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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, 01 Dec 2010 19:23:20 +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/01/t1291231462xj5i0qhl8i0mxu1.htm/, Retrieved Sat, 04 May 2024 21:28:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104173, Retrieved Sat, 04 May 2024 21:28:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-12-01 19:23:20] [9d4f9c24554023ef0148ede5dd3a4d11] [Current]
-    D      [Multiple Regression] [] [2010-12-01 19:53:48] [4cec9a0c6d7fcfe819c8df12b51eb7f5]
-             [Multiple Regression] [] [2010-12-01 20:51:08] [f82dc80ca9fc4fd83b66f6024d510f8c]
-               [Multiple Regression] [] [2010-12-29 14:37:31] [4cec9a0c6d7fcfe819c8df12b51eb7f5]
-   PD        [Multiple Regression] [] [2010-12-03 10:07:03] [c91278f1cd2d8b4eeb874e50bb706c21]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	1	2	14	3	6	3	6	2	4	2	4
2	2	4	18	5	10	4	8	1	2	2	4
2	3	6	11	3	6	2	4	2	4	4	8
1	4	4	12	3	3	2	2	2	2	3	3
2	5	10	16	4	8	4	8	1	2	3	6
2	6	12	18	5	10	4	8	1	2	2	4
2	7	14	14	4	8	4	8	2	4	4	8
2	8	16	14	4	8	4	8	3	6	3	6
2	9	18	15	4	8	3	6	2	4	2	4
2	10	20	15	4	8	3	6	2	4	2	4
1	11	11	17	4	4	5	5	2	2	2	2
2	12	24	19	5	10	4	8	1	2	1	2
1	13	13	10	2	2	2	2	4	4	2	2
2	14	28	16	4	8	3	6	2	4	1	2
2	15	30	18	5	10	5	10	2	4	2	4
1	16	16	14	4	4	4	4	3	3	3	3
1	17	17	14	4	4	3	3	3	3	2	2
2	18	36	17	4	8	4	8	1	2	2	4
1	19	19	14	4	4	2	2	1	1	3	3
2	20	40	16	5	10	3	6	2	4	2	4
1	21	21	18	4	4	4	4	1	1	1	1
2	22	44	11	3	6	2	4	3	6	3	6
2	23	46	14	3	6	5	10	2	4	4	8
2	24	48	12	3	6	3	6	3	6	3	6
1	25	25	17	5	5	4	4	2	2	2	2
2	26	52	9	2	4	3	6	4	8	4	8
1	27	27	16	4	4	4	4	2	2	2	2
2	28	56	14	4	8	4	8	2	4	4	8
2	29	58	15	4	8	4	8	2	4	3	6
1	30	30	11	3	3	2	2	2	2	4	4
2	31	62	16	4	8	4	8	2	4	2	4
1	32	32	13	3	3	4	4	3	3	3	3
2	33	66	17	4	8	4	8	2	4	1	2
2	34	68	15	4	8	3	6	2	4	2	4
1	35	35	14	4	4	4	4	3	3	3	3
1	36	36	16	4	4	4	4	2	2	2	2
1	37	37	9	2	2	3	3	4	4	4	4
1	38	38	15	4	4	3	3	2	2	2	2
2	39	78	17	5	10	4	8	2	4	2	4
1	40	40	13	3	3	4	4	4	4	2	2
1	41	41	15	4	4	4	4	3	3	2	2
2	42	84	16	4	8	4	8	2	4	2	4
1	43	43	16	5	5	4	4	3	3	2	2
1	44	44	12	3	3	4	4	4	4	3	3
2	45	90	12	4	8		0	2	4	2	4
2	46	92	11	3	6	3	6	3	6	4	8
2	47	94	15	4	8	4	8	3	6	2	4
2	48	96	15	4	8	3	6	2	4	2	4
2	49	98	17	5	10	4	8	1	2	3	6
1	50	50	13	4	4	3	3	2	2	4	4
2	51	102	16	3	6	4	8	1	2	2	4
1	52	52	14	3	3	3	3	2	2	2	2
1	53	53	11	4	4	2	2	3	3	4	4
2	54	108	12	3	6	3	6	4	8	2	4
1	55	55	12	4	4	4	4	5	5	3	3
2	56	112	15	4	8	4	8	3	6	2	4
2	57	114	16	4	8	4	8	2	4	2	4
2	58	116	15	3	6	4	8	2	4	2	4
1	59	59	12	3	3	3	3	3	3	3	3
2	60	120	12	3	6	3	6	2	4	4	8
1	61	61	8	3	3	2	2	4	4	5	5
1	62	62	13	4	4	3	3	3	3	3	3
2	63	126	11	4	8	2	4	2	4	5	10
2	64	128	14	4	8	3	6	2	4	3	6
2	65	130	15	4	8	4	8	2	4	3	6
1	66	66	10	3	3	2	2	3	3	4	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104173&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104173&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104173&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
PSS[t] = + 14.7010144744564 -0.213486826461162G[t] + 0.0485974878790153T[t] -0.0288204218007257`T-G`[t] + 0.696344101971695HPP[t] -0.142272713875854`HPP-G`[t] -0.655973025889067TGYW[t] + 1.06170320950822`TGYW-G`[t] -0.789069557634572POP[t] -0.157775203711889`POP-G`[t] -0.294494920037662IDT[t] -0.584187019346881`IDT-G `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PSS[t] =  +  14.7010144744564 -0.213486826461162G[t] +  0.0485974878790153T[t] -0.0288204218007257`T-G`[t] +  0.696344101971695HPP[t] -0.142272713875854`HPP-G`[t] -0.655973025889067TGYW[t] +  1.06170320950822`TGYW-G`[t] -0.789069557634572POP[t] -0.157775203711889`POP-G`[t] -0.294494920037662IDT[t] -0.584187019346881`IDT-G
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104173&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PSS[t] =  +  14.7010144744564 -0.213486826461162G[t] +  0.0485974878790153T[t] -0.0288204218007257`T-G`[t] +  0.696344101971695HPP[t] -0.142272713875854`HPP-G`[t] -0.655973025889067TGYW[t] +  1.06170320950822`TGYW-G`[t] -0.789069557634572POP[t] -0.157775203711889`POP-G`[t] -0.294494920037662IDT[t] -0.584187019346881`IDT-G
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104173&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104173&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
PSS[t] = + 14.7010144744564 -0.213486826461162G[t] + 0.0485974878790153T[t] -0.0288204218007257`T-G`[t] + 0.696344101971695HPP[t] -0.142272713875854`HPP-G`[t] -0.655973025889067TGYW[t] + 1.06170320950822`TGYW-G`[t] -0.789069557634572POP[t] -0.157775203711889`POP-G`[t] -0.294494920037662IDT[t] -0.584187019346881`IDT-G `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.70101447445640.61965923.724400
G-0.2134868264611620.01374-15.537200
T0.04859748787901530.0170172.85580.0060810.00304
`T-G`-0.02882042180072570.01082-2.66360.0101680.005084
HPP0.6963441019716950.1569344.43724.5e-052.3e-05
`HPP-G`-0.1422727138758540.100438-1.41650.1623650.081182
TGYW-0.6559730258890670.145994-4.49323.7e-051.9e-05
`TGYW-G`1.061703209508220.10175810.433600
POP-0.7890695576345720.110111-7.166100
`POP-G`-0.1577752037118890.110529-1.42750.1592060.079603
IDT-0.2944949200376620.134578-2.18830.0329950.016497
`IDT-G `-0.5841870193468810.094525-6.180200

\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) & 14.7010144744564 & 0.619659 & 23.7244 & 0 & 0 \tabularnewline
G & -0.213486826461162 & 0.01374 & -15.5372 & 0 & 0 \tabularnewline
T & 0.0485974878790153 & 0.017017 & 2.8558 & 0.006081 & 0.00304 \tabularnewline
`T-G` & -0.0288204218007257 & 0.01082 & -2.6636 & 0.010168 & 0.005084 \tabularnewline
HPP & 0.696344101971695 & 0.156934 & 4.4372 & 4.5e-05 & 2.3e-05 \tabularnewline
`HPP-G` & -0.142272713875854 & 0.100438 & -1.4165 & 0.162365 & 0.081182 \tabularnewline
TGYW & -0.655973025889067 & 0.145994 & -4.4932 & 3.7e-05 & 1.9e-05 \tabularnewline
`TGYW-G` & 1.06170320950822 & 0.101758 & 10.4336 & 0 & 0 \tabularnewline
POP & -0.789069557634572 & 0.110111 & -7.1661 & 0 & 0 \tabularnewline
`POP-G` & -0.157775203711889 & 0.110529 & -1.4275 & 0.159206 & 0.079603 \tabularnewline
IDT & -0.294494920037662 & 0.134578 & -2.1883 & 0.032995 & 0.016497 \tabularnewline
`IDT-G
` & -0.584187019346881 & 0.094525 & -6.1802 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104173&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]14.7010144744564[/C][C]0.619659[/C][C]23.7244[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]G[/C][C]-0.213486826461162[/C][C]0.01374[/C][C]-15.5372[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]T[/C][C]0.0485974878790153[/C][C]0.017017[/C][C]2.8558[/C][C]0.006081[/C][C]0.00304[/C][/ROW]
[ROW][C]`T-G`[/C][C]-0.0288204218007257[/C][C]0.01082[/C][C]-2.6636[/C][C]0.010168[/C][C]0.005084[/C][/ROW]
[ROW][C]HPP[/C][C]0.696344101971695[/C][C]0.156934[/C][C]4.4372[/C][C]4.5e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]`HPP-G`[/C][C]-0.142272713875854[/C][C]0.100438[/C][C]-1.4165[/C][C]0.162365[/C][C]0.081182[/C][/ROW]
[ROW][C]TGYW[/C][C]-0.655973025889067[/C][C]0.145994[/C][C]-4.4932[/C][C]3.7e-05[/C][C]1.9e-05[/C][/ROW]
[ROW][C]`TGYW-G`[/C][C]1.06170320950822[/C][C]0.101758[/C][C]10.4336[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]POP[/C][C]-0.789069557634572[/C][C]0.110111[/C][C]-7.1661[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`POP-G`[/C][C]-0.157775203711889[/C][C]0.110529[/C][C]-1.4275[/C][C]0.159206[/C][C]0.079603[/C][/ROW]
[ROW][C]IDT[/C][C]-0.294494920037662[/C][C]0.134578[/C][C]-2.1883[/C][C]0.032995[/C][C]0.016497[/C][/ROW]
[ROW][C]`IDT-G
`[/C][C]-0.584187019346881[/C][C]0.094525[/C][C]-6.1802[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104173&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104173&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)14.70101447445640.61965923.724400
G-0.2134868264611620.01374-15.537200
T0.04859748787901530.0170172.85580.0060810.00304
`T-G`-0.02882042180072570.01082-2.66360.0101680.005084
HPP0.6963441019716950.1569344.43724.5e-052.3e-05
`HPP-G`-0.1422727138758540.100438-1.41650.1623650.081182
TGYW-0.6559730258890670.145994-4.49323.7e-051.9e-05
`TGYW-G`1.061703209508220.10175810.433600
POP-0.7890695576345720.110111-7.166100
`POP-G`-0.1577752037118890.110529-1.42750.1592060.079603
IDT-0.2944949200376620.134578-2.18830.0329950.016497
`IDT-G `-0.5841870193468810.094525-6.180200






Multiple Linear Regression - Regression Statistics
Multiple R0.99123303097312
R-squared0.98254292169216
Adjusted R-squared0.978986850185008
F-TEST (value)276.300102434920
F-TEST (DF numerator)11
F-TEST (DF denominator)54
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.801797935945067
Sum Squared Residuals34.7155162246316

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99123303097312 \tabularnewline
R-squared & 0.98254292169216 \tabularnewline
Adjusted R-squared & 0.978986850185008 \tabularnewline
F-TEST (value) & 276.300102434920 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.801797935945067 \tabularnewline
Sum Squared Residuals & 34.7155162246316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104173&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99123303097312[/C][/ROW]
[ROW][C]R-squared[/C][C]0.98254292169216[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.978986850185008[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]276.300102434920[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/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]0.801797935945067[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.7155162246316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104173&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104173&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 R0.99123303097312
R-squared0.98254292169216
Adjusted R-squared0.978986850185008
F-TEST (value)276.300102434920
F-TEST (DF numerator)11
F-TEST (DF denominator)54
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.801797935945067
Sum Squared Residuals34.7155162246316






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.7677158202741-0.767715820274149
21818.1543231711774-0.154323171177378
31110.35645779823900.643542201760957
41212.5105751029876-0.51057510298765
51616.2525254710587-0.252525471058658
61818.1181497482876-0.118149748287631
71413.6669498358240.333050164175989
81414.0161554737746-0.0161554737746486
91515.1071676487146-0.107167648714623
101515.0981242929922-0.0981242929921869
111715.29895844387351.70104155612650
121919.5267585726844-0.526758572684439
131011.1194897262880-1.11948972628803
141616.5248198288339-0.524819828833866
151818.3995729748547-0.39957297485472
161413.16658688991480.833413110085201
171413.65931571175850.340684288241517
181717.5978308053984-0.597830805398411
191414.3081472436043-0.308147243604294
201615.41948940998780.580510590012188
211816.91652562176831.08347437823174
221110.54288303318580.457116966814171
231414.5778908631724-0.57789086317241
241211.99222971486830.0077702851316808
251715.72417857344631.27582142655375
2698.994855405413680.00514459458631516
271615.2096613175070.790338682493012
281413.47703936565290.522960634347149
291514.93086496866180.0691350313381611
301112.1460968816386-1.14609688163863
311616.3756472159484-0.375647215948391
321312.92894855907160.0710514409284098
331717.8204294632349-0.820429463234943
341514.88108375565370.118916244346281
351413.54235114540230.4576488545977
361615.38765491221160.612345087788406
37910.2425056170170-1.24250561701704
381515.0214788607490-0.0214788607490249
391716.71509904438890.284900955611112
401313.019002265736-0.0190022657359885
411514.53969548125660.46030451874342
421616.2761703030016-0.276170303001593
431615.1333210015090.866678998491
441212.2194285906646-0.219428590664604
451211.81950857311430.180491426885667
4633.14523108604716-0.145231086047158
4744.34565707400495-0.345657074004954
4846.20001989471674-2.20001989471674
4956.31841452002216-1.31841452002216
5044.04033402058695-0.0403340205869518
5134.07603987631705-1.07603987631705
5234.47411808649622-1.47411808649622
5344.67418624008053-0.674186240080527
5433.74735705978176-0.74735705978176
5543.71663308908250.283366910917499
5643.299030417676770.700969582323229
5743.670354050933840.329645949066157
5832.774381437434930.225618562565073
5932.613710068799860.386289931200144
6032.58894767951040.411052320489598
6133.14973924232481-0.149739242324807
6242.786565733224391.21343426677561
6343.784847455665020.215152544334983
6443.621405661480150.378594338519852
6542.606261642669181.39373835733082
6631.863101158341651.13689884165835

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.7677158202741 & -0.767715820274149 \tabularnewline
2 & 18 & 18.1543231711774 & -0.154323171177378 \tabularnewline
3 & 11 & 10.3564577982390 & 0.643542201760957 \tabularnewline
4 & 12 & 12.5105751029876 & -0.51057510298765 \tabularnewline
5 & 16 & 16.2525254710587 & -0.252525471058658 \tabularnewline
6 & 18 & 18.1181497482876 & -0.118149748287631 \tabularnewline
7 & 14 & 13.666949835824 & 0.333050164175989 \tabularnewline
8 & 14 & 14.0161554737746 & -0.0161554737746486 \tabularnewline
9 & 15 & 15.1071676487146 & -0.107167648714623 \tabularnewline
10 & 15 & 15.0981242929922 & -0.0981242929921869 \tabularnewline
11 & 17 & 15.2989584438735 & 1.70104155612650 \tabularnewline
12 & 19 & 19.5267585726844 & -0.526758572684439 \tabularnewline
13 & 10 & 11.1194897262880 & -1.11948972628803 \tabularnewline
14 & 16 & 16.5248198288339 & -0.524819828833866 \tabularnewline
15 & 18 & 18.3995729748547 & -0.39957297485472 \tabularnewline
16 & 14 & 13.1665868899148 & 0.833413110085201 \tabularnewline
17 & 14 & 13.6593157117585 & 0.340684288241517 \tabularnewline
18 & 17 & 17.5978308053984 & -0.597830805398411 \tabularnewline
19 & 14 & 14.3081472436043 & -0.308147243604294 \tabularnewline
20 & 16 & 15.4194894099878 & 0.580510590012188 \tabularnewline
21 & 18 & 16.9165256217683 & 1.08347437823174 \tabularnewline
22 & 11 & 10.5428830331858 & 0.457116966814171 \tabularnewline
23 & 14 & 14.5778908631724 & -0.57789086317241 \tabularnewline
24 & 12 & 11.9922297148683 & 0.0077702851316808 \tabularnewline
25 & 17 & 15.7241785734463 & 1.27582142655375 \tabularnewline
26 & 9 & 8.99485540541368 & 0.00514459458631516 \tabularnewline
27 & 16 & 15.209661317507 & 0.790338682493012 \tabularnewline
28 & 14 & 13.4770393656529 & 0.522960634347149 \tabularnewline
29 & 15 & 14.9308649686618 & 0.0691350313381611 \tabularnewline
30 & 11 & 12.1460968816386 & -1.14609688163863 \tabularnewline
31 & 16 & 16.3756472159484 & -0.375647215948391 \tabularnewline
32 & 13 & 12.9289485590716 & 0.0710514409284098 \tabularnewline
33 & 17 & 17.8204294632349 & -0.820429463234943 \tabularnewline
34 & 15 & 14.8810837556537 & 0.118916244346281 \tabularnewline
35 & 14 & 13.5423511454023 & 0.4576488545977 \tabularnewline
36 & 16 & 15.3876549122116 & 0.612345087788406 \tabularnewline
37 & 9 & 10.2425056170170 & -1.24250561701704 \tabularnewline
38 & 15 & 15.0214788607490 & -0.0214788607490249 \tabularnewline
39 & 17 & 16.7150990443889 & 0.284900955611112 \tabularnewline
40 & 13 & 13.019002265736 & -0.0190022657359885 \tabularnewline
41 & 15 & 14.5396954812566 & 0.46030451874342 \tabularnewline
42 & 16 & 16.2761703030016 & -0.276170303001593 \tabularnewline
43 & 16 & 15.133321001509 & 0.866678998491 \tabularnewline
44 & 12 & 12.2194285906646 & -0.219428590664604 \tabularnewline
45 & 12 & 11.8195085731143 & 0.180491426885667 \tabularnewline
46 & 3 & 3.14523108604716 & -0.145231086047158 \tabularnewline
47 & 4 & 4.34565707400495 & -0.345657074004954 \tabularnewline
48 & 4 & 6.20001989471674 & -2.20001989471674 \tabularnewline
49 & 5 & 6.31841452002216 & -1.31841452002216 \tabularnewline
50 & 4 & 4.04033402058695 & -0.0403340205869518 \tabularnewline
51 & 3 & 4.07603987631705 & -1.07603987631705 \tabularnewline
52 & 3 & 4.47411808649622 & -1.47411808649622 \tabularnewline
53 & 4 & 4.67418624008053 & -0.674186240080527 \tabularnewline
54 & 3 & 3.74735705978176 & -0.74735705978176 \tabularnewline
55 & 4 & 3.7166330890825 & 0.283366910917499 \tabularnewline
56 & 4 & 3.29903041767677 & 0.700969582323229 \tabularnewline
57 & 4 & 3.67035405093384 & 0.329645949066157 \tabularnewline
58 & 3 & 2.77438143743493 & 0.225618562565073 \tabularnewline
59 & 3 & 2.61371006879986 & 0.386289931200144 \tabularnewline
60 & 3 & 2.5889476795104 & 0.411052320489598 \tabularnewline
61 & 3 & 3.14973924232481 & -0.149739242324807 \tabularnewline
62 & 4 & 2.78656573322439 & 1.21343426677561 \tabularnewline
63 & 4 & 3.78484745566502 & 0.215152544334983 \tabularnewline
64 & 4 & 3.62140566148015 & 0.378594338519852 \tabularnewline
65 & 4 & 2.60626164266918 & 1.39373835733082 \tabularnewline
66 & 3 & 1.86310115834165 & 1.13689884165835 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104173&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]14[/C][C]14.7677158202741[/C][C]-0.767715820274149[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]18.1543231711774[/C][C]-0.154323171177378[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]10.3564577982390[/C][C]0.643542201760957[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.5105751029876[/C][C]-0.51057510298765[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]16.2525254710587[/C][C]-0.252525471058658[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]18.1181497482876[/C][C]-0.118149748287631[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]13.666949835824[/C][C]0.333050164175989[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.0161554737746[/C][C]-0.0161554737746486[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.1071676487146[/C][C]-0.107167648714623[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.0981242929922[/C][C]-0.0981242929921869[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.2989584438735[/C][C]1.70104155612650[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]19.5267585726844[/C][C]-0.526758572684439[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]11.1194897262880[/C][C]-1.11948972628803[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]16.5248198288339[/C][C]-0.524819828833866[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]18.3995729748547[/C][C]-0.39957297485472[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.1665868899148[/C][C]0.833413110085201[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.6593157117585[/C][C]0.340684288241517[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]17.5978308053984[/C][C]-0.597830805398411[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.3081472436043[/C][C]-0.308147243604294[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4194894099878[/C][C]0.580510590012188[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]16.9165256217683[/C][C]1.08347437823174[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]10.5428830331858[/C][C]0.457116966814171[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.5778908631724[/C][C]-0.57789086317241[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.9922297148683[/C][C]0.0077702851316808[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.7241785734463[/C][C]1.27582142655375[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]8.99485540541368[/C][C]0.00514459458631516[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.209661317507[/C][C]0.790338682493012[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.4770393656529[/C][C]0.522960634347149[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.9308649686618[/C][C]0.0691350313381611[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]12.1460968816386[/C][C]-1.14609688163863[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]16.3756472159484[/C][C]-0.375647215948391[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.9289485590716[/C][C]0.0710514409284098[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]17.8204294632349[/C][C]-0.820429463234943[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.8810837556537[/C][C]0.118916244346281[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.5423511454023[/C][C]0.4576488545977[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.3876549122116[/C][C]0.612345087788406[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.2425056170170[/C][C]-1.24250561701704[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]15.0214788607490[/C][C]-0.0214788607490249[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]16.7150990443889[/C][C]0.284900955611112[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]13.019002265736[/C][C]-0.0190022657359885[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]14.5396954812566[/C][C]0.46030451874342[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]16.2761703030016[/C][C]-0.276170303001593[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.133321001509[/C][C]0.866678998491[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.2194285906646[/C][C]-0.219428590664604[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]11.8195085731143[/C][C]0.180491426885667[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]3.14523108604716[/C][C]-0.145231086047158[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.34565707400495[/C][C]-0.345657074004954[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]6.20001989471674[/C][C]-2.20001989471674[/C][/ROW]
[ROW][C]49[/C][C]5[/C][C]6.31841452002216[/C][C]-1.31841452002216[/C][/ROW]
[ROW][C]50[/C][C]4[/C][C]4.04033402058695[/C][C]-0.0403340205869518[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]4.07603987631705[/C][C]-1.07603987631705[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]4.47411808649622[/C][C]-1.47411808649622[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.67418624008053[/C][C]-0.674186240080527[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]3.74735705978176[/C][C]-0.74735705978176[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]3.7166330890825[/C][C]0.283366910917499[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]3.29903041767677[/C][C]0.700969582323229[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]3.67035405093384[/C][C]0.329645949066157[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.77438143743493[/C][C]0.225618562565073[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]2.61371006879986[/C][C]0.386289931200144[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]2.5889476795104[/C][C]0.411052320489598[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]3.14973924232481[/C][C]-0.149739242324807[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]2.78656573322439[/C][C]1.21343426677561[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.78484745566502[/C][C]0.215152544334983[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]3.62140566148015[/C][C]0.378594338519852[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]2.60626164266918[/C][C]1.39373835733082[/C][/ROW]
[ROW][C]66[/C][C]3[/C][C]1.86310115834165[/C][C]1.13689884165835[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104173&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104173&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
11414.7677158202741-0.767715820274149
21818.1543231711774-0.154323171177378
31110.35645779823900.643542201760957
41212.5105751029876-0.51057510298765
51616.2525254710587-0.252525471058658
61818.1181497482876-0.118149748287631
71413.6669498358240.333050164175989
81414.0161554737746-0.0161554737746486
91515.1071676487146-0.107167648714623
101515.0981242929922-0.0981242929921869
111715.29895844387351.70104155612650
121919.5267585726844-0.526758572684439
131011.1194897262880-1.11948972628803
141616.5248198288339-0.524819828833866
151818.3995729748547-0.39957297485472
161413.16658688991480.833413110085201
171413.65931571175850.340684288241517
181717.5978308053984-0.597830805398411
191414.3081472436043-0.308147243604294
201615.41948940998780.580510590012188
211816.91652562176831.08347437823174
221110.54288303318580.457116966814171
231414.5778908631724-0.57789086317241
241211.99222971486830.0077702851316808
251715.72417857344631.27582142655375
2698.994855405413680.00514459458631516
271615.2096613175070.790338682493012
281413.47703936565290.522960634347149
291514.93086496866180.0691350313381611
301112.1460968816386-1.14609688163863
311616.3756472159484-0.375647215948391
321312.92894855907160.0710514409284098
331717.8204294632349-0.820429463234943
341514.88108375565370.118916244346281
351413.54235114540230.4576488545977
361615.38765491221160.612345087788406
37910.2425056170170-1.24250561701704
381515.0214788607490-0.0214788607490249
391716.71509904438890.284900955611112
401313.019002265736-0.0190022657359885
411514.53969548125660.46030451874342
421616.2761703030016-0.276170303001593
431615.1333210015090.866678998491
441212.2194285906646-0.219428590664604
451211.81950857311430.180491426885667
4633.14523108604716-0.145231086047158
4744.34565707400495-0.345657074004954
4846.20001989471674-2.20001989471674
4956.31841452002216-1.31841452002216
5044.04033402058695-0.0403340205869518
5134.07603987631705-1.07603987631705
5234.47411808649622-1.47411808649622
5344.67418624008053-0.674186240080527
5433.74735705978176-0.74735705978176
5543.71663308908250.283366910917499
5643.299030417676770.700969582323229
5743.670354050933840.329645949066157
5832.774381437434930.225618562565073
5932.613710068799860.386289931200144
6032.58894767951040.411052320489598
6133.14973924232481-0.149739242324807
6242.786565733224391.21343426677561
6343.784847455665020.215152544334983
6443.621405661480150.378594338519852
6542.606261642669181.39373835733082
6631.863101158341651.13689884165835






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
151.00489494034282e-452.00978988068565e-451
161.83697553139090e-603.67395106278181e-601
173.49914216649658e-746.99828433299317e-741
182.87745481376475e-885.7549096275295e-881
197.88047576183506e-1021.57609515236701e-1011
204.41833035673249e-1208.83666071346498e-1201
214.89965276183681e-1389.79930552367363e-1381
221.34370497216944e-1462.68740994433888e-1461
234.70916751899335e-1629.4183350379867e-1621
245.54879927060798e-1781.10975985412160e-1771
251.59002359740615e-1963.1800471948123e-1961
261.14650321024298e-2032.29300642048595e-2031
278.15242740362624e-2231.63048548072525e-2221
281.99638093890283e-2353.99276187780566e-2351
291.09407494592744e-2492.18814989185488e-2491
306.03313659772278e-2581.20662731954456e-2571
313.5188664351616e-2887.0377328703232e-2881
327.22277340874178e-2901.44455468174836e-2891
332.32677969909686e-3084.65355939819372e-3081
345.61209167111072e-3201.12241833422214e-3191
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
4514.02811470584559e-1282.01405735292280e-128
4614.784238973527e-1182.3921194867635e-118
4719.31144006844364e-1004.65572003422182e-100
4812.25907481145191e-881.12953740572596e-88
4911.42351471209035e-757.11757356045177e-76
5011.12677186633582e-605.63385933167909e-61
5111.03964351399275e-445.19821756996373e-45

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 1.00489494034282e-45 & 2.00978988068565e-45 & 1 \tabularnewline
16 & 1.83697553139090e-60 & 3.67395106278181e-60 & 1 \tabularnewline
17 & 3.49914216649658e-74 & 6.99828433299317e-74 & 1 \tabularnewline
18 & 2.87745481376475e-88 & 5.7549096275295e-88 & 1 \tabularnewline
19 & 7.88047576183506e-102 & 1.57609515236701e-101 & 1 \tabularnewline
20 & 4.41833035673249e-120 & 8.83666071346498e-120 & 1 \tabularnewline
21 & 4.89965276183681e-138 & 9.79930552367363e-138 & 1 \tabularnewline
22 & 1.34370497216944e-146 & 2.68740994433888e-146 & 1 \tabularnewline
23 & 4.70916751899335e-162 & 9.4183350379867e-162 & 1 \tabularnewline
24 & 5.54879927060798e-178 & 1.10975985412160e-177 & 1 \tabularnewline
25 & 1.59002359740615e-196 & 3.1800471948123e-196 & 1 \tabularnewline
26 & 1.14650321024298e-203 & 2.29300642048595e-203 & 1 \tabularnewline
27 & 8.15242740362624e-223 & 1.63048548072525e-222 & 1 \tabularnewline
28 & 1.99638093890283e-235 & 3.99276187780566e-235 & 1 \tabularnewline
29 & 1.09407494592744e-249 & 2.18814989185488e-249 & 1 \tabularnewline
30 & 6.03313659772278e-258 & 1.20662731954456e-257 & 1 \tabularnewline
31 & 3.5188664351616e-288 & 7.0377328703232e-288 & 1 \tabularnewline
32 & 7.22277340874178e-290 & 1.44455468174836e-289 & 1 \tabularnewline
33 & 2.32677969909686e-308 & 4.65355939819372e-308 & 1 \tabularnewline
34 & 5.61209167111072e-320 & 1.12241833422214e-319 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 1 & 4.02811470584559e-128 & 2.01405735292280e-128 \tabularnewline
46 & 1 & 4.784238973527e-118 & 2.3921194867635e-118 \tabularnewline
47 & 1 & 9.31144006844364e-100 & 4.65572003422182e-100 \tabularnewline
48 & 1 & 2.25907481145191e-88 & 1.12953740572596e-88 \tabularnewline
49 & 1 & 1.42351471209035e-75 & 7.11757356045177e-76 \tabularnewline
50 & 1 & 1.12677186633582e-60 & 5.63385933167909e-61 \tabularnewline
51 & 1 & 1.03964351399275e-44 & 5.19821756996373e-45 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104173&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]15[/C][C]1.00489494034282e-45[/C][C]2.00978988068565e-45[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]1.83697553139090e-60[/C][C]3.67395106278181e-60[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]3.49914216649658e-74[/C][C]6.99828433299317e-74[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]2.87745481376475e-88[/C][C]5.7549096275295e-88[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]7.88047576183506e-102[/C][C]1.57609515236701e-101[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]4.41833035673249e-120[/C][C]8.83666071346498e-120[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]4.89965276183681e-138[/C][C]9.79930552367363e-138[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]1.34370497216944e-146[/C][C]2.68740994433888e-146[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]4.70916751899335e-162[/C][C]9.4183350379867e-162[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]5.54879927060798e-178[/C][C]1.10975985412160e-177[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.59002359740615e-196[/C][C]3.1800471948123e-196[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]1.14650321024298e-203[/C][C]2.29300642048595e-203[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]8.15242740362624e-223[/C][C]1.63048548072525e-222[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.99638093890283e-235[/C][C]3.99276187780566e-235[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]1.09407494592744e-249[/C][C]2.18814989185488e-249[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]6.03313659772278e-258[/C][C]1.20662731954456e-257[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]3.5188664351616e-288[/C][C]7.0377328703232e-288[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]7.22277340874178e-290[/C][C]1.44455468174836e-289[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]2.32677969909686e-308[/C][C]4.65355939819372e-308[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]5.61209167111072e-320[/C][C]1.12241833422214e-319[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]4.02811470584559e-128[/C][C]2.01405735292280e-128[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]4.784238973527e-118[/C][C]2.3921194867635e-118[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]9.31144006844364e-100[/C][C]4.65572003422182e-100[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]2.25907481145191e-88[/C][C]1.12953740572596e-88[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.42351471209035e-75[/C][C]7.11757356045177e-76[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]1.12677186633582e-60[/C][C]5.63385933167909e-61[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.03964351399275e-44[/C][C]5.19821756996373e-45[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104173&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104173&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
151.00489494034282e-452.00978988068565e-451
161.83697553139090e-603.67395106278181e-601
173.49914216649658e-746.99828433299317e-741
182.87745481376475e-885.7549096275295e-881
197.88047576183506e-1021.57609515236701e-1011
204.41833035673249e-1208.83666071346498e-1201
214.89965276183681e-1389.79930552367363e-1381
221.34370497216944e-1462.68740994433888e-1461
234.70916751899335e-1629.4183350379867e-1621
245.54879927060798e-1781.10975985412160e-1771
251.59002359740615e-1963.1800471948123e-1961
261.14650321024298e-2032.29300642048595e-2031
278.15242740362624e-2231.63048548072525e-2221
281.99638093890283e-2353.99276187780566e-2351
291.09407494592744e-2492.18814989185488e-2491
306.03313659772278e-2581.20662731954456e-2571
313.5188664351616e-2887.0377328703232e-2881
327.22277340874178e-2901.44455468174836e-2891
332.32677969909686e-3084.65355939819372e-3081
345.61209167111072e-3201.12241833422214e-3191
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
4514.02811470584559e-1282.01405735292280e-128
4614.784238973527e-1182.3921194867635e-118
4719.31144006844364e-1004.65572003422182e-100
4812.25907481145191e-881.12953740572596e-88
4911.42351471209035e-757.11757356045177e-76
5011.12677186633582e-605.63385933167909e-61
5111.03964351399275e-445.19821756996373e-45






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level371NOK
5% type I error level371NOK
10% type I error level371NOK

\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 & 37 & 1 & NOK \tabularnewline
5% type I error level & 37 & 1 & NOK \tabularnewline
10% type I error level & 37 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104173&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]37[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104173&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104173&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 level371NOK
5% type I error level371NOK
10% type I error level371NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}