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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 21 Dec 2011 09:53:50 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/21/t1324479525lytyvqc0tyfxod3.htm/, Retrieved Tue, 07 May 2024 06:04:56 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158795, Retrieved Tue, 07 May 2024 06:04:56 +0000

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User-defined keywords

Estimated Impact

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

-       [Multiple Regression] [] [2011-12-21 14:53:50] [1e640daebbc6b5a89eef23229b5a56d5] [Current]
-   P     [Multiple Regression] [] [2011-12-21 15:00:06] [a2638725f7f7c6bd63902ba17eba666b] 

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Dataseries X:

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Histogram

Boxplots

5.50	518	117	401
5.40	534	120	413
5.90	528	116	413
5.80	478	87	390
5.10	469	84	385
4.10	490	93	397
4.40	493	95	398
3.60	508	101	406
3.50	517	105	412
3.10	514	105	409
2.90	510	106	404
2.20	527	115	412
1.40	542	124	418
1.20	565	130	434
1.30	555	124	431
1.30	499	93	406
1.30	511	95	416
1.80	526	102	424
1.80	532	105	427
1.80	549	111	438
1.70	561	117	444
2.10	557	116	442
2.00	566	123	443
1.70	588	134	453
1.90	620	149	471
2.30	626	150	476
2.40	620	144	476
2.50	573	112	461
2.80	573	111	462
2.60	574	114	460
2.20	580	117	463
2.80	590	123	467
2.80	593	125	468
2.80	597	132	465
2.30	595	137	459
2.20	612	147	465
3.00	628	157	471
2.90	629	157	472
2.70	621	149	472
2.70	569	113	456
2.30	567	112	455
2.40	573	117	456
2.80	584	122	462
2.30	589	127	463
2.00	591	130	461
1.90	595	135	461
2.30	594	139	455
2.70	611	149	462
1.80	613	161	452
2.00	611	162	449
2.10	594	153	441
2.00	543	116	427
2.40	537	114	423
1.70	544	120	424
1.00	555	126	430
1.20	561	133	428
1.40	562	136	426
1.70	555	137	418
1.80	547	138	410
1.40	565	148	418
1.70	578	158	420
1.60	580	159	421
1.40	569	151	419
1.50	507	111	396
0.90	501	108	392
1.50	509	114	396
1.70	510	118	392
1.60	517	123	394
1.20	519	127	392

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'AstonUniversity' @ aston.wessa.net

\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 & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158795&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158795&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158795&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	3 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 1.07920669683229 -0.00602839840963902HIPC[t] + 0.986893059914533`<25jaar`[t] + 1.00169618355604`>25jaar`[t] + 0.0835921876063798M1[t] + 0.4341935621186M2[t] -0.317960237533354M3[t] -0.232987808370073M4[t] -0.25062036977429M5[t] -0.512800109966768M6[t] -0.466998245142893M7[t] -0.231262002042783M8[t] -0.185885415692573M9[t] -0.571477882939537M10[t] -0.516512921467451M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  1.07920669683229 -0.00602839840963902HIPC[t] +  0.986893059914533`<25jaar`[t] +  1.00169618355604`>25jaar`[t] +  0.0835921876063798M1[t] +  0.4341935621186M2[t] -0.317960237533354M3[t] -0.232987808370073M4[t] -0.25062036977429M5[t] -0.512800109966768M6[t] -0.466998245142893M7[t] -0.231262002042783M8[t] -0.185885415692573M9[t] -0.571477882939537M10[t] -0.516512921467451M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158795&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  1.07920669683229 -0.00602839840963902HIPC[t] +  0.986893059914533`<25jaar`[t] +  1.00169618355604`>25jaar`[t] +  0.0835921876063798M1[t] +  0.4341935621186M2[t] -0.317960237533354M3[t] -0.232987808370073M4[t] -0.25062036977429M5[t] -0.512800109966768M6[t] -0.466998245142893M7[t] -0.231262002042783M8[t] -0.185885415692573M9[t] -0.571477882939537M10[t] -0.516512921467451M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158795&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158795&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
werkloosheid[t] = + 1.07920669683229 -0.00602839840963902HIPC[t] + 0.986893059914533`<25jaar`[t] + 1.00169618355604`>25jaar`[t] + 0.0835921876063798M1[t] + 0.4341935621186M2[t] -0.317960237533354M3[t] -0.232987808370073M4[t] -0.25062036977429M5[t] -0.512800109966768M6[t] -0.466998245142893M7[t] -0.231262002042783M8[t] -0.185885415692573M9[t] -0.571477882939537M10[t] -0.516512921467451M11[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)	1.07920669683229	1.035613	1.0421	0.302011	0.151005
HIPC	-0.00602839840963902	0.05962	-0.1011	0.919835	0.459917
`<25jaar`	0.986893059914533	0.006059	162.8804	0	0
`>25jaar`	1.00169618355604	0.002502	400.3969	0	0
M1	0.0835921876063798	0.279863	0.2987	0.766323	0.383161
M2	0.4341935621186	0.281231	1.5439	0.128452	0.064226
M3	-0.317960237533354	0.275813	-1.1528	0.254066	0.127033
M4	-0.232987808370073	0.318284	-0.732	0.467326	0.233663
M5	-0.25062036977429	0.324478	-0.7724	0.443257	0.221628
M6	-0.512800109966768	0.309904	-1.6547	0.103786	0.051893
M7	-0.466998245142893	0.301152	-1.5507	0.126813	0.063407
M8	-0.231262002042783	0.290408	-0.7963	0.429324	0.214662
M9	-0.185885415692573	0.285031	-0.6522	0.517066	0.258533
M10	-0.571477882939537	0.293862	-1.9447	0.057025	0.028512
M11	-0.516512921467451	0.289061	-1.7869	0.079572	0.039786

\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) & 1.07920669683229 & 1.035613 & 1.0421 & 0.302011 & 0.151005 \tabularnewline
HIPC & -0.00602839840963902 & 0.05962 & -0.1011 & 0.919835 & 0.459917 \tabularnewline
`<25jaar` & 0.986893059914533 & 0.006059 & 162.8804 & 0 & 0 \tabularnewline
`>25jaar` & 1.00169618355604 & 0.002502 & 400.3969 & 0 & 0 \tabularnewline
M1 & 0.0835921876063798 & 0.279863 & 0.2987 & 0.766323 & 0.383161 \tabularnewline
M2 & 0.4341935621186 & 0.281231 & 1.5439 & 0.128452 & 0.064226 \tabularnewline
M3 & -0.317960237533354 & 0.275813 & -1.1528 & 0.254066 & 0.127033 \tabularnewline
M4 & -0.232987808370073 & 0.318284 & -0.732 & 0.467326 & 0.233663 \tabularnewline
M5 & -0.25062036977429 & 0.324478 & -0.7724 & 0.443257 & 0.221628 \tabularnewline
M6 & -0.512800109966768 & 0.309904 & -1.6547 & 0.103786 & 0.051893 \tabularnewline
M7 & -0.466998245142893 & 0.301152 & -1.5507 & 0.126813 & 0.063407 \tabularnewline
M8 & -0.231262002042783 & 0.290408 & -0.7963 & 0.429324 & 0.214662 \tabularnewline
M9 & -0.185885415692573 & 0.285031 & -0.6522 & 0.517066 & 0.258533 \tabularnewline
M10 & -0.571477882939537 & 0.293862 & -1.9447 & 0.057025 & 0.028512 \tabularnewline
M11 & -0.516512921467451 & 0.289061 & -1.7869 & 0.079572 & 0.039786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158795&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]1.07920669683229[/C][C]1.035613[/C][C]1.0421[/C][C]0.302011[/C][C]0.151005[/C][/ROW]
[ROW][C]HIPC[/C][C]-0.00602839840963902[/C][C]0.05962[/C][C]-0.1011[/C][C]0.919835[/C][C]0.459917[/C][/ROW]
[ROW][C]`<25jaar`[/C][C]0.986893059914533[/C][C]0.006059[/C][C]162.8804[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`>25jaar`[/C][C]1.00169618355604[/C][C]0.002502[/C][C]400.3969[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.0835921876063798[/C][C]0.279863[/C][C]0.2987[/C][C]0.766323[/C][C]0.383161[/C][/ROW]
[ROW][C]M2[/C][C]0.4341935621186[/C][C]0.281231[/C][C]1.5439[/C][C]0.128452[/C][C]0.064226[/C][/ROW]
[ROW][C]M3[/C][C]-0.317960237533354[/C][C]0.275813[/C][C]-1.1528[/C][C]0.254066[/C][C]0.127033[/C][/ROW]
[ROW][C]M4[/C][C]-0.232987808370073[/C][C]0.318284[/C][C]-0.732[/C][C]0.467326[/C][C]0.233663[/C][/ROW]
[ROW][C]M5[/C][C]-0.25062036977429[/C][C]0.324478[/C][C]-0.7724[/C][C]0.443257[/C][C]0.221628[/C][/ROW]
[ROW][C]M6[/C][C]-0.512800109966768[/C][C]0.309904[/C][C]-1.6547[/C][C]0.103786[/C][C]0.051893[/C][/ROW]
[ROW][C]M7[/C][C]-0.466998245142893[/C][C]0.301152[/C][C]-1.5507[/C][C]0.126813[/C][C]0.063407[/C][/ROW]
[ROW][C]M8[/C][C]-0.231262002042783[/C][C]0.290408[/C][C]-0.7963[/C][C]0.429324[/C][C]0.214662[/C][/ROW]
[ROW][C]M9[/C][C]-0.185885415692573[/C][C]0.285031[/C][C]-0.6522[/C][C]0.517066[/C][C]0.258533[/C][/ROW]
[ROW][C]M10[/C][C]-0.571477882939537[/C][C]0.293862[/C][C]-1.9447[/C][C]0.057025[/C][C]0.028512[/C][/ROW]
[ROW][C]M11[/C][C]-0.516512921467451[/C][C]0.289061[/C][C]-1.7869[/C][C]0.079572[/C][C]0.039786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158795&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158795&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)	1.07920669683229	1.035613	1.0421	0.302011	0.151005
HIPC	-0.00602839840963902	0.05962	-0.1011	0.919835	0.459917
`<25jaar`	0.986893059914533	0.006059	162.8804	0	0
`>25jaar`	1.00169618355604	0.002502	400.3969	0	0
M1	0.0835921876063798	0.279863	0.2987	0.766323	0.383161
M2	0.4341935621186	0.281231	1.5439	0.128452	0.064226
M3	-0.317960237533354	0.275813	-1.1528	0.254066	0.127033
M4	-0.232987808370073	0.318284	-0.732	0.467326	0.233663
M5	-0.25062036977429	0.324478	-0.7724	0.443257	0.221628
M6	-0.512800109966768	0.309904	-1.6547	0.103786	0.051893
M7	-0.466998245142893	0.301152	-1.5507	0.126813	0.063407
M8	-0.231262002042783	0.290408	-0.7963	0.429324	0.214662
M9	-0.185885415692573	0.285031	-0.6522	0.517066	0.258533
M10	-0.571477882939537	0.293862	-1.9447	0.057025	0.028512
M11	-0.516512921467451	0.289061	-1.7869	0.079572	0.039786

Multiple Linear Regression - Regression Statistics
Multiple R	0.999950721938766
R-squared	0.99990144630586
Adjusted R-squared	0.99987589534812
F-TEST (value)	39133.6190401468
F-TEST (DF numerator)	14
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.450991632836941
Sum Squared Residuals	10.9832464560023

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999950721938766 \tabularnewline
R-squared & 0.99990144630586 \tabularnewline
Adjusted R-squared & 0.99987589534812 \tabularnewline
F-TEST (value) & 39133.6190401468 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.450991632836941 \tabularnewline
Sum Squared Residuals & 10.9832464560023 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158795&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999950721938766[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99990144630586[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99987589534812[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]39133.6190401468[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.450991632836941[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.9832464560023[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158795&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158795&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	0.999950721938766
R-squared	0.99990144630586
Adjusted R-squared	0.99987589534812
F-TEST (value)	39133.6190401468
F-TEST (DF numerator)	14
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.450991632836941
Sum Squared Residuals	10.9832464560023

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	518	518.276300309158	-0.27630030915755
2	534	533.608537905927	0.391462094073181
3	528	528.905797667412	-0.905797667411849
4	478	477.332461977106	0.667538022894229
5	469	469.349889197065	-0.349889197064548
6	490	489.996129597185	0.00387040281504461
7	493	493.015605245871	-0.0156052458710455
8	508	507.191092035634	0.808907964365636
9	517	517.19482080282	-0.1948208028199
10	514	513.806551144269	0.193448855731323
11	510	509.841133927557	0.158866072442967
12	527	527.25747373559	-0.257473735590341
13	542	542.238103282491	-0.238103282491454
14	565	564.538407633069	0.461592366930592
15	555	554.859204083421	0.140795916578821
16	499	499.308087066333	-0.308087066332982
17	511	511.281202460318	-0.281202460318204
18	526	525.937829408771	0.0621705912290423
19	532	531.949399004007	0.05060099599346
20	549	549.12515162571	-0.125151625710267
21	561	561.102666512725	-0.102666512724869
22	557	557.724377259087	-0.72437725908744
23	566	565.689892663358	0.310107336641741
24	588	587.080999598969	0.91900040103116
25	620	619.99731330962	0.00268669038003056
26	626	626.340877302463	-0.340877302463072
27	620	619.666762303483	0.333237696517055
28	573	573.1451112222	-0.145111222199634
29	573	573.140473264914	-0.140473264914043
30	574	573.836786017035	0.163213982964989
31	580	579.850766971634	0.149233028365537
32	590	590.0110292694	-0.0110292694001255
33	593	593.031888159135	-0.0318881591354447
34	597	596.549458560622	0.450541439377904
35	595	595.531725919535	-0.531725919535437
36	612	611.927949381325	0.072050618674589
37	628	627.885826550686	0.114173449314361
38	629	629.238726948595	-0.238726948594866
39	621	620.592634349309	0.40736565069142
40	569	569.122317684652	-0.122317684652045
41	567	567.118507239141	-0.118507239141119
42	573	572.791886142236	0.208113857763615
43	584	583.779919048605	0.22008095139471
44	589	589.954830974039	-0.95483097403893
45	591	590.959302892544	0.0406971074564472
46	595	595.50877856471	-0.508778564710214
47	594	593.49872730514	0.501272694859649
48	611	610.893632751282	0.10636724871846
49	613	612.808405380871	0.191594619129385
50	611	611.139605584947	-0.139605584947323
51	594	593.491241937775	0.508758062224703
52	543	543.038027420157	-0.0380274201572939
53	537	537.037412645336	-0.0374126453360052
54	544	543.702507327073	0.297492672926489
55	555	555.684064531608	-0.684064531607558
56	561	560.823454147315	0.176545852684604
57	562	561.824911866615	0.175088133384799
58	555	554.410834471312	0.589165528688428
59	547	547.438520184409	-0.43852018440892
60	565	565.839944532834	-0.839944532833867
61	578	577.794051167175	0.205948832825227
62	580	580.133844624998	-0.133844624998512
63	569	569.4843596586	-0.484359658600151
64	507	507.053994629552	-0.0539946295522733
65	501	500.072515193226	0.927484806773918
66	509	509.734861507699	-0.734861507699181
67	510	509.720245198275	0.279754801724897
68	517	516.894441947901	0.105558052099082
69	519	518.886409766161	0.113590233838968

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 518 & 518.276300309158 & -0.27630030915755 \tabularnewline
2 & 534 & 533.608537905927 & 0.391462094073181 \tabularnewline
3 & 528 & 528.905797667412 & -0.905797667411849 \tabularnewline
4 & 478 & 477.332461977106 & 0.667538022894229 \tabularnewline
5 & 469 & 469.349889197065 & -0.349889197064548 \tabularnewline
6 & 490 & 489.996129597185 & 0.00387040281504461 \tabularnewline
7 & 493 & 493.015605245871 & -0.0156052458710455 \tabularnewline
8 & 508 & 507.191092035634 & 0.808907964365636 \tabularnewline
9 & 517 & 517.19482080282 & -0.1948208028199 \tabularnewline
10 & 514 & 513.806551144269 & 0.193448855731323 \tabularnewline
11 & 510 & 509.841133927557 & 0.158866072442967 \tabularnewline
12 & 527 & 527.25747373559 & -0.257473735590341 \tabularnewline
13 & 542 & 542.238103282491 & -0.238103282491454 \tabularnewline
14 & 565 & 564.538407633069 & 0.461592366930592 \tabularnewline
15 & 555 & 554.859204083421 & 0.140795916578821 \tabularnewline
16 & 499 & 499.308087066333 & -0.308087066332982 \tabularnewline
17 & 511 & 511.281202460318 & -0.281202460318204 \tabularnewline
18 & 526 & 525.937829408771 & 0.0621705912290423 \tabularnewline
19 & 532 & 531.949399004007 & 0.05060099599346 \tabularnewline
20 & 549 & 549.12515162571 & -0.125151625710267 \tabularnewline
21 & 561 & 561.102666512725 & -0.102666512724869 \tabularnewline
22 & 557 & 557.724377259087 & -0.72437725908744 \tabularnewline
23 & 566 & 565.689892663358 & 0.310107336641741 \tabularnewline
24 & 588 & 587.080999598969 & 0.91900040103116 \tabularnewline
25 & 620 & 619.99731330962 & 0.00268669038003056 \tabularnewline
26 & 626 & 626.340877302463 & -0.340877302463072 \tabularnewline
27 & 620 & 619.666762303483 & 0.333237696517055 \tabularnewline
28 & 573 & 573.1451112222 & -0.145111222199634 \tabularnewline
29 & 573 & 573.140473264914 & -0.140473264914043 \tabularnewline
30 & 574 & 573.836786017035 & 0.163213982964989 \tabularnewline
31 & 580 & 579.850766971634 & 0.149233028365537 \tabularnewline
32 & 590 & 590.0110292694 & -0.0110292694001255 \tabularnewline
33 & 593 & 593.031888159135 & -0.0318881591354447 \tabularnewline
34 & 597 & 596.549458560622 & 0.450541439377904 \tabularnewline
35 & 595 & 595.531725919535 & -0.531725919535437 \tabularnewline
36 & 612 & 611.927949381325 & 0.072050618674589 \tabularnewline
37 & 628 & 627.885826550686 & 0.114173449314361 \tabularnewline
38 & 629 & 629.238726948595 & -0.238726948594866 \tabularnewline
39 & 621 & 620.592634349309 & 0.40736565069142 \tabularnewline
40 & 569 & 569.122317684652 & -0.122317684652045 \tabularnewline
41 & 567 & 567.118507239141 & -0.118507239141119 \tabularnewline
42 & 573 & 572.791886142236 & 0.208113857763615 \tabularnewline
43 & 584 & 583.779919048605 & 0.22008095139471 \tabularnewline
44 & 589 & 589.954830974039 & -0.95483097403893 \tabularnewline
45 & 591 & 590.959302892544 & 0.0406971074564472 \tabularnewline
46 & 595 & 595.50877856471 & -0.508778564710214 \tabularnewline
47 & 594 & 593.49872730514 & 0.501272694859649 \tabularnewline
48 & 611 & 610.893632751282 & 0.10636724871846 \tabularnewline
49 & 613 & 612.808405380871 & 0.191594619129385 \tabularnewline
50 & 611 & 611.139605584947 & -0.139605584947323 \tabularnewline
51 & 594 & 593.491241937775 & 0.508758062224703 \tabularnewline
52 & 543 & 543.038027420157 & -0.0380274201572939 \tabularnewline
53 & 537 & 537.037412645336 & -0.0374126453360052 \tabularnewline
54 & 544 & 543.702507327073 & 0.297492672926489 \tabularnewline
55 & 555 & 555.684064531608 & -0.684064531607558 \tabularnewline
56 & 561 & 560.823454147315 & 0.176545852684604 \tabularnewline
57 & 562 & 561.824911866615 & 0.175088133384799 \tabularnewline
58 & 555 & 554.410834471312 & 0.589165528688428 \tabularnewline
59 & 547 & 547.438520184409 & -0.43852018440892 \tabularnewline
60 & 565 & 565.839944532834 & -0.839944532833867 \tabularnewline
61 & 578 & 577.794051167175 & 0.205948832825227 \tabularnewline
62 & 580 & 580.133844624998 & -0.133844624998512 \tabularnewline
63 & 569 & 569.4843596586 & -0.484359658600151 \tabularnewline
64 & 507 & 507.053994629552 & -0.0539946295522733 \tabularnewline
65 & 501 & 500.072515193226 & 0.927484806773918 \tabularnewline
66 & 509 & 509.734861507699 & -0.734861507699181 \tabularnewline
67 & 510 & 509.720245198275 & 0.279754801724897 \tabularnewline
68 & 517 & 516.894441947901 & 0.105558052099082 \tabularnewline
69 & 519 & 518.886409766161 & 0.113590233838968 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158795&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]518[/C][C]518.276300309158[/C][C]-0.27630030915755[/C][/ROW]
[ROW][C]2[/C][C]534[/C][C]533.608537905927[/C][C]0.391462094073181[/C][/ROW]
[ROW][C]3[/C][C]528[/C][C]528.905797667412[/C][C]-0.905797667411849[/C][/ROW]
[ROW][C]4[/C][C]478[/C][C]477.332461977106[/C][C]0.667538022894229[/C][/ROW]
[ROW][C]5[/C][C]469[/C][C]469.349889197065[/C][C]-0.349889197064548[/C][/ROW]
[ROW][C]6[/C][C]490[/C][C]489.996129597185[/C][C]0.00387040281504461[/C][/ROW]
[ROW][C]7[/C][C]493[/C][C]493.015605245871[/C][C]-0.0156052458710455[/C][/ROW]
[ROW][C]8[/C][C]508[/C][C]507.191092035634[/C][C]0.808907964365636[/C][/ROW]
[ROW][C]9[/C][C]517[/C][C]517.19482080282[/C][C]-0.1948208028199[/C][/ROW]
[ROW][C]10[/C][C]514[/C][C]513.806551144269[/C][C]0.193448855731323[/C][/ROW]
[ROW][C]11[/C][C]510[/C][C]509.841133927557[/C][C]0.158866072442967[/C][/ROW]
[ROW][C]12[/C][C]527[/C][C]527.25747373559[/C][C]-0.257473735590341[/C][/ROW]
[ROW][C]13[/C][C]542[/C][C]542.238103282491[/C][C]-0.238103282491454[/C][/ROW]
[ROW][C]14[/C][C]565[/C][C]564.538407633069[/C][C]0.461592366930592[/C][/ROW]
[ROW][C]15[/C][C]555[/C][C]554.859204083421[/C][C]0.140795916578821[/C][/ROW]
[ROW][C]16[/C][C]499[/C][C]499.308087066333[/C][C]-0.308087066332982[/C][/ROW]
[ROW][C]17[/C][C]511[/C][C]511.281202460318[/C][C]-0.281202460318204[/C][/ROW]
[ROW][C]18[/C][C]526[/C][C]525.937829408771[/C][C]0.0621705912290423[/C][/ROW]
[ROW][C]19[/C][C]532[/C][C]531.949399004007[/C][C]0.05060099599346[/C][/ROW]
[ROW][C]20[/C][C]549[/C][C]549.12515162571[/C][C]-0.125151625710267[/C][/ROW]
[ROW][C]21[/C][C]561[/C][C]561.102666512725[/C][C]-0.102666512724869[/C][/ROW]
[ROW][C]22[/C][C]557[/C][C]557.724377259087[/C][C]-0.72437725908744[/C][/ROW]
[ROW][C]23[/C][C]566[/C][C]565.689892663358[/C][C]0.310107336641741[/C][/ROW]
[ROW][C]24[/C][C]588[/C][C]587.080999598969[/C][C]0.91900040103116[/C][/ROW]
[ROW][C]25[/C][C]620[/C][C]619.99731330962[/C][C]0.00268669038003056[/C][/ROW]
[ROW][C]26[/C][C]626[/C][C]626.340877302463[/C][C]-0.340877302463072[/C][/ROW]
[ROW][C]27[/C][C]620[/C][C]619.666762303483[/C][C]0.333237696517055[/C][/ROW]
[ROW][C]28[/C][C]573[/C][C]573.1451112222[/C][C]-0.145111222199634[/C][/ROW]
[ROW][C]29[/C][C]573[/C][C]573.140473264914[/C][C]-0.140473264914043[/C][/ROW]
[ROW][C]30[/C][C]574[/C][C]573.836786017035[/C][C]0.163213982964989[/C][/ROW]
[ROW][C]31[/C][C]580[/C][C]579.850766971634[/C][C]0.149233028365537[/C][/ROW]
[ROW][C]32[/C][C]590[/C][C]590.0110292694[/C][C]-0.0110292694001255[/C][/ROW]
[ROW][C]33[/C][C]593[/C][C]593.031888159135[/C][C]-0.0318881591354447[/C][/ROW]
[ROW][C]34[/C][C]597[/C][C]596.549458560622[/C][C]0.450541439377904[/C][/ROW]
[ROW][C]35[/C][C]595[/C][C]595.531725919535[/C][C]-0.531725919535437[/C][/ROW]
[ROW][C]36[/C][C]612[/C][C]611.927949381325[/C][C]0.072050618674589[/C][/ROW]
[ROW][C]37[/C][C]628[/C][C]627.885826550686[/C][C]0.114173449314361[/C][/ROW]
[ROW][C]38[/C][C]629[/C][C]629.238726948595[/C][C]-0.238726948594866[/C][/ROW]
[ROW][C]39[/C][C]621[/C][C]620.592634349309[/C][C]0.40736565069142[/C][/ROW]
[ROW][C]40[/C][C]569[/C][C]569.122317684652[/C][C]-0.122317684652045[/C][/ROW]
[ROW][C]41[/C][C]567[/C][C]567.118507239141[/C][C]-0.118507239141119[/C][/ROW]
[ROW][C]42[/C][C]573[/C][C]572.791886142236[/C][C]0.208113857763615[/C][/ROW]
[ROW][C]43[/C][C]584[/C][C]583.779919048605[/C][C]0.22008095139471[/C][/ROW]
[ROW][C]44[/C][C]589[/C][C]589.954830974039[/C][C]-0.95483097403893[/C][/ROW]
[ROW][C]45[/C][C]591[/C][C]590.959302892544[/C][C]0.0406971074564472[/C][/ROW]
[ROW][C]46[/C][C]595[/C][C]595.50877856471[/C][C]-0.508778564710214[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.49872730514[/C][C]0.501272694859649[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.893632751282[/C][C]0.10636724871846[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.808405380871[/C][C]0.191594619129385[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]611.139605584947[/C][C]-0.139605584947323[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.491241937775[/C][C]0.508758062224703[/C][/ROW]
[ROW][C]52[/C][C]543[/C][C]543.038027420157[/C][C]-0.0380274201572939[/C][/ROW]
[ROW][C]53[/C][C]537[/C][C]537.037412645336[/C][C]-0.0374126453360052[/C][/ROW]
[ROW][C]54[/C][C]544[/C][C]543.702507327073[/C][C]0.297492672926489[/C][/ROW]
[ROW][C]55[/C][C]555[/C][C]555.684064531608[/C][C]-0.684064531607558[/C][/ROW]
[ROW][C]56[/C][C]561[/C][C]560.823454147315[/C][C]0.176545852684604[/C][/ROW]
[ROW][C]57[/C][C]562[/C][C]561.824911866615[/C][C]0.175088133384799[/C][/ROW]
[ROW][C]58[/C][C]555[/C][C]554.410834471312[/C][C]0.589165528688428[/C][/ROW]
[ROW][C]59[/C][C]547[/C][C]547.438520184409[/C][C]-0.43852018440892[/C][/ROW]
[ROW][C]60[/C][C]565[/C][C]565.839944532834[/C][C]-0.839944532833867[/C][/ROW]
[ROW][C]61[/C][C]578[/C][C]577.794051167175[/C][C]0.205948832825227[/C][/ROW]
[ROW][C]62[/C][C]580[/C][C]580.133844624998[/C][C]-0.133844624998512[/C][/ROW]
[ROW][C]63[/C][C]569[/C][C]569.4843596586[/C][C]-0.484359658600151[/C][/ROW]
[ROW][C]64[/C][C]507[/C][C]507.053994629552[/C][C]-0.0539946295522733[/C][/ROW]
[ROW][C]65[/C][C]501[/C][C]500.072515193226[/C][C]0.927484806773918[/C][/ROW]
[ROW][C]66[/C][C]509[/C][C]509.734861507699[/C][C]-0.734861507699181[/C][/ROW]
[ROW][C]67[/C][C]510[/C][C]509.720245198275[/C][C]0.279754801724897[/C][/ROW]
[ROW][C]68[/C][C]517[/C][C]516.894441947901[/C][C]0.105558052099082[/C][/ROW]
[ROW][C]69[/C][C]519[/C][C]518.886409766161[/C][C]0.113590233838968[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158795&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158795&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	518	518.276300309158	-0.27630030915755
2	534	533.608537905927	0.391462094073181
3	528	528.905797667412	-0.905797667411849
4	478	477.332461977106	0.667538022894229
5	469	469.349889197065	-0.349889197064548
6	490	489.996129597185	0.00387040281504461
7	493	493.015605245871	-0.0156052458710455
8	508	507.191092035634	0.808907964365636
9	517	517.19482080282	-0.1948208028199
10	514	513.806551144269	0.193448855731323
11	510	509.841133927557	0.158866072442967
12	527	527.25747373559	-0.257473735590341
13	542	542.238103282491	-0.238103282491454
14	565	564.538407633069	0.461592366930592
15	555	554.859204083421	0.140795916578821
16	499	499.308087066333	-0.308087066332982
17	511	511.281202460318	-0.281202460318204
18	526	525.937829408771	0.0621705912290423
19	532	531.949399004007	0.05060099599346
20	549	549.12515162571	-0.125151625710267
21	561	561.102666512725	-0.102666512724869
22	557	557.724377259087	-0.72437725908744
23	566	565.689892663358	0.310107336641741
24	588	587.080999598969	0.91900040103116
25	620	619.99731330962	0.00268669038003056
26	626	626.340877302463	-0.340877302463072
27	620	619.666762303483	0.333237696517055
28	573	573.1451112222	-0.145111222199634
29	573	573.140473264914	-0.140473264914043
30	574	573.836786017035	0.163213982964989
31	580	579.850766971634	0.149233028365537
32	590	590.0110292694	-0.0110292694001255
33	593	593.031888159135	-0.0318881591354447
34	597	596.549458560622	0.450541439377904
35	595	595.531725919535	-0.531725919535437
36	612	611.927949381325	0.072050618674589
37	628	627.885826550686	0.114173449314361
38	629	629.238726948595	-0.238726948594866
39	621	620.592634349309	0.40736565069142
40	569	569.122317684652	-0.122317684652045
41	567	567.118507239141	-0.118507239141119
42	573	572.791886142236	0.208113857763615
43	584	583.779919048605	0.22008095139471
44	589	589.954830974039	-0.95483097403893
45	591	590.959302892544	0.0406971074564472
46	595	595.50877856471	-0.508778564710214
47	594	593.49872730514	0.501272694859649
48	611	610.893632751282	0.10636724871846
49	613	612.808405380871	0.191594619129385
50	611	611.139605584947	-0.139605584947323
51	594	593.491241937775	0.508758062224703
52	543	543.038027420157	-0.0380274201572939
53	537	537.037412645336	-0.0374126453360052
54	544	543.702507327073	0.297492672926489
55	555	555.684064531608	-0.684064531607558
56	561	560.823454147315	0.176545852684604
57	562	561.824911866615	0.175088133384799
58	555	554.410834471312	0.589165528688428
59	547	547.438520184409	-0.43852018440892
60	565	565.839944532834	-0.839944532833867
61	578	577.794051167175	0.205948832825227
62	580	580.133844624998	-0.133844624998512
63	569	569.4843596586	-0.484359658600151
64	507	507.053994629552	-0.0539946295522733
65	501	500.072515193226	0.927484806773918
66	509	509.734861507699	-0.734861507699181
67	510	509.720245198275	0.279754801724897
68	517	516.894441947901	0.105558052099082
69	519	518.886409766161	0.113590233838968

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.69381168049899	0.61237663900202	0.30618831950101
19	0.532238888084449	0.935522223831103	0.467761111915551
20	0.512741555266785	0.97451688946643	0.487258444733215
21	0.389778034832297	0.779556069664594	0.610221965167703
22	0.460557695089998	0.921115390179996	0.539442304910002
23	0.375432104093792	0.750864208187585	0.624567895906208
24	0.399803670873029	0.799607341746057	0.600196329126971
25	0.445491251568149	0.890982503136297	0.554508748431851
26	0.683096553345775	0.63380689330845	0.316903446654225
27	0.676892904367437	0.646214191265126	0.323107095632563
28	0.593177638711453	0.813644722577093	0.406822361288547
29	0.572258725497401	0.855482549005198	0.427741274502599
30	0.505944141572917	0.988111716854165	0.494055858427083
31	0.443299744219911	0.886599488439821	0.55670025578009
32	0.377758066027202	0.755516132054405	0.622241933972798
33	0.301559601112728	0.603119202225457	0.698440398887272
34	0.245165927772274	0.490331855544548	0.754834072227726
35	0.361784145004091	0.723568290008182	0.638215854995909
36	0.332281572963433	0.664563145926866	0.667718427036567
37	0.262058356095422	0.524116712190845	0.737941643904578
38	0.216804098119316	0.433608196238632	0.783195901880684
39	0.195305714358611	0.390611428717221	0.80469428564139
40	0.140220085095808	0.280440170191615	0.859779914904192
41	0.107916684626995	0.21583336925399	0.892083315373005
42	0.0814669065236066	0.162933813047213	0.918533093476393
43	0.0563083842709629	0.112616768541926	0.943691615729037
44	0.156598039608886	0.313196079217771	0.843401960391114
45	0.106099277636934	0.212198555273868	0.893900722363066
46	0.287161846969434	0.574323693938868	0.712838153030566
47	0.21986577503981	0.439731550079621	0.78013422496019
48	0.160023627395021	0.320047254790041	0.83997637260498
49	0.0986466262504927	0.197293252500985	0.901353373749507
50	0.054753550495494	0.109507100990988	0.945246449504506
51	0.0849657683257698	0.16993153665154	0.91503423167423

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.69381168049899 & 0.61237663900202 & 0.30618831950101 \tabularnewline
19 & 0.532238888084449 & 0.935522223831103 & 0.467761111915551 \tabularnewline
20 & 0.512741555266785 & 0.97451688946643 & 0.487258444733215 \tabularnewline
21 & 0.389778034832297 & 0.779556069664594 & 0.610221965167703 \tabularnewline
22 & 0.460557695089998 & 0.921115390179996 & 0.539442304910002 \tabularnewline
23 & 0.375432104093792 & 0.750864208187585 & 0.624567895906208 \tabularnewline
24 & 0.399803670873029 & 0.799607341746057 & 0.600196329126971 \tabularnewline
25 & 0.445491251568149 & 0.890982503136297 & 0.554508748431851 \tabularnewline
26 & 0.683096553345775 & 0.63380689330845 & 0.316903446654225 \tabularnewline
27 & 0.676892904367437 & 0.646214191265126 & 0.323107095632563 \tabularnewline
28 & 0.593177638711453 & 0.813644722577093 & 0.406822361288547 \tabularnewline
29 & 0.572258725497401 & 0.855482549005198 & 0.427741274502599 \tabularnewline
30 & 0.505944141572917 & 0.988111716854165 & 0.494055858427083 \tabularnewline
31 & 0.443299744219911 & 0.886599488439821 & 0.55670025578009 \tabularnewline
32 & 0.377758066027202 & 0.755516132054405 & 0.622241933972798 \tabularnewline
33 & 0.301559601112728 & 0.603119202225457 & 0.698440398887272 \tabularnewline
34 & 0.245165927772274 & 0.490331855544548 & 0.754834072227726 \tabularnewline
35 & 0.361784145004091 & 0.723568290008182 & 0.638215854995909 \tabularnewline
36 & 0.332281572963433 & 0.664563145926866 & 0.667718427036567 \tabularnewline
37 & 0.262058356095422 & 0.524116712190845 & 0.737941643904578 \tabularnewline
38 & 0.216804098119316 & 0.433608196238632 & 0.783195901880684 \tabularnewline
39 & 0.195305714358611 & 0.390611428717221 & 0.80469428564139 \tabularnewline
40 & 0.140220085095808 & 0.280440170191615 & 0.859779914904192 \tabularnewline
41 & 0.107916684626995 & 0.21583336925399 & 0.892083315373005 \tabularnewline
42 & 0.0814669065236066 & 0.162933813047213 & 0.918533093476393 \tabularnewline
43 & 0.0563083842709629 & 0.112616768541926 & 0.943691615729037 \tabularnewline
44 & 0.156598039608886 & 0.313196079217771 & 0.843401960391114 \tabularnewline
45 & 0.106099277636934 & 0.212198555273868 & 0.893900722363066 \tabularnewline
46 & 0.287161846969434 & 0.574323693938868 & 0.712838153030566 \tabularnewline
47 & 0.21986577503981 & 0.439731550079621 & 0.78013422496019 \tabularnewline
48 & 0.160023627395021 & 0.320047254790041 & 0.83997637260498 \tabularnewline
49 & 0.0986466262504927 & 0.197293252500985 & 0.901353373749507 \tabularnewline
50 & 0.054753550495494 & 0.109507100990988 & 0.945246449504506 \tabularnewline
51 & 0.0849657683257698 & 0.16993153665154 & 0.91503423167423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158795&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]18[/C][C]0.69381168049899[/C][C]0.61237663900202[/C][C]0.30618831950101[/C][/ROW]
[ROW][C]19[/C][C]0.532238888084449[/C][C]0.935522223831103[/C][C]0.467761111915551[/C][/ROW]
[ROW][C]20[/C][C]0.512741555266785[/C][C]0.97451688946643[/C][C]0.487258444733215[/C][/ROW]
[ROW][C]21[/C][C]0.389778034832297[/C][C]0.779556069664594[/C][C]0.610221965167703[/C][/ROW]
[ROW][C]22[/C][C]0.460557695089998[/C][C]0.921115390179996[/C][C]0.539442304910002[/C][/ROW]
[ROW][C]23[/C][C]0.375432104093792[/C][C]0.750864208187585[/C][C]0.624567895906208[/C][/ROW]
[ROW][C]24[/C][C]0.399803670873029[/C][C]0.799607341746057[/C][C]0.600196329126971[/C][/ROW]
[ROW][C]25[/C][C]0.445491251568149[/C][C]0.890982503136297[/C][C]0.554508748431851[/C][/ROW]
[ROW][C]26[/C][C]0.683096553345775[/C][C]0.63380689330845[/C][C]0.316903446654225[/C][/ROW]
[ROW][C]27[/C][C]0.676892904367437[/C][C]0.646214191265126[/C][C]0.323107095632563[/C][/ROW]
[ROW][C]28[/C][C]0.593177638711453[/C][C]0.813644722577093[/C][C]0.406822361288547[/C][/ROW]
[ROW][C]29[/C][C]0.572258725497401[/C][C]0.855482549005198[/C][C]0.427741274502599[/C][/ROW]
[ROW][C]30[/C][C]0.505944141572917[/C][C]0.988111716854165[/C][C]0.494055858427083[/C][/ROW]
[ROW][C]31[/C][C]0.443299744219911[/C][C]0.886599488439821[/C][C]0.55670025578009[/C][/ROW]
[ROW][C]32[/C][C]0.377758066027202[/C][C]0.755516132054405[/C][C]0.622241933972798[/C][/ROW]
[ROW][C]33[/C][C]0.301559601112728[/C][C]0.603119202225457[/C][C]0.698440398887272[/C][/ROW]
[ROW][C]34[/C][C]0.245165927772274[/C][C]0.490331855544548[/C][C]0.754834072227726[/C][/ROW]
[ROW][C]35[/C][C]0.361784145004091[/C][C]0.723568290008182[/C][C]0.638215854995909[/C][/ROW]
[ROW][C]36[/C][C]0.332281572963433[/C][C]0.664563145926866[/C][C]0.667718427036567[/C][/ROW]
[ROW][C]37[/C][C]0.262058356095422[/C][C]0.524116712190845[/C][C]0.737941643904578[/C][/ROW]
[ROW][C]38[/C][C]0.216804098119316[/C][C]0.433608196238632[/C][C]0.783195901880684[/C][/ROW]
[ROW][C]39[/C][C]0.195305714358611[/C][C]0.390611428717221[/C][C]0.80469428564139[/C][/ROW]
[ROW][C]40[/C][C]0.140220085095808[/C][C]0.280440170191615[/C][C]0.859779914904192[/C][/ROW]
[ROW][C]41[/C][C]0.107916684626995[/C][C]0.21583336925399[/C][C]0.892083315373005[/C][/ROW]
[ROW][C]42[/C][C]0.0814669065236066[/C][C]0.162933813047213[/C][C]0.918533093476393[/C][/ROW]
[ROW][C]43[/C][C]0.0563083842709629[/C][C]0.112616768541926[/C][C]0.943691615729037[/C][/ROW]
[ROW][C]44[/C][C]0.156598039608886[/C][C]0.313196079217771[/C][C]0.843401960391114[/C][/ROW]
[ROW][C]45[/C][C]0.106099277636934[/C][C]0.212198555273868[/C][C]0.893900722363066[/C][/ROW]
[ROW][C]46[/C][C]0.287161846969434[/C][C]0.574323693938868[/C][C]0.712838153030566[/C][/ROW]
[ROW][C]47[/C][C]0.21986577503981[/C][C]0.439731550079621[/C][C]0.78013422496019[/C][/ROW]
[ROW][C]48[/C][C]0.160023627395021[/C][C]0.320047254790041[/C][C]0.83997637260498[/C][/ROW]
[ROW][C]49[/C][C]0.0986466262504927[/C][C]0.197293252500985[/C][C]0.901353373749507[/C][/ROW]
[ROW][C]50[/C][C]0.054753550495494[/C][C]0.109507100990988[/C][C]0.945246449504506[/C][/ROW]
[ROW][C]51[/C][C]0.0849657683257698[/C][C]0.16993153665154[/C][C]0.91503423167423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158795&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158795&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.69381168049899	0.61237663900202	0.30618831950101
19	0.532238888084449	0.935522223831103	0.467761111915551
20	0.512741555266785	0.97451688946643	0.487258444733215
21	0.389778034832297	0.779556069664594	0.610221965167703
22	0.460557695089998	0.921115390179996	0.539442304910002
23	0.375432104093792	0.750864208187585	0.624567895906208
24	0.399803670873029	0.799607341746057	0.600196329126971
25	0.445491251568149	0.890982503136297	0.554508748431851
26	0.683096553345775	0.63380689330845	0.316903446654225
27	0.676892904367437	0.646214191265126	0.323107095632563
28	0.593177638711453	0.813644722577093	0.406822361288547
29	0.572258725497401	0.855482549005198	0.427741274502599
30	0.505944141572917	0.988111716854165	0.494055858427083
31	0.443299744219911	0.886599488439821	0.55670025578009
32	0.377758066027202	0.755516132054405	0.622241933972798
33	0.301559601112728	0.603119202225457	0.698440398887272
34	0.245165927772274	0.490331855544548	0.754834072227726
35	0.361784145004091	0.723568290008182	0.638215854995909
36	0.332281572963433	0.664563145926866	0.667718427036567
37	0.262058356095422	0.524116712190845	0.737941643904578
38	0.216804098119316	0.433608196238632	0.783195901880684
39	0.195305714358611	0.390611428717221	0.80469428564139
40	0.140220085095808	0.280440170191615	0.859779914904192
41	0.107916684626995	0.21583336925399	0.892083315373005
42	0.0814669065236066	0.162933813047213	0.918533093476393
43	0.0563083842709629	0.112616768541926	0.943691615729037
44	0.156598039608886	0.313196079217771	0.843401960391114
45	0.106099277636934	0.212198555273868	0.893900722363066
46	0.287161846969434	0.574323693938868	0.712838153030566
47	0.21986577503981	0.439731550079621	0.78013422496019
48	0.160023627395021	0.320047254790041	0.83997637260498
49	0.0986466262504927	0.197293252500985	0.901353373749507
50	0.054753550495494	0.109507100990988	0.945246449504506
51	0.0849657683257698	0.16993153665154	0.91503423167423

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158795&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158795&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158795&T=6

As an alternative you can also use a QR Code:

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

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

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Include Monthly 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