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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 15 Nov 2012 13:53:09 -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/2012/Nov/15/t1353005630pd1ekvm03fk4icf.htm/, Retrieved Thu, 02 May 2024 02:37:15 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=189752, Retrieved Thu, 02 May 2024 02:37:15 +0000

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-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [WS7] [2012-11-15 18:53:09] [25a095d064099991cfda4df2228bcf08] [Current]

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81,03418763	86,9582902	96,70529891	91,03094482	108,8042818	101,1181891	76,91132508	107,0596158	98,17363849	98,35209361	97,9529321	83,2372117
75,57811005	85,22376406	101,9671936	95,33878814	100,2948505	89,11042103	71,76907399	94,84588782	99,82971016	106,6083541	86,19600326	83,34439697
87,48461288	88,46764167	83,64850573	116,6609723	113,9731354	116,1423525	87,82130934	101,9143869	105,0648085	113,7370878	94,38255317	77,53178472
70,08822129	94,86731581	91,37253152	107,1159807	93,00710269	106,0731409	95,12880307	93,0214225	107,3747148	97,45453956	96,82302931	81,05566774
80,64057443	93,39758573	107,2426702	87,77148401	85,11832356	105,6396865	90,08809408	93,314885	111,6843171	98,9275759	90,32562214	82,85770806
80,62006534	91,71872099	104,6089069	92,75212893	104,4065395	112,53674	83,25290756	106,3179769	98,2143621	103,1814547	116,166169	106,2240281
92,07506756	93,40957156	105,7013167	103,7920925	115,0920116	116,4988348	90,60106593	90,1868579	112,4506913	115,7650424	109,6292291	98,14249275
95,35451786	93,62942851	100,2702348	107,5725034	118,5993989	121,850154	100,2690906	126,250847	115,6487048	120,3390609	114,0805695	101,0355033
111,6243272	138,3315968	117,0363334	121,6054175	102,5754339	126,9136027	106,6582769	114,0263698	120,3619819	116,3221561	95,10612615	86,66980479
73,86994214	87,80551874	84,55711639	100,8709724	105,2033306	114,6573879	95,2487207	95,95035428	116,4142423	113,1168774	90,10656705	74,93251007
70,12572009	79,08771587	111,6504762	106,2864397	93,20692976	131,9272518	96,34695328	118,9540799	116,852669	116,408917	107,1056264	69,34637502
83,76252569	104,4370702	95,90533101	87,4540976	120,2090211	113,3939671	91,49981439	117,3157082	112,6583452	111,8202652	108,0836359	92,27191975
94,33648426	84,47557766	97,0634478	107,1001249	117,5684821	116,7592148	119,2512914	96,09472564				
80,70643133	86,73397302	96,63408089	91,07505238	108,495847	100,8528302	76,81077119	106,8159236	97,86448	98,04264189	97,79934907	83,14052298
75,1804784	84,95503186	101,7737828	95,14619541	100,1327704	88,90603429	71,73301977	94,71047401	99,691576	106,3983142	85,97164711	83,29817682
87,3267658	88,33555694	83,46510197	116,6726419	114,0297716	116,1683495	87,83095647	101,9208518	105,0009584	113,7120457	94,34459373	77,48834416
69,7291201	94,75166366	91,21504271	107,0691658	92,92788609	106,1255894	95,3334692	93,06043959	107,342928	97,31838584	96,82733826	80,9840039
80,47641488	93,32698232	107,2251227	87,66187014	85,11262144	105,6247458	90,27609764	93,35347186	111,6756466	98,83932759	90,26834944	82,83714441
80,48691981	91,67145078	104,5515212	92,65386947	104,5076365	112,5332138	83,44859642	106,3168348	98,04254924	103,1541942	116,2573843	106,3758294
91,88891551	93,26420157	105,4865555	103,7469448	115,1602279	116,5841112	90,86416304	90,06464366	112,414701	115,6686118	109,628304	98,25476342
95,14753431	93,48986694	100,0513673	107,502911	118,7103306	121,9848295	100,4509598	126,3964684	115,6802309	120,2987469	114,1273272	101,223233
111,574245	138,5618362	117,1253668	121,6519456	102,6052069	127,0744692	106,8969711	114,2084566	120,4033666	116,4372117	95,17980199	86,75437473
73,6354974	87,7044599	84,30497685	100,8181207	105,3119321	114,7713115	95,55182384	95,98571197	116,5534704	113,2577127	90,19511869	74,95429835
69,86368901	79,05669445	111,7608934	106,4609228	93,36272533	132,2440491	96,55174999	119,166206	116,9901561	116,5750517	107,2657825	69,29949411
83,80548612	104,6260341	95,79568818	87,46220803	120,4705533	113,6496368	91,83981744	117,5425486	112,90271	112,0264811	108,2871454	92,44769876
94,38817844	84,41064517	97,09471042	107,365507	117,8911619	117,004753	119,8699351	96,24489788				
75,53712948	83,25531785	94,4671407	83,30858876	92,77356523	89,84481743	74,76499198	81,63547596	92,7959853	92,81553176	91,68027536	83,50134269
92,64933567	91,21494917	99,55152963	87,66614111	95,36196568	98,40548282	80,08639762	84,3015	92,39638434	98,06359806	92,11791656	79,97388374
88,66776457	87,36036288	95,9938241	94,24946026	92,07264103	93,71693627	83,4939354	80,97211155	96,37340364	101,6077124	91,59765717	83,94344633
91,66220019	90,40879752	97,95256596	95,52531784	90,46363541	97,19194915	87,96991755	79,91597689	102,0295106	104,2541349	92,12465123	95,82501972
89,07761242	92,2280028	107,4883614	99,62666941	92,11448497	108,851218	89,76715851	89,45548657	107,6371514	105,5669505	100,8474367	102,8009102
96,06585959	94,70342606	107,3050986	102,8797151	96,14356683	109,7983819	85,33850481	90,0277052	109,2217248	101,2463276	104,8017257	102,4679637
97,70639206	98,90651229	114,9188733	97,51609439	107,3179375	112,3321762	88,49906216	92,8638523	108,8458658	112,3456099	107,3276982	101,7300127
104,9679287	103,2885483	116,5999496	103,5955298	108,892749	117,0273622	100,8743135	101,003588	109,7605727	121,0160753	114,0654863	105,4714021
112,5035625	113,9257931	115,3031274	120,3659852	111,0795586	120,1441565	106,0743641	95,96547038	119,4084072	117,3452339	98,61591954	99,59951505
87,35765571	90,75630928	101,2317436	93,24050558	95,17461744	101,9705765	87,0239155	86,26625168	105,0533145	104,191095	99,1660366	95,07457298
92,53418755	99,12195623	113,5292964	104,7225358	100,4292779	116,2962226	94,1409653	94,906152	115,0959495	110,0720247	108,3459488	103,6658781
102,7537343	107,7198137	126,5222707	108,6080902	117,0826572	112,2422987	94,66474704	102,8153563	119,0240288	110,6443152	109,0752805	105,2393813
103,3325	103,5302529	116,8463198	101,2305215	105,4350306	110,3277847	97,8849154	100,5687108				
77,29100464	83,71833364	94,52908527	83,37015983	91,65538151	88,72022649	74,71250925	81,16634557	91,95179881	92,65630205	91,97270508	84,55623629
92,63704227	90,61798722	98,46232811	86,79260838	93,49756438	96,24452097	79,4770082	83,53373192	91,11952765	97,264638	92,21171313	81,2012148
89,37287322	87,18567205	95,33859408	92,99301261	90,94619048	92,44125394	83,26650452	81,27337492	95,04431829	100,5091819	91,66595477	84,7409361
92,49785302	90,42883912	97,04879426	94,51973011	89,69459327	95,75308431	87,69072293	80,85558483	100,8156912	103,2498408	91,90564925	96,07088678
90,06381565	91,72304517	105,7557666	97,89197637	91,2549727	106,3390419	89,66575399	89,07249335	105,6817864	104,0537901	100,211708	102,5806489
96,48082269	95,02540173	106,5506033	102,1635793	96,19846045	108,4437787	86,85967243	90,61282609	108,0021874	101,2442327	104,7114018	103,7070335
99,26477172	99,61879347	114,0644769	97,15870495	106,1789643	110,7159742	89,31288033	93,12025091	107,8393264	111,862353	106,9952868	102,607595
105,9019862	103,8032421	115,9432469	103,7289489	108,2163749	115,7006479	100,4828691	101,103678	108,723868	119,7824651	113,7705064	105,9378123
112,3953146	112,9137395	113,5407096	117,5791072	109,3852137	117,9946509	105,6537745	96,27981673	117,5188789	116,5534812	99,21968577	100,7782938
90,15940639	92,06632721	101,8355342	93,59005029	95,83303081	102,0087188	89,05352626	88,85866903	105,9591558	105,2117443	100,1878355	97,43346979
95,23902588	100,5784294	114,1557567	105,095025	101,4275161	115,5554292	95,42773489	96,46035237	114,7883551	110,5053701	109,0335698	104,7322212
103,948059	107,5512439	125,0182101	108,1997767	116,0154521	111,2322927	95,58841472	103,4130547	117,9747332	110,5999955	109,2719442	105,7472281
103,8851935	103,0618697	115,4093993	100,7954475	105,0780277	108,9070656	97,83565609	100,7179532				
77,51981343	83,81722698	94,57631206	83,52696409	91,72388945	88,8935344	74,92056118	81,32162004	92,02672818	92,72873997	91,9884162	84,73901849
92,69304744	90,61280594	98,51534114	86,90165063	93,55303102	96,35309487	79,6901409	83,6994862	91,18289133	97,25260599	92,28359093	81,36420395
89,51966307	87,21051677	95,34681031	93,01950302	91,09420883	92,43439659	83,43891123	81,49103634	95,13068815	100,5482013	91,72437065	84,88596607
92,6404867	90,48557641	97,08726211	94,62979359	89,81819271	95,83704279	87,88779864	81,13171768	100,8212701	103,2641128	91,9599457	96,12056486
90,22904457	91,73284742	105,6933284	97,94737916	91,43487452	106,2425816	89,76344344	89,16708818	105,5870127	103,9988594	100,2454749	102,5412952
96,4967059	95,00650211	106,5401097	102,1291675	96,31840945	108,3803867	87,02126023	90,68735517	107,8497647	101,1987184	104,6581007	103,7135195
99,2902562	99,5741627	113,8975008	97,14675363	106,1786321	110,6778162	89,55193338	93,25396992	107,6165582	111,6742991	106,9362751	102,608225
105,8379865	103,6815615	115,8361795	103,8233484	108,1536635	115,530632	100,47252	101,1121663	108,6331569	119,4980426	113,6430874	105,8776908
112,3099082	112,6939362	113,376877	117,2837591	109,3704239	117,7501393	105,6176446	96,29952596	117,2506038	116,370522	99,17995995	100,7702181
90,40433012	92,11094438	101,8166353	93,74312344	96,00866777	102,0139534	89,21144197	89,03872497	105,8952086	105,1586259	100,233378	97,51678452
95,37541949	100,553915	114,0176275	105,1112121	101,4480037	115,4969022	95,50820162	96,51091766	114,6081296	110,346609	108,9632928	104,7489924
103,9335197	107,4101668	124,7816543	108,1234638	115,8918041	111,1833626	95,60844252	103,3318739	117,7356386	110,460683	109,1974592	105,7747233

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'George Udny Yule' @ yule.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189752&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189752&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189752&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	8 seconds
R Server	'George Udny Yule' @ yule.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
Maart[t] = + 26.9631083775115 + 0.33098020691659Januari[t] + 0.229091084400067Februari[t] + 0.0730654961934324April[t] -0.0541294587441554Mei[t] + 0.179952198871172Juni[t] -0.376125406787961Juli[t] + 0.0964404103799477Augustus[t] + 0.363889505058277September[t] + 0.10640615358495Oktober[t] -0.0262306680046949November[t] -0.232224505457716`December\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Maart[t] =  +  26.9631083775115 +  0.33098020691659Januari[t] +  0.229091084400067Februari[t] +  0.0730654961934324April[t] -0.0541294587441554Mei[t] +  0.179952198871172Juni[t] -0.376125406787961Juli[t] +  0.0964404103799477Augustus[t] +  0.363889505058277September[t] +  0.10640615358495Oktober[t] -0.0262306680046949November[t] -0.232224505457716`December\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189752&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Maart[t] =  +  26.9631083775115 +  0.33098020691659Januari[t] +  0.229091084400067Februari[t] +  0.0730654961934324April[t] -0.0541294587441554Mei[t] +  0.179952198871172Juni[t] -0.376125406787961Juli[t] +  0.0964404103799477Augustus[t] +  0.363889505058277September[t] +  0.10640615358495Oktober[t] -0.0262306680046949November[t] -0.232224505457716`December\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189752&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189752&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
Maart[t] = + 26.9631083775115 + 0.33098020691659Januari[t] + 0.229091084400067Februari[t] + 0.0730654961934324April[t] -0.0541294587441554Mei[t] + 0.179952198871172Juni[t] -0.376125406787961Juli[t] + 0.0964404103799477Augustus[t] + 0.363889505058277September[t] + 0.10640615358495Oktober[t] -0.0262306680046949November[t] -0.232224505457716`December\r`[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)	26.9631083775115	18.073206	1.4919	0.141772	0.070886
Januari	0.33098020691659	0.194678	1.7001	0.095079	0.04754
Februari	0.229091084400067	0.155864	1.4698	0.147637	0.073819
April	0.0730654961934324	0.186461	0.3919	0.696768	0.348384
Mei	-0.0541294587441554	0.197053	-0.2747	0.784639	0.392319
Juni	0.179952198871172	0.218437	0.8238	0.413806	0.206903
Juli	-0.376125406787961	0.163398	-2.3019	0.025378	0.012689
Augustus	0.0964404103799477	0.168288	0.5731	0.569069	0.284535
September	0.363889505058277	0.193786	1.8778	0.066022	0.033011
Oktober	0.10640615358495	0.19801	0.5374	0.593299	0.296649
November	-0.0262306680046949	0.184383	-0.1423	0.887423	0.443711
`December\r`	-0.232224505457716	0.183777	-1.2636	0.212001	0.106

\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) & 26.9631083775115 & 18.073206 & 1.4919 & 0.141772 & 0.070886 \tabularnewline
Januari & 0.33098020691659 & 0.194678 & 1.7001 & 0.095079 & 0.04754 \tabularnewline
Februari & 0.229091084400067 & 0.155864 & 1.4698 & 0.147637 & 0.073819 \tabularnewline
April & 0.0730654961934324 & 0.186461 & 0.3919 & 0.696768 & 0.348384 \tabularnewline
Mei & -0.0541294587441554 & 0.197053 & -0.2747 & 0.784639 & 0.392319 \tabularnewline
Juni & 0.179952198871172 & 0.218437 & 0.8238 & 0.413806 & 0.206903 \tabularnewline
Juli & -0.376125406787961 & 0.163398 & -2.3019 & 0.025378 & 0.012689 \tabularnewline
Augustus & 0.0964404103799477 & 0.168288 & 0.5731 & 0.569069 & 0.284535 \tabularnewline
September & 0.363889505058277 & 0.193786 & 1.8778 & 0.066022 & 0.033011 \tabularnewline
Oktober & 0.10640615358495 & 0.19801 & 0.5374 & 0.593299 & 0.296649 \tabularnewline
November & -0.0262306680046949 & 0.184383 & -0.1423 & 0.887423 & 0.443711 \tabularnewline
`December\r` & -0.232224505457716 & 0.183777 & -1.2636 & 0.212001 & 0.106 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189752&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]26.9631083775115[/C][C]18.073206[/C][C]1.4919[/C][C]0.141772[/C][C]0.070886[/C][/ROW]
[ROW][C]Januari[/C][C]0.33098020691659[/C][C]0.194678[/C][C]1.7001[/C][C]0.095079[/C][C]0.04754[/C][/ROW]
[ROW][C]Februari[/C][C]0.229091084400067[/C][C]0.155864[/C][C]1.4698[/C][C]0.147637[/C][C]0.073819[/C][/ROW]
[ROW][C]April[/C][C]0.0730654961934324[/C][C]0.186461[/C][C]0.3919[/C][C]0.696768[/C][C]0.348384[/C][/ROW]
[ROW][C]Mei[/C][C]-0.0541294587441554[/C][C]0.197053[/C][C]-0.2747[/C][C]0.784639[/C][C]0.392319[/C][/ROW]
[ROW][C]Juni[/C][C]0.179952198871172[/C][C]0.218437[/C][C]0.8238[/C][C]0.413806[/C][C]0.206903[/C][/ROW]
[ROW][C]Juli[/C][C]-0.376125406787961[/C][C]0.163398[/C][C]-2.3019[/C][C]0.025378[/C][C]0.012689[/C][/ROW]
[ROW][C]Augustus[/C][C]0.0964404103799477[/C][C]0.168288[/C][C]0.5731[/C][C]0.569069[/C][C]0.284535[/C][/ROW]
[ROW][C]September[/C][C]0.363889505058277[/C][C]0.193786[/C][C]1.8778[/C][C]0.066022[/C][C]0.033011[/C][/ROW]
[ROW][C]Oktober[/C][C]0.10640615358495[/C][C]0.19801[/C][C]0.5374[/C][C]0.593299[/C][C]0.296649[/C][/ROW]
[ROW][C]November[/C][C]-0.0262306680046949[/C][C]0.184383[/C][C]-0.1423[/C][C]0.887423[/C][C]0.443711[/C][/ROW]
[ROW][C]`December\r`[/C][C]-0.232224505457716[/C][C]0.183777[/C][C]-1.2636[/C][C]0.212001[/C][C]0.106[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189752&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189752&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)	26.9631083775115	18.073206	1.4919	0.141772	0.070886
Januari	0.33098020691659	0.194678	1.7001	0.095079	0.04754
Februari	0.229091084400067	0.155864	1.4698	0.147637	0.073819
April	0.0730654961934324	0.186461	0.3919	0.696768	0.348384
Mei	-0.0541294587441554	0.197053	-0.2747	0.784639	0.392319
Juni	0.179952198871172	0.218437	0.8238	0.413806	0.206903
Juli	-0.376125406787961	0.163398	-2.3019	0.025378	0.012689
Augustus	0.0964404103799477	0.168288	0.5731	0.569069	0.284535
September	0.363889505058277	0.193786	1.8778	0.066022	0.033011
Oktober	0.10640615358495	0.19801	0.5374	0.593299	0.296649
November	-0.0262306680046949	0.184383	-0.1423	0.887423	0.443711
`December\r`	-0.232224505457716	0.183777	-1.2636	0.212001	0.106

Multiple Linear Regression - Regression Statistics
Multiple R	0.655316246477304
R-squared	0.429439382897103
Adjusted R-squared	0.308743867740721
F-TEST (value)	3.55803927213608
F-TEST (DF numerator)	11
F-TEST (DF denominator)	52
p-value	0.000904320685802373
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.64679673822833
Sum Squared Residuals	4839.15574005202

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.655316246477304 \tabularnewline
R-squared & 0.429439382897103 \tabularnewline
Adjusted R-squared & 0.308743867740721 \tabularnewline
F-TEST (value) & 3.55803927213608 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0.000904320685802373 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.64679673822833 \tabularnewline
Sum Squared Residuals & 4839.15574005202 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189752&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.655316246477304[/C][/ROW]
[ROW][C]R-squared[/C][C]0.429439382897103[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.308743867740721[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.55803927213608[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]0.000904320685802373[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.64679673822833[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4839.15574005202[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189752&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189752&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	0.655316246477304
R-squared	0.429439382897103
Adjusted R-squared	0.308743867740721
F-TEST (value)	3.55803927213608
F-TEST (DF numerator)	11
F-TEST (DF denominator)	52
p-value	0.000904320685802373
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.64679673822833
Sum Squared Residuals	4839.15574005202

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	96.70529891	98.3504376936718	-1.64513878367176
2	101.9671936	97.2826403026991	4.6845532973009
3	83.64850573	106.091240037889	-22.4427343078893
4	91.37253152	95.0444455576572	-3.67191403765719
5	107.2426702	100.537115498901	6.70555470109904
6	104.6089069	93.978634529497	10.630272370503
7	105.7013167	103.3467016014	2.35461509860036
8	100.2702348	106.235322171457	-5.96508737145696
9	117.0363334	126.204443697656	-9.16811029765581
10	84.55711639	101.898056018729	-17.3409396287293
11	111.6504762	105.981002666073	5.66947353392662
12	95.90533101	104.430186652326	-8.5248556423264
13	97.0634478	79.3382926595076	17.7251551404924
14	76.81077119	88.9933055825499	-12.1825343925499
15	71.73301977	88.733418666929	-17.000398896929
16	87.83095647	93.7305726741712	-5.89961620417121
17	95.3334692	87.9707513635464	7.36271783645359
18	90.27609764	86.7141439343632	3.56195370563681
19	83.44859642	91.3906536005087	-7.94205718050874
20	90.86416304	98.3213379223825	-7.45717488238255
21	100.4509598	109.678563513383	-9.22760371338314
22	106.8969711	95.8862403122717	11.0107307877283
23	95.55182384	88.68193104238	6.86989279761996
24	96.55174999	96.6555395627497	-0.1037895727497
25	91.83981744	99.5440153556143	-7.70419791561435
26	119.8699351	105.616167307211	14.2537677927894
27	91.68027536	90.9455655332525	0.734709826747473
28	92.11791656	92.240718307079	-0.122801747079055
29	91.59765717	94.3439078654115	-2.74625069541151
30	92.12465123	94.5454196225489	-2.42076839254892
31	100.8474367	99.2119396898344	1.63549701016559
32	104.8017257	99.603129750446	5.19859594955401
33	107.3276982	101.174854282298	6.15284391770187
34	114.0654863	109.509686874201	4.5557994257994
35	98.61591954	106.331967436153	-7.7160478961534
36	99.1660366	97.1870972991023	1.97893930089766
37	108.3459488	102.745946083061	5.60000271693914
38	109.0752805	102.429858112514	6.64542238748638
39	94.52908527	89.81705302709	4.71203224290997
40	98.46232811	97.3779087676135	1.08441934238654
41	95.33859408	94.7403739871368	0.598220092863209
42	97.04879426	95.3429635843063	1.70583067569369
43	105.7557666	97.0769841941256	8.67878240587438
44	106.5506033	101.750497628165	4.80010567183463
45	114.0644769	103.812280349083	10.2521965509174
46	115.9432469	105.016619922426	10.9266269775737
47	113.5407096	112.640976515905	0.899733084094858
48	101.8355342	97.4756100368458	4.35992416315416
49	114.1557567	104.26721733095	9.88853936905017
50	125.0182101	108.944081553605	16.0741285463948
51	115.4093993	94.3968375466625	21.0125617533376
52	74.92056118	89.5138611764104	-14.5932999964104
53	79.6901409	89.5708061176897	-9.88066521768967
54	83.43891123	89.6917382947124	-6.25282706471238
55	87.88779864	89.4572853001157	-1.5694866601157
56	89.76344344	91.976119324131	-2.21267588413104
57	87.02126023	94.4869000676099	-7.46563983760989
58	89.55193338	100.616924080935	-11.064990700935
59	100.47252	102.354806701823	-1.88228670182253
60	105.6176446	102.441061059464	3.17658354053556
61	89.21144197	91.5174489448193	-2.30600697481927
62	95.50820162	97.6903258707097	-2.18212425070966
63	95.60844252	96.0399356660687	-0.431493146068669
64	76.91132508	89.28798910017	-12.37666402017

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 96.70529891 & 98.3504376936718 & -1.64513878367176 \tabularnewline
2 & 101.9671936 & 97.2826403026991 & 4.6845532973009 \tabularnewline
3 & 83.64850573 & 106.091240037889 & -22.4427343078893 \tabularnewline
4 & 91.37253152 & 95.0444455576572 & -3.67191403765719 \tabularnewline
5 & 107.2426702 & 100.537115498901 & 6.70555470109904 \tabularnewline
6 & 104.6089069 & 93.978634529497 & 10.630272370503 \tabularnewline
7 & 105.7013167 & 103.3467016014 & 2.35461509860036 \tabularnewline
8 & 100.2702348 & 106.235322171457 & -5.96508737145696 \tabularnewline
9 & 117.0363334 & 126.204443697656 & -9.16811029765581 \tabularnewline
10 & 84.55711639 & 101.898056018729 & -17.3409396287293 \tabularnewline
11 & 111.6504762 & 105.981002666073 & 5.66947353392662 \tabularnewline
12 & 95.90533101 & 104.430186652326 & -8.5248556423264 \tabularnewline
13 & 97.0634478 & 79.3382926595076 & 17.7251551404924 \tabularnewline
14 & 76.81077119 & 88.9933055825499 & -12.1825343925499 \tabularnewline
15 & 71.73301977 & 88.733418666929 & -17.000398896929 \tabularnewline
16 & 87.83095647 & 93.7305726741712 & -5.89961620417121 \tabularnewline
17 & 95.3334692 & 87.9707513635464 & 7.36271783645359 \tabularnewline
18 & 90.27609764 & 86.7141439343632 & 3.56195370563681 \tabularnewline
19 & 83.44859642 & 91.3906536005087 & -7.94205718050874 \tabularnewline
20 & 90.86416304 & 98.3213379223825 & -7.45717488238255 \tabularnewline
21 & 100.4509598 & 109.678563513383 & -9.22760371338314 \tabularnewline
22 & 106.8969711 & 95.8862403122717 & 11.0107307877283 \tabularnewline
23 & 95.55182384 & 88.68193104238 & 6.86989279761996 \tabularnewline
24 & 96.55174999 & 96.6555395627497 & -0.1037895727497 \tabularnewline
25 & 91.83981744 & 99.5440153556143 & -7.70419791561435 \tabularnewline
26 & 119.8699351 & 105.616167307211 & 14.2537677927894 \tabularnewline
27 & 91.68027536 & 90.9455655332525 & 0.734709826747473 \tabularnewline
28 & 92.11791656 & 92.240718307079 & -0.122801747079055 \tabularnewline
29 & 91.59765717 & 94.3439078654115 & -2.74625069541151 \tabularnewline
30 & 92.12465123 & 94.5454196225489 & -2.42076839254892 \tabularnewline
31 & 100.8474367 & 99.2119396898344 & 1.63549701016559 \tabularnewline
32 & 104.8017257 & 99.603129750446 & 5.19859594955401 \tabularnewline
33 & 107.3276982 & 101.174854282298 & 6.15284391770187 \tabularnewline
34 & 114.0654863 & 109.509686874201 & 4.5557994257994 \tabularnewline
35 & 98.61591954 & 106.331967436153 & -7.7160478961534 \tabularnewline
36 & 99.1660366 & 97.1870972991023 & 1.97893930089766 \tabularnewline
37 & 108.3459488 & 102.745946083061 & 5.60000271693914 \tabularnewline
38 & 109.0752805 & 102.429858112514 & 6.64542238748638 \tabularnewline
39 & 94.52908527 & 89.81705302709 & 4.71203224290997 \tabularnewline
40 & 98.46232811 & 97.3779087676135 & 1.08441934238654 \tabularnewline
41 & 95.33859408 & 94.7403739871368 & 0.598220092863209 \tabularnewline
42 & 97.04879426 & 95.3429635843063 & 1.70583067569369 \tabularnewline
43 & 105.7557666 & 97.0769841941256 & 8.67878240587438 \tabularnewline
44 & 106.5506033 & 101.750497628165 & 4.80010567183463 \tabularnewline
45 & 114.0644769 & 103.812280349083 & 10.2521965509174 \tabularnewline
46 & 115.9432469 & 105.016619922426 & 10.9266269775737 \tabularnewline
47 & 113.5407096 & 112.640976515905 & 0.899733084094858 \tabularnewline
48 & 101.8355342 & 97.4756100368458 & 4.35992416315416 \tabularnewline
49 & 114.1557567 & 104.26721733095 & 9.88853936905017 \tabularnewline
50 & 125.0182101 & 108.944081553605 & 16.0741285463948 \tabularnewline
51 & 115.4093993 & 94.3968375466625 & 21.0125617533376 \tabularnewline
52 & 74.92056118 & 89.5138611764104 & -14.5932999964104 \tabularnewline
53 & 79.6901409 & 89.5708061176897 & -9.88066521768967 \tabularnewline
54 & 83.43891123 & 89.6917382947124 & -6.25282706471238 \tabularnewline
55 & 87.88779864 & 89.4572853001157 & -1.5694866601157 \tabularnewline
56 & 89.76344344 & 91.976119324131 & -2.21267588413104 \tabularnewline
57 & 87.02126023 & 94.4869000676099 & -7.46563983760989 \tabularnewline
58 & 89.55193338 & 100.616924080935 & -11.064990700935 \tabularnewline
59 & 100.47252 & 102.354806701823 & -1.88228670182253 \tabularnewline
60 & 105.6176446 & 102.441061059464 & 3.17658354053556 \tabularnewline
61 & 89.21144197 & 91.5174489448193 & -2.30600697481927 \tabularnewline
62 & 95.50820162 & 97.6903258707097 & -2.18212425070966 \tabularnewline
63 & 95.60844252 & 96.0399356660687 & -0.431493146068669 \tabularnewline
64 & 76.91132508 & 89.28798910017 & -12.37666402017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189752&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]96.70529891[/C][C]98.3504376936718[/C][C]-1.64513878367176[/C][/ROW]
[ROW][C]2[/C][C]101.9671936[/C][C]97.2826403026991[/C][C]4.6845532973009[/C][/ROW]
[ROW][C]3[/C][C]83.64850573[/C][C]106.091240037889[/C][C]-22.4427343078893[/C][/ROW]
[ROW][C]4[/C][C]91.37253152[/C][C]95.0444455576572[/C][C]-3.67191403765719[/C][/ROW]
[ROW][C]5[/C][C]107.2426702[/C][C]100.537115498901[/C][C]6.70555470109904[/C][/ROW]
[ROW][C]6[/C][C]104.6089069[/C][C]93.978634529497[/C][C]10.630272370503[/C][/ROW]
[ROW][C]7[/C][C]105.7013167[/C][C]103.3467016014[/C][C]2.35461509860036[/C][/ROW]
[ROW][C]8[/C][C]100.2702348[/C][C]106.235322171457[/C][C]-5.96508737145696[/C][/ROW]
[ROW][C]9[/C][C]117.0363334[/C][C]126.204443697656[/C][C]-9.16811029765581[/C][/ROW]
[ROW][C]10[/C][C]84.55711639[/C][C]101.898056018729[/C][C]-17.3409396287293[/C][/ROW]
[ROW][C]11[/C][C]111.6504762[/C][C]105.981002666073[/C][C]5.66947353392662[/C][/ROW]
[ROW][C]12[/C][C]95.90533101[/C][C]104.430186652326[/C][C]-8.5248556423264[/C][/ROW]
[ROW][C]13[/C][C]97.0634478[/C][C]79.3382926595076[/C][C]17.7251551404924[/C][/ROW]
[ROW][C]14[/C][C]76.81077119[/C][C]88.9933055825499[/C][C]-12.1825343925499[/C][/ROW]
[ROW][C]15[/C][C]71.73301977[/C][C]88.733418666929[/C][C]-17.000398896929[/C][/ROW]
[ROW][C]16[/C][C]87.83095647[/C][C]93.7305726741712[/C][C]-5.89961620417121[/C][/ROW]
[ROW][C]17[/C][C]95.3334692[/C][C]87.9707513635464[/C][C]7.36271783645359[/C][/ROW]
[ROW][C]18[/C][C]90.27609764[/C][C]86.7141439343632[/C][C]3.56195370563681[/C][/ROW]
[ROW][C]19[/C][C]83.44859642[/C][C]91.3906536005087[/C][C]-7.94205718050874[/C][/ROW]
[ROW][C]20[/C][C]90.86416304[/C][C]98.3213379223825[/C][C]-7.45717488238255[/C][/ROW]
[ROW][C]21[/C][C]100.4509598[/C][C]109.678563513383[/C][C]-9.22760371338314[/C][/ROW]
[ROW][C]22[/C][C]106.8969711[/C][C]95.8862403122717[/C][C]11.0107307877283[/C][/ROW]
[ROW][C]23[/C][C]95.55182384[/C][C]88.68193104238[/C][C]6.86989279761996[/C][/ROW]
[ROW][C]24[/C][C]96.55174999[/C][C]96.6555395627497[/C][C]-0.1037895727497[/C][/ROW]
[ROW][C]25[/C][C]91.83981744[/C][C]99.5440153556143[/C][C]-7.70419791561435[/C][/ROW]
[ROW][C]26[/C][C]119.8699351[/C][C]105.616167307211[/C][C]14.2537677927894[/C][/ROW]
[ROW][C]27[/C][C]91.68027536[/C][C]90.9455655332525[/C][C]0.734709826747473[/C][/ROW]
[ROW][C]28[/C][C]92.11791656[/C][C]92.240718307079[/C][C]-0.122801747079055[/C][/ROW]
[ROW][C]29[/C][C]91.59765717[/C][C]94.3439078654115[/C][C]-2.74625069541151[/C][/ROW]
[ROW][C]30[/C][C]92.12465123[/C][C]94.5454196225489[/C][C]-2.42076839254892[/C][/ROW]
[ROW][C]31[/C][C]100.8474367[/C][C]99.2119396898344[/C][C]1.63549701016559[/C][/ROW]
[ROW][C]32[/C][C]104.8017257[/C][C]99.603129750446[/C][C]5.19859594955401[/C][/ROW]
[ROW][C]33[/C][C]107.3276982[/C][C]101.174854282298[/C][C]6.15284391770187[/C][/ROW]
[ROW][C]34[/C][C]114.0654863[/C][C]109.509686874201[/C][C]4.5557994257994[/C][/ROW]
[ROW][C]35[/C][C]98.61591954[/C][C]106.331967436153[/C][C]-7.7160478961534[/C][/ROW]
[ROW][C]36[/C][C]99.1660366[/C][C]97.1870972991023[/C][C]1.97893930089766[/C][/ROW]
[ROW][C]37[/C][C]108.3459488[/C][C]102.745946083061[/C][C]5.60000271693914[/C][/ROW]
[ROW][C]38[/C][C]109.0752805[/C][C]102.429858112514[/C][C]6.64542238748638[/C][/ROW]
[ROW][C]39[/C][C]94.52908527[/C][C]89.81705302709[/C][C]4.71203224290997[/C][/ROW]
[ROW][C]40[/C][C]98.46232811[/C][C]97.3779087676135[/C][C]1.08441934238654[/C][/ROW]
[ROW][C]41[/C][C]95.33859408[/C][C]94.7403739871368[/C][C]0.598220092863209[/C][/ROW]
[ROW][C]42[/C][C]97.04879426[/C][C]95.3429635843063[/C][C]1.70583067569369[/C][/ROW]
[ROW][C]43[/C][C]105.7557666[/C][C]97.0769841941256[/C][C]8.67878240587438[/C][/ROW]
[ROW][C]44[/C][C]106.5506033[/C][C]101.750497628165[/C][C]4.80010567183463[/C][/ROW]
[ROW][C]45[/C][C]114.0644769[/C][C]103.812280349083[/C][C]10.2521965509174[/C][/ROW]
[ROW][C]46[/C][C]115.9432469[/C][C]105.016619922426[/C][C]10.9266269775737[/C][/ROW]
[ROW][C]47[/C][C]113.5407096[/C][C]112.640976515905[/C][C]0.899733084094858[/C][/ROW]
[ROW][C]48[/C][C]101.8355342[/C][C]97.4756100368458[/C][C]4.35992416315416[/C][/ROW]
[ROW][C]49[/C][C]114.1557567[/C][C]104.26721733095[/C][C]9.88853936905017[/C][/ROW]
[ROW][C]50[/C][C]125.0182101[/C][C]108.944081553605[/C][C]16.0741285463948[/C][/ROW]
[ROW][C]51[/C][C]115.4093993[/C][C]94.3968375466625[/C][C]21.0125617533376[/C][/ROW]
[ROW][C]52[/C][C]74.92056118[/C][C]89.5138611764104[/C][C]-14.5932999964104[/C][/ROW]
[ROW][C]53[/C][C]79.6901409[/C][C]89.5708061176897[/C][C]-9.88066521768967[/C][/ROW]
[ROW][C]54[/C][C]83.43891123[/C][C]89.6917382947124[/C][C]-6.25282706471238[/C][/ROW]
[ROW][C]55[/C][C]87.88779864[/C][C]89.4572853001157[/C][C]-1.5694866601157[/C][/ROW]
[ROW][C]56[/C][C]89.76344344[/C][C]91.976119324131[/C][C]-2.21267588413104[/C][/ROW]
[ROW][C]57[/C][C]87.02126023[/C][C]94.4869000676099[/C][C]-7.46563983760989[/C][/ROW]
[ROW][C]58[/C][C]89.55193338[/C][C]100.616924080935[/C][C]-11.064990700935[/C][/ROW]
[ROW][C]59[/C][C]100.47252[/C][C]102.354806701823[/C][C]-1.88228670182253[/C][/ROW]
[ROW][C]60[/C][C]105.6176446[/C][C]102.441061059464[/C][C]3.17658354053556[/C][/ROW]
[ROW][C]61[/C][C]89.21144197[/C][C]91.5174489448193[/C][C]-2.30600697481927[/C][/ROW]
[ROW][C]62[/C][C]95.50820162[/C][C]97.6903258707097[/C][C]-2.18212425070966[/C][/ROW]
[ROW][C]63[/C][C]95.60844252[/C][C]96.0399356660687[/C][C]-0.431493146068669[/C][/ROW]
[ROW][C]64[/C][C]76.91132508[/C][C]89.28798910017[/C][C]-12.37666402017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189752&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189752&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	96.70529891	98.3504376936718	-1.64513878367176
2	101.9671936	97.2826403026991	4.6845532973009
3	83.64850573	106.091240037889	-22.4427343078893
4	91.37253152	95.0444455576572	-3.67191403765719
5	107.2426702	100.537115498901	6.70555470109904
6	104.6089069	93.978634529497	10.630272370503
7	105.7013167	103.3467016014	2.35461509860036
8	100.2702348	106.235322171457	-5.96508737145696
9	117.0363334	126.204443697656	-9.16811029765581
10	84.55711639	101.898056018729	-17.3409396287293
11	111.6504762	105.981002666073	5.66947353392662
12	95.90533101	104.430186652326	-8.5248556423264
13	97.0634478	79.3382926595076	17.7251551404924
14	76.81077119	88.9933055825499	-12.1825343925499
15	71.73301977	88.733418666929	-17.000398896929
16	87.83095647	93.7305726741712	-5.89961620417121
17	95.3334692	87.9707513635464	7.36271783645359
18	90.27609764	86.7141439343632	3.56195370563681
19	83.44859642	91.3906536005087	-7.94205718050874
20	90.86416304	98.3213379223825	-7.45717488238255
21	100.4509598	109.678563513383	-9.22760371338314
22	106.8969711	95.8862403122717	11.0107307877283
23	95.55182384	88.68193104238	6.86989279761996
24	96.55174999	96.6555395627497	-0.1037895727497
25	91.83981744	99.5440153556143	-7.70419791561435
26	119.8699351	105.616167307211	14.2537677927894
27	91.68027536	90.9455655332525	0.734709826747473
28	92.11791656	92.240718307079	-0.122801747079055
29	91.59765717	94.3439078654115	-2.74625069541151
30	92.12465123	94.5454196225489	-2.42076839254892
31	100.8474367	99.2119396898344	1.63549701016559
32	104.8017257	99.603129750446	5.19859594955401
33	107.3276982	101.174854282298	6.15284391770187
34	114.0654863	109.509686874201	4.5557994257994
35	98.61591954	106.331967436153	-7.7160478961534
36	99.1660366	97.1870972991023	1.97893930089766
37	108.3459488	102.745946083061	5.60000271693914
38	109.0752805	102.429858112514	6.64542238748638
39	94.52908527	89.81705302709	4.71203224290997
40	98.46232811	97.3779087676135	1.08441934238654
41	95.33859408	94.7403739871368	0.598220092863209
42	97.04879426	95.3429635843063	1.70583067569369
43	105.7557666	97.0769841941256	8.67878240587438
44	106.5506033	101.750497628165	4.80010567183463
45	114.0644769	103.812280349083	10.2521965509174
46	115.9432469	105.016619922426	10.9266269775737
47	113.5407096	112.640976515905	0.899733084094858
48	101.8355342	97.4756100368458	4.35992416315416
49	114.1557567	104.26721733095	9.88853936905017
50	125.0182101	108.944081553605	16.0741285463948
51	115.4093993	94.3968375466625	21.0125617533376
52	74.92056118	89.5138611764104	-14.5932999964104
53	79.6901409	89.5708061176897	-9.88066521768967
54	83.43891123	89.6917382947124	-6.25282706471238
55	87.88779864	89.4572853001157	-1.5694866601157
56	89.76344344	91.976119324131	-2.21267588413104
57	87.02126023	94.4869000676099	-7.46563983760989
58	89.55193338	100.616924080935	-11.064990700935
59	100.47252	102.354806701823	-1.88228670182253
60	105.6176446	102.441061059464	3.17658354053556
61	89.21144197	91.5174489448193	-2.30600697481927
62	95.50820162	97.6903258707097	-2.18212425070966
63	95.60844252	96.0399356660687	-0.431493146068669
64	76.91132508	89.28798910017	-12.37666402017

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
15	0.999052205847391	0.00189558830521759	0.000947794152608796
16	0.997330922015149	0.00533815596970308	0.00266907798485154
17	0.996157117202193	0.00768576559561325	0.00384288279780663
18	0.992210751707258	0.0155784965854844	0.0077892482927422
19	0.991986954359232	0.0160260912815356	0.00801304564076782
20	0.987284219251866	0.0254315614962679	0.0127157807481339
21	0.98817124199841	0.0236575160031791	0.0118287580015896
22	0.991043970053403	0.0179120598931933	0.00895602994659665
23	0.99258478684676	0.0148304263064791	0.00741521315323957
24	0.990769255811356	0.018461488377289	0.00923074418864448
25	0.99048691584459	0.0190261683108209	0.00951308415541046
26	0.999884081699966	0.000231836600068144	0.000115918300034072
27	0.999723240722643	0.000553518554714667	0.000276759277357334
28	0.999472770679832	0.00105445864033508	0.000527229320167541
29	0.999016660990897	0.0019666780182067	0.000983339009103352
30	0.997996283661117	0.00400743267776533	0.00200371633888266
31	0.998215851473377	0.00356829705324629	0.00178414852662315
32	0.99743387990183	0.00513224019634042	0.00256612009817021
33	0.997317409230859	0.005365181538282	0.002682590769141
34	0.997866885321977	0.00426622935604646	0.00213311467802323
35	0.995941843407088	0.00811631318582325	0.00405815659291162
36	0.995926208496724	0.00814758300655302	0.00407379150327651
37	0.993083582258048	0.0138328354839037	0.00691641774195186
38	0.998599942113632	0.00280011577273683	0.00140005788636842
39	0.99720836632216	0.00558326735567896	0.00279163367783948
40	0.995007873026365	0.00998425394727099	0.0049921269736355
41	0.99180479478413	0.016390410431741	0.00819520521587051
42	0.985224132287855	0.0295517354242893	0.0147758677121447
43	0.972989840194763	0.0540203196104743	0.0270101598052371
44	0.958712941469293	0.0825741170614146	0.0412870585307073
45	0.928043962754691	0.143912074490617	0.0719560372453087
46	0.876830768430757	0.246338463138486	0.123169231569243
47	0.824805627456278	0.350388745087444	0.175194372543722
48	0.738749677919631	0.522500644160738	0.261250322080369
49	0.89367835036777	0.212643299264461	0.10632164963223

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.999052205847391 & 0.00189558830521759 & 0.000947794152608796 \tabularnewline
16 & 0.997330922015149 & 0.00533815596970308 & 0.00266907798485154 \tabularnewline
17 & 0.996157117202193 & 0.00768576559561325 & 0.00384288279780663 \tabularnewline
18 & 0.992210751707258 & 0.0155784965854844 & 0.0077892482927422 \tabularnewline
19 & 0.991986954359232 & 0.0160260912815356 & 0.00801304564076782 \tabularnewline
20 & 0.987284219251866 & 0.0254315614962679 & 0.0127157807481339 \tabularnewline
21 & 0.98817124199841 & 0.0236575160031791 & 0.0118287580015896 \tabularnewline
22 & 0.991043970053403 & 0.0179120598931933 & 0.00895602994659665 \tabularnewline
23 & 0.99258478684676 & 0.0148304263064791 & 0.00741521315323957 \tabularnewline
24 & 0.990769255811356 & 0.018461488377289 & 0.00923074418864448 \tabularnewline
25 & 0.99048691584459 & 0.0190261683108209 & 0.00951308415541046 \tabularnewline
26 & 0.999884081699966 & 0.000231836600068144 & 0.000115918300034072 \tabularnewline
27 & 0.999723240722643 & 0.000553518554714667 & 0.000276759277357334 \tabularnewline
28 & 0.999472770679832 & 0.00105445864033508 & 0.000527229320167541 \tabularnewline
29 & 0.999016660990897 & 0.0019666780182067 & 0.000983339009103352 \tabularnewline
30 & 0.997996283661117 & 0.00400743267776533 & 0.00200371633888266 \tabularnewline
31 & 0.998215851473377 & 0.00356829705324629 & 0.00178414852662315 \tabularnewline
32 & 0.99743387990183 & 0.00513224019634042 & 0.00256612009817021 \tabularnewline
33 & 0.997317409230859 & 0.005365181538282 & 0.002682590769141 \tabularnewline
34 & 0.997866885321977 & 0.00426622935604646 & 0.00213311467802323 \tabularnewline
35 & 0.995941843407088 & 0.00811631318582325 & 0.00405815659291162 \tabularnewline
36 & 0.995926208496724 & 0.00814758300655302 & 0.00407379150327651 \tabularnewline
37 & 0.993083582258048 & 0.0138328354839037 & 0.00691641774195186 \tabularnewline
38 & 0.998599942113632 & 0.00280011577273683 & 0.00140005788636842 \tabularnewline
39 & 0.99720836632216 & 0.00558326735567896 & 0.00279163367783948 \tabularnewline
40 & 0.995007873026365 & 0.00998425394727099 & 0.0049921269736355 \tabularnewline
41 & 0.99180479478413 & 0.016390410431741 & 0.00819520521587051 \tabularnewline
42 & 0.985224132287855 & 0.0295517354242893 & 0.0147758677121447 \tabularnewline
43 & 0.972989840194763 & 0.0540203196104743 & 0.0270101598052371 \tabularnewline
44 & 0.958712941469293 & 0.0825741170614146 & 0.0412870585307073 \tabularnewline
45 & 0.928043962754691 & 0.143912074490617 & 0.0719560372453087 \tabularnewline
46 & 0.876830768430757 & 0.246338463138486 & 0.123169231569243 \tabularnewline
47 & 0.824805627456278 & 0.350388745087444 & 0.175194372543722 \tabularnewline
48 & 0.738749677919631 & 0.522500644160738 & 0.261250322080369 \tabularnewline
49 & 0.89367835036777 & 0.212643299264461 & 0.10632164963223 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189752&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]15[/C][C]0.999052205847391[/C][C]0.00189558830521759[/C][C]0.000947794152608796[/C][/ROW]
[ROW][C]16[/C][C]0.997330922015149[/C][C]0.00533815596970308[/C][C]0.00266907798485154[/C][/ROW]
[ROW][C]17[/C][C]0.996157117202193[/C][C]0.00768576559561325[/C][C]0.00384288279780663[/C][/ROW]
[ROW][C]18[/C][C]0.992210751707258[/C][C]0.0155784965854844[/C][C]0.0077892482927422[/C][/ROW]
[ROW][C]19[/C][C]0.991986954359232[/C][C]0.0160260912815356[/C][C]0.00801304564076782[/C][/ROW]
[ROW][C]20[/C][C]0.987284219251866[/C][C]0.0254315614962679[/C][C]0.0127157807481339[/C][/ROW]
[ROW][C]21[/C][C]0.98817124199841[/C][C]0.0236575160031791[/C][C]0.0118287580015896[/C][/ROW]
[ROW][C]22[/C][C]0.991043970053403[/C][C]0.0179120598931933[/C][C]0.00895602994659665[/C][/ROW]
[ROW][C]23[/C][C]0.99258478684676[/C][C]0.0148304263064791[/C][C]0.00741521315323957[/C][/ROW]
[ROW][C]24[/C][C]0.990769255811356[/C][C]0.018461488377289[/C][C]0.00923074418864448[/C][/ROW]
[ROW][C]25[/C][C]0.99048691584459[/C][C]0.0190261683108209[/C][C]0.00951308415541046[/C][/ROW]
[ROW][C]26[/C][C]0.999884081699966[/C][C]0.000231836600068144[/C][C]0.000115918300034072[/C][/ROW]
[ROW][C]27[/C][C]0.999723240722643[/C][C]0.000553518554714667[/C][C]0.000276759277357334[/C][/ROW]
[ROW][C]28[/C][C]0.999472770679832[/C][C]0.00105445864033508[/C][C]0.000527229320167541[/C][/ROW]
[ROW][C]29[/C][C]0.999016660990897[/C][C]0.0019666780182067[/C][C]0.000983339009103352[/C][/ROW]
[ROW][C]30[/C][C]0.997996283661117[/C][C]0.00400743267776533[/C][C]0.00200371633888266[/C][/ROW]
[ROW][C]31[/C][C]0.998215851473377[/C][C]0.00356829705324629[/C][C]0.00178414852662315[/C][/ROW]
[ROW][C]32[/C][C]0.99743387990183[/C][C]0.00513224019634042[/C][C]0.00256612009817021[/C][/ROW]
[ROW][C]33[/C][C]0.997317409230859[/C][C]0.005365181538282[/C][C]0.002682590769141[/C][/ROW]
[ROW][C]34[/C][C]0.997866885321977[/C][C]0.00426622935604646[/C][C]0.00213311467802323[/C][/ROW]
[ROW][C]35[/C][C]0.995941843407088[/C][C]0.00811631318582325[/C][C]0.00405815659291162[/C][/ROW]
[ROW][C]36[/C][C]0.995926208496724[/C][C]0.00814758300655302[/C][C]0.00407379150327651[/C][/ROW]
[ROW][C]37[/C][C]0.993083582258048[/C][C]0.0138328354839037[/C][C]0.00691641774195186[/C][/ROW]
[ROW][C]38[/C][C]0.998599942113632[/C][C]0.00280011577273683[/C][C]0.00140005788636842[/C][/ROW]
[ROW][C]39[/C][C]0.99720836632216[/C][C]0.00558326735567896[/C][C]0.00279163367783948[/C][/ROW]
[ROW][C]40[/C][C]0.995007873026365[/C][C]0.00998425394727099[/C][C]0.0049921269736355[/C][/ROW]
[ROW][C]41[/C][C]0.99180479478413[/C][C]0.016390410431741[/C][C]0.00819520521587051[/C][/ROW]
[ROW][C]42[/C][C]0.985224132287855[/C][C]0.0295517354242893[/C][C]0.0147758677121447[/C][/ROW]
[ROW][C]43[/C][C]0.972989840194763[/C][C]0.0540203196104743[/C][C]0.0270101598052371[/C][/ROW]
[ROW][C]44[/C][C]0.958712941469293[/C][C]0.0825741170614146[/C][C]0.0412870585307073[/C][/ROW]
[ROW][C]45[/C][C]0.928043962754691[/C][C]0.143912074490617[/C][C]0.0719560372453087[/C][/ROW]
[ROW][C]46[/C][C]0.876830768430757[/C][C]0.246338463138486[/C][C]0.123169231569243[/C][/ROW]
[ROW][C]47[/C][C]0.824805627456278[/C][C]0.350388745087444[/C][C]0.175194372543722[/C][/ROW]
[ROW][C]48[/C][C]0.738749677919631[/C][C]0.522500644160738[/C][C]0.261250322080369[/C][/ROW]
[ROW][C]49[/C][C]0.89367835036777[/C][C]0.212643299264461[/C][C]0.10632164963223[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189752&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189752&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
15	0.999052205847391	0.00189558830521759	0.000947794152608796
16	0.997330922015149	0.00533815596970308	0.00266907798485154
17	0.996157117202193	0.00768576559561325	0.00384288279780663
18	0.992210751707258	0.0155784965854844	0.0077892482927422
19	0.991986954359232	0.0160260912815356	0.00801304564076782
20	0.987284219251866	0.0254315614962679	0.0127157807481339
21	0.98817124199841	0.0236575160031791	0.0118287580015896
22	0.991043970053403	0.0179120598931933	0.00895602994659665
23	0.99258478684676	0.0148304263064791	0.00741521315323957
24	0.990769255811356	0.018461488377289	0.00923074418864448
25	0.99048691584459	0.0190261683108209	0.00951308415541046
26	0.999884081699966	0.000231836600068144	0.000115918300034072
27	0.999723240722643	0.000553518554714667	0.000276759277357334
28	0.999472770679832	0.00105445864033508	0.000527229320167541
29	0.999016660990897	0.0019666780182067	0.000983339009103352
30	0.997996283661117	0.00400743267776533	0.00200371633888266
31	0.998215851473377	0.00356829705324629	0.00178414852662315
32	0.99743387990183	0.00513224019634042	0.00256612009817021
33	0.997317409230859	0.005365181538282	0.002682590769141
34	0.997866885321977	0.00426622935604646	0.00213311467802323
35	0.995941843407088	0.00811631318582325	0.00405815659291162
36	0.995926208496724	0.00814758300655302	0.00407379150327651
37	0.993083582258048	0.0138328354839037	0.00691641774195186
38	0.998599942113632	0.00280011577273683	0.00140005788636842
39	0.99720836632216	0.00558326735567896	0.00279163367783948
40	0.995007873026365	0.00998425394727099	0.0049921269736355
41	0.99180479478413	0.016390410431741	0.00819520521587051
42	0.985224132287855	0.0295517354242893	0.0147758677121447
43	0.972989840194763	0.0540203196104743	0.0270101598052371
44	0.958712941469293	0.0825741170614146	0.0412870585307073
45	0.928043962754691	0.143912074490617	0.0719560372453087
46	0.876830768430757	0.246338463138486	0.123169231569243
47	0.824805627456278	0.350388745087444	0.175194372543722
48	0.738749677919631	0.522500644160738	0.261250322080369
49	0.89367835036777	0.212643299264461	0.10632164963223

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	17	0.485714285714286	NOK
5% type I error level	28	0.8	NOK
10% type I error level	30	0.857142857142857	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 & 17 & 0.485714285714286 & NOK \tabularnewline
5% type I error level & 28 & 0.8 & NOK \tabularnewline
10% type I error level & 30 & 0.857142857142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189752&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]17[/C][C]0.485714285714286[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.8[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.857142857142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189752&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189752&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	17	0.485714285714286	NOK
5% type I error level	28	0.8	NOK
10% type I error level	30	0.857142857142857	NOK

Figure 1

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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 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal 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