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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 15 Dec 2009 12:04:43 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/15/t1260904166dn14gavu69zefv4.htm/, Retrieved Thu, 31 Oct 2024 23:11:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68079, Retrieved Thu, 31 Oct 2024 23:11:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Scatterplot prijs...] [2009-12-12 17:13:39] [8733f8ed033058987ec00f5e71b74854]
- RMPD  [Multiple Regression] [Multiple Regression] [2009-12-12 23:11:16] [8733f8ed033058987ec00f5e71b74854]
-         [Multiple Regression] [Multiple Regression] [2009-12-13 11:19:41] [8733f8ed033058987ec00f5e71b74854]
-    D      [Multiple Regression] [Multiple Regression] [2009-12-14 22:31:19] [8733f8ed033058987ec00f5e71b74854]
-    D        [Multiple Regression] [Multiple Regression] [2009-12-14 23:44:49] [8733f8ed033058987ec00f5e71b74854]
-   P             [Multiple Regression] [Multiple Regression] [2009-12-15 19:04:43] [c6e373ff11c42d4585d53e9e88ed5606] [Current]
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Dataseries X:
107.1	0	96.3	87.0	96.8
115.2	0	107.1	96.3	87.0
106.1	0	115.2	107.1	96.3
89.5	0	106.1	115.2	107.1
91.3	0	89.5	106.1	115.2
97.6	0	91.3	89.5	106.1
100.7	0	97.6	91.3	89.5
104.6	0	100.7	97.6	91.3
94.7	0	104.6	100.7	97.6
101.8	0	94.7	104.6	100.7
102.5	0	101.8	94.7	104.6
105.3	0	102.5	101.8	94.7
110.3	0	105.3	102.5	101.8
109.8	0	110.3	105.3	102.5
117.3	0	109.8	110.3	105.3
118.8	0	117.3	109.8	110.3
131.3	0	118.8	117.3	109.8
125.9	0	131.3	118.8	117.3
133.1	0	125.9	131.3	118.8
147.0	0	133.1	125.9	131.3
145.8	0	147.0	133.1	125.9
164.4	0	145.8	147.0	133.1
149.8	0	164.4	145.8	147.0
137.7	0	149.8	164.4	145.8
151.7	0	137.7	149.8	164.4
156.8	0	151.7	137.7	149.8
180.0	0	156.8	151.7	137.7
180.4	0	180.0	156.8	151.7
170.4	0	180.4	180.0	156.8
191.6	0	170.4	180.4	180.0
199.5	0	191.6	170.4	180.4
218.2	0	199.5	191.6	170.4
217.5	0	218.2	199.5	191.6
205.0	0	217.5	218.2	199.5
194.0	0	205.0	217.5	218.2
199.3	0	194.0	205.0	217.5
219.3	0	199.3	194.0	205.0
211.1	0	219.3	199.3	194.0
215.2	0	211.1	219.3	199.3
240.2	0	215.2	211.1	219.3
242.2	0	240.2	215.2	211.1
240.7	0	242.2	240.2	215.2
255.4	0	240.7	242.2	240.2
253.0	0	255.4	240.7	242.2
218.2	0	253.0	255.4	240.7
203.7	0	218.2	253.0	255.4
205.6	0	203.7	218.2	253.0
215.6	0	205.6	203.7	218.2
188.5	0	215.6	205.6	203.7
202.9	0	188.5	215.6	205.6
214.0	0	202.9	188.5	215.6
230.3	0	214.0	202.9	188.5
230.0	0	230.3	214.0	202.9
241.0	0	230.0	230.3	214.0
259.6	1	241.0	230.0	230.3
247.8	1	259.6	241.0	230.0
270.3	1	247.8	259.6	241.0
289.7	1	270.3	247.8	259.6
322.7	1	289.7	270.3	247.8
315.0	1	322.7	289.7	270.3
320.2	1	315.0	322.7	289.7
329.5	1	320.2	315.0	322.7
360.6	1	329.5	320.2	315.0
382.2	1	360.6	329.5	320.2
435.4	1	382.2	360.6	329.5
464.0	1	435.4	382.2	360.6
468.8	1	464.0	435.4	382.2
403.0	1	468.8	464.0	435.4
351.6	1	403.0	468.8	464.0
252.0	1	351.6	403.0	468.8
188.0	1	252.0	351.6	403.0
146.5	1	188.0	252.0	351.6
152.9	1	146.5	188.0	252.0
148.1	1	152.9	146.5	188.0
165.1	1	148.1	152.9	146.5
177.0	1	165.1	148.1	152.9
206.1	1	177.0	165.1	148.1
244.9	1	206.1	177.0	165.1
228.6	1	244.9	206.1	177.0
253.4	1	228.6	244.9	206.1
241.1	1	253.4	228.6	244.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68079&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68079&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68079&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 15.1707908539916 + 2.58019404216615D[t] + 1.2970254593017Y1[t] -0.140546777544707Y2[t] -0.292873046833665Y3[t] + 0.300136115162012t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  15.1707908539916 +  2.58019404216615D[t] +  1.2970254593017Y1[t] -0.140546777544707Y2[t] -0.292873046833665Y3[t] +  0.300136115162012t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68079&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  15.1707908539916 +  2.58019404216615D[t] +  1.2970254593017Y1[t] -0.140546777544707Y2[t] -0.292873046833665Y3[t] +  0.300136115162012t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68079&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68079&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 15.1707908539916 + 2.58019404216615D[t] + 1.2970254593017Y1[t] -0.140546777544707Y2[t] -0.292873046833665Y3[t] + 0.300136115162012t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.17079085399166.1343862.47310.015660.00783
D2.580194042166157.6361260.33790.7363880.368194
Y11.29702545930170.11275811.502700
Y2-0.1405467775447070.190226-0.73880.462310.231155
Y3-0.2928730468336650.114306-2.56220.0124070.006204
t0.3001361151620120.1750541.71450.0905610.04528

\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) & 15.1707908539916 & 6.134386 & 2.4731 & 0.01566 & 0.00783 \tabularnewline
D & 2.58019404216615 & 7.636126 & 0.3379 & 0.736388 & 0.368194 \tabularnewline
Y1 & 1.2970254593017 & 0.112758 & 11.5027 & 0 & 0 \tabularnewline
Y2 & -0.140546777544707 & 0.190226 & -0.7388 & 0.46231 & 0.231155 \tabularnewline
Y3 & -0.292873046833665 & 0.114306 & -2.5622 & 0.012407 & 0.006204 \tabularnewline
t & 0.300136115162012 & 0.175054 & 1.7145 & 0.090561 & 0.04528 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68079&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]15.1707908539916[/C][C]6.134386[/C][C]2.4731[/C][C]0.01566[/C][C]0.00783[/C][/ROW]
[ROW][C]D[/C][C]2.58019404216615[/C][C]7.636126[/C][C]0.3379[/C][C]0.736388[/C][C]0.368194[/C][/ROW]
[ROW][C]Y1[/C][C]1.2970254593017[/C][C]0.112758[/C][C]11.5027[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.140546777544707[/C][C]0.190226[/C][C]-0.7388[/C][C]0.46231[/C][C]0.231155[/C][/ROW]
[ROW][C]Y3[/C][C]-0.292873046833665[/C][C]0.114306[/C][C]-2.5622[/C][C]0.012407[/C][C]0.006204[/C][/ROW]
[ROW][C]t[/C][C]0.300136115162012[/C][C]0.175054[/C][C]1.7145[/C][C]0.090561[/C][C]0.04528[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68079&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68079&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.17079085399166.1343862.47310.015660.00783
D2.580194042166157.6361260.33790.7363880.368194
Y11.29702545930170.11275811.502700
Y2-0.1405467775447070.190226-0.73880.462310.231155
Y3-0.2928730468336650.114306-2.56220.0124070.006204
t0.3001361151620120.1750541.71450.0905610.04528







Multiple Linear Regression - Regression Statistics
Multiple R0.978714772026642
R-squared0.957882604983162
Adjusted R-squared0.955074778648706
F-TEST (value)341.147382667025
F-TEST (DF numerator)5
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation18.5544241079048
Sum Squared Residuals25819.9990482000

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.978714772026642 \tabularnewline
R-squared & 0.957882604983162 \tabularnewline
Adjusted R-squared & 0.955074778648706 \tabularnewline
F-TEST (value) & 341.147382667025 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 75 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 18.5544241079048 \tabularnewline
Sum Squared Residuals & 25819.9990482000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68079&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.978714772026642[/C][/ROW]
[ROW][C]R-squared[/C][C]0.957882604983162[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.955074778648706[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]341.147382667025[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]75[/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]18.5544241079048[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25819.9990482000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68079&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68079&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.978714772026642
R-squared0.957882604983162
Adjusted R-squared0.955074778648706
F-TEST (value)341.147382667025
F-TEST (DF numerator)5
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation18.5544241079048
Sum Squared Residuals25819.9990482000







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1107.199.79679812001937.30320187998065
2115.2115.667880023444-0.467880023443807
3106.1122.232297825913-16.1322978259132
489.5106.428044457514-16.9280444575143
591.384.10426194457227.1957380554278
697.691.73726511990575.86273488009425
7100.7104.817370006527-4.11737000652681
8104.6107.725668862692-3.12566886269187
994.7110.803409063690-16.1034090636898
10101.896.80695425415634.99304574584372
11102.5106.565179345402-4.06517934540167
12105.3109.674794325161-4.37479432516073
13110.3111.428820349567-1.12882034956719
14109.8117.615541651329-7.81554165132895
15117.3115.7443866179821.55561338201768
16118.8124.378121832511-5.57812183251111
17131.3125.7161318284575.58386817154281
18125.9139.821718167321-13.9217181673209
19133.1130.9217725126942.17822748730556
20147137.6585314481499.34146855185073
21145.8156.556899102185-10.7568991021848
22164.4151.23831852111113.161681478889
23149.8171.760848961350-21.9608489613503
24137.7150.861690964576-13.1616909645764
25151.7132.07236330323419.6276366967656
26156.8156.5074183406830.292581659317393
27180164.99849327934515.0015067206552
28180.4190.572608829157-10.1726088291569
29170.4186.637217350151-16.2372173501507
30191.6167.11622547473724.4837745252632
31199.5196.2016198838083.29838011619154
32218.2206.69739591184311.5026040881572
33217.5223.932679980470-6.43267998046969
34205218.382976464049-13.3829764640485
35194197.091951106431-3.09195110643108
36199.3185.08665302136714.2133469786332
37219.3197.46795170924021.8320482907596
38211.1226.185302604620-15.0853026046198
39215.2211.4866672543953.71333274560472
40240.2212.39963039188827.8003696081124
41242.2246.950720185695-4.75072018569482
42240.7245.130458288825-4.43045828882452
43255.4235.88213648910319.5178635108971
44253254.873620928650-1.87362092864971
45218.2250.434167881831-32.2341678818310
46203.7201.6298964909462.07010350905378
47205.6188.71708661719016.8829133828098
48215.6203.71148140923511.8885185907648
49188.5220.961492419167-32.4614924191674
50202.9184.15031202282218.7496879771777
51214204.0077019550549.99229804494628
52230.3224.6178066410135.68219335898689
53230240.282016637642-10.2820166376418
54241234.6512418211816.34875817881907
55259.6247.06718540070212.5328145992977
56247.8270.033842419934-22.2338424199343
57270.3249.19330453783421.1066954621656
58289.7274.88752679120614.8124732087940
59322.7300.64355627470222.0564437252977
60315334.429281508696-19.4292815086957
61320.2314.4225408196865.77745918031379
62329.5312.88460896480016.6153910351997
63360.6326.77136106885533.8286389311451
64382.2364.57896409359917.621035906401
65435.4385.80012601248449.5998739875157
66464442.95785441100421.042145588996
67468.8466.5497722852092.25022771479086
68403453.47514667569-50.4751466756897
69351.6359.380213897142-7.78021389714243
70252300.855428741837-48.8554287418372
71188198.466979958003-10.4669799580030
72146.5144.8096203285591.69037967144065
73152.9129.44834911019523.4516508898050
74148.1162.626014430348-14.5260144303478
75165.1167.955160408173-2.85516040817264
76177189.104966363943-12.1049663639427
77206.1203.8562008513362.24379914866349
78244.9235.2484293832249.65157061677632
79228.6278.29805283542-49.6980528354201
80253.4243.480853332379.91914666762991
81241.1266.874659095047-25.7746590950468

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 107.1 & 99.7967981200193 & 7.30320187998065 \tabularnewline
2 & 115.2 & 115.667880023444 & -0.467880023443807 \tabularnewline
3 & 106.1 & 122.232297825913 & -16.1322978259132 \tabularnewline
4 & 89.5 & 106.428044457514 & -16.9280444575143 \tabularnewline
5 & 91.3 & 84.1042619445722 & 7.1957380554278 \tabularnewline
6 & 97.6 & 91.7372651199057 & 5.86273488009425 \tabularnewline
7 & 100.7 & 104.817370006527 & -4.11737000652681 \tabularnewline
8 & 104.6 & 107.725668862692 & -3.12566886269187 \tabularnewline
9 & 94.7 & 110.803409063690 & -16.1034090636898 \tabularnewline
10 & 101.8 & 96.8069542541563 & 4.99304574584372 \tabularnewline
11 & 102.5 & 106.565179345402 & -4.06517934540167 \tabularnewline
12 & 105.3 & 109.674794325161 & -4.37479432516073 \tabularnewline
13 & 110.3 & 111.428820349567 & -1.12882034956719 \tabularnewline
14 & 109.8 & 117.615541651329 & -7.81554165132895 \tabularnewline
15 & 117.3 & 115.744386617982 & 1.55561338201768 \tabularnewline
16 & 118.8 & 124.378121832511 & -5.57812183251111 \tabularnewline
17 & 131.3 & 125.716131828457 & 5.58386817154281 \tabularnewline
18 & 125.9 & 139.821718167321 & -13.9217181673209 \tabularnewline
19 & 133.1 & 130.921772512694 & 2.17822748730556 \tabularnewline
20 & 147 & 137.658531448149 & 9.34146855185073 \tabularnewline
21 & 145.8 & 156.556899102185 & -10.7568991021848 \tabularnewline
22 & 164.4 & 151.238318521111 & 13.161681478889 \tabularnewline
23 & 149.8 & 171.760848961350 & -21.9608489613503 \tabularnewline
24 & 137.7 & 150.861690964576 & -13.1616909645764 \tabularnewline
25 & 151.7 & 132.072363303234 & 19.6276366967656 \tabularnewline
26 & 156.8 & 156.507418340683 & 0.292581659317393 \tabularnewline
27 & 180 & 164.998493279345 & 15.0015067206552 \tabularnewline
28 & 180.4 & 190.572608829157 & -10.1726088291569 \tabularnewline
29 & 170.4 & 186.637217350151 & -16.2372173501507 \tabularnewline
30 & 191.6 & 167.116225474737 & 24.4837745252632 \tabularnewline
31 & 199.5 & 196.201619883808 & 3.29838011619154 \tabularnewline
32 & 218.2 & 206.697395911843 & 11.5026040881572 \tabularnewline
33 & 217.5 & 223.932679980470 & -6.43267998046969 \tabularnewline
34 & 205 & 218.382976464049 & -13.3829764640485 \tabularnewline
35 & 194 & 197.091951106431 & -3.09195110643108 \tabularnewline
36 & 199.3 & 185.086653021367 & 14.2133469786332 \tabularnewline
37 & 219.3 & 197.467951709240 & 21.8320482907596 \tabularnewline
38 & 211.1 & 226.185302604620 & -15.0853026046198 \tabularnewline
39 & 215.2 & 211.486667254395 & 3.71333274560472 \tabularnewline
40 & 240.2 & 212.399630391888 & 27.8003696081124 \tabularnewline
41 & 242.2 & 246.950720185695 & -4.75072018569482 \tabularnewline
42 & 240.7 & 245.130458288825 & -4.43045828882452 \tabularnewline
43 & 255.4 & 235.882136489103 & 19.5178635108971 \tabularnewline
44 & 253 & 254.873620928650 & -1.87362092864971 \tabularnewline
45 & 218.2 & 250.434167881831 & -32.2341678818310 \tabularnewline
46 & 203.7 & 201.629896490946 & 2.07010350905378 \tabularnewline
47 & 205.6 & 188.717086617190 & 16.8829133828098 \tabularnewline
48 & 215.6 & 203.711481409235 & 11.8885185907648 \tabularnewline
49 & 188.5 & 220.961492419167 & -32.4614924191674 \tabularnewline
50 & 202.9 & 184.150312022822 & 18.7496879771777 \tabularnewline
51 & 214 & 204.007701955054 & 9.99229804494628 \tabularnewline
52 & 230.3 & 224.617806641013 & 5.68219335898689 \tabularnewline
53 & 230 & 240.282016637642 & -10.2820166376418 \tabularnewline
54 & 241 & 234.651241821181 & 6.34875817881907 \tabularnewline
55 & 259.6 & 247.067185400702 & 12.5328145992977 \tabularnewline
56 & 247.8 & 270.033842419934 & -22.2338424199343 \tabularnewline
57 & 270.3 & 249.193304537834 & 21.1066954621656 \tabularnewline
58 & 289.7 & 274.887526791206 & 14.8124732087940 \tabularnewline
59 & 322.7 & 300.643556274702 & 22.0564437252977 \tabularnewline
60 & 315 & 334.429281508696 & -19.4292815086957 \tabularnewline
61 & 320.2 & 314.422540819686 & 5.77745918031379 \tabularnewline
62 & 329.5 & 312.884608964800 & 16.6153910351997 \tabularnewline
63 & 360.6 & 326.771361068855 & 33.8286389311451 \tabularnewline
64 & 382.2 & 364.578964093599 & 17.621035906401 \tabularnewline
65 & 435.4 & 385.800126012484 & 49.5998739875157 \tabularnewline
66 & 464 & 442.957854411004 & 21.042145588996 \tabularnewline
67 & 468.8 & 466.549772285209 & 2.25022771479086 \tabularnewline
68 & 403 & 453.47514667569 & -50.4751466756897 \tabularnewline
69 & 351.6 & 359.380213897142 & -7.78021389714243 \tabularnewline
70 & 252 & 300.855428741837 & -48.8554287418372 \tabularnewline
71 & 188 & 198.466979958003 & -10.4669799580030 \tabularnewline
72 & 146.5 & 144.809620328559 & 1.69037967144065 \tabularnewline
73 & 152.9 & 129.448349110195 & 23.4516508898050 \tabularnewline
74 & 148.1 & 162.626014430348 & -14.5260144303478 \tabularnewline
75 & 165.1 & 167.955160408173 & -2.85516040817264 \tabularnewline
76 & 177 & 189.104966363943 & -12.1049663639427 \tabularnewline
77 & 206.1 & 203.856200851336 & 2.24379914866349 \tabularnewline
78 & 244.9 & 235.248429383224 & 9.65157061677632 \tabularnewline
79 & 228.6 & 278.29805283542 & -49.6980528354201 \tabularnewline
80 & 253.4 & 243.48085333237 & 9.91914666762991 \tabularnewline
81 & 241.1 & 266.874659095047 & -25.7746590950468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68079&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]107.1[/C][C]99.7967981200193[/C][C]7.30320187998065[/C][/ROW]
[ROW][C]2[/C][C]115.2[/C][C]115.667880023444[/C][C]-0.467880023443807[/C][/ROW]
[ROW][C]3[/C][C]106.1[/C][C]122.232297825913[/C][C]-16.1322978259132[/C][/ROW]
[ROW][C]4[/C][C]89.5[/C][C]106.428044457514[/C][C]-16.9280444575143[/C][/ROW]
[ROW][C]5[/C][C]91.3[/C][C]84.1042619445722[/C][C]7.1957380554278[/C][/ROW]
[ROW][C]6[/C][C]97.6[/C][C]91.7372651199057[/C][C]5.86273488009425[/C][/ROW]
[ROW][C]7[/C][C]100.7[/C][C]104.817370006527[/C][C]-4.11737000652681[/C][/ROW]
[ROW][C]8[/C][C]104.6[/C][C]107.725668862692[/C][C]-3.12566886269187[/C][/ROW]
[ROW][C]9[/C][C]94.7[/C][C]110.803409063690[/C][C]-16.1034090636898[/C][/ROW]
[ROW][C]10[/C][C]101.8[/C][C]96.8069542541563[/C][C]4.99304574584372[/C][/ROW]
[ROW][C]11[/C][C]102.5[/C][C]106.565179345402[/C][C]-4.06517934540167[/C][/ROW]
[ROW][C]12[/C][C]105.3[/C][C]109.674794325161[/C][C]-4.37479432516073[/C][/ROW]
[ROW][C]13[/C][C]110.3[/C][C]111.428820349567[/C][C]-1.12882034956719[/C][/ROW]
[ROW][C]14[/C][C]109.8[/C][C]117.615541651329[/C][C]-7.81554165132895[/C][/ROW]
[ROW][C]15[/C][C]117.3[/C][C]115.744386617982[/C][C]1.55561338201768[/C][/ROW]
[ROW][C]16[/C][C]118.8[/C][C]124.378121832511[/C][C]-5.57812183251111[/C][/ROW]
[ROW][C]17[/C][C]131.3[/C][C]125.716131828457[/C][C]5.58386817154281[/C][/ROW]
[ROW][C]18[/C][C]125.9[/C][C]139.821718167321[/C][C]-13.9217181673209[/C][/ROW]
[ROW][C]19[/C][C]133.1[/C][C]130.921772512694[/C][C]2.17822748730556[/C][/ROW]
[ROW][C]20[/C][C]147[/C][C]137.658531448149[/C][C]9.34146855185073[/C][/ROW]
[ROW][C]21[/C][C]145.8[/C][C]156.556899102185[/C][C]-10.7568991021848[/C][/ROW]
[ROW][C]22[/C][C]164.4[/C][C]151.238318521111[/C][C]13.161681478889[/C][/ROW]
[ROW][C]23[/C][C]149.8[/C][C]171.760848961350[/C][C]-21.9608489613503[/C][/ROW]
[ROW][C]24[/C][C]137.7[/C][C]150.861690964576[/C][C]-13.1616909645764[/C][/ROW]
[ROW][C]25[/C][C]151.7[/C][C]132.072363303234[/C][C]19.6276366967656[/C][/ROW]
[ROW][C]26[/C][C]156.8[/C][C]156.507418340683[/C][C]0.292581659317393[/C][/ROW]
[ROW][C]27[/C][C]180[/C][C]164.998493279345[/C][C]15.0015067206552[/C][/ROW]
[ROW][C]28[/C][C]180.4[/C][C]190.572608829157[/C][C]-10.1726088291569[/C][/ROW]
[ROW][C]29[/C][C]170.4[/C][C]186.637217350151[/C][C]-16.2372173501507[/C][/ROW]
[ROW][C]30[/C][C]191.6[/C][C]167.116225474737[/C][C]24.4837745252632[/C][/ROW]
[ROW][C]31[/C][C]199.5[/C][C]196.201619883808[/C][C]3.29838011619154[/C][/ROW]
[ROW][C]32[/C][C]218.2[/C][C]206.697395911843[/C][C]11.5026040881572[/C][/ROW]
[ROW][C]33[/C][C]217.5[/C][C]223.932679980470[/C][C]-6.43267998046969[/C][/ROW]
[ROW][C]34[/C][C]205[/C][C]218.382976464049[/C][C]-13.3829764640485[/C][/ROW]
[ROW][C]35[/C][C]194[/C][C]197.091951106431[/C][C]-3.09195110643108[/C][/ROW]
[ROW][C]36[/C][C]199.3[/C][C]185.086653021367[/C][C]14.2133469786332[/C][/ROW]
[ROW][C]37[/C][C]219.3[/C][C]197.467951709240[/C][C]21.8320482907596[/C][/ROW]
[ROW][C]38[/C][C]211.1[/C][C]226.185302604620[/C][C]-15.0853026046198[/C][/ROW]
[ROW][C]39[/C][C]215.2[/C][C]211.486667254395[/C][C]3.71333274560472[/C][/ROW]
[ROW][C]40[/C][C]240.2[/C][C]212.399630391888[/C][C]27.8003696081124[/C][/ROW]
[ROW][C]41[/C][C]242.2[/C][C]246.950720185695[/C][C]-4.75072018569482[/C][/ROW]
[ROW][C]42[/C][C]240.7[/C][C]245.130458288825[/C][C]-4.43045828882452[/C][/ROW]
[ROW][C]43[/C][C]255.4[/C][C]235.882136489103[/C][C]19.5178635108971[/C][/ROW]
[ROW][C]44[/C][C]253[/C][C]254.873620928650[/C][C]-1.87362092864971[/C][/ROW]
[ROW][C]45[/C][C]218.2[/C][C]250.434167881831[/C][C]-32.2341678818310[/C][/ROW]
[ROW][C]46[/C][C]203.7[/C][C]201.629896490946[/C][C]2.07010350905378[/C][/ROW]
[ROW][C]47[/C][C]205.6[/C][C]188.717086617190[/C][C]16.8829133828098[/C][/ROW]
[ROW][C]48[/C][C]215.6[/C][C]203.711481409235[/C][C]11.8885185907648[/C][/ROW]
[ROW][C]49[/C][C]188.5[/C][C]220.961492419167[/C][C]-32.4614924191674[/C][/ROW]
[ROW][C]50[/C][C]202.9[/C][C]184.150312022822[/C][C]18.7496879771777[/C][/ROW]
[ROW][C]51[/C][C]214[/C][C]204.007701955054[/C][C]9.99229804494628[/C][/ROW]
[ROW][C]52[/C][C]230.3[/C][C]224.617806641013[/C][C]5.68219335898689[/C][/ROW]
[ROW][C]53[/C][C]230[/C][C]240.282016637642[/C][C]-10.2820166376418[/C][/ROW]
[ROW][C]54[/C][C]241[/C][C]234.651241821181[/C][C]6.34875817881907[/C][/ROW]
[ROW][C]55[/C][C]259.6[/C][C]247.067185400702[/C][C]12.5328145992977[/C][/ROW]
[ROW][C]56[/C][C]247.8[/C][C]270.033842419934[/C][C]-22.2338424199343[/C][/ROW]
[ROW][C]57[/C][C]270.3[/C][C]249.193304537834[/C][C]21.1066954621656[/C][/ROW]
[ROW][C]58[/C][C]289.7[/C][C]274.887526791206[/C][C]14.8124732087940[/C][/ROW]
[ROW][C]59[/C][C]322.7[/C][C]300.643556274702[/C][C]22.0564437252977[/C][/ROW]
[ROW][C]60[/C][C]315[/C][C]334.429281508696[/C][C]-19.4292815086957[/C][/ROW]
[ROW][C]61[/C][C]320.2[/C][C]314.422540819686[/C][C]5.77745918031379[/C][/ROW]
[ROW][C]62[/C][C]329.5[/C][C]312.884608964800[/C][C]16.6153910351997[/C][/ROW]
[ROW][C]63[/C][C]360.6[/C][C]326.771361068855[/C][C]33.8286389311451[/C][/ROW]
[ROW][C]64[/C][C]382.2[/C][C]364.578964093599[/C][C]17.621035906401[/C][/ROW]
[ROW][C]65[/C][C]435.4[/C][C]385.800126012484[/C][C]49.5998739875157[/C][/ROW]
[ROW][C]66[/C][C]464[/C][C]442.957854411004[/C][C]21.042145588996[/C][/ROW]
[ROW][C]67[/C][C]468.8[/C][C]466.549772285209[/C][C]2.25022771479086[/C][/ROW]
[ROW][C]68[/C][C]403[/C][C]453.47514667569[/C][C]-50.4751466756897[/C][/ROW]
[ROW][C]69[/C][C]351.6[/C][C]359.380213897142[/C][C]-7.78021389714243[/C][/ROW]
[ROW][C]70[/C][C]252[/C][C]300.855428741837[/C][C]-48.8554287418372[/C][/ROW]
[ROW][C]71[/C][C]188[/C][C]198.466979958003[/C][C]-10.4669799580030[/C][/ROW]
[ROW][C]72[/C][C]146.5[/C][C]144.809620328559[/C][C]1.69037967144065[/C][/ROW]
[ROW][C]73[/C][C]152.9[/C][C]129.448349110195[/C][C]23.4516508898050[/C][/ROW]
[ROW][C]74[/C][C]148.1[/C][C]162.626014430348[/C][C]-14.5260144303478[/C][/ROW]
[ROW][C]75[/C][C]165.1[/C][C]167.955160408173[/C][C]-2.85516040817264[/C][/ROW]
[ROW][C]76[/C][C]177[/C][C]189.104966363943[/C][C]-12.1049663639427[/C][/ROW]
[ROW][C]77[/C][C]206.1[/C][C]203.856200851336[/C][C]2.24379914866349[/C][/ROW]
[ROW][C]78[/C][C]244.9[/C][C]235.248429383224[/C][C]9.65157061677632[/C][/ROW]
[ROW][C]79[/C][C]228.6[/C][C]278.29805283542[/C][C]-49.6980528354201[/C][/ROW]
[ROW][C]80[/C][C]253.4[/C][C]243.48085333237[/C][C]9.91914666762991[/C][/ROW]
[ROW][C]81[/C][C]241.1[/C][C]266.874659095047[/C][C]-25.7746590950468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68079&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68079&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1107.199.79679812001937.30320187998065
2115.2115.667880023444-0.467880023443807
3106.1122.232297825913-16.1322978259132
489.5106.428044457514-16.9280444575143
591.384.10426194457227.1957380554278
697.691.73726511990575.86273488009425
7100.7104.817370006527-4.11737000652681
8104.6107.725668862692-3.12566886269187
994.7110.803409063690-16.1034090636898
10101.896.80695425415634.99304574584372
11102.5106.565179345402-4.06517934540167
12105.3109.674794325161-4.37479432516073
13110.3111.428820349567-1.12882034956719
14109.8117.615541651329-7.81554165132895
15117.3115.7443866179821.55561338201768
16118.8124.378121832511-5.57812183251111
17131.3125.7161318284575.58386817154281
18125.9139.821718167321-13.9217181673209
19133.1130.9217725126942.17822748730556
20147137.6585314481499.34146855185073
21145.8156.556899102185-10.7568991021848
22164.4151.23831852111113.161681478889
23149.8171.760848961350-21.9608489613503
24137.7150.861690964576-13.1616909645764
25151.7132.07236330323419.6276366967656
26156.8156.5074183406830.292581659317393
27180164.99849327934515.0015067206552
28180.4190.572608829157-10.1726088291569
29170.4186.637217350151-16.2372173501507
30191.6167.11622547473724.4837745252632
31199.5196.2016198838083.29838011619154
32218.2206.69739591184311.5026040881572
33217.5223.932679980470-6.43267998046969
34205218.382976464049-13.3829764640485
35194197.091951106431-3.09195110643108
36199.3185.08665302136714.2133469786332
37219.3197.46795170924021.8320482907596
38211.1226.185302604620-15.0853026046198
39215.2211.4866672543953.71333274560472
40240.2212.39963039188827.8003696081124
41242.2246.950720185695-4.75072018569482
42240.7245.130458288825-4.43045828882452
43255.4235.88213648910319.5178635108971
44253254.873620928650-1.87362092864971
45218.2250.434167881831-32.2341678818310
46203.7201.6298964909462.07010350905378
47205.6188.71708661719016.8829133828098
48215.6203.71148140923511.8885185907648
49188.5220.961492419167-32.4614924191674
50202.9184.15031202282218.7496879771777
51214204.0077019550549.99229804494628
52230.3224.6178066410135.68219335898689
53230240.282016637642-10.2820166376418
54241234.6512418211816.34875817881907
55259.6247.06718540070212.5328145992977
56247.8270.033842419934-22.2338424199343
57270.3249.19330453783421.1066954621656
58289.7274.88752679120614.8124732087940
59322.7300.64355627470222.0564437252977
60315334.429281508696-19.4292815086957
61320.2314.4225408196865.77745918031379
62329.5312.88460896480016.6153910351997
63360.6326.77136106885533.8286389311451
64382.2364.57896409359917.621035906401
65435.4385.80012601248449.5998739875157
66464442.95785441100421.042145588996
67468.8466.5497722852092.25022771479086
68403453.47514667569-50.4751466756897
69351.6359.380213897142-7.78021389714243
70252300.855428741837-48.8554287418372
71188198.466979958003-10.4669799580030
72146.5144.8096203285591.69037967144065
73152.9129.44834911019523.4516508898050
74148.1162.626014430348-14.5260144303478
75165.1167.955160408173-2.85516040817264
76177189.104966363943-12.1049663639427
77206.1203.8562008513362.24379914866349
78244.9235.2484293832249.65157061677632
79228.6278.29805283542-49.6980528354201
80253.4243.480853332379.91914666762991
81241.1266.874659095047-25.7746590950468







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.02230446951759460.04460893903518920.977695530482405
100.01432628707217590.02865257414435170.985673712927824
110.007537196239786280.01507439247957260.992462803760214
120.002554527183124880.005109054366249760.997445472816875
130.00202244715647350.0040448943129470.997977552843527
140.0007632817730932240.001526563546186450.999236718226907
150.0009200109973752570.001840021994750510.999079989002625
160.0003619331446799820.0007238662893599640.99963806685532
170.0006809826287366770.001361965257473350.999319017371263
180.0002865021986587110.0005730043973174220.999713497801341
190.0002265751802658810.0004531503605317610.999773424819734
200.0001925547289611950.000385109457922390.999807445271039
218.03599389054704e-050.0001607198778109410.999919640061095
220.0001555239498699020.0003110478997398030.99984447605013
230.0002651520803159900.0005303041606319810.999734847919684
240.0002364233804145970.0004728467608291930.999763576619585
250.0001481899296122450.000296379859224490.999851810070388
266.41929908395996e-050.0001283859816791990.99993580700916
270.000168812811205640.000337625622411280.999831187188794
288.80625258630217e-050.0001761250517260430.999911937474137
296.7636498666953e-050.0001352729973339060.999932363501333
309.79538793228484e-050.0001959077586456970.999902046120677
314.78995994222496e-059.57991988444991e-050.999952100400578
326.88650748277579e-050.0001377301496555160.999931134925172
333.65537540240901e-057.31075080481801e-050.999963446245976
343.89079716364752e-057.78159432729505e-050.999961092028363
353.42426127995193e-056.84852255990387e-050.9999657573872
361.53029244667751e-053.06058489335501e-050.999984697075533
371.21800553574767e-052.43601107149534e-050.999987819944643
381.23070600818785e-052.46141201637571e-050.999987692939918
395.83025304774715e-061.16605060954943e-050.999994169746952
408.96665696003511e-061.79333139200702e-050.99999103334304
414.32367556395301e-068.64735112790601e-060.999995676324436
422.11257081655950e-064.22514163311901e-060.999997887429183
431.52600449770315e-063.05200899540631e-060.999998473995502
447.038922921419e-071.4077845842838e-060.999999296107708
452.18288626453530e-054.36577252907060e-050.999978171137355
462.40806152129645e-054.8161230425929e-050.999975919384787
471.83930006808305e-053.67860013616611e-050.99998160699932
489.5895617048996e-061.91791234097992e-050.999990410438295
490.0001825100055950500.0003650200111901000.999817489994405
500.0001134212897736950.0002268425795473910.999886578710226
516.13296360600287e-050.0001226592721200570.99993867036394
523.38716547974193e-056.77433095948387e-050.999966128345203
531.97693057429495e-053.9538611485899e-050.999980230694257
549.76853525306571e-061.95370705061314e-050.999990231464747
554.5704691866225e-069.140938373245e-060.999995429530813
563.72365684482919e-057.44731368965839e-050.999962763431552
573.21014241942270e-056.42028483884541e-050.999967898575806
581.83229603378106e-053.66459206756212e-050.999981677039662
592.11520315738060e-054.23040631476119e-050.999978847968426
600.0002000181448771990.0004000362897543990.999799981855123
610.000544234144785250.00108846828957050.999455765855215
620.0004231895961544210.0008463791923088430.999576810403846
630.0003957401364849960.0007914802729699920.999604259863515
640.0002546855333501030.0005093710667002050.99974531446665
650.002993411588894420.005986823177788840.997006588411106
660.01450776590724980.02901553181449960.98549223409275
670.08522187699172320.1704437539834460.914778123008277
680.1658721938728860.3317443877457710.834127806127114
690.3685676552294190.7371353104588390.63143234477058
700.5023710328555880.9952579342888240.497628967144412
710.3671514915911720.7343029831823430.632848508408828
720.2292090029483590.4584180058967170.770790997051641

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0223044695175946 & 0.0446089390351892 & 0.977695530482405 \tabularnewline
10 & 0.0143262870721759 & 0.0286525741443517 & 0.985673712927824 \tabularnewline
11 & 0.00753719623978628 & 0.0150743924795726 & 0.992462803760214 \tabularnewline
12 & 0.00255452718312488 & 0.00510905436624976 & 0.997445472816875 \tabularnewline
13 & 0.0020224471564735 & 0.004044894312947 & 0.997977552843527 \tabularnewline
14 & 0.000763281773093224 & 0.00152656354618645 & 0.999236718226907 \tabularnewline
15 & 0.000920010997375257 & 0.00184002199475051 & 0.999079989002625 \tabularnewline
16 & 0.000361933144679982 & 0.000723866289359964 & 0.99963806685532 \tabularnewline
17 & 0.000680982628736677 & 0.00136196525747335 & 0.999319017371263 \tabularnewline
18 & 0.000286502198658711 & 0.000573004397317422 & 0.999713497801341 \tabularnewline
19 & 0.000226575180265881 & 0.000453150360531761 & 0.999773424819734 \tabularnewline
20 & 0.000192554728961195 & 0.00038510945792239 & 0.999807445271039 \tabularnewline
21 & 8.03599389054704e-05 & 0.000160719877810941 & 0.999919640061095 \tabularnewline
22 & 0.000155523949869902 & 0.000311047899739803 & 0.99984447605013 \tabularnewline
23 & 0.000265152080315990 & 0.000530304160631981 & 0.999734847919684 \tabularnewline
24 & 0.000236423380414597 & 0.000472846760829193 & 0.999763576619585 \tabularnewline
25 & 0.000148189929612245 & 0.00029637985922449 & 0.999851810070388 \tabularnewline
26 & 6.41929908395996e-05 & 0.000128385981679199 & 0.99993580700916 \tabularnewline
27 & 0.00016881281120564 & 0.00033762562241128 & 0.999831187188794 \tabularnewline
28 & 8.80625258630217e-05 & 0.000176125051726043 & 0.999911937474137 \tabularnewline
29 & 6.7636498666953e-05 & 0.000135272997333906 & 0.999932363501333 \tabularnewline
30 & 9.79538793228484e-05 & 0.000195907758645697 & 0.999902046120677 \tabularnewline
31 & 4.78995994222496e-05 & 9.57991988444991e-05 & 0.999952100400578 \tabularnewline
32 & 6.88650748277579e-05 & 0.000137730149655516 & 0.999931134925172 \tabularnewline
33 & 3.65537540240901e-05 & 7.31075080481801e-05 & 0.999963446245976 \tabularnewline
34 & 3.89079716364752e-05 & 7.78159432729505e-05 & 0.999961092028363 \tabularnewline
35 & 3.42426127995193e-05 & 6.84852255990387e-05 & 0.9999657573872 \tabularnewline
36 & 1.53029244667751e-05 & 3.06058489335501e-05 & 0.999984697075533 \tabularnewline
37 & 1.21800553574767e-05 & 2.43601107149534e-05 & 0.999987819944643 \tabularnewline
38 & 1.23070600818785e-05 & 2.46141201637571e-05 & 0.999987692939918 \tabularnewline
39 & 5.83025304774715e-06 & 1.16605060954943e-05 & 0.999994169746952 \tabularnewline
40 & 8.96665696003511e-06 & 1.79333139200702e-05 & 0.99999103334304 \tabularnewline
41 & 4.32367556395301e-06 & 8.64735112790601e-06 & 0.999995676324436 \tabularnewline
42 & 2.11257081655950e-06 & 4.22514163311901e-06 & 0.999997887429183 \tabularnewline
43 & 1.52600449770315e-06 & 3.05200899540631e-06 & 0.999998473995502 \tabularnewline
44 & 7.038922921419e-07 & 1.4077845842838e-06 & 0.999999296107708 \tabularnewline
45 & 2.18288626453530e-05 & 4.36577252907060e-05 & 0.999978171137355 \tabularnewline
46 & 2.40806152129645e-05 & 4.8161230425929e-05 & 0.999975919384787 \tabularnewline
47 & 1.83930006808305e-05 & 3.67860013616611e-05 & 0.99998160699932 \tabularnewline
48 & 9.5895617048996e-06 & 1.91791234097992e-05 & 0.999990410438295 \tabularnewline
49 & 0.000182510005595050 & 0.000365020011190100 & 0.999817489994405 \tabularnewline
50 & 0.000113421289773695 & 0.000226842579547391 & 0.999886578710226 \tabularnewline
51 & 6.13296360600287e-05 & 0.000122659272120057 & 0.99993867036394 \tabularnewline
52 & 3.38716547974193e-05 & 6.77433095948387e-05 & 0.999966128345203 \tabularnewline
53 & 1.97693057429495e-05 & 3.9538611485899e-05 & 0.999980230694257 \tabularnewline
54 & 9.76853525306571e-06 & 1.95370705061314e-05 & 0.999990231464747 \tabularnewline
55 & 4.5704691866225e-06 & 9.140938373245e-06 & 0.999995429530813 \tabularnewline
56 & 3.72365684482919e-05 & 7.44731368965839e-05 & 0.999962763431552 \tabularnewline
57 & 3.21014241942270e-05 & 6.42028483884541e-05 & 0.999967898575806 \tabularnewline
58 & 1.83229603378106e-05 & 3.66459206756212e-05 & 0.999981677039662 \tabularnewline
59 & 2.11520315738060e-05 & 4.23040631476119e-05 & 0.999978847968426 \tabularnewline
60 & 0.000200018144877199 & 0.000400036289754399 & 0.999799981855123 \tabularnewline
61 & 0.00054423414478525 & 0.0010884682895705 & 0.999455765855215 \tabularnewline
62 & 0.000423189596154421 & 0.000846379192308843 & 0.999576810403846 \tabularnewline
63 & 0.000395740136484996 & 0.000791480272969992 & 0.999604259863515 \tabularnewline
64 & 0.000254685533350103 & 0.000509371066700205 & 0.99974531446665 \tabularnewline
65 & 0.00299341158889442 & 0.00598682317778884 & 0.997006588411106 \tabularnewline
66 & 0.0145077659072498 & 0.0290155318144996 & 0.98549223409275 \tabularnewline
67 & 0.0852218769917232 & 0.170443753983446 & 0.914778123008277 \tabularnewline
68 & 0.165872193872886 & 0.331744387745771 & 0.834127806127114 \tabularnewline
69 & 0.368567655229419 & 0.737135310458839 & 0.63143234477058 \tabularnewline
70 & 0.502371032855588 & 0.995257934288824 & 0.497628967144412 \tabularnewline
71 & 0.367151491591172 & 0.734302983182343 & 0.632848508408828 \tabularnewline
72 & 0.229209002948359 & 0.458418005896717 & 0.770790997051641 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68079&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]9[/C][C]0.0223044695175946[/C][C]0.0446089390351892[/C][C]0.977695530482405[/C][/ROW]
[ROW][C]10[/C][C]0.0143262870721759[/C][C]0.0286525741443517[/C][C]0.985673712927824[/C][/ROW]
[ROW][C]11[/C][C]0.00753719623978628[/C][C]0.0150743924795726[/C][C]0.992462803760214[/C][/ROW]
[ROW][C]12[/C][C]0.00255452718312488[/C][C]0.00510905436624976[/C][C]0.997445472816875[/C][/ROW]
[ROW][C]13[/C][C]0.0020224471564735[/C][C]0.004044894312947[/C][C]0.997977552843527[/C][/ROW]
[ROW][C]14[/C][C]0.000763281773093224[/C][C]0.00152656354618645[/C][C]0.999236718226907[/C][/ROW]
[ROW][C]15[/C][C]0.000920010997375257[/C][C]0.00184002199475051[/C][C]0.999079989002625[/C][/ROW]
[ROW][C]16[/C][C]0.000361933144679982[/C][C]0.000723866289359964[/C][C]0.99963806685532[/C][/ROW]
[ROW][C]17[/C][C]0.000680982628736677[/C][C]0.00136196525747335[/C][C]0.999319017371263[/C][/ROW]
[ROW][C]18[/C][C]0.000286502198658711[/C][C]0.000573004397317422[/C][C]0.999713497801341[/C][/ROW]
[ROW][C]19[/C][C]0.000226575180265881[/C][C]0.000453150360531761[/C][C]0.999773424819734[/C][/ROW]
[ROW][C]20[/C][C]0.000192554728961195[/C][C]0.00038510945792239[/C][C]0.999807445271039[/C][/ROW]
[ROW][C]21[/C][C]8.03599389054704e-05[/C][C]0.000160719877810941[/C][C]0.999919640061095[/C][/ROW]
[ROW][C]22[/C][C]0.000155523949869902[/C][C]0.000311047899739803[/C][C]0.99984447605013[/C][/ROW]
[ROW][C]23[/C][C]0.000265152080315990[/C][C]0.000530304160631981[/C][C]0.999734847919684[/C][/ROW]
[ROW][C]24[/C][C]0.000236423380414597[/C][C]0.000472846760829193[/C][C]0.999763576619585[/C][/ROW]
[ROW][C]25[/C][C]0.000148189929612245[/C][C]0.00029637985922449[/C][C]0.999851810070388[/C][/ROW]
[ROW][C]26[/C][C]6.41929908395996e-05[/C][C]0.000128385981679199[/C][C]0.99993580700916[/C][/ROW]
[ROW][C]27[/C][C]0.00016881281120564[/C][C]0.00033762562241128[/C][C]0.999831187188794[/C][/ROW]
[ROW][C]28[/C][C]8.80625258630217e-05[/C][C]0.000176125051726043[/C][C]0.999911937474137[/C][/ROW]
[ROW][C]29[/C][C]6.7636498666953e-05[/C][C]0.000135272997333906[/C][C]0.999932363501333[/C][/ROW]
[ROW][C]30[/C][C]9.79538793228484e-05[/C][C]0.000195907758645697[/C][C]0.999902046120677[/C][/ROW]
[ROW][C]31[/C][C]4.78995994222496e-05[/C][C]9.57991988444991e-05[/C][C]0.999952100400578[/C][/ROW]
[ROW][C]32[/C][C]6.88650748277579e-05[/C][C]0.000137730149655516[/C][C]0.999931134925172[/C][/ROW]
[ROW][C]33[/C][C]3.65537540240901e-05[/C][C]7.31075080481801e-05[/C][C]0.999963446245976[/C][/ROW]
[ROW][C]34[/C][C]3.89079716364752e-05[/C][C]7.78159432729505e-05[/C][C]0.999961092028363[/C][/ROW]
[ROW][C]35[/C][C]3.42426127995193e-05[/C][C]6.84852255990387e-05[/C][C]0.9999657573872[/C][/ROW]
[ROW][C]36[/C][C]1.53029244667751e-05[/C][C]3.06058489335501e-05[/C][C]0.999984697075533[/C][/ROW]
[ROW][C]37[/C][C]1.21800553574767e-05[/C][C]2.43601107149534e-05[/C][C]0.999987819944643[/C][/ROW]
[ROW][C]38[/C][C]1.23070600818785e-05[/C][C]2.46141201637571e-05[/C][C]0.999987692939918[/C][/ROW]
[ROW][C]39[/C][C]5.83025304774715e-06[/C][C]1.16605060954943e-05[/C][C]0.999994169746952[/C][/ROW]
[ROW][C]40[/C][C]8.96665696003511e-06[/C][C]1.79333139200702e-05[/C][C]0.99999103334304[/C][/ROW]
[ROW][C]41[/C][C]4.32367556395301e-06[/C][C]8.64735112790601e-06[/C][C]0.999995676324436[/C][/ROW]
[ROW][C]42[/C][C]2.11257081655950e-06[/C][C]4.22514163311901e-06[/C][C]0.999997887429183[/C][/ROW]
[ROW][C]43[/C][C]1.52600449770315e-06[/C][C]3.05200899540631e-06[/C][C]0.999998473995502[/C][/ROW]
[ROW][C]44[/C][C]7.038922921419e-07[/C][C]1.4077845842838e-06[/C][C]0.999999296107708[/C][/ROW]
[ROW][C]45[/C][C]2.18288626453530e-05[/C][C]4.36577252907060e-05[/C][C]0.999978171137355[/C][/ROW]
[ROW][C]46[/C][C]2.40806152129645e-05[/C][C]4.8161230425929e-05[/C][C]0.999975919384787[/C][/ROW]
[ROW][C]47[/C][C]1.83930006808305e-05[/C][C]3.67860013616611e-05[/C][C]0.99998160699932[/C][/ROW]
[ROW][C]48[/C][C]9.5895617048996e-06[/C][C]1.91791234097992e-05[/C][C]0.999990410438295[/C][/ROW]
[ROW][C]49[/C][C]0.000182510005595050[/C][C]0.000365020011190100[/C][C]0.999817489994405[/C][/ROW]
[ROW][C]50[/C][C]0.000113421289773695[/C][C]0.000226842579547391[/C][C]0.999886578710226[/C][/ROW]
[ROW][C]51[/C][C]6.13296360600287e-05[/C][C]0.000122659272120057[/C][C]0.99993867036394[/C][/ROW]
[ROW][C]52[/C][C]3.38716547974193e-05[/C][C]6.77433095948387e-05[/C][C]0.999966128345203[/C][/ROW]
[ROW][C]53[/C][C]1.97693057429495e-05[/C][C]3.9538611485899e-05[/C][C]0.999980230694257[/C][/ROW]
[ROW][C]54[/C][C]9.76853525306571e-06[/C][C]1.95370705061314e-05[/C][C]0.999990231464747[/C][/ROW]
[ROW][C]55[/C][C]4.5704691866225e-06[/C][C]9.140938373245e-06[/C][C]0.999995429530813[/C][/ROW]
[ROW][C]56[/C][C]3.72365684482919e-05[/C][C]7.44731368965839e-05[/C][C]0.999962763431552[/C][/ROW]
[ROW][C]57[/C][C]3.21014241942270e-05[/C][C]6.42028483884541e-05[/C][C]0.999967898575806[/C][/ROW]
[ROW][C]58[/C][C]1.83229603378106e-05[/C][C]3.66459206756212e-05[/C][C]0.999981677039662[/C][/ROW]
[ROW][C]59[/C][C]2.11520315738060e-05[/C][C]4.23040631476119e-05[/C][C]0.999978847968426[/C][/ROW]
[ROW][C]60[/C][C]0.000200018144877199[/C][C]0.000400036289754399[/C][C]0.999799981855123[/C][/ROW]
[ROW][C]61[/C][C]0.00054423414478525[/C][C]0.0010884682895705[/C][C]0.999455765855215[/C][/ROW]
[ROW][C]62[/C][C]0.000423189596154421[/C][C]0.000846379192308843[/C][C]0.999576810403846[/C][/ROW]
[ROW][C]63[/C][C]0.000395740136484996[/C][C]0.000791480272969992[/C][C]0.999604259863515[/C][/ROW]
[ROW][C]64[/C][C]0.000254685533350103[/C][C]0.000509371066700205[/C][C]0.99974531446665[/C][/ROW]
[ROW][C]65[/C][C]0.00299341158889442[/C][C]0.00598682317778884[/C][C]0.997006588411106[/C][/ROW]
[ROW][C]66[/C][C]0.0145077659072498[/C][C]0.0290155318144996[/C][C]0.98549223409275[/C][/ROW]
[ROW][C]67[/C][C]0.0852218769917232[/C][C]0.170443753983446[/C][C]0.914778123008277[/C][/ROW]
[ROW][C]68[/C][C]0.165872193872886[/C][C]0.331744387745771[/C][C]0.834127806127114[/C][/ROW]
[ROW][C]69[/C][C]0.368567655229419[/C][C]0.737135310458839[/C][C]0.63143234477058[/C][/ROW]
[ROW][C]70[/C][C]0.502371032855588[/C][C]0.995257934288824[/C][C]0.497628967144412[/C][/ROW]
[ROW][C]71[/C][C]0.367151491591172[/C][C]0.734302983182343[/C][C]0.632848508408828[/C][/ROW]
[ROW][C]72[/C][C]0.229209002948359[/C][C]0.458418005896717[/C][C]0.770790997051641[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68079&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68079&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.02230446951759460.04460893903518920.977695530482405
100.01432628707217590.02865257414435170.985673712927824
110.007537196239786280.01507439247957260.992462803760214
120.002554527183124880.005109054366249760.997445472816875
130.00202244715647350.0040448943129470.997977552843527
140.0007632817730932240.001526563546186450.999236718226907
150.0009200109973752570.001840021994750510.999079989002625
160.0003619331446799820.0007238662893599640.99963806685532
170.0006809826287366770.001361965257473350.999319017371263
180.0002865021986587110.0005730043973174220.999713497801341
190.0002265751802658810.0004531503605317610.999773424819734
200.0001925547289611950.000385109457922390.999807445271039
218.03599389054704e-050.0001607198778109410.999919640061095
220.0001555239498699020.0003110478997398030.99984447605013
230.0002651520803159900.0005303041606319810.999734847919684
240.0002364233804145970.0004728467608291930.999763576619585
250.0001481899296122450.000296379859224490.999851810070388
266.41929908395996e-050.0001283859816791990.99993580700916
270.000168812811205640.000337625622411280.999831187188794
288.80625258630217e-050.0001761250517260430.999911937474137
296.7636498666953e-050.0001352729973339060.999932363501333
309.79538793228484e-050.0001959077586456970.999902046120677
314.78995994222496e-059.57991988444991e-050.999952100400578
326.88650748277579e-050.0001377301496555160.999931134925172
333.65537540240901e-057.31075080481801e-050.999963446245976
343.89079716364752e-057.78159432729505e-050.999961092028363
353.42426127995193e-056.84852255990387e-050.9999657573872
361.53029244667751e-053.06058489335501e-050.999984697075533
371.21800553574767e-052.43601107149534e-050.999987819944643
381.23070600818785e-052.46141201637571e-050.999987692939918
395.83025304774715e-061.16605060954943e-050.999994169746952
408.96665696003511e-061.79333139200702e-050.99999103334304
414.32367556395301e-068.64735112790601e-060.999995676324436
422.11257081655950e-064.22514163311901e-060.999997887429183
431.52600449770315e-063.05200899540631e-060.999998473995502
447.038922921419e-071.4077845842838e-060.999999296107708
452.18288626453530e-054.36577252907060e-050.999978171137355
462.40806152129645e-054.8161230425929e-050.999975919384787
471.83930006808305e-053.67860013616611e-050.99998160699932
489.5895617048996e-061.91791234097992e-050.999990410438295
490.0001825100055950500.0003650200111901000.999817489994405
500.0001134212897736950.0002268425795473910.999886578710226
516.13296360600287e-050.0001226592721200570.99993867036394
523.38716547974193e-056.77433095948387e-050.999966128345203
531.97693057429495e-053.9538611485899e-050.999980230694257
549.76853525306571e-061.95370705061314e-050.999990231464747
554.5704691866225e-069.140938373245e-060.999995429530813
563.72365684482919e-057.44731368965839e-050.999962763431552
573.21014241942270e-056.42028483884541e-050.999967898575806
581.83229603378106e-053.66459206756212e-050.999981677039662
592.11520315738060e-054.23040631476119e-050.999978847968426
600.0002000181448771990.0004000362897543990.999799981855123
610.000544234144785250.00108846828957050.999455765855215
620.0004231895961544210.0008463791923088430.999576810403846
630.0003957401364849960.0007914802729699920.999604259863515
640.0002546855333501030.0005093710667002050.99974531446665
650.002993411588894420.005986823177788840.997006588411106
660.01450776590724980.02901553181449960.98549223409275
670.08522187699172320.1704437539834460.914778123008277
680.1658721938728860.3317443877457710.834127806127114
690.3685676552294190.7371353104588390.63143234477058
700.5023710328555880.9952579342888240.497628967144412
710.3671514915911720.7343029831823430.632848508408828
720.2292090029483590.4584180058967170.770790997051641







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level540.84375NOK
5% type I error level580.90625NOK
10% type I error level580.90625NOK

\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 & 54 & 0.84375 & NOK \tabularnewline
5% type I error level & 58 & 0.90625 & NOK \tabularnewline
10% type I error level & 58 & 0.90625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68079&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]54[/C][C]0.84375[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]58[/C][C]0.90625[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]58[/C][C]0.90625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68079&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68079&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level540.84375NOK
5% type I error level580.90625NOK
10% type I error level580.90625NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}