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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 23 Dec 2011 03:30:54 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t1324629092ulxboejmecjqxj8.htm/, Retrieved Mon, 29 Apr 2024 19:31:11 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160190, Retrieved Mon, 29 Apr 2024 19:31:11 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

122

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2011-12-23 08:30:54] [3542f7682a242002513e4b562d4475ed] [Current]
-   P       [Multiple Regression] [] [2011-12-23 12:05:34] [74be16979710d4c4e7c6647856088456] 
- R PD      [Multiple Regression] [MR] [2011-12-23 12:13:36] [a9671b130b33f9fcb98554992ce4582f] 

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Dataseries X:

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Histogram

Boxplots

210907	79	30	94	112285	144	145
120982	58	28	103	84786	103	101
176508	60	38	93	83123	98	98
179321	108	30	103	101193	135	132
123185	49	22	51	38361	61	60
52746	0	26	70	68504	39	38
385534	121	25	91	119182	150	144
33170	1	18	22	22807	5	5
101645	20	11	38	17140	28	28
149061	43	26	93	116174	84	84
165446	69	25	60	57635	80	79
237213	78	38	123	66198	130	127
173326	86	44	148	71701	82	78
133131	44	30	90	57793	60	60
258873	104	40	124	80444	131	131
180083	63	34	70	53855	84	84
324799	158	47	168	97668	140	133
230964	102	30	115	133824	151	150
236785	77	31	71	101481	91	91
135473	82	23	66	99645	138	132
202925	115	36	134	114789	150	136
215147	101	36	117	99052	124	124
344297	80	30	108	67654	119	118
153935	50	25	84	65553	73	70
132943	83	39	156	97500	110	107
174724	123	34	120	69112	123	119
174415	73	31	114	82753	90	89
225548	81	31	94	85323	116	112
223632	105	33	120	72654	113	108
124817	47	25	81	30727	56	52
221698	105	33	110	77873	115	112
210767	94	35	133	117478	119	116
170266	44	42	122	74007	129	123
260561	114	43	158	90183	127	125
84853	38	30	109	61542	27	27
294424	107	33	124	101494	175	162
101011	30	13	39	27570	35	32
215641	71	32	92	55813	64	64
325107	84	36	126	79215	96	92
7176	0	0	0	1423	0	0
167542	59	28	70	55461	84	83
106408	33	14	37	31081	41	41
96560	42	17	38	22996	47	47
265769	96	32	120	83122	126	120
269651	106	30	93	70106	105	105
149112	56	35	95	60578	80	79
175824	57	20	77	39992	70	65
152871	59	28	90	79892	73	70

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160190&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160190&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160190&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
X5[t] = + 0.822382366636281 -4.4504311416862e-06Y[t] + 0.0105913466602133X1[t] -0.221866252035594X2[t] + 0.0667948630590553X3[t] -2.92105665097489e-05X4[t] + 1.04519097864273X6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X5[t] =  +  0.822382366636281 -4.4504311416862e-06Y[t] +  0.0105913466602133X1[t] -0.221866252035594X2[t] +  0.0667948630590553X3[t] -2.92105665097489e-05X4[t] +  1.04519097864273X6[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160190&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X5[t] =  +  0.822382366636281 -4.4504311416862e-06Y[t] +  0.0105913466602133X1[t] -0.221866252035594X2[t] +  0.0667948630590553X3[t] -2.92105665097489e-05X4[t] +  1.04519097864273X6[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160190&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160190&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
X5[t] = + 0.822382366636281 -4.4504311416862e-06Y[t] + 0.0105913466602133X1[t] -0.221866252035594X2[t] + 0.0667948630590553X3[t] -2.92105665097489e-05X4[t] + 1.04519097864273X6[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.822382366636281	1.479106	0.556	0.581233	0.290616
Y	-4.4504311416862e-06	9e-06	-0.4954	0.622963	0.311481
X1	0.0105913466602133	0.02759	0.3839	0.703044	0.351522
X2	-0.221866252035594	0.104961	-2.1138	0.040661	0.020331
X3	0.0667948630590553	0.030118	2.2178	0.032169	0.016085
X4	-2.92105665097489e-05	2.5e-05	-1.1552	0.254709	0.127354
X6	1.04519097864273	0.026359	39.6521	0	0

\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) & 0.822382366636281 & 1.479106 & 0.556 & 0.581233 & 0.290616 \tabularnewline
Y & -4.4504311416862e-06 & 9e-06 & -0.4954 & 0.622963 & 0.311481 \tabularnewline
X1 & 0.0105913466602133 & 0.02759 & 0.3839 & 0.703044 & 0.351522 \tabularnewline
X2 & -0.221866252035594 & 0.104961 & -2.1138 & 0.040661 & 0.020331 \tabularnewline
X3 & 0.0667948630590553 & 0.030118 & 2.2178 & 0.032169 & 0.016085 \tabularnewline
X4 & -2.92105665097489e-05 & 2.5e-05 & -1.1552 & 0.254709 & 0.127354 \tabularnewline
X6 & 1.04519097864273 & 0.026359 & 39.6521 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160190&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]0.822382366636281[/C][C]1.479106[/C][C]0.556[/C][C]0.581233[/C][C]0.290616[/C][/ROW]
[ROW][C]Y[/C][C]-4.4504311416862e-06[/C][C]9e-06[/C][C]-0.4954[/C][C]0.622963[/C][C]0.311481[/C][/ROW]
[ROW][C]X1[/C][C]0.0105913466602133[/C][C]0.02759[/C][C]0.3839[/C][C]0.703044[/C][C]0.351522[/C][/ROW]
[ROW][C]X2[/C][C]-0.221866252035594[/C][C]0.104961[/C][C]-2.1138[/C][C]0.040661[/C][C]0.020331[/C][/ROW]
[ROW][C]X3[/C][C]0.0667948630590553[/C][C]0.030118[/C][C]2.2178[/C][C]0.032169[/C][C]0.016085[/C][/ROW]
[ROW][C]X4[/C][C]-2.92105665097489e-05[/C][C]2.5e-05[/C][C]-1.1552[/C][C]0.254709[/C][C]0.127354[/C][/ROW]
[ROW][C]X6[/C][C]1.04519097864273[/C][C]0.026359[/C][C]39.6521[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160190&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160190&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.822382366636281	1.479106	0.556	0.581233	0.290616
Y	-4.4504311416862e-06	9e-06	-0.4954	0.622963	0.311481
X1	0.0105913466602133	0.02759	0.3839	0.703044	0.351522
X2	-0.221866252035594	0.104961	-2.1138	0.040661	0.020331
X3	0.0667948630590553	0.030118	2.2178	0.032169	0.016085
X4	-2.92105665097489e-05	2.5e-05	-1.1552	0.254709	0.127354
X6	1.04519097864273	0.026359	39.6521	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.998071940196716
R-squared	0.996147597808037
Adjusted R-squared	0.995583831633603
F-TEST (value)	1766.95169554782
F-TEST (DF numerator)	6
F-TEST (DF denominator)	41
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.72631923434946
Sum Squared Residuals	304.745479270938

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998071940196716 \tabularnewline
R-squared & 0.996147597808037 \tabularnewline
Adjusted R-squared & 0.995583831633603 \tabularnewline
F-TEST (value) & 1766.95169554782 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.72631923434946 \tabularnewline
Sum Squared Residuals & 304.745479270938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160190&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998071940196716[/C][/ROW]
[ROW][C]R-squared[/C][C]0.996147597808037[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.995583831633603[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1766.95169554782[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/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]2.72631923434946[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]304.745479270938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160190&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160190&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 R	0.998071940196716
R-squared	0.996147597808037
Adjusted R-squared	0.995583831633603
F-TEST (value)	1766.95169554782
F-TEST (DF numerator)	6
F-TEST (DF denominator)	41
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.72631923434946
Sum Squared Residuals	304.745479270938

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	144	148.615984681125	-4.61598468112516
2	103	104.653516001451	-1.65351600145106
3	98	98.4539771404292	-0.45397714042923
4	135	136.401379701215	-1.40137970121476
5	61	60.90952464071	0.090475359290019
6	39	37.2109743270851	1.7890256729149
7	150	147.945946217015	2.05405378298484
8	5	2.72099686581108	2.27900313418892
9	28	29.4441995423157	-1.44419954231565
10	84	85.4603581204667	-1.46035812046672
11	80	80.1644510501631	-0.16445105016307
12	130	131.183231068445	-1.18323106844525
13	82	80.5158568993419	1.48414310065807
14	60	61.0747538338747	-1.07475383387467
15	131	134.749902286143	-3.74990228614276
16	84	84.0432852064716	-0.0432852064716211
17	140	138.001605252418	1.9983947475817
18	151	154.769803982302	-3.7698039823015
19	91	90.5967637421903	0.403236257809663
20	138	135.448018980771	2.55198101922894
21	150	140.893531446089	9.1064685539106
22	124	127.47274169288	-3.47274169287999
23	119	122.051601471166	-3.05160147116553
24	73	71.9795130164996	1.02048698350044
25	110	111.86442976006	-1.86442976006048
26	123	124.178377658782	-1.17837765878167
27	90	92.160824389705	-2.16082438970496
28	116	114.64641535909	1.35358464090963
29	113	112.391373393008	0.608626606991689
30	56	54.0807916150248	1.91920838497518
31	115	115.760345864202	-0.760345864202308
32	119	119.808917487989	-0.808917487989119
33	129	125.757939195993	3.24206080400651
34	127	129.898102433784	-2.89810243378403
35	27	27.8943533576387	-0.894353357638746
36	175	167.962560725749	7.03743927425124
37	35	33.0520946471237	1.94790535287628
38	64	64.921973178507	-0.921973178507033
39	96	94.5378118501347	1.46218814986532
40	0	0.748879436620154	-0.748879436620154
41	84	84.2958270405348	-0.295827040534832
42	41	42.0085542408478	-1.00855424084778
43	47	48.056213615713	-1.05621361571298
44	126	124.566905241573	1.43309475842658
45	105	107.99815339001	-2.99815339001022
46	80	80.1326478753214	-0.132647875321407
47	70	68.1187005719673	1.88129942803268
48	73	71.3958905042405	1.6041094957595

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 144 & 148.615984681125 & -4.61598468112516 \tabularnewline
2 & 103 & 104.653516001451 & -1.65351600145106 \tabularnewline
3 & 98 & 98.4539771404292 & -0.45397714042923 \tabularnewline
4 & 135 & 136.401379701215 & -1.40137970121476 \tabularnewline
5 & 61 & 60.90952464071 & 0.090475359290019 \tabularnewline
6 & 39 & 37.2109743270851 & 1.7890256729149 \tabularnewline
7 & 150 & 147.945946217015 & 2.05405378298484 \tabularnewline
8 & 5 & 2.72099686581108 & 2.27900313418892 \tabularnewline
9 & 28 & 29.4441995423157 & -1.44419954231565 \tabularnewline
10 & 84 & 85.4603581204667 & -1.46035812046672 \tabularnewline
11 & 80 & 80.1644510501631 & -0.16445105016307 \tabularnewline
12 & 130 & 131.183231068445 & -1.18323106844525 \tabularnewline
13 & 82 & 80.5158568993419 & 1.48414310065807 \tabularnewline
14 & 60 & 61.0747538338747 & -1.07475383387467 \tabularnewline
15 & 131 & 134.749902286143 & -3.74990228614276 \tabularnewline
16 & 84 & 84.0432852064716 & -0.0432852064716211 \tabularnewline
17 & 140 & 138.001605252418 & 1.9983947475817 \tabularnewline
18 & 151 & 154.769803982302 & -3.7698039823015 \tabularnewline
19 & 91 & 90.5967637421903 & 0.403236257809663 \tabularnewline
20 & 138 & 135.448018980771 & 2.55198101922894 \tabularnewline
21 & 150 & 140.893531446089 & 9.1064685539106 \tabularnewline
22 & 124 & 127.47274169288 & -3.47274169287999 \tabularnewline
23 & 119 & 122.051601471166 & -3.05160147116553 \tabularnewline
24 & 73 & 71.9795130164996 & 1.02048698350044 \tabularnewline
25 & 110 & 111.86442976006 & -1.86442976006048 \tabularnewline
26 & 123 & 124.178377658782 & -1.17837765878167 \tabularnewline
27 & 90 & 92.160824389705 & -2.16082438970496 \tabularnewline
28 & 116 & 114.64641535909 & 1.35358464090963 \tabularnewline
29 & 113 & 112.391373393008 & 0.608626606991689 \tabularnewline
30 & 56 & 54.0807916150248 & 1.91920838497518 \tabularnewline
31 & 115 & 115.760345864202 & -0.760345864202308 \tabularnewline
32 & 119 & 119.808917487989 & -0.808917487989119 \tabularnewline
33 & 129 & 125.757939195993 & 3.24206080400651 \tabularnewline
34 & 127 & 129.898102433784 & -2.89810243378403 \tabularnewline
35 & 27 & 27.8943533576387 & -0.894353357638746 \tabularnewline
36 & 175 & 167.962560725749 & 7.03743927425124 \tabularnewline
37 & 35 & 33.0520946471237 & 1.94790535287628 \tabularnewline
38 & 64 & 64.921973178507 & -0.921973178507033 \tabularnewline
39 & 96 & 94.5378118501347 & 1.46218814986532 \tabularnewline
40 & 0 & 0.748879436620154 & -0.748879436620154 \tabularnewline
41 & 84 & 84.2958270405348 & -0.295827040534832 \tabularnewline
42 & 41 & 42.0085542408478 & -1.00855424084778 \tabularnewline
43 & 47 & 48.056213615713 & -1.05621361571298 \tabularnewline
44 & 126 & 124.566905241573 & 1.43309475842658 \tabularnewline
45 & 105 & 107.99815339001 & -2.99815339001022 \tabularnewline
46 & 80 & 80.1326478753214 & -0.132647875321407 \tabularnewline
47 & 70 & 68.1187005719673 & 1.88129942803268 \tabularnewline
48 & 73 & 71.3958905042405 & 1.6041094957595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160190&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]144[/C][C]148.615984681125[/C][C]-4.61598468112516[/C][/ROW]
[ROW][C]2[/C][C]103[/C][C]104.653516001451[/C][C]-1.65351600145106[/C][/ROW]
[ROW][C]3[/C][C]98[/C][C]98.4539771404292[/C][C]-0.45397714042923[/C][/ROW]
[ROW][C]4[/C][C]135[/C][C]136.401379701215[/C][C]-1.40137970121476[/C][/ROW]
[ROW][C]5[/C][C]61[/C][C]60.90952464071[/C][C]0.090475359290019[/C][/ROW]
[ROW][C]6[/C][C]39[/C][C]37.2109743270851[/C][C]1.7890256729149[/C][/ROW]
[ROW][C]7[/C][C]150[/C][C]147.945946217015[/C][C]2.05405378298484[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]2.72099686581108[/C][C]2.27900313418892[/C][/ROW]
[ROW][C]9[/C][C]28[/C][C]29.4441995423157[/C][C]-1.44419954231565[/C][/ROW]
[ROW][C]10[/C][C]84[/C][C]85.4603581204667[/C][C]-1.46035812046672[/C][/ROW]
[ROW][C]11[/C][C]80[/C][C]80.1644510501631[/C][C]-0.16445105016307[/C][/ROW]
[ROW][C]12[/C][C]130[/C][C]131.183231068445[/C][C]-1.18323106844525[/C][/ROW]
[ROW][C]13[/C][C]82[/C][C]80.5158568993419[/C][C]1.48414310065807[/C][/ROW]
[ROW][C]14[/C][C]60[/C][C]61.0747538338747[/C][C]-1.07475383387467[/C][/ROW]
[ROW][C]15[/C][C]131[/C][C]134.749902286143[/C][C]-3.74990228614276[/C][/ROW]
[ROW][C]16[/C][C]84[/C][C]84.0432852064716[/C][C]-0.0432852064716211[/C][/ROW]
[ROW][C]17[/C][C]140[/C][C]138.001605252418[/C][C]1.9983947475817[/C][/ROW]
[ROW][C]18[/C][C]151[/C][C]154.769803982302[/C][C]-3.7698039823015[/C][/ROW]
[ROW][C]19[/C][C]91[/C][C]90.5967637421903[/C][C]0.403236257809663[/C][/ROW]
[ROW][C]20[/C][C]138[/C][C]135.448018980771[/C][C]2.55198101922894[/C][/ROW]
[ROW][C]21[/C][C]150[/C][C]140.893531446089[/C][C]9.1064685539106[/C][/ROW]
[ROW][C]22[/C][C]124[/C][C]127.47274169288[/C][C]-3.47274169287999[/C][/ROW]
[ROW][C]23[/C][C]119[/C][C]122.051601471166[/C][C]-3.05160147116553[/C][/ROW]
[ROW][C]24[/C][C]73[/C][C]71.9795130164996[/C][C]1.02048698350044[/C][/ROW]
[ROW][C]25[/C][C]110[/C][C]111.86442976006[/C][C]-1.86442976006048[/C][/ROW]
[ROW][C]26[/C][C]123[/C][C]124.178377658782[/C][C]-1.17837765878167[/C][/ROW]
[ROW][C]27[/C][C]90[/C][C]92.160824389705[/C][C]-2.16082438970496[/C][/ROW]
[ROW][C]28[/C][C]116[/C][C]114.64641535909[/C][C]1.35358464090963[/C][/ROW]
[ROW][C]29[/C][C]113[/C][C]112.391373393008[/C][C]0.608626606991689[/C][/ROW]
[ROW][C]30[/C][C]56[/C][C]54.0807916150248[/C][C]1.91920838497518[/C][/ROW]
[ROW][C]31[/C][C]115[/C][C]115.760345864202[/C][C]-0.760345864202308[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]119.808917487989[/C][C]-0.808917487989119[/C][/ROW]
[ROW][C]33[/C][C]129[/C][C]125.757939195993[/C][C]3.24206080400651[/C][/ROW]
[ROW][C]34[/C][C]127[/C][C]129.898102433784[/C][C]-2.89810243378403[/C][/ROW]
[ROW][C]35[/C][C]27[/C][C]27.8943533576387[/C][C]-0.894353357638746[/C][/ROW]
[ROW][C]36[/C][C]175[/C][C]167.962560725749[/C][C]7.03743927425124[/C][/ROW]
[ROW][C]37[/C][C]35[/C][C]33.0520946471237[/C][C]1.94790535287628[/C][/ROW]
[ROW][C]38[/C][C]64[/C][C]64.921973178507[/C][C]-0.921973178507033[/C][/ROW]
[ROW][C]39[/C][C]96[/C][C]94.5378118501347[/C][C]1.46218814986532[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.748879436620154[/C][C]-0.748879436620154[/C][/ROW]
[ROW][C]41[/C][C]84[/C][C]84.2958270405348[/C][C]-0.295827040534832[/C][/ROW]
[ROW][C]42[/C][C]41[/C][C]42.0085542408478[/C][C]-1.00855424084778[/C][/ROW]
[ROW][C]43[/C][C]47[/C][C]48.056213615713[/C][C]-1.05621361571298[/C][/ROW]
[ROW][C]44[/C][C]126[/C][C]124.566905241573[/C][C]1.43309475842658[/C][/ROW]
[ROW][C]45[/C][C]105[/C][C]107.99815339001[/C][C]-2.99815339001022[/C][/ROW]
[ROW][C]46[/C][C]80[/C][C]80.1326478753214[/C][C]-0.132647875321407[/C][/ROW]
[ROW][C]47[/C][C]70[/C][C]68.1187005719673[/C][C]1.88129942803268[/C][/ROW]
[ROW][C]48[/C][C]73[/C][C]71.3958905042405[/C][C]1.6041094957595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160190&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160190&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	144	148.615984681125	-4.61598468112516
2	103	104.653516001451	-1.65351600145106
3	98	98.4539771404292	-0.45397714042923
4	135	136.401379701215	-1.40137970121476
5	61	60.90952464071	0.090475359290019
6	39	37.2109743270851	1.7890256729149
7	150	147.945946217015	2.05405378298484
8	5	2.72099686581108	2.27900313418892
9	28	29.4441995423157	-1.44419954231565
10	84	85.4603581204667	-1.46035812046672
11	80	80.1644510501631	-0.16445105016307
12	130	131.183231068445	-1.18323106844525
13	82	80.5158568993419	1.48414310065807
14	60	61.0747538338747	-1.07475383387467
15	131	134.749902286143	-3.74990228614276
16	84	84.0432852064716	-0.0432852064716211
17	140	138.001605252418	1.9983947475817
18	151	154.769803982302	-3.7698039823015
19	91	90.5967637421903	0.403236257809663
20	138	135.448018980771	2.55198101922894
21	150	140.893531446089	9.1064685539106
22	124	127.47274169288	-3.47274169287999
23	119	122.051601471166	-3.05160147116553
24	73	71.9795130164996	1.02048698350044
25	110	111.86442976006	-1.86442976006048
26	123	124.178377658782	-1.17837765878167
27	90	92.160824389705	-2.16082438970496
28	116	114.64641535909	1.35358464090963
29	113	112.391373393008	0.608626606991689
30	56	54.0807916150248	1.91920838497518
31	115	115.760345864202	-0.760345864202308
32	119	119.808917487989	-0.808917487989119
33	129	125.757939195993	3.24206080400651
34	127	129.898102433784	-2.89810243378403
35	27	27.8943533576387	-0.894353357638746
36	175	167.962560725749	7.03743927425124
37	35	33.0520946471237	1.94790535287628
38	64	64.921973178507	-0.921973178507033
39	96	94.5378118501347	1.46218814986532
40	0	0.748879436620154	-0.748879436620154
41	84	84.2958270405348	-0.295827040534832
42	41	42.0085542408478	-1.00855424084778
43	47	48.056213615713	-1.05621361571298
44	126	124.566905241573	1.43309475842658
45	105	107.99815339001	-2.99815339001022
46	80	80.1326478753214	-0.132647875321407
47	70	68.1187005719673	1.88129942803268
48	73	71.3958905042405	1.6041094957595

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.225239398631372	0.450478797262743	0.774760601368628
11	0.116934173229176	0.233868346458353	0.883065826770824
12	0.0530271906753877	0.106054381350775	0.946972809324612
13	0.043092431958987	0.0861848639179741	0.956907568041013
14	0.0322857247061832	0.0645714494123663	0.967714275293817
15	0.0564218675707825	0.112843735141565	0.943578132429218
16	0.0282442869252096	0.0564885738504191	0.97175571307479
17	0.016036479972045	0.0320729599440901	0.983963520027955
18	0.0208408458786152	0.0416816917572303	0.979159154121385
19	0.0139195776282065	0.0278391552564131	0.986080422371793
20	0.0944244239331796	0.188848847866359	0.90557557606682
21	0.878393032823772	0.243213934352456	0.121606967176228
22	0.918607370089518	0.162785259820965	0.0813926299104823
23	0.987158207295458	0.0256835854090834	0.0128417927045417
24	0.976978421203681	0.0460431575926374	0.0230215787963187
25	0.971739217080743	0.0565215658385143	0.0282607829192571
26	0.965225944381653	0.0695481112366945	0.0347740556183473
27	0.971132077733105	0.0577358445337891	0.0288679222668946
28	0.954119379994273	0.091761240011454	0.045880620005727
29	0.938672631854101	0.122654736291798	0.061327368145899
30	0.949584293135122	0.100831413729757	0.0504157068648784
31	0.92776566689346	0.144468666213081	0.0722343331065405
32	0.949659818447875	0.10068036310425	0.0503401815521249
33	0.967167183454304	0.0656656330913912	0.0328328165456956
34	0.965522163884748	0.0689556722305042	0.0344778361152521
35	0.933819686089202	0.132360627821595	0.0661803139107976
36	0.978146112840611	0.0437077743187776	0.0218538871593888
37	0.984833860534761	0.0303322789304774	0.0151661394652387
38	0.945945978633163	0.108108042733674	0.054054021366837

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.225239398631372 & 0.450478797262743 & 0.774760601368628 \tabularnewline
11 & 0.116934173229176 & 0.233868346458353 & 0.883065826770824 \tabularnewline
12 & 0.0530271906753877 & 0.106054381350775 & 0.946972809324612 \tabularnewline
13 & 0.043092431958987 & 0.0861848639179741 & 0.956907568041013 \tabularnewline
14 & 0.0322857247061832 & 0.0645714494123663 & 0.967714275293817 \tabularnewline
15 & 0.0564218675707825 & 0.112843735141565 & 0.943578132429218 \tabularnewline
16 & 0.0282442869252096 & 0.0564885738504191 & 0.97175571307479 \tabularnewline
17 & 0.016036479972045 & 0.0320729599440901 & 0.983963520027955 \tabularnewline
18 & 0.0208408458786152 & 0.0416816917572303 & 0.979159154121385 \tabularnewline
19 & 0.0139195776282065 & 0.0278391552564131 & 0.986080422371793 \tabularnewline
20 & 0.0944244239331796 & 0.188848847866359 & 0.90557557606682 \tabularnewline
21 & 0.878393032823772 & 0.243213934352456 & 0.121606967176228 \tabularnewline
22 & 0.918607370089518 & 0.162785259820965 & 0.0813926299104823 \tabularnewline
23 & 0.987158207295458 & 0.0256835854090834 & 0.0128417927045417 \tabularnewline
24 & 0.976978421203681 & 0.0460431575926374 & 0.0230215787963187 \tabularnewline
25 & 0.971739217080743 & 0.0565215658385143 & 0.0282607829192571 \tabularnewline
26 & 0.965225944381653 & 0.0695481112366945 & 0.0347740556183473 \tabularnewline
27 & 0.971132077733105 & 0.0577358445337891 & 0.0288679222668946 \tabularnewline
28 & 0.954119379994273 & 0.091761240011454 & 0.045880620005727 \tabularnewline
29 & 0.938672631854101 & 0.122654736291798 & 0.061327368145899 \tabularnewline
30 & 0.949584293135122 & 0.100831413729757 & 0.0504157068648784 \tabularnewline
31 & 0.92776566689346 & 0.144468666213081 & 0.0722343331065405 \tabularnewline
32 & 0.949659818447875 & 0.10068036310425 & 0.0503401815521249 \tabularnewline
33 & 0.967167183454304 & 0.0656656330913912 & 0.0328328165456956 \tabularnewline
34 & 0.965522163884748 & 0.0689556722305042 & 0.0344778361152521 \tabularnewline
35 & 0.933819686089202 & 0.132360627821595 & 0.0661803139107976 \tabularnewline
36 & 0.978146112840611 & 0.0437077743187776 & 0.0218538871593888 \tabularnewline
37 & 0.984833860534761 & 0.0303322789304774 & 0.0151661394652387 \tabularnewline
38 & 0.945945978633163 & 0.108108042733674 & 0.054054021366837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160190&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]10[/C][C]0.225239398631372[/C][C]0.450478797262743[/C][C]0.774760601368628[/C][/ROW]
[ROW][C]11[/C][C]0.116934173229176[/C][C]0.233868346458353[/C][C]0.883065826770824[/C][/ROW]
[ROW][C]12[/C][C]0.0530271906753877[/C][C]0.106054381350775[/C][C]0.946972809324612[/C][/ROW]
[ROW][C]13[/C][C]0.043092431958987[/C][C]0.0861848639179741[/C][C]0.956907568041013[/C][/ROW]
[ROW][C]14[/C][C]0.0322857247061832[/C][C]0.0645714494123663[/C][C]0.967714275293817[/C][/ROW]
[ROW][C]15[/C][C]0.0564218675707825[/C][C]0.112843735141565[/C][C]0.943578132429218[/C][/ROW]
[ROW][C]16[/C][C]0.0282442869252096[/C][C]0.0564885738504191[/C][C]0.97175571307479[/C][/ROW]
[ROW][C]17[/C][C]0.016036479972045[/C][C]0.0320729599440901[/C][C]0.983963520027955[/C][/ROW]
[ROW][C]18[/C][C]0.0208408458786152[/C][C]0.0416816917572303[/C][C]0.979159154121385[/C][/ROW]
[ROW][C]19[/C][C]0.0139195776282065[/C][C]0.0278391552564131[/C][C]0.986080422371793[/C][/ROW]
[ROW][C]20[/C][C]0.0944244239331796[/C][C]0.188848847866359[/C][C]0.90557557606682[/C][/ROW]
[ROW][C]21[/C][C]0.878393032823772[/C][C]0.243213934352456[/C][C]0.121606967176228[/C][/ROW]
[ROW][C]22[/C][C]0.918607370089518[/C][C]0.162785259820965[/C][C]0.0813926299104823[/C][/ROW]
[ROW][C]23[/C][C]0.987158207295458[/C][C]0.0256835854090834[/C][C]0.0128417927045417[/C][/ROW]
[ROW][C]24[/C][C]0.976978421203681[/C][C]0.0460431575926374[/C][C]0.0230215787963187[/C][/ROW]
[ROW][C]25[/C][C]0.971739217080743[/C][C]0.0565215658385143[/C][C]0.0282607829192571[/C][/ROW]
[ROW][C]26[/C][C]0.965225944381653[/C][C]0.0695481112366945[/C][C]0.0347740556183473[/C][/ROW]
[ROW][C]27[/C][C]0.971132077733105[/C][C]0.0577358445337891[/C][C]0.0288679222668946[/C][/ROW]
[ROW][C]28[/C][C]0.954119379994273[/C][C]0.091761240011454[/C][C]0.045880620005727[/C][/ROW]
[ROW][C]29[/C][C]0.938672631854101[/C][C]0.122654736291798[/C][C]0.061327368145899[/C][/ROW]
[ROW][C]30[/C][C]0.949584293135122[/C][C]0.100831413729757[/C][C]0.0504157068648784[/C][/ROW]
[ROW][C]31[/C][C]0.92776566689346[/C][C]0.144468666213081[/C][C]0.0722343331065405[/C][/ROW]
[ROW][C]32[/C][C]0.949659818447875[/C][C]0.10068036310425[/C][C]0.0503401815521249[/C][/ROW]
[ROW][C]33[/C][C]0.967167183454304[/C][C]0.0656656330913912[/C][C]0.0328328165456956[/C][/ROW]
[ROW][C]34[/C][C]0.965522163884748[/C][C]0.0689556722305042[/C][C]0.0344778361152521[/C][/ROW]
[ROW][C]35[/C][C]0.933819686089202[/C][C]0.132360627821595[/C][C]0.0661803139107976[/C][/ROW]
[ROW][C]36[/C][C]0.978146112840611[/C][C]0.0437077743187776[/C][C]0.0218538871593888[/C][/ROW]
[ROW][C]37[/C][C]0.984833860534761[/C][C]0.0303322789304774[/C][C]0.0151661394652387[/C][/ROW]
[ROW][C]38[/C][C]0.945945978633163[/C][C]0.108108042733674[/C][C]0.054054021366837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160190&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160190&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.225239398631372	0.450478797262743	0.774760601368628
11	0.116934173229176	0.233868346458353	0.883065826770824
12	0.0530271906753877	0.106054381350775	0.946972809324612
13	0.043092431958987	0.0861848639179741	0.956907568041013
14	0.0322857247061832	0.0645714494123663	0.967714275293817
15	0.0564218675707825	0.112843735141565	0.943578132429218
16	0.0282442869252096	0.0564885738504191	0.97175571307479
17	0.016036479972045	0.0320729599440901	0.983963520027955
18	0.0208408458786152	0.0416816917572303	0.979159154121385
19	0.0139195776282065	0.0278391552564131	0.986080422371793
20	0.0944244239331796	0.188848847866359	0.90557557606682
21	0.878393032823772	0.243213934352456	0.121606967176228
22	0.918607370089518	0.162785259820965	0.0813926299104823
23	0.987158207295458	0.0256835854090834	0.0128417927045417
24	0.976978421203681	0.0460431575926374	0.0230215787963187
25	0.971739217080743	0.0565215658385143	0.0282607829192571
26	0.965225944381653	0.0695481112366945	0.0347740556183473
27	0.971132077733105	0.0577358445337891	0.0288679222668946
28	0.954119379994273	0.091761240011454	0.045880620005727
29	0.938672631854101	0.122654736291798	0.061327368145899
30	0.949584293135122	0.100831413729757	0.0504157068648784
31	0.92776566689346	0.144468666213081	0.0722343331065405
32	0.949659818447875	0.10068036310425	0.0503401815521249
33	0.967167183454304	0.0656656330913912	0.0328328165456956
34	0.965522163884748	0.0689556722305042	0.0344778361152521
35	0.933819686089202	0.132360627821595	0.0661803139107976
36	0.978146112840611	0.0437077743187776	0.0218538871593888
37	0.984833860534761	0.0303322789304774	0.0151661394652387
38	0.945945978633163	0.108108042733674	0.054054021366837

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	7	0.241379310344828	NOK
10% type I error level	16	0.551724137931034	NOK

\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 & 7 & 0.241379310344828 & NOK \tabularnewline
10% type I error level & 16 & 0.551724137931034 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160190&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]7[/C][C]0.241379310344828[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.551724137931034[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160190&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160190&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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	7	0.241379310344828	NOK
10% type I error level	16	0.551724137931034	NOK

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code