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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 13:38:54 -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/19/t1258663194avt1561g1ekoyrr.htm/, Retrieved Sat, 20 Apr 2024 09:34:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57942, Retrieved Sat, 20 Apr 2024 09:34:58 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

190

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

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

Feedback Forum

2009-11-21 08:20:16 [] [reply] 
de adjusted R²square is 63%, wat betekent dat dit model 63% verklaart van de spreiding van de variabele, maw 63% wordt verklaard door de verandering van de rentevoet.
2009-11-21 08:23:55 [d41d8cd98f00b204e9800998ecf8427e] [reply] 
sorry, feedback hierboven hoort bij model 1

Post a new message

Dataseries X:

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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=57942&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=57942&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57942&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
Tprod[t] = + 104.239014386218 -0.949163093002725Rente[t] -0.429150643259583M1[t] -0.389285793353394M2[t] -0.376879098097416M3[t] -0.35979416242337M4[t] -0.266505879121328M5[t] -0.169285793353391M6[t] -0.0816924886093766M7[t] -0.0635908147953803M8[t] -0.103455664701495M9[t] -0.0986422741895337M10[t] -0.037490385955704M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tprod[t] =  +  104.239014386218 -0.949163093002725Rente[t] -0.429150643259583M1[t] -0.389285793353394M2[t] -0.376879098097416M3[t] -0.35979416242337M4[t] -0.266505879121328M5[t] -0.169285793353391M6[t] -0.0816924886093766M7[t] -0.0635908147953803M8[t] -0.103455664701495M9[t] -0.0986422741895337M10[t] -0.037490385955704M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57942&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tprod[t] =  +  104.239014386218 -0.949163093002725Rente[t] -0.429150643259583M1[t] -0.389285793353394M2[t] -0.376879098097416M3[t] -0.35979416242337M4[t] -0.266505879121328M5[t] -0.169285793353391M6[t] -0.0816924886093766M7[t] -0.0635908147953803M8[t] -0.103455664701495M9[t] -0.0986422741895337M10[t] -0.037490385955704M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57942&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57942&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Tprod[t] = + 104.239014386218 -0.949163093002725Rente[t] -0.429150643259583M1[t] -0.389285793353394M2[t] -0.376879098097416M3[t] -0.35979416242337M4[t] -0.266505879121328M5[t] -0.169285793353391M6[t] -0.0816924886093766M7[t] -0.0635908147953803M8[t] -0.103455664701495M9[t] -0.0986422741895337M10[t] -0.037490385955704M11[t] + 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)	104.239014386218	0.460177	226.5196	0	0
Rente	-0.949163093002725	0.101565	-9.3454	0	0
M1	-0.429150643259583	0.49181	-0.8726	0.38732	0.19366
M2	-0.389285793353394	0.491987	-0.7913	0.432772	0.216386
M3	-0.376879098097416	0.49195	-0.7661	0.447452	0.223726
M4	-0.35979416242337	0.492035	-0.7312	0.468264	0.234132
M5	-0.266505879121328	0.492105	-0.5416	0.590677	0.295339
M6	-0.169285793353391	0.491987	-0.3441	0.732316	0.366158
M7	-0.0816924886093766	0.492025	-0.166	0.868843	0.434421
M8	-0.0635908147953803	0.492015	-0.1292	0.897715	0.448857
M9	-0.103455664701495	0.491833	-0.2103	0.834306	0.417153
M10	-0.0986422741895337	0.491773	-0.2006	0.841889	0.420944
M11	-0.037490385955704	0.491592	-0.0763	0.939533	0.469767

\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) & 104.239014386218 & 0.460177 & 226.5196 & 0 & 0 \tabularnewline
Rente & -0.949163093002725 & 0.101565 & -9.3454 & 0 & 0 \tabularnewline
M1 & -0.429150643259583 & 0.49181 & -0.8726 & 0.38732 & 0.19366 \tabularnewline
M2 & -0.389285793353394 & 0.491987 & -0.7913 & 0.432772 & 0.216386 \tabularnewline
M3 & -0.376879098097416 & 0.49195 & -0.7661 & 0.447452 & 0.223726 \tabularnewline
M4 & -0.35979416242337 & 0.492035 & -0.7312 & 0.468264 & 0.234132 \tabularnewline
M5 & -0.266505879121328 & 0.492105 & -0.5416 & 0.590677 & 0.295339 \tabularnewline
M6 & -0.169285793353391 & 0.491987 & -0.3441 & 0.732316 & 0.366158 \tabularnewline
M7 & -0.0816924886093766 & 0.492025 & -0.166 & 0.868843 & 0.434421 \tabularnewline
M8 & -0.0635908147953803 & 0.492015 & -0.1292 & 0.897715 & 0.448857 \tabularnewline
M9 & -0.103455664701495 & 0.491833 & -0.2103 & 0.834306 & 0.417153 \tabularnewline
M10 & -0.0986422741895337 & 0.491773 & -0.2006 & 0.841889 & 0.420944 \tabularnewline
M11 & -0.037490385955704 & 0.491592 & -0.0763 & 0.939533 & 0.469767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57942&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]104.239014386218[/C][C]0.460177[/C][C]226.5196[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Rente[/C][C]-0.949163093002725[/C][C]0.101565[/C][C]-9.3454[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.429150643259583[/C][C]0.49181[/C][C]-0.8726[/C][C]0.38732[/C][C]0.19366[/C][/ROW]
[ROW][C]M2[/C][C]-0.389285793353394[/C][C]0.491987[/C][C]-0.7913[/C][C]0.432772[/C][C]0.216386[/C][/ROW]
[ROW][C]M3[/C][C]-0.376879098097416[/C][C]0.49195[/C][C]-0.7661[/C][C]0.447452[/C][C]0.223726[/C][/ROW]
[ROW][C]M4[/C][C]-0.35979416242337[/C][C]0.492035[/C][C]-0.7312[/C][C]0.468264[/C][C]0.234132[/C][/ROW]
[ROW][C]M5[/C][C]-0.266505879121328[/C][C]0.492105[/C][C]-0.5416[/C][C]0.590677[/C][C]0.295339[/C][/ROW]
[ROW][C]M6[/C][C]-0.169285793353391[/C][C]0.491987[/C][C]-0.3441[/C][C]0.732316[/C][C]0.366158[/C][/ROW]
[ROW][C]M7[/C][C]-0.0816924886093766[/C][C]0.492025[/C][C]-0.166[/C][C]0.868843[/C][C]0.434421[/C][/ROW]
[ROW][C]M8[/C][C]-0.0635908147953803[/C][C]0.492015[/C][C]-0.1292[/C][C]0.897715[/C][C]0.448857[/C][/ROW]
[ROW][C]M9[/C][C]-0.103455664701495[/C][C]0.491833[/C][C]-0.2103[/C][C]0.834306[/C][C]0.417153[/C][/ROW]
[ROW][C]M10[/C][C]-0.0986422741895337[/C][C]0.491773[/C][C]-0.2006[/C][C]0.841889[/C][C]0.420944[/C][/ROW]
[ROW][C]M11[/C][C]-0.037490385955704[/C][C]0.491592[/C][C]-0.0763[/C][C]0.939533[/C][C]0.469767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57942&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57942&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)	104.239014386218	0.460177	226.5196	0	0
Rente	-0.949163093002725	0.101565	-9.3454	0	0
M1	-0.429150643259583	0.49181	-0.8726	0.38732	0.19366
M2	-0.389285793353394	0.491987	-0.7913	0.432772	0.216386
M3	-0.376879098097416	0.49195	-0.7661	0.447452	0.223726
M4	-0.35979416242337	0.492035	-0.7312	0.468264	0.234132
M5	-0.266505879121328	0.492105	-0.5416	0.590677	0.295339
M6	-0.169285793353391	0.491987	-0.3441	0.732316	0.366158
M7	-0.0816924886093766	0.492025	-0.166	0.868843	0.434421
M8	-0.0635908147953803	0.492015	-0.1292	0.897715	0.448857
M9	-0.103455664701495	0.491833	-0.2103	0.834306	0.417153
M10	-0.0986422741895337	0.491773	-0.2006	0.841889	0.420944
M11	-0.037490385955704	0.491592	-0.0763	0.939533	0.469767

Multiple Linear Regression - Regression Statistics
Multiple R	0.811936829502331
R-squared	0.659241415102298
Adjusted R-squared	0.572239223213523
F-TEST (value)	7.57729662656181
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	1.54445368716338e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.77708225964867
Sum Squared Residuals	28.3812713982522

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.811936829502331 \tabularnewline
R-squared & 0.659241415102298 \tabularnewline
Adjusted R-squared & 0.572239223213523 \tabularnewline
F-TEST (value) & 7.57729662656181 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 1.54445368716338e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.77708225964867 \tabularnewline
Sum Squared Residuals & 28.3812713982522 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57942&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.811936829502331[/C][/ROW]
[ROW][C]R-squared[/C][C]0.659241415102298[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.572239223213523[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.57729662656181[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]1.54445368716338e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.77708225964867[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]28.3812713982522[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57942&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57942&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	0.811936829502331
R-squared	0.659241415102298
Adjusted R-squared	0.572239223213523
F-TEST (value)	7.57729662656181
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	1.54445368716338e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.77708225964867
Sum Squared Residuals	28.3812713982522

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.2	100.962374463951	0.237625536049289
2	101.1	100.802915064326	0.297084935674034
3	100.7	100.663455664701	0.0365443352985084
4	100.1	100.547657767355	-0.447657767355165
5	99.9	100.413146908337	-0.513146908336542
6	99.7	100.168668280624	-0.4686682806235
7	99.5	100.123378752347	-0.623378752347136
8	99.2	100.141480426161	-0.94148042616113
9	99	99.8643248030043	-0.864324803004337
10	99	99.6793055749158	-0.679305574915753
11	99.3	99.6929993084995	-0.39299930849945
12	99.5	99.7304896944551	-0.230489694455151
13	99.7	99.3013390511956	0.398660948804435
14	100	99.3412039011017	0.658796098898244
15	100.4	99.3536105963577	1.04638940364227
16	100.6	99.3706955320318	1.22930446796821
17	100.7	99.6253415411443	1.07465845885572
18	100.7	99.7984946743524	0.901505325647562
19	100.6	99.8860879790965	0.713912020903539
20	100.5	99.9136812838405	0.586318716159522
21	100.6	100.310431456716	0.289568543284378
22	100.5	100.581010513268	-0.081010513268341
23	100.4	100.983861114983	-0.583861114983145
24	100.3	101.154234333959	-0.85423433395924
25	100.4	100.725083690700	-0.325083690699647
26	100.4	100.764948540606	-0.364948540605836
27	100.4	100.777355235862	-0.377355235861815
28	100.4	100.794440171536	-0.394440171535861
29	100.4	100.887728454838	-0.487728454837904
30	100.5	100.984948540606	-0.484948540605845
31	100.6	101.072541845350	-0.472541845349865
32	100.6	101.090643519164	-0.490643519163862
33	100.5	101.050778669258	-0.550778669257742
34	100.5	101.055592059770	-0.555592059769703
35	100.7	101.116743948004	-0.41674394800353
36	101.1	101.533899571160	-0.433899571160332
37	101.5	101.199665237201	0.300334762798985
38	101.9	101.239530087107	0.660469912892802
39	102.1	101.441769400964	0.658230599036267
40	102.1	101.506312491288	0.593687508712085
41	102.1	101.59960077459	0.500399225410042
42	102.4	102.076486097559	0.323513902441028
43	102.8	102.258995711603	0.541004288396733
44	103.1	102.277097385417	0.822902614582734
45	103.1	102.237232535511	0.862767464488848
46	102.9	102.242045926023	0.657954073976898
47	102.4	102.303197814257	0.0968021857430683
48	101.9	102.340688200213	-0.440688200212636
49	101.3	101.911537556953	-0.61153755695306
50	100.7	101.951402406859	-1.25140240685924
51	100.6	101.963809102115	-1.36380910211523
52	101	101.980894037789	-0.980894037789271
53	101.5	102.074182321091	-0.574182321091314
54	101.9	102.171402406859	-0.271402406859245
55	102.1	102.258995711603	-0.158995711603270
56	102.3	102.277097385417	0.0229026145827360
57	102.5	102.237232535511	0.262767464488854
58	102.9	102.242045926023	0.657954073976898
59	103.6	102.303197814257	1.29680218574306
60	104.3	102.340688200213	1.95931179978736

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.2 & 100.962374463951 & 0.237625536049289 \tabularnewline
2 & 101.1 & 100.802915064326 & 0.297084935674034 \tabularnewline
3 & 100.7 & 100.663455664701 & 0.0365443352985084 \tabularnewline
4 & 100.1 & 100.547657767355 & -0.447657767355165 \tabularnewline
5 & 99.9 & 100.413146908337 & -0.513146908336542 \tabularnewline
6 & 99.7 & 100.168668280624 & -0.4686682806235 \tabularnewline
7 & 99.5 & 100.123378752347 & -0.623378752347136 \tabularnewline
8 & 99.2 & 100.141480426161 & -0.94148042616113 \tabularnewline
9 & 99 & 99.8643248030043 & -0.864324803004337 \tabularnewline
10 & 99 & 99.6793055749158 & -0.679305574915753 \tabularnewline
11 & 99.3 & 99.6929993084995 & -0.39299930849945 \tabularnewline
12 & 99.5 & 99.7304896944551 & -0.230489694455151 \tabularnewline
13 & 99.7 & 99.3013390511956 & 0.398660948804435 \tabularnewline
14 & 100 & 99.3412039011017 & 0.658796098898244 \tabularnewline
15 & 100.4 & 99.3536105963577 & 1.04638940364227 \tabularnewline
16 & 100.6 & 99.3706955320318 & 1.22930446796821 \tabularnewline
17 & 100.7 & 99.6253415411443 & 1.07465845885572 \tabularnewline
18 & 100.7 & 99.7984946743524 & 0.901505325647562 \tabularnewline
19 & 100.6 & 99.8860879790965 & 0.713912020903539 \tabularnewline
20 & 100.5 & 99.9136812838405 & 0.586318716159522 \tabularnewline
21 & 100.6 & 100.310431456716 & 0.289568543284378 \tabularnewline
22 & 100.5 & 100.581010513268 & -0.081010513268341 \tabularnewline
23 & 100.4 & 100.983861114983 & -0.583861114983145 \tabularnewline
24 & 100.3 & 101.154234333959 & -0.85423433395924 \tabularnewline
25 & 100.4 & 100.725083690700 & -0.325083690699647 \tabularnewline
26 & 100.4 & 100.764948540606 & -0.364948540605836 \tabularnewline
27 & 100.4 & 100.777355235862 & -0.377355235861815 \tabularnewline
28 & 100.4 & 100.794440171536 & -0.394440171535861 \tabularnewline
29 & 100.4 & 100.887728454838 & -0.487728454837904 \tabularnewline
30 & 100.5 & 100.984948540606 & -0.484948540605845 \tabularnewline
31 & 100.6 & 101.072541845350 & -0.472541845349865 \tabularnewline
32 & 100.6 & 101.090643519164 & -0.490643519163862 \tabularnewline
33 & 100.5 & 101.050778669258 & -0.550778669257742 \tabularnewline
34 & 100.5 & 101.055592059770 & -0.555592059769703 \tabularnewline
35 & 100.7 & 101.116743948004 & -0.41674394800353 \tabularnewline
36 & 101.1 & 101.533899571160 & -0.433899571160332 \tabularnewline
37 & 101.5 & 101.199665237201 & 0.300334762798985 \tabularnewline
38 & 101.9 & 101.239530087107 & 0.660469912892802 \tabularnewline
39 & 102.1 & 101.441769400964 & 0.658230599036267 \tabularnewline
40 & 102.1 & 101.506312491288 & 0.593687508712085 \tabularnewline
41 & 102.1 & 101.59960077459 & 0.500399225410042 \tabularnewline
42 & 102.4 & 102.076486097559 & 0.323513902441028 \tabularnewline
43 & 102.8 & 102.258995711603 & 0.541004288396733 \tabularnewline
44 & 103.1 & 102.277097385417 & 0.822902614582734 \tabularnewline
45 & 103.1 & 102.237232535511 & 0.862767464488848 \tabularnewline
46 & 102.9 & 102.242045926023 & 0.657954073976898 \tabularnewline
47 & 102.4 & 102.303197814257 & 0.0968021857430683 \tabularnewline
48 & 101.9 & 102.340688200213 & -0.440688200212636 \tabularnewline
49 & 101.3 & 101.911537556953 & -0.61153755695306 \tabularnewline
50 & 100.7 & 101.951402406859 & -1.25140240685924 \tabularnewline
51 & 100.6 & 101.963809102115 & -1.36380910211523 \tabularnewline
52 & 101 & 101.980894037789 & -0.980894037789271 \tabularnewline
53 & 101.5 & 102.074182321091 & -0.574182321091314 \tabularnewline
54 & 101.9 & 102.171402406859 & -0.271402406859245 \tabularnewline
55 & 102.1 & 102.258995711603 & -0.158995711603270 \tabularnewline
56 & 102.3 & 102.277097385417 & 0.0229026145827360 \tabularnewline
57 & 102.5 & 102.237232535511 & 0.262767464488854 \tabularnewline
58 & 102.9 & 102.242045926023 & 0.657954073976898 \tabularnewline
59 & 103.6 & 102.303197814257 & 1.29680218574306 \tabularnewline
60 & 104.3 & 102.340688200213 & 1.95931179978736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57942&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]101.2[/C][C]100.962374463951[/C][C]0.237625536049289[/C][/ROW]
[ROW][C]2[/C][C]101.1[/C][C]100.802915064326[/C][C]0.297084935674034[/C][/ROW]
[ROW][C]3[/C][C]100.7[/C][C]100.663455664701[/C][C]0.0365443352985084[/C][/ROW]
[ROW][C]4[/C][C]100.1[/C][C]100.547657767355[/C][C]-0.447657767355165[/C][/ROW]
[ROW][C]5[/C][C]99.9[/C][C]100.413146908337[/C][C]-0.513146908336542[/C][/ROW]
[ROW][C]6[/C][C]99.7[/C][C]100.168668280624[/C][C]-0.4686682806235[/C][/ROW]
[ROW][C]7[/C][C]99.5[/C][C]100.123378752347[/C][C]-0.623378752347136[/C][/ROW]
[ROW][C]8[/C][C]99.2[/C][C]100.141480426161[/C][C]-0.94148042616113[/C][/ROW]
[ROW][C]9[/C][C]99[/C][C]99.8643248030043[/C][C]-0.864324803004337[/C][/ROW]
[ROW][C]10[/C][C]99[/C][C]99.6793055749158[/C][C]-0.679305574915753[/C][/ROW]
[ROW][C]11[/C][C]99.3[/C][C]99.6929993084995[/C][C]-0.39299930849945[/C][/ROW]
[ROW][C]12[/C][C]99.5[/C][C]99.7304896944551[/C][C]-0.230489694455151[/C][/ROW]
[ROW][C]13[/C][C]99.7[/C][C]99.3013390511956[/C][C]0.398660948804435[/C][/ROW]
[ROW][C]14[/C][C]100[/C][C]99.3412039011017[/C][C]0.658796098898244[/C][/ROW]
[ROW][C]15[/C][C]100.4[/C][C]99.3536105963577[/C][C]1.04638940364227[/C][/ROW]
[ROW][C]16[/C][C]100.6[/C][C]99.3706955320318[/C][C]1.22930446796821[/C][/ROW]
[ROW][C]17[/C][C]100.7[/C][C]99.6253415411443[/C][C]1.07465845885572[/C][/ROW]
[ROW][C]18[/C][C]100.7[/C][C]99.7984946743524[/C][C]0.901505325647562[/C][/ROW]
[ROW][C]19[/C][C]100.6[/C][C]99.8860879790965[/C][C]0.713912020903539[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]99.9136812838405[/C][C]0.586318716159522[/C][/ROW]
[ROW][C]21[/C][C]100.6[/C][C]100.310431456716[/C][C]0.289568543284378[/C][/ROW]
[ROW][C]22[/C][C]100.5[/C][C]100.581010513268[/C][C]-0.081010513268341[/C][/ROW]
[ROW][C]23[/C][C]100.4[/C][C]100.983861114983[/C][C]-0.583861114983145[/C][/ROW]
[ROW][C]24[/C][C]100.3[/C][C]101.154234333959[/C][C]-0.85423433395924[/C][/ROW]
[ROW][C]25[/C][C]100.4[/C][C]100.725083690700[/C][C]-0.325083690699647[/C][/ROW]
[ROW][C]26[/C][C]100.4[/C][C]100.764948540606[/C][C]-0.364948540605836[/C][/ROW]
[ROW][C]27[/C][C]100.4[/C][C]100.777355235862[/C][C]-0.377355235861815[/C][/ROW]
[ROW][C]28[/C][C]100.4[/C][C]100.794440171536[/C][C]-0.394440171535861[/C][/ROW]
[ROW][C]29[/C][C]100.4[/C][C]100.887728454838[/C][C]-0.487728454837904[/C][/ROW]
[ROW][C]30[/C][C]100.5[/C][C]100.984948540606[/C][C]-0.484948540605845[/C][/ROW]
[ROW][C]31[/C][C]100.6[/C][C]101.072541845350[/C][C]-0.472541845349865[/C][/ROW]
[ROW][C]32[/C][C]100.6[/C][C]101.090643519164[/C][C]-0.490643519163862[/C][/ROW]
[ROW][C]33[/C][C]100.5[/C][C]101.050778669258[/C][C]-0.550778669257742[/C][/ROW]
[ROW][C]34[/C][C]100.5[/C][C]101.055592059770[/C][C]-0.555592059769703[/C][/ROW]
[ROW][C]35[/C][C]100.7[/C][C]101.116743948004[/C][C]-0.41674394800353[/C][/ROW]
[ROW][C]36[/C][C]101.1[/C][C]101.533899571160[/C][C]-0.433899571160332[/C][/ROW]
[ROW][C]37[/C][C]101.5[/C][C]101.199665237201[/C][C]0.300334762798985[/C][/ROW]
[ROW][C]38[/C][C]101.9[/C][C]101.239530087107[/C][C]0.660469912892802[/C][/ROW]
[ROW][C]39[/C][C]102.1[/C][C]101.441769400964[/C][C]0.658230599036267[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]101.506312491288[/C][C]0.593687508712085[/C][/ROW]
[ROW][C]41[/C][C]102.1[/C][C]101.59960077459[/C][C]0.500399225410042[/C][/ROW]
[ROW][C]42[/C][C]102.4[/C][C]102.076486097559[/C][C]0.323513902441028[/C][/ROW]
[ROW][C]43[/C][C]102.8[/C][C]102.258995711603[/C][C]0.541004288396733[/C][/ROW]
[ROW][C]44[/C][C]103.1[/C][C]102.277097385417[/C][C]0.822902614582734[/C][/ROW]
[ROW][C]45[/C][C]103.1[/C][C]102.237232535511[/C][C]0.862767464488848[/C][/ROW]
[ROW][C]46[/C][C]102.9[/C][C]102.242045926023[/C][C]0.657954073976898[/C][/ROW]
[ROW][C]47[/C][C]102.4[/C][C]102.303197814257[/C][C]0.0968021857430683[/C][/ROW]
[ROW][C]48[/C][C]101.9[/C][C]102.340688200213[/C][C]-0.440688200212636[/C][/ROW]
[ROW][C]49[/C][C]101.3[/C][C]101.911537556953[/C][C]-0.61153755695306[/C][/ROW]
[ROW][C]50[/C][C]100.7[/C][C]101.951402406859[/C][C]-1.25140240685924[/C][/ROW]
[ROW][C]51[/C][C]100.6[/C][C]101.963809102115[/C][C]-1.36380910211523[/C][/ROW]
[ROW][C]52[/C][C]101[/C][C]101.980894037789[/C][C]-0.980894037789271[/C][/ROW]
[ROW][C]53[/C][C]101.5[/C][C]102.074182321091[/C][C]-0.574182321091314[/C][/ROW]
[ROW][C]54[/C][C]101.9[/C][C]102.171402406859[/C][C]-0.271402406859245[/C][/ROW]
[ROW][C]55[/C][C]102.1[/C][C]102.258995711603[/C][C]-0.158995711603270[/C][/ROW]
[ROW][C]56[/C][C]102.3[/C][C]102.277097385417[/C][C]0.0229026145827360[/C][/ROW]
[ROW][C]57[/C][C]102.5[/C][C]102.237232535511[/C][C]0.262767464488854[/C][/ROW]
[ROW][C]58[/C][C]102.9[/C][C]102.242045926023[/C][C]0.657954073976898[/C][/ROW]
[ROW][C]59[/C][C]103.6[/C][C]102.303197814257[/C][C]1.29680218574306[/C][/ROW]
[ROW][C]60[/C][C]104.3[/C][C]102.340688200213[/C][C]1.95931179978736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57942&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57942&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	101.2	100.962374463951	0.237625536049289
2	101.1	100.802915064326	0.297084935674034
3	100.7	100.663455664701	0.0365443352985084
4	100.1	100.547657767355	-0.447657767355165
5	99.9	100.413146908337	-0.513146908336542
6	99.7	100.168668280624	-0.4686682806235
7	99.5	100.123378752347	-0.623378752347136
8	99.2	100.141480426161	-0.94148042616113
9	99	99.8643248030043	-0.864324803004337
10	99	99.6793055749158	-0.679305574915753
11	99.3	99.6929993084995	-0.39299930849945
12	99.5	99.7304896944551	-0.230489694455151
13	99.7	99.3013390511956	0.398660948804435
14	100	99.3412039011017	0.658796098898244
15	100.4	99.3536105963577	1.04638940364227
16	100.6	99.3706955320318	1.22930446796821
17	100.7	99.6253415411443	1.07465845885572
18	100.7	99.7984946743524	0.901505325647562
19	100.6	99.8860879790965	0.713912020903539
20	100.5	99.9136812838405	0.586318716159522
21	100.6	100.310431456716	0.289568543284378
22	100.5	100.581010513268	-0.081010513268341
23	100.4	100.983861114983	-0.583861114983145
24	100.3	101.154234333959	-0.85423433395924
25	100.4	100.725083690700	-0.325083690699647
26	100.4	100.764948540606	-0.364948540605836
27	100.4	100.777355235862	-0.377355235861815
28	100.4	100.794440171536	-0.394440171535861
29	100.4	100.887728454838	-0.487728454837904
30	100.5	100.984948540606	-0.484948540605845
31	100.6	101.072541845350	-0.472541845349865
32	100.6	101.090643519164	-0.490643519163862
33	100.5	101.050778669258	-0.550778669257742
34	100.5	101.055592059770	-0.555592059769703
35	100.7	101.116743948004	-0.41674394800353
36	101.1	101.533899571160	-0.433899571160332
37	101.5	101.199665237201	0.300334762798985
38	101.9	101.239530087107	0.660469912892802
39	102.1	101.441769400964	0.658230599036267
40	102.1	101.506312491288	0.593687508712085
41	102.1	101.59960077459	0.500399225410042
42	102.4	102.076486097559	0.323513902441028
43	102.8	102.258995711603	0.541004288396733
44	103.1	102.277097385417	0.822902614582734
45	103.1	102.237232535511	0.862767464488848
46	102.9	102.242045926023	0.657954073976898
47	102.4	102.303197814257	0.0968021857430683
48	101.9	102.340688200213	-0.440688200212636
49	101.3	101.911537556953	-0.61153755695306
50	100.7	101.951402406859	-1.25140240685924
51	100.6	101.963809102115	-1.36380910211523
52	101	101.980894037789	-0.980894037789271
53	101.5	102.074182321091	-0.574182321091314
54	101.9	102.171402406859	-0.271402406859245
55	102.1	102.258995711603	-0.158995711603270
56	102.3	102.277097385417	0.0229026145827360
57	102.5	102.237232535511	0.262767464488854
58	102.9	102.242045926023	0.657954073976898
59	103.6	102.303197814257	1.29680218574306
60	104.3	102.340688200213	1.95931179978736

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.310535172926722	0.621070345853444	0.689464827073278
17	0.367070722331768	0.734141444663536	0.632929277668232
18	0.40803609784493	0.81607219568986	0.59196390215507
19	0.450205515440260	0.900411030880519	0.54979448455974
20	0.525403245678041	0.949193508643918	0.474596754321959
21	0.584776897928257	0.830446204143486	0.415223102071743
22	0.559633362405663	0.880733275188675	0.440366637594337
23	0.472848271027772	0.945696542055544	0.527151728972228
24	0.393985090425049	0.787970180850097	0.606014909574951
25	0.305518483760296	0.611036967520592	0.694481516239704
26	0.236717165607875	0.473434331215749	0.763282834392125
27	0.180154029960253	0.360308059920505	0.819845970039747
28	0.125256022698981	0.250512045397962	0.874743977301019
29	0.0822495734847573	0.164499146969515	0.917750426515243
30	0.0504243856351433	0.100848771270287	0.949575614364857
31	0.0297346467966931	0.0594692935933862	0.970265353203307
32	0.0183576898787099	0.0367153797574197	0.98164231012129
33	0.0123361745475252	0.0246723490950505	0.987663825452475
34	0.0109066399323343	0.0218132798646686	0.989093360067666
35	0.0205022624493418	0.0410045248986836	0.979497737550658
36	0.177526622175992	0.355053244351985	0.822473377824008
37	0.185873924441009	0.371747848882018	0.814126075558991
38	0.159589218121650	0.319178436243301	0.84041078187835
39	0.135663122865382	0.271326245730764	0.864336877134618
40	0.107365958627557	0.214731917255113	0.892634041372443
41	0.0796688807366614	0.159337761473323	0.920331119263339
42	0.0499417203704934	0.0998834407409868	0.950058279629507
43	0.0392018293923445	0.078403658784689	0.960798170607656
44	0.0342409380334964	0.0684818760669927	0.965759061966504

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.310535172926722 & 0.621070345853444 & 0.689464827073278 \tabularnewline
17 & 0.367070722331768 & 0.734141444663536 & 0.632929277668232 \tabularnewline
18 & 0.40803609784493 & 0.81607219568986 & 0.59196390215507 \tabularnewline
19 & 0.450205515440260 & 0.900411030880519 & 0.54979448455974 \tabularnewline
20 & 0.525403245678041 & 0.949193508643918 & 0.474596754321959 \tabularnewline
21 & 0.584776897928257 & 0.830446204143486 & 0.415223102071743 \tabularnewline
22 & 0.559633362405663 & 0.880733275188675 & 0.440366637594337 \tabularnewline
23 & 0.472848271027772 & 0.945696542055544 & 0.527151728972228 \tabularnewline
24 & 0.393985090425049 & 0.787970180850097 & 0.606014909574951 \tabularnewline
25 & 0.305518483760296 & 0.611036967520592 & 0.694481516239704 \tabularnewline
26 & 0.236717165607875 & 0.473434331215749 & 0.763282834392125 \tabularnewline
27 & 0.180154029960253 & 0.360308059920505 & 0.819845970039747 \tabularnewline
28 & 0.125256022698981 & 0.250512045397962 & 0.874743977301019 \tabularnewline
29 & 0.0822495734847573 & 0.164499146969515 & 0.917750426515243 \tabularnewline
30 & 0.0504243856351433 & 0.100848771270287 & 0.949575614364857 \tabularnewline
31 & 0.0297346467966931 & 0.0594692935933862 & 0.970265353203307 \tabularnewline
32 & 0.0183576898787099 & 0.0367153797574197 & 0.98164231012129 \tabularnewline
33 & 0.0123361745475252 & 0.0246723490950505 & 0.987663825452475 \tabularnewline
34 & 0.0109066399323343 & 0.0218132798646686 & 0.989093360067666 \tabularnewline
35 & 0.0205022624493418 & 0.0410045248986836 & 0.979497737550658 \tabularnewline
36 & 0.177526622175992 & 0.355053244351985 & 0.822473377824008 \tabularnewline
37 & 0.185873924441009 & 0.371747848882018 & 0.814126075558991 \tabularnewline
38 & 0.159589218121650 & 0.319178436243301 & 0.84041078187835 \tabularnewline
39 & 0.135663122865382 & 0.271326245730764 & 0.864336877134618 \tabularnewline
40 & 0.107365958627557 & 0.214731917255113 & 0.892634041372443 \tabularnewline
41 & 0.0796688807366614 & 0.159337761473323 & 0.920331119263339 \tabularnewline
42 & 0.0499417203704934 & 0.0998834407409868 & 0.950058279629507 \tabularnewline
43 & 0.0392018293923445 & 0.078403658784689 & 0.960798170607656 \tabularnewline
44 & 0.0342409380334964 & 0.0684818760669927 & 0.965759061966504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57942&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]16[/C][C]0.310535172926722[/C][C]0.621070345853444[/C][C]0.689464827073278[/C][/ROW]
[ROW][C]17[/C][C]0.367070722331768[/C][C]0.734141444663536[/C][C]0.632929277668232[/C][/ROW]
[ROW][C]18[/C][C]0.40803609784493[/C][C]0.81607219568986[/C][C]0.59196390215507[/C][/ROW]
[ROW][C]19[/C][C]0.450205515440260[/C][C]0.900411030880519[/C][C]0.54979448455974[/C][/ROW]
[ROW][C]20[/C][C]0.525403245678041[/C][C]0.949193508643918[/C][C]0.474596754321959[/C][/ROW]
[ROW][C]21[/C][C]0.584776897928257[/C][C]0.830446204143486[/C][C]0.415223102071743[/C][/ROW]
[ROW][C]22[/C][C]0.559633362405663[/C][C]0.880733275188675[/C][C]0.440366637594337[/C][/ROW]
[ROW][C]23[/C][C]0.472848271027772[/C][C]0.945696542055544[/C][C]0.527151728972228[/C][/ROW]
[ROW][C]24[/C][C]0.393985090425049[/C][C]0.787970180850097[/C][C]0.606014909574951[/C][/ROW]
[ROW][C]25[/C][C]0.305518483760296[/C][C]0.611036967520592[/C][C]0.694481516239704[/C][/ROW]
[ROW][C]26[/C][C]0.236717165607875[/C][C]0.473434331215749[/C][C]0.763282834392125[/C][/ROW]
[ROW][C]27[/C][C]0.180154029960253[/C][C]0.360308059920505[/C][C]0.819845970039747[/C][/ROW]
[ROW][C]28[/C][C]0.125256022698981[/C][C]0.250512045397962[/C][C]0.874743977301019[/C][/ROW]
[ROW][C]29[/C][C]0.0822495734847573[/C][C]0.164499146969515[/C][C]0.917750426515243[/C][/ROW]
[ROW][C]30[/C][C]0.0504243856351433[/C][C]0.100848771270287[/C][C]0.949575614364857[/C][/ROW]
[ROW][C]31[/C][C]0.0297346467966931[/C][C]0.0594692935933862[/C][C]0.970265353203307[/C][/ROW]
[ROW][C]32[/C][C]0.0183576898787099[/C][C]0.0367153797574197[/C][C]0.98164231012129[/C][/ROW]
[ROW][C]33[/C][C]0.0123361745475252[/C][C]0.0246723490950505[/C][C]0.987663825452475[/C][/ROW]
[ROW][C]34[/C][C]0.0109066399323343[/C][C]0.0218132798646686[/C][C]0.989093360067666[/C][/ROW]
[ROW][C]35[/C][C]0.0205022624493418[/C][C]0.0410045248986836[/C][C]0.979497737550658[/C][/ROW]
[ROW][C]36[/C][C]0.177526622175992[/C][C]0.355053244351985[/C][C]0.822473377824008[/C][/ROW]
[ROW][C]37[/C][C]0.185873924441009[/C][C]0.371747848882018[/C][C]0.814126075558991[/C][/ROW]
[ROW][C]38[/C][C]0.159589218121650[/C][C]0.319178436243301[/C][C]0.84041078187835[/C][/ROW]
[ROW][C]39[/C][C]0.135663122865382[/C][C]0.271326245730764[/C][C]0.864336877134618[/C][/ROW]
[ROW][C]40[/C][C]0.107365958627557[/C][C]0.214731917255113[/C][C]0.892634041372443[/C][/ROW]
[ROW][C]41[/C][C]0.0796688807366614[/C][C]0.159337761473323[/C][C]0.920331119263339[/C][/ROW]
[ROW][C]42[/C][C]0.0499417203704934[/C][C]0.0998834407409868[/C][C]0.950058279629507[/C][/ROW]
[ROW][C]43[/C][C]0.0392018293923445[/C][C]0.078403658784689[/C][C]0.960798170607656[/C][/ROW]
[ROW][C]44[/C][C]0.0342409380334964[/C][C]0.0684818760669927[/C][C]0.965759061966504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57942&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57942&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
16	0.310535172926722	0.621070345853444	0.689464827073278
17	0.367070722331768	0.734141444663536	0.632929277668232
18	0.40803609784493	0.81607219568986	0.59196390215507
19	0.450205515440260	0.900411030880519	0.54979448455974
20	0.525403245678041	0.949193508643918	0.474596754321959
21	0.584776897928257	0.830446204143486	0.415223102071743
22	0.559633362405663	0.880733275188675	0.440366637594337
23	0.472848271027772	0.945696542055544	0.527151728972228
24	0.393985090425049	0.787970180850097	0.606014909574951
25	0.305518483760296	0.611036967520592	0.694481516239704
26	0.236717165607875	0.473434331215749	0.763282834392125
27	0.180154029960253	0.360308059920505	0.819845970039747
28	0.125256022698981	0.250512045397962	0.874743977301019
29	0.0822495734847573	0.164499146969515	0.917750426515243
30	0.0504243856351433	0.100848771270287	0.949575614364857
31	0.0297346467966931	0.0594692935933862	0.970265353203307
32	0.0183576898787099	0.0367153797574197	0.98164231012129
33	0.0123361745475252	0.0246723490950505	0.987663825452475
34	0.0109066399323343	0.0218132798646686	0.989093360067666
35	0.0205022624493418	0.0410045248986836	0.979497737550658
36	0.177526622175992	0.355053244351985	0.822473377824008
37	0.185873924441009	0.371747848882018	0.814126075558991
38	0.159589218121650	0.319178436243301	0.84041078187835
39	0.135663122865382	0.271326245730764	0.864336877134618
40	0.107365958627557	0.214731917255113	0.892634041372443
41	0.0796688807366614	0.159337761473323	0.920331119263339
42	0.0499417203704934	0.0998834407409868	0.950058279629507
43	0.0392018293923445	0.078403658784689	0.960798170607656
44	0.0342409380334964	0.0684818760669927	0.965759061966504

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	4	0.137931034482759	NOK
10% type I error level	8	0.275862068965517	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 & 0 & 0 & OK \tabularnewline
5% type I error level & 4 & 0.137931034482759 & NOK \tabularnewline
10% type I error level & 8 & 0.275862068965517 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57942&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]4[/C][C]0.137931034482759[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.275862068965517[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57942&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57942&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	4	0.137931034482759	NOK
10% type I error level	8	0.275862068965517	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code