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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 28 Jan 2011 13:26:07 +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/2011/Jan/28/t1296221130ly5aki24camij83.htm/, Retrieved Thu, 16 May 2024 06:38:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117881, Retrieved Thu, 16 May 2024 06:38:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact179

Tree of Dependent Computations

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-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [ws7TimDamen] [2011-01-28 13:26:07] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6654,00	5712,00	3,30	38,60	645,00	3,00	5,00	3,00
1,00	6,60	8,30	4,50	42,00	3,00	1,00	3,00
3,39	44,50	12,50	14,00	60,00	1,00	1,00	1,00
0,92	5,70	16,50		25,00	5,00	2,00	3,00
2547,00	4603,00	3,90	69,00	624,00	3,00	5,00	4,00
10,55	179,50	9,80	27,00	180,00	4,00	4,00	4,00
0,02	0,30	19,70	19,00	35,00	1,00	1,00	1,00
160,00	169,00	6,20	30,40	392,00	4,00	5,00	4,00
3,30	25,60	14,50	28,00	63,00	1,00	2,00	1,00
52,16	440,00	9,70	50,00	230,00	1,00	1,00	1,00
0,43	6,40	12,50	7,00	112,00	5,00	4,00	4,00
465,00	423,00	3,90	30,00	281,00	5,00	5,00	5,00
0,55	2,40	10,30			2,00	1,00	2,00
187,10	419,00	3,10	40,00	365,00	5,00	5,00	5,00
0,08	1,20	8,40	3,50	42,00	1,00	1,00	1,00
3,00	25,00	8,60	50,00	28,00	2,00	2,00	2,00
0,79	3,50	10,70	6,00	42,00	2,00	2,00	2,00
0,20	5,00	10,70	10,40	120,00	2,00	2,00	2,00
1,41	17,50	6,10	34,00		1,00	2,00	1,00
60,00	81,00	18,10	7,00		1,00	1,00	1,00
529,00	680,00		28,00	400,00	5,00	5,00	5,00
27,66	115,00	3,80	20,00	148,00	5,00	5,00	5,00
0,12	1,00	14,40	3,90	16,00	3,00	1,00	2,00
207,00	406,00	12,00	39,30	252,00	1,00	4,00	1,00
85,00	325,00	6,20	41,00	310,00	1,00	3,00	1,00
36,33	119,50	13,00	16,20	63,00	1,00	1,00	1,00
0,10	4,00	13,80	9,00	28,00	5,00	1,00	3,00
1,04	5,50	8,20	7,60	68,00	5,00	3,00	4,00
521,00	655,00	2,90	46,00	336,00	5,00	5,00	5,00
100,00	157,00	10,80	22,40	100,00	1,00	1,00	1,00
35,00	56,00		16,30	33,00	3,00	5,00	4,00
0,01	0,14	9,10	2,60	21,50	5,00	2,00	4,00
0,01	0,25	19,90	24,00	50,00	1,00	1,00	1,00
62,00	1320,00	8,00	100,00	267,00	1,00	1,00	1,00
0,12	3,00	10,60		30,00	2,00	1,00	1,00
1,35	8,10	11,20		45,00	3,00	1,00	3,00
0,02	0,40	13,20	3,20	19,00	4,00	1,00	3,00
0,05	0,33	12,80	2,00	30,00	4,00	1,00	3,00
1,70	6,30	19,40	5,00	12,00	2,00	1,00	1,00
3,50	10,80	17,40	6,50	120,00	2,00	1,00	1,00
250,00	490,00		23,60	440,00	5,00	5,00	5,00
0,48	15,50	17,00	12,00	140,00	2,00	2,00	2,00
10,00	115,00	10,90	20,20	170,00	4,00	4,00	4,00
1,62	11,40	13,70	13,00	17,00	2,00	1,00	2,00
192,00	180,00	8,40	27,00	115,00	4,00	4,00	4,00
2,50	12,10	8,40	18,00	31,00	5,00	5,00	5,00
4,29	39,20	12,50	13,70	63,00	2,00	2,00	2,00
0,28	1,90	13,20	4,70	21,00	3,00	1,00	3,00
4,24	50,40	9,80	9,80	52,00	1,00	1,00	1,00
6,80	179,00	9,60	29,00	164,00	2,00	3,00	2,00
0,75	12,30	6,60	7,00	225,00	2,00	2,00	2,00
3,60	21,00	5,40	6,00	225,00	3,00	2,00	3,00
14,83	98,20	2,60	17,00	150,00	5,00	5,00	5,00
55,50	175,00	3,80	20,00	151,00	5,00	5,00	5,00
1,40	12,50	11,00	12,70	90,00	2,00	2,00	2,00
0,06	1,00	10,30	3,50		3,00	1,00	2,00
0,90	2,60	13,30	4,50	60,00	2,00	1,00	2,00
2,00	12,30	5,40	7,50	200,00	3,00	1,00	3,00
0,10	2,50	15,80	2,30	46,00	3,00	2,00	2,00
4,19	58,00	10,30	24,00	210,00	4,00	3,00	4,00
3,50	3,90	19,40	3,00	14,00	2,00	1,00	1,00
4,05	17,00		13,00	38,00	3,00	1,00	1,00

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ www.wessa.org
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ www.wessa.org \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117881&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ www.wessa.org[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117881&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117881&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 time4 seconds
R Server'Gwilym Jenkins' @ www.wessa.org
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 52.800717586225 -0.0530935358697208G[t] + 0.141950777249531H[t] + 0.431528586556607J[t] -0.818834514415419Z[t] + 0.95259716088965P[t] -0.0594488639737405B[t] -0.0226698680966238D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  52.800717586225 -0.0530935358697208G[t] +  0.141950777249531H[t] +  0.431528586556607J[t] -0.818834514415419Z[t] +  0.95259716088965P[t] -0.0594488639737405B[t] -0.0226698680966238D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117881&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  52.800717586225 -0.0530935358697208G[t] +  0.141950777249531H[t] +  0.431528586556607J[t] -0.818834514415419Z[t] +  0.95259716088965P[t] -0.0594488639737405B[t] -0.0226698680966238D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117881&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117881&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
Y[t] = + 52.800717586225 -0.0530935358697208G[t] + 0.141950777249531H[t] + 0.431528586556607J[t] -0.818834514415419Z[t] + 0.95259716088965P[t] -0.0594488639737405B[t] -0.0226698680966238D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)52.80071758622528.4617671.85510.0690380.034519
G-0.05309353586972080.050659-1.04810.2992780.149639
H0.1419507772495310.0770151.84310.0707980.035399
J0.4315285865566070.2417031.78540.0798190.039909
Z-0.8188345144154190.269448-3.03890.0036550.001827
P0.952597160889650.315673.01770.0038810.00194
B-0.05944886397374050.31613-0.18810.8515410.42577
D-0.02266986809662380.071648-0.31640.7529130.376457

\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) & 52.800717586225 & 28.461767 & 1.8551 & 0.069038 & 0.034519 \tabularnewline
G & -0.0530935358697208 & 0.050659 & -1.0481 & 0.299278 & 0.149639 \tabularnewline
H & 0.141950777249531 & 0.077015 & 1.8431 & 0.070798 & 0.035399 \tabularnewline
J & 0.431528586556607 & 0.241703 & 1.7854 & 0.079819 & 0.039909 \tabularnewline
Z & -0.818834514415419 & 0.269448 & -3.0389 & 0.003655 & 0.001827 \tabularnewline
P & 0.95259716088965 & 0.31567 & 3.0177 & 0.003881 & 0.00194 \tabularnewline
B & -0.0594488639737405 & 0.31613 & -0.1881 & 0.851541 & 0.42577 \tabularnewline
D & -0.0226698680966238 & 0.071648 & -0.3164 & 0.752913 & 0.376457 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117881&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]52.800717586225[/C][C]28.461767[/C][C]1.8551[/C][C]0.069038[/C][C]0.034519[/C][/ROW]
[ROW][C]G[/C][C]-0.0530935358697208[/C][C]0.050659[/C][C]-1.0481[/C][C]0.299278[/C][C]0.149639[/C][/ROW]
[ROW][C]H[/C][C]0.141950777249531[/C][C]0.077015[/C][C]1.8431[/C][C]0.070798[/C][C]0.035399[/C][/ROW]
[ROW][C]J[/C][C]0.431528586556607[/C][C]0.241703[/C][C]1.7854[/C][C]0.079819[/C][C]0.039909[/C][/ROW]
[ROW][C]Z[/C][C]-0.818834514415419[/C][C]0.269448[/C][C]-3.0389[/C][C]0.003655[/C][C]0.001827[/C][/ROW]
[ROW][C]P[/C][C]0.95259716088965[/C][C]0.31567[/C][C]3.0177[/C][C]0.003881[/C][C]0.00194[/C][/ROW]
[ROW][C]B[/C][C]-0.0594488639737405[/C][C]0.31613[/C][C]-0.1881[/C][C]0.851541[/C][C]0.42577[/C][/ROW]
[ROW][C]D[/C][C]-0.0226698680966238[/C][C]0.071648[/C][C]-0.3164[/C][C]0.752913[/C][C]0.376457[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117881&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117881&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)52.80071758622528.4617671.85510.0690380.034519
G-0.05309353586972080.050659-1.04810.2992780.149639
H0.1419507772495310.0770151.84310.0707980.035399
J0.4315285865566070.2417031.78540.0798190.039909
Z-0.8188345144154190.269448-3.03890.0036550.001827
P0.952597160889650.315673.01770.0038810.00194
B-0.05944886397374050.31613-0.18810.8515410.42577
D-0.02266986809662380.071648-0.31640.7529130.376457






Multiple Linear Regression - Regression Statistics
Multiple R0.408875273496465
R-squared0.167178989276809
Adjusted R-squared0.0592207101089881
F-TEST (value)1.54855181617826
F-TEST (DF numerator)7
F-TEST (DF denominator)54
p-value0.171002963389419
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation182.895543423538
Sum Squared Residuals1806342.10942632

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.408875273496465 \tabularnewline
R-squared & 0.167178989276809 \tabularnewline
Adjusted R-squared & 0.0592207101089881 \tabularnewline
F-TEST (value) & 1.54855181617826 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0.171002963389419 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 182.895543423538 \tabularnewline
Sum Squared Residuals & 1806342.10942632 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117881&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.408875273496465[/C][/ROW]
[ROW][C]R-squared[/C][C]0.167178989276809[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0592207101089881[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.54855181617826[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0.171002963389419[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]182.895543423538[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1806342.10942632[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117881&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117881&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 R0.408875273496465
R-squared0.167178989276809
Adjusted R-squared0.0592207101089881
F-TEST (value)1.54855181617826
F-TEST (DF numerator)7
F-TEST (DF denominator)54
p-value0.171002963389419
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation182.895543423538
Sum Squared Residuals1806342.10942632






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13.31.340448760075221.95955123992478
28.323.9656612286646-15.6656612286646
312.516.7193478629171-4.21934786291705
416.54.2417267431429712.258273256857
56980.068137717384-11.0681377173841
627122.633492827485-95.6334928274848
71967.1318552538017-48.1318552538017
830.4215.042252499552-184.642252499552
92880.5305609146148-52.5305609146148
1050130.132624990107-80.1326249901066
11791.5034373172743-84.5034373172743
1230152.514393252062-122.514393252062
132206.180823869891-204.180823869891
145100.366922452312-95.366922452312
15159.3661259449969-58.3661259449969
16253.647963844569-51.647963844569
17257.3201846834447-55.3201846834447
18268.6725469093306-66.6725469093306
19278.3646023829961-76.3646023829961
201-272.859447111505273.859447111505
2127.66109.899382101551-82.2393821015514
220.1244.5812528716985-44.4612528716985
23207240.839402407335-33.8394024073354
2485208.504976758037-123.504976758037
2536.33105.370330869949-69.0403308699494
260.150.1112297911574-50.0112297911574
271.0451.9163069220226-50.8763069220226
28521357.21714595565163.78285404435
29100127.522199265282-27.5221992652822
303594.8521834667575-59.8521834667575
310.1474.5259582537691-74.3859582537691
320.2589.0728927894371-88.8228927894371
331320237.3786727162041082.6213272838
34334.5969013183716-31.5969013183716
3511.271.6149226319296-60.4149226319296
363.260.3500525940483-57.1500525940483
37265.0063980004226-63.0063980004226
38559.5745511573128-54.5745511573128
396.5100.068693553696-93.5686935536956
4044032.5814611080996407.4185388919
4114051.530595798646688.4694042013534
4217056.9958250218699113.00417497813
431740.3733550891803-23.3733550891803
4411558.025640238876556.9743597611235
453156.5926075847301-25.5926075847301
466355.15263807093747.84736192906255
472154.7059697584223-33.7059697584223
485249.77465112046092.22534887953914
4916456.3959141081895107.60408589181
5022553.8844550159586171.115544984041
5122552.7726176697146172.227382330285
5215050.635654903908199.3643450960919
5315157.897832099293893.1021679007062
549055.123809228853734.8761907711463
55351.9458025976498-48.9458025976498
56260.9242588746894-58.9242588746894
57378.3561502006467-75.3561502006467
58358.7435747037409-55.7435747037409
59482.0178277023031-78.0178277023031
60256.7941214610438-54.7941214610438
61143.2794300633246-42.2794300633246
62525.1486918117129-20.1486918117129

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.3 & 1.34044876007522 & 1.95955123992478 \tabularnewline
2 & 8.3 & 23.9656612286646 & -15.6656612286646 \tabularnewline
3 & 12.5 & 16.7193478629171 & -4.21934786291705 \tabularnewline
4 & 16.5 & 4.24172674314297 & 12.258273256857 \tabularnewline
5 & 69 & 80.068137717384 & -11.0681377173841 \tabularnewline
6 & 27 & 122.633492827485 & -95.6334928274848 \tabularnewline
7 & 19 & 67.1318552538017 & -48.1318552538017 \tabularnewline
8 & 30.4 & 215.042252499552 & -184.642252499552 \tabularnewline
9 & 28 & 80.5305609146148 & -52.5305609146148 \tabularnewline
10 & 50 & 130.132624990107 & -80.1326249901066 \tabularnewline
11 & 7 & 91.5034373172743 & -84.5034373172743 \tabularnewline
12 & 30 & 152.514393252062 & -122.514393252062 \tabularnewline
13 & 2 & 206.180823869891 & -204.180823869891 \tabularnewline
14 & 5 & 100.366922452312 & -95.366922452312 \tabularnewline
15 & 1 & 59.3661259449969 & -58.3661259449969 \tabularnewline
16 & 2 & 53.647963844569 & -51.647963844569 \tabularnewline
17 & 2 & 57.3201846834447 & -55.3201846834447 \tabularnewline
18 & 2 & 68.6725469093306 & -66.6725469093306 \tabularnewline
19 & 2 & 78.3646023829961 & -76.3646023829961 \tabularnewline
20 & 1 & -272.859447111505 & 273.859447111505 \tabularnewline
21 & 27.66 & 109.899382101551 & -82.2393821015514 \tabularnewline
22 & 0.12 & 44.5812528716985 & -44.4612528716985 \tabularnewline
23 & 207 & 240.839402407335 & -33.8394024073354 \tabularnewline
24 & 85 & 208.504976758037 & -123.504976758037 \tabularnewline
25 & 36.33 & 105.370330869949 & -69.0403308699494 \tabularnewline
26 & 0.1 & 50.1112297911574 & -50.0112297911574 \tabularnewline
27 & 1.04 & 51.9163069220226 & -50.8763069220226 \tabularnewline
28 & 521 & 357.21714595565 & 163.78285404435 \tabularnewline
29 & 100 & 127.522199265282 & -27.5221992652822 \tabularnewline
30 & 35 & 94.8521834667575 & -59.8521834667575 \tabularnewline
31 & 0.14 & 74.5259582537691 & -74.3859582537691 \tabularnewline
32 & 0.25 & 89.0728927894371 & -88.8228927894371 \tabularnewline
33 & 1320 & 237.378672716204 & 1082.6213272838 \tabularnewline
34 & 3 & 34.5969013183716 & -31.5969013183716 \tabularnewline
35 & 11.2 & 71.6149226319296 & -60.4149226319296 \tabularnewline
36 & 3.2 & 60.3500525940483 & -57.1500525940483 \tabularnewline
37 & 2 & 65.0063980004226 & -63.0063980004226 \tabularnewline
38 & 5 & 59.5745511573128 & -54.5745511573128 \tabularnewline
39 & 6.5 & 100.068693553696 & -93.5686935536956 \tabularnewline
40 & 440 & 32.5814611080996 & 407.4185388919 \tabularnewline
41 & 140 & 51.5305957986466 & 88.4694042013534 \tabularnewline
42 & 170 & 56.9958250218699 & 113.00417497813 \tabularnewline
43 & 17 & 40.3733550891803 & -23.3733550891803 \tabularnewline
44 & 115 & 58.0256402388765 & 56.9743597611235 \tabularnewline
45 & 31 & 56.5926075847301 & -25.5926075847301 \tabularnewline
46 & 63 & 55.1526380709374 & 7.84736192906255 \tabularnewline
47 & 21 & 54.7059697584223 & -33.7059697584223 \tabularnewline
48 & 52 & 49.7746511204609 & 2.22534887953914 \tabularnewline
49 & 164 & 56.3959141081895 & 107.60408589181 \tabularnewline
50 & 225 & 53.8844550159586 & 171.115544984041 \tabularnewline
51 & 225 & 52.7726176697146 & 172.227382330285 \tabularnewline
52 & 150 & 50.6356549039081 & 99.3643450960919 \tabularnewline
53 & 151 & 57.8978320992938 & 93.1021679007062 \tabularnewline
54 & 90 & 55.1238092288537 & 34.8761907711463 \tabularnewline
55 & 3 & 51.9458025976498 & -48.9458025976498 \tabularnewline
56 & 2 & 60.9242588746894 & -58.9242588746894 \tabularnewline
57 & 3 & 78.3561502006467 & -75.3561502006467 \tabularnewline
58 & 3 & 58.7435747037409 & -55.7435747037409 \tabularnewline
59 & 4 & 82.0178277023031 & -78.0178277023031 \tabularnewline
60 & 2 & 56.7941214610438 & -54.7941214610438 \tabularnewline
61 & 1 & 43.2794300633246 & -42.2794300633246 \tabularnewline
62 & 5 & 25.1486918117129 & -20.1486918117129 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117881&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]3.3[/C][C]1.34044876007522[/C][C]1.95955123992478[/C][/ROW]
[ROW][C]2[/C][C]8.3[/C][C]23.9656612286646[/C][C]-15.6656612286646[/C][/ROW]
[ROW][C]3[/C][C]12.5[/C][C]16.7193478629171[/C][C]-4.21934786291705[/C][/ROW]
[ROW][C]4[/C][C]16.5[/C][C]4.24172674314297[/C][C]12.258273256857[/C][/ROW]
[ROW][C]5[/C][C]69[/C][C]80.068137717384[/C][C]-11.0681377173841[/C][/ROW]
[ROW][C]6[/C][C]27[/C][C]122.633492827485[/C][C]-95.6334928274848[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]67.1318552538017[/C][C]-48.1318552538017[/C][/ROW]
[ROW][C]8[/C][C]30.4[/C][C]215.042252499552[/C][C]-184.642252499552[/C][/ROW]
[ROW][C]9[/C][C]28[/C][C]80.5305609146148[/C][C]-52.5305609146148[/C][/ROW]
[ROW][C]10[/C][C]50[/C][C]130.132624990107[/C][C]-80.1326249901066[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]91.5034373172743[/C][C]-84.5034373172743[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]152.514393252062[/C][C]-122.514393252062[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]206.180823869891[/C][C]-204.180823869891[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]100.366922452312[/C][C]-95.366922452312[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]59.3661259449969[/C][C]-58.3661259449969[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]53.647963844569[/C][C]-51.647963844569[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]57.3201846834447[/C][C]-55.3201846834447[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]68.6725469093306[/C][C]-66.6725469093306[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]78.3646023829961[/C][C]-76.3646023829961[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]-272.859447111505[/C][C]273.859447111505[/C][/ROW]
[ROW][C]21[/C][C]27.66[/C][C]109.899382101551[/C][C]-82.2393821015514[/C][/ROW]
[ROW][C]22[/C][C]0.12[/C][C]44.5812528716985[/C][C]-44.4612528716985[/C][/ROW]
[ROW][C]23[/C][C]207[/C][C]240.839402407335[/C][C]-33.8394024073354[/C][/ROW]
[ROW][C]24[/C][C]85[/C][C]208.504976758037[/C][C]-123.504976758037[/C][/ROW]
[ROW][C]25[/C][C]36.33[/C][C]105.370330869949[/C][C]-69.0403308699494[/C][/ROW]
[ROW][C]26[/C][C]0.1[/C][C]50.1112297911574[/C][C]-50.0112297911574[/C][/ROW]
[ROW][C]27[/C][C]1.04[/C][C]51.9163069220226[/C][C]-50.8763069220226[/C][/ROW]
[ROW][C]28[/C][C]521[/C][C]357.21714595565[/C][C]163.78285404435[/C][/ROW]
[ROW][C]29[/C][C]100[/C][C]127.522199265282[/C][C]-27.5221992652822[/C][/ROW]
[ROW][C]30[/C][C]35[/C][C]94.8521834667575[/C][C]-59.8521834667575[/C][/ROW]
[ROW][C]31[/C][C]0.14[/C][C]74.5259582537691[/C][C]-74.3859582537691[/C][/ROW]
[ROW][C]32[/C][C]0.25[/C][C]89.0728927894371[/C][C]-88.8228927894371[/C][/ROW]
[ROW][C]33[/C][C]1320[/C][C]237.378672716204[/C][C]1082.6213272838[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]34.5969013183716[/C][C]-31.5969013183716[/C][/ROW]
[ROW][C]35[/C][C]11.2[/C][C]71.6149226319296[/C][C]-60.4149226319296[/C][/ROW]
[ROW][C]36[/C][C]3.2[/C][C]60.3500525940483[/C][C]-57.1500525940483[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]65.0063980004226[/C][C]-63.0063980004226[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]59.5745511573128[/C][C]-54.5745511573128[/C][/ROW]
[ROW][C]39[/C][C]6.5[/C][C]100.068693553696[/C][C]-93.5686935536956[/C][/ROW]
[ROW][C]40[/C][C]440[/C][C]32.5814611080996[/C][C]407.4185388919[/C][/ROW]
[ROW][C]41[/C][C]140[/C][C]51.5305957986466[/C][C]88.4694042013534[/C][/ROW]
[ROW][C]42[/C][C]170[/C][C]56.9958250218699[/C][C]113.00417497813[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]40.3733550891803[/C][C]-23.3733550891803[/C][/ROW]
[ROW][C]44[/C][C]115[/C][C]58.0256402388765[/C][C]56.9743597611235[/C][/ROW]
[ROW][C]45[/C][C]31[/C][C]56.5926075847301[/C][C]-25.5926075847301[/C][/ROW]
[ROW][C]46[/C][C]63[/C][C]55.1526380709374[/C][C]7.84736192906255[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]54.7059697584223[/C][C]-33.7059697584223[/C][/ROW]
[ROW][C]48[/C][C]52[/C][C]49.7746511204609[/C][C]2.22534887953914[/C][/ROW]
[ROW][C]49[/C][C]164[/C][C]56.3959141081895[/C][C]107.60408589181[/C][/ROW]
[ROW][C]50[/C][C]225[/C][C]53.8844550159586[/C][C]171.115544984041[/C][/ROW]
[ROW][C]51[/C][C]225[/C][C]52.7726176697146[/C][C]172.227382330285[/C][/ROW]
[ROW][C]52[/C][C]150[/C][C]50.6356549039081[/C][C]99.3643450960919[/C][/ROW]
[ROW][C]53[/C][C]151[/C][C]57.8978320992938[/C][C]93.1021679007062[/C][/ROW]
[ROW][C]54[/C][C]90[/C][C]55.1238092288537[/C][C]34.8761907711463[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]51.9458025976498[/C][C]-48.9458025976498[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]60.9242588746894[/C][C]-58.9242588746894[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]78.3561502006467[/C][C]-75.3561502006467[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]58.7435747037409[/C][C]-55.7435747037409[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]82.0178277023031[/C][C]-78.0178277023031[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]56.7941214610438[/C][C]-54.7941214610438[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]43.2794300633246[/C][C]-42.2794300633246[/C][/ROW]
[ROW][C]62[/C][C]5[/C][C]25.1486918117129[/C][C]-20.1486918117129[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117881&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117881&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
13.31.340448760075221.95955123992478
28.323.9656612286646-15.6656612286646
312.516.7193478629171-4.21934786291705
416.54.2417267431429712.258273256857
56980.068137717384-11.0681377173841
627122.633492827485-95.6334928274848
71967.1318552538017-48.1318552538017
830.4215.042252499552-184.642252499552
92880.5305609146148-52.5305609146148
1050130.132624990107-80.1326249901066
11791.5034373172743-84.5034373172743
1230152.514393252062-122.514393252062
132206.180823869891-204.180823869891
145100.366922452312-95.366922452312
15159.3661259449969-58.3661259449969
16253.647963844569-51.647963844569
17257.3201846834447-55.3201846834447
18268.6725469093306-66.6725469093306
19278.3646023829961-76.3646023829961
201-272.859447111505273.859447111505
2127.66109.899382101551-82.2393821015514
220.1244.5812528716985-44.4612528716985
23207240.839402407335-33.8394024073354
2485208.504976758037-123.504976758037
2536.33105.370330869949-69.0403308699494
260.150.1112297911574-50.0112297911574
271.0451.9163069220226-50.8763069220226
28521357.21714595565163.78285404435
29100127.522199265282-27.5221992652822
303594.8521834667575-59.8521834667575
310.1474.5259582537691-74.3859582537691
320.2589.0728927894371-88.8228927894371
331320237.3786727162041082.6213272838
34334.5969013183716-31.5969013183716
3511.271.6149226319296-60.4149226319296
363.260.3500525940483-57.1500525940483
37265.0063980004226-63.0063980004226
38559.5745511573128-54.5745511573128
396.5100.068693553696-93.5686935536956
4044032.5814611080996407.4185388919
4114051.530595798646688.4694042013534
4217056.9958250218699113.00417497813
431740.3733550891803-23.3733550891803
4411558.025640238876556.9743597611235
453156.5926075847301-25.5926075847301
466355.15263807093747.84736192906255
472154.7059697584223-33.7059697584223
485249.77465112046092.22534887953914
4916456.3959141081895107.60408589181
5022553.8844550159586171.115544984041
5122552.7726176697146172.227382330285
5215050.635654903908199.3643450960919
5315157.897832099293893.1021679007062
549055.123809228853734.8761907711463
55351.9458025976498-48.9458025976498
56260.9242588746894-58.9242588746894
57378.3561502006467-75.3561502006467
58358.7435747037409-55.7435747037409
59482.0178277023031-78.0178277023031
60256.7941214610438-54.7941214610438
61143.2794300633246-42.2794300633246
62525.1486918117129-20.1486918117129






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
119.34980396202463e-050.0001869960792404930.99990650196038
124.9928729711795e-069.985745942359e-060.999995007127029
133.30321599403568e-076.60643198807136e-070.9999996696784
141.08860532501465e-072.17721065002931e-070.999999891139468
151.86049433880525e-083.72098867761051e-080.999999981395057
161.39983197095132e-092.79966394190265e-090.999999998600168
178.69367004262015e-111.73873400852403e-100.999999999913063
184.412419711157e-128.824839422314e-120.999999999995588
193.20723346722299e-136.41446693444597e-130.99999999999968
202.84434219671068e-115.68868439342136e-110.999999999971557
214.51459447273108e-129.02918894546216e-120.999999999995485
225.02717353043964e-131.00543470608793e-120.999999999999497
237.78612100103067e-071.55722420020613e-060.9999992213879
245.54497019631512e-071.10899403926302e-060.99999944550298
251.48037081216945e-072.9607416243389e-070.999999851962919
263.51756018233107e-087.03512036466215e-080.999999964824398
278.66248803434673e-091.73249760686935e-080.999999991337512
280.001558495591168760.003116991182337510.998441504408831
290.001631488014361010.003262976028722020.998368511985639
300.00094018315300960.00188036630601920.99905981684699
310.0009693693741836770.001938738748367350.999030630625816
320.002745402769171660.005490805538343310.997254597230828
330.8570349416518780.2859301166962440.142965058348122
340.8677464089972680.2645071820054650.132253591002733
350.8169108503326550.3661782993346910.183089149667345
360.796111161715820.407777676568360.20388883828418
370.7901758394002860.4196483211994280.209824160599714
380.7901089597182560.4197820805634870.209891040281744
390.9020430755442430.1959138489115140.0979569244557572
400.9968725289723640.006254942055271270.00312747102763563
410.994951224214580.01009755157083840.0050487757854192
420.9924081582385990.01518368352280220.0075918417614011
430.9877997126060060.02440057478798730.0122002873939936
440.9763253724122770.04734925517544560.0236746275877228
450.9817486606436170.03650267871276590.0182513393563829
460.9648502956062660.07029940878746860.0351497043937343
470.959618119728420.0807637605431620.040381880271581
480.9426106731108160.1147786537783670.0573893268891836
490.9117876888574850.176424622285030.088212311142515
500.9598322720535370.08033545589292680.0401677279464634
510.985687783289180.02862443342164230.0143122167108212

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 9.34980396202463e-05 & 0.000186996079240493 & 0.99990650196038 \tabularnewline
12 & 4.9928729711795e-06 & 9.985745942359e-06 & 0.999995007127029 \tabularnewline
13 & 3.30321599403568e-07 & 6.60643198807136e-07 & 0.9999996696784 \tabularnewline
14 & 1.08860532501465e-07 & 2.17721065002931e-07 & 0.999999891139468 \tabularnewline
15 & 1.86049433880525e-08 & 3.72098867761051e-08 & 0.999999981395057 \tabularnewline
16 & 1.39983197095132e-09 & 2.79966394190265e-09 & 0.999999998600168 \tabularnewline
17 & 8.69367004262015e-11 & 1.73873400852403e-10 & 0.999999999913063 \tabularnewline
18 & 4.412419711157e-12 & 8.824839422314e-12 & 0.999999999995588 \tabularnewline
19 & 3.20723346722299e-13 & 6.41446693444597e-13 & 0.99999999999968 \tabularnewline
20 & 2.84434219671068e-11 & 5.68868439342136e-11 & 0.999999999971557 \tabularnewline
21 & 4.51459447273108e-12 & 9.02918894546216e-12 & 0.999999999995485 \tabularnewline
22 & 5.02717353043964e-13 & 1.00543470608793e-12 & 0.999999999999497 \tabularnewline
23 & 7.78612100103067e-07 & 1.55722420020613e-06 & 0.9999992213879 \tabularnewline
24 & 5.54497019631512e-07 & 1.10899403926302e-06 & 0.99999944550298 \tabularnewline
25 & 1.48037081216945e-07 & 2.9607416243389e-07 & 0.999999851962919 \tabularnewline
26 & 3.51756018233107e-08 & 7.03512036466215e-08 & 0.999999964824398 \tabularnewline
27 & 8.66248803434673e-09 & 1.73249760686935e-08 & 0.999999991337512 \tabularnewline
28 & 0.00155849559116876 & 0.00311699118233751 & 0.998441504408831 \tabularnewline
29 & 0.00163148801436101 & 0.00326297602872202 & 0.998368511985639 \tabularnewline
30 & 0.0009401831530096 & 0.0018803663060192 & 0.99905981684699 \tabularnewline
31 & 0.000969369374183677 & 0.00193873874836735 & 0.999030630625816 \tabularnewline
32 & 0.00274540276917166 & 0.00549080553834331 & 0.997254597230828 \tabularnewline
33 & 0.857034941651878 & 0.285930116696244 & 0.142965058348122 \tabularnewline
34 & 0.867746408997268 & 0.264507182005465 & 0.132253591002733 \tabularnewline
35 & 0.816910850332655 & 0.366178299334691 & 0.183089149667345 \tabularnewline
36 & 0.79611116171582 & 0.40777767656836 & 0.20388883828418 \tabularnewline
37 & 0.790175839400286 & 0.419648321199428 & 0.209824160599714 \tabularnewline
38 & 0.790108959718256 & 0.419782080563487 & 0.209891040281744 \tabularnewline
39 & 0.902043075544243 & 0.195913848911514 & 0.0979569244557572 \tabularnewline
40 & 0.996872528972364 & 0.00625494205527127 & 0.00312747102763563 \tabularnewline
41 & 0.99495122421458 & 0.0100975515708384 & 0.0050487757854192 \tabularnewline
42 & 0.992408158238599 & 0.0151836835228022 & 0.0075918417614011 \tabularnewline
43 & 0.987799712606006 & 0.0244005747879873 & 0.0122002873939936 \tabularnewline
44 & 0.976325372412277 & 0.0473492551754456 & 0.0236746275877228 \tabularnewline
45 & 0.981748660643617 & 0.0365026787127659 & 0.0182513393563829 \tabularnewline
46 & 0.964850295606266 & 0.0702994087874686 & 0.0351497043937343 \tabularnewline
47 & 0.95961811972842 & 0.080763760543162 & 0.040381880271581 \tabularnewline
48 & 0.942610673110816 & 0.114778653778367 & 0.0573893268891836 \tabularnewline
49 & 0.911787688857485 & 0.17642462228503 & 0.088212311142515 \tabularnewline
50 & 0.959832272053537 & 0.0803354558929268 & 0.0401677279464634 \tabularnewline
51 & 0.98568778328918 & 0.0286244334216423 & 0.0143122167108212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117881&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]11[/C][C]9.34980396202463e-05[/C][C]0.000186996079240493[/C][C]0.99990650196038[/C][/ROW]
[ROW][C]12[/C][C]4.9928729711795e-06[/C][C]9.985745942359e-06[/C][C]0.999995007127029[/C][/ROW]
[ROW][C]13[/C][C]3.30321599403568e-07[/C][C]6.60643198807136e-07[/C][C]0.9999996696784[/C][/ROW]
[ROW][C]14[/C][C]1.08860532501465e-07[/C][C]2.17721065002931e-07[/C][C]0.999999891139468[/C][/ROW]
[ROW][C]15[/C][C]1.86049433880525e-08[/C][C]3.72098867761051e-08[/C][C]0.999999981395057[/C][/ROW]
[ROW][C]16[/C][C]1.39983197095132e-09[/C][C]2.79966394190265e-09[/C][C]0.999999998600168[/C][/ROW]
[ROW][C]17[/C][C]8.69367004262015e-11[/C][C]1.73873400852403e-10[/C][C]0.999999999913063[/C][/ROW]
[ROW][C]18[/C][C]4.412419711157e-12[/C][C]8.824839422314e-12[/C][C]0.999999999995588[/C][/ROW]
[ROW][C]19[/C][C]3.20723346722299e-13[/C][C]6.41446693444597e-13[/C][C]0.99999999999968[/C][/ROW]
[ROW][C]20[/C][C]2.84434219671068e-11[/C][C]5.68868439342136e-11[/C][C]0.999999999971557[/C][/ROW]
[ROW][C]21[/C][C]4.51459447273108e-12[/C][C]9.02918894546216e-12[/C][C]0.999999999995485[/C][/ROW]
[ROW][C]22[/C][C]5.02717353043964e-13[/C][C]1.00543470608793e-12[/C][C]0.999999999999497[/C][/ROW]
[ROW][C]23[/C][C]7.78612100103067e-07[/C][C]1.55722420020613e-06[/C][C]0.9999992213879[/C][/ROW]
[ROW][C]24[/C][C]5.54497019631512e-07[/C][C]1.10899403926302e-06[/C][C]0.99999944550298[/C][/ROW]
[ROW][C]25[/C][C]1.48037081216945e-07[/C][C]2.9607416243389e-07[/C][C]0.999999851962919[/C][/ROW]
[ROW][C]26[/C][C]3.51756018233107e-08[/C][C]7.03512036466215e-08[/C][C]0.999999964824398[/C][/ROW]
[ROW][C]27[/C][C]8.66248803434673e-09[/C][C]1.73249760686935e-08[/C][C]0.999999991337512[/C][/ROW]
[ROW][C]28[/C][C]0.00155849559116876[/C][C]0.00311699118233751[/C][C]0.998441504408831[/C][/ROW]
[ROW][C]29[/C][C]0.00163148801436101[/C][C]0.00326297602872202[/C][C]0.998368511985639[/C][/ROW]
[ROW][C]30[/C][C]0.0009401831530096[/C][C]0.0018803663060192[/C][C]0.99905981684699[/C][/ROW]
[ROW][C]31[/C][C]0.000969369374183677[/C][C]0.00193873874836735[/C][C]0.999030630625816[/C][/ROW]
[ROW][C]32[/C][C]0.00274540276917166[/C][C]0.00549080553834331[/C][C]0.997254597230828[/C][/ROW]
[ROW][C]33[/C][C]0.857034941651878[/C][C]0.285930116696244[/C][C]0.142965058348122[/C][/ROW]
[ROW][C]34[/C][C]0.867746408997268[/C][C]0.264507182005465[/C][C]0.132253591002733[/C][/ROW]
[ROW][C]35[/C][C]0.816910850332655[/C][C]0.366178299334691[/C][C]0.183089149667345[/C][/ROW]
[ROW][C]36[/C][C]0.79611116171582[/C][C]0.40777767656836[/C][C]0.20388883828418[/C][/ROW]
[ROW][C]37[/C][C]0.790175839400286[/C][C]0.419648321199428[/C][C]0.209824160599714[/C][/ROW]
[ROW][C]38[/C][C]0.790108959718256[/C][C]0.419782080563487[/C][C]0.209891040281744[/C][/ROW]
[ROW][C]39[/C][C]0.902043075544243[/C][C]0.195913848911514[/C][C]0.0979569244557572[/C][/ROW]
[ROW][C]40[/C][C]0.996872528972364[/C][C]0.00625494205527127[/C][C]0.00312747102763563[/C][/ROW]
[ROW][C]41[/C][C]0.99495122421458[/C][C]0.0100975515708384[/C][C]0.0050487757854192[/C][/ROW]
[ROW][C]42[/C][C]0.992408158238599[/C][C]0.0151836835228022[/C][C]0.0075918417614011[/C][/ROW]
[ROW][C]43[/C][C]0.987799712606006[/C][C]0.0244005747879873[/C][C]0.0122002873939936[/C][/ROW]
[ROW][C]44[/C][C]0.976325372412277[/C][C]0.0473492551754456[/C][C]0.0236746275877228[/C][/ROW]
[ROW][C]45[/C][C]0.981748660643617[/C][C]0.0365026787127659[/C][C]0.0182513393563829[/C][/ROW]
[ROW][C]46[/C][C]0.964850295606266[/C][C]0.0702994087874686[/C][C]0.0351497043937343[/C][/ROW]
[ROW][C]47[/C][C]0.95961811972842[/C][C]0.080763760543162[/C][C]0.040381880271581[/C][/ROW]
[ROW][C]48[/C][C]0.942610673110816[/C][C]0.114778653778367[/C][C]0.0573893268891836[/C][/ROW]
[ROW][C]49[/C][C]0.911787688857485[/C][C]0.17642462228503[/C][C]0.088212311142515[/C][/ROW]
[ROW][C]50[/C][C]0.959832272053537[/C][C]0.0803354558929268[/C][C]0.0401677279464634[/C][/ROW]
[ROW][C]51[/C][C]0.98568778328918[/C][C]0.0286244334216423[/C][C]0.0143122167108212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117881&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117881&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
119.34980396202463e-050.0001869960792404930.99990650196038
124.9928729711795e-069.985745942359e-060.999995007127029
133.30321599403568e-076.60643198807136e-070.9999996696784
141.08860532501465e-072.17721065002931e-070.999999891139468
151.86049433880525e-083.72098867761051e-080.999999981395057
161.39983197095132e-092.79966394190265e-090.999999998600168
178.69367004262015e-111.73873400852403e-100.999999999913063
184.412419711157e-128.824839422314e-120.999999999995588
193.20723346722299e-136.41446693444597e-130.99999999999968
202.84434219671068e-115.68868439342136e-110.999999999971557
214.51459447273108e-129.02918894546216e-120.999999999995485
225.02717353043964e-131.00543470608793e-120.999999999999497
237.78612100103067e-071.55722420020613e-060.9999992213879
245.54497019631512e-071.10899403926302e-060.99999944550298
251.48037081216945e-072.9607416243389e-070.999999851962919
263.51756018233107e-087.03512036466215e-080.999999964824398
278.66248803434673e-091.73249760686935e-080.999999991337512
280.001558495591168760.003116991182337510.998441504408831
290.001631488014361010.003262976028722020.998368511985639
300.00094018315300960.00188036630601920.99905981684699
310.0009693693741836770.001938738748367350.999030630625816
320.002745402769171660.005490805538343310.997254597230828
330.8570349416518780.2859301166962440.142965058348122
340.8677464089972680.2645071820054650.132253591002733
350.8169108503326550.3661782993346910.183089149667345
360.796111161715820.407777676568360.20388883828418
370.7901758394002860.4196483211994280.209824160599714
380.7901089597182560.4197820805634870.209891040281744
390.9020430755442430.1959138489115140.0979569244557572
400.9968725289723640.006254942055271270.00312747102763563
410.994951224214580.01009755157083840.0050487757854192
420.9924081582385990.01518368352280220.0075918417614011
430.9877997126060060.02440057478798730.0122002873939936
440.9763253724122770.04734925517544560.0236746275877228
450.9817486606436170.03650267871276590.0182513393563829
460.9648502956062660.07029940878746860.0351497043937343
470.959618119728420.0807637605431620.040381880271581
480.9426106731108160.1147786537783670.0573893268891836
490.9117876888574850.176424622285030.088212311142515
500.9598322720535370.08033545589292680.0401677279464634
510.985687783289180.02862443342164230.0143122167108212






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.560975609756098NOK
5% type I error level290.707317073170732NOK
10% type I error level320.780487804878049NOK

\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 & 23 & 0.560975609756098 & NOK \tabularnewline
5% type I error level & 29 & 0.707317073170732 & NOK \tabularnewline
10% type I error level & 32 & 0.780487804878049 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117881&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]23[/C][C]0.560975609756098[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.707317073170732[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.780487804878049[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117881&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117881&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 level230.560975609756098NOK
5% type I error level290.707317073170732NOK
10% type I error level320.780487804878049NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}