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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 21 Dec 2010 19:59:58 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t12929628934xr2wl9zhzgu3k3.htm/, Retrieved Sun, 19 May 2024 19:19:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113941, Retrieved Sun, 19 May 2024 19:19:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [regression] [2010-12-21 19:59:58] [8690b0a5633f6ac5ed8a33b8894b072f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-999,00	38,60	6654,00	5712,00	645,00	3,30	3,00	5,00	3,00
6,30	4,50	1,00	6600,00	42,00	8,30	3,00	1,00	3,00
-999,00	14,00	3,39	44,50	60,00	12,50	1,00	1,00	1,00
-999,00	-999,00	0,92	5,70	25,00	16,50	5,00	2,00	3,00
2,10	69,00	2547,00	4603,00	624,00	3,90	3,00	5,00	4,00
9,10	27,00	10,55	179,50	180,00	9,80	4,00	4,00	4,00
15,80	19,00	0,02	0,30	35,00	19,70	1,00	1,00	1,00
5,20	30,40	160,00	169,00	392,00	6,20	4,00	5,00	4,00
10,90	28,00	3,30	25,60	63,00	14,50	1,00	2,00	1,00
8,30	50,00	52,16	440,00	230,00	9,70	1,00	1,00	1,00
11,00	7,00	0,43	6,40	112,00	12,50	5,00	4,00	4,00
3,20	30,00	465,00	423,00	281,00	3,90	5,00	5,00	5,00
7,60	-999,00	0,55	2,40	-999,00	10,30	2,00	1,00	2,00
-999,00	40,00	187,10	419,00	365,00	3,10	5,00	5,00	5,00
6,30	3,50	0,08	1,20	42,00	8,40	1,00	1,00	1,00
8,60	50,00	3,00	25,00	28,00	8,60	2,00	2,00	2,00
6,60	6,00	0,79	3500,00	42,00	10,70	2,00	2,00	2,00
9,50	10,40	0,20	5,00	120,00	10,70	2,00	2,00	2,00
4,80	34,00	1,41	17,50	-999,00	6,10	1,00	2,00	1,00
12,00	7,00	60,00	81,00	-999,00	18,10	1,00	1,00	1,00
-999,00	28,00	529,00	680,00	400,00	-999,00	5,00	5,00	5,00
3,30	20,00	27,66	115,00	148,00	3,80	5,00	5,00	5,00
11,00	3,90	0,12	1,00	16,00	14,40	3,00	1,00	2,00
-999,00	39,30	207,00	406,00	252,00	12,00	1,00	4,00	1,00
4,70	41,00	85,00	325,00	310,00	6,20	1,00	3,00	1,00
-999,00	16,20	36,33	119,50	63,00	13,00	1,00	1,00	1,00
10,40	9,00	0,10	4,00	28,00	13,80	5,00	1,00	3,00
7,40	7,60	1,04	5,50	68,00	8,20	5,00	3,00	4,00
2,10	46,00	521,00	655,00	336,00	2,90	5,00	5,00	5,00
-999,00	22,40	100,00	157,00	100,00	10,80	1,00	1,00	1,00
-999,00	16,30	35,00	56,00	33,00	-999,00	3,00	5,00	4,00
7,70	2,60	0,01	0,14	21,50	9,10	5,00	2,00	4,00
17,90	24,00	0,01	0,25	50,00	19,90	1,00	1,00	1,00
6,10	100,00	62,00	1320,00	267,00	8,00	1,00	1,00	1,00
8,20	-999,00	0,12	3,00	30,00	10,60	2,00	1,00	1,00
8,40	-999,00	1,35	8,10	45,00	11,20	3,00	1,00	3,00
11,90	3,20	0,02	0,40	19,00	13,20	4,00	1,00	3,00
10,80	2,00	0,05	0,33	30,00	12,80	4,00	1,00	3,00
13,80	5,00	1,70	6,30	12,00	19,40	2,00	1,00	1,00
14,30	6,50	3,50	10,80	120,00	17,40	2,00	1,00	1,00
-999,00	23,60	250,00	490,00	440,00	-999,00	5,00	5,00	5,00
15,20	12,00	0,48	15,50	140,00	17,00	2,00	2,00	2,00
10,00	20,20	10,00	115,00	170,00	10,90	4,00	4,00	4,00
11,90	13,00	1,62	11,40	17,00	13,70	2,00	1,00	2,00
6,50	27,00	192,00	180,00	115,00	8,40	4,00	4,00	4,00
7,50	18,00	2,50	12,10	31,00	8,40	5,00	5,00	5,00
-999,00	13,70	4,29	39,20	63,00	12,50	2,00	2,00	2,00
10,60	4,70	0,28	1,90	21,00	13,20	3,00	1,00	3,00
7,40	9,80	4,24	50,40	52,00	9,80	1,00	1,00	1,00
8,40	29,00	6,80	179,00	164,00	9,60	2,00	3,00	2,00
5,70	7,00	0,75	12,30	225,00	6,60	2,00	2,00	2,00
4,90	6,00	3,60	21,00	225,00	5,40	3,00	2,00	3,00
-999,00	17,00	14,83	98,20	150,00	2,60	5,00	5,00	5,00
3,20	20,00	55,50	175,00	151,00	3,80	5,00	5,00	5,00
-999,00	12,70	1,40	12,50	90,00	11,00	2,00	2,00	2,00
8,10	3,50	0,06	1,00	-999,00	10,30	3,00	1,00	2,00
11,00	4,50	0,90	2,60	60,00	13,30	2,00	1,00	2,00
4,90	7,50	2,00	12,30	200,00	5,40	3,00	1,00	3,00
13,20	2,30	0,10	2,50	46,00	15,80	3,00	2,00	2,00
9,70	24,00	4,19	58,00	210,00	10,30	4,00	3,00	4,00
12,80	3,00	3,50	3,90	14,00	19,40	2,00	1,00	1,00
-999,00	13,00	4,05	17,00	38,00	-999,00	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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113941&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113941&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113941&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
SWS[t] = -192.398252454159 + 0.196980459486057L[t] -0.0894302120127955Wb[t] + 0.0270719269611047Wbr[t] -0.162991037668553Tg[t] + 0.78500630910943Ts[t] -25.9723351193777P[t] -83.0275670875686S[t] + 121.616239149268D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  -192.398252454159 +  0.196980459486057L[t] -0.0894302120127955Wb[t] +  0.0270719269611047Wbr[t] -0.162991037668553Tg[t] +  0.78500630910943Ts[t] -25.9723351193777P[t] -83.0275670875686S[t] +  121.616239149268D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113941&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  -192.398252454159 +  0.196980459486057L[t] -0.0894302120127955Wb[t] +  0.0270719269611047Wbr[t] -0.162991037668553Tg[t] +  0.78500630910943Ts[t] -25.9723351193777P[t] -83.0275670875686S[t] +  121.616239149268D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113941&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113941&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
SWS[t] = -192.398252454159 + 0.196980459486057L[t] -0.0894302120127955Wb[t] + 0.0270719269611047Wbr[t] -0.162991037668553Tg[t] + 0.78500630910943Ts[t] -25.9723351193777P[t] -83.0275670875686S[t] + 121.616239149268D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-192.398252454159111.812203-1.72070.0911340.045567
L0.1969804594860570.2008380.98080.3311510.165575
Wb-0.08943021201279550.076477-1.16940.2474840.123742
Wbr0.02707192696110470.050410.5370.5934880.296744
Tg-0.1629910376685530.176564-0.92310.3601240.180062
Ts0.785006309109430.1982893.95890.0002260.000113
P-25.972335119377791.790596-0.2830.7783160.389158
S-83.027567087568660.60128-1.37010.1764430.088222
D121.616239149268120.3788181.01030.3169520.158476

\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) & -192.398252454159 & 111.812203 & -1.7207 & 0.091134 & 0.045567 \tabularnewline
L & 0.196980459486057 & 0.200838 & 0.9808 & 0.331151 & 0.165575 \tabularnewline
Wb & -0.0894302120127955 & 0.076477 & -1.1694 & 0.247484 & 0.123742 \tabularnewline
Wbr & 0.0270719269611047 & 0.05041 & 0.537 & 0.593488 & 0.296744 \tabularnewline
Tg & -0.162991037668553 & 0.176564 & -0.9231 & 0.360124 & 0.180062 \tabularnewline
Ts & 0.78500630910943 & 0.198289 & 3.9589 & 0.000226 & 0.000113 \tabularnewline
P & -25.9723351193777 & 91.790596 & -0.283 & 0.778316 & 0.389158 \tabularnewline
S & -83.0275670875686 & 60.60128 & -1.3701 & 0.176443 & 0.088222 \tabularnewline
D & 121.616239149268 & 120.378818 & 1.0103 & 0.316952 & 0.158476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113941&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]-192.398252454159[/C][C]111.812203[/C][C]-1.7207[/C][C]0.091134[/C][C]0.045567[/C][/ROW]
[ROW][C]L[/C][C]0.196980459486057[/C][C]0.200838[/C][C]0.9808[/C][C]0.331151[/C][C]0.165575[/C][/ROW]
[ROW][C]Wb[/C][C]-0.0894302120127955[/C][C]0.076477[/C][C]-1.1694[/C][C]0.247484[/C][C]0.123742[/C][/ROW]
[ROW][C]Wbr[/C][C]0.0270719269611047[/C][C]0.05041[/C][C]0.537[/C][C]0.593488[/C][C]0.296744[/C][/ROW]
[ROW][C]Tg[/C][C]-0.162991037668553[/C][C]0.176564[/C][C]-0.9231[/C][C]0.360124[/C][C]0.180062[/C][/ROW]
[ROW][C]Ts[/C][C]0.78500630910943[/C][C]0.198289[/C][C]3.9589[/C][C]0.000226[/C][C]0.000113[/C][/ROW]
[ROW][C]P[/C][C]-25.9723351193777[/C][C]91.790596[/C][C]-0.283[/C][C]0.778316[/C][C]0.389158[/C][/ROW]
[ROW][C]S[/C][C]-83.0275670875686[/C][C]60.60128[/C][C]-1.3701[/C][C]0.176443[/C][C]0.088222[/C][/ROW]
[ROW][C]D[/C][C]121.616239149268[/C][C]120.378818[/C][C]1.0103[/C][C]0.316952[/C][C]0.158476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113941&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113941&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)-192.398252454159111.812203-1.72070.0911340.045567
L0.1969804594860570.2008380.98080.3311510.165575
Wb-0.08943021201279550.076477-1.16940.2474840.123742
Wbr0.02707192696110470.050410.5370.5934880.296744
Tg-0.1629910376685530.176564-0.92310.3601240.180062
Ts0.785006309109430.1982893.95890.0002260.000113
P-25.972335119377791.790596-0.2830.7783160.389158
S-83.027567087568660.60128-1.37010.1764430.088222
D121.616239149268120.3788181.01030.3169520.158476






Multiple Linear Regression - Regression Statistics
Multiple R0.58753404519096
R-squared0.345196254258452
Adjusted R-squared0.246357953014445
F-TEST (value)3.49253528150235
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0.00262481877877097
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation368.755482812332
Sum Squared Residuals7206972.12352026

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.58753404519096 \tabularnewline
R-squared & 0.345196254258452 \tabularnewline
Adjusted R-squared & 0.246357953014445 \tabularnewline
F-TEST (value) & 3.49253528150235 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0.00262481877877097 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 368.755482812332 \tabularnewline
Sum Squared Residuals & 7206972.12352026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113941&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.58753404519096[/C][/ROW]
[ROW][C]R-squared[/C][C]0.345196254258452[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.246357953014445[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.49253528150235[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]0.00262481877877097[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]368.755482812332[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7206972.12352026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113941&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113941&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 R0.58753404519096
R-squared0.345196254258452
Adjusted R-squared0.246357953014445
F-TEST (value)3.49253528150235
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0.00262481877877097
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation368.755482812332
Sum Squared Residuals7206972.12352026






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-999-855.973412473636-143.026587526364
26.3190.647521130438-184.347521130438
3-999-176.089540144232-822.910459855768
4-999-311.299961457735-687.700038542265
52.1-387.208038042801389.308038042801
69.1-154.343835077033163.443835077033
715.8-166.273015836697182.073015836697
85.2-287.731391719281292.931391719281
910.9-255.781951994266266.681951994266
108.3-192.559301998642200.859301998642
1111-174.833988601473185.833988601473
123.2-196.279734418709199.479734418709
137.6-109.992092885708117.592092885708
14-999-183.884813824783-815.115186175217
156.3-179.318722593804185.618722593804
168.6-154.720742853602163.320742853602
176.6-69.748657390831776.3486573908317
189.5-176.158865211214185.658865211214
194.8-88.147899737547592.9478997375475
2012-4.5393781065888316.5393781065888
21-999-1002.118504565903.11850456590095
223.3-145.876976216908149.176976216908
2311-100.629548259331111.629548259331
24-999-460.297802040344-538.702197959656
254.7-382.224225167548386.924225167548
26-999-176.668089753433-822.331910246567
2710.4-32.297270857804342.6972708578043
287.4-87.971071873559695.3710718735596
292.1-201.604965265552203.704965265552
30-999-187.883317516210-811.11668248379
31-999-986.99139171745-12.0086082825502
327.72.304189436226615.39581056377340
3317.9-167.576437136702185.476437136702
346.1-167.132155703995173.232155703995
358.2-399.035909755841407.235909755841
368.4-183.721560689520192.121560689520
3711.9-6.5618113699934318.4618113699934
3810.8-8.909669800622219.7096698006222
3913.8-191.477596609651205.277596609651
4014.3-210.394321317143224.694321317143
41-999-989.697497065422-9.30250293457835
4215.2-173.898762708289189.098762708289
4310-154.886837557217164.886837557217
4411.9-73.429783690298985.329783690299
456.5-161.062002467572167.562002467572
467.5-124.125593856811131.625593856811
47-999-164.245238856468-834.754761143532
4810.619.3973683985945-8.79736839859451
497.4-177.648738118461185.048738118461
508.4-259.43743245783267.83743245783
515.7-197.012745145804202.712745145804
524.9-102.527179486006107.427179486006
53-999-147.043325994456-851.956674005544
543.2-147.231370814683150.431370814683
55-999-170.484853933813-828.515146066187
568.161.5144027358274-53.4144027358274
5711-82.600577943930593.6005779439305
584.9-15.221803192835520.1218031928355
5913.2-187.720609884699200.920609884699
609.7-79.124900320797588.8249003207975
6112.8-192.423486610290205.223486610290
62-999-1019.4827716093120.4827716093122

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -999 & -855.973412473636 & -143.026587526364 \tabularnewline
2 & 6.3 & 190.647521130438 & -184.347521130438 \tabularnewline
3 & -999 & -176.089540144232 & -822.910459855768 \tabularnewline
4 & -999 & -311.299961457735 & -687.700038542265 \tabularnewline
5 & 2.1 & -387.208038042801 & 389.308038042801 \tabularnewline
6 & 9.1 & -154.343835077033 & 163.443835077033 \tabularnewline
7 & 15.8 & -166.273015836697 & 182.073015836697 \tabularnewline
8 & 5.2 & -287.731391719281 & 292.931391719281 \tabularnewline
9 & 10.9 & -255.781951994266 & 266.681951994266 \tabularnewline
10 & 8.3 & -192.559301998642 & 200.859301998642 \tabularnewline
11 & 11 & -174.833988601473 & 185.833988601473 \tabularnewline
12 & 3.2 & -196.279734418709 & 199.479734418709 \tabularnewline
13 & 7.6 & -109.992092885708 & 117.592092885708 \tabularnewline
14 & -999 & -183.884813824783 & -815.115186175217 \tabularnewline
15 & 6.3 & -179.318722593804 & 185.618722593804 \tabularnewline
16 & 8.6 & -154.720742853602 & 163.320742853602 \tabularnewline
17 & 6.6 & -69.7486573908317 & 76.3486573908317 \tabularnewline
18 & 9.5 & -176.158865211214 & 185.658865211214 \tabularnewline
19 & 4.8 & -88.1478997375475 & 92.9478997375475 \tabularnewline
20 & 12 & -4.53937810658883 & 16.5393781065888 \tabularnewline
21 & -999 & -1002.11850456590 & 3.11850456590095 \tabularnewline
22 & 3.3 & -145.876976216908 & 149.176976216908 \tabularnewline
23 & 11 & -100.629548259331 & 111.629548259331 \tabularnewline
24 & -999 & -460.297802040344 & -538.702197959656 \tabularnewline
25 & 4.7 & -382.224225167548 & 386.924225167548 \tabularnewline
26 & -999 & -176.668089753433 & -822.331910246567 \tabularnewline
27 & 10.4 & -32.2972708578043 & 42.6972708578043 \tabularnewline
28 & 7.4 & -87.9710718735596 & 95.3710718735596 \tabularnewline
29 & 2.1 & -201.604965265552 & 203.704965265552 \tabularnewline
30 & -999 & -187.883317516210 & -811.11668248379 \tabularnewline
31 & -999 & -986.99139171745 & -12.0086082825502 \tabularnewline
32 & 7.7 & 2.30418943622661 & 5.39581056377340 \tabularnewline
33 & 17.9 & -167.576437136702 & 185.476437136702 \tabularnewline
34 & 6.1 & -167.132155703995 & 173.232155703995 \tabularnewline
35 & 8.2 & -399.035909755841 & 407.235909755841 \tabularnewline
36 & 8.4 & -183.721560689520 & 192.121560689520 \tabularnewline
37 & 11.9 & -6.56181136999343 & 18.4618113699934 \tabularnewline
38 & 10.8 & -8.9096698006222 & 19.7096698006222 \tabularnewline
39 & 13.8 & -191.477596609651 & 205.277596609651 \tabularnewline
40 & 14.3 & -210.394321317143 & 224.694321317143 \tabularnewline
41 & -999 & -989.697497065422 & -9.30250293457835 \tabularnewline
42 & 15.2 & -173.898762708289 & 189.098762708289 \tabularnewline
43 & 10 & -154.886837557217 & 164.886837557217 \tabularnewline
44 & 11.9 & -73.4297836902989 & 85.329783690299 \tabularnewline
45 & 6.5 & -161.062002467572 & 167.562002467572 \tabularnewline
46 & 7.5 & -124.125593856811 & 131.625593856811 \tabularnewline
47 & -999 & -164.245238856468 & -834.754761143532 \tabularnewline
48 & 10.6 & 19.3973683985945 & -8.79736839859451 \tabularnewline
49 & 7.4 & -177.648738118461 & 185.048738118461 \tabularnewline
50 & 8.4 & -259.43743245783 & 267.83743245783 \tabularnewline
51 & 5.7 & -197.012745145804 & 202.712745145804 \tabularnewline
52 & 4.9 & -102.527179486006 & 107.427179486006 \tabularnewline
53 & -999 & -147.043325994456 & -851.956674005544 \tabularnewline
54 & 3.2 & -147.231370814683 & 150.431370814683 \tabularnewline
55 & -999 & -170.484853933813 & -828.515146066187 \tabularnewline
56 & 8.1 & 61.5144027358274 & -53.4144027358274 \tabularnewline
57 & 11 & -82.6005779439305 & 93.6005779439305 \tabularnewline
58 & 4.9 & -15.2218031928355 & 20.1218031928355 \tabularnewline
59 & 13.2 & -187.720609884699 & 200.920609884699 \tabularnewline
60 & 9.7 & -79.1249003207975 & 88.8249003207975 \tabularnewline
61 & 12.8 & -192.423486610290 & 205.223486610290 \tabularnewline
62 & -999 & -1019.48277160931 & 20.4827716093122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113941&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]-999[/C][C]-855.973412473636[/C][C]-143.026587526364[/C][/ROW]
[ROW][C]2[/C][C]6.3[/C][C]190.647521130438[/C][C]-184.347521130438[/C][/ROW]
[ROW][C]3[/C][C]-999[/C][C]-176.089540144232[/C][C]-822.910459855768[/C][/ROW]
[ROW][C]4[/C][C]-999[/C][C]-311.299961457735[/C][C]-687.700038542265[/C][/ROW]
[ROW][C]5[/C][C]2.1[/C][C]-387.208038042801[/C][C]389.308038042801[/C][/ROW]
[ROW][C]6[/C][C]9.1[/C][C]-154.343835077033[/C][C]163.443835077033[/C][/ROW]
[ROW][C]7[/C][C]15.8[/C][C]-166.273015836697[/C][C]182.073015836697[/C][/ROW]
[ROW][C]8[/C][C]5.2[/C][C]-287.731391719281[/C][C]292.931391719281[/C][/ROW]
[ROW][C]9[/C][C]10.9[/C][C]-255.781951994266[/C][C]266.681951994266[/C][/ROW]
[ROW][C]10[/C][C]8.3[/C][C]-192.559301998642[/C][C]200.859301998642[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]-174.833988601473[/C][C]185.833988601473[/C][/ROW]
[ROW][C]12[/C][C]3.2[/C][C]-196.279734418709[/C][C]199.479734418709[/C][/ROW]
[ROW][C]13[/C][C]7.6[/C][C]-109.992092885708[/C][C]117.592092885708[/C][/ROW]
[ROW][C]14[/C][C]-999[/C][C]-183.884813824783[/C][C]-815.115186175217[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]-179.318722593804[/C][C]185.618722593804[/C][/ROW]
[ROW][C]16[/C][C]8.6[/C][C]-154.720742853602[/C][C]163.320742853602[/C][/ROW]
[ROW][C]17[/C][C]6.6[/C][C]-69.7486573908317[/C][C]76.3486573908317[/C][/ROW]
[ROW][C]18[/C][C]9.5[/C][C]-176.158865211214[/C][C]185.658865211214[/C][/ROW]
[ROW][C]19[/C][C]4.8[/C][C]-88.1478997375475[/C][C]92.9478997375475[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]-4.53937810658883[/C][C]16.5393781065888[/C][/ROW]
[ROW][C]21[/C][C]-999[/C][C]-1002.11850456590[/C][C]3.11850456590095[/C][/ROW]
[ROW][C]22[/C][C]3.3[/C][C]-145.876976216908[/C][C]149.176976216908[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]-100.629548259331[/C][C]111.629548259331[/C][/ROW]
[ROW][C]24[/C][C]-999[/C][C]-460.297802040344[/C][C]-538.702197959656[/C][/ROW]
[ROW][C]25[/C][C]4.7[/C][C]-382.224225167548[/C][C]386.924225167548[/C][/ROW]
[ROW][C]26[/C][C]-999[/C][C]-176.668089753433[/C][C]-822.331910246567[/C][/ROW]
[ROW][C]27[/C][C]10.4[/C][C]-32.2972708578043[/C][C]42.6972708578043[/C][/ROW]
[ROW][C]28[/C][C]7.4[/C][C]-87.9710718735596[/C][C]95.3710718735596[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]-201.604965265552[/C][C]203.704965265552[/C][/ROW]
[ROW][C]30[/C][C]-999[/C][C]-187.883317516210[/C][C]-811.11668248379[/C][/ROW]
[ROW][C]31[/C][C]-999[/C][C]-986.99139171745[/C][C]-12.0086082825502[/C][/ROW]
[ROW][C]32[/C][C]7.7[/C][C]2.30418943622661[/C][C]5.39581056377340[/C][/ROW]
[ROW][C]33[/C][C]17.9[/C][C]-167.576437136702[/C][C]185.476437136702[/C][/ROW]
[ROW][C]34[/C][C]6.1[/C][C]-167.132155703995[/C][C]173.232155703995[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]-399.035909755841[/C][C]407.235909755841[/C][/ROW]
[ROW][C]36[/C][C]8.4[/C][C]-183.721560689520[/C][C]192.121560689520[/C][/ROW]
[ROW][C]37[/C][C]11.9[/C][C]-6.56181136999343[/C][C]18.4618113699934[/C][/ROW]
[ROW][C]38[/C][C]10.8[/C][C]-8.9096698006222[/C][C]19.7096698006222[/C][/ROW]
[ROW][C]39[/C][C]13.8[/C][C]-191.477596609651[/C][C]205.277596609651[/C][/ROW]
[ROW][C]40[/C][C]14.3[/C][C]-210.394321317143[/C][C]224.694321317143[/C][/ROW]
[ROW][C]41[/C][C]-999[/C][C]-989.697497065422[/C][C]-9.30250293457835[/C][/ROW]
[ROW][C]42[/C][C]15.2[/C][C]-173.898762708289[/C][C]189.098762708289[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]-154.886837557217[/C][C]164.886837557217[/C][/ROW]
[ROW][C]44[/C][C]11.9[/C][C]-73.4297836902989[/C][C]85.329783690299[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]-161.062002467572[/C][C]167.562002467572[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]-124.125593856811[/C][C]131.625593856811[/C][/ROW]
[ROW][C]47[/C][C]-999[/C][C]-164.245238856468[/C][C]-834.754761143532[/C][/ROW]
[ROW][C]48[/C][C]10.6[/C][C]19.3973683985945[/C][C]-8.79736839859451[/C][/ROW]
[ROW][C]49[/C][C]7.4[/C][C]-177.648738118461[/C][C]185.048738118461[/C][/ROW]
[ROW][C]50[/C][C]8.4[/C][C]-259.43743245783[/C][C]267.83743245783[/C][/ROW]
[ROW][C]51[/C][C]5.7[/C][C]-197.012745145804[/C][C]202.712745145804[/C][/ROW]
[ROW][C]52[/C][C]4.9[/C][C]-102.527179486006[/C][C]107.427179486006[/C][/ROW]
[ROW][C]53[/C][C]-999[/C][C]-147.043325994456[/C][C]-851.956674005544[/C][/ROW]
[ROW][C]54[/C][C]3.2[/C][C]-147.231370814683[/C][C]150.431370814683[/C][/ROW]
[ROW][C]55[/C][C]-999[/C][C]-170.484853933813[/C][C]-828.515146066187[/C][/ROW]
[ROW][C]56[/C][C]8.1[/C][C]61.5144027358274[/C][C]-53.4144027358274[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]-82.6005779439305[/C][C]93.6005779439305[/C][/ROW]
[ROW][C]58[/C][C]4.9[/C][C]-15.2218031928355[/C][C]20.1218031928355[/C][/ROW]
[ROW][C]59[/C][C]13.2[/C][C]-187.720609884699[/C][C]200.920609884699[/C][/ROW]
[ROW][C]60[/C][C]9.7[/C][C]-79.1249003207975[/C][C]88.8249003207975[/C][/ROW]
[ROW][C]61[/C][C]12.8[/C][C]-192.423486610290[/C][C]205.223486610290[/C][/ROW]
[ROW][C]62[/C][C]-999[/C][C]-1019.48277160931[/C][C]20.4827716093122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113941&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113941&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
1-999-855.973412473636-143.026587526364
26.3190.647521130438-184.347521130438
3-999-176.089540144232-822.910459855768
4-999-311.299961457735-687.700038542265
52.1-387.208038042801389.308038042801
69.1-154.343835077033163.443835077033
715.8-166.273015836697182.073015836697
85.2-287.731391719281292.931391719281
910.9-255.781951994266266.681951994266
108.3-192.559301998642200.859301998642
1111-174.833988601473185.833988601473
123.2-196.279734418709199.479734418709
137.6-109.992092885708117.592092885708
14-999-183.884813824783-815.115186175217
156.3-179.318722593804185.618722593804
168.6-154.720742853602163.320742853602
176.6-69.748657390831776.3486573908317
189.5-176.158865211214185.658865211214
194.8-88.147899737547592.9478997375475
2012-4.5393781065888316.5393781065888
21-999-1002.118504565903.11850456590095
223.3-145.876976216908149.176976216908
2311-100.629548259331111.629548259331
24-999-460.297802040344-538.702197959656
254.7-382.224225167548386.924225167548
26-999-176.668089753433-822.331910246567
2710.4-32.297270857804342.6972708578043
287.4-87.971071873559695.3710718735596
292.1-201.604965265552203.704965265552
30-999-187.883317516210-811.11668248379
31-999-986.99139171745-12.0086082825502
327.72.304189436226615.39581056377340
3317.9-167.576437136702185.476437136702
346.1-167.132155703995173.232155703995
358.2-399.035909755841407.235909755841
368.4-183.721560689520192.121560689520
3711.9-6.5618113699934318.4618113699934
3810.8-8.909669800622219.7096698006222
3913.8-191.477596609651205.277596609651
4014.3-210.394321317143224.694321317143
41-999-989.697497065422-9.30250293457835
4215.2-173.898762708289189.098762708289
4310-154.886837557217164.886837557217
4411.9-73.429783690298985.329783690299
456.5-161.062002467572167.562002467572
467.5-124.125593856811131.625593856811
47-999-164.245238856468-834.754761143532
4810.619.3973683985945-8.79736839859451
497.4-177.648738118461185.048738118461
508.4-259.43743245783267.83743245783
515.7-197.012745145804202.712745145804
524.9-102.527179486006107.427179486006
53-999-147.043325994456-851.956674005544
543.2-147.231370814683150.431370814683
55-999-170.484853933813-828.515146066187
568.161.5144027358274-53.4144027358274
5711-82.600577943930593.6005779439305
584.9-15.221803192835520.1218031928355
5913.2-187.720609884699200.920609884699
609.7-79.124900320797588.8249003207975
6112.8-192.423486610290205.223486610290
62-999-1019.4827716093120.4827716093122






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7346108741304110.5307782517391780.265389125869589
130.7586434489101560.4827131021796880.241356551089844
140.9323006538289560.1353986923420880.0676993461710439
150.9259384157834650.1481231684330700.0740615842165349
160.888728319704480.2225433605910410.111271680295521
170.8478816784228310.3042366431543370.152118321577169
180.801227978706090.397544042587820.19877202129391
190.7669833667323740.4660332665352520.233016633267626
200.7141584461646380.5716831076707240.285841553835362
210.6510220070942850.697955985811430.348977992905715
220.5682925941884620.8634148116230750.431707405811538
230.5494664715902430.9010670568195140.450533528409757
240.619934518470820.7601309630583610.380065481529181
250.6430391024197310.7139217951605370.356960897580269
260.8598152445313920.2803695109372150.140184755468608
270.820733604695250.3585327906094990.179266395304750
280.7600469222773360.4799061554453270.239953077722664
290.700424613235650.59915077352870.29957538676435
300.8910556402639620.2178887194720760.108944359736038
310.8699591547846940.2600816904306130.130040845215306
320.8185641298933550.3628717402132890.181435870106645
330.780474706384730.4390505872305390.219525293615270
340.7389776833729510.5220446332540970.261022316627049
350.7477971090897250.504405781820550.252202890910275
360.6819666370196980.6360667259606050.318033362980302
370.6104317923213980.7791364153572040.389568207678602
380.5527542122998620.8944915754002760.447245787700138
390.4777669237127320.9555338474254640.522233076287268
400.4034737404628240.8069474809256480.596526259537176
410.3141260531347340.6282521062694670.685873946865266
420.2746963780769340.5493927561538690.725303621923066
430.2119110881713350.423822176342670.788088911828665
440.1504070422793010.3008140845586020.8495929577207
450.09775267183616440.1955053436723290.902247328163836
460.3290788848376660.6581577696753320.670921115162334
470.5252345517748900.949530896450220.47476544822511
480.4031656459890740.8063312919781490.596834354010926
490.2740776304555690.5481552609111370.725922369544431
500.1665603902602450.3331207805204890.833439609739755

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.734610874130411 & 0.530778251739178 & 0.265389125869589 \tabularnewline
13 & 0.758643448910156 & 0.482713102179688 & 0.241356551089844 \tabularnewline
14 & 0.932300653828956 & 0.135398692342088 & 0.0676993461710439 \tabularnewline
15 & 0.925938415783465 & 0.148123168433070 & 0.0740615842165349 \tabularnewline
16 & 0.88872831970448 & 0.222543360591041 & 0.111271680295521 \tabularnewline
17 & 0.847881678422831 & 0.304236643154337 & 0.152118321577169 \tabularnewline
18 & 0.80122797870609 & 0.39754404258782 & 0.19877202129391 \tabularnewline
19 & 0.766983366732374 & 0.466033266535252 & 0.233016633267626 \tabularnewline
20 & 0.714158446164638 & 0.571683107670724 & 0.285841553835362 \tabularnewline
21 & 0.651022007094285 & 0.69795598581143 & 0.348977992905715 \tabularnewline
22 & 0.568292594188462 & 0.863414811623075 & 0.431707405811538 \tabularnewline
23 & 0.549466471590243 & 0.901067056819514 & 0.450533528409757 \tabularnewline
24 & 0.61993451847082 & 0.760130963058361 & 0.380065481529181 \tabularnewline
25 & 0.643039102419731 & 0.713921795160537 & 0.356960897580269 \tabularnewline
26 & 0.859815244531392 & 0.280369510937215 & 0.140184755468608 \tabularnewline
27 & 0.82073360469525 & 0.358532790609499 & 0.179266395304750 \tabularnewline
28 & 0.760046922277336 & 0.479906155445327 & 0.239953077722664 \tabularnewline
29 & 0.70042461323565 & 0.5991507735287 & 0.29957538676435 \tabularnewline
30 & 0.891055640263962 & 0.217888719472076 & 0.108944359736038 \tabularnewline
31 & 0.869959154784694 & 0.260081690430613 & 0.130040845215306 \tabularnewline
32 & 0.818564129893355 & 0.362871740213289 & 0.181435870106645 \tabularnewline
33 & 0.78047470638473 & 0.439050587230539 & 0.219525293615270 \tabularnewline
34 & 0.738977683372951 & 0.522044633254097 & 0.261022316627049 \tabularnewline
35 & 0.747797109089725 & 0.50440578182055 & 0.252202890910275 \tabularnewline
36 & 0.681966637019698 & 0.636066725960605 & 0.318033362980302 \tabularnewline
37 & 0.610431792321398 & 0.779136415357204 & 0.389568207678602 \tabularnewline
38 & 0.552754212299862 & 0.894491575400276 & 0.447245787700138 \tabularnewline
39 & 0.477766923712732 & 0.955533847425464 & 0.522233076287268 \tabularnewline
40 & 0.403473740462824 & 0.806947480925648 & 0.596526259537176 \tabularnewline
41 & 0.314126053134734 & 0.628252106269467 & 0.685873946865266 \tabularnewline
42 & 0.274696378076934 & 0.549392756153869 & 0.725303621923066 \tabularnewline
43 & 0.211911088171335 & 0.42382217634267 & 0.788088911828665 \tabularnewline
44 & 0.150407042279301 & 0.300814084558602 & 0.8495929577207 \tabularnewline
45 & 0.0977526718361644 & 0.195505343672329 & 0.902247328163836 \tabularnewline
46 & 0.329078884837666 & 0.658157769675332 & 0.670921115162334 \tabularnewline
47 & 0.525234551774890 & 0.94953089645022 & 0.47476544822511 \tabularnewline
48 & 0.403165645989074 & 0.806331291978149 & 0.596834354010926 \tabularnewline
49 & 0.274077630455569 & 0.548155260911137 & 0.725922369544431 \tabularnewline
50 & 0.166560390260245 & 0.333120780520489 & 0.833439609739755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113941&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]12[/C][C]0.734610874130411[/C][C]0.530778251739178[/C][C]0.265389125869589[/C][/ROW]
[ROW][C]13[/C][C]0.758643448910156[/C][C]0.482713102179688[/C][C]0.241356551089844[/C][/ROW]
[ROW][C]14[/C][C]0.932300653828956[/C][C]0.135398692342088[/C][C]0.0676993461710439[/C][/ROW]
[ROW][C]15[/C][C]0.925938415783465[/C][C]0.148123168433070[/C][C]0.0740615842165349[/C][/ROW]
[ROW][C]16[/C][C]0.88872831970448[/C][C]0.222543360591041[/C][C]0.111271680295521[/C][/ROW]
[ROW][C]17[/C][C]0.847881678422831[/C][C]0.304236643154337[/C][C]0.152118321577169[/C][/ROW]
[ROW][C]18[/C][C]0.80122797870609[/C][C]0.39754404258782[/C][C]0.19877202129391[/C][/ROW]
[ROW][C]19[/C][C]0.766983366732374[/C][C]0.466033266535252[/C][C]0.233016633267626[/C][/ROW]
[ROW][C]20[/C][C]0.714158446164638[/C][C]0.571683107670724[/C][C]0.285841553835362[/C][/ROW]
[ROW][C]21[/C][C]0.651022007094285[/C][C]0.69795598581143[/C][C]0.348977992905715[/C][/ROW]
[ROW][C]22[/C][C]0.568292594188462[/C][C]0.863414811623075[/C][C]0.431707405811538[/C][/ROW]
[ROW][C]23[/C][C]0.549466471590243[/C][C]0.901067056819514[/C][C]0.450533528409757[/C][/ROW]
[ROW][C]24[/C][C]0.61993451847082[/C][C]0.760130963058361[/C][C]0.380065481529181[/C][/ROW]
[ROW][C]25[/C][C]0.643039102419731[/C][C]0.713921795160537[/C][C]0.356960897580269[/C][/ROW]
[ROW][C]26[/C][C]0.859815244531392[/C][C]0.280369510937215[/C][C]0.140184755468608[/C][/ROW]
[ROW][C]27[/C][C]0.82073360469525[/C][C]0.358532790609499[/C][C]0.179266395304750[/C][/ROW]
[ROW][C]28[/C][C]0.760046922277336[/C][C]0.479906155445327[/C][C]0.239953077722664[/C][/ROW]
[ROW][C]29[/C][C]0.70042461323565[/C][C]0.5991507735287[/C][C]0.29957538676435[/C][/ROW]
[ROW][C]30[/C][C]0.891055640263962[/C][C]0.217888719472076[/C][C]0.108944359736038[/C][/ROW]
[ROW][C]31[/C][C]0.869959154784694[/C][C]0.260081690430613[/C][C]0.130040845215306[/C][/ROW]
[ROW][C]32[/C][C]0.818564129893355[/C][C]0.362871740213289[/C][C]0.181435870106645[/C][/ROW]
[ROW][C]33[/C][C]0.78047470638473[/C][C]0.439050587230539[/C][C]0.219525293615270[/C][/ROW]
[ROW][C]34[/C][C]0.738977683372951[/C][C]0.522044633254097[/C][C]0.261022316627049[/C][/ROW]
[ROW][C]35[/C][C]0.747797109089725[/C][C]0.50440578182055[/C][C]0.252202890910275[/C][/ROW]
[ROW][C]36[/C][C]0.681966637019698[/C][C]0.636066725960605[/C][C]0.318033362980302[/C][/ROW]
[ROW][C]37[/C][C]0.610431792321398[/C][C]0.779136415357204[/C][C]0.389568207678602[/C][/ROW]
[ROW][C]38[/C][C]0.552754212299862[/C][C]0.894491575400276[/C][C]0.447245787700138[/C][/ROW]
[ROW][C]39[/C][C]0.477766923712732[/C][C]0.955533847425464[/C][C]0.522233076287268[/C][/ROW]
[ROW][C]40[/C][C]0.403473740462824[/C][C]0.806947480925648[/C][C]0.596526259537176[/C][/ROW]
[ROW][C]41[/C][C]0.314126053134734[/C][C]0.628252106269467[/C][C]0.685873946865266[/C][/ROW]
[ROW][C]42[/C][C]0.274696378076934[/C][C]0.549392756153869[/C][C]0.725303621923066[/C][/ROW]
[ROW][C]43[/C][C]0.211911088171335[/C][C]0.42382217634267[/C][C]0.788088911828665[/C][/ROW]
[ROW][C]44[/C][C]0.150407042279301[/C][C]0.300814084558602[/C][C]0.8495929577207[/C][/ROW]
[ROW][C]45[/C][C]0.0977526718361644[/C][C]0.195505343672329[/C][C]0.902247328163836[/C][/ROW]
[ROW][C]46[/C][C]0.329078884837666[/C][C]0.658157769675332[/C][C]0.670921115162334[/C][/ROW]
[ROW][C]47[/C][C]0.525234551774890[/C][C]0.94953089645022[/C][C]0.47476544822511[/C][/ROW]
[ROW][C]48[/C][C]0.403165645989074[/C][C]0.806331291978149[/C][C]0.596834354010926[/C][/ROW]
[ROW][C]49[/C][C]0.274077630455569[/C][C]0.548155260911137[/C][C]0.725922369544431[/C][/ROW]
[ROW][C]50[/C][C]0.166560390260245[/C][C]0.333120780520489[/C][C]0.833439609739755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113941&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113941&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
120.7346108741304110.5307782517391780.265389125869589
130.7586434489101560.4827131021796880.241356551089844
140.9323006538289560.1353986923420880.0676993461710439
150.9259384157834650.1481231684330700.0740615842165349
160.888728319704480.2225433605910410.111271680295521
170.8478816784228310.3042366431543370.152118321577169
180.801227978706090.397544042587820.19877202129391
190.7669833667323740.4660332665352520.233016633267626
200.7141584461646380.5716831076707240.285841553835362
210.6510220070942850.697955985811430.348977992905715
220.5682925941884620.8634148116230750.431707405811538
230.5494664715902430.9010670568195140.450533528409757
240.619934518470820.7601309630583610.380065481529181
250.6430391024197310.7139217951605370.356960897580269
260.8598152445313920.2803695109372150.140184755468608
270.820733604695250.3585327906094990.179266395304750
280.7600469222773360.4799061554453270.239953077722664
290.700424613235650.59915077352870.29957538676435
300.8910556402639620.2178887194720760.108944359736038
310.8699591547846940.2600816904306130.130040845215306
320.8185641298933550.3628717402132890.181435870106645
330.780474706384730.4390505872305390.219525293615270
340.7389776833729510.5220446332540970.261022316627049
350.7477971090897250.504405781820550.252202890910275
360.6819666370196980.6360667259606050.318033362980302
370.6104317923213980.7791364153572040.389568207678602
380.5527542122998620.8944915754002760.447245787700138
390.4777669237127320.9555338474254640.522233076287268
400.4034737404628240.8069474809256480.596526259537176
410.3141260531347340.6282521062694670.685873946865266
420.2746963780769340.5493927561538690.725303621923066
430.2119110881713350.423822176342670.788088911828665
440.1504070422793010.3008140845586020.8495929577207
450.09775267183616440.1955053436723290.902247328163836
460.3290788848376660.6581577696753320.670921115162334
470.5252345517748900.949530896450220.47476544822511
480.4031656459890740.8063312919781490.596834354010926
490.2740776304555690.5481552609111370.725922369544431
500.1665603902602450.3331207805204890.833439609739755






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=113941&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=113941&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113941&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 2
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Figure 5
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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}