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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 computationMon, 15 Dec 2014 19:08:53 +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/15/t14186705397s0saklwi4ovfqw.htm/, Retrieved Thu, 16 May 2024 23:29:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268923, Retrieved Thu, 16 May 2024 23:29:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact55

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD    [Multiple Regression] [] [2014-12-15 19:08:53] [457d039f1491608548baeb848eb0333c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
21	16	20	25	20	23	16,1
20	19	17	19	24	21	12,7
14	9	7	25	18	22	12,3
18	22	16	25	26	24	11,6
23	24	20	28	20	25	12,1
17	13	8	19	17	23	12,6
21	17	21	26	21	24	4,3
14	12	8	20	21	24	5,6
25	19	17	27	18	27	9,6
22	16	17	18	20	18	12,6
26	25	21	28	22	21	18,9
11	12	10	14	19	23	11,6
24	20	22	27	8	27	14,6
28	19	19	24	15	20	13,85
23	18	17	26	18	25	15,9
15	18	17	23	20	23	10,95
20	23	11	21	22	24	15,1
28	28	24	24	26	25	15,95
19	21	16	24	23	22	14,6
20	18	15	24	23	27	17,6
17	17	13	23	16	24	15,35
12	14	9	20	23	19	12,9
20	21	12	27	20	25	12,6
18	14	14	19	19	24	10,35
21	24	22	25	26	24	15,4
24	16	19	25	9	24	9,6
17	17	16	17	13	17	14,85
22	21	20	19	17	14	13,6
18	11	9	16	17	15	12,65
21	18	11	26	20	28	16,6
21	19	14	24	18	24	11,2
15	11	11	19	16	25	15,85
19	14	13	20	23	26	11,15
22	21	19	25	23	23	15,6
25	23	19	27	28	25	13,1
21	21	23	25	22	22	12,4
21	19	17	22	18	24	14,9
21	18	8	24	15	20	11,2
22	19	16	26	24	28	14,6
18	18	14	23	18	26	14,75
17	20	19	25	25	24	7,85
18	12	12	21	14	20	7,85
19	15	18	17	16	16	10,95
15	14	15	20	13	20	9,95
24	18	20	22	19	23	14,9
19	19	12	23	16	18	13,4
23	24	19	21	23	23	16,85
23	21	18	24	20	26	10,95
17	22	8	25	26	24	12,2
22	20	18	21	21	23	15,2
16	16	13	16	25	24	8,1
24	24	19	22	25	24	7,4
15	16	12	19	15	20	6,7
17	19	16	25	20	20	12,6
19	15	9	16	15	21	13,3
28	28	28	28	28	28	11,1
26	21	20	21	11	10	8,2
15	18	16	22	22	22	11,4
26	22	22	24	22	19	6,4
15	16	12	20	21	25	11,9
26	20	17	23	24	22	13,8
22	23	18	22	25	21	11,7
20	18	15	21	23	26	10,9
22	21	21	21	22	24	9,9
20	19	19	27	21	24	9,0
17	20	14	23	25	23	10,8
22	17	11	27	18	23	13,0
17	8	10	25	26	25	10,8
18	14	11	24	12	24	10,9
24	21	17	20	20	23	13,3
23	20	14	19	24	27	10,1
20	18	16	21	22	23	14,3
22	24	15	18	23	23	9,3
21	15	10	27	19	20	14,5
19	18	16	23	22	26	7,7
20	17	15	22	14	15	4,35
6	6	4	24	5	27	12,7
15	22	9	19	25	23	18,1
18	20	18	25	21	23	17,85
21	17	12	24	9	22	17,1
20	23	17	23	23	21	16,1
25	22	20	27	24	25	14,7
16	20	16	24	16	24	10,6
20	20	15	26	20	22	12,6
14	13	10	21	15	28	16,2
22	16	16	25	18	22	13,6
20	16	15	19	21	23	14,1
17	15	16	20	21	19	14,5
22	19	9	27	20	25	14,75
20	24	19	23	24	23	14,8
17	9	7	18	15	28	12,45
22	22	23	23	24	14	12,65
17	15	14	21	18	23	17,35
22	22	10	23	24	24	8,6
25	24	12	21	15	15	16,1
19	21	7	24	20	26	17,75
24	25	20	26	26	21	15,25
17	26	9	24	26	26	17,65
26	28	19	20	16	16	13,6
21	16	14	25	11	21	18,25
21	21	14	23	18	19	16
19	22	15	20	19	21	18,25
27	24	22	23	21	17	18,95
22	22	17	18	24	17	16,1
22	21	13	23	19	16	15,4
21	20	15	21	21	18	15,4
19	17	11	19	19	17	13,35
21	23	7	18	23	27	19,1
8	17	22	5	6	8	19,25
17	19	15	28	23	28	12,75
25	19	11	27	20	24	9,85
24	23	10	23	23	25	15,25
17	16	16	19	12	22	12,4
22	21	16	24	24	22	18,15
19	20	14	19	19	21	12,35
19	19	10	23	28	21	15,6
23	22	17	25	23	26	18,4
20	18	12	26	21	26	12,85
23	23	8	23	25	19	9,5
21	20	17	22	18	21	4,5
23	23	17	22	28	24	13,6
11	13	7	17	9	6	11,7
18	10	13	24	12	24	14,05
24	24	15	22	25	21	13,35
20	21	15	23	24	26	11,85
14	16	11	20	22	23	13,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268923&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268923&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268923&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 time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOTAAL[t] = + 11.4693 -0.057895I1[t] + 0.336279I2[t] -0.105361I3[t] -0.0572523E1[t] -0.117733E2[t] + 0.0729723E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOTAAL[t] =  +  11.4693 -0.057895I1[t] +  0.336279I2[t] -0.105361I3[t] -0.0572523E1[t] -0.117733E2[t] +  0.0729723E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268923&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOTAAL[t] =  +  11.4693 -0.057895I1[t] +  0.336279I2[t] -0.105361I3[t] -0.0572523E1[t] -0.117733E2[t] +  0.0729723E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268923&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268923&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
TOTAAL[t] = + 11.4693 -0.057895I1[t] + 0.336279I2[t] -0.105361I3[t] -0.0572523E1[t] -0.117733E2[t] + 0.0729723E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.46932.337234.9072.9644e-061.4822e-06
I1-0.0578950.1091-0.53070.5966440.298322
I20.3362790.1080523.1120.002325740.00116287
I3-0.1053610.0798969-1.3190.1897970.0948983
E1-0.05725230.106481-0.53770.5918040.295902
E2-0.1177330.0771885-1.5250.1298480.0649238
E30.07297230.09084430.80330.4234220.211711

\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) & 11.4693 & 2.33723 & 4.907 & 2.9644e-06 & 1.4822e-06 \tabularnewline
I1 & -0.057895 & 0.1091 & -0.5307 & 0.596644 & 0.298322 \tabularnewline
I2 & 0.336279 & 0.108052 & 3.112 & 0.00232574 & 0.00116287 \tabularnewline
I3 & -0.105361 & 0.0798969 & -1.319 & 0.189797 & 0.0948983 \tabularnewline
E1 & -0.0572523 & 0.106481 & -0.5377 & 0.591804 & 0.295902 \tabularnewline
E2 & -0.117733 & 0.0771885 & -1.525 & 0.129848 & 0.0649238 \tabularnewline
E3 & 0.0729723 & 0.0908443 & 0.8033 & 0.423422 & 0.211711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268923&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]11.4693[/C][C]2.33723[/C][C]4.907[/C][C]2.9644e-06[/C][C]1.4822e-06[/C][/ROW]
[ROW][C]I1[/C][C]-0.057895[/C][C]0.1091[/C][C]-0.5307[/C][C]0.596644[/C][C]0.298322[/C][/ROW]
[ROW][C]I2[/C][C]0.336279[/C][C]0.108052[/C][C]3.112[/C][C]0.00232574[/C][C]0.00116287[/C][/ROW]
[ROW][C]I3[/C][C]-0.105361[/C][C]0.0798969[/C][C]-1.319[/C][C]0.189797[/C][C]0.0948983[/C][/ROW]
[ROW][C]E1[/C][C]-0.0572523[/C][C]0.106481[/C][C]-0.5377[/C][C]0.591804[/C][C]0.295902[/C][/ROW]
[ROW][C]E2[/C][C]-0.117733[/C][C]0.0771885[/C][C]-1.525[/C][C]0.129848[/C][C]0.0649238[/C][/ROW]
[ROW][C]E3[/C][C]0.0729723[/C][C]0.0908443[/C][C]0.8033[/C][C]0.423422[/C][C]0.211711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268923&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268923&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)11.46932.337234.9072.9644e-061.4822e-06
I1-0.0578950.1091-0.53070.5966440.298322
I20.3362790.1080523.1120.002325740.00116287
I3-0.1053610.0798969-1.3190.1897970.0948983
E1-0.05725230.106481-0.53770.5918040.295902
E2-0.1177330.0771885-1.5250.1298480.0649238
E30.07297230.09084430.80330.4234220.211711






Multiple Linear Regression - Regression Statistics
Multiple R0.287043
R-squared0.0823938
Adjusted R-squared0.0361279
F-TEST (value)1.78088
F-TEST (DF numerator)6
F-TEST (DF denominator)119
p-value0.108703
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.20559
Sum Squared Residuals1222.82

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.287043 \tabularnewline
R-squared & 0.0823938 \tabularnewline
Adjusted R-squared & 0.0361279 \tabularnewline
F-TEST (value) & 1.78088 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 119 \tabularnewline
p-value & 0.108703 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.20559 \tabularnewline
Sum Squared Residuals & 1222.82 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268923&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.287043[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0823938[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0361279[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.78088[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]119[/C][/ROW]
[ROW][C]p-value[/C][C]0.108703[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.20559[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1222.82[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268923&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268923&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 R0.287043
R-squared0.0823938
Adjusted R-squared0.0361279
F-TEST (value)1.78088
F-TEST (DF numerator)6
F-TEST (DF denominator)119
p-value0.108703
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.20559
Sum Squared Residuals1222.82






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116.111.41914.68088
212.712.52860.171424
312.311.00261.29737
411.613.3985-1.7985
512.113.9677-1.86775
612.612.6029-0.00291279
74.311.548-7.24803
85.611.9851-6.38511
99.612.9253-3.32531
1012.611.71320.886784
1118.913.49765.40237
1211.612.4541-0.854078
1314.613.970.629996
1413.8512.55511.29495
1515.912.61613.28387
1610.9512.8696-1.91964
1715.114.84570.254265
1815.9514.12461.82544
1914.613.26881.33117
2017.612.67234.92768
2115.3513.38291.96708
2212.912.06780.832232
2312.614.0327-1.43274
2410.3512.0866-1.73664
2515.413.26522.13479
269.612.7188-3.11883
2714.8513.25271.59726
2813.613.08260.517419
2912.6511.35511.29492
3016.613.34753.25246
3111.213.4258-2.22581
3215.8511.99373.85626
3311.1511.7519-0.601865
3415.612.79482.80522
3513.112.73640.363567
3612.412.476-0.0759941
3714.913.22421.67576
3811.213.783-2.58301
3914.612.62821.97181
4014.7513.46641.28358
417.8512.5855-4.73549
427.8511.8071-3.95707
4310.9511.8275-0.877499
449.9512.5122-2.56221
4514.912.20752.69252
4613.413.6072-0.207211
4716.8513.97472.87527
4810.9513.4716-2.52162
4912.214.2993-2.09929
5015.213.02832.17166
518.112.4457-4.34571
527.413.6971-6.29709
536.713.3226-6.62264
5412.612.8621-0.262066
5513.313.3156-0.0155961
5611.113.4576-2.35755
578.213.151-4.951
5811.412.7238-1.32381
596.412.4665-6.06649
6011.912.9239-1.02386
6113.812.36141.43855
6211.713.363-1.66305
6310.912.7711-1.87111
649.913.0038-3.10378
65912.4319-3.43195
6610.813.1538-2.35383
671312.76670.233283
6810.89.453641.34636
6910.912.9406-2.04059
7013.313.5292-0.229175
7110.113.4451-3.34509
7214.312.56461.73544
739.314.6258-5.32583
7414.511.92082.57923
757.712.7269-5.02687
764.3512.6345-8.28448
7712.712.7257-0.0256706
7818.114.6983.40201
7917.8513.03094.81909
8017.113.87763.22237
8116.113.76242.33759
8214.712.76571.93428
8310.614.0763-3.47631
8412.613.2187-0.618713
8516.213.05173.1483
8613.611.94521.65483
8714.112.22961.8704
8814.511.61252.88749
8914.7513.56051.18952
9014.813.91620.883819
9112.4512.02070.429261
9212.6512.04960.600361
9317.3512.41114.93893
948.614.1491-5.54906
9516.114.95461.14545
9617.7514.86222.88783
9715.2513.36241.88765
9817.6515.74221.90776
9913.615.5167-1.91674
10018.2512.96495.28506
1011613.79082.20924
10218.2514.33743.91256
10318.9513.11025.8398
10416.113.1872.91302
10515.413.50161.89842
10615.413.03752.36254
10713.3512.84290.507146
10819.115.48223.61777
10919.2513.9965.25397
11012.7513.0263-0.276251
1119.8513.1031-3.2531
11215.2514.56030.689749
11312.413.2845-0.884549
11418.1512.97745.17259
11512.3513.8275-1.4775
11615.612.62412.97594
11718.413.50284.89719
11812.8513.0364-0.186396
1199.514.1556-4.65557
1204.513.3416-8.8416
12113.613.27620.323765
12211.712.8715-1.17148
12314.0511.38472.66525
12413.3513.8996-0.549622
12511.8513.5477-1.69771
12613.212.82340.376565

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16.1 & 11.4191 & 4.68088 \tabularnewline
2 & 12.7 & 12.5286 & 0.171424 \tabularnewline
3 & 12.3 & 11.0026 & 1.29737 \tabularnewline
4 & 11.6 & 13.3985 & -1.7985 \tabularnewline
5 & 12.1 & 13.9677 & -1.86775 \tabularnewline
6 & 12.6 & 12.6029 & -0.00291279 \tabularnewline
7 & 4.3 & 11.548 & -7.24803 \tabularnewline
8 & 5.6 & 11.9851 & -6.38511 \tabularnewline
9 & 9.6 & 12.9253 & -3.32531 \tabularnewline
10 & 12.6 & 11.7132 & 0.886784 \tabularnewline
11 & 18.9 & 13.4976 & 5.40237 \tabularnewline
12 & 11.6 & 12.4541 & -0.854078 \tabularnewline
13 & 14.6 & 13.97 & 0.629996 \tabularnewline
14 & 13.85 & 12.5551 & 1.29495 \tabularnewline
15 & 15.9 & 12.6161 & 3.28387 \tabularnewline
16 & 10.95 & 12.8696 & -1.91964 \tabularnewline
17 & 15.1 & 14.8457 & 0.254265 \tabularnewline
18 & 15.95 & 14.1246 & 1.82544 \tabularnewline
19 & 14.6 & 13.2688 & 1.33117 \tabularnewline
20 & 17.6 & 12.6723 & 4.92768 \tabularnewline
21 & 15.35 & 13.3829 & 1.96708 \tabularnewline
22 & 12.9 & 12.0678 & 0.832232 \tabularnewline
23 & 12.6 & 14.0327 & -1.43274 \tabularnewline
24 & 10.35 & 12.0866 & -1.73664 \tabularnewline
25 & 15.4 & 13.2652 & 2.13479 \tabularnewline
26 & 9.6 & 12.7188 & -3.11883 \tabularnewline
27 & 14.85 & 13.2527 & 1.59726 \tabularnewline
28 & 13.6 & 13.0826 & 0.517419 \tabularnewline
29 & 12.65 & 11.3551 & 1.29492 \tabularnewline
30 & 16.6 & 13.3475 & 3.25246 \tabularnewline
31 & 11.2 & 13.4258 & -2.22581 \tabularnewline
32 & 15.85 & 11.9937 & 3.85626 \tabularnewline
33 & 11.15 & 11.7519 & -0.601865 \tabularnewline
34 & 15.6 & 12.7948 & 2.80522 \tabularnewline
35 & 13.1 & 12.7364 & 0.363567 \tabularnewline
36 & 12.4 & 12.476 & -0.0759941 \tabularnewline
37 & 14.9 & 13.2242 & 1.67576 \tabularnewline
38 & 11.2 & 13.783 & -2.58301 \tabularnewline
39 & 14.6 & 12.6282 & 1.97181 \tabularnewline
40 & 14.75 & 13.4664 & 1.28358 \tabularnewline
41 & 7.85 & 12.5855 & -4.73549 \tabularnewline
42 & 7.85 & 11.8071 & -3.95707 \tabularnewline
43 & 10.95 & 11.8275 & -0.877499 \tabularnewline
44 & 9.95 & 12.5122 & -2.56221 \tabularnewline
45 & 14.9 & 12.2075 & 2.69252 \tabularnewline
46 & 13.4 & 13.6072 & -0.207211 \tabularnewline
47 & 16.85 & 13.9747 & 2.87527 \tabularnewline
48 & 10.95 & 13.4716 & -2.52162 \tabularnewline
49 & 12.2 & 14.2993 & -2.09929 \tabularnewline
50 & 15.2 & 13.0283 & 2.17166 \tabularnewline
51 & 8.1 & 12.4457 & -4.34571 \tabularnewline
52 & 7.4 & 13.6971 & -6.29709 \tabularnewline
53 & 6.7 & 13.3226 & -6.62264 \tabularnewline
54 & 12.6 & 12.8621 & -0.262066 \tabularnewline
55 & 13.3 & 13.3156 & -0.0155961 \tabularnewline
56 & 11.1 & 13.4576 & -2.35755 \tabularnewline
57 & 8.2 & 13.151 & -4.951 \tabularnewline
58 & 11.4 & 12.7238 & -1.32381 \tabularnewline
59 & 6.4 & 12.4665 & -6.06649 \tabularnewline
60 & 11.9 & 12.9239 & -1.02386 \tabularnewline
61 & 13.8 & 12.3614 & 1.43855 \tabularnewline
62 & 11.7 & 13.363 & -1.66305 \tabularnewline
63 & 10.9 & 12.7711 & -1.87111 \tabularnewline
64 & 9.9 & 13.0038 & -3.10378 \tabularnewline
65 & 9 & 12.4319 & -3.43195 \tabularnewline
66 & 10.8 & 13.1538 & -2.35383 \tabularnewline
67 & 13 & 12.7667 & 0.233283 \tabularnewline
68 & 10.8 & 9.45364 & 1.34636 \tabularnewline
69 & 10.9 & 12.9406 & -2.04059 \tabularnewline
70 & 13.3 & 13.5292 & -0.229175 \tabularnewline
71 & 10.1 & 13.4451 & -3.34509 \tabularnewline
72 & 14.3 & 12.5646 & 1.73544 \tabularnewline
73 & 9.3 & 14.6258 & -5.32583 \tabularnewline
74 & 14.5 & 11.9208 & 2.57923 \tabularnewline
75 & 7.7 & 12.7269 & -5.02687 \tabularnewline
76 & 4.35 & 12.6345 & -8.28448 \tabularnewline
77 & 12.7 & 12.7257 & -0.0256706 \tabularnewline
78 & 18.1 & 14.698 & 3.40201 \tabularnewline
79 & 17.85 & 13.0309 & 4.81909 \tabularnewline
80 & 17.1 & 13.8776 & 3.22237 \tabularnewline
81 & 16.1 & 13.7624 & 2.33759 \tabularnewline
82 & 14.7 & 12.7657 & 1.93428 \tabularnewline
83 & 10.6 & 14.0763 & -3.47631 \tabularnewline
84 & 12.6 & 13.2187 & -0.618713 \tabularnewline
85 & 16.2 & 13.0517 & 3.1483 \tabularnewline
86 & 13.6 & 11.9452 & 1.65483 \tabularnewline
87 & 14.1 & 12.2296 & 1.8704 \tabularnewline
88 & 14.5 & 11.6125 & 2.88749 \tabularnewline
89 & 14.75 & 13.5605 & 1.18952 \tabularnewline
90 & 14.8 & 13.9162 & 0.883819 \tabularnewline
91 & 12.45 & 12.0207 & 0.429261 \tabularnewline
92 & 12.65 & 12.0496 & 0.600361 \tabularnewline
93 & 17.35 & 12.4111 & 4.93893 \tabularnewline
94 & 8.6 & 14.1491 & -5.54906 \tabularnewline
95 & 16.1 & 14.9546 & 1.14545 \tabularnewline
96 & 17.75 & 14.8622 & 2.88783 \tabularnewline
97 & 15.25 & 13.3624 & 1.88765 \tabularnewline
98 & 17.65 & 15.7422 & 1.90776 \tabularnewline
99 & 13.6 & 15.5167 & -1.91674 \tabularnewline
100 & 18.25 & 12.9649 & 5.28506 \tabularnewline
101 & 16 & 13.7908 & 2.20924 \tabularnewline
102 & 18.25 & 14.3374 & 3.91256 \tabularnewline
103 & 18.95 & 13.1102 & 5.8398 \tabularnewline
104 & 16.1 & 13.187 & 2.91302 \tabularnewline
105 & 15.4 & 13.5016 & 1.89842 \tabularnewline
106 & 15.4 & 13.0375 & 2.36254 \tabularnewline
107 & 13.35 & 12.8429 & 0.507146 \tabularnewline
108 & 19.1 & 15.4822 & 3.61777 \tabularnewline
109 & 19.25 & 13.996 & 5.25397 \tabularnewline
110 & 12.75 & 13.0263 & -0.276251 \tabularnewline
111 & 9.85 & 13.1031 & -3.2531 \tabularnewline
112 & 15.25 & 14.5603 & 0.689749 \tabularnewline
113 & 12.4 & 13.2845 & -0.884549 \tabularnewline
114 & 18.15 & 12.9774 & 5.17259 \tabularnewline
115 & 12.35 & 13.8275 & -1.4775 \tabularnewline
116 & 15.6 & 12.6241 & 2.97594 \tabularnewline
117 & 18.4 & 13.5028 & 4.89719 \tabularnewline
118 & 12.85 & 13.0364 & -0.186396 \tabularnewline
119 & 9.5 & 14.1556 & -4.65557 \tabularnewline
120 & 4.5 & 13.3416 & -8.8416 \tabularnewline
121 & 13.6 & 13.2762 & 0.323765 \tabularnewline
122 & 11.7 & 12.8715 & -1.17148 \tabularnewline
123 & 14.05 & 11.3847 & 2.66525 \tabularnewline
124 & 13.35 & 13.8996 & -0.549622 \tabularnewline
125 & 11.85 & 13.5477 & -1.69771 \tabularnewline
126 & 13.2 & 12.8234 & 0.376565 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268923&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]16.1[/C][C]11.4191[/C][C]4.68088[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]12.5286[/C][C]0.171424[/C][/ROW]
[ROW][C]3[/C][C]12.3[/C][C]11.0026[/C][C]1.29737[/C][/ROW]
[ROW][C]4[/C][C]11.6[/C][C]13.3985[/C][C]-1.7985[/C][/ROW]
[ROW][C]5[/C][C]12.1[/C][C]13.9677[/C][C]-1.86775[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.6029[/C][C]-0.00291279[/C][/ROW]
[ROW][C]7[/C][C]4.3[/C][C]11.548[/C][C]-7.24803[/C][/ROW]
[ROW][C]8[/C][C]5.6[/C][C]11.9851[/C][C]-6.38511[/C][/ROW]
[ROW][C]9[/C][C]9.6[/C][C]12.9253[/C][C]-3.32531[/C][/ROW]
[ROW][C]10[/C][C]12.6[/C][C]11.7132[/C][C]0.886784[/C][/ROW]
[ROW][C]11[/C][C]18.9[/C][C]13.4976[/C][C]5.40237[/C][/ROW]
[ROW][C]12[/C][C]11.6[/C][C]12.4541[/C][C]-0.854078[/C][/ROW]
[ROW][C]13[/C][C]14.6[/C][C]13.97[/C][C]0.629996[/C][/ROW]
[ROW][C]14[/C][C]13.85[/C][C]12.5551[/C][C]1.29495[/C][/ROW]
[ROW][C]15[/C][C]15.9[/C][C]12.6161[/C][C]3.28387[/C][/ROW]
[ROW][C]16[/C][C]10.95[/C][C]12.8696[/C][C]-1.91964[/C][/ROW]
[ROW][C]17[/C][C]15.1[/C][C]14.8457[/C][C]0.254265[/C][/ROW]
[ROW][C]18[/C][C]15.95[/C][C]14.1246[/C][C]1.82544[/C][/ROW]
[ROW][C]19[/C][C]14.6[/C][C]13.2688[/C][C]1.33117[/C][/ROW]
[ROW][C]20[/C][C]17.6[/C][C]12.6723[/C][C]4.92768[/C][/ROW]
[ROW][C]21[/C][C]15.35[/C][C]13.3829[/C][C]1.96708[/C][/ROW]
[ROW][C]22[/C][C]12.9[/C][C]12.0678[/C][C]0.832232[/C][/ROW]
[ROW][C]23[/C][C]12.6[/C][C]14.0327[/C][C]-1.43274[/C][/ROW]
[ROW][C]24[/C][C]10.35[/C][C]12.0866[/C][C]-1.73664[/C][/ROW]
[ROW][C]25[/C][C]15.4[/C][C]13.2652[/C][C]2.13479[/C][/ROW]
[ROW][C]26[/C][C]9.6[/C][C]12.7188[/C][C]-3.11883[/C][/ROW]
[ROW][C]27[/C][C]14.85[/C][C]13.2527[/C][C]1.59726[/C][/ROW]
[ROW][C]28[/C][C]13.6[/C][C]13.0826[/C][C]0.517419[/C][/ROW]
[ROW][C]29[/C][C]12.65[/C][C]11.3551[/C][C]1.29492[/C][/ROW]
[ROW][C]30[/C][C]16.6[/C][C]13.3475[/C][C]3.25246[/C][/ROW]
[ROW][C]31[/C][C]11.2[/C][C]13.4258[/C][C]-2.22581[/C][/ROW]
[ROW][C]32[/C][C]15.85[/C][C]11.9937[/C][C]3.85626[/C][/ROW]
[ROW][C]33[/C][C]11.15[/C][C]11.7519[/C][C]-0.601865[/C][/ROW]
[ROW][C]34[/C][C]15.6[/C][C]12.7948[/C][C]2.80522[/C][/ROW]
[ROW][C]35[/C][C]13.1[/C][C]12.7364[/C][C]0.363567[/C][/ROW]
[ROW][C]36[/C][C]12.4[/C][C]12.476[/C][C]-0.0759941[/C][/ROW]
[ROW][C]37[/C][C]14.9[/C][C]13.2242[/C][C]1.67576[/C][/ROW]
[ROW][C]38[/C][C]11.2[/C][C]13.783[/C][C]-2.58301[/C][/ROW]
[ROW][C]39[/C][C]14.6[/C][C]12.6282[/C][C]1.97181[/C][/ROW]
[ROW][C]40[/C][C]14.75[/C][C]13.4664[/C][C]1.28358[/C][/ROW]
[ROW][C]41[/C][C]7.85[/C][C]12.5855[/C][C]-4.73549[/C][/ROW]
[ROW][C]42[/C][C]7.85[/C][C]11.8071[/C][C]-3.95707[/C][/ROW]
[ROW][C]43[/C][C]10.95[/C][C]11.8275[/C][C]-0.877499[/C][/ROW]
[ROW][C]44[/C][C]9.95[/C][C]12.5122[/C][C]-2.56221[/C][/ROW]
[ROW][C]45[/C][C]14.9[/C][C]12.2075[/C][C]2.69252[/C][/ROW]
[ROW][C]46[/C][C]13.4[/C][C]13.6072[/C][C]-0.207211[/C][/ROW]
[ROW][C]47[/C][C]16.85[/C][C]13.9747[/C][C]2.87527[/C][/ROW]
[ROW][C]48[/C][C]10.95[/C][C]13.4716[/C][C]-2.52162[/C][/ROW]
[ROW][C]49[/C][C]12.2[/C][C]14.2993[/C][C]-2.09929[/C][/ROW]
[ROW][C]50[/C][C]15.2[/C][C]13.0283[/C][C]2.17166[/C][/ROW]
[ROW][C]51[/C][C]8.1[/C][C]12.4457[/C][C]-4.34571[/C][/ROW]
[ROW][C]52[/C][C]7.4[/C][C]13.6971[/C][C]-6.29709[/C][/ROW]
[ROW][C]53[/C][C]6.7[/C][C]13.3226[/C][C]-6.62264[/C][/ROW]
[ROW][C]54[/C][C]12.6[/C][C]12.8621[/C][C]-0.262066[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]13.3156[/C][C]-0.0155961[/C][/ROW]
[ROW][C]56[/C][C]11.1[/C][C]13.4576[/C][C]-2.35755[/C][/ROW]
[ROW][C]57[/C][C]8.2[/C][C]13.151[/C][C]-4.951[/C][/ROW]
[ROW][C]58[/C][C]11.4[/C][C]12.7238[/C][C]-1.32381[/C][/ROW]
[ROW][C]59[/C][C]6.4[/C][C]12.4665[/C][C]-6.06649[/C][/ROW]
[ROW][C]60[/C][C]11.9[/C][C]12.9239[/C][C]-1.02386[/C][/ROW]
[ROW][C]61[/C][C]13.8[/C][C]12.3614[/C][C]1.43855[/C][/ROW]
[ROW][C]62[/C][C]11.7[/C][C]13.363[/C][C]-1.66305[/C][/ROW]
[ROW][C]63[/C][C]10.9[/C][C]12.7711[/C][C]-1.87111[/C][/ROW]
[ROW][C]64[/C][C]9.9[/C][C]13.0038[/C][C]-3.10378[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]12.4319[/C][C]-3.43195[/C][/ROW]
[ROW][C]66[/C][C]10.8[/C][C]13.1538[/C][C]-2.35383[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]12.7667[/C][C]0.233283[/C][/ROW]
[ROW][C]68[/C][C]10.8[/C][C]9.45364[/C][C]1.34636[/C][/ROW]
[ROW][C]69[/C][C]10.9[/C][C]12.9406[/C][C]-2.04059[/C][/ROW]
[ROW][C]70[/C][C]13.3[/C][C]13.5292[/C][C]-0.229175[/C][/ROW]
[ROW][C]71[/C][C]10.1[/C][C]13.4451[/C][C]-3.34509[/C][/ROW]
[ROW][C]72[/C][C]14.3[/C][C]12.5646[/C][C]1.73544[/C][/ROW]
[ROW][C]73[/C][C]9.3[/C][C]14.6258[/C][C]-5.32583[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]11.9208[/C][C]2.57923[/C][/ROW]
[ROW][C]75[/C][C]7.7[/C][C]12.7269[/C][C]-5.02687[/C][/ROW]
[ROW][C]76[/C][C]4.35[/C][C]12.6345[/C][C]-8.28448[/C][/ROW]
[ROW][C]77[/C][C]12.7[/C][C]12.7257[/C][C]-0.0256706[/C][/ROW]
[ROW][C]78[/C][C]18.1[/C][C]14.698[/C][C]3.40201[/C][/ROW]
[ROW][C]79[/C][C]17.85[/C][C]13.0309[/C][C]4.81909[/C][/ROW]
[ROW][C]80[/C][C]17.1[/C][C]13.8776[/C][C]3.22237[/C][/ROW]
[ROW][C]81[/C][C]16.1[/C][C]13.7624[/C][C]2.33759[/C][/ROW]
[ROW][C]82[/C][C]14.7[/C][C]12.7657[/C][C]1.93428[/C][/ROW]
[ROW][C]83[/C][C]10.6[/C][C]14.0763[/C][C]-3.47631[/C][/ROW]
[ROW][C]84[/C][C]12.6[/C][C]13.2187[/C][C]-0.618713[/C][/ROW]
[ROW][C]85[/C][C]16.2[/C][C]13.0517[/C][C]3.1483[/C][/ROW]
[ROW][C]86[/C][C]13.6[/C][C]11.9452[/C][C]1.65483[/C][/ROW]
[ROW][C]87[/C][C]14.1[/C][C]12.2296[/C][C]1.8704[/C][/ROW]
[ROW][C]88[/C][C]14.5[/C][C]11.6125[/C][C]2.88749[/C][/ROW]
[ROW][C]89[/C][C]14.75[/C][C]13.5605[/C][C]1.18952[/C][/ROW]
[ROW][C]90[/C][C]14.8[/C][C]13.9162[/C][C]0.883819[/C][/ROW]
[ROW][C]91[/C][C]12.45[/C][C]12.0207[/C][C]0.429261[/C][/ROW]
[ROW][C]92[/C][C]12.65[/C][C]12.0496[/C][C]0.600361[/C][/ROW]
[ROW][C]93[/C][C]17.35[/C][C]12.4111[/C][C]4.93893[/C][/ROW]
[ROW][C]94[/C][C]8.6[/C][C]14.1491[/C][C]-5.54906[/C][/ROW]
[ROW][C]95[/C][C]16.1[/C][C]14.9546[/C][C]1.14545[/C][/ROW]
[ROW][C]96[/C][C]17.75[/C][C]14.8622[/C][C]2.88783[/C][/ROW]
[ROW][C]97[/C][C]15.25[/C][C]13.3624[/C][C]1.88765[/C][/ROW]
[ROW][C]98[/C][C]17.65[/C][C]15.7422[/C][C]1.90776[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]15.5167[/C][C]-1.91674[/C][/ROW]
[ROW][C]100[/C][C]18.25[/C][C]12.9649[/C][C]5.28506[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]13.7908[/C][C]2.20924[/C][/ROW]
[ROW][C]102[/C][C]18.25[/C][C]14.3374[/C][C]3.91256[/C][/ROW]
[ROW][C]103[/C][C]18.95[/C][C]13.1102[/C][C]5.8398[/C][/ROW]
[ROW][C]104[/C][C]16.1[/C][C]13.187[/C][C]2.91302[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]13.5016[/C][C]1.89842[/C][/ROW]
[ROW][C]106[/C][C]15.4[/C][C]13.0375[/C][C]2.36254[/C][/ROW]
[ROW][C]107[/C][C]13.35[/C][C]12.8429[/C][C]0.507146[/C][/ROW]
[ROW][C]108[/C][C]19.1[/C][C]15.4822[/C][C]3.61777[/C][/ROW]
[ROW][C]109[/C][C]19.25[/C][C]13.996[/C][C]5.25397[/C][/ROW]
[ROW][C]110[/C][C]12.75[/C][C]13.0263[/C][C]-0.276251[/C][/ROW]
[ROW][C]111[/C][C]9.85[/C][C]13.1031[/C][C]-3.2531[/C][/ROW]
[ROW][C]112[/C][C]15.25[/C][C]14.5603[/C][C]0.689749[/C][/ROW]
[ROW][C]113[/C][C]12.4[/C][C]13.2845[/C][C]-0.884549[/C][/ROW]
[ROW][C]114[/C][C]18.15[/C][C]12.9774[/C][C]5.17259[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]13.8275[/C][C]-1.4775[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]12.6241[/C][C]2.97594[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]13.5028[/C][C]4.89719[/C][/ROW]
[ROW][C]118[/C][C]12.85[/C][C]13.0364[/C][C]-0.186396[/C][/ROW]
[ROW][C]119[/C][C]9.5[/C][C]14.1556[/C][C]-4.65557[/C][/ROW]
[ROW][C]120[/C][C]4.5[/C][C]13.3416[/C][C]-8.8416[/C][/ROW]
[ROW][C]121[/C][C]13.6[/C][C]13.2762[/C][C]0.323765[/C][/ROW]
[ROW][C]122[/C][C]11.7[/C][C]12.8715[/C][C]-1.17148[/C][/ROW]
[ROW][C]123[/C][C]14.05[/C][C]11.3847[/C][C]2.66525[/C][/ROW]
[ROW][C]124[/C][C]13.35[/C][C]13.8996[/C][C]-0.549622[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]13.5477[/C][C]-1.69771[/C][/ROW]
[ROW][C]126[/C][C]13.2[/C][C]12.8234[/C][C]0.376565[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268923&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268923&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
116.111.41914.68088
212.712.52860.171424
312.311.00261.29737
411.613.3985-1.7985
512.113.9677-1.86775
612.612.6029-0.00291279
74.311.548-7.24803
85.611.9851-6.38511
99.612.9253-3.32531
1012.611.71320.886784
1118.913.49765.40237
1211.612.4541-0.854078
1314.613.970.629996
1413.8512.55511.29495
1515.912.61613.28387
1610.9512.8696-1.91964
1715.114.84570.254265
1815.9514.12461.82544
1914.613.26881.33117
2017.612.67234.92768
2115.3513.38291.96708
2212.912.06780.832232
2312.614.0327-1.43274
2410.3512.0866-1.73664
2515.413.26522.13479
269.612.7188-3.11883
2714.8513.25271.59726
2813.613.08260.517419
2912.6511.35511.29492
3016.613.34753.25246
3111.213.4258-2.22581
3215.8511.99373.85626
3311.1511.7519-0.601865
3415.612.79482.80522
3513.112.73640.363567
3612.412.476-0.0759941
3714.913.22421.67576
3811.213.783-2.58301
3914.612.62821.97181
4014.7513.46641.28358
417.8512.5855-4.73549
427.8511.8071-3.95707
4310.9511.8275-0.877499
449.9512.5122-2.56221
4514.912.20752.69252
4613.413.6072-0.207211
4716.8513.97472.87527
4810.9513.4716-2.52162
4912.214.2993-2.09929
5015.213.02832.17166
518.112.4457-4.34571
527.413.6971-6.29709
536.713.3226-6.62264
5412.612.8621-0.262066
5513.313.3156-0.0155961
5611.113.4576-2.35755
578.213.151-4.951
5811.412.7238-1.32381
596.412.4665-6.06649
6011.912.9239-1.02386
6113.812.36141.43855
6211.713.363-1.66305
6310.912.7711-1.87111
649.913.0038-3.10378
65912.4319-3.43195
6610.813.1538-2.35383
671312.76670.233283
6810.89.453641.34636
6910.912.9406-2.04059
7013.313.5292-0.229175
7110.113.4451-3.34509
7214.312.56461.73544
739.314.6258-5.32583
7414.511.92082.57923
757.712.7269-5.02687
764.3512.6345-8.28448
7712.712.7257-0.0256706
7818.114.6983.40201
7917.8513.03094.81909
8017.113.87763.22237
8116.113.76242.33759
8214.712.76571.93428
8310.614.0763-3.47631
8412.613.2187-0.618713
8516.213.05173.1483
8613.611.94521.65483
8714.112.22961.8704
8814.511.61252.88749
8914.7513.56051.18952
9014.813.91620.883819
9112.4512.02070.429261
9212.6512.04960.600361
9317.3512.41114.93893
948.614.1491-5.54906
9516.114.95461.14545
9617.7514.86222.88783
9715.2513.36241.88765
9817.6515.74221.90776
9913.615.5167-1.91674
10018.2512.96495.28506
1011613.79082.20924
10218.2514.33743.91256
10318.9513.11025.8398
10416.113.1872.91302
10515.413.50161.89842
10615.413.03752.36254
10713.3512.84290.507146
10819.115.48223.61777
10919.2513.9965.25397
11012.7513.0263-0.276251
1119.8513.1031-3.2531
11215.2514.56030.689749
11312.413.2845-0.884549
11418.1512.97745.17259
11512.3513.8275-1.4775
11615.612.62412.97594
11718.413.50284.89719
11812.8513.0364-0.186396
1199.514.1556-4.65557
1204.513.3416-8.8416
12113.613.27620.323765
12211.712.8715-1.17148
12314.0511.38472.66525
12413.3513.8996-0.549622
12511.8513.5477-1.69771
12613.212.82340.376565






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9362610.1274780.0637392
110.8864920.2270160.113508
120.8474250.305150.152575
130.7753860.4492280.224614
140.7328520.5342960.267148
150.8138350.372330.186165
160.7657820.4684360.234218
170.6885910.6228180.311409
180.6690410.6619180.330959
190.5886460.8227070.411354
200.8086550.382690.191345
210.7749530.4500950.225047
220.7143980.5712040.285602
230.6696340.6607330.330366
240.6056760.7886470.394324
250.5606160.8787680.439384
260.5502480.8995050.449752
270.4826790.9653570.517321
280.4428230.8856450.557177
290.3770960.7541930.622904
300.3813840.7627690.618616
310.3588460.7176920.641154
320.4371720.8743440.562828
330.3762370.7524750.623763
340.3475480.6950960.652452
350.2946360.5892730.705364
360.2432160.4864330.756784
370.2071710.4143420.792829
380.2054970.4109950.794503
390.1762960.3525920.823704
400.1467460.2934920.853254
410.188480.3769610.81152
420.2032070.4064140.796793
430.1672630.3345260.832737
440.1440350.2880690.855965
450.12790.25580.8721
460.1001780.2003560.899822
470.08684230.1736850.913158
480.08539340.1707870.914607
490.07462580.1492520.925374
500.0617810.1235620.938219
510.08477740.1695550.915223
520.1893370.3786740.810663
530.3012030.6024060.698797
540.2567890.5135780.743211
550.2156080.4312150.784392
560.2040450.4080890.795955
570.2590360.5180720.740964
580.2239660.4479320.776034
590.3407530.6815060.659247
600.2986290.5972570.701371
610.2588340.5176680.741166
620.2274890.4549780.772511
630.2045750.409150.795425
640.2044650.408930.795535
650.2160380.4320760.783962
660.2027880.4055760.797212
670.1669390.3338780.833061
680.1362460.2724930.863754
690.1198720.2397440.880128
700.09548470.1909690.904515
710.1046120.2092250.895388
720.08777540.1755510.912225
730.1353750.2707510.864625
740.1248370.2496750.875163
750.2047090.4094180.795291
760.4986120.9972240.501388
770.4505720.9011450.549428
780.4760620.9521230.523938
790.5296610.9406790.470339
800.5273640.9452720.472636
810.4980660.9961330.501934
820.4488940.8977870.551106
830.4881920.9763830.511808
840.4413640.8827280.558636
850.4142470.8284930.585753
860.3632840.7265680.636716
870.3186770.6373530.681323
880.2931370.5862750.706863
890.2495360.4990720.750464
900.2107430.4214870.789257
910.1713520.3427040.828648
920.142640.2852810.85736
930.1606270.3212550.839373
940.2458870.4917730.754113
950.208920.417840.79108
960.2044380.4088770.795562
970.1654220.3308450.834578
980.1538870.3077730.846113
990.1409180.2818360.859082
1000.202940.4058790.79706
1010.1791760.3583530.820824
1020.192730.385460.80727
1030.2607390.5214770.739261
1040.2134780.4269570.786522
1050.2268960.4537920.773104
1060.19790.39580.8021
1070.1459220.2918450.854078
1080.1485030.2970070.851497
1090.1902450.380490.809755
1100.1407340.2814690.859266
1110.1286140.2572290.871386
1120.1400420.2800830.859958
1130.1096790.2193570.890321
1140.1042940.2085890.895706
1150.08845220.1769040.911548
1160.07574480.151490.924255

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.936261 & 0.127478 & 0.0637392 \tabularnewline
11 & 0.886492 & 0.227016 & 0.113508 \tabularnewline
12 & 0.847425 & 0.30515 & 0.152575 \tabularnewline
13 & 0.775386 & 0.449228 & 0.224614 \tabularnewline
14 & 0.732852 & 0.534296 & 0.267148 \tabularnewline
15 & 0.813835 & 0.37233 & 0.186165 \tabularnewline
16 & 0.765782 & 0.468436 & 0.234218 \tabularnewline
17 & 0.688591 & 0.622818 & 0.311409 \tabularnewline
18 & 0.669041 & 0.661918 & 0.330959 \tabularnewline
19 & 0.588646 & 0.822707 & 0.411354 \tabularnewline
20 & 0.808655 & 0.38269 & 0.191345 \tabularnewline
21 & 0.774953 & 0.450095 & 0.225047 \tabularnewline
22 & 0.714398 & 0.571204 & 0.285602 \tabularnewline
23 & 0.669634 & 0.660733 & 0.330366 \tabularnewline
24 & 0.605676 & 0.788647 & 0.394324 \tabularnewline
25 & 0.560616 & 0.878768 & 0.439384 \tabularnewline
26 & 0.550248 & 0.899505 & 0.449752 \tabularnewline
27 & 0.482679 & 0.965357 & 0.517321 \tabularnewline
28 & 0.442823 & 0.885645 & 0.557177 \tabularnewline
29 & 0.377096 & 0.754193 & 0.622904 \tabularnewline
30 & 0.381384 & 0.762769 & 0.618616 \tabularnewline
31 & 0.358846 & 0.717692 & 0.641154 \tabularnewline
32 & 0.437172 & 0.874344 & 0.562828 \tabularnewline
33 & 0.376237 & 0.752475 & 0.623763 \tabularnewline
34 & 0.347548 & 0.695096 & 0.652452 \tabularnewline
35 & 0.294636 & 0.589273 & 0.705364 \tabularnewline
36 & 0.243216 & 0.486433 & 0.756784 \tabularnewline
37 & 0.207171 & 0.414342 & 0.792829 \tabularnewline
38 & 0.205497 & 0.410995 & 0.794503 \tabularnewline
39 & 0.176296 & 0.352592 & 0.823704 \tabularnewline
40 & 0.146746 & 0.293492 & 0.853254 \tabularnewline
41 & 0.18848 & 0.376961 & 0.81152 \tabularnewline
42 & 0.203207 & 0.406414 & 0.796793 \tabularnewline
43 & 0.167263 & 0.334526 & 0.832737 \tabularnewline
44 & 0.144035 & 0.288069 & 0.855965 \tabularnewline
45 & 0.1279 & 0.2558 & 0.8721 \tabularnewline
46 & 0.100178 & 0.200356 & 0.899822 \tabularnewline
47 & 0.0868423 & 0.173685 & 0.913158 \tabularnewline
48 & 0.0853934 & 0.170787 & 0.914607 \tabularnewline
49 & 0.0746258 & 0.149252 & 0.925374 \tabularnewline
50 & 0.061781 & 0.123562 & 0.938219 \tabularnewline
51 & 0.0847774 & 0.169555 & 0.915223 \tabularnewline
52 & 0.189337 & 0.378674 & 0.810663 \tabularnewline
53 & 0.301203 & 0.602406 & 0.698797 \tabularnewline
54 & 0.256789 & 0.513578 & 0.743211 \tabularnewline
55 & 0.215608 & 0.431215 & 0.784392 \tabularnewline
56 & 0.204045 & 0.408089 & 0.795955 \tabularnewline
57 & 0.259036 & 0.518072 & 0.740964 \tabularnewline
58 & 0.223966 & 0.447932 & 0.776034 \tabularnewline
59 & 0.340753 & 0.681506 & 0.659247 \tabularnewline
60 & 0.298629 & 0.597257 & 0.701371 \tabularnewline
61 & 0.258834 & 0.517668 & 0.741166 \tabularnewline
62 & 0.227489 & 0.454978 & 0.772511 \tabularnewline
63 & 0.204575 & 0.40915 & 0.795425 \tabularnewline
64 & 0.204465 & 0.40893 & 0.795535 \tabularnewline
65 & 0.216038 & 0.432076 & 0.783962 \tabularnewline
66 & 0.202788 & 0.405576 & 0.797212 \tabularnewline
67 & 0.166939 & 0.333878 & 0.833061 \tabularnewline
68 & 0.136246 & 0.272493 & 0.863754 \tabularnewline
69 & 0.119872 & 0.239744 & 0.880128 \tabularnewline
70 & 0.0954847 & 0.190969 & 0.904515 \tabularnewline
71 & 0.104612 & 0.209225 & 0.895388 \tabularnewline
72 & 0.0877754 & 0.175551 & 0.912225 \tabularnewline
73 & 0.135375 & 0.270751 & 0.864625 \tabularnewline
74 & 0.124837 & 0.249675 & 0.875163 \tabularnewline
75 & 0.204709 & 0.409418 & 0.795291 \tabularnewline
76 & 0.498612 & 0.997224 & 0.501388 \tabularnewline
77 & 0.450572 & 0.901145 & 0.549428 \tabularnewline
78 & 0.476062 & 0.952123 & 0.523938 \tabularnewline
79 & 0.529661 & 0.940679 & 0.470339 \tabularnewline
80 & 0.527364 & 0.945272 & 0.472636 \tabularnewline
81 & 0.498066 & 0.996133 & 0.501934 \tabularnewline
82 & 0.448894 & 0.897787 & 0.551106 \tabularnewline
83 & 0.488192 & 0.976383 & 0.511808 \tabularnewline
84 & 0.441364 & 0.882728 & 0.558636 \tabularnewline
85 & 0.414247 & 0.828493 & 0.585753 \tabularnewline
86 & 0.363284 & 0.726568 & 0.636716 \tabularnewline
87 & 0.318677 & 0.637353 & 0.681323 \tabularnewline
88 & 0.293137 & 0.586275 & 0.706863 \tabularnewline
89 & 0.249536 & 0.499072 & 0.750464 \tabularnewline
90 & 0.210743 & 0.421487 & 0.789257 \tabularnewline
91 & 0.171352 & 0.342704 & 0.828648 \tabularnewline
92 & 0.14264 & 0.285281 & 0.85736 \tabularnewline
93 & 0.160627 & 0.321255 & 0.839373 \tabularnewline
94 & 0.245887 & 0.491773 & 0.754113 \tabularnewline
95 & 0.20892 & 0.41784 & 0.79108 \tabularnewline
96 & 0.204438 & 0.408877 & 0.795562 \tabularnewline
97 & 0.165422 & 0.330845 & 0.834578 \tabularnewline
98 & 0.153887 & 0.307773 & 0.846113 \tabularnewline
99 & 0.140918 & 0.281836 & 0.859082 \tabularnewline
100 & 0.20294 & 0.405879 & 0.79706 \tabularnewline
101 & 0.179176 & 0.358353 & 0.820824 \tabularnewline
102 & 0.19273 & 0.38546 & 0.80727 \tabularnewline
103 & 0.260739 & 0.521477 & 0.739261 \tabularnewline
104 & 0.213478 & 0.426957 & 0.786522 \tabularnewline
105 & 0.226896 & 0.453792 & 0.773104 \tabularnewline
106 & 0.1979 & 0.3958 & 0.8021 \tabularnewline
107 & 0.145922 & 0.291845 & 0.854078 \tabularnewline
108 & 0.148503 & 0.297007 & 0.851497 \tabularnewline
109 & 0.190245 & 0.38049 & 0.809755 \tabularnewline
110 & 0.140734 & 0.281469 & 0.859266 \tabularnewline
111 & 0.128614 & 0.257229 & 0.871386 \tabularnewline
112 & 0.140042 & 0.280083 & 0.859958 \tabularnewline
113 & 0.109679 & 0.219357 & 0.890321 \tabularnewline
114 & 0.104294 & 0.208589 & 0.895706 \tabularnewline
115 & 0.0884522 & 0.176904 & 0.911548 \tabularnewline
116 & 0.0757448 & 0.15149 & 0.924255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268923&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]10[/C][C]0.936261[/C][C]0.127478[/C][C]0.0637392[/C][/ROW]
[ROW][C]11[/C][C]0.886492[/C][C]0.227016[/C][C]0.113508[/C][/ROW]
[ROW][C]12[/C][C]0.847425[/C][C]0.30515[/C][C]0.152575[/C][/ROW]
[ROW][C]13[/C][C]0.775386[/C][C]0.449228[/C][C]0.224614[/C][/ROW]
[ROW][C]14[/C][C]0.732852[/C][C]0.534296[/C][C]0.267148[/C][/ROW]
[ROW][C]15[/C][C]0.813835[/C][C]0.37233[/C][C]0.186165[/C][/ROW]
[ROW][C]16[/C][C]0.765782[/C][C]0.468436[/C][C]0.234218[/C][/ROW]
[ROW][C]17[/C][C]0.688591[/C][C]0.622818[/C][C]0.311409[/C][/ROW]
[ROW][C]18[/C][C]0.669041[/C][C]0.661918[/C][C]0.330959[/C][/ROW]
[ROW][C]19[/C][C]0.588646[/C][C]0.822707[/C][C]0.411354[/C][/ROW]
[ROW][C]20[/C][C]0.808655[/C][C]0.38269[/C][C]0.191345[/C][/ROW]
[ROW][C]21[/C][C]0.774953[/C][C]0.450095[/C][C]0.225047[/C][/ROW]
[ROW][C]22[/C][C]0.714398[/C][C]0.571204[/C][C]0.285602[/C][/ROW]
[ROW][C]23[/C][C]0.669634[/C][C]0.660733[/C][C]0.330366[/C][/ROW]
[ROW][C]24[/C][C]0.605676[/C][C]0.788647[/C][C]0.394324[/C][/ROW]
[ROW][C]25[/C][C]0.560616[/C][C]0.878768[/C][C]0.439384[/C][/ROW]
[ROW][C]26[/C][C]0.550248[/C][C]0.899505[/C][C]0.449752[/C][/ROW]
[ROW][C]27[/C][C]0.482679[/C][C]0.965357[/C][C]0.517321[/C][/ROW]
[ROW][C]28[/C][C]0.442823[/C][C]0.885645[/C][C]0.557177[/C][/ROW]
[ROW][C]29[/C][C]0.377096[/C][C]0.754193[/C][C]0.622904[/C][/ROW]
[ROW][C]30[/C][C]0.381384[/C][C]0.762769[/C][C]0.618616[/C][/ROW]
[ROW][C]31[/C][C]0.358846[/C][C]0.717692[/C][C]0.641154[/C][/ROW]
[ROW][C]32[/C][C]0.437172[/C][C]0.874344[/C][C]0.562828[/C][/ROW]
[ROW][C]33[/C][C]0.376237[/C][C]0.752475[/C][C]0.623763[/C][/ROW]
[ROW][C]34[/C][C]0.347548[/C][C]0.695096[/C][C]0.652452[/C][/ROW]
[ROW][C]35[/C][C]0.294636[/C][C]0.589273[/C][C]0.705364[/C][/ROW]
[ROW][C]36[/C][C]0.243216[/C][C]0.486433[/C][C]0.756784[/C][/ROW]
[ROW][C]37[/C][C]0.207171[/C][C]0.414342[/C][C]0.792829[/C][/ROW]
[ROW][C]38[/C][C]0.205497[/C][C]0.410995[/C][C]0.794503[/C][/ROW]
[ROW][C]39[/C][C]0.176296[/C][C]0.352592[/C][C]0.823704[/C][/ROW]
[ROW][C]40[/C][C]0.146746[/C][C]0.293492[/C][C]0.853254[/C][/ROW]
[ROW][C]41[/C][C]0.18848[/C][C]0.376961[/C][C]0.81152[/C][/ROW]
[ROW][C]42[/C][C]0.203207[/C][C]0.406414[/C][C]0.796793[/C][/ROW]
[ROW][C]43[/C][C]0.167263[/C][C]0.334526[/C][C]0.832737[/C][/ROW]
[ROW][C]44[/C][C]0.144035[/C][C]0.288069[/C][C]0.855965[/C][/ROW]
[ROW][C]45[/C][C]0.1279[/C][C]0.2558[/C][C]0.8721[/C][/ROW]
[ROW][C]46[/C][C]0.100178[/C][C]0.200356[/C][C]0.899822[/C][/ROW]
[ROW][C]47[/C][C]0.0868423[/C][C]0.173685[/C][C]0.913158[/C][/ROW]
[ROW][C]48[/C][C]0.0853934[/C][C]0.170787[/C][C]0.914607[/C][/ROW]
[ROW][C]49[/C][C]0.0746258[/C][C]0.149252[/C][C]0.925374[/C][/ROW]
[ROW][C]50[/C][C]0.061781[/C][C]0.123562[/C][C]0.938219[/C][/ROW]
[ROW][C]51[/C][C]0.0847774[/C][C]0.169555[/C][C]0.915223[/C][/ROW]
[ROW][C]52[/C][C]0.189337[/C][C]0.378674[/C][C]0.810663[/C][/ROW]
[ROW][C]53[/C][C]0.301203[/C][C]0.602406[/C][C]0.698797[/C][/ROW]
[ROW][C]54[/C][C]0.256789[/C][C]0.513578[/C][C]0.743211[/C][/ROW]
[ROW][C]55[/C][C]0.215608[/C][C]0.431215[/C][C]0.784392[/C][/ROW]
[ROW][C]56[/C][C]0.204045[/C][C]0.408089[/C][C]0.795955[/C][/ROW]
[ROW][C]57[/C][C]0.259036[/C][C]0.518072[/C][C]0.740964[/C][/ROW]
[ROW][C]58[/C][C]0.223966[/C][C]0.447932[/C][C]0.776034[/C][/ROW]
[ROW][C]59[/C][C]0.340753[/C][C]0.681506[/C][C]0.659247[/C][/ROW]
[ROW][C]60[/C][C]0.298629[/C][C]0.597257[/C][C]0.701371[/C][/ROW]
[ROW][C]61[/C][C]0.258834[/C][C]0.517668[/C][C]0.741166[/C][/ROW]
[ROW][C]62[/C][C]0.227489[/C][C]0.454978[/C][C]0.772511[/C][/ROW]
[ROW][C]63[/C][C]0.204575[/C][C]0.40915[/C][C]0.795425[/C][/ROW]
[ROW][C]64[/C][C]0.204465[/C][C]0.40893[/C][C]0.795535[/C][/ROW]
[ROW][C]65[/C][C]0.216038[/C][C]0.432076[/C][C]0.783962[/C][/ROW]
[ROW][C]66[/C][C]0.202788[/C][C]0.405576[/C][C]0.797212[/C][/ROW]
[ROW][C]67[/C][C]0.166939[/C][C]0.333878[/C][C]0.833061[/C][/ROW]
[ROW][C]68[/C][C]0.136246[/C][C]0.272493[/C][C]0.863754[/C][/ROW]
[ROW][C]69[/C][C]0.119872[/C][C]0.239744[/C][C]0.880128[/C][/ROW]
[ROW][C]70[/C][C]0.0954847[/C][C]0.190969[/C][C]0.904515[/C][/ROW]
[ROW][C]71[/C][C]0.104612[/C][C]0.209225[/C][C]0.895388[/C][/ROW]
[ROW][C]72[/C][C]0.0877754[/C][C]0.175551[/C][C]0.912225[/C][/ROW]
[ROW][C]73[/C][C]0.135375[/C][C]0.270751[/C][C]0.864625[/C][/ROW]
[ROW][C]74[/C][C]0.124837[/C][C]0.249675[/C][C]0.875163[/C][/ROW]
[ROW][C]75[/C][C]0.204709[/C][C]0.409418[/C][C]0.795291[/C][/ROW]
[ROW][C]76[/C][C]0.498612[/C][C]0.997224[/C][C]0.501388[/C][/ROW]
[ROW][C]77[/C][C]0.450572[/C][C]0.901145[/C][C]0.549428[/C][/ROW]
[ROW][C]78[/C][C]0.476062[/C][C]0.952123[/C][C]0.523938[/C][/ROW]
[ROW][C]79[/C][C]0.529661[/C][C]0.940679[/C][C]0.470339[/C][/ROW]
[ROW][C]80[/C][C]0.527364[/C][C]0.945272[/C][C]0.472636[/C][/ROW]
[ROW][C]81[/C][C]0.498066[/C][C]0.996133[/C][C]0.501934[/C][/ROW]
[ROW][C]82[/C][C]0.448894[/C][C]0.897787[/C][C]0.551106[/C][/ROW]
[ROW][C]83[/C][C]0.488192[/C][C]0.976383[/C][C]0.511808[/C][/ROW]
[ROW][C]84[/C][C]0.441364[/C][C]0.882728[/C][C]0.558636[/C][/ROW]
[ROW][C]85[/C][C]0.414247[/C][C]0.828493[/C][C]0.585753[/C][/ROW]
[ROW][C]86[/C][C]0.363284[/C][C]0.726568[/C][C]0.636716[/C][/ROW]
[ROW][C]87[/C][C]0.318677[/C][C]0.637353[/C][C]0.681323[/C][/ROW]
[ROW][C]88[/C][C]0.293137[/C][C]0.586275[/C][C]0.706863[/C][/ROW]
[ROW][C]89[/C][C]0.249536[/C][C]0.499072[/C][C]0.750464[/C][/ROW]
[ROW][C]90[/C][C]0.210743[/C][C]0.421487[/C][C]0.789257[/C][/ROW]
[ROW][C]91[/C][C]0.171352[/C][C]0.342704[/C][C]0.828648[/C][/ROW]
[ROW][C]92[/C][C]0.14264[/C][C]0.285281[/C][C]0.85736[/C][/ROW]
[ROW][C]93[/C][C]0.160627[/C][C]0.321255[/C][C]0.839373[/C][/ROW]
[ROW][C]94[/C][C]0.245887[/C][C]0.491773[/C][C]0.754113[/C][/ROW]
[ROW][C]95[/C][C]0.20892[/C][C]0.41784[/C][C]0.79108[/C][/ROW]
[ROW][C]96[/C][C]0.204438[/C][C]0.408877[/C][C]0.795562[/C][/ROW]
[ROW][C]97[/C][C]0.165422[/C][C]0.330845[/C][C]0.834578[/C][/ROW]
[ROW][C]98[/C][C]0.153887[/C][C]0.307773[/C][C]0.846113[/C][/ROW]
[ROW][C]99[/C][C]0.140918[/C][C]0.281836[/C][C]0.859082[/C][/ROW]
[ROW][C]100[/C][C]0.20294[/C][C]0.405879[/C][C]0.79706[/C][/ROW]
[ROW][C]101[/C][C]0.179176[/C][C]0.358353[/C][C]0.820824[/C][/ROW]
[ROW][C]102[/C][C]0.19273[/C][C]0.38546[/C][C]0.80727[/C][/ROW]
[ROW][C]103[/C][C]0.260739[/C][C]0.521477[/C][C]0.739261[/C][/ROW]
[ROW][C]104[/C][C]0.213478[/C][C]0.426957[/C][C]0.786522[/C][/ROW]
[ROW][C]105[/C][C]0.226896[/C][C]0.453792[/C][C]0.773104[/C][/ROW]
[ROW][C]106[/C][C]0.1979[/C][C]0.3958[/C][C]0.8021[/C][/ROW]
[ROW][C]107[/C][C]0.145922[/C][C]0.291845[/C][C]0.854078[/C][/ROW]
[ROW][C]108[/C][C]0.148503[/C][C]0.297007[/C][C]0.851497[/C][/ROW]
[ROW][C]109[/C][C]0.190245[/C][C]0.38049[/C][C]0.809755[/C][/ROW]
[ROW][C]110[/C][C]0.140734[/C][C]0.281469[/C][C]0.859266[/C][/ROW]
[ROW][C]111[/C][C]0.128614[/C][C]0.257229[/C][C]0.871386[/C][/ROW]
[ROW][C]112[/C][C]0.140042[/C][C]0.280083[/C][C]0.859958[/C][/ROW]
[ROW][C]113[/C][C]0.109679[/C][C]0.219357[/C][C]0.890321[/C][/ROW]
[ROW][C]114[/C][C]0.104294[/C][C]0.208589[/C][C]0.895706[/C][/ROW]
[ROW][C]115[/C][C]0.0884522[/C][C]0.176904[/C][C]0.911548[/C][/ROW]
[ROW][C]116[/C][C]0.0757448[/C][C]0.15149[/C][C]0.924255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268923&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268923&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
100.9362610.1274780.0637392
110.8864920.2270160.113508
120.8474250.305150.152575
130.7753860.4492280.224614
140.7328520.5342960.267148
150.8138350.372330.186165
160.7657820.4684360.234218
170.6885910.6228180.311409
180.6690410.6619180.330959
190.5886460.8227070.411354
200.8086550.382690.191345
210.7749530.4500950.225047
220.7143980.5712040.285602
230.6696340.6607330.330366
240.6056760.7886470.394324
250.5606160.8787680.439384
260.5502480.8995050.449752
270.4826790.9653570.517321
280.4428230.8856450.557177
290.3770960.7541930.622904
300.3813840.7627690.618616
310.3588460.7176920.641154
320.4371720.8743440.562828
330.3762370.7524750.623763
340.3475480.6950960.652452
350.2946360.5892730.705364
360.2432160.4864330.756784
370.2071710.4143420.792829
380.2054970.4109950.794503
390.1762960.3525920.823704
400.1467460.2934920.853254
410.188480.3769610.81152
420.2032070.4064140.796793
430.1672630.3345260.832737
440.1440350.2880690.855965
450.12790.25580.8721
460.1001780.2003560.899822
470.08684230.1736850.913158
480.08539340.1707870.914607
490.07462580.1492520.925374
500.0617810.1235620.938219
510.08477740.1695550.915223
520.1893370.3786740.810663
530.3012030.6024060.698797
540.2567890.5135780.743211
550.2156080.4312150.784392
560.2040450.4080890.795955
570.2590360.5180720.740964
580.2239660.4479320.776034
590.3407530.6815060.659247
600.2986290.5972570.701371
610.2588340.5176680.741166
620.2274890.4549780.772511
630.2045750.409150.795425
640.2044650.408930.795535
650.2160380.4320760.783962
660.2027880.4055760.797212
670.1669390.3338780.833061
680.1362460.2724930.863754
690.1198720.2397440.880128
700.09548470.1909690.904515
710.1046120.2092250.895388
720.08777540.1755510.912225
730.1353750.2707510.864625
740.1248370.2496750.875163
750.2047090.4094180.795291
760.4986120.9972240.501388
770.4505720.9011450.549428
780.4760620.9521230.523938
790.5296610.9406790.470339
800.5273640.9452720.472636
810.4980660.9961330.501934
820.4488940.8977870.551106
830.4881920.9763830.511808
840.4413640.8827280.558636
850.4142470.8284930.585753
860.3632840.7265680.636716
870.3186770.6373530.681323
880.2931370.5862750.706863
890.2495360.4990720.750464
900.2107430.4214870.789257
910.1713520.3427040.828648
920.142640.2852810.85736
930.1606270.3212550.839373
940.2458870.4917730.754113
950.208920.417840.79108
960.2044380.4088770.795562
970.1654220.3308450.834578
980.1538870.3077730.846113
990.1409180.2818360.859082
1000.202940.4058790.79706
1010.1791760.3583530.820824
1020.192730.385460.80727
1030.2607390.5214770.739261
1040.2134780.4269570.786522
1050.2268960.4537920.773104
1060.19790.39580.8021
1070.1459220.2918450.854078
1080.1485030.2970070.851497
1090.1902450.380490.809755
1100.1407340.2814690.859266
1110.1286140.2572290.871386
1120.1400420.2800830.859958
1130.1096790.2193570.890321
1140.1042940.2085890.895706
1150.08845220.1769040.911548
1160.07574480.151490.924255






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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268923&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268923&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268923&T=6

As an alternative you can also use a QR Code:  
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 level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 7 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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')
}