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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 07:38:49 -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/t1258728675g8xw73tuowqsyi7.htm/, Retrieved Thu, 28 Mar 2024 15:39:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58230, Retrieved Thu, 28 Mar 2024 15:39:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198
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]
- RM D  [Multiple Regression] [Workshop 7 - mult...] [2009-11-20 10:14:52] [74be16979710d4c4e7c6647856088456]
-    D      [Multiple Regression] [Workshop 7 - auto...] [2009-11-20 14:38:49] [d904c6aa144b8c40108ebe5ec22fe1a0] [Current]
-    D        [Multiple Regression] [Workshop 7 - mode...] [2009-11-20 18:37:37] [1646a2766cb8c4a6f9d3b2fffef409b3]
-   P           [Multiple Regression] [] [2009-11-21 19:58:43] [74be16979710d4c4e7c6647856088456]
-               [Multiple Regression] [] [2009-11-22 16:14:27] [74be16979710d4c4e7c6647856088456]
-                 [Multiple Regression] [] [2009-11-25 12:35:32] [74be16979710d4c4e7c6647856088456]
-               [Multiple Regression] [] [2009-11-22 17:08:38] [3af9fa3d2c04a43d660a9a466bdfbaa0]
-               [Multiple Regression] [workshop 7-model ...] [2009-11-22 19:52:12] [24c4941ee50deadff4640c9c09cc70cb]
-               [Multiple Regression] [] [2009-12-17 09:17:03] [68cb6e9d2b1cb3475e83bcdfaf88b501]
-             [Multiple Regression] [] [2009-11-22 12:47:37] [74be16979710d4c4e7c6647856088456]
-             [Multiple Regression] [] [2009-11-22 16:17:38] [74be16979710d4c4e7c6647856088456]
-               [Multiple Regression] [] [2009-11-25 12:36:59] [74be16979710d4c4e7c6647856088456]
-             [Multiple Regression] [] [2009-11-22 17:14:11] [3af9fa3d2c04a43d660a9a466bdfbaa0]
-             [Multiple Regression] [workshop 7-autoco...] [2009-11-22 19:54:41] [24c4941ee50deadff4640c9c09cc70cb]
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Dataseries X:
267413	0	262813	269645
267366	0	267413	267037
264777	0	267366	258113
258863	0	264777	262813
254844	0	258863	267413
254868	0	254844	267366
277267	0	254868	264777
285351	0	277267	258863
286602	0	285351	254844
283042	0	286602	254868
276687	0	283042	277267
277915	0	276687	285351
277128	0	277915	286602
277103	0	277128	283042
275037	0	277103	276687
270150	0	275037	277915
267140	0	270150	277128
264993	0	267140	277103
287259	0	264993	275037
291186	0	287259	270150
292300	0	291186	267140
288186	0	292300	264993
281477	0	288186	287259
282656	0	281477	291186
280190	0	282656	292300
280408	0	280190	288186
276836	0	280408	281477
275216	0	276836	282656
274352	0	275216	280190
271311	0	274352	280408
289802	0	271311	276836
290726	0	289802	275216
292300	0	290726	274352
278506	0	292300	271311
269826	0	278506	289802
265861	0	269826	290726
269034	0	265861	292300
264176	0	269034	278506
255198	0	264176	269826
253353	0	255198	265861
246057	0	253353	269034
235372	0	246057	264176
258556	0	235372	255198
260993	0	258556	253353
254663	0	260993	246057
250643	0	254663	235372
243422	0	250643	258556
247105	0	243422	260993
248541	0	247105	254663
245039	0	248541	250643
237080	0	245039	243422
237085	0	237080	247105
225554	0	237085	248541
226839	0	225554	245039
247934	0	226839	237080
248333	1	247934	237085
246969	1	248333	225554
245098	1	246969	226839
246263	1	245098	247934
255765	1	246263	248333
264319	1	255765	246969
268347	1	264319	245098
273046	1	268347	246263
273963	1	273046	255765
267430	1	273963	264319
271993	1	267430	268347
292710	1	271993	273046
295881	1	292710	273963




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58230&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]4 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=58230&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58230&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 20183.8908363885 + 4420.45606204759x[t] + 1.07278534683268y1[t] -0.128942160563676y4[t] -431.278490014834M1[t] -4299.96610657626M2[t] -7684.23161222854M3[t] -5830.93324768869M4[t] -8608.0669432388M5[t] -4351.97049942333M6[t] + 18422.3835583305M7[t] -2290.05787685714M8[t] -6937.72173064447M9[t] -11912.1094256655M10[t] -8764.12912603719M11[t] -67.3332347975218t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  20183.8908363885 +  4420.45606204759x[t] +  1.07278534683268y1[t] -0.128942160563676y4[t] -431.278490014834M1[t] -4299.96610657626M2[t] -7684.23161222854M3[t] -5830.93324768869M4[t] -8608.0669432388M5[t] -4351.97049942333M6[t] +  18422.3835583305M7[t] -2290.05787685714M8[t] -6937.72173064447M9[t] -11912.1094256655M10[t] -8764.12912603719M11[t] -67.3332347975218t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58230&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  20183.8908363885 +  4420.45606204759x[t] +  1.07278534683268y1[t] -0.128942160563676y4[t] -431.278490014834M1[t] -4299.96610657626M2[t] -7684.23161222854M3[t] -5830.93324768869M4[t] -8608.0669432388M5[t] -4351.97049942333M6[t] +  18422.3835583305M7[t] -2290.05787685714M8[t] -6937.72173064447M9[t] -11912.1094256655M10[t] -8764.12912603719M11[t] -67.3332347975218t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58230&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58230&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] = + 20183.8908363885 + 4420.45606204759x[t] + 1.07278534683268y1[t] -0.128942160563676y4[t] -431.278490014834M1[t] -4299.96610657626M2[t] -7684.23161222854M3[t] -5830.93324768869M4[t] -8608.0669432388M5[t] -4351.97049942333M6[t] + 18422.3835583305M7[t] -2290.05787685714M8[t] -6937.72173064447M9[t] -11912.1094256655M10[t] -8764.12912603719M11[t] -67.3332347975218t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)20183.89083638859546.4364812.11430.0393050.019652
x4420.456062047591933.2546252.28650.0263250.013163
y11.072785346832680.07385114.526400
y4-0.1289421605636760.078557-1.64140.1067520.053376
M1-431.2784900148342018.035954-0.21370.8316070.415804
M2-4299.966106576262159.582509-1.99110.0517350.025867
M3-7684.231612228542360.116241-3.25590.0019920.000996
M4-5830.933247688692160.531412-2.69880.0093630.004681
M5-8608.06694323882059.70848-4.17930.0001125.6e-05
M6-4351.970499423332020.839733-2.15350.0359330.017967
M718422.38355833052046.6702799.001100
M8-2290.057876857142684.606209-0.8530.3975530.198777
M9-6937.721730644473251.784405-2.13350.037620.01881
M10-11912.10942566553395.39403-3.50830.0009390.00047
M11-8764.129126037192182.651035-4.01540.0001929.6e-05
t-67.333234797521833.724977-1.99650.0511230.025561

\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) & 20183.8908363885 & 9546.436481 & 2.1143 & 0.039305 & 0.019652 \tabularnewline
x & 4420.45606204759 & 1933.254625 & 2.2865 & 0.026325 & 0.013163 \tabularnewline
y1 & 1.07278534683268 & 0.073851 & 14.5264 & 0 & 0 \tabularnewline
y4 & -0.128942160563676 & 0.078557 & -1.6414 & 0.106752 & 0.053376 \tabularnewline
M1 & -431.278490014834 & 2018.035954 & -0.2137 & 0.831607 & 0.415804 \tabularnewline
M2 & -4299.96610657626 & 2159.582509 & -1.9911 & 0.051735 & 0.025867 \tabularnewline
M3 & -7684.23161222854 & 2360.116241 & -3.2559 & 0.001992 & 0.000996 \tabularnewline
M4 & -5830.93324768869 & 2160.531412 & -2.6988 & 0.009363 & 0.004681 \tabularnewline
M5 & -8608.0669432388 & 2059.70848 & -4.1793 & 0.000112 & 5.6e-05 \tabularnewline
M6 & -4351.97049942333 & 2020.839733 & -2.1535 & 0.035933 & 0.017967 \tabularnewline
M7 & 18422.3835583305 & 2046.670279 & 9.0011 & 0 & 0 \tabularnewline
M8 & -2290.05787685714 & 2684.606209 & -0.853 & 0.397553 & 0.198777 \tabularnewline
M9 & -6937.72173064447 & 3251.784405 & -2.1335 & 0.03762 & 0.01881 \tabularnewline
M10 & -11912.1094256655 & 3395.39403 & -3.5083 & 0.000939 & 0.00047 \tabularnewline
M11 & -8764.12912603719 & 2182.651035 & -4.0154 & 0.000192 & 9.6e-05 \tabularnewline
t & -67.3332347975218 & 33.724977 & -1.9965 & 0.051123 & 0.025561 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58230&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]20183.8908363885[/C][C]9546.436481[/C][C]2.1143[/C][C]0.039305[/C][C]0.019652[/C][/ROW]
[ROW][C]x[/C][C]4420.45606204759[/C][C]1933.254625[/C][C]2.2865[/C][C]0.026325[/C][C]0.013163[/C][/ROW]
[ROW][C]y1[/C][C]1.07278534683268[/C][C]0.073851[/C][C]14.5264[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y4[/C][C]-0.128942160563676[/C][C]0.078557[/C][C]-1.6414[/C][C]0.106752[/C][C]0.053376[/C][/ROW]
[ROW][C]M1[/C][C]-431.278490014834[/C][C]2018.035954[/C][C]-0.2137[/C][C]0.831607[/C][C]0.415804[/C][/ROW]
[ROW][C]M2[/C][C]-4299.96610657626[/C][C]2159.582509[/C][C]-1.9911[/C][C]0.051735[/C][C]0.025867[/C][/ROW]
[ROW][C]M3[/C][C]-7684.23161222854[/C][C]2360.116241[/C][C]-3.2559[/C][C]0.001992[/C][C]0.000996[/C][/ROW]
[ROW][C]M4[/C][C]-5830.93324768869[/C][C]2160.531412[/C][C]-2.6988[/C][C]0.009363[/C][C]0.004681[/C][/ROW]
[ROW][C]M5[/C][C]-8608.0669432388[/C][C]2059.70848[/C][C]-4.1793[/C][C]0.000112[/C][C]5.6e-05[/C][/ROW]
[ROW][C]M6[/C][C]-4351.97049942333[/C][C]2020.839733[/C][C]-2.1535[/C][C]0.035933[/C][C]0.017967[/C][/ROW]
[ROW][C]M7[/C][C]18422.3835583305[/C][C]2046.670279[/C][C]9.0011[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-2290.05787685714[/C][C]2684.606209[/C][C]-0.853[/C][C]0.397553[/C][C]0.198777[/C][/ROW]
[ROW][C]M9[/C][C]-6937.72173064447[/C][C]3251.784405[/C][C]-2.1335[/C][C]0.03762[/C][C]0.01881[/C][/ROW]
[ROW][C]M10[/C][C]-11912.1094256655[/C][C]3395.39403[/C][C]-3.5083[/C][C]0.000939[/C][C]0.00047[/C][/ROW]
[ROW][C]M11[/C][C]-8764.12912603719[/C][C]2182.651035[/C][C]-4.0154[/C][C]0.000192[/C][C]9.6e-05[/C][/ROW]
[ROW][C]t[/C][C]-67.3332347975218[/C][C]33.724977[/C][C]-1.9965[/C][C]0.051123[/C][C]0.025561[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58230&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58230&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)20183.89083638859546.4364812.11430.0393050.019652
x4420.456062047591933.2546252.28650.0263250.013163
y11.072785346832680.07385114.526400
y4-0.1289421605636760.078557-1.64140.1067520.053376
M1-431.2784900148342018.035954-0.21370.8316070.415804
M2-4299.966106576262159.582509-1.99110.0517350.025867
M3-7684.231612228542360.116241-3.25590.0019920.000996
M4-5830.933247688692160.531412-2.69880.0093630.004681
M5-8608.06694323882059.70848-4.17930.0001125.6e-05
M6-4351.970499423332020.839733-2.15350.0359330.017967
M718422.38355833052046.6702799.001100
M8-2290.057876857142684.606209-0.8530.3975530.198777
M9-6937.721730644473251.784405-2.13350.037620.01881
M10-11912.10942566553395.39403-3.50830.0009390.00047
M11-8764.129126037192182.651035-4.01540.0001929.6e-05
t-67.333234797521833.724977-1.99650.0511230.025561







Multiple Linear Regression - Regression Statistics
Multiple R0.985621546213983
R-squared0.971449832361242
Adjusted R-squared0.963214207080831
F-TEST (value)117.957021997093
F-TEST (DF numerator)15
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3303.31748786203
Sum Squared Residuals567419134.131984

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985621546213983 \tabularnewline
R-squared & 0.971449832361242 \tabularnewline
Adjusted R-squared & 0.963214207080831 \tabularnewline
F-TEST (value) & 117.957021997093 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3303.31748786203 \tabularnewline
Sum Squared Residuals & 567419134.131984 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58230&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985621546213983[/C][/ROW]
[ROW][C]R-squared[/C][C]0.971449832361242[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.963214207080831[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]117.957021997093[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/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]3303.31748786203[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]567419134.131984[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58230&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58230&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 R0.985621546213983
R-squared0.971449832361242
Adjusted R-squared0.963214207080831
F-TEST (value)117.957021997093
F-TEST (DF numerator)15
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3303.31748786203
Sum Squared Residuals567419134.131984







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1267413266858.605583522554.394416478448
2267366268193.678482343-827.678482342807
3264777265842.338671462-1065.33867146211
4258863264244.834383605-5381.83438360534
5254844254462.780973496381.219026503680
6254868254346.08015514521.919844859791
7277267277412.67908012-145.679080119890
8285351281424.7873314143926.21266858648
9286602285900.405529929701.594470070513
10283042282197.644457145844.35554285488
11276687278571.000232786-1884.00023278576
12277915279407.876818907-1492.87681890696
13277128280065.33885714-2937.33885713999
14277103275744.0700294301358.92997056959
15275037273085.0790856921951.92091430805
16270150272496.328715706-2346.32871570576
17267140264510.6372757502629.36272424957
18264993265473.540144816-480.54014481609
19287259286143.6853318471115.31466815279
20291186289880.6895331131305.31046688680
21292300289766.6364048372533.36359516304
22288186286196.8371701201989.16282987976
23281477281993.019170971-516.019170970558
24282656282986.142305776-330.142305776204
25280190283608.702938012-3418.70293801165
26280408277557.6614699222850.33853007773
27276836275205.0028903041630.99710969631
28275216273006.9559538552209.04404614490
29274352268742.5481295895609.45187041144
30271311271976.31540794-665.315407940186
31289802291881.577388712-2079.57738871178
32290726291147.562867123-421.562867122867
33292300287535.2254657384764.77453426157
34278506284574.181782109-6068.18178210868
35269826270472.558281747-646.558281746506
36265861269738.434806118-3877.43480611765
37269034264783.2742203864250.72577961351
38264176266029.829437343-1853.82943734299
39255198258485.857435673-3287.85743567272
40253353251151.6113881862201.38861181379
41246057245918.722017464138.277982536256
42235372242906.844352009-7534.84435200876
43258556255308.7964615993247.20353840141
44260993259638.3755588221354.62444117772
45254663258478.518363941-3815.51836394125
46250643248023.8131742952619.18682570528
47243422243802.46809435-380.468094349849
48247105244438.4489508172666.55104918297
49248541248707.109534758-166.109534757523
50245039246829.955926916-1790.95592691628
51237080240552.554243289-3472.55424328873
52237085233325.3268202343759.67317976628
53225554230301.062874051-4747.06287405083
54226839222571.0936950354267.9063049649
55247934247682.894344598251.105655402272
56248333253953.337917293-5620.33791729274
57246969251153.214235554-4184.21423555387
58245098244482.523416331615.476583668764
59246263242835.9542201473427.04577985268
60255765252731.0971183823033.90288161785
61264319262601.9688661831717.03113381720
62268347268083.804654045263.19534595475
63273046268803.1676735814242.8323264192
64273963274404.942738414-441.942738413858
65267430271441.24872965-4011.24872965012
66271993268102.1262450603890.87375494034
67292710295098.367393125-2388.3673931248
68295881296425.246792235-544.246792235398

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 267413 & 266858.605583522 & 554.394416478448 \tabularnewline
2 & 267366 & 268193.678482343 & -827.678482342807 \tabularnewline
3 & 264777 & 265842.338671462 & -1065.33867146211 \tabularnewline
4 & 258863 & 264244.834383605 & -5381.83438360534 \tabularnewline
5 & 254844 & 254462.780973496 & 381.219026503680 \tabularnewline
6 & 254868 & 254346.08015514 & 521.919844859791 \tabularnewline
7 & 277267 & 277412.67908012 & -145.679080119890 \tabularnewline
8 & 285351 & 281424.787331414 & 3926.21266858648 \tabularnewline
9 & 286602 & 285900.405529929 & 701.594470070513 \tabularnewline
10 & 283042 & 282197.644457145 & 844.35554285488 \tabularnewline
11 & 276687 & 278571.000232786 & -1884.00023278576 \tabularnewline
12 & 277915 & 279407.876818907 & -1492.87681890696 \tabularnewline
13 & 277128 & 280065.33885714 & -2937.33885713999 \tabularnewline
14 & 277103 & 275744.070029430 & 1358.92997056959 \tabularnewline
15 & 275037 & 273085.079085692 & 1951.92091430805 \tabularnewline
16 & 270150 & 272496.328715706 & -2346.32871570576 \tabularnewline
17 & 267140 & 264510.637275750 & 2629.36272424957 \tabularnewline
18 & 264993 & 265473.540144816 & -480.54014481609 \tabularnewline
19 & 287259 & 286143.685331847 & 1115.31466815279 \tabularnewline
20 & 291186 & 289880.689533113 & 1305.31046688680 \tabularnewline
21 & 292300 & 289766.636404837 & 2533.36359516304 \tabularnewline
22 & 288186 & 286196.837170120 & 1989.16282987976 \tabularnewline
23 & 281477 & 281993.019170971 & -516.019170970558 \tabularnewline
24 & 282656 & 282986.142305776 & -330.142305776204 \tabularnewline
25 & 280190 & 283608.702938012 & -3418.70293801165 \tabularnewline
26 & 280408 & 277557.661469922 & 2850.33853007773 \tabularnewline
27 & 276836 & 275205.002890304 & 1630.99710969631 \tabularnewline
28 & 275216 & 273006.955953855 & 2209.04404614490 \tabularnewline
29 & 274352 & 268742.548129589 & 5609.45187041144 \tabularnewline
30 & 271311 & 271976.31540794 & -665.315407940186 \tabularnewline
31 & 289802 & 291881.577388712 & -2079.57738871178 \tabularnewline
32 & 290726 & 291147.562867123 & -421.562867122867 \tabularnewline
33 & 292300 & 287535.225465738 & 4764.77453426157 \tabularnewline
34 & 278506 & 284574.181782109 & -6068.18178210868 \tabularnewline
35 & 269826 & 270472.558281747 & -646.558281746506 \tabularnewline
36 & 265861 & 269738.434806118 & -3877.43480611765 \tabularnewline
37 & 269034 & 264783.274220386 & 4250.72577961351 \tabularnewline
38 & 264176 & 266029.829437343 & -1853.82943734299 \tabularnewline
39 & 255198 & 258485.857435673 & -3287.85743567272 \tabularnewline
40 & 253353 & 251151.611388186 & 2201.38861181379 \tabularnewline
41 & 246057 & 245918.722017464 & 138.277982536256 \tabularnewline
42 & 235372 & 242906.844352009 & -7534.84435200876 \tabularnewline
43 & 258556 & 255308.796461599 & 3247.20353840141 \tabularnewline
44 & 260993 & 259638.375558822 & 1354.62444117772 \tabularnewline
45 & 254663 & 258478.518363941 & -3815.51836394125 \tabularnewline
46 & 250643 & 248023.813174295 & 2619.18682570528 \tabularnewline
47 & 243422 & 243802.46809435 & -380.468094349849 \tabularnewline
48 & 247105 & 244438.448950817 & 2666.55104918297 \tabularnewline
49 & 248541 & 248707.109534758 & -166.109534757523 \tabularnewline
50 & 245039 & 246829.955926916 & -1790.95592691628 \tabularnewline
51 & 237080 & 240552.554243289 & -3472.55424328873 \tabularnewline
52 & 237085 & 233325.326820234 & 3759.67317976628 \tabularnewline
53 & 225554 & 230301.062874051 & -4747.06287405083 \tabularnewline
54 & 226839 & 222571.093695035 & 4267.9063049649 \tabularnewline
55 & 247934 & 247682.894344598 & 251.105655402272 \tabularnewline
56 & 248333 & 253953.337917293 & -5620.33791729274 \tabularnewline
57 & 246969 & 251153.214235554 & -4184.21423555387 \tabularnewline
58 & 245098 & 244482.523416331 & 615.476583668764 \tabularnewline
59 & 246263 & 242835.954220147 & 3427.04577985268 \tabularnewline
60 & 255765 & 252731.097118382 & 3033.90288161785 \tabularnewline
61 & 264319 & 262601.968866183 & 1717.03113381720 \tabularnewline
62 & 268347 & 268083.804654045 & 263.19534595475 \tabularnewline
63 & 273046 & 268803.167673581 & 4242.8323264192 \tabularnewline
64 & 273963 & 274404.942738414 & -441.942738413858 \tabularnewline
65 & 267430 & 271441.24872965 & -4011.24872965012 \tabularnewline
66 & 271993 & 268102.126245060 & 3890.87375494034 \tabularnewline
67 & 292710 & 295098.367393125 & -2388.3673931248 \tabularnewline
68 & 295881 & 296425.246792235 & -544.246792235398 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58230&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]267413[/C][C]266858.605583522[/C][C]554.394416478448[/C][/ROW]
[ROW][C]2[/C][C]267366[/C][C]268193.678482343[/C][C]-827.678482342807[/C][/ROW]
[ROW][C]3[/C][C]264777[/C][C]265842.338671462[/C][C]-1065.33867146211[/C][/ROW]
[ROW][C]4[/C][C]258863[/C][C]264244.834383605[/C][C]-5381.83438360534[/C][/ROW]
[ROW][C]5[/C][C]254844[/C][C]254462.780973496[/C][C]381.219026503680[/C][/ROW]
[ROW][C]6[/C][C]254868[/C][C]254346.08015514[/C][C]521.919844859791[/C][/ROW]
[ROW][C]7[/C][C]277267[/C][C]277412.67908012[/C][C]-145.679080119890[/C][/ROW]
[ROW][C]8[/C][C]285351[/C][C]281424.787331414[/C][C]3926.21266858648[/C][/ROW]
[ROW][C]9[/C][C]286602[/C][C]285900.405529929[/C][C]701.594470070513[/C][/ROW]
[ROW][C]10[/C][C]283042[/C][C]282197.644457145[/C][C]844.35554285488[/C][/ROW]
[ROW][C]11[/C][C]276687[/C][C]278571.000232786[/C][C]-1884.00023278576[/C][/ROW]
[ROW][C]12[/C][C]277915[/C][C]279407.876818907[/C][C]-1492.87681890696[/C][/ROW]
[ROW][C]13[/C][C]277128[/C][C]280065.33885714[/C][C]-2937.33885713999[/C][/ROW]
[ROW][C]14[/C][C]277103[/C][C]275744.070029430[/C][C]1358.92997056959[/C][/ROW]
[ROW][C]15[/C][C]275037[/C][C]273085.079085692[/C][C]1951.92091430805[/C][/ROW]
[ROW][C]16[/C][C]270150[/C][C]272496.328715706[/C][C]-2346.32871570576[/C][/ROW]
[ROW][C]17[/C][C]267140[/C][C]264510.637275750[/C][C]2629.36272424957[/C][/ROW]
[ROW][C]18[/C][C]264993[/C][C]265473.540144816[/C][C]-480.54014481609[/C][/ROW]
[ROW][C]19[/C][C]287259[/C][C]286143.685331847[/C][C]1115.31466815279[/C][/ROW]
[ROW][C]20[/C][C]291186[/C][C]289880.689533113[/C][C]1305.31046688680[/C][/ROW]
[ROW][C]21[/C][C]292300[/C][C]289766.636404837[/C][C]2533.36359516304[/C][/ROW]
[ROW][C]22[/C][C]288186[/C][C]286196.837170120[/C][C]1989.16282987976[/C][/ROW]
[ROW][C]23[/C][C]281477[/C][C]281993.019170971[/C][C]-516.019170970558[/C][/ROW]
[ROW][C]24[/C][C]282656[/C][C]282986.142305776[/C][C]-330.142305776204[/C][/ROW]
[ROW][C]25[/C][C]280190[/C][C]283608.702938012[/C][C]-3418.70293801165[/C][/ROW]
[ROW][C]26[/C][C]280408[/C][C]277557.661469922[/C][C]2850.33853007773[/C][/ROW]
[ROW][C]27[/C][C]276836[/C][C]275205.002890304[/C][C]1630.99710969631[/C][/ROW]
[ROW][C]28[/C][C]275216[/C][C]273006.955953855[/C][C]2209.04404614490[/C][/ROW]
[ROW][C]29[/C][C]274352[/C][C]268742.548129589[/C][C]5609.45187041144[/C][/ROW]
[ROW][C]30[/C][C]271311[/C][C]271976.31540794[/C][C]-665.315407940186[/C][/ROW]
[ROW][C]31[/C][C]289802[/C][C]291881.577388712[/C][C]-2079.57738871178[/C][/ROW]
[ROW][C]32[/C][C]290726[/C][C]291147.562867123[/C][C]-421.562867122867[/C][/ROW]
[ROW][C]33[/C][C]292300[/C][C]287535.225465738[/C][C]4764.77453426157[/C][/ROW]
[ROW][C]34[/C][C]278506[/C][C]284574.181782109[/C][C]-6068.18178210868[/C][/ROW]
[ROW][C]35[/C][C]269826[/C][C]270472.558281747[/C][C]-646.558281746506[/C][/ROW]
[ROW][C]36[/C][C]265861[/C][C]269738.434806118[/C][C]-3877.43480611765[/C][/ROW]
[ROW][C]37[/C][C]269034[/C][C]264783.274220386[/C][C]4250.72577961351[/C][/ROW]
[ROW][C]38[/C][C]264176[/C][C]266029.829437343[/C][C]-1853.82943734299[/C][/ROW]
[ROW][C]39[/C][C]255198[/C][C]258485.857435673[/C][C]-3287.85743567272[/C][/ROW]
[ROW][C]40[/C][C]253353[/C][C]251151.611388186[/C][C]2201.38861181379[/C][/ROW]
[ROW][C]41[/C][C]246057[/C][C]245918.722017464[/C][C]138.277982536256[/C][/ROW]
[ROW][C]42[/C][C]235372[/C][C]242906.844352009[/C][C]-7534.84435200876[/C][/ROW]
[ROW][C]43[/C][C]258556[/C][C]255308.796461599[/C][C]3247.20353840141[/C][/ROW]
[ROW][C]44[/C][C]260993[/C][C]259638.375558822[/C][C]1354.62444117772[/C][/ROW]
[ROW][C]45[/C][C]254663[/C][C]258478.518363941[/C][C]-3815.51836394125[/C][/ROW]
[ROW][C]46[/C][C]250643[/C][C]248023.813174295[/C][C]2619.18682570528[/C][/ROW]
[ROW][C]47[/C][C]243422[/C][C]243802.46809435[/C][C]-380.468094349849[/C][/ROW]
[ROW][C]48[/C][C]247105[/C][C]244438.448950817[/C][C]2666.55104918297[/C][/ROW]
[ROW][C]49[/C][C]248541[/C][C]248707.109534758[/C][C]-166.109534757523[/C][/ROW]
[ROW][C]50[/C][C]245039[/C][C]246829.955926916[/C][C]-1790.95592691628[/C][/ROW]
[ROW][C]51[/C][C]237080[/C][C]240552.554243289[/C][C]-3472.55424328873[/C][/ROW]
[ROW][C]52[/C][C]237085[/C][C]233325.326820234[/C][C]3759.67317976628[/C][/ROW]
[ROW][C]53[/C][C]225554[/C][C]230301.062874051[/C][C]-4747.06287405083[/C][/ROW]
[ROW][C]54[/C][C]226839[/C][C]222571.093695035[/C][C]4267.9063049649[/C][/ROW]
[ROW][C]55[/C][C]247934[/C][C]247682.894344598[/C][C]251.105655402272[/C][/ROW]
[ROW][C]56[/C][C]248333[/C][C]253953.337917293[/C][C]-5620.33791729274[/C][/ROW]
[ROW][C]57[/C][C]246969[/C][C]251153.214235554[/C][C]-4184.21423555387[/C][/ROW]
[ROW][C]58[/C][C]245098[/C][C]244482.523416331[/C][C]615.476583668764[/C][/ROW]
[ROW][C]59[/C][C]246263[/C][C]242835.954220147[/C][C]3427.04577985268[/C][/ROW]
[ROW][C]60[/C][C]255765[/C][C]252731.097118382[/C][C]3033.90288161785[/C][/ROW]
[ROW][C]61[/C][C]264319[/C][C]262601.968866183[/C][C]1717.03113381720[/C][/ROW]
[ROW][C]62[/C][C]268347[/C][C]268083.804654045[/C][C]263.19534595475[/C][/ROW]
[ROW][C]63[/C][C]273046[/C][C]268803.167673581[/C][C]4242.8323264192[/C][/ROW]
[ROW][C]64[/C][C]273963[/C][C]274404.942738414[/C][C]-441.942738413858[/C][/ROW]
[ROW][C]65[/C][C]267430[/C][C]271441.24872965[/C][C]-4011.24872965012[/C][/ROW]
[ROW][C]66[/C][C]271993[/C][C]268102.126245060[/C][C]3890.87375494034[/C][/ROW]
[ROW][C]67[/C][C]292710[/C][C]295098.367393125[/C][C]-2388.3673931248[/C][/ROW]
[ROW][C]68[/C][C]295881[/C][C]296425.246792235[/C][C]-544.246792235398[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58230&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58230&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
1267413266858.605583522554.394416478448
2267366268193.678482343-827.678482342807
3264777265842.338671462-1065.33867146211
4258863264244.834383605-5381.83438360534
5254844254462.780973496381.219026503680
6254868254346.08015514521.919844859791
7277267277412.67908012-145.679080119890
8285351281424.7873314143926.21266858648
9286602285900.405529929701.594470070513
10283042282197.644457145844.35554285488
11276687278571.000232786-1884.00023278576
12277915279407.876818907-1492.87681890696
13277128280065.33885714-2937.33885713999
14277103275744.0700294301358.92997056959
15275037273085.0790856921951.92091430805
16270150272496.328715706-2346.32871570576
17267140264510.6372757502629.36272424957
18264993265473.540144816-480.54014481609
19287259286143.6853318471115.31466815279
20291186289880.6895331131305.31046688680
21292300289766.6364048372533.36359516304
22288186286196.8371701201989.16282987976
23281477281993.019170971-516.019170970558
24282656282986.142305776-330.142305776204
25280190283608.702938012-3418.70293801165
26280408277557.6614699222850.33853007773
27276836275205.0028903041630.99710969631
28275216273006.9559538552209.04404614490
29274352268742.5481295895609.45187041144
30271311271976.31540794-665.315407940186
31289802291881.577388712-2079.57738871178
32290726291147.562867123-421.562867122867
33292300287535.2254657384764.77453426157
34278506284574.181782109-6068.18178210868
35269826270472.558281747-646.558281746506
36265861269738.434806118-3877.43480611765
37269034264783.2742203864250.72577961351
38264176266029.829437343-1853.82943734299
39255198258485.857435673-3287.85743567272
40253353251151.6113881862201.38861181379
41246057245918.722017464138.277982536256
42235372242906.844352009-7534.84435200876
43258556255308.7964615993247.20353840141
44260993259638.3755588221354.62444117772
45254663258478.518363941-3815.51836394125
46250643248023.8131742952619.18682570528
47243422243802.46809435-380.468094349849
48247105244438.4489508172666.55104918297
49248541248707.109534758-166.109534757523
50245039246829.955926916-1790.95592691628
51237080240552.554243289-3472.55424328873
52237085233325.3268202343759.67317976628
53225554230301.062874051-4747.06287405083
54226839222571.0936950354267.9063049649
55247934247682.894344598251.105655402272
56248333253953.337917293-5620.33791729274
57246969251153.214235554-4184.21423555387
58245098244482.523416331615.476583668764
59246263242835.9542201473427.04577985268
60255765252731.0971183823033.90288161785
61264319262601.9688661831717.03113381720
62268347268083.804654045263.19534595475
63273046268803.1676735814242.8323264192
64273963274404.942738414-441.942738413858
65267430271441.24872965-4011.24872965012
66271993268102.1262450603890.87375494034
67292710295098.367393125-2388.3673931248
68295881296425.246792235-544.246792235398







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.01709145265085570.03418290530171140.982908547349144
200.07068442240377050.1413688448075410.92931557759623
210.04299045107581820.08598090215163640.957009548924182
220.02079522438275090.04159044876550180.97920477561725
230.008437689527833310.01687537905566660.991562310472167
240.002949485004738890.005898970009477770.997050514995261
250.003337565491164530.006675130982329060.996662434508836
260.001372735977562690.002745471955125390.998627264022437
270.0005403426918157250.001080685383631450.999459657308184
280.000856138796150010.001712277592300020.99914386120385
290.002191494557094730.004382989114189460.997808505442905
300.0009640583878172180.001928116775634440.999035941612183
310.0008160634362171510.001632126872434300.999183936563783
320.002675202035520880.005350404071041760.997324797964479
330.007098514407691930.01419702881538390.992901485592308
340.09380213567359130.1876042713471830.906197864326409
350.05938671155592810.1187734231118560.940613288444072
360.05914109398520050.1182821879704010.9408589060148
370.08501383598672990.1700276719734600.91498616401327
380.0573687825660790.1147375651321580.94263121743392
390.04281961212404150.0856392242480830.957180387875958
400.05143719279094420.1028743855818880.948562807209056
410.07163672054603330.1432734410920670.928363279453967
420.3757749022453280.7515498044906560.624225097754672
430.4902087694558470.9804175389116940.509791230544153
440.6692124215387880.6615751569224240.330787578461212
450.6217423693560110.7565152612879780.378257630643989
460.6875288309315240.6249423381369520.312471169068476
470.5599213349642950.880157330071410.440078665035705
480.5459842993591960.9080314012816080.454015700640804
490.5092960137613240.9814079724773510.490703986238676

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.0170914526508557 & 0.0341829053017114 & 0.982908547349144 \tabularnewline
20 & 0.0706844224037705 & 0.141368844807541 & 0.92931557759623 \tabularnewline
21 & 0.0429904510758182 & 0.0859809021516364 & 0.957009548924182 \tabularnewline
22 & 0.0207952243827509 & 0.0415904487655018 & 0.97920477561725 \tabularnewline
23 & 0.00843768952783331 & 0.0168753790556666 & 0.991562310472167 \tabularnewline
24 & 0.00294948500473889 & 0.00589897000947777 & 0.997050514995261 \tabularnewline
25 & 0.00333756549116453 & 0.00667513098232906 & 0.996662434508836 \tabularnewline
26 & 0.00137273597756269 & 0.00274547195512539 & 0.998627264022437 \tabularnewline
27 & 0.000540342691815725 & 0.00108068538363145 & 0.999459657308184 \tabularnewline
28 & 0.00085613879615001 & 0.00171227759230002 & 0.99914386120385 \tabularnewline
29 & 0.00219149455709473 & 0.00438298911418946 & 0.997808505442905 \tabularnewline
30 & 0.000964058387817218 & 0.00192811677563444 & 0.999035941612183 \tabularnewline
31 & 0.000816063436217151 & 0.00163212687243430 & 0.999183936563783 \tabularnewline
32 & 0.00267520203552088 & 0.00535040407104176 & 0.997324797964479 \tabularnewline
33 & 0.00709851440769193 & 0.0141970288153839 & 0.992901485592308 \tabularnewline
34 & 0.0938021356735913 & 0.187604271347183 & 0.906197864326409 \tabularnewline
35 & 0.0593867115559281 & 0.118773423111856 & 0.940613288444072 \tabularnewline
36 & 0.0591410939852005 & 0.118282187970401 & 0.9408589060148 \tabularnewline
37 & 0.0850138359867299 & 0.170027671973460 & 0.91498616401327 \tabularnewline
38 & 0.057368782566079 & 0.114737565132158 & 0.94263121743392 \tabularnewline
39 & 0.0428196121240415 & 0.085639224248083 & 0.957180387875958 \tabularnewline
40 & 0.0514371927909442 & 0.102874385581888 & 0.948562807209056 \tabularnewline
41 & 0.0716367205460333 & 0.143273441092067 & 0.928363279453967 \tabularnewline
42 & 0.375774902245328 & 0.751549804490656 & 0.624225097754672 \tabularnewline
43 & 0.490208769455847 & 0.980417538911694 & 0.509791230544153 \tabularnewline
44 & 0.669212421538788 & 0.661575156922424 & 0.330787578461212 \tabularnewline
45 & 0.621742369356011 & 0.756515261287978 & 0.378257630643989 \tabularnewline
46 & 0.687528830931524 & 0.624942338136952 & 0.312471169068476 \tabularnewline
47 & 0.559921334964295 & 0.88015733007141 & 0.440078665035705 \tabularnewline
48 & 0.545984299359196 & 0.908031401281608 & 0.454015700640804 \tabularnewline
49 & 0.509296013761324 & 0.981407972477351 & 0.490703986238676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58230&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]19[/C][C]0.0170914526508557[/C][C]0.0341829053017114[/C][C]0.982908547349144[/C][/ROW]
[ROW][C]20[/C][C]0.0706844224037705[/C][C]0.141368844807541[/C][C]0.92931557759623[/C][/ROW]
[ROW][C]21[/C][C]0.0429904510758182[/C][C]0.0859809021516364[/C][C]0.957009548924182[/C][/ROW]
[ROW][C]22[/C][C]0.0207952243827509[/C][C]0.0415904487655018[/C][C]0.97920477561725[/C][/ROW]
[ROW][C]23[/C][C]0.00843768952783331[/C][C]0.0168753790556666[/C][C]0.991562310472167[/C][/ROW]
[ROW][C]24[/C][C]0.00294948500473889[/C][C]0.00589897000947777[/C][C]0.997050514995261[/C][/ROW]
[ROW][C]25[/C][C]0.00333756549116453[/C][C]0.00667513098232906[/C][C]0.996662434508836[/C][/ROW]
[ROW][C]26[/C][C]0.00137273597756269[/C][C]0.00274547195512539[/C][C]0.998627264022437[/C][/ROW]
[ROW][C]27[/C][C]0.000540342691815725[/C][C]0.00108068538363145[/C][C]0.999459657308184[/C][/ROW]
[ROW][C]28[/C][C]0.00085613879615001[/C][C]0.00171227759230002[/C][C]0.99914386120385[/C][/ROW]
[ROW][C]29[/C][C]0.00219149455709473[/C][C]0.00438298911418946[/C][C]0.997808505442905[/C][/ROW]
[ROW][C]30[/C][C]0.000964058387817218[/C][C]0.00192811677563444[/C][C]0.999035941612183[/C][/ROW]
[ROW][C]31[/C][C]0.000816063436217151[/C][C]0.00163212687243430[/C][C]0.999183936563783[/C][/ROW]
[ROW][C]32[/C][C]0.00267520203552088[/C][C]0.00535040407104176[/C][C]0.997324797964479[/C][/ROW]
[ROW][C]33[/C][C]0.00709851440769193[/C][C]0.0141970288153839[/C][C]0.992901485592308[/C][/ROW]
[ROW][C]34[/C][C]0.0938021356735913[/C][C]0.187604271347183[/C][C]0.906197864326409[/C][/ROW]
[ROW][C]35[/C][C]0.0593867115559281[/C][C]0.118773423111856[/C][C]0.940613288444072[/C][/ROW]
[ROW][C]36[/C][C]0.0591410939852005[/C][C]0.118282187970401[/C][C]0.9408589060148[/C][/ROW]
[ROW][C]37[/C][C]0.0850138359867299[/C][C]0.170027671973460[/C][C]0.91498616401327[/C][/ROW]
[ROW][C]38[/C][C]0.057368782566079[/C][C]0.114737565132158[/C][C]0.94263121743392[/C][/ROW]
[ROW][C]39[/C][C]0.0428196121240415[/C][C]0.085639224248083[/C][C]0.957180387875958[/C][/ROW]
[ROW][C]40[/C][C]0.0514371927909442[/C][C]0.102874385581888[/C][C]0.948562807209056[/C][/ROW]
[ROW][C]41[/C][C]0.0716367205460333[/C][C]0.143273441092067[/C][C]0.928363279453967[/C][/ROW]
[ROW][C]42[/C][C]0.375774902245328[/C][C]0.751549804490656[/C][C]0.624225097754672[/C][/ROW]
[ROW][C]43[/C][C]0.490208769455847[/C][C]0.980417538911694[/C][C]0.509791230544153[/C][/ROW]
[ROW][C]44[/C][C]0.669212421538788[/C][C]0.661575156922424[/C][C]0.330787578461212[/C][/ROW]
[ROW][C]45[/C][C]0.621742369356011[/C][C]0.756515261287978[/C][C]0.378257630643989[/C][/ROW]
[ROW][C]46[/C][C]0.687528830931524[/C][C]0.624942338136952[/C][C]0.312471169068476[/C][/ROW]
[ROW][C]47[/C][C]0.559921334964295[/C][C]0.88015733007141[/C][C]0.440078665035705[/C][/ROW]
[ROW][C]48[/C][C]0.545984299359196[/C][C]0.908031401281608[/C][C]0.454015700640804[/C][/ROW]
[ROW][C]49[/C][C]0.509296013761324[/C][C]0.981407972477351[/C][C]0.490703986238676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58230&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58230&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
190.01709145265085570.03418290530171140.982908547349144
200.07068442240377050.1413688448075410.92931557759623
210.04299045107581820.08598090215163640.957009548924182
220.02079522438275090.04159044876550180.97920477561725
230.008437689527833310.01687537905566660.991562310472167
240.002949485004738890.005898970009477770.997050514995261
250.003337565491164530.006675130982329060.996662434508836
260.001372735977562690.002745471955125390.998627264022437
270.0005403426918157250.001080685383631450.999459657308184
280.000856138796150010.001712277592300020.99914386120385
290.002191494557094730.004382989114189460.997808505442905
300.0009640583878172180.001928116775634440.999035941612183
310.0008160634362171510.001632126872434300.999183936563783
320.002675202035520880.005350404071041760.997324797964479
330.007098514407691930.01419702881538390.992901485592308
340.09380213567359130.1876042713471830.906197864326409
350.05938671155592810.1187734231118560.940613288444072
360.05914109398520050.1182821879704010.9408589060148
370.08501383598672990.1700276719734600.91498616401327
380.0573687825660790.1147375651321580.94263121743392
390.04281961212404150.0856392242480830.957180387875958
400.05143719279094420.1028743855818880.948562807209056
410.07163672054603330.1432734410920670.928363279453967
420.3757749022453280.7515498044906560.624225097754672
430.4902087694558470.9804175389116940.509791230544153
440.6692124215387880.6615751569224240.330787578461212
450.6217423693560110.7565152612879780.378257630643989
460.6875288309315240.6249423381369520.312471169068476
470.5599213349642950.880157330071410.440078665035705
480.5459842993591960.9080314012816080.454015700640804
490.5092960137613240.9814079724773510.490703986238676







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.290322580645161NOK
5% type I error level130.419354838709677NOK
10% type I error level150.483870967741935NOK

\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 & 9 & 0.290322580645161 & NOK \tabularnewline
5% type I error level & 13 & 0.419354838709677 & NOK \tabularnewline
10% type I error level & 15 & 0.483870967741935 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58230&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]9[/C][C]0.290322580645161[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.419354838709677[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.483870967741935[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58230&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58230&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 level90.290322580645161NOK
5% type I error level130.419354838709677NOK
10% type I error level150.483870967741935NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}