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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 computationFri, 19 Nov 2010 14:51:10 +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/19/t1290178166rq4ufcpe0ecnc2u.htm/, Retrieved Fri, 29 Mar 2024 09:49:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98016, Retrieved Fri, 29 Mar 2024 09:49:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-11-19 14:51:10] [1638ccfec791c539017705f3e680eb33] [Current]
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Dataseries X:
8	78	284	9.100000381	109
9.300000191	68	433	8.699999809	144
7.5	70	739	7.199999809	113
8.899999619	96	1792	8.899999619	97
10.19999981	74	477	8.300000191	206
8.300000191	111	362	10.89999962	124
8.800000191	77	671	10	152
8.800000191	168	636	9.100000381	162
10.69999981	82	329	8.699999809	150
11.69999981	89	634	7.599999905	134
8.5	149	631	10.80000019	292
8.300000191	60	257	9.5	108
8.199999809	96	284	8.800000191	111
7.900000095	83	603	9.5	182
10.30000019	130	686	8.699999809	129
7.400000095	145	345	11.19999981	158
9.600000381	112	1357	9.699999809	186
9.300000191	131	544	9.600000381	177
10.60000038	80	205	9.100000381	127
9.699999809	130	1264	9.199999809	179
11.60000038	140	688	8.300000191	80
8.100000381	154	354	8.399999619	103
9.800000191	118	1632	9.399999619	101
7.400000095	94	348	9.800000191	117
9.399999619	119	370	10.39999962	88
11.19999981	153	648	9.899999619	78
9.100000381	116	366	9.199999809	102
10.5	97	540	10.30000019	95
11.89999962	176	680	8.899999619	80
8.399999619	75	345	9.600000381	92
5	134	525	10.30000019	126
9.800000191	161	870	10.39999962	108
9.800000191	111	669	9.699999809	77
10.80000019	114	452	9.600000381	60
10.10000038	142	430	10.69999981	71
10.89999962	238	822	10.30000019	86
9.199999809	78	190	10.69999981	93
8.300000191	196	867	9.600000381	106
7.300000191	125	969	10.5	162
9.399999619	82	499	7.699999809	95
9.399999619	125	925	10.19999981	91
9.800000191	129	353	9.899999619	52
3.599999905	84	288	8.399999619	110
8.399999619	183	718	10.39999962	69
10.80000019	119	540	9.199999809	57
10.10000038	180	668	13	106
9	82	347	8.800000191	40
10	71	345	9.199999809	50
11.30000019	118	463	7.800000191	35
11.30000019	121	728	8.199999809	86
12.80000019	68	383	7.400000095	57
10	112	316	10.39999962	57
6.699999809	109	388	8.899999619	94




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98016&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98016&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98016&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'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
X1[t] = + 12.2662551669423 + 0.00739161503025132X2[t] + 0.0005837156555437X3[t] -0.330230236582663X4[t] -0.00946288487507264X5[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X1[t] =  +  12.2662551669423 +  0.00739161503025132X2[t] +  0.0005837156555437X3[t] -0.330230236582663X4[t] -0.00946288487507264X5[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98016&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X1[t] =  +  12.2662551669423 +  0.00739161503025132X2[t] +  0.0005837156555437X3[t] -0.330230236582663X4[t] -0.00946288487507264X5[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98016&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98016&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
X1[t] = + 12.2662551669423 + 0.00739161503025132X2[t] + 0.0005837156555437X3[t] -0.330230236582663X4[t] -0.00946288487507264X5[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.26625516694232.0201476.07200
X20.007391615030251320.0069341.06610.2917340.145867
X30.00058371565554370.0007220.80860.4227530.211377
X4-0.3302302365826630.234552-1.40790.1655980.082799
X5-0.009462884875072640.004887-1.93640.0587170.029358

\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) & 12.2662551669423 & 2.020147 & 6.072 & 0 & 0 \tabularnewline
X2 & 0.00739161503025132 & 0.006934 & 1.0661 & 0.291734 & 0.145867 \tabularnewline
X3 & 0.0005837156555437 & 0.000722 & 0.8086 & 0.422753 & 0.211377 \tabularnewline
X4 & -0.330230236582663 & 0.234552 & -1.4079 & 0.165598 & 0.082799 \tabularnewline
X5 & -0.00946288487507264 & 0.004887 & -1.9364 & 0.058717 & 0.029358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98016&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]12.2662551669423[/C][C]2.020147[/C][C]6.072[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X2[/C][C]0.00739161503025132[/C][C]0.006934[/C][C]1.0661[/C][C]0.291734[/C][C]0.145867[/C][/ROW]
[ROW][C]X3[/C][C]0.0005837156555437[/C][C]0.000722[/C][C]0.8086[/C][C]0.422753[/C][C]0.211377[/C][/ROW]
[ROW][C]X4[/C][C]-0.330230236582663[/C][C]0.234552[/C][C]-1.4079[/C][C]0.165598[/C][C]0.082799[/C][/ROW]
[ROW][C]X5[/C][C]-0.00946288487507264[/C][C]0.004887[/C][C]-1.9364[/C][C]0.058717[/C][C]0.029358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98016&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98016&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)12.26625516694232.0201476.07200
X20.007391615030251320.0069341.06610.2917340.145867
X30.00058371565554370.0007220.80860.4227530.211377
X4-0.3302302365826630.234552-1.40790.1655980.082799
X5-0.009462884875072640.004887-1.93640.0587170.029358







Multiple Linear Regression - Regression Statistics
Multiple R0.379083047311434
R-squared0.143703956758923
Adjusted R-squared0.0723459531555002
F-TEST (value)2.01384497186283
F-TEST (DF numerator)4
F-TEST (DF denominator)48
p-value0.107455004361819
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.60126238508799
Sum Squared Residuals123.073978843088

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.379083047311434 \tabularnewline
R-squared & 0.143703956758923 \tabularnewline
Adjusted R-squared & 0.0723459531555002 \tabularnewline
F-TEST (value) & 2.01384497186283 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.107455004361819 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.60126238508799 \tabularnewline
Sum Squared Residuals & 123.073978843088 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98016&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.379083047311434[/C][/ROW]
[ROW][C]R-squared[/C][C]0.143703956758923[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0723459531555002[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.01384497186283[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.107455004361819[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.60126238508799[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]123.073978843088[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98016&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98016&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 R0.379083047311434
R-squared0.143703956758923
Adjusted R-squared0.0723459531555002
F-TEST (value)2.01384497186283
F-TEST (DF numerator)4
F-TEST (DF denominator)48
p-value0.107455004361819
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.60126238508799
Sum Squared Residuals123.073978843088







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
188.97202665537339-0.97202665537339
29.3000001918.785975450644120.51402474035588
37.59.76807045730224-2.26807045730224
48.89999961910.1649198519307-1.26492023293067
510.199999818.401401735900161.79859807409984
68.3000001918.52512232483443-0.225122133834433
78.8000001918.486421862303760.313578328696236
88.8000001919.34120702046856-0.541206829468555
910.699999818.771974323640661.92802548635934
1011.699999819.516408290333232.18359151966677
118.57.406281383740071.09371861625993
128.3000001918.70058817818893-0.400587987188927
138.1999998099.18521908988633-0.985219280886327
147.9000000958.37230745994745-0.472307364947453
1510.300000199.533878916498350.766121273501653
167.4000000958.34570685024772-0.945706755247721
179.6000003818.922888376361840.677112004638156
189.3000001918.706957032621820.593043158378185
1910.600000388.770364420894651.82963595910535
209.6999998099.233007203357640.466992605642356
2111.6000003810.20473582547561.39526455452438
228.1000003819.8625882200543-1.7625878390543
239.80000019110.0311742199176-0.231174028917586
247.4000000958.82078611594752-1.42078602094752
259.3999996199.094703944114740.305295674885261
2611.199999819.768035774756721.43196403524328
279.1000003819.33399006963647-0.233989688636476
2810.58.998102716193161.50189728380684
2911.8999996210.26802628826241.63197333173758
308.3999996198.98121238985572-0.581212770855718
3158.96948730635205-3.96948730635205
329.8000001919.50775190565570.292248285344308
339.8000001919.545354841700440.254645349299557
3410.800000199.634755267181021.16524492281898
3510.100000389.361533938300830.738466441699168
3610.8999996210.29009421419760.609905405802424
379.1999998098.540195351782660.659804457217337
388.30000019110.0478169924589-1.74781680145892
397.3000001918.7554226820658-1.45542249106579
409.3999996199.72189488979475-0.321895270794744
419.3999996199.50067315307057-0.10067353407057
429.8000001919.664475902397190.135524288602814
433.5999999059.24040974054532-5.64040983554532
448.3999996199.95069516680641-1.55069554780641
4510.800000199.88356125817010.9164389318299
4610.100000388.690609057958371.40939132204163
4799.79037539189222-0.790375391892218
48109.481179378012530.518820621987475
4911.3000001910.50172920998240.79827098001764
5011.3000001910.06388960667841.23611058332162
5112.8000001910.00935986510992.79064032489013
52109.304791424630870.695208575369128
536.6999998099.4698627215658-2.7698629125658

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8 & 8.97202665537339 & -0.97202665537339 \tabularnewline
2 & 9.300000191 & 8.78597545064412 & 0.51402474035588 \tabularnewline
3 & 7.5 & 9.76807045730224 & -2.26807045730224 \tabularnewline
4 & 8.899999619 & 10.1649198519307 & -1.26492023293067 \tabularnewline
5 & 10.19999981 & 8.40140173590016 & 1.79859807409984 \tabularnewline
6 & 8.300000191 & 8.52512232483443 & -0.225122133834433 \tabularnewline
7 & 8.800000191 & 8.48642186230376 & 0.313578328696236 \tabularnewline
8 & 8.800000191 & 9.34120702046856 & -0.541206829468555 \tabularnewline
9 & 10.69999981 & 8.77197432364066 & 1.92802548635934 \tabularnewline
10 & 11.69999981 & 9.51640829033323 & 2.18359151966677 \tabularnewline
11 & 8.5 & 7.40628138374007 & 1.09371861625993 \tabularnewline
12 & 8.300000191 & 8.70058817818893 & -0.400587987188927 \tabularnewline
13 & 8.199999809 & 9.18521908988633 & -0.985219280886327 \tabularnewline
14 & 7.900000095 & 8.37230745994745 & -0.472307364947453 \tabularnewline
15 & 10.30000019 & 9.53387891649835 & 0.766121273501653 \tabularnewline
16 & 7.400000095 & 8.34570685024772 & -0.945706755247721 \tabularnewline
17 & 9.600000381 & 8.92288837636184 & 0.677112004638156 \tabularnewline
18 & 9.300000191 & 8.70695703262182 & 0.593043158378185 \tabularnewline
19 & 10.60000038 & 8.77036442089465 & 1.82963595910535 \tabularnewline
20 & 9.699999809 & 9.23300720335764 & 0.466992605642356 \tabularnewline
21 & 11.60000038 & 10.2047358254756 & 1.39526455452438 \tabularnewline
22 & 8.100000381 & 9.8625882200543 & -1.7625878390543 \tabularnewline
23 & 9.800000191 & 10.0311742199176 & -0.231174028917586 \tabularnewline
24 & 7.400000095 & 8.82078611594752 & -1.42078602094752 \tabularnewline
25 & 9.399999619 & 9.09470394411474 & 0.305295674885261 \tabularnewline
26 & 11.19999981 & 9.76803577475672 & 1.43196403524328 \tabularnewline
27 & 9.100000381 & 9.33399006963647 & -0.233989688636476 \tabularnewline
28 & 10.5 & 8.99810271619316 & 1.50189728380684 \tabularnewline
29 & 11.89999962 & 10.2680262882624 & 1.63197333173758 \tabularnewline
30 & 8.399999619 & 8.98121238985572 & -0.581212770855718 \tabularnewline
31 & 5 & 8.96948730635205 & -3.96948730635205 \tabularnewline
32 & 9.800000191 & 9.5077519056557 & 0.292248285344308 \tabularnewline
33 & 9.800000191 & 9.54535484170044 & 0.254645349299557 \tabularnewline
34 & 10.80000019 & 9.63475526718102 & 1.16524492281898 \tabularnewline
35 & 10.10000038 & 9.36153393830083 & 0.738466441699168 \tabularnewline
36 & 10.89999962 & 10.2900942141976 & 0.609905405802424 \tabularnewline
37 & 9.199999809 & 8.54019535178266 & 0.659804457217337 \tabularnewline
38 & 8.300000191 & 10.0478169924589 & -1.74781680145892 \tabularnewline
39 & 7.300000191 & 8.7554226820658 & -1.45542249106579 \tabularnewline
40 & 9.399999619 & 9.72189488979475 & -0.321895270794744 \tabularnewline
41 & 9.399999619 & 9.50067315307057 & -0.10067353407057 \tabularnewline
42 & 9.800000191 & 9.66447590239719 & 0.135524288602814 \tabularnewline
43 & 3.599999905 & 9.24040974054532 & -5.64040983554532 \tabularnewline
44 & 8.399999619 & 9.95069516680641 & -1.55069554780641 \tabularnewline
45 & 10.80000019 & 9.8835612581701 & 0.9164389318299 \tabularnewline
46 & 10.10000038 & 8.69060905795837 & 1.40939132204163 \tabularnewline
47 & 9 & 9.79037539189222 & -0.790375391892218 \tabularnewline
48 & 10 & 9.48117937801253 & 0.518820621987475 \tabularnewline
49 & 11.30000019 & 10.5017292099824 & 0.79827098001764 \tabularnewline
50 & 11.30000019 & 10.0638896066784 & 1.23611058332162 \tabularnewline
51 & 12.80000019 & 10.0093598651099 & 2.79064032489013 \tabularnewline
52 & 10 & 9.30479142463087 & 0.695208575369128 \tabularnewline
53 & 6.699999809 & 9.4698627215658 & -2.7698629125658 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98016&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]8[/C][C]8.97202665537339[/C][C]-0.97202665537339[/C][/ROW]
[ROW][C]2[/C][C]9.300000191[/C][C]8.78597545064412[/C][C]0.51402474035588[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]9.76807045730224[/C][C]-2.26807045730224[/C][/ROW]
[ROW][C]4[/C][C]8.899999619[/C][C]10.1649198519307[/C][C]-1.26492023293067[/C][/ROW]
[ROW][C]5[/C][C]10.19999981[/C][C]8.40140173590016[/C][C]1.79859807409984[/C][/ROW]
[ROW][C]6[/C][C]8.300000191[/C][C]8.52512232483443[/C][C]-0.225122133834433[/C][/ROW]
[ROW][C]7[/C][C]8.800000191[/C][C]8.48642186230376[/C][C]0.313578328696236[/C][/ROW]
[ROW][C]8[/C][C]8.800000191[/C][C]9.34120702046856[/C][C]-0.541206829468555[/C][/ROW]
[ROW][C]9[/C][C]10.69999981[/C][C]8.77197432364066[/C][C]1.92802548635934[/C][/ROW]
[ROW][C]10[/C][C]11.69999981[/C][C]9.51640829033323[/C][C]2.18359151966677[/C][/ROW]
[ROW][C]11[/C][C]8.5[/C][C]7.40628138374007[/C][C]1.09371861625993[/C][/ROW]
[ROW][C]12[/C][C]8.300000191[/C][C]8.70058817818893[/C][C]-0.400587987188927[/C][/ROW]
[ROW][C]13[/C][C]8.199999809[/C][C]9.18521908988633[/C][C]-0.985219280886327[/C][/ROW]
[ROW][C]14[/C][C]7.900000095[/C][C]8.37230745994745[/C][C]-0.472307364947453[/C][/ROW]
[ROW][C]15[/C][C]10.30000019[/C][C]9.53387891649835[/C][C]0.766121273501653[/C][/ROW]
[ROW][C]16[/C][C]7.400000095[/C][C]8.34570685024772[/C][C]-0.945706755247721[/C][/ROW]
[ROW][C]17[/C][C]9.600000381[/C][C]8.92288837636184[/C][C]0.677112004638156[/C][/ROW]
[ROW][C]18[/C][C]9.300000191[/C][C]8.70695703262182[/C][C]0.593043158378185[/C][/ROW]
[ROW][C]19[/C][C]10.60000038[/C][C]8.77036442089465[/C][C]1.82963595910535[/C][/ROW]
[ROW][C]20[/C][C]9.699999809[/C][C]9.23300720335764[/C][C]0.466992605642356[/C][/ROW]
[ROW][C]21[/C][C]11.60000038[/C][C]10.2047358254756[/C][C]1.39526455452438[/C][/ROW]
[ROW][C]22[/C][C]8.100000381[/C][C]9.8625882200543[/C][C]-1.7625878390543[/C][/ROW]
[ROW][C]23[/C][C]9.800000191[/C][C]10.0311742199176[/C][C]-0.231174028917586[/C][/ROW]
[ROW][C]24[/C][C]7.400000095[/C][C]8.82078611594752[/C][C]-1.42078602094752[/C][/ROW]
[ROW][C]25[/C][C]9.399999619[/C][C]9.09470394411474[/C][C]0.305295674885261[/C][/ROW]
[ROW][C]26[/C][C]11.19999981[/C][C]9.76803577475672[/C][C]1.43196403524328[/C][/ROW]
[ROW][C]27[/C][C]9.100000381[/C][C]9.33399006963647[/C][C]-0.233989688636476[/C][/ROW]
[ROW][C]28[/C][C]10.5[/C][C]8.99810271619316[/C][C]1.50189728380684[/C][/ROW]
[ROW][C]29[/C][C]11.89999962[/C][C]10.2680262882624[/C][C]1.63197333173758[/C][/ROW]
[ROW][C]30[/C][C]8.399999619[/C][C]8.98121238985572[/C][C]-0.581212770855718[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]8.96948730635205[/C][C]-3.96948730635205[/C][/ROW]
[ROW][C]32[/C][C]9.800000191[/C][C]9.5077519056557[/C][C]0.292248285344308[/C][/ROW]
[ROW][C]33[/C][C]9.800000191[/C][C]9.54535484170044[/C][C]0.254645349299557[/C][/ROW]
[ROW][C]34[/C][C]10.80000019[/C][C]9.63475526718102[/C][C]1.16524492281898[/C][/ROW]
[ROW][C]35[/C][C]10.10000038[/C][C]9.36153393830083[/C][C]0.738466441699168[/C][/ROW]
[ROW][C]36[/C][C]10.89999962[/C][C]10.2900942141976[/C][C]0.609905405802424[/C][/ROW]
[ROW][C]37[/C][C]9.199999809[/C][C]8.54019535178266[/C][C]0.659804457217337[/C][/ROW]
[ROW][C]38[/C][C]8.300000191[/C][C]10.0478169924589[/C][C]-1.74781680145892[/C][/ROW]
[ROW][C]39[/C][C]7.300000191[/C][C]8.7554226820658[/C][C]-1.45542249106579[/C][/ROW]
[ROW][C]40[/C][C]9.399999619[/C][C]9.72189488979475[/C][C]-0.321895270794744[/C][/ROW]
[ROW][C]41[/C][C]9.399999619[/C][C]9.50067315307057[/C][C]-0.10067353407057[/C][/ROW]
[ROW][C]42[/C][C]9.800000191[/C][C]9.66447590239719[/C][C]0.135524288602814[/C][/ROW]
[ROW][C]43[/C][C]3.599999905[/C][C]9.24040974054532[/C][C]-5.64040983554532[/C][/ROW]
[ROW][C]44[/C][C]8.399999619[/C][C]9.95069516680641[/C][C]-1.55069554780641[/C][/ROW]
[ROW][C]45[/C][C]10.80000019[/C][C]9.8835612581701[/C][C]0.9164389318299[/C][/ROW]
[ROW][C]46[/C][C]10.10000038[/C][C]8.69060905795837[/C][C]1.40939132204163[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]9.79037539189222[/C][C]-0.790375391892218[/C][/ROW]
[ROW][C]48[/C][C]10[/C][C]9.48117937801253[/C][C]0.518820621987475[/C][/ROW]
[ROW][C]49[/C][C]11.30000019[/C][C]10.5017292099824[/C][C]0.79827098001764[/C][/ROW]
[ROW][C]50[/C][C]11.30000019[/C][C]10.0638896066784[/C][C]1.23611058332162[/C][/ROW]
[ROW][C]51[/C][C]12.80000019[/C][C]10.0093598651099[/C][C]2.79064032489013[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]9.30479142463087[/C][C]0.695208575369128[/C][/ROW]
[ROW][C]53[/C][C]6.699999809[/C][C]9.4698627215658[/C][C]-2.7698629125658[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98016&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98016&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
188.97202665537339-0.97202665537339
29.3000001918.785975450644120.51402474035588
37.59.76807045730224-2.26807045730224
48.89999961910.1649198519307-1.26492023293067
510.199999818.401401735900161.79859807409984
68.3000001918.52512232483443-0.225122133834433
78.8000001918.486421862303760.313578328696236
88.8000001919.34120702046856-0.541206829468555
910.699999818.771974323640661.92802548635934
1011.699999819.516408290333232.18359151966677
118.57.406281383740071.09371861625993
128.3000001918.70058817818893-0.400587987188927
138.1999998099.18521908988633-0.985219280886327
147.9000000958.37230745994745-0.472307364947453
1510.300000199.533878916498350.766121273501653
167.4000000958.34570685024772-0.945706755247721
179.6000003818.922888376361840.677112004638156
189.3000001918.706957032621820.593043158378185
1910.600000388.770364420894651.82963595910535
209.6999998099.233007203357640.466992605642356
2111.6000003810.20473582547561.39526455452438
228.1000003819.8625882200543-1.7625878390543
239.80000019110.0311742199176-0.231174028917586
247.4000000958.82078611594752-1.42078602094752
259.3999996199.094703944114740.305295674885261
2611.199999819.768035774756721.43196403524328
279.1000003819.33399006963647-0.233989688636476
2810.58.998102716193161.50189728380684
2911.8999996210.26802628826241.63197333173758
308.3999996198.98121238985572-0.581212770855718
3158.96948730635205-3.96948730635205
329.8000001919.50775190565570.292248285344308
339.8000001919.545354841700440.254645349299557
3410.800000199.634755267181021.16524492281898
3510.100000389.361533938300830.738466441699168
3610.8999996210.29009421419760.609905405802424
379.1999998098.540195351782660.659804457217337
388.30000019110.0478169924589-1.74781680145892
397.3000001918.7554226820658-1.45542249106579
409.3999996199.72189488979475-0.321895270794744
419.3999996199.50067315307057-0.10067353407057
429.8000001919.664475902397190.135524288602814
433.5999999059.24040974054532-5.64040983554532
448.3999996199.95069516680641-1.55069554780641
4510.800000199.88356125817010.9164389318299
4610.100000388.690609057958371.40939132204163
4799.79037539189222-0.790375391892218
48109.481179378012530.518820621987475
4911.3000001910.50172920998240.79827098001764
5011.3000001910.06388960667841.23611058332162
5112.8000001910.00935986510992.79064032489013
52109.304791424630870.695208575369128
536.6999998099.4698627215658-2.7698629125658







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.04735538259296880.09471076518593760.952644617407031
90.1843188607871970.3686377215743950.815681139212803
100.4565423210595150.913084642119030.543457678940485
110.4468006527855770.8936013055711530.553199347214423
120.3320828837338710.6641657674677430.667917116266129
130.2480006813609650.496001362721930.751999318639035
140.2067254261810440.4134508523620880.793274573818956
150.1787992297512280.3575984595024570.821200770248772
160.1231539595203480.2463079190406960.876846040479652
170.08844398605025940.1768879721005190.91155601394974
180.07078760711941030.1415752142388210.92921239288059
190.139207689781130.2784153795622610.86079231021887
200.1379167099626150.275833419925230.862083290037385
210.1816577204684940.3633154409369880.818342279531506
220.19592613711280.3918522742255990.8040738628872
230.1823450432198560.3646900864397110.817654956780144
240.1532096058060060.3064192116120130.846790394193994
250.1346999575841580.2693999151683160.865300042415842
260.1656925845349050.331385169069810.834307415465095
270.1439309533882270.2878619067764530.856069046611773
280.1686717950720840.3373435901441680.831328204927916
290.2204695707338620.4409391414677240.779530429266138
300.165356686343190.3307133726863810.83464331365681
310.3913148536245480.7826297072490970.608685146375452
320.3170615747521590.6341231495043170.682938425247841
330.2508797861470440.5017595722940870.749120213852956
340.2114003696876960.4228007393753920.788599630312304
350.1651846598740560.3303693197481120.834815340125944
360.1590967008547520.3181934017095040.840903299145248
370.1588643971845530.3177287943691070.841135602815447
380.1312961789373040.2625923578746080.868703821062696
390.09541634905518750.1908326981103750.904583650944813
400.07491764678453920.1498352935690780.92508235321546
410.1249321199989850.2498642399979710.875067880001015
420.0957703763748790.1915407527497580.904229623625121
430.3126827732759410.6253655465518830.687317226724059
440.3332874182818790.6665748365637580.666712581718121
450.2117619495536250.4235238991072510.788238050446375

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0473553825929688 & 0.0947107651859376 & 0.952644617407031 \tabularnewline
9 & 0.184318860787197 & 0.368637721574395 & 0.815681139212803 \tabularnewline
10 & 0.456542321059515 & 0.91308464211903 & 0.543457678940485 \tabularnewline
11 & 0.446800652785577 & 0.893601305571153 & 0.553199347214423 \tabularnewline
12 & 0.332082883733871 & 0.664165767467743 & 0.667917116266129 \tabularnewline
13 & 0.248000681360965 & 0.49600136272193 & 0.751999318639035 \tabularnewline
14 & 0.206725426181044 & 0.413450852362088 & 0.793274573818956 \tabularnewline
15 & 0.178799229751228 & 0.357598459502457 & 0.821200770248772 \tabularnewline
16 & 0.123153959520348 & 0.246307919040696 & 0.876846040479652 \tabularnewline
17 & 0.0884439860502594 & 0.176887972100519 & 0.91155601394974 \tabularnewline
18 & 0.0707876071194103 & 0.141575214238821 & 0.92921239288059 \tabularnewline
19 & 0.13920768978113 & 0.278415379562261 & 0.86079231021887 \tabularnewline
20 & 0.137916709962615 & 0.27583341992523 & 0.862083290037385 \tabularnewline
21 & 0.181657720468494 & 0.363315440936988 & 0.818342279531506 \tabularnewline
22 & 0.1959261371128 & 0.391852274225599 & 0.8040738628872 \tabularnewline
23 & 0.182345043219856 & 0.364690086439711 & 0.817654956780144 \tabularnewline
24 & 0.153209605806006 & 0.306419211612013 & 0.846790394193994 \tabularnewline
25 & 0.134699957584158 & 0.269399915168316 & 0.865300042415842 \tabularnewline
26 & 0.165692584534905 & 0.33138516906981 & 0.834307415465095 \tabularnewline
27 & 0.143930953388227 & 0.287861906776453 & 0.856069046611773 \tabularnewline
28 & 0.168671795072084 & 0.337343590144168 & 0.831328204927916 \tabularnewline
29 & 0.220469570733862 & 0.440939141467724 & 0.779530429266138 \tabularnewline
30 & 0.16535668634319 & 0.330713372686381 & 0.83464331365681 \tabularnewline
31 & 0.391314853624548 & 0.782629707249097 & 0.608685146375452 \tabularnewline
32 & 0.317061574752159 & 0.634123149504317 & 0.682938425247841 \tabularnewline
33 & 0.250879786147044 & 0.501759572294087 & 0.749120213852956 \tabularnewline
34 & 0.211400369687696 & 0.422800739375392 & 0.788599630312304 \tabularnewline
35 & 0.165184659874056 & 0.330369319748112 & 0.834815340125944 \tabularnewline
36 & 0.159096700854752 & 0.318193401709504 & 0.840903299145248 \tabularnewline
37 & 0.158864397184553 & 0.317728794369107 & 0.841135602815447 \tabularnewline
38 & 0.131296178937304 & 0.262592357874608 & 0.868703821062696 \tabularnewline
39 & 0.0954163490551875 & 0.190832698110375 & 0.904583650944813 \tabularnewline
40 & 0.0749176467845392 & 0.149835293569078 & 0.92508235321546 \tabularnewline
41 & 0.124932119998985 & 0.249864239997971 & 0.875067880001015 \tabularnewline
42 & 0.095770376374879 & 0.191540752749758 & 0.904229623625121 \tabularnewline
43 & 0.312682773275941 & 0.625365546551883 & 0.687317226724059 \tabularnewline
44 & 0.333287418281879 & 0.666574836563758 & 0.666712581718121 \tabularnewline
45 & 0.211761949553625 & 0.423523899107251 & 0.788238050446375 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98016&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]8[/C][C]0.0473553825929688[/C][C]0.0947107651859376[/C][C]0.952644617407031[/C][/ROW]
[ROW][C]9[/C][C]0.184318860787197[/C][C]0.368637721574395[/C][C]0.815681139212803[/C][/ROW]
[ROW][C]10[/C][C]0.456542321059515[/C][C]0.91308464211903[/C][C]0.543457678940485[/C][/ROW]
[ROW][C]11[/C][C]0.446800652785577[/C][C]0.893601305571153[/C][C]0.553199347214423[/C][/ROW]
[ROW][C]12[/C][C]0.332082883733871[/C][C]0.664165767467743[/C][C]0.667917116266129[/C][/ROW]
[ROW][C]13[/C][C]0.248000681360965[/C][C]0.49600136272193[/C][C]0.751999318639035[/C][/ROW]
[ROW][C]14[/C][C]0.206725426181044[/C][C]0.413450852362088[/C][C]0.793274573818956[/C][/ROW]
[ROW][C]15[/C][C]0.178799229751228[/C][C]0.357598459502457[/C][C]0.821200770248772[/C][/ROW]
[ROW][C]16[/C][C]0.123153959520348[/C][C]0.246307919040696[/C][C]0.876846040479652[/C][/ROW]
[ROW][C]17[/C][C]0.0884439860502594[/C][C]0.176887972100519[/C][C]0.91155601394974[/C][/ROW]
[ROW][C]18[/C][C]0.0707876071194103[/C][C]0.141575214238821[/C][C]0.92921239288059[/C][/ROW]
[ROW][C]19[/C][C]0.13920768978113[/C][C]0.278415379562261[/C][C]0.86079231021887[/C][/ROW]
[ROW][C]20[/C][C]0.137916709962615[/C][C]0.27583341992523[/C][C]0.862083290037385[/C][/ROW]
[ROW][C]21[/C][C]0.181657720468494[/C][C]0.363315440936988[/C][C]0.818342279531506[/C][/ROW]
[ROW][C]22[/C][C]0.1959261371128[/C][C]0.391852274225599[/C][C]0.8040738628872[/C][/ROW]
[ROW][C]23[/C][C]0.182345043219856[/C][C]0.364690086439711[/C][C]0.817654956780144[/C][/ROW]
[ROW][C]24[/C][C]0.153209605806006[/C][C]0.306419211612013[/C][C]0.846790394193994[/C][/ROW]
[ROW][C]25[/C][C]0.134699957584158[/C][C]0.269399915168316[/C][C]0.865300042415842[/C][/ROW]
[ROW][C]26[/C][C]0.165692584534905[/C][C]0.33138516906981[/C][C]0.834307415465095[/C][/ROW]
[ROW][C]27[/C][C]0.143930953388227[/C][C]0.287861906776453[/C][C]0.856069046611773[/C][/ROW]
[ROW][C]28[/C][C]0.168671795072084[/C][C]0.337343590144168[/C][C]0.831328204927916[/C][/ROW]
[ROW][C]29[/C][C]0.220469570733862[/C][C]0.440939141467724[/C][C]0.779530429266138[/C][/ROW]
[ROW][C]30[/C][C]0.16535668634319[/C][C]0.330713372686381[/C][C]0.83464331365681[/C][/ROW]
[ROW][C]31[/C][C]0.391314853624548[/C][C]0.782629707249097[/C][C]0.608685146375452[/C][/ROW]
[ROW][C]32[/C][C]0.317061574752159[/C][C]0.634123149504317[/C][C]0.682938425247841[/C][/ROW]
[ROW][C]33[/C][C]0.250879786147044[/C][C]0.501759572294087[/C][C]0.749120213852956[/C][/ROW]
[ROW][C]34[/C][C]0.211400369687696[/C][C]0.422800739375392[/C][C]0.788599630312304[/C][/ROW]
[ROW][C]35[/C][C]0.165184659874056[/C][C]0.330369319748112[/C][C]0.834815340125944[/C][/ROW]
[ROW][C]36[/C][C]0.159096700854752[/C][C]0.318193401709504[/C][C]0.840903299145248[/C][/ROW]
[ROW][C]37[/C][C]0.158864397184553[/C][C]0.317728794369107[/C][C]0.841135602815447[/C][/ROW]
[ROW][C]38[/C][C]0.131296178937304[/C][C]0.262592357874608[/C][C]0.868703821062696[/C][/ROW]
[ROW][C]39[/C][C]0.0954163490551875[/C][C]0.190832698110375[/C][C]0.904583650944813[/C][/ROW]
[ROW][C]40[/C][C]0.0749176467845392[/C][C]0.149835293569078[/C][C]0.92508235321546[/C][/ROW]
[ROW][C]41[/C][C]0.124932119998985[/C][C]0.249864239997971[/C][C]0.875067880001015[/C][/ROW]
[ROW][C]42[/C][C]0.095770376374879[/C][C]0.191540752749758[/C][C]0.904229623625121[/C][/ROW]
[ROW][C]43[/C][C]0.312682773275941[/C][C]0.625365546551883[/C][C]0.687317226724059[/C][/ROW]
[ROW][C]44[/C][C]0.333287418281879[/C][C]0.666574836563758[/C][C]0.666712581718121[/C][/ROW]
[ROW][C]45[/C][C]0.211761949553625[/C][C]0.423523899107251[/C][C]0.788238050446375[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98016&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98016&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
80.04735538259296880.09471076518593760.952644617407031
90.1843188607871970.3686377215743950.815681139212803
100.4565423210595150.913084642119030.543457678940485
110.4468006527855770.8936013055711530.553199347214423
120.3320828837338710.6641657674677430.667917116266129
130.2480006813609650.496001362721930.751999318639035
140.2067254261810440.4134508523620880.793274573818956
150.1787992297512280.3575984595024570.821200770248772
160.1231539595203480.2463079190406960.876846040479652
170.08844398605025940.1768879721005190.91155601394974
180.07078760711941030.1415752142388210.92921239288059
190.139207689781130.2784153795622610.86079231021887
200.1379167099626150.275833419925230.862083290037385
210.1816577204684940.3633154409369880.818342279531506
220.19592613711280.3918522742255990.8040738628872
230.1823450432198560.3646900864397110.817654956780144
240.1532096058060060.3064192116120130.846790394193994
250.1346999575841580.2693999151683160.865300042415842
260.1656925845349050.331385169069810.834307415465095
270.1439309533882270.2878619067764530.856069046611773
280.1686717950720840.3373435901441680.831328204927916
290.2204695707338620.4409391414677240.779530429266138
300.165356686343190.3307133726863810.83464331365681
310.3913148536245480.7826297072490970.608685146375452
320.3170615747521590.6341231495043170.682938425247841
330.2508797861470440.5017595722940870.749120213852956
340.2114003696876960.4228007393753920.788599630312304
350.1651846598740560.3303693197481120.834815340125944
360.1590967008547520.3181934017095040.840903299145248
370.1588643971845530.3177287943691070.841135602815447
380.1312961789373040.2625923578746080.868703821062696
390.09541634905518750.1908326981103750.904583650944813
400.07491764678453920.1498352935690780.92508235321546
410.1249321199989850.2498642399979710.875067880001015
420.0957703763748790.1915407527497580.904229623625121
430.3126827732759410.6253655465518830.687317226724059
440.3332874182818790.6665748365637580.666712581718121
450.2117619495536250.4235238991072510.788238050446375







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0263157894736842OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0263157894736842 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98016&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0263157894736842[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98016&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98016&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 level10.0263157894736842OK



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')
}