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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 computationSun, 18 Nov 2012 09:35:13 -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/18/t1353249336ltecl1rb21jujz9.htm/, Retrieved Mon, 29 Apr 2024 20:01:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190193, Retrieved Mon, 29 Apr 2024 20:01:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [ws7] [2012-11-18 10:59:23] [7722d8427d2b2c713c1f0d5525f2f86c]
- R PD  [Multiple Regression] [ws7 goed] [2012-11-18 13:15:15] [7722d8427d2b2c713c1f0d5525f2f86c]
-    D      [Multiple Regression] [ws maand] [2012-11-18 14:35:13] [2bcb0f1dab9cffb75c9fd882cacbd29a] [Current]
-   PD        [Multiple Regression] [ws7 trend] [2012-11-18 14:44:48] [7722d8427d2b2c713c1f0d5525f2f86c]
-               [Multiple Regression] [] [2012-11-20 22:37:08] [74be16979710d4c4e7c6647856088456]
-    D        [Multiple Regression] [] [2012-11-20 22:34:22] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100,5	99,5	101,5	467	9
99	93,5	99,2	460	9
104,1	104,6	107,8	448	9
98,6	95,3	92,3	443	9
101,4	102,8	99,2	436	9
102,1	103,3	101,6	431	9
93	100,2	87	484	9
96,9	107,9	71,4	510	9
91,2	107,5	104,7	513	9
96,9	119,8	115,1	503	9
94	112	102,5	471	9
90,4	102,1	75,3	471	9
105,2	105,3	96,7	476	9
103,4	101,3	94,6	475	9
111,7	108,4	98,6	470	9
114,2	107,4	99,5	461	9
111,4	109,1	92	455	9
106,3	109,5	93,6	456	9
111,8	111,4	89,3	517	9
101,5	110,1	66,9	525	9
103	117	108,8	523	9
105,2	129,6	113,2	519	9
101,1	113,5	105,5	509	9
100,7	113,3	77,8	512	9
116,7	110,1	102,1	519	9
109	107,4	97	517	9
119,5	110,1	95,5	510	9
115,1	112,5	99,3	509	9
107,1	106	86,4	501	9
109,7	117,6	92,4	507	9
110,4	117,8	85,7	569	9
105	113,5	61,9	580	9
115,8	121,2	104,9	578	9
116,4	130,4	107,9	565	9
111,1	115,2	95,6	547	9
119,5	117,9	79,8	555	9
110,9	110,7	94,8	562	9
115,1	107,6	93,7	561	9
125,2	124,3	108,1	555	9
116	115,1	96,9	544	9
112,9	112,5	88,8	537	9
121,7	127,9	106,7	543	10
123,2	117,4	86,8	594	10
116,6	119,3	69,8	611	10
136,2	130,4	110,9	613	10
120,9	126	105,4	611	10
119,6	125,4	99,2	594	10
125,9	130,5	84,4	595	10
116,1	115,9	87,2	591	10
107,5	108,7	91,9	589	10
116,7	124	97,9	584	10
112,5	119,4	94,5	573	10
113	118,6	85	567	10
126,4	131,3	100,3	569	10
114,1	111,1	78,7	621	10
112,5	124,8	65,8	629	10
112,4	132,3	104,8	628	10
113,1	126,7	96	612	10
116,3	131,7	103,3	595	10
111,7	130,9	82,9	597	10
118,8	122,1	91,4	593	10
116,5	113,2	94,5	590	10
125,1	133,6	109,3	580	10
113,1	119,2	92,1	574	10
119,6	129,4	99,3	573	10
114,4	131,4	109,6	573	10
114	117,1	87,5	620	10
117,8	130,5	73,1	626	10
117	132,3	110,7	620	10
120,9	140,8	111,6	588	10
115	137,5	110,7	566	10
117,3	128,6	84	557	10
119,4	126,7	101,6	561	10
114,9	120,8	102,1	549	10
125,8	139,3	113,9	532	10
117,6	128,6	99	526	10
117,6	131,3	100,4	511	10
114,9	136,3	109,5	499	10
121,9	128,8	93,1	555	10
117	133,2	77	565	10
106,4	136,3	108	542	10
110,5	151,1	119,9	527	10
113,6	145	105,9	510	11
114,2	134,4	78,2	514	11
125,4	135,7	100,3	517	11
124,6	128,7	102,2	508	11
120,2	129,2	97	493	11
120,8	138,6	101,3	490	11
111,4	132,7	89,2	469	11
124,1	132,5	93,3	478	11
120,2	137,3	88,5	528	11
125,5	127,1	61,5	534	11
116	143,7	96,3	518	11
117	149,9	95,4	506	11
105,7	131,6	79,9	502	11
102	138,8	66,7	516	11
106,4	122,5	71,2	528	11
96,9	122	73,1	533	11
107,6	135,6	81	536	11
98,8	133,4	77,2	537	11
101,1	127,3	67,7	524	11
105,7	138,9	76,7	536	11
104,6	131,4	73,3	587	11
103,2	131,6	54,1	597	11
101,6	135,8	85	581	11
106,7	141,6	85,9	564	11
99,5	132,6	79,3	558	11
101	132,3	67,2	575	11
104,9	120,6	72,4	580	11
118,4	123,8	76,1	575	11
129	145,1	89,8	563	11
123,7	135	84	552	11
127,6	127,6	75,4	537	11
129,4	142	90	545	11
128,3	130,1	76,8	601	11
124,8	131	59,6	604	11
125,2	141,3	92,1	586	11
129,6	139,6	88,4	564	11
124,8	142,2	82,8	549	11
121,9	140	69,4	551	11
129,2	132	73,4	556	11

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'Sir Maurice George Kendall' @ kendall.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 & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190193&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190193&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190193&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'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 419.778003374043 + 1.76129274906834chemie[t] + 1.60376814161943vm[t] -1.20989112286638textiel[t] -16.5723552810496maand[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  419.778003374043 +  1.76129274906834chemie[t] +  1.60376814161943vm[t] -1.20989112286638textiel[t] -16.5723552810496maand[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190193&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  419.778003374043 +  1.76129274906834chemie[t] +  1.60376814161943vm[t] -1.20989112286638textiel[t] -16.5723552810496maand[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190193&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190193&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
werkloosheid[t] = + 419.778003374043 + 1.76129274906834chemie[t] + 1.60376814161943vm[t] -1.20989112286638textiel[t] -16.5723552810496maand[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)419.77800337404370.3596655.966200
chemie1.761292749068340.4474163.93660.0001417.1e-05
vm1.603768141619430.5328633.00970.0032090.001604
textiel-1.209891122866380.327323-3.69630.0003350.000168
maand-16.57235528104968.666188-1.91230.0583040.029152

\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) & 419.778003374043 & 70.359665 & 5.9662 & 0 & 0 \tabularnewline
chemie & 1.76129274906834 & 0.447416 & 3.9366 & 0.000141 & 7.1e-05 \tabularnewline
vm & 1.60376814161943 & 0.532863 & 3.0097 & 0.003209 & 0.001604 \tabularnewline
textiel & -1.20989112286638 & 0.327323 & -3.6963 & 0.000335 & 0.000168 \tabularnewline
maand & -16.5723552810496 & 8.666188 & -1.9123 & 0.058304 & 0.029152 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190193&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]419.778003374043[/C][C]70.359665[/C][C]5.9662[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]chemie[/C][C]1.76129274906834[/C][C]0.447416[/C][C]3.9366[/C][C]0.000141[/C][C]7.1e-05[/C][/ROW]
[ROW][C]vm[/C][C]1.60376814161943[/C][C]0.532863[/C][C]3.0097[/C][C]0.003209[/C][C]0.001604[/C][/ROW]
[ROW][C]textiel[/C][C]-1.20989112286638[/C][C]0.327323[/C][C]-3.6963[/C][C]0.000335[/C][C]0.000168[/C][/ROW]
[ROW][C]maand[/C][C]-16.5723552810496[/C][C]8.666188[/C][C]-1.9123[/C][C]0.058304[/C][C]0.029152[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190193&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190193&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)419.77800337404370.3596655.966200
chemie1.761292749068340.4474163.93660.0001417.1e-05
vm1.603768141619430.5328633.00970.0032090.001604
textiel-1.209891122866380.327323-3.69630.0003350.000168
maand-16.57235528104968.666188-1.91230.0583040.029152






Multiple Linear Regression - Regression Statistics
Multiple R0.551026185417988
R-squared0.303629857016299
Adjusted R-squared0.279617093465137
F-TEST (value)12.6445195018632
F-TEST (DF numerator)4
F-TEST (DF denominator)116
p-value1.42720724127798e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39.9751882720934
Sum Squared Residuals185369.81857716

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.551026185417988 \tabularnewline
R-squared & 0.303629857016299 \tabularnewline
Adjusted R-squared & 0.279617093465137 \tabularnewline
F-TEST (value) & 12.6445195018632 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 116 \tabularnewline
p-value & 1.42720724127798e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 39.9751882720934 \tabularnewline
Sum Squared Residuals & 185369.81857716 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190193&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.551026185417988[/C][/ROW]
[ROW][C]R-squared[/C][C]0.303629857016299[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.279617093465137[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.6445195018632[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]116[/C][/ROW]
[ROW][C]p-value[/C][C]1.42720724127798e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]39.9751882720934[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]185369.81857716[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190193&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190193&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 R0.551026185417988
R-squared0.303629857016299
Adjusted R-squared0.279617093465137
F-TEST (value)12.6445195018632
F-TEST (DF numerator)4
F-TEST (DF denominator)116
p-value1.42720724127798e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39.9751882720934
Sum Squared Residuals185369.81857716






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1467484.40770824616-17.40770824616
2460474.925909855434-14.9259098554338
3448491.305265591007-43.3052655910071
4443485.456424158499-42.4564241584994
5436494.068056170259-58.0680561702585
6431493.199106470537-62.1991064705367
7484489.864071608844-5.86407160884383
8510527.956429537396-17.9564295373956
9513476.98617921960836.0138207803923
10503494.1690283534068.83097164659409
11471491.796516024593-20.7965160245925
12471502.48759606788-31.4875960678798
13476507.795116777933-31.7951167779328
14475500.750488621151-25.7504886211514
15470521.916407752451-51.9164077524511
16461523.626969472923-62.6269694729228
17455530.495939037782-75.4959390377823
18456520.219027477595-64.2190274775954
19517538.155828894874-21.1558288948736
20525545.031176147571-20.0311761475714
21523508.04467740024714.9553225997534
22519526.80347909199-7.8034790919897
23509503.0776733868085.92232661319225
24512535.566386762255-23.5663867622554
25519529.214658408514-10.2146584085135
26517517.492974984933-0.49297498493337
27510542.131559516823-32.131559516823
28509533.633328693917-24.6333286939167
29501524.72608926582-23.72608926582
30507540.649814118985-33.6498141189848
31569550.30974319486118.6902568051387
32580562.69796806514917.3020319348514
33578542.04362616230235.9563738376982
34565554.22539534604310.7746046539575
35547535.39492883462111.6050711653786
36555573.636241650457-18.6362416504568
37562528.79362654581333.2063734541866
38561532.55025508803328.4497449119668
39555559.699807649392-4.69980764939207
40544542.2920280311681.70797196883199
41537542.462341436063-5.46234143606337
42543544.430340628446-1.43034062844613
43594554.30954761008639.6904523899144
44611566.3003240240444.69967597596
45613568.89696312794744.1030368720532
46611541.54700541984169.4529945801592
47594545.79638892285248.2036110771481
48595582.97813938266412.0218606173359
49591538.91476043012552.0852395698753
50589506.53402389100582.465976108995
51584540.01622301201343.9837769879872
52573529.35508983222243.6449101677779
53567540.44668736069126.5533126393087
54569565.9045314169183.09546858308171
55621537.97816239657983.021837603421
56629572.73931302323256.2606869767677
57628537.40569101868290.5943089813178
58612540.30453623118571.6954637688146
59595545.12730853937749.8726914606234
60597560.42412628684136.575873713159
61593548.53207061461144.4679293853891
62590526.45689835045563.5431016495451
63580556.41449746305723.5855025369434
64574532.99485054821941.0051494517815
65573552.09047237704320.909527622957
66573533.67740779960339.3225922003972
67620536.77760009016583.2223999098354
68626582.38343780360143.6165621963994
69620538.36928003948581.6307199605151
70588557.78144895403730.2185510459631
71566543.18628887776922.8137111222307
72557565.267818720746-8.26781872074593
73561544.62529026226416.3747097377358
74549526.63229529446922.3677047055312
75532561.22338162945-29.2233816294499
76526547.647839702471-21.6478397024707
77511550.28416611283-39.2841661128302
78499542.537507180359-43.5375071803588
79555562.6805097767-7.68050977670014
80565580.58600220754-15.5860022075395
81542529.38135549757712.6186445024225
82527545.940719902615-18.9407199026153
83510541.983862199928-31.9838621999283
84514559.554679651602-45.5546796516022
85517554.627463209926-37.6274632099258
86508539.693258885889-31.6932588858889
87493539.036888699703-46.0368886997031
88490549.966553052041-59.9665530520414
89469538.587851761928-69.5878517619276
90478555.674962443019-77.6749624430194
91528562.311485191185-34.3114851911849
92534587.954962034121-53.9549620341212
93518555.741020993104-37.7410209931044
94506568.534578230793-62.534578230793
95502538.036325579114-36.036325579114
96516559.037235849057-43.0372358490574
97528535.200993183663-7.20099318366256
98533515.36803486325717.6319651367425
99536546.466974133669-10.4669741336686
100537532.0368942971974.9631057028033
101524537.798847623406-13.798847623406
102536553.615484606108-17.6154846061083
103587543.76343133773343.2365686622669
104597564.84828467639632.1517153236041
105581531.38040677611749.6195932238831
106564548.57595300717815.4240469928216
107558529.4460133502328.5539866497704
108575546.2465046180328.7534953819705
109580528.06002524354351.9399747564566
110575552.49293825454322.5070617454574
111563588.747394427892-25.7473944278915
112552570.231853140098-18.2318531400981
113537575.638074270132-38.6380742701316
114545584.238252063925-39.2382520639253
115601579.18655197651521.8134480234848
116604595.2755459955358.72445400446476
117586573.17741346068512.8225865393147
118564582.677292870439-18.6772928704385
119549585.168275131173-36.1682751311728
120551592.744777293721-41.7447772937214
121556587.932504737499-31.9325047374993

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 467 & 484.40770824616 & -17.40770824616 \tabularnewline
2 & 460 & 474.925909855434 & -14.9259098554338 \tabularnewline
3 & 448 & 491.305265591007 & -43.3052655910071 \tabularnewline
4 & 443 & 485.456424158499 & -42.4564241584994 \tabularnewline
5 & 436 & 494.068056170259 & -58.0680561702585 \tabularnewline
6 & 431 & 493.199106470537 & -62.1991064705367 \tabularnewline
7 & 484 & 489.864071608844 & -5.86407160884383 \tabularnewline
8 & 510 & 527.956429537396 & -17.9564295373956 \tabularnewline
9 & 513 & 476.986179219608 & 36.0138207803923 \tabularnewline
10 & 503 & 494.169028353406 & 8.83097164659409 \tabularnewline
11 & 471 & 491.796516024593 & -20.7965160245925 \tabularnewline
12 & 471 & 502.48759606788 & -31.4875960678798 \tabularnewline
13 & 476 & 507.795116777933 & -31.7951167779328 \tabularnewline
14 & 475 & 500.750488621151 & -25.7504886211514 \tabularnewline
15 & 470 & 521.916407752451 & -51.9164077524511 \tabularnewline
16 & 461 & 523.626969472923 & -62.6269694729228 \tabularnewline
17 & 455 & 530.495939037782 & -75.4959390377823 \tabularnewline
18 & 456 & 520.219027477595 & -64.2190274775954 \tabularnewline
19 & 517 & 538.155828894874 & -21.1558288948736 \tabularnewline
20 & 525 & 545.031176147571 & -20.0311761475714 \tabularnewline
21 & 523 & 508.044677400247 & 14.9553225997534 \tabularnewline
22 & 519 & 526.80347909199 & -7.8034790919897 \tabularnewline
23 & 509 & 503.077673386808 & 5.92232661319225 \tabularnewline
24 & 512 & 535.566386762255 & -23.5663867622554 \tabularnewline
25 & 519 & 529.214658408514 & -10.2146584085135 \tabularnewline
26 & 517 & 517.492974984933 & -0.49297498493337 \tabularnewline
27 & 510 & 542.131559516823 & -32.131559516823 \tabularnewline
28 & 509 & 533.633328693917 & -24.6333286939167 \tabularnewline
29 & 501 & 524.72608926582 & -23.72608926582 \tabularnewline
30 & 507 & 540.649814118985 & -33.6498141189848 \tabularnewline
31 & 569 & 550.309743194861 & 18.6902568051387 \tabularnewline
32 & 580 & 562.697968065149 & 17.3020319348514 \tabularnewline
33 & 578 & 542.043626162302 & 35.9563738376982 \tabularnewline
34 & 565 & 554.225395346043 & 10.7746046539575 \tabularnewline
35 & 547 & 535.394928834621 & 11.6050711653786 \tabularnewline
36 & 555 & 573.636241650457 & -18.6362416504568 \tabularnewline
37 & 562 & 528.793626545813 & 33.2063734541866 \tabularnewline
38 & 561 & 532.550255088033 & 28.4497449119668 \tabularnewline
39 & 555 & 559.699807649392 & -4.69980764939207 \tabularnewline
40 & 544 & 542.292028031168 & 1.70797196883199 \tabularnewline
41 & 537 & 542.462341436063 & -5.46234143606337 \tabularnewline
42 & 543 & 544.430340628446 & -1.43034062844613 \tabularnewline
43 & 594 & 554.309547610086 & 39.6904523899144 \tabularnewline
44 & 611 & 566.30032402404 & 44.69967597596 \tabularnewline
45 & 613 & 568.896963127947 & 44.1030368720532 \tabularnewline
46 & 611 & 541.547005419841 & 69.4529945801592 \tabularnewline
47 & 594 & 545.796388922852 & 48.2036110771481 \tabularnewline
48 & 595 & 582.978139382664 & 12.0218606173359 \tabularnewline
49 & 591 & 538.914760430125 & 52.0852395698753 \tabularnewline
50 & 589 & 506.534023891005 & 82.465976108995 \tabularnewline
51 & 584 & 540.016223012013 & 43.9837769879872 \tabularnewline
52 & 573 & 529.355089832222 & 43.6449101677779 \tabularnewline
53 & 567 & 540.446687360691 & 26.5533126393087 \tabularnewline
54 & 569 & 565.904531416918 & 3.09546858308171 \tabularnewline
55 & 621 & 537.978162396579 & 83.021837603421 \tabularnewline
56 & 629 & 572.739313023232 & 56.2606869767677 \tabularnewline
57 & 628 & 537.405691018682 & 90.5943089813178 \tabularnewline
58 & 612 & 540.304536231185 & 71.6954637688146 \tabularnewline
59 & 595 & 545.127308539377 & 49.8726914606234 \tabularnewline
60 & 597 & 560.424126286841 & 36.575873713159 \tabularnewline
61 & 593 & 548.532070614611 & 44.4679293853891 \tabularnewline
62 & 590 & 526.456898350455 & 63.5431016495451 \tabularnewline
63 & 580 & 556.414497463057 & 23.5855025369434 \tabularnewline
64 & 574 & 532.994850548219 & 41.0051494517815 \tabularnewline
65 & 573 & 552.090472377043 & 20.909527622957 \tabularnewline
66 & 573 & 533.677407799603 & 39.3225922003972 \tabularnewline
67 & 620 & 536.777600090165 & 83.2223999098354 \tabularnewline
68 & 626 & 582.383437803601 & 43.6165621963994 \tabularnewline
69 & 620 & 538.369280039485 & 81.6307199605151 \tabularnewline
70 & 588 & 557.781448954037 & 30.2185510459631 \tabularnewline
71 & 566 & 543.186288877769 & 22.8137111222307 \tabularnewline
72 & 557 & 565.267818720746 & -8.26781872074593 \tabularnewline
73 & 561 & 544.625290262264 & 16.3747097377358 \tabularnewline
74 & 549 & 526.632295294469 & 22.3677047055312 \tabularnewline
75 & 532 & 561.22338162945 & -29.2233816294499 \tabularnewline
76 & 526 & 547.647839702471 & -21.6478397024707 \tabularnewline
77 & 511 & 550.28416611283 & -39.2841661128302 \tabularnewline
78 & 499 & 542.537507180359 & -43.5375071803588 \tabularnewline
79 & 555 & 562.6805097767 & -7.68050977670014 \tabularnewline
80 & 565 & 580.58600220754 & -15.5860022075395 \tabularnewline
81 & 542 & 529.381355497577 & 12.6186445024225 \tabularnewline
82 & 527 & 545.940719902615 & -18.9407199026153 \tabularnewline
83 & 510 & 541.983862199928 & -31.9838621999283 \tabularnewline
84 & 514 & 559.554679651602 & -45.5546796516022 \tabularnewline
85 & 517 & 554.627463209926 & -37.6274632099258 \tabularnewline
86 & 508 & 539.693258885889 & -31.6932588858889 \tabularnewline
87 & 493 & 539.036888699703 & -46.0368886997031 \tabularnewline
88 & 490 & 549.966553052041 & -59.9665530520414 \tabularnewline
89 & 469 & 538.587851761928 & -69.5878517619276 \tabularnewline
90 & 478 & 555.674962443019 & -77.6749624430194 \tabularnewline
91 & 528 & 562.311485191185 & -34.3114851911849 \tabularnewline
92 & 534 & 587.954962034121 & -53.9549620341212 \tabularnewline
93 & 518 & 555.741020993104 & -37.7410209931044 \tabularnewline
94 & 506 & 568.534578230793 & -62.534578230793 \tabularnewline
95 & 502 & 538.036325579114 & -36.036325579114 \tabularnewline
96 & 516 & 559.037235849057 & -43.0372358490574 \tabularnewline
97 & 528 & 535.200993183663 & -7.20099318366256 \tabularnewline
98 & 533 & 515.368034863257 & 17.6319651367425 \tabularnewline
99 & 536 & 546.466974133669 & -10.4669741336686 \tabularnewline
100 & 537 & 532.036894297197 & 4.9631057028033 \tabularnewline
101 & 524 & 537.798847623406 & -13.798847623406 \tabularnewline
102 & 536 & 553.615484606108 & -17.6154846061083 \tabularnewline
103 & 587 & 543.763431337733 & 43.2365686622669 \tabularnewline
104 & 597 & 564.848284676396 & 32.1517153236041 \tabularnewline
105 & 581 & 531.380406776117 & 49.6195932238831 \tabularnewline
106 & 564 & 548.575953007178 & 15.4240469928216 \tabularnewline
107 & 558 & 529.44601335023 & 28.5539866497704 \tabularnewline
108 & 575 & 546.24650461803 & 28.7534953819705 \tabularnewline
109 & 580 & 528.060025243543 & 51.9399747564566 \tabularnewline
110 & 575 & 552.492938254543 & 22.5070617454574 \tabularnewline
111 & 563 & 588.747394427892 & -25.7473944278915 \tabularnewline
112 & 552 & 570.231853140098 & -18.2318531400981 \tabularnewline
113 & 537 & 575.638074270132 & -38.6380742701316 \tabularnewline
114 & 545 & 584.238252063925 & -39.2382520639253 \tabularnewline
115 & 601 & 579.186551976515 & 21.8134480234848 \tabularnewline
116 & 604 & 595.275545995535 & 8.72445400446476 \tabularnewline
117 & 586 & 573.177413460685 & 12.8225865393147 \tabularnewline
118 & 564 & 582.677292870439 & -18.6772928704385 \tabularnewline
119 & 549 & 585.168275131173 & -36.1682751311728 \tabularnewline
120 & 551 & 592.744777293721 & -41.7447772937214 \tabularnewline
121 & 556 & 587.932504737499 & -31.9325047374993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190193&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]467[/C][C]484.40770824616[/C][C]-17.40770824616[/C][/ROW]
[ROW][C]2[/C][C]460[/C][C]474.925909855434[/C][C]-14.9259098554338[/C][/ROW]
[ROW][C]3[/C][C]448[/C][C]491.305265591007[/C][C]-43.3052655910071[/C][/ROW]
[ROW][C]4[/C][C]443[/C][C]485.456424158499[/C][C]-42.4564241584994[/C][/ROW]
[ROW][C]5[/C][C]436[/C][C]494.068056170259[/C][C]-58.0680561702585[/C][/ROW]
[ROW][C]6[/C][C]431[/C][C]493.199106470537[/C][C]-62.1991064705367[/C][/ROW]
[ROW][C]7[/C][C]484[/C][C]489.864071608844[/C][C]-5.86407160884383[/C][/ROW]
[ROW][C]8[/C][C]510[/C][C]527.956429537396[/C][C]-17.9564295373956[/C][/ROW]
[ROW][C]9[/C][C]513[/C][C]476.986179219608[/C][C]36.0138207803923[/C][/ROW]
[ROW][C]10[/C][C]503[/C][C]494.169028353406[/C][C]8.83097164659409[/C][/ROW]
[ROW][C]11[/C][C]471[/C][C]491.796516024593[/C][C]-20.7965160245925[/C][/ROW]
[ROW][C]12[/C][C]471[/C][C]502.48759606788[/C][C]-31.4875960678798[/C][/ROW]
[ROW][C]13[/C][C]476[/C][C]507.795116777933[/C][C]-31.7951167779328[/C][/ROW]
[ROW][C]14[/C][C]475[/C][C]500.750488621151[/C][C]-25.7504886211514[/C][/ROW]
[ROW][C]15[/C][C]470[/C][C]521.916407752451[/C][C]-51.9164077524511[/C][/ROW]
[ROW][C]16[/C][C]461[/C][C]523.626969472923[/C][C]-62.6269694729228[/C][/ROW]
[ROW][C]17[/C][C]455[/C][C]530.495939037782[/C][C]-75.4959390377823[/C][/ROW]
[ROW][C]18[/C][C]456[/C][C]520.219027477595[/C][C]-64.2190274775954[/C][/ROW]
[ROW][C]19[/C][C]517[/C][C]538.155828894874[/C][C]-21.1558288948736[/C][/ROW]
[ROW][C]20[/C][C]525[/C][C]545.031176147571[/C][C]-20.0311761475714[/C][/ROW]
[ROW][C]21[/C][C]523[/C][C]508.044677400247[/C][C]14.9553225997534[/C][/ROW]
[ROW][C]22[/C][C]519[/C][C]526.80347909199[/C][C]-7.8034790919897[/C][/ROW]
[ROW][C]23[/C][C]509[/C][C]503.077673386808[/C][C]5.92232661319225[/C][/ROW]
[ROW][C]24[/C][C]512[/C][C]535.566386762255[/C][C]-23.5663867622554[/C][/ROW]
[ROW][C]25[/C][C]519[/C][C]529.214658408514[/C][C]-10.2146584085135[/C][/ROW]
[ROW][C]26[/C][C]517[/C][C]517.492974984933[/C][C]-0.49297498493337[/C][/ROW]
[ROW][C]27[/C][C]510[/C][C]542.131559516823[/C][C]-32.131559516823[/C][/ROW]
[ROW][C]28[/C][C]509[/C][C]533.633328693917[/C][C]-24.6333286939167[/C][/ROW]
[ROW][C]29[/C][C]501[/C][C]524.72608926582[/C][C]-23.72608926582[/C][/ROW]
[ROW][C]30[/C][C]507[/C][C]540.649814118985[/C][C]-33.6498141189848[/C][/ROW]
[ROW][C]31[/C][C]569[/C][C]550.309743194861[/C][C]18.6902568051387[/C][/ROW]
[ROW][C]32[/C][C]580[/C][C]562.697968065149[/C][C]17.3020319348514[/C][/ROW]
[ROW][C]33[/C][C]578[/C][C]542.043626162302[/C][C]35.9563738376982[/C][/ROW]
[ROW][C]34[/C][C]565[/C][C]554.225395346043[/C][C]10.7746046539575[/C][/ROW]
[ROW][C]35[/C][C]547[/C][C]535.394928834621[/C][C]11.6050711653786[/C][/ROW]
[ROW][C]36[/C][C]555[/C][C]573.636241650457[/C][C]-18.6362416504568[/C][/ROW]
[ROW][C]37[/C][C]562[/C][C]528.793626545813[/C][C]33.2063734541866[/C][/ROW]
[ROW][C]38[/C][C]561[/C][C]532.550255088033[/C][C]28.4497449119668[/C][/ROW]
[ROW][C]39[/C][C]555[/C][C]559.699807649392[/C][C]-4.69980764939207[/C][/ROW]
[ROW][C]40[/C][C]544[/C][C]542.292028031168[/C][C]1.70797196883199[/C][/ROW]
[ROW][C]41[/C][C]537[/C][C]542.462341436063[/C][C]-5.46234143606337[/C][/ROW]
[ROW][C]42[/C][C]543[/C][C]544.430340628446[/C][C]-1.43034062844613[/C][/ROW]
[ROW][C]43[/C][C]594[/C][C]554.309547610086[/C][C]39.6904523899144[/C][/ROW]
[ROW][C]44[/C][C]611[/C][C]566.30032402404[/C][C]44.69967597596[/C][/ROW]
[ROW][C]45[/C][C]613[/C][C]568.896963127947[/C][C]44.1030368720532[/C][/ROW]
[ROW][C]46[/C][C]611[/C][C]541.547005419841[/C][C]69.4529945801592[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]545.796388922852[/C][C]48.2036110771481[/C][/ROW]
[ROW][C]48[/C][C]595[/C][C]582.978139382664[/C][C]12.0218606173359[/C][/ROW]
[ROW][C]49[/C][C]591[/C][C]538.914760430125[/C][C]52.0852395698753[/C][/ROW]
[ROW][C]50[/C][C]589[/C][C]506.534023891005[/C][C]82.465976108995[/C][/ROW]
[ROW][C]51[/C][C]584[/C][C]540.016223012013[/C][C]43.9837769879872[/C][/ROW]
[ROW][C]52[/C][C]573[/C][C]529.355089832222[/C][C]43.6449101677779[/C][/ROW]
[ROW][C]53[/C][C]567[/C][C]540.446687360691[/C][C]26.5533126393087[/C][/ROW]
[ROW][C]54[/C][C]569[/C][C]565.904531416918[/C][C]3.09546858308171[/C][/ROW]
[ROW][C]55[/C][C]621[/C][C]537.978162396579[/C][C]83.021837603421[/C][/ROW]
[ROW][C]56[/C][C]629[/C][C]572.739313023232[/C][C]56.2606869767677[/C][/ROW]
[ROW][C]57[/C][C]628[/C][C]537.405691018682[/C][C]90.5943089813178[/C][/ROW]
[ROW][C]58[/C][C]612[/C][C]540.304536231185[/C][C]71.6954637688146[/C][/ROW]
[ROW][C]59[/C][C]595[/C][C]545.127308539377[/C][C]49.8726914606234[/C][/ROW]
[ROW][C]60[/C][C]597[/C][C]560.424126286841[/C][C]36.575873713159[/C][/ROW]
[ROW][C]61[/C][C]593[/C][C]548.532070614611[/C][C]44.4679293853891[/C][/ROW]
[ROW][C]62[/C][C]590[/C][C]526.456898350455[/C][C]63.5431016495451[/C][/ROW]
[ROW][C]63[/C][C]580[/C][C]556.414497463057[/C][C]23.5855025369434[/C][/ROW]
[ROW][C]64[/C][C]574[/C][C]532.994850548219[/C][C]41.0051494517815[/C][/ROW]
[ROW][C]65[/C][C]573[/C][C]552.090472377043[/C][C]20.909527622957[/C][/ROW]
[ROW][C]66[/C][C]573[/C][C]533.677407799603[/C][C]39.3225922003972[/C][/ROW]
[ROW][C]67[/C][C]620[/C][C]536.777600090165[/C][C]83.2223999098354[/C][/ROW]
[ROW][C]68[/C][C]626[/C][C]582.383437803601[/C][C]43.6165621963994[/C][/ROW]
[ROW][C]69[/C][C]620[/C][C]538.369280039485[/C][C]81.6307199605151[/C][/ROW]
[ROW][C]70[/C][C]588[/C][C]557.781448954037[/C][C]30.2185510459631[/C][/ROW]
[ROW][C]71[/C][C]566[/C][C]543.186288877769[/C][C]22.8137111222307[/C][/ROW]
[ROW][C]72[/C][C]557[/C][C]565.267818720746[/C][C]-8.26781872074593[/C][/ROW]
[ROW][C]73[/C][C]561[/C][C]544.625290262264[/C][C]16.3747097377358[/C][/ROW]
[ROW][C]74[/C][C]549[/C][C]526.632295294469[/C][C]22.3677047055312[/C][/ROW]
[ROW][C]75[/C][C]532[/C][C]561.22338162945[/C][C]-29.2233816294499[/C][/ROW]
[ROW][C]76[/C][C]526[/C][C]547.647839702471[/C][C]-21.6478397024707[/C][/ROW]
[ROW][C]77[/C][C]511[/C][C]550.28416611283[/C][C]-39.2841661128302[/C][/ROW]
[ROW][C]78[/C][C]499[/C][C]542.537507180359[/C][C]-43.5375071803588[/C][/ROW]
[ROW][C]79[/C][C]555[/C][C]562.6805097767[/C][C]-7.68050977670014[/C][/ROW]
[ROW][C]80[/C][C]565[/C][C]580.58600220754[/C][C]-15.5860022075395[/C][/ROW]
[ROW][C]81[/C][C]542[/C][C]529.381355497577[/C][C]12.6186445024225[/C][/ROW]
[ROW][C]82[/C][C]527[/C][C]545.940719902615[/C][C]-18.9407199026153[/C][/ROW]
[ROW][C]83[/C][C]510[/C][C]541.983862199928[/C][C]-31.9838621999283[/C][/ROW]
[ROW][C]84[/C][C]514[/C][C]559.554679651602[/C][C]-45.5546796516022[/C][/ROW]
[ROW][C]85[/C][C]517[/C][C]554.627463209926[/C][C]-37.6274632099258[/C][/ROW]
[ROW][C]86[/C][C]508[/C][C]539.693258885889[/C][C]-31.6932588858889[/C][/ROW]
[ROW][C]87[/C][C]493[/C][C]539.036888699703[/C][C]-46.0368886997031[/C][/ROW]
[ROW][C]88[/C][C]490[/C][C]549.966553052041[/C][C]-59.9665530520414[/C][/ROW]
[ROW][C]89[/C][C]469[/C][C]538.587851761928[/C][C]-69.5878517619276[/C][/ROW]
[ROW][C]90[/C][C]478[/C][C]555.674962443019[/C][C]-77.6749624430194[/C][/ROW]
[ROW][C]91[/C][C]528[/C][C]562.311485191185[/C][C]-34.3114851911849[/C][/ROW]
[ROW][C]92[/C][C]534[/C][C]587.954962034121[/C][C]-53.9549620341212[/C][/ROW]
[ROW][C]93[/C][C]518[/C][C]555.741020993104[/C][C]-37.7410209931044[/C][/ROW]
[ROW][C]94[/C][C]506[/C][C]568.534578230793[/C][C]-62.534578230793[/C][/ROW]
[ROW][C]95[/C][C]502[/C][C]538.036325579114[/C][C]-36.036325579114[/C][/ROW]
[ROW][C]96[/C][C]516[/C][C]559.037235849057[/C][C]-43.0372358490574[/C][/ROW]
[ROW][C]97[/C][C]528[/C][C]535.200993183663[/C][C]-7.20099318366256[/C][/ROW]
[ROW][C]98[/C][C]533[/C][C]515.368034863257[/C][C]17.6319651367425[/C][/ROW]
[ROW][C]99[/C][C]536[/C][C]546.466974133669[/C][C]-10.4669741336686[/C][/ROW]
[ROW][C]100[/C][C]537[/C][C]532.036894297197[/C][C]4.9631057028033[/C][/ROW]
[ROW][C]101[/C][C]524[/C][C]537.798847623406[/C][C]-13.798847623406[/C][/ROW]
[ROW][C]102[/C][C]536[/C][C]553.615484606108[/C][C]-17.6154846061083[/C][/ROW]
[ROW][C]103[/C][C]587[/C][C]543.763431337733[/C][C]43.2365686622669[/C][/ROW]
[ROW][C]104[/C][C]597[/C][C]564.848284676396[/C][C]32.1517153236041[/C][/ROW]
[ROW][C]105[/C][C]581[/C][C]531.380406776117[/C][C]49.6195932238831[/C][/ROW]
[ROW][C]106[/C][C]564[/C][C]548.575953007178[/C][C]15.4240469928216[/C][/ROW]
[ROW][C]107[/C][C]558[/C][C]529.44601335023[/C][C]28.5539866497704[/C][/ROW]
[ROW][C]108[/C][C]575[/C][C]546.24650461803[/C][C]28.7534953819705[/C][/ROW]
[ROW][C]109[/C][C]580[/C][C]528.060025243543[/C][C]51.9399747564566[/C][/ROW]
[ROW][C]110[/C][C]575[/C][C]552.492938254543[/C][C]22.5070617454574[/C][/ROW]
[ROW][C]111[/C][C]563[/C][C]588.747394427892[/C][C]-25.7473944278915[/C][/ROW]
[ROW][C]112[/C][C]552[/C][C]570.231853140098[/C][C]-18.2318531400981[/C][/ROW]
[ROW][C]113[/C][C]537[/C][C]575.638074270132[/C][C]-38.6380742701316[/C][/ROW]
[ROW][C]114[/C][C]545[/C][C]584.238252063925[/C][C]-39.2382520639253[/C][/ROW]
[ROW][C]115[/C][C]601[/C][C]579.186551976515[/C][C]21.8134480234848[/C][/ROW]
[ROW][C]116[/C][C]604[/C][C]595.275545995535[/C][C]8.72445400446476[/C][/ROW]
[ROW][C]117[/C][C]586[/C][C]573.177413460685[/C][C]12.8225865393147[/C][/ROW]
[ROW][C]118[/C][C]564[/C][C]582.677292870439[/C][C]-18.6772928704385[/C][/ROW]
[ROW][C]119[/C][C]549[/C][C]585.168275131173[/C][C]-36.1682751311728[/C][/ROW]
[ROW][C]120[/C][C]551[/C][C]592.744777293721[/C][C]-41.7447772937214[/C][/ROW]
[ROW][C]121[/C][C]556[/C][C]587.932504737499[/C][C]-31.9325047374993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190193&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190193&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
1467484.40770824616-17.40770824616
2460474.925909855434-14.9259098554338
3448491.305265591007-43.3052655910071
4443485.456424158499-42.4564241584994
5436494.068056170259-58.0680561702585
6431493.199106470537-62.1991064705367
7484489.864071608844-5.86407160884383
8510527.956429537396-17.9564295373956
9513476.98617921960836.0138207803923
10503494.1690283534068.83097164659409
11471491.796516024593-20.7965160245925
12471502.48759606788-31.4875960678798
13476507.795116777933-31.7951167779328
14475500.750488621151-25.7504886211514
15470521.916407752451-51.9164077524511
16461523.626969472923-62.6269694729228
17455530.495939037782-75.4959390377823
18456520.219027477595-64.2190274775954
19517538.155828894874-21.1558288948736
20525545.031176147571-20.0311761475714
21523508.04467740024714.9553225997534
22519526.80347909199-7.8034790919897
23509503.0776733868085.92232661319225
24512535.566386762255-23.5663867622554
25519529.214658408514-10.2146584085135
26517517.492974984933-0.49297498493337
27510542.131559516823-32.131559516823
28509533.633328693917-24.6333286939167
29501524.72608926582-23.72608926582
30507540.649814118985-33.6498141189848
31569550.30974319486118.6902568051387
32580562.69796806514917.3020319348514
33578542.04362616230235.9563738376982
34565554.22539534604310.7746046539575
35547535.39492883462111.6050711653786
36555573.636241650457-18.6362416504568
37562528.79362654581333.2063734541866
38561532.55025508803328.4497449119668
39555559.699807649392-4.69980764939207
40544542.2920280311681.70797196883199
41537542.462341436063-5.46234143606337
42543544.430340628446-1.43034062844613
43594554.30954761008639.6904523899144
44611566.3003240240444.69967597596
45613568.89696312794744.1030368720532
46611541.54700541984169.4529945801592
47594545.79638892285248.2036110771481
48595582.97813938266412.0218606173359
49591538.91476043012552.0852395698753
50589506.53402389100582.465976108995
51584540.01622301201343.9837769879872
52573529.35508983222243.6449101677779
53567540.44668736069126.5533126393087
54569565.9045314169183.09546858308171
55621537.97816239657983.021837603421
56629572.73931302323256.2606869767677
57628537.40569101868290.5943089813178
58612540.30453623118571.6954637688146
59595545.12730853937749.8726914606234
60597560.42412628684136.575873713159
61593548.53207061461144.4679293853891
62590526.45689835045563.5431016495451
63580556.41449746305723.5855025369434
64574532.99485054821941.0051494517815
65573552.09047237704320.909527622957
66573533.67740779960339.3225922003972
67620536.77760009016583.2223999098354
68626582.38343780360143.6165621963994
69620538.36928003948581.6307199605151
70588557.78144895403730.2185510459631
71566543.18628887776922.8137111222307
72557565.267818720746-8.26781872074593
73561544.62529026226416.3747097377358
74549526.63229529446922.3677047055312
75532561.22338162945-29.2233816294499
76526547.647839702471-21.6478397024707
77511550.28416611283-39.2841661128302
78499542.537507180359-43.5375071803588
79555562.6805097767-7.68050977670014
80565580.58600220754-15.5860022075395
81542529.38135549757712.6186445024225
82527545.940719902615-18.9407199026153
83510541.983862199928-31.9838621999283
84514559.554679651602-45.5546796516022
85517554.627463209926-37.6274632099258
86508539.693258885889-31.6932588858889
87493539.036888699703-46.0368886997031
88490549.966553052041-59.9665530520414
89469538.587851761928-69.5878517619276
90478555.674962443019-77.6749624430194
91528562.311485191185-34.3114851911849
92534587.954962034121-53.9549620341212
93518555.741020993104-37.7410209931044
94506568.534578230793-62.534578230793
95502538.036325579114-36.036325579114
96516559.037235849057-43.0372358490574
97528535.200993183663-7.20099318366256
98533515.36803486325717.6319651367425
99536546.466974133669-10.4669741336686
100537532.0368942971974.9631057028033
101524537.798847623406-13.798847623406
102536553.615484606108-17.6154846061083
103587543.76343133773343.2365686622669
104597564.84828467639632.1517153236041
105581531.38040677611749.6195932238831
106564548.57595300717815.4240469928216
107558529.4460133502328.5539866497704
108575546.2465046180328.7534953819705
109580528.06002524354351.9399747564566
110575552.49293825454322.5070617454574
111563588.747394427892-25.7473944278915
112552570.231853140098-18.2318531400981
113537575.638074270132-38.6380742701316
114545584.238252063925-39.2382520639253
115601579.18655197651521.8134480234848
116604595.2755459955358.72445400446476
117586573.17741346068512.8225865393147
118564582.677292870439-18.6772928704385
119549585.168275131173-36.1682751311728
120551592.744777293721-41.7447772937214
121556587.932504737499-31.9325047374993






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.187784373597670.375568747195340.81221562640233
90.09281844926060790.1856368985212160.907181550739392
100.0384870925452160.0769741850904320.961512907454784
110.04852270781940990.09704541563881970.95147729218059
120.05314634814354560.1062926962870910.946853651856454
130.04367783969422960.08735567938845920.95632216030577
140.03180767160395470.06361534320790940.968192328396045
150.01986443650857980.03972887301715950.98013556349142
160.01210478066511430.02420956133022850.987895219334886
170.009263754061040170.01852750812208030.99073624593896
180.007975508244085870.01595101648817170.992024491755914
190.01943306918175740.03886613836351480.980566930818243
200.01416740449348770.02833480898697540.985832595506512
210.01504900890728470.03009801781456940.984950991092715
220.009120933764114010.0182418675282280.990879066235886
230.007000924865475190.01400184973095040.992999075134525
240.004559856530819080.009119713061638150.995440143469181
250.0124687135387040.02493742707740790.987531286461296
260.01881807520233830.03763615040467670.981181924797662
270.01879454235746980.03758908471493970.98120545764253
280.01660222751275930.03320445502551860.983397772487241
290.01759456350833050.03518912701666110.982405436491669
300.01754223553382660.03508447106765320.982457764466173
310.02891719285103370.05783438570206740.971082807148966
320.03587493374337040.07174986748674090.96412506625663
330.06299404986915210.1259880997383040.937005950130848
340.04609916063853830.09219832127707660.953900839361462
350.0481589746902950.096317949380590.951841025309705
360.04502753884565210.09005507769130420.954972461154348
370.09098293948932810.1819658789786560.909017060510672
380.1695615716020020.3391231432040040.830438428397998
390.1545401117980850.309080223596170.845459888201915
400.1806692921531720.3613385843063450.819330707846828
410.2814308625183050.562861725036610.718569137481695
420.2446994231890660.4893988463781330.755300576810934
430.2677074716177630.5354149432355270.732292528382237
440.2370294521012420.4740589042024840.762970547898758
450.2294326292016550.458865258403310.770567370798345
460.2524187124535960.5048374249071910.747581287546404
470.2167654698006440.4335309396012880.783234530199356
480.2181994950152790.4363989900305570.781800504984721
490.1969248685304970.3938497370609930.803075131469503
500.2476558094270260.4953116188540510.752344190572974
510.2084049891451120.4168099782902230.791595010854888
520.1712327162582590.3424654325165170.828767283741741
530.1526656597313240.3053313194626490.847334340268676
540.1681764793075590.3363529586151180.831823520692441
550.2048285699047230.4096571398094470.795171430095277
560.1706417893622270.3412835787244530.829358210637773
570.2384574812653860.4769149625307720.761542518734614
580.2389077019060370.4778154038120740.761092298093963
590.2251696255854290.4503392511708570.774830374414571
600.2042892741892620.4085785483785230.795710725810738
610.1727091413636740.3454182827273470.827290858636326
620.1631474075833080.3262948151666160.836852592416692
630.1597076305094530.3194152610189050.840292369490547
640.1301405314699330.2602810629398670.869859468530067
650.1189204897009380.2378409794018770.881079510299062
660.1084981954629360.2169963909258720.891501804537064
670.1538655693480460.3077311386960920.846134430651954
680.1431772727016210.2863545454032430.856822727298379
690.3119417170582560.6238834341165130.688058282941744
700.3869456923987210.7738913847974410.613054307601279
710.4309581444282510.8619162888565030.569041855571749
720.4711894857280630.9423789714561270.528810514271937
730.4695165281338520.9390330562677030.530483471866148
740.4594549490188540.9189098980377080.540545050981146
750.5439189542348470.9121620915303070.456081045765153
760.5820423794826640.8359152410346730.417957620517336
770.677155568468590.645688863062820.32284443153141
780.7621945571334380.4756108857331240.237805442866562
790.7408613059169980.5182773881660050.259138694083002
800.7536249847297740.4927500305404510.246375015270226
810.7125116547381750.5749766905236490.287488345261825
820.6862378747232750.627524250553450.313762125276725
830.7661298430565120.4677403138869770.233870156943488
840.8603871775644430.2792256448711140.139612822435557
850.8792548850428720.2414902299142570.120745114957128
860.8800233784452610.2399532431094780.119976621554739
870.888401279103430.223197441793140.11159872089657
880.9038578711008950.1922842577982110.0961421288991054
890.9580063900156130.08398721996877290.0419936099843865
900.9871556789122410.02568864217551810.0128443210877591
910.9841748381758060.03165032364838790.0158251618241939
920.9900181162814840.01996376743703270.00998188371851635
930.9867384424111920.02652311517761620.0132615575888081
940.9886506832688320.02269863346233640.0113493167311682
950.9932450370091570.01350992598168690.00675496299084343
960.9957467432990610.008506513401878090.00425325670093904
970.9956585639857560.008682872028487140.00434143601424357
980.9951113769903690.00977724601926140.0048886230096307
990.993395359254830.01320928149033930.00660464074516966
1000.9917736622704730.01645267545905380.00822633772952692
1010.9975342751651070.004931449669786370.00246572483489319
1020.9981809977239920.003638004552016590.0018190022760083
1030.9967746103945750.006450779210850740.00322538960542537
1040.9942829602070140.01143407958597140.00571703979298569
1050.9911562367498210.01768752650035720.00884376325017861
1060.9823833537181560.03523329256368760.0176166462818438
1070.9680421088435320.06391578231293680.0319578911564684
1080.9403700090566140.1192599818867710.0596299909433856
1090.8967726062160230.2064547875679550.103227393783977
1100.8260313003574250.347937399285150.173968699642575
1110.725484149613530.5490317007729410.27451585038647
1120.6051376402966170.7897247194067660.394862359703383
1130.8700292292807880.2599415414384240.129970770719212

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.18778437359767 & 0.37556874719534 & 0.81221562640233 \tabularnewline
9 & 0.0928184492606079 & 0.185636898521216 & 0.907181550739392 \tabularnewline
10 & 0.038487092545216 & 0.076974185090432 & 0.961512907454784 \tabularnewline
11 & 0.0485227078194099 & 0.0970454156388197 & 0.95147729218059 \tabularnewline
12 & 0.0531463481435456 & 0.106292696287091 & 0.946853651856454 \tabularnewline
13 & 0.0436778396942296 & 0.0873556793884592 & 0.95632216030577 \tabularnewline
14 & 0.0318076716039547 & 0.0636153432079094 & 0.968192328396045 \tabularnewline
15 & 0.0198644365085798 & 0.0397288730171595 & 0.98013556349142 \tabularnewline
16 & 0.0121047806651143 & 0.0242095613302285 & 0.987895219334886 \tabularnewline
17 & 0.00926375406104017 & 0.0185275081220803 & 0.99073624593896 \tabularnewline
18 & 0.00797550824408587 & 0.0159510164881717 & 0.992024491755914 \tabularnewline
19 & 0.0194330691817574 & 0.0388661383635148 & 0.980566930818243 \tabularnewline
20 & 0.0141674044934877 & 0.0283348089869754 & 0.985832595506512 \tabularnewline
21 & 0.0150490089072847 & 0.0300980178145694 & 0.984950991092715 \tabularnewline
22 & 0.00912093376411401 & 0.018241867528228 & 0.990879066235886 \tabularnewline
23 & 0.00700092486547519 & 0.0140018497309504 & 0.992999075134525 \tabularnewline
24 & 0.00455985653081908 & 0.00911971306163815 & 0.995440143469181 \tabularnewline
25 & 0.012468713538704 & 0.0249374270774079 & 0.987531286461296 \tabularnewline
26 & 0.0188180752023383 & 0.0376361504046767 & 0.981181924797662 \tabularnewline
27 & 0.0187945423574698 & 0.0375890847149397 & 0.98120545764253 \tabularnewline
28 & 0.0166022275127593 & 0.0332044550255186 & 0.983397772487241 \tabularnewline
29 & 0.0175945635083305 & 0.0351891270166611 & 0.982405436491669 \tabularnewline
30 & 0.0175422355338266 & 0.0350844710676532 & 0.982457764466173 \tabularnewline
31 & 0.0289171928510337 & 0.0578343857020674 & 0.971082807148966 \tabularnewline
32 & 0.0358749337433704 & 0.0717498674867409 & 0.96412506625663 \tabularnewline
33 & 0.0629940498691521 & 0.125988099738304 & 0.937005950130848 \tabularnewline
34 & 0.0460991606385383 & 0.0921983212770766 & 0.953900839361462 \tabularnewline
35 & 0.048158974690295 & 0.09631794938059 & 0.951841025309705 \tabularnewline
36 & 0.0450275388456521 & 0.0900550776913042 & 0.954972461154348 \tabularnewline
37 & 0.0909829394893281 & 0.181965878978656 & 0.909017060510672 \tabularnewline
38 & 0.169561571602002 & 0.339123143204004 & 0.830438428397998 \tabularnewline
39 & 0.154540111798085 & 0.30908022359617 & 0.845459888201915 \tabularnewline
40 & 0.180669292153172 & 0.361338584306345 & 0.819330707846828 \tabularnewline
41 & 0.281430862518305 & 0.56286172503661 & 0.718569137481695 \tabularnewline
42 & 0.244699423189066 & 0.489398846378133 & 0.755300576810934 \tabularnewline
43 & 0.267707471617763 & 0.535414943235527 & 0.732292528382237 \tabularnewline
44 & 0.237029452101242 & 0.474058904202484 & 0.762970547898758 \tabularnewline
45 & 0.229432629201655 & 0.45886525840331 & 0.770567370798345 \tabularnewline
46 & 0.252418712453596 & 0.504837424907191 & 0.747581287546404 \tabularnewline
47 & 0.216765469800644 & 0.433530939601288 & 0.783234530199356 \tabularnewline
48 & 0.218199495015279 & 0.436398990030557 & 0.781800504984721 \tabularnewline
49 & 0.196924868530497 & 0.393849737060993 & 0.803075131469503 \tabularnewline
50 & 0.247655809427026 & 0.495311618854051 & 0.752344190572974 \tabularnewline
51 & 0.208404989145112 & 0.416809978290223 & 0.791595010854888 \tabularnewline
52 & 0.171232716258259 & 0.342465432516517 & 0.828767283741741 \tabularnewline
53 & 0.152665659731324 & 0.305331319462649 & 0.847334340268676 \tabularnewline
54 & 0.168176479307559 & 0.336352958615118 & 0.831823520692441 \tabularnewline
55 & 0.204828569904723 & 0.409657139809447 & 0.795171430095277 \tabularnewline
56 & 0.170641789362227 & 0.341283578724453 & 0.829358210637773 \tabularnewline
57 & 0.238457481265386 & 0.476914962530772 & 0.761542518734614 \tabularnewline
58 & 0.238907701906037 & 0.477815403812074 & 0.761092298093963 \tabularnewline
59 & 0.225169625585429 & 0.450339251170857 & 0.774830374414571 \tabularnewline
60 & 0.204289274189262 & 0.408578548378523 & 0.795710725810738 \tabularnewline
61 & 0.172709141363674 & 0.345418282727347 & 0.827290858636326 \tabularnewline
62 & 0.163147407583308 & 0.326294815166616 & 0.836852592416692 \tabularnewline
63 & 0.159707630509453 & 0.319415261018905 & 0.840292369490547 \tabularnewline
64 & 0.130140531469933 & 0.260281062939867 & 0.869859468530067 \tabularnewline
65 & 0.118920489700938 & 0.237840979401877 & 0.881079510299062 \tabularnewline
66 & 0.108498195462936 & 0.216996390925872 & 0.891501804537064 \tabularnewline
67 & 0.153865569348046 & 0.307731138696092 & 0.846134430651954 \tabularnewline
68 & 0.143177272701621 & 0.286354545403243 & 0.856822727298379 \tabularnewline
69 & 0.311941717058256 & 0.623883434116513 & 0.688058282941744 \tabularnewline
70 & 0.386945692398721 & 0.773891384797441 & 0.613054307601279 \tabularnewline
71 & 0.430958144428251 & 0.861916288856503 & 0.569041855571749 \tabularnewline
72 & 0.471189485728063 & 0.942378971456127 & 0.528810514271937 \tabularnewline
73 & 0.469516528133852 & 0.939033056267703 & 0.530483471866148 \tabularnewline
74 & 0.459454949018854 & 0.918909898037708 & 0.540545050981146 \tabularnewline
75 & 0.543918954234847 & 0.912162091530307 & 0.456081045765153 \tabularnewline
76 & 0.582042379482664 & 0.835915241034673 & 0.417957620517336 \tabularnewline
77 & 0.67715556846859 & 0.64568886306282 & 0.32284443153141 \tabularnewline
78 & 0.762194557133438 & 0.475610885733124 & 0.237805442866562 \tabularnewline
79 & 0.740861305916998 & 0.518277388166005 & 0.259138694083002 \tabularnewline
80 & 0.753624984729774 & 0.492750030540451 & 0.246375015270226 \tabularnewline
81 & 0.712511654738175 & 0.574976690523649 & 0.287488345261825 \tabularnewline
82 & 0.686237874723275 & 0.62752425055345 & 0.313762125276725 \tabularnewline
83 & 0.766129843056512 & 0.467740313886977 & 0.233870156943488 \tabularnewline
84 & 0.860387177564443 & 0.279225644871114 & 0.139612822435557 \tabularnewline
85 & 0.879254885042872 & 0.241490229914257 & 0.120745114957128 \tabularnewline
86 & 0.880023378445261 & 0.239953243109478 & 0.119976621554739 \tabularnewline
87 & 0.88840127910343 & 0.22319744179314 & 0.11159872089657 \tabularnewline
88 & 0.903857871100895 & 0.192284257798211 & 0.0961421288991054 \tabularnewline
89 & 0.958006390015613 & 0.0839872199687729 & 0.0419936099843865 \tabularnewline
90 & 0.987155678912241 & 0.0256886421755181 & 0.0128443210877591 \tabularnewline
91 & 0.984174838175806 & 0.0316503236483879 & 0.0158251618241939 \tabularnewline
92 & 0.990018116281484 & 0.0199637674370327 & 0.00998188371851635 \tabularnewline
93 & 0.986738442411192 & 0.0265231151776162 & 0.0132615575888081 \tabularnewline
94 & 0.988650683268832 & 0.0226986334623364 & 0.0113493167311682 \tabularnewline
95 & 0.993245037009157 & 0.0135099259816869 & 0.00675496299084343 \tabularnewline
96 & 0.995746743299061 & 0.00850651340187809 & 0.00425325670093904 \tabularnewline
97 & 0.995658563985756 & 0.00868287202848714 & 0.00434143601424357 \tabularnewline
98 & 0.995111376990369 & 0.0097772460192614 & 0.0048886230096307 \tabularnewline
99 & 0.99339535925483 & 0.0132092814903393 & 0.00660464074516966 \tabularnewline
100 & 0.991773662270473 & 0.0164526754590538 & 0.00822633772952692 \tabularnewline
101 & 0.997534275165107 & 0.00493144966978637 & 0.00246572483489319 \tabularnewline
102 & 0.998180997723992 & 0.00363800455201659 & 0.0018190022760083 \tabularnewline
103 & 0.996774610394575 & 0.00645077921085074 & 0.00322538960542537 \tabularnewline
104 & 0.994282960207014 & 0.0114340795859714 & 0.00571703979298569 \tabularnewline
105 & 0.991156236749821 & 0.0176875265003572 & 0.00884376325017861 \tabularnewline
106 & 0.982383353718156 & 0.0352332925636876 & 0.0176166462818438 \tabularnewline
107 & 0.968042108843532 & 0.0639157823129368 & 0.0319578911564684 \tabularnewline
108 & 0.940370009056614 & 0.119259981886771 & 0.0596299909433856 \tabularnewline
109 & 0.896772606216023 & 0.206454787567955 & 0.103227393783977 \tabularnewline
110 & 0.826031300357425 & 0.34793739928515 & 0.173968699642575 \tabularnewline
111 & 0.72548414961353 & 0.549031700772941 & 0.27451585038647 \tabularnewline
112 & 0.605137640296617 & 0.789724719406766 & 0.394862359703383 \tabularnewline
113 & 0.870029229280788 & 0.259941541438424 & 0.129970770719212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190193&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]0.18778437359767[/C][C]0.37556874719534[/C][C]0.81221562640233[/C][/ROW]
[ROW][C]9[/C][C]0.0928184492606079[/C][C]0.185636898521216[/C][C]0.907181550739392[/C][/ROW]
[ROW][C]10[/C][C]0.038487092545216[/C][C]0.076974185090432[/C][C]0.961512907454784[/C][/ROW]
[ROW][C]11[/C][C]0.0485227078194099[/C][C]0.0970454156388197[/C][C]0.95147729218059[/C][/ROW]
[ROW][C]12[/C][C]0.0531463481435456[/C][C]0.106292696287091[/C][C]0.946853651856454[/C][/ROW]
[ROW][C]13[/C][C]0.0436778396942296[/C][C]0.0873556793884592[/C][C]0.95632216030577[/C][/ROW]
[ROW][C]14[/C][C]0.0318076716039547[/C][C]0.0636153432079094[/C][C]0.968192328396045[/C][/ROW]
[ROW][C]15[/C][C]0.0198644365085798[/C][C]0.0397288730171595[/C][C]0.98013556349142[/C][/ROW]
[ROW][C]16[/C][C]0.0121047806651143[/C][C]0.0242095613302285[/C][C]0.987895219334886[/C][/ROW]
[ROW][C]17[/C][C]0.00926375406104017[/C][C]0.0185275081220803[/C][C]0.99073624593896[/C][/ROW]
[ROW][C]18[/C][C]0.00797550824408587[/C][C]0.0159510164881717[/C][C]0.992024491755914[/C][/ROW]
[ROW][C]19[/C][C]0.0194330691817574[/C][C]0.0388661383635148[/C][C]0.980566930818243[/C][/ROW]
[ROW][C]20[/C][C]0.0141674044934877[/C][C]0.0283348089869754[/C][C]0.985832595506512[/C][/ROW]
[ROW][C]21[/C][C]0.0150490089072847[/C][C]0.0300980178145694[/C][C]0.984950991092715[/C][/ROW]
[ROW][C]22[/C][C]0.00912093376411401[/C][C]0.018241867528228[/C][C]0.990879066235886[/C][/ROW]
[ROW][C]23[/C][C]0.00700092486547519[/C][C]0.0140018497309504[/C][C]0.992999075134525[/C][/ROW]
[ROW][C]24[/C][C]0.00455985653081908[/C][C]0.00911971306163815[/C][C]0.995440143469181[/C][/ROW]
[ROW][C]25[/C][C]0.012468713538704[/C][C]0.0249374270774079[/C][C]0.987531286461296[/C][/ROW]
[ROW][C]26[/C][C]0.0188180752023383[/C][C]0.0376361504046767[/C][C]0.981181924797662[/C][/ROW]
[ROW][C]27[/C][C]0.0187945423574698[/C][C]0.0375890847149397[/C][C]0.98120545764253[/C][/ROW]
[ROW][C]28[/C][C]0.0166022275127593[/C][C]0.0332044550255186[/C][C]0.983397772487241[/C][/ROW]
[ROW][C]29[/C][C]0.0175945635083305[/C][C]0.0351891270166611[/C][C]0.982405436491669[/C][/ROW]
[ROW][C]30[/C][C]0.0175422355338266[/C][C]0.0350844710676532[/C][C]0.982457764466173[/C][/ROW]
[ROW][C]31[/C][C]0.0289171928510337[/C][C]0.0578343857020674[/C][C]0.971082807148966[/C][/ROW]
[ROW][C]32[/C][C]0.0358749337433704[/C][C]0.0717498674867409[/C][C]0.96412506625663[/C][/ROW]
[ROW][C]33[/C][C]0.0629940498691521[/C][C]0.125988099738304[/C][C]0.937005950130848[/C][/ROW]
[ROW][C]34[/C][C]0.0460991606385383[/C][C]0.0921983212770766[/C][C]0.953900839361462[/C][/ROW]
[ROW][C]35[/C][C]0.048158974690295[/C][C]0.09631794938059[/C][C]0.951841025309705[/C][/ROW]
[ROW][C]36[/C][C]0.0450275388456521[/C][C]0.0900550776913042[/C][C]0.954972461154348[/C][/ROW]
[ROW][C]37[/C][C]0.0909829394893281[/C][C]0.181965878978656[/C][C]0.909017060510672[/C][/ROW]
[ROW][C]38[/C][C]0.169561571602002[/C][C]0.339123143204004[/C][C]0.830438428397998[/C][/ROW]
[ROW][C]39[/C][C]0.154540111798085[/C][C]0.30908022359617[/C][C]0.845459888201915[/C][/ROW]
[ROW][C]40[/C][C]0.180669292153172[/C][C]0.361338584306345[/C][C]0.819330707846828[/C][/ROW]
[ROW][C]41[/C][C]0.281430862518305[/C][C]0.56286172503661[/C][C]0.718569137481695[/C][/ROW]
[ROW][C]42[/C][C]0.244699423189066[/C][C]0.489398846378133[/C][C]0.755300576810934[/C][/ROW]
[ROW][C]43[/C][C]0.267707471617763[/C][C]0.535414943235527[/C][C]0.732292528382237[/C][/ROW]
[ROW][C]44[/C][C]0.237029452101242[/C][C]0.474058904202484[/C][C]0.762970547898758[/C][/ROW]
[ROW][C]45[/C][C]0.229432629201655[/C][C]0.45886525840331[/C][C]0.770567370798345[/C][/ROW]
[ROW][C]46[/C][C]0.252418712453596[/C][C]0.504837424907191[/C][C]0.747581287546404[/C][/ROW]
[ROW][C]47[/C][C]0.216765469800644[/C][C]0.433530939601288[/C][C]0.783234530199356[/C][/ROW]
[ROW][C]48[/C][C]0.218199495015279[/C][C]0.436398990030557[/C][C]0.781800504984721[/C][/ROW]
[ROW][C]49[/C][C]0.196924868530497[/C][C]0.393849737060993[/C][C]0.803075131469503[/C][/ROW]
[ROW][C]50[/C][C]0.247655809427026[/C][C]0.495311618854051[/C][C]0.752344190572974[/C][/ROW]
[ROW][C]51[/C][C]0.208404989145112[/C][C]0.416809978290223[/C][C]0.791595010854888[/C][/ROW]
[ROW][C]52[/C][C]0.171232716258259[/C][C]0.342465432516517[/C][C]0.828767283741741[/C][/ROW]
[ROW][C]53[/C][C]0.152665659731324[/C][C]0.305331319462649[/C][C]0.847334340268676[/C][/ROW]
[ROW][C]54[/C][C]0.168176479307559[/C][C]0.336352958615118[/C][C]0.831823520692441[/C][/ROW]
[ROW][C]55[/C][C]0.204828569904723[/C][C]0.409657139809447[/C][C]0.795171430095277[/C][/ROW]
[ROW][C]56[/C][C]0.170641789362227[/C][C]0.341283578724453[/C][C]0.829358210637773[/C][/ROW]
[ROW][C]57[/C][C]0.238457481265386[/C][C]0.476914962530772[/C][C]0.761542518734614[/C][/ROW]
[ROW][C]58[/C][C]0.238907701906037[/C][C]0.477815403812074[/C][C]0.761092298093963[/C][/ROW]
[ROW][C]59[/C][C]0.225169625585429[/C][C]0.450339251170857[/C][C]0.774830374414571[/C][/ROW]
[ROW][C]60[/C][C]0.204289274189262[/C][C]0.408578548378523[/C][C]0.795710725810738[/C][/ROW]
[ROW][C]61[/C][C]0.172709141363674[/C][C]0.345418282727347[/C][C]0.827290858636326[/C][/ROW]
[ROW][C]62[/C][C]0.163147407583308[/C][C]0.326294815166616[/C][C]0.836852592416692[/C][/ROW]
[ROW][C]63[/C][C]0.159707630509453[/C][C]0.319415261018905[/C][C]0.840292369490547[/C][/ROW]
[ROW][C]64[/C][C]0.130140531469933[/C][C]0.260281062939867[/C][C]0.869859468530067[/C][/ROW]
[ROW][C]65[/C][C]0.118920489700938[/C][C]0.237840979401877[/C][C]0.881079510299062[/C][/ROW]
[ROW][C]66[/C][C]0.108498195462936[/C][C]0.216996390925872[/C][C]0.891501804537064[/C][/ROW]
[ROW][C]67[/C][C]0.153865569348046[/C][C]0.307731138696092[/C][C]0.846134430651954[/C][/ROW]
[ROW][C]68[/C][C]0.143177272701621[/C][C]0.286354545403243[/C][C]0.856822727298379[/C][/ROW]
[ROW][C]69[/C][C]0.311941717058256[/C][C]0.623883434116513[/C][C]0.688058282941744[/C][/ROW]
[ROW][C]70[/C][C]0.386945692398721[/C][C]0.773891384797441[/C][C]0.613054307601279[/C][/ROW]
[ROW][C]71[/C][C]0.430958144428251[/C][C]0.861916288856503[/C][C]0.569041855571749[/C][/ROW]
[ROW][C]72[/C][C]0.471189485728063[/C][C]0.942378971456127[/C][C]0.528810514271937[/C][/ROW]
[ROW][C]73[/C][C]0.469516528133852[/C][C]0.939033056267703[/C][C]0.530483471866148[/C][/ROW]
[ROW][C]74[/C][C]0.459454949018854[/C][C]0.918909898037708[/C][C]0.540545050981146[/C][/ROW]
[ROW][C]75[/C][C]0.543918954234847[/C][C]0.912162091530307[/C][C]0.456081045765153[/C][/ROW]
[ROW][C]76[/C][C]0.582042379482664[/C][C]0.835915241034673[/C][C]0.417957620517336[/C][/ROW]
[ROW][C]77[/C][C]0.67715556846859[/C][C]0.64568886306282[/C][C]0.32284443153141[/C][/ROW]
[ROW][C]78[/C][C]0.762194557133438[/C][C]0.475610885733124[/C][C]0.237805442866562[/C][/ROW]
[ROW][C]79[/C][C]0.740861305916998[/C][C]0.518277388166005[/C][C]0.259138694083002[/C][/ROW]
[ROW][C]80[/C][C]0.753624984729774[/C][C]0.492750030540451[/C][C]0.246375015270226[/C][/ROW]
[ROW][C]81[/C][C]0.712511654738175[/C][C]0.574976690523649[/C][C]0.287488345261825[/C][/ROW]
[ROW][C]82[/C][C]0.686237874723275[/C][C]0.62752425055345[/C][C]0.313762125276725[/C][/ROW]
[ROW][C]83[/C][C]0.766129843056512[/C][C]0.467740313886977[/C][C]0.233870156943488[/C][/ROW]
[ROW][C]84[/C][C]0.860387177564443[/C][C]0.279225644871114[/C][C]0.139612822435557[/C][/ROW]
[ROW][C]85[/C][C]0.879254885042872[/C][C]0.241490229914257[/C][C]0.120745114957128[/C][/ROW]
[ROW][C]86[/C][C]0.880023378445261[/C][C]0.239953243109478[/C][C]0.119976621554739[/C][/ROW]
[ROW][C]87[/C][C]0.88840127910343[/C][C]0.22319744179314[/C][C]0.11159872089657[/C][/ROW]
[ROW][C]88[/C][C]0.903857871100895[/C][C]0.192284257798211[/C][C]0.0961421288991054[/C][/ROW]
[ROW][C]89[/C][C]0.958006390015613[/C][C]0.0839872199687729[/C][C]0.0419936099843865[/C][/ROW]
[ROW][C]90[/C][C]0.987155678912241[/C][C]0.0256886421755181[/C][C]0.0128443210877591[/C][/ROW]
[ROW][C]91[/C][C]0.984174838175806[/C][C]0.0316503236483879[/C][C]0.0158251618241939[/C][/ROW]
[ROW][C]92[/C][C]0.990018116281484[/C][C]0.0199637674370327[/C][C]0.00998188371851635[/C][/ROW]
[ROW][C]93[/C][C]0.986738442411192[/C][C]0.0265231151776162[/C][C]0.0132615575888081[/C][/ROW]
[ROW][C]94[/C][C]0.988650683268832[/C][C]0.0226986334623364[/C][C]0.0113493167311682[/C][/ROW]
[ROW][C]95[/C][C]0.993245037009157[/C][C]0.0135099259816869[/C][C]0.00675496299084343[/C][/ROW]
[ROW][C]96[/C][C]0.995746743299061[/C][C]0.00850651340187809[/C][C]0.00425325670093904[/C][/ROW]
[ROW][C]97[/C][C]0.995658563985756[/C][C]0.00868287202848714[/C][C]0.00434143601424357[/C][/ROW]
[ROW][C]98[/C][C]0.995111376990369[/C][C]0.0097772460192614[/C][C]0.0048886230096307[/C][/ROW]
[ROW][C]99[/C][C]0.99339535925483[/C][C]0.0132092814903393[/C][C]0.00660464074516966[/C][/ROW]
[ROW][C]100[/C][C]0.991773662270473[/C][C]0.0164526754590538[/C][C]0.00822633772952692[/C][/ROW]
[ROW][C]101[/C][C]0.997534275165107[/C][C]0.00493144966978637[/C][C]0.00246572483489319[/C][/ROW]
[ROW][C]102[/C][C]0.998180997723992[/C][C]0.00363800455201659[/C][C]0.0018190022760083[/C][/ROW]
[ROW][C]103[/C][C]0.996774610394575[/C][C]0.00645077921085074[/C][C]0.00322538960542537[/C][/ROW]
[ROW][C]104[/C][C]0.994282960207014[/C][C]0.0114340795859714[/C][C]0.00571703979298569[/C][/ROW]
[ROW][C]105[/C][C]0.991156236749821[/C][C]0.0176875265003572[/C][C]0.00884376325017861[/C][/ROW]
[ROW][C]106[/C][C]0.982383353718156[/C][C]0.0352332925636876[/C][C]0.0176166462818438[/C][/ROW]
[ROW][C]107[/C][C]0.968042108843532[/C][C]0.0639157823129368[/C][C]0.0319578911564684[/C][/ROW]
[ROW][C]108[/C][C]0.940370009056614[/C][C]0.119259981886771[/C][C]0.0596299909433856[/C][/ROW]
[ROW][C]109[/C][C]0.896772606216023[/C][C]0.206454787567955[/C][C]0.103227393783977[/C][/ROW]
[ROW][C]110[/C][C]0.826031300357425[/C][C]0.34793739928515[/C][C]0.173968699642575[/C][/ROW]
[ROW][C]111[/C][C]0.72548414961353[/C][C]0.549031700772941[/C][C]0.27451585038647[/C][/ROW]
[ROW][C]112[/C][C]0.605137640296617[/C][C]0.789724719406766[/C][C]0.394862359703383[/C][/ROW]
[ROW][C]113[/C][C]0.870029229280788[/C][C]0.259941541438424[/C][C]0.129970770719212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190193&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190193&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
80.187784373597670.375568747195340.81221562640233
90.09281844926060790.1856368985212160.907181550739392
100.0384870925452160.0769741850904320.961512907454784
110.04852270781940990.09704541563881970.95147729218059
120.05314634814354560.1062926962870910.946853651856454
130.04367783969422960.08735567938845920.95632216030577
140.03180767160395470.06361534320790940.968192328396045
150.01986443650857980.03972887301715950.98013556349142
160.01210478066511430.02420956133022850.987895219334886
170.009263754061040170.01852750812208030.99073624593896
180.007975508244085870.01595101648817170.992024491755914
190.01943306918175740.03886613836351480.980566930818243
200.01416740449348770.02833480898697540.985832595506512
210.01504900890728470.03009801781456940.984950991092715
220.009120933764114010.0182418675282280.990879066235886
230.007000924865475190.01400184973095040.992999075134525
240.004559856530819080.009119713061638150.995440143469181
250.0124687135387040.02493742707740790.987531286461296
260.01881807520233830.03763615040467670.981181924797662
270.01879454235746980.03758908471493970.98120545764253
280.01660222751275930.03320445502551860.983397772487241
290.01759456350833050.03518912701666110.982405436491669
300.01754223553382660.03508447106765320.982457764466173
310.02891719285103370.05783438570206740.971082807148966
320.03587493374337040.07174986748674090.96412506625663
330.06299404986915210.1259880997383040.937005950130848
340.04609916063853830.09219832127707660.953900839361462
350.0481589746902950.096317949380590.951841025309705
360.04502753884565210.09005507769130420.954972461154348
370.09098293948932810.1819658789786560.909017060510672
380.1695615716020020.3391231432040040.830438428397998
390.1545401117980850.309080223596170.845459888201915
400.1806692921531720.3613385843063450.819330707846828
410.2814308625183050.562861725036610.718569137481695
420.2446994231890660.4893988463781330.755300576810934
430.2677074716177630.5354149432355270.732292528382237
440.2370294521012420.4740589042024840.762970547898758
450.2294326292016550.458865258403310.770567370798345
460.2524187124535960.5048374249071910.747581287546404
470.2167654698006440.4335309396012880.783234530199356
480.2181994950152790.4363989900305570.781800504984721
490.1969248685304970.3938497370609930.803075131469503
500.2476558094270260.4953116188540510.752344190572974
510.2084049891451120.4168099782902230.791595010854888
520.1712327162582590.3424654325165170.828767283741741
530.1526656597313240.3053313194626490.847334340268676
540.1681764793075590.3363529586151180.831823520692441
550.2048285699047230.4096571398094470.795171430095277
560.1706417893622270.3412835787244530.829358210637773
570.2384574812653860.4769149625307720.761542518734614
580.2389077019060370.4778154038120740.761092298093963
590.2251696255854290.4503392511708570.774830374414571
600.2042892741892620.4085785483785230.795710725810738
610.1727091413636740.3454182827273470.827290858636326
620.1631474075833080.3262948151666160.836852592416692
630.1597076305094530.3194152610189050.840292369490547
640.1301405314699330.2602810629398670.869859468530067
650.1189204897009380.2378409794018770.881079510299062
660.1084981954629360.2169963909258720.891501804537064
670.1538655693480460.3077311386960920.846134430651954
680.1431772727016210.2863545454032430.856822727298379
690.3119417170582560.6238834341165130.688058282941744
700.3869456923987210.7738913847974410.613054307601279
710.4309581444282510.8619162888565030.569041855571749
720.4711894857280630.9423789714561270.528810514271937
730.4695165281338520.9390330562677030.530483471866148
740.4594549490188540.9189098980377080.540545050981146
750.5439189542348470.9121620915303070.456081045765153
760.5820423794826640.8359152410346730.417957620517336
770.677155568468590.645688863062820.32284443153141
780.7621945571334380.4756108857331240.237805442866562
790.7408613059169980.5182773881660050.259138694083002
800.7536249847297740.4927500305404510.246375015270226
810.7125116547381750.5749766905236490.287488345261825
820.6862378747232750.627524250553450.313762125276725
830.7661298430565120.4677403138869770.233870156943488
840.8603871775644430.2792256448711140.139612822435557
850.8792548850428720.2414902299142570.120745114957128
860.8800233784452610.2399532431094780.119976621554739
870.888401279103430.223197441793140.11159872089657
880.9038578711008950.1922842577982110.0961421288991054
890.9580063900156130.08398721996877290.0419936099843865
900.9871556789122410.02568864217551810.0128443210877591
910.9841748381758060.03165032364838790.0158251618241939
920.9900181162814840.01996376743703270.00998188371851635
930.9867384424111920.02652311517761620.0132615575888081
940.9886506832688320.02269863346233640.0113493167311682
950.9932450370091570.01350992598168690.00675496299084343
960.9957467432990610.008506513401878090.00425325670093904
970.9956585639857560.008682872028487140.00434143601424357
980.9951113769903690.00977724601926140.0048886230096307
990.993395359254830.01320928149033930.00660464074516966
1000.9917736622704730.01645267545905380.00822633772952692
1010.9975342751651070.004931449669786370.00246572483489319
1020.9981809977239920.003638004552016590.0018190022760083
1030.9967746103945750.006450779210850740.00322538960542537
1040.9942829602070140.01143407958597140.00571703979298569
1050.9911562367498210.01768752650035720.00884376325017861
1060.9823833537181560.03523329256368760.0176166462818438
1070.9680421088435320.06391578231293680.0319578911564684
1080.9403700090566140.1192599818867710.0596299909433856
1090.8967726062160230.2064547875679550.103227393783977
1100.8260313003574250.347937399285150.173968699642575
1110.725484149613530.5490317007729410.27451585038647
1120.6051376402966170.7897247194067660.394862359703383
1130.8700292292807880.2599415414384240.129970770719212






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0660377358490566NOK
5% type I error level330.311320754716981NOK
10% type I error level440.415094339622642NOK

\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 & 7 & 0.0660377358490566 & NOK \tabularnewline
5% type I error level & 33 & 0.311320754716981 & NOK \tabularnewline
10% type I error level & 44 & 0.415094339622642 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190193&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]7[/C][C]0.0660377358490566[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]33[/C][C]0.311320754716981[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.415094339622642[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190193&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190193&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 level70.0660377358490566NOK
5% type I error level330.311320754716981NOK
10% type I error level440.415094339622642NOK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 9
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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 = 4 ; 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')
}