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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 computationTue, 23 Nov 2010 17:54:30 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290534924cvj8bl86qdyv89y.htm/, Retrieved Fri, 29 Mar 2024 00:41:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99513, Retrieved Fri, 29 Mar 2024 00:41:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS7: Mini-tutorial b] [2010-11-19 11:46:42] [1fd136673b2a4fecb5c545b9b4a05d64]
-    D    [Multiple Regression] [ws7 mini-tutorial] [2010-11-23 17:54:30] [2953e4eb3235e2fd3d6373a16d27c72f] [Current]
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Dataseries X:
73	2	71.91	5.11	50	3
28	6	6.06	3.53	48	5
40	5	8.1	4.52	63	11
79	3	79.38	3.72	113	13
75	3	65.34	5.99	128	11
21	3	34.62	3.15	52	7
16	2	26.26	3.17	104	1
81	2	60.92	3.5	40	1
90	2	39.56	3.39	89	11
87	5	65.61	4.15	97	3
99	3	56.49	4.5	29	9
54	3	56.19	3.31	36	5
53	5	80.3	3.09	114	11
6	4	61.2	5.31	49	9
71	5	58.2	4.24	57	7
93	6	75.91	5.06	82	4
82	3	73.66	4.72	34	10
32	4	73.87	4.58	36	13
93	4	87.21	5.3	89	9
24	4	64.29	5.11	69	5
96	5	71.82	4.05	35	8
88	4	89.31	4.62	65	12
83	2	1.41	4.66	70	8
23	6	35.17	4.66	60	5
23	5	34.68	2.76	57	9
20	5	41.08	5.1	127	11
33	3	30.57	4.97	96	8
88	2	68.84	2.87	61	9
42	6	7.17	5.14	127	10
98	2	71.05	4.98	36	1
34	4	23.32	4.55	55	9
59	3	61.39	5.45	75	2
26	6	8.41	4.36	42	3
64	4	65.88	4.78	64	4
13	1	64.06	4.74	83	3
6	2	26.8	5.44	56	1
49	4	12.78	5.78	114	5
3	5	23.84	2.92	33	4
87	6	42.69	4.22	91	2
77	2	54.94	3.93	127	2
70	4	89.99	3.01	45	10
76	4	5.68	3.22	80	6
82	4	72.64	5.12	40	9
12	2	45.92	3.04	115	7
44	3	24.96	5.82	33	1
63	5	18.17	3.11	127	13
35	1	29.12	3.87	45	9
69	1	40.08	3.75	74	11
10	5	1.08	4.82	105	10
36	2	57.52	2.83	60	7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99513&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99513&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99513&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
slaagkans[t] = + 21.2435270406808 -0.189021294324606verzekeraar[t] + 0.546658570171708kost[t] + 0.788682667921521grootte[t] + 0.0270026552096039snelheid[t] + 0.345099845668745maand[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
slaagkans[t] =  +  21.2435270406808 -0.189021294324606verzekeraar[t] +  0.546658570171708kost[t] +  0.788682667921521grootte[t] +  0.0270026552096039snelheid[t] +  0.345099845668745maand[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99513&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]slaagkans[t] =  +  21.2435270406808 -0.189021294324606verzekeraar[t] +  0.546658570171708kost[t] +  0.788682667921521grootte[t] +  0.0270026552096039snelheid[t] +  0.345099845668745maand[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99513&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99513&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
slaagkans[t] = + 21.2435270406808 -0.189021294324606verzekeraar[t] + 0.546658570171708kost[t] + 0.788682667921521grootte[t] + 0.0270026552096039snelheid[t] + 0.345099845668745maand[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)21.243527040680824.7104730.85970.3946160.197308
verzekeraar-0.1890212943246062.766775-0.06830.9458420.472921
kost0.5466585701717080.1589083.44010.0012840.000642
grootte0.7886826679215214.4479240.17730.8600750.430037
snelheid0.02700265520960390.1341530.20130.8414050.420703
maand0.3450998456687451.1345910.30420.7624380.381219

\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) & 21.2435270406808 & 24.710473 & 0.8597 & 0.394616 & 0.197308 \tabularnewline
verzekeraar & -0.189021294324606 & 2.766775 & -0.0683 & 0.945842 & 0.472921 \tabularnewline
kost & 0.546658570171708 & 0.158908 & 3.4401 & 0.001284 & 0.000642 \tabularnewline
grootte & 0.788682667921521 & 4.447924 & 0.1773 & 0.860075 & 0.430037 \tabularnewline
snelheid & 0.0270026552096039 & 0.134153 & 0.2013 & 0.841405 & 0.420703 \tabularnewline
maand & 0.345099845668745 & 1.134591 & 0.3042 & 0.762438 & 0.381219 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99513&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]21.2435270406808[/C][C]24.710473[/C][C]0.8597[/C][C]0.394616[/C][C]0.197308[/C][/ROW]
[ROW][C]verzekeraar[/C][C]-0.189021294324606[/C][C]2.766775[/C][C]-0.0683[/C][C]0.945842[/C][C]0.472921[/C][/ROW]
[ROW][C]kost[/C][C]0.546658570171708[/C][C]0.158908[/C][C]3.4401[/C][C]0.001284[/C][C]0.000642[/C][/ROW]
[ROW][C]grootte[/C][C]0.788682667921521[/C][C]4.447924[/C][C]0.1773[/C][C]0.860075[/C][C]0.430037[/C][/ROW]
[ROW][C]snelheid[/C][C]0.0270026552096039[/C][C]0.134153[/C][C]0.2013[/C][C]0.841405[/C][C]0.420703[/C][/ROW]
[ROW][C]maand[/C][C]0.345099845668745[/C][C]1.134591[/C][C]0.3042[/C][C]0.762438[/C][C]0.381219[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99513&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99513&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)21.243527040680824.7104730.85970.3946160.197308
verzekeraar-0.1890212943246062.766775-0.06830.9458420.472921
kost0.5466585701717080.1589083.44010.0012840.000642
grootte0.7886826679215214.4479240.17730.8600750.430037
snelheid0.02700265520960390.1341530.20130.8414050.420703
maand0.3450998456687451.1345910.30420.7624380.381219







Multiple Linear Regression - Regression Statistics
Multiple R0.479791384134689
R-squared0.230199772289880
Adjusted R-squared0.142722473686458
F-TEST (value)2.63153727841424
F-TEST (DF numerator)5
F-TEST (DF denominator)44
p-value0.0363218943091439
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27.7904116050644
Sum Squared Residuals33981.5069958716

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.479791384134689 \tabularnewline
R-squared & 0.230199772289880 \tabularnewline
Adjusted R-squared & 0.142722473686458 \tabularnewline
F-TEST (value) & 2.63153727841424 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0.0363218943091439 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 27.7904116050644 \tabularnewline
Sum Squared Residuals & 33981.5069958716 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99513&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.479791384134689[/C][/ROW]
[ROW][C]R-squared[/C][C]0.230199772289880[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.142722473686458[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.63153727841424[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]0.0363218943091439[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]27.7904116050644[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33981.5069958716[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99513&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99513&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 R0.479791384134689
R-squared0.230199772289880
Adjusted R-squared0.142722473686458
F-TEST (value)2.63153727841424
F-TEST (DF numerator)5
F-TEST (DF denominator)44
p-value0.0363218943091439
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27.7904116050644
Sum Squared Residuals33981.5069958716







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17366.59130296364446.4086970363556
22829.2278267061414-1.22782670614140
34033.78846622701526.21153377298481
47974.54171801498414.45828198501587
57568.37178148276186.62821851723822
62145.9059702515849-24.9059702515849
71640.8742385495194-24.8742385495194
88158.353519938670322.6464800613297
99051.364266348289338.6357336517107
108763.092279522235723.9077204777643
119958.995253404450840.0047465955492
125456.701342662365-2.70134266236496
135373.5065341939744-20.5065341939744
14662.5598800408435-56.5598800408435
157159.41281413166711.5871858683329
169369.191602746012923.8083972539871
178269.035004362958512.9649956370415
183269.9396706422864-37.9396706422864
199377.850688832714515.1493111672855
202463.2509722106068-39.2509722106068
219666.459495581558129.5405044184419
228878.84960342786429.15039657213578
238329.962518898510253.0374811014898
242346.3563009611064-23.3563009611064
252346.0783539040421-23.0783539040421
262054.0028717520872-34.0028717520872
273346.660622172898-13.660622172898
288865.514040258391322.4859597416087
294234.96310580428817.03689419571192
309864.950410982195133.0495890178049
313441.4150705063765-7.41507050637646
325961.2495521527782-2.24955215277821
332630.3148653378247-4.31486533782468
346463.3797809350490.620219064951007
351363.0883295169072-50.0883295169072
36641.663616383533-35.663616383533
374936.836126133001812.1638738669982
38337.9052012768741-34.9052012768741
398749.921935809403637.0780641905964
407758.11796608515418.882033914846
417076.7212993646978-6.72129936469776
427630.362832223445845.6371677765542
438268.420780479816313.5792195201837
441253.8866455735835-41.8866455735835
454440.14738166408183.85261833591824
466340.59964509162722.4003549083730
473544.3464233300635-9.3464233300635
486951.716436031410817.2835639685892
491030.9765395379208-20.9765395379208
503658.5771155907836-22.5771155907836

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 73 & 66.5913029636444 & 6.4086970363556 \tabularnewline
2 & 28 & 29.2278267061414 & -1.22782670614140 \tabularnewline
3 & 40 & 33.7884662270152 & 6.21153377298481 \tabularnewline
4 & 79 & 74.5417180149841 & 4.45828198501587 \tabularnewline
5 & 75 & 68.3717814827618 & 6.62821851723822 \tabularnewline
6 & 21 & 45.9059702515849 & -24.9059702515849 \tabularnewline
7 & 16 & 40.8742385495194 & -24.8742385495194 \tabularnewline
8 & 81 & 58.3535199386703 & 22.6464800613297 \tabularnewline
9 & 90 & 51.3642663482893 & 38.6357336517107 \tabularnewline
10 & 87 & 63.0922795222357 & 23.9077204777643 \tabularnewline
11 & 99 & 58.9952534044508 & 40.0047465955492 \tabularnewline
12 & 54 & 56.701342662365 & -2.70134266236496 \tabularnewline
13 & 53 & 73.5065341939744 & -20.5065341939744 \tabularnewline
14 & 6 & 62.5598800408435 & -56.5598800408435 \tabularnewline
15 & 71 & 59.412814131667 & 11.5871858683329 \tabularnewline
16 & 93 & 69.1916027460129 & 23.8083972539871 \tabularnewline
17 & 82 & 69.0350043629585 & 12.9649956370415 \tabularnewline
18 & 32 & 69.9396706422864 & -37.9396706422864 \tabularnewline
19 & 93 & 77.8506888327145 & 15.1493111672855 \tabularnewline
20 & 24 & 63.2509722106068 & -39.2509722106068 \tabularnewline
21 & 96 & 66.4594955815581 & 29.5405044184419 \tabularnewline
22 & 88 & 78.8496034278642 & 9.15039657213578 \tabularnewline
23 & 83 & 29.9625188985102 & 53.0374811014898 \tabularnewline
24 & 23 & 46.3563009611064 & -23.3563009611064 \tabularnewline
25 & 23 & 46.0783539040421 & -23.0783539040421 \tabularnewline
26 & 20 & 54.0028717520872 & -34.0028717520872 \tabularnewline
27 & 33 & 46.660622172898 & -13.660622172898 \tabularnewline
28 & 88 & 65.5140402583913 & 22.4859597416087 \tabularnewline
29 & 42 & 34.9631058042881 & 7.03689419571192 \tabularnewline
30 & 98 & 64.9504109821951 & 33.0495890178049 \tabularnewline
31 & 34 & 41.4150705063765 & -7.41507050637646 \tabularnewline
32 & 59 & 61.2495521527782 & -2.24955215277821 \tabularnewline
33 & 26 & 30.3148653378247 & -4.31486533782468 \tabularnewline
34 & 64 & 63.379780935049 & 0.620219064951007 \tabularnewline
35 & 13 & 63.0883295169072 & -50.0883295169072 \tabularnewline
36 & 6 & 41.663616383533 & -35.663616383533 \tabularnewline
37 & 49 & 36.8361261330018 & 12.1638738669982 \tabularnewline
38 & 3 & 37.9052012768741 & -34.9052012768741 \tabularnewline
39 & 87 & 49.9219358094036 & 37.0780641905964 \tabularnewline
40 & 77 & 58.117966085154 & 18.882033914846 \tabularnewline
41 & 70 & 76.7212993646978 & -6.72129936469776 \tabularnewline
42 & 76 & 30.3628322234458 & 45.6371677765542 \tabularnewline
43 & 82 & 68.4207804798163 & 13.5792195201837 \tabularnewline
44 & 12 & 53.8866455735835 & -41.8866455735835 \tabularnewline
45 & 44 & 40.1473816640818 & 3.85261833591824 \tabularnewline
46 & 63 & 40.599645091627 & 22.4003549083730 \tabularnewline
47 & 35 & 44.3464233300635 & -9.3464233300635 \tabularnewline
48 & 69 & 51.7164360314108 & 17.2835639685892 \tabularnewline
49 & 10 & 30.9765395379208 & -20.9765395379208 \tabularnewline
50 & 36 & 58.5771155907836 & -22.5771155907836 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99513&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]73[/C][C]66.5913029636444[/C][C]6.4086970363556[/C][/ROW]
[ROW][C]2[/C][C]28[/C][C]29.2278267061414[/C][C]-1.22782670614140[/C][/ROW]
[ROW][C]3[/C][C]40[/C][C]33.7884662270152[/C][C]6.21153377298481[/C][/ROW]
[ROW][C]4[/C][C]79[/C][C]74.5417180149841[/C][C]4.45828198501587[/C][/ROW]
[ROW][C]5[/C][C]75[/C][C]68.3717814827618[/C][C]6.62821851723822[/C][/ROW]
[ROW][C]6[/C][C]21[/C][C]45.9059702515849[/C][C]-24.9059702515849[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]40.8742385495194[/C][C]-24.8742385495194[/C][/ROW]
[ROW][C]8[/C][C]81[/C][C]58.3535199386703[/C][C]22.6464800613297[/C][/ROW]
[ROW][C]9[/C][C]90[/C][C]51.3642663482893[/C][C]38.6357336517107[/C][/ROW]
[ROW][C]10[/C][C]87[/C][C]63.0922795222357[/C][C]23.9077204777643[/C][/ROW]
[ROW][C]11[/C][C]99[/C][C]58.9952534044508[/C][C]40.0047465955492[/C][/ROW]
[ROW][C]12[/C][C]54[/C][C]56.701342662365[/C][C]-2.70134266236496[/C][/ROW]
[ROW][C]13[/C][C]53[/C][C]73.5065341939744[/C][C]-20.5065341939744[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]62.5598800408435[/C][C]-56.5598800408435[/C][/ROW]
[ROW][C]15[/C][C]71[/C][C]59.412814131667[/C][C]11.5871858683329[/C][/ROW]
[ROW][C]16[/C][C]93[/C][C]69.1916027460129[/C][C]23.8083972539871[/C][/ROW]
[ROW][C]17[/C][C]82[/C][C]69.0350043629585[/C][C]12.9649956370415[/C][/ROW]
[ROW][C]18[/C][C]32[/C][C]69.9396706422864[/C][C]-37.9396706422864[/C][/ROW]
[ROW][C]19[/C][C]93[/C][C]77.8506888327145[/C][C]15.1493111672855[/C][/ROW]
[ROW][C]20[/C][C]24[/C][C]63.2509722106068[/C][C]-39.2509722106068[/C][/ROW]
[ROW][C]21[/C][C]96[/C][C]66.4594955815581[/C][C]29.5405044184419[/C][/ROW]
[ROW][C]22[/C][C]88[/C][C]78.8496034278642[/C][C]9.15039657213578[/C][/ROW]
[ROW][C]23[/C][C]83[/C][C]29.9625188985102[/C][C]53.0374811014898[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]46.3563009611064[/C][C]-23.3563009611064[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]46.0783539040421[/C][C]-23.0783539040421[/C][/ROW]
[ROW][C]26[/C][C]20[/C][C]54.0028717520872[/C][C]-34.0028717520872[/C][/ROW]
[ROW][C]27[/C][C]33[/C][C]46.660622172898[/C][C]-13.660622172898[/C][/ROW]
[ROW][C]28[/C][C]88[/C][C]65.5140402583913[/C][C]22.4859597416087[/C][/ROW]
[ROW][C]29[/C][C]42[/C][C]34.9631058042881[/C][C]7.03689419571192[/C][/ROW]
[ROW][C]30[/C][C]98[/C][C]64.9504109821951[/C][C]33.0495890178049[/C][/ROW]
[ROW][C]31[/C][C]34[/C][C]41.4150705063765[/C][C]-7.41507050637646[/C][/ROW]
[ROW][C]32[/C][C]59[/C][C]61.2495521527782[/C][C]-2.24955215277821[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]30.3148653378247[/C][C]-4.31486533782468[/C][/ROW]
[ROW][C]34[/C][C]64[/C][C]63.379780935049[/C][C]0.620219064951007[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]63.0883295169072[/C][C]-50.0883295169072[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]41.663616383533[/C][C]-35.663616383533[/C][/ROW]
[ROW][C]37[/C][C]49[/C][C]36.8361261330018[/C][C]12.1638738669982[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]37.9052012768741[/C][C]-34.9052012768741[/C][/ROW]
[ROW][C]39[/C][C]87[/C][C]49.9219358094036[/C][C]37.0780641905964[/C][/ROW]
[ROW][C]40[/C][C]77[/C][C]58.117966085154[/C][C]18.882033914846[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]76.7212993646978[/C][C]-6.72129936469776[/C][/ROW]
[ROW][C]42[/C][C]76[/C][C]30.3628322234458[/C][C]45.6371677765542[/C][/ROW]
[ROW][C]43[/C][C]82[/C][C]68.4207804798163[/C][C]13.5792195201837[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]53.8866455735835[/C][C]-41.8866455735835[/C][/ROW]
[ROW][C]45[/C][C]44[/C][C]40.1473816640818[/C][C]3.85261833591824[/C][/ROW]
[ROW][C]46[/C][C]63[/C][C]40.599645091627[/C][C]22.4003549083730[/C][/ROW]
[ROW][C]47[/C][C]35[/C][C]44.3464233300635[/C][C]-9.3464233300635[/C][/ROW]
[ROW][C]48[/C][C]69[/C][C]51.7164360314108[/C][C]17.2835639685892[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]30.9765395379208[/C][C]-20.9765395379208[/C][/ROW]
[ROW][C]50[/C][C]36[/C][C]58.5771155907836[/C][C]-22.5771155907836[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99513&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99513&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
17366.59130296364446.4086970363556
22829.2278267061414-1.22782670614140
34033.78846622701526.21153377298481
47974.54171801498414.45828198501587
57568.37178148276186.62821851723822
62145.9059702515849-24.9059702515849
71640.8742385495194-24.8742385495194
88158.353519938670322.6464800613297
99051.364266348289338.6357336517107
108763.092279522235723.9077204777643
119958.995253404450840.0047465955492
125456.701342662365-2.70134266236496
135373.5065341939744-20.5065341939744
14662.5598800408435-56.5598800408435
157159.41281413166711.5871858683329
169369.191602746012923.8083972539871
178269.035004362958512.9649956370415
183269.9396706422864-37.9396706422864
199377.850688832714515.1493111672855
202463.2509722106068-39.2509722106068
219666.459495581558129.5405044184419
228878.84960342786429.15039657213578
238329.962518898510253.0374811014898
242346.3563009611064-23.3563009611064
252346.0783539040421-23.0783539040421
262054.0028717520872-34.0028717520872
273346.660622172898-13.660622172898
288865.514040258391322.4859597416087
294234.96310580428817.03689419571192
309864.950410982195133.0495890178049
313441.4150705063765-7.41507050637646
325961.2495521527782-2.24955215277821
332630.3148653378247-4.31486533782468
346463.3797809350490.620219064951007
351363.0883295169072-50.0883295169072
36641.663616383533-35.663616383533
374936.836126133001812.1638738669982
38337.9052012768741-34.9052012768741
398749.921935809403637.0780641905964
407758.11796608515418.882033914846
417076.7212993646978-6.72129936469776
427630.362832223445845.6371677765542
438268.420780479816313.5792195201837
441253.8866455735835-41.8866455735835
454440.14738166408183.85261833591824
466340.59964509162722.4003549083730
473544.3464233300635-9.3464233300635
486951.716436031410817.2835639685892
491030.9765395379208-20.9765395379208
503658.5771155907836-22.5771155907836







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5370328187783770.9259343624432460.462967181221623
100.4878840275374560.9757680550749120.512115972462544
110.4118896101808910.8237792203617830.588110389819109
120.328296214485420.656592428970840.67170378551458
130.2937722014372800.5875444028745590.70622779856272
140.732444120379350.53511175924130.26755587962065
150.6537194443776390.6925611112447230.346280555622361
160.6146209979079520.7707580041840970.385379002092048
170.5217571429367930.9564857141264140.478242857063207
180.5837841429514040.8324317140971910.416215857048596
190.504297375829040.991405248341920.49570262417096
200.586223215888360.8275535682232790.413776784111640
210.5804861258229950.8390277483540110.419513874177006
220.4974537396744290.9949074793488570.502546260325571
230.6695425172400560.6609149655198880.330457482759944
240.6380185305332550.723962938933490.361981469466745
250.6015502561694360.7968994876611280.398449743830564
260.6431746233205320.7136507533589350.356825376679468
270.575800849716420.848398300567160.42419915028358
280.540262545808320.919474908383360.45973745419168
290.4706840126884820.9413680253769640.529315987311518
300.5485785050791090.9028429898417820.451421494920891
310.4595331555853780.9190663111707560.540466844414622
320.3685137316623660.7370274633247320.631486268337634
330.2854932119509970.5709864239019940.714506788049003
340.2049347960320720.4098695920641440.795065203967928
350.3177797788954490.6355595577908980.682220221104551
360.3401493639584460.6802987279168930.659850636041554
370.2584750409504770.5169500819009540.741524959049523
380.3303950651390850.660790130278170.669604934860915
390.270079581311170.540159162622340.72992041868883
400.3677448873035380.7354897746070770.632255112696462
410.2629762279884800.5259524559769590.73702377201152

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.537032818778377 & 0.925934362443246 & 0.462967181221623 \tabularnewline
10 & 0.487884027537456 & 0.975768055074912 & 0.512115972462544 \tabularnewline
11 & 0.411889610180891 & 0.823779220361783 & 0.588110389819109 \tabularnewline
12 & 0.32829621448542 & 0.65659242897084 & 0.67170378551458 \tabularnewline
13 & 0.293772201437280 & 0.587544402874559 & 0.70622779856272 \tabularnewline
14 & 0.73244412037935 & 0.5351117592413 & 0.26755587962065 \tabularnewline
15 & 0.653719444377639 & 0.692561111244723 & 0.346280555622361 \tabularnewline
16 & 0.614620997907952 & 0.770758004184097 & 0.385379002092048 \tabularnewline
17 & 0.521757142936793 & 0.956485714126414 & 0.478242857063207 \tabularnewline
18 & 0.583784142951404 & 0.832431714097191 & 0.416215857048596 \tabularnewline
19 & 0.50429737582904 & 0.99140524834192 & 0.49570262417096 \tabularnewline
20 & 0.58622321588836 & 0.827553568223279 & 0.413776784111640 \tabularnewline
21 & 0.580486125822995 & 0.839027748354011 & 0.419513874177006 \tabularnewline
22 & 0.497453739674429 & 0.994907479348857 & 0.502546260325571 \tabularnewline
23 & 0.669542517240056 & 0.660914965519888 & 0.330457482759944 \tabularnewline
24 & 0.638018530533255 & 0.72396293893349 & 0.361981469466745 \tabularnewline
25 & 0.601550256169436 & 0.796899487661128 & 0.398449743830564 \tabularnewline
26 & 0.643174623320532 & 0.713650753358935 & 0.356825376679468 \tabularnewline
27 & 0.57580084971642 & 0.84839830056716 & 0.42419915028358 \tabularnewline
28 & 0.54026254580832 & 0.91947490838336 & 0.45973745419168 \tabularnewline
29 & 0.470684012688482 & 0.941368025376964 & 0.529315987311518 \tabularnewline
30 & 0.548578505079109 & 0.902842989841782 & 0.451421494920891 \tabularnewline
31 & 0.459533155585378 & 0.919066311170756 & 0.540466844414622 \tabularnewline
32 & 0.368513731662366 & 0.737027463324732 & 0.631486268337634 \tabularnewline
33 & 0.285493211950997 & 0.570986423901994 & 0.714506788049003 \tabularnewline
34 & 0.204934796032072 & 0.409869592064144 & 0.795065203967928 \tabularnewline
35 & 0.317779778895449 & 0.635559557790898 & 0.682220221104551 \tabularnewline
36 & 0.340149363958446 & 0.680298727916893 & 0.659850636041554 \tabularnewline
37 & 0.258475040950477 & 0.516950081900954 & 0.741524959049523 \tabularnewline
38 & 0.330395065139085 & 0.66079013027817 & 0.669604934860915 \tabularnewline
39 & 0.27007958131117 & 0.54015916262234 & 0.72992041868883 \tabularnewline
40 & 0.367744887303538 & 0.735489774607077 & 0.632255112696462 \tabularnewline
41 & 0.262976227988480 & 0.525952455976959 & 0.73702377201152 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99513&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]9[/C][C]0.537032818778377[/C][C]0.925934362443246[/C][C]0.462967181221623[/C][/ROW]
[ROW][C]10[/C][C]0.487884027537456[/C][C]0.975768055074912[/C][C]0.512115972462544[/C][/ROW]
[ROW][C]11[/C][C]0.411889610180891[/C][C]0.823779220361783[/C][C]0.588110389819109[/C][/ROW]
[ROW][C]12[/C][C]0.32829621448542[/C][C]0.65659242897084[/C][C]0.67170378551458[/C][/ROW]
[ROW][C]13[/C][C]0.293772201437280[/C][C]0.587544402874559[/C][C]0.70622779856272[/C][/ROW]
[ROW][C]14[/C][C]0.73244412037935[/C][C]0.5351117592413[/C][C]0.26755587962065[/C][/ROW]
[ROW][C]15[/C][C]0.653719444377639[/C][C]0.692561111244723[/C][C]0.346280555622361[/C][/ROW]
[ROW][C]16[/C][C]0.614620997907952[/C][C]0.770758004184097[/C][C]0.385379002092048[/C][/ROW]
[ROW][C]17[/C][C]0.521757142936793[/C][C]0.956485714126414[/C][C]0.478242857063207[/C][/ROW]
[ROW][C]18[/C][C]0.583784142951404[/C][C]0.832431714097191[/C][C]0.416215857048596[/C][/ROW]
[ROW][C]19[/C][C]0.50429737582904[/C][C]0.99140524834192[/C][C]0.49570262417096[/C][/ROW]
[ROW][C]20[/C][C]0.58622321588836[/C][C]0.827553568223279[/C][C]0.413776784111640[/C][/ROW]
[ROW][C]21[/C][C]0.580486125822995[/C][C]0.839027748354011[/C][C]0.419513874177006[/C][/ROW]
[ROW][C]22[/C][C]0.497453739674429[/C][C]0.994907479348857[/C][C]0.502546260325571[/C][/ROW]
[ROW][C]23[/C][C]0.669542517240056[/C][C]0.660914965519888[/C][C]0.330457482759944[/C][/ROW]
[ROW][C]24[/C][C]0.638018530533255[/C][C]0.72396293893349[/C][C]0.361981469466745[/C][/ROW]
[ROW][C]25[/C][C]0.601550256169436[/C][C]0.796899487661128[/C][C]0.398449743830564[/C][/ROW]
[ROW][C]26[/C][C]0.643174623320532[/C][C]0.713650753358935[/C][C]0.356825376679468[/C][/ROW]
[ROW][C]27[/C][C]0.57580084971642[/C][C]0.84839830056716[/C][C]0.42419915028358[/C][/ROW]
[ROW][C]28[/C][C]0.54026254580832[/C][C]0.91947490838336[/C][C]0.45973745419168[/C][/ROW]
[ROW][C]29[/C][C]0.470684012688482[/C][C]0.941368025376964[/C][C]0.529315987311518[/C][/ROW]
[ROW][C]30[/C][C]0.548578505079109[/C][C]0.902842989841782[/C][C]0.451421494920891[/C][/ROW]
[ROW][C]31[/C][C]0.459533155585378[/C][C]0.919066311170756[/C][C]0.540466844414622[/C][/ROW]
[ROW][C]32[/C][C]0.368513731662366[/C][C]0.737027463324732[/C][C]0.631486268337634[/C][/ROW]
[ROW][C]33[/C][C]0.285493211950997[/C][C]0.570986423901994[/C][C]0.714506788049003[/C][/ROW]
[ROW][C]34[/C][C]0.204934796032072[/C][C]0.409869592064144[/C][C]0.795065203967928[/C][/ROW]
[ROW][C]35[/C][C]0.317779778895449[/C][C]0.635559557790898[/C][C]0.682220221104551[/C][/ROW]
[ROW][C]36[/C][C]0.340149363958446[/C][C]0.680298727916893[/C][C]0.659850636041554[/C][/ROW]
[ROW][C]37[/C][C]0.258475040950477[/C][C]0.516950081900954[/C][C]0.741524959049523[/C][/ROW]
[ROW][C]38[/C][C]0.330395065139085[/C][C]0.66079013027817[/C][C]0.669604934860915[/C][/ROW]
[ROW][C]39[/C][C]0.27007958131117[/C][C]0.54015916262234[/C][C]0.72992041868883[/C][/ROW]
[ROW][C]40[/C][C]0.367744887303538[/C][C]0.735489774607077[/C][C]0.632255112696462[/C][/ROW]
[ROW][C]41[/C][C]0.262976227988480[/C][C]0.525952455976959[/C][C]0.73702377201152[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99513&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99513&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
90.5370328187783770.9259343624432460.462967181221623
100.4878840275374560.9757680550749120.512115972462544
110.4118896101808910.8237792203617830.588110389819109
120.328296214485420.656592428970840.67170378551458
130.2937722014372800.5875444028745590.70622779856272
140.732444120379350.53511175924130.26755587962065
150.6537194443776390.6925611112447230.346280555622361
160.6146209979079520.7707580041840970.385379002092048
170.5217571429367930.9564857141264140.478242857063207
180.5837841429514040.8324317140971910.416215857048596
190.504297375829040.991405248341920.49570262417096
200.586223215888360.8275535682232790.413776784111640
210.5804861258229950.8390277483540110.419513874177006
220.4974537396744290.9949074793488570.502546260325571
230.6695425172400560.6609149655198880.330457482759944
240.6380185305332550.723962938933490.361981469466745
250.6015502561694360.7968994876611280.398449743830564
260.6431746233205320.7136507533589350.356825376679468
270.575800849716420.848398300567160.42419915028358
280.540262545808320.919474908383360.45973745419168
290.4706840126884820.9413680253769640.529315987311518
300.5485785050791090.9028429898417820.451421494920891
310.4595331555853780.9190663111707560.540466844414622
320.3685137316623660.7370274633247320.631486268337634
330.2854932119509970.5709864239019940.714506788049003
340.2049347960320720.4098695920641440.795065203967928
350.3177797788954490.6355595577908980.682220221104551
360.3401493639584460.6802987279168930.659850636041554
370.2584750409504770.5169500819009540.741524959049523
380.3303950651390850.660790130278170.669604934860915
390.270079581311170.540159162622340.72992041868883
400.3677448873035380.7354897746070770.632255112696462
410.2629762279884800.5259524559769590.73702377201152







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99513&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99513&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99513&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 level00OK
10% type I error level00OK



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')
}