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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 27 Nov 2009 08:55:13 -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/27/t1259337555yesomtjp13p9kky.htm/, Retrieved Mon, 29 Apr 2024 19:48:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60925, Retrieved Mon, 29 Apr 2024 19:48:34 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

136

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]
-    D    [Multiple Regression] [WS 7 4] [2009-11-14 13:58:56] [6e4e01d7eb22a9f33d58ebb35753a195]
-   PD      [Multiple Regression] [ws7 4] [2009-11-18 20:49:31] [6e4e01d7eb22a9f33d58ebb35753a195]
-    D          [Multiple Regression] [WS7: Correctie] [2009-11-27 15:55:13] [b9056af0304697100f456398102f1287] [Current]

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103.91	89.00	103.88	103.77	103.66	103.64	103.63
103.91	86.40	103.91	103.88	103.77	103.66	103.64
103.92	84.50	103.91	103.91	103.88	103.77	103.66
104.05	82.70	103.92	103.91	103.91	103.88	103.77
104.23	80.80	104.05	103.92	103.91	103.91	103.88
104.30	81.80	104.23	104.05	103.92	103.91	103.91
104.31	81.80	104.30	104.23	104.05	103.92	103.91
104.31	82.90	104.31	104.30	104.23	104.05	103.92
104.34	83.80	104.31	104.31	104.30	104.23	104.05
104.55	86.20	104.34	104.31	104.31	104.30	104.23
104.65	86.10	104.55	104.34	104.31	104.31	104.30
104.73	86.20	104.65	104.55	104.34	104.31	104.31
104.75	88.80	104.73	104.65	104.55	104.34	104.31
104.75	89.60	104.75	104.73	104.65	104.55	104.34
104.76	87.80	104.75	104.75	104.73	104.65	104.55
104.94	88.30	104.76	104.75	104.75	104.73	104.65
105.29	88.60	104.94	104.76	104.75	104.75	104.73
105.38	91.00	105.29	104.94	104.76	104.75	104.75
105.43	91.50	105.38	105.29	104.94	104.76	104.75
105.43	95.40	105.43	105.38	105.29	104.94	104.76
105.42	98.70	105.43	105.43	105.38	105.29	104.94
105.52	99.90	105.42	105.43	105.43	105.38	105.29
105.69	98.60	105.52	105.42	105.43	105.43	105.38
105.72	100.30	105.69	105.52	105.42	105.43	105.43
105.74	100.20	105.72	105.69	105.52	105.42	105.43
105.74	100.40	105.74	105.72	105.69	105.52	105.42
105.74	101.40	105.74	105.74	105.72	105.69	105.52
105.95	103.00	105.74	105.74	105.74	105.72	105.69
106.17	109.10	105.95	105.74	105.74	105.74	105.72
106.34	111.40	106.17	105.95	105.74	105.74	105.74
106.37	114.10	106.34	106.17	105.95	105.74	105.74
106.37	121.80	106.37	106.34	106.17	105.95	105.74
106.36	127.60	106.37	106.37	106.34	106.17	105.95
106.44	129.90	106.36	106.37	106.37	106.34	106.17
106.29	128.00	106.44	106.36	106.37	106.37	106.34
106.23	123.50	106.29	106.44	106.36	106.37	106.37
106.23	124.00	106.23	106.29	106.44	106.36	106.37
106.23	127.40	106.23	106.23	106.29	106.44	106.36
106.23	127.60	106.23	106.23	106.23	106.29	106.44
106.34	128.40	106.23	106.23	106.23	106.23	106.29
106.44	131.40	106.34	106.23	106.23	106.23	106.23
106.44	135.10	106.44	106.34	106.23	106.23	106.23
106.48	134.00	106.44	106.44	106.34	106.23	106.23
106.50	144.50	106.48	106.44	106.44	106.34	106.23
106.57	147.30	106.50	106.48	106.44	106.44	106.34
106.40	150.90	106.57	106.50	106.48	106.44	106.44
106.37	148.70	106.40	106.57	106.50	106.48	106.44
106.25	141.40	106.37	106.40	106.57	106.50	106.48
106.21	138.90	106.25	106.37	106.40	106.57	106.50
106.21	139.80	106.21	106.25	106.37	106.40	106.57
106.24	145.60	106.21	106.21	106.25	106.37	106.40
106.19	147.90	106.24	106.21	106.21	106.25	106.37
106.08	148.50	106.19	106.24	106.21	106.21	106.25
106.13	151.10	106.08	106.19	106.24	106.21	106.21
106.09	157.50	106.13	106.08	106.19	106.24	106.21

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=60925&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=60925&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60925&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] = -0.882021848595453 -0.00452130769895071X[t] + 1.28776785397041Y1[t] -0.416249054274984Y2[t] -0.111638282342769Y3[t] + 0.271637226912727Y4[t] -0.0188616710391742Y5[t] + 0.0035299223429012t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -0.882021848595453 -0.00452130769895071X[t] +  1.28776785397041Y1[t] -0.416249054274984Y2[t] -0.111638282342769Y3[t] +  0.271637226912727Y4[t] -0.0188616710391742Y5[t] +  0.0035299223429012t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60925&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -0.882021848595453 -0.00452130769895071X[t] +  1.28776785397041Y1[t] -0.416249054274984Y2[t] -0.111638282342769Y3[t] +  0.271637226912727Y4[t] -0.0188616710391742Y5[t] +  0.0035299223429012t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60925&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60925&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] = -0.882021848595453 -0.00452130769895071X[t] + 1.28776785397041Y1[t] -0.416249054274984Y2[t] -0.111638282342769Y3[t] + 0.271637226912727Y4[t] -0.0188616710391742Y5[t] + 0.0035299223429012t + 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)	-0.882021848595453	4.04946	-0.2178	0.828518	0.414259
X	-0.00452130769895071	0.001859	-2.4321	0.018876	0.009438
Y1	1.28776785397041	0.138486	9.2989	0	0
Y2	-0.416249054274984	0.229841	-1.811	0.07653	0.038265
Y3	-0.111638282342769	0.243676	-0.4581	0.648961	0.32448
Y4	0.271637226912727	0.236105	1.1505	0.255761	0.12788
Y5	-0.0188616710391742	0.149062	-0.1265	0.899847	0.449924
t	0.0035299223429012	0.003991	0.8845	0.38095	0.190475

\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) & -0.882021848595453 & 4.04946 & -0.2178 & 0.828518 & 0.414259 \tabularnewline
X & -0.00452130769895071 & 0.001859 & -2.4321 & 0.018876 & 0.009438 \tabularnewline
Y1 & 1.28776785397041 & 0.138486 & 9.2989 & 0 & 0 \tabularnewline
Y2 & -0.416249054274984 & 0.229841 & -1.811 & 0.07653 & 0.038265 \tabularnewline
Y3 & -0.111638282342769 & 0.243676 & -0.4581 & 0.648961 & 0.32448 \tabularnewline
Y4 & 0.271637226912727 & 0.236105 & 1.1505 & 0.255761 & 0.12788 \tabularnewline
Y5 & -0.0188616710391742 & 0.149062 & -0.1265 & 0.899847 & 0.449924 \tabularnewline
t & 0.0035299223429012 & 0.003991 & 0.8845 & 0.38095 & 0.190475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60925&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]-0.882021848595453[/C][C]4.04946[/C][C]-0.2178[/C][C]0.828518[/C][C]0.414259[/C][/ROW]
[ROW][C]X[/C][C]-0.00452130769895071[/C][C]0.001859[/C][C]-2.4321[/C][C]0.018876[/C][C]0.009438[/C][/ROW]
[ROW][C]Y1[/C][C]1.28776785397041[/C][C]0.138486[/C][C]9.2989[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.416249054274984[/C][C]0.229841[/C][C]-1.811[/C][C]0.07653[/C][C]0.038265[/C][/ROW]
[ROW][C]Y3[/C][C]-0.111638282342769[/C][C]0.243676[/C][C]-0.4581[/C][C]0.648961[/C][C]0.32448[/C][/ROW]
[ROW][C]Y4[/C][C]0.271637226912727[/C][C]0.236105[/C][C]1.1505[/C][C]0.255761[/C][C]0.12788[/C][/ROW]
[ROW][C]Y5[/C][C]-0.0188616710391742[/C][C]0.149062[/C][C]-0.1265[/C][C]0.899847[/C][C]0.449924[/C][/ROW]
[ROW][C]t[/C][C]0.0035299223429012[/C][C]0.003991[/C][C]0.8845[/C][C]0.38095[/C][C]0.190475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60925&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60925&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)	-0.882021848595453	4.04946	-0.2178	0.828518	0.414259
X	-0.00452130769895071	0.001859	-2.4321	0.018876	0.009438
Y1	1.28776785397041	0.138486	9.2989	0	0
Y2	-0.416249054274984	0.229841	-1.811	0.07653	0.038265
Y3	-0.111638282342769	0.243676	-0.4581	0.648961	0.32448
Y4	0.271637226912727	0.236105	1.1505	0.255761	0.12788
Y5	-0.0188616710391742	0.149062	-0.1265	0.899847	0.449924
t	0.0035299223429012	0.003991	0.8845	0.38095	0.190475

Multiple Linear Regression - Regression Statistics
Multiple R	0.996238049982534
R-squared	0.992490252233001
Adjusted R-squared	0.99137177916132
F-TEST (value)	887.361776838902
F-TEST (DF numerator)	7
F-TEST (DF denominator)	47
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.07894860758537
Sum Squared Residuals	0.292945484064431

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.996238049982534 \tabularnewline
R-squared & 0.992490252233001 \tabularnewline
Adjusted R-squared & 0.99137177916132 \tabularnewline
F-TEST (value) & 887.361776838902 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.07894860758537 \tabularnewline
Sum Squared Residuals & 0.292945484064431 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60925&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.996238049982534[/C][/ROW]
[ROW][C]R-squared[/C][C]0.992490252233001[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99137177916132[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]887.361776838902[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/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]0.07894860758537[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.292945484064431[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60925&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60925&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	0.996238049982534
R-squared	0.992490252233001
Adjusted R-squared	0.99137177916132
F-TEST (value)	887.361776838902
F-TEST (DF numerator)	7
F-TEST (DF denominator)	47
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.07894860758537
Sum Squared Residuals	0.292945484064431

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	103.91	103.923694876667	-0.0136948766666860
2	103.91	103.924789755446	-0.0147897554456499
3	103.92	103.941645341270	-0.0216453412702114
4	104.05	103.990647458687	0.0593525413132544
5	104.23	104.172089529124	0.0579104708758845
6	104.3	104.347101747472	-0.0471017474724066
7	104.31	104.354053985388	-0.0440539853882922
8	104.31	104.351380045969	-0.0413800459693747
9	104.34	104.385306304686	-0.0453063046856742
10	104.55	104.431121246444	0.118878753556377
11	104.65	104.694443132558	-0.0444431325583265
12	104.73	104.73534764295	-0.0053476429499675
13	104.75	104.773223765681	-0.0232237656811312
14	104.75	104.810906213889	-0.0609062138884949
15	104.76	104.828521218190	-0.0685212181896317
16	104.94	104.860280210625	0.0797197893749793
17	105.29	105.094013274685	0.195986725314739
18	105.38	105.460992361427	-0.0809923614266413
19	105.43	105.415095049229	0.0149049507714379
20	105.43	105.437550534673	-0.00755053467327394
21	105.42	105.486978172117	-0.0669781721174468
22	105.52	105.484468698123	0.0355313018768063
23	105.69	105.638699907367	0.051300092633371
24	105.72	105.812012535640	-0.0920125356402588
25	105.74	105.769985084642	-0.02998508464202
26	105.74	105.794252462300	-0.0542524622996764
27	105.74	105.825879108859	-0.0858791088590926
28	105.95	105.824884805968	0.125115194032468
29	106.17	106.076132895088	0.0938671049122893
30	106.34	106.264783202778	0.0752167972220204
31	106.37	106.360007298276	0.00999270172379286
32	106.37	106.329077243266	0.0409227567341805
33	106.36	106.330716840331	0.0292831596691363
34	106.44	106.349649688903	0.090350311097268
35	106.29	106.473896647465	-0.183896647464736
36	106.23	106.271857884708	-0.0418578847076219
37	106.23	106.246651005248	-0.0166510052475141
38	106.23	106.298448761885	-0.0684487618853065
39	106.23	106.265518201909	-0.0355182019089434
40	106.34	106.251962095134	0.0880379048662037
41	106.44	106.384714258579	0.0552857414210537
42	106.44	106.454504731863	-0.0145047318625162
43	106.48	106.409102976189	0.0708970238109437
44	106.5	106.435386148578	0.064613851422073
45	106.57	106.460450743149	0.109549256850864
46	106.4	106.523171028071	-0.123171028070596
47	106.37	106.297222581807	0.0727774181933546
48	106.25	106.362750951892	-0.112750951892213
49	106.21	106.273155353096	-0.0631553530956664
50	106.21	106.226905773786	-0.0169057737860680
51	106.24	106.229316034796	0.0106839652035338
52	106.19	106.233514899246	-0.0435148992462501
53	106.08	106.148854084091	-0.0688540840912035
54	106.13	106.017191913565	0.112808086434877
55	106.09	106.115692286228	-0.0256922862280168

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.91 & 103.923694876667 & -0.0136948766666860 \tabularnewline
2 & 103.91 & 103.924789755446 & -0.0147897554456499 \tabularnewline
3 & 103.92 & 103.941645341270 & -0.0216453412702114 \tabularnewline
4 & 104.05 & 103.990647458687 & 0.0593525413132544 \tabularnewline
5 & 104.23 & 104.172089529124 & 0.0579104708758845 \tabularnewline
6 & 104.3 & 104.347101747472 & -0.0471017474724066 \tabularnewline
7 & 104.31 & 104.354053985388 & -0.0440539853882922 \tabularnewline
8 & 104.31 & 104.351380045969 & -0.0413800459693747 \tabularnewline
9 & 104.34 & 104.385306304686 & -0.0453063046856742 \tabularnewline
10 & 104.55 & 104.431121246444 & 0.118878753556377 \tabularnewline
11 & 104.65 & 104.694443132558 & -0.0444431325583265 \tabularnewline
12 & 104.73 & 104.73534764295 & -0.0053476429499675 \tabularnewline
13 & 104.75 & 104.773223765681 & -0.0232237656811312 \tabularnewline
14 & 104.75 & 104.810906213889 & -0.0609062138884949 \tabularnewline
15 & 104.76 & 104.828521218190 & -0.0685212181896317 \tabularnewline
16 & 104.94 & 104.860280210625 & 0.0797197893749793 \tabularnewline
17 & 105.29 & 105.094013274685 & 0.195986725314739 \tabularnewline
18 & 105.38 & 105.460992361427 & -0.0809923614266413 \tabularnewline
19 & 105.43 & 105.415095049229 & 0.0149049507714379 \tabularnewline
20 & 105.43 & 105.437550534673 & -0.00755053467327394 \tabularnewline
21 & 105.42 & 105.486978172117 & -0.0669781721174468 \tabularnewline
22 & 105.52 & 105.484468698123 & 0.0355313018768063 \tabularnewline
23 & 105.69 & 105.638699907367 & 0.051300092633371 \tabularnewline
24 & 105.72 & 105.812012535640 & -0.0920125356402588 \tabularnewline
25 & 105.74 & 105.769985084642 & -0.02998508464202 \tabularnewline
26 & 105.74 & 105.794252462300 & -0.0542524622996764 \tabularnewline
27 & 105.74 & 105.825879108859 & -0.0858791088590926 \tabularnewline
28 & 105.95 & 105.824884805968 & 0.125115194032468 \tabularnewline
29 & 106.17 & 106.076132895088 & 0.0938671049122893 \tabularnewline
30 & 106.34 & 106.264783202778 & 0.0752167972220204 \tabularnewline
31 & 106.37 & 106.360007298276 & 0.00999270172379286 \tabularnewline
32 & 106.37 & 106.329077243266 & 0.0409227567341805 \tabularnewline
33 & 106.36 & 106.330716840331 & 0.0292831596691363 \tabularnewline
34 & 106.44 & 106.349649688903 & 0.090350311097268 \tabularnewline
35 & 106.29 & 106.473896647465 & -0.183896647464736 \tabularnewline
36 & 106.23 & 106.271857884708 & -0.0418578847076219 \tabularnewline
37 & 106.23 & 106.246651005248 & -0.0166510052475141 \tabularnewline
38 & 106.23 & 106.298448761885 & -0.0684487618853065 \tabularnewline
39 & 106.23 & 106.265518201909 & -0.0355182019089434 \tabularnewline
40 & 106.34 & 106.251962095134 & 0.0880379048662037 \tabularnewline
41 & 106.44 & 106.384714258579 & 0.0552857414210537 \tabularnewline
42 & 106.44 & 106.454504731863 & -0.0145047318625162 \tabularnewline
43 & 106.48 & 106.409102976189 & 0.0708970238109437 \tabularnewline
44 & 106.5 & 106.435386148578 & 0.064613851422073 \tabularnewline
45 & 106.57 & 106.460450743149 & 0.109549256850864 \tabularnewline
46 & 106.4 & 106.523171028071 & -0.123171028070596 \tabularnewline
47 & 106.37 & 106.297222581807 & 0.0727774181933546 \tabularnewline
48 & 106.25 & 106.362750951892 & -0.112750951892213 \tabularnewline
49 & 106.21 & 106.273155353096 & -0.0631553530956664 \tabularnewline
50 & 106.21 & 106.226905773786 & -0.0169057737860680 \tabularnewline
51 & 106.24 & 106.229316034796 & 0.0106839652035338 \tabularnewline
52 & 106.19 & 106.233514899246 & -0.0435148992462501 \tabularnewline
53 & 106.08 & 106.148854084091 & -0.0688540840912035 \tabularnewline
54 & 106.13 & 106.017191913565 & 0.112808086434877 \tabularnewline
55 & 106.09 & 106.115692286228 & -0.0256922862280168 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60925&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]103.91[/C][C]103.923694876667[/C][C]-0.0136948766666860[/C][/ROW]
[ROW][C]2[/C][C]103.91[/C][C]103.924789755446[/C][C]-0.0147897554456499[/C][/ROW]
[ROW][C]3[/C][C]103.92[/C][C]103.941645341270[/C][C]-0.0216453412702114[/C][/ROW]
[ROW][C]4[/C][C]104.05[/C][C]103.990647458687[/C][C]0.0593525413132544[/C][/ROW]
[ROW][C]5[/C][C]104.23[/C][C]104.172089529124[/C][C]0.0579104708758845[/C][/ROW]
[ROW][C]6[/C][C]104.3[/C][C]104.347101747472[/C][C]-0.0471017474724066[/C][/ROW]
[ROW][C]7[/C][C]104.31[/C][C]104.354053985388[/C][C]-0.0440539853882922[/C][/ROW]
[ROW][C]8[/C][C]104.31[/C][C]104.351380045969[/C][C]-0.0413800459693747[/C][/ROW]
[ROW][C]9[/C][C]104.34[/C][C]104.385306304686[/C][C]-0.0453063046856742[/C][/ROW]
[ROW][C]10[/C][C]104.55[/C][C]104.431121246444[/C][C]0.118878753556377[/C][/ROW]
[ROW][C]11[/C][C]104.65[/C][C]104.694443132558[/C][C]-0.0444431325583265[/C][/ROW]
[ROW][C]12[/C][C]104.73[/C][C]104.73534764295[/C][C]-0.0053476429499675[/C][/ROW]
[ROW][C]13[/C][C]104.75[/C][C]104.773223765681[/C][C]-0.0232237656811312[/C][/ROW]
[ROW][C]14[/C][C]104.75[/C][C]104.810906213889[/C][C]-0.0609062138884949[/C][/ROW]
[ROW][C]15[/C][C]104.76[/C][C]104.828521218190[/C][C]-0.0685212181896317[/C][/ROW]
[ROW][C]16[/C][C]104.94[/C][C]104.860280210625[/C][C]0.0797197893749793[/C][/ROW]
[ROW][C]17[/C][C]105.29[/C][C]105.094013274685[/C][C]0.195986725314739[/C][/ROW]
[ROW][C]18[/C][C]105.38[/C][C]105.460992361427[/C][C]-0.0809923614266413[/C][/ROW]
[ROW][C]19[/C][C]105.43[/C][C]105.415095049229[/C][C]0.0149049507714379[/C][/ROW]
[ROW][C]20[/C][C]105.43[/C][C]105.437550534673[/C][C]-0.00755053467327394[/C][/ROW]
[ROW][C]21[/C][C]105.42[/C][C]105.486978172117[/C][C]-0.0669781721174468[/C][/ROW]
[ROW][C]22[/C][C]105.52[/C][C]105.484468698123[/C][C]0.0355313018768063[/C][/ROW]
[ROW][C]23[/C][C]105.69[/C][C]105.638699907367[/C][C]0.051300092633371[/C][/ROW]
[ROW][C]24[/C][C]105.72[/C][C]105.812012535640[/C][C]-0.0920125356402588[/C][/ROW]
[ROW][C]25[/C][C]105.74[/C][C]105.769985084642[/C][C]-0.02998508464202[/C][/ROW]
[ROW][C]26[/C][C]105.74[/C][C]105.794252462300[/C][C]-0.0542524622996764[/C][/ROW]
[ROW][C]27[/C][C]105.74[/C][C]105.825879108859[/C][C]-0.0858791088590926[/C][/ROW]
[ROW][C]28[/C][C]105.95[/C][C]105.824884805968[/C][C]0.125115194032468[/C][/ROW]
[ROW][C]29[/C][C]106.17[/C][C]106.076132895088[/C][C]0.0938671049122893[/C][/ROW]
[ROW][C]30[/C][C]106.34[/C][C]106.264783202778[/C][C]0.0752167972220204[/C][/ROW]
[ROW][C]31[/C][C]106.37[/C][C]106.360007298276[/C][C]0.00999270172379286[/C][/ROW]
[ROW][C]32[/C][C]106.37[/C][C]106.329077243266[/C][C]0.0409227567341805[/C][/ROW]
[ROW][C]33[/C][C]106.36[/C][C]106.330716840331[/C][C]0.0292831596691363[/C][/ROW]
[ROW][C]34[/C][C]106.44[/C][C]106.349649688903[/C][C]0.090350311097268[/C][/ROW]
[ROW][C]35[/C][C]106.29[/C][C]106.473896647465[/C][C]-0.183896647464736[/C][/ROW]
[ROW][C]36[/C][C]106.23[/C][C]106.271857884708[/C][C]-0.0418578847076219[/C][/ROW]
[ROW][C]37[/C][C]106.23[/C][C]106.246651005248[/C][C]-0.0166510052475141[/C][/ROW]
[ROW][C]38[/C][C]106.23[/C][C]106.298448761885[/C][C]-0.0684487618853065[/C][/ROW]
[ROW][C]39[/C][C]106.23[/C][C]106.265518201909[/C][C]-0.0355182019089434[/C][/ROW]
[ROW][C]40[/C][C]106.34[/C][C]106.251962095134[/C][C]0.0880379048662037[/C][/ROW]
[ROW][C]41[/C][C]106.44[/C][C]106.384714258579[/C][C]0.0552857414210537[/C][/ROW]
[ROW][C]42[/C][C]106.44[/C][C]106.454504731863[/C][C]-0.0145047318625162[/C][/ROW]
[ROW][C]43[/C][C]106.48[/C][C]106.409102976189[/C][C]0.0708970238109437[/C][/ROW]
[ROW][C]44[/C][C]106.5[/C][C]106.435386148578[/C][C]0.064613851422073[/C][/ROW]
[ROW][C]45[/C][C]106.57[/C][C]106.460450743149[/C][C]0.109549256850864[/C][/ROW]
[ROW][C]46[/C][C]106.4[/C][C]106.523171028071[/C][C]-0.123171028070596[/C][/ROW]
[ROW][C]47[/C][C]106.37[/C][C]106.297222581807[/C][C]0.0727774181933546[/C][/ROW]
[ROW][C]48[/C][C]106.25[/C][C]106.362750951892[/C][C]-0.112750951892213[/C][/ROW]
[ROW][C]49[/C][C]106.21[/C][C]106.273155353096[/C][C]-0.0631553530956664[/C][/ROW]
[ROW][C]50[/C][C]106.21[/C][C]106.226905773786[/C][C]-0.0169057737860680[/C][/ROW]
[ROW][C]51[/C][C]106.24[/C][C]106.229316034796[/C][C]0.0106839652035338[/C][/ROW]
[ROW][C]52[/C][C]106.19[/C][C]106.233514899246[/C][C]-0.0435148992462501[/C][/ROW]
[ROW][C]53[/C][C]106.08[/C][C]106.148854084091[/C][C]-0.0688540840912035[/C][/ROW]
[ROW][C]54[/C][C]106.13[/C][C]106.017191913565[/C][C]0.112808086434877[/C][/ROW]
[ROW][C]55[/C][C]106.09[/C][C]106.115692286228[/C][C]-0.0256922862280168[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60925&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60925&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	103.91	103.923694876667	-0.0136948766666860
2	103.91	103.924789755446	-0.0147897554456499
3	103.92	103.941645341270	-0.0216453412702114
4	104.05	103.990647458687	0.0593525413132544
5	104.23	104.172089529124	0.0579104708758845
6	104.3	104.347101747472	-0.0471017474724066
7	104.31	104.354053985388	-0.0440539853882922
8	104.31	104.351380045969	-0.0413800459693747
9	104.34	104.385306304686	-0.0453063046856742
10	104.55	104.431121246444	0.118878753556377
11	104.65	104.694443132558	-0.0444431325583265
12	104.73	104.73534764295	-0.0053476429499675
13	104.75	104.773223765681	-0.0232237656811312
14	104.75	104.810906213889	-0.0609062138884949
15	104.76	104.828521218190	-0.0685212181896317
16	104.94	104.860280210625	0.0797197893749793
17	105.29	105.094013274685	0.195986725314739
18	105.38	105.460992361427	-0.0809923614266413
19	105.43	105.415095049229	0.0149049507714379
20	105.43	105.437550534673	-0.00755053467327394
21	105.42	105.486978172117	-0.0669781721174468
22	105.52	105.484468698123	0.0355313018768063
23	105.69	105.638699907367	0.051300092633371
24	105.72	105.812012535640	-0.0920125356402588
25	105.74	105.769985084642	-0.02998508464202
26	105.74	105.794252462300	-0.0542524622996764
27	105.74	105.825879108859	-0.0858791088590926
28	105.95	105.824884805968	0.125115194032468
29	106.17	106.076132895088	0.0938671049122893
30	106.34	106.264783202778	0.0752167972220204
31	106.37	106.360007298276	0.00999270172379286
32	106.37	106.329077243266	0.0409227567341805
33	106.36	106.330716840331	0.0292831596691363
34	106.44	106.349649688903	0.090350311097268
35	106.29	106.473896647465	-0.183896647464736
36	106.23	106.271857884708	-0.0418578847076219
37	106.23	106.246651005248	-0.0166510052475141
38	106.23	106.298448761885	-0.0684487618853065
39	106.23	106.265518201909	-0.0355182019089434
40	106.34	106.251962095134	0.0880379048662037
41	106.44	106.384714258579	0.0552857414210537
42	106.44	106.454504731863	-0.0145047318625162
43	106.48	106.409102976189	0.0708970238109437
44	106.5	106.435386148578	0.064613851422073
45	106.57	106.460450743149	0.109549256850864
46	106.4	106.523171028071	-0.123171028070596
47	106.37	106.297222581807	0.0727774181933546
48	106.25	106.362750951892	-0.112750951892213
49	106.21	106.273155353096	-0.0631553530956664
50	106.21	106.226905773786	-0.0169057737860680
51	106.24	106.229316034796	0.0106839652035338
52	106.19	106.233514899246	-0.0435148992462501
53	106.08	106.148854084091	-0.0688540840912035
54	106.13	106.017191913565	0.112808086434877
55	106.09	106.115692286228	-0.0256922862280168

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.071265950870845	0.14253190174169	0.928734049129155
12	0.0218894858032215	0.0437789716064431	0.978110514196779
13	0.00814670263366301	0.0162934052673260	0.991853297366337
14	0.00493308734657163	0.00986617469314327	0.995066912653428
15	0.00216667796840162	0.00433335593680323	0.997833322031598
16	0.000635912631242297	0.00127182526248459	0.999364087368758
17	0.106632233816174	0.213264467632349	0.893367766183826
18	0.0938331915573314	0.187666383114663	0.906166808442669
19	0.238414354198204	0.476828708396409	0.761585645801796
20	0.197930614162767	0.395861228325534	0.802069385837233
21	0.16020519792714	0.32041039585428	0.83979480207286
22	0.150141424871728	0.300282849743456	0.849858575128272
23	0.108163703551091	0.216327407102182	0.891836296448909
24	0.16718194738908	0.33436389477816	0.83281805261092
25	0.151798772285462	0.303597544570924	0.848201227714538
26	0.173767408281790	0.347534816563580	0.82623259171821
27	0.339235750299163	0.678471500598327	0.660764249700837
28	0.294231866630903	0.588463733261806	0.705768133369097
29	0.238637280098202	0.477274560196403	0.761362719901798
30	0.200214296265097	0.400428592530195	0.799785703734903
31	0.147472425502857	0.294944851005715	0.852527574497143
32	0.133016256970895	0.266032513941791	0.866983743029105
33	0.124449598043578	0.248899196087157	0.875550401956422
34	0.0920529696497502	0.184105939299500	0.90794703035025
35	0.556351878180809	0.887296243638382	0.443648121819191
36	0.627049447585381	0.745901104829238	0.372950552414619
37	0.592274321104015	0.81545135779197	0.407725678895985
38	0.648157640811814	0.703684718376371	0.351842359188186
39	0.686429692755743	0.627140614488514	0.313570307244257
40	0.696157143246208	0.607685713507583	0.303842856753792
41	0.64875431329439	0.702491373411219	0.351245686705610
42	0.851394470904742	0.297211058190516	0.148605529095258
43	0.755849871146904	0.488300257706191	0.244150128853096
44	0.955787335316	0.0884253293680012	0.0442126646840006

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.071265950870845 & 0.14253190174169 & 0.928734049129155 \tabularnewline
12 & 0.0218894858032215 & 0.0437789716064431 & 0.978110514196779 \tabularnewline
13 & 0.00814670263366301 & 0.0162934052673260 & 0.991853297366337 \tabularnewline
14 & 0.00493308734657163 & 0.00986617469314327 & 0.995066912653428 \tabularnewline
15 & 0.00216667796840162 & 0.00433335593680323 & 0.997833322031598 \tabularnewline
16 & 0.000635912631242297 & 0.00127182526248459 & 0.999364087368758 \tabularnewline
17 & 0.106632233816174 & 0.213264467632349 & 0.893367766183826 \tabularnewline
18 & 0.0938331915573314 & 0.187666383114663 & 0.906166808442669 \tabularnewline
19 & 0.238414354198204 & 0.476828708396409 & 0.761585645801796 \tabularnewline
20 & 0.197930614162767 & 0.395861228325534 & 0.802069385837233 \tabularnewline
21 & 0.16020519792714 & 0.32041039585428 & 0.83979480207286 \tabularnewline
22 & 0.150141424871728 & 0.300282849743456 & 0.849858575128272 \tabularnewline
23 & 0.108163703551091 & 0.216327407102182 & 0.891836296448909 \tabularnewline
24 & 0.16718194738908 & 0.33436389477816 & 0.83281805261092 \tabularnewline
25 & 0.151798772285462 & 0.303597544570924 & 0.848201227714538 \tabularnewline
26 & 0.173767408281790 & 0.347534816563580 & 0.82623259171821 \tabularnewline
27 & 0.339235750299163 & 0.678471500598327 & 0.660764249700837 \tabularnewline
28 & 0.294231866630903 & 0.588463733261806 & 0.705768133369097 \tabularnewline
29 & 0.238637280098202 & 0.477274560196403 & 0.761362719901798 \tabularnewline
30 & 0.200214296265097 & 0.400428592530195 & 0.799785703734903 \tabularnewline
31 & 0.147472425502857 & 0.294944851005715 & 0.852527574497143 \tabularnewline
32 & 0.133016256970895 & 0.266032513941791 & 0.866983743029105 \tabularnewline
33 & 0.124449598043578 & 0.248899196087157 & 0.875550401956422 \tabularnewline
34 & 0.0920529696497502 & 0.184105939299500 & 0.90794703035025 \tabularnewline
35 & 0.556351878180809 & 0.887296243638382 & 0.443648121819191 \tabularnewline
36 & 0.627049447585381 & 0.745901104829238 & 0.372950552414619 \tabularnewline
37 & 0.592274321104015 & 0.81545135779197 & 0.407725678895985 \tabularnewline
38 & 0.648157640811814 & 0.703684718376371 & 0.351842359188186 \tabularnewline
39 & 0.686429692755743 & 0.627140614488514 & 0.313570307244257 \tabularnewline
40 & 0.696157143246208 & 0.607685713507583 & 0.303842856753792 \tabularnewline
41 & 0.64875431329439 & 0.702491373411219 & 0.351245686705610 \tabularnewline
42 & 0.851394470904742 & 0.297211058190516 & 0.148605529095258 \tabularnewline
43 & 0.755849871146904 & 0.488300257706191 & 0.244150128853096 \tabularnewline
44 & 0.955787335316 & 0.0884253293680012 & 0.0442126646840006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60925&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]11[/C][C]0.071265950870845[/C][C]0.14253190174169[/C][C]0.928734049129155[/C][/ROW]
[ROW][C]12[/C][C]0.0218894858032215[/C][C]0.0437789716064431[/C][C]0.978110514196779[/C][/ROW]
[ROW][C]13[/C][C]0.00814670263366301[/C][C]0.0162934052673260[/C][C]0.991853297366337[/C][/ROW]
[ROW][C]14[/C][C]0.00493308734657163[/C][C]0.00986617469314327[/C][C]0.995066912653428[/C][/ROW]
[ROW][C]15[/C][C]0.00216667796840162[/C][C]0.00433335593680323[/C][C]0.997833322031598[/C][/ROW]
[ROW][C]16[/C][C]0.000635912631242297[/C][C]0.00127182526248459[/C][C]0.999364087368758[/C][/ROW]
[ROW][C]17[/C][C]0.106632233816174[/C][C]0.213264467632349[/C][C]0.893367766183826[/C][/ROW]
[ROW][C]18[/C][C]0.0938331915573314[/C][C]0.187666383114663[/C][C]0.906166808442669[/C][/ROW]
[ROW][C]19[/C][C]0.238414354198204[/C][C]0.476828708396409[/C][C]0.761585645801796[/C][/ROW]
[ROW][C]20[/C][C]0.197930614162767[/C][C]0.395861228325534[/C][C]0.802069385837233[/C][/ROW]
[ROW][C]21[/C][C]0.16020519792714[/C][C]0.32041039585428[/C][C]0.83979480207286[/C][/ROW]
[ROW][C]22[/C][C]0.150141424871728[/C][C]0.300282849743456[/C][C]0.849858575128272[/C][/ROW]
[ROW][C]23[/C][C]0.108163703551091[/C][C]0.216327407102182[/C][C]0.891836296448909[/C][/ROW]
[ROW][C]24[/C][C]0.16718194738908[/C][C]0.33436389477816[/C][C]0.83281805261092[/C][/ROW]
[ROW][C]25[/C][C]0.151798772285462[/C][C]0.303597544570924[/C][C]0.848201227714538[/C][/ROW]
[ROW][C]26[/C][C]0.173767408281790[/C][C]0.347534816563580[/C][C]0.82623259171821[/C][/ROW]
[ROW][C]27[/C][C]0.339235750299163[/C][C]0.678471500598327[/C][C]0.660764249700837[/C][/ROW]
[ROW][C]28[/C][C]0.294231866630903[/C][C]0.588463733261806[/C][C]0.705768133369097[/C][/ROW]
[ROW][C]29[/C][C]0.238637280098202[/C][C]0.477274560196403[/C][C]0.761362719901798[/C][/ROW]
[ROW][C]30[/C][C]0.200214296265097[/C][C]0.400428592530195[/C][C]0.799785703734903[/C][/ROW]
[ROW][C]31[/C][C]0.147472425502857[/C][C]0.294944851005715[/C][C]0.852527574497143[/C][/ROW]
[ROW][C]32[/C][C]0.133016256970895[/C][C]0.266032513941791[/C][C]0.866983743029105[/C][/ROW]
[ROW][C]33[/C][C]0.124449598043578[/C][C]0.248899196087157[/C][C]0.875550401956422[/C][/ROW]
[ROW][C]34[/C][C]0.0920529696497502[/C][C]0.184105939299500[/C][C]0.90794703035025[/C][/ROW]
[ROW][C]35[/C][C]0.556351878180809[/C][C]0.887296243638382[/C][C]0.443648121819191[/C][/ROW]
[ROW][C]36[/C][C]0.627049447585381[/C][C]0.745901104829238[/C][C]0.372950552414619[/C][/ROW]
[ROW][C]37[/C][C]0.592274321104015[/C][C]0.81545135779197[/C][C]0.407725678895985[/C][/ROW]
[ROW][C]38[/C][C]0.648157640811814[/C][C]0.703684718376371[/C][C]0.351842359188186[/C][/ROW]
[ROW][C]39[/C][C]0.686429692755743[/C][C]0.627140614488514[/C][C]0.313570307244257[/C][/ROW]
[ROW][C]40[/C][C]0.696157143246208[/C][C]0.607685713507583[/C][C]0.303842856753792[/C][/ROW]
[ROW][C]41[/C][C]0.64875431329439[/C][C]0.702491373411219[/C][C]0.351245686705610[/C][/ROW]
[ROW][C]42[/C][C]0.851394470904742[/C][C]0.297211058190516[/C][C]0.148605529095258[/C][/ROW]
[ROW][C]43[/C][C]0.755849871146904[/C][C]0.488300257706191[/C][C]0.244150128853096[/C][/ROW]
[ROW][C]44[/C][C]0.955787335316[/C][C]0.0884253293680012[/C][C]0.0442126646840006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60925&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60925&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
11	0.071265950870845	0.14253190174169	0.928734049129155
12	0.0218894858032215	0.0437789716064431	0.978110514196779
13	0.00814670263366301	0.0162934052673260	0.991853297366337
14	0.00493308734657163	0.00986617469314327	0.995066912653428
15	0.00216667796840162	0.00433335593680323	0.997833322031598
16	0.000635912631242297	0.00127182526248459	0.999364087368758
17	0.106632233816174	0.213264467632349	0.893367766183826
18	0.0938331915573314	0.187666383114663	0.906166808442669
19	0.238414354198204	0.476828708396409	0.761585645801796
20	0.197930614162767	0.395861228325534	0.802069385837233
21	0.16020519792714	0.32041039585428	0.83979480207286
22	0.150141424871728	0.300282849743456	0.849858575128272
23	0.108163703551091	0.216327407102182	0.891836296448909
24	0.16718194738908	0.33436389477816	0.83281805261092
25	0.151798772285462	0.303597544570924	0.848201227714538
26	0.173767408281790	0.347534816563580	0.82623259171821
27	0.339235750299163	0.678471500598327	0.660764249700837
28	0.294231866630903	0.588463733261806	0.705768133369097
29	0.238637280098202	0.477274560196403	0.761362719901798
30	0.200214296265097	0.400428592530195	0.799785703734903
31	0.147472425502857	0.294944851005715	0.852527574497143
32	0.133016256970895	0.266032513941791	0.866983743029105
33	0.124449598043578	0.248899196087157	0.875550401956422
34	0.0920529696497502	0.184105939299500	0.90794703035025
35	0.556351878180809	0.887296243638382	0.443648121819191
36	0.627049447585381	0.745901104829238	0.372950552414619
37	0.592274321104015	0.81545135779197	0.407725678895985
38	0.648157640811814	0.703684718376371	0.351842359188186
39	0.686429692755743	0.627140614488514	0.313570307244257
40	0.696157143246208	0.607685713507583	0.303842856753792
41	0.64875431329439	0.702491373411219	0.351245686705610
42	0.851394470904742	0.297211058190516	0.148605529095258
43	0.755849871146904	0.488300257706191	0.244150128853096
44	0.955787335316	0.0884253293680012	0.0442126646840006

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.088235294117647	NOK
5% type I error level	5	0.147058823529412	NOK
10% type I error level	6	0.176470588235294	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 & 0.088235294117647 & NOK \tabularnewline
5% type I error level & 5 & 0.147058823529412 & NOK \tabularnewline
10% type I error level & 6 & 0.176470588235294 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60925&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]0.088235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]5[/C][C]0.147058823529412[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.176470588235294[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60925&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60925&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	0.088235294117647	NOK
5% type I error level	5	0.147058823529412	NOK
10% type I error level	6	0.176470588235294	NOK

Figure 1

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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 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; 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