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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 computationFri, 20 Nov 2009 05:32:50 -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/Nov/20/t1258721490344k64nr1y1qk6a.htm/, Retrieved Fri, 29 Mar 2024 00:04:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58097, Retrieved Fri, 29 Mar 2024 00:04:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Model 1] [2009-11-20 12:32:50] [cf272a759dc2b193d9a85354803ede7b] [Current]
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Dataseries X:
108.5	98.71
112.3	98.54
116.6	98.2
115.5	96.92
120.1	99.06
132.9	99.65
128.1	99.82
129.3	99.99
132.5	100.33
131	99.31
124.9	101.1
120.8	101.1
122	100.93
122.1	100.85
127.4	100.93
135.2	99.6
137.3	101.88
135	101.81
136	102.38
138.4	102.74
134.7	102.82
138.4	101.72
133.9	103.47
133.6	102.98
141.2	102.68
151.8	102.9
155.4	103.03
156.6	101.29
161.6	103.69
160.7	103.68
156	104.2
159.5	104.08
168.7	104.16
169.9	103.05
169.9	104.66
185.9	104.46
190.8	104.95
195.8	105.85
211.9	106.23
227.1	104.86
251.3	107.44
256.7	108.23
251.9	108.45
251.2	109.39
270.3	110.15
267.2	109.13
243	110.28
229.9	110.17
187.2	109.99
178.2	109.26
175.2	109.11
192.4	107.06
187	109.53
184	108.92
194.1	109.24
212.7	109.12
217.5	109
200.5	107.23
205.9	109.49
196.5	109.04
206.3	109.02




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=58097&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=58097&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58097&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] = -873.66330396259 + 10.0004964841749X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -873.66330396259 +  10.0004964841749X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58097&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -873.66330396259 +  10.0004964841749X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58097&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58097&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] = -873.66330396259 + 10.0004964841749X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-873.6633039625977.616918-11.256100
X10.00049648417490.74301813.459300

\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) & -873.66330396259 & 77.616918 & -11.2561 & 0 & 0 \tabularnewline
X & 10.0004964841749 & 0.743018 & 13.4593 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58097&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]-873.66330396259[/C][C]77.616918[/C][C]-11.2561[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]10.0004964841749[/C][C]0.743018[/C][C]13.4593[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58097&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58097&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)-873.6633039625977.616918-11.256100
X10.00049648417490.74301813.459300







Multiple Linear Regression - Regression Statistics
Multiple R0.868517469066465
R-squared0.754322594073617
Adjusted R-squared0.750158570244357
F-TEST (value)181.152324050830
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.2647862679449
Sum Squared Residuals29247.5217458788

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.868517469066465 \tabularnewline
R-squared & 0.754322594073617 \tabularnewline
Adjusted R-squared & 0.750158570244357 \tabularnewline
F-TEST (value) & 181.152324050830 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 22.2647862679449 \tabularnewline
Sum Squared Residuals & 29247.5217458788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58097&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.868517469066465[/C][/ROW]
[ROW][C]R-squared[/C][C]0.754322594073617[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.750158570244357[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]181.152324050830[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/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]22.2647862679449[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]29247.5217458788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58097&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58097&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 R0.868517469066465
R-squared0.754322594073617
Adjusted R-squared0.750158570244357
F-TEST (value)181.152324050830
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.2647862679449
Sum Squared Residuals29247.5217458788







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1108.5113.485703990315-4.98570399031535
2112.3111.7856195880050.51438041199518
3116.6108.3854507833858.21454921661466
4115.595.584815283641419.9151847163585
5120.1116.9858777597763.11412224022424
6132.9122.88617068543910.0138293145610
7128.1124.5862550877493.51374491225140
8129.3126.2863394900583.01366050994167
9132.5129.6865082946782.81349170532216
10131119.48600188081911.5139981191805
11124.9137.386890587492-12.4868905874925
12120.8137.386890587492-16.5868905874925
13122135.686806185183-13.6868061851829
14122.1134.886766466449-12.7867664664488
15127.4135.686806185183-8.28680618518287
16135.2122.3861458612312.8138541387699
17137.3145.187277845149-7.88727784514891
18135144.487243091257-9.48724309125675
19136150.187526087236-14.1875260872364
20138.4153.787704821539-15.3877048215393
21134.7154.587744540273-19.8877445402733
22138.4143.587198407681-5.18719840768097
23133.9161.088067254987-27.1880672549871
24133.6156.187823977741-22.5878239777414
25141.2153.187675032489-11.9876750324890
26151.8155.387784259007-3.58778425900742
27155.4156.687848801950-1.28784880195012
28156.6139.28698491948617.3130150805142
29161.6163.288176481506-1.68817648150553
30160.7163.188171516664-2.48817151666388
31156168.388429688435-12.3884296884348
32159.5167.188370110334-7.68837011033374
33168.7167.9884098290680.711590170932269
34169.9156.88785873163413.0121412683664
35169.9172.988658071155-3.08865807115517
36185.9170.9885587743214.9114412256798
37190.8175.88880205156614.9111979484341
38195.8184.88924888732310.9107511126767
39211.9188.68943755131023.2105624486902
40227.1174.98875736799052.1112426320098
41251.3200.79003829716150.5099617028386
42256.7208.69043051966048.0095694803403
43251.9210.89053974617841.0094602538219
44251.2220.29100644130330.9089935586975
45270.3227.89138376927542.4086162307245
46267.2217.69087735541749.509122644583
47243229.19144831221813.8085516877818
48229.9228.0913936989591.80860630104105
49187.2226.291304331807-39.0913043318074
50178.2218.99094189836-40.7909418983598
51175.2217.490867425734-42.2908674257335
52192.4196.989849633175-4.589849633175
53187221.691075949087-34.691075949087
54184215.590773093740-31.5907730937403
55194.1218.790931968676-24.6909319686762
56212.7217.590872390575-4.89087239057535
57217.5216.3908128124741.10918718752570
58200.5198.6899340354851.81006596451524
59205.9221.29105608972-15.3910560897199
60196.5216.790832671841-20.2908326718414
61206.3216.590822742158-10.2908227421577

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 108.5 & 113.485703990315 & -4.98570399031535 \tabularnewline
2 & 112.3 & 111.785619588005 & 0.51438041199518 \tabularnewline
3 & 116.6 & 108.385450783385 & 8.21454921661466 \tabularnewline
4 & 115.5 & 95.5848152836414 & 19.9151847163585 \tabularnewline
5 & 120.1 & 116.985877759776 & 3.11412224022424 \tabularnewline
6 & 132.9 & 122.886170685439 & 10.0138293145610 \tabularnewline
7 & 128.1 & 124.586255087749 & 3.51374491225140 \tabularnewline
8 & 129.3 & 126.286339490058 & 3.01366050994167 \tabularnewline
9 & 132.5 & 129.686508294678 & 2.81349170532216 \tabularnewline
10 & 131 & 119.486001880819 & 11.5139981191805 \tabularnewline
11 & 124.9 & 137.386890587492 & -12.4868905874925 \tabularnewline
12 & 120.8 & 137.386890587492 & -16.5868905874925 \tabularnewline
13 & 122 & 135.686806185183 & -13.6868061851829 \tabularnewline
14 & 122.1 & 134.886766466449 & -12.7867664664488 \tabularnewline
15 & 127.4 & 135.686806185183 & -8.28680618518287 \tabularnewline
16 & 135.2 & 122.38614586123 & 12.8138541387699 \tabularnewline
17 & 137.3 & 145.187277845149 & -7.88727784514891 \tabularnewline
18 & 135 & 144.487243091257 & -9.48724309125675 \tabularnewline
19 & 136 & 150.187526087236 & -14.1875260872364 \tabularnewline
20 & 138.4 & 153.787704821539 & -15.3877048215393 \tabularnewline
21 & 134.7 & 154.587744540273 & -19.8877445402733 \tabularnewline
22 & 138.4 & 143.587198407681 & -5.18719840768097 \tabularnewline
23 & 133.9 & 161.088067254987 & -27.1880672549871 \tabularnewline
24 & 133.6 & 156.187823977741 & -22.5878239777414 \tabularnewline
25 & 141.2 & 153.187675032489 & -11.9876750324890 \tabularnewline
26 & 151.8 & 155.387784259007 & -3.58778425900742 \tabularnewline
27 & 155.4 & 156.687848801950 & -1.28784880195012 \tabularnewline
28 & 156.6 & 139.286984919486 & 17.3130150805142 \tabularnewline
29 & 161.6 & 163.288176481506 & -1.68817648150553 \tabularnewline
30 & 160.7 & 163.188171516664 & -2.48817151666388 \tabularnewline
31 & 156 & 168.388429688435 & -12.3884296884348 \tabularnewline
32 & 159.5 & 167.188370110334 & -7.68837011033374 \tabularnewline
33 & 168.7 & 167.988409829068 & 0.711590170932269 \tabularnewline
34 & 169.9 & 156.887858731634 & 13.0121412683664 \tabularnewline
35 & 169.9 & 172.988658071155 & -3.08865807115517 \tabularnewline
36 & 185.9 & 170.98855877432 & 14.9114412256798 \tabularnewline
37 & 190.8 & 175.888802051566 & 14.9111979484341 \tabularnewline
38 & 195.8 & 184.889248887323 & 10.9107511126767 \tabularnewline
39 & 211.9 & 188.689437551310 & 23.2105624486902 \tabularnewline
40 & 227.1 & 174.988757367990 & 52.1112426320098 \tabularnewline
41 & 251.3 & 200.790038297161 & 50.5099617028386 \tabularnewline
42 & 256.7 & 208.690430519660 & 48.0095694803403 \tabularnewline
43 & 251.9 & 210.890539746178 & 41.0094602538219 \tabularnewline
44 & 251.2 & 220.291006441303 & 30.9089935586975 \tabularnewline
45 & 270.3 & 227.891383769275 & 42.4086162307245 \tabularnewline
46 & 267.2 & 217.690877355417 & 49.509122644583 \tabularnewline
47 & 243 & 229.191448312218 & 13.8085516877818 \tabularnewline
48 & 229.9 & 228.091393698959 & 1.80860630104105 \tabularnewline
49 & 187.2 & 226.291304331807 & -39.0913043318074 \tabularnewline
50 & 178.2 & 218.99094189836 & -40.7909418983598 \tabularnewline
51 & 175.2 & 217.490867425734 & -42.2908674257335 \tabularnewline
52 & 192.4 & 196.989849633175 & -4.589849633175 \tabularnewline
53 & 187 & 221.691075949087 & -34.691075949087 \tabularnewline
54 & 184 & 215.590773093740 & -31.5907730937403 \tabularnewline
55 & 194.1 & 218.790931968676 & -24.6909319686762 \tabularnewline
56 & 212.7 & 217.590872390575 & -4.89087239057535 \tabularnewline
57 & 217.5 & 216.390812812474 & 1.10918718752570 \tabularnewline
58 & 200.5 & 198.689934035485 & 1.81006596451524 \tabularnewline
59 & 205.9 & 221.29105608972 & -15.3910560897199 \tabularnewline
60 & 196.5 & 216.790832671841 & -20.2908326718414 \tabularnewline
61 & 206.3 & 216.590822742158 & -10.2908227421577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58097&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]108.5[/C][C]113.485703990315[/C][C]-4.98570399031535[/C][/ROW]
[ROW][C]2[/C][C]112.3[/C][C]111.785619588005[/C][C]0.51438041199518[/C][/ROW]
[ROW][C]3[/C][C]116.6[/C][C]108.385450783385[/C][C]8.21454921661466[/C][/ROW]
[ROW][C]4[/C][C]115.5[/C][C]95.5848152836414[/C][C]19.9151847163585[/C][/ROW]
[ROW][C]5[/C][C]120.1[/C][C]116.985877759776[/C][C]3.11412224022424[/C][/ROW]
[ROW][C]6[/C][C]132.9[/C][C]122.886170685439[/C][C]10.0138293145610[/C][/ROW]
[ROW][C]7[/C][C]128.1[/C][C]124.586255087749[/C][C]3.51374491225140[/C][/ROW]
[ROW][C]8[/C][C]129.3[/C][C]126.286339490058[/C][C]3.01366050994167[/C][/ROW]
[ROW][C]9[/C][C]132.5[/C][C]129.686508294678[/C][C]2.81349170532216[/C][/ROW]
[ROW][C]10[/C][C]131[/C][C]119.486001880819[/C][C]11.5139981191805[/C][/ROW]
[ROW][C]11[/C][C]124.9[/C][C]137.386890587492[/C][C]-12.4868905874925[/C][/ROW]
[ROW][C]12[/C][C]120.8[/C][C]137.386890587492[/C][C]-16.5868905874925[/C][/ROW]
[ROW][C]13[/C][C]122[/C][C]135.686806185183[/C][C]-13.6868061851829[/C][/ROW]
[ROW][C]14[/C][C]122.1[/C][C]134.886766466449[/C][C]-12.7867664664488[/C][/ROW]
[ROW][C]15[/C][C]127.4[/C][C]135.686806185183[/C][C]-8.28680618518287[/C][/ROW]
[ROW][C]16[/C][C]135.2[/C][C]122.38614586123[/C][C]12.8138541387699[/C][/ROW]
[ROW][C]17[/C][C]137.3[/C][C]145.187277845149[/C][C]-7.88727784514891[/C][/ROW]
[ROW][C]18[/C][C]135[/C][C]144.487243091257[/C][C]-9.48724309125675[/C][/ROW]
[ROW][C]19[/C][C]136[/C][C]150.187526087236[/C][C]-14.1875260872364[/C][/ROW]
[ROW][C]20[/C][C]138.4[/C][C]153.787704821539[/C][C]-15.3877048215393[/C][/ROW]
[ROW][C]21[/C][C]134.7[/C][C]154.587744540273[/C][C]-19.8877445402733[/C][/ROW]
[ROW][C]22[/C][C]138.4[/C][C]143.587198407681[/C][C]-5.18719840768097[/C][/ROW]
[ROW][C]23[/C][C]133.9[/C][C]161.088067254987[/C][C]-27.1880672549871[/C][/ROW]
[ROW][C]24[/C][C]133.6[/C][C]156.187823977741[/C][C]-22.5878239777414[/C][/ROW]
[ROW][C]25[/C][C]141.2[/C][C]153.187675032489[/C][C]-11.9876750324890[/C][/ROW]
[ROW][C]26[/C][C]151.8[/C][C]155.387784259007[/C][C]-3.58778425900742[/C][/ROW]
[ROW][C]27[/C][C]155.4[/C][C]156.687848801950[/C][C]-1.28784880195012[/C][/ROW]
[ROW][C]28[/C][C]156.6[/C][C]139.286984919486[/C][C]17.3130150805142[/C][/ROW]
[ROW][C]29[/C][C]161.6[/C][C]163.288176481506[/C][C]-1.68817648150553[/C][/ROW]
[ROW][C]30[/C][C]160.7[/C][C]163.188171516664[/C][C]-2.48817151666388[/C][/ROW]
[ROW][C]31[/C][C]156[/C][C]168.388429688435[/C][C]-12.3884296884348[/C][/ROW]
[ROW][C]32[/C][C]159.5[/C][C]167.188370110334[/C][C]-7.68837011033374[/C][/ROW]
[ROW][C]33[/C][C]168.7[/C][C]167.988409829068[/C][C]0.711590170932269[/C][/ROW]
[ROW][C]34[/C][C]169.9[/C][C]156.887858731634[/C][C]13.0121412683664[/C][/ROW]
[ROW][C]35[/C][C]169.9[/C][C]172.988658071155[/C][C]-3.08865807115517[/C][/ROW]
[ROW][C]36[/C][C]185.9[/C][C]170.98855877432[/C][C]14.9114412256798[/C][/ROW]
[ROW][C]37[/C][C]190.8[/C][C]175.888802051566[/C][C]14.9111979484341[/C][/ROW]
[ROW][C]38[/C][C]195.8[/C][C]184.889248887323[/C][C]10.9107511126767[/C][/ROW]
[ROW][C]39[/C][C]211.9[/C][C]188.689437551310[/C][C]23.2105624486902[/C][/ROW]
[ROW][C]40[/C][C]227.1[/C][C]174.988757367990[/C][C]52.1112426320098[/C][/ROW]
[ROW][C]41[/C][C]251.3[/C][C]200.790038297161[/C][C]50.5099617028386[/C][/ROW]
[ROW][C]42[/C][C]256.7[/C][C]208.690430519660[/C][C]48.0095694803403[/C][/ROW]
[ROW][C]43[/C][C]251.9[/C][C]210.890539746178[/C][C]41.0094602538219[/C][/ROW]
[ROW][C]44[/C][C]251.2[/C][C]220.291006441303[/C][C]30.9089935586975[/C][/ROW]
[ROW][C]45[/C][C]270.3[/C][C]227.891383769275[/C][C]42.4086162307245[/C][/ROW]
[ROW][C]46[/C][C]267.2[/C][C]217.690877355417[/C][C]49.509122644583[/C][/ROW]
[ROW][C]47[/C][C]243[/C][C]229.191448312218[/C][C]13.8085516877818[/C][/ROW]
[ROW][C]48[/C][C]229.9[/C][C]228.091393698959[/C][C]1.80860630104105[/C][/ROW]
[ROW][C]49[/C][C]187.2[/C][C]226.291304331807[/C][C]-39.0913043318074[/C][/ROW]
[ROW][C]50[/C][C]178.2[/C][C]218.99094189836[/C][C]-40.7909418983598[/C][/ROW]
[ROW][C]51[/C][C]175.2[/C][C]217.490867425734[/C][C]-42.2908674257335[/C][/ROW]
[ROW][C]52[/C][C]192.4[/C][C]196.989849633175[/C][C]-4.589849633175[/C][/ROW]
[ROW][C]53[/C][C]187[/C][C]221.691075949087[/C][C]-34.691075949087[/C][/ROW]
[ROW][C]54[/C][C]184[/C][C]215.590773093740[/C][C]-31.5907730937403[/C][/ROW]
[ROW][C]55[/C][C]194.1[/C][C]218.790931968676[/C][C]-24.6909319686762[/C][/ROW]
[ROW][C]56[/C][C]212.7[/C][C]217.590872390575[/C][C]-4.89087239057535[/C][/ROW]
[ROW][C]57[/C][C]217.5[/C][C]216.390812812474[/C][C]1.10918718752570[/C][/ROW]
[ROW][C]58[/C][C]200.5[/C][C]198.689934035485[/C][C]1.81006596451524[/C][/ROW]
[ROW][C]59[/C][C]205.9[/C][C]221.29105608972[/C][C]-15.3910560897199[/C][/ROW]
[ROW][C]60[/C][C]196.5[/C][C]216.790832671841[/C][C]-20.2908326718414[/C][/ROW]
[ROW][C]61[/C][C]206.3[/C][C]216.590822742158[/C][C]-10.2908227421577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58097&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58097&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
1108.5113.485703990315-4.98570399031535
2112.3111.7856195880050.51438041199518
3116.6108.3854507833858.21454921661466
4115.595.584815283641419.9151847163585
5120.1116.9858777597763.11412224022424
6132.9122.88617068543910.0138293145610
7128.1124.5862550877493.51374491225140
8129.3126.2863394900583.01366050994167
9132.5129.6865082946782.81349170532216
10131119.48600188081911.5139981191805
11124.9137.386890587492-12.4868905874925
12120.8137.386890587492-16.5868905874925
13122135.686806185183-13.6868061851829
14122.1134.886766466449-12.7867664664488
15127.4135.686806185183-8.28680618518287
16135.2122.3861458612312.8138541387699
17137.3145.187277845149-7.88727784514891
18135144.487243091257-9.48724309125675
19136150.187526087236-14.1875260872364
20138.4153.787704821539-15.3877048215393
21134.7154.587744540273-19.8877445402733
22138.4143.587198407681-5.18719840768097
23133.9161.088067254987-27.1880672549871
24133.6156.187823977741-22.5878239777414
25141.2153.187675032489-11.9876750324890
26151.8155.387784259007-3.58778425900742
27155.4156.687848801950-1.28784880195012
28156.6139.28698491948617.3130150805142
29161.6163.288176481506-1.68817648150553
30160.7163.188171516664-2.48817151666388
31156168.388429688435-12.3884296884348
32159.5167.188370110334-7.68837011033374
33168.7167.9884098290680.711590170932269
34169.9156.88785873163413.0121412683664
35169.9172.988658071155-3.08865807115517
36185.9170.9885587743214.9114412256798
37190.8175.88880205156614.9111979484341
38195.8184.88924888732310.9107511126767
39211.9188.68943755131023.2105624486902
40227.1174.98875736799052.1112426320098
41251.3200.79003829716150.5099617028386
42256.7208.69043051966048.0095694803403
43251.9210.89053974617841.0094602538219
44251.2220.29100644130330.9089935586975
45270.3227.89138376927542.4086162307245
46267.2217.69087735541749.509122644583
47243229.19144831221813.8085516877818
48229.9228.0913936989591.80860630104105
49187.2226.291304331807-39.0913043318074
50178.2218.99094189836-40.7909418983598
51175.2217.490867425734-42.2908674257335
52192.4196.989849633175-4.589849633175
53187221.691075949087-34.691075949087
54184215.590773093740-31.5907730937403
55194.1218.790931968676-24.6909319686762
56212.7217.590872390575-4.89087239057535
57217.5216.3908128124741.10918718752570
58200.5198.6899340354851.81006596451524
59205.9221.29105608972-15.3910560897199
60196.5216.790832671841-20.2908326718414
61206.3216.590822742158-10.2908227421577







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.01436473117863590.02872946235727180.985635268821364
60.02522112467299530.05044224934599070.974778875327005
70.008008439818096980.01601687963619400.991991560181903
80.002290056685059350.004580113370118710.997709943314941
90.0006261412360781940.001252282472156390.999373858763922
100.0002787725127653520.0005575450255307050.999721227487235
110.0001788586353719120.0003577172707438240.999821141364628
120.0001241320887937370.0002482641775874730.999875867911206
134.7801849389359e-059.5603698778718e-050.99995219815061
141.59531565081441e-053.19063130162881e-050.999984046843492
154.15915919858439e-068.31831839716879e-060.999995840840801
164.71777624499913e-069.43555248999826e-060.999995282223755
171.78266967915192e-063.56533935830383e-060.99999821733032
185.15082077065081e-071.03016415413016e-060.999999484917923
191.39216197907072e-072.78432395814145e-070.999999860783802
203.80902030835625e-087.6180406167125e-080.999999961909797
211.08902535520152e-082.17805071040303e-080.999999989109746
223.84208394818238e-097.68416789636475e-090.999999996157916
231.73508238769923e-093.47016477539846e-090.999999998264918
246.11915514520135e-101.22383102904027e-090.999999999388084
252.38911529340144e-104.77823058680288e-100.999999999761088
265.60130907505944e-101.12026181501189e-090.99999999943987
271.44930997885129e-092.89861995770258e-090.99999999855069
282.11692018178332e-084.23384036356664e-080.999999978830798
292.73202714068092e-085.46405428136183e-080.999999972679729
302.29734246525285e-084.59468493050569e-080.999999977026575
311.10707523352930e-082.21415046705860e-080.999999988929248
326.53527651273406e-091.30705530254681e-080.999999993464723
338.05462982439929e-091.61092596487986e-080.99999999194537
342.65085920087019e-085.30171840174039e-080.999999973491408
352.15693688316131e-084.31387376632262e-080.999999978430631
369.50203553411203e-081.90040710682241e-070.999999904979645
372.51350481295656e-075.02700962591312e-070.999999748649519
383.20849929433431e-076.41699858866861e-070.99999967915007
398.9435072159145e-071.7887014431829e-060.999999105649278
404.20379789486717e-058.40759578973433e-050.999957962021051
410.0004441522111256010.0008883044222512030.999555847788874
420.002233575109069240.004467150218138490.99776642489093
430.006006666164101360.01201333232820270.993993333835899
440.008508829867010480.01701765973402100.99149117013299
450.03514535888660070.07029071777320140.9648546411134
460.3530499776702350.706099955340470.646950022329765
470.6064094810164830.7871810379670340.393590518983517
480.8326604365193910.3346791269612170.167339563480608
490.8775765075429230.2448469849141530.122423492457077
500.9270947085917940.1458105828164120.0729052914082059
510.9737569479426970.05248610411460520.0262430520573026
520.9523952499864890.09520950002702270.0476047500135113
530.9533084984895130.09338300302097490.0466915015104874
540.9767465585531910.04650688289361730.0232534414468087
550.9742341094457240.05153178110855220.0257658905542761
560.933069948770340.1338601024593180.0669300512296592

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0143647311786359 & 0.0287294623572718 & 0.985635268821364 \tabularnewline
6 & 0.0252211246729953 & 0.0504422493459907 & 0.974778875327005 \tabularnewline
7 & 0.00800843981809698 & 0.0160168796361940 & 0.991991560181903 \tabularnewline
8 & 0.00229005668505935 & 0.00458011337011871 & 0.997709943314941 \tabularnewline
9 & 0.000626141236078194 & 0.00125228247215639 & 0.999373858763922 \tabularnewline
10 & 0.000278772512765352 & 0.000557545025530705 & 0.999721227487235 \tabularnewline
11 & 0.000178858635371912 & 0.000357717270743824 & 0.999821141364628 \tabularnewline
12 & 0.000124132088793737 & 0.000248264177587473 & 0.999875867911206 \tabularnewline
13 & 4.7801849389359e-05 & 9.5603698778718e-05 & 0.99995219815061 \tabularnewline
14 & 1.59531565081441e-05 & 3.19063130162881e-05 & 0.999984046843492 \tabularnewline
15 & 4.15915919858439e-06 & 8.31831839716879e-06 & 0.999995840840801 \tabularnewline
16 & 4.71777624499913e-06 & 9.43555248999826e-06 & 0.999995282223755 \tabularnewline
17 & 1.78266967915192e-06 & 3.56533935830383e-06 & 0.99999821733032 \tabularnewline
18 & 5.15082077065081e-07 & 1.03016415413016e-06 & 0.999999484917923 \tabularnewline
19 & 1.39216197907072e-07 & 2.78432395814145e-07 & 0.999999860783802 \tabularnewline
20 & 3.80902030835625e-08 & 7.6180406167125e-08 & 0.999999961909797 \tabularnewline
21 & 1.08902535520152e-08 & 2.17805071040303e-08 & 0.999999989109746 \tabularnewline
22 & 3.84208394818238e-09 & 7.68416789636475e-09 & 0.999999996157916 \tabularnewline
23 & 1.73508238769923e-09 & 3.47016477539846e-09 & 0.999999998264918 \tabularnewline
24 & 6.11915514520135e-10 & 1.22383102904027e-09 & 0.999999999388084 \tabularnewline
25 & 2.38911529340144e-10 & 4.77823058680288e-10 & 0.999999999761088 \tabularnewline
26 & 5.60130907505944e-10 & 1.12026181501189e-09 & 0.99999999943987 \tabularnewline
27 & 1.44930997885129e-09 & 2.89861995770258e-09 & 0.99999999855069 \tabularnewline
28 & 2.11692018178332e-08 & 4.23384036356664e-08 & 0.999999978830798 \tabularnewline
29 & 2.73202714068092e-08 & 5.46405428136183e-08 & 0.999999972679729 \tabularnewline
30 & 2.29734246525285e-08 & 4.59468493050569e-08 & 0.999999977026575 \tabularnewline
31 & 1.10707523352930e-08 & 2.21415046705860e-08 & 0.999999988929248 \tabularnewline
32 & 6.53527651273406e-09 & 1.30705530254681e-08 & 0.999999993464723 \tabularnewline
33 & 8.05462982439929e-09 & 1.61092596487986e-08 & 0.99999999194537 \tabularnewline
34 & 2.65085920087019e-08 & 5.30171840174039e-08 & 0.999999973491408 \tabularnewline
35 & 2.15693688316131e-08 & 4.31387376632262e-08 & 0.999999978430631 \tabularnewline
36 & 9.50203553411203e-08 & 1.90040710682241e-07 & 0.999999904979645 \tabularnewline
37 & 2.51350481295656e-07 & 5.02700962591312e-07 & 0.999999748649519 \tabularnewline
38 & 3.20849929433431e-07 & 6.41699858866861e-07 & 0.99999967915007 \tabularnewline
39 & 8.9435072159145e-07 & 1.7887014431829e-06 & 0.999999105649278 \tabularnewline
40 & 4.20379789486717e-05 & 8.40759578973433e-05 & 0.999957962021051 \tabularnewline
41 & 0.000444152211125601 & 0.000888304422251203 & 0.999555847788874 \tabularnewline
42 & 0.00223357510906924 & 0.00446715021813849 & 0.99776642489093 \tabularnewline
43 & 0.00600666616410136 & 0.0120133323282027 & 0.993993333835899 \tabularnewline
44 & 0.00850882986701048 & 0.0170176597340210 & 0.99149117013299 \tabularnewline
45 & 0.0351453588866007 & 0.0702907177732014 & 0.9648546411134 \tabularnewline
46 & 0.353049977670235 & 0.70609995534047 & 0.646950022329765 \tabularnewline
47 & 0.606409481016483 & 0.787181037967034 & 0.393590518983517 \tabularnewline
48 & 0.832660436519391 & 0.334679126961217 & 0.167339563480608 \tabularnewline
49 & 0.877576507542923 & 0.244846984914153 & 0.122423492457077 \tabularnewline
50 & 0.927094708591794 & 0.145810582816412 & 0.0729052914082059 \tabularnewline
51 & 0.973756947942697 & 0.0524861041146052 & 0.0262430520573026 \tabularnewline
52 & 0.952395249986489 & 0.0952095000270227 & 0.0476047500135113 \tabularnewline
53 & 0.953308498489513 & 0.0933830030209749 & 0.0466915015104874 \tabularnewline
54 & 0.976746558553191 & 0.0465068828936173 & 0.0232534414468087 \tabularnewline
55 & 0.974234109445724 & 0.0515317811085522 & 0.0257658905542761 \tabularnewline
56 & 0.93306994877034 & 0.133860102459318 & 0.0669300512296592 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58097&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]5[/C][C]0.0143647311786359[/C][C]0.0287294623572718[/C][C]0.985635268821364[/C][/ROW]
[ROW][C]6[/C][C]0.0252211246729953[/C][C]0.0504422493459907[/C][C]0.974778875327005[/C][/ROW]
[ROW][C]7[/C][C]0.00800843981809698[/C][C]0.0160168796361940[/C][C]0.991991560181903[/C][/ROW]
[ROW][C]8[/C][C]0.00229005668505935[/C][C]0.00458011337011871[/C][C]0.997709943314941[/C][/ROW]
[ROW][C]9[/C][C]0.000626141236078194[/C][C]0.00125228247215639[/C][C]0.999373858763922[/C][/ROW]
[ROW][C]10[/C][C]0.000278772512765352[/C][C]0.000557545025530705[/C][C]0.999721227487235[/C][/ROW]
[ROW][C]11[/C][C]0.000178858635371912[/C][C]0.000357717270743824[/C][C]0.999821141364628[/C][/ROW]
[ROW][C]12[/C][C]0.000124132088793737[/C][C]0.000248264177587473[/C][C]0.999875867911206[/C][/ROW]
[ROW][C]13[/C][C]4.7801849389359e-05[/C][C]9.5603698778718e-05[/C][C]0.99995219815061[/C][/ROW]
[ROW][C]14[/C][C]1.59531565081441e-05[/C][C]3.19063130162881e-05[/C][C]0.999984046843492[/C][/ROW]
[ROW][C]15[/C][C]4.15915919858439e-06[/C][C]8.31831839716879e-06[/C][C]0.999995840840801[/C][/ROW]
[ROW][C]16[/C][C]4.71777624499913e-06[/C][C]9.43555248999826e-06[/C][C]0.999995282223755[/C][/ROW]
[ROW][C]17[/C][C]1.78266967915192e-06[/C][C]3.56533935830383e-06[/C][C]0.99999821733032[/C][/ROW]
[ROW][C]18[/C][C]5.15082077065081e-07[/C][C]1.03016415413016e-06[/C][C]0.999999484917923[/C][/ROW]
[ROW][C]19[/C][C]1.39216197907072e-07[/C][C]2.78432395814145e-07[/C][C]0.999999860783802[/C][/ROW]
[ROW][C]20[/C][C]3.80902030835625e-08[/C][C]7.6180406167125e-08[/C][C]0.999999961909797[/C][/ROW]
[ROW][C]21[/C][C]1.08902535520152e-08[/C][C]2.17805071040303e-08[/C][C]0.999999989109746[/C][/ROW]
[ROW][C]22[/C][C]3.84208394818238e-09[/C][C]7.68416789636475e-09[/C][C]0.999999996157916[/C][/ROW]
[ROW][C]23[/C][C]1.73508238769923e-09[/C][C]3.47016477539846e-09[/C][C]0.999999998264918[/C][/ROW]
[ROW][C]24[/C][C]6.11915514520135e-10[/C][C]1.22383102904027e-09[/C][C]0.999999999388084[/C][/ROW]
[ROW][C]25[/C][C]2.38911529340144e-10[/C][C]4.77823058680288e-10[/C][C]0.999999999761088[/C][/ROW]
[ROW][C]26[/C][C]5.60130907505944e-10[/C][C]1.12026181501189e-09[/C][C]0.99999999943987[/C][/ROW]
[ROW][C]27[/C][C]1.44930997885129e-09[/C][C]2.89861995770258e-09[/C][C]0.99999999855069[/C][/ROW]
[ROW][C]28[/C][C]2.11692018178332e-08[/C][C]4.23384036356664e-08[/C][C]0.999999978830798[/C][/ROW]
[ROW][C]29[/C][C]2.73202714068092e-08[/C][C]5.46405428136183e-08[/C][C]0.999999972679729[/C][/ROW]
[ROW][C]30[/C][C]2.29734246525285e-08[/C][C]4.59468493050569e-08[/C][C]0.999999977026575[/C][/ROW]
[ROW][C]31[/C][C]1.10707523352930e-08[/C][C]2.21415046705860e-08[/C][C]0.999999988929248[/C][/ROW]
[ROW][C]32[/C][C]6.53527651273406e-09[/C][C]1.30705530254681e-08[/C][C]0.999999993464723[/C][/ROW]
[ROW][C]33[/C][C]8.05462982439929e-09[/C][C]1.61092596487986e-08[/C][C]0.99999999194537[/C][/ROW]
[ROW][C]34[/C][C]2.65085920087019e-08[/C][C]5.30171840174039e-08[/C][C]0.999999973491408[/C][/ROW]
[ROW][C]35[/C][C]2.15693688316131e-08[/C][C]4.31387376632262e-08[/C][C]0.999999978430631[/C][/ROW]
[ROW][C]36[/C][C]9.50203553411203e-08[/C][C]1.90040710682241e-07[/C][C]0.999999904979645[/C][/ROW]
[ROW][C]37[/C][C]2.51350481295656e-07[/C][C]5.02700962591312e-07[/C][C]0.999999748649519[/C][/ROW]
[ROW][C]38[/C][C]3.20849929433431e-07[/C][C]6.41699858866861e-07[/C][C]0.99999967915007[/C][/ROW]
[ROW][C]39[/C][C]8.9435072159145e-07[/C][C]1.7887014431829e-06[/C][C]0.999999105649278[/C][/ROW]
[ROW][C]40[/C][C]4.20379789486717e-05[/C][C]8.40759578973433e-05[/C][C]0.999957962021051[/C][/ROW]
[ROW][C]41[/C][C]0.000444152211125601[/C][C]0.000888304422251203[/C][C]0.999555847788874[/C][/ROW]
[ROW][C]42[/C][C]0.00223357510906924[/C][C]0.00446715021813849[/C][C]0.99776642489093[/C][/ROW]
[ROW][C]43[/C][C]0.00600666616410136[/C][C]0.0120133323282027[/C][C]0.993993333835899[/C][/ROW]
[ROW][C]44[/C][C]0.00850882986701048[/C][C]0.0170176597340210[/C][C]0.99149117013299[/C][/ROW]
[ROW][C]45[/C][C]0.0351453588866007[/C][C]0.0702907177732014[/C][C]0.9648546411134[/C][/ROW]
[ROW][C]46[/C][C]0.353049977670235[/C][C]0.70609995534047[/C][C]0.646950022329765[/C][/ROW]
[ROW][C]47[/C][C]0.606409481016483[/C][C]0.787181037967034[/C][C]0.393590518983517[/C][/ROW]
[ROW][C]48[/C][C]0.832660436519391[/C][C]0.334679126961217[/C][C]0.167339563480608[/C][/ROW]
[ROW][C]49[/C][C]0.877576507542923[/C][C]0.244846984914153[/C][C]0.122423492457077[/C][/ROW]
[ROW][C]50[/C][C]0.927094708591794[/C][C]0.145810582816412[/C][C]0.0729052914082059[/C][/ROW]
[ROW][C]51[/C][C]0.973756947942697[/C][C]0.0524861041146052[/C][C]0.0262430520573026[/C][/ROW]
[ROW][C]52[/C][C]0.952395249986489[/C][C]0.0952095000270227[/C][C]0.0476047500135113[/C][/ROW]
[ROW][C]53[/C][C]0.953308498489513[/C][C]0.0933830030209749[/C][C]0.0466915015104874[/C][/ROW]
[ROW][C]54[/C][C]0.976746558553191[/C][C]0.0465068828936173[/C][C]0.0232534414468087[/C][/ROW]
[ROW][C]55[/C][C]0.974234109445724[/C][C]0.0515317811085522[/C][C]0.0257658905542761[/C][/ROW]
[ROW][C]56[/C][C]0.93306994877034[/C][C]0.133860102459318[/C][C]0.0669300512296592[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58097&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58097&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
50.01436473117863590.02872946235727180.985635268821364
60.02522112467299530.05044224934599070.974778875327005
70.008008439818096980.01601687963619400.991991560181903
80.002290056685059350.004580113370118710.997709943314941
90.0006261412360781940.001252282472156390.999373858763922
100.0002787725127653520.0005575450255307050.999721227487235
110.0001788586353719120.0003577172707438240.999821141364628
120.0001241320887937370.0002482641775874730.999875867911206
134.7801849389359e-059.5603698778718e-050.99995219815061
141.59531565081441e-053.19063130162881e-050.999984046843492
154.15915919858439e-068.31831839716879e-060.999995840840801
164.71777624499913e-069.43555248999826e-060.999995282223755
171.78266967915192e-063.56533935830383e-060.99999821733032
185.15082077065081e-071.03016415413016e-060.999999484917923
191.39216197907072e-072.78432395814145e-070.999999860783802
203.80902030835625e-087.6180406167125e-080.999999961909797
211.08902535520152e-082.17805071040303e-080.999999989109746
223.84208394818238e-097.68416789636475e-090.999999996157916
231.73508238769923e-093.47016477539846e-090.999999998264918
246.11915514520135e-101.22383102904027e-090.999999999388084
252.38911529340144e-104.77823058680288e-100.999999999761088
265.60130907505944e-101.12026181501189e-090.99999999943987
271.44930997885129e-092.89861995770258e-090.99999999855069
282.11692018178332e-084.23384036356664e-080.999999978830798
292.73202714068092e-085.46405428136183e-080.999999972679729
302.29734246525285e-084.59468493050569e-080.999999977026575
311.10707523352930e-082.21415046705860e-080.999999988929248
326.53527651273406e-091.30705530254681e-080.999999993464723
338.05462982439929e-091.61092596487986e-080.99999999194537
342.65085920087019e-085.30171840174039e-080.999999973491408
352.15693688316131e-084.31387376632262e-080.999999978430631
369.50203553411203e-081.90040710682241e-070.999999904979645
372.51350481295656e-075.02700962591312e-070.999999748649519
383.20849929433431e-076.41699858866861e-070.99999967915007
398.9435072159145e-071.7887014431829e-060.999999105649278
404.20379789486717e-058.40759578973433e-050.999957962021051
410.0004441522111256010.0008883044222512030.999555847788874
420.002233575109069240.004467150218138490.99776642489093
430.006006666164101360.01201333232820270.993993333835899
440.008508829867010480.01701765973402100.99149117013299
450.03514535888660070.07029071777320140.9648546411134
460.3530499776702350.706099955340470.646950022329765
470.6064094810164830.7871810379670340.393590518983517
480.8326604365193910.3346791269612170.167339563480608
490.8775765075429230.2448469849141530.122423492457077
500.9270947085917940.1458105828164120.0729052914082059
510.9737569479426970.05248610411460520.0262430520573026
520.9523952499864890.09520950002702270.0476047500135113
530.9533084984895130.09338300302097490.0466915015104874
540.9767465585531910.04650688289361730.0232534414468087
550.9742341094457240.05153178110855220.0257658905542761
560.933069948770340.1338601024593180.0669300512296592







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level350.673076923076923NOK
5% type I error level400.769230769230769NOK
10% type I error level460.884615384615385NOK

\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 & 35 & 0.673076923076923 & NOK \tabularnewline
5% type I error level & 40 & 0.769230769230769 & NOK \tabularnewline
10% type I error level & 46 & 0.884615384615385 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58097&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]35[/C][C]0.673076923076923[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]40[/C][C]0.769230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.884615384615385[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58097&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58097&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 level350.673076923076923NOK
5% type I error level400.769230769230769NOK
10% type I error level460.884615384615385NOK



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