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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, 20 Nov 2012 19:23:34 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/20/t1353457439yz8iq9y5sh6vopf.htm/, Retrieved Mon, 29 Apr 2024 18:31:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191336, Retrieved Mon, 29 Apr 2024 18:31:31 +0000
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Original text written by user:Cijfers uit: Belgostat, (2012)  Indexcijfers van de consumptieprijzen - synthese tabel Geraadpleegd op 18/11/2012, op www.nbb.be/belgostat
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100

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]
- R PD  [Multiple Regression] [conusumptieprijzen] [2012-11-20 22:46:29] [3dc52aaca1c2323e282536a0c7c26bc2]
-    D    [Multiple Regression] [consumptiegoederen3] [2012-11-21 00:14:23] [3dc52aaca1c2323e282536a0c7c26bc2]
-    D        [Multiple Regression] [consumptieprijs-i...] [2012-11-21 00:23:34] [2f047a68beb18e789d06219c4ebd4599] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103,48	101,94	108,42	106,34	100,17
103,93	102,62	108,62	106,35	102,01
103,89	102,71	109,43	106,61	100,30
104,40	103,39	110,25	108,03	99,94
104,79	104,51	110,59	108,50	100,16
104,77	104,09	110,71	108,49	100,18
105,13	104,29	111,33	109,09	99,98
105,26	104,57	111,45	109,21	100,04
104,96	105,39	110,93	107,20	100,05
104,75	105,15	110,58	106,15	100,11
105,01	106,13	110,75	106,25	100,11
105,15	105,46	111,26	106,52	101,03
105,20	106,47	111,08	105,16	100,84
105,77	106,62	111,92	105,68	102,68
105,78	106,52	111,02	107,01	101,27
106,26	108,04	111,21	107,90	100,28
106,13	107,15	110,71	108,12	100,82
106,12	107,32	110,43	108,43	100,87
106,57	107,76	110,73	109,02	101,23
106,44	107,26	111,07	108,39	101,09
106,54	107,89	111,55	108,65	101,22
107,10	109,08	112,47	109,55	101,33
108,10	110,40	114,97	111,69	101,30
108,40	111,03	115,65	110,76	102,39
108,84	112,05	117,44	110,78	101,69
109,62	112,28	120,13	110,76	103,75
110,42	112,80	122,87	112,38	102,99
110,67	114,17	123,67	112,86	100,80
111,66	114,92	125,68	114,74	102,21
112,28	114,65	127,68	116,21	102,45
112,87	115,49	128,41	116,86	102,49
112,18	114,67	127,03	114,51	102,40
112,36	114,71	128,57	114,11	102,99
112,16	115,15	127,54	112,12	103,19
111,49	115,03	126,27	108,90	103,35
111,25	115,07	125,69	106,62	104,44
111,36	116,46	125,80	105,95	103,42
111,74	116,37	124,36	107,03	105,81
111,10	116,20	121,18	107,10	104,25
111,33	116,50	121,08	108,00	103,78
111,25	116,38	119,98	108,24	104,53
111,04	115,44	117,58	109,72	105,01
110,97	114,96	117,29	109,53	104,83
111,31	114,48	119,02	110,64	104,55
111,02	114,30	117,76	110,03	105,16
111,07	114,66	118,06	109,38	105,06
111,36	114,97	118,76	110,62	105,20
111,54	114,79	119,04	110,57	105,87
112,05	116,16	120,34	111,52	105,41
112,52	116,52	120,74	111,47	107,89
112,94	117,14	122,26	112,97	106,06
113,33	117,27	123,41	114,24	105,50
113,78	117,58	124,12	114,97	106,71
113,77	117,21	124,29	114,82	106,34
113,82	117,08	124,02	114,61	106,11
113,89	117,06	124,35	114,68	106,15
114,25	117,55	125,56	114,90	106,61
114,41	117,61	125,99	115,05	106,63
114,55	117,74	126,35	115,67	106,27
115,00	117,87	127,53	117,17	105,59
115,66	118,59	128,42	118,17	107,09
116,33	119,09	130,11	118,61	108,53
116,91	118,93	132,15	120,38	108,01
117,20	119,62	132,91	121,27	106,52
117,59	120,09	133,84	121,55	107,27
117,95	120,38	135,52	121,08	107,58
118,09	120,49	135,29	121,01	107,36
117,99	120,02	135,13	121,15	107,23
118,31	120,17	136,43	121,84	107,54
118,49	120,58	136,29	121,83	107,64
118,96	121,54	137,32	121,86	108,23
119,01	121,52	137,30	121,56	108,42
119,88	121,81	138,38	122,81	109,33
120,59	122,85	139,39	123,24	111,30
120,85	122,97	140,03	124,52	110,52
120,93	122,96	140,05	125,03	109,86
120,89	123,40	139,47	123,56	110,94
120,61	123,23	138,31	122,58	111,35
120,83	123,24	138,50	122,95	111,01
121,36	123,72	139,31	124,73	110,84
121,57	123,99	139,66	125,75	110,79
121,79	125,10	139,63	125,16	110,87

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191336&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191336&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191336&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 time8 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Algemeen_indexcijfer[t] = + 5.24052941401015 + 0.325965307070096Voedingsmiddelen_en_dranken[t] + 0.127262119422997Huisv_wat_elektr_gas_ed[t] + 0.195712621991814Vervoer[t] + 0.3023564700636Recreatie_en_cultuur[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Algemeen_indexcijfer[t] =  +  5.24052941401015 +  0.325965307070096Voedingsmiddelen_en_dranken[t] +  0.127262119422997Huisv_wat_elektr_gas_ed[t] +  0.195712621991814Vervoer[t] +  0.3023564700636Recreatie_en_cultuur[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191336&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Algemeen_indexcijfer[t] =  +  5.24052941401015 +  0.325965307070096Voedingsmiddelen_en_dranken[t] +  0.127262119422997Huisv_wat_elektr_gas_ed[t] +  0.195712621991814Vervoer[t] +  0.3023564700636Recreatie_en_cultuur[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191336&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191336&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
Algemeen_indexcijfer[t] = + 5.24052941401015 + 0.325965307070096Voedingsmiddelen_en_dranken[t] + 0.127262119422997Huisv_wat_elektr_gas_ed[t] + 0.195712621991814Vervoer[t] + 0.3023564700636Recreatie_en_cultuur[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.240529414010151.0105895.18562e-061e-06
Voedingsmiddelen_en_dranken0.3259653070700960.01421522.931400
Huisv_wat_elektr_gas_ed0.1272621194229970.00949713.400100
Vervoer0.1957126219918140.01017419.23700
Recreatie_en_cultuur0.30235647006360.01853916.309300

\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) & 5.24052941401015 & 1.010589 & 5.1856 & 2e-06 & 1e-06 \tabularnewline
Voedingsmiddelen_en_dranken & 0.325965307070096 & 0.014215 & 22.9314 & 0 & 0 \tabularnewline
Huisv_wat_elektr_gas_ed & 0.127262119422997 & 0.009497 & 13.4001 & 0 & 0 \tabularnewline
Vervoer & 0.195712621991814 & 0.010174 & 19.237 & 0 & 0 \tabularnewline
Recreatie_en_cultuur & 0.3023564700636 & 0.018539 & 16.3093 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191336&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]5.24052941401015[/C][C]1.010589[/C][C]5.1856[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Voedingsmiddelen_en_dranken[/C][C]0.325965307070096[/C][C]0.014215[/C][C]22.9314[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Huisv_wat_elektr_gas_ed[/C][C]0.127262119422997[/C][C]0.009497[/C][C]13.4001[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vervoer[/C][C]0.195712621991814[/C][C]0.010174[/C][C]19.237[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Recreatie_en_cultuur[/C][C]0.3023564700636[/C][C]0.018539[/C][C]16.3093[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191336&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191336&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)5.240529414010151.0105895.18562e-061e-06
Voedingsmiddelen_en_dranken0.3259653070700960.01421522.931400
Huisv_wat_elektr_gas_ed0.1272621194229970.00949713.400100
Vervoer0.1957126219918140.01017419.23700
Recreatie_en_cultuur0.30235647006360.01853916.309300






Multiple Linear Regression - Regression Statistics
Multiple R0.999349273275883
R-squared0.998698969997036
Adjusted R-squared0.998631384022856
F-TEST (value)14776.7193136538
F-TEST (DF numerator)4
F-TEST (DF denominator)77
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.197034738835453
Sum Squared Residuals2.98934699971254

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999349273275883 \tabularnewline
R-squared & 0.998698969997036 \tabularnewline
Adjusted R-squared & 0.998631384022856 \tabularnewline
F-TEST (value) & 14776.7193136538 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 77 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.197034738835453 \tabularnewline
Sum Squared Residuals & 2.98934699971254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191336&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999349273275883[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998698969997036[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.998631384022856[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14776.7193136538[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]77[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.197034738835453[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.98934699971254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191336&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191336&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 R0.999349273275883
R-squared0.998698969997036
Adjusted R-squared0.998631384022856
F-TEST (value)14776.7193136538
F-TEST (DF numerator)4
F-TEST (DF denominator)77
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.197034738835453
Sum Squared Residuals2.98934699971254






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1103.48103.3663196334570.11368036654271
2103.93104.171721497287-0.241721497286525
3103.89103.8379964095650.05200359043542
4104.4104.3330713503050.0669286496954208
5104.79104.899924970577-0.109924970577051
6104.77104.78238099912-0.0123809991197342
7105.13104.9834328537580.146567146241618
8105.26105.1316014969120.128398503088412
9104.96104.9423579411060.0176420588937885
10104.75104.6322276607240.117772339276253
11105.01104.9928794841540.0171205158464782
12105.15105.170396769719-0.0203967697185871
13105.2105.1530976531420.0469023468577021
14105.77105.966999097871-0.19699909787091
15105.78105.6538418241430.126158175857373
16106.26106.0483402217890.211659778210708
17106.13105.9009293094580.229070690542041
18106.12105.9964987545420.123501245457929
19106.57106.4024209016780.167579098322109
20106.44106.1170785110830.32292148891709
21106.54106.4737140946860.0662859053137618
22107.1107.188094531468-0.0880945314684471
23108.1108.346278352319-0.246278352319042
24108.4108.785730550898-0.385730550897768
25108.84109.138279081272-0.298279081271744
26109.62110.174526279037-0.554526279036908
27110.42110.779989976311-0.359989976310771
28110.67110.760153531652-0.0901535316519877
29111.66112.054686724129-0.394686724129074
30112.28112.581463437209-0.301463437209371
31112.87113.087483105424-0.217483105424252
32112.18112.1574330848370.0225669151634435
33112.36112.466560629572-0.106560629571573
34112.16112.1499085579260.0100914420742504
35111.49111.3673522218070.122647778193335
36111.25111.1899225790520.0600774209478868
37111.36111.2174821328170.142517867183311
38111.74111.938889398414-0.198889398414437
39111.1111.0208055466880.0791944533123987
40111.33111.1399027457290.190097254270934
41111.25111.2345369593410.01546304065891
42111.04111.057486270258-0.0174862702584169
43110.97110.7725073454420.197492654557786
44111.31110.9687886634430.341211336556543
45111.02110.8148173850220.205182614978342
46111.07110.8128946800930.257105319907244
47111.36111.2880409659590.0719590340406671
48111.54111.4577938079680.0822061920318296
49112.05112.116650048567-0.0666500485670679
50112.52113.02496082154-0.504960821539641
51112.94113.160754326217-0.220754326217389
52113.33113.428716660167-0.0987166601669308
53113.78114.12884355298-0.348843552979966
54113.77113.888642162444-0.118642162443642
55113.82113.7012642615470.118735738452586
56113.89113.7625355971580.12746440284243
57114.25114.258386515191-0.00838651519119547
58114.41114.3680711676670.0419288323326658
59114.55114.4687545169910.081245483009247
60115114.7492658411730.250734158826522
61115.66115.746471475638-0.0864714756376288
62116.33116.646033981566-0.316033981565524
63116.91117.04268023255-0.132680232549663
64117.2117.0879885983670.112011401632554
65117.59117.641112950459-0.0511129504591858
66117.95117.951188823524-0.00118882352371146
67118.09117.8775564128810.212443587119288
68117.99117.6920842054210.297915794579315
69118.31118.1351919716250.174808028374841
70118.49118.2792995715910.210700428408878
71118.96118.9075679453810.0524320546186158
72119.01118.8972373395660.112762660433951
73119.88119.6489955328410.231004467159134
74120.59120.796332866293-0.206332866292751
75120.85120.931570569072-0.0815705690718062
76120.93120.8311143253630.0988856746365965
77120.89120.93957446455-0.0495744645496375
78120.61120.668704086991-0.0587040869911428
79120.83120.6657560130680.164243986932437
80121.36121.2222695444280.137730455571548
81121.57121.5393309700640.0306690299360985
82121.79121.806052667959-0.016052667958921

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.48 & 103.366319633457 & 0.11368036654271 \tabularnewline
2 & 103.93 & 104.171721497287 & -0.241721497286525 \tabularnewline
3 & 103.89 & 103.837996409565 & 0.05200359043542 \tabularnewline
4 & 104.4 & 104.333071350305 & 0.0669286496954208 \tabularnewline
5 & 104.79 & 104.899924970577 & -0.109924970577051 \tabularnewline
6 & 104.77 & 104.78238099912 & -0.0123809991197342 \tabularnewline
7 & 105.13 & 104.983432853758 & 0.146567146241618 \tabularnewline
8 & 105.26 & 105.131601496912 & 0.128398503088412 \tabularnewline
9 & 104.96 & 104.942357941106 & 0.0176420588937885 \tabularnewline
10 & 104.75 & 104.632227660724 & 0.117772339276253 \tabularnewline
11 & 105.01 & 104.992879484154 & 0.0171205158464782 \tabularnewline
12 & 105.15 & 105.170396769719 & -0.0203967697185871 \tabularnewline
13 & 105.2 & 105.153097653142 & 0.0469023468577021 \tabularnewline
14 & 105.77 & 105.966999097871 & -0.19699909787091 \tabularnewline
15 & 105.78 & 105.653841824143 & 0.126158175857373 \tabularnewline
16 & 106.26 & 106.048340221789 & 0.211659778210708 \tabularnewline
17 & 106.13 & 105.900929309458 & 0.229070690542041 \tabularnewline
18 & 106.12 & 105.996498754542 & 0.123501245457929 \tabularnewline
19 & 106.57 & 106.402420901678 & 0.167579098322109 \tabularnewline
20 & 106.44 & 106.117078511083 & 0.32292148891709 \tabularnewline
21 & 106.54 & 106.473714094686 & 0.0662859053137618 \tabularnewline
22 & 107.1 & 107.188094531468 & -0.0880945314684471 \tabularnewline
23 & 108.1 & 108.346278352319 & -0.246278352319042 \tabularnewline
24 & 108.4 & 108.785730550898 & -0.385730550897768 \tabularnewline
25 & 108.84 & 109.138279081272 & -0.298279081271744 \tabularnewline
26 & 109.62 & 110.174526279037 & -0.554526279036908 \tabularnewline
27 & 110.42 & 110.779989976311 & -0.359989976310771 \tabularnewline
28 & 110.67 & 110.760153531652 & -0.0901535316519877 \tabularnewline
29 & 111.66 & 112.054686724129 & -0.394686724129074 \tabularnewline
30 & 112.28 & 112.581463437209 & -0.301463437209371 \tabularnewline
31 & 112.87 & 113.087483105424 & -0.217483105424252 \tabularnewline
32 & 112.18 & 112.157433084837 & 0.0225669151634435 \tabularnewline
33 & 112.36 & 112.466560629572 & -0.106560629571573 \tabularnewline
34 & 112.16 & 112.149908557926 & 0.0100914420742504 \tabularnewline
35 & 111.49 & 111.367352221807 & 0.122647778193335 \tabularnewline
36 & 111.25 & 111.189922579052 & 0.0600774209478868 \tabularnewline
37 & 111.36 & 111.217482132817 & 0.142517867183311 \tabularnewline
38 & 111.74 & 111.938889398414 & -0.198889398414437 \tabularnewline
39 & 111.1 & 111.020805546688 & 0.0791944533123987 \tabularnewline
40 & 111.33 & 111.139902745729 & 0.190097254270934 \tabularnewline
41 & 111.25 & 111.234536959341 & 0.01546304065891 \tabularnewline
42 & 111.04 & 111.057486270258 & -0.0174862702584169 \tabularnewline
43 & 110.97 & 110.772507345442 & 0.197492654557786 \tabularnewline
44 & 111.31 & 110.968788663443 & 0.341211336556543 \tabularnewline
45 & 111.02 & 110.814817385022 & 0.205182614978342 \tabularnewline
46 & 111.07 & 110.812894680093 & 0.257105319907244 \tabularnewline
47 & 111.36 & 111.288040965959 & 0.0719590340406671 \tabularnewline
48 & 111.54 & 111.457793807968 & 0.0822061920318296 \tabularnewline
49 & 112.05 & 112.116650048567 & -0.0666500485670679 \tabularnewline
50 & 112.52 & 113.02496082154 & -0.504960821539641 \tabularnewline
51 & 112.94 & 113.160754326217 & -0.220754326217389 \tabularnewline
52 & 113.33 & 113.428716660167 & -0.0987166601669308 \tabularnewline
53 & 113.78 & 114.12884355298 & -0.348843552979966 \tabularnewline
54 & 113.77 & 113.888642162444 & -0.118642162443642 \tabularnewline
55 & 113.82 & 113.701264261547 & 0.118735738452586 \tabularnewline
56 & 113.89 & 113.762535597158 & 0.12746440284243 \tabularnewline
57 & 114.25 & 114.258386515191 & -0.00838651519119547 \tabularnewline
58 & 114.41 & 114.368071167667 & 0.0419288323326658 \tabularnewline
59 & 114.55 & 114.468754516991 & 0.081245483009247 \tabularnewline
60 & 115 & 114.749265841173 & 0.250734158826522 \tabularnewline
61 & 115.66 & 115.746471475638 & -0.0864714756376288 \tabularnewline
62 & 116.33 & 116.646033981566 & -0.316033981565524 \tabularnewline
63 & 116.91 & 117.04268023255 & -0.132680232549663 \tabularnewline
64 & 117.2 & 117.087988598367 & 0.112011401632554 \tabularnewline
65 & 117.59 & 117.641112950459 & -0.0511129504591858 \tabularnewline
66 & 117.95 & 117.951188823524 & -0.00118882352371146 \tabularnewline
67 & 118.09 & 117.877556412881 & 0.212443587119288 \tabularnewline
68 & 117.99 & 117.692084205421 & 0.297915794579315 \tabularnewline
69 & 118.31 & 118.135191971625 & 0.174808028374841 \tabularnewline
70 & 118.49 & 118.279299571591 & 0.210700428408878 \tabularnewline
71 & 118.96 & 118.907567945381 & 0.0524320546186158 \tabularnewline
72 & 119.01 & 118.897237339566 & 0.112762660433951 \tabularnewline
73 & 119.88 & 119.648995532841 & 0.231004467159134 \tabularnewline
74 & 120.59 & 120.796332866293 & -0.206332866292751 \tabularnewline
75 & 120.85 & 120.931570569072 & -0.0815705690718062 \tabularnewline
76 & 120.93 & 120.831114325363 & 0.0988856746365965 \tabularnewline
77 & 120.89 & 120.93957446455 & -0.0495744645496375 \tabularnewline
78 & 120.61 & 120.668704086991 & -0.0587040869911428 \tabularnewline
79 & 120.83 & 120.665756013068 & 0.164243986932437 \tabularnewline
80 & 121.36 & 121.222269544428 & 0.137730455571548 \tabularnewline
81 & 121.57 & 121.539330970064 & 0.0306690299360985 \tabularnewline
82 & 121.79 & 121.806052667959 & -0.016052667958921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191336&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]103.48[/C][C]103.366319633457[/C][C]0.11368036654271[/C][/ROW]
[ROW][C]2[/C][C]103.93[/C][C]104.171721497287[/C][C]-0.241721497286525[/C][/ROW]
[ROW][C]3[/C][C]103.89[/C][C]103.837996409565[/C][C]0.05200359043542[/C][/ROW]
[ROW][C]4[/C][C]104.4[/C][C]104.333071350305[/C][C]0.0669286496954208[/C][/ROW]
[ROW][C]5[/C][C]104.79[/C][C]104.899924970577[/C][C]-0.109924970577051[/C][/ROW]
[ROW][C]6[/C][C]104.77[/C][C]104.78238099912[/C][C]-0.0123809991197342[/C][/ROW]
[ROW][C]7[/C][C]105.13[/C][C]104.983432853758[/C][C]0.146567146241618[/C][/ROW]
[ROW][C]8[/C][C]105.26[/C][C]105.131601496912[/C][C]0.128398503088412[/C][/ROW]
[ROW][C]9[/C][C]104.96[/C][C]104.942357941106[/C][C]0.0176420588937885[/C][/ROW]
[ROW][C]10[/C][C]104.75[/C][C]104.632227660724[/C][C]0.117772339276253[/C][/ROW]
[ROW][C]11[/C][C]105.01[/C][C]104.992879484154[/C][C]0.0171205158464782[/C][/ROW]
[ROW][C]12[/C][C]105.15[/C][C]105.170396769719[/C][C]-0.0203967697185871[/C][/ROW]
[ROW][C]13[/C][C]105.2[/C][C]105.153097653142[/C][C]0.0469023468577021[/C][/ROW]
[ROW][C]14[/C][C]105.77[/C][C]105.966999097871[/C][C]-0.19699909787091[/C][/ROW]
[ROW][C]15[/C][C]105.78[/C][C]105.653841824143[/C][C]0.126158175857373[/C][/ROW]
[ROW][C]16[/C][C]106.26[/C][C]106.048340221789[/C][C]0.211659778210708[/C][/ROW]
[ROW][C]17[/C][C]106.13[/C][C]105.900929309458[/C][C]0.229070690542041[/C][/ROW]
[ROW][C]18[/C][C]106.12[/C][C]105.996498754542[/C][C]0.123501245457929[/C][/ROW]
[ROW][C]19[/C][C]106.57[/C][C]106.402420901678[/C][C]0.167579098322109[/C][/ROW]
[ROW][C]20[/C][C]106.44[/C][C]106.117078511083[/C][C]0.32292148891709[/C][/ROW]
[ROW][C]21[/C][C]106.54[/C][C]106.473714094686[/C][C]0.0662859053137618[/C][/ROW]
[ROW][C]22[/C][C]107.1[/C][C]107.188094531468[/C][C]-0.0880945314684471[/C][/ROW]
[ROW][C]23[/C][C]108.1[/C][C]108.346278352319[/C][C]-0.246278352319042[/C][/ROW]
[ROW][C]24[/C][C]108.4[/C][C]108.785730550898[/C][C]-0.385730550897768[/C][/ROW]
[ROW][C]25[/C][C]108.84[/C][C]109.138279081272[/C][C]-0.298279081271744[/C][/ROW]
[ROW][C]26[/C][C]109.62[/C][C]110.174526279037[/C][C]-0.554526279036908[/C][/ROW]
[ROW][C]27[/C][C]110.42[/C][C]110.779989976311[/C][C]-0.359989976310771[/C][/ROW]
[ROW][C]28[/C][C]110.67[/C][C]110.760153531652[/C][C]-0.0901535316519877[/C][/ROW]
[ROW][C]29[/C][C]111.66[/C][C]112.054686724129[/C][C]-0.394686724129074[/C][/ROW]
[ROW][C]30[/C][C]112.28[/C][C]112.581463437209[/C][C]-0.301463437209371[/C][/ROW]
[ROW][C]31[/C][C]112.87[/C][C]113.087483105424[/C][C]-0.217483105424252[/C][/ROW]
[ROW][C]32[/C][C]112.18[/C][C]112.157433084837[/C][C]0.0225669151634435[/C][/ROW]
[ROW][C]33[/C][C]112.36[/C][C]112.466560629572[/C][C]-0.106560629571573[/C][/ROW]
[ROW][C]34[/C][C]112.16[/C][C]112.149908557926[/C][C]0.0100914420742504[/C][/ROW]
[ROW][C]35[/C][C]111.49[/C][C]111.367352221807[/C][C]0.122647778193335[/C][/ROW]
[ROW][C]36[/C][C]111.25[/C][C]111.189922579052[/C][C]0.0600774209478868[/C][/ROW]
[ROW][C]37[/C][C]111.36[/C][C]111.217482132817[/C][C]0.142517867183311[/C][/ROW]
[ROW][C]38[/C][C]111.74[/C][C]111.938889398414[/C][C]-0.198889398414437[/C][/ROW]
[ROW][C]39[/C][C]111.1[/C][C]111.020805546688[/C][C]0.0791944533123987[/C][/ROW]
[ROW][C]40[/C][C]111.33[/C][C]111.139902745729[/C][C]0.190097254270934[/C][/ROW]
[ROW][C]41[/C][C]111.25[/C][C]111.234536959341[/C][C]0.01546304065891[/C][/ROW]
[ROW][C]42[/C][C]111.04[/C][C]111.057486270258[/C][C]-0.0174862702584169[/C][/ROW]
[ROW][C]43[/C][C]110.97[/C][C]110.772507345442[/C][C]0.197492654557786[/C][/ROW]
[ROW][C]44[/C][C]111.31[/C][C]110.968788663443[/C][C]0.341211336556543[/C][/ROW]
[ROW][C]45[/C][C]111.02[/C][C]110.814817385022[/C][C]0.205182614978342[/C][/ROW]
[ROW][C]46[/C][C]111.07[/C][C]110.812894680093[/C][C]0.257105319907244[/C][/ROW]
[ROW][C]47[/C][C]111.36[/C][C]111.288040965959[/C][C]0.0719590340406671[/C][/ROW]
[ROW][C]48[/C][C]111.54[/C][C]111.457793807968[/C][C]0.0822061920318296[/C][/ROW]
[ROW][C]49[/C][C]112.05[/C][C]112.116650048567[/C][C]-0.0666500485670679[/C][/ROW]
[ROW][C]50[/C][C]112.52[/C][C]113.02496082154[/C][C]-0.504960821539641[/C][/ROW]
[ROW][C]51[/C][C]112.94[/C][C]113.160754326217[/C][C]-0.220754326217389[/C][/ROW]
[ROW][C]52[/C][C]113.33[/C][C]113.428716660167[/C][C]-0.0987166601669308[/C][/ROW]
[ROW][C]53[/C][C]113.78[/C][C]114.12884355298[/C][C]-0.348843552979966[/C][/ROW]
[ROW][C]54[/C][C]113.77[/C][C]113.888642162444[/C][C]-0.118642162443642[/C][/ROW]
[ROW][C]55[/C][C]113.82[/C][C]113.701264261547[/C][C]0.118735738452586[/C][/ROW]
[ROW][C]56[/C][C]113.89[/C][C]113.762535597158[/C][C]0.12746440284243[/C][/ROW]
[ROW][C]57[/C][C]114.25[/C][C]114.258386515191[/C][C]-0.00838651519119547[/C][/ROW]
[ROW][C]58[/C][C]114.41[/C][C]114.368071167667[/C][C]0.0419288323326658[/C][/ROW]
[ROW][C]59[/C][C]114.55[/C][C]114.468754516991[/C][C]0.081245483009247[/C][/ROW]
[ROW][C]60[/C][C]115[/C][C]114.749265841173[/C][C]0.250734158826522[/C][/ROW]
[ROW][C]61[/C][C]115.66[/C][C]115.746471475638[/C][C]-0.0864714756376288[/C][/ROW]
[ROW][C]62[/C][C]116.33[/C][C]116.646033981566[/C][C]-0.316033981565524[/C][/ROW]
[ROW][C]63[/C][C]116.91[/C][C]117.04268023255[/C][C]-0.132680232549663[/C][/ROW]
[ROW][C]64[/C][C]117.2[/C][C]117.087988598367[/C][C]0.112011401632554[/C][/ROW]
[ROW][C]65[/C][C]117.59[/C][C]117.641112950459[/C][C]-0.0511129504591858[/C][/ROW]
[ROW][C]66[/C][C]117.95[/C][C]117.951188823524[/C][C]-0.00118882352371146[/C][/ROW]
[ROW][C]67[/C][C]118.09[/C][C]117.877556412881[/C][C]0.212443587119288[/C][/ROW]
[ROW][C]68[/C][C]117.99[/C][C]117.692084205421[/C][C]0.297915794579315[/C][/ROW]
[ROW][C]69[/C][C]118.31[/C][C]118.135191971625[/C][C]0.174808028374841[/C][/ROW]
[ROW][C]70[/C][C]118.49[/C][C]118.279299571591[/C][C]0.210700428408878[/C][/ROW]
[ROW][C]71[/C][C]118.96[/C][C]118.907567945381[/C][C]0.0524320546186158[/C][/ROW]
[ROW][C]72[/C][C]119.01[/C][C]118.897237339566[/C][C]0.112762660433951[/C][/ROW]
[ROW][C]73[/C][C]119.88[/C][C]119.648995532841[/C][C]0.231004467159134[/C][/ROW]
[ROW][C]74[/C][C]120.59[/C][C]120.796332866293[/C][C]-0.206332866292751[/C][/ROW]
[ROW][C]75[/C][C]120.85[/C][C]120.931570569072[/C][C]-0.0815705690718062[/C][/ROW]
[ROW][C]76[/C][C]120.93[/C][C]120.831114325363[/C][C]0.0988856746365965[/C][/ROW]
[ROW][C]77[/C][C]120.89[/C][C]120.93957446455[/C][C]-0.0495744645496375[/C][/ROW]
[ROW][C]78[/C][C]120.61[/C][C]120.668704086991[/C][C]-0.0587040869911428[/C][/ROW]
[ROW][C]79[/C][C]120.83[/C][C]120.665756013068[/C][C]0.164243986932437[/C][/ROW]
[ROW][C]80[/C][C]121.36[/C][C]121.222269544428[/C][C]0.137730455571548[/C][/ROW]
[ROW][C]81[/C][C]121.57[/C][C]121.539330970064[/C][C]0.0306690299360985[/C][/ROW]
[ROW][C]82[/C][C]121.79[/C][C]121.806052667959[/C][C]-0.016052667958921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191336&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191336&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
1103.48103.3663196334570.11368036654271
2103.93104.171721497287-0.241721497286525
3103.89103.8379964095650.05200359043542
4104.4104.3330713503050.0669286496954208
5104.79104.899924970577-0.109924970577051
6104.77104.78238099912-0.0123809991197342
7105.13104.9834328537580.146567146241618
8105.26105.1316014969120.128398503088412
9104.96104.9423579411060.0176420588937885
10104.75104.6322276607240.117772339276253
11105.01104.9928794841540.0171205158464782
12105.15105.170396769719-0.0203967697185871
13105.2105.1530976531420.0469023468577021
14105.77105.966999097871-0.19699909787091
15105.78105.6538418241430.126158175857373
16106.26106.0483402217890.211659778210708
17106.13105.9009293094580.229070690542041
18106.12105.9964987545420.123501245457929
19106.57106.4024209016780.167579098322109
20106.44106.1170785110830.32292148891709
21106.54106.4737140946860.0662859053137618
22107.1107.188094531468-0.0880945314684471
23108.1108.346278352319-0.246278352319042
24108.4108.785730550898-0.385730550897768
25108.84109.138279081272-0.298279081271744
26109.62110.174526279037-0.554526279036908
27110.42110.779989976311-0.359989976310771
28110.67110.760153531652-0.0901535316519877
29111.66112.054686724129-0.394686724129074
30112.28112.581463437209-0.301463437209371
31112.87113.087483105424-0.217483105424252
32112.18112.1574330848370.0225669151634435
33112.36112.466560629572-0.106560629571573
34112.16112.1499085579260.0100914420742504
35111.49111.3673522218070.122647778193335
36111.25111.1899225790520.0600774209478868
37111.36111.2174821328170.142517867183311
38111.74111.938889398414-0.198889398414437
39111.1111.0208055466880.0791944533123987
40111.33111.1399027457290.190097254270934
41111.25111.2345369593410.01546304065891
42111.04111.057486270258-0.0174862702584169
43110.97110.7725073454420.197492654557786
44111.31110.9687886634430.341211336556543
45111.02110.8148173850220.205182614978342
46111.07110.8128946800930.257105319907244
47111.36111.2880409659590.0719590340406671
48111.54111.4577938079680.0822061920318296
49112.05112.116650048567-0.0666500485670679
50112.52113.02496082154-0.504960821539641
51112.94113.160754326217-0.220754326217389
52113.33113.428716660167-0.0987166601669308
53113.78114.12884355298-0.348843552979966
54113.77113.888642162444-0.118642162443642
55113.82113.7012642615470.118735738452586
56113.89113.7625355971580.12746440284243
57114.25114.258386515191-0.00838651519119547
58114.41114.3680711676670.0419288323326658
59114.55114.4687545169910.081245483009247
60115114.7492658411730.250734158826522
61115.66115.746471475638-0.0864714756376288
62116.33116.646033981566-0.316033981565524
63116.91117.04268023255-0.132680232549663
64117.2117.0879885983670.112011401632554
65117.59117.641112950459-0.0511129504591858
66117.95117.951188823524-0.00118882352371146
67118.09117.8775564128810.212443587119288
68117.99117.6920842054210.297915794579315
69118.31118.1351919716250.174808028374841
70118.49118.2792995715910.210700428408878
71118.96118.9075679453810.0524320546186158
72119.01118.8972373395660.112762660433951
73119.88119.6489955328410.231004467159134
74120.59120.796332866293-0.206332866292751
75120.85120.931570569072-0.0815705690718062
76120.93120.8311143253630.0988856746365965
77120.89120.93957446455-0.0495744645496375
78120.61120.668704086991-0.0587040869911428
79120.83120.6657560130680.164243986932437
80121.36121.2222695444280.137730455571548
81121.57121.5393309700640.0306690299360985
82121.79121.806052667959-0.016052667958921






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.02479312731216630.04958625462433260.975206872687834
90.02089388873273940.04178777746547870.979106111267261
100.007716775292984050.01543355058596810.992283224707016
110.00246435939324740.004928718786494790.997535640606753
120.0008076897002446750.001615379400489350.999192310299755
130.0002542800957380880.0005085601914761770.999745719904262
146.80516983209037e-050.0001361033966418070.999931948301679
150.004697485828869920.009394971657739840.99530251417113
160.003103358563556920.006206717127113830.996896641436443
170.002694960779968430.005389921559936870.997305039220032
180.001344464994845860.002688929989691710.998655535005154
190.0007292808961296290.001458561792259260.99927071910387
200.004060237134116330.008120474268232670.995939762865884
210.003814125568036570.007628251136073140.996185874431963
220.009392657140193110.01878531428038620.990607342859807
230.01189138468177450.0237827693635490.988108615318225
240.007756031189386270.01551206237877250.992243968810614
250.004975048213228940.009950096426457880.995024951786771
260.009885647147253980.0197712942945080.990114352852746
270.0311053932696660.06221078653933210.968894606730334
280.02682383311888310.05364766623776620.973176166881117
290.03468036657449490.06936073314898980.965319633425505
300.06522717605845160.1304543521169030.934772823941548
310.1339477684758850.2678955369517710.866052231524115
320.2896709165008930.5793418330017860.710329083499107
330.4202723404210760.8405446808421520.579727659578924
340.5395503076664540.9208993846670930.460449692333546
350.5510479887641650.8979040224716690.448952011235835
360.5036392869831840.9927214260336320.496360713016816
370.4412552736368090.8825105472736190.558744726363191
380.4539839489672460.9079678979344910.546016051032754
390.4351141986148250.8702283972296490.564885801385175
400.4318768226190750.8637536452381510.568123177380925
410.4518663229431260.9037326458862520.548133677056874
420.4581629228734010.9163258457468020.541837077126599
430.5801220677444580.8397558645110830.419877932255542
440.8191396870626320.3617206258747360.180860312937368
450.895572216086990.2088555678260210.10442778391301
460.9486024112177450.102795177564510.0513975887822552
470.9493482797421330.1013034405157350.0506517202578675
480.9786203169895220.04275936602095510.0213796830104775
490.9682821550221870.06343568995562520.0317178449778126
500.9767833251079040.04643334978419210.023216674892096
510.9761073438168070.04778531236638640.0238926561831932
520.9741832451439490.05163350971210160.0258167548560508
530.99032876840910.01934246318179930.00967123159089966
540.9889407971001140.02211840579977290.0110592028998865
550.990308288752990.01938342249401920.00969171124700962
560.9923683773646980.01526324527060450.00763162263530224
570.9883873500460550.0232252999078890.0116126499539445
580.9842365188669430.03152696226611450.0157634811330573
590.9800939081321940.03981218373561170.0199060918678058
600.9921972699622950.01560546007540950.00780273003770476
610.988252625824120.02349474835175940.0117473741758797
620.9817455801223050.03650883975538910.0182544198776946
630.9710709016366980.05785819672660370.0289290983633019
640.9596500662748170.08069986745036650.0403499337251833
650.9790144052100530.04197118957989490.0209855947899474
660.9903878238643250.01922435227134920.0096121761356746
670.9850846242078390.02983075158432290.0149153757921614
680.9757175533155150.04856489336897020.0242824466844851
690.9599025997375790.0801948005248420.040097400262421
700.9411848074483110.1176303851033780.058815192551689
710.9348411731108810.1303176537782370.0651588268891187
720.9688992211210540.06220155775789170.0311007788789459
730.9665496475968810.06690070480623850.0334503524031192
740.9056359450458230.1887281099083540.0943640549541768

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0247931273121663 & 0.0495862546243326 & 0.975206872687834 \tabularnewline
9 & 0.0208938887327394 & 0.0417877774654787 & 0.979106111267261 \tabularnewline
10 & 0.00771677529298405 & 0.0154335505859681 & 0.992283224707016 \tabularnewline
11 & 0.0024643593932474 & 0.00492871878649479 & 0.997535640606753 \tabularnewline
12 & 0.000807689700244675 & 0.00161537940048935 & 0.999192310299755 \tabularnewline
13 & 0.000254280095738088 & 0.000508560191476177 & 0.999745719904262 \tabularnewline
14 & 6.80516983209037e-05 & 0.000136103396641807 & 0.999931948301679 \tabularnewline
15 & 0.00469748582886992 & 0.00939497165773984 & 0.99530251417113 \tabularnewline
16 & 0.00310335856355692 & 0.00620671712711383 & 0.996896641436443 \tabularnewline
17 & 0.00269496077996843 & 0.00538992155993687 & 0.997305039220032 \tabularnewline
18 & 0.00134446499484586 & 0.00268892998969171 & 0.998655535005154 \tabularnewline
19 & 0.000729280896129629 & 0.00145856179225926 & 0.99927071910387 \tabularnewline
20 & 0.00406023713411633 & 0.00812047426823267 & 0.995939762865884 \tabularnewline
21 & 0.00381412556803657 & 0.00762825113607314 & 0.996185874431963 \tabularnewline
22 & 0.00939265714019311 & 0.0187853142803862 & 0.990607342859807 \tabularnewline
23 & 0.0118913846817745 & 0.023782769363549 & 0.988108615318225 \tabularnewline
24 & 0.00775603118938627 & 0.0155120623787725 & 0.992243968810614 \tabularnewline
25 & 0.00497504821322894 & 0.00995009642645788 & 0.995024951786771 \tabularnewline
26 & 0.00988564714725398 & 0.019771294294508 & 0.990114352852746 \tabularnewline
27 & 0.031105393269666 & 0.0622107865393321 & 0.968894606730334 \tabularnewline
28 & 0.0268238331188831 & 0.0536476662377662 & 0.973176166881117 \tabularnewline
29 & 0.0346803665744949 & 0.0693607331489898 & 0.965319633425505 \tabularnewline
30 & 0.0652271760584516 & 0.130454352116903 & 0.934772823941548 \tabularnewline
31 & 0.133947768475885 & 0.267895536951771 & 0.866052231524115 \tabularnewline
32 & 0.289670916500893 & 0.579341833001786 & 0.710329083499107 \tabularnewline
33 & 0.420272340421076 & 0.840544680842152 & 0.579727659578924 \tabularnewline
34 & 0.539550307666454 & 0.920899384667093 & 0.460449692333546 \tabularnewline
35 & 0.551047988764165 & 0.897904022471669 & 0.448952011235835 \tabularnewline
36 & 0.503639286983184 & 0.992721426033632 & 0.496360713016816 \tabularnewline
37 & 0.441255273636809 & 0.882510547273619 & 0.558744726363191 \tabularnewline
38 & 0.453983948967246 & 0.907967897934491 & 0.546016051032754 \tabularnewline
39 & 0.435114198614825 & 0.870228397229649 & 0.564885801385175 \tabularnewline
40 & 0.431876822619075 & 0.863753645238151 & 0.568123177380925 \tabularnewline
41 & 0.451866322943126 & 0.903732645886252 & 0.548133677056874 \tabularnewline
42 & 0.458162922873401 & 0.916325845746802 & 0.541837077126599 \tabularnewline
43 & 0.580122067744458 & 0.839755864511083 & 0.419877932255542 \tabularnewline
44 & 0.819139687062632 & 0.361720625874736 & 0.180860312937368 \tabularnewline
45 & 0.89557221608699 & 0.208855567826021 & 0.10442778391301 \tabularnewline
46 & 0.948602411217745 & 0.10279517756451 & 0.0513975887822552 \tabularnewline
47 & 0.949348279742133 & 0.101303440515735 & 0.0506517202578675 \tabularnewline
48 & 0.978620316989522 & 0.0427593660209551 & 0.0213796830104775 \tabularnewline
49 & 0.968282155022187 & 0.0634356899556252 & 0.0317178449778126 \tabularnewline
50 & 0.976783325107904 & 0.0464333497841921 & 0.023216674892096 \tabularnewline
51 & 0.976107343816807 & 0.0477853123663864 & 0.0238926561831932 \tabularnewline
52 & 0.974183245143949 & 0.0516335097121016 & 0.0258167548560508 \tabularnewline
53 & 0.9903287684091 & 0.0193424631817993 & 0.00967123159089966 \tabularnewline
54 & 0.988940797100114 & 0.0221184057997729 & 0.0110592028998865 \tabularnewline
55 & 0.99030828875299 & 0.0193834224940192 & 0.00969171124700962 \tabularnewline
56 & 0.992368377364698 & 0.0152632452706045 & 0.00763162263530224 \tabularnewline
57 & 0.988387350046055 & 0.023225299907889 & 0.0116126499539445 \tabularnewline
58 & 0.984236518866943 & 0.0315269622661145 & 0.0157634811330573 \tabularnewline
59 & 0.980093908132194 & 0.0398121837356117 & 0.0199060918678058 \tabularnewline
60 & 0.992197269962295 & 0.0156054600754095 & 0.00780273003770476 \tabularnewline
61 & 0.98825262582412 & 0.0234947483517594 & 0.0117473741758797 \tabularnewline
62 & 0.981745580122305 & 0.0365088397553891 & 0.0182544198776946 \tabularnewline
63 & 0.971070901636698 & 0.0578581967266037 & 0.0289290983633019 \tabularnewline
64 & 0.959650066274817 & 0.0806998674503665 & 0.0403499337251833 \tabularnewline
65 & 0.979014405210053 & 0.0419711895798949 & 0.0209855947899474 \tabularnewline
66 & 0.990387823864325 & 0.0192243522713492 & 0.0096121761356746 \tabularnewline
67 & 0.985084624207839 & 0.0298307515843229 & 0.0149153757921614 \tabularnewline
68 & 0.975717553315515 & 0.0485648933689702 & 0.0242824466844851 \tabularnewline
69 & 0.959902599737579 & 0.080194800524842 & 0.040097400262421 \tabularnewline
70 & 0.941184807448311 & 0.117630385103378 & 0.058815192551689 \tabularnewline
71 & 0.934841173110881 & 0.130317653778237 & 0.0651588268891187 \tabularnewline
72 & 0.968899221121054 & 0.0622015577578917 & 0.0311007788789459 \tabularnewline
73 & 0.966549647596881 & 0.0669007048062385 & 0.0334503524031192 \tabularnewline
74 & 0.905635945045823 & 0.188728109908354 & 0.0943640549541768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191336&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.0247931273121663[/C][C]0.0495862546243326[/C][C]0.975206872687834[/C][/ROW]
[ROW][C]9[/C][C]0.0208938887327394[/C][C]0.0417877774654787[/C][C]0.979106111267261[/C][/ROW]
[ROW][C]10[/C][C]0.00771677529298405[/C][C]0.0154335505859681[/C][C]0.992283224707016[/C][/ROW]
[ROW][C]11[/C][C]0.0024643593932474[/C][C]0.00492871878649479[/C][C]0.997535640606753[/C][/ROW]
[ROW][C]12[/C][C]0.000807689700244675[/C][C]0.00161537940048935[/C][C]0.999192310299755[/C][/ROW]
[ROW][C]13[/C][C]0.000254280095738088[/C][C]0.000508560191476177[/C][C]0.999745719904262[/C][/ROW]
[ROW][C]14[/C][C]6.80516983209037e-05[/C][C]0.000136103396641807[/C][C]0.999931948301679[/C][/ROW]
[ROW][C]15[/C][C]0.00469748582886992[/C][C]0.00939497165773984[/C][C]0.99530251417113[/C][/ROW]
[ROW][C]16[/C][C]0.00310335856355692[/C][C]0.00620671712711383[/C][C]0.996896641436443[/C][/ROW]
[ROW][C]17[/C][C]0.00269496077996843[/C][C]0.00538992155993687[/C][C]0.997305039220032[/C][/ROW]
[ROW][C]18[/C][C]0.00134446499484586[/C][C]0.00268892998969171[/C][C]0.998655535005154[/C][/ROW]
[ROW][C]19[/C][C]0.000729280896129629[/C][C]0.00145856179225926[/C][C]0.99927071910387[/C][/ROW]
[ROW][C]20[/C][C]0.00406023713411633[/C][C]0.00812047426823267[/C][C]0.995939762865884[/C][/ROW]
[ROW][C]21[/C][C]0.00381412556803657[/C][C]0.00762825113607314[/C][C]0.996185874431963[/C][/ROW]
[ROW][C]22[/C][C]0.00939265714019311[/C][C]0.0187853142803862[/C][C]0.990607342859807[/C][/ROW]
[ROW][C]23[/C][C]0.0118913846817745[/C][C]0.023782769363549[/C][C]0.988108615318225[/C][/ROW]
[ROW][C]24[/C][C]0.00775603118938627[/C][C]0.0155120623787725[/C][C]0.992243968810614[/C][/ROW]
[ROW][C]25[/C][C]0.00497504821322894[/C][C]0.00995009642645788[/C][C]0.995024951786771[/C][/ROW]
[ROW][C]26[/C][C]0.00988564714725398[/C][C]0.019771294294508[/C][C]0.990114352852746[/C][/ROW]
[ROW][C]27[/C][C]0.031105393269666[/C][C]0.0622107865393321[/C][C]0.968894606730334[/C][/ROW]
[ROW][C]28[/C][C]0.0268238331188831[/C][C]0.0536476662377662[/C][C]0.973176166881117[/C][/ROW]
[ROW][C]29[/C][C]0.0346803665744949[/C][C]0.0693607331489898[/C][C]0.965319633425505[/C][/ROW]
[ROW][C]30[/C][C]0.0652271760584516[/C][C]0.130454352116903[/C][C]0.934772823941548[/C][/ROW]
[ROW][C]31[/C][C]0.133947768475885[/C][C]0.267895536951771[/C][C]0.866052231524115[/C][/ROW]
[ROW][C]32[/C][C]0.289670916500893[/C][C]0.579341833001786[/C][C]0.710329083499107[/C][/ROW]
[ROW][C]33[/C][C]0.420272340421076[/C][C]0.840544680842152[/C][C]0.579727659578924[/C][/ROW]
[ROW][C]34[/C][C]0.539550307666454[/C][C]0.920899384667093[/C][C]0.460449692333546[/C][/ROW]
[ROW][C]35[/C][C]0.551047988764165[/C][C]0.897904022471669[/C][C]0.448952011235835[/C][/ROW]
[ROW][C]36[/C][C]0.503639286983184[/C][C]0.992721426033632[/C][C]0.496360713016816[/C][/ROW]
[ROW][C]37[/C][C]0.441255273636809[/C][C]0.882510547273619[/C][C]0.558744726363191[/C][/ROW]
[ROW][C]38[/C][C]0.453983948967246[/C][C]0.907967897934491[/C][C]0.546016051032754[/C][/ROW]
[ROW][C]39[/C][C]0.435114198614825[/C][C]0.870228397229649[/C][C]0.564885801385175[/C][/ROW]
[ROW][C]40[/C][C]0.431876822619075[/C][C]0.863753645238151[/C][C]0.568123177380925[/C][/ROW]
[ROW][C]41[/C][C]0.451866322943126[/C][C]0.903732645886252[/C][C]0.548133677056874[/C][/ROW]
[ROW][C]42[/C][C]0.458162922873401[/C][C]0.916325845746802[/C][C]0.541837077126599[/C][/ROW]
[ROW][C]43[/C][C]0.580122067744458[/C][C]0.839755864511083[/C][C]0.419877932255542[/C][/ROW]
[ROW][C]44[/C][C]0.819139687062632[/C][C]0.361720625874736[/C][C]0.180860312937368[/C][/ROW]
[ROW][C]45[/C][C]0.89557221608699[/C][C]0.208855567826021[/C][C]0.10442778391301[/C][/ROW]
[ROW][C]46[/C][C]0.948602411217745[/C][C]0.10279517756451[/C][C]0.0513975887822552[/C][/ROW]
[ROW][C]47[/C][C]0.949348279742133[/C][C]0.101303440515735[/C][C]0.0506517202578675[/C][/ROW]
[ROW][C]48[/C][C]0.978620316989522[/C][C]0.0427593660209551[/C][C]0.0213796830104775[/C][/ROW]
[ROW][C]49[/C][C]0.968282155022187[/C][C]0.0634356899556252[/C][C]0.0317178449778126[/C][/ROW]
[ROW][C]50[/C][C]0.976783325107904[/C][C]0.0464333497841921[/C][C]0.023216674892096[/C][/ROW]
[ROW][C]51[/C][C]0.976107343816807[/C][C]0.0477853123663864[/C][C]0.0238926561831932[/C][/ROW]
[ROW][C]52[/C][C]0.974183245143949[/C][C]0.0516335097121016[/C][C]0.0258167548560508[/C][/ROW]
[ROW][C]53[/C][C]0.9903287684091[/C][C]0.0193424631817993[/C][C]0.00967123159089966[/C][/ROW]
[ROW][C]54[/C][C]0.988940797100114[/C][C]0.0221184057997729[/C][C]0.0110592028998865[/C][/ROW]
[ROW][C]55[/C][C]0.99030828875299[/C][C]0.0193834224940192[/C][C]0.00969171124700962[/C][/ROW]
[ROW][C]56[/C][C]0.992368377364698[/C][C]0.0152632452706045[/C][C]0.00763162263530224[/C][/ROW]
[ROW][C]57[/C][C]0.988387350046055[/C][C]0.023225299907889[/C][C]0.0116126499539445[/C][/ROW]
[ROW][C]58[/C][C]0.984236518866943[/C][C]0.0315269622661145[/C][C]0.0157634811330573[/C][/ROW]
[ROW][C]59[/C][C]0.980093908132194[/C][C]0.0398121837356117[/C][C]0.0199060918678058[/C][/ROW]
[ROW][C]60[/C][C]0.992197269962295[/C][C]0.0156054600754095[/C][C]0.00780273003770476[/C][/ROW]
[ROW][C]61[/C][C]0.98825262582412[/C][C]0.0234947483517594[/C][C]0.0117473741758797[/C][/ROW]
[ROW][C]62[/C][C]0.981745580122305[/C][C]0.0365088397553891[/C][C]0.0182544198776946[/C][/ROW]
[ROW][C]63[/C][C]0.971070901636698[/C][C]0.0578581967266037[/C][C]0.0289290983633019[/C][/ROW]
[ROW][C]64[/C][C]0.959650066274817[/C][C]0.0806998674503665[/C][C]0.0403499337251833[/C][/ROW]
[ROW][C]65[/C][C]0.979014405210053[/C][C]0.0419711895798949[/C][C]0.0209855947899474[/C][/ROW]
[ROW][C]66[/C][C]0.990387823864325[/C][C]0.0192243522713492[/C][C]0.0096121761356746[/C][/ROW]
[ROW][C]67[/C][C]0.985084624207839[/C][C]0.0298307515843229[/C][C]0.0149153757921614[/C][/ROW]
[ROW][C]68[/C][C]0.975717553315515[/C][C]0.0485648933689702[/C][C]0.0242824466844851[/C][/ROW]
[ROW][C]69[/C][C]0.959902599737579[/C][C]0.080194800524842[/C][C]0.040097400262421[/C][/ROW]
[ROW][C]70[/C][C]0.941184807448311[/C][C]0.117630385103378[/C][C]0.058815192551689[/C][/ROW]
[ROW][C]71[/C][C]0.934841173110881[/C][C]0.130317653778237[/C][C]0.0651588268891187[/C][/ROW]
[ROW][C]72[/C][C]0.968899221121054[/C][C]0.0622015577578917[/C][C]0.0311007788789459[/C][/ROW]
[ROW][C]73[/C][C]0.966549647596881[/C][C]0.0669007048062385[/C][C]0.0334503524031192[/C][/ROW]
[ROW][C]74[/C][C]0.905635945045823[/C][C]0.188728109908354[/C][C]0.0943640549541768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191336&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191336&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
80.02479312731216630.04958625462433260.975206872687834
90.02089388873273940.04178777746547870.979106111267261
100.007716775292984050.01543355058596810.992283224707016
110.00246435939324740.004928718786494790.997535640606753
120.0008076897002446750.001615379400489350.999192310299755
130.0002542800957380880.0005085601914761770.999745719904262
146.80516983209037e-050.0001361033966418070.999931948301679
150.004697485828869920.009394971657739840.99530251417113
160.003103358563556920.006206717127113830.996896641436443
170.002694960779968430.005389921559936870.997305039220032
180.001344464994845860.002688929989691710.998655535005154
190.0007292808961296290.001458561792259260.99927071910387
200.004060237134116330.008120474268232670.995939762865884
210.003814125568036570.007628251136073140.996185874431963
220.009392657140193110.01878531428038620.990607342859807
230.01189138468177450.0237827693635490.988108615318225
240.007756031189386270.01551206237877250.992243968810614
250.004975048213228940.009950096426457880.995024951786771
260.009885647147253980.0197712942945080.990114352852746
270.0311053932696660.06221078653933210.968894606730334
280.02682383311888310.05364766623776620.973176166881117
290.03468036657449490.06936073314898980.965319633425505
300.06522717605845160.1304543521169030.934772823941548
310.1339477684758850.2678955369517710.866052231524115
320.2896709165008930.5793418330017860.710329083499107
330.4202723404210760.8405446808421520.579727659578924
340.5395503076664540.9208993846670930.460449692333546
350.5510479887641650.8979040224716690.448952011235835
360.5036392869831840.9927214260336320.496360713016816
370.4412552736368090.8825105472736190.558744726363191
380.4539839489672460.9079678979344910.546016051032754
390.4351141986148250.8702283972296490.564885801385175
400.4318768226190750.8637536452381510.568123177380925
410.4518663229431260.9037326458862520.548133677056874
420.4581629228734010.9163258457468020.541837077126599
430.5801220677444580.8397558645110830.419877932255542
440.8191396870626320.3617206258747360.180860312937368
450.895572216086990.2088555678260210.10442778391301
460.9486024112177450.102795177564510.0513975887822552
470.9493482797421330.1013034405157350.0506517202578675
480.9786203169895220.04275936602095510.0213796830104775
490.9682821550221870.06343568995562520.0317178449778126
500.9767833251079040.04643334978419210.023216674892096
510.9761073438168070.04778531236638640.0238926561831932
520.9741832451439490.05163350971210160.0258167548560508
530.99032876840910.01934246318179930.00967123159089966
540.9889407971001140.02211840579977290.0110592028998865
550.990308288752990.01938342249401920.00969171124700962
560.9923683773646980.01526324527060450.00763162263530224
570.9883873500460550.0232252999078890.0116126499539445
580.9842365188669430.03152696226611450.0157634811330573
590.9800939081321940.03981218373561170.0199060918678058
600.9921972699622950.01560546007540950.00780273003770476
610.988252625824120.02349474835175940.0117473741758797
620.9817455801223050.03650883975538910.0182544198776946
630.9710709016366980.05785819672660370.0289290983633019
640.9596500662748170.08069986745036650.0403499337251833
650.9790144052100530.04197118957989490.0209855947899474
660.9903878238643250.01922435227134920.0096121761356746
670.9850846242078390.02983075158432290.0149153757921614
680.9757175533155150.04856489336897020.0242824466844851
690.9599025997375790.0801948005248420.040097400262421
700.9411848074483110.1176303851033780.058815192551689
710.9348411731108810.1303176537782370.0651588268891187
720.9688992211210540.06220155775789170.0311007788789459
730.9665496475968810.06690070480623850.0334503524031192
740.9056359450458230.1887281099083540.0943640549541768






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.17910447761194NOK
5% type I error level360.537313432835821NOK
10% type I error level460.686567164179104NOK

\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 & 12 & 0.17910447761194 & NOK \tabularnewline
5% type I error level & 36 & 0.537313432835821 & NOK \tabularnewline
10% type I error level & 46 & 0.686567164179104 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191336&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]12[/C][C]0.17910447761194[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.537313432835821[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.686567164179104[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191336&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191336&T=6

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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 level120.17910447761194NOK
5% type I error level360.537313432835821NOK
10% type I error level460.686567164179104NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}