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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 10 Dec 2011 09:37:21 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/10/t1323527994l22rahcwc8pxjc3.htm/, Retrieved Sat, 04 May 2024 23:08:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153558, Retrieved Sat, 04 May 2024 23:08:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [Multiple regressi...] [2011-12-10 14:37:21] [8432dc408001a08517818ba7ac24bdb0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	1418	210907	56	396	81	3	79	30	115	94	112285	24188	146283	144	145
4	2172	179321	89	967	125	2	108	30	116	103	101193	32287	96933	135	132
0	1583	149061	44	656	66	5	43	26	100	93	116174	27101	95757	84	84
0	1764	237213	84	655	74	0	78	38	140	123	66198	19716	143983	130	127
-4	1495	173326	88	465	49	7	86	44	166	148	71701	17753	75851	82	78
4	1373	133131	55	525	52	7	44	30	99	90	57793	9028	59238	60	60
4	2187	258873	60	885	88	3	104	40	139	124	80444	18653	93163	131	131
0	4041	324799	154	1436	108	0	158	47	181	168	97668	29498	151511	140	133
-1	1706	230964	53	612	43	4	102	30	116	115	133824	27563	136368	151	150
0	2152	236785	119	865	75	3	77	31	116	71	101481	18293	112642	91	91
1	2242	344297	75	963	86	1	80	30	108	108	67654	16116	127766	119	118
0	2515	174724	92	966	135	0	123	34	129	120	69112	26569	85646	123	119
3	2147	174415	100	801	63	0	73	31	118	114	82753	24785	98579	90	89
-1	1638	223632	73	513	52	0	105	33	125	120	72654	23825	131741	113	108
4	2452	294424	77	992	59	2	107	33	127	124	101494	34461	171975	175	162
3	2662	325107	99	937	64	0	84	36	136	126	79215	24919	159676	96	92
1	865	106408	30	260	32	1	33	14	46	37	31081	12558	58391	41	41
0	1793	96560	76	503	129	0	42	17	54	38	22996	7784	31580	47	47
-2	2527	265769	146	927	37	2	96	32	124	120	83122	28522	136815	126	120
-3	2747	269651	67	1269	31	10	106	30	115	93	70106	22265	120642	105	105
-4	1324	149112	56	537	65	6	56	35	128	95	60578	14459	69107	80	79
2	1383	152871	58	532	74	5	59	28	97	90	79892	22240	108016	73	70
2	4308	362301	119	1635	715	2	76	34	125	110	100708	11912	79336	68	67
-4	1831	183167	66	557	66	0	91	39	149	138	82875	18220	93176	127	127
3	3373	277965	89	1178	106	8	115	39	149	133	139077	19199	161632	154	152
2	2352	218946	41	866	112	1	76	29	108	96	80670	25239	102996	112	109
2	2144	244052	68	574	66	5	101	44	166	164	143558	29801	160604	137	133
0	4691	341570	168	1276	190	1	94	21	80	78	117105	18450	158051	135	123
5	2694	233328	132	825	165	5	92	28	107	102	120733	34861	162647	230	230
-2	1769	206161	71	663	61	12	75	28	107	99	73107	16688	60622	71	68
0	3148	311473	112	1069	53	8	128	38	146	129	132068	24683	179566	147	147
-2	1954	207176	70	711	38	8	56	32	123	114	87011	21436	96144	105	101
-3	1226	196553	57	503	50	2	41	29	111	99	95260	30546	129847	107	108
2	1496	143246	103	464	42	5	67	27	105	104	106671	15977	71180	116	114
2	1943	182192	52	657	53	12	77	40	155	138	70054	14251	86767	89	88
2	1762	194979	62	577	50	7	66	40	155	151	74011	16851	93487	84	83
0	1403	167488	45	619	77	2	69	28	104	72	83737	21113	82981	113	113
4	1425	143756	46	479	57	0	105	34	132	120	69094	17401	73815	120	118
4	1857	275541	63	817	73	4	116	33	127	115	93133	23958	94552	110	110
2	1420	152299	53	537	63	0	62	33	122	98	61370	14587	67808	78	76
2	1644	193339	78	465	47	2	100	35	87	71	84651	20537	106175	145	141
-4	1054	130585	46	299	57	5	67	29	109	107	95364	30495	76669	91	91
3	937	112611	41	248	36	0	46	20	78	73	26706	7117	57283	48	48
3	2547	148446	91	905	63	1	135	37	141	129	126846	33473	72413	150	144
2	1626	182079	63	512	63	2	124	33	124	118	102860	21115	96971	181	168
-1	1964	243060	63	786	110	4	58	29	112	104	111813	32902	120336	121	117
-3	1381	162765	32	489	56	2	68	28	108	107	120293	25131	93913	99	100
0	1290	85574	34	351	71	0	37	21	78	36	24266	6943	32036	40	37
1	1982	225060	93	669	56	7	93	41	158	139	109825	31808	102255	87	87
-3	1590	133328	55	506	79	0	56	20	78	56	40909	17014	63506	66	64
3	1281	100750	72	407	67	0	83	30	119	93	140867	6440	68370	58	58
0	1272	101523	42	316	76	0	59	22	88	87	61056	18647	50517	77	76
0	1944	243511	71	603	65	0	133	42	155	110	101338	20556	103950	130	129
0	1605	152474	65	577	45	0	106	32	123	83	65567	22392	84396	101	101
3	1386	132487	41	411	97	0	71	36	136	98	40735	8388	55515	120	89
-3	2395	317394	86	975	53	1	116	31	117	82	91413	22120	209056	195	193
0	2699	244749	95	964	144	2	98	33	124	115	76643	20923	142775	106	101
-4	1606	184510	49	537	60	7	64	40	151	140	110681	20237	68847	83	82
2	1204	128423	64	369	89	8	32	38	145	120	92696	3769	20112	37	36
-1	1138	97839	38	417	42	2	25	24	87	66	94785	12252	61023	77	75
3	1111	172494	52	389	52	0	46	43	165	139	86687	21721	112494	144	131
2	2186	229242	247	719	128	4	63	31	120	119	91721	17939	78876	95	90
5	3604	351619	139	1277	142	4	95	40	150	141	115168	23436	170745	169	166
2	3261	324598	110	1402	128	0	113	37	136	133	135777	34538	122037	134	133
-2	1641	195838	67	564	50	1	111	31	116	98	102372	25515	112283	197	196
0	2312	254488	83	747	50	10	120	39	150	117	103772	29402	120691	140	136
3	2201	199476	70	861	46	2	87	32	118	105	135400	28732	122422	125	123
-2	961	92499	32	319	57	0	25	18	71	55	21399	5250	25899	21	21
0	1900	224330	83	612	52	1	131	39	144	132	130115	28608	139296	167	163
6	1645	181633	70	564	48	2	47	30	110	73	64466	14817	89455	96	96
-3	2429	271856	103	824	91	1	109	37	147	86	54990	16714	147866	151	151
3	872	95227	34	239	70	0	37	32	111	48	34777	1669	14336	23	23
0	1018	98146	40	459	37	0	15	17	68	48	27114	7768	30059	21	14
-2	1403	118612	46	454	72	2	54	12	48	43	30080	7936	41907	90	87
1	616	65475	18	225	24	2	16	13	51	46	69008	7294	35885	60	56
0	1232	108446	60	389	90	1	22	17	68	65	46300	13275	55764	26	25
2	1255	121848	39	339	45	0	37	17	64	52	30594	5401	35619	41	41
2	995	76302	31	333	26	0	29	20	76	68	30976	8702	40557	35	33
-3	2048	98104	54	636	132	2	55	17	66	47	25568	8030	44197	68	68
-2	301	30989	14	93	35	0	5	17	68	41	4154	1278	4103	6	6
1	628	31774	23	170	48	1	0	17	66	47	4143	1574	4694	0	0
-4	1597	150580	77	530	124	0	27	22	83	71	45588	9653	62991	41	39
0	717	54157	19	201	35	0	37	15	55	30	18625	7067	24261	38	37
1	652	59382	49	227	49	0	29	12	41	24	26263	1514	21425	47	47
0	733	84105	20	261	45	0	17	17	66	63	20055	5432	27184	34	34

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153558&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153558&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153558&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Som_TEST[t] = -0.887990440534397 -0.000578934155785486pageviews[t] -7.5346218287732e-06time_in_rfc[t] + 0.00311202351276826logins[t] + 0.00386989340635107compendium_views_info[t] -0.00106316987190134compendium_views_pr[t] -0.138676685012885shared_compendiums[t] -0.0128263290757282blogged_computations[t] + 0.322685527585549compendiums_reviewed[t] -0.0928765921873651feedback_messages_p1[t] + 0.0259471097388446feedback_messages_p120[t] + 2.01584864710495e-05totsize[t] -9.50727328695808e-05totrevisions[t] -4.24610259015999e-06totseconds[t] + 0.0720047248264554tothyperlinks[t] -0.0592482532580352totblogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Som_TEST[t] =  -0.887990440534397 -0.000578934155785486pageviews[t] -7.5346218287732e-06time_in_rfc[t] +  0.00311202351276826logins[t] +  0.00386989340635107compendium_views_info[t] -0.00106316987190134compendium_views_pr[t] -0.138676685012885shared_compendiums[t] -0.0128263290757282blogged_computations[t] +  0.322685527585549compendiums_reviewed[t] -0.0928765921873651feedback_messages_p1[t] +  0.0259471097388446feedback_messages_p120[t] +  2.01584864710495e-05totsize[t] -9.50727328695808e-05totrevisions[t] -4.24610259015999e-06totseconds[t] +  0.0720047248264554tothyperlinks[t] -0.0592482532580352totblogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153558&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Som_TEST[t] =  -0.887990440534397 -0.000578934155785486pageviews[t] -7.5346218287732e-06time_in_rfc[t] +  0.00311202351276826logins[t] +  0.00386989340635107compendium_views_info[t] -0.00106316987190134compendium_views_pr[t] -0.138676685012885shared_compendiums[t] -0.0128263290757282blogged_computations[t] +  0.322685527585549compendiums_reviewed[t] -0.0928765921873651feedback_messages_p1[t] +  0.0259471097388446feedback_messages_p120[t] +  2.01584864710495e-05totsize[t] -9.50727328695808e-05totrevisions[t] -4.24610259015999e-06totseconds[t] +  0.0720047248264554tothyperlinks[t] -0.0592482532580352totblogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153558&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153558&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Som_TEST[t] = -0.887990440534397 -0.000578934155785486pageviews[t] -7.5346218287732e-06time_in_rfc[t] + 0.00311202351276826logins[t] + 0.00386989340635107compendium_views_info[t] -0.00106316987190134compendium_views_pr[t] -0.138676685012885shared_compendiums[t] -0.0128263290757282blogged_computations[t] + 0.322685527585549compendiums_reviewed[t] -0.0928765921873651feedback_messages_p1[t] + 0.0259471097388446feedback_messages_p120[t] + 2.01584864710495e-05totsize[t] -9.50727328695808e-05totrevisions[t] -4.24610259015999e-06totseconds[t] + 0.0720047248264554tothyperlinks[t] -0.0592482532580352totblogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.8879904405343971.135898-0.78180.4370350.218517
pageviews-0.0005789341557854860.001424-0.40640.6856870.342843
time_in_rfc-7.5346218287732e-061.2e-05-0.64390.5217960.260898
logins0.003112023512768260.0115990.26830.789270.394635
compendium_views_info0.003869893406351070.0034041.13690.2595070.129753
compendium_views_pr-0.001063169871901340.005386-0.19740.8440940.422047
shared_compendiums-0.1386766850128850.105825-1.31040.1943960.097198
blogged_computations-0.01282632907572820.01859-0.690.4925380.246269
compendiums_reviewed0.3226855275855490.1738051.85660.0676380.033819
feedback_messages_p1-0.09287659218736510.050718-1.83120.0713810.03569
feedback_messages_p1200.02594710973884460.024771.04750.2985070.149253
totsize2.01584864710495e-051.4e-051.41870.16050.08025
totrevisions-9.50727328695808e-056.3e-05-1.50280.137460.06873
totseconds-4.24610259015999e-061.9e-05-0.22260.8244980.412249
tothyperlinks0.07200472482645540.0681051.05730.2940790.14704
totblogs-0.05924825325803520.07188-0.82430.4126260.206313

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.887990440534397 & 1.135898 & -0.7818 & 0.437035 & 0.218517 \tabularnewline
pageviews & -0.000578934155785486 & 0.001424 & -0.4064 & 0.685687 & 0.342843 \tabularnewline
time_in_rfc & -7.5346218287732e-06 & 1.2e-05 & -0.6439 & 0.521796 & 0.260898 \tabularnewline
logins & 0.00311202351276826 & 0.011599 & 0.2683 & 0.78927 & 0.394635 \tabularnewline
compendium_views_info & 0.00386989340635107 & 0.003404 & 1.1369 & 0.259507 & 0.129753 \tabularnewline
compendium_views_pr & -0.00106316987190134 & 0.005386 & -0.1974 & 0.844094 & 0.422047 \tabularnewline
shared_compendiums & -0.138676685012885 & 0.105825 & -1.3104 & 0.194396 & 0.097198 \tabularnewline
blogged_computations & -0.0128263290757282 & 0.01859 & -0.69 & 0.492538 & 0.246269 \tabularnewline
compendiums_reviewed & 0.322685527585549 & 0.173805 & 1.8566 & 0.067638 & 0.033819 \tabularnewline
feedback_messages_p1 & -0.0928765921873651 & 0.050718 & -1.8312 & 0.071381 & 0.03569 \tabularnewline
feedback_messages_p120 & 0.0259471097388446 & 0.02477 & 1.0475 & 0.298507 & 0.149253 \tabularnewline
totsize & 2.01584864710495e-05 & 1.4e-05 & 1.4187 & 0.1605 & 0.08025 \tabularnewline
totrevisions & -9.50727328695808e-05 & 6.3e-05 & -1.5028 & 0.13746 & 0.06873 \tabularnewline
totseconds & -4.24610259015999e-06 & 1.9e-05 & -0.2226 & 0.824498 & 0.412249 \tabularnewline
tothyperlinks & 0.0720047248264554 & 0.068105 & 1.0573 & 0.294079 & 0.14704 \tabularnewline
totblogs & -0.0592482532580352 & 0.07188 & -0.8243 & 0.412626 & 0.206313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153558&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.887990440534397[/C][C]1.135898[/C][C]-0.7818[/C][C]0.437035[/C][C]0.218517[/C][/ROW]
[ROW][C]pageviews[/C][C]-0.000578934155785486[/C][C]0.001424[/C][C]-0.4064[/C][C]0.685687[/C][C]0.342843[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]-7.5346218287732e-06[/C][C]1.2e-05[/C][C]-0.6439[/C][C]0.521796[/C][C]0.260898[/C][/ROW]
[ROW][C]logins[/C][C]0.00311202351276826[/C][C]0.011599[/C][C]0.2683[/C][C]0.78927[/C][C]0.394635[/C][/ROW]
[ROW][C]compendium_views_info[/C][C]0.00386989340635107[/C][C]0.003404[/C][C]1.1369[/C][C]0.259507[/C][C]0.129753[/C][/ROW]
[ROW][C]compendium_views_pr[/C][C]-0.00106316987190134[/C][C]0.005386[/C][C]-0.1974[/C][C]0.844094[/C][C]0.422047[/C][/ROW]
[ROW][C]shared_compendiums[/C][C]-0.138676685012885[/C][C]0.105825[/C][C]-1.3104[/C][C]0.194396[/C][C]0.097198[/C][/ROW]
[ROW][C]blogged_computations[/C][C]-0.0128263290757282[/C][C]0.01859[/C][C]-0.69[/C][C]0.492538[/C][C]0.246269[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]0.322685527585549[/C][C]0.173805[/C][C]1.8566[/C][C]0.067638[/C][C]0.033819[/C][/ROW]
[ROW][C]feedback_messages_p1[/C][C]-0.0928765921873651[/C][C]0.050718[/C][C]-1.8312[/C][C]0.071381[/C][C]0.03569[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]0.0259471097388446[/C][C]0.02477[/C][C]1.0475[/C][C]0.298507[/C][C]0.149253[/C][/ROW]
[ROW][C]totsize[/C][C]2.01584864710495e-05[/C][C]1.4e-05[/C][C]1.4187[/C][C]0.1605[/C][C]0.08025[/C][/ROW]
[ROW][C]totrevisions[/C][C]-9.50727328695808e-05[/C][C]6.3e-05[/C][C]-1.5028[/C][C]0.13746[/C][C]0.06873[/C][/ROW]
[ROW][C]totseconds[/C][C]-4.24610259015999e-06[/C][C]1.9e-05[/C][C]-0.2226[/C][C]0.824498[/C][C]0.412249[/C][/ROW]
[ROW][C]tothyperlinks[/C][C]0.0720047248264554[/C][C]0.068105[/C][C]1.0573[/C][C]0.294079[/C][C]0.14704[/C][/ROW]
[ROW][C]totblogs[/C][C]-0.0592482532580352[/C][C]0.07188[/C][C]-0.8243[/C][C]0.412626[/C][C]0.206313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153558&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153558&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.8879904405343971.135898-0.78180.4370350.218517
pageviews-0.0005789341557854860.001424-0.40640.6856870.342843
time_in_rfc-7.5346218287732e-061.2e-05-0.64390.5217960.260898
logins0.003112023512768260.0115990.26830.789270.394635
compendium_views_info0.003869893406351070.0034041.13690.2595070.129753
compendium_views_pr-0.001063169871901340.005386-0.19740.8440940.422047
shared_compendiums-0.1386766850128850.105825-1.31040.1943960.097198
blogged_computations-0.01282632907572820.01859-0.690.4925380.246269
compendiums_reviewed0.3226855275855490.1738051.85660.0676380.033819
feedback_messages_p1-0.09287659218736510.050718-1.83120.0713810.03569
feedback_messages_p1200.02594710973884460.024771.04750.2985070.149253
totsize2.01584864710495e-051.4e-051.41870.16050.08025
totrevisions-9.50727328695808e-056.3e-05-1.50280.137460.06873
totseconds-4.24610259015999e-061.9e-05-0.22260.8244980.412249
tothyperlinks0.07200472482645540.0681051.05730.2940790.14704
totblogs-0.05924825325803520.07188-0.82430.4126260.206313






Multiple Linear Regression - Regression Statistics
Multiple R0.409336235531031
R-squared0.167556153718716
Adjusted R-squared-0.0134098998206935
F-TEST (value)0.925898257941658
F-TEST (DF numerator)15
F-TEST (DF denominator)69
p-value0.540345260833694
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.47848954590598
Sum Squared Residuals423.8608196124

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.409336235531031 \tabularnewline
R-squared & 0.167556153718716 \tabularnewline
Adjusted R-squared & -0.0134098998206935 \tabularnewline
F-TEST (value) & 0.925898257941658 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 69 \tabularnewline
p-value & 0.540345260833694 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.47848954590598 \tabularnewline
Sum Squared Residuals & 423.8608196124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153558&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.409336235531031[/C][/ROW]
[ROW][C]R-squared[/C][C]0.167556153718716[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0134098998206935[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.925898257941658[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]69[/C][/ROW]
[ROW][C]p-value[/C][C]0.540345260833694[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.47848954590598[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]423.8608196124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153558&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153558&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.409336235531031
R-squared0.167556153718716
Adjusted R-squared-0.0134098998206935
F-TEST (value)0.925898257941658
F-TEST (DF numerator)15
F-TEST (DF denominator)69
p-value0.540345260833694
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.47848954590598
Sum Squared Residuals423.8608196124






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12-0.5474858058730742.54748580587307
240.765111315904573.23488868409543
300.378453992711137-0.378453992711137
401.15605997907744-1.15605997907744
5-40.227350335137185-4.22735033513719
641.568099971318622.43190002868138
742.002192368139531.99780763186047
801.6567023849773-1.6567023849773
9-10.38316614752204-1.38316614752204
1000.377350027566335-0.377350027566335
1111.24233761441058-0.242337614410577
1201.05643158322927-1.05643158322927
1331.064907787252661.93509221274734
14-1-0.180358365718305-0.819641634281695
1540.9529390906811833.04706090931882
1630.1762034377635362.82379656223646
171-0.7739256117135471.77392561171355
1800.498941777127645-0.498941777127645
19-20.409018669982476-2.40901866998248
20-3-0.633509974465415-2.36649002553458
21-40.3585635096246-4.3585635096246
2220.3777972890267561.62220271097324
2322.27679584453082-0.276795844530823
24-41.28511695698761-5.28511695698761
2531.775527124407221.22447287559278
2620.5609623180977311.43903768190227
2720.80517269767331.1948273023267
2801.47579154051971-1.47579154051971
2950.4557843577525074.54421564224749
30-2-0.990362411447923-1.00963758855208
3100.0982257858899872-0.0982257858899872
32-20.26532966014413-2.26532966014413
33-3-0.423267196076195-2.5767328039238
3421.271712939887590.728287060112407
352-0.4136535041468682.41365350414687
3620.2315539707205551.76844602927944
3700.343174225113895-0.343174225113895
3840.6929158888725943.30708411112741
394-0.3629653310065144.36296533100651
4021.059642431762540.940357568237464
4123.58154207188476-1.58154207188476
42-4-0.926074696169884-3.07392530383012
433-0.4856153548315313.48561535483153
4431.894299847000581.10570015299942
4521.968536166320060.0314638336799443
46-10.0154951324145685-1.01549513241457
47-30.490403857476764-3.49040385747676
480-0.5183319139107180.518331913910718
491-1.048212547741672.04821254774167
50-3-0.925768107311072-2.07423189268893
5131.992875365155791.00712463484421
520-0.4049835790953130.404983579095313
5300.307105088029028-0.307105088029028
540-0.7595986375000170.759598637500017
5532.699404816410710.300595183589289
56-30.414346907707941-3.41434690770794
5700.757997154524645-0.757997154524645
58-40.815767655733207-4.81576765573321
5921.324755506893950.67524449310605
60-11.7695646379527-2.7695646379527
6132.160444690015630.839555309984365
6221.433883280684350.566116719315646
6352.163012112273552.83698788772645
6422.42381848383212-0.423818483832124
65-20.967756319419255-2.96775631941925
660-1.477884337057811.47788433705781
6731.724483229560341.27551677043966
68-2-0.457121126084578-1.54287887391542
6901.38871920778929-1.38871920778929
7060.3542756103840675.64572438961593
71-3-1.13091865902322-1.86908134097678
7230.4083952290391272.59160477096087
7300.23074064640743-0.23074064640743
74-2-0.210738838791504-1.7892611612085
7510.8801274243348710.119872575665129
760-0.5607761094150330.560776109415033
772-0.2559693585281852.25596935852818
7820.2959678248956171.70403217510438
79-3-0.299596297687811-2.70040370231219
80-2-0.738384522020822-1.26161547797918
811-0.4619626186773611.46196261867736
82-40.473276554593716-4.47327655459372
830-0.7309109236172010.730910923617201
8410.4750999783283950.524900021671605
8500.0569480050360825-0.0569480050360825

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & -0.547485805873074 & 2.54748580587307 \tabularnewline
2 & 4 & 0.76511131590457 & 3.23488868409543 \tabularnewline
3 & 0 & 0.378453992711137 & -0.378453992711137 \tabularnewline
4 & 0 & 1.15605997907744 & -1.15605997907744 \tabularnewline
5 & -4 & 0.227350335137185 & -4.22735033513719 \tabularnewline
6 & 4 & 1.56809997131862 & 2.43190002868138 \tabularnewline
7 & 4 & 2.00219236813953 & 1.99780763186047 \tabularnewline
8 & 0 & 1.6567023849773 & -1.6567023849773 \tabularnewline
9 & -1 & 0.38316614752204 & -1.38316614752204 \tabularnewline
10 & 0 & 0.377350027566335 & -0.377350027566335 \tabularnewline
11 & 1 & 1.24233761441058 & -0.242337614410577 \tabularnewline
12 & 0 & 1.05643158322927 & -1.05643158322927 \tabularnewline
13 & 3 & 1.06490778725266 & 1.93509221274734 \tabularnewline
14 & -1 & -0.180358365718305 & -0.819641634281695 \tabularnewline
15 & 4 & 0.952939090681183 & 3.04706090931882 \tabularnewline
16 & 3 & 0.176203437763536 & 2.82379656223646 \tabularnewline
17 & 1 & -0.773925611713547 & 1.77392561171355 \tabularnewline
18 & 0 & 0.498941777127645 & -0.498941777127645 \tabularnewline
19 & -2 & 0.409018669982476 & -2.40901866998248 \tabularnewline
20 & -3 & -0.633509974465415 & -2.36649002553458 \tabularnewline
21 & -4 & 0.3585635096246 & -4.3585635096246 \tabularnewline
22 & 2 & 0.377797289026756 & 1.62220271097324 \tabularnewline
23 & 2 & 2.27679584453082 & -0.276795844530823 \tabularnewline
24 & -4 & 1.28511695698761 & -5.28511695698761 \tabularnewline
25 & 3 & 1.77552712440722 & 1.22447287559278 \tabularnewline
26 & 2 & 0.560962318097731 & 1.43903768190227 \tabularnewline
27 & 2 & 0.8051726976733 & 1.1948273023267 \tabularnewline
28 & 0 & 1.47579154051971 & -1.47579154051971 \tabularnewline
29 & 5 & 0.455784357752507 & 4.54421564224749 \tabularnewline
30 & -2 & -0.990362411447923 & -1.00963758855208 \tabularnewline
31 & 0 & 0.0982257858899872 & -0.0982257858899872 \tabularnewline
32 & -2 & 0.26532966014413 & -2.26532966014413 \tabularnewline
33 & -3 & -0.423267196076195 & -2.5767328039238 \tabularnewline
34 & 2 & 1.27171293988759 & 0.728287060112407 \tabularnewline
35 & 2 & -0.413653504146868 & 2.41365350414687 \tabularnewline
36 & 2 & 0.231553970720555 & 1.76844602927944 \tabularnewline
37 & 0 & 0.343174225113895 & -0.343174225113895 \tabularnewline
38 & 4 & 0.692915888872594 & 3.30708411112741 \tabularnewline
39 & 4 & -0.362965331006514 & 4.36296533100651 \tabularnewline
40 & 2 & 1.05964243176254 & 0.940357568237464 \tabularnewline
41 & 2 & 3.58154207188476 & -1.58154207188476 \tabularnewline
42 & -4 & -0.926074696169884 & -3.07392530383012 \tabularnewline
43 & 3 & -0.485615354831531 & 3.48561535483153 \tabularnewline
44 & 3 & 1.89429984700058 & 1.10570015299942 \tabularnewline
45 & 2 & 1.96853616632006 & 0.0314638336799443 \tabularnewline
46 & -1 & 0.0154951324145685 & -1.01549513241457 \tabularnewline
47 & -3 & 0.490403857476764 & -3.49040385747676 \tabularnewline
48 & 0 & -0.518331913910718 & 0.518331913910718 \tabularnewline
49 & 1 & -1.04821254774167 & 2.04821254774167 \tabularnewline
50 & -3 & -0.925768107311072 & -2.07423189268893 \tabularnewline
51 & 3 & 1.99287536515579 & 1.00712463484421 \tabularnewline
52 & 0 & -0.404983579095313 & 0.404983579095313 \tabularnewline
53 & 0 & 0.307105088029028 & -0.307105088029028 \tabularnewline
54 & 0 & -0.759598637500017 & 0.759598637500017 \tabularnewline
55 & 3 & 2.69940481641071 & 0.300595183589289 \tabularnewline
56 & -3 & 0.414346907707941 & -3.41434690770794 \tabularnewline
57 & 0 & 0.757997154524645 & -0.757997154524645 \tabularnewline
58 & -4 & 0.815767655733207 & -4.81576765573321 \tabularnewline
59 & 2 & 1.32475550689395 & 0.67524449310605 \tabularnewline
60 & -1 & 1.7695646379527 & -2.7695646379527 \tabularnewline
61 & 3 & 2.16044469001563 & 0.839555309984365 \tabularnewline
62 & 2 & 1.43388328068435 & 0.566116719315646 \tabularnewline
63 & 5 & 2.16301211227355 & 2.83698788772645 \tabularnewline
64 & 2 & 2.42381848383212 & -0.423818483832124 \tabularnewline
65 & -2 & 0.967756319419255 & -2.96775631941925 \tabularnewline
66 & 0 & -1.47788433705781 & 1.47788433705781 \tabularnewline
67 & 3 & 1.72448322956034 & 1.27551677043966 \tabularnewline
68 & -2 & -0.457121126084578 & -1.54287887391542 \tabularnewline
69 & 0 & 1.38871920778929 & -1.38871920778929 \tabularnewline
70 & 6 & 0.354275610384067 & 5.64572438961593 \tabularnewline
71 & -3 & -1.13091865902322 & -1.86908134097678 \tabularnewline
72 & 3 & 0.408395229039127 & 2.59160477096087 \tabularnewline
73 & 0 & 0.23074064640743 & -0.23074064640743 \tabularnewline
74 & -2 & -0.210738838791504 & -1.7892611612085 \tabularnewline
75 & 1 & 0.880127424334871 & 0.119872575665129 \tabularnewline
76 & 0 & -0.560776109415033 & 0.560776109415033 \tabularnewline
77 & 2 & -0.255969358528185 & 2.25596935852818 \tabularnewline
78 & 2 & 0.295967824895617 & 1.70403217510438 \tabularnewline
79 & -3 & -0.299596297687811 & -2.70040370231219 \tabularnewline
80 & -2 & -0.738384522020822 & -1.26161547797918 \tabularnewline
81 & 1 & -0.461962618677361 & 1.46196261867736 \tabularnewline
82 & -4 & 0.473276554593716 & -4.47327655459372 \tabularnewline
83 & 0 & -0.730910923617201 & 0.730910923617201 \tabularnewline
84 & 1 & 0.475099978328395 & 0.524900021671605 \tabularnewline
85 & 0 & 0.0569480050360825 & -0.0569480050360825 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153558&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]2[/C][C]-0.547485805873074[/C][C]2.54748580587307[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]0.76511131590457[/C][C]3.23488868409543[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.378453992711137[/C][C]-0.378453992711137[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]1.15605997907744[/C][C]-1.15605997907744[/C][/ROW]
[ROW][C]5[/C][C]-4[/C][C]0.227350335137185[/C][C]-4.22735033513719[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]1.56809997131862[/C][C]2.43190002868138[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]2.00219236813953[/C][C]1.99780763186047[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]1.6567023849773[/C][C]-1.6567023849773[/C][/ROW]
[ROW][C]9[/C][C]-1[/C][C]0.38316614752204[/C][C]-1.38316614752204[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.377350027566335[/C][C]-0.377350027566335[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.24233761441058[/C][C]-0.242337614410577[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]1.05643158322927[/C][C]-1.05643158322927[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]1.06490778725266[/C][C]1.93509221274734[/C][/ROW]
[ROW][C]14[/C][C]-1[/C][C]-0.180358365718305[/C][C]-0.819641634281695[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]0.952939090681183[/C][C]3.04706090931882[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]0.176203437763536[/C][C]2.82379656223646[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]-0.773925611713547[/C][C]1.77392561171355[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.498941777127645[/C][C]-0.498941777127645[/C][/ROW]
[ROW][C]19[/C][C]-2[/C][C]0.409018669982476[/C][C]-2.40901866998248[/C][/ROW]
[ROW][C]20[/C][C]-3[/C][C]-0.633509974465415[/C][C]-2.36649002553458[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]0.3585635096246[/C][C]-4.3585635096246[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]0.377797289026756[/C][C]1.62220271097324[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.27679584453082[/C][C]-0.276795844530823[/C][/ROW]
[ROW][C]24[/C][C]-4[/C][C]1.28511695698761[/C][C]-5.28511695698761[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]1.77552712440722[/C][C]1.22447287559278[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]0.560962318097731[/C][C]1.43903768190227[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]0.8051726976733[/C][C]1.1948273023267[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]1.47579154051971[/C][C]-1.47579154051971[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]0.455784357752507[/C][C]4.54421564224749[/C][/ROW]
[ROW][C]30[/C][C]-2[/C][C]-0.990362411447923[/C][C]-1.00963758855208[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0982257858899872[/C][C]-0.0982257858899872[/C][/ROW]
[ROW][C]32[/C][C]-2[/C][C]0.26532966014413[/C][C]-2.26532966014413[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]-0.423267196076195[/C][C]-2.5767328039238[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.27171293988759[/C][C]0.728287060112407[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]-0.413653504146868[/C][C]2.41365350414687[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]0.231553970720555[/C][C]1.76844602927944[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.343174225113895[/C][C]-0.343174225113895[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]0.692915888872594[/C][C]3.30708411112741[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]-0.362965331006514[/C][C]4.36296533100651[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]1.05964243176254[/C][C]0.940357568237464[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]3.58154207188476[/C][C]-1.58154207188476[/C][/ROW]
[ROW][C]42[/C][C]-4[/C][C]-0.926074696169884[/C][C]-3.07392530383012[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]-0.485615354831531[/C][C]3.48561535483153[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]1.89429984700058[/C][C]1.10570015299942[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.96853616632006[/C][C]0.0314638336799443[/C][/ROW]
[ROW][C]46[/C][C]-1[/C][C]0.0154951324145685[/C][C]-1.01549513241457[/C][/ROW]
[ROW][C]47[/C][C]-3[/C][C]0.490403857476764[/C][C]-3.49040385747676[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.518331913910718[/C][C]0.518331913910718[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]-1.04821254774167[/C][C]2.04821254774167[/C][/ROW]
[ROW][C]50[/C][C]-3[/C][C]-0.925768107311072[/C][C]-2.07423189268893[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]1.99287536515579[/C][C]1.00712463484421[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]-0.404983579095313[/C][C]0.404983579095313[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.307105088029028[/C][C]-0.307105088029028[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]-0.759598637500017[/C][C]0.759598637500017[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]2.69940481641071[/C][C]0.300595183589289[/C][/ROW]
[ROW][C]56[/C][C]-3[/C][C]0.414346907707941[/C][C]-3.41434690770794[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.757997154524645[/C][C]-0.757997154524645[/C][/ROW]
[ROW][C]58[/C][C]-4[/C][C]0.815767655733207[/C][C]-4.81576765573321[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]1.32475550689395[/C][C]0.67524449310605[/C][/ROW]
[ROW][C]60[/C][C]-1[/C][C]1.7695646379527[/C][C]-2.7695646379527[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]2.16044469001563[/C][C]0.839555309984365[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]1.43388328068435[/C][C]0.566116719315646[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]2.16301211227355[/C][C]2.83698788772645[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]2.42381848383212[/C][C]-0.423818483832124[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]0.967756319419255[/C][C]-2.96775631941925[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-1.47788433705781[/C][C]1.47788433705781[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]1.72448322956034[/C][C]1.27551677043966[/C][/ROW]
[ROW][C]68[/C][C]-2[/C][C]-0.457121126084578[/C][C]-1.54287887391542[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]1.38871920778929[/C][C]-1.38871920778929[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]0.354275610384067[/C][C]5.64572438961593[/C][/ROW]
[ROW][C]71[/C][C]-3[/C][C]-1.13091865902322[/C][C]-1.86908134097678[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]0.408395229039127[/C][C]2.59160477096087[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.23074064640743[/C][C]-0.23074064640743[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]-0.210738838791504[/C][C]-1.7892611612085[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.880127424334871[/C][C]0.119872575665129[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]-0.560776109415033[/C][C]0.560776109415033[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]-0.255969358528185[/C][C]2.25596935852818[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]0.295967824895617[/C][C]1.70403217510438[/C][/ROW]
[ROW][C]79[/C][C]-3[/C][C]-0.299596297687811[/C][C]-2.70040370231219[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-0.738384522020822[/C][C]-1.26161547797918[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]-0.461962618677361[/C][C]1.46196261867736[/C][/ROW]
[ROW][C]82[/C][C]-4[/C][C]0.473276554593716[/C][C]-4.47327655459372[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.730910923617201[/C][C]0.730910923617201[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.475099978328395[/C][C]0.524900021671605[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0569480050360825[/C][C]-0.0569480050360825[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153558&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153558&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12-0.5474858058730742.54748580587307
240.765111315904573.23488868409543
300.378453992711137-0.378453992711137
401.15605997907744-1.15605997907744
5-40.227350335137185-4.22735033513719
641.568099971318622.43190002868138
742.002192368139531.99780763186047
801.6567023849773-1.6567023849773
9-10.38316614752204-1.38316614752204
1000.377350027566335-0.377350027566335
1111.24233761441058-0.242337614410577
1201.05643158322927-1.05643158322927
1331.064907787252661.93509221274734
14-1-0.180358365718305-0.819641634281695
1540.9529390906811833.04706090931882
1630.1762034377635362.82379656223646
171-0.7739256117135471.77392561171355
1800.498941777127645-0.498941777127645
19-20.409018669982476-2.40901866998248
20-3-0.633509974465415-2.36649002553458
21-40.3585635096246-4.3585635096246
2220.3777972890267561.62220271097324
2322.27679584453082-0.276795844530823
24-41.28511695698761-5.28511695698761
2531.775527124407221.22447287559278
2620.5609623180977311.43903768190227
2720.80517269767331.1948273023267
2801.47579154051971-1.47579154051971
2950.4557843577525074.54421564224749
30-2-0.990362411447923-1.00963758855208
3100.0982257858899872-0.0982257858899872
32-20.26532966014413-2.26532966014413
33-3-0.423267196076195-2.5767328039238
3421.271712939887590.728287060112407
352-0.4136535041468682.41365350414687
3620.2315539707205551.76844602927944
3700.343174225113895-0.343174225113895
3840.6929158888725943.30708411112741
394-0.3629653310065144.36296533100651
4021.059642431762540.940357568237464
4123.58154207188476-1.58154207188476
42-4-0.926074696169884-3.07392530383012
433-0.4856153548315313.48561535483153
4431.894299847000581.10570015299942
4521.968536166320060.0314638336799443
46-10.0154951324145685-1.01549513241457
47-30.490403857476764-3.49040385747676
480-0.5183319139107180.518331913910718
491-1.048212547741672.04821254774167
50-3-0.925768107311072-2.07423189268893
5131.992875365155791.00712463484421
520-0.4049835790953130.404983579095313
5300.307105088029028-0.307105088029028
540-0.7595986375000170.759598637500017
5532.699404816410710.300595183589289
56-30.414346907707941-3.41434690770794
5700.757997154524645-0.757997154524645
58-40.815767655733207-4.81576765573321
5921.324755506893950.67524449310605
60-11.7695646379527-2.7695646379527
6132.160444690015630.839555309984365
6221.433883280684350.566116719315646
6352.163012112273552.83698788772645
6422.42381848383212-0.423818483832124
65-20.967756319419255-2.96775631941925
660-1.477884337057811.47788433705781
6731.724483229560341.27551677043966
68-2-0.457121126084578-1.54287887391542
6901.38871920778929-1.38871920778929
7060.3542756103840675.64572438961593
71-3-1.13091865902322-1.86908134097678
7230.4083952290391272.59160477096087
7300.23074064640743-0.23074064640743
74-2-0.210738838791504-1.7892611612085
7510.8801274243348710.119872575665129
760-0.5607761094150330.560776109415033
772-0.2559693585281852.25596935852818
7820.2959678248956171.70403217510438
79-3-0.299596297687811-2.70040370231219
80-2-0.738384522020822-1.26161547797918
811-0.4619626186773611.46196261867736
82-40.473276554593716-4.47327655459372
830-0.7309109236172010.730910923617201
8410.4750999783283950.524900021671605
8500.0569480050360825-0.0569480050360825






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.6909271481695120.6181457036609750.309072851830488
200.5487059572637530.9025880854724940.451294042736247
210.484201330304850.9684026606097010.51579866969515
220.5927908195954710.8144183608090570.407209180404529
230.4713188729191670.9426377458383330.528681127080833
240.4889175655559330.9778351311118660.511082434444067
250.4966273313526440.9932546627052870.503372668647356
260.4074465061464970.8148930122929940.592553493853503
270.3265385552120780.6530771104241550.673461444787922
280.2901497099928690.5802994199857370.709850290007131
290.4174314849742730.8348629699485470.582568515025727
300.5000416637226310.9999166725547390.499958336277369
310.4212759334726740.8425518669453490.578724066527326
320.3922738815481720.7845477630963440.607726118451828
330.5133591289260460.9732817421479080.486640871073954
340.5251523804131970.9496952391736060.474847619586803
350.745450638611390.509098722777220.25454936138861
360.7512745269496630.4974509461006740.248725473050337
370.7065895108291420.5868209783417170.293410489170858
380.7806321072058070.4387357855883870.219367892794193
390.9119471108782050.176105778243590.0880528891217952
400.8929706627001910.2140586745996180.107029337299809
410.8920768743647080.2158462512705850.107923125635292
420.8844492323825160.2311015352349680.115550767617484
430.8886503242250010.2226993515499970.111349675774999
440.8568706170634850.286258765873030.143129382936515
450.8359930926506940.3280138146986120.164006907349306
460.8492689829880640.3014620340238710.150731017011936
470.8551417188955220.2897165622089550.144858281104478
480.8152772458941860.3694455082116290.184722754105814
490.8201315992739480.3597368014521040.179868400726052
500.8041067313696350.391786537260730.195893268630365
510.8088085065609850.3823829868780310.191191493439015
520.7852185001718760.4295629996562480.214781499828124
530.7163382606534210.5673234786931580.283661739346579
540.6624491207006860.6751017585986280.337550879299314
550.5915875245042660.8168249509914670.408412475495734
560.6547145202354990.6905709595290020.345285479764501
570.5697456312420660.8605087375158680.430254368757934
580.8641035126846590.2717929746306830.135896487315341
590.849294067409920.3014118651801610.15070593259008
600.9554847495272230.08903050094555440.0445152504727772
610.9537369703141610.09252605937167770.0462630296858389
620.9125208074246150.1749583851507710.0874791925753853
630.8727086913514850.254582617297030.127291308648515
640.8646235795815860.2707528408368290.135376420418414
650.810256196992080.379487606015840.18974380300792
660.8403238203097160.3193523593805680.159676179690284

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.690927148169512 & 0.618145703660975 & 0.309072851830488 \tabularnewline
20 & 0.548705957263753 & 0.902588085472494 & 0.451294042736247 \tabularnewline
21 & 0.48420133030485 & 0.968402660609701 & 0.51579866969515 \tabularnewline
22 & 0.592790819595471 & 0.814418360809057 & 0.407209180404529 \tabularnewline
23 & 0.471318872919167 & 0.942637745838333 & 0.528681127080833 \tabularnewline
24 & 0.488917565555933 & 0.977835131111866 & 0.511082434444067 \tabularnewline
25 & 0.496627331352644 & 0.993254662705287 & 0.503372668647356 \tabularnewline
26 & 0.407446506146497 & 0.814893012292994 & 0.592553493853503 \tabularnewline
27 & 0.326538555212078 & 0.653077110424155 & 0.673461444787922 \tabularnewline
28 & 0.290149709992869 & 0.580299419985737 & 0.709850290007131 \tabularnewline
29 & 0.417431484974273 & 0.834862969948547 & 0.582568515025727 \tabularnewline
30 & 0.500041663722631 & 0.999916672554739 & 0.499958336277369 \tabularnewline
31 & 0.421275933472674 & 0.842551866945349 & 0.578724066527326 \tabularnewline
32 & 0.392273881548172 & 0.784547763096344 & 0.607726118451828 \tabularnewline
33 & 0.513359128926046 & 0.973281742147908 & 0.486640871073954 \tabularnewline
34 & 0.525152380413197 & 0.949695239173606 & 0.474847619586803 \tabularnewline
35 & 0.74545063861139 & 0.50909872277722 & 0.25454936138861 \tabularnewline
36 & 0.751274526949663 & 0.497450946100674 & 0.248725473050337 \tabularnewline
37 & 0.706589510829142 & 0.586820978341717 & 0.293410489170858 \tabularnewline
38 & 0.780632107205807 & 0.438735785588387 & 0.219367892794193 \tabularnewline
39 & 0.911947110878205 & 0.17610577824359 & 0.0880528891217952 \tabularnewline
40 & 0.892970662700191 & 0.214058674599618 & 0.107029337299809 \tabularnewline
41 & 0.892076874364708 & 0.215846251270585 & 0.107923125635292 \tabularnewline
42 & 0.884449232382516 & 0.231101535234968 & 0.115550767617484 \tabularnewline
43 & 0.888650324225001 & 0.222699351549997 & 0.111349675774999 \tabularnewline
44 & 0.856870617063485 & 0.28625876587303 & 0.143129382936515 \tabularnewline
45 & 0.835993092650694 & 0.328013814698612 & 0.164006907349306 \tabularnewline
46 & 0.849268982988064 & 0.301462034023871 & 0.150731017011936 \tabularnewline
47 & 0.855141718895522 & 0.289716562208955 & 0.144858281104478 \tabularnewline
48 & 0.815277245894186 & 0.369445508211629 & 0.184722754105814 \tabularnewline
49 & 0.820131599273948 & 0.359736801452104 & 0.179868400726052 \tabularnewline
50 & 0.804106731369635 & 0.39178653726073 & 0.195893268630365 \tabularnewline
51 & 0.808808506560985 & 0.382382986878031 & 0.191191493439015 \tabularnewline
52 & 0.785218500171876 & 0.429562999656248 & 0.214781499828124 \tabularnewline
53 & 0.716338260653421 & 0.567323478693158 & 0.283661739346579 \tabularnewline
54 & 0.662449120700686 & 0.675101758598628 & 0.337550879299314 \tabularnewline
55 & 0.591587524504266 & 0.816824950991467 & 0.408412475495734 \tabularnewline
56 & 0.654714520235499 & 0.690570959529002 & 0.345285479764501 \tabularnewline
57 & 0.569745631242066 & 0.860508737515868 & 0.430254368757934 \tabularnewline
58 & 0.864103512684659 & 0.271792974630683 & 0.135896487315341 \tabularnewline
59 & 0.84929406740992 & 0.301411865180161 & 0.15070593259008 \tabularnewline
60 & 0.955484749527223 & 0.0890305009455544 & 0.0445152504727772 \tabularnewline
61 & 0.953736970314161 & 0.0925260593716777 & 0.0462630296858389 \tabularnewline
62 & 0.912520807424615 & 0.174958385150771 & 0.0874791925753853 \tabularnewline
63 & 0.872708691351485 & 0.25458261729703 & 0.127291308648515 \tabularnewline
64 & 0.864623579581586 & 0.270752840836829 & 0.135376420418414 \tabularnewline
65 & 0.81025619699208 & 0.37948760601584 & 0.18974380300792 \tabularnewline
66 & 0.840323820309716 & 0.319352359380568 & 0.159676179690284 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153558&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]19[/C][C]0.690927148169512[/C][C]0.618145703660975[/C][C]0.309072851830488[/C][/ROW]
[ROW][C]20[/C][C]0.548705957263753[/C][C]0.902588085472494[/C][C]0.451294042736247[/C][/ROW]
[ROW][C]21[/C][C]0.48420133030485[/C][C]0.968402660609701[/C][C]0.51579866969515[/C][/ROW]
[ROW][C]22[/C][C]0.592790819595471[/C][C]0.814418360809057[/C][C]0.407209180404529[/C][/ROW]
[ROW][C]23[/C][C]0.471318872919167[/C][C]0.942637745838333[/C][C]0.528681127080833[/C][/ROW]
[ROW][C]24[/C][C]0.488917565555933[/C][C]0.977835131111866[/C][C]0.511082434444067[/C][/ROW]
[ROW][C]25[/C][C]0.496627331352644[/C][C]0.993254662705287[/C][C]0.503372668647356[/C][/ROW]
[ROW][C]26[/C][C]0.407446506146497[/C][C]0.814893012292994[/C][C]0.592553493853503[/C][/ROW]
[ROW][C]27[/C][C]0.326538555212078[/C][C]0.653077110424155[/C][C]0.673461444787922[/C][/ROW]
[ROW][C]28[/C][C]0.290149709992869[/C][C]0.580299419985737[/C][C]0.709850290007131[/C][/ROW]
[ROW][C]29[/C][C]0.417431484974273[/C][C]0.834862969948547[/C][C]0.582568515025727[/C][/ROW]
[ROW][C]30[/C][C]0.500041663722631[/C][C]0.999916672554739[/C][C]0.499958336277369[/C][/ROW]
[ROW][C]31[/C][C]0.421275933472674[/C][C]0.842551866945349[/C][C]0.578724066527326[/C][/ROW]
[ROW][C]32[/C][C]0.392273881548172[/C][C]0.784547763096344[/C][C]0.607726118451828[/C][/ROW]
[ROW][C]33[/C][C]0.513359128926046[/C][C]0.973281742147908[/C][C]0.486640871073954[/C][/ROW]
[ROW][C]34[/C][C]0.525152380413197[/C][C]0.949695239173606[/C][C]0.474847619586803[/C][/ROW]
[ROW][C]35[/C][C]0.74545063861139[/C][C]0.50909872277722[/C][C]0.25454936138861[/C][/ROW]
[ROW][C]36[/C][C]0.751274526949663[/C][C]0.497450946100674[/C][C]0.248725473050337[/C][/ROW]
[ROW][C]37[/C][C]0.706589510829142[/C][C]0.586820978341717[/C][C]0.293410489170858[/C][/ROW]
[ROW][C]38[/C][C]0.780632107205807[/C][C]0.438735785588387[/C][C]0.219367892794193[/C][/ROW]
[ROW][C]39[/C][C]0.911947110878205[/C][C]0.17610577824359[/C][C]0.0880528891217952[/C][/ROW]
[ROW][C]40[/C][C]0.892970662700191[/C][C]0.214058674599618[/C][C]0.107029337299809[/C][/ROW]
[ROW][C]41[/C][C]0.892076874364708[/C][C]0.215846251270585[/C][C]0.107923125635292[/C][/ROW]
[ROW][C]42[/C][C]0.884449232382516[/C][C]0.231101535234968[/C][C]0.115550767617484[/C][/ROW]
[ROW][C]43[/C][C]0.888650324225001[/C][C]0.222699351549997[/C][C]0.111349675774999[/C][/ROW]
[ROW][C]44[/C][C]0.856870617063485[/C][C]0.28625876587303[/C][C]0.143129382936515[/C][/ROW]
[ROW][C]45[/C][C]0.835993092650694[/C][C]0.328013814698612[/C][C]0.164006907349306[/C][/ROW]
[ROW][C]46[/C][C]0.849268982988064[/C][C]0.301462034023871[/C][C]0.150731017011936[/C][/ROW]
[ROW][C]47[/C][C]0.855141718895522[/C][C]0.289716562208955[/C][C]0.144858281104478[/C][/ROW]
[ROW][C]48[/C][C]0.815277245894186[/C][C]0.369445508211629[/C][C]0.184722754105814[/C][/ROW]
[ROW][C]49[/C][C]0.820131599273948[/C][C]0.359736801452104[/C][C]0.179868400726052[/C][/ROW]
[ROW][C]50[/C][C]0.804106731369635[/C][C]0.39178653726073[/C][C]0.195893268630365[/C][/ROW]
[ROW][C]51[/C][C]0.808808506560985[/C][C]0.382382986878031[/C][C]0.191191493439015[/C][/ROW]
[ROW][C]52[/C][C]0.785218500171876[/C][C]0.429562999656248[/C][C]0.214781499828124[/C][/ROW]
[ROW][C]53[/C][C]0.716338260653421[/C][C]0.567323478693158[/C][C]0.283661739346579[/C][/ROW]
[ROW][C]54[/C][C]0.662449120700686[/C][C]0.675101758598628[/C][C]0.337550879299314[/C][/ROW]
[ROW][C]55[/C][C]0.591587524504266[/C][C]0.816824950991467[/C][C]0.408412475495734[/C][/ROW]
[ROW][C]56[/C][C]0.654714520235499[/C][C]0.690570959529002[/C][C]0.345285479764501[/C][/ROW]
[ROW][C]57[/C][C]0.569745631242066[/C][C]0.860508737515868[/C][C]0.430254368757934[/C][/ROW]
[ROW][C]58[/C][C]0.864103512684659[/C][C]0.271792974630683[/C][C]0.135896487315341[/C][/ROW]
[ROW][C]59[/C][C]0.84929406740992[/C][C]0.301411865180161[/C][C]0.15070593259008[/C][/ROW]
[ROW][C]60[/C][C]0.955484749527223[/C][C]0.0890305009455544[/C][C]0.0445152504727772[/C][/ROW]
[ROW][C]61[/C][C]0.953736970314161[/C][C]0.0925260593716777[/C][C]0.0462630296858389[/C][/ROW]
[ROW][C]62[/C][C]0.912520807424615[/C][C]0.174958385150771[/C][C]0.0874791925753853[/C][/ROW]
[ROW][C]63[/C][C]0.872708691351485[/C][C]0.25458261729703[/C][C]0.127291308648515[/C][/ROW]
[ROW][C]64[/C][C]0.864623579581586[/C][C]0.270752840836829[/C][C]0.135376420418414[/C][/ROW]
[ROW][C]65[/C][C]0.81025619699208[/C][C]0.37948760601584[/C][C]0.18974380300792[/C][/ROW]
[ROW][C]66[/C][C]0.840323820309716[/C][C]0.319352359380568[/C][C]0.159676179690284[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153558&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153558&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.6909271481695120.6181457036609750.309072851830488
200.5487059572637530.9025880854724940.451294042736247
210.484201330304850.9684026606097010.51579866969515
220.5927908195954710.8144183608090570.407209180404529
230.4713188729191670.9426377458383330.528681127080833
240.4889175655559330.9778351311118660.511082434444067
250.4966273313526440.9932546627052870.503372668647356
260.4074465061464970.8148930122929940.592553493853503
270.3265385552120780.6530771104241550.673461444787922
280.2901497099928690.5802994199857370.709850290007131
290.4174314849742730.8348629699485470.582568515025727
300.5000416637226310.9999166725547390.499958336277369
310.4212759334726740.8425518669453490.578724066527326
320.3922738815481720.7845477630963440.607726118451828
330.5133591289260460.9732817421479080.486640871073954
340.5251523804131970.9496952391736060.474847619586803
350.745450638611390.509098722777220.25454936138861
360.7512745269496630.4974509461006740.248725473050337
370.7065895108291420.5868209783417170.293410489170858
380.7806321072058070.4387357855883870.219367892794193
390.9119471108782050.176105778243590.0880528891217952
400.8929706627001910.2140586745996180.107029337299809
410.8920768743647080.2158462512705850.107923125635292
420.8844492323825160.2311015352349680.115550767617484
430.8886503242250010.2226993515499970.111349675774999
440.8568706170634850.286258765873030.143129382936515
450.8359930926506940.3280138146986120.164006907349306
460.8492689829880640.3014620340238710.150731017011936
470.8551417188955220.2897165622089550.144858281104478
480.8152772458941860.3694455082116290.184722754105814
490.8201315992739480.3597368014521040.179868400726052
500.8041067313696350.391786537260730.195893268630365
510.8088085065609850.3823829868780310.191191493439015
520.7852185001718760.4295629996562480.214781499828124
530.7163382606534210.5673234786931580.283661739346579
540.6624491207006860.6751017585986280.337550879299314
550.5915875245042660.8168249509914670.408412475495734
560.6547145202354990.6905709595290020.345285479764501
570.5697456312420660.8605087375158680.430254368757934
580.8641035126846590.2717929746306830.135896487315341
590.849294067409920.3014118651801610.15070593259008
600.9554847495272230.08903050094555440.0445152504727772
610.9537369703141610.09252605937167770.0462630296858389
620.9125208074246150.1749583851507710.0874791925753853
630.8727086913514850.254582617297030.127291308648515
640.8646235795815860.2707528408368290.135376420418414
650.810256196992080.379487606015840.18974380300792
660.8403238203097160.3193523593805680.159676179690284






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0416666666666667OK

\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 & 2 & 0.0416666666666667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153558&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]2[/C][C]0.0416666666666667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153558&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153558&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0416666666666667OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 7
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}