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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 31 Oct 2012 12:36:28 -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/Oct/31/t1351701477hun3qmv2tb9oc2k.htm/, Retrieved Fri, 07 Oct 2022 00:18:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185452, Retrieved Fri, 07 Oct 2022 00:18:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127
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] [bdee33f3d7ceb254f97215ce68b6a08e] [Current]
- RMPD    [Skewness and Kurtosis Test] [] [2012-10-31 22:34:37] [83c7ccdb194e46f99f0902896e3c3ab1]
- R  D    [Multiple Regression] [] [2012-11-03 12:10:12] [74be16979710d4c4e7c6647856088456]
- 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]
- RMPD    [Skewness and Kurtosis Test] [] [2012-11-03 12:22:04] [83c7ccdb194e46f99f0902896e3c3ab1]
- R         [Skewness and Kurtosis Test] [paper (kutosis/sk...] [2012-12-18 21:26:08] [5b6f1874b4b9118bec0ed9a6d3e17ca8]
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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
143.8	13271	9115	8190	-279	83	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
1.1	3614	367	90	14,1	24,6	1	0	3614	367	90	14,1	24,6	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
4.8	1022	1754	1370	72	119,5	0	1	0	0	0	0	0	1022	1754	1370	72
20.8	1093	1679	1070	100,9	164,5	0	1	0	0	0	0	0	1093	1679	1070	100,9
19.4	1529	1295	444	25,6	137	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
79.4	19788	9084	10636	1092,9	2576,8	1	0	19788	9084	10636	1092,9	2576,8	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
3.8	1117	1038	478	59,7	91,7	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
13.2	1128	1516	430	-47	26,7	0	1	0	0	0	0	0	1128	1516	430	-47
2.8	1633	701	679	74,3	135,9	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
6.2	5651	1254	2002	310,7	407,9	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
3.8	278	205	853	44,8	50,5	1	0	278	205	853	44,8	50,5	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
12.6	866	1487	944	71,7	115,4	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
87.3	6914	7029	7957	400,6	754,7	0	1	0	0	0	0	0	6914	7029	7957	400,6
16.0	862	1601	1093	66,9	106,8	1	0	862	1601	1093	66,9	106,8	0	0	0	0
0.7	401	176	1084	55,6	57	1	0	401	176	1084	55,6	57	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
15.4	799	1140	683	57,6	89,2	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
2.1	2548	264	181	22,2	26,2	1	0	2548	264	181	22,2	26,2	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
6.4	3494	1653	1442	160,9	320,3	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
304.0	26432	28285	33172	2336	3562	0	1	0	0	0	0	0	26432	28285	33172	2336
18.6	623	2247	797	57	93,8	1	0	623	2247	797	57	93,8	0	0	0	0
65.0	1608	6615	829	56,1	134	1	0	1608	6615	829	56,1	134	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
83.0	5769	6571	9462	482	792	0	1	0	0	0	0	0	5769	6571	9462	482
62.0	6259	4152	3090	283,7	524,5	1	0	6259	4152	3090	283,7	524,5	0	0	0	0
1.6	1654	451	779	84,8	130,4	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
23.3	999	1878	393	-173,5	-108,1	1	0	999	1878	393	-173,5	-108,1	0	0	0	0
4.6	1679	1354	687	93,8	154,6	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
1.9	223	557	1040	60,6	63,7	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
2.4	3720	356	211	26,6	34,8	1	0	3720	356	211	26,6	34,8	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
15.8	33406	3222	1413	201,7	246,7	1	0	33406	3222	1413	201,7	246,7	0	0	0	0
0.6	1257	355	181	167,5	304	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
9.0	12505	1302	702	108,4	131,4	1	0	12505	1302	702	108,4	131,4	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
7.4	8998	882	988	93	119	1	0	8998	882	988	93	119	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
25.2	2366	3305	1117	131,2	256,5	0	1	0	0	0	0	0	2366	3305	1117	131,2
25.4	2448	3484	1036	48,8	257,1	1	0	2448	3484	1036	48,8	257,1	0	0	0	0
3.5	1440	1617	639	81,7	126,4	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
37.5	4084	4346	3023	302,7	521,7	0	1	0	0	0	0	0	4084	4346	3023	302,7
3.4	3010	749	1120	146,3	209,2	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
6.1	707	706	275	61,4	77,8	1	0	707	706	275	61,4	77,8	0	0	0	0
4.9	3086	1739	1507	202,7	335,2	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
7.0	11052	1097	606	64,9	97,6	1	0	11052	1097	606	64,9	97,6	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
43.5	1112	3689	542	30,3	96,9	1	0	1112	3689	542	30,3	96,9	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
5.4	478	672	866	67,1	101,6	0	1	0	0	0	0	0	478	672	866	67,1
49.5	10348	5721	1915	223,6	322,5	0	1	0	0	0	0	0	10348	5721	1915	223,6
29.1	2769	3725	663	-208,4	12,4	1	0	2769	3725	663	-208,4	12,4	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
0.8	4989	518	53	-3,1	-0,3	1	0	4989	518	53	-3,1	-0,3	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
2.3	1995	2662	341	34,7	100,7	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
10.3	952	1307	309	35,4	92,8	1	0	952	1307	309	35,4	92,8	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
118.1	2535	5958	1921	177	288	1	0	2535	5958	1921	177	288	0	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185452&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185452&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185452&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
wn[t] = -0.482589666805368 -0.0019639816208048ta[t] + 0.000918919891368292omzet[t] + 0.0155105975931151mw[t] -0.0940278026115134winst[t] + 0.024674319298947cf[t] + 4.03359881439576dienst[t] -2.56006294870041product[t] + 0.000568604447350252ta_d[t] + 0.00917513969824316omzet_d[t] -0.0112623094879224mw_d[t] + 0.253854810908797winst_d[t] -0.104044736313532cf_d[t] -0.00116508725179709ta_p[t] + 0.0146516776327829omzet_p[t] -0.0215121429961511mw_p[t] + 0.113719105649213cf_p[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
wn[t] =  -0.482589666805368 -0.0019639816208048ta[t] +  0.000918919891368292omzet[t] +  0.0155105975931151mw[t] -0.0940278026115134winst[t] +  0.024674319298947cf[t] +  4.03359881439576dienst[t] -2.56006294870041product[t] +  0.000568604447350252ta_d[t] +  0.00917513969824316omzet_d[t] -0.0112623094879224mw_d[t] +  0.253854810908797winst_d[t] -0.104044736313532cf_d[t] -0.00116508725179709ta_p[t] +  0.0146516776327829omzet_p[t] -0.0215121429961511mw_p[t] +  0.113719105649213cf_p[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185452&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]wn[t] =  -0.482589666805368 -0.0019639816208048ta[t] +  0.000918919891368292omzet[t] +  0.0155105975931151mw[t] -0.0940278026115134winst[t] +  0.024674319298947cf[t] +  4.03359881439576dienst[t] -2.56006294870041product[t] +  0.000568604447350252ta_d[t] +  0.00917513969824316omzet_d[t] -0.0112623094879224mw_d[t] +  0.253854810908797winst_d[t] -0.104044736313532cf_d[t] -0.00116508725179709ta_p[t] +  0.0146516776327829omzet_p[t] -0.0215121429961511mw_p[t] +  0.113719105649213cf_p[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185452&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185452&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] = -0.482589666805368 -0.0019639816208048ta[t] + 0.000918919891368292omzet[t] + 0.0155105975931151mw[t] -0.0940278026115134winst[t] + 0.024674319298947cf[t] + 4.03359881439576dienst[t] -2.56006294870041product[t] + 0.000568604447350252ta_d[t] + 0.00917513969824316omzet_d[t] -0.0112623094879224mw_d[t] + 0.253854810908797winst_d[t] -0.104044736313532cf_d[t] -0.00116508725179709ta_p[t] + 0.0146516776327829omzet_p[t] -0.0215121429961511mw_p[t] + 0.113719105649213cf_p[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.4825896668053684.785358-0.10080.9199970.459999
ta-0.00196398162080480.002703-0.72670.470140.23507
omzet0.0009189198913682920.002660.34540.730950.365475
mw0.01551059759311510.0044183.51050.000840.00042
winst-0.09402780261151340.053701-1.7510.0848990.042449
cf0.0246743192989470.0414040.59590.5533890.276695
dienst4.033598814395765.650310.71390.4779850.238993
product-2.560062948700416.747182-0.37940.7056670.352834
ta_d0.0005686044473502520.002720.20910.8350730.417537
omzet_d0.009175139698243160.0027733.30840.0015660.000783
mw_d-0.01126230948792240.005497-2.04870.044730.022365
winst_d0.2538548109087970.0629394.03340.0001537.7e-05
cf_d-0.1040447363135320.047296-2.19990.0315540.015777
ta_p-0.001165087251797090.003447-0.3380.736530.368265
omzet_p0.01465167763278290.0029744.92637e-063e-06
mw_p-0.02151214299615110.005409-3.97690.0001859.3e-05
cf_p0.1137191056492130.0325763.49080.0008930.000446

\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) & -0.482589666805368 & 4.785358 & -0.1008 & 0.919997 & 0.459999 \tabularnewline
ta & -0.0019639816208048 & 0.002703 & -0.7267 & 0.47014 & 0.23507 \tabularnewline
omzet & 0.000918919891368292 & 0.00266 & 0.3454 & 0.73095 & 0.365475 \tabularnewline
mw & 0.0155105975931151 & 0.004418 & 3.5105 & 0.00084 & 0.00042 \tabularnewline
winst & -0.0940278026115134 & 0.053701 & -1.751 & 0.084899 & 0.042449 \tabularnewline
cf & 0.024674319298947 & 0.041404 & 0.5959 & 0.553389 & 0.276695 \tabularnewline
dienst & 4.03359881439576 & 5.65031 & 0.7139 & 0.477985 & 0.238993 \tabularnewline
product & -2.56006294870041 & 6.747182 & -0.3794 & 0.705667 & 0.352834 \tabularnewline
ta_d & 0.000568604447350252 & 0.00272 & 0.2091 & 0.835073 & 0.417537 \tabularnewline
omzet_d & 0.00917513969824316 & 0.002773 & 3.3084 & 0.001566 & 0.000783 \tabularnewline
mw_d & -0.0112623094879224 & 0.005497 & -2.0487 & 0.04473 & 0.022365 \tabularnewline
winst_d & 0.253854810908797 & 0.062939 & 4.0334 & 0.000153 & 7.7e-05 \tabularnewline
cf_d & -0.104044736313532 & 0.047296 & -2.1999 & 0.031554 & 0.015777 \tabularnewline
ta_p & -0.00116508725179709 & 0.003447 & -0.338 & 0.73653 & 0.368265 \tabularnewline
omzet_p & 0.0146516776327829 & 0.002974 & 4.9263 & 7e-06 & 3e-06 \tabularnewline
mw_p & -0.0215121429961511 & 0.005409 & -3.9769 & 0.000185 & 9.3e-05 \tabularnewline
cf_p & 0.113719105649213 & 0.032576 & 3.4908 & 0.000893 & 0.000446 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185452&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]-0.482589666805368[/C][C]4.785358[/C][C]-0.1008[/C][C]0.919997[/C][C]0.459999[/C][/ROW]
[ROW][C]ta[/C][C]-0.0019639816208048[/C][C]0.002703[/C][C]-0.7267[/C][C]0.47014[/C][C]0.23507[/C][/ROW]
[ROW][C]omzet[/C][C]0.000918919891368292[/C][C]0.00266[/C][C]0.3454[/C][C]0.73095[/C][C]0.365475[/C][/ROW]
[ROW][C]mw[/C][C]0.0155105975931151[/C][C]0.004418[/C][C]3.5105[/C][C]0.00084[/C][C]0.00042[/C][/ROW]
[ROW][C]winst[/C][C]-0.0940278026115134[/C][C]0.053701[/C][C]-1.751[/C][C]0.084899[/C][C]0.042449[/C][/ROW]
[ROW][C]cf[/C][C]0.024674319298947[/C][C]0.041404[/C][C]0.5959[/C][C]0.553389[/C][C]0.276695[/C][/ROW]
[ROW][C]dienst[/C][C]4.03359881439576[/C][C]5.65031[/C][C]0.7139[/C][C]0.477985[/C][C]0.238993[/C][/ROW]
[ROW][C]product[/C][C]-2.56006294870041[/C][C]6.747182[/C][C]-0.3794[/C][C]0.705667[/C][C]0.352834[/C][/ROW]
[ROW][C]ta_d[/C][C]0.000568604447350252[/C][C]0.00272[/C][C]0.2091[/C][C]0.835073[/C][C]0.417537[/C][/ROW]
[ROW][C]omzet_d[/C][C]0.00917513969824316[/C][C]0.002773[/C][C]3.3084[/C][C]0.001566[/C][C]0.000783[/C][/ROW]
[ROW][C]mw_d[/C][C]-0.0112623094879224[/C][C]0.005497[/C][C]-2.0487[/C][C]0.04473[/C][C]0.022365[/C][/ROW]
[ROW][C]winst_d[/C][C]0.253854810908797[/C][C]0.062939[/C][C]4.0334[/C][C]0.000153[/C][C]7.7e-05[/C][/ROW]
[ROW][C]cf_d[/C][C]-0.104044736313532[/C][C]0.047296[/C][C]-2.1999[/C][C]0.031554[/C][C]0.015777[/C][/ROW]
[ROW][C]ta_p[/C][C]-0.00116508725179709[/C][C]0.003447[/C][C]-0.338[/C][C]0.73653[/C][C]0.368265[/C][/ROW]
[ROW][C]omzet_p[/C][C]0.0146516776327829[/C][C]0.002974[/C][C]4.9263[/C][C]7e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]mw_p[/C][C]-0.0215121429961511[/C][C]0.005409[/C][C]-3.9769[/C][C]0.000185[/C][C]9.3e-05[/C][/ROW]
[ROW][C]cf_p[/C][C]0.113719105649213[/C][C]0.032576[/C][C]3.4908[/C][C]0.000893[/C][C]0.000446[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185452&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185452&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)-0.4825896668053684.785358-0.10080.9199970.459999
ta-0.00196398162080480.002703-0.72670.470140.23507
omzet0.0009189198913682920.002660.34540.730950.365475
mw0.01551059759311510.0044183.51050.000840.00042
winst-0.09402780261151340.053701-1.7510.0848990.042449
cf0.0246743192989470.0414040.59590.5533890.276695
dienst4.033598814395765.650310.71390.4779850.238993
product-2.560062948700416.747182-0.37940.7056670.352834
ta_d0.0005686044473502520.002720.20910.8350730.417537
omzet_d0.009175139698243160.0027733.30840.0015660.000783
mw_d-0.01126230948792240.005497-2.04870.044730.022365
winst_d0.2538548109087970.0629394.03340.0001537.7e-05
cf_d-0.1040447363135320.047296-2.19990.0315540.015777
ta_p-0.001165087251797090.003447-0.3380.736530.368265
omzet_p0.01465167763278290.0029744.92637e-063e-06
mw_p-0.02151214299615110.005409-3.97690.0001859.3e-05
cf_p0.1137191056492130.0325763.49080.0008930.000446







Multiple Linear Regression - Regression Statistics
Multiple R0.983587811537261
R-squared0.967444983004658
Adjusted R-squared0.959043688296182
F-TEST (value)115.154272832338
F-TEST (DF numerator)16
F-TEST (DF denominator)62
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13.0541447793876
Sum Squared Residuals10565.4631471151

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.983587811537261 \tabularnewline
R-squared & 0.967444983004658 \tabularnewline
Adjusted R-squared & 0.959043688296182 \tabularnewline
F-TEST (value) & 115.154272832338 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 62 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 13.0541447793876 \tabularnewline
Sum Squared Residuals & 10565.4631471151 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185452&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.983587811537261[/C][/ROW]
[ROW][C]R-squared[/C][C]0.967444983004658[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.959043688296182[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]115.154272832338[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]62[/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]13.0541447793876[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10565.4631471151[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185452&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185452&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 R0.983587811537261
R-squared0.967444983004658
Adjusted R-squared0.959043688296182
F-TEST (value)115.154272832338
F-TEST (DF numerator)16
F-TEST (DF denominator)62
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13.0541447793876
Sum Squared Residuals10565.4631471151







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
118.220.26404578253-2.06404578253
2143.8137.1428847713546.65711522864641
323.424.7116538565276-1.31165385652759
41.12.89603040001339-1.79603040001339
549.567.9700692903106-18.4700692903106
64.817.2145048268355-12.4145048268355
720.819.30443276972231.49556723027767
819.45.5644590227501513.8355409772499
92.15.14979696402705-3.04979696402705
1079.482.9717620430496-3.57176204304957
112.815.5320953176455-12.7320953176455
123.82.340722623311061.45927737668894
134.14.27884690996464-0.178846909964642
1413.214.185432102017-0.985432102017027
152.83.85306121468615-1.05306121468615
1648.559.0564749163737-10.5564749163737
176.21.473708689862244.72629131013776
1810.829.7226312212085-18.9226312212085
193.812.0082101754515-8.20821017545145
2021.922.7617239661268-0.861723966126757
2112.69.930663255795932.66933674420407
22128114.18075961372713.8192403862728
2387.363.524443196443623.7755568035564
241625.3678286439466-9.36782864394663
250.713.7354295863332-13.0354295863332
2622.510.168020349062712.3319796509373
2715.46.374443701471959.02555629852805
2835.34568489161739-2.34568489161739
292.14.89801464674306-2.79801464674306
304.14.59699492966701-0.496994929667013
316.49.31462589106662-2.91462589106662
3226.633.7938048771319-7.19380487713189
33304289.46969504390214.530304956098
3418.630.4141210432007-11.8141210432007
356569.9319369626831-4.93193696268312
3666.248.611687415860317.5883125841397
378353.466791735061729.5332082649383
386253.56822760983998.43177239016013
391.64.01014670355359-2.41014670355359
40400.2410.044595748462-9.84459574846234
4123.33.6332046256382119.6667953743618
424.63.114725149903351.48527485009665
43164.6177.365482455016-12.7654824550164
441.911.5959716091722-9.69597160917218
4557.573.1681290524712-15.6681290524712
462.44.33938797503698-1.93938797503698
4777.370.3124897393746.98751026062598
4815.88.119356077597197.68064392240282
490.6-8.066343708916048.66634370891604
503.50.583955867120682.91604413287932
5199.12255633276976-0.122556332769761
526248.380762386718113.6192376132819
537.49.51450669372579-2.11450669372579
5415.67.136065745144698.46393425485531
5525.243.2235308927727-18.0235308927727
5625.427.0974797046171-1.69747970461711
573.53.52320461160504-0.0232046116050384
5827.325.78793898531271.51206101468734
5937.552.5385250031436-15.0385250031436
603.43.071566032771230.328433967228775
6114.320.2890446520356-5.9890446520356
626.114.4975226508702-8.39752265087017
634.97.64043155495818-2.74043155495818
643.311.9548980636366-8.65489806363659
6574.403166725991472.59683327400853
668.29.46268943677432-1.26268943677433
6743.538.69067265249444.80932734750565
6848.557.1892260418941-8.6892260418941
695.44.555952955193550.844047044806452
7049.554.5246150128051-5.02461501280509
7129.15.8120550392053123.2879449607947
722.628.0529687030544-25.4529687030544
730.81.5717019656024-0.771701965602397
74184.8188.872107929342-4.072107929342
752.32.55648473254814-0.256484732548137
76819.8890184314072-11.8890184314072
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 & 20.26404578253 & -2.06404578253 \tabularnewline
2 & 143.8 & 137.142884771354 & 6.65711522864641 \tabularnewline
3 & 23.4 & 24.7116538565276 & -1.31165385652759 \tabularnewline
4 & 1.1 & 2.89603040001339 & -1.79603040001339 \tabularnewline
5 & 49.5 & 67.9700692903106 & -18.4700692903106 \tabularnewline
6 & 4.8 & 17.2145048268355 & -12.4145048268355 \tabularnewline
7 & 20.8 & 19.3044327697223 & 1.49556723027767 \tabularnewline
8 & 19.4 & 5.56445902275015 & 13.8355409772499 \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 & 2.34072262331106 & 1.45927737668894 \tabularnewline
13 & 4.1 & 4.27884690996464 & -0.178846909964642 \tabularnewline
14 & 13.2 & 14.185432102017 & -0.985432102017027 \tabularnewline
15 & 2.8 & 3.85306121468615 & -1.05306121468615 \tabularnewline
16 & 48.5 & 59.0564749163737 & -10.5564749163737 \tabularnewline
17 & 6.2 & 1.47370868986224 & 4.72629131013776 \tabularnewline
18 & 10.8 & 29.7226312212085 & -18.9226312212085 \tabularnewline
19 & 3.8 & 12.0082101754515 & -8.20821017545145 \tabularnewline
20 & 21.9 & 22.7617239661268 & -0.861723966126757 \tabularnewline
21 & 12.6 & 9.93066325579593 & 2.66933674420407 \tabularnewline
22 & 128 & 114.180759613727 & 13.8192403862728 \tabularnewline
23 & 87.3 & 63.5244431964436 & 23.7755568035564 \tabularnewline
24 & 16 & 25.3678286439466 & -9.36782864394663 \tabularnewline
25 & 0.7 & 13.7354295863332 & -13.0354295863332 \tabularnewline
26 & 22.5 & 10.1680203490627 & 12.3319796509373 \tabularnewline
27 & 15.4 & 6.37444370147195 & 9.02555629852805 \tabularnewline
28 & 3 & 5.34568489161739 & -2.34568489161739 \tabularnewline
29 & 2.1 & 4.89801464674306 & -2.79801464674306 \tabularnewline
30 & 4.1 & 4.59699492966701 & -0.496994929667013 \tabularnewline
31 & 6.4 & 9.31462589106662 & -2.91462589106662 \tabularnewline
32 & 26.6 & 33.7938048771319 & -7.19380487713189 \tabularnewline
33 & 304 & 289.469695043902 & 14.530304956098 \tabularnewline
34 & 18.6 & 30.4141210432007 & -11.8141210432007 \tabularnewline
35 & 65 & 69.9319369626831 & -4.93193696268312 \tabularnewline
36 & 66.2 & 48.6116874158603 & 17.5883125841397 \tabularnewline
37 & 83 & 53.4667917350617 & 29.5332082649383 \tabularnewline
38 & 62 & 53.5682276098399 & 8.43177239016013 \tabularnewline
39 & 1.6 & 4.01014670355359 & -2.41014670355359 \tabularnewline
40 & 400.2 & 410.044595748462 & -9.84459574846234 \tabularnewline
41 & 23.3 & 3.63320462563821 & 19.6667953743618 \tabularnewline
42 & 4.6 & 3.11472514990335 & 1.48527485009665 \tabularnewline
43 & 164.6 & 177.365482455016 & -12.7654824550164 \tabularnewline
44 & 1.9 & 11.5959716091722 & -9.69597160917218 \tabularnewline
45 & 57.5 & 73.1681290524712 & -15.6681290524712 \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 & -8.06634370891604 & 8.66634370891604 \tabularnewline
50 & 3.5 & 0.58395586712068 & 2.91604413287932 \tabularnewline
51 & 9 & 9.12255633276976 & -0.122556332769761 \tabularnewline
52 & 62 & 48.3807623867181 & 13.6192376132819 \tabularnewline
53 & 7.4 & 9.51450669372579 & -2.11450669372579 \tabularnewline
54 & 15.6 & 7.13606574514469 & 8.46393425485531 \tabularnewline
55 & 25.2 & 43.2235308927727 & -18.0235308927727 \tabularnewline
56 & 25.4 & 27.0974797046171 & -1.69747970461711 \tabularnewline
57 & 3.5 & 3.52320461160504 & -0.0232046116050384 \tabularnewline
58 & 27.3 & 25.7879389853127 & 1.51206101468734 \tabularnewline
59 & 37.5 & 52.5385250031436 & -15.0385250031436 \tabularnewline
60 & 3.4 & 3.07156603277123 & 0.328433967228775 \tabularnewline
61 & 14.3 & 20.2890446520356 & -5.9890446520356 \tabularnewline
62 & 6.1 & 14.4975226508702 & -8.39752265087017 \tabularnewline
63 & 4.9 & 7.64043155495818 & -2.74043155495818 \tabularnewline
64 & 3.3 & 11.9548980636366 & -8.65489806363659 \tabularnewline
65 & 7 & 4.40316672599147 & 2.59683327400853 \tabularnewline
66 & 8.2 & 9.46268943677432 & -1.26268943677433 \tabularnewline
67 & 43.5 & 38.6906726524944 & 4.80932734750565 \tabularnewline
68 & 48.5 & 57.1892260418941 & -8.6892260418941 \tabularnewline
69 & 5.4 & 4.55595295519355 & 0.844047044806452 \tabularnewline
70 & 49.5 & 54.5246150128051 & -5.02461501280509 \tabularnewline
71 & 29.1 & 5.81205503920531 & 23.2879449607947 \tabularnewline
72 & 2.6 & 28.0529687030544 & -25.4529687030544 \tabularnewline
73 & 0.8 & 1.5717019656024 & -0.771701965602397 \tabularnewline
74 & 184.8 & 188.872107929342 & -4.072107929342 \tabularnewline
75 & 2.3 & 2.55648473254814 & -0.256484732548137 \tabularnewline
76 & 8 & 19.8890184314072 & -11.8890184314072 \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=185452&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]20.26404578253[/C][C]-2.06404578253[/C][/ROW]
[ROW][C]2[/C][C]143.8[/C][C]137.142884771354[/C][C]6.65711522864641[/C][/ROW]
[ROW][C]3[/C][C]23.4[/C][C]24.7116538565276[/C][C]-1.31165385652759[/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]17.2145048268355[/C][C]-12.4145048268355[/C][/ROW]
[ROW][C]7[/C][C]20.8[/C][C]19.3044327697223[/C][C]1.49556723027767[/C][/ROW]
[ROW][C]8[/C][C]19.4[/C][C]5.56445902275015[/C][C]13.8355409772499[/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]2.34072262331106[/C][C]1.45927737668894[/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]14.185432102017[/C][C]-0.985432102017027[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]3.85306121468615[/C][C]-1.05306121468615[/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]1.47370868986224[/C][C]4.72629131013776[/C][/ROW]
[ROW][C]18[/C][C]10.8[/C][C]29.7226312212085[/C][C]-18.9226312212085[/C][/ROW]
[ROW][C]19[/C][C]3.8[/C][C]12.0082101754515[/C][C]-8.20821017545145[/C][/ROW]
[ROW][C]20[/C][C]21.9[/C][C]22.7617239661268[/C][C]-0.861723966126757[/C][/ROW]
[ROW][C]21[/C][C]12.6[/C][C]9.93066325579593[/C][C]2.66933674420407[/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]63.5244431964436[/C][C]23.7755568035564[/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]10.1680203490627[/C][C]12.3319796509373[/C][/ROW]
[ROW][C]27[/C][C]15.4[/C][C]6.37444370147195[/C][C]9.02555629852805[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]5.34568489161739[/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]-0.496994929667013[/C][/ROW]
[ROW][C]31[/C][C]6.4[/C][C]9.31462589106662[/C][C]-2.91462589106662[/C][/ROW]
[ROW][C]32[/C][C]26.6[/C][C]33.7938048771319[/C][C]-7.19380487713189[/C][/ROW]
[ROW][C]33[/C][C]304[/C][C]289.469695043902[/C][C]14.530304956098[/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]-4.93193696268312[/C][/ROW]
[ROW][C]36[/C][C]66.2[/C][C]48.6116874158603[/C][C]17.5883125841397[/C][/ROW]
[ROW][C]37[/C][C]83[/C][C]53.4667917350617[/C][C]29.5332082649383[/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]4.01014670355359[/C][C]-2.41014670355359[/C][/ROW]
[ROW][C]40[/C][C]400.2[/C][C]410.044595748462[/C][C]-9.84459574846234[/C][/ROW]
[ROW][C]41[/C][C]23.3[/C][C]3.63320462563821[/C][C]19.6667953743618[/C][/ROW]
[ROW][C]42[/C][C]4.6[/C][C]3.11472514990335[/C][C]1.48527485009665[/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]11.5959716091722[/C][C]-9.69597160917218[/C][/ROW]
[ROW][C]45[/C][C]57.5[/C][C]73.1681290524712[/C][C]-15.6681290524712[/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]-8.06634370891604[/C][C]8.66634370891604[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]0.58395586712068[/C][C]2.91604413287932[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]9.12255633276976[/C][C]-0.122556332769761[/C][/ROW]
[ROW][C]52[/C][C]62[/C][C]48.3807623867181[/C][C]13.6192376132819[/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]43.2235308927727[/C][C]-18.0235308927727[/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]3.52320461160504[/C][C]-0.0232046116050384[/C][/ROW]
[ROW][C]58[/C][C]27.3[/C][C]25.7879389853127[/C][C]1.51206101468734[/C][/ROW]
[ROW][C]59[/C][C]37.5[/C][C]52.5385250031436[/C][C]-15.0385250031436[/C][/ROW]
[ROW][C]60[/C][C]3.4[/C][C]3.07156603277123[/C][C]0.328433967228775[/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]7.64043155495818[/C][C]-2.74043155495818[/C][/ROW]
[ROW][C]64[/C][C]3.3[/C][C]11.9548980636366[/C][C]-8.65489806363659[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]4.40316672599147[/C][C]2.59683327400853[/C][/ROW]
[ROW][C]66[/C][C]8.2[/C][C]9.46268943677432[/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]4.55595295519355[/C][C]0.844047044806452[/C][/ROW]
[ROW][C]70[/C][C]49.5[/C][C]54.5246150128051[/C][C]-5.02461501280509[/C][/ROW]
[ROW][C]71[/C][C]29.1[/C][C]5.81205503920531[/C][C]23.2879449607947[/C][/ROW]
[ROW][C]72[/C][C]2.6[/C][C]28.0529687030544[/C][C]-25.4529687030544[/C][/ROW]
[ROW][C]73[/C][C]0.8[/C][C]1.5717019656024[/C][C]-0.771701965602397[/C][/ROW]
[ROW][C]74[/C][C]184.8[/C][C]188.872107929342[/C][C]-4.072107929342[/C][/ROW]
[ROW][C]75[/C][C]2.3[/C][C]2.55648473254814[/C][C]-0.256484732548137[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]19.8890184314072[/C][C]-11.8890184314072[/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=185452&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185452&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
118.220.26404578253-2.06404578253
2143.8137.1428847713546.65711522864641
323.424.7116538565276-1.31165385652759
41.12.89603040001339-1.79603040001339
549.567.9700692903106-18.4700692903106
64.817.2145048268355-12.4145048268355
720.819.30443276972231.49556723027767
819.45.5644590227501513.8355409772499
92.15.14979696402705-3.04979696402705
1079.482.9717620430496-3.57176204304957
112.815.5320953176455-12.7320953176455
123.82.340722623311061.45927737668894
134.14.27884690996464-0.178846909964642
1413.214.185432102017-0.985432102017027
152.83.85306121468615-1.05306121468615
1648.559.0564749163737-10.5564749163737
176.21.473708689862244.72629131013776
1810.829.7226312212085-18.9226312212085
193.812.0082101754515-8.20821017545145
2021.922.7617239661268-0.861723966126757
2112.69.930663255795932.66933674420407
22128114.18075961372713.8192403862728
2387.363.524443196443623.7755568035564
241625.3678286439466-9.36782864394663
250.713.7354295863332-13.0354295863332
2622.510.168020349062712.3319796509373
2715.46.374443701471959.02555629852805
2835.34568489161739-2.34568489161739
292.14.89801464674306-2.79801464674306
304.14.59699492966701-0.496994929667013
316.49.31462589106662-2.91462589106662
3226.633.7938048771319-7.19380487713189
33304289.46969504390214.530304956098
3418.630.4141210432007-11.8141210432007
356569.9319369626831-4.93193696268312
3666.248.611687415860317.5883125841397
378353.466791735061729.5332082649383
386253.56822760983998.43177239016013
391.64.01014670355359-2.41014670355359
40400.2410.044595748462-9.84459574846234
4123.33.6332046256382119.6667953743618
424.63.114725149903351.48527485009665
43164.6177.365482455016-12.7654824550164
441.911.5959716091722-9.69597160917218
4557.573.1681290524712-15.6681290524712
462.44.33938797503698-1.93938797503698
4777.370.3124897393746.98751026062598
4815.88.119356077597197.68064392240282
490.6-8.066343708916048.66634370891604
503.50.583955867120682.91604413287932
5199.12255633276976-0.122556332769761
526248.380762386718113.6192376132819
537.49.51450669372579-2.11450669372579
5415.67.136065745144698.46393425485531
5525.243.2235308927727-18.0235308927727
5625.427.0974797046171-1.69747970461711
573.53.52320461160504-0.0232046116050384
5827.325.78793898531271.51206101468734
5937.552.5385250031436-15.0385250031436
603.43.071566032771230.328433967228775
6114.320.2890446520356-5.9890446520356
626.114.4975226508702-8.39752265087017
634.97.64043155495818-2.74043155495818
643.311.9548980636366-8.65489806363659
6574.403166725991472.59683327400853
668.29.46268943677432-1.26268943677433
6743.538.69067265249444.80932734750565
6848.557.1892260418941-8.6892260418941
695.44.555952955193550.844047044806452
7049.554.5246150128051-5.02461501280509
7129.15.8120550392053123.2879449607947
722.628.0529687030544-25.4529687030544
730.81.5717019656024-0.771701965602397
74184.8188.872107929342-4.072107929342
752.32.55648473254814-0.256484732548137
76819.8890184314072-11.8890184314072
7710.315.0205683813587-4.72056838135867
785028.758120264214121.2418797357859
79118.173.745796866281944.3542031337181







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.4151179829322690.8302359658645380.584882017067731
210.2561373890346180.5122747780692360.743862610965382
220.4704536471804320.9409072943608630.529546352819569
230.3786470230279580.7572940460559160.621352976972042
240.2803902849663010.5607805699326020.719609715033699
250.2692058853182850.538411770636570.730794114681715
260.193918907096710.387837814193420.80608109290329
270.1415453749885380.2830907499770770.858454625011462
280.08920393162621490.178407863252430.910796068373785
290.05720040061149620.1144008012229920.942799599388504
300.03343977182248780.06687954364497560.966560228177512
310.01959624832833510.03919249665667010.980403751671665
320.03337798887593820.06675597775187640.966622011124062
330.02686586997942310.05373173995884620.973134130020577
340.01799654485476330.03599308970952660.982003455145237
350.01374485689628730.02748971379257450.986255143103713
360.01917315382533360.03834630765066720.980826846174666
370.05473768987375020.10947537974750.94526231012625
380.04096352023846630.08192704047693260.959036479761534
390.02890852270315310.05781704540630620.971091477296847
400.05828277616793230.1165655523358650.941717223832068
410.1715303799858030.3430607599716060.828469620014197
420.1234856761677530.2469713523355060.876514323832247
430.2966477258068630.5932954516137260.703352274193137
440.3273854689096080.6547709378192170.672614531090392
450.7097552304845580.5804895390308840.290244769515442
460.637699718775140.7246005624497210.36230028122486
470.6550646972766070.6898706054467850.344935302723393
480.643452822960230.713094354079540.35654717703977
490.5965665739423610.8068668521152770.403433426057639
500.5023267328252240.9953465343495520.497673267174776
510.4046734198830790.8093468397661570.595326580116921
520.3135239110503680.6270478221007350.686476088949632
530.2392525724292330.4785051448584660.760747427570767
540.188571906844040.3771438136880790.81142809315596
550.1647108717729210.3294217435458430.835289128227079
560.1893590050742330.3787180101484660.810640994925767
570.1182800693065530.2365601386131050.881719930693447
580.0642458414752870.1284916829505740.935754158524713
590.04382447934996370.08764895869992730.956175520650036

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.415117982932269 & 0.830235965864538 & 0.584882017067731 \tabularnewline
21 & 0.256137389034618 & 0.512274778069236 & 0.743862610965382 \tabularnewline
22 & 0.470453647180432 & 0.940907294360863 & 0.529546352819569 \tabularnewline
23 & 0.378647023027958 & 0.757294046055916 & 0.621352976972042 \tabularnewline
24 & 0.280390284966301 & 0.560780569932602 & 0.719609715033699 \tabularnewline
25 & 0.269205885318285 & 0.53841177063657 & 0.730794114681715 \tabularnewline
26 & 0.19391890709671 & 0.38783781419342 & 0.80608109290329 \tabularnewline
27 & 0.141545374988538 & 0.283090749977077 & 0.858454625011462 \tabularnewline
28 & 0.0892039316262149 & 0.17840786325243 & 0.910796068373785 \tabularnewline
29 & 0.0572004006114962 & 0.114400801222992 & 0.942799599388504 \tabularnewline
30 & 0.0334397718224878 & 0.0668795436449756 & 0.966560228177512 \tabularnewline
31 & 0.0195962483283351 & 0.0391924966566701 & 0.980403751671665 \tabularnewline
32 & 0.0333779888759382 & 0.0667559777518764 & 0.966622011124062 \tabularnewline
33 & 0.0268658699794231 & 0.0537317399588462 & 0.973134130020577 \tabularnewline
34 & 0.0179965448547633 & 0.0359930897095266 & 0.982003455145237 \tabularnewline
35 & 0.0137448568962873 & 0.0274897137925745 & 0.986255143103713 \tabularnewline
36 & 0.0191731538253336 & 0.0383463076506672 & 0.980826846174666 \tabularnewline
37 & 0.0547376898737502 & 0.1094753797475 & 0.94526231012625 \tabularnewline
38 & 0.0409635202384663 & 0.0819270404769326 & 0.959036479761534 \tabularnewline
39 & 0.0289085227031531 & 0.0578170454063062 & 0.971091477296847 \tabularnewline
40 & 0.0582827761679323 & 0.116565552335865 & 0.941717223832068 \tabularnewline
41 & 0.171530379985803 & 0.343060759971606 & 0.828469620014197 \tabularnewline
42 & 0.123485676167753 & 0.246971352335506 & 0.876514323832247 \tabularnewline
43 & 0.296647725806863 & 0.593295451613726 & 0.703352274193137 \tabularnewline
44 & 0.327385468909608 & 0.654770937819217 & 0.672614531090392 \tabularnewline
45 & 0.709755230484558 & 0.580489539030884 & 0.290244769515442 \tabularnewline
46 & 0.63769971877514 & 0.724600562449721 & 0.36230028122486 \tabularnewline
47 & 0.655064697276607 & 0.689870605446785 & 0.344935302723393 \tabularnewline
48 & 0.64345282296023 & 0.71309435407954 & 0.35654717703977 \tabularnewline
49 & 0.596566573942361 & 0.806866852115277 & 0.403433426057639 \tabularnewline
50 & 0.502326732825224 & 0.995346534349552 & 0.497673267174776 \tabularnewline
51 & 0.404673419883079 & 0.809346839766157 & 0.595326580116921 \tabularnewline
52 & 0.313523911050368 & 0.627047822100735 & 0.686476088949632 \tabularnewline
53 & 0.239252572429233 & 0.478505144858466 & 0.760747427570767 \tabularnewline
54 & 0.18857190684404 & 0.377143813688079 & 0.81142809315596 \tabularnewline
55 & 0.164710871772921 & 0.329421743545843 & 0.835289128227079 \tabularnewline
56 & 0.189359005074233 & 0.378718010148466 & 0.810640994925767 \tabularnewline
57 & 0.118280069306553 & 0.236560138613105 & 0.881719930693447 \tabularnewline
58 & 0.064245841475287 & 0.128491682950574 & 0.935754158524713 \tabularnewline
59 & 0.0438244793499637 & 0.0876489586999273 & 0.956175520650036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185452&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]20[/C][C]0.415117982932269[/C][C]0.830235965864538[/C][C]0.584882017067731[/C][/ROW]
[ROW][C]21[/C][C]0.256137389034618[/C][C]0.512274778069236[/C][C]0.743862610965382[/C][/ROW]
[ROW][C]22[/C][C]0.470453647180432[/C][C]0.940907294360863[/C][C]0.529546352819569[/C][/ROW]
[ROW][C]23[/C][C]0.378647023027958[/C][C]0.757294046055916[/C][C]0.621352976972042[/C][/ROW]
[ROW][C]24[/C][C]0.280390284966301[/C][C]0.560780569932602[/C][C]0.719609715033699[/C][/ROW]
[ROW][C]25[/C][C]0.269205885318285[/C][C]0.53841177063657[/C][C]0.730794114681715[/C][/ROW]
[ROW][C]26[/C][C]0.19391890709671[/C][C]0.38783781419342[/C][C]0.80608109290329[/C][/ROW]
[ROW][C]27[/C][C]0.141545374988538[/C][C]0.283090749977077[/C][C]0.858454625011462[/C][/ROW]
[ROW][C]28[/C][C]0.0892039316262149[/C][C]0.17840786325243[/C][C]0.910796068373785[/C][/ROW]
[ROW][C]29[/C][C]0.0572004006114962[/C][C]0.114400801222992[/C][C]0.942799599388504[/C][/ROW]
[ROW][C]30[/C][C]0.0334397718224878[/C][C]0.0668795436449756[/C][C]0.966560228177512[/C][/ROW]
[ROW][C]31[/C][C]0.0195962483283351[/C][C]0.0391924966566701[/C][C]0.980403751671665[/C][/ROW]
[ROW][C]32[/C][C]0.0333779888759382[/C][C]0.0667559777518764[/C][C]0.966622011124062[/C][/ROW]
[ROW][C]33[/C][C]0.0268658699794231[/C][C]0.0537317399588462[/C][C]0.973134130020577[/C][/ROW]
[ROW][C]34[/C][C]0.0179965448547633[/C][C]0.0359930897095266[/C][C]0.982003455145237[/C][/ROW]
[ROW][C]35[/C][C]0.0137448568962873[/C][C]0.0274897137925745[/C][C]0.986255143103713[/C][/ROW]
[ROW][C]36[/C][C]0.0191731538253336[/C][C]0.0383463076506672[/C][C]0.980826846174666[/C][/ROW]
[ROW][C]37[/C][C]0.0547376898737502[/C][C]0.1094753797475[/C][C]0.94526231012625[/C][/ROW]
[ROW][C]38[/C][C]0.0409635202384663[/C][C]0.0819270404769326[/C][C]0.959036479761534[/C][/ROW]
[ROW][C]39[/C][C]0.0289085227031531[/C][C]0.0578170454063062[/C][C]0.971091477296847[/C][/ROW]
[ROW][C]40[/C][C]0.0582827761679323[/C][C]0.116565552335865[/C][C]0.941717223832068[/C][/ROW]
[ROW][C]41[/C][C]0.171530379985803[/C][C]0.343060759971606[/C][C]0.828469620014197[/C][/ROW]
[ROW][C]42[/C][C]0.123485676167753[/C][C]0.246971352335506[/C][C]0.876514323832247[/C][/ROW]
[ROW][C]43[/C][C]0.296647725806863[/C][C]0.593295451613726[/C][C]0.703352274193137[/C][/ROW]
[ROW][C]44[/C][C]0.327385468909608[/C][C]0.654770937819217[/C][C]0.672614531090392[/C][/ROW]
[ROW][C]45[/C][C]0.709755230484558[/C][C]0.580489539030884[/C][C]0.290244769515442[/C][/ROW]
[ROW][C]46[/C][C]0.63769971877514[/C][C]0.724600562449721[/C][C]0.36230028122486[/C][/ROW]
[ROW][C]47[/C][C]0.655064697276607[/C][C]0.689870605446785[/C][C]0.344935302723393[/C][/ROW]
[ROW][C]48[/C][C]0.64345282296023[/C][C]0.71309435407954[/C][C]0.35654717703977[/C][/ROW]
[ROW][C]49[/C][C]0.596566573942361[/C][C]0.806866852115277[/C][C]0.403433426057639[/C][/ROW]
[ROW][C]50[/C][C]0.502326732825224[/C][C]0.995346534349552[/C][C]0.497673267174776[/C][/ROW]
[ROW][C]51[/C][C]0.404673419883079[/C][C]0.809346839766157[/C][C]0.595326580116921[/C][/ROW]
[ROW][C]52[/C][C]0.313523911050368[/C][C]0.627047822100735[/C][C]0.686476088949632[/C][/ROW]
[ROW][C]53[/C][C]0.239252572429233[/C][C]0.478505144858466[/C][C]0.760747427570767[/C][/ROW]
[ROW][C]54[/C][C]0.18857190684404[/C][C]0.377143813688079[/C][C]0.81142809315596[/C][/ROW]
[ROW][C]55[/C][C]0.164710871772921[/C][C]0.329421743545843[/C][C]0.835289128227079[/C][/ROW]
[ROW][C]56[/C][C]0.189359005074233[/C][C]0.378718010148466[/C][C]0.810640994925767[/C][/ROW]
[ROW][C]57[/C][C]0.118280069306553[/C][C]0.236560138613105[/C][C]0.881719930693447[/C][/ROW]
[ROW][C]58[/C][C]0.064245841475287[/C][C]0.128491682950574[/C][C]0.935754158524713[/C][/ROW]
[ROW][C]59[/C][C]0.0438244793499637[/C][C]0.0876489586999273[/C][C]0.956175520650036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185452&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185452&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
200.4151179829322690.8302359658645380.584882017067731
210.2561373890346180.5122747780692360.743862610965382
220.4704536471804320.9409072943608630.529546352819569
230.3786470230279580.7572940460559160.621352976972042
240.2803902849663010.5607805699326020.719609715033699
250.2692058853182850.538411770636570.730794114681715
260.193918907096710.387837814193420.80608109290329
270.1415453749885380.2830907499770770.858454625011462
280.08920393162621490.178407863252430.910796068373785
290.05720040061149620.1144008012229920.942799599388504
300.03343977182248780.06687954364497560.966560228177512
310.01959624832833510.03919249665667010.980403751671665
320.03337798887593820.06675597775187640.966622011124062
330.02686586997942310.05373173995884620.973134130020577
340.01799654485476330.03599308970952660.982003455145237
350.01374485689628730.02748971379257450.986255143103713
360.01917315382533360.03834630765066720.980826846174666
370.05473768987375020.10947537974750.94526231012625
380.04096352023846630.08192704047693260.959036479761534
390.02890852270315310.05781704540630620.971091477296847
400.05828277616793230.1165655523358650.941717223832068
410.1715303799858030.3430607599716060.828469620014197
420.1234856761677530.2469713523355060.876514323832247
430.2966477258068630.5932954516137260.703352274193137
440.3273854689096080.6547709378192170.672614531090392
450.7097552304845580.5804895390308840.290244769515442
460.637699718775140.7246005624497210.36230028122486
470.6550646972766070.6898706054467850.344935302723393
480.643452822960230.713094354079540.35654717703977
490.5965665739423610.8068668521152770.403433426057639
500.5023267328252240.9953465343495520.497673267174776
510.4046734198830790.8093468397661570.595326580116921
520.3135239110503680.6270478221007350.686476088949632
530.2392525724292330.4785051448584660.760747427570767
540.188571906844040.3771438136880790.81142809315596
550.1647108717729210.3294217435458430.835289128227079
560.1893590050742330.3787180101484660.810640994925767
570.1182800693065530.2365601386131050.881719930693447
580.0642458414752870.1284916829505740.935754158524713
590.04382447934996370.08764895869992730.956175520650036







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.1NOK
10% type I error level100.25NOK

\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 & 4 & 0.1 & NOK \tabularnewline
10% type I error level & 10 & 0.25 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185452&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]4[/C][C]0.1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.25[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185452&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185452&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 level40.1NOK
10% type I error level100.25NOK



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')
}