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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 23:46:10 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/24/t1290556050buxxdees1gdynww.htm/, Retrieved Thu, 25 Apr 2024 13:28:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99692, Retrieved Thu, 25 Apr 2024 13:28:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact185

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Multiple Regressi...] [2010-11-23 23:46:10] [278a0539dc236556c5f30b5bc56ff9eb] [Current]
-   PD      [Multiple Regression] [Werkloosheid] [2010-11-29 23:29:58] [b8e188bcc949964bed729335b3416734]
-   P         [Multiple Regression] [] [2010-11-30 19:58:06] [b659239b537e56f17142ee5c56ad6265]
-    D        [Multiple Regression] [ACF Nieuwbouw] [2010-12-18 23:41:03] [b8e188bcc949964bed729335b3416734]
-   P           [Multiple Regression] [Regression Analys...] [2010-12-20 22:02:17] [b8e188bcc949964bed729335b3416734]
-   P           [Multiple Regression] [Regression Analys...] [2010-12-20 22:02:17] [b8e188bcc949964bed729335b3416734]
- R           [Multiple Regression] [] [2012-11-10 13:07:14] [74be16979710d4c4e7c6647856088456]
-             [Multiple Regression] [] [2012-11-12 14:55:54] [1337bb1ecd4655261bf98bac1776aa01]
- RMP         [Classical Decomposition] [] [2012-11-12 15:10:43] [1337bb1ecd4655261bf98bac1776aa01]
-   PD      [Multiple Regression] [Multiple Regressi...] [2010-11-30 00:01:16] [b8e188bcc949964bed729335b3416734]
-   P         [Multiple Regression] [] [2010-11-30 20:01:58] [b659239b537e56f17142ee5c56ad6265]
- RM          [Multiple Regression] [] [2012-11-10 13:17:02] [74be16979710d4c4e7c6647856088456]
-             [Multiple Regression] [] [2012-11-12 15:36:26] [1337bb1ecd4655261bf98bac1776aa01]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	7	1	7	7	1	7	7	4	5	2	5	6	2	5	6
5	6	1	5	5	1	5	5	7	7	1	7	7	1	7	6
6	6	2	5	6	1	4	5	5	4	1	7	7	1	4	7
5	6	2	5	6	2	5	6	5	6	1	6	7	1	6	7
6	7	1	7	5	1	6	7	5	6	2	5	6	3	6	6
6	7	1	5	6	1	5	7	7	7	1	7	7	1	6	7
6	7	1	3	7	2	7	7	3	7	1	7	7	1	6	7
6	6	1	6	6	1	5	6	3	3	1	5	5	1	4	4
4	6	1	5	6	1	4	5	6	7	1	7	6	1	6	7
6	7	1	3	6	1	6	6	5	5	1	7	6	1	5	6
6	6	1	7	7	1	7	7	6	6	1	7	6	1	6	6
3	4	1	7	7	1	4	7	4	5	3	4	3	3	4	5
5	6	2	6	7	2	6	6	4	4	1	5	5	1	6	7
5	7	1	7	7	0	5	7	6	6	2	6	6	2	5	5
2	5	1	4	5	1	2	6	5	7	1	5	7	1	5	5
3	6	1	7	7	2	5	7	7	7	1	7	7	1	7	7
6	5	1	7	6	1	6	5	6	6	1	7	6	2	7	5
6	5	1	7	6	1	6	5	7	3	1	6	5	1	6	6
5	6	1	3	6	1	5	7	5	5	1	4	6	2	4	5
7	6	1	5	6	1	5	6	5	4	3	7	7	3	6	7
5	5	1	5	5	1	6	6	2	6	3	6	7	2	4	7
5	4	4	5	3	6	5	1	6	7	1	6	7	1	6	6
5	6	1	7	7	1	5	7	1	4	1	7	7	1	6	6
5	7	1	7	6	1	5	6	5	3	2	7	7	1	6	7
5	7	1	6	7	1	5	7	6	4	1	7	6	1	5	4
6	5	1	6	7	1	7	6	6	7	1	7	6	1	5	6
5	6	2	7	6	2	5	6	6	6	1	6	6	2	6	6
5	6	4	6	6	4	3	6	5	6	1	5	7	1	6	7
6	6	2	5	6	2	5	6	6	6	1	6	7	1	6	7
4	6	2	5	6	2	4	5	5	6	1	6	6	2	6	6
4	5	1	3	5	1	6	5	6	7	1	7	7	2	5	6
6	6	2	7	7	1	5	7	7	7	1	7	7	1	6	7
3	5	1	6	4	1	4	3	4	6	1	6	2	1	3	3
6	6	1	5	5	2	5	6	5	7	1	7	6	1	7	4
5	6	1	5	6	1	5	5	3	6	2	6	5	2	5	6
6	7	1	7	7	1	6	6	7	5	1	7	6	1	6	6
7	4	1	6	7	1	5	7	7	5	1	5	6	1	7	5
4	4	3	6	6	1	5	6	6	6	1	6	6	1	6	5
5	5	1	7	6	1	5	5	6	6	1	6	5	1	4	6
4	6	4	5	4	4	4	5	6	6	3	7	6	2	7	6
5	6	1	6	7	1	5	6	5	7	1	5	6	1	5	4
3	6	1	5	7	2	5	7	5	5	1	6	5	1	5	5
5	7	1	5	7	1	5	7	4	5	2	5	5	2	5	5
6	6	1	6	5	3	6	5	4	6	1	3	7	2	4	7
6	7	1	7	7	2	6	7	6	4	1	7	5	2	5	5
4	5	2	6	5	2	4	5	5	7	2	6	6	1	6	7
4	4	2	5	5	2	4	5	4	3	1	5	5	1	4	6
6	6	1	6	6	1	5	5	6	6	1	6	6	1	6	6
6	5	1	6	6	1	6	6	4	5	2	6	7	2	6	6
5	7	1	7	6	1	6	6	4	6	1	2	6	7	2	5
6	6	1	7	7	2	6	7	4	5	1	6	7	1	5	6
4	5	4	5	5	3	4	7	6	6	1	7	6	2	5	7
4	7	3	3	7	2	6	7	3	5	1	7	7	4	4	7
5	6	2	6	6	2	5	7	6	7	1	6	7	1	6	6
3	2	1	6	5	1	4	2	5	5	1	5	6	1	6	6
6	7	1	6	7	3	6	6	4	6	2	5	7	3	6	7
6	7	1	6	7	1	6	6	7	7	1	6	6	2	7	5
4	7	2	6	6	1	4	6	6	6	1	6	5	1	5	6
5	7	1	7	7	1	5	7	5	5	2	6	4	3	5	5
5	5	2	6	5	1	5	5	6	7	1	7	7	1	7	7
4	5	1	6	6	1	6	7	6	7	1	6	6	1	6	6
6	5	2	5	6	2	6	6	5	6	2	6	5	1	5	6
5	6	1	6	6	1	6	6	5	4	1	5	5	1	4	5
4	5	2	6	5	3	5	5	0	0	0	0	0	0	0	0
6	5	1	6	7	2	5	6	0	0	0	0	0	0	0	0
5	7	1	4	7	1	7	7	0	0	0	0	0	0	0	0
6	6	1	6	6	1	6	6	0	0	0	0	0	0	0	0
5	7	1	7	7	1	7	7	0	0	0	0	0	0	0	0
6	6	1	7	7	2	6	7	0	0	0	0	0	0	0	0
5	5	1	5	4	1	5	5	0	0	0	0	0	0	0	0
4	5	2	5	5	2	4	6	0	0	0	0	0	0	0	0
6	7	1	7	7	1	6	7	0	0	0	0	0	0	0	0
5	5	2	7	7	2	3	7	0	0	0	0	0	0	0	0
5	7	2	5	6	4	5	7	0	0	0	0	0	0	0	0
3	3	2	5	7	1	5	7	0	0	0	0	0	0	0	0
5	7	2	3	0	0	5	7	0	0	0	0	0	0	0	0
4	5	2	6	6	2	5	6	0	0	0	0	0	0	0	0
5	6	2	5	6	1	5	5	0	0	0	0	0	0	0	0
5	4	4	4	3	3	3	5	0	0	0	0	0	0	0	0
7	7	1	7	7	1	7	7	0	0	0	0	0	0	0	0
7	5	1	7	7	1	6	6	0	0	0	0	0	0	0	0
5	7	1	2	6	2	4	6	0	0	0	0	0	0	0	0
4	5	3	6	6	2	4	6	0	0	0	0	0	0	0	0
6	6	2	4	6	3	6	6	0	0	0	0	0	0	0	0
5	7	5	7	7	3	5	7	0	0	0	0	0	0	0	0
5	6	2	6	7	2	6	6	0	0	0	0	0	0	0	0
4	6	1	2	6	2	5	7	0	0	0	0	0	0	0	0
5	7	2	7	7	2	5	5	0	0	0	0	0	0	0	0
2	7	1	7	7	2	2	5	0	0	0	0	0	0	0	0
7	7	1	5	7	5	6	7	0	0	0	0	0	0	0	0
4	5	1	6	6	1	5	5	0	0	0	0	0	0	0	0
5	6	1	5	7	2	5	7	0	0	0	0	0	0	0	0
5	7	1	6	7	2	6	7	0	0	0	0	0	0	0	0
7	6	1	7	5	1	7	5	0	0	0	0	0	0	0	0
2	6	2	6	6	2	6	6	0	0	0	0	0	0	0	0
4	4	4	7	7	4	4	7	0	0	0	0	0	0	0	0
6	7	1	6	7	3	6	6	0	0	0	0	0	0	0	0
5	6	1	5	6	1	6	5	0	0	0	0	0	0	0	0
5	5	1	5	6	1	5	5	0	0	0	0	0	0	0	0
4	6	1	4	5	1	5	7	0	0	0	0	0	0	0	0
4	5	5	4	6	4	5	7	0	0	0	0	0	0	0	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99692&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99692&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99692&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
Q1_2[t] = + 0.683448246864695 + 0.198926748843929Q1_3[t] -0.0928214103551924Q1_5[t] + 0.0904997968487101Q1_7[t] -0.075972113093604Q1_8[t] + 0.116383027938451Q1_12[t] + 0.580006004424285Q1_16[t] -0.00989775749926517Q1_22[t] + 0.124131261356080Q1_2v[t] -0.261297359223972Q1_3v[t] + 0.227200441858429Q1_5v[t] -0.128064165925015Q1_7v[t] + 0.308882429796853Q1_8v[t] -0.158991872319213Q1_12v[t] + 0.0889780949233021Q1_16v[t] -0.102927253819690Q1_22v[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Q1_2[t] =  +  0.683448246864695 +  0.198926748843929Q1_3[t] -0.0928214103551924Q1_5[t] +  0.0904997968487101Q1_7[t] -0.075972113093604Q1_8[t] +  0.116383027938451Q1_12[t] +  0.580006004424285Q1_16[t] -0.00989775749926517Q1_22[t] +  0.124131261356080Q1_2v[t] -0.261297359223972Q1_3v[t] +  0.227200441858429Q1_5v[t] -0.128064165925015Q1_7v[t] +  0.308882429796853Q1_8v[t] -0.158991872319213Q1_12v[t] +  0.0889780949233021Q1_16v[t] -0.102927253819690Q1_22v[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99692&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Q1_2[t] =  +  0.683448246864695 +  0.198926748843929Q1_3[t] -0.0928214103551924Q1_5[t] +  0.0904997968487101Q1_7[t] -0.075972113093604Q1_8[t] +  0.116383027938451Q1_12[t] +  0.580006004424285Q1_16[t] -0.00989775749926517Q1_22[t] +  0.124131261356080Q1_2v[t] -0.261297359223972Q1_3v[t] +  0.227200441858429Q1_5v[t] -0.128064165925015Q1_7v[t] +  0.308882429796853Q1_8v[t] -0.158991872319213Q1_12v[t] +  0.0889780949233021Q1_16v[t] -0.102927253819690Q1_22v[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99692&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99692&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Q1_2[t] = + 0.683448246864695 + 0.198926748843929Q1_3[t] -0.0928214103551924Q1_5[t] + 0.0904997968487101Q1_7[t] -0.075972113093604Q1_8[t] + 0.116383027938451Q1_12[t] + 0.580006004424285Q1_16[t] -0.00989775749926517Q1_22[t] + 0.124131261356080Q1_2v[t] -0.261297359223972Q1_3v[t] + 0.227200441858429Q1_5v[t] -0.128064165925015Q1_7v[t] + 0.308882429796853Q1_8v[t] -0.158991872319213Q1_12v[t] + 0.0889780949233021Q1_16v[t] -0.102927253819690Q1_22v[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6834482468646950.9262710.73780.4626380.231319
Q1_30.1989267488439290.1066261.86570.0655390.032769
Q1_5-0.09282141035519240.14066-0.65990.5111020.255551
Q1_70.09049979684871010.0867631.04310.2998750.149938
Q1_8-0.0759721130936040.111699-0.68020.4982570.249129
Q1_120.1163830279384510.1253180.92870.3556720.177836
Q1_160.5800060044242850.1019825.687300
Q1_22-0.009897757499265170.11257-0.08790.9301430.465071
Q1_2v0.1241312613560800.1126681.10170.2736810.136841
Q1_3v-0.2612973592239720.107925-2.42110.0176010.008801
Q1_5v0.2272004418584290.2392370.94970.3449640.172482
Q1_7v-0.1280641659250150.129997-0.98510.3273560.163678
Q1_8v0.3088824297968530.1613011.91490.0588620.029431
Q1_12v-0.1589918723192130.142698-1.11420.2683390.13417
Q1_16v0.08897809492330210.1613120.55160.5826760.291338
Q1_22v-0.1029272538196900.153545-0.67030.5044590.252229

\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) & 0.683448246864695 & 0.926271 & 0.7378 & 0.462638 & 0.231319 \tabularnewline
Q1_3 & 0.198926748843929 & 0.106626 & 1.8657 & 0.065539 & 0.032769 \tabularnewline
Q1_5 & -0.0928214103551924 & 0.14066 & -0.6599 & 0.511102 & 0.255551 \tabularnewline
Q1_7 & 0.0904997968487101 & 0.086763 & 1.0431 & 0.299875 & 0.149938 \tabularnewline
Q1_8 & -0.075972113093604 & 0.111699 & -0.6802 & 0.498257 & 0.249129 \tabularnewline
Q1_12 & 0.116383027938451 & 0.125318 & 0.9287 & 0.355672 & 0.177836 \tabularnewline
Q1_16 & 0.580006004424285 & 0.101982 & 5.6873 & 0 & 0 \tabularnewline
Q1_22 & -0.00989775749926517 & 0.11257 & -0.0879 & 0.930143 & 0.465071 \tabularnewline
Q1_2v & 0.124131261356080 & 0.112668 & 1.1017 & 0.273681 & 0.136841 \tabularnewline
Q1_3v & -0.261297359223972 & 0.107925 & -2.4211 & 0.017601 & 0.008801 \tabularnewline
Q1_5v & 0.227200441858429 & 0.239237 & 0.9497 & 0.344964 & 0.172482 \tabularnewline
Q1_7v & -0.128064165925015 & 0.129997 & -0.9851 & 0.327356 & 0.163678 \tabularnewline
Q1_8v & 0.308882429796853 & 0.161301 & 1.9149 & 0.058862 & 0.029431 \tabularnewline
Q1_12v & -0.158991872319213 & 0.142698 & -1.1142 & 0.268339 & 0.13417 \tabularnewline
Q1_16v & 0.0889780949233021 & 0.161312 & 0.5516 & 0.582676 & 0.291338 \tabularnewline
Q1_22v & -0.102927253819690 & 0.153545 & -0.6703 & 0.504459 & 0.252229 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99692&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]0.683448246864695[/C][C]0.926271[/C][C]0.7378[/C][C]0.462638[/C][C]0.231319[/C][/ROW]
[ROW][C]Q1_3[/C][C]0.198926748843929[/C][C]0.106626[/C][C]1.8657[/C][C]0.065539[/C][C]0.032769[/C][/ROW]
[ROW][C]Q1_5[/C][C]-0.0928214103551924[/C][C]0.14066[/C][C]-0.6599[/C][C]0.511102[/C][C]0.255551[/C][/ROW]
[ROW][C]Q1_7[/C][C]0.0904997968487101[/C][C]0.086763[/C][C]1.0431[/C][C]0.299875[/C][C]0.149938[/C][/ROW]
[ROW][C]Q1_8[/C][C]-0.075972113093604[/C][C]0.111699[/C][C]-0.6802[/C][C]0.498257[/C][C]0.249129[/C][/ROW]
[ROW][C]Q1_12[/C][C]0.116383027938451[/C][C]0.125318[/C][C]0.9287[/C][C]0.355672[/C][C]0.177836[/C][/ROW]
[ROW][C]Q1_16[/C][C]0.580006004424285[/C][C]0.101982[/C][C]5.6873[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Q1_22[/C][C]-0.00989775749926517[/C][C]0.11257[/C][C]-0.0879[/C][C]0.930143[/C][C]0.465071[/C][/ROW]
[ROW][C]Q1_2v[/C][C]0.124131261356080[/C][C]0.112668[/C][C]1.1017[/C][C]0.273681[/C][C]0.136841[/C][/ROW]
[ROW][C]Q1_3v[/C][C]-0.261297359223972[/C][C]0.107925[/C][C]-2.4211[/C][C]0.017601[/C][C]0.008801[/C][/ROW]
[ROW][C]Q1_5v[/C][C]0.227200441858429[/C][C]0.239237[/C][C]0.9497[/C][C]0.344964[/C][C]0.172482[/C][/ROW]
[ROW][C]Q1_7v[/C][C]-0.128064165925015[/C][C]0.129997[/C][C]-0.9851[/C][C]0.327356[/C][C]0.163678[/C][/ROW]
[ROW][C]Q1_8v[/C][C]0.308882429796853[/C][C]0.161301[/C][C]1.9149[/C][C]0.058862[/C][C]0.029431[/C][/ROW]
[ROW][C]Q1_12v[/C][C]-0.158991872319213[/C][C]0.142698[/C][C]-1.1142[/C][C]0.268339[/C][C]0.13417[/C][/ROW]
[ROW][C]Q1_16v[/C][C]0.0889780949233021[/C][C]0.161312[/C][C]0.5516[/C][C]0.582676[/C][C]0.291338[/C][/ROW]
[ROW][C]Q1_22v[/C][C]-0.102927253819690[/C][C]0.153545[/C][C]-0.6703[/C][C]0.504459[/C][C]0.252229[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99692&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99692&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.6834482468646950.9262710.73780.4626380.231319
Q1_30.1989267488439290.1066261.86570.0655390.032769
Q1_5-0.09282141035519240.14066-0.65990.5111020.255551
Q1_70.09049979684871010.0867631.04310.2998750.149938
Q1_8-0.0759721130936040.111699-0.68020.4982570.249129
Q1_120.1163830279384510.1253180.92870.3556720.177836
Q1_160.5800060044242850.1019825.687300
Q1_22-0.009897757499265170.11257-0.08790.9301430.465071
Q1_2v0.1241312613560800.1126681.10170.2736810.136841
Q1_3v-0.2612973592239720.107925-2.42110.0176010.008801
Q1_5v0.2272004418584290.2392370.94970.3449640.172482
Q1_7v-0.1280641659250150.129997-0.98510.3273560.163678
Q1_8v0.3088824297968530.1613011.91490.0588620.029431
Q1_12v-0.1589918723192130.142698-1.11420.2683390.13417
Q1_16v0.08897809492330210.1613120.55160.5826760.291338
Q1_22v-0.1029272538196900.153545-0.67030.5044590.252229






Multiple Linear Regression - Regression Statistics
Multiple R0.673124912686542
R-squared0.453097148079265
Adjusted R-squared0.356584880093253
F-TEST (value)4.69471039831883
F-TEST (DF numerator)15
F-TEST (DF denominator)85
p-value1.81266670795655e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.90392117898313
Sum Squared Residuals69.4512473142114

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.673124912686542 \tabularnewline
R-squared & 0.453097148079265 \tabularnewline
Adjusted R-squared & 0.356584880093253 \tabularnewline
F-TEST (value) & 4.69471039831883 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 85 \tabularnewline
p-value & 1.81266670795655e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.90392117898313 \tabularnewline
Sum Squared Residuals & 69.4512473142114 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99692&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.673124912686542[/C][/ROW]
[ROW][C]R-squared[/C][C]0.453097148079265[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.356584880093253[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.69471039831883[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]85[/C][/ROW]
[ROW][C]p-value[/C][C]1.81266670795655e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.90392117898313[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]69.4512473142114[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99692&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99692&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.673124912686542
R-squared0.453097148079265
Adjusted R-squared0.356584880093253
F-TEST (value)4.69471039831883
F-TEST (DF numerator)15
F-TEST (DF denominator)85
p-value1.81266670795655e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.90392117898313
Sum Squared Residuals69.4512473142114






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
176.558704710353650.441295289646353
255.20280688402397-0.202806884023974
364.619775372521051.38022462747895
455.0896922847082-0.0896922847081988
565.923463056852770.0765369431472323
665.114060656032780.885939343967223
765.636958940604450.363041059395548
865.333384896877180.66661510312282
943.921909726610160.07809027338984
1065.502363749094220.497636250905775
1166.11138431853196-0.111384318531962
1233.50548105551907-0.505481055519066
1355.59298873631076-0.592988736310762
1455.24413792494949-0.244137924949490
1523.09630144067017-1.09630144067017
1635.22552251065442-2.22552251065442
1765.461132669779660.538867330220338
1866.15542426851204-0.155424268512041
1954.952683022678970.0473169773210339
2075.59707835872631.40292164127370
2155.2682410732817-0.268241073281701
2255.2148943607927-0.214894360792697
2355.16219315114779-0.162193151147789
2456.32908536327162-1.32908536327162
2555.69927039284263-0.699270392842633
2665.481580076191310.518419923808686
2755.02987609146532-0.0298760914653231
2854.195367473799870.804632526200128
2965.213823546064280.786176453935721
3044.1546369894868-0.154636989486803
3144.94180722238501-0.941807222385013
3264.927339977437471.07266002256253
3333.03556652955895-0.0355665295589454
3464.958001709593531.04199829040647
3554.292158770753940.707841229246058
3666.12563144103092-0.125631441030924
3775.29648131631991.70351868368010
3844.59423748477401-0.594237484774013
3954.489438735213160.510561264786845
4044.89315052491175-0.893150524911754
4154.858346394320.141653605679998
4234.85705273681687-1.85705273681687
4355.01173793183049-0.0117379318304941
4465.978385569120850.021614430879145
4565.915357666118320.0846423338816765
4644.14414985171917-0.144149851719168
4744.29273383980916-0.292733839809155
4865.084704306851830.915295693148168
4965.84601164078080.153988359219196
5055.0012968806686-0.00129688066859569
5166.00876467935651-0.00876467935650707
5243.746788792654090.253211207345913
5344.73897302711348-0.738973027113485
5455.13605548000944-0.136055480009441
5534.07988695643614-1.07988695643614
5666.0055067618141-0.0055067618141013
5765.673514568083060.326485431916937
5844.20304535869686-0.203045358696862
5954.720930318842910.279069681157086
6054.774500006497790.225499993502207
6145.18469068820968-1.18469068820968
6265.094156281449660.905843718550336
6355.79742881096996-0.797428810969963
6444.85526770443865-0.855267704438649
6564.669864103168921.33013589683108
6655.92044923057021-0.920449230570208
6765.408385941592280.591614058407717
6856.19194862111634-1.19194862111634
6965.529398895786580.470601104213425
7054.700795375161830.299204624838168
7144.05848111772794-0.0584811177279371
7265.611942616692050.388057383307947
7353.49763272331461.5023672766854
7455.18323680512411-0.183236805124113
7533.96240861283944-0.96240861283944
7654.992537778234510.0074622217654881
7744.65301480590733-0.653014805907328
7854.654956487463360.345043512536640
7953.281630758525551.71836924147445
8076.191948621116340.808051378883662
8175.223986876503461.77601312349654
8254.201684522131250.798315477868746
8343.980187391127850.0198126088721492
8465.367330993416570.632669006583427
8554.89341702672390.106582973276100
8655.35597544608194-0.355975446081937
8744.57286602021234-0.572866020212344
8855.07529374484956-0.075293744849557
8923.42809714193190-1.42809714193190
9075.896475134748441.10352486525156
9144.63935094582333-0.639350945823334
9254.768393297664870.23160670233513
9355.63782584778179-0.637825847781794
9476.164761613458150.835238386541853
9525.43194755917554-3.43194755917554
9643.925835214061470.0741647859385272
9765.764106633219510.23589336678049
9855.32778390224284-0.327783902242838
9954.548851148974620.451148851025376
10044.71345469906492-0.713454699064917
10144.41641927952197-0.416419279521968

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7 & 6.55870471035365 & 0.441295289646353 \tabularnewline
2 & 5 & 5.20280688402397 & -0.202806884023974 \tabularnewline
3 & 6 & 4.61977537252105 & 1.38022462747895 \tabularnewline
4 & 5 & 5.0896922847082 & -0.0896922847081988 \tabularnewline
5 & 6 & 5.92346305685277 & 0.0765369431472323 \tabularnewline
6 & 6 & 5.11406065603278 & 0.885939343967223 \tabularnewline
7 & 6 & 5.63695894060445 & 0.363041059395548 \tabularnewline
8 & 6 & 5.33338489687718 & 0.66661510312282 \tabularnewline
9 & 4 & 3.92190972661016 & 0.07809027338984 \tabularnewline
10 & 6 & 5.50236374909422 & 0.497636250905775 \tabularnewline
11 & 6 & 6.11138431853196 & -0.111384318531962 \tabularnewline
12 & 3 & 3.50548105551907 & -0.505481055519066 \tabularnewline
13 & 5 & 5.59298873631076 & -0.592988736310762 \tabularnewline
14 & 5 & 5.24413792494949 & -0.244137924949490 \tabularnewline
15 & 2 & 3.09630144067017 & -1.09630144067017 \tabularnewline
16 & 3 & 5.22552251065442 & -2.22552251065442 \tabularnewline
17 & 6 & 5.46113266977966 & 0.538867330220338 \tabularnewline
18 & 6 & 6.15542426851204 & -0.155424268512041 \tabularnewline
19 & 5 & 4.95268302267897 & 0.0473169773210339 \tabularnewline
20 & 7 & 5.5970783587263 & 1.40292164127370 \tabularnewline
21 & 5 & 5.2682410732817 & -0.268241073281701 \tabularnewline
22 & 5 & 5.2148943607927 & -0.214894360792697 \tabularnewline
23 & 5 & 5.16219315114779 & -0.162193151147789 \tabularnewline
24 & 5 & 6.32908536327162 & -1.32908536327162 \tabularnewline
25 & 5 & 5.69927039284263 & -0.699270392842633 \tabularnewline
26 & 6 & 5.48158007619131 & 0.518419923808686 \tabularnewline
27 & 5 & 5.02987609146532 & -0.0298760914653231 \tabularnewline
28 & 5 & 4.19536747379987 & 0.804632526200128 \tabularnewline
29 & 6 & 5.21382354606428 & 0.786176453935721 \tabularnewline
30 & 4 & 4.1546369894868 & -0.154636989486803 \tabularnewline
31 & 4 & 4.94180722238501 & -0.941807222385013 \tabularnewline
32 & 6 & 4.92733997743747 & 1.07266002256253 \tabularnewline
33 & 3 & 3.03556652955895 & -0.0355665295589454 \tabularnewline
34 & 6 & 4.95800170959353 & 1.04199829040647 \tabularnewline
35 & 5 & 4.29215877075394 & 0.707841229246058 \tabularnewline
36 & 6 & 6.12563144103092 & -0.125631441030924 \tabularnewline
37 & 7 & 5.2964813163199 & 1.70351868368010 \tabularnewline
38 & 4 & 4.59423748477401 & -0.594237484774013 \tabularnewline
39 & 5 & 4.48943873521316 & 0.510561264786845 \tabularnewline
40 & 4 & 4.89315052491175 & -0.893150524911754 \tabularnewline
41 & 5 & 4.85834639432 & 0.141653605679998 \tabularnewline
42 & 3 & 4.85705273681687 & -1.85705273681687 \tabularnewline
43 & 5 & 5.01173793183049 & -0.0117379318304941 \tabularnewline
44 & 6 & 5.97838556912085 & 0.021614430879145 \tabularnewline
45 & 6 & 5.91535766611832 & 0.0846423338816765 \tabularnewline
46 & 4 & 4.14414985171917 & -0.144149851719168 \tabularnewline
47 & 4 & 4.29273383980916 & -0.292733839809155 \tabularnewline
48 & 6 & 5.08470430685183 & 0.915295693148168 \tabularnewline
49 & 6 & 5.8460116407808 & 0.153988359219196 \tabularnewline
50 & 5 & 5.0012968806686 & -0.00129688066859569 \tabularnewline
51 & 6 & 6.00876467935651 & -0.00876467935650707 \tabularnewline
52 & 4 & 3.74678879265409 & 0.253211207345913 \tabularnewline
53 & 4 & 4.73897302711348 & -0.738973027113485 \tabularnewline
54 & 5 & 5.13605548000944 & -0.136055480009441 \tabularnewline
55 & 3 & 4.07988695643614 & -1.07988695643614 \tabularnewline
56 & 6 & 6.0055067618141 & -0.0055067618141013 \tabularnewline
57 & 6 & 5.67351456808306 & 0.326485431916937 \tabularnewline
58 & 4 & 4.20304535869686 & -0.203045358696862 \tabularnewline
59 & 5 & 4.72093031884291 & 0.279069681157086 \tabularnewline
60 & 5 & 4.77450000649779 & 0.225499993502207 \tabularnewline
61 & 4 & 5.18469068820968 & -1.18469068820968 \tabularnewline
62 & 6 & 5.09415628144966 & 0.905843718550336 \tabularnewline
63 & 5 & 5.79742881096996 & -0.797428810969963 \tabularnewline
64 & 4 & 4.85526770443865 & -0.855267704438649 \tabularnewline
65 & 6 & 4.66986410316892 & 1.33013589683108 \tabularnewline
66 & 5 & 5.92044923057021 & -0.920449230570208 \tabularnewline
67 & 6 & 5.40838594159228 & 0.591614058407717 \tabularnewline
68 & 5 & 6.19194862111634 & -1.19194862111634 \tabularnewline
69 & 6 & 5.52939889578658 & 0.470601104213425 \tabularnewline
70 & 5 & 4.70079537516183 & 0.299204624838168 \tabularnewline
71 & 4 & 4.05848111772794 & -0.0584811177279371 \tabularnewline
72 & 6 & 5.61194261669205 & 0.388057383307947 \tabularnewline
73 & 5 & 3.4976327233146 & 1.5023672766854 \tabularnewline
74 & 5 & 5.18323680512411 & -0.183236805124113 \tabularnewline
75 & 3 & 3.96240861283944 & -0.96240861283944 \tabularnewline
76 & 5 & 4.99253777823451 & 0.0074622217654881 \tabularnewline
77 & 4 & 4.65301480590733 & -0.653014805907328 \tabularnewline
78 & 5 & 4.65495648746336 & 0.345043512536640 \tabularnewline
79 & 5 & 3.28163075852555 & 1.71836924147445 \tabularnewline
80 & 7 & 6.19194862111634 & 0.808051378883662 \tabularnewline
81 & 7 & 5.22398687650346 & 1.77601312349654 \tabularnewline
82 & 5 & 4.20168452213125 & 0.798315477868746 \tabularnewline
83 & 4 & 3.98018739112785 & 0.0198126088721492 \tabularnewline
84 & 6 & 5.36733099341657 & 0.632669006583427 \tabularnewline
85 & 5 & 4.8934170267239 & 0.106582973276100 \tabularnewline
86 & 5 & 5.35597544608194 & -0.355975446081937 \tabularnewline
87 & 4 & 4.57286602021234 & -0.572866020212344 \tabularnewline
88 & 5 & 5.07529374484956 & -0.075293744849557 \tabularnewline
89 & 2 & 3.42809714193190 & -1.42809714193190 \tabularnewline
90 & 7 & 5.89647513474844 & 1.10352486525156 \tabularnewline
91 & 4 & 4.63935094582333 & -0.639350945823334 \tabularnewline
92 & 5 & 4.76839329766487 & 0.23160670233513 \tabularnewline
93 & 5 & 5.63782584778179 & -0.637825847781794 \tabularnewline
94 & 7 & 6.16476161345815 & 0.835238386541853 \tabularnewline
95 & 2 & 5.43194755917554 & -3.43194755917554 \tabularnewline
96 & 4 & 3.92583521406147 & 0.0741647859385272 \tabularnewline
97 & 6 & 5.76410663321951 & 0.23589336678049 \tabularnewline
98 & 5 & 5.32778390224284 & -0.327783902242838 \tabularnewline
99 & 5 & 4.54885114897462 & 0.451148851025376 \tabularnewline
100 & 4 & 4.71345469906492 & -0.713454699064917 \tabularnewline
101 & 4 & 4.41641927952197 & -0.416419279521968 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99692&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]7[/C][C]6.55870471035365[/C][C]0.441295289646353[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]5.20280688402397[/C][C]-0.202806884023974[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]4.61977537252105[/C][C]1.38022462747895[/C][/ROW]
[ROW][C]4[/C][C]5[/C][C]5.0896922847082[/C][C]-0.0896922847081988[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]5.92346305685277[/C][C]0.0765369431472323[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.11406065603278[/C][C]0.885939343967223[/C][/ROW]
[ROW][C]7[/C][C]6[/C][C]5.63695894060445[/C][C]0.363041059395548[/C][/ROW]
[ROW][C]8[/C][C]6[/C][C]5.33338489687718[/C][C]0.66661510312282[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.92190972661016[/C][C]0.07809027338984[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]5.50236374909422[/C][C]0.497636250905775[/C][/ROW]
[ROW][C]11[/C][C]6[/C][C]6.11138431853196[/C][C]-0.111384318531962[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]3.50548105551907[/C][C]-0.505481055519066[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]5.59298873631076[/C][C]-0.592988736310762[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]5.24413792494949[/C][C]-0.244137924949490[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]3.09630144067017[/C][C]-1.09630144067017[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]5.22552251065442[/C][C]-2.22552251065442[/C][/ROW]
[ROW][C]17[/C][C]6[/C][C]5.46113266977966[/C][C]0.538867330220338[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]6.15542426851204[/C][C]-0.155424268512041[/C][/ROW]
[ROW][C]19[/C][C]5[/C][C]4.95268302267897[/C][C]0.0473169773210339[/C][/ROW]
[ROW][C]20[/C][C]7[/C][C]5.5970783587263[/C][C]1.40292164127370[/C][/ROW]
[ROW][C]21[/C][C]5[/C][C]5.2682410732817[/C][C]-0.268241073281701[/C][/ROW]
[ROW][C]22[/C][C]5[/C][C]5.2148943607927[/C][C]-0.214894360792697[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]5.16219315114779[/C][C]-0.162193151147789[/C][/ROW]
[ROW][C]24[/C][C]5[/C][C]6.32908536327162[/C][C]-1.32908536327162[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]5.69927039284263[/C][C]-0.699270392842633[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]5.48158007619131[/C][C]0.518419923808686[/C][/ROW]
[ROW][C]27[/C][C]5[/C][C]5.02987609146532[/C][C]-0.0298760914653231[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]4.19536747379987[/C][C]0.804632526200128[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]5.21382354606428[/C][C]0.786176453935721[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.1546369894868[/C][C]-0.154636989486803[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.94180722238501[/C][C]-0.941807222385013[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]4.92733997743747[/C][C]1.07266002256253[/C][/ROW]
[ROW][C]33[/C][C]3[/C][C]3.03556652955895[/C][C]-0.0355665295589454[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]4.95800170959353[/C][C]1.04199829040647[/C][/ROW]
[ROW][C]35[/C][C]5[/C][C]4.29215877075394[/C][C]0.707841229246058[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]6.12563144103092[/C][C]-0.125631441030924[/C][/ROW]
[ROW][C]37[/C][C]7[/C][C]5.2964813163199[/C][C]1.70351868368010[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.59423748477401[/C][C]-0.594237484774013[/C][/ROW]
[ROW][C]39[/C][C]5[/C][C]4.48943873521316[/C][C]0.510561264786845[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.89315052491175[/C][C]-0.893150524911754[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.85834639432[/C][C]0.141653605679998[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]4.85705273681687[/C][C]-1.85705273681687[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]5.01173793183049[/C][C]-0.0117379318304941[/C][/ROW]
[ROW][C]44[/C][C]6[/C][C]5.97838556912085[/C][C]0.021614430879145[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]5.91535766611832[/C][C]0.0846423338816765[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]4.14414985171917[/C][C]-0.144149851719168[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.29273383980916[/C][C]-0.292733839809155[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]5.08470430685183[/C][C]0.915295693148168[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]5.8460116407808[/C][C]0.153988359219196[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]5.0012968806686[/C][C]-0.00129688066859569[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]6.00876467935651[/C][C]-0.00876467935650707[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.74678879265409[/C][C]0.253211207345913[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.73897302711348[/C][C]-0.738973027113485[/C][/ROW]
[ROW][C]54[/C][C]5[/C][C]5.13605548000944[/C][C]-0.136055480009441[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]4.07988695643614[/C][C]-1.07988695643614[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]6.0055067618141[/C][C]-0.0055067618141013[/C][/ROW]
[ROW][C]57[/C][C]6[/C][C]5.67351456808306[/C][C]0.326485431916937[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.20304535869686[/C][C]-0.203045358696862[/C][/ROW]
[ROW][C]59[/C][C]5[/C][C]4.72093031884291[/C][C]0.279069681157086[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]4.77450000649779[/C][C]0.225499993502207[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]5.18469068820968[/C][C]-1.18469068820968[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]5.09415628144966[/C][C]0.905843718550336[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]5.79742881096996[/C][C]-0.797428810969963[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]4.85526770443865[/C][C]-0.855267704438649[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]4.66986410316892[/C][C]1.33013589683108[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.92044923057021[/C][C]-0.920449230570208[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]5.40838594159228[/C][C]0.591614058407717[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]6.19194862111634[/C][C]-1.19194862111634[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]5.52939889578658[/C][C]0.470601104213425[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]4.70079537516183[/C][C]0.299204624838168[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]4.05848111772794[/C][C]-0.0584811177279371[/C][/ROW]
[ROW][C]72[/C][C]6[/C][C]5.61194261669205[/C][C]0.388057383307947[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]3.4976327233146[/C][C]1.5023672766854[/C][/ROW]
[ROW][C]74[/C][C]5[/C][C]5.18323680512411[/C][C]-0.183236805124113[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]3.96240861283944[/C][C]-0.96240861283944[/C][/ROW]
[ROW][C]76[/C][C]5[/C][C]4.99253777823451[/C][C]0.0074622217654881[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]4.65301480590733[/C][C]-0.653014805907328[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]4.65495648746336[/C][C]0.345043512536640[/C][/ROW]
[ROW][C]79[/C][C]5[/C][C]3.28163075852555[/C][C]1.71836924147445[/C][/ROW]
[ROW][C]80[/C][C]7[/C][C]6.19194862111634[/C][C]0.808051378883662[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]5.22398687650346[/C][C]1.77601312349654[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]4.20168452213125[/C][C]0.798315477868746[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]3.98018739112785[/C][C]0.0198126088721492[/C][/ROW]
[ROW][C]84[/C][C]6[/C][C]5.36733099341657[/C][C]0.632669006583427[/C][/ROW]
[ROW][C]85[/C][C]5[/C][C]4.8934170267239[/C][C]0.106582973276100[/C][/ROW]
[ROW][C]86[/C][C]5[/C][C]5.35597544608194[/C][C]-0.355975446081937[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]4.57286602021234[/C][C]-0.572866020212344[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]5.07529374484956[/C][C]-0.075293744849557[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]3.42809714193190[/C][C]-1.42809714193190[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]5.89647513474844[/C][C]1.10352486525156[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]4.63935094582333[/C][C]-0.639350945823334[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]4.76839329766487[/C][C]0.23160670233513[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]5.63782584778179[/C][C]-0.637825847781794[/C][/ROW]
[ROW][C]94[/C][C]7[/C][C]6.16476161345815[/C][C]0.835238386541853[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]5.43194755917554[/C][C]-3.43194755917554[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]3.92583521406147[/C][C]0.0741647859385272[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]5.76410663321951[/C][C]0.23589336678049[/C][/ROW]
[ROW][C]98[/C][C]5[/C][C]5.32778390224284[/C][C]-0.327783902242838[/C][/ROW]
[ROW][C]99[/C][C]5[/C][C]4.54885114897462[/C][C]0.451148851025376[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]4.71345469906492[/C][C]-0.713454699064917[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]4.41641927952197[/C][C]-0.416419279521968[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99692&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99692&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
176.558704710353650.441295289646353
255.20280688402397-0.202806884023974
364.619775372521051.38022462747895
455.0896922847082-0.0896922847081988
565.923463056852770.0765369431472323
665.114060656032780.885939343967223
765.636958940604450.363041059395548
865.333384896877180.66661510312282
943.921909726610160.07809027338984
1065.502363749094220.497636250905775
1166.11138431853196-0.111384318531962
1233.50548105551907-0.505481055519066
1355.59298873631076-0.592988736310762
1455.24413792494949-0.244137924949490
1523.09630144067017-1.09630144067017
1635.22552251065442-2.22552251065442
1765.461132669779660.538867330220338
1866.15542426851204-0.155424268512041
1954.952683022678970.0473169773210339
2075.59707835872631.40292164127370
2155.2682410732817-0.268241073281701
2255.2148943607927-0.214894360792697
2355.16219315114779-0.162193151147789
2456.32908536327162-1.32908536327162
2555.69927039284263-0.699270392842633
2665.481580076191310.518419923808686
2755.02987609146532-0.0298760914653231
2854.195367473799870.804632526200128
2965.213823546064280.786176453935721
3044.1546369894868-0.154636989486803
3144.94180722238501-0.941807222385013
3264.927339977437471.07266002256253
3333.03556652955895-0.0355665295589454
3464.958001709593531.04199829040647
3554.292158770753940.707841229246058
3666.12563144103092-0.125631441030924
3775.29648131631991.70351868368010
3844.59423748477401-0.594237484774013
3954.489438735213160.510561264786845
4044.89315052491175-0.893150524911754
4154.858346394320.141653605679998
4234.85705273681687-1.85705273681687
4355.01173793183049-0.0117379318304941
4465.978385569120850.021614430879145
4565.915357666118320.0846423338816765
4644.14414985171917-0.144149851719168
4744.29273383980916-0.292733839809155
4865.084704306851830.915295693148168
4965.84601164078080.153988359219196
5055.0012968806686-0.00129688066859569
5166.00876467935651-0.00876467935650707
5243.746788792654090.253211207345913
5344.73897302711348-0.738973027113485
5455.13605548000944-0.136055480009441
5534.07988695643614-1.07988695643614
5666.0055067618141-0.0055067618141013
5765.673514568083060.326485431916937
5844.20304535869686-0.203045358696862
5954.720930318842910.279069681157086
6054.774500006497790.225499993502207
6145.18469068820968-1.18469068820968
6265.094156281449660.905843718550336
6355.79742881096996-0.797428810969963
6444.85526770443865-0.855267704438649
6564.669864103168921.33013589683108
6655.92044923057021-0.920449230570208
6765.408385941592280.591614058407717
6856.19194862111634-1.19194862111634
6965.529398895786580.470601104213425
7054.700795375161830.299204624838168
7144.05848111772794-0.0584811177279371
7265.611942616692050.388057383307947
7353.49763272331461.5023672766854
7455.18323680512411-0.183236805124113
7533.96240861283944-0.96240861283944
7654.992537778234510.0074622217654881
7744.65301480590733-0.653014805907328
7854.654956487463360.345043512536640
7953.281630758525551.71836924147445
8076.191948621116340.808051378883662
8175.223986876503461.77601312349654
8254.201684522131250.798315477868746
8343.980187391127850.0198126088721492
8465.367330993416570.632669006583427
8554.89341702672390.106582973276100
8655.35597544608194-0.355975446081937
8744.57286602021234-0.572866020212344
8855.07529374484956-0.075293744849557
8923.42809714193190-1.42809714193190
9075.896475134748441.10352486525156
9144.63935094582333-0.639350945823334
9254.768393297664870.23160670233513
9355.63782584778179-0.637825847781794
9476.164761613458150.835238386541853
9525.43194755917554-3.43194755917554
9643.925835214061470.0741647859385272
9765.764106633219510.23589336678049
9855.32778390224284-0.327783902242838
9954.548851148974620.451148851025376
10044.71345469906492-0.713454699064917
10144.41641927952197-0.416419279521968






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.5289245606111650.942150878777670.471075439388835
200.4257719336642310.8515438673284620.574228066335769
210.4618119030163530.9236238060327060.538188096983647
220.3709452473800060.7418904947600130.629054752619994
230.2659450772071210.5318901544142410.73405492279288
240.2804636111337870.5609272222675740.719536388866213
250.2284211227402080.4568422454804160.771578877259792
260.1613666733344230.3227333466688460.838633326665577
270.1141060681969470.2282121363938950.885893931803053
280.1635964512997200.3271929025994390.83640354870028
290.1446866789244670.2893733578489340.855313321075533
300.1401563468368360.2803126936736710.859843653163164
310.3245579838664810.6491159677329610.675442016133519
320.2842798836721470.5685597673442930.715720116327853
330.219630825308620.439261650617240.78036917469138
340.2509240361011140.5018480722022280.749075963898886
350.2174689807351260.4349379614702510.782531019264874
360.1657463896782560.3314927793565110.834253610321744
370.2674830227112060.5349660454224120.732516977288794
380.552801685997520.894396628004960.44719831400248
390.5267455073774470.9465089852451070.473254492622553
400.526901028991240.946197942017520.47309897100876
410.455464180975610.910928361951220.54453581902439
420.5784724706145840.8430550587708330.421527529385416
430.5089811786246330.9820376427507340.491018821375367
440.4452933573630880.8905867147261750.554706642636912
450.3926648335240620.7853296670481250.607335166475938
460.3334758556740660.6669517113481320.666524144325934
470.2760285793257210.5520571586514420.723971420674279
480.2749923054433090.5499846108866180.725007694556691
490.220618926719510.441237853439020.77938107328049
500.1876959338707640.3753918677415280.812304066129236
510.1473170730270140.2946341460540290.852682926972986
520.1249880263243150.2499760526486290.875011973675685
530.1119106263804830.2238212527609650.888089373619517
540.0831148251047170.1662296502094340.916885174895283
550.1159391853078010.2318783706156020.884060814692199
560.08682063781467910.1736412756293580.91317936218532
570.06251099612379680.1250219922475940.937489003876203
580.04593917345018220.09187834690036440.954060826549818
590.03262051016807750.0652410203361550.967379489831923
600.02176437550857430.04352875101714860.978235624491426
610.01944936291129350.03889872582258710.980550637088706
620.01490795175882950.02981590351765900.98509204824117
630.01136506139674550.02273012279349100.988634938603254
640.01142006359739340.02284012719478690.988579936402607
650.01573003314180380.03146006628360760.984269966858196
660.01388071426030250.02776142852060490.986119285739698
670.01025522691575750.02051045383151490.989744773084243
680.01054407729316980.02108815458633970.98945592270683
690.00718796094011970.01437592188023940.99281203905988
700.00440951610969230.00881903221938460.995590483890308
710.002494227953836190.004988455907672370.997505772046164
720.001533164853472920.003066329706945840.998466835146527
730.003535054917919120.007070109835838240.99646494508208
740.001948071354513260.003896142709026530.998051928645487
750.001412720429518110.002825440859036230.998587279570482
760.0006845047610693710.001369009522138740.99931549523893
770.0004244825301909740.0008489650603819490.999575517469809
780.0002006093050272820.0004012186100545640.999799390694973
790.0007029230251257640.001405846050251530.999297076974874
800.0003737418109946020.0007474836219892050.999626258189005
810.001006575444997650.002013150889995310.998993424555002
820.0005887961518047180.001177592303609440.999411203848195

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.528924560611165 & 0.94215087877767 & 0.471075439388835 \tabularnewline
20 & 0.425771933664231 & 0.851543867328462 & 0.574228066335769 \tabularnewline
21 & 0.461811903016353 & 0.923623806032706 & 0.538188096983647 \tabularnewline
22 & 0.370945247380006 & 0.741890494760013 & 0.629054752619994 \tabularnewline
23 & 0.265945077207121 & 0.531890154414241 & 0.73405492279288 \tabularnewline
24 & 0.280463611133787 & 0.560927222267574 & 0.719536388866213 \tabularnewline
25 & 0.228421122740208 & 0.456842245480416 & 0.771578877259792 \tabularnewline
26 & 0.161366673334423 & 0.322733346668846 & 0.838633326665577 \tabularnewline
27 & 0.114106068196947 & 0.228212136393895 & 0.885893931803053 \tabularnewline
28 & 0.163596451299720 & 0.327192902599439 & 0.83640354870028 \tabularnewline
29 & 0.144686678924467 & 0.289373357848934 & 0.855313321075533 \tabularnewline
30 & 0.140156346836836 & 0.280312693673671 & 0.859843653163164 \tabularnewline
31 & 0.324557983866481 & 0.649115967732961 & 0.675442016133519 \tabularnewline
32 & 0.284279883672147 & 0.568559767344293 & 0.715720116327853 \tabularnewline
33 & 0.21963082530862 & 0.43926165061724 & 0.78036917469138 \tabularnewline
34 & 0.250924036101114 & 0.501848072202228 & 0.749075963898886 \tabularnewline
35 & 0.217468980735126 & 0.434937961470251 & 0.782531019264874 \tabularnewline
36 & 0.165746389678256 & 0.331492779356511 & 0.834253610321744 \tabularnewline
37 & 0.267483022711206 & 0.534966045422412 & 0.732516977288794 \tabularnewline
38 & 0.55280168599752 & 0.89439662800496 & 0.44719831400248 \tabularnewline
39 & 0.526745507377447 & 0.946508985245107 & 0.473254492622553 \tabularnewline
40 & 0.52690102899124 & 0.94619794201752 & 0.47309897100876 \tabularnewline
41 & 0.45546418097561 & 0.91092836195122 & 0.54453581902439 \tabularnewline
42 & 0.578472470614584 & 0.843055058770833 & 0.421527529385416 \tabularnewline
43 & 0.508981178624633 & 0.982037642750734 & 0.491018821375367 \tabularnewline
44 & 0.445293357363088 & 0.890586714726175 & 0.554706642636912 \tabularnewline
45 & 0.392664833524062 & 0.785329667048125 & 0.607335166475938 \tabularnewline
46 & 0.333475855674066 & 0.666951711348132 & 0.666524144325934 \tabularnewline
47 & 0.276028579325721 & 0.552057158651442 & 0.723971420674279 \tabularnewline
48 & 0.274992305443309 & 0.549984610886618 & 0.725007694556691 \tabularnewline
49 & 0.22061892671951 & 0.44123785343902 & 0.77938107328049 \tabularnewline
50 & 0.187695933870764 & 0.375391867741528 & 0.812304066129236 \tabularnewline
51 & 0.147317073027014 & 0.294634146054029 & 0.852682926972986 \tabularnewline
52 & 0.124988026324315 & 0.249976052648629 & 0.875011973675685 \tabularnewline
53 & 0.111910626380483 & 0.223821252760965 & 0.888089373619517 \tabularnewline
54 & 0.083114825104717 & 0.166229650209434 & 0.916885174895283 \tabularnewline
55 & 0.115939185307801 & 0.231878370615602 & 0.884060814692199 \tabularnewline
56 & 0.0868206378146791 & 0.173641275629358 & 0.91317936218532 \tabularnewline
57 & 0.0625109961237968 & 0.125021992247594 & 0.937489003876203 \tabularnewline
58 & 0.0459391734501822 & 0.0918783469003644 & 0.954060826549818 \tabularnewline
59 & 0.0326205101680775 & 0.065241020336155 & 0.967379489831923 \tabularnewline
60 & 0.0217643755085743 & 0.0435287510171486 & 0.978235624491426 \tabularnewline
61 & 0.0194493629112935 & 0.0388987258225871 & 0.980550637088706 \tabularnewline
62 & 0.0149079517588295 & 0.0298159035176590 & 0.98509204824117 \tabularnewline
63 & 0.0113650613967455 & 0.0227301227934910 & 0.988634938603254 \tabularnewline
64 & 0.0114200635973934 & 0.0228401271947869 & 0.988579936402607 \tabularnewline
65 & 0.0157300331418038 & 0.0314600662836076 & 0.984269966858196 \tabularnewline
66 & 0.0138807142603025 & 0.0277614285206049 & 0.986119285739698 \tabularnewline
67 & 0.0102552269157575 & 0.0205104538315149 & 0.989744773084243 \tabularnewline
68 & 0.0105440772931698 & 0.0210881545863397 & 0.98945592270683 \tabularnewline
69 & 0.0071879609401197 & 0.0143759218802394 & 0.99281203905988 \tabularnewline
70 & 0.0044095161096923 & 0.0088190322193846 & 0.995590483890308 \tabularnewline
71 & 0.00249422795383619 & 0.00498845590767237 & 0.997505772046164 \tabularnewline
72 & 0.00153316485347292 & 0.00306632970694584 & 0.998466835146527 \tabularnewline
73 & 0.00353505491791912 & 0.00707010983583824 & 0.99646494508208 \tabularnewline
74 & 0.00194807135451326 & 0.00389614270902653 & 0.998051928645487 \tabularnewline
75 & 0.00141272042951811 & 0.00282544085903623 & 0.998587279570482 \tabularnewline
76 & 0.000684504761069371 & 0.00136900952213874 & 0.99931549523893 \tabularnewline
77 & 0.000424482530190974 & 0.000848965060381949 & 0.999575517469809 \tabularnewline
78 & 0.000200609305027282 & 0.000401218610054564 & 0.999799390694973 \tabularnewline
79 & 0.000702923025125764 & 0.00140584605025153 & 0.999297076974874 \tabularnewline
80 & 0.000373741810994602 & 0.000747483621989205 & 0.999626258189005 \tabularnewline
81 & 0.00100657544499765 & 0.00201315088999531 & 0.998993424555002 \tabularnewline
82 & 0.000588796151804718 & 0.00117759230360944 & 0.999411203848195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99692&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]0.528924560611165[/C][C]0.94215087877767[/C][C]0.471075439388835[/C][/ROW]
[ROW][C]20[/C][C]0.425771933664231[/C][C]0.851543867328462[/C][C]0.574228066335769[/C][/ROW]
[ROW][C]21[/C][C]0.461811903016353[/C][C]0.923623806032706[/C][C]0.538188096983647[/C][/ROW]
[ROW][C]22[/C][C]0.370945247380006[/C][C]0.741890494760013[/C][C]0.629054752619994[/C][/ROW]
[ROW][C]23[/C][C]0.265945077207121[/C][C]0.531890154414241[/C][C]0.73405492279288[/C][/ROW]
[ROW][C]24[/C][C]0.280463611133787[/C][C]0.560927222267574[/C][C]0.719536388866213[/C][/ROW]
[ROW][C]25[/C][C]0.228421122740208[/C][C]0.456842245480416[/C][C]0.771578877259792[/C][/ROW]
[ROW][C]26[/C][C]0.161366673334423[/C][C]0.322733346668846[/C][C]0.838633326665577[/C][/ROW]
[ROW][C]27[/C][C]0.114106068196947[/C][C]0.228212136393895[/C][C]0.885893931803053[/C][/ROW]
[ROW][C]28[/C][C]0.163596451299720[/C][C]0.327192902599439[/C][C]0.83640354870028[/C][/ROW]
[ROW][C]29[/C][C]0.144686678924467[/C][C]0.289373357848934[/C][C]0.855313321075533[/C][/ROW]
[ROW][C]30[/C][C]0.140156346836836[/C][C]0.280312693673671[/C][C]0.859843653163164[/C][/ROW]
[ROW][C]31[/C][C]0.324557983866481[/C][C]0.649115967732961[/C][C]0.675442016133519[/C][/ROW]
[ROW][C]32[/C][C]0.284279883672147[/C][C]0.568559767344293[/C][C]0.715720116327853[/C][/ROW]
[ROW][C]33[/C][C]0.21963082530862[/C][C]0.43926165061724[/C][C]0.78036917469138[/C][/ROW]
[ROW][C]34[/C][C]0.250924036101114[/C][C]0.501848072202228[/C][C]0.749075963898886[/C][/ROW]
[ROW][C]35[/C][C]0.217468980735126[/C][C]0.434937961470251[/C][C]0.782531019264874[/C][/ROW]
[ROW][C]36[/C][C]0.165746389678256[/C][C]0.331492779356511[/C][C]0.834253610321744[/C][/ROW]
[ROW][C]37[/C][C]0.267483022711206[/C][C]0.534966045422412[/C][C]0.732516977288794[/C][/ROW]
[ROW][C]38[/C][C]0.55280168599752[/C][C]0.89439662800496[/C][C]0.44719831400248[/C][/ROW]
[ROW][C]39[/C][C]0.526745507377447[/C][C]0.946508985245107[/C][C]0.473254492622553[/C][/ROW]
[ROW][C]40[/C][C]0.52690102899124[/C][C]0.94619794201752[/C][C]0.47309897100876[/C][/ROW]
[ROW][C]41[/C][C]0.45546418097561[/C][C]0.91092836195122[/C][C]0.54453581902439[/C][/ROW]
[ROW][C]42[/C][C]0.578472470614584[/C][C]0.843055058770833[/C][C]0.421527529385416[/C][/ROW]
[ROW][C]43[/C][C]0.508981178624633[/C][C]0.982037642750734[/C][C]0.491018821375367[/C][/ROW]
[ROW][C]44[/C][C]0.445293357363088[/C][C]0.890586714726175[/C][C]0.554706642636912[/C][/ROW]
[ROW][C]45[/C][C]0.392664833524062[/C][C]0.785329667048125[/C][C]0.607335166475938[/C][/ROW]
[ROW][C]46[/C][C]0.333475855674066[/C][C]0.666951711348132[/C][C]0.666524144325934[/C][/ROW]
[ROW][C]47[/C][C]0.276028579325721[/C][C]0.552057158651442[/C][C]0.723971420674279[/C][/ROW]
[ROW][C]48[/C][C]0.274992305443309[/C][C]0.549984610886618[/C][C]0.725007694556691[/C][/ROW]
[ROW][C]49[/C][C]0.22061892671951[/C][C]0.44123785343902[/C][C]0.77938107328049[/C][/ROW]
[ROW][C]50[/C][C]0.187695933870764[/C][C]0.375391867741528[/C][C]0.812304066129236[/C][/ROW]
[ROW][C]51[/C][C]0.147317073027014[/C][C]0.294634146054029[/C][C]0.852682926972986[/C][/ROW]
[ROW][C]52[/C][C]0.124988026324315[/C][C]0.249976052648629[/C][C]0.875011973675685[/C][/ROW]
[ROW][C]53[/C][C]0.111910626380483[/C][C]0.223821252760965[/C][C]0.888089373619517[/C][/ROW]
[ROW][C]54[/C][C]0.083114825104717[/C][C]0.166229650209434[/C][C]0.916885174895283[/C][/ROW]
[ROW][C]55[/C][C]0.115939185307801[/C][C]0.231878370615602[/C][C]0.884060814692199[/C][/ROW]
[ROW][C]56[/C][C]0.0868206378146791[/C][C]0.173641275629358[/C][C]0.91317936218532[/C][/ROW]
[ROW][C]57[/C][C]0.0625109961237968[/C][C]0.125021992247594[/C][C]0.937489003876203[/C][/ROW]
[ROW][C]58[/C][C]0.0459391734501822[/C][C]0.0918783469003644[/C][C]0.954060826549818[/C][/ROW]
[ROW][C]59[/C][C]0.0326205101680775[/C][C]0.065241020336155[/C][C]0.967379489831923[/C][/ROW]
[ROW][C]60[/C][C]0.0217643755085743[/C][C]0.0435287510171486[/C][C]0.978235624491426[/C][/ROW]
[ROW][C]61[/C][C]0.0194493629112935[/C][C]0.0388987258225871[/C][C]0.980550637088706[/C][/ROW]
[ROW][C]62[/C][C]0.0149079517588295[/C][C]0.0298159035176590[/C][C]0.98509204824117[/C][/ROW]
[ROW][C]63[/C][C]0.0113650613967455[/C][C]0.0227301227934910[/C][C]0.988634938603254[/C][/ROW]
[ROW][C]64[/C][C]0.0114200635973934[/C][C]0.0228401271947869[/C][C]0.988579936402607[/C][/ROW]
[ROW][C]65[/C][C]0.0157300331418038[/C][C]0.0314600662836076[/C][C]0.984269966858196[/C][/ROW]
[ROW][C]66[/C][C]0.0138807142603025[/C][C]0.0277614285206049[/C][C]0.986119285739698[/C][/ROW]
[ROW][C]67[/C][C]0.0102552269157575[/C][C]0.0205104538315149[/C][C]0.989744773084243[/C][/ROW]
[ROW][C]68[/C][C]0.0105440772931698[/C][C]0.0210881545863397[/C][C]0.98945592270683[/C][/ROW]
[ROW][C]69[/C][C]0.0071879609401197[/C][C]0.0143759218802394[/C][C]0.99281203905988[/C][/ROW]
[ROW][C]70[/C][C]0.0044095161096923[/C][C]0.0088190322193846[/C][C]0.995590483890308[/C][/ROW]
[ROW][C]71[/C][C]0.00249422795383619[/C][C]0.00498845590767237[/C][C]0.997505772046164[/C][/ROW]
[ROW][C]72[/C][C]0.00153316485347292[/C][C]0.00306632970694584[/C][C]0.998466835146527[/C][/ROW]
[ROW][C]73[/C][C]0.00353505491791912[/C][C]0.00707010983583824[/C][C]0.99646494508208[/C][/ROW]
[ROW][C]74[/C][C]0.00194807135451326[/C][C]0.00389614270902653[/C][C]0.998051928645487[/C][/ROW]
[ROW][C]75[/C][C]0.00141272042951811[/C][C]0.00282544085903623[/C][C]0.998587279570482[/C][/ROW]
[ROW][C]76[/C][C]0.000684504761069371[/C][C]0.00136900952213874[/C][C]0.99931549523893[/C][/ROW]
[ROW][C]77[/C][C]0.000424482530190974[/C][C]0.000848965060381949[/C][C]0.999575517469809[/C][/ROW]
[ROW][C]78[/C][C]0.000200609305027282[/C][C]0.000401218610054564[/C][C]0.999799390694973[/C][/ROW]
[ROW][C]79[/C][C]0.000702923025125764[/C][C]0.00140584605025153[/C][C]0.999297076974874[/C][/ROW]
[ROW][C]80[/C][C]0.000373741810994602[/C][C]0.000747483621989205[/C][C]0.999626258189005[/C][/ROW]
[ROW][C]81[/C][C]0.00100657544499765[/C][C]0.00201315088999531[/C][C]0.998993424555002[/C][/ROW]
[ROW][C]82[/C][C]0.000588796151804718[/C][C]0.00117759230360944[/C][C]0.999411203848195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99692&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99692&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.5289245606111650.942150878777670.471075439388835
200.4257719336642310.8515438673284620.574228066335769
210.4618119030163530.9236238060327060.538188096983647
220.3709452473800060.7418904947600130.629054752619994
230.2659450772071210.5318901544142410.73405492279288
240.2804636111337870.5609272222675740.719536388866213
250.2284211227402080.4568422454804160.771578877259792
260.1613666733344230.3227333466688460.838633326665577
270.1141060681969470.2282121363938950.885893931803053
280.1635964512997200.3271929025994390.83640354870028
290.1446866789244670.2893733578489340.855313321075533
300.1401563468368360.2803126936736710.859843653163164
310.3245579838664810.6491159677329610.675442016133519
320.2842798836721470.5685597673442930.715720116327853
330.219630825308620.439261650617240.78036917469138
340.2509240361011140.5018480722022280.749075963898886
350.2174689807351260.4349379614702510.782531019264874
360.1657463896782560.3314927793565110.834253610321744
370.2674830227112060.5349660454224120.732516977288794
380.552801685997520.894396628004960.44719831400248
390.5267455073774470.9465089852451070.473254492622553
400.526901028991240.946197942017520.47309897100876
410.455464180975610.910928361951220.54453581902439
420.5784724706145840.8430550587708330.421527529385416
430.5089811786246330.9820376427507340.491018821375367
440.4452933573630880.8905867147261750.554706642636912
450.3926648335240620.7853296670481250.607335166475938
460.3334758556740660.6669517113481320.666524144325934
470.2760285793257210.5520571586514420.723971420674279
480.2749923054433090.5499846108866180.725007694556691
490.220618926719510.441237853439020.77938107328049
500.1876959338707640.3753918677415280.812304066129236
510.1473170730270140.2946341460540290.852682926972986
520.1249880263243150.2499760526486290.875011973675685
530.1119106263804830.2238212527609650.888089373619517
540.0831148251047170.1662296502094340.916885174895283
550.1159391853078010.2318783706156020.884060814692199
560.08682063781467910.1736412756293580.91317936218532
570.06251099612379680.1250219922475940.937489003876203
580.04593917345018220.09187834690036440.954060826549818
590.03262051016807750.0652410203361550.967379489831923
600.02176437550857430.04352875101714860.978235624491426
610.01944936291129350.03889872582258710.980550637088706
620.01490795175882950.02981590351765900.98509204824117
630.01136506139674550.02273012279349100.988634938603254
640.01142006359739340.02284012719478690.988579936402607
650.01573003314180380.03146006628360760.984269966858196
660.01388071426030250.02776142852060490.986119285739698
670.01025522691575750.02051045383151490.989744773084243
680.01054407729316980.02108815458633970.98945592270683
690.00718796094011970.01437592188023940.99281203905988
700.00440951610969230.00881903221938460.995590483890308
710.002494227953836190.004988455907672370.997505772046164
720.001533164853472920.003066329706945840.998466835146527
730.003535054917919120.007070109835838240.99646494508208
740.001948071354513260.003896142709026530.998051928645487
750.001412720429518110.002825440859036230.998587279570482
760.0006845047610693710.001369009522138740.99931549523893
770.0004244825301909740.0008489650603819490.999575517469809
780.0002006093050272820.0004012186100545640.999799390694973
790.0007029230251257640.001405846050251530.999297076974874
800.0003737418109946020.0007474836219892050.999626258189005
810.001006575444997650.002013150889995310.998993424555002
820.0005887961518047180.001177592303609440.999411203848195






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.203125NOK
5% type I error level230.359375NOK
10% type I error level250.390625NOK

\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 & 13 & 0.203125 & NOK \tabularnewline
5% type I error level & 23 & 0.359375 & NOK \tabularnewline
10% type I error level & 25 & 0.390625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99692&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]13[/C][C]0.203125[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.359375[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.390625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99692&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99692&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.203125NOK
5% type I error level230.359375NOK
10% type I error level250.390625NOK


Figures (Output of Computation)

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Input Parameters & R Code

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')
}