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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 23 Nov 2009 11:40:20 -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/23/t1259001695609080u3v0mlc5r.htm/, Retrieved Sun, 28 Apr 2024 21:57:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58856, Retrieved Sun, 28 Apr 2024 21:57:08 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

188

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 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-20 07:57:23] [5d885a68c2332cc44f6191ec94766bfa]
-    D        [Multiple Regression] [Verbetering_Model5] [2009-11-23 18:40:20] [82f29a5d509ab8039aab37a0145f886d] [Current]

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Dataseries X:

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Histogram

Boxplots

100.25	1.8	100.03
99.6	2.7	100.25
100.16	2.3	99.6
100.49	1.9	100.16
99.72	2	100.49
100.14	2.3	99.72
98.48	2.8	100.14
100.38	2.4	98.48
101.45	2.3	100.38
98.42	2.7	101.45
98.6	2.7	98.42
100.06	2.9	98.6
98.62	3	100.06
100.84	2.2	98.62
100.02	2.3	100.84
97.95	2.8	100.02
98.32	2.8	97.95
98.27	2.8	98.32
97.22	2.2	98.27
99.28	2.6	97.22
100.38	2.8	99.28
99.02	2.5	100.38
100.32	2.4	99.02
99.81	2.3	100.32
100.6	1.9	99.81
101.19	1.7	100.6
100.47	2	101.19
101.77	2.1	100.47
102.32	1.7	101.77
102.39	1.8	102.32
101.16	1.8	102.39
100.63	1.8	101.16
101.48	1.3	100.63
101.44	1.3	101.48
100.09	1.3	101.44
100.7	1.2	100.09
100.78	1.4	100.7
99.81	2.2	100.78
98.45	2.9	99.81
98.49	3.1	98.45
97.48	3.5	98.49
97.91	3.6	97.48
96.94	4.4	97.91
98.53	4.1	96.94
96.82	5.1	98.53
95.76	5.8	96.82
95.27	5.9	95.76
97.32	5.4	95.27
96.68	5.5	97.32
97.87	4.8	96.68
97.42	3.2	97.87
97.94	2.7	97.42
99.52	2.1	97.94
100.99	1.9	99.52
99.92	0.6	100.99
101.97	0.7	99.92
101.58	-0.2	101.97
99.54	-1	101.58
100.83	-1.7	99.54

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58856&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58856&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58856&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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 54.4671841739803 -0.510590757417604X[t] + 0.473872331863727Y1[t] -0.717448617283483M1[t] -0.141020655860973M2[t] -1.00988898244975M3[t] -0.72507879670461M4[t] -0.63690956869757M5[t] -0.199910499327358M6[t] -1.67235240181616M7[t] + 0.294576516509678M8[t] -0.215513166477157M9[t] -1.80210443582655M10[t] -0.967334170364718M11[t] -0.00660123971353016t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  54.4671841739803 -0.510590757417604X[t] +  0.473872331863727Y1[t] -0.717448617283483M1[t] -0.141020655860973M2[t] -1.00988898244975M3[t] -0.72507879670461M4[t] -0.63690956869757M5[t] -0.199910499327358M6[t] -1.67235240181616M7[t] +  0.294576516509678M8[t] -0.215513166477157M9[t] -1.80210443582655M10[t] -0.967334170364718M11[t] -0.00660123971353016t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58856&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  54.4671841739803 -0.510590757417604X[t] +  0.473872331863727Y1[t] -0.717448617283483M1[t] -0.141020655860973M2[t] -1.00988898244975M3[t] -0.72507879670461M4[t] -0.63690956869757M5[t] -0.199910499327358M6[t] -1.67235240181616M7[t] +  0.294576516509678M8[t] -0.215513166477157M9[t] -1.80210443582655M10[t] -0.967334170364718M11[t] -0.00660123971353016t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58856&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58856&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
Y[t] = + 54.4671841739803 -0.510590757417604X[t] + 0.473872331863727Y1[t] -0.717448617283483M1[t] -0.141020655860973M2[t] -1.00988898244975M3[t] -0.72507879670461M4[t] -0.63690956869757M5[t] -0.199910499327358M6[t] -1.67235240181616M7[t] + 0.294576516509678M8[t] -0.215513166477157M9[t] -1.80210443582655M10[t] -0.967334170364718M11[t] -0.00660123971353016t + 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)	54.4671841739803	12.014833	4.5333	4.4e-05	2.2e-05
X	-0.510590757417604	0.126577	-4.0338	0.000215	0.000108
Y1	0.473872331863727	0.118113	4.012	0.00023	0.000115
M1	-0.717448617283483	0.590856	-1.2143	0.231128	0.115564
M2	-0.141020655860973	0.587854	-0.2399	0.811529	0.405765
M3	-1.00988898244975	0.594107	-1.6998	0.09622	0.04811
M4	-0.72507879670461	0.58596	-1.2374	0.222494	0.111247
M5	-0.63690956869757	0.586093	-1.0867	0.283085	0.141542
M6	-0.199910499327358	0.5878	-0.3401	0.7354	0.3677
M7	-1.67235240181616	0.595058	-2.8104	0.007358	0.003679
M8	0.294576516509678	0.586924	0.5019	0.618241	0.309121
M9	-0.215513166477157	0.600171	-0.3591	0.721248	0.360624
M10	-1.80210443582655	0.60604	-2.9736	0.004762	0.002381
M11	-0.967334170364718	0.589491	-1.641	0.107935	0.053967
t	-0.00660123971353016	0.007182	-0.9192	0.363025	0.181513

\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) & 54.4671841739803 & 12.014833 & 4.5333 & 4.4e-05 & 2.2e-05 \tabularnewline
X & -0.510590757417604 & 0.126577 & -4.0338 & 0.000215 & 0.000108 \tabularnewline
Y1 & 0.473872331863727 & 0.118113 & 4.012 & 0.00023 & 0.000115 \tabularnewline
M1 & -0.717448617283483 & 0.590856 & -1.2143 & 0.231128 & 0.115564 \tabularnewline
M2 & -0.141020655860973 & 0.587854 & -0.2399 & 0.811529 & 0.405765 \tabularnewline
M3 & -1.00988898244975 & 0.594107 & -1.6998 & 0.09622 & 0.04811 \tabularnewline
M4 & -0.72507879670461 & 0.58596 & -1.2374 & 0.222494 & 0.111247 \tabularnewline
M5 & -0.63690956869757 & 0.586093 & -1.0867 & 0.283085 & 0.141542 \tabularnewline
M6 & -0.199910499327358 & 0.5878 & -0.3401 & 0.7354 & 0.3677 \tabularnewline
M7 & -1.67235240181616 & 0.595058 & -2.8104 & 0.007358 & 0.003679 \tabularnewline
M8 & 0.294576516509678 & 0.586924 & 0.5019 & 0.618241 & 0.309121 \tabularnewline
M9 & -0.215513166477157 & 0.600171 & -0.3591 & 0.721248 & 0.360624 \tabularnewline
M10 & -1.80210443582655 & 0.60604 & -2.9736 & 0.004762 & 0.002381 \tabularnewline
M11 & -0.967334170364718 & 0.589491 & -1.641 & 0.107935 & 0.053967 \tabularnewline
t & -0.00660123971353016 & 0.007182 & -0.9192 & 0.363025 & 0.181513 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58856&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]54.4671841739803[/C][C]12.014833[/C][C]4.5333[/C][C]4.4e-05[/C][C]2.2e-05[/C][/ROW]
[ROW][C]X[/C][C]-0.510590757417604[/C][C]0.126577[/C][C]-4.0338[/C][C]0.000215[/C][C]0.000108[/C][/ROW]
[ROW][C]Y1[/C][C]0.473872331863727[/C][C]0.118113[/C][C]4.012[/C][C]0.00023[/C][C]0.000115[/C][/ROW]
[ROW][C]M1[/C][C]-0.717448617283483[/C][C]0.590856[/C][C]-1.2143[/C][C]0.231128[/C][C]0.115564[/C][/ROW]
[ROW][C]M2[/C][C]-0.141020655860973[/C][C]0.587854[/C][C]-0.2399[/C][C]0.811529[/C][C]0.405765[/C][/ROW]
[ROW][C]M3[/C][C]-1.00988898244975[/C][C]0.594107[/C][C]-1.6998[/C][C]0.09622[/C][C]0.04811[/C][/ROW]
[ROW][C]M4[/C][C]-0.72507879670461[/C][C]0.58596[/C][C]-1.2374[/C][C]0.222494[/C][C]0.111247[/C][/ROW]
[ROW][C]M5[/C][C]-0.63690956869757[/C][C]0.586093[/C][C]-1.0867[/C][C]0.283085[/C][C]0.141542[/C][/ROW]
[ROW][C]M6[/C][C]-0.199910499327358[/C][C]0.5878[/C][C]-0.3401[/C][C]0.7354[/C][C]0.3677[/C][/ROW]
[ROW][C]M7[/C][C]-1.67235240181616[/C][C]0.595058[/C][C]-2.8104[/C][C]0.007358[/C][C]0.003679[/C][/ROW]
[ROW][C]M8[/C][C]0.294576516509678[/C][C]0.586924[/C][C]0.5019[/C][C]0.618241[/C][C]0.309121[/C][/ROW]
[ROW][C]M9[/C][C]-0.215513166477157[/C][C]0.600171[/C][C]-0.3591[/C][C]0.721248[/C][C]0.360624[/C][/ROW]
[ROW][C]M10[/C][C]-1.80210443582655[/C][C]0.60604[/C][C]-2.9736[/C][C]0.004762[/C][C]0.002381[/C][/ROW]
[ROW][C]M11[/C][C]-0.967334170364718[/C][C]0.589491[/C][C]-1.641[/C][C]0.107935[/C][C]0.053967[/C][/ROW]
[ROW][C]t[/C][C]-0.00660123971353016[/C][C]0.007182[/C][C]-0.9192[/C][C]0.363025[/C][C]0.181513[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58856&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58856&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)	54.4671841739803	12.014833	4.5333	4.4e-05	2.2e-05
X	-0.510590757417604	0.126577	-4.0338	0.000215	0.000108
Y1	0.473872331863727	0.118113	4.012	0.00023	0.000115
M1	-0.717448617283483	0.590856	-1.2143	0.231128	0.115564
M2	-0.141020655860973	0.587854	-0.2399	0.811529	0.405765
M3	-1.00988898244975	0.594107	-1.6998	0.09622	0.04811
M4	-0.72507879670461	0.58596	-1.2374	0.222494	0.111247
M5	-0.63690956869757	0.586093	-1.0867	0.283085	0.141542
M6	-0.199910499327358	0.5878	-0.3401	0.7354	0.3677
M7	-1.67235240181616	0.595058	-2.8104	0.007358	0.003679
M8	0.294576516509678	0.586924	0.5019	0.618241	0.309121
M9	-0.215513166477157	0.600171	-0.3591	0.721248	0.360624
M10	-1.80210443582655	0.60604	-2.9736	0.004762	0.002381
M11	-0.967334170364718	0.589491	-1.641	0.107935	0.053967
t	-0.00660123971353016	0.007182	-0.9192	0.363025	0.181513

Multiple Linear Regression - Regression Statistics
Multiple R	0.889364739685644
R-squared	0.790969640196113
Adjusted R-squared	0.724459980258513
F-TEST (value)	11.8925527650901
F-TEST (DF numerator)	14
F-TEST (DF denominator)	44
p-value	1.09689812788361e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.869724287959638
Sum Squared Residuals	33.2824948309435

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.889364739685644 \tabularnewline
R-squared & 0.790969640196113 \tabularnewline
Adjusted R-squared & 0.724459980258513 \tabularnewline
F-TEST (value) & 11.8925527650901 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 1.09689812788361e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.869724287959638 \tabularnewline
Sum Squared Residuals & 33.2824948309435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58856&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.889364739685644[/C][/ROW]
[ROW][C]R-squared[/C][C]0.790969640196113[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.724459980258513[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.8925527650901[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]1.09689812788361e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.869724287959638[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33.2824948309435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58856&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58856&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	0.889364739685644
R-squared	0.790969640196113
Adjusted R-squared	0.724459980258513
F-TEST (value)	11.8925527650901
F-TEST (DF numerator)	14
F-TEST (DF denominator)	44
p-value	1.09689812788361e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.869724287959638
Sum Squared Residuals	33.2824948309435

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.25	100.22552030996	0.0244796900400029
2	99.6	100.440067263003	-0.84006726300334
3	100.16	99.4608169839566	0.699183016043347
4	100.49	100.208630738799	0.28136926120101
5	99.72	100.395517520866	-0.675517520865765
6	100.14	100.307856427762	-0.167856427762095
7	98.48	98.7725442862337	-0.292544286233722
8	100.38	100.150480196919	0.229519803080704
9	101.45	100.585205780502	0.864794219498239
10	98.42	99.294820363566	-0.874820363565989
11	98.6	98.6871562237672	-0.0871562237672058
12	100.06	99.6310680226703	0.428931977329668
13	98.62	99.5478126944526	-0.9278126944526
14	100.84	99.843735864212	0.996264135788101
15	100.02	99.9692037989053	0.0507962010946843
16	97.95	99.6035420540999	-1.65354205409985
17	98.32	98.7041943154355	-0.384194315435464
18	98.27	99.3099249078817	-1.03992490788172
19	97.22	98.1135426035368	-0.893542603536754
20	99.28	99.3720680307251	-0.09206803072511
21	100.38	99.7294359601805	0.650564039819492
22	99.02	98.810680243393	0.209319756607038
23	100.32	99.0454419735483	1.27455802645164
24	99.81	100.673268011364	-0.863268011364142
25	100.6	99.9117795680837	0.688220431916318
26	101.19	100.958083583449	0.231916416551480
27	100.47	100.209021465721	0.260978534279466
28	101.77	100.094983257068	1.6750167429315
29	102.32	100.996821579752	1.3231784202481
30	102.39	101.636790116192	0.75320988380814
31	101.16	100.19091803722	0.969081962780007
32	100.63	101.56838274764	-0.93838274763992
33	101.48	101.055834867761	0.424165132239428
34	101.44	99.8654338407818	1.57456615921817
35	100.09	100.674647973256	-0.584647973255571
36	100.7	101.046712331632	-0.346712331632491
37	100.78	100.509606445589	0.270393554411168
38	99.81	100.708870347913	-0.898870347912826
39	98.45	99.0163310895104	-0.566331089510384
40	98.49	98.5479555127238	-0.0579555127238091
41	97.48	98.4442420913248	-0.964242091324815
42	97.91	98.3449697900574	-0.434969790057382
43	96.94	96.6612191446224	0.278780855377638
44	98.53	98.3150678885521	0.214932111447863
45	96.82	98.0412432160975	-1.22124321609750
46	95.76	95.2803154893553	0.479684510644732
47	95.27	95.5551207675863	-0.285120767586275
48	97.32	96.538951634333	0.781048365666965
49	96.68	96.735280981915	-0.0552809819148877
50	97.87	97.3592429414234	0.510757058576585
51	97.42	97.8646266619071	-0.444626661907113
52	97.94	98.1848884373088	-0.244888437308847
53	99.52	98.819224492622	0.700775507377942
54	100.99	100.100458758107	0.889541241893053
55	99.92	99.9817759283872	-0.0617759283871687
56	101.97	101.384001136164	0.585998863836462
57	101.58	102.298280175460	-0.718280175459655
58	99.54	100.928750062904	-1.38875006290395
59	100.83	101.147633061843	-0.317633061842588

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.25 & 100.22552030996 & 0.0244796900400029 \tabularnewline
2 & 99.6 & 100.440067263003 & -0.84006726300334 \tabularnewline
3 & 100.16 & 99.4608169839566 & 0.699183016043347 \tabularnewline
4 & 100.49 & 100.208630738799 & 0.28136926120101 \tabularnewline
5 & 99.72 & 100.395517520866 & -0.675517520865765 \tabularnewline
6 & 100.14 & 100.307856427762 & -0.167856427762095 \tabularnewline
7 & 98.48 & 98.7725442862337 & -0.292544286233722 \tabularnewline
8 & 100.38 & 100.150480196919 & 0.229519803080704 \tabularnewline
9 & 101.45 & 100.585205780502 & 0.864794219498239 \tabularnewline
10 & 98.42 & 99.294820363566 & -0.874820363565989 \tabularnewline
11 & 98.6 & 98.6871562237672 & -0.0871562237672058 \tabularnewline
12 & 100.06 & 99.6310680226703 & 0.428931977329668 \tabularnewline
13 & 98.62 & 99.5478126944526 & -0.9278126944526 \tabularnewline
14 & 100.84 & 99.843735864212 & 0.996264135788101 \tabularnewline
15 & 100.02 & 99.9692037989053 & 0.0507962010946843 \tabularnewline
16 & 97.95 & 99.6035420540999 & -1.65354205409985 \tabularnewline
17 & 98.32 & 98.7041943154355 & -0.384194315435464 \tabularnewline
18 & 98.27 & 99.3099249078817 & -1.03992490788172 \tabularnewline
19 & 97.22 & 98.1135426035368 & -0.893542603536754 \tabularnewline
20 & 99.28 & 99.3720680307251 & -0.09206803072511 \tabularnewline
21 & 100.38 & 99.7294359601805 & 0.650564039819492 \tabularnewline
22 & 99.02 & 98.810680243393 & 0.209319756607038 \tabularnewline
23 & 100.32 & 99.0454419735483 & 1.27455802645164 \tabularnewline
24 & 99.81 & 100.673268011364 & -0.863268011364142 \tabularnewline
25 & 100.6 & 99.9117795680837 & 0.688220431916318 \tabularnewline
26 & 101.19 & 100.958083583449 & 0.231916416551480 \tabularnewline
27 & 100.47 & 100.209021465721 & 0.260978534279466 \tabularnewline
28 & 101.77 & 100.094983257068 & 1.6750167429315 \tabularnewline
29 & 102.32 & 100.996821579752 & 1.3231784202481 \tabularnewline
30 & 102.39 & 101.636790116192 & 0.75320988380814 \tabularnewline
31 & 101.16 & 100.19091803722 & 0.969081962780007 \tabularnewline
32 & 100.63 & 101.56838274764 & -0.93838274763992 \tabularnewline
33 & 101.48 & 101.055834867761 & 0.424165132239428 \tabularnewline
34 & 101.44 & 99.8654338407818 & 1.57456615921817 \tabularnewline
35 & 100.09 & 100.674647973256 & -0.584647973255571 \tabularnewline
36 & 100.7 & 101.046712331632 & -0.346712331632491 \tabularnewline
37 & 100.78 & 100.509606445589 & 0.270393554411168 \tabularnewline
38 & 99.81 & 100.708870347913 & -0.898870347912826 \tabularnewline
39 & 98.45 & 99.0163310895104 & -0.566331089510384 \tabularnewline
40 & 98.49 & 98.5479555127238 & -0.0579555127238091 \tabularnewline
41 & 97.48 & 98.4442420913248 & -0.964242091324815 \tabularnewline
42 & 97.91 & 98.3449697900574 & -0.434969790057382 \tabularnewline
43 & 96.94 & 96.6612191446224 & 0.278780855377638 \tabularnewline
44 & 98.53 & 98.3150678885521 & 0.214932111447863 \tabularnewline
45 & 96.82 & 98.0412432160975 & -1.22124321609750 \tabularnewline
46 & 95.76 & 95.2803154893553 & 0.479684510644732 \tabularnewline
47 & 95.27 & 95.5551207675863 & -0.285120767586275 \tabularnewline
48 & 97.32 & 96.538951634333 & 0.781048365666965 \tabularnewline
49 & 96.68 & 96.735280981915 & -0.0552809819148877 \tabularnewline
50 & 97.87 & 97.3592429414234 & 0.510757058576585 \tabularnewline
51 & 97.42 & 97.8646266619071 & -0.444626661907113 \tabularnewline
52 & 97.94 & 98.1848884373088 & -0.244888437308847 \tabularnewline
53 & 99.52 & 98.819224492622 & 0.700775507377942 \tabularnewline
54 & 100.99 & 100.100458758107 & 0.889541241893053 \tabularnewline
55 & 99.92 & 99.9817759283872 & -0.0617759283871687 \tabularnewline
56 & 101.97 & 101.384001136164 & 0.585998863836462 \tabularnewline
57 & 101.58 & 102.298280175460 & -0.718280175459655 \tabularnewline
58 & 99.54 & 100.928750062904 & -1.38875006290395 \tabularnewline
59 & 100.83 & 101.147633061843 & -0.317633061842588 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58856&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]100.25[/C][C]100.22552030996[/C][C]0.0244796900400029[/C][/ROW]
[ROW][C]2[/C][C]99.6[/C][C]100.440067263003[/C][C]-0.84006726300334[/C][/ROW]
[ROW][C]3[/C][C]100.16[/C][C]99.4608169839566[/C][C]0.699183016043347[/C][/ROW]
[ROW][C]4[/C][C]100.49[/C][C]100.208630738799[/C][C]0.28136926120101[/C][/ROW]
[ROW][C]5[/C][C]99.72[/C][C]100.395517520866[/C][C]-0.675517520865765[/C][/ROW]
[ROW][C]6[/C][C]100.14[/C][C]100.307856427762[/C][C]-0.167856427762095[/C][/ROW]
[ROW][C]7[/C][C]98.48[/C][C]98.7725442862337[/C][C]-0.292544286233722[/C][/ROW]
[ROW][C]8[/C][C]100.38[/C][C]100.150480196919[/C][C]0.229519803080704[/C][/ROW]
[ROW][C]9[/C][C]101.45[/C][C]100.585205780502[/C][C]0.864794219498239[/C][/ROW]
[ROW][C]10[/C][C]98.42[/C][C]99.294820363566[/C][C]-0.874820363565989[/C][/ROW]
[ROW][C]11[/C][C]98.6[/C][C]98.6871562237672[/C][C]-0.0871562237672058[/C][/ROW]
[ROW][C]12[/C][C]100.06[/C][C]99.6310680226703[/C][C]0.428931977329668[/C][/ROW]
[ROW][C]13[/C][C]98.62[/C][C]99.5478126944526[/C][C]-0.9278126944526[/C][/ROW]
[ROW][C]14[/C][C]100.84[/C][C]99.843735864212[/C][C]0.996264135788101[/C][/ROW]
[ROW][C]15[/C][C]100.02[/C][C]99.9692037989053[/C][C]0.0507962010946843[/C][/ROW]
[ROW][C]16[/C][C]97.95[/C][C]99.6035420540999[/C][C]-1.65354205409985[/C][/ROW]
[ROW][C]17[/C][C]98.32[/C][C]98.7041943154355[/C][C]-0.384194315435464[/C][/ROW]
[ROW][C]18[/C][C]98.27[/C][C]99.3099249078817[/C][C]-1.03992490788172[/C][/ROW]
[ROW][C]19[/C][C]97.22[/C][C]98.1135426035368[/C][C]-0.893542603536754[/C][/ROW]
[ROW][C]20[/C][C]99.28[/C][C]99.3720680307251[/C][C]-0.09206803072511[/C][/ROW]
[ROW][C]21[/C][C]100.38[/C][C]99.7294359601805[/C][C]0.650564039819492[/C][/ROW]
[ROW][C]22[/C][C]99.02[/C][C]98.810680243393[/C][C]0.209319756607038[/C][/ROW]
[ROW][C]23[/C][C]100.32[/C][C]99.0454419735483[/C][C]1.27455802645164[/C][/ROW]
[ROW][C]24[/C][C]99.81[/C][C]100.673268011364[/C][C]-0.863268011364142[/C][/ROW]
[ROW][C]25[/C][C]100.6[/C][C]99.9117795680837[/C][C]0.688220431916318[/C][/ROW]
[ROW][C]26[/C][C]101.19[/C][C]100.958083583449[/C][C]0.231916416551480[/C][/ROW]
[ROW][C]27[/C][C]100.47[/C][C]100.209021465721[/C][C]0.260978534279466[/C][/ROW]
[ROW][C]28[/C][C]101.77[/C][C]100.094983257068[/C][C]1.6750167429315[/C][/ROW]
[ROW][C]29[/C][C]102.32[/C][C]100.996821579752[/C][C]1.3231784202481[/C][/ROW]
[ROW][C]30[/C][C]102.39[/C][C]101.636790116192[/C][C]0.75320988380814[/C][/ROW]
[ROW][C]31[/C][C]101.16[/C][C]100.19091803722[/C][C]0.969081962780007[/C][/ROW]
[ROW][C]32[/C][C]100.63[/C][C]101.56838274764[/C][C]-0.93838274763992[/C][/ROW]
[ROW][C]33[/C][C]101.48[/C][C]101.055834867761[/C][C]0.424165132239428[/C][/ROW]
[ROW][C]34[/C][C]101.44[/C][C]99.8654338407818[/C][C]1.57456615921817[/C][/ROW]
[ROW][C]35[/C][C]100.09[/C][C]100.674647973256[/C][C]-0.584647973255571[/C][/ROW]
[ROW][C]36[/C][C]100.7[/C][C]101.046712331632[/C][C]-0.346712331632491[/C][/ROW]
[ROW][C]37[/C][C]100.78[/C][C]100.509606445589[/C][C]0.270393554411168[/C][/ROW]
[ROW][C]38[/C][C]99.81[/C][C]100.708870347913[/C][C]-0.898870347912826[/C][/ROW]
[ROW][C]39[/C][C]98.45[/C][C]99.0163310895104[/C][C]-0.566331089510384[/C][/ROW]
[ROW][C]40[/C][C]98.49[/C][C]98.5479555127238[/C][C]-0.0579555127238091[/C][/ROW]
[ROW][C]41[/C][C]97.48[/C][C]98.4442420913248[/C][C]-0.964242091324815[/C][/ROW]
[ROW][C]42[/C][C]97.91[/C][C]98.3449697900574[/C][C]-0.434969790057382[/C][/ROW]
[ROW][C]43[/C][C]96.94[/C][C]96.6612191446224[/C][C]0.278780855377638[/C][/ROW]
[ROW][C]44[/C][C]98.53[/C][C]98.3150678885521[/C][C]0.214932111447863[/C][/ROW]
[ROW][C]45[/C][C]96.82[/C][C]98.0412432160975[/C][C]-1.22124321609750[/C][/ROW]
[ROW][C]46[/C][C]95.76[/C][C]95.2803154893553[/C][C]0.479684510644732[/C][/ROW]
[ROW][C]47[/C][C]95.27[/C][C]95.5551207675863[/C][C]-0.285120767586275[/C][/ROW]
[ROW][C]48[/C][C]97.32[/C][C]96.538951634333[/C][C]0.781048365666965[/C][/ROW]
[ROW][C]49[/C][C]96.68[/C][C]96.735280981915[/C][C]-0.0552809819148877[/C][/ROW]
[ROW][C]50[/C][C]97.87[/C][C]97.3592429414234[/C][C]0.510757058576585[/C][/ROW]
[ROW][C]51[/C][C]97.42[/C][C]97.8646266619071[/C][C]-0.444626661907113[/C][/ROW]
[ROW][C]52[/C][C]97.94[/C][C]98.1848884373088[/C][C]-0.244888437308847[/C][/ROW]
[ROW][C]53[/C][C]99.52[/C][C]98.819224492622[/C][C]0.700775507377942[/C][/ROW]
[ROW][C]54[/C][C]100.99[/C][C]100.100458758107[/C][C]0.889541241893053[/C][/ROW]
[ROW][C]55[/C][C]99.92[/C][C]99.9817759283872[/C][C]-0.0617759283871687[/C][/ROW]
[ROW][C]56[/C][C]101.97[/C][C]101.384001136164[/C][C]0.585998863836462[/C][/ROW]
[ROW][C]57[/C][C]101.58[/C][C]102.298280175460[/C][C]-0.718280175459655[/C][/ROW]
[ROW][C]58[/C][C]99.54[/C][C]100.928750062904[/C][C]-1.38875006290395[/C][/ROW]
[ROW][C]59[/C][C]100.83[/C][C]101.147633061843[/C][C]-0.317633061842588[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58856&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58856&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	100.25	100.22552030996	0.0244796900400029
2	99.6	100.440067263003	-0.84006726300334
3	100.16	99.4608169839566	0.699183016043347
4	100.49	100.208630738799	0.28136926120101
5	99.72	100.395517520866	-0.675517520865765
6	100.14	100.307856427762	-0.167856427762095
7	98.48	98.7725442862337	-0.292544286233722
8	100.38	100.150480196919	0.229519803080704
9	101.45	100.585205780502	0.864794219498239
10	98.42	99.294820363566	-0.874820363565989
11	98.6	98.6871562237672	-0.0871562237672058
12	100.06	99.6310680226703	0.428931977329668
13	98.62	99.5478126944526	-0.9278126944526
14	100.84	99.843735864212	0.996264135788101
15	100.02	99.9692037989053	0.0507962010946843
16	97.95	99.6035420540999	-1.65354205409985
17	98.32	98.7041943154355	-0.384194315435464
18	98.27	99.3099249078817	-1.03992490788172
19	97.22	98.1135426035368	-0.893542603536754
20	99.28	99.3720680307251	-0.09206803072511
21	100.38	99.7294359601805	0.650564039819492
22	99.02	98.810680243393	0.209319756607038
23	100.32	99.0454419735483	1.27455802645164
24	99.81	100.673268011364	-0.863268011364142
25	100.6	99.9117795680837	0.688220431916318
26	101.19	100.958083583449	0.231916416551480
27	100.47	100.209021465721	0.260978534279466
28	101.77	100.094983257068	1.6750167429315
29	102.32	100.996821579752	1.3231784202481
30	102.39	101.636790116192	0.75320988380814
31	101.16	100.19091803722	0.969081962780007
32	100.63	101.56838274764	-0.93838274763992
33	101.48	101.055834867761	0.424165132239428
34	101.44	99.8654338407818	1.57456615921817
35	100.09	100.674647973256	-0.584647973255571
36	100.7	101.046712331632	-0.346712331632491
37	100.78	100.509606445589	0.270393554411168
38	99.81	100.708870347913	-0.898870347912826
39	98.45	99.0163310895104	-0.566331089510384
40	98.49	98.5479555127238	-0.0579555127238091
41	97.48	98.4442420913248	-0.964242091324815
42	97.91	98.3449697900574	-0.434969790057382
43	96.94	96.6612191446224	0.278780855377638
44	98.53	98.3150678885521	0.214932111447863
45	96.82	98.0412432160975	-1.22124321609750
46	95.76	95.2803154893553	0.479684510644732
47	95.27	95.5551207675863	-0.285120767586275
48	97.32	96.538951634333	0.781048365666965
49	96.68	96.735280981915	-0.0552809819148877
50	97.87	97.3592429414234	0.510757058576585
51	97.42	97.8646266619071	-0.444626661907113
52	97.94	98.1848884373088	-0.244888437308847
53	99.52	98.819224492622	0.700775507377942
54	100.99	100.100458758107	0.889541241893053
55	99.92	99.9817759283872	-0.0617759283871687
56	101.97	101.384001136164	0.585998863836462
57	101.58	102.298280175460	-0.718280175459655
58	99.54	100.928750062904	-1.38875006290395
59	100.83	101.147633061843	-0.317633061842588

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.264194454161243	0.528388908322486	0.735805545838757
19	0.502032030912094	0.995935938175812	0.497967969087906
20	0.372002866932961	0.744005733865921	0.62799713306704
21	0.248924636890084	0.497849273780167	0.751075363109916
22	0.224208480814764	0.448416961629528	0.775791519185236
23	0.26025796232125	0.5205159246425	0.73974203767875
24	0.315714155124867	0.631428310249733	0.684285844875133
25	0.269589643954673	0.539179287909346	0.730410356045327
26	0.184297417791826	0.368594835583652	0.815702582208174
27	0.120214247551822	0.240428495103644	0.879785752448178
28	0.365307127250607	0.730614254501214	0.634692872749393
29	0.445128920322775	0.89025784064555	0.554871079677225
30	0.384620295228626	0.769240590457252	0.615379704771374
31	0.360717729882443	0.721435459764886	0.639282270117557
32	0.462456267940862	0.924912535881724	0.537543732059138
33	0.504491613257794	0.99101677348441	0.495508386742206
34	0.800425631221686	0.399148737556627	0.199574368778314
35	0.823384280714955	0.353231438570091	0.176615719285045
36	0.756701839307065	0.48659632138587	0.243298160692935
37	0.786660160999019	0.426679678001963	0.213339839000981
38	0.698000435706552	0.603999128586896	0.301999564293448
39	0.632752075097222	0.734495849805555	0.367247924902778
40	0.830033164377217	0.339933671245565	0.169966835622783
41	0.94472911331322	0.110541773373562	0.0552708866867809

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.264194454161243 & 0.528388908322486 & 0.735805545838757 \tabularnewline
19 & 0.502032030912094 & 0.995935938175812 & 0.497967969087906 \tabularnewline
20 & 0.372002866932961 & 0.744005733865921 & 0.62799713306704 \tabularnewline
21 & 0.248924636890084 & 0.497849273780167 & 0.751075363109916 \tabularnewline
22 & 0.224208480814764 & 0.448416961629528 & 0.775791519185236 \tabularnewline
23 & 0.26025796232125 & 0.5205159246425 & 0.73974203767875 \tabularnewline
24 & 0.315714155124867 & 0.631428310249733 & 0.684285844875133 \tabularnewline
25 & 0.269589643954673 & 0.539179287909346 & 0.730410356045327 \tabularnewline
26 & 0.184297417791826 & 0.368594835583652 & 0.815702582208174 \tabularnewline
27 & 0.120214247551822 & 0.240428495103644 & 0.879785752448178 \tabularnewline
28 & 0.365307127250607 & 0.730614254501214 & 0.634692872749393 \tabularnewline
29 & 0.445128920322775 & 0.89025784064555 & 0.554871079677225 \tabularnewline
30 & 0.384620295228626 & 0.769240590457252 & 0.615379704771374 \tabularnewline
31 & 0.360717729882443 & 0.721435459764886 & 0.639282270117557 \tabularnewline
32 & 0.462456267940862 & 0.924912535881724 & 0.537543732059138 \tabularnewline
33 & 0.504491613257794 & 0.99101677348441 & 0.495508386742206 \tabularnewline
34 & 0.800425631221686 & 0.399148737556627 & 0.199574368778314 \tabularnewline
35 & 0.823384280714955 & 0.353231438570091 & 0.176615719285045 \tabularnewline
36 & 0.756701839307065 & 0.48659632138587 & 0.243298160692935 \tabularnewline
37 & 0.786660160999019 & 0.426679678001963 & 0.213339839000981 \tabularnewline
38 & 0.698000435706552 & 0.603999128586896 & 0.301999564293448 \tabularnewline
39 & 0.632752075097222 & 0.734495849805555 & 0.367247924902778 \tabularnewline
40 & 0.830033164377217 & 0.339933671245565 & 0.169966835622783 \tabularnewline
41 & 0.94472911331322 & 0.110541773373562 & 0.0552708866867809 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58856&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]18[/C][C]0.264194454161243[/C][C]0.528388908322486[/C][C]0.735805545838757[/C][/ROW]
[ROW][C]19[/C][C]0.502032030912094[/C][C]0.995935938175812[/C][C]0.497967969087906[/C][/ROW]
[ROW][C]20[/C][C]0.372002866932961[/C][C]0.744005733865921[/C][C]0.62799713306704[/C][/ROW]
[ROW][C]21[/C][C]0.248924636890084[/C][C]0.497849273780167[/C][C]0.751075363109916[/C][/ROW]
[ROW][C]22[/C][C]0.224208480814764[/C][C]0.448416961629528[/C][C]0.775791519185236[/C][/ROW]
[ROW][C]23[/C][C]0.26025796232125[/C][C]0.5205159246425[/C][C]0.73974203767875[/C][/ROW]
[ROW][C]24[/C][C]0.315714155124867[/C][C]0.631428310249733[/C][C]0.684285844875133[/C][/ROW]
[ROW][C]25[/C][C]0.269589643954673[/C][C]0.539179287909346[/C][C]0.730410356045327[/C][/ROW]
[ROW][C]26[/C][C]0.184297417791826[/C][C]0.368594835583652[/C][C]0.815702582208174[/C][/ROW]
[ROW][C]27[/C][C]0.120214247551822[/C][C]0.240428495103644[/C][C]0.879785752448178[/C][/ROW]
[ROW][C]28[/C][C]0.365307127250607[/C][C]0.730614254501214[/C][C]0.634692872749393[/C][/ROW]
[ROW][C]29[/C][C]0.445128920322775[/C][C]0.89025784064555[/C][C]0.554871079677225[/C][/ROW]
[ROW][C]30[/C][C]0.384620295228626[/C][C]0.769240590457252[/C][C]0.615379704771374[/C][/ROW]
[ROW][C]31[/C][C]0.360717729882443[/C][C]0.721435459764886[/C][C]0.639282270117557[/C][/ROW]
[ROW][C]32[/C][C]0.462456267940862[/C][C]0.924912535881724[/C][C]0.537543732059138[/C][/ROW]
[ROW][C]33[/C][C]0.504491613257794[/C][C]0.99101677348441[/C][C]0.495508386742206[/C][/ROW]
[ROW][C]34[/C][C]0.800425631221686[/C][C]0.399148737556627[/C][C]0.199574368778314[/C][/ROW]
[ROW][C]35[/C][C]0.823384280714955[/C][C]0.353231438570091[/C][C]0.176615719285045[/C][/ROW]
[ROW][C]36[/C][C]0.756701839307065[/C][C]0.48659632138587[/C][C]0.243298160692935[/C][/ROW]
[ROW][C]37[/C][C]0.786660160999019[/C][C]0.426679678001963[/C][C]0.213339839000981[/C][/ROW]
[ROW][C]38[/C][C]0.698000435706552[/C][C]0.603999128586896[/C][C]0.301999564293448[/C][/ROW]
[ROW][C]39[/C][C]0.632752075097222[/C][C]0.734495849805555[/C][C]0.367247924902778[/C][/ROW]
[ROW][C]40[/C][C]0.830033164377217[/C][C]0.339933671245565[/C][C]0.169966835622783[/C][/ROW]
[ROW][C]41[/C][C]0.94472911331322[/C][C]0.110541773373562[/C][C]0.0552708866867809[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58856&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58856&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
18	0.264194454161243	0.528388908322486	0.735805545838757
19	0.502032030912094	0.995935938175812	0.497967969087906
20	0.372002866932961	0.744005733865921	0.62799713306704
21	0.248924636890084	0.497849273780167	0.751075363109916
22	0.224208480814764	0.448416961629528	0.775791519185236
23	0.26025796232125	0.5205159246425	0.73974203767875
24	0.315714155124867	0.631428310249733	0.684285844875133
25	0.269589643954673	0.539179287909346	0.730410356045327
26	0.184297417791826	0.368594835583652	0.815702582208174
27	0.120214247551822	0.240428495103644	0.879785752448178
28	0.365307127250607	0.730614254501214	0.634692872749393
29	0.445128920322775	0.89025784064555	0.554871079677225
30	0.384620295228626	0.769240590457252	0.615379704771374
31	0.360717729882443	0.721435459764886	0.639282270117557
32	0.462456267940862	0.924912535881724	0.537543732059138
33	0.504491613257794	0.99101677348441	0.495508386742206
34	0.800425631221686	0.399148737556627	0.199574368778314
35	0.823384280714955	0.353231438570091	0.176615719285045
36	0.756701839307065	0.48659632138587	0.243298160692935
37	0.786660160999019	0.426679678001963	0.213339839000981
38	0.698000435706552	0.603999128586896	0.301999564293448
39	0.632752075097222	0.734495849805555	0.367247924902778
40	0.830033164377217	0.339933671245565	0.169966835622783
41	0.94472911331322	0.110541773373562	0.0552708866867809

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58856&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58856&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58856&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

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 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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