Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 17 Dec 2014 08:02:13 +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/2014/Dec/17/t1418803643edbf5k8spj6qruk.htm/, Retrieved Thu, 31 Oct 2024 23:24:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269979, Retrieved Thu, 31 Oct 2024 23:24:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-17 08:02:13] [80e094d39007183c022472d38ca26b6f] [Current]
- RMPD    [Central Tendency] [] [2014-12-17 11:49:34] [92c92161c1f9605aa7c2b3c0cb9f0216]
- RMPD    [Central Tendency] [] [2014-12-17 11:49:34] [92c92161c1f9605aa7c2b3c0cb9f0216]
- RMPD    [Central Tendency] [] [2014-12-17 11:49:34] [92c92161c1f9605aa7c2b3c0cb9f0216]
- RMPD    [Central Tendency] [] [2014-12-17 12:56:14] [92c92161c1f9605aa7c2b3c0cb9f0216]
Feedback Forum

Post a new message
Dataseries X:
0	0	93,75
0	1	53,13
0	1	75,00
0	1	62,50
0	1	78,13
0	1	62,50
0	0	84,38
0	0	56,25
0	0	87,50
0	1	65,63
0	0	84,38
0	1	68,75
0	0	87,50
0	1	78,13
0	0	65,63
0	0	68,75
0	1	87,50
0	0	62,50
0	1	90,63
0	1	62,50
0	1	62,50
0	0	71,88
0	0	56,25
0	0	56,25
0	1	59,38
0	0	78,13
0	0	78,13
0	0	78,13
0	0	75,00
0	1	59,38
0	1	81,25
0	1	31,25
0	1	53,13
0	0	40,63
0	0	53,13
0	1	93,75
0	0	12,50
0	0	50,00
0	0	65,63
0	1	68,75
0	0	62,50
0	0	68,75
0	1	71,88
0	1	50,00
0	0	0,00
0	1	56,25
0	1	78,13
0	0	56,25
0	1	56,25
0	1	75,00
0	0	90,63
0	0	46,88
0	0	68,75
0	1	71,88
0	1	75,00
0	0	68,75
0	1	46,88
0	0	53,13
0	1	62,50
0	0	84,38
0	1	81,25
0	1	71,88
0	1	71,88
0	0	46,88
0	0	81,25
0	1	68,75
0	0	56,25
0	1	46,88
0	1	68,75
0	0	84,38
0	1	31,25
0	1	62,50
0	0	53,13
0	1	71,88
0	0	59,38
0	0	40,63
0	1	84,38
0	1	71,88
0	0	50,00
0	1	78,13
0	0	6,25
0	0	81,25
0	1	62,50
0	0	68,75
0	1	75,00
1	1	71,88
1	1	68,75
1	1	65,63
1	1	78,13
1	1	84,38
1	0	71,88
1	1	71,88
1	0	56,25
1	0	56,25
1	1	71,88
1	1	59,38
1	1	46,88
1	1	62,50
1	1	50,00
1	1	78,13
1	1	78,13
1	0	59,38
1	1	59,38
1	1	50,00
1	1	59,38
1	1	59,38
1	1	71,88
1	1	65,63
1	0	68,75
1	1	59,38
1	1	62,50
1	1	9,38
1	1	71,88
1	0	43,75
1	0	71,88
1	0	62,50
1	1	46,88
1	0	40,63
1	0	50,00
1	0	21,88
1	1	75,00
1	0	53,13
1	1	75,00
1	1	75,00
1	0	59,38
1	1	87,50
1	0	71,88
1	0	59,38
1	1	71,88
1	1	78,13
1	1	78,13
1	1	62,50
1	0	50,00
1	0	62,50
1	1	78,13
1	0	78,13
1	1	71,88
1	0	53,13
1	1	62,50
1	1	50,00
1	1	71,88
1	0	37,50
1	0	75,00
1	1	34,38
1	0	43,75
1	1	71,88
1	0	56,25
1	1	90,63
1	1	50,00
1	0	59,38
1	0	50,00
1	1	71,88
1	0	59,38
1	0	12,50
1	0	62,50
1	1	62,50
1	1	12,50
1	1	75,00
1	1	50,00
1	1	9,38
1	0	75,00
1	1	71,88
1	0	53,13
1	1	62,50
1	0	68,75
1	1	59,38
1	1	75,00
1	0	59,38
1	1	84,38
1	0	68,75
1	0	71,88




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269979&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269979&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269979&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
NumeracyScore[t] = + 62.1387 -4.24238Student[t] + 5.51513Gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
NumeracyScore[t] =  +  62.1387 -4.24238Student[t] +  5.51513Gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269979&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]NumeracyScore[t] =  +  62.1387 -4.24238Student[t] +  5.51513Gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269979&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269979&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
NumeracyScore[t] = + 62.1387 -4.24238Student[t] + 5.51513Gender[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)62.13872.2899327.142.67114e-631.33557e-63
Student-4.242382.63182-1.6120.1088480.054424
Gender5.515132.648172.0830.03880090.0194005

\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) & 62.1387 & 2.28993 & 27.14 & 2.67114e-63 & 1.33557e-63 \tabularnewline
Student & -4.24238 & 2.63182 & -1.612 & 0.108848 & 0.054424 \tabularnewline
Gender & 5.51513 & 2.64817 & 2.083 & 0.0388009 & 0.0194005 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269979&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]62.1387[/C][C]2.28993[/C][C]27.14[/C][C]2.67114e-63[/C][C]1.33557e-63[/C][/ROW]
[ROW][C]Student[/C][C]-4.24238[/C][C]2.63182[/C][C]-1.612[/C][C]0.108848[/C][C]0.054424[/C][/ROW]
[ROW][C]Gender[/C][C]5.51513[/C][C]2.64817[/C][C]2.083[/C][C]0.0388009[/C][C]0.0194005[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269979&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269979&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)62.13872.2899327.142.67114e-631.33557e-63
Student-4.242382.63182-1.6120.1088480.054424
Gender5.515132.648172.0830.03880090.0194005







Multiple Linear Regression - Regression Statistics
Multiple R0.190567
R-squared0.0363158
Adjusted R-squared0.0248434
F-TEST (value)3.16549
F-TEST (DF numerator)2
F-TEST (DF denominator)168
p-value0.0447216
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.1223
Sum Squared Residuals49252.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.190567 \tabularnewline
R-squared & 0.0363158 \tabularnewline
Adjusted R-squared & 0.0248434 \tabularnewline
F-TEST (value) & 3.16549 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 168 \tabularnewline
p-value & 0.0447216 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 17.1223 \tabularnewline
Sum Squared Residuals & 49252.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269979&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.190567[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0363158[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0248434[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.16549[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]168[/C][/ROW]
[ROW][C]p-value[/C][C]0.0447216[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]17.1223[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]49252.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269979&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269979&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 R0.190567
R-squared0.0363158
Adjusted R-squared0.0248434
F-TEST (value)3.16549
F-TEST (DF numerator)2
F-TEST (DF denominator)168
p-value0.0447216
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.1223
Sum Squared Residuals49252.9







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
193.7562.138731.6113
253.1367.6538-14.5238
37567.65387.34617
462.567.6538-5.15383
578.1367.653810.4762
662.567.6538-5.15383
784.3862.138722.2413
856.2562.1387-5.8887
987.562.138725.3613
1065.6367.6538-2.02383
1184.3862.138722.2413
1268.7567.65381.09617
1387.562.138725.3613
1478.1367.653810.4762
1565.6362.13873.4913
1668.7562.13876.6113
1787.567.653819.8462
1862.562.13870.361303
1990.6367.653822.9762
2062.567.6538-5.15383
2162.567.6538-5.15383
2271.8862.13879.7413
2356.2562.1387-5.8887
2456.2562.1387-5.8887
2559.3867.6538-8.27383
2678.1362.138715.9913
2778.1362.138715.9913
2878.1362.138715.9913
297562.138712.8613
3059.3867.6538-8.27383
3181.2567.653813.5962
3231.2567.6538-36.4038
3353.1367.6538-14.5238
3440.6362.1387-21.5087
3553.1362.1387-9.0087
3693.7567.653826.0962
3712.562.1387-49.6387
385062.1387-12.1387
3965.6362.13873.4913
4068.7567.65381.09617
4162.562.13870.361303
4268.7562.13876.6113
4371.8867.65384.22617
445067.6538-17.6538
45062.1387-62.1387
4656.2567.6538-11.4038
4778.1367.653810.4762
4856.2562.1387-5.8887
4956.2567.6538-11.4038
507567.65387.34617
5190.6362.138728.4913
5246.8862.1387-15.2587
5368.7562.13876.6113
5471.8867.65384.22617
557567.65387.34617
5668.7562.13876.6113
5746.8867.6538-20.7738
5853.1362.1387-9.0087
5962.567.6538-5.15383
6084.3862.138722.2413
6181.2567.653813.5962
6271.8867.65384.22617
6371.8867.65384.22617
6446.8862.1387-15.2587
6581.2562.138719.1113
6668.7567.65381.09617
6756.2562.1387-5.8887
6846.8867.6538-20.7738
6968.7567.65381.09617
7084.3862.138722.2413
7131.2567.6538-36.4038
7262.567.6538-5.15383
7353.1362.1387-9.0087
7471.8867.65384.22617
7559.3862.1387-2.7587
7640.6362.1387-21.5087
7784.3867.653816.7262
7871.8867.65384.22617
795062.1387-12.1387
8078.1367.653810.4762
816.2562.1387-55.8887
8281.2562.138719.1113
8362.567.6538-5.15383
8468.7562.13876.6113
857567.65387.34617
8671.8863.41148.46855
8768.7563.41145.33855
8865.6363.41142.21855
8978.1363.411414.7186
9084.3863.411420.9686
9171.8857.896313.9837
9271.8863.41148.46855
9356.2557.8963-1.64631
9456.2557.8963-1.64631
9571.8863.41148.46855
9659.3863.4114-4.03145
9746.8863.4114-16.5314
9862.563.4114-0.911448
995063.4114-13.4114
10078.1363.411414.7186
10178.1363.411414.7186
10259.3857.89631.48369
10359.3863.4114-4.03145
1045063.4114-13.4114
10559.3863.4114-4.03145
10659.3863.4114-4.03145
10771.8863.41148.46855
10865.6363.41142.21855
10968.7557.896310.8537
11059.3863.4114-4.03145
11162.563.4114-0.911448
1129.3863.4114-54.0314
11371.8863.41148.46855
11443.7557.8963-14.1463
11571.8857.896313.9837
11662.557.89634.60369
11746.8863.4114-16.5314
11840.6357.8963-17.2663
1195057.8963-7.89631
12021.8857.8963-36.0163
1217563.411411.5886
12253.1357.8963-4.76631
1237563.411411.5886
1247563.411411.5886
12559.3857.89631.48369
12687.563.411424.0886
12771.8857.896313.9837
12859.3857.89631.48369
12971.8863.41148.46855
13078.1363.411414.7186
13178.1363.411414.7186
13262.563.4114-0.911448
1335057.8963-7.89631
13462.557.89634.60369
13578.1363.411414.7186
13678.1357.896320.2337
13771.8863.41148.46855
13853.1357.8963-4.76631
13962.563.4114-0.911448
1405063.4114-13.4114
14171.8863.41148.46855
14237.557.8963-20.3963
1437557.896317.1037
14434.3863.4114-29.0314
14543.7557.8963-14.1463
14671.8863.41148.46855
14756.2557.8963-1.64631
14890.6363.411427.2186
1495063.4114-13.4114
15059.3857.89631.48369
1515057.8963-7.89631
15271.8863.41148.46855
15359.3857.89631.48369
15412.557.8963-45.3963
15562.557.89634.60369
15662.563.4114-0.911448
15712.563.4114-50.9114
1587563.411411.5886
1595063.4114-13.4114
1609.3863.4114-54.0314
1617557.896317.1037
16271.8863.41148.46855
16353.1357.8963-4.76631
16462.563.4114-0.911448
16568.7557.896310.8537
16659.3863.4114-4.03145
1677563.411411.5886
16859.3857.89631.48369
16984.3863.411420.9686
17068.7557.896310.8537
17171.8857.896313.9837

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 93.75 & 62.1387 & 31.6113 \tabularnewline
2 & 53.13 & 67.6538 & -14.5238 \tabularnewline
3 & 75 & 67.6538 & 7.34617 \tabularnewline
4 & 62.5 & 67.6538 & -5.15383 \tabularnewline
5 & 78.13 & 67.6538 & 10.4762 \tabularnewline
6 & 62.5 & 67.6538 & -5.15383 \tabularnewline
7 & 84.38 & 62.1387 & 22.2413 \tabularnewline
8 & 56.25 & 62.1387 & -5.8887 \tabularnewline
9 & 87.5 & 62.1387 & 25.3613 \tabularnewline
10 & 65.63 & 67.6538 & -2.02383 \tabularnewline
11 & 84.38 & 62.1387 & 22.2413 \tabularnewline
12 & 68.75 & 67.6538 & 1.09617 \tabularnewline
13 & 87.5 & 62.1387 & 25.3613 \tabularnewline
14 & 78.13 & 67.6538 & 10.4762 \tabularnewline
15 & 65.63 & 62.1387 & 3.4913 \tabularnewline
16 & 68.75 & 62.1387 & 6.6113 \tabularnewline
17 & 87.5 & 67.6538 & 19.8462 \tabularnewline
18 & 62.5 & 62.1387 & 0.361303 \tabularnewline
19 & 90.63 & 67.6538 & 22.9762 \tabularnewline
20 & 62.5 & 67.6538 & -5.15383 \tabularnewline
21 & 62.5 & 67.6538 & -5.15383 \tabularnewline
22 & 71.88 & 62.1387 & 9.7413 \tabularnewline
23 & 56.25 & 62.1387 & -5.8887 \tabularnewline
24 & 56.25 & 62.1387 & -5.8887 \tabularnewline
25 & 59.38 & 67.6538 & -8.27383 \tabularnewline
26 & 78.13 & 62.1387 & 15.9913 \tabularnewline
27 & 78.13 & 62.1387 & 15.9913 \tabularnewline
28 & 78.13 & 62.1387 & 15.9913 \tabularnewline
29 & 75 & 62.1387 & 12.8613 \tabularnewline
30 & 59.38 & 67.6538 & -8.27383 \tabularnewline
31 & 81.25 & 67.6538 & 13.5962 \tabularnewline
32 & 31.25 & 67.6538 & -36.4038 \tabularnewline
33 & 53.13 & 67.6538 & -14.5238 \tabularnewline
34 & 40.63 & 62.1387 & -21.5087 \tabularnewline
35 & 53.13 & 62.1387 & -9.0087 \tabularnewline
36 & 93.75 & 67.6538 & 26.0962 \tabularnewline
37 & 12.5 & 62.1387 & -49.6387 \tabularnewline
38 & 50 & 62.1387 & -12.1387 \tabularnewline
39 & 65.63 & 62.1387 & 3.4913 \tabularnewline
40 & 68.75 & 67.6538 & 1.09617 \tabularnewline
41 & 62.5 & 62.1387 & 0.361303 \tabularnewline
42 & 68.75 & 62.1387 & 6.6113 \tabularnewline
43 & 71.88 & 67.6538 & 4.22617 \tabularnewline
44 & 50 & 67.6538 & -17.6538 \tabularnewline
45 & 0 & 62.1387 & -62.1387 \tabularnewline
46 & 56.25 & 67.6538 & -11.4038 \tabularnewline
47 & 78.13 & 67.6538 & 10.4762 \tabularnewline
48 & 56.25 & 62.1387 & -5.8887 \tabularnewline
49 & 56.25 & 67.6538 & -11.4038 \tabularnewline
50 & 75 & 67.6538 & 7.34617 \tabularnewline
51 & 90.63 & 62.1387 & 28.4913 \tabularnewline
52 & 46.88 & 62.1387 & -15.2587 \tabularnewline
53 & 68.75 & 62.1387 & 6.6113 \tabularnewline
54 & 71.88 & 67.6538 & 4.22617 \tabularnewline
55 & 75 & 67.6538 & 7.34617 \tabularnewline
56 & 68.75 & 62.1387 & 6.6113 \tabularnewline
57 & 46.88 & 67.6538 & -20.7738 \tabularnewline
58 & 53.13 & 62.1387 & -9.0087 \tabularnewline
59 & 62.5 & 67.6538 & -5.15383 \tabularnewline
60 & 84.38 & 62.1387 & 22.2413 \tabularnewline
61 & 81.25 & 67.6538 & 13.5962 \tabularnewline
62 & 71.88 & 67.6538 & 4.22617 \tabularnewline
63 & 71.88 & 67.6538 & 4.22617 \tabularnewline
64 & 46.88 & 62.1387 & -15.2587 \tabularnewline
65 & 81.25 & 62.1387 & 19.1113 \tabularnewline
66 & 68.75 & 67.6538 & 1.09617 \tabularnewline
67 & 56.25 & 62.1387 & -5.8887 \tabularnewline
68 & 46.88 & 67.6538 & -20.7738 \tabularnewline
69 & 68.75 & 67.6538 & 1.09617 \tabularnewline
70 & 84.38 & 62.1387 & 22.2413 \tabularnewline
71 & 31.25 & 67.6538 & -36.4038 \tabularnewline
72 & 62.5 & 67.6538 & -5.15383 \tabularnewline
73 & 53.13 & 62.1387 & -9.0087 \tabularnewline
74 & 71.88 & 67.6538 & 4.22617 \tabularnewline
75 & 59.38 & 62.1387 & -2.7587 \tabularnewline
76 & 40.63 & 62.1387 & -21.5087 \tabularnewline
77 & 84.38 & 67.6538 & 16.7262 \tabularnewline
78 & 71.88 & 67.6538 & 4.22617 \tabularnewline
79 & 50 & 62.1387 & -12.1387 \tabularnewline
80 & 78.13 & 67.6538 & 10.4762 \tabularnewline
81 & 6.25 & 62.1387 & -55.8887 \tabularnewline
82 & 81.25 & 62.1387 & 19.1113 \tabularnewline
83 & 62.5 & 67.6538 & -5.15383 \tabularnewline
84 & 68.75 & 62.1387 & 6.6113 \tabularnewline
85 & 75 & 67.6538 & 7.34617 \tabularnewline
86 & 71.88 & 63.4114 & 8.46855 \tabularnewline
87 & 68.75 & 63.4114 & 5.33855 \tabularnewline
88 & 65.63 & 63.4114 & 2.21855 \tabularnewline
89 & 78.13 & 63.4114 & 14.7186 \tabularnewline
90 & 84.38 & 63.4114 & 20.9686 \tabularnewline
91 & 71.88 & 57.8963 & 13.9837 \tabularnewline
92 & 71.88 & 63.4114 & 8.46855 \tabularnewline
93 & 56.25 & 57.8963 & -1.64631 \tabularnewline
94 & 56.25 & 57.8963 & -1.64631 \tabularnewline
95 & 71.88 & 63.4114 & 8.46855 \tabularnewline
96 & 59.38 & 63.4114 & -4.03145 \tabularnewline
97 & 46.88 & 63.4114 & -16.5314 \tabularnewline
98 & 62.5 & 63.4114 & -0.911448 \tabularnewline
99 & 50 & 63.4114 & -13.4114 \tabularnewline
100 & 78.13 & 63.4114 & 14.7186 \tabularnewline
101 & 78.13 & 63.4114 & 14.7186 \tabularnewline
102 & 59.38 & 57.8963 & 1.48369 \tabularnewline
103 & 59.38 & 63.4114 & -4.03145 \tabularnewline
104 & 50 & 63.4114 & -13.4114 \tabularnewline
105 & 59.38 & 63.4114 & -4.03145 \tabularnewline
106 & 59.38 & 63.4114 & -4.03145 \tabularnewline
107 & 71.88 & 63.4114 & 8.46855 \tabularnewline
108 & 65.63 & 63.4114 & 2.21855 \tabularnewline
109 & 68.75 & 57.8963 & 10.8537 \tabularnewline
110 & 59.38 & 63.4114 & -4.03145 \tabularnewline
111 & 62.5 & 63.4114 & -0.911448 \tabularnewline
112 & 9.38 & 63.4114 & -54.0314 \tabularnewline
113 & 71.88 & 63.4114 & 8.46855 \tabularnewline
114 & 43.75 & 57.8963 & -14.1463 \tabularnewline
115 & 71.88 & 57.8963 & 13.9837 \tabularnewline
116 & 62.5 & 57.8963 & 4.60369 \tabularnewline
117 & 46.88 & 63.4114 & -16.5314 \tabularnewline
118 & 40.63 & 57.8963 & -17.2663 \tabularnewline
119 & 50 & 57.8963 & -7.89631 \tabularnewline
120 & 21.88 & 57.8963 & -36.0163 \tabularnewline
121 & 75 & 63.4114 & 11.5886 \tabularnewline
122 & 53.13 & 57.8963 & -4.76631 \tabularnewline
123 & 75 & 63.4114 & 11.5886 \tabularnewline
124 & 75 & 63.4114 & 11.5886 \tabularnewline
125 & 59.38 & 57.8963 & 1.48369 \tabularnewline
126 & 87.5 & 63.4114 & 24.0886 \tabularnewline
127 & 71.88 & 57.8963 & 13.9837 \tabularnewline
128 & 59.38 & 57.8963 & 1.48369 \tabularnewline
129 & 71.88 & 63.4114 & 8.46855 \tabularnewline
130 & 78.13 & 63.4114 & 14.7186 \tabularnewline
131 & 78.13 & 63.4114 & 14.7186 \tabularnewline
132 & 62.5 & 63.4114 & -0.911448 \tabularnewline
133 & 50 & 57.8963 & -7.89631 \tabularnewline
134 & 62.5 & 57.8963 & 4.60369 \tabularnewline
135 & 78.13 & 63.4114 & 14.7186 \tabularnewline
136 & 78.13 & 57.8963 & 20.2337 \tabularnewline
137 & 71.88 & 63.4114 & 8.46855 \tabularnewline
138 & 53.13 & 57.8963 & -4.76631 \tabularnewline
139 & 62.5 & 63.4114 & -0.911448 \tabularnewline
140 & 50 & 63.4114 & -13.4114 \tabularnewline
141 & 71.88 & 63.4114 & 8.46855 \tabularnewline
142 & 37.5 & 57.8963 & -20.3963 \tabularnewline
143 & 75 & 57.8963 & 17.1037 \tabularnewline
144 & 34.38 & 63.4114 & -29.0314 \tabularnewline
145 & 43.75 & 57.8963 & -14.1463 \tabularnewline
146 & 71.88 & 63.4114 & 8.46855 \tabularnewline
147 & 56.25 & 57.8963 & -1.64631 \tabularnewline
148 & 90.63 & 63.4114 & 27.2186 \tabularnewline
149 & 50 & 63.4114 & -13.4114 \tabularnewline
150 & 59.38 & 57.8963 & 1.48369 \tabularnewline
151 & 50 & 57.8963 & -7.89631 \tabularnewline
152 & 71.88 & 63.4114 & 8.46855 \tabularnewline
153 & 59.38 & 57.8963 & 1.48369 \tabularnewline
154 & 12.5 & 57.8963 & -45.3963 \tabularnewline
155 & 62.5 & 57.8963 & 4.60369 \tabularnewline
156 & 62.5 & 63.4114 & -0.911448 \tabularnewline
157 & 12.5 & 63.4114 & -50.9114 \tabularnewline
158 & 75 & 63.4114 & 11.5886 \tabularnewline
159 & 50 & 63.4114 & -13.4114 \tabularnewline
160 & 9.38 & 63.4114 & -54.0314 \tabularnewline
161 & 75 & 57.8963 & 17.1037 \tabularnewline
162 & 71.88 & 63.4114 & 8.46855 \tabularnewline
163 & 53.13 & 57.8963 & -4.76631 \tabularnewline
164 & 62.5 & 63.4114 & -0.911448 \tabularnewline
165 & 68.75 & 57.8963 & 10.8537 \tabularnewline
166 & 59.38 & 63.4114 & -4.03145 \tabularnewline
167 & 75 & 63.4114 & 11.5886 \tabularnewline
168 & 59.38 & 57.8963 & 1.48369 \tabularnewline
169 & 84.38 & 63.4114 & 20.9686 \tabularnewline
170 & 68.75 & 57.8963 & 10.8537 \tabularnewline
171 & 71.88 & 57.8963 & 13.9837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269979&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]93.75[/C][C]62.1387[/C][C]31.6113[/C][/ROW]
[ROW][C]2[/C][C]53.13[/C][C]67.6538[/C][C]-14.5238[/C][/ROW]
[ROW][C]3[/C][C]75[/C][C]67.6538[/C][C]7.34617[/C][/ROW]
[ROW][C]4[/C][C]62.5[/C][C]67.6538[/C][C]-5.15383[/C][/ROW]
[ROW][C]5[/C][C]78.13[/C][C]67.6538[/C][C]10.4762[/C][/ROW]
[ROW][C]6[/C][C]62.5[/C][C]67.6538[/C][C]-5.15383[/C][/ROW]
[ROW][C]7[/C][C]84.38[/C][C]62.1387[/C][C]22.2413[/C][/ROW]
[ROW][C]8[/C][C]56.25[/C][C]62.1387[/C][C]-5.8887[/C][/ROW]
[ROW][C]9[/C][C]87.5[/C][C]62.1387[/C][C]25.3613[/C][/ROW]
[ROW][C]10[/C][C]65.63[/C][C]67.6538[/C][C]-2.02383[/C][/ROW]
[ROW][C]11[/C][C]84.38[/C][C]62.1387[/C][C]22.2413[/C][/ROW]
[ROW][C]12[/C][C]68.75[/C][C]67.6538[/C][C]1.09617[/C][/ROW]
[ROW][C]13[/C][C]87.5[/C][C]62.1387[/C][C]25.3613[/C][/ROW]
[ROW][C]14[/C][C]78.13[/C][C]67.6538[/C][C]10.4762[/C][/ROW]
[ROW][C]15[/C][C]65.63[/C][C]62.1387[/C][C]3.4913[/C][/ROW]
[ROW][C]16[/C][C]68.75[/C][C]62.1387[/C][C]6.6113[/C][/ROW]
[ROW][C]17[/C][C]87.5[/C][C]67.6538[/C][C]19.8462[/C][/ROW]
[ROW][C]18[/C][C]62.5[/C][C]62.1387[/C][C]0.361303[/C][/ROW]
[ROW][C]19[/C][C]90.63[/C][C]67.6538[/C][C]22.9762[/C][/ROW]
[ROW][C]20[/C][C]62.5[/C][C]67.6538[/C][C]-5.15383[/C][/ROW]
[ROW][C]21[/C][C]62.5[/C][C]67.6538[/C][C]-5.15383[/C][/ROW]
[ROW][C]22[/C][C]71.88[/C][C]62.1387[/C][C]9.7413[/C][/ROW]
[ROW][C]23[/C][C]56.25[/C][C]62.1387[/C][C]-5.8887[/C][/ROW]
[ROW][C]24[/C][C]56.25[/C][C]62.1387[/C][C]-5.8887[/C][/ROW]
[ROW][C]25[/C][C]59.38[/C][C]67.6538[/C][C]-8.27383[/C][/ROW]
[ROW][C]26[/C][C]78.13[/C][C]62.1387[/C][C]15.9913[/C][/ROW]
[ROW][C]27[/C][C]78.13[/C][C]62.1387[/C][C]15.9913[/C][/ROW]
[ROW][C]28[/C][C]78.13[/C][C]62.1387[/C][C]15.9913[/C][/ROW]
[ROW][C]29[/C][C]75[/C][C]62.1387[/C][C]12.8613[/C][/ROW]
[ROW][C]30[/C][C]59.38[/C][C]67.6538[/C][C]-8.27383[/C][/ROW]
[ROW][C]31[/C][C]81.25[/C][C]67.6538[/C][C]13.5962[/C][/ROW]
[ROW][C]32[/C][C]31.25[/C][C]67.6538[/C][C]-36.4038[/C][/ROW]
[ROW][C]33[/C][C]53.13[/C][C]67.6538[/C][C]-14.5238[/C][/ROW]
[ROW][C]34[/C][C]40.63[/C][C]62.1387[/C][C]-21.5087[/C][/ROW]
[ROW][C]35[/C][C]53.13[/C][C]62.1387[/C][C]-9.0087[/C][/ROW]
[ROW][C]36[/C][C]93.75[/C][C]67.6538[/C][C]26.0962[/C][/ROW]
[ROW][C]37[/C][C]12.5[/C][C]62.1387[/C][C]-49.6387[/C][/ROW]
[ROW][C]38[/C][C]50[/C][C]62.1387[/C][C]-12.1387[/C][/ROW]
[ROW][C]39[/C][C]65.63[/C][C]62.1387[/C][C]3.4913[/C][/ROW]
[ROW][C]40[/C][C]68.75[/C][C]67.6538[/C][C]1.09617[/C][/ROW]
[ROW][C]41[/C][C]62.5[/C][C]62.1387[/C][C]0.361303[/C][/ROW]
[ROW][C]42[/C][C]68.75[/C][C]62.1387[/C][C]6.6113[/C][/ROW]
[ROW][C]43[/C][C]71.88[/C][C]67.6538[/C][C]4.22617[/C][/ROW]
[ROW][C]44[/C][C]50[/C][C]67.6538[/C][C]-17.6538[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]62.1387[/C][C]-62.1387[/C][/ROW]
[ROW][C]46[/C][C]56.25[/C][C]67.6538[/C][C]-11.4038[/C][/ROW]
[ROW][C]47[/C][C]78.13[/C][C]67.6538[/C][C]10.4762[/C][/ROW]
[ROW][C]48[/C][C]56.25[/C][C]62.1387[/C][C]-5.8887[/C][/ROW]
[ROW][C]49[/C][C]56.25[/C][C]67.6538[/C][C]-11.4038[/C][/ROW]
[ROW][C]50[/C][C]75[/C][C]67.6538[/C][C]7.34617[/C][/ROW]
[ROW][C]51[/C][C]90.63[/C][C]62.1387[/C][C]28.4913[/C][/ROW]
[ROW][C]52[/C][C]46.88[/C][C]62.1387[/C][C]-15.2587[/C][/ROW]
[ROW][C]53[/C][C]68.75[/C][C]62.1387[/C][C]6.6113[/C][/ROW]
[ROW][C]54[/C][C]71.88[/C][C]67.6538[/C][C]4.22617[/C][/ROW]
[ROW][C]55[/C][C]75[/C][C]67.6538[/C][C]7.34617[/C][/ROW]
[ROW][C]56[/C][C]68.75[/C][C]62.1387[/C][C]6.6113[/C][/ROW]
[ROW][C]57[/C][C]46.88[/C][C]67.6538[/C][C]-20.7738[/C][/ROW]
[ROW][C]58[/C][C]53.13[/C][C]62.1387[/C][C]-9.0087[/C][/ROW]
[ROW][C]59[/C][C]62.5[/C][C]67.6538[/C][C]-5.15383[/C][/ROW]
[ROW][C]60[/C][C]84.38[/C][C]62.1387[/C][C]22.2413[/C][/ROW]
[ROW][C]61[/C][C]81.25[/C][C]67.6538[/C][C]13.5962[/C][/ROW]
[ROW][C]62[/C][C]71.88[/C][C]67.6538[/C][C]4.22617[/C][/ROW]
[ROW][C]63[/C][C]71.88[/C][C]67.6538[/C][C]4.22617[/C][/ROW]
[ROW][C]64[/C][C]46.88[/C][C]62.1387[/C][C]-15.2587[/C][/ROW]
[ROW][C]65[/C][C]81.25[/C][C]62.1387[/C][C]19.1113[/C][/ROW]
[ROW][C]66[/C][C]68.75[/C][C]67.6538[/C][C]1.09617[/C][/ROW]
[ROW][C]67[/C][C]56.25[/C][C]62.1387[/C][C]-5.8887[/C][/ROW]
[ROW][C]68[/C][C]46.88[/C][C]67.6538[/C][C]-20.7738[/C][/ROW]
[ROW][C]69[/C][C]68.75[/C][C]67.6538[/C][C]1.09617[/C][/ROW]
[ROW][C]70[/C][C]84.38[/C][C]62.1387[/C][C]22.2413[/C][/ROW]
[ROW][C]71[/C][C]31.25[/C][C]67.6538[/C][C]-36.4038[/C][/ROW]
[ROW][C]72[/C][C]62.5[/C][C]67.6538[/C][C]-5.15383[/C][/ROW]
[ROW][C]73[/C][C]53.13[/C][C]62.1387[/C][C]-9.0087[/C][/ROW]
[ROW][C]74[/C][C]71.88[/C][C]67.6538[/C][C]4.22617[/C][/ROW]
[ROW][C]75[/C][C]59.38[/C][C]62.1387[/C][C]-2.7587[/C][/ROW]
[ROW][C]76[/C][C]40.63[/C][C]62.1387[/C][C]-21.5087[/C][/ROW]
[ROW][C]77[/C][C]84.38[/C][C]67.6538[/C][C]16.7262[/C][/ROW]
[ROW][C]78[/C][C]71.88[/C][C]67.6538[/C][C]4.22617[/C][/ROW]
[ROW][C]79[/C][C]50[/C][C]62.1387[/C][C]-12.1387[/C][/ROW]
[ROW][C]80[/C][C]78.13[/C][C]67.6538[/C][C]10.4762[/C][/ROW]
[ROW][C]81[/C][C]6.25[/C][C]62.1387[/C][C]-55.8887[/C][/ROW]
[ROW][C]82[/C][C]81.25[/C][C]62.1387[/C][C]19.1113[/C][/ROW]
[ROW][C]83[/C][C]62.5[/C][C]67.6538[/C][C]-5.15383[/C][/ROW]
[ROW][C]84[/C][C]68.75[/C][C]62.1387[/C][C]6.6113[/C][/ROW]
[ROW][C]85[/C][C]75[/C][C]67.6538[/C][C]7.34617[/C][/ROW]
[ROW][C]86[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]87[/C][C]68.75[/C][C]63.4114[/C][C]5.33855[/C][/ROW]
[ROW][C]88[/C][C]65.63[/C][C]63.4114[/C][C]2.21855[/C][/ROW]
[ROW][C]89[/C][C]78.13[/C][C]63.4114[/C][C]14.7186[/C][/ROW]
[ROW][C]90[/C][C]84.38[/C][C]63.4114[/C][C]20.9686[/C][/ROW]
[ROW][C]91[/C][C]71.88[/C][C]57.8963[/C][C]13.9837[/C][/ROW]
[ROW][C]92[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]93[/C][C]56.25[/C][C]57.8963[/C][C]-1.64631[/C][/ROW]
[ROW][C]94[/C][C]56.25[/C][C]57.8963[/C][C]-1.64631[/C][/ROW]
[ROW][C]95[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]96[/C][C]59.38[/C][C]63.4114[/C][C]-4.03145[/C][/ROW]
[ROW][C]97[/C][C]46.88[/C][C]63.4114[/C][C]-16.5314[/C][/ROW]
[ROW][C]98[/C][C]62.5[/C][C]63.4114[/C][C]-0.911448[/C][/ROW]
[ROW][C]99[/C][C]50[/C][C]63.4114[/C][C]-13.4114[/C][/ROW]
[ROW][C]100[/C][C]78.13[/C][C]63.4114[/C][C]14.7186[/C][/ROW]
[ROW][C]101[/C][C]78.13[/C][C]63.4114[/C][C]14.7186[/C][/ROW]
[ROW][C]102[/C][C]59.38[/C][C]57.8963[/C][C]1.48369[/C][/ROW]
[ROW][C]103[/C][C]59.38[/C][C]63.4114[/C][C]-4.03145[/C][/ROW]
[ROW][C]104[/C][C]50[/C][C]63.4114[/C][C]-13.4114[/C][/ROW]
[ROW][C]105[/C][C]59.38[/C][C]63.4114[/C][C]-4.03145[/C][/ROW]
[ROW][C]106[/C][C]59.38[/C][C]63.4114[/C][C]-4.03145[/C][/ROW]
[ROW][C]107[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]108[/C][C]65.63[/C][C]63.4114[/C][C]2.21855[/C][/ROW]
[ROW][C]109[/C][C]68.75[/C][C]57.8963[/C][C]10.8537[/C][/ROW]
[ROW][C]110[/C][C]59.38[/C][C]63.4114[/C][C]-4.03145[/C][/ROW]
[ROW][C]111[/C][C]62.5[/C][C]63.4114[/C][C]-0.911448[/C][/ROW]
[ROW][C]112[/C][C]9.38[/C][C]63.4114[/C][C]-54.0314[/C][/ROW]
[ROW][C]113[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]114[/C][C]43.75[/C][C]57.8963[/C][C]-14.1463[/C][/ROW]
[ROW][C]115[/C][C]71.88[/C][C]57.8963[/C][C]13.9837[/C][/ROW]
[ROW][C]116[/C][C]62.5[/C][C]57.8963[/C][C]4.60369[/C][/ROW]
[ROW][C]117[/C][C]46.88[/C][C]63.4114[/C][C]-16.5314[/C][/ROW]
[ROW][C]118[/C][C]40.63[/C][C]57.8963[/C][C]-17.2663[/C][/ROW]
[ROW][C]119[/C][C]50[/C][C]57.8963[/C][C]-7.89631[/C][/ROW]
[ROW][C]120[/C][C]21.88[/C][C]57.8963[/C][C]-36.0163[/C][/ROW]
[ROW][C]121[/C][C]75[/C][C]63.4114[/C][C]11.5886[/C][/ROW]
[ROW][C]122[/C][C]53.13[/C][C]57.8963[/C][C]-4.76631[/C][/ROW]
[ROW][C]123[/C][C]75[/C][C]63.4114[/C][C]11.5886[/C][/ROW]
[ROW][C]124[/C][C]75[/C][C]63.4114[/C][C]11.5886[/C][/ROW]
[ROW][C]125[/C][C]59.38[/C][C]57.8963[/C][C]1.48369[/C][/ROW]
[ROW][C]126[/C][C]87.5[/C][C]63.4114[/C][C]24.0886[/C][/ROW]
[ROW][C]127[/C][C]71.88[/C][C]57.8963[/C][C]13.9837[/C][/ROW]
[ROW][C]128[/C][C]59.38[/C][C]57.8963[/C][C]1.48369[/C][/ROW]
[ROW][C]129[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]130[/C][C]78.13[/C][C]63.4114[/C][C]14.7186[/C][/ROW]
[ROW][C]131[/C][C]78.13[/C][C]63.4114[/C][C]14.7186[/C][/ROW]
[ROW][C]132[/C][C]62.5[/C][C]63.4114[/C][C]-0.911448[/C][/ROW]
[ROW][C]133[/C][C]50[/C][C]57.8963[/C][C]-7.89631[/C][/ROW]
[ROW][C]134[/C][C]62.5[/C][C]57.8963[/C][C]4.60369[/C][/ROW]
[ROW][C]135[/C][C]78.13[/C][C]63.4114[/C][C]14.7186[/C][/ROW]
[ROW][C]136[/C][C]78.13[/C][C]57.8963[/C][C]20.2337[/C][/ROW]
[ROW][C]137[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]138[/C][C]53.13[/C][C]57.8963[/C][C]-4.76631[/C][/ROW]
[ROW][C]139[/C][C]62.5[/C][C]63.4114[/C][C]-0.911448[/C][/ROW]
[ROW][C]140[/C][C]50[/C][C]63.4114[/C][C]-13.4114[/C][/ROW]
[ROW][C]141[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]142[/C][C]37.5[/C][C]57.8963[/C][C]-20.3963[/C][/ROW]
[ROW][C]143[/C][C]75[/C][C]57.8963[/C][C]17.1037[/C][/ROW]
[ROW][C]144[/C][C]34.38[/C][C]63.4114[/C][C]-29.0314[/C][/ROW]
[ROW][C]145[/C][C]43.75[/C][C]57.8963[/C][C]-14.1463[/C][/ROW]
[ROW][C]146[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]147[/C][C]56.25[/C][C]57.8963[/C][C]-1.64631[/C][/ROW]
[ROW][C]148[/C][C]90.63[/C][C]63.4114[/C][C]27.2186[/C][/ROW]
[ROW][C]149[/C][C]50[/C][C]63.4114[/C][C]-13.4114[/C][/ROW]
[ROW][C]150[/C][C]59.38[/C][C]57.8963[/C][C]1.48369[/C][/ROW]
[ROW][C]151[/C][C]50[/C][C]57.8963[/C][C]-7.89631[/C][/ROW]
[ROW][C]152[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]153[/C][C]59.38[/C][C]57.8963[/C][C]1.48369[/C][/ROW]
[ROW][C]154[/C][C]12.5[/C][C]57.8963[/C][C]-45.3963[/C][/ROW]
[ROW][C]155[/C][C]62.5[/C][C]57.8963[/C][C]4.60369[/C][/ROW]
[ROW][C]156[/C][C]62.5[/C][C]63.4114[/C][C]-0.911448[/C][/ROW]
[ROW][C]157[/C][C]12.5[/C][C]63.4114[/C][C]-50.9114[/C][/ROW]
[ROW][C]158[/C][C]75[/C][C]63.4114[/C][C]11.5886[/C][/ROW]
[ROW][C]159[/C][C]50[/C][C]63.4114[/C][C]-13.4114[/C][/ROW]
[ROW][C]160[/C][C]9.38[/C][C]63.4114[/C][C]-54.0314[/C][/ROW]
[ROW][C]161[/C][C]75[/C][C]57.8963[/C][C]17.1037[/C][/ROW]
[ROW][C]162[/C][C]71.88[/C][C]63.4114[/C][C]8.46855[/C][/ROW]
[ROW][C]163[/C][C]53.13[/C][C]57.8963[/C][C]-4.76631[/C][/ROW]
[ROW][C]164[/C][C]62.5[/C][C]63.4114[/C][C]-0.911448[/C][/ROW]
[ROW][C]165[/C][C]68.75[/C][C]57.8963[/C][C]10.8537[/C][/ROW]
[ROW][C]166[/C][C]59.38[/C][C]63.4114[/C][C]-4.03145[/C][/ROW]
[ROW][C]167[/C][C]75[/C][C]63.4114[/C][C]11.5886[/C][/ROW]
[ROW][C]168[/C][C]59.38[/C][C]57.8963[/C][C]1.48369[/C][/ROW]
[ROW][C]169[/C][C]84.38[/C][C]63.4114[/C][C]20.9686[/C][/ROW]
[ROW][C]170[/C][C]68.75[/C][C]57.8963[/C][C]10.8537[/C][/ROW]
[ROW][C]171[/C][C]71.88[/C][C]57.8963[/C][C]13.9837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269979&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269979&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
193.7562.138731.6113
253.1367.6538-14.5238
37567.65387.34617
462.567.6538-5.15383
578.1367.653810.4762
662.567.6538-5.15383
784.3862.138722.2413
856.2562.1387-5.8887
987.562.138725.3613
1065.6367.6538-2.02383
1184.3862.138722.2413
1268.7567.65381.09617
1387.562.138725.3613
1478.1367.653810.4762
1565.6362.13873.4913
1668.7562.13876.6113
1787.567.653819.8462
1862.562.13870.361303
1990.6367.653822.9762
2062.567.6538-5.15383
2162.567.6538-5.15383
2271.8862.13879.7413
2356.2562.1387-5.8887
2456.2562.1387-5.8887
2559.3867.6538-8.27383
2678.1362.138715.9913
2778.1362.138715.9913
2878.1362.138715.9913
297562.138712.8613
3059.3867.6538-8.27383
3181.2567.653813.5962
3231.2567.6538-36.4038
3353.1367.6538-14.5238
3440.6362.1387-21.5087
3553.1362.1387-9.0087
3693.7567.653826.0962
3712.562.1387-49.6387
385062.1387-12.1387
3965.6362.13873.4913
4068.7567.65381.09617
4162.562.13870.361303
4268.7562.13876.6113
4371.8867.65384.22617
445067.6538-17.6538
45062.1387-62.1387
4656.2567.6538-11.4038
4778.1367.653810.4762
4856.2562.1387-5.8887
4956.2567.6538-11.4038
507567.65387.34617
5190.6362.138728.4913
5246.8862.1387-15.2587
5368.7562.13876.6113
5471.8867.65384.22617
557567.65387.34617
5668.7562.13876.6113
5746.8867.6538-20.7738
5853.1362.1387-9.0087
5962.567.6538-5.15383
6084.3862.138722.2413
6181.2567.653813.5962
6271.8867.65384.22617
6371.8867.65384.22617
6446.8862.1387-15.2587
6581.2562.138719.1113
6668.7567.65381.09617
6756.2562.1387-5.8887
6846.8867.6538-20.7738
6968.7567.65381.09617
7084.3862.138722.2413
7131.2567.6538-36.4038
7262.567.6538-5.15383
7353.1362.1387-9.0087
7471.8867.65384.22617
7559.3862.1387-2.7587
7640.6362.1387-21.5087
7784.3867.653816.7262
7871.8867.65384.22617
795062.1387-12.1387
8078.1367.653810.4762
816.2562.1387-55.8887
8281.2562.138719.1113
8362.567.6538-5.15383
8468.7562.13876.6113
857567.65387.34617
8671.8863.41148.46855
8768.7563.41145.33855
8865.6363.41142.21855
8978.1363.411414.7186
9084.3863.411420.9686
9171.8857.896313.9837
9271.8863.41148.46855
9356.2557.8963-1.64631
9456.2557.8963-1.64631
9571.8863.41148.46855
9659.3863.4114-4.03145
9746.8863.4114-16.5314
9862.563.4114-0.911448
995063.4114-13.4114
10078.1363.411414.7186
10178.1363.411414.7186
10259.3857.89631.48369
10359.3863.4114-4.03145
1045063.4114-13.4114
10559.3863.4114-4.03145
10659.3863.4114-4.03145
10771.8863.41148.46855
10865.6363.41142.21855
10968.7557.896310.8537
11059.3863.4114-4.03145
11162.563.4114-0.911448
1129.3863.4114-54.0314
11371.8863.41148.46855
11443.7557.8963-14.1463
11571.8857.896313.9837
11662.557.89634.60369
11746.8863.4114-16.5314
11840.6357.8963-17.2663
1195057.8963-7.89631
12021.8857.8963-36.0163
1217563.411411.5886
12253.1357.8963-4.76631
1237563.411411.5886
1247563.411411.5886
12559.3857.89631.48369
12687.563.411424.0886
12771.8857.896313.9837
12859.3857.89631.48369
12971.8863.41148.46855
13078.1363.411414.7186
13178.1363.411414.7186
13262.563.4114-0.911448
1335057.8963-7.89631
13462.557.89634.60369
13578.1363.411414.7186
13678.1357.896320.2337
13771.8863.41148.46855
13853.1357.8963-4.76631
13962.563.4114-0.911448
1405063.4114-13.4114
14171.8863.41148.46855
14237.557.8963-20.3963
1437557.896317.1037
14434.3863.4114-29.0314
14543.7557.8963-14.1463
14671.8863.41148.46855
14756.2557.8963-1.64631
14890.6363.411427.2186
1495063.4114-13.4114
15059.3857.89631.48369
1515057.8963-7.89631
15271.8863.41148.46855
15359.3857.89631.48369
15412.557.8963-45.3963
15562.557.89634.60369
15662.563.4114-0.911448
15712.563.4114-50.9114
1587563.411411.5886
1595063.4114-13.4114
1609.3863.4114-54.0314
1617557.896317.1037
16271.8863.41148.46855
16353.1357.8963-4.76631
16462.563.4114-0.911448
16568.7557.896310.8537
16659.3863.4114-4.03145
1677563.411411.5886
16859.3857.89631.48369
16984.3863.411420.9686
17068.7557.896310.8537
17171.8857.896313.9837







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.297840.5956790.70216
70.1866780.3733550.813322
80.4480040.8960070.551996
90.3572250.7144490.642775
100.2481050.496210.751895
110.1726910.3453820.827309
120.1112230.2224460.888777
130.07762820.1552560.922372
140.06379350.1275870.936206
150.0690420.1380840.930958
160.05564020.111280.94436
170.07466940.1493390.925331
180.07522470.1504490.924775
190.1006330.2012670.899367
200.07978380.1595680.920216
210.06140520.122810.938595
220.04376410.08752810.956236
230.05450270.1090050.945497
240.05981140.1196230.940189
250.0496520.09930410.950348
260.03737460.07474920.962625
270.02781740.05563480.972183
280.02050660.04101320.979493
290.01427930.02855860.985721
300.01130720.02261440.988693
310.01008680.02017350.989913
320.06595980.131920.93404
330.06199030.1239810.93801
340.133480.266960.86652
350.1379750.2759490.862025
360.1997630.3995260.800237
370.6854650.629070.314535
380.6772740.6454520.322726
390.6297820.7404360.370218
400.5789480.8421030.421052
410.5298760.9402480.470124
420.4823070.9646140.517693
430.4340270.8680530.565973
440.4358030.8716050.564197
450.9169640.1660710.0830357
460.9044470.1911060.0955531
470.8907770.2184470.109223
480.8699130.2601730.130087
490.8532580.2934830.146742
500.8296890.3406230.170311
510.8706630.2586740.129337
520.8663540.2672930.133646
530.8427160.3145680.157284
540.8148650.370270.185135
550.7886360.4227270.211364
560.7583290.4833420.241671
570.7697340.4605320.230266
580.7444650.5110710.255535
590.7082420.5835170.291758
600.731170.537660.26883
610.7197650.560470.280235
620.683620.6327590.31638
630.6458820.7082360.354118
640.6369640.7260730.363036
650.6493460.7013090.350654
660.6076010.7847980.392399
670.5696220.8607570.430378
680.5813570.8372850.418643
690.5378170.9243660.462183
700.5749540.8500910.425046
710.7013290.5973420.298671
720.6632390.6735230.336761
730.6338010.7323990.366199
740.5955730.8088530.404427
750.55430.8913990.4457
760.5766980.8466030.423302
770.5792980.8414040.420702
780.5406410.9187190.459359
790.5153110.9693770.484689
800.4938560.9877110.506144
810.8508330.2983350.149167
820.8490520.3018960.150948
830.8280130.3439750.171987
840.8001190.3997610.199881
850.7710590.4578820.228941
860.740410.5191810.25959
870.7053480.5893040.294652
880.6672730.6654530.332727
890.6465830.7068340.353417
900.6486230.7027530.351377
910.6245590.7508830.375441
920.5888310.8223390.411169
930.5552260.8895480.444774
940.5185030.9629940.481497
950.4813860.9627720.518614
960.4462750.8925490.553725
970.4548320.9096640.545168
980.4127320.8254630.587268
990.4012940.8025880.598706
1000.3848570.7697140.615143
1010.368640.737280.63136
1020.3284210.6568430.671579
1030.2929730.5859450.707027
1040.2812720.5625430.718728
1050.2474260.4948530.752574
1060.2156630.4313250.784337
1070.1901830.3803670.809817
1080.1612590.3225180.838741
1090.1439380.2878770.856062
1100.1213870.2427740.878613
1110.1000680.2001360.899932
1120.3944080.7888150.605592
1130.3587490.7174980.641251
1140.3428410.6856810.657159
1150.3294370.6588750.670563
1160.2918410.5836820.708159
1170.2913360.5826710.708664
1180.2871070.5742150.712893
1190.2542690.5085370.745731
1200.3886940.7773880.611306
1210.3612570.7225140.638743
1220.3198510.6397010.680149
1230.2944080.5888160.705592
1240.2701830.5403660.729817
1250.2309280.4618570.769072
1260.2657660.5315310.734234
1270.2498110.4996220.750189
1280.2115110.4230220.788489
1290.1849460.3698920.815054
1300.1767810.3535620.823219
1310.1703670.3407330.829633
1320.1396240.2792480.860376
1330.1176790.2353580.882321
1340.09483730.1896750.905163
1350.09160480.183210.908395
1360.09776220.1955240.902238
1370.08394310.1678860.916057
1380.06569910.1313980.934301
1390.05017730.1003550.949823
1400.04152070.08304130.958479
1410.03439480.06878960.965605
1420.036660.073320.96334
1430.03487940.06975890.965121
1440.04800790.09601580.951992
1450.04170220.08340440.958298
1460.03353050.0670610.966469
1470.02360390.04720770.976396
1480.04305240.08610480.956948
1490.0328910.06578210.967109
1500.0225670.0451340.977433
1510.01626140.03252270.983739
1520.0131180.02623610.986882
1530.008288960.01657790.991711
1540.07718610.1543720.922814
1550.05396220.1079240.946038
1560.03729990.07459970.9627
1570.2204830.4409670.779517
1580.1970530.3941050.802947
1590.1543520.3087040.845648
1600.9631640.07367280.0368364
1610.9510630.09787320.0489366
1620.9057280.1885450.0942723
1630.8977540.2044920.102246
1640.8506920.2986170.149308
1650.7168290.5663420.283171

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.29784 & 0.595679 & 0.70216 \tabularnewline
7 & 0.186678 & 0.373355 & 0.813322 \tabularnewline
8 & 0.448004 & 0.896007 & 0.551996 \tabularnewline
9 & 0.357225 & 0.714449 & 0.642775 \tabularnewline
10 & 0.248105 & 0.49621 & 0.751895 \tabularnewline
11 & 0.172691 & 0.345382 & 0.827309 \tabularnewline
12 & 0.111223 & 0.222446 & 0.888777 \tabularnewline
13 & 0.0776282 & 0.155256 & 0.922372 \tabularnewline
14 & 0.0637935 & 0.127587 & 0.936206 \tabularnewline
15 & 0.069042 & 0.138084 & 0.930958 \tabularnewline
16 & 0.0556402 & 0.11128 & 0.94436 \tabularnewline
17 & 0.0746694 & 0.149339 & 0.925331 \tabularnewline
18 & 0.0752247 & 0.150449 & 0.924775 \tabularnewline
19 & 0.100633 & 0.201267 & 0.899367 \tabularnewline
20 & 0.0797838 & 0.159568 & 0.920216 \tabularnewline
21 & 0.0614052 & 0.12281 & 0.938595 \tabularnewline
22 & 0.0437641 & 0.0875281 & 0.956236 \tabularnewline
23 & 0.0545027 & 0.109005 & 0.945497 \tabularnewline
24 & 0.0598114 & 0.119623 & 0.940189 \tabularnewline
25 & 0.049652 & 0.0993041 & 0.950348 \tabularnewline
26 & 0.0373746 & 0.0747492 & 0.962625 \tabularnewline
27 & 0.0278174 & 0.0556348 & 0.972183 \tabularnewline
28 & 0.0205066 & 0.0410132 & 0.979493 \tabularnewline
29 & 0.0142793 & 0.0285586 & 0.985721 \tabularnewline
30 & 0.0113072 & 0.0226144 & 0.988693 \tabularnewline
31 & 0.0100868 & 0.0201735 & 0.989913 \tabularnewline
32 & 0.0659598 & 0.13192 & 0.93404 \tabularnewline
33 & 0.0619903 & 0.123981 & 0.93801 \tabularnewline
34 & 0.13348 & 0.26696 & 0.86652 \tabularnewline
35 & 0.137975 & 0.275949 & 0.862025 \tabularnewline
36 & 0.199763 & 0.399526 & 0.800237 \tabularnewline
37 & 0.685465 & 0.62907 & 0.314535 \tabularnewline
38 & 0.677274 & 0.645452 & 0.322726 \tabularnewline
39 & 0.629782 & 0.740436 & 0.370218 \tabularnewline
40 & 0.578948 & 0.842103 & 0.421052 \tabularnewline
41 & 0.529876 & 0.940248 & 0.470124 \tabularnewline
42 & 0.482307 & 0.964614 & 0.517693 \tabularnewline
43 & 0.434027 & 0.868053 & 0.565973 \tabularnewline
44 & 0.435803 & 0.871605 & 0.564197 \tabularnewline
45 & 0.916964 & 0.166071 & 0.0830357 \tabularnewline
46 & 0.904447 & 0.191106 & 0.0955531 \tabularnewline
47 & 0.890777 & 0.218447 & 0.109223 \tabularnewline
48 & 0.869913 & 0.260173 & 0.130087 \tabularnewline
49 & 0.853258 & 0.293483 & 0.146742 \tabularnewline
50 & 0.829689 & 0.340623 & 0.170311 \tabularnewline
51 & 0.870663 & 0.258674 & 0.129337 \tabularnewline
52 & 0.866354 & 0.267293 & 0.133646 \tabularnewline
53 & 0.842716 & 0.314568 & 0.157284 \tabularnewline
54 & 0.814865 & 0.37027 & 0.185135 \tabularnewline
55 & 0.788636 & 0.422727 & 0.211364 \tabularnewline
56 & 0.758329 & 0.483342 & 0.241671 \tabularnewline
57 & 0.769734 & 0.460532 & 0.230266 \tabularnewline
58 & 0.744465 & 0.511071 & 0.255535 \tabularnewline
59 & 0.708242 & 0.583517 & 0.291758 \tabularnewline
60 & 0.73117 & 0.53766 & 0.26883 \tabularnewline
61 & 0.719765 & 0.56047 & 0.280235 \tabularnewline
62 & 0.68362 & 0.632759 & 0.31638 \tabularnewline
63 & 0.645882 & 0.708236 & 0.354118 \tabularnewline
64 & 0.636964 & 0.726073 & 0.363036 \tabularnewline
65 & 0.649346 & 0.701309 & 0.350654 \tabularnewline
66 & 0.607601 & 0.784798 & 0.392399 \tabularnewline
67 & 0.569622 & 0.860757 & 0.430378 \tabularnewline
68 & 0.581357 & 0.837285 & 0.418643 \tabularnewline
69 & 0.537817 & 0.924366 & 0.462183 \tabularnewline
70 & 0.574954 & 0.850091 & 0.425046 \tabularnewline
71 & 0.701329 & 0.597342 & 0.298671 \tabularnewline
72 & 0.663239 & 0.673523 & 0.336761 \tabularnewline
73 & 0.633801 & 0.732399 & 0.366199 \tabularnewline
74 & 0.595573 & 0.808853 & 0.404427 \tabularnewline
75 & 0.5543 & 0.891399 & 0.4457 \tabularnewline
76 & 0.576698 & 0.846603 & 0.423302 \tabularnewline
77 & 0.579298 & 0.841404 & 0.420702 \tabularnewline
78 & 0.540641 & 0.918719 & 0.459359 \tabularnewline
79 & 0.515311 & 0.969377 & 0.484689 \tabularnewline
80 & 0.493856 & 0.987711 & 0.506144 \tabularnewline
81 & 0.850833 & 0.298335 & 0.149167 \tabularnewline
82 & 0.849052 & 0.301896 & 0.150948 \tabularnewline
83 & 0.828013 & 0.343975 & 0.171987 \tabularnewline
84 & 0.800119 & 0.399761 & 0.199881 \tabularnewline
85 & 0.771059 & 0.457882 & 0.228941 \tabularnewline
86 & 0.74041 & 0.519181 & 0.25959 \tabularnewline
87 & 0.705348 & 0.589304 & 0.294652 \tabularnewline
88 & 0.667273 & 0.665453 & 0.332727 \tabularnewline
89 & 0.646583 & 0.706834 & 0.353417 \tabularnewline
90 & 0.648623 & 0.702753 & 0.351377 \tabularnewline
91 & 0.624559 & 0.750883 & 0.375441 \tabularnewline
92 & 0.588831 & 0.822339 & 0.411169 \tabularnewline
93 & 0.555226 & 0.889548 & 0.444774 \tabularnewline
94 & 0.518503 & 0.962994 & 0.481497 \tabularnewline
95 & 0.481386 & 0.962772 & 0.518614 \tabularnewline
96 & 0.446275 & 0.892549 & 0.553725 \tabularnewline
97 & 0.454832 & 0.909664 & 0.545168 \tabularnewline
98 & 0.412732 & 0.825463 & 0.587268 \tabularnewline
99 & 0.401294 & 0.802588 & 0.598706 \tabularnewline
100 & 0.384857 & 0.769714 & 0.615143 \tabularnewline
101 & 0.36864 & 0.73728 & 0.63136 \tabularnewline
102 & 0.328421 & 0.656843 & 0.671579 \tabularnewline
103 & 0.292973 & 0.585945 & 0.707027 \tabularnewline
104 & 0.281272 & 0.562543 & 0.718728 \tabularnewline
105 & 0.247426 & 0.494853 & 0.752574 \tabularnewline
106 & 0.215663 & 0.431325 & 0.784337 \tabularnewline
107 & 0.190183 & 0.380367 & 0.809817 \tabularnewline
108 & 0.161259 & 0.322518 & 0.838741 \tabularnewline
109 & 0.143938 & 0.287877 & 0.856062 \tabularnewline
110 & 0.121387 & 0.242774 & 0.878613 \tabularnewline
111 & 0.100068 & 0.200136 & 0.899932 \tabularnewline
112 & 0.394408 & 0.788815 & 0.605592 \tabularnewline
113 & 0.358749 & 0.717498 & 0.641251 \tabularnewline
114 & 0.342841 & 0.685681 & 0.657159 \tabularnewline
115 & 0.329437 & 0.658875 & 0.670563 \tabularnewline
116 & 0.291841 & 0.583682 & 0.708159 \tabularnewline
117 & 0.291336 & 0.582671 & 0.708664 \tabularnewline
118 & 0.287107 & 0.574215 & 0.712893 \tabularnewline
119 & 0.254269 & 0.508537 & 0.745731 \tabularnewline
120 & 0.388694 & 0.777388 & 0.611306 \tabularnewline
121 & 0.361257 & 0.722514 & 0.638743 \tabularnewline
122 & 0.319851 & 0.639701 & 0.680149 \tabularnewline
123 & 0.294408 & 0.588816 & 0.705592 \tabularnewline
124 & 0.270183 & 0.540366 & 0.729817 \tabularnewline
125 & 0.230928 & 0.461857 & 0.769072 \tabularnewline
126 & 0.265766 & 0.531531 & 0.734234 \tabularnewline
127 & 0.249811 & 0.499622 & 0.750189 \tabularnewline
128 & 0.211511 & 0.423022 & 0.788489 \tabularnewline
129 & 0.184946 & 0.369892 & 0.815054 \tabularnewline
130 & 0.176781 & 0.353562 & 0.823219 \tabularnewline
131 & 0.170367 & 0.340733 & 0.829633 \tabularnewline
132 & 0.139624 & 0.279248 & 0.860376 \tabularnewline
133 & 0.117679 & 0.235358 & 0.882321 \tabularnewline
134 & 0.0948373 & 0.189675 & 0.905163 \tabularnewline
135 & 0.0916048 & 0.18321 & 0.908395 \tabularnewline
136 & 0.0977622 & 0.195524 & 0.902238 \tabularnewline
137 & 0.0839431 & 0.167886 & 0.916057 \tabularnewline
138 & 0.0656991 & 0.131398 & 0.934301 \tabularnewline
139 & 0.0501773 & 0.100355 & 0.949823 \tabularnewline
140 & 0.0415207 & 0.0830413 & 0.958479 \tabularnewline
141 & 0.0343948 & 0.0687896 & 0.965605 \tabularnewline
142 & 0.03666 & 0.07332 & 0.96334 \tabularnewline
143 & 0.0348794 & 0.0697589 & 0.965121 \tabularnewline
144 & 0.0480079 & 0.0960158 & 0.951992 \tabularnewline
145 & 0.0417022 & 0.0834044 & 0.958298 \tabularnewline
146 & 0.0335305 & 0.067061 & 0.966469 \tabularnewline
147 & 0.0236039 & 0.0472077 & 0.976396 \tabularnewline
148 & 0.0430524 & 0.0861048 & 0.956948 \tabularnewline
149 & 0.032891 & 0.0657821 & 0.967109 \tabularnewline
150 & 0.022567 & 0.045134 & 0.977433 \tabularnewline
151 & 0.0162614 & 0.0325227 & 0.983739 \tabularnewline
152 & 0.013118 & 0.0262361 & 0.986882 \tabularnewline
153 & 0.00828896 & 0.0165779 & 0.991711 \tabularnewline
154 & 0.0771861 & 0.154372 & 0.922814 \tabularnewline
155 & 0.0539622 & 0.107924 & 0.946038 \tabularnewline
156 & 0.0372999 & 0.0745997 & 0.9627 \tabularnewline
157 & 0.220483 & 0.440967 & 0.779517 \tabularnewline
158 & 0.197053 & 0.394105 & 0.802947 \tabularnewline
159 & 0.154352 & 0.308704 & 0.845648 \tabularnewline
160 & 0.963164 & 0.0736728 & 0.0368364 \tabularnewline
161 & 0.951063 & 0.0978732 & 0.0489366 \tabularnewline
162 & 0.905728 & 0.188545 & 0.0942723 \tabularnewline
163 & 0.897754 & 0.204492 & 0.102246 \tabularnewline
164 & 0.850692 & 0.298617 & 0.149308 \tabularnewline
165 & 0.716829 & 0.566342 & 0.283171 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269979&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]6[/C][C]0.29784[/C][C]0.595679[/C][C]0.70216[/C][/ROW]
[ROW][C]7[/C][C]0.186678[/C][C]0.373355[/C][C]0.813322[/C][/ROW]
[ROW][C]8[/C][C]0.448004[/C][C]0.896007[/C][C]0.551996[/C][/ROW]
[ROW][C]9[/C][C]0.357225[/C][C]0.714449[/C][C]0.642775[/C][/ROW]
[ROW][C]10[/C][C]0.248105[/C][C]0.49621[/C][C]0.751895[/C][/ROW]
[ROW][C]11[/C][C]0.172691[/C][C]0.345382[/C][C]0.827309[/C][/ROW]
[ROW][C]12[/C][C]0.111223[/C][C]0.222446[/C][C]0.888777[/C][/ROW]
[ROW][C]13[/C][C]0.0776282[/C][C]0.155256[/C][C]0.922372[/C][/ROW]
[ROW][C]14[/C][C]0.0637935[/C][C]0.127587[/C][C]0.936206[/C][/ROW]
[ROW][C]15[/C][C]0.069042[/C][C]0.138084[/C][C]0.930958[/C][/ROW]
[ROW][C]16[/C][C]0.0556402[/C][C]0.11128[/C][C]0.94436[/C][/ROW]
[ROW][C]17[/C][C]0.0746694[/C][C]0.149339[/C][C]0.925331[/C][/ROW]
[ROW][C]18[/C][C]0.0752247[/C][C]0.150449[/C][C]0.924775[/C][/ROW]
[ROW][C]19[/C][C]0.100633[/C][C]0.201267[/C][C]0.899367[/C][/ROW]
[ROW][C]20[/C][C]0.0797838[/C][C]0.159568[/C][C]0.920216[/C][/ROW]
[ROW][C]21[/C][C]0.0614052[/C][C]0.12281[/C][C]0.938595[/C][/ROW]
[ROW][C]22[/C][C]0.0437641[/C][C]0.0875281[/C][C]0.956236[/C][/ROW]
[ROW][C]23[/C][C]0.0545027[/C][C]0.109005[/C][C]0.945497[/C][/ROW]
[ROW][C]24[/C][C]0.0598114[/C][C]0.119623[/C][C]0.940189[/C][/ROW]
[ROW][C]25[/C][C]0.049652[/C][C]0.0993041[/C][C]0.950348[/C][/ROW]
[ROW][C]26[/C][C]0.0373746[/C][C]0.0747492[/C][C]0.962625[/C][/ROW]
[ROW][C]27[/C][C]0.0278174[/C][C]0.0556348[/C][C]0.972183[/C][/ROW]
[ROW][C]28[/C][C]0.0205066[/C][C]0.0410132[/C][C]0.979493[/C][/ROW]
[ROW][C]29[/C][C]0.0142793[/C][C]0.0285586[/C][C]0.985721[/C][/ROW]
[ROW][C]30[/C][C]0.0113072[/C][C]0.0226144[/C][C]0.988693[/C][/ROW]
[ROW][C]31[/C][C]0.0100868[/C][C]0.0201735[/C][C]0.989913[/C][/ROW]
[ROW][C]32[/C][C]0.0659598[/C][C]0.13192[/C][C]0.93404[/C][/ROW]
[ROW][C]33[/C][C]0.0619903[/C][C]0.123981[/C][C]0.93801[/C][/ROW]
[ROW][C]34[/C][C]0.13348[/C][C]0.26696[/C][C]0.86652[/C][/ROW]
[ROW][C]35[/C][C]0.137975[/C][C]0.275949[/C][C]0.862025[/C][/ROW]
[ROW][C]36[/C][C]0.199763[/C][C]0.399526[/C][C]0.800237[/C][/ROW]
[ROW][C]37[/C][C]0.685465[/C][C]0.62907[/C][C]0.314535[/C][/ROW]
[ROW][C]38[/C][C]0.677274[/C][C]0.645452[/C][C]0.322726[/C][/ROW]
[ROW][C]39[/C][C]0.629782[/C][C]0.740436[/C][C]0.370218[/C][/ROW]
[ROW][C]40[/C][C]0.578948[/C][C]0.842103[/C][C]0.421052[/C][/ROW]
[ROW][C]41[/C][C]0.529876[/C][C]0.940248[/C][C]0.470124[/C][/ROW]
[ROW][C]42[/C][C]0.482307[/C][C]0.964614[/C][C]0.517693[/C][/ROW]
[ROW][C]43[/C][C]0.434027[/C][C]0.868053[/C][C]0.565973[/C][/ROW]
[ROW][C]44[/C][C]0.435803[/C][C]0.871605[/C][C]0.564197[/C][/ROW]
[ROW][C]45[/C][C]0.916964[/C][C]0.166071[/C][C]0.0830357[/C][/ROW]
[ROW][C]46[/C][C]0.904447[/C][C]0.191106[/C][C]0.0955531[/C][/ROW]
[ROW][C]47[/C][C]0.890777[/C][C]0.218447[/C][C]0.109223[/C][/ROW]
[ROW][C]48[/C][C]0.869913[/C][C]0.260173[/C][C]0.130087[/C][/ROW]
[ROW][C]49[/C][C]0.853258[/C][C]0.293483[/C][C]0.146742[/C][/ROW]
[ROW][C]50[/C][C]0.829689[/C][C]0.340623[/C][C]0.170311[/C][/ROW]
[ROW][C]51[/C][C]0.870663[/C][C]0.258674[/C][C]0.129337[/C][/ROW]
[ROW][C]52[/C][C]0.866354[/C][C]0.267293[/C][C]0.133646[/C][/ROW]
[ROW][C]53[/C][C]0.842716[/C][C]0.314568[/C][C]0.157284[/C][/ROW]
[ROW][C]54[/C][C]0.814865[/C][C]0.37027[/C][C]0.185135[/C][/ROW]
[ROW][C]55[/C][C]0.788636[/C][C]0.422727[/C][C]0.211364[/C][/ROW]
[ROW][C]56[/C][C]0.758329[/C][C]0.483342[/C][C]0.241671[/C][/ROW]
[ROW][C]57[/C][C]0.769734[/C][C]0.460532[/C][C]0.230266[/C][/ROW]
[ROW][C]58[/C][C]0.744465[/C][C]0.511071[/C][C]0.255535[/C][/ROW]
[ROW][C]59[/C][C]0.708242[/C][C]0.583517[/C][C]0.291758[/C][/ROW]
[ROW][C]60[/C][C]0.73117[/C][C]0.53766[/C][C]0.26883[/C][/ROW]
[ROW][C]61[/C][C]0.719765[/C][C]0.56047[/C][C]0.280235[/C][/ROW]
[ROW][C]62[/C][C]0.68362[/C][C]0.632759[/C][C]0.31638[/C][/ROW]
[ROW][C]63[/C][C]0.645882[/C][C]0.708236[/C][C]0.354118[/C][/ROW]
[ROW][C]64[/C][C]0.636964[/C][C]0.726073[/C][C]0.363036[/C][/ROW]
[ROW][C]65[/C][C]0.649346[/C][C]0.701309[/C][C]0.350654[/C][/ROW]
[ROW][C]66[/C][C]0.607601[/C][C]0.784798[/C][C]0.392399[/C][/ROW]
[ROW][C]67[/C][C]0.569622[/C][C]0.860757[/C][C]0.430378[/C][/ROW]
[ROW][C]68[/C][C]0.581357[/C][C]0.837285[/C][C]0.418643[/C][/ROW]
[ROW][C]69[/C][C]0.537817[/C][C]0.924366[/C][C]0.462183[/C][/ROW]
[ROW][C]70[/C][C]0.574954[/C][C]0.850091[/C][C]0.425046[/C][/ROW]
[ROW][C]71[/C][C]0.701329[/C][C]0.597342[/C][C]0.298671[/C][/ROW]
[ROW][C]72[/C][C]0.663239[/C][C]0.673523[/C][C]0.336761[/C][/ROW]
[ROW][C]73[/C][C]0.633801[/C][C]0.732399[/C][C]0.366199[/C][/ROW]
[ROW][C]74[/C][C]0.595573[/C][C]0.808853[/C][C]0.404427[/C][/ROW]
[ROW][C]75[/C][C]0.5543[/C][C]0.891399[/C][C]0.4457[/C][/ROW]
[ROW][C]76[/C][C]0.576698[/C][C]0.846603[/C][C]0.423302[/C][/ROW]
[ROW][C]77[/C][C]0.579298[/C][C]0.841404[/C][C]0.420702[/C][/ROW]
[ROW][C]78[/C][C]0.540641[/C][C]0.918719[/C][C]0.459359[/C][/ROW]
[ROW][C]79[/C][C]0.515311[/C][C]0.969377[/C][C]0.484689[/C][/ROW]
[ROW][C]80[/C][C]0.493856[/C][C]0.987711[/C][C]0.506144[/C][/ROW]
[ROW][C]81[/C][C]0.850833[/C][C]0.298335[/C][C]0.149167[/C][/ROW]
[ROW][C]82[/C][C]0.849052[/C][C]0.301896[/C][C]0.150948[/C][/ROW]
[ROW][C]83[/C][C]0.828013[/C][C]0.343975[/C][C]0.171987[/C][/ROW]
[ROW][C]84[/C][C]0.800119[/C][C]0.399761[/C][C]0.199881[/C][/ROW]
[ROW][C]85[/C][C]0.771059[/C][C]0.457882[/C][C]0.228941[/C][/ROW]
[ROW][C]86[/C][C]0.74041[/C][C]0.519181[/C][C]0.25959[/C][/ROW]
[ROW][C]87[/C][C]0.705348[/C][C]0.589304[/C][C]0.294652[/C][/ROW]
[ROW][C]88[/C][C]0.667273[/C][C]0.665453[/C][C]0.332727[/C][/ROW]
[ROW][C]89[/C][C]0.646583[/C][C]0.706834[/C][C]0.353417[/C][/ROW]
[ROW][C]90[/C][C]0.648623[/C][C]0.702753[/C][C]0.351377[/C][/ROW]
[ROW][C]91[/C][C]0.624559[/C][C]0.750883[/C][C]0.375441[/C][/ROW]
[ROW][C]92[/C][C]0.588831[/C][C]0.822339[/C][C]0.411169[/C][/ROW]
[ROW][C]93[/C][C]0.555226[/C][C]0.889548[/C][C]0.444774[/C][/ROW]
[ROW][C]94[/C][C]0.518503[/C][C]0.962994[/C][C]0.481497[/C][/ROW]
[ROW][C]95[/C][C]0.481386[/C][C]0.962772[/C][C]0.518614[/C][/ROW]
[ROW][C]96[/C][C]0.446275[/C][C]0.892549[/C][C]0.553725[/C][/ROW]
[ROW][C]97[/C][C]0.454832[/C][C]0.909664[/C][C]0.545168[/C][/ROW]
[ROW][C]98[/C][C]0.412732[/C][C]0.825463[/C][C]0.587268[/C][/ROW]
[ROW][C]99[/C][C]0.401294[/C][C]0.802588[/C][C]0.598706[/C][/ROW]
[ROW][C]100[/C][C]0.384857[/C][C]0.769714[/C][C]0.615143[/C][/ROW]
[ROW][C]101[/C][C]0.36864[/C][C]0.73728[/C][C]0.63136[/C][/ROW]
[ROW][C]102[/C][C]0.328421[/C][C]0.656843[/C][C]0.671579[/C][/ROW]
[ROW][C]103[/C][C]0.292973[/C][C]0.585945[/C][C]0.707027[/C][/ROW]
[ROW][C]104[/C][C]0.281272[/C][C]0.562543[/C][C]0.718728[/C][/ROW]
[ROW][C]105[/C][C]0.247426[/C][C]0.494853[/C][C]0.752574[/C][/ROW]
[ROW][C]106[/C][C]0.215663[/C][C]0.431325[/C][C]0.784337[/C][/ROW]
[ROW][C]107[/C][C]0.190183[/C][C]0.380367[/C][C]0.809817[/C][/ROW]
[ROW][C]108[/C][C]0.161259[/C][C]0.322518[/C][C]0.838741[/C][/ROW]
[ROW][C]109[/C][C]0.143938[/C][C]0.287877[/C][C]0.856062[/C][/ROW]
[ROW][C]110[/C][C]0.121387[/C][C]0.242774[/C][C]0.878613[/C][/ROW]
[ROW][C]111[/C][C]0.100068[/C][C]0.200136[/C][C]0.899932[/C][/ROW]
[ROW][C]112[/C][C]0.394408[/C][C]0.788815[/C][C]0.605592[/C][/ROW]
[ROW][C]113[/C][C]0.358749[/C][C]0.717498[/C][C]0.641251[/C][/ROW]
[ROW][C]114[/C][C]0.342841[/C][C]0.685681[/C][C]0.657159[/C][/ROW]
[ROW][C]115[/C][C]0.329437[/C][C]0.658875[/C][C]0.670563[/C][/ROW]
[ROW][C]116[/C][C]0.291841[/C][C]0.583682[/C][C]0.708159[/C][/ROW]
[ROW][C]117[/C][C]0.291336[/C][C]0.582671[/C][C]0.708664[/C][/ROW]
[ROW][C]118[/C][C]0.287107[/C][C]0.574215[/C][C]0.712893[/C][/ROW]
[ROW][C]119[/C][C]0.254269[/C][C]0.508537[/C][C]0.745731[/C][/ROW]
[ROW][C]120[/C][C]0.388694[/C][C]0.777388[/C][C]0.611306[/C][/ROW]
[ROW][C]121[/C][C]0.361257[/C][C]0.722514[/C][C]0.638743[/C][/ROW]
[ROW][C]122[/C][C]0.319851[/C][C]0.639701[/C][C]0.680149[/C][/ROW]
[ROW][C]123[/C][C]0.294408[/C][C]0.588816[/C][C]0.705592[/C][/ROW]
[ROW][C]124[/C][C]0.270183[/C][C]0.540366[/C][C]0.729817[/C][/ROW]
[ROW][C]125[/C][C]0.230928[/C][C]0.461857[/C][C]0.769072[/C][/ROW]
[ROW][C]126[/C][C]0.265766[/C][C]0.531531[/C][C]0.734234[/C][/ROW]
[ROW][C]127[/C][C]0.249811[/C][C]0.499622[/C][C]0.750189[/C][/ROW]
[ROW][C]128[/C][C]0.211511[/C][C]0.423022[/C][C]0.788489[/C][/ROW]
[ROW][C]129[/C][C]0.184946[/C][C]0.369892[/C][C]0.815054[/C][/ROW]
[ROW][C]130[/C][C]0.176781[/C][C]0.353562[/C][C]0.823219[/C][/ROW]
[ROW][C]131[/C][C]0.170367[/C][C]0.340733[/C][C]0.829633[/C][/ROW]
[ROW][C]132[/C][C]0.139624[/C][C]0.279248[/C][C]0.860376[/C][/ROW]
[ROW][C]133[/C][C]0.117679[/C][C]0.235358[/C][C]0.882321[/C][/ROW]
[ROW][C]134[/C][C]0.0948373[/C][C]0.189675[/C][C]0.905163[/C][/ROW]
[ROW][C]135[/C][C]0.0916048[/C][C]0.18321[/C][C]0.908395[/C][/ROW]
[ROW][C]136[/C][C]0.0977622[/C][C]0.195524[/C][C]0.902238[/C][/ROW]
[ROW][C]137[/C][C]0.0839431[/C][C]0.167886[/C][C]0.916057[/C][/ROW]
[ROW][C]138[/C][C]0.0656991[/C][C]0.131398[/C][C]0.934301[/C][/ROW]
[ROW][C]139[/C][C]0.0501773[/C][C]0.100355[/C][C]0.949823[/C][/ROW]
[ROW][C]140[/C][C]0.0415207[/C][C]0.0830413[/C][C]0.958479[/C][/ROW]
[ROW][C]141[/C][C]0.0343948[/C][C]0.0687896[/C][C]0.965605[/C][/ROW]
[ROW][C]142[/C][C]0.03666[/C][C]0.07332[/C][C]0.96334[/C][/ROW]
[ROW][C]143[/C][C]0.0348794[/C][C]0.0697589[/C][C]0.965121[/C][/ROW]
[ROW][C]144[/C][C]0.0480079[/C][C]0.0960158[/C][C]0.951992[/C][/ROW]
[ROW][C]145[/C][C]0.0417022[/C][C]0.0834044[/C][C]0.958298[/C][/ROW]
[ROW][C]146[/C][C]0.0335305[/C][C]0.067061[/C][C]0.966469[/C][/ROW]
[ROW][C]147[/C][C]0.0236039[/C][C]0.0472077[/C][C]0.976396[/C][/ROW]
[ROW][C]148[/C][C]0.0430524[/C][C]0.0861048[/C][C]0.956948[/C][/ROW]
[ROW][C]149[/C][C]0.032891[/C][C]0.0657821[/C][C]0.967109[/C][/ROW]
[ROW][C]150[/C][C]0.022567[/C][C]0.045134[/C][C]0.977433[/C][/ROW]
[ROW][C]151[/C][C]0.0162614[/C][C]0.0325227[/C][C]0.983739[/C][/ROW]
[ROW][C]152[/C][C]0.013118[/C][C]0.0262361[/C][C]0.986882[/C][/ROW]
[ROW][C]153[/C][C]0.00828896[/C][C]0.0165779[/C][C]0.991711[/C][/ROW]
[ROW][C]154[/C][C]0.0771861[/C][C]0.154372[/C][C]0.922814[/C][/ROW]
[ROW][C]155[/C][C]0.0539622[/C][C]0.107924[/C][C]0.946038[/C][/ROW]
[ROW][C]156[/C][C]0.0372999[/C][C]0.0745997[/C][C]0.9627[/C][/ROW]
[ROW][C]157[/C][C]0.220483[/C][C]0.440967[/C][C]0.779517[/C][/ROW]
[ROW][C]158[/C][C]0.197053[/C][C]0.394105[/C][C]0.802947[/C][/ROW]
[ROW][C]159[/C][C]0.154352[/C][C]0.308704[/C][C]0.845648[/C][/ROW]
[ROW][C]160[/C][C]0.963164[/C][C]0.0736728[/C][C]0.0368364[/C][/ROW]
[ROW][C]161[/C][C]0.951063[/C][C]0.0978732[/C][C]0.0489366[/C][/ROW]
[ROW][C]162[/C][C]0.905728[/C][C]0.188545[/C][C]0.0942723[/C][/ROW]
[ROW][C]163[/C][C]0.897754[/C][C]0.204492[/C][C]0.102246[/C][/ROW]
[ROW][C]164[/C][C]0.850692[/C][C]0.298617[/C][C]0.149308[/C][/ROW]
[ROW][C]165[/C][C]0.716829[/C][C]0.566342[/C][C]0.283171[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269979&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269979&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
60.297840.5956790.70216
70.1866780.3733550.813322
80.4480040.8960070.551996
90.3572250.7144490.642775
100.2481050.496210.751895
110.1726910.3453820.827309
120.1112230.2224460.888777
130.07762820.1552560.922372
140.06379350.1275870.936206
150.0690420.1380840.930958
160.05564020.111280.94436
170.07466940.1493390.925331
180.07522470.1504490.924775
190.1006330.2012670.899367
200.07978380.1595680.920216
210.06140520.122810.938595
220.04376410.08752810.956236
230.05450270.1090050.945497
240.05981140.1196230.940189
250.0496520.09930410.950348
260.03737460.07474920.962625
270.02781740.05563480.972183
280.02050660.04101320.979493
290.01427930.02855860.985721
300.01130720.02261440.988693
310.01008680.02017350.989913
320.06595980.131920.93404
330.06199030.1239810.93801
340.133480.266960.86652
350.1379750.2759490.862025
360.1997630.3995260.800237
370.6854650.629070.314535
380.6772740.6454520.322726
390.6297820.7404360.370218
400.5789480.8421030.421052
410.5298760.9402480.470124
420.4823070.9646140.517693
430.4340270.8680530.565973
440.4358030.8716050.564197
450.9169640.1660710.0830357
460.9044470.1911060.0955531
470.8907770.2184470.109223
480.8699130.2601730.130087
490.8532580.2934830.146742
500.8296890.3406230.170311
510.8706630.2586740.129337
520.8663540.2672930.133646
530.8427160.3145680.157284
540.8148650.370270.185135
550.7886360.4227270.211364
560.7583290.4833420.241671
570.7697340.4605320.230266
580.7444650.5110710.255535
590.7082420.5835170.291758
600.731170.537660.26883
610.7197650.560470.280235
620.683620.6327590.31638
630.6458820.7082360.354118
640.6369640.7260730.363036
650.6493460.7013090.350654
660.6076010.7847980.392399
670.5696220.8607570.430378
680.5813570.8372850.418643
690.5378170.9243660.462183
700.5749540.8500910.425046
710.7013290.5973420.298671
720.6632390.6735230.336761
730.6338010.7323990.366199
740.5955730.8088530.404427
750.55430.8913990.4457
760.5766980.8466030.423302
770.5792980.8414040.420702
780.5406410.9187190.459359
790.5153110.9693770.484689
800.4938560.9877110.506144
810.8508330.2983350.149167
820.8490520.3018960.150948
830.8280130.3439750.171987
840.8001190.3997610.199881
850.7710590.4578820.228941
860.740410.5191810.25959
870.7053480.5893040.294652
880.6672730.6654530.332727
890.6465830.7068340.353417
900.6486230.7027530.351377
910.6245590.7508830.375441
920.5888310.8223390.411169
930.5552260.8895480.444774
940.5185030.9629940.481497
950.4813860.9627720.518614
960.4462750.8925490.553725
970.4548320.9096640.545168
980.4127320.8254630.587268
990.4012940.8025880.598706
1000.3848570.7697140.615143
1010.368640.737280.63136
1020.3284210.6568430.671579
1030.2929730.5859450.707027
1040.2812720.5625430.718728
1050.2474260.4948530.752574
1060.2156630.4313250.784337
1070.1901830.3803670.809817
1080.1612590.3225180.838741
1090.1439380.2878770.856062
1100.1213870.2427740.878613
1110.1000680.2001360.899932
1120.3944080.7888150.605592
1130.3587490.7174980.641251
1140.3428410.6856810.657159
1150.3294370.6588750.670563
1160.2918410.5836820.708159
1170.2913360.5826710.708664
1180.2871070.5742150.712893
1190.2542690.5085370.745731
1200.3886940.7773880.611306
1210.3612570.7225140.638743
1220.3198510.6397010.680149
1230.2944080.5888160.705592
1240.2701830.5403660.729817
1250.2309280.4618570.769072
1260.2657660.5315310.734234
1270.2498110.4996220.750189
1280.2115110.4230220.788489
1290.1849460.3698920.815054
1300.1767810.3535620.823219
1310.1703670.3407330.829633
1320.1396240.2792480.860376
1330.1176790.2353580.882321
1340.09483730.1896750.905163
1350.09160480.183210.908395
1360.09776220.1955240.902238
1370.08394310.1678860.916057
1380.06569910.1313980.934301
1390.05017730.1003550.949823
1400.04152070.08304130.958479
1410.03439480.06878960.965605
1420.036660.073320.96334
1430.03487940.06975890.965121
1440.04800790.09601580.951992
1450.04170220.08340440.958298
1460.03353050.0670610.966469
1470.02360390.04720770.976396
1480.04305240.08610480.956948
1490.0328910.06578210.967109
1500.0225670.0451340.977433
1510.01626140.03252270.983739
1520.0131180.02623610.986882
1530.008288960.01657790.991711
1540.07718610.1543720.922814
1550.05396220.1079240.946038
1560.03729990.07459970.9627
1570.2204830.4409670.779517
1580.1970530.3941050.802947
1590.1543520.3087040.845648
1600.9631640.07367280.0368364
1610.9510630.09787320.0489366
1620.9057280.1885450.0942723
1630.8977540.2044920.102246
1640.8506920.2986170.149308
1650.7168290.5663420.283171







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.05625NOK
10% type I error level250.15625NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 9 & 0.05625 & NOK \tabularnewline
10% type I error level & 25 & 0.15625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269979&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.05625[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.15625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269979&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269979&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 level00OK
5% type I error level90.05625NOK
10% type I error level250.15625NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}