760.709712818496912
820.2658286839551700.5316573679103410.73417131604483
830.2800048416101180.5600096832202350.719995158389882
840.2554154858796300.5108309717592610.74458451412037
850.241821853095620.483643706191240.75817814690438
860.2480676193543580.4961352387087160.751932380645642
870.2520035392186870.5040070784373730.747996460781313
880.2928762763121840.5857525526243680.707123723687816
890.2539436447708610.5078872895417210.74605635522914
900.2189107479041280.4378214958082570.781089252095872
910.2708815401628960.5417630803257930.729118459837104
920.2460808566615280.4921617133230560.753919143338472
930.2173936454491750.4347872908983490.782606354550825
940.2182480432594230.4364960865188460.781751956740577
950.1883995884139890.3767991768279790.81160041158601
960.1788384201006610.3576768402013220.821161579899339
970.1717554521915830.3435109043831660.828244547808417
980.1774187593922090.3548375187844180.822581240607791
990.2022079174076720.4044158348153450.797792082592328
1000.1930373262140670.3860746524281340.806962673785933
1010.1671108394509870.3342216789019740.832889160549013
1020.1383625979434100.2767251958868200.86163740205659
1030.1931306431702190.3862612863404390.80686935682978
1040.2078884102448120.4157768204896240.792111589755188
1050.1808592873577510.3617185747155010.81914071264225
1060.1561860601525690.3123721203051380.84381393984743
1070.1504612866375430.3009225732750860.849538713362457
1080.1232187862267580.2464375724535160.876781213773242
1090.1361453125613810.2722906251227620.86385468743862
1100.4218007027463040.8436014054926080.578199297253696
1110.3994845158471860.7989690316943730.600515484152814
1120.3776735768226690.7553471536453380.622326423177331
1130.3460217108979820.6920434217959650.653978289102018
1140.3052110554030910.6104221108061830.694788944596909
1150.2731195704945670.5462391409891350.726880429505433
1160.2327185368889010.4654370737778020.767281463111099
1170.1936617425544690.3873234851089370.806338257445531
1180.2341616203637070.4683232407274130.765838379636293
1190.2109824019260020.4219648038520040.789017598073998
1200.1759129203148090.3518258406296170.824087079685191
1210.1421435418467210.2842870836934420.857856458153279
1220.1304079702828290.2608159405656590.86959202971717
1230.1021180122794230.2042360245588450.897881987720577
1240.0920577234578290.1841154469156580.907942276542171
1250.07489508281497510.1497901656299500.925104917185025
1260.0803289797234290.1606579594468580.919671020276571
1270.06035652509836040.1207130501967210.93964347490164
1280.06705445921007450.1341089184201490.932945540789926
1290.09306505724185160.1861301144837030.906934942758148
1300.3543831287866140.7087662575732280.645616871213386
1310.305144483138020.610288966276040.69485551686198
1320.246415197660010.492830395320020.75358480233999
1330.4686048755605270.9372097511210540.531395124439473
1340.4410754679320210.8821509358640420.558924532067979
1350.3720449620220810.7440899240441620.62795503797792
1360.3305585381713010.6611170763426020.669441461828699
1370.2661051281187740.5322102562375470.733894871881226
1380.2005834785555360.4011669571110720.799416521444464
1390.1495473868872670.2990947737745340.850452613112733
1400.1079300596938190.2158601193876370.892069940306181
1410.07006605129973360.1401321025994670.929933948700266
1420.0424656113307760.0849312226615520.957534388669224
1430.03187073597952030.06374147195904070.96812926402048
1440.7189902726935810.5620194546128380.281009727306419
1450.5773325549946870.8453348900106260.422667445005313
1460.4271041583578890.8542083167157770.572895841642111






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0145985401459854OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0145985401459854 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103761&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0145985401459854[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103761&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103761&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 level00OK
5% type I error level00OK
10% type I error level20.0145985401459854OK


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')
}