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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 03 Nov 2012 08:10:12 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/03/t1351944653634hktebb9mwgcg.htm/, Retrieved Sun, 03 Jul 2022 14:34:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185711, Retrieved Sun, 03 Jul 2022 14:34:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-10-31 16:36:28] [83c7ccdb194e46f99f0902896e3c3ab1]
- R  D    [Multiple Regression] [] [2012-11-03 12:10:12] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R  D      [Multiple Regression] [] [2012-12-04 08:48:16] [83c7ccdb194e46f99f0902896e3c3ab1]
- RMPD      [Paired and Unpaired Two Samples Tests about the Mean] [Paper: Deel 2 - U...] [2012-12-08 16:30:36] [3e359e71f3147107dc62f6199da5411f]
-  M        [Multiple Regression] [paper 2012 (regre...] [2012-12-18 15:55:25] [5b6f1874b4b9118bec0ed9a6d3e17ca8]
-  M        [Multiple Regression] [Paper deel 3] [2012-12-20 10:32:40] [f069503e2a3869e5c2794f1841bac1ef]
-    D      [Multiple Regression] [Paper deel 3] [2012-12-20 11:44:35] [f069503e2a3869e5c2794f1841bac1ef]
-   PD      [Multiple Regression] [Maandelijkse gebo...] [2012-12-20 18:35:23] [77d02b0cf2cecd023ffa9a06f056f18d]
-             [Multiple Regression] [Linear trend] [2012-12-20 18:49:04] [77d02b0cf2cecd023ffa9a06f056f18d]
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Dataseries X:
18.2	2687	1870	1890	145,7	352,2	0	0	0	0	0	0	0	0	0	0	0	0
143.8	13271	9115	8190	-279	83	0	0	0	0	0	0	0	0	0	0	0	0
23.4	13621	4848	4572	485	898,9	0	0	0	0	0	0	0	0	0	0	0	0
1.1	3614	367	90	14,1	24,6	1	0	3614	367	90	14,1	24,6	0	0	0	0	0
49.5	6425	6131	2448	345,8	682,5	1	0	6425	6131	2448	345,8	682,5	0	0	0	0	0
4.8	1022	1754	1370	72	119,5	0	1	0	0	0	0	0	1022	1754	1370	72	119,5
20.8	1093	1679	1070	100,9	164,5	0	1	0	0	0	0	0	1093	1679	1070	100,9	164,5
19.4	1529	1295	444	25,6	137	0	0	0	0	0	0	0	0	0	0	0	0
2.1	2788	271	304	23,5	28,9	1	0	2788	271	304	23,5	28,9	0	0	0	0	0
79.4	19788	9084	10636	1092,9	2576,8	1	0	19788	9084	10636	1092,9	2576,8	0	0	0	0	0
2.8	327	542	959	54,1	72,5	1	0	327	542	959	54,1	72,5	0	0	0	0	0
3.8	1117	1038	478	59,7	91,7	0	0	0	0	0	0	0	0	0	0	0	0
4.1	5401	550	376	25,6	37,5	1	0	5401	550	376	25,6	37,5	0	0	0	0	0
13.2	1128	1516	430	-47	26,7	0	1	0	0	0	0	0	1128	1516	430	-47	26,7
2.8	1633	701	679	74,3	135,9	0	0	0	0	0	0	0	0	0	0	0	0
48.5	44736	16197	4653	-732,5	-651,9	1	0	44736	16197	4653	-732,5	-651,9	0	0	0	0	0
6.2	5651	1254	2002	310,7	407,9	0	0	0	0	0	0	0	0	0	0	0	0
10.8	5835	4053	1601	-93,8	173,8	0	0	0	0	0	0	0	0	0	0	0	0
3.8	278	205	853	44,8	50,5	1	0	278	205	853	44,8	50,5	0	0	0	0	0
21.9	5074	2557	1892	239,9	578,3	1	0	5074	2557	1892	239,9	578,3	0	0	0	0	0
12.6	866	1487	944	71,7	115,4	0	0	0	0	0	0	0	0	0	0	0	0
128.0	4418	8793	4459	283,6	456,5	1	0	4418	8793	4459	283,6	456,5	0	0	0	0	0
87.3	6914	7029	7957	400,6	754,7	0	1	0	0	0	0	0	6914	7029	7957	400,6	754,7
16.0	862	1601	1093	66,9	106,8	1	0	862	1601	1093	66,9	106,8	0	0	0	0	0
0.7	401	176	1084	55,6	57	1	0	401	176	1084	55,6	57	0	0	0	0	0
22.5	430	1155	1045	55,7	70,8	0	1	0	0	0	0	0	430	1155	1045	55,7	70,8
15.4	799	1140	683	57,6	89,2	0	0	0	0	0	0	0	0	0	0	0	0
3.0	4789	453	367	40,2	51,4	1	0	4789	453	367	40,2	51,4	0	0	0	0	0
2.1	2548	264	181	22,2	26,2	1	0	2548	264	181	22,2	26,2	0	0	0	0	0
4.1	5249	527	346	37,8	56,2	1	0	5249	527	346	37,8	56,2	0	0	0	0	0
6.4	3494	1653	1442	160,9	320,3	0	0	0	0	0	0	0	0	0	0	0	0
26.6	1804	2564	483	70,5	164,9	0	1	0	0	0	0	0	1804	2564	483	70,5	164,9
304.0	26432	28285	33172	2336	3562	0	1	0	0	0	0	0	26432	28285	33172	2336	3562
18.6	623	2247	797	57	93,8	1	0	623	2247	797	57	93,8	0	0	0	0	0
65.0	1608	6615	829	56,1	134	1	0	1608	6615	829	56,1	134	0	0	0	0	0
66.2	4662	4781	2988	28,7	371,5	0	1	0	0	0	0	0	4662	4781	2988	28,7	371,5
83.0	5769	6571	9462	482	792	0	1	0	0	0	0	0	5769	6571	9462	482	792
62.0	6259	4152	3090	283,7	524,5	1	0	6259	4152	3090	283,7	524,5	0	0	0	0	0
1.6	1654	451	779	84,8	130,4	0	0	0	0	0	0	0	0	0	0	0	0
400.2	52634	50056	95697	6555	9874	0	1	0	0	0	0	0	52634	50056	95697	6555	9874
23.3	999	1878	393	-173,5	-108,1	1	0	999	1878	393	-173,5	-108,1	0	0	0	0	0
4.6	1679	1354	687	93,8	154,6	0	0	0	0	0	0	0	0	0	0	0	0
164.6	4178	17124	2091	180,8	390,4	1	0	4178	17124	2091	180,8	390,4	0	0	0	0	0
1.9	223	557	1040	60,6	63,7	0	0	0	0	0	0	0	0	0	0	0	0
57.5	6307	8199	598	-771,5	-524,3	0	1	0	0	0	0	0	6307	8199	598	-771,5	-524,3
2.4	3720	356	211	26,6	34,8	1	0	3720	356	211	26,6	34,8	0	0	0	0	0
77.3	3442	5080	2673	235,4	361,5	1	0	3442	5080	2673	235,4	361,5	0	0	0	0	0
15.8	33406	3222	1413	201,7	246,7	1	0	33406	3222	1413	201,7	246,7	0	0	0	0	0
0.6	1257	355	181	167,5	304	0	0	0	0	0	0	0	0	0	0	0	0
3.5	1743	597	717	121,6	172,4	0	0	0	0	0	0	0	0	0	0	0	0
9.0	12505	1302	702	108,4	131,4	1	0	12505	1302	702	108,4	131,4	0	0	0	0	0
62.0	3940	4317	3940	315,2	566,3	0	1	0	0	0	0	0	3940	4317	3940	315,2	566,3
7.4	8998	882	988	93	119	1	0	8998	882	988	93	119	0	0	0	0	0
15.6	21419	2516	930	107,6	164,7	1	0	21419	2516	930	107,6	164,7	0	0	0	0	0
25.2	2366	3305	1117	131,2	256,5	0	1	0	0	0	0	0	2366	3305	1117	131,2	256,5
25.4	2448	3484	1036	48,8	257,1	1	0	2448	3484	1036	48,8	257,1	0	0	0	0	0
3.5	1440	1617	639	81,7	126,4	0	0	0	0	0	0	0	0	0	0	0	0
27.3	14045	15636	2754	418	1462	0	0	0	0	0	0	0	0	0	0	0	0
37.5	4084	4346	3023	302,7	521,7	0	1	0	0	0	0	0	4084	4346	3023	302,7	521,7
3.4	3010	749	1120	146,3	209,2	0	0	0	0	0	0	0	0	0	0	0	0
14.3	1286	1734	361	69,2	145,7	1	0	1286	1734	361	69,2	145,7	0	0	0	0	0
6.1	707	706	275	61,4	77,8	1	0	707	706	275	61,4	77,8	0	0	0	0	0
4.9	3086	1739	1507	202,7	335,2	0	0	0	0	0	0	0	0	0	0	0	0
3.3	252	312	883	41,7	60,6	1	0	252	312	883	41,7	60,6	0	0	0	0	0
7.0	11052	1097	606	64,9	97,6	1	0	11052	1097	606	64,9	97,6	0	0	0	0	0
8.2	9672	1037	829	92,6	118,2	1	0	9672	1037	829	92,6	118,2	0	0	0	0	0
43.5	1112	3689	542	30,3	96,9	1	0	1112	3689	542	30,3	96,9	0	0	0	0	0
48.5	1104	5123	910	63,7	133,3	1	0	1104	5123	910	63,7	133,3	0	0	0	0	0
5.4	478	672	866	67,1	101,6	0	1	0	0	0	0	0	478	672	866	67,1	101,6
49.5	10348	5721	1915	223,6	322,5	0	1	0	0	0	0	0	10348	5721	1915	223,6	322,5
29.1	2769	3725	663	-208,4	12,4	1	0	2769	3725	663	-208,4	12,4	0	0	0	0	0
2.6	752	2149	101	11,1	15,2	0	1	0	0	0	0	0	752	2149	101	11,1	15,2
0.8	4989	518	53	-3,1	-0,3	1	0	4989	518	53	-3,1	-0,3	0	0	0	0	0
184.8	10528	14992	5377	312,7	710,7	0	1	0	0	0	0	0	10528	14992	5377	312,7	710,7
2.3	1995	2662	341	34,7	100,7	0	0	0	0	0	0	0	0	0	0	0	0
8.0	2286	2235	2306	195,3	219	0	0	0	0	0	0	0	0	0	0	0	0
10.3	952	1307	309	35,4	92,8	1	0	952	1307	309	35,4	92,8	0	0	0	0	0
50.0	2957	2806	457	40,6	93,5	1	0	2957	2806	457	40,6	93,5	0	0	0	0	0
118.1	2535	5958	1921	177	288	1	0	2535	5958	1921	177	288	0	0	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 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 & 13 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185711&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]13 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=185711&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185711&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
wn[t] = -0.418850835801199 -0.00225081430349453ta[t] + 0.000353090163285318omzet[t] + 0.0160241336628865mw[t] -0.107337318460469winst[t] + 0.0363906856461829cf[t] + 3.96985998339162dienst[t] -2.35771541331388product[t] + 0.000855437130039981ta_d[t] + 0.00974096942632612omzet_d[t] -0.0117758455576938mw_d[t] + 0.267164326757753winst_d[t] -0.115761102660768cf_d[t] -0.000880137267368733ta_p[t] + 0.0153333181085418omzet_p[t] -0.0216815834484814mw_p[t] + 0.13504041258139winst_p[t] -0.0209937936157664cf_p[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
wn[t] =  -0.418850835801199 -0.00225081430349453ta[t] +  0.000353090163285318omzet[t] +  0.0160241336628865mw[t] -0.107337318460469winst[t] +  0.0363906856461829cf[t] +  3.96985998339162dienst[t] -2.35771541331388product[t] +  0.000855437130039981ta_d[t] +  0.00974096942632612omzet_d[t] -0.0117758455576938mw_d[t] +  0.267164326757753winst_d[t] -0.115761102660768cf_d[t] -0.000880137267368733ta_p[t] +  0.0153333181085418omzet_p[t] -0.0216815834484814mw_p[t] +  0.13504041258139winst_p[t] -0.0209937936157664cf_p[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185711&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]wn[t] =  -0.418850835801199 -0.00225081430349453ta[t] +  0.000353090163285318omzet[t] +  0.0160241336628865mw[t] -0.107337318460469winst[t] +  0.0363906856461829cf[t] +  3.96985998339162dienst[t] -2.35771541331388product[t] +  0.000855437130039981ta_d[t] +  0.00974096942632612omzet_d[t] -0.0117758455576938mw_d[t] +  0.267164326757753winst_d[t] -0.115761102660768cf_d[t] -0.000880137267368733ta_p[t] +  0.0153333181085418omzet_p[t] -0.0216815834484814mw_p[t] +  0.13504041258139winst_p[t] -0.0209937936157664cf_p[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185711&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185711&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
wn[t] = -0.418850835801199 -0.00225081430349453ta[t] + 0.000353090163285318omzet[t] + 0.0160241336628865mw[t] -0.107337318460469winst[t] + 0.0363906856461829cf[t] + 3.96985998339162dienst[t] -2.35771541331388product[t] + 0.000855437130039981ta_d[t] + 0.00974096942632612omzet_d[t] -0.0117758455576938mw_d[t] + 0.267164326757753winst_d[t] -0.115761102660768cf_d[t] -0.000880137267368733ta_p[t] + 0.0153333181085418omzet_p[t] -0.0216815834484814mw_p[t] + 0.13504041258139winst_p[t] -0.0209937936157664cf_p[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.4188508358011994.828696-0.08670.9311610.46558
ta-0.002250814303494530.002955-0.76160.4492150.224608
omzet0.0003530901632853180.0035090.10060.9201780.460089
mw0.01602413366288650.0049043.26790.0017820.000891
winst-0.1073373184604690.075926-1.41370.1625310.081265
cf0.03639068564618290.0627610.57980.5641630.282082
dienst3.969859983391625.6992320.69660.4887240.244362
product-2.357715413313886.846833-0.34440.7317660.365883
ta_d0.0008554371300399810.0029710.28790.7743820.387191
omzet_d0.009740969426326120.0035972.70830.0087640.004382
mw_d-0.01177584555769380.005908-1.99310.0507250.025363
winst_d0.2671643267577530.0828183.22590.002020.00101
cf_d-0.1157611026607680.066855-1.73150.0884110.044205
ta_p-0.0008801372673687330.003656-0.24070.810570.405285
omzet_p0.01533331810854180.0040523.78370.0003550.000177
mw_p-0.02168158344848140.005493-3.94740.0002070.000104
winst_p0.135040412581390.0914181.47720.1447750.072387
cf_p-0.02099379361576640.084011-0.24990.803510.401755

\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) & -0.418850835801199 & 4.828696 & -0.0867 & 0.931161 & 0.46558 \tabularnewline
ta & -0.00225081430349453 & 0.002955 & -0.7616 & 0.449215 & 0.224608 \tabularnewline
omzet & 0.000353090163285318 & 0.003509 & 0.1006 & 0.920178 & 0.460089 \tabularnewline
mw & 0.0160241336628865 & 0.004904 & 3.2679 & 0.001782 & 0.000891 \tabularnewline
winst & -0.107337318460469 & 0.075926 & -1.4137 & 0.162531 & 0.081265 \tabularnewline
cf & 0.0363906856461829 & 0.062761 & 0.5798 & 0.564163 & 0.282082 \tabularnewline
dienst & 3.96985998339162 & 5.699232 & 0.6966 & 0.488724 & 0.244362 \tabularnewline
product & -2.35771541331388 & 6.846833 & -0.3444 & 0.731766 & 0.365883 \tabularnewline
ta_d & 0.000855437130039981 & 0.002971 & 0.2879 & 0.774382 & 0.387191 \tabularnewline
omzet_d & 0.00974096942632612 & 0.003597 & 2.7083 & 0.008764 & 0.004382 \tabularnewline
mw_d & -0.0117758455576938 & 0.005908 & -1.9931 & 0.050725 & 0.025363 \tabularnewline
winst_d & 0.267164326757753 & 0.082818 & 3.2259 & 0.00202 & 0.00101 \tabularnewline
cf_d & -0.115761102660768 & 0.066855 & -1.7315 & 0.088411 & 0.044205 \tabularnewline
ta_p & -0.000880137267368733 & 0.003656 & -0.2407 & 0.81057 & 0.405285 \tabularnewline
omzet_p & 0.0153333181085418 & 0.004052 & 3.7837 & 0.000355 & 0.000177 \tabularnewline
mw_p & -0.0216815834484814 & 0.005493 & -3.9474 & 0.000207 & 0.000104 \tabularnewline
winst_p & 0.13504041258139 & 0.091418 & 1.4772 & 0.144775 & 0.072387 \tabularnewline
cf_p & -0.0209937936157664 & 0.084011 & -0.2499 & 0.80351 & 0.401755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185711&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]-0.418850835801199[/C][C]4.828696[/C][C]-0.0867[/C][C]0.931161[/C][C]0.46558[/C][/ROW]
[ROW][C]ta[/C][C]-0.00225081430349453[/C][C]0.002955[/C][C]-0.7616[/C][C]0.449215[/C][C]0.224608[/C][/ROW]
[ROW][C]omzet[/C][C]0.000353090163285318[/C][C]0.003509[/C][C]0.1006[/C][C]0.920178[/C][C]0.460089[/C][/ROW]
[ROW][C]mw[/C][C]0.0160241336628865[/C][C]0.004904[/C][C]3.2679[/C][C]0.001782[/C][C]0.000891[/C][/ROW]
[ROW][C]winst[/C][C]-0.107337318460469[/C][C]0.075926[/C][C]-1.4137[/C][C]0.162531[/C][C]0.081265[/C][/ROW]
[ROW][C]cf[/C][C]0.0363906856461829[/C][C]0.062761[/C][C]0.5798[/C][C]0.564163[/C][C]0.282082[/C][/ROW]
[ROW][C]dienst[/C][C]3.96985998339162[/C][C]5.699232[/C][C]0.6966[/C][C]0.488724[/C][C]0.244362[/C][/ROW]
[ROW][C]product[/C][C]-2.35771541331388[/C][C]6.846833[/C][C]-0.3444[/C][C]0.731766[/C][C]0.365883[/C][/ROW]
[ROW][C]ta_d[/C][C]0.000855437130039981[/C][C]0.002971[/C][C]0.2879[/C][C]0.774382[/C][C]0.387191[/C][/ROW]
[ROW][C]omzet_d[/C][C]0.00974096942632612[/C][C]0.003597[/C][C]2.7083[/C][C]0.008764[/C][C]0.004382[/C][/ROW]
[ROW][C]mw_d[/C][C]-0.0117758455576938[/C][C]0.005908[/C][C]-1.9931[/C][C]0.050725[/C][C]0.025363[/C][/ROW]
[ROW][C]winst_d[/C][C]0.267164326757753[/C][C]0.082818[/C][C]3.2259[/C][C]0.00202[/C][C]0.00101[/C][/ROW]
[ROW][C]cf_d[/C][C]-0.115761102660768[/C][C]0.066855[/C][C]-1.7315[/C][C]0.088411[/C][C]0.044205[/C][/ROW]
[ROW][C]ta_p[/C][C]-0.000880137267368733[/C][C]0.003656[/C][C]-0.2407[/C][C]0.81057[/C][C]0.405285[/C][/ROW]
[ROW][C]omzet_p[/C][C]0.0153333181085418[/C][C]0.004052[/C][C]3.7837[/C][C]0.000355[/C][C]0.000177[/C][/ROW]
[ROW][C]mw_p[/C][C]-0.0216815834484814[/C][C]0.005493[/C][C]-3.9474[/C][C]0.000207[/C][C]0.000104[/C][/ROW]
[ROW][C]winst_p[/C][C]0.13504041258139[/C][C]0.091418[/C][C]1.4772[/C][C]0.144775[/C][C]0.072387[/C][/ROW]
[ROW][C]cf_p[/C][C]-0.0209937936157664[/C][C]0.084011[/C][C]-0.2499[/C][C]0.80351[/C][C]0.401755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185711&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185711&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.4188508358011994.828696-0.08670.9311610.46558
ta-0.002250814303494530.002955-0.76160.4492150.224608
omzet0.0003530901632853180.0035090.10060.9201780.460089
mw0.01602413366288650.0049043.26790.0017820.000891
winst-0.1073373184604690.075926-1.41370.1625310.081265
cf0.03639068564618290.0627610.57980.5641630.282082
dienst3.969859983391625.6992320.69660.4887240.244362
product-2.357715413313886.846833-0.34440.7317660.365883
ta_d0.0008554371300399810.0029710.28790.7743820.387191
omzet_d0.009740969426326120.0035972.70830.0087640.004382
mw_d-0.01177584555769380.005908-1.99310.0507250.025363
winst_d0.2671643267577530.0828183.22590.002020.00101
cf_d-0.1157611026607680.066855-1.73150.0884110.044205
ta_p-0.0008801372673687330.003656-0.24070.810570.405285
omzet_p0.01533331810854180.0040523.78370.0003550.000177
mw_p-0.02168158344848140.005493-3.94740.0002070.000104
winst_p0.135040412581390.0914181.47720.1447750.072387
cf_p-0.02099379361576640.084011-0.24990.803510.401755







Multiple Linear Regression - Regression Statistics
Multiple R0.983604735621981
R-squared0.967478275937988
Adjusted R-squared0.958414844642017
F-TEST (value)106.745254015231
F-TEST (DF numerator)17
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13.1539797625996
Sum Squared Residuals10554.6581992877

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.983604735621981 \tabularnewline
R-squared & 0.967478275937988 \tabularnewline
Adjusted R-squared & 0.958414844642017 \tabularnewline
F-TEST (value) & 106.745254015231 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 61 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 13.1539797625996 \tabularnewline
Sum Squared Residuals & 10554.6581992877 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185711&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.983604735621981[/C][/ROW]
[ROW][C]R-squared[/C][C]0.967478275937988[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.958414844642017[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]106.745254015231[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]61[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]13.1539797625996[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10554.6581992877[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185711&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185711&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.983604735621981
R-squared0.967478275937988
Adjusted R-squared0.958414844642017
F-TEST (value)106.745254015231
F-TEST (DF numerator)17
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13.1539797625996
Sum Squared Residuals10554.6581992877







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
118.221.6568545438032-3.45685454380321
2143.8137.1342028390136.66579716098685
323.424.5499156286502-1.14991562865021
41.12.89603040001339-1.79603040001339
549.567.9700692903106-18.4700692903106
64.817.6214065223235-12.8214065223235
720.819.4133428375471.38665716245305
819.45.949309782870813.4506902171292
92.15.14979696402705-3.04979696402705
1079.482.9717620430496-3.57176204304957
112.815.5320953176455-12.7320953176455
123.82.022021029210231.77797897078977
134.14.27884690996464-0.178846909964642
1413.214.1486635047642-0.948663504764192
152.84.0038037858585-1.20380378585851
1648.559.0564749163737-10.5564749163737
176.20.8789440224198925.32105597758011
1810.829.9263017662835-19.1263017662835
193.812.0082101754515-8.20821017545146
2021.922.7617239661268-0.861723966126758
2112.69.787170617896462.81282938210354
22128114.18075961372713.8192403862728
2387.363.537364308826823.7626356911732
241625.3678286439466-9.36782864394663
250.713.7354295863332-13.0354295863332
2622.510.716053401716111.7839465982839
2715.46.193174229919849.20682577008016
2835.3456848916174-2.34568489161739
292.14.89801464674306-2.79801464674306
304.14.59699492966701-0.496994929667014
316.49.79262484176468-3.39262484176468
3226.633.5546153109106-6.95461531091064
33304290.04541278951713.9545872104826
3418.630.4141210432007-11.8141210432007
356569.9319369626831-4.93193696268311
3666.247.234219706338618.9657802936614
378354.252802875825428.7471971241746
386253.56822760983998.43177239016013
391.64.14348689606351-2.54348689606351
40400.2409.849502963537-9.64950296353657
4123.33.633204625638219.6667953743618
424.62.846455385430641.75354461456936
43164.6177.365482455016-12.7654824550164
441.911.7544329818287-9.85443298182874
4557.573.2607010365384-15.7607010365384
462.44.33938797503698-1.93938797503698
4777.370.3124897393746.98751026062598
4815.88.119356077597197.68064392240282
490.6-7.138641620034157.73864162003415
503.50.5796147775875922.92038522241241
5199.12255633276976-0.122556332769764
526247.766632139656514.2333678603435
537.49.51450669372579-2.11450669372579
5415.67.136065745144698.46393425485531
5525.242.9237590165683-17.7237590165683
5625.427.0974797046171-1.69747970461711
573.52.980668519240640.519331480759363
5827.325.956027470581.34397252942004
5937.551.9255723456576-14.4255723456576
603.42.927174091828580.472825908171421
6114.320.2890446520356-5.9890446520356
626.114.4975226508702-8.39752265087017
634.97.83841282420111-2.93841282420111
643.311.9548980636366-8.65489806363658
6574.403166725991462.59683327400854
668.29.46268943677433-1.26268943677433
6743.538.69067265249444.80932734750565
6848.557.1892260418941-8.6892260418941
695.44.791955590158960.608044409841041
7049.554.892181804548-5.39218180454801
7129.15.8120550392053223.2879449607947
722.628.5491842210118-25.9491842210118
730.81.5717019656024-0.7717019656024
74184.8188.616629624554-3.81662962455432
752.31.434867316429330.865132683570666
76819.1831782691536-11.1831782691536
7710.315.0205683813587-4.72056838135867
785028.758120264214121.2418797357859
79118.173.745796866281944.3542031337181

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18.2 & 21.6568545438032 & -3.45685454380321 \tabularnewline
2 & 143.8 & 137.134202839013 & 6.66579716098685 \tabularnewline
3 & 23.4 & 24.5499156286502 & -1.14991562865021 \tabularnewline
4 & 1.1 & 2.89603040001339 & -1.79603040001339 \tabularnewline
5 & 49.5 & 67.9700692903106 & -18.4700692903106 \tabularnewline
6 & 4.8 & 17.6214065223235 & -12.8214065223235 \tabularnewline
7 & 20.8 & 19.413342837547 & 1.38665716245305 \tabularnewline
8 & 19.4 & 5.9493097828708 & 13.4506902171292 \tabularnewline
9 & 2.1 & 5.14979696402705 & -3.04979696402705 \tabularnewline
10 & 79.4 & 82.9717620430496 & -3.57176204304957 \tabularnewline
11 & 2.8 & 15.5320953176455 & -12.7320953176455 \tabularnewline
12 & 3.8 & 2.02202102921023 & 1.77797897078977 \tabularnewline
13 & 4.1 & 4.27884690996464 & -0.178846909964642 \tabularnewline
14 & 13.2 & 14.1486635047642 & -0.948663504764192 \tabularnewline
15 & 2.8 & 4.0038037858585 & -1.20380378585851 \tabularnewline
16 & 48.5 & 59.0564749163737 & -10.5564749163737 \tabularnewline
17 & 6.2 & 0.878944022419892 & 5.32105597758011 \tabularnewline
18 & 10.8 & 29.9263017662835 & -19.1263017662835 \tabularnewline
19 & 3.8 & 12.0082101754515 & -8.20821017545146 \tabularnewline
20 & 21.9 & 22.7617239661268 & -0.861723966126758 \tabularnewline
21 & 12.6 & 9.78717061789646 & 2.81282938210354 \tabularnewline
22 & 128 & 114.180759613727 & 13.8192403862728 \tabularnewline
23 & 87.3 & 63.5373643088268 & 23.7626356911732 \tabularnewline
24 & 16 & 25.3678286439466 & -9.36782864394663 \tabularnewline
25 & 0.7 & 13.7354295863332 & -13.0354295863332 \tabularnewline
26 & 22.5 & 10.7160534017161 & 11.7839465982839 \tabularnewline
27 & 15.4 & 6.19317422991984 & 9.20682577008016 \tabularnewline
28 & 3 & 5.3456848916174 & -2.34568489161739 \tabularnewline
29 & 2.1 & 4.89801464674306 & -2.79801464674306 \tabularnewline
30 & 4.1 & 4.59699492966701 & -0.496994929667014 \tabularnewline
31 & 6.4 & 9.79262484176468 & -3.39262484176468 \tabularnewline
32 & 26.6 & 33.5546153109106 & -6.95461531091064 \tabularnewline
33 & 304 & 290.045412789517 & 13.9545872104826 \tabularnewline
34 & 18.6 & 30.4141210432007 & -11.8141210432007 \tabularnewline
35 & 65 & 69.9319369626831 & -4.93193696268311 \tabularnewline
36 & 66.2 & 47.2342197063386 & 18.9657802936614 \tabularnewline
37 & 83 & 54.2528028758254 & 28.7471971241746 \tabularnewline
38 & 62 & 53.5682276098399 & 8.43177239016013 \tabularnewline
39 & 1.6 & 4.14348689606351 & -2.54348689606351 \tabularnewline
40 & 400.2 & 409.849502963537 & -9.64950296353657 \tabularnewline
41 & 23.3 & 3.6332046256382 & 19.6667953743618 \tabularnewline
42 & 4.6 & 2.84645538543064 & 1.75354461456936 \tabularnewline
43 & 164.6 & 177.365482455016 & -12.7654824550164 \tabularnewline
44 & 1.9 & 11.7544329818287 & -9.85443298182874 \tabularnewline
45 & 57.5 & 73.2607010365384 & -15.7607010365384 \tabularnewline
46 & 2.4 & 4.33938797503698 & -1.93938797503698 \tabularnewline
47 & 77.3 & 70.312489739374 & 6.98751026062598 \tabularnewline
48 & 15.8 & 8.11935607759719 & 7.68064392240282 \tabularnewline
49 & 0.6 & -7.13864162003415 & 7.73864162003415 \tabularnewline
50 & 3.5 & 0.579614777587592 & 2.92038522241241 \tabularnewline
51 & 9 & 9.12255633276976 & -0.122556332769764 \tabularnewline
52 & 62 & 47.7666321396565 & 14.2333678603435 \tabularnewline
53 & 7.4 & 9.51450669372579 & -2.11450669372579 \tabularnewline
54 & 15.6 & 7.13606574514469 & 8.46393425485531 \tabularnewline
55 & 25.2 & 42.9237590165683 & -17.7237590165683 \tabularnewline
56 & 25.4 & 27.0974797046171 & -1.69747970461711 \tabularnewline
57 & 3.5 & 2.98066851924064 & 0.519331480759363 \tabularnewline
58 & 27.3 & 25.95602747058 & 1.34397252942004 \tabularnewline
59 & 37.5 & 51.9255723456576 & -14.4255723456576 \tabularnewline
60 & 3.4 & 2.92717409182858 & 0.472825908171421 \tabularnewline
61 & 14.3 & 20.2890446520356 & -5.9890446520356 \tabularnewline
62 & 6.1 & 14.4975226508702 & -8.39752265087017 \tabularnewline
63 & 4.9 & 7.83841282420111 & -2.93841282420111 \tabularnewline
64 & 3.3 & 11.9548980636366 & -8.65489806363658 \tabularnewline
65 & 7 & 4.40316672599146 & 2.59683327400854 \tabularnewline
66 & 8.2 & 9.46268943677433 & -1.26268943677433 \tabularnewline
67 & 43.5 & 38.6906726524944 & 4.80932734750565 \tabularnewline
68 & 48.5 & 57.1892260418941 & -8.6892260418941 \tabularnewline
69 & 5.4 & 4.79195559015896 & 0.608044409841041 \tabularnewline
70 & 49.5 & 54.892181804548 & -5.39218180454801 \tabularnewline
71 & 29.1 & 5.81205503920532 & 23.2879449607947 \tabularnewline
72 & 2.6 & 28.5491842210118 & -25.9491842210118 \tabularnewline
73 & 0.8 & 1.5717019656024 & -0.7717019656024 \tabularnewline
74 & 184.8 & 188.616629624554 & -3.81662962455432 \tabularnewline
75 & 2.3 & 1.43486731642933 & 0.865132683570666 \tabularnewline
76 & 8 & 19.1831782691536 & -11.1831782691536 \tabularnewline
77 & 10.3 & 15.0205683813587 & -4.72056838135867 \tabularnewline
78 & 50 & 28.7581202642141 & 21.2418797357859 \tabularnewline
79 & 118.1 & 73.7457968662819 & 44.3542031337181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185711&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]18.2[/C][C]21.6568545438032[/C][C]-3.45685454380321[/C][/ROW]
[ROW][C]2[/C][C]143.8[/C][C]137.134202839013[/C][C]6.66579716098685[/C][/ROW]
[ROW][C]3[/C][C]23.4[/C][C]24.5499156286502[/C][C]-1.14991562865021[/C][/ROW]
[ROW][C]4[/C][C]1.1[/C][C]2.89603040001339[/C][C]-1.79603040001339[/C][/ROW]
[ROW][C]5[/C][C]49.5[/C][C]67.9700692903106[/C][C]-18.4700692903106[/C][/ROW]
[ROW][C]6[/C][C]4.8[/C][C]17.6214065223235[/C][C]-12.8214065223235[/C][/ROW]
[ROW][C]7[/C][C]20.8[/C][C]19.413342837547[/C][C]1.38665716245305[/C][/ROW]
[ROW][C]8[/C][C]19.4[/C][C]5.9493097828708[/C][C]13.4506902171292[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]5.14979696402705[/C][C]-3.04979696402705[/C][/ROW]
[ROW][C]10[/C][C]79.4[/C][C]82.9717620430496[/C][C]-3.57176204304957[/C][/ROW]
[ROW][C]11[/C][C]2.8[/C][C]15.5320953176455[/C][C]-12.7320953176455[/C][/ROW]
[ROW][C]12[/C][C]3.8[/C][C]2.02202102921023[/C][C]1.77797897078977[/C][/ROW]
[ROW][C]13[/C][C]4.1[/C][C]4.27884690996464[/C][C]-0.178846909964642[/C][/ROW]
[ROW][C]14[/C][C]13.2[/C][C]14.1486635047642[/C][C]-0.948663504764192[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]4.0038037858585[/C][C]-1.20380378585851[/C][/ROW]
[ROW][C]16[/C][C]48.5[/C][C]59.0564749163737[/C][C]-10.5564749163737[/C][/ROW]
[ROW][C]17[/C][C]6.2[/C][C]0.878944022419892[/C][C]5.32105597758011[/C][/ROW]
[ROW][C]18[/C][C]10.8[/C][C]29.9263017662835[/C][C]-19.1263017662835[/C][/ROW]
[ROW][C]19[/C][C]3.8[/C][C]12.0082101754515[/C][C]-8.20821017545146[/C][/ROW]
[ROW][C]20[/C][C]21.9[/C][C]22.7617239661268[/C][C]-0.861723966126758[/C][/ROW]
[ROW][C]21[/C][C]12.6[/C][C]9.78717061789646[/C][C]2.81282938210354[/C][/ROW]
[ROW][C]22[/C][C]128[/C][C]114.180759613727[/C][C]13.8192403862728[/C][/ROW]
[ROW][C]23[/C][C]87.3[/C][C]63.5373643088268[/C][C]23.7626356911732[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]25.3678286439466[/C][C]-9.36782864394663[/C][/ROW]
[ROW][C]25[/C][C]0.7[/C][C]13.7354295863332[/C][C]-13.0354295863332[/C][/ROW]
[ROW][C]26[/C][C]22.5[/C][C]10.7160534017161[/C][C]11.7839465982839[/C][/ROW]
[ROW][C]27[/C][C]15.4[/C][C]6.19317422991984[/C][C]9.20682577008016[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]5.3456848916174[/C][C]-2.34568489161739[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]4.89801464674306[/C][C]-2.79801464674306[/C][/ROW]
[ROW][C]30[/C][C]4.1[/C][C]4.59699492966701[/C][C]-0.496994929667014[/C][/ROW]
[ROW][C]31[/C][C]6.4[/C][C]9.79262484176468[/C][C]-3.39262484176468[/C][/ROW]
[ROW][C]32[/C][C]26.6[/C][C]33.5546153109106[/C][C]-6.95461531091064[/C][/ROW]
[ROW][C]33[/C][C]304[/C][C]290.045412789517[/C][C]13.9545872104826[/C][/ROW]
[ROW][C]34[/C][C]18.6[/C][C]30.4141210432007[/C][C]-11.8141210432007[/C][/ROW]
[ROW][C]35[/C][C]65[/C][C]69.9319369626831[/C][C]-4.93193696268311[/C][/ROW]
[ROW][C]36[/C][C]66.2[/C][C]47.2342197063386[/C][C]18.9657802936614[/C][/ROW]
[ROW][C]37[/C][C]83[/C][C]54.2528028758254[/C][C]28.7471971241746[/C][/ROW]
[ROW][C]38[/C][C]62[/C][C]53.5682276098399[/C][C]8.43177239016013[/C][/ROW]
[ROW][C]39[/C][C]1.6[/C][C]4.14348689606351[/C][C]-2.54348689606351[/C][/ROW]
[ROW][C]40[/C][C]400.2[/C][C]409.849502963537[/C][C]-9.64950296353657[/C][/ROW]
[ROW][C]41[/C][C]23.3[/C][C]3.6332046256382[/C][C]19.6667953743618[/C][/ROW]
[ROW][C]42[/C][C]4.6[/C][C]2.84645538543064[/C][C]1.75354461456936[/C][/ROW]
[ROW][C]43[/C][C]164.6[/C][C]177.365482455016[/C][C]-12.7654824550164[/C][/ROW]
[ROW][C]44[/C][C]1.9[/C][C]11.7544329818287[/C][C]-9.85443298182874[/C][/ROW]
[ROW][C]45[/C][C]57.5[/C][C]73.2607010365384[/C][C]-15.7607010365384[/C][/ROW]
[ROW][C]46[/C][C]2.4[/C][C]4.33938797503698[/C][C]-1.93938797503698[/C][/ROW]
[ROW][C]47[/C][C]77.3[/C][C]70.312489739374[/C][C]6.98751026062598[/C][/ROW]
[ROW][C]48[/C][C]15.8[/C][C]8.11935607759719[/C][C]7.68064392240282[/C][/ROW]
[ROW][C]49[/C][C]0.6[/C][C]-7.13864162003415[/C][C]7.73864162003415[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]0.579614777587592[/C][C]2.92038522241241[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]9.12255633276976[/C][C]-0.122556332769764[/C][/ROW]
[ROW][C]52[/C][C]62[/C][C]47.7666321396565[/C][C]14.2333678603435[/C][/ROW]
[ROW][C]53[/C][C]7.4[/C][C]9.51450669372579[/C][C]-2.11450669372579[/C][/ROW]
[ROW][C]54[/C][C]15.6[/C][C]7.13606574514469[/C][C]8.46393425485531[/C][/ROW]
[ROW][C]55[/C][C]25.2[/C][C]42.9237590165683[/C][C]-17.7237590165683[/C][/ROW]
[ROW][C]56[/C][C]25.4[/C][C]27.0974797046171[/C][C]-1.69747970461711[/C][/ROW]
[ROW][C]57[/C][C]3.5[/C][C]2.98066851924064[/C][C]0.519331480759363[/C][/ROW]
[ROW][C]58[/C][C]27.3[/C][C]25.95602747058[/C][C]1.34397252942004[/C][/ROW]
[ROW][C]59[/C][C]37.5[/C][C]51.9255723456576[/C][C]-14.4255723456576[/C][/ROW]
[ROW][C]60[/C][C]3.4[/C][C]2.92717409182858[/C][C]0.472825908171421[/C][/ROW]
[ROW][C]61[/C][C]14.3[/C][C]20.2890446520356[/C][C]-5.9890446520356[/C][/ROW]
[ROW][C]62[/C][C]6.1[/C][C]14.4975226508702[/C][C]-8.39752265087017[/C][/ROW]
[ROW][C]63[/C][C]4.9[/C][C]7.83841282420111[/C][C]-2.93841282420111[/C][/ROW]
[ROW][C]64[/C][C]3.3[/C][C]11.9548980636366[/C][C]-8.65489806363658[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]4.40316672599146[/C][C]2.59683327400854[/C][/ROW]
[ROW][C]66[/C][C]8.2[/C][C]9.46268943677433[/C][C]-1.26268943677433[/C][/ROW]
[ROW][C]67[/C][C]43.5[/C][C]38.6906726524944[/C][C]4.80932734750565[/C][/ROW]
[ROW][C]68[/C][C]48.5[/C][C]57.1892260418941[/C][C]-8.6892260418941[/C][/ROW]
[ROW][C]69[/C][C]5.4[/C][C]4.79195559015896[/C][C]0.608044409841041[/C][/ROW]
[ROW][C]70[/C][C]49.5[/C][C]54.892181804548[/C][C]-5.39218180454801[/C][/ROW]
[ROW][C]71[/C][C]29.1[/C][C]5.81205503920532[/C][C]23.2879449607947[/C][/ROW]
[ROW][C]72[/C][C]2.6[/C][C]28.5491842210118[/C][C]-25.9491842210118[/C][/ROW]
[ROW][C]73[/C][C]0.8[/C][C]1.5717019656024[/C][C]-0.7717019656024[/C][/ROW]
[ROW][C]74[/C][C]184.8[/C][C]188.616629624554[/C][C]-3.81662962455432[/C][/ROW]
[ROW][C]75[/C][C]2.3[/C][C]1.43486731642933[/C][C]0.865132683570666[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]19.1831782691536[/C][C]-11.1831782691536[/C][/ROW]
[ROW][C]77[/C][C]10.3[/C][C]15.0205683813587[/C][C]-4.72056838135867[/C][/ROW]
[ROW][C]78[/C][C]50[/C][C]28.7581202642141[/C][C]21.2418797357859[/C][/ROW]
[ROW][C]79[/C][C]118.1[/C][C]73.7457968662819[/C][C]44.3542031337181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185711&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185711&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
118.221.6568545438032-3.45685454380321
2143.8137.1342028390136.66579716098685
323.424.5499156286502-1.14991562865021
41.12.89603040001339-1.79603040001339
549.567.9700692903106-18.4700692903106
64.817.6214065223235-12.8214065223235
720.819.4133428375471.38665716245305
819.45.949309782870813.4506902171292
92.15.14979696402705-3.04979696402705
1079.482.9717620430496-3.57176204304957
112.815.5320953176455-12.7320953176455
123.82.022021029210231.77797897078977
134.14.27884690996464-0.178846909964642
1413.214.1486635047642-0.948663504764192
152.84.0038037858585-1.20380378585851
1648.559.0564749163737-10.5564749163737
176.20.8789440224198925.32105597758011
1810.829.9263017662835-19.1263017662835
193.812.0082101754515-8.20821017545146
2021.922.7617239661268-0.861723966126758
2112.69.787170617896462.81282938210354
22128114.18075961372713.8192403862728
2387.363.537364308826823.7626356911732
241625.3678286439466-9.36782864394663
250.713.7354295863332-13.0354295863332
2622.510.716053401716111.7839465982839
2715.46.193174229919849.20682577008016
2835.3456848916174-2.34568489161739
292.14.89801464674306-2.79801464674306
304.14.59699492966701-0.496994929667014
316.49.79262484176468-3.39262484176468
3226.633.5546153109106-6.95461531091064
33304290.04541278951713.9545872104826
3418.630.4141210432007-11.8141210432007
356569.9319369626831-4.93193696268311
3666.247.234219706338618.9657802936614
378354.252802875825428.7471971241746
386253.56822760983998.43177239016013
391.64.14348689606351-2.54348689606351
40400.2409.849502963537-9.64950296353657
4123.33.633204625638219.6667953743618
424.62.846455385430641.75354461456936
43164.6177.365482455016-12.7654824550164
441.911.7544329818287-9.85443298182874
4557.573.2607010365384-15.7607010365384
462.44.33938797503698-1.93938797503698
4777.370.3124897393746.98751026062598
4815.88.119356077597197.68064392240282
490.6-7.138641620034157.73864162003415
503.50.5796147775875922.92038522241241
5199.12255633276976-0.122556332769764
526247.766632139656514.2333678603435
537.49.51450669372579-2.11450669372579
5415.67.136065745144698.46393425485531
5525.242.9237590165683-17.7237590165683
5625.427.0974797046171-1.69747970461711
573.52.980668519240640.519331480759363
5827.325.956027470581.34397252942004
5937.551.9255723456576-14.4255723456576
603.42.927174091828580.472825908171421
6114.320.2890446520356-5.9890446520356
626.114.4975226508702-8.39752265087017
634.97.83841282420111-2.93841282420111
643.311.9548980636366-8.65489806363658
6574.403166725991462.59683327400854
668.29.46268943677433-1.26268943677433
6743.538.69067265249444.80932734750565
6848.557.1892260418941-8.6892260418941
695.44.791955590158960.608044409841041
7049.554.892181804548-5.39218180454801
7129.15.8120550392053223.2879449607947
722.628.5491842210118-25.9491842210118
730.81.5717019656024-0.7717019656024
74184.8188.616629624554-3.81662962455432
752.31.434867316429330.865132683570666
76819.1831782691536-11.1831782691536
7710.315.0205683813587-4.72056838135867
785028.758120264214121.2418797357859
79118.173.745796866281944.3542031337181







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.4039645410181350.807929082036270.596035458981865
220.5930966168166440.8138067663667120.406903383183356
230.4863456445671290.9726912891342590.513654355432871
240.3700677878243020.7401355756486050.629932212175698
250.3468733444117420.6937466888234840.653126655588258
260.2547697748695350.5095395497390710.745230225130465
270.1885581143674960.3771162287349930.811441885632504
280.1216574328142630.2433148656285250.878342567185737
290.07914967476780730.1582993495356150.920850325232193
300.04708059031939170.09416118063878340.952919409680608
310.02791616003734960.05583232007469920.97208383996265
320.01533401391590650.03066802783181290.984665986084094
330.02608667070664990.05217334141329980.97391332929335
340.01702650274172960.03405300548345920.98297349725827
350.01286694779380660.02573389558761330.987133052206193
360.01793020548110580.03586041096221160.982069794518894
370.04927411221800040.09854822443600080.950725887782
380.03639979871327340.07279959742654680.963600201286727
390.02416464256633310.04832928513266630.975835357433667
400.06904152493090830.1380830498618170.930958475069092
410.1882216637509180.3764433275018360.811778336249082
420.1357019133535230.2714038267070450.864298086646477
430.3168546100908510.6337092201817020.683145389909149
440.3177054110072950.6354108220145890.682294588992705
450.6643446883834840.6713106232330320.335655311616516
460.5862907999740370.8274184000519250.413709200025963
470.6005832067690310.7988335864619380.399416793230969
480.58566683492960.8286663301408010.4143331650704
490.5147943408487740.9704113183024520.485205659151226
500.4174770679832190.8349541359664370.582522932016781
510.3215762960824730.6431525921649460.678423703917527
520.2429243910622470.4858487821244930.757075608937753
530.1760061723617740.3520123447235480.823993827638226
540.1317589687611470.2635179375222950.868241031238853
550.09461809354985760.1892361870997150.905381906450142
560.107280469839460.2145609396789190.89271953016054
570.0572304589944430.1144609179888860.942769541005557
580.02594876870104770.05189753740209530.974051231298952

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.403964541018135 & 0.80792908203627 & 0.596035458981865 \tabularnewline
22 & 0.593096616816644 & 0.813806766366712 & 0.406903383183356 \tabularnewline
23 & 0.486345644567129 & 0.972691289134259 & 0.513654355432871 \tabularnewline
24 & 0.370067787824302 & 0.740135575648605 & 0.629932212175698 \tabularnewline
25 & 0.346873344411742 & 0.693746688823484 & 0.653126655588258 \tabularnewline
26 & 0.254769774869535 & 0.509539549739071 & 0.745230225130465 \tabularnewline
27 & 0.188558114367496 & 0.377116228734993 & 0.811441885632504 \tabularnewline
28 & 0.121657432814263 & 0.243314865628525 & 0.878342567185737 \tabularnewline
29 & 0.0791496747678073 & 0.158299349535615 & 0.920850325232193 \tabularnewline
30 & 0.0470805903193917 & 0.0941611806387834 & 0.952919409680608 \tabularnewline
31 & 0.0279161600373496 & 0.0558323200746992 & 0.97208383996265 \tabularnewline
32 & 0.0153340139159065 & 0.0306680278318129 & 0.984665986084094 \tabularnewline
33 & 0.0260866707066499 & 0.0521733414132998 & 0.97391332929335 \tabularnewline
34 & 0.0170265027417296 & 0.0340530054834592 & 0.98297349725827 \tabularnewline
35 & 0.0128669477938066 & 0.0257338955876133 & 0.987133052206193 \tabularnewline
36 & 0.0179302054811058 & 0.0358604109622116 & 0.982069794518894 \tabularnewline
37 & 0.0492741122180004 & 0.0985482244360008 & 0.950725887782 \tabularnewline
38 & 0.0363997987132734 & 0.0727995974265468 & 0.963600201286727 \tabularnewline
39 & 0.0241646425663331 & 0.0483292851326663 & 0.975835357433667 \tabularnewline
40 & 0.0690415249309083 & 0.138083049861817 & 0.930958475069092 \tabularnewline
41 & 0.188221663750918 & 0.376443327501836 & 0.811778336249082 \tabularnewline
42 & 0.135701913353523 & 0.271403826707045 & 0.864298086646477 \tabularnewline
43 & 0.316854610090851 & 0.633709220181702 & 0.683145389909149 \tabularnewline
44 & 0.317705411007295 & 0.635410822014589 & 0.682294588992705 \tabularnewline
45 & 0.664344688383484 & 0.671310623233032 & 0.335655311616516 \tabularnewline
46 & 0.586290799974037 & 0.827418400051925 & 0.413709200025963 \tabularnewline
47 & 0.600583206769031 & 0.798833586461938 & 0.399416793230969 \tabularnewline
48 & 0.5856668349296 & 0.828666330140801 & 0.4143331650704 \tabularnewline
49 & 0.514794340848774 & 0.970411318302452 & 0.485205659151226 \tabularnewline
50 & 0.417477067983219 & 0.834954135966437 & 0.582522932016781 \tabularnewline
51 & 0.321576296082473 & 0.643152592164946 & 0.678423703917527 \tabularnewline
52 & 0.242924391062247 & 0.485848782124493 & 0.757075608937753 \tabularnewline
53 & 0.176006172361774 & 0.352012344723548 & 0.823993827638226 \tabularnewline
54 & 0.131758968761147 & 0.263517937522295 & 0.868241031238853 \tabularnewline
55 & 0.0946180935498576 & 0.189236187099715 & 0.905381906450142 \tabularnewline
56 & 0.10728046983946 & 0.214560939678919 & 0.89271953016054 \tabularnewline
57 & 0.057230458994443 & 0.114460917988886 & 0.942769541005557 \tabularnewline
58 & 0.0259487687010477 & 0.0518975374020953 & 0.974051231298952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185711&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]21[/C][C]0.403964541018135[/C][C]0.80792908203627[/C][C]0.596035458981865[/C][/ROW]
[ROW][C]22[/C][C]0.593096616816644[/C][C]0.813806766366712[/C][C]0.406903383183356[/C][/ROW]
[ROW][C]23[/C][C]0.486345644567129[/C][C]0.972691289134259[/C][C]0.513654355432871[/C][/ROW]
[ROW][C]24[/C][C]0.370067787824302[/C][C]0.740135575648605[/C][C]0.629932212175698[/C][/ROW]
[ROW][C]25[/C][C]0.346873344411742[/C][C]0.693746688823484[/C][C]0.653126655588258[/C][/ROW]
[ROW][C]26[/C][C]0.254769774869535[/C][C]0.509539549739071[/C][C]0.745230225130465[/C][/ROW]
[ROW][C]27[/C][C]0.188558114367496[/C][C]0.377116228734993[/C][C]0.811441885632504[/C][/ROW]
[ROW][C]28[/C][C]0.121657432814263[/C][C]0.243314865628525[/C][C]0.878342567185737[/C][/ROW]
[ROW][C]29[/C][C]0.0791496747678073[/C][C]0.158299349535615[/C][C]0.920850325232193[/C][/ROW]
[ROW][C]30[/C][C]0.0470805903193917[/C][C]0.0941611806387834[/C][C]0.952919409680608[/C][/ROW]
[ROW][C]31[/C][C]0.0279161600373496[/C][C]0.0558323200746992[/C][C]0.97208383996265[/C][/ROW]
[ROW][C]32[/C][C]0.0153340139159065[/C][C]0.0306680278318129[/C][C]0.984665986084094[/C][/ROW]
[ROW][C]33[/C][C]0.0260866707066499[/C][C]0.0521733414132998[/C][C]0.97391332929335[/C][/ROW]
[ROW][C]34[/C][C]0.0170265027417296[/C][C]0.0340530054834592[/C][C]0.98297349725827[/C][/ROW]
[ROW][C]35[/C][C]0.0128669477938066[/C][C]0.0257338955876133[/C][C]0.987133052206193[/C][/ROW]
[ROW][C]36[/C][C]0.0179302054811058[/C][C]0.0358604109622116[/C][C]0.982069794518894[/C][/ROW]
[ROW][C]37[/C][C]0.0492741122180004[/C][C]0.0985482244360008[/C][C]0.950725887782[/C][/ROW]
[ROW][C]38[/C][C]0.0363997987132734[/C][C]0.0727995974265468[/C][C]0.963600201286727[/C][/ROW]
[ROW][C]39[/C][C]0.0241646425663331[/C][C]0.0483292851326663[/C][C]0.975835357433667[/C][/ROW]
[ROW][C]40[/C][C]0.0690415249309083[/C][C]0.138083049861817[/C][C]0.930958475069092[/C][/ROW]
[ROW][C]41[/C][C]0.188221663750918[/C][C]0.376443327501836[/C][C]0.811778336249082[/C][/ROW]
[ROW][C]42[/C][C]0.135701913353523[/C][C]0.271403826707045[/C][C]0.864298086646477[/C][/ROW]
[ROW][C]43[/C][C]0.316854610090851[/C][C]0.633709220181702[/C][C]0.683145389909149[/C][/ROW]
[ROW][C]44[/C][C]0.317705411007295[/C][C]0.635410822014589[/C][C]0.682294588992705[/C][/ROW]
[ROW][C]45[/C][C]0.664344688383484[/C][C]0.671310623233032[/C][C]0.335655311616516[/C][/ROW]
[ROW][C]46[/C][C]0.586290799974037[/C][C]0.827418400051925[/C][C]0.413709200025963[/C][/ROW]
[ROW][C]47[/C][C]0.600583206769031[/C][C]0.798833586461938[/C][C]0.399416793230969[/C][/ROW]
[ROW][C]48[/C][C]0.5856668349296[/C][C]0.828666330140801[/C][C]0.4143331650704[/C][/ROW]
[ROW][C]49[/C][C]0.514794340848774[/C][C]0.970411318302452[/C][C]0.485205659151226[/C][/ROW]
[ROW][C]50[/C][C]0.417477067983219[/C][C]0.834954135966437[/C][C]0.582522932016781[/C][/ROW]
[ROW][C]51[/C][C]0.321576296082473[/C][C]0.643152592164946[/C][C]0.678423703917527[/C][/ROW]
[ROW][C]52[/C][C]0.242924391062247[/C][C]0.485848782124493[/C][C]0.757075608937753[/C][/ROW]
[ROW][C]53[/C][C]0.176006172361774[/C][C]0.352012344723548[/C][C]0.823993827638226[/C][/ROW]
[ROW][C]54[/C][C]0.131758968761147[/C][C]0.263517937522295[/C][C]0.868241031238853[/C][/ROW]
[ROW][C]55[/C][C]0.0946180935498576[/C][C]0.189236187099715[/C][C]0.905381906450142[/C][/ROW]
[ROW][C]56[/C][C]0.10728046983946[/C][C]0.214560939678919[/C][C]0.89271953016054[/C][/ROW]
[ROW][C]57[/C][C]0.057230458994443[/C][C]0.114460917988886[/C][C]0.942769541005557[/C][/ROW]
[ROW][C]58[/C][C]0.0259487687010477[/C][C]0.0518975374020953[/C][C]0.974051231298952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185711&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185711&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.4039645410181350.807929082036270.596035458981865
220.5930966168166440.8138067663667120.406903383183356
230.4863456445671290.9726912891342590.513654355432871
240.3700677878243020.7401355756486050.629932212175698
250.3468733444117420.6937466888234840.653126655588258
260.2547697748695350.5095395497390710.745230225130465
270.1885581143674960.3771162287349930.811441885632504
280.1216574328142630.2433148656285250.878342567185737
290.07914967476780730.1582993495356150.920850325232193
300.04708059031939170.09416118063878340.952919409680608
310.02791616003734960.05583232007469920.97208383996265
320.01533401391590650.03066802783181290.984665986084094
330.02608667070664990.05217334141329980.97391332929335
340.01702650274172960.03405300548345920.98297349725827
350.01286694779380660.02573389558761330.987133052206193
360.01793020548110580.03586041096221160.982069794518894
370.04927411221800040.09854822443600080.950725887782
380.03639979871327340.07279959742654680.963600201286727
390.02416464256633310.04832928513266630.975835357433667
400.06904152493090830.1380830498618170.930958475069092
410.1882216637509180.3764433275018360.811778336249082
420.1357019133535230.2714038267070450.864298086646477
430.3168546100908510.6337092201817020.683145389909149
440.3177054110072950.6354108220145890.682294588992705
450.6643446883834840.6713106232330320.335655311616516
460.5862907999740370.8274184000519250.413709200025963
470.6005832067690310.7988335864619380.399416793230969
480.58566683492960.8286663301408010.4143331650704
490.5147943408487740.9704113183024520.485205659151226
500.4174770679832190.8349541359664370.582522932016781
510.3215762960824730.6431525921649460.678423703917527
520.2429243910622470.4858487821244930.757075608937753
530.1760061723617740.3520123447235480.823993827638226
540.1317589687611470.2635179375222950.868241031238853
550.09461809354985760.1892361870997150.905381906450142
560.107280469839460.2145609396789190.89271953016054
570.0572304589944430.1144609179888860.942769541005557
580.02594876870104770.05189753740209530.974051231298952







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.131578947368421NOK
10% type I error level110.289473684210526NOK

\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 & 5 & 0.131578947368421 & NOK \tabularnewline
10% type I error level & 11 & 0.289473684210526 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185711&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]5[/C][C]0.131578947368421[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.289473684210526[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185711&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185711&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.131578947368421NOK
10% type I error level110.289473684210526NOK



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')
}