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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 computationWed, 22 Dec 2010 07:24:23 +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/22/t129300252652zos0glzbwomok.htm/, Retrieved Sun, 05 May 2024 21:13:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114053, Retrieved Sun, 05 May 2024 21:13:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-12-22 07:12:18] [47138a5b35b45ef255ae0d42cb04d202]
-   P       [Multiple Regression] [] [2010-12-22 07:24:23] [0dfe009a651fec1e160584d659799586] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-999.00	-999.00	38.60	6654.00	5712.00	645.00	3.00	5.00	3.00
6.30	2.00	4.50	1.00	6600.00	42.00	3.00	1.00	3.00
-999.00	-999.00	14.00	3.39	44.50	60.00	1.00	1.00	1.00
-999.00	-999.00	-999.00	0.92	5.70	25.00	5.00	2.00	3.00
2.10	1.80	69.00	2547.00	4603.00	624.00	3.00	5.00	4.00
9.10	0.70	27.00	10.55	179.50	180.00	4.00	4.00	4.00
15.80	3.90	19.00	0.02	0.30	35.00	1.00	1.00	1.00
5.20	1.00	30.40	160.00	169.00	392.00	4.00	5.00	4.00
10.90	3.60	28.00	3.30	25.60	63.00	1.00	2.00	1.00
8.30	1.40	50.00	52.16	440.00	230.00	1.00	1.00	1.00
11.00	1.50	7.00	0.43	6.40	112.00	5.00	4.00	4.00
3.20	0.70	30.00	465.00	423.00	281.00	5.00	5.00	5.00
7.60	2.70	-999.00	0.55	2.40	-999.00	2.00	1.00	2.00
-999.00	-999.00	40.00	187.10	419.00	365.00	5.00	5.00	5.00
6.30	2.10	3.50	0.08	1.20	42.00	1.00	1.00	1.00
8.60	0.00	50.00	3.00	25.00	28.00	2.00	2.00	2.00
6.60	4.10	6.00	0.79	3500.00	42.00	2.00	2.00	2.00
9.50	1.20	10.40	0.20	5.00	120.00	2.00	2.00	2.00
4.80	1.30	34.00	1.41	17.50	-999.00	1.00	2.00	1.00
12.00	6.10	7.00	60.00	81.00	-999.00	1.00	1.00	1.00
-999.00	0.30	28.00	529.00	680.00	400.00	5.00	5.00	5.00
3.30	0.50	20.00	27.66	115.00	148.00	5.00	5.00	5.00
11.00	3.40	3.90	0.12	1.00	16.00	3.00	1.00	2.00
-999.00	-999.00	39.30	207.00	406.00	252.00	1.00	4.00	1.00
4.70	1.50	41.00	85.00	325.00	310.00	1.00	3.00	1.00
-999.00	-999.00	16.20	36.33	119.50	63.00	1.00	1.00	1.00
10.40	3.40	9.00	0.10	4.00	28.00	5.00	1.00	3.00
7.40	0.80	7.60	1.04	5.50	68.00	5.00	3.00	4.00
2.10	0.80	46.00	521.00	655.00	336.00	5.00	5.00	5.00
-999.00	-999.00	22.40	100.00	157.00	100.00	1.00	1.00	1.00
-999.00	-999.00	16.30	35.00	56.00	33.00	3.00	5.00	4.00
7.70	1.40	2.60	0.01	0.14	21.50	5.00	2.00	4.00
17.90	2.00	24.00	0.01	0.25	50.00	1.00	1.00	1.00
6.10	1.90	100.00	62.00	1320.00	267.00	1.00	1.00	1.00
8.20	2.40	-999.00	0.12	3.00	30.00	2.00	1.00	1.00
8.40	2.80	-999.00	1.35	8.10	45.00	3.00	1.00	3.00
11.90	1.30	3.20	0.02	0.40	19.00	4.00	1.00	3.00
10.80	2.00	2.00	0.05	0.33	30.00	4.00	1.00	3.00
13.80	5.60	5.00	1.70	6.30	12.00	2.00	1.00	1.00
14.30	3.10	6.50	3.50	10.80	120.00	2.00	1.00	1.00
-999.00	1.00	23.60	250.00	490.00	440.00	5.00	5.00	5.00
15.20	1.80	12.00	0.48	15.50	140.00	2.00	2.00	2.00
10.00	0.90	20.20	10.00	115.00	170.00	4.00	4.00	4.00
11.90	1.80	13.00	1.62	11.40	17.00	2.00	1.00	2.00
6.50	1.90	27.00	192.00	180.00	115.00	4.00	4.00	4.00
7.50	0.90	18.00	2.50	12.10	31.00	5.00	5.00	5.00
-999.00	-999.00	13.70	4.29	39.20	63.00	2.00	2.00	2.00
10.60	2.60	4.70	0.28	1.90	21.00	3.00	1.00	3.00
7.40	2.40	9.80	4.24	50.40	52.00	1.00	1.00	1.00
8.40	1.20	29.00	6.80	179.00	164.00	2.00	3.00	2.00
5.70	0.90	7.00	0.75	12.30	225.00	2.00	2.00	2.00
4.90	0.50	6.00	3.60	21.00	225.00	3.00	2.00	3.00
-999.00	-999.00	17.00	14.83	98.20	150.00	5.00	5.00	5.00
3.20	0.60	20.00	55.50	175.00	151.00	5.00	5.00	5.00
-999.00	-999.00	12.70	1.40	12.50	90.00	2.00	2.00	2.00
8.10	2.20	3.50	0.06	1.00	-999.00	3.00	1.00	2.00
11.00	2.30	4.50	0.90	2.60	60.00	2.00	1.00	2.00
4.90	0.50	7.50	2.00	12.30	200.00	3.00	1.00	3.00
13.20	2.60	2.30	0.10	2.50	46.00	3.00	2.00	2.00
9.70	0.60	24.00	4.19	58.00	210.00	4.00	3.00	4.00
12.80	6.60	3.00	3.50	3.90	14.00	2.00	1.00	1.00
-999.00	-999.00	13.00	4.05	17.00	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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114053&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114053&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114053&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
PS[t] = -88.2745862297677 + 0.85459151446654SWS[t] + 0.0141510400094479L[t] -0.0210073835664919Wb[t] + 0.00242742421534027Wbr[t] + 0.0344938766541348Tg[t] -9.18488699988641P[t] -1.64139874995659S[t] + 44.1134440268902D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  -88.2745862297677 +  0.85459151446654SWS[t] +  0.0141510400094479L[t] -0.0210073835664919Wb[t] +  0.00242742421534027Wbr[t] +  0.0344938766541348Tg[t] -9.18488699988641P[t] -1.64139874995659S[t] +  44.1134440268902D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114053&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  -88.2745862297677 +  0.85459151446654SWS[t] +  0.0141510400094479L[t] -0.0210073835664919Wb[t] +  0.00242742421534027Wbr[t] +  0.0344938766541348Tg[t] -9.18488699988641P[t] -1.64139874995659S[t] +  44.1134440268902D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114053&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114053&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
PS[t] = -88.2745862297677 + 0.85459151446654SWS[t] + 0.0141510400094479L[t] -0.0210073835664919Wb[t] + 0.00242742421534027Wbr[t] + 0.0344938766541348Tg[t] -9.18488699988641P[t] -1.64139874995659S[t] + 44.1134440268902D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-88.274586229767751.817459-1.70360.0943190.04716
SWS0.854591514466540.05567615.349300
L0.01415104000944790.0931990.15180.8798920.439946
Wb-0.02100738356649190.035368-0.5940.5550670.277533
Wbr0.002427424215340270.0233060.10420.9174410.458721
Tg0.03449387665413480.0820840.42020.6760180.338009
P-9.1848869998864142.276019-0.21730.828840.41442
S-1.6413987499565928.36796-0.05790.9540770.477038
D44.113444026890256.0648450.78680.4348860.217443

\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) & -88.2745862297677 & 51.817459 & -1.7036 & 0.094319 & 0.04716 \tabularnewline
SWS & 0.85459151446654 & 0.055676 & 15.3493 & 0 & 0 \tabularnewline
L & 0.0141510400094479 & 0.093199 & 0.1518 & 0.879892 & 0.439946 \tabularnewline
Wb & -0.0210073835664919 & 0.035368 & -0.594 & 0.555067 & 0.277533 \tabularnewline
Wbr & 0.00242742421534027 & 0.023306 & 0.1042 & 0.917441 & 0.458721 \tabularnewline
Tg & 0.0344938766541348 & 0.082084 & 0.4202 & 0.676018 & 0.338009 \tabularnewline
P & -9.18488699988641 & 42.276019 & -0.2173 & 0.82884 & 0.41442 \tabularnewline
S & -1.64139874995659 & 28.36796 & -0.0579 & 0.954077 & 0.477038 \tabularnewline
D & 44.1134440268902 & 56.064845 & 0.7868 & 0.434886 & 0.217443 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114053&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]-88.2745862297677[/C][C]51.817459[/C][C]-1.7036[/C][C]0.094319[/C][C]0.04716[/C][/ROW]
[ROW][C]SWS[/C][C]0.85459151446654[/C][C]0.055676[/C][C]15.3493[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]0.0141510400094479[/C][C]0.093199[/C][C]0.1518[/C][C]0.879892[/C][C]0.439946[/C][/ROW]
[ROW][C]Wb[/C][C]-0.0210073835664919[/C][C]0.035368[/C][C]-0.594[/C][C]0.555067[/C][C]0.277533[/C][/ROW]
[ROW][C]Wbr[/C][C]0.00242742421534027[/C][C]0.023306[/C][C]0.1042[/C][C]0.917441[/C][C]0.458721[/C][/ROW]
[ROW][C]Tg[/C][C]0.0344938766541348[/C][C]0.082084[/C][C]0.4202[/C][C]0.676018[/C][C]0.338009[/C][/ROW]
[ROW][C]P[/C][C]-9.18488699988641[/C][C]42.276019[/C][C]-0.2173[/C][C]0.82884[/C][C]0.41442[/C][/ROW]
[ROW][C]S[/C][C]-1.64139874995659[/C][C]28.36796[/C][C]-0.0579[/C][C]0.954077[/C][C]0.477038[/C][/ROW]
[ROW][C]D[/C][C]44.1134440268902[/C][C]56.064845[/C][C]0.7868[/C][C]0.434886[/C][C]0.217443[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114053&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114053&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)-88.274586229767751.817459-1.70360.0943190.04716
SWS0.854591514466540.05567615.349300
L0.01415104000944790.0931990.15180.8798920.439946
Wb-0.02100738356649190.035368-0.5940.5550670.277533
Wbr0.002427424215340270.0233060.10420.9174410.458721
Tg0.03449387665413480.0820840.42020.6760180.338009
P-9.1848869998864142.276019-0.21730.828840.41442
S-1.6413987499565928.36796-0.05790.9540770.477038
D44.113444026890256.0648450.78680.4348860.217443






Multiple Linear Regression - Regression Statistics
Multiple R0.917484263475886
R-squared0.84177737372589
Adjusted R-squared0.817894713156213
F-TEST (value)35.2463818371503
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation170.137619942577
Sum Squared Residuals1534180.91514542

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.917484263475886 \tabularnewline
R-squared & 0.84177737372589 \tabularnewline
Adjusted R-squared & 0.817894713156213 \tabularnewline
F-TEST (value) & 35.2463818371503 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 170.137619942577 \tabularnewline
Sum Squared Residuals & 1534180.91514542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114053&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.917484263475886[/C][/ROW]
[ROW][C]R-squared[/C][C]0.84177737372589[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.817894713156213[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]35.2463818371503[/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[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]170.137619942577[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1534180.91514542[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114053&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114053&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 R0.917484263475886
R-squared0.84177737372589
Adjusted R-squared0.817894713156213
F-TEST (value)35.2463818371503
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation170.137619942577
Sum Squared Residuals1534180.91514542






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-999-948.555734397744-50.4442656022556
2237.7660275796213-35.7660275796213
3-999-906.419798398121-92.5802016018793
4-999-872.158442128454-126.841557871546
51.834.3804058209188-32.5804058209188
60.759.4559002880929-58.7559002880929
73.9-40.008418501481743.9084185014817
8158.6773685901522-57.6773685901522
93.6-44.751618151373548.3516181513736
101.4-39.280853223803440.6808532238034
111.549.0585403430403-47.5585403430403
120.782.2715756265265-81.5715756265265
132.7-62.16597540553564.865975405535
14-999-765.33280193653-233.667198063469
152.1-48.103997633701550.2039976337015
160-12.67973864978912.679738649789
174.1-6.046927699990510.1469276999905
181.2-9.2872786292834510.4872786292835
191.3-86.492175337457587.7921753374575
206.1-80.156476929083686.2564769290836
210.3-770.844195454929771.144195454929
220.586.0675612535236-85.5675612535236
233.4-19.236253645780922.6362536457809
24-999-907.762948532285-91.2370514677145
251.5-43.97706566463845.477065664638
26-999-906.795110878667-92.2048891213327
273.46.48846071687163-3.08846071687163
280.846.0991705063717-45.2991705063717
290.882.8418537549773-82.0418537549773
30-999-906.677612698009-92.322387301991
31-999-800.54975061075-198.45024938925
321.446.330852857483-44.930852857483
332-37.625524268617839.6255242686178
341.9-37.247708463749239.1477084637492
352.4-70.261975817177772.6619758171777
362.89.45489247113303-6.65489247113303
371.316.555655930026-15.255655930026
38215.9772565180949-13.9772565180949
395.6-51.914690112578257.5146901125782
403.1-47.767718998134950.8677189981349
411-764.126855550669765.126855550669
421.8-3.683981912863875.48398191286387
430.959.6388520115793-58.7388520115793
441.8-9.1252318056446810.9252318056447
451.951.1812843319288-49.2812843319288
460.985.8715237845048-84.9715237845048
47-999-873.065175796665-125.934824203335
482.624.717967491024-22.117967491024
492.4-46.697818093429349.0978180934293
501.2-9.8040650462652111.0040650462652
510.9-8.954816735793849.85481673579384
520.525.2371635871362-24.7371635871362
53-999-770.234235058669-228.765764941331
540.685.6463836264685-85.0463836264685
55-999-872.152093055055-126.847906944945
562.2-56.730253814670658.9302538146706
572.3-8.537647329544210.8376473295442
580.526.0499352037865-25.5499352037865
592.6-17.981315144307820.5813151443078
600.662.4410920436442-61.8410920436442
616.6-52.772235062291959.3722350622919
62-999-925.64320776337-73.3567922366302

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -999 & -948.555734397744 & -50.4442656022556 \tabularnewline
2 & 2 & 37.7660275796213 & -35.7660275796213 \tabularnewline
3 & -999 & -906.419798398121 & -92.5802016018793 \tabularnewline
4 & -999 & -872.158442128454 & -126.841557871546 \tabularnewline
5 & 1.8 & 34.3804058209188 & -32.5804058209188 \tabularnewline
6 & 0.7 & 59.4559002880929 & -58.7559002880929 \tabularnewline
7 & 3.9 & -40.0084185014817 & 43.9084185014817 \tabularnewline
8 & 1 & 58.6773685901522 & -57.6773685901522 \tabularnewline
9 & 3.6 & -44.7516181513735 & 48.3516181513736 \tabularnewline
10 & 1.4 & -39.2808532238034 & 40.6808532238034 \tabularnewline
11 & 1.5 & 49.0585403430403 & -47.5585403430403 \tabularnewline
12 & 0.7 & 82.2715756265265 & -81.5715756265265 \tabularnewline
13 & 2.7 & -62.165975405535 & 64.865975405535 \tabularnewline
14 & -999 & -765.33280193653 & -233.667198063469 \tabularnewline
15 & 2.1 & -48.1039976337015 & 50.2039976337015 \tabularnewline
16 & 0 & -12.679738649789 & 12.679738649789 \tabularnewline
17 & 4.1 & -6.0469276999905 & 10.1469276999905 \tabularnewline
18 & 1.2 & -9.28727862928345 & 10.4872786292835 \tabularnewline
19 & 1.3 & -86.4921753374575 & 87.7921753374575 \tabularnewline
20 & 6.1 & -80.1564769290836 & 86.2564769290836 \tabularnewline
21 & 0.3 & -770.844195454929 & 771.144195454929 \tabularnewline
22 & 0.5 & 86.0675612535236 & -85.5675612535236 \tabularnewline
23 & 3.4 & -19.2362536457809 & 22.6362536457809 \tabularnewline
24 & -999 & -907.762948532285 & -91.2370514677145 \tabularnewline
25 & 1.5 & -43.977065664638 & 45.477065664638 \tabularnewline
26 & -999 & -906.795110878667 & -92.2048891213327 \tabularnewline
27 & 3.4 & 6.48846071687163 & -3.08846071687163 \tabularnewline
28 & 0.8 & 46.0991705063717 & -45.2991705063717 \tabularnewline
29 & 0.8 & 82.8418537549773 & -82.0418537549773 \tabularnewline
30 & -999 & -906.677612698009 & -92.322387301991 \tabularnewline
31 & -999 & -800.54975061075 & -198.45024938925 \tabularnewline
32 & 1.4 & 46.330852857483 & -44.930852857483 \tabularnewline
33 & 2 & -37.6255242686178 & 39.6255242686178 \tabularnewline
34 & 1.9 & -37.2477084637492 & 39.1477084637492 \tabularnewline
35 & 2.4 & -70.2619758171777 & 72.6619758171777 \tabularnewline
36 & 2.8 & 9.45489247113303 & -6.65489247113303 \tabularnewline
37 & 1.3 & 16.555655930026 & -15.255655930026 \tabularnewline
38 & 2 & 15.9772565180949 & -13.9772565180949 \tabularnewline
39 & 5.6 & -51.9146901125782 & 57.5146901125782 \tabularnewline
40 & 3.1 & -47.7677189981349 & 50.8677189981349 \tabularnewline
41 & 1 & -764.126855550669 & 765.126855550669 \tabularnewline
42 & 1.8 & -3.68398191286387 & 5.48398191286387 \tabularnewline
43 & 0.9 & 59.6388520115793 & -58.7388520115793 \tabularnewline
44 & 1.8 & -9.12523180564468 & 10.9252318056447 \tabularnewline
45 & 1.9 & 51.1812843319288 & -49.2812843319288 \tabularnewline
46 & 0.9 & 85.8715237845048 & -84.9715237845048 \tabularnewline
47 & -999 & -873.065175796665 & -125.934824203335 \tabularnewline
48 & 2.6 & 24.717967491024 & -22.117967491024 \tabularnewline
49 & 2.4 & -46.6978180934293 & 49.0978180934293 \tabularnewline
50 & 1.2 & -9.80406504626521 & 11.0040650462652 \tabularnewline
51 & 0.9 & -8.95481673579384 & 9.85481673579384 \tabularnewline
52 & 0.5 & 25.2371635871362 & -24.7371635871362 \tabularnewline
53 & -999 & -770.234235058669 & -228.765764941331 \tabularnewline
54 & 0.6 & 85.6463836264685 & -85.0463836264685 \tabularnewline
55 & -999 & -872.152093055055 & -126.847906944945 \tabularnewline
56 & 2.2 & -56.7302538146706 & 58.9302538146706 \tabularnewline
57 & 2.3 & -8.5376473295442 & 10.8376473295442 \tabularnewline
58 & 0.5 & 26.0499352037865 & -25.5499352037865 \tabularnewline
59 & 2.6 & -17.9813151443078 & 20.5813151443078 \tabularnewline
60 & 0.6 & 62.4410920436442 & -61.8410920436442 \tabularnewline
61 & 6.6 & -52.7722350622919 & 59.3722350622919 \tabularnewline
62 & -999 & -925.64320776337 & -73.3567922366302 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114053&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]-948.555734397744[/C][C]-50.4442656022556[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]37.7660275796213[/C][C]-35.7660275796213[/C][/ROW]
[ROW][C]3[/C][C]-999[/C][C]-906.419798398121[/C][C]-92.5802016018793[/C][/ROW]
[ROW][C]4[/C][C]-999[/C][C]-872.158442128454[/C][C]-126.841557871546[/C][/ROW]
[ROW][C]5[/C][C]1.8[/C][C]34.3804058209188[/C][C]-32.5804058209188[/C][/ROW]
[ROW][C]6[/C][C]0.7[/C][C]59.4559002880929[/C][C]-58.7559002880929[/C][/ROW]
[ROW][C]7[/C][C]3.9[/C][C]-40.0084185014817[/C][C]43.9084185014817[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]58.6773685901522[/C][C]-57.6773685901522[/C][/ROW]
[ROW][C]9[/C][C]3.6[/C][C]-44.7516181513735[/C][C]48.3516181513736[/C][/ROW]
[ROW][C]10[/C][C]1.4[/C][C]-39.2808532238034[/C][C]40.6808532238034[/C][/ROW]
[ROW][C]11[/C][C]1.5[/C][C]49.0585403430403[/C][C]-47.5585403430403[/C][/ROW]
[ROW][C]12[/C][C]0.7[/C][C]82.2715756265265[/C][C]-81.5715756265265[/C][/ROW]
[ROW][C]13[/C][C]2.7[/C][C]-62.165975405535[/C][C]64.865975405535[/C][/ROW]
[ROW][C]14[/C][C]-999[/C][C]-765.33280193653[/C][C]-233.667198063469[/C][/ROW]
[ROW][C]15[/C][C]2.1[/C][C]-48.1039976337015[/C][C]50.2039976337015[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]-12.679738649789[/C][C]12.679738649789[/C][/ROW]
[ROW][C]17[/C][C]4.1[/C][C]-6.0469276999905[/C][C]10.1469276999905[/C][/ROW]
[ROW][C]18[/C][C]1.2[/C][C]-9.28727862928345[/C][C]10.4872786292835[/C][/ROW]
[ROW][C]19[/C][C]1.3[/C][C]-86.4921753374575[/C][C]87.7921753374575[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]-80.1564769290836[/C][C]86.2564769290836[/C][/ROW]
[ROW][C]21[/C][C]0.3[/C][C]-770.844195454929[/C][C]771.144195454929[/C][/ROW]
[ROW][C]22[/C][C]0.5[/C][C]86.0675612535236[/C][C]-85.5675612535236[/C][/ROW]
[ROW][C]23[/C][C]3.4[/C][C]-19.2362536457809[/C][C]22.6362536457809[/C][/ROW]
[ROW][C]24[/C][C]-999[/C][C]-907.762948532285[/C][C]-91.2370514677145[/C][/ROW]
[ROW][C]25[/C][C]1.5[/C][C]-43.977065664638[/C][C]45.477065664638[/C][/ROW]
[ROW][C]26[/C][C]-999[/C][C]-906.795110878667[/C][C]-92.2048891213327[/C][/ROW]
[ROW][C]27[/C][C]3.4[/C][C]6.48846071687163[/C][C]-3.08846071687163[/C][/ROW]
[ROW][C]28[/C][C]0.8[/C][C]46.0991705063717[/C][C]-45.2991705063717[/C][/ROW]
[ROW][C]29[/C][C]0.8[/C][C]82.8418537549773[/C][C]-82.0418537549773[/C][/ROW]
[ROW][C]30[/C][C]-999[/C][C]-906.677612698009[/C][C]-92.322387301991[/C][/ROW]
[ROW][C]31[/C][C]-999[/C][C]-800.54975061075[/C][C]-198.45024938925[/C][/ROW]
[ROW][C]32[/C][C]1.4[/C][C]46.330852857483[/C][C]-44.930852857483[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]-37.6255242686178[/C][C]39.6255242686178[/C][/ROW]
[ROW][C]34[/C][C]1.9[/C][C]-37.2477084637492[/C][C]39.1477084637492[/C][/ROW]
[ROW][C]35[/C][C]2.4[/C][C]-70.2619758171777[/C][C]72.6619758171777[/C][/ROW]
[ROW][C]36[/C][C]2.8[/C][C]9.45489247113303[/C][C]-6.65489247113303[/C][/ROW]
[ROW][C]37[/C][C]1.3[/C][C]16.555655930026[/C][C]-15.255655930026[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]15.9772565180949[/C][C]-13.9772565180949[/C][/ROW]
[ROW][C]39[/C][C]5.6[/C][C]-51.9146901125782[/C][C]57.5146901125782[/C][/ROW]
[ROW][C]40[/C][C]3.1[/C][C]-47.7677189981349[/C][C]50.8677189981349[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]-764.126855550669[/C][C]765.126855550669[/C][/ROW]
[ROW][C]42[/C][C]1.8[/C][C]-3.68398191286387[/C][C]5.48398191286387[/C][/ROW]
[ROW][C]43[/C][C]0.9[/C][C]59.6388520115793[/C][C]-58.7388520115793[/C][/ROW]
[ROW][C]44[/C][C]1.8[/C][C]-9.12523180564468[/C][C]10.9252318056447[/C][/ROW]
[ROW][C]45[/C][C]1.9[/C][C]51.1812843319288[/C][C]-49.2812843319288[/C][/ROW]
[ROW][C]46[/C][C]0.9[/C][C]85.8715237845048[/C][C]-84.9715237845048[/C][/ROW]
[ROW][C]47[/C][C]-999[/C][C]-873.065175796665[/C][C]-125.934824203335[/C][/ROW]
[ROW][C]48[/C][C]2.6[/C][C]24.717967491024[/C][C]-22.117967491024[/C][/ROW]
[ROW][C]49[/C][C]2.4[/C][C]-46.6978180934293[/C][C]49.0978180934293[/C][/ROW]
[ROW][C]50[/C][C]1.2[/C][C]-9.80406504626521[/C][C]11.0040650462652[/C][/ROW]
[ROW][C]51[/C][C]0.9[/C][C]-8.95481673579384[/C][C]9.85481673579384[/C][/ROW]
[ROW][C]52[/C][C]0.5[/C][C]25.2371635871362[/C][C]-24.7371635871362[/C][/ROW]
[ROW][C]53[/C][C]-999[/C][C]-770.234235058669[/C][C]-228.765764941331[/C][/ROW]
[ROW][C]54[/C][C]0.6[/C][C]85.6463836264685[/C][C]-85.0463836264685[/C][/ROW]
[ROW][C]55[/C][C]-999[/C][C]-872.152093055055[/C][C]-126.847906944945[/C][/ROW]
[ROW][C]56[/C][C]2.2[/C][C]-56.7302538146706[/C][C]58.9302538146706[/C][/ROW]
[ROW][C]57[/C][C]2.3[/C][C]-8.5376473295442[/C][C]10.8376473295442[/C][/ROW]
[ROW][C]58[/C][C]0.5[/C][C]26.0499352037865[/C][C]-25.5499352037865[/C][/ROW]
[ROW][C]59[/C][C]2.6[/C][C]-17.9813151443078[/C][C]20.5813151443078[/C][/ROW]
[ROW][C]60[/C][C]0.6[/C][C]62.4410920436442[/C][C]-61.8410920436442[/C][/ROW]
[ROW][C]61[/C][C]6.6[/C][C]-52.7722350622919[/C][C]59.3722350622919[/C][/ROW]
[ROW][C]62[/C][C]-999[/C][C]-925.64320776337[/C][C]-73.3567922366302[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114053&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114053&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
1-999-948.555734397744-50.4442656022556
2237.7660275796213-35.7660275796213
3-999-906.419798398121-92.5802016018793
4-999-872.158442128454-126.841557871546
51.834.3804058209188-32.5804058209188
60.759.4559002880929-58.7559002880929
73.9-40.008418501481743.9084185014817
8158.6773685901522-57.6773685901522
93.6-44.751618151373548.3516181513736
101.4-39.280853223803440.6808532238034
111.549.0585403430403-47.5585403430403
120.782.2715756265265-81.5715756265265
132.7-62.16597540553564.865975405535
14-999-765.33280193653-233.667198063469
152.1-48.103997633701550.2039976337015
160-12.67973864978912.679738649789
174.1-6.046927699990510.1469276999905
181.2-9.2872786292834510.4872786292835
191.3-86.492175337457587.7921753374575
206.1-80.156476929083686.2564769290836
210.3-770.844195454929771.144195454929
220.586.0675612535236-85.5675612535236
233.4-19.236253645780922.6362536457809
24-999-907.762948532285-91.2370514677145
251.5-43.97706566463845.477065664638
26-999-906.795110878667-92.2048891213327
273.46.48846071687163-3.08846071687163
280.846.0991705063717-45.2991705063717
290.882.8418537549773-82.0418537549773
30-999-906.677612698009-92.322387301991
31-999-800.54975061075-198.45024938925
321.446.330852857483-44.930852857483
332-37.625524268617839.6255242686178
341.9-37.247708463749239.1477084637492
352.4-70.261975817177772.6619758171777
362.89.45489247113303-6.65489247113303
371.316.555655930026-15.255655930026
38215.9772565180949-13.9772565180949
395.6-51.914690112578257.5146901125782
403.1-47.767718998134950.8677189981349
411-764.126855550669765.126855550669
421.8-3.683981912863875.48398191286387
430.959.6388520115793-58.7388520115793
441.8-9.1252318056446810.9252318056447
451.951.1812843319288-49.2812843319288
460.985.8715237845048-84.9715237845048
47-999-873.065175796665-125.934824203335
482.624.717967491024-22.117967491024
492.4-46.697818093429349.0978180934293
501.2-9.8040650462652111.0040650462652
510.9-8.954816735793849.85481673579384
520.525.2371635871362-24.7371635871362
53-999-770.234235058669-228.765764941331
540.685.6463836264685-85.0463836264685
55-999-872.152093055055-126.847906944945
562.2-56.730253814670658.9302538146706
572.3-8.537647329544210.8376473295442
580.526.0499352037865-25.5499352037865
592.6-17.981315144307820.5813151443078
600.662.4410920436442-61.8410920436442
616.6-52.772235062291959.3722350622919
62-999-925.64320776337-73.3567922366302






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
125.36097740204375e-061.07219548040875e-050.999994639022598
139.94207442238263e-081.98841488447653e-070.999999900579256
143.20571633008404e-096.41143266016807e-090.999999996794284
151.43235542769201e-102.86471085538403e-100.999999999856764
162.33405127437989e-124.66810254875978e-120.999999999997666
174.98649667663022e-149.97299335326044e-140.99999999999995
187.72314644137518e-161.54462928827504e-151
192.31599083003535e-174.6319816600707e-171
203.89231698142251e-197.78463396284501e-191
210.9752541452332650.04949170953346960.0247458547667348
220.96321579012930.07356841974139970.0367842098706999
230.9429265727364580.1141468545270840.0570734272635419
240.9213446736532320.1573106526935360.0786553263467682
250.8957687918758930.2084624162482140.104231208124107
260.8752970082832590.2494059834334830.124702991716741
270.8272375591067870.3455248817864250.172762440893213
280.7699901285161740.4600197429676510.230009871483826
290.9605587031527770.07888259369444690.0394412968472235
300.974488023263090.05102395347381750.0255119767369088
310.973501133961390.05299773207722140.0264988660386107
320.9577495370302270.0845009259395450.0422504629697725
330.9354127552351490.1291744895297020.0645872447648511
340.9950914123748480.009817175250304560.00490858762515228
350.9916433606942920.0167132786114150.00835663930570748
360.9984372998645420.003125400270916690.00156270013545835
370.9968237074181680.006352585163664470.00317629258183223
380.9945002498052420.0109995003895170.00549975019475848
390.9891954081677370.02160918366452570.0108045918322628
400.97958224858830.0408355028233980.020417751411699
4117.67736743576465e-213.83868371788232e-21
4218.10011098887522e-204.05005549443761e-20
4313.6863971545603e-181.84319857728015e-18
4411.8761645851048e-169.38082292552399e-17
450.9999999999999976.3986562081802e-153.1993281040901e-15
460.9999999999998053.88939445767372e-131.94469722883686e-13
470.9999999999848243.03514858741087e-111.51757429370543e-11
480.9999999991558081.68838441101757e-098.44192205508783e-10
490.9999999265925431.46814914756527e-077.34074573782636e-08
500.9999947849920481.04300159049688e-055.21500795248441e-06

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 5.36097740204375e-06 & 1.07219548040875e-05 & 0.999994639022598 \tabularnewline
13 & 9.94207442238263e-08 & 1.98841488447653e-07 & 0.999999900579256 \tabularnewline
14 & 3.20571633008404e-09 & 6.41143266016807e-09 & 0.999999996794284 \tabularnewline
15 & 1.43235542769201e-10 & 2.86471085538403e-10 & 0.999999999856764 \tabularnewline
16 & 2.33405127437989e-12 & 4.66810254875978e-12 & 0.999999999997666 \tabularnewline
17 & 4.98649667663022e-14 & 9.97299335326044e-14 & 0.99999999999995 \tabularnewline
18 & 7.72314644137518e-16 & 1.54462928827504e-15 & 1 \tabularnewline
19 & 2.31599083003535e-17 & 4.6319816600707e-17 & 1 \tabularnewline
20 & 3.89231698142251e-19 & 7.78463396284501e-19 & 1 \tabularnewline
21 & 0.975254145233265 & 0.0494917095334696 & 0.0247458547667348 \tabularnewline
22 & 0.9632157901293 & 0.0735684197413997 & 0.0367842098706999 \tabularnewline
23 & 0.942926572736458 & 0.114146854527084 & 0.0570734272635419 \tabularnewline
24 & 0.921344673653232 & 0.157310652693536 & 0.0786553263467682 \tabularnewline
25 & 0.895768791875893 & 0.208462416248214 & 0.104231208124107 \tabularnewline
26 & 0.875297008283259 & 0.249405983433483 & 0.124702991716741 \tabularnewline
27 & 0.827237559106787 & 0.345524881786425 & 0.172762440893213 \tabularnewline
28 & 0.769990128516174 & 0.460019742967651 & 0.230009871483826 \tabularnewline
29 & 0.960558703152777 & 0.0788825936944469 & 0.0394412968472235 \tabularnewline
30 & 0.97448802326309 & 0.0510239534738175 & 0.0255119767369088 \tabularnewline
31 & 0.97350113396139 & 0.0529977320772214 & 0.0264988660386107 \tabularnewline
32 & 0.957749537030227 & 0.084500925939545 & 0.0422504629697725 \tabularnewline
33 & 0.935412755235149 & 0.129174489529702 & 0.0645872447648511 \tabularnewline
34 & 0.995091412374848 & 0.00981717525030456 & 0.00490858762515228 \tabularnewline
35 & 0.991643360694292 & 0.016713278611415 & 0.00835663930570748 \tabularnewline
36 & 0.998437299864542 & 0.00312540027091669 & 0.00156270013545835 \tabularnewline
37 & 0.996823707418168 & 0.00635258516366447 & 0.00317629258183223 \tabularnewline
38 & 0.994500249805242 & 0.010999500389517 & 0.00549975019475848 \tabularnewline
39 & 0.989195408167737 & 0.0216091836645257 & 0.0108045918322628 \tabularnewline
40 & 0.9795822485883 & 0.040835502823398 & 0.020417751411699 \tabularnewline
41 & 1 & 7.67736743576465e-21 & 3.83868371788232e-21 \tabularnewline
42 & 1 & 8.10011098887522e-20 & 4.05005549443761e-20 \tabularnewline
43 & 1 & 3.6863971545603e-18 & 1.84319857728015e-18 \tabularnewline
44 & 1 & 1.8761645851048e-16 & 9.38082292552399e-17 \tabularnewline
45 & 0.999999999999997 & 6.3986562081802e-15 & 3.1993281040901e-15 \tabularnewline
46 & 0.999999999999805 & 3.88939445767372e-13 & 1.94469722883686e-13 \tabularnewline
47 & 0.999999999984824 & 3.03514858741087e-11 & 1.51757429370543e-11 \tabularnewline
48 & 0.999999999155808 & 1.68838441101757e-09 & 8.44192205508783e-10 \tabularnewline
49 & 0.999999926592543 & 1.46814914756527e-07 & 7.34074573782636e-08 \tabularnewline
50 & 0.999994784992048 & 1.04300159049688e-05 & 5.21500795248441e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114053&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]5.36097740204375e-06[/C][C]1.07219548040875e-05[/C][C]0.999994639022598[/C][/ROW]
[ROW][C]13[/C][C]9.94207442238263e-08[/C][C]1.98841488447653e-07[/C][C]0.999999900579256[/C][/ROW]
[ROW][C]14[/C][C]3.20571633008404e-09[/C][C]6.41143266016807e-09[/C][C]0.999999996794284[/C][/ROW]
[ROW][C]15[/C][C]1.43235542769201e-10[/C][C]2.86471085538403e-10[/C][C]0.999999999856764[/C][/ROW]
[ROW][C]16[/C][C]2.33405127437989e-12[/C][C]4.66810254875978e-12[/C][C]0.999999999997666[/C][/ROW]
[ROW][C]17[/C][C]4.98649667663022e-14[/C][C]9.97299335326044e-14[/C][C]0.99999999999995[/C][/ROW]
[ROW][C]18[/C][C]7.72314644137518e-16[/C][C]1.54462928827504e-15[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]2.31599083003535e-17[/C][C]4.6319816600707e-17[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]3.89231698142251e-19[/C][C]7.78463396284501e-19[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0.975254145233265[/C][C]0.0494917095334696[/C][C]0.0247458547667348[/C][/ROW]
[ROW][C]22[/C][C]0.9632157901293[/C][C]0.0735684197413997[/C][C]0.0367842098706999[/C][/ROW]
[ROW][C]23[/C][C]0.942926572736458[/C][C]0.114146854527084[/C][C]0.0570734272635419[/C][/ROW]
[ROW][C]24[/C][C]0.921344673653232[/C][C]0.157310652693536[/C][C]0.0786553263467682[/C][/ROW]
[ROW][C]25[/C][C]0.895768791875893[/C][C]0.208462416248214[/C][C]0.104231208124107[/C][/ROW]
[ROW][C]26[/C][C]0.875297008283259[/C][C]0.249405983433483[/C][C]0.124702991716741[/C][/ROW]
[ROW][C]27[/C][C]0.827237559106787[/C][C]0.345524881786425[/C][C]0.172762440893213[/C][/ROW]
[ROW][C]28[/C][C]0.769990128516174[/C][C]0.460019742967651[/C][C]0.230009871483826[/C][/ROW]
[ROW][C]29[/C][C]0.960558703152777[/C][C]0.0788825936944469[/C][C]0.0394412968472235[/C][/ROW]
[ROW][C]30[/C][C]0.97448802326309[/C][C]0.0510239534738175[/C][C]0.0255119767369088[/C][/ROW]
[ROW][C]31[/C][C]0.97350113396139[/C][C]0.0529977320772214[/C][C]0.0264988660386107[/C][/ROW]
[ROW][C]32[/C][C]0.957749537030227[/C][C]0.084500925939545[/C][C]0.0422504629697725[/C][/ROW]
[ROW][C]33[/C][C]0.935412755235149[/C][C]0.129174489529702[/C][C]0.0645872447648511[/C][/ROW]
[ROW][C]34[/C][C]0.995091412374848[/C][C]0.00981717525030456[/C][C]0.00490858762515228[/C][/ROW]
[ROW][C]35[/C][C]0.991643360694292[/C][C]0.016713278611415[/C][C]0.00835663930570748[/C][/ROW]
[ROW][C]36[/C][C]0.998437299864542[/C][C]0.00312540027091669[/C][C]0.00156270013545835[/C][/ROW]
[ROW][C]37[/C][C]0.996823707418168[/C][C]0.00635258516366447[/C][C]0.00317629258183223[/C][/ROW]
[ROW][C]38[/C][C]0.994500249805242[/C][C]0.010999500389517[/C][C]0.00549975019475848[/C][/ROW]
[ROW][C]39[/C][C]0.989195408167737[/C][C]0.0216091836645257[/C][C]0.0108045918322628[/C][/ROW]
[ROW][C]40[/C][C]0.9795822485883[/C][C]0.040835502823398[/C][C]0.020417751411699[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]7.67736743576465e-21[/C][C]3.83868371788232e-21[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]8.10011098887522e-20[/C][C]4.05005549443761e-20[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]3.6863971545603e-18[/C][C]1.84319857728015e-18[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.8761645851048e-16[/C][C]9.38082292552399e-17[/C][/ROW]
[ROW][C]45[/C][C]0.999999999999997[/C][C]6.3986562081802e-15[/C][C]3.1993281040901e-15[/C][/ROW]
[ROW][C]46[/C][C]0.999999999999805[/C][C]3.88939445767372e-13[/C][C]1.94469722883686e-13[/C][/ROW]
[ROW][C]47[/C][C]0.999999999984824[/C][C]3.03514858741087e-11[/C][C]1.51757429370543e-11[/C][/ROW]
[ROW][C]48[/C][C]0.999999999155808[/C][C]1.68838441101757e-09[/C][C]8.44192205508783e-10[/C][/ROW]
[ROW][C]49[/C][C]0.999999926592543[/C][C]1.46814914756527e-07[/C][C]7.34074573782636e-08[/C][/ROW]
[ROW][C]50[/C][C]0.999994784992048[/C][C]1.04300159049688e-05[/C][C]5.21500795248441e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114053&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114053&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
125.36097740204375e-061.07219548040875e-050.999994639022598
139.94207442238263e-081.98841488447653e-070.999999900579256
143.20571633008404e-096.41143266016807e-090.999999996794284
151.43235542769201e-102.86471085538403e-100.999999999856764
162.33405127437989e-124.66810254875978e-120.999999999997666
174.98649667663022e-149.97299335326044e-140.99999999999995
187.72314644137518e-161.54462928827504e-151
192.31599083003535e-174.6319816600707e-171
203.89231698142251e-197.78463396284501e-191
210.9752541452332650.04949170953346960.0247458547667348
220.96321579012930.07356841974139970.0367842098706999
230.9429265727364580.1141468545270840.0570734272635419
240.9213446736532320.1573106526935360.0786553263467682
250.8957687918758930.2084624162482140.104231208124107
260.8752970082832590.2494059834334830.124702991716741
270.8272375591067870.3455248817864250.172762440893213
280.7699901285161740.4600197429676510.230009871483826
290.9605587031527770.07888259369444690.0394412968472235
300.974488023263090.05102395347381750.0255119767369088
310.973501133961390.05299773207722140.0264988660386107
320.9577495370302270.0845009259395450.0422504629697725
330.9354127552351490.1291744895297020.0645872447648511
340.9950914123748480.009817175250304560.00490858762515228
350.9916433606942920.0167132786114150.00835663930570748
360.9984372998645420.003125400270916690.00156270013545835
370.9968237074181680.006352585163664470.00317629258183223
380.9945002498052420.0109995003895170.00549975019475848
390.9891954081677370.02160918366452570.0108045918322628
400.97958224858830.0408355028233980.020417751411699
4117.67736743576465e-213.83868371788232e-21
4218.10011098887522e-204.05005549443761e-20
4313.6863971545603e-181.84319857728015e-18
4411.8761645851048e-169.38082292552399e-17
450.9999999999999976.3986562081802e-153.1993281040901e-15
460.9999999999998053.88939445767372e-131.94469722883686e-13
470.9999999999848243.03514858741087e-111.51757429370543e-11
480.9999999991558081.68838441101757e-098.44192205508783e-10
490.9999999265925431.46814914756527e-077.34074573782636e-08
500.9999947849920481.04300159049688e-055.21500795248441e-06






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.564102564102564NOK
5% type I error level270.692307692307692NOK
10% type I error level320.82051282051282NOK

\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 & 22 & 0.564102564102564 & NOK \tabularnewline
5% type I error level & 27 & 0.692307692307692 & NOK \tabularnewline
10% type I error level & 32 & 0.82051282051282 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114053&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]22[/C][C]0.564102564102564[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.692307692307692[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.82051282051282[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114053&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114053&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 level220.564102564102564NOK
5% type I error level270.692307692307692NOK
10% type I error level320.82051282051282NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}