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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 08:42:18 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587318021x3xrihq1kza4g8.htm/, Retrieved Thu, 18 Apr 2024 00:17:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58281, Retrieved Thu, 18 Apr 2024 00:17:42 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

151

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
F R PD    [Multiple Regression] [Model 1] [2009-11-19 20:32:12] [c0117c881d5fcd069841276db0c34efe]
F    D      [Multiple Regression] [Model 2] [2009-11-19 20:38:54] [c0117c881d5fcd069841276db0c34efe]
-             [Multiple Regression] [Model 3] [2009-11-19 20:41:09] [c0117c881d5fcd069841276db0c34efe]
-    D          [Multiple Regression] [Model 4] [2009-11-19 20:47:35] [c0117c881d5fcd069841276db0c34efe]
-   PD              [Multiple Regression] [Model 5] [2009-11-20 15:42:18] [d5837f25ec8937f9733a894c487f865c] [Current]

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Dataseries X:

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3.37	100.7	101.1	101.2
3.51	100.1	100.7	101.1
3.75	99.9	100.1	100.7
4.11	99.7	99.9	100.1
4.25	99.5	99.7	99.9
4.25	99.2	99.5	99.7
4.5	99	99.2	99.5
4.7	99	99	99.2
4.75	99.3	99	99
4.75	99.5	99.3	99
4.75	99.7	99.5	99.3
4.75	100	99.7	99.5
4.75	100.4	100	99.7
4.75	100.6	100.4	100
4.58	100.7	100.6	100.4
4.5	100.7	100.7	100.6
4.5	100.6	100.7	100.7
4.49	100.5	100.6	100.7
4.03	100.6	100.5	100.6
3.75	100.5	100.6	100.5
3.39	100.4	100.5	100.6
3.25	100.3	100.4	100.5
3.25	100.4	100.3	100.4
3.25	100.4	100.4	100.3
3.25	100.4	100.4	100.4
3.25	100.4	100.4	100.4
3.25	100.4	100.4	100.4
3.25	100.5	100.4	100.4
3.25	100.6	100.5	100.4
3.25	100.6	100.6	100.5
3.25	100.5	100.6	100.6
3.25	100.5	100.5	100.6
3.25	100.7	100.5	100.5
2.85	101.1	100.7	100.5
2.75	101.5	101.1	100.7
2.75	101.9	101.5	101.1
2.55	102.1	101.9	101.5
2.5	102.1	102.1	101.9
2.5	102.1	102.1	102.1
2.1	102.4	102.1	102.1
2	102.8	102.4	102.1
2	103.1	102.8	102.4
2	103.1	103.1	102.8
2	102.9	103.1	103.1
2	102.4	102.9	103.1
2	101.9	102.4	102.9
2	101.3	101.9	102.4
2	100.7	101.3	101.9
2	100.6	100.7	101.3
2	101	100.6	100.7
2	101.5	101	100.6
2	101.9	101.5	101
2	102.1	101.9	101.5
2	102.3	102.1	101.9
2	102.5	102.3	102.1
2	102.9	102.5	102.3
2	103.6	102.9	102.5
2	104.3	103.6	102.9

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58281&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58281&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58281&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
tprod[t] = + 12.9908192957064 + 0.00822602769769642rente[t] + 1.71770940750858y1[t] -0.849061836576314y2[t] + 0.00908552672304065t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tprod[t] =  +  12.9908192957064 +  0.00822602769769642rente[t] +  1.71770940750858y1[t] -0.849061836576314y2[t] +  0.00908552672304065t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58281&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]tprod[t] =  +  12.9908192957064 +  0.00822602769769642rente[t] +  1.71770940750858y1[t] -0.849061836576314y2[t] +  0.00908552672304065t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58281&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58281&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
tprod[t] = + 12.9908192957064 + 0.00822602769769642rente[t] + 1.71770940750858y1[t] -0.849061836576314y2[t] + 0.00908552672304065t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	12.9908192957064	3.641248	3.5677	0.000774	0.000387
rente	0.00822602769769642	0.059448	0.1384	0.890471	0.445235
y1	1.71770940750858	0.080118	21.4397	0	0
y2	-0.849061836576314	0.085439	-9.9377	0	0
t	0.00908552672304065	0.003184	2.8535	0.006156	0.003078

\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) & 12.9908192957064 & 3.641248 & 3.5677 & 0.000774 & 0.000387 \tabularnewline
rente & 0.00822602769769642 & 0.059448 & 0.1384 & 0.890471 & 0.445235 \tabularnewline
y1 & 1.71770940750858 & 0.080118 & 21.4397 & 0 & 0 \tabularnewline
y2 & -0.849061836576314 & 0.085439 & -9.9377 & 0 & 0 \tabularnewline
t & 0.00908552672304065 & 0.003184 & 2.8535 & 0.006156 & 0.003078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58281&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]12.9908192957064[/C][C]3.641248[/C][C]3.5677[/C][C]0.000774[/C][C]0.000387[/C][/ROW]
[ROW][C]rente[/C][C]0.00822602769769642[/C][C]0.059448[/C][C]0.1384[/C][C]0.890471[/C][C]0.445235[/C][/ROW]
[ROW][C]y1[/C][C]1.71770940750858[/C][C]0.080118[/C][C]21.4397[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y2[/C][C]-0.849061836576314[/C][C]0.085439[/C][C]-9.9377[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.00908552672304065[/C][C]0.003184[/C][C]2.8535[/C][C]0.006156[/C][C]0.003078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58281&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58281&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	12.9908192957064	3.641248	3.5677	0.000774	0.000387
rente	0.00822602769769642	0.059448	0.1384	0.890471	0.445235
y1	1.71770940750858	0.080118	21.4397	0	0
y2	-0.849061836576314	0.085439	-9.9377	0	0
t	0.00908552672304065	0.003184	2.8535	0.006156	0.003078

Multiple Linear Regression - Regression Statistics
Multiple R	0.992463563783446
R-squared	0.984983925437739
Adjusted R-squared	0.98385063679153
F-TEST (value)	869.137733562538
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.153589057440373
Sum Squared Residuals	1.25024872396738

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.992463563783446 \tabularnewline
R-squared & 0.984983925437739 \tabularnewline
Adjusted R-squared & 0.98385063679153 \tabularnewline
F-TEST (value) & 869.137733562538 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.153589057440373 \tabularnewline
Sum Squared Residuals & 1.25024872396738 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58281&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.992463563783446[/C][/ROW]
[ROW][C]R-squared[/C][C]0.984983925437739[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.98385063679153[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]869.137733562538[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/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]0.153589057440373[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.25024872396738[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58281&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58281&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.992463563783446
R-squared	0.984983925437739
Adjusted R-squared	0.98385063679153
F-TEST (value)	869.137733562538
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.153589057440373
Sum Squared Residuals	1.25024872396738

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.7	100.762989773365	-0.062989773365053
2	100.1	100.17104936462	-0.071049364619917
3	99.9	99.4911082281157	0.408891771884256
4	99.7	99.669050345254	0.0309496547459434
5	99.5	99.5055580016683	-0.00555800166831006
6	99.2	99.3409140142049	-0.140914014204893
7	99	99.006555592915	-0.00655559291505667
8	99	98.9284629946488	0.0715370053511918
9	99.3	99.107772190072	0.192227809927998
10	99.5	99.6321705390476	-0.132170539047608
11	99.7	99.7300793962995	-0.0300793962994748
12	100	99.912894437209	0.0871055627910264
13	100.4	100.267480418869	0.132519581130688
14	100.6	100.708931157623	-0.108931157622913
15	100.7	100.720535406509	-0.0205354065085027
16	100.7	100.730921424451	-0.0309214244513468
17	100.6	100.655100767517	-0.0551007675167573
18	100.5	100.492333093212	0.00766690678805705
19	100.6	100.410769890101	0.189230109899161
20	100.5	100.674229253477	-0.174229253476994
21	100.4	100.423676285820	-0.0236762858203833
22	100.3	100.344745411573	-0.0447454115725334
23	100.4	100.266966181202	0.133033818797680
24	100.4	100.532728832334	-0.132728832333871
25	100.4	100.456908175399	-0.0569081753992734
26	100.4	100.465993702122	-0.065993702122314
27	100.4	100.475079228845	-0.0750792288453547
28	100.5	100.484164755568	0.0158352444315989
29	100.6	100.665021223042	-0.0650212230422954
30	100.6	100.760971506859	-0.160971506858558
31	100.5	100.685150849924	-0.185150849923966
32	100.5	100.522465435896	-0.0224654358961586
33	100.7	100.616457146277	0.083542853723177
34	101.1	100.965794143423	0.134205856577486
35	101.5	101.491328463064	0.00867153693606944
36	101.9	101.847873018160	0.0521269818401117
37	102.1	102.202772367716	-0.102772367716312
38	102.1	102.215363739926	-0.115363739925633
39	102.1	102.054636899333	0.0453631006665791
40	102.4	102.060432014977	0.339567985022629
41	102.8	102.584007761183	0.215992238816756
42	103.1	103.025458499937	0.0745415000631995
43	103.1	103.210232114282	-0.110232114281891
44	102.9	102.964599090032	-0.064599090032029
45	102.4	102.630142735253	-0.230142735253373
46	101.9	101.950185925537	-0.0501859255373783
47	101.3	101.524947666794	-0.224947666794295
48	100.7	100.927938467300	-0.227938467300325
49	100.6	100.415835451464	0.184164548535967
50	101	100.762587139382	0.237412860618021
51	101.5	101.543662612766	-0.043662612766099
52	101.9	102.071978108613	-0.171978108612893
53	102.1	102.343616480051	-0.243616480051229
54	102.3	102.356619153645	-0.0566191536454327
55	102.5	102.539434194555	-0.039434194554938
56	102.9	102.722249235464	0.177750764535572
57	103.6	103.248606157876	0.351393842124344
58	104.3	104.120463535224	0.179536464775851

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.7 & 100.762989773365 & -0.062989773365053 \tabularnewline
2 & 100.1 & 100.17104936462 & -0.071049364619917 \tabularnewline
3 & 99.9 & 99.4911082281157 & 0.408891771884256 \tabularnewline
4 & 99.7 & 99.669050345254 & 0.0309496547459434 \tabularnewline
5 & 99.5 & 99.5055580016683 & -0.00555800166831006 \tabularnewline
6 & 99.2 & 99.3409140142049 & -0.140914014204893 \tabularnewline
7 & 99 & 99.006555592915 & -0.00655559291505667 \tabularnewline
8 & 99 & 98.9284629946488 & 0.0715370053511918 \tabularnewline
9 & 99.3 & 99.107772190072 & 0.192227809927998 \tabularnewline
10 & 99.5 & 99.6321705390476 & -0.132170539047608 \tabularnewline
11 & 99.7 & 99.7300793962995 & -0.0300793962994748 \tabularnewline
12 & 100 & 99.912894437209 & 0.0871055627910264 \tabularnewline
13 & 100.4 & 100.267480418869 & 0.132519581130688 \tabularnewline
14 & 100.6 & 100.708931157623 & -0.108931157622913 \tabularnewline
15 & 100.7 & 100.720535406509 & -0.0205354065085027 \tabularnewline
16 & 100.7 & 100.730921424451 & -0.0309214244513468 \tabularnewline
17 & 100.6 & 100.655100767517 & -0.0551007675167573 \tabularnewline
18 & 100.5 & 100.492333093212 & 0.00766690678805705 \tabularnewline
19 & 100.6 & 100.410769890101 & 0.189230109899161 \tabularnewline
20 & 100.5 & 100.674229253477 & -0.174229253476994 \tabularnewline
21 & 100.4 & 100.423676285820 & -0.0236762858203833 \tabularnewline
22 & 100.3 & 100.344745411573 & -0.0447454115725334 \tabularnewline
23 & 100.4 & 100.266966181202 & 0.133033818797680 \tabularnewline
24 & 100.4 & 100.532728832334 & -0.132728832333871 \tabularnewline
25 & 100.4 & 100.456908175399 & -0.0569081753992734 \tabularnewline
26 & 100.4 & 100.465993702122 & -0.065993702122314 \tabularnewline
27 & 100.4 & 100.475079228845 & -0.0750792288453547 \tabularnewline
28 & 100.5 & 100.484164755568 & 0.0158352444315989 \tabularnewline
29 & 100.6 & 100.665021223042 & -0.0650212230422954 \tabularnewline
30 & 100.6 & 100.760971506859 & -0.160971506858558 \tabularnewline
31 & 100.5 & 100.685150849924 & -0.185150849923966 \tabularnewline
32 & 100.5 & 100.522465435896 & -0.0224654358961586 \tabularnewline
33 & 100.7 & 100.616457146277 & 0.083542853723177 \tabularnewline
34 & 101.1 & 100.965794143423 & 0.134205856577486 \tabularnewline
35 & 101.5 & 101.491328463064 & 0.00867153693606944 \tabularnewline
36 & 101.9 & 101.847873018160 & 0.0521269818401117 \tabularnewline
37 & 102.1 & 102.202772367716 & -0.102772367716312 \tabularnewline
38 & 102.1 & 102.215363739926 & -0.115363739925633 \tabularnewline
39 & 102.1 & 102.054636899333 & 0.0453631006665791 \tabularnewline
40 & 102.4 & 102.060432014977 & 0.339567985022629 \tabularnewline
41 & 102.8 & 102.584007761183 & 0.215992238816756 \tabularnewline
42 & 103.1 & 103.025458499937 & 0.0745415000631995 \tabularnewline
43 & 103.1 & 103.210232114282 & -0.110232114281891 \tabularnewline
44 & 102.9 & 102.964599090032 & -0.064599090032029 \tabularnewline
45 & 102.4 & 102.630142735253 & -0.230142735253373 \tabularnewline
46 & 101.9 & 101.950185925537 & -0.0501859255373783 \tabularnewline
47 & 101.3 & 101.524947666794 & -0.224947666794295 \tabularnewline
48 & 100.7 & 100.927938467300 & -0.227938467300325 \tabularnewline
49 & 100.6 & 100.415835451464 & 0.184164548535967 \tabularnewline
50 & 101 & 100.762587139382 & 0.237412860618021 \tabularnewline
51 & 101.5 & 101.543662612766 & -0.043662612766099 \tabularnewline
52 & 101.9 & 102.071978108613 & -0.171978108612893 \tabularnewline
53 & 102.1 & 102.343616480051 & -0.243616480051229 \tabularnewline
54 & 102.3 & 102.356619153645 & -0.0566191536454327 \tabularnewline
55 & 102.5 & 102.539434194555 & -0.039434194554938 \tabularnewline
56 & 102.9 & 102.722249235464 & 0.177750764535572 \tabularnewline
57 & 103.6 & 103.248606157876 & 0.351393842124344 \tabularnewline
58 & 104.3 & 104.120463535224 & 0.179536464775851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58281&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]100.7[/C][C]100.762989773365[/C][C]-0.062989773365053[/C][/ROW]
[ROW][C]2[/C][C]100.1[/C][C]100.17104936462[/C][C]-0.071049364619917[/C][/ROW]
[ROW][C]3[/C][C]99.9[/C][C]99.4911082281157[/C][C]0.408891771884256[/C][/ROW]
[ROW][C]4[/C][C]99.7[/C][C]99.669050345254[/C][C]0.0309496547459434[/C][/ROW]
[ROW][C]5[/C][C]99.5[/C][C]99.5055580016683[/C][C]-0.00555800166831006[/C][/ROW]
[ROW][C]6[/C][C]99.2[/C][C]99.3409140142049[/C][C]-0.140914014204893[/C][/ROW]
[ROW][C]7[/C][C]99[/C][C]99.006555592915[/C][C]-0.00655559291505667[/C][/ROW]
[ROW][C]8[/C][C]99[/C][C]98.9284629946488[/C][C]0.0715370053511918[/C][/ROW]
[ROW][C]9[/C][C]99.3[/C][C]99.107772190072[/C][C]0.192227809927998[/C][/ROW]
[ROW][C]10[/C][C]99.5[/C][C]99.6321705390476[/C][C]-0.132170539047608[/C][/ROW]
[ROW][C]11[/C][C]99.7[/C][C]99.7300793962995[/C][C]-0.0300793962994748[/C][/ROW]
[ROW][C]12[/C][C]100[/C][C]99.912894437209[/C][C]0.0871055627910264[/C][/ROW]
[ROW][C]13[/C][C]100.4[/C][C]100.267480418869[/C][C]0.132519581130688[/C][/ROW]
[ROW][C]14[/C][C]100.6[/C][C]100.708931157623[/C][C]-0.108931157622913[/C][/ROW]
[ROW][C]15[/C][C]100.7[/C][C]100.720535406509[/C][C]-0.0205354065085027[/C][/ROW]
[ROW][C]16[/C][C]100.7[/C][C]100.730921424451[/C][C]-0.0309214244513468[/C][/ROW]
[ROW][C]17[/C][C]100.6[/C][C]100.655100767517[/C][C]-0.0551007675167573[/C][/ROW]
[ROW][C]18[/C][C]100.5[/C][C]100.492333093212[/C][C]0.00766690678805705[/C][/ROW]
[ROW][C]19[/C][C]100.6[/C][C]100.410769890101[/C][C]0.189230109899161[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]100.674229253477[/C][C]-0.174229253476994[/C][/ROW]
[ROW][C]21[/C][C]100.4[/C][C]100.423676285820[/C][C]-0.0236762858203833[/C][/ROW]
[ROW][C]22[/C][C]100.3[/C][C]100.344745411573[/C][C]-0.0447454115725334[/C][/ROW]
[ROW][C]23[/C][C]100.4[/C][C]100.266966181202[/C][C]0.133033818797680[/C][/ROW]
[ROW][C]24[/C][C]100.4[/C][C]100.532728832334[/C][C]-0.132728832333871[/C][/ROW]
[ROW][C]25[/C][C]100.4[/C][C]100.456908175399[/C][C]-0.0569081753992734[/C][/ROW]
[ROW][C]26[/C][C]100.4[/C][C]100.465993702122[/C][C]-0.065993702122314[/C][/ROW]
[ROW][C]27[/C][C]100.4[/C][C]100.475079228845[/C][C]-0.0750792288453547[/C][/ROW]
[ROW][C]28[/C][C]100.5[/C][C]100.484164755568[/C][C]0.0158352444315989[/C][/ROW]
[ROW][C]29[/C][C]100.6[/C][C]100.665021223042[/C][C]-0.0650212230422954[/C][/ROW]
[ROW][C]30[/C][C]100.6[/C][C]100.760971506859[/C][C]-0.160971506858558[/C][/ROW]
[ROW][C]31[/C][C]100.5[/C][C]100.685150849924[/C][C]-0.185150849923966[/C][/ROW]
[ROW][C]32[/C][C]100.5[/C][C]100.522465435896[/C][C]-0.0224654358961586[/C][/ROW]
[ROW][C]33[/C][C]100.7[/C][C]100.616457146277[/C][C]0.083542853723177[/C][/ROW]
[ROW][C]34[/C][C]101.1[/C][C]100.965794143423[/C][C]0.134205856577486[/C][/ROW]
[ROW][C]35[/C][C]101.5[/C][C]101.491328463064[/C][C]0.00867153693606944[/C][/ROW]
[ROW][C]36[/C][C]101.9[/C][C]101.847873018160[/C][C]0.0521269818401117[/C][/ROW]
[ROW][C]37[/C][C]102.1[/C][C]102.202772367716[/C][C]-0.102772367716312[/C][/ROW]
[ROW][C]38[/C][C]102.1[/C][C]102.215363739926[/C][C]-0.115363739925633[/C][/ROW]
[ROW][C]39[/C][C]102.1[/C][C]102.054636899333[/C][C]0.0453631006665791[/C][/ROW]
[ROW][C]40[/C][C]102.4[/C][C]102.060432014977[/C][C]0.339567985022629[/C][/ROW]
[ROW][C]41[/C][C]102.8[/C][C]102.584007761183[/C][C]0.215992238816756[/C][/ROW]
[ROW][C]42[/C][C]103.1[/C][C]103.025458499937[/C][C]0.0745415000631995[/C][/ROW]
[ROW][C]43[/C][C]103.1[/C][C]103.210232114282[/C][C]-0.110232114281891[/C][/ROW]
[ROW][C]44[/C][C]102.9[/C][C]102.964599090032[/C][C]-0.064599090032029[/C][/ROW]
[ROW][C]45[/C][C]102.4[/C][C]102.630142735253[/C][C]-0.230142735253373[/C][/ROW]
[ROW][C]46[/C][C]101.9[/C][C]101.950185925537[/C][C]-0.0501859255373783[/C][/ROW]
[ROW][C]47[/C][C]101.3[/C][C]101.524947666794[/C][C]-0.224947666794295[/C][/ROW]
[ROW][C]48[/C][C]100.7[/C][C]100.927938467300[/C][C]-0.227938467300325[/C][/ROW]
[ROW][C]49[/C][C]100.6[/C][C]100.415835451464[/C][C]0.184164548535967[/C][/ROW]
[ROW][C]50[/C][C]101[/C][C]100.762587139382[/C][C]0.237412860618021[/C][/ROW]
[ROW][C]51[/C][C]101.5[/C][C]101.543662612766[/C][C]-0.043662612766099[/C][/ROW]
[ROW][C]52[/C][C]101.9[/C][C]102.071978108613[/C][C]-0.171978108612893[/C][/ROW]
[ROW][C]53[/C][C]102.1[/C][C]102.343616480051[/C][C]-0.243616480051229[/C][/ROW]
[ROW][C]54[/C][C]102.3[/C][C]102.356619153645[/C][C]-0.0566191536454327[/C][/ROW]
[ROW][C]55[/C][C]102.5[/C][C]102.539434194555[/C][C]-0.039434194554938[/C][/ROW]
[ROW][C]56[/C][C]102.9[/C][C]102.722249235464[/C][C]0.177750764535572[/C][/ROW]
[ROW][C]57[/C][C]103.6[/C][C]103.248606157876[/C][C]0.351393842124344[/C][/ROW]
[ROW][C]58[/C][C]104.3[/C][C]104.120463535224[/C][C]0.179536464775851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58281&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58281&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.7	100.762989773365	-0.062989773365053
2	100.1	100.17104936462	-0.071049364619917
3	99.9	99.4911082281157	0.408891771884256
4	99.7	99.669050345254	0.0309496547459434
5	99.5	99.5055580016683	-0.00555800166831006
6	99.2	99.3409140142049	-0.140914014204893
7	99	99.006555592915	-0.00655559291505667
8	99	98.9284629946488	0.0715370053511918
9	99.3	99.107772190072	0.192227809927998
10	99.5	99.6321705390476	-0.132170539047608
11	99.7	99.7300793962995	-0.0300793962994748
12	100	99.912894437209	0.0871055627910264
13	100.4	100.267480418869	0.132519581130688
14	100.6	100.708931157623	-0.108931157622913
15	100.7	100.720535406509	-0.0205354065085027
16	100.7	100.730921424451	-0.0309214244513468
17	100.6	100.655100767517	-0.0551007675167573
18	100.5	100.492333093212	0.00766690678805705
19	100.6	100.410769890101	0.189230109899161
20	100.5	100.674229253477	-0.174229253476994
21	100.4	100.423676285820	-0.0236762858203833
22	100.3	100.344745411573	-0.0447454115725334
23	100.4	100.266966181202	0.133033818797680
24	100.4	100.532728832334	-0.132728832333871
25	100.4	100.456908175399	-0.0569081753992734
26	100.4	100.465993702122	-0.065993702122314
27	100.4	100.475079228845	-0.0750792288453547
28	100.5	100.484164755568	0.0158352444315989
29	100.6	100.665021223042	-0.0650212230422954
30	100.6	100.760971506859	-0.160971506858558
31	100.5	100.685150849924	-0.185150849923966
32	100.5	100.522465435896	-0.0224654358961586
33	100.7	100.616457146277	0.083542853723177
34	101.1	100.965794143423	0.134205856577486
35	101.5	101.491328463064	0.00867153693606944
36	101.9	101.847873018160	0.0521269818401117
37	102.1	102.202772367716	-0.102772367716312
38	102.1	102.215363739926	-0.115363739925633
39	102.1	102.054636899333	0.0453631006665791
40	102.4	102.060432014977	0.339567985022629
41	102.8	102.584007761183	0.215992238816756
42	103.1	103.025458499937	0.0745415000631995
43	103.1	103.210232114282	-0.110232114281891
44	102.9	102.964599090032	-0.064599090032029
45	102.4	102.630142735253	-0.230142735253373
46	101.9	101.950185925537	-0.0501859255373783
47	101.3	101.524947666794	-0.224947666794295
48	100.7	100.927938467300	-0.227938467300325
49	100.6	100.415835451464	0.184164548535967
50	101	100.762587139382	0.237412860618021
51	101.5	101.543662612766	-0.043662612766099
52	101.9	102.071978108613	-0.171978108612893
53	102.1	102.343616480051	-0.243616480051229
54	102.3	102.356619153645	-0.0566191536454327
55	102.5	102.539434194555	-0.039434194554938
56	102.9	102.722249235464	0.177750764535572
57	103.6	103.248606157876	0.351393842124344
58	104.3	104.120463535224	0.179536464775851

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.559511774984523	0.880976450030955	0.440488225015477
9	0.836521855502964	0.326956288994072	0.163478144497036
10	0.745483415163598	0.509033169672804	0.254516584836402
11	0.635149374753572	0.729701250492857	0.364850625246428
12	0.523746082529187	0.952507834941626	0.476253917470813
13	0.426493273265642	0.852986546531284	0.573506726734358
14	0.445741022594597	0.891482045189195	0.554258977405403
15	0.404646620098383	0.809293240196765	0.595353379901617
16	0.342242782490781	0.684485564981562	0.657757217509219
17	0.282301246858076	0.564602493716151	0.717698753141924
18	0.206113240920712	0.412226481841423	0.793886759079288
19	0.237583420066054	0.475166840132108	0.762416579933946
20	0.240033386596318	0.480066773192636	0.759966613403682
21	0.176882594588919	0.353765189177839	0.823117405411081
22	0.124544526983814	0.249089053967628	0.875455473016186
23	0.134918283884376	0.269836567768752	0.865081716115624
24	0.102022074909373	0.204044149818746	0.897977925090627
25	0.0688416707011073	0.137683341402215	0.931158329298893
26	0.0451449215336397	0.0902898430672794	0.95485507846636
27	0.0289087478070724	0.0578174956141449	0.971091252192928
28	0.0188992705785260	0.0377985411570519	0.981100729421474
29	0.0110820878850113	0.0221641757700226	0.988917912114989
30	0.0085756642230614	0.0171513284461228	0.991424335776939
31	0.0095382150288086	0.0190764300576172	0.990461784971191
32	0.00546666794050588	0.0109333358810118	0.994533332059494
33	0.00416697604882772	0.00833395209765544	0.995833023951172
34	0.00855758550309936	0.0171151710061987	0.9914424144969
35	0.00697827941798127	0.0139565588359625	0.993021720582019
36	0.00602549283527164	0.0120509856705433	0.993974507164728
37	0.00361472228435484	0.00722944456870968	0.996385277715645
38	0.00271531047543415	0.00543062095086831	0.997284689524566
39	0.00666681626034248	0.0133336325206850	0.993333183739658
40	0.0149187595399062	0.0298375190798124	0.985081240460094
41	0.0397830753337333	0.0795661506674665	0.960216924666267
42	0.0537816919434082	0.107563383886816	0.946218308056592
43	0.0490762789923609	0.0981525579847217	0.95092372100764
44	0.070105598972849	0.140211197945698	0.929894401027151
45	0.0748503970522165	0.149700794104433	0.925149602947783
46	0.135811889852485	0.271623779704971	0.864188110147515
47	0.165804602285627	0.331609204571253	0.834195397714373
48	0.110897507652459	0.221795015304918	0.88910249234754
49	0.116296644335282	0.232593288670563	0.883703355664718
50	0.808370413123654	0.383259173752691	0.191629586876346

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.559511774984523 & 0.880976450030955 & 0.440488225015477 \tabularnewline
9 & 0.836521855502964 & 0.326956288994072 & 0.163478144497036 \tabularnewline
10 & 0.745483415163598 & 0.509033169672804 & 0.254516584836402 \tabularnewline
11 & 0.635149374753572 & 0.729701250492857 & 0.364850625246428 \tabularnewline
12 & 0.523746082529187 & 0.952507834941626 & 0.476253917470813 \tabularnewline
13 & 0.426493273265642 & 0.852986546531284 & 0.573506726734358 \tabularnewline
14 & 0.445741022594597 & 0.891482045189195 & 0.554258977405403 \tabularnewline
15 & 0.404646620098383 & 0.809293240196765 & 0.595353379901617 \tabularnewline
16 & 0.342242782490781 & 0.684485564981562 & 0.657757217509219 \tabularnewline
17 & 0.282301246858076 & 0.564602493716151 & 0.717698753141924 \tabularnewline
18 & 0.206113240920712 & 0.412226481841423 & 0.793886759079288 \tabularnewline
19 & 0.237583420066054 & 0.475166840132108 & 0.762416579933946 \tabularnewline
20 & 0.240033386596318 & 0.480066773192636 & 0.759966613403682 \tabularnewline
21 & 0.176882594588919 & 0.353765189177839 & 0.823117405411081 \tabularnewline
22 & 0.124544526983814 & 0.249089053967628 & 0.875455473016186 \tabularnewline
23 & 0.134918283884376 & 0.269836567768752 & 0.865081716115624 \tabularnewline
24 & 0.102022074909373 & 0.204044149818746 & 0.897977925090627 \tabularnewline
25 & 0.0688416707011073 & 0.137683341402215 & 0.931158329298893 \tabularnewline
26 & 0.0451449215336397 & 0.0902898430672794 & 0.95485507846636 \tabularnewline
27 & 0.0289087478070724 & 0.0578174956141449 & 0.971091252192928 \tabularnewline
28 & 0.0188992705785260 & 0.0377985411570519 & 0.981100729421474 \tabularnewline
29 & 0.0110820878850113 & 0.0221641757700226 & 0.988917912114989 \tabularnewline
30 & 0.0085756642230614 & 0.0171513284461228 & 0.991424335776939 \tabularnewline
31 & 0.0095382150288086 & 0.0190764300576172 & 0.990461784971191 \tabularnewline
32 & 0.00546666794050588 & 0.0109333358810118 & 0.994533332059494 \tabularnewline
33 & 0.00416697604882772 & 0.00833395209765544 & 0.995833023951172 \tabularnewline
34 & 0.00855758550309936 & 0.0171151710061987 & 0.9914424144969 \tabularnewline
35 & 0.00697827941798127 & 0.0139565588359625 & 0.993021720582019 \tabularnewline
36 & 0.00602549283527164 & 0.0120509856705433 & 0.993974507164728 \tabularnewline
37 & 0.00361472228435484 & 0.00722944456870968 & 0.996385277715645 \tabularnewline
38 & 0.00271531047543415 & 0.00543062095086831 & 0.997284689524566 \tabularnewline
39 & 0.00666681626034248 & 0.0133336325206850 & 0.993333183739658 \tabularnewline
40 & 0.0149187595399062 & 0.0298375190798124 & 0.985081240460094 \tabularnewline
41 & 0.0397830753337333 & 0.0795661506674665 & 0.960216924666267 \tabularnewline
42 & 0.0537816919434082 & 0.107563383886816 & 0.946218308056592 \tabularnewline
43 & 0.0490762789923609 & 0.0981525579847217 & 0.95092372100764 \tabularnewline
44 & 0.070105598972849 & 0.140211197945698 & 0.929894401027151 \tabularnewline
45 & 0.0748503970522165 & 0.149700794104433 & 0.925149602947783 \tabularnewline
46 & 0.135811889852485 & 0.271623779704971 & 0.864188110147515 \tabularnewline
47 & 0.165804602285627 & 0.331609204571253 & 0.834195397714373 \tabularnewline
48 & 0.110897507652459 & 0.221795015304918 & 0.88910249234754 \tabularnewline
49 & 0.116296644335282 & 0.232593288670563 & 0.883703355664718 \tabularnewline
50 & 0.808370413123654 & 0.383259173752691 & 0.191629586876346 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58281&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]8[/C][C]0.559511774984523[/C][C]0.880976450030955[/C][C]0.440488225015477[/C][/ROW]
[ROW][C]9[/C][C]0.836521855502964[/C][C]0.326956288994072[/C][C]0.163478144497036[/C][/ROW]
[ROW][C]10[/C][C]0.745483415163598[/C][C]0.509033169672804[/C][C]0.254516584836402[/C][/ROW]
[ROW][C]11[/C][C]0.635149374753572[/C][C]0.729701250492857[/C][C]0.364850625246428[/C][/ROW]
[ROW][C]12[/C][C]0.523746082529187[/C][C]0.952507834941626[/C][C]0.476253917470813[/C][/ROW]
[ROW][C]13[/C][C]0.426493273265642[/C][C]0.852986546531284[/C][C]0.573506726734358[/C][/ROW]
[ROW][C]14[/C][C]0.445741022594597[/C][C]0.891482045189195[/C][C]0.554258977405403[/C][/ROW]
[ROW][C]15[/C][C]0.404646620098383[/C][C]0.809293240196765[/C][C]0.595353379901617[/C][/ROW]
[ROW][C]16[/C][C]0.342242782490781[/C][C]0.684485564981562[/C][C]0.657757217509219[/C][/ROW]
[ROW][C]17[/C][C]0.282301246858076[/C][C]0.564602493716151[/C][C]0.717698753141924[/C][/ROW]
[ROW][C]18[/C][C]0.206113240920712[/C][C]0.412226481841423[/C][C]0.793886759079288[/C][/ROW]
[ROW][C]19[/C][C]0.237583420066054[/C][C]0.475166840132108[/C][C]0.762416579933946[/C][/ROW]
[ROW][C]20[/C][C]0.240033386596318[/C][C]0.480066773192636[/C][C]0.759966613403682[/C][/ROW]
[ROW][C]21[/C][C]0.176882594588919[/C][C]0.353765189177839[/C][C]0.823117405411081[/C][/ROW]
[ROW][C]22[/C][C]0.124544526983814[/C][C]0.249089053967628[/C][C]0.875455473016186[/C][/ROW]
[ROW][C]23[/C][C]0.134918283884376[/C][C]0.269836567768752[/C][C]0.865081716115624[/C][/ROW]
[ROW][C]24[/C][C]0.102022074909373[/C][C]0.204044149818746[/C][C]0.897977925090627[/C][/ROW]
[ROW][C]25[/C][C]0.0688416707011073[/C][C]0.137683341402215[/C][C]0.931158329298893[/C][/ROW]
[ROW][C]26[/C][C]0.0451449215336397[/C][C]0.0902898430672794[/C][C]0.95485507846636[/C][/ROW]
[ROW][C]27[/C][C]0.0289087478070724[/C][C]0.0578174956141449[/C][C]0.971091252192928[/C][/ROW]
[ROW][C]28[/C][C]0.0188992705785260[/C][C]0.0377985411570519[/C][C]0.981100729421474[/C][/ROW]
[ROW][C]29[/C][C]0.0110820878850113[/C][C]0.0221641757700226[/C][C]0.988917912114989[/C][/ROW]
[ROW][C]30[/C][C]0.0085756642230614[/C][C]0.0171513284461228[/C][C]0.991424335776939[/C][/ROW]
[ROW][C]31[/C][C]0.0095382150288086[/C][C]0.0190764300576172[/C][C]0.990461784971191[/C][/ROW]
[ROW][C]32[/C][C]0.00546666794050588[/C][C]0.0109333358810118[/C][C]0.994533332059494[/C][/ROW]
[ROW][C]33[/C][C]0.00416697604882772[/C][C]0.00833395209765544[/C][C]0.995833023951172[/C][/ROW]
[ROW][C]34[/C][C]0.00855758550309936[/C][C]0.0171151710061987[/C][C]0.9914424144969[/C][/ROW]
[ROW][C]35[/C][C]0.00697827941798127[/C][C]0.0139565588359625[/C][C]0.993021720582019[/C][/ROW]
[ROW][C]36[/C][C]0.00602549283527164[/C][C]0.0120509856705433[/C][C]0.993974507164728[/C][/ROW]
[ROW][C]37[/C][C]0.00361472228435484[/C][C]0.00722944456870968[/C][C]0.996385277715645[/C][/ROW]
[ROW][C]38[/C][C]0.00271531047543415[/C][C]0.00543062095086831[/C][C]0.997284689524566[/C][/ROW]
[ROW][C]39[/C][C]0.00666681626034248[/C][C]0.0133336325206850[/C][C]0.993333183739658[/C][/ROW]
[ROW][C]40[/C][C]0.0149187595399062[/C][C]0.0298375190798124[/C][C]0.985081240460094[/C][/ROW]
[ROW][C]41[/C][C]0.0397830753337333[/C][C]0.0795661506674665[/C][C]0.960216924666267[/C][/ROW]
[ROW][C]42[/C][C]0.0537816919434082[/C][C]0.107563383886816[/C][C]0.946218308056592[/C][/ROW]
[ROW][C]43[/C][C]0.0490762789923609[/C][C]0.0981525579847217[/C][C]0.95092372100764[/C][/ROW]
[ROW][C]44[/C][C]0.070105598972849[/C][C]0.140211197945698[/C][C]0.929894401027151[/C][/ROW]
[ROW][C]45[/C][C]0.0748503970522165[/C][C]0.149700794104433[/C][C]0.925149602947783[/C][/ROW]
[ROW][C]46[/C][C]0.135811889852485[/C][C]0.271623779704971[/C][C]0.864188110147515[/C][/ROW]
[ROW][C]47[/C][C]0.165804602285627[/C][C]0.331609204571253[/C][C]0.834195397714373[/C][/ROW]
[ROW][C]48[/C][C]0.110897507652459[/C][C]0.221795015304918[/C][C]0.88910249234754[/C][/ROW]
[ROW][C]49[/C][C]0.116296644335282[/C][C]0.232593288670563[/C][C]0.883703355664718[/C][/ROW]
[ROW][C]50[/C][C]0.808370413123654[/C][C]0.383259173752691[/C][C]0.191629586876346[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58281&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58281&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.559511774984523	0.880976450030955	0.440488225015477
9	0.836521855502964	0.326956288994072	0.163478144497036
10	0.745483415163598	0.509033169672804	0.254516584836402
11	0.635149374753572	0.729701250492857	0.364850625246428
12	0.523746082529187	0.952507834941626	0.476253917470813
13	0.426493273265642	0.852986546531284	0.573506726734358
14	0.445741022594597	0.891482045189195	0.554258977405403
15	0.404646620098383	0.809293240196765	0.595353379901617
16	0.342242782490781	0.684485564981562	0.657757217509219
17	0.282301246858076	0.564602493716151	0.717698753141924
18	0.206113240920712	0.412226481841423	0.793886759079288
19	0.237583420066054	0.475166840132108	0.762416579933946
20	0.240033386596318	0.480066773192636	0.759966613403682
21	0.176882594588919	0.353765189177839	0.823117405411081
22	0.124544526983814	0.249089053967628	0.875455473016186
23	0.134918283884376	0.269836567768752	0.865081716115624
24	0.102022074909373	0.204044149818746	0.897977925090627
25	0.0688416707011073	0.137683341402215	0.931158329298893
26	0.0451449215336397	0.0902898430672794	0.95485507846636
27	0.0289087478070724	0.0578174956141449	0.971091252192928
28	0.0188992705785260	0.0377985411570519	0.981100729421474
29	0.0110820878850113	0.0221641757700226	0.988917912114989
30	0.0085756642230614	0.0171513284461228	0.991424335776939
31	0.0095382150288086	0.0190764300576172	0.990461784971191
32	0.00546666794050588	0.0109333358810118	0.994533332059494
33	0.00416697604882772	0.00833395209765544	0.995833023951172
34	0.00855758550309936	0.0171151710061987	0.9914424144969
35	0.00697827941798127	0.0139565588359625	0.993021720582019
36	0.00602549283527164	0.0120509856705433	0.993974507164728
37	0.00361472228435484	0.00722944456870968	0.996385277715645
38	0.00271531047543415	0.00543062095086831	0.997284689524566
39	0.00666681626034248	0.0133336325206850	0.993333183739658
40	0.0149187595399062	0.0298375190798124	0.985081240460094
41	0.0397830753337333	0.0795661506674665	0.960216924666267
42	0.0537816919434082	0.107563383886816	0.946218308056592
43	0.0490762789923609	0.0981525579847217	0.95092372100764
44	0.070105598972849	0.140211197945698	0.929894401027151
45	0.0748503970522165	0.149700794104433	0.925149602947783
46	0.135811889852485	0.271623779704971	0.864188110147515
47	0.165804602285627	0.331609204571253	0.834195397714373
48	0.110897507652459	0.221795015304918	0.88910249234754
49	0.116296644335282	0.232593288670563	0.883703355664718
50	0.808370413123654	0.383259173752691	0.191629586876346

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.0697674418604651	NOK
5% type I error level	13	0.302325581395349	NOK
10% type I error level	17	0.395348837209302	NOK

\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 & 3 & 0.0697674418604651 & NOK \tabularnewline
5% type I error level & 13 & 0.302325581395349 & NOK \tabularnewline
10% type I error level & 17 & 0.395348837209302 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58281&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]3[/C][C]0.0697674418604651[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.302325581395349[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.395348837209302[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58281&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58281&T=6

As an alternative you can also use a 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 tests	OK/NOK
1% type I error level	3	0.0697674418604651	NOK
5% type I error level	13	0.302325581395349	NOK
10% type I error level	17	0.395348837209302	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code