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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 computationThu, 15 Dec 2011 06:40:27 -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/15/t1323949248hfpqdarpqq4pydq.htm/, Retrieved Wed, 08 May 2024 10:25:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155364, Retrieved Wed, 08 May 2024 10:25:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-15 11:40:27] [bb550f50666f8cd9962562839f8255be] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	30	1418	56	396	81	115	94	24188	146283	144	145
4	30	2172	89	967	125	116	103	32287	96933	135	132
0	26	1583	44	656	66	100	93	27101	95757	84	84
0	38	1764	84	655	74	140	123	19716	143983	130	127
-4	44	1495	88	465	49	166	148	17753	75851	82	78
4	30	1373	55	525	52	99	90	9028	59238	60	60
4	40	2187	60	885	88	139	124	18653	93163	131	131
0	47	4041	154	1436	108	181	168	29498	151511	140	133
-1	30	1706	53	612	43	116	115	27563	136368	151	150
0	31	2152	119	865	75	116	71	18293	112642	91	91
1	30	2242	75	963	86	108	108	16116	127766	119	118
0	34	2515	92	966	135	129	120	26569	85646	123	119
3	31	2147	100	801	63	118	114	24785	98579	90	89
-1	33	1638	73	513	52	125	120	23825	131741	113	108
4	33	2452	77	992	59	127	124	34461	171975	175	162
3	36	2662	99	937	64	136	126	24919	159676	96	92
1	14	865	30	260	32	46	37	12558	58391	41	41
0	17	1793	76	503	129	54	38	7784	31580	47	47
-2	32	2527	146	927	37	124	120	28522	136815	126	120
-3	30	2747	67	1269	31	115	93	22265	120642	105	105
-4	35	1324	56	537	65	128	95	14459	69107	80	79
2	28	1383	58	532	74	97	90	22240	108016	73	70
2	34	4308	119	1635	715	125	110	11912	79336	68	67
-4	39	1831	66	557	66	149	138	18220	93176	127	127
3	39	3373	89	1178	106	149	133	19199	161632	154	152
2	29	2352	41	866	112	108	96	25239	102996	112	109
2	44	2144	68	574	66	166	164	29801	160604	137	133
0	21	4691	168	1276	190	80	78	18450	158051	135	123
5	28	2694	132	825	165	107	102	34861	162647	230	230
-2	28	1769	71	663	61	107	99	16688	60622	71	68
0	38	3148	112	1069	53	146	129	24683	179566	147	147
-2	32	1954	70	711	38	123	114	21436	96144	105	101
-3	29	1226	57	503	50	111	99	30546	129847	107	108
2	27	1496	103	464	42	105	104	15977	71180	116	114
2	40	1943	52	657	53	155	138	14251	86767	89	88
2	40	1762	62	577	50	155	151	16851	93487	84	83
0	28	1403	45	619	77	104	72	21113	82981	113	113
4	34	1425	46	479	57	132	120	17401	73815	120	118
4	33	1857	63	817	73	127	115	23958	94552	110	110
2	33	1420	53	537	63	122	98	14587	67808	78	76
2	35	1644	78	465	47	87	71	20537	106175	145	141
-4	29	1054	46	299	57	109	107	30495	76669	91	91
3	20	937	41	248	36	78	73	7117	57283	48	48
3	37	2547	91	905	63	141	129	33473	72413	150	144
2	33	1626	63	512	63	124	118	21115	96971	181	168
-1	29	1964	63	786	110	112	104	32902	120336	121	117
-3	28	1381	32	489	56	108	107	25131	93913	99	100
0	21	1290	34	351	71	78	36	6943	32036	40	37
1	41	1982	93	669	56	158	139	31808	102255	87	87
-3	20	1590	55	506	79	78	56	17014	63506	66	64
3	30	1281	72	407	67	119	93	6440	68370	58	58
0	22	1272	42	316	76	88	87	18647	50517	77	76
0	42	1944	71	603	65	155	110	20556	103950	130	129
0	32	1605	65	577	45	123	83	22392	84396	101	101
3	36	1386	41	411	97	136	98	8388	55515	120	89
-3	31	2395	86	975	53	117	82	22120	209056	195	193
0	33	2699	95	964	144	124	115	20923	142775	106	101
-4	40	1606	49	537	60	151	140	20237	68847	83	82
2	38	1204	64	369	89	145	120	3769	20112	37	36
-1	24	1138	38	417	42	87	66	12252	61023	77	75
3	43	1111	52	389	52	165	139	21721	112494	144	131
2	31	2186	247	719	128	120	119	17939	78876	95	90
5	40	3604	139	1277	142	150	141	23436	170745	169	166
2	37	3261	110	1402	128	136	133	34538	122037	134	133
-2	31	1641	67	564	50	116	98	25515	112283	197	196
0	39	2312	83	747	50	150	117	29402	120691	140	136
3	32	2201	70	861	46	118	105	28732	122422	125	123
-2	18	961	32	319	57	71	55	5250	25899	21	21
0	39	1900	83	612	52	144	132	28608	139296	167	163
6	30	1645	70	564	48	110	73	14817	89455	96	96
-3	37	2429	103	824	91	147	86	16714	147866	151	151
3	32	872	34	239	70	111	48	1669	14336	23	23
0	17	1018	40	459	37	68	48	7768	30059	21	14
-2	12	1403	46	454	72	48	43	7936	41907	90	87
1	13	616	18	225	24	51	46	7294	35885	60	56
0	17	1232	60	389	90	68	65	13275	55764	26	25
2	17	1255	39	339	45	64	52	5401	35619	41	41
2	20	995	31	333	26	76	68	8702	40557	35	33
-3	17	2048	54	636	132	66	47	8030	44197	68	68
-2	17	301	14	93	35	68	41	1278	4103	6	6
1	17	628	23	170	48	66	47	1574	4694	0	0
-4	22	1597	77	530	124	83	71	9653	62991	41	39
0	15	717	19	201	35	55	30	7067	24261	38	37
1	12	652	49	227	49	41	24	1514	21425	47	47
0	17	733	20	261	45	66	63	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 time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155364&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155364&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155364&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
score[t] = -0.499196751044553 + 0.282710837368043comp_rev[t] -0.000698879751712639pageview[t] + 0.00396592077682703logins[t] + 0.00224768012879452comp_info[t] + 0.00133820189015943comp_view_pr[t] -0.089307464930844feedback_pc[t] + 0.0262587394294302feedbackp120[t] -6.29345910915495e-05revisions[t] -7.27600641965381e-06sec[t] + 0.0781974664703195hyperl[t] -0.0657258098288154blogs[t] -0.00102669152057652t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
score[t] =  -0.499196751044553 +  0.282710837368043comp_rev[t] -0.000698879751712639pageview[t] +  0.00396592077682703logins[t] +  0.00224768012879452comp_info[t] +  0.00133820189015943comp_view_pr[t] -0.089307464930844feedback_pc[t] +  0.0262587394294302feedbackp120[t] -6.29345910915495e-05revisions[t] -7.27600641965381e-06sec[t] +  0.0781974664703195hyperl[t] -0.0657258098288154blogs[t] -0.00102669152057652t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155364&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]score[t] =  -0.499196751044553 +  0.282710837368043comp_rev[t] -0.000698879751712639pageview[t] +  0.00396592077682703logins[t] +  0.00224768012879452comp_info[t] +  0.00133820189015943comp_view_pr[t] -0.089307464930844feedback_pc[t] +  0.0262587394294302feedbackp120[t] -6.29345910915495e-05revisions[t] -7.27600641965381e-06sec[t] +  0.0781974664703195hyperl[t] -0.0657258098288154blogs[t] -0.00102669152057652t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155364&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155364&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
score[t] = -0.499196751044553 + 0.282710837368043comp_rev[t] -0.000698879751712639pageview[t] + 0.00396592077682703logins[t] + 0.00224768012879452comp_info[t] + 0.00133820189015943comp_view_pr[t] -0.089307464930844feedback_pc[t] + 0.0262587394294302feedbackp120[t] -6.29345910915495e-05revisions[t] -7.27600641965381e-06sec[t] + 0.0781974664703195hyperl[t] -0.0657258098288154blogs[t] -0.00102669152057652t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.4991967510445531.472621-0.3390.7356080.367804
comp_rev0.2827108373680430.1759521.60680.1124860.056243
pageview-0.0006988797517126390.001359-0.51440.6085690.304284
logins0.003965920776827030.0114980.34490.7311530.365577
comp_info0.002247680128794520.0031190.72060.4734680.236734
comp_view_pr0.001338201890159430.0048670.2750.7841340.392067
feedback_pc-0.0893074649308440.052311-1.70720.0920880.046044
feedbackp1200.02625873942943020.0244981.07190.2873610.143681
revisions-6.29345910915495e-055.9e-05-1.06410.290820.14541
sec-7.27600641965381e-061.4e-05-0.52030.6044340.302217
hyperl0.07819746647031950.0660391.18410.2402660.120133
blogs-0.06572580982881540.068554-0.95880.3408920.170446
t-0.001026691520576520.013727-0.07480.9405860.470293

\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) & -0.499196751044553 & 1.472621 & -0.339 & 0.735608 & 0.367804 \tabularnewline
comp_rev & 0.282710837368043 & 0.175952 & 1.6068 & 0.112486 & 0.056243 \tabularnewline
pageview & -0.000698879751712639 & 0.001359 & -0.5144 & 0.608569 & 0.304284 \tabularnewline
logins & 0.00396592077682703 & 0.011498 & 0.3449 & 0.731153 & 0.365577 \tabularnewline
comp_info & 0.00224768012879452 & 0.003119 & 0.7206 & 0.473468 & 0.236734 \tabularnewline
comp_view_pr & 0.00133820189015943 & 0.004867 & 0.275 & 0.784134 & 0.392067 \tabularnewline
feedback_pc & -0.089307464930844 & 0.052311 & -1.7072 & 0.092088 & 0.046044 \tabularnewline
feedbackp120 & 0.0262587394294302 & 0.024498 & 1.0719 & 0.287361 & 0.143681 \tabularnewline
revisions & -6.29345910915495e-05 & 5.9e-05 & -1.0641 & 0.29082 & 0.14541 \tabularnewline
sec & -7.27600641965381e-06 & 1.4e-05 & -0.5203 & 0.604434 & 0.302217 \tabularnewline
hyperl & 0.0781974664703195 & 0.066039 & 1.1841 & 0.240266 & 0.120133 \tabularnewline
blogs & -0.0657258098288154 & 0.068554 & -0.9588 & 0.340892 & 0.170446 \tabularnewline
t & -0.00102669152057652 & 0.013727 & -0.0748 & 0.940586 & 0.470293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155364&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]-0.499196751044553[/C][C]1.472621[/C][C]-0.339[/C][C]0.735608[/C][C]0.367804[/C][/ROW]
[ROW][C]comp_rev[/C][C]0.282710837368043[/C][C]0.175952[/C][C]1.6068[/C][C]0.112486[/C][C]0.056243[/C][/ROW]
[ROW][C]pageview[/C][C]-0.000698879751712639[/C][C]0.001359[/C][C]-0.5144[/C][C]0.608569[/C][C]0.304284[/C][/ROW]
[ROW][C]logins[/C][C]0.00396592077682703[/C][C]0.011498[/C][C]0.3449[/C][C]0.731153[/C][C]0.365577[/C][/ROW]
[ROW][C]comp_info[/C][C]0.00224768012879452[/C][C]0.003119[/C][C]0.7206[/C][C]0.473468[/C][C]0.236734[/C][/ROW]
[ROW][C]comp_view_pr[/C][C]0.00133820189015943[/C][C]0.004867[/C][C]0.275[/C][C]0.784134[/C][C]0.392067[/C][/ROW]
[ROW][C]feedback_pc[/C][C]-0.089307464930844[/C][C]0.052311[/C][C]-1.7072[/C][C]0.092088[/C][C]0.046044[/C][/ROW]
[ROW][C]feedbackp120[/C][C]0.0262587394294302[/C][C]0.024498[/C][C]1.0719[/C][C]0.287361[/C][C]0.143681[/C][/ROW]
[ROW][C]revisions[/C][C]-6.29345910915495e-05[/C][C]5.9e-05[/C][C]-1.0641[/C][C]0.29082[/C][C]0.14541[/C][/ROW]
[ROW][C]sec[/C][C]-7.27600641965381e-06[/C][C]1.4e-05[/C][C]-0.5203[/C][C]0.604434[/C][C]0.302217[/C][/ROW]
[ROW][C]hyperl[/C][C]0.0781974664703195[/C][C]0.066039[/C][C]1.1841[/C][C]0.240266[/C][C]0.120133[/C][/ROW]
[ROW][C]blogs[/C][C]-0.0657258098288154[/C][C]0.068554[/C][C]-0.9588[/C][C]0.340892[/C][C]0.170446[/C][/ROW]
[ROW][C]t[/C][C]-0.00102669152057652[/C][C]0.013727[/C][C]-0.0748[/C][C]0.940586[/C][C]0.470293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155364&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155364&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)-0.4991967510445531.472621-0.3390.7356080.367804
comp_rev0.2827108373680430.1759521.60680.1124860.056243
pageview-0.0006988797517126390.001359-0.51440.6085690.304284
logins0.003965920776827030.0114980.34490.7311530.365577
comp_info0.002247680128794520.0031190.72060.4734680.236734
comp_view_pr0.001338201890159430.0048670.2750.7841340.392067
feedback_pc-0.0893074649308440.052311-1.70720.0920880.046044
feedbackp1200.02625873942943020.0244981.07190.2873610.143681
revisions-6.29345910915495e-055.9e-05-1.06410.290820.14541
sec-7.27600641965381e-061.4e-05-0.52030.6044340.302217
hyperl0.07819746647031950.0660391.18410.2402660.120133
blogs-0.06572580982881540.068554-0.95880.3408920.170446
t-0.001026691520576520.013727-0.07480.9405860.470293






Multiple Linear Regression - Regression Statistics
Multiple R0.340994467989689
R-squared0.116277227199571
Adjusted R-squared-0.0310099016005005
F-TEST (value)0.789459528112646
F-TEST (DF numerator)12
F-TEST (DF denominator)72
p-value0.659527126988585
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49991900544623
Sum Squared Residuals449.970842432971

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.340994467989689 \tabularnewline
R-squared & 0.116277227199571 \tabularnewline
Adjusted R-squared & -0.0310099016005005 \tabularnewline
F-TEST (value) & 0.789459528112646 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 72 \tabularnewline
p-value & 0.659527126988585 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.49991900544623 \tabularnewline
Sum Squared Residuals & 449.970842432971 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155364&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.340994467989689[/C][/ROW]
[ROW][C]R-squared[/C][C]0.116277227199571[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0310099016005005[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.789459528112646[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]72[/C][/ROW]
[ROW][C]p-value[/C][C]0.659527126988585[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.49991900544623[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]449.970842432971[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155364&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155364&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 R0.340994467989689
R-squared0.116277227199571
Adjusted R-squared-0.0310099016005005
F-TEST (value)0.789459528112646
F-TEST (DF numerator)12
F-TEST (DF denominator)72
p-value0.659527126988585
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49991900544623
Sum Squared Residuals449.970842432971






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12-0.4478047123859512.44780471238595
240.6444380686813083.35556193131869
30-0.3642051657380740.364205165738074
401.16811221242509-1.16811221242509
5-41.02752876997283-5.02752876997283
641.755107892726172.24489210727383
742.242873383158581.75712661684142
801.43286235874269-1.43286235874269
9-10.306070498571491-1.30607049857149
100-0.06408497598679530.0640849759867953
1111.77770223454573-0.777702234545731
1201.19178258186378-1.19178258186378
1330.3985830342299332.60141696577007
14-10.450857658898126-1.45085765889813
1541.24616679263622.7538332073638
1630.2787933273113822.72120667268862
171-0.2574508431026181.25745084310262
1800.68167448408828-0.68167448408828
19-20.726330606500459-2.72633060650046
20-30.40347650582784-3.40347650582784
21-40.678773006723897-4.6787730067239
2220.5748653580075451.42513464199246
2322.49420036654377-0.494200366543774
24-40.900443148380033-4.90044314838003
2531.139490884862011.86050911513799
2620.4197607764201671.58023922357983
2720.4709783688911091.52902163110889
2800.98395599910834-0.98395599910834
2950.7163818562906054.2836181437094
30-20.638053274347394-2.63805327434739
3100.25167521040634-0.25167521040634
32-20.608146618289501-2.6081466182895
33-3-0.679678757632504-2.3203212423675
3420.9695589109650171.03044108903498
3520.5978117293142371.40218827068576
3620.6455930940448491.35440690595515
3700.150454573745771-0.150454573745771
3840.7717129845097353.22828701549027
3940.5301379523851593.46986204761484
4020.6689769799202151.33102302007978
4123.72293528598277-1.72293528598277
42-4-0.416498843219283-3.58350115678072
433-0.09092710258735613.09092710258736
4431.041880269258091.95811973074191
4522.23443871306974-0.234438713069744
46-1-0.00247121485747037-0.99752878514253
47-3-0.227210085146399-2.7727899148536
480-0.4889888308007020.488988830800702
491-0.5174840875535221.51748408755352
50-3-0.620114742796504-2.3798852572035
513-0.04041502467245823.04041502467246
520-0.3329889439331330.332988943933133
5300.368357270551449-0.368357270551449
540-0.5836806235859970.583680623585997
5532.899300181529630.100699818470365
56-30.491471437464192-3.49147143746419
5700.862331975823799-0.862331975823799
58-40.626045307612824-4.62604530761282
5920.8892098208189281.11079017918107
60-10.413146964330556-1.41314696433056
6131.347525449638391.65247455036161
6221.658832200805110.341167799194891
6351.731340862439053.26865913756095
6421.397085782637090.602914217362911
65-20.965145744954859-2.96514574495486
660-0.1254105401400370.125410540140037
6730.4252672604989462.57473273950105
68-2-0.385185655024749-1.61481434497525
6901.03974800563306-1.03974800563306
7060.07711139280108185.92288860719892
71-3-0.541503466942159-2.45849653305784
7230.05470842874267222.94529157125733
730-0.03777314888668680.0377731488866868
74-20.493688993570536-2.49368899357054
7510.2220433342624750.777956665737525
760-0.6167165272389710.616716527238971
772-0.1103674743458892.11036747434589
7820.009203725398441831.99079627460156
79-3-0.0242016468826201-2.97579835311738
80-2-0.705969654286968-1.29403034571303
811-0.4709600753047531.47096007530475
82-40.21153532933823-4.21153532933823
830-0.4766546750054720.476654675005472
8410.4251471492843150.574852850715685
8500.0779144282752659-0.0779144282752659

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & -0.447804712385951 & 2.44780471238595 \tabularnewline
2 & 4 & 0.644438068681308 & 3.35556193131869 \tabularnewline
3 & 0 & -0.364205165738074 & 0.364205165738074 \tabularnewline
4 & 0 & 1.16811221242509 & -1.16811221242509 \tabularnewline
5 & -4 & 1.02752876997283 & -5.02752876997283 \tabularnewline
6 & 4 & 1.75510789272617 & 2.24489210727383 \tabularnewline
7 & 4 & 2.24287338315858 & 1.75712661684142 \tabularnewline
8 & 0 & 1.43286235874269 & -1.43286235874269 \tabularnewline
9 & -1 & 0.306070498571491 & -1.30607049857149 \tabularnewline
10 & 0 & -0.0640849759867953 & 0.0640849759867953 \tabularnewline
11 & 1 & 1.77770223454573 & -0.777702234545731 \tabularnewline
12 & 0 & 1.19178258186378 & -1.19178258186378 \tabularnewline
13 & 3 & 0.398583034229933 & 2.60141696577007 \tabularnewline
14 & -1 & 0.450857658898126 & -1.45085765889813 \tabularnewline
15 & 4 & 1.2461667926362 & 2.7538332073638 \tabularnewline
16 & 3 & 0.278793327311382 & 2.72120667268862 \tabularnewline
17 & 1 & -0.257450843102618 & 1.25745084310262 \tabularnewline
18 & 0 & 0.68167448408828 & -0.68167448408828 \tabularnewline
19 & -2 & 0.726330606500459 & -2.72633060650046 \tabularnewline
20 & -3 & 0.40347650582784 & -3.40347650582784 \tabularnewline
21 & -4 & 0.678773006723897 & -4.6787730067239 \tabularnewline
22 & 2 & 0.574865358007545 & 1.42513464199246 \tabularnewline
23 & 2 & 2.49420036654377 & -0.494200366543774 \tabularnewline
24 & -4 & 0.900443148380033 & -4.90044314838003 \tabularnewline
25 & 3 & 1.13949088486201 & 1.86050911513799 \tabularnewline
26 & 2 & 0.419760776420167 & 1.58023922357983 \tabularnewline
27 & 2 & 0.470978368891109 & 1.52902163110889 \tabularnewline
28 & 0 & 0.98395599910834 & -0.98395599910834 \tabularnewline
29 & 5 & 0.716381856290605 & 4.2836181437094 \tabularnewline
30 & -2 & 0.638053274347394 & -2.63805327434739 \tabularnewline
31 & 0 & 0.25167521040634 & -0.25167521040634 \tabularnewline
32 & -2 & 0.608146618289501 & -2.6081466182895 \tabularnewline
33 & -3 & -0.679678757632504 & -2.3203212423675 \tabularnewline
34 & 2 & 0.969558910965017 & 1.03044108903498 \tabularnewline
35 & 2 & 0.597811729314237 & 1.40218827068576 \tabularnewline
36 & 2 & 0.645593094044849 & 1.35440690595515 \tabularnewline
37 & 0 & 0.150454573745771 & -0.150454573745771 \tabularnewline
38 & 4 & 0.771712984509735 & 3.22828701549027 \tabularnewline
39 & 4 & 0.530137952385159 & 3.46986204761484 \tabularnewline
40 & 2 & 0.668976979920215 & 1.33102302007978 \tabularnewline
41 & 2 & 3.72293528598277 & -1.72293528598277 \tabularnewline
42 & -4 & -0.416498843219283 & -3.58350115678072 \tabularnewline
43 & 3 & -0.0909271025873561 & 3.09092710258736 \tabularnewline
44 & 3 & 1.04188026925809 & 1.95811973074191 \tabularnewline
45 & 2 & 2.23443871306974 & -0.234438713069744 \tabularnewline
46 & -1 & -0.00247121485747037 & -0.99752878514253 \tabularnewline
47 & -3 & -0.227210085146399 & -2.7727899148536 \tabularnewline
48 & 0 & -0.488988830800702 & 0.488988830800702 \tabularnewline
49 & 1 & -0.517484087553522 & 1.51748408755352 \tabularnewline
50 & -3 & -0.620114742796504 & -2.3798852572035 \tabularnewline
51 & 3 & -0.0404150246724582 & 3.04041502467246 \tabularnewline
52 & 0 & -0.332988943933133 & 0.332988943933133 \tabularnewline
53 & 0 & 0.368357270551449 & -0.368357270551449 \tabularnewline
54 & 0 & -0.583680623585997 & 0.583680623585997 \tabularnewline
55 & 3 & 2.89930018152963 & 0.100699818470365 \tabularnewline
56 & -3 & 0.491471437464192 & -3.49147143746419 \tabularnewline
57 & 0 & 0.862331975823799 & -0.862331975823799 \tabularnewline
58 & -4 & 0.626045307612824 & -4.62604530761282 \tabularnewline
59 & 2 & 0.889209820818928 & 1.11079017918107 \tabularnewline
60 & -1 & 0.413146964330556 & -1.41314696433056 \tabularnewline
61 & 3 & 1.34752544963839 & 1.65247455036161 \tabularnewline
62 & 2 & 1.65883220080511 & 0.341167799194891 \tabularnewline
63 & 5 & 1.73134086243905 & 3.26865913756095 \tabularnewline
64 & 2 & 1.39708578263709 & 0.602914217362911 \tabularnewline
65 & -2 & 0.965145744954859 & -2.96514574495486 \tabularnewline
66 & 0 & -0.125410540140037 & 0.125410540140037 \tabularnewline
67 & 3 & 0.425267260498946 & 2.57473273950105 \tabularnewline
68 & -2 & -0.385185655024749 & -1.61481434497525 \tabularnewline
69 & 0 & 1.03974800563306 & -1.03974800563306 \tabularnewline
70 & 6 & 0.0771113928010818 & 5.92288860719892 \tabularnewline
71 & -3 & -0.541503466942159 & -2.45849653305784 \tabularnewline
72 & 3 & 0.0547084287426722 & 2.94529157125733 \tabularnewline
73 & 0 & -0.0377731488866868 & 0.0377731488866868 \tabularnewline
74 & -2 & 0.493688993570536 & -2.49368899357054 \tabularnewline
75 & 1 & 0.222043334262475 & 0.777956665737525 \tabularnewline
76 & 0 & -0.616716527238971 & 0.616716527238971 \tabularnewline
77 & 2 & -0.110367474345889 & 2.11036747434589 \tabularnewline
78 & 2 & 0.00920372539844183 & 1.99079627460156 \tabularnewline
79 & -3 & -0.0242016468826201 & -2.97579835311738 \tabularnewline
80 & -2 & -0.705969654286968 & -1.29403034571303 \tabularnewline
81 & 1 & -0.470960075304753 & 1.47096007530475 \tabularnewline
82 & -4 & 0.21153532933823 & -4.21153532933823 \tabularnewline
83 & 0 & -0.476654675005472 & 0.476654675005472 \tabularnewline
84 & 1 & 0.425147149284315 & 0.574852850715685 \tabularnewline
85 & 0 & 0.0779144282752659 & -0.0779144282752659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155364&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]-0.447804712385951[/C][C]2.44780471238595[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]0.644438068681308[/C][C]3.35556193131869[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.364205165738074[/C][C]0.364205165738074[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]1.16811221242509[/C][C]-1.16811221242509[/C][/ROW]
[ROW][C]5[/C][C]-4[/C][C]1.02752876997283[/C][C]-5.02752876997283[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]1.75510789272617[/C][C]2.24489210727383[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]2.24287338315858[/C][C]1.75712661684142[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]1.43286235874269[/C][C]-1.43286235874269[/C][/ROW]
[ROW][C]9[/C][C]-1[/C][C]0.306070498571491[/C][C]-1.30607049857149[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0640849759867953[/C][C]0.0640849759867953[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.77770223454573[/C][C]-0.777702234545731[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]1.19178258186378[/C][C]-1.19178258186378[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]0.398583034229933[/C][C]2.60141696577007[/C][/ROW]
[ROW][C]14[/C][C]-1[/C][C]0.450857658898126[/C][C]-1.45085765889813[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]1.2461667926362[/C][C]2.7538332073638[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]0.278793327311382[/C][C]2.72120667268862[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]-0.257450843102618[/C][C]1.25745084310262[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.68167448408828[/C][C]-0.68167448408828[/C][/ROW]
[ROW][C]19[/C][C]-2[/C][C]0.726330606500459[/C][C]-2.72633060650046[/C][/ROW]
[ROW][C]20[/C][C]-3[/C][C]0.40347650582784[/C][C]-3.40347650582784[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]0.678773006723897[/C][C]-4.6787730067239[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]0.574865358007545[/C][C]1.42513464199246[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.49420036654377[/C][C]-0.494200366543774[/C][/ROW]
[ROW][C]24[/C][C]-4[/C][C]0.900443148380033[/C][C]-4.90044314838003[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]1.13949088486201[/C][C]1.86050911513799[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]0.419760776420167[/C][C]1.58023922357983[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]0.470978368891109[/C][C]1.52902163110889[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.98395599910834[/C][C]-0.98395599910834[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]0.716381856290605[/C][C]4.2836181437094[/C][/ROW]
[ROW][C]30[/C][C]-2[/C][C]0.638053274347394[/C][C]-2.63805327434739[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.25167521040634[/C][C]-0.25167521040634[/C][/ROW]
[ROW][C]32[/C][C]-2[/C][C]0.608146618289501[/C][C]-2.6081466182895[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]-0.679678757632504[/C][C]-2.3203212423675[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]0.969558910965017[/C][C]1.03044108903498[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]0.597811729314237[/C][C]1.40218827068576[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]0.645593094044849[/C][C]1.35440690595515[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.150454573745771[/C][C]-0.150454573745771[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]0.771712984509735[/C][C]3.22828701549027[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]0.530137952385159[/C][C]3.46986204761484[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]0.668976979920215[/C][C]1.33102302007978[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]3.72293528598277[/C][C]-1.72293528598277[/C][/ROW]
[ROW][C]42[/C][C]-4[/C][C]-0.416498843219283[/C][C]-3.58350115678072[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]-0.0909271025873561[/C][C]3.09092710258736[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]1.04188026925809[/C][C]1.95811973074191[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]2.23443871306974[/C][C]-0.234438713069744[/C][/ROW]
[ROW][C]46[/C][C]-1[/C][C]-0.00247121485747037[/C][C]-0.99752878514253[/C][/ROW]
[ROW][C]47[/C][C]-3[/C][C]-0.227210085146399[/C][C]-2.7727899148536[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.488988830800702[/C][C]0.488988830800702[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]-0.517484087553522[/C][C]1.51748408755352[/C][/ROW]
[ROW][C]50[/C][C]-3[/C][C]-0.620114742796504[/C][C]-2.3798852572035[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]-0.0404150246724582[/C][C]3.04041502467246[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]-0.332988943933133[/C][C]0.332988943933133[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.368357270551449[/C][C]-0.368357270551449[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]-0.583680623585997[/C][C]0.583680623585997[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]2.89930018152963[/C][C]0.100699818470365[/C][/ROW]
[ROW][C]56[/C][C]-3[/C][C]0.491471437464192[/C][C]-3.49147143746419[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.862331975823799[/C][C]-0.862331975823799[/C][/ROW]
[ROW][C]58[/C][C]-4[/C][C]0.626045307612824[/C][C]-4.62604530761282[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]0.889209820818928[/C][C]1.11079017918107[/C][/ROW]
[ROW][C]60[/C][C]-1[/C][C]0.413146964330556[/C][C]-1.41314696433056[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]1.34752544963839[/C][C]1.65247455036161[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]1.65883220080511[/C][C]0.341167799194891[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]1.73134086243905[/C][C]3.26865913756095[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.39708578263709[/C][C]0.602914217362911[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]0.965145744954859[/C][C]-2.96514574495486[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-0.125410540140037[/C][C]0.125410540140037[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]0.425267260498946[/C][C]2.57473273950105[/C][/ROW]
[ROW][C]68[/C][C]-2[/C][C]-0.385185655024749[/C][C]-1.61481434497525[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]1.03974800563306[/C][C]-1.03974800563306[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]0.0771113928010818[/C][C]5.92288860719892[/C][/ROW]
[ROW][C]71[/C][C]-3[/C][C]-0.541503466942159[/C][C]-2.45849653305784[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]0.0547084287426722[/C][C]2.94529157125733[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]-0.0377731488866868[/C][C]0.0377731488866868[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]0.493688993570536[/C][C]-2.49368899357054[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.222043334262475[/C][C]0.777956665737525[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]-0.616716527238971[/C][C]0.616716527238971[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]-0.110367474345889[/C][C]2.11036747434589[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]0.00920372539844183[/C][C]1.99079627460156[/C][/ROW]
[ROW][C]79[/C][C]-3[/C][C]-0.0242016468826201[/C][C]-2.97579835311738[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-0.705969654286968[/C][C]-1.29403034571303[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]-0.470960075304753[/C][C]1.47096007530475[/C][/ROW]
[ROW][C]82[/C][C]-4[/C][C]0.21153532933823[/C][C]-4.21153532933823[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.476654675005472[/C][C]0.476654675005472[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.425147149284315[/C][C]0.574852850715685[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0779144282752659[/C][C]-0.0779144282752659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155364&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155364&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
12-0.4478047123859512.44780471238595
240.6444380686813083.35556193131869
30-0.3642051657380740.364205165738074
401.16811221242509-1.16811221242509
5-41.02752876997283-5.02752876997283
641.755107892726172.24489210727383
742.242873383158581.75712661684142
801.43286235874269-1.43286235874269
9-10.306070498571491-1.30607049857149
100-0.06408497598679530.0640849759867953
1111.77770223454573-0.777702234545731
1201.19178258186378-1.19178258186378
1330.3985830342299332.60141696577007
14-10.450857658898126-1.45085765889813
1541.24616679263622.7538332073638
1630.2787933273113822.72120667268862
171-0.2574508431026181.25745084310262
1800.68167448408828-0.68167448408828
19-20.726330606500459-2.72633060650046
20-30.40347650582784-3.40347650582784
21-40.678773006723897-4.6787730067239
2220.5748653580075451.42513464199246
2322.49420036654377-0.494200366543774
24-40.900443148380033-4.90044314838003
2531.139490884862011.86050911513799
2620.4197607764201671.58023922357983
2720.4709783688911091.52902163110889
2800.98395599910834-0.98395599910834
2950.7163818562906054.2836181437094
30-20.638053274347394-2.63805327434739
3100.25167521040634-0.25167521040634
32-20.608146618289501-2.6081466182895
33-3-0.679678757632504-2.3203212423675
3420.9695589109650171.03044108903498
3520.5978117293142371.40218827068576
3620.6455930940448491.35440690595515
3700.150454573745771-0.150454573745771
3840.7717129845097353.22828701549027
3940.5301379523851593.46986204761484
4020.6689769799202151.33102302007978
4123.72293528598277-1.72293528598277
42-4-0.416498843219283-3.58350115678072
433-0.09092710258735613.09092710258736
4431.041880269258091.95811973074191
4522.23443871306974-0.234438713069744
46-1-0.00247121485747037-0.99752878514253
47-3-0.227210085146399-2.7727899148536
480-0.4889888308007020.488988830800702
491-0.5174840875535221.51748408755352
50-3-0.620114742796504-2.3798852572035
513-0.04041502467245823.04041502467246
520-0.3329889439331330.332988943933133
5300.368357270551449-0.368357270551449
540-0.5836806235859970.583680623585997
5532.899300181529630.100699818470365
56-30.491471437464192-3.49147143746419
5700.862331975823799-0.862331975823799
58-40.626045307612824-4.62604530761282
5920.8892098208189281.11079017918107
60-10.413146964330556-1.41314696433056
6131.347525449638391.65247455036161
6221.658832200805110.341167799194891
6351.731340862439053.26865913756095
6421.397085782637090.602914217362911
65-20.965145744954859-2.96514574495486
660-0.1254105401400370.125410540140037
6730.4252672604989462.57473273950105
68-2-0.385185655024749-1.61481434497525
6901.03974800563306-1.03974800563306
7060.07711139280108185.92288860719892
71-3-0.541503466942159-2.45849653305784
7230.05470842874267222.94529157125733
730-0.03777314888668680.0377731488866868
74-20.493688993570536-2.49368899357054
7510.2220433342624750.777956665737525
760-0.6167165272389710.616716527238971
772-0.1103674743458892.11036747434589
7820.009203725398441831.99079627460156
79-3-0.0242016468826201-2.97579835311738
80-2-0.705969654286968-1.29403034571303
811-0.4709600753047531.47096007530475
82-40.21153532933823-4.21153532933823
830-0.4766546750054720.476654675005472
8410.4251471492843150.574852850715685
8500.0779144282752659-0.0779144282752659






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.4104709961994930.8209419923989860.589529003800507
170.2591181832439310.5182363664878610.740881816756069
180.171256492334130.342512984668260.82874350766587
190.284277881891810.5685557637836210.71572211810819
200.1958270882827210.3916541765654430.804172911717279
210.1498204935391780.2996409870783560.850179506460822
220.1848936851688670.3697873703377330.815106314831133
230.1212848090253990.2425696180507970.878715190974601
240.1778363942170470.3556727884340940.822163605782953
250.334657360190410.6693147203808210.66534263980959
260.2634990537731610.5269981075463220.736500946226839
270.2030079166108880.4060158332217760.796992083389112
280.2046241687325160.4092483374650330.795375831267484
290.2711821311697460.5423642623394920.728817868830254
300.3361103992426480.6722207984852960.663889600757352
310.273026161161830.546052322323660.72697383883817
320.3020824894994510.6041649789989030.697917510500549
330.3653476097614650.7306952195229310.634652390238535
340.5977903557559110.8044192884881780.402209644244089
350.7766486978672050.446702604265590.223351302132795
360.8006697670611760.3986604658776480.199330232938824
370.7484114264062250.5031771471875490.251588573593775
380.8082211871264560.3835576257470870.191778812873544
390.8429082199731320.3141835600537360.157091780026868
400.8110573430099190.3778853139801620.188942656990081
410.8511163007189520.2977673985620970.148883699281048
420.8737593357369380.2524813285261250.126240664263062
430.8657798799358290.2684402401283420.134220120064171
440.8454503690195820.3090992619608360.154549630980418
450.8067161469894170.3865677060211650.193283853010583
460.8014292334748920.3971415330502150.198570766525108
470.7918368213424630.4163263573150740.208163178657537
480.7385515111113560.5228969777772880.261448488888644
490.7075676121096730.5848647757806540.292432387890327
500.6770789287447550.6458421425104890.322921071255245
510.7280016455567730.5439967088864540.271998354443227
520.7710993581816390.4578012836367220.228900641818361
530.709473030286320.5810539394273590.29052696971368
540.6875159684658260.6249680630683470.312484031534174
550.6559654180869830.6880691638260340.344034581913017
560.7333064986215170.5333870027569670.266693501378483
570.6596978785786120.6806042428427750.340302121421388
580.8488112628846320.3023774742307360.151188737115368
590.7947119745909870.4105760508180250.205288025409013
600.8579452066786230.2841095866427550.142054793321377
610.9380502754639980.1238994490720030.0619497245360016
620.8991995581039320.2016008837921350.100800441896068
630.9160337153107050.167932569378590.0839662846892948
640.871133982298050.2577320354039010.12886601770195
650.8099762448695230.3800475102609530.190023755130477
660.7088794571122270.5822410857755460.291120542887773
670.6558069783074550.6883860433850890.344193021692545
680.9566914852983450.08661702940331050.0433085147016552
690.8826906009027220.2346187981945550.117309399097278

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.410470996199493 & 0.820941992398986 & 0.589529003800507 \tabularnewline
17 & 0.259118183243931 & 0.518236366487861 & 0.740881816756069 \tabularnewline
18 & 0.17125649233413 & 0.34251298466826 & 0.82874350766587 \tabularnewline
19 & 0.28427788189181 & 0.568555763783621 & 0.71572211810819 \tabularnewline
20 & 0.195827088282721 & 0.391654176565443 & 0.804172911717279 \tabularnewline
21 & 0.149820493539178 & 0.299640987078356 & 0.850179506460822 \tabularnewline
22 & 0.184893685168867 & 0.369787370337733 & 0.815106314831133 \tabularnewline
23 & 0.121284809025399 & 0.242569618050797 & 0.878715190974601 \tabularnewline
24 & 0.177836394217047 & 0.355672788434094 & 0.822163605782953 \tabularnewline
25 & 0.33465736019041 & 0.669314720380821 & 0.66534263980959 \tabularnewline
26 & 0.263499053773161 & 0.526998107546322 & 0.736500946226839 \tabularnewline
27 & 0.203007916610888 & 0.406015833221776 & 0.796992083389112 \tabularnewline
28 & 0.204624168732516 & 0.409248337465033 & 0.795375831267484 \tabularnewline
29 & 0.271182131169746 & 0.542364262339492 & 0.728817868830254 \tabularnewline
30 & 0.336110399242648 & 0.672220798485296 & 0.663889600757352 \tabularnewline
31 & 0.27302616116183 & 0.54605232232366 & 0.72697383883817 \tabularnewline
32 & 0.302082489499451 & 0.604164978998903 & 0.697917510500549 \tabularnewline
33 & 0.365347609761465 & 0.730695219522931 & 0.634652390238535 \tabularnewline
34 & 0.597790355755911 & 0.804419288488178 & 0.402209644244089 \tabularnewline
35 & 0.776648697867205 & 0.44670260426559 & 0.223351302132795 \tabularnewline
36 & 0.800669767061176 & 0.398660465877648 & 0.199330232938824 \tabularnewline
37 & 0.748411426406225 & 0.503177147187549 & 0.251588573593775 \tabularnewline
38 & 0.808221187126456 & 0.383557625747087 & 0.191778812873544 \tabularnewline
39 & 0.842908219973132 & 0.314183560053736 & 0.157091780026868 \tabularnewline
40 & 0.811057343009919 & 0.377885313980162 & 0.188942656990081 \tabularnewline
41 & 0.851116300718952 & 0.297767398562097 & 0.148883699281048 \tabularnewline
42 & 0.873759335736938 & 0.252481328526125 & 0.126240664263062 \tabularnewline
43 & 0.865779879935829 & 0.268440240128342 & 0.134220120064171 \tabularnewline
44 & 0.845450369019582 & 0.309099261960836 & 0.154549630980418 \tabularnewline
45 & 0.806716146989417 & 0.386567706021165 & 0.193283853010583 \tabularnewline
46 & 0.801429233474892 & 0.397141533050215 & 0.198570766525108 \tabularnewline
47 & 0.791836821342463 & 0.416326357315074 & 0.208163178657537 \tabularnewline
48 & 0.738551511111356 & 0.522896977777288 & 0.261448488888644 \tabularnewline
49 & 0.707567612109673 & 0.584864775780654 & 0.292432387890327 \tabularnewline
50 & 0.677078928744755 & 0.645842142510489 & 0.322921071255245 \tabularnewline
51 & 0.728001645556773 & 0.543996708886454 & 0.271998354443227 \tabularnewline
52 & 0.771099358181639 & 0.457801283636722 & 0.228900641818361 \tabularnewline
53 & 0.70947303028632 & 0.581053939427359 & 0.29052696971368 \tabularnewline
54 & 0.687515968465826 & 0.624968063068347 & 0.312484031534174 \tabularnewline
55 & 0.655965418086983 & 0.688069163826034 & 0.344034581913017 \tabularnewline
56 & 0.733306498621517 & 0.533387002756967 & 0.266693501378483 \tabularnewline
57 & 0.659697878578612 & 0.680604242842775 & 0.340302121421388 \tabularnewline
58 & 0.848811262884632 & 0.302377474230736 & 0.151188737115368 \tabularnewline
59 & 0.794711974590987 & 0.410576050818025 & 0.205288025409013 \tabularnewline
60 & 0.857945206678623 & 0.284109586642755 & 0.142054793321377 \tabularnewline
61 & 0.938050275463998 & 0.123899449072003 & 0.0619497245360016 \tabularnewline
62 & 0.899199558103932 & 0.201600883792135 & 0.100800441896068 \tabularnewline
63 & 0.916033715310705 & 0.16793256937859 & 0.0839662846892948 \tabularnewline
64 & 0.87113398229805 & 0.257732035403901 & 0.12886601770195 \tabularnewline
65 & 0.809976244869523 & 0.380047510260953 & 0.190023755130477 \tabularnewline
66 & 0.708879457112227 & 0.582241085775546 & 0.291120542887773 \tabularnewline
67 & 0.655806978307455 & 0.688386043385089 & 0.344193021692545 \tabularnewline
68 & 0.956691485298345 & 0.0866170294033105 & 0.0433085147016552 \tabularnewline
69 & 0.882690600902722 & 0.234618798194555 & 0.117309399097278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155364&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.410470996199493[/C][C]0.820941992398986[/C][C]0.589529003800507[/C][/ROW]
[ROW][C]17[/C][C]0.259118183243931[/C][C]0.518236366487861[/C][C]0.740881816756069[/C][/ROW]
[ROW][C]18[/C][C]0.17125649233413[/C][C]0.34251298466826[/C][C]0.82874350766587[/C][/ROW]
[ROW][C]19[/C][C]0.28427788189181[/C][C]0.568555763783621[/C][C]0.71572211810819[/C][/ROW]
[ROW][C]20[/C][C]0.195827088282721[/C][C]0.391654176565443[/C][C]0.804172911717279[/C][/ROW]
[ROW][C]21[/C][C]0.149820493539178[/C][C]0.299640987078356[/C][C]0.850179506460822[/C][/ROW]
[ROW][C]22[/C][C]0.184893685168867[/C][C]0.369787370337733[/C][C]0.815106314831133[/C][/ROW]
[ROW][C]23[/C][C]0.121284809025399[/C][C]0.242569618050797[/C][C]0.878715190974601[/C][/ROW]
[ROW][C]24[/C][C]0.177836394217047[/C][C]0.355672788434094[/C][C]0.822163605782953[/C][/ROW]
[ROW][C]25[/C][C]0.33465736019041[/C][C]0.669314720380821[/C][C]0.66534263980959[/C][/ROW]
[ROW][C]26[/C][C]0.263499053773161[/C][C]0.526998107546322[/C][C]0.736500946226839[/C][/ROW]
[ROW][C]27[/C][C]0.203007916610888[/C][C]0.406015833221776[/C][C]0.796992083389112[/C][/ROW]
[ROW][C]28[/C][C]0.204624168732516[/C][C]0.409248337465033[/C][C]0.795375831267484[/C][/ROW]
[ROW][C]29[/C][C]0.271182131169746[/C][C]0.542364262339492[/C][C]0.728817868830254[/C][/ROW]
[ROW][C]30[/C][C]0.336110399242648[/C][C]0.672220798485296[/C][C]0.663889600757352[/C][/ROW]
[ROW][C]31[/C][C]0.27302616116183[/C][C]0.54605232232366[/C][C]0.72697383883817[/C][/ROW]
[ROW][C]32[/C][C]0.302082489499451[/C][C]0.604164978998903[/C][C]0.697917510500549[/C][/ROW]
[ROW][C]33[/C][C]0.365347609761465[/C][C]0.730695219522931[/C][C]0.634652390238535[/C][/ROW]
[ROW][C]34[/C][C]0.597790355755911[/C][C]0.804419288488178[/C][C]0.402209644244089[/C][/ROW]
[ROW][C]35[/C][C]0.776648697867205[/C][C]0.44670260426559[/C][C]0.223351302132795[/C][/ROW]
[ROW][C]36[/C][C]0.800669767061176[/C][C]0.398660465877648[/C][C]0.199330232938824[/C][/ROW]
[ROW][C]37[/C][C]0.748411426406225[/C][C]0.503177147187549[/C][C]0.251588573593775[/C][/ROW]
[ROW][C]38[/C][C]0.808221187126456[/C][C]0.383557625747087[/C][C]0.191778812873544[/C][/ROW]
[ROW][C]39[/C][C]0.842908219973132[/C][C]0.314183560053736[/C][C]0.157091780026868[/C][/ROW]
[ROW][C]40[/C][C]0.811057343009919[/C][C]0.377885313980162[/C][C]0.188942656990081[/C][/ROW]
[ROW][C]41[/C][C]0.851116300718952[/C][C]0.297767398562097[/C][C]0.148883699281048[/C][/ROW]
[ROW][C]42[/C][C]0.873759335736938[/C][C]0.252481328526125[/C][C]0.126240664263062[/C][/ROW]
[ROW][C]43[/C][C]0.865779879935829[/C][C]0.268440240128342[/C][C]0.134220120064171[/C][/ROW]
[ROW][C]44[/C][C]0.845450369019582[/C][C]0.309099261960836[/C][C]0.154549630980418[/C][/ROW]
[ROW][C]45[/C][C]0.806716146989417[/C][C]0.386567706021165[/C][C]0.193283853010583[/C][/ROW]
[ROW][C]46[/C][C]0.801429233474892[/C][C]0.397141533050215[/C][C]0.198570766525108[/C][/ROW]
[ROW][C]47[/C][C]0.791836821342463[/C][C]0.416326357315074[/C][C]0.208163178657537[/C][/ROW]
[ROW][C]48[/C][C]0.738551511111356[/C][C]0.522896977777288[/C][C]0.261448488888644[/C][/ROW]
[ROW][C]49[/C][C]0.707567612109673[/C][C]0.584864775780654[/C][C]0.292432387890327[/C][/ROW]
[ROW][C]50[/C][C]0.677078928744755[/C][C]0.645842142510489[/C][C]0.322921071255245[/C][/ROW]
[ROW][C]51[/C][C]0.728001645556773[/C][C]0.543996708886454[/C][C]0.271998354443227[/C][/ROW]
[ROW][C]52[/C][C]0.771099358181639[/C][C]0.457801283636722[/C][C]0.228900641818361[/C][/ROW]
[ROW][C]53[/C][C]0.70947303028632[/C][C]0.581053939427359[/C][C]0.29052696971368[/C][/ROW]
[ROW][C]54[/C][C]0.687515968465826[/C][C]0.624968063068347[/C][C]0.312484031534174[/C][/ROW]
[ROW][C]55[/C][C]0.655965418086983[/C][C]0.688069163826034[/C][C]0.344034581913017[/C][/ROW]
[ROW][C]56[/C][C]0.733306498621517[/C][C]0.533387002756967[/C][C]0.266693501378483[/C][/ROW]
[ROW][C]57[/C][C]0.659697878578612[/C][C]0.680604242842775[/C][C]0.340302121421388[/C][/ROW]
[ROW][C]58[/C][C]0.848811262884632[/C][C]0.302377474230736[/C][C]0.151188737115368[/C][/ROW]
[ROW][C]59[/C][C]0.794711974590987[/C][C]0.410576050818025[/C][C]0.205288025409013[/C][/ROW]
[ROW][C]60[/C][C]0.857945206678623[/C][C]0.284109586642755[/C][C]0.142054793321377[/C][/ROW]
[ROW][C]61[/C][C]0.938050275463998[/C][C]0.123899449072003[/C][C]0.0619497245360016[/C][/ROW]
[ROW][C]62[/C][C]0.899199558103932[/C][C]0.201600883792135[/C][C]0.100800441896068[/C][/ROW]
[ROW][C]63[/C][C]0.916033715310705[/C][C]0.16793256937859[/C][C]0.0839662846892948[/C][/ROW]
[ROW][C]64[/C][C]0.87113398229805[/C][C]0.257732035403901[/C][C]0.12886601770195[/C][/ROW]
[ROW][C]65[/C][C]0.809976244869523[/C][C]0.380047510260953[/C][C]0.190023755130477[/C][/ROW]
[ROW][C]66[/C][C]0.708879457112227[/C][C]0.582241085775546[/C][C]0.291120542887773[/C][/ROW]
[ROW][C]67[/C][C]0.655806978307455[/C][C]0.688386043385089[/C][C]0.344193021692545[/C][/ROW]
[ROW][C]68[/C][C]0.956691485298345[/C][C]0.0866170294033105[/C][C]0.0433085147016552[/C][/ROW]
[ROW][C]69[/C][C]0.882690600902722[/C][C]0.234618798194555[/C][C]0.117309399097278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155364&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155364&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
160.4104709961994930.8209419923989860.589529003800507
170.2591181832439310.5182363664878610.740881816756069
180.171256492334130.342512984668260.82874350766587
190.284277881891810.5685557637836210.71572211810819
200.1958270882827210.3916541765654430.804172911717279
210.1498204935391780.2996409870783560.850179506460822
220.1848936851688670.3697873703377330.815106314831133
230.1212848090253990.2425696180507970.878715190974601
240.1778363942170470.3556727884340940.822163605782953
250.334657360190410.6693147203808210.66534263980959
260.2634990537731610.5269981075463220.736500946226839
270.2030079166108880.4060158332217760.796992083389112
280.2046241687325160.4092483374650330.795375831267484
290.2711821311697460.5423642623394920.728817868830254
300.3361103992426480.6722207984852960.663889600757352
310.273026161161830.546052322323660.72697383883817
320.3020824894994510.6041649789989030.697917510500549
330.3653476097614650.7306952195229310.634652390238535
340.5977903557559110.8044192884881780.402209644244089
350.7766486978672050.446702604265590.223351302132795
360.8006697670611760.3986604658776480.199330232938824
370.7484114264062250.5031771471875490.251588573593775
380.8082211871264560.3835576257470870.191778812873544
390.8429082199731320.3141835600537360.157091780026868
400.8110573430099190.3778853139801620.188942656990081
410.8511163007189520.2977673985620970.148883699281048
420.8737593357369380.2524813285261250.126240664263062
430.8657798799358290.2684402401283420.134220120064171
440.8454503690195820.3090992619608360.154549630980418
450.8067161469894170.3865677060211650.193283853010583
460.8014292334748920.3971415330502150.198570766525108
470.7918368213424630.4163263573150740.208163178657537
480.7385515111113560.5228969777772880.261448488888644
490.7075676121096730.5848647757806540.292432387890327
500.6770789287447550.6458421425104890.322921071255245
510.7280016455567730.5439967088864540.271998354443227
520.7710993581816390.4578012836367220.228900641818361
530.709473030286320.5810539394273590.29052696971368
540.6875159684658260.6249680630683470.312484031534174
550.6559654180869830.6880691638260340.344034581913017
560.7333064986215170.5333870027569670.266693501378483
570.6596978785786120.6806042428427750.340302121421388
580.8488112628846320.3023774742307360.151188737115368
590.7947119745909870.4105760508180250.205288025409013
600.8579452066786230.2841095866427550.142054793321377
610.9380502754639980.1238994490720030.0619497245360016
620.8991995581039320.2016008837921350.100800441896068
630.9160337153107050.167932569378590.0839662846892948
640.871133982298050.2577320354039010.12886601770195
650.8099762448695230.3800475102609530.190023755130477
660.7088794571122270.5822410857755460.291120542887773
670.6558069783074550.6883860433850890.344193021692545
680.9566914852983450.08661702940331050.0433085147016552
690.8826906009027220.2346187981945550.117309399097278






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 level10.0185185185185185OK

\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 & 1 & 0.0185185185185185 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155364&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]1[/C][C]0.0185185185185185[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155364&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155364&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 level10.0185185185185185OK


Figures (Output of Computation)

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