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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 14 Dec 2010 23:18:51 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/15/t1292369015gbqcqnegvsk41az.htm/, Retrieved Fri, 03 May 2024 14:31:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110273, Retrieved Fri, 03 May 2024 14:31:38 +0000

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Estimated Impact

149

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Workshop 7 - Firs...] [2010-11-19 10:36:36] [8b017ffbf7b0eded54d8efebfb3e4cfa]
-   P     [Multiple Regression] [Workshop 7 - Firs...] [2010-11-19 10:41:24] [8b017ffbf7b0eded54d8efebfb3e4cfa]
- R PD      [Multiple Regression] [] [2010-11-23 16:27:22] [1ad9dd03b6c5806e9fe90049663fcef1]
- RMPD        [Pearson Correlation] [correlatie SWS - ...] [2010-12-14 21:53:29] [ca5ab8c53423c489dac59e1a1d654047]
- RM D            [Multiple Regression] [multiple regressi...] [2010-12-14 23:18:51] [5f761c4a622da19727fd2adf71158b48] [Current]

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2	4,5	1	6,6	42	3	1	3
1,8	69	2547	4603	624	3	5	4
0,7	27	10,55	179,5	180	4	4	4
3,9	19	0,023	0,3	35	1	1	1
1	30,4	160	169	392	4	5	4
3,6	28	3,3	25,6	63	1	2	1
1,4	50	52,16	440	230	1	1	1
1,5	7	0,425	6,4	112	5	4	4
0,7	30	465	423	281	5	5	5
2,1	3,5	0,075	1,2	42	1	1	1
0	50	3	25	28	2	2	2
4,1	6	0,785	3,5	42	2	2	2
1,2	10,4	0,2	5	120	2	2	2
0,5	20	27,66	115	148	5	5	5
3,4	3,9	0,12	1	16	3	1	2
1,5	41	85	325	310	1	3	1
3,4	9	0,101	4	28	5	1	3
0,8	7,6	1,04	5,5	68	5	3	4
0,8	46	521	655	336	5	5	5
1,4	2,6	0,005	0,14	21,5	5	2	4
2	24	0,01	0,25	50	1	1	1
1,9	100	62	1320	267	1	1	1
1,3	3,2	0,023	0,4	19	4	1	3
2	2	0,048	0,33	30	4	1	3
5,6	5	1,7	6,3	12	2	1	1
3,1	6,5	3,5	10,8	120	2	1	1
1,8	12	0,48	15,5	140	2	2	2
0,9	20,2	10	115	170	4	4	4
1,8	13	1,62	11,4	17	2	1	2
1,9	27	192	180	115	4	4	4
0,9	18	2,5	12,1	31	5	5	5
2,6	4,7	0,28	1,9	21	3	1	3
2,4	9,8	4,235	50,4	52	1	1	1
1,2	29	6,8	179	164	2	3	2
0,9	7	0,75	12,3	225	2	2	2
0,5	6	3,6	21	225	3	2	3
0,6	20	55,5	175	151	5	5	5
2,3	4,5	0,9	2,6	60	2	1	2
0,5	7,5	2	12,3	200	3	1	3
2,6	2,3	0,104	2,5	46	3	2	2
0,6	24	4,19	58	210	4	3	4
6,6	3	3,5	3,9	14	2	1	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110273&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110273&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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.77763738061614 -0.0133949634614039LS[t] + 0.00137031210793506BW[t] + 0.000296123778267419BRW[t] -0.00500701775504269GT[t] + 0.900361471653856PI[t] + 0.360021324193483SEI[t] -1.73845778307009OD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.77763738061614 -0.0133949634614039LS[t] +  0.00137031210793506BW[t] +  0.000296123778267419BRW[t] -0.00500701775504269GT[t] +  0.900361471653856PI[t] +  0.360021324193483SEI[t] -1.73845778307009OD[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110273&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.77763738061614 -0.0133949634614039LS[t] +  0.00137031210793506BW[t] +  0.000296123778267419BRW[t] -0.00500701775504269GT[t] +  0.900361471653856PI[t] +  0.360021324193483SEI[t] -1.73845778307009OD[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110273&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
PS[t] = + 3.77763738061614 -0.0133949634614039LS[t] + 0.00137031210793506BW[t] + 0.000296123778267419BRW[t] -0.00500701775504269GT[t] + 0.900361471653856PI[t] + 0.360021324193483SEI[t] -1.73845778307009OD[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)	3.77763738061614	0.413268	9.1409	0	0
LS	-0.0133949634614039	0.014311	-0.936	0.355869	0.177935
BW	0.00137031210793506	0.001831	0.7483	0.459434	0.229717
BRW	0.000296123778267419	0.001099	0.2694	0.789227	0.394614
GT	-0.00500701775504269	0.002158	-2.3197	0.026497	0.013248
PI	0.900361471653856	0.337906	2.6645	0.011704	0.005852
SEI	0.360021324193483	0.211701	1.7006	0.098147	0.049074
OD	-1.73845778307009	0.419569	-4.1434	0.000214	0.000107

\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) & 3.77763738061614 & 0.413268 & 9.1409 & 0 & 0 \tabularnewline
LS & -0.0133949634614039 & 0.014311 & -0.936 & 0.355869 & 0.177935 \tabularnewline
BW & 0.00137031210793506 & 0.001831 & 0.7483 & 0.459434 & 0.229717 \tabularnewline
BRW & 0.000296123778267419 & 0.001099 & 0.2694 & 0.789227 & 0.394614 \tabularnewline
GT & -0.00500701775504269 & 0.002158 & -2.3197 & 0.026497 & 0.013248 \tabularnewline
PI & 0.900361471653856 & 0.337906 & 2.6645 & 0.011704 & 0.005852 \tabularnewline
SEI & 0.360021324193483 & 0.211701 & 1.7006 & 0.098147 & 0.049074 \tabularnewline
OD & -1.73845778307009 & 0.419569 & -4.1434 & 0.000214 & 0.000107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110273&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]3.77763738061614[/C][C]0.413268[/C][C]9.1409[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]LS[/C][C]-0.0133949634614039[/C][C]0.014311[/C][C]-0.936[/C][C]0.355869[/C][C]0.177935[/C][/ROW]
[ROW][C]BW[/C][C]0.00137031210793506[/C][C]0.001831[/C][C]0.7483[/C][C]0.459434[/C][C]0.229717[/C][/ROW]
[ROW][C]BRW[/C][C]0.000296123778267419[/C][C]0.001099[/C][C]0.2694[/C][C]0.789227[/C][C]0.394614[/C][/ROW]
[ROW][C]GT[/C][C]-0.00500701775504269[/C][C]0.002158[/C][C]-2.3197[/C][C]0.026497[/C][C]0.013248[/C][/ROW]
[ROW][C]PI[/C][C]0.900361471653856[/C][C]0.337906[/C][C]2.6645[/C][C]0.011704[/C][C]0.005852[/C][/ROW]
[ROW][C]SEI[/C][C]0.360021324193483[/C][C]0.211701[/C][C]1.7006[/C][C]0.098147[/C][C]0.049074[/C][/ROW]
[ROW][C]OD[/C][C]-1.73845778307009[/C][C]0.419569[/C][C]-4.1434[/C][C]0.000214[/C][C]0.000107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110273&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110273&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)	3.77763738061614	0.413268	9.1409	0	0
LS	-0.0133949634614039	0.014311	-0.936	0.355869	0.177935
BW	0.00137031210793506	0.001831	0.7483	0.459434	0.229717
BRW	0.000296123778267419	0.001099	0.2694	0.789227	0.394614
GT	-0.00500701775504269	0.002158	-2.3197	0.026497	0.013248
PI	0.900361471653856	0.337906	2.6645	0.011704	0.005852
SEI	0.360021324193483	0.211701	1.7006	0.098147	0.049074
OD	-1.73845778307009	0.419569	-4.1434	0.000214	0.000107

Multiple Linear Regression - Regression Statistics
Multiple R	0.787651358957768
R-squared	0.620394663268018
Adjusted R-squared	0.54224062335261
F-TEST (value)	7.93810101102281
F-TEST (DF numerator)	7
F-TEST (DF denominator)	34
p-value	1.06575639513551e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.939636820152983
Sum Squared Residuals	30.0191900287651

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.787651358957768 \tabularnewline
R-squared & 0.620394663268018 \tabularnewline
Adjusted R-squared & 0.54224062335261 \tabularnewline
F-TEST (value) & 7.93810101102281 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 1.06575639513551e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.939636820152983 \tabularnewline
Sum Squared Residuals & 30.0191900287651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110273&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.787651358957768[/C][/ROW]
[ROW][C]R-squared[/C][C]0.620394663268018[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.54224062335261[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.93810101102281[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]1.06575639513551e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.939636820152983[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]30.0191900287651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110273&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110273&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	0.787651358957768
R-squared	0.620394663268018
Adjusted R-squared	0.54224062335261
F-TEST (value)	7.93810101102281
F-TEST (DF numerator)	7
F-TEST (DF denominator)	34
p-value	1.06575639513551e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.939636820152983
Sum Squared Residuals	30.0191900287651

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2	1.3561224183173	0.643877581682701
2	1.8	2.12960841655679	-0.329608416556788
3	0.7	0.670021233297254	0.0299787667027456
4	3.9	2.86993282051218	1.03006717948782
5	1	0.124695762512002	0.875304237487998
6	3.6	2.98118542077970	0.618814579220296
7	1.4	1.67997007865093	-0.279970078650931
8	1.5	2.11362574541116	-0.613625745411159
9	0.7	0.340897039890184	0.359102960109816
10	2.1	3.0428433975087	-0.942843397508697
11	0	2.02305676673973	-2.02305676673973
12	4.1	2.53293500791908	1.56706499208092
13	1.2	2.08309233687984	-0.883092336879836
14	0.5	0.450281614934216	0.0497183850657844
15	3.4	3.22993547328207	0.170064526717932
16	1.5	2.13095279291095	-0.630952792910953
17	3.4	3.16466444221077	0.235335557789233
18	0.8	1.96645245490865	-1.16645245490865
19	0.8	-0.0033701574172218	0.803370157417222
20	1.4	1.90322677714845	-0.503226777148446
21	2	2.72782011663321	-0.727820116633206
22	1.9	1.09903504466157	0.800964955338435
23	1.3	2.38588398848226	-1.08588398848226
24	2	2.34689427846869	-0.346894278468690
25	5.6	4.07705994506629	1.52294005493371
26	3.1	3.52000870112606	-0.420008701126056
27	1.8	1.96501302730277	-0.165013027302765
28	0.9	0.791323507027615	0.108676492972385
29	1.8	2.20780797183028	-0.407807971830278
30	1.9	1.24426858124898	0.65573141875102
31	0.9	0.997944429777654	-0.0979444297776535
32	2.6	1.45621239200535	1.14378760799465
33	2.4	2.92865473841119	-0.528654738411193
34	1.2	2.03422815680023	-0.83422815680023
35	0.9	1.60581372360984	-0.705813723609843
36	0.5	0.787594042033552	-0.287594042033552
37	0.6	0.491177477450044	0.108822522549956
38	2.3	2.10277088381891	0.197229116181091
39	0.5	0.527886940280407	-0.0278869402804072
40	2.6	3.46160046703619	-0.861600467036191
41	0.6	0.155280042770744	0.444719957229256
42	6.6	4.09559170120545	2.50440829879455

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.3561224183173 & 0.643877581682701 \tabularnewline
2 & 1.8 & 2.12960841655679 & -0.329608416556788 \tabularnewline
3 & 0.7 & 0.670021233297254 & 0.0299787667027456 \tabularnewline
4 & 3.9 & 2.86993282051218 & 1.03006717948782 \tabularnewline
5 & 1 & 0.124695762512002 & 0.875304237487998 \tabularnewline
6 & 3.6 & 2.98118542077970 & 0.618814579220296 \tabularnewline
7 & 1.4 & 1.67997007865093 & -0.279970078650931 \tabularnewline
8 & 1.5 & 2.11362574541116 & -0.613625745411159 \tabularnewline
9 & 0.7 & 0.340897039890184 & 0.359102960109816 \tabularnewline
10 & 2.1 & 3.0428433975087 & -0.942843397508697 \tabularnewline
11 & 0 & 2.02305676673973 & -2.02305676673973 \tabularnewline
12 & 4.1 & 2.53293500791908 & 1.56706499208092 \tabularnewline
13 & 1.2 & 2.08309233687984 & -0.883092336879836 \tabularnewline
14 & 0.5 & 0.450281614934216 & 0.0497183850657844 \tabularnewline
15 & 3.4 & 3.22993547328207 & 0.170064526717932 \tabularnewline
16 & 1.5 & 2.13095279291095 & -0.630952792910953 \tabularnewline
17 & 3.4 & 3.16466444221077 & 0.235335557789233 \tabularnewline
18 & 0.8 & 1.96645245490865 & -1.16645245490865 \tabularnewline
19 & 0.8 & -0.0033701574172218 & 0.803370157417222 \tabularnewline
20 & 1.4 & 1.90322677714845 & -0.503226777148446 \tabularnewline
21 & 2 & 2.72782011663321 & -0.727820116633206 \tabularnewline
22 & 1.9 & 1.09903504466157 & 0.800964955338435 \tabularnewline
23 & 1.3 & 2.38588398848226 & -1.08588398848226 \tabularnewline
24 & 2 & 2.34689427846869 & -0.346894278468690 \tabularnewline
25 & 5.6 & 4.07705994506629 & 1.52294005493371 \tabularnewline
26 & 3.1 & 3.52000870112606 & -0.420008701126056 \tabularnewline
27 & 1.8 & 1.96501302730277 & -0.165013027302765 \tabularnewline
28 & 0.9 & 0.791323507027615 & 0.108676492972385 \tabularnewline
29 & 1.8 & 2.20780797183028 & -0.407807971830278 \tabularnewline
30 & 1.9 & 1.24426858124898 & 0.65573141875102 \tabularnewline
31 & 0.9 & 0.997944429777654 & -0.0979444297776535 \tabularnewline
32 & 2.6 & 1.45621239200535 & 1.14378760799465 \tabularnewline
33 & 2.4 & 2.92865473841119 & -0.528654738411193 \tabularnewline
34 & 1.2 & 2.03422815680023 & -0.83422815680023 \tabularnewline
35 & 0.9 & 1.60581372360984 & -0.705813723609843 \tabularnewline
36 & 0.5 & 0.787594042033552 & -0.287594042033552 \tabularnewline
37 & 0.6 & 0.491177477450044 & 0.108822522549956 \tabularnewline
38 & 2.3 & 2.10277088381891 & 0.197229116181091 \tabularnewline
39 & 0.5 & 0.527886940280407 & -0.0278869402804072 \tabularnewline
40 & 2.6 & 3.46160046703619 & -0.861600467036191 \tabularnewline
41 & 0.6 & 0.155280042770744 & 0.444719957229256 \tabularnewline
42 & 6.6 & 4.09559170120545 & 2.50440829879455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110273&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]2[/C][C]1.3561224183173[/C][C]0.643877581682701[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]2.12960841655679[/C][C]-0.329608416556788[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.670021233297254[/C][C]0.0299787667027456[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]2.86993282051218[/C][C]1.03006717948782[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.124695762512002[/C][C]0.875304237487998[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]2.98118542077970[/C][C]0.618814579220296[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]1.67997007865093[/C][C]-0.279970078650931[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]2.11362574541116[/C][C]-0.613625745411159[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.340897039890184[/C][C]0.359102960109816[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]3.0428433975087[/C][C]-0.942843397508697[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]2.02305676673973[/C][C]-2.02305676673973[/C][/ROW]
[ROW][C]12[/C][C]4.1[/C][C]2.53293500791908[/C][C]1.56706499208092[/C][/ROW]
[ROW][C]13[/C][C]1.2[/C][C]2.08309233687984[/C][C]-0.883092336879836[/C][/ROW]
[ROW][C]14[/C][C]0.5[/C][C]0.450281614934216[/C][C]0.0497183850657844[/C][/ROW]
[ROW][C]15[/C][C]3.4[/C][C]3.22993547328207[/C][C]0.170064526717932[/C][/ROW]
[ROW][C]16[/C][C]1.5[/C][C]2.13095279291095[/C][C]-0.630952792910953[/C][/ROW]
[ROW][C]17[/C][C]3.4[/C][C]3.16466444221077[/C][C]0.235335557789233[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]1.96645245490865[/C][C]-1.16645245490865[/C][/ROW]
[ROW][C]19[/C][C]0.8[/C][C]-0.0033701574172218[/C][C]0.803370157417222[/C][/ROW]
[ROW][C]20[/C][C]1.4[/C][C]1.90322677714845[/C][C]-0.503226777148446[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]2.72782011663321[/C][C]-0.727820116633206[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.09903504466157[/C][C]0.800964955338435[/C][/ROW]
[ROW][C]23[/C][C]1.3[/C][C]2.38588398848226[/C][C]-1.08588398848226[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]2.34689427846869[/C][C]-0.346894278468690[/C][/ROW]
[ROW][C]25[/C][C]5.6[/C][C]4.07705994506629[/C][C]1.52294005493371[/C][/ROW]
[ROW][C]26[/C][C]3.1[/C][C]3.52000870112606[/C][C]-0.420008701126056[/C][/ROW]
[ROW][C]27[/C][C]1.8[/C][C]1.96501302730277[/C][C]-0.165013027302765[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]0.791323507027615[/C][C]0.108676492972385[/C][/ROW]
[ROW][C]29[/C][C]1.8[/C][C]2.20780797183028[/C][C]-0.407807971830278[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.24426858124898[/C][C]0.65573141875102[/C][/ROW]
[ROW][C]31[/C][C]0.9[/C][C]0.997944429777654[/C][C]-0.0979444297776535[/C][/ROW]
[ROW][C]32[/C][C]2.6[/C][C]1.45621239200535[/C][C]1.14378760799465[/C][/ROW]
[ROW][C]33[/C][C]2.4[/C][C]2.92865473841119[/C][C]-0.528654738411193[/C][/ROW]
[ROW][C]34[/C][C]1.2[/C][C]2.03422815680023[/C][C]-0.83422815680023[/C][/ROW]
[ROW][C]35[/C][C]0.9[/C][C]1.60581372360984[/C][C]-0.705813723609843[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]0.787594042033552[/C][C]-0.287594042033552[/C][/ROW]
[ROW][C]37[/C][C]0.6[/C][C]0.491177477450044[/C][C]0.108822522549956[/C][/ROW]
[ROW][C]38[/C][C]2.3[/C][C]2.10277088381891[/C][C]0.197229116181091[/C][/ROW]
[ROW][C]39[/C][C]0.5[/C][C]0.527886940280407[/C][C]-0.0278869402804072[/C][/ROW]
[ROW][C]40[/C][C]2.6[/C][C]3.46160046703619[/C][C]-0.861600467036191[/C][/ROW]
[ROW][C]41[/C][C]0.6[/C][C]0.155280042770744[/C][C]0.444719957229256[/C][/ROW]
[ROW][C]42[/C][C]6.6[/C][C]4.09559170120545[/C][C]2.50440829879455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110273&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110273&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	2	1.3561224183173	0.643877581682701
2	1.8	2.12960841655679	-0.329608416556788
3	0.7	0.670021233297254	0.0299787667027456
4	3.9	2.86993282051218	1.03006717948782
5	1	0.124695762512002	0.875304237487998
6	3.6	2.98118542077970	0.618814579220296
7	1.4	1.67997007865093	-0.279970078650931
8	1.5	2.11362574541116	-0.613625745411159
9	0.7	0.340897039890184	0.359102960109816
10	2.1	3.0428433975087	-0.942843397508697
11	0	2.02305676673973	-2.02305676673973
12	4.1	2.53293500791908	1.56706499208092
13	1.2	2.08309233687984	-0.883092336879836
14	0.5	0.450281614934216	0.0497183850657844
15	3.4	3.22993547328207	0.170064526717932
16	1.5	2.13095279291095	-0.630952792910953
17	3.4	3.16466444221077	0.235335557789233
18	0.8	1.96645245490865	-1.16645245490865
19	0.8	-0.0033701574172218	0.803370157417222
20	1.4	1.90322677714845	-0.503226777148446
21	2	2.72782011663321	-0.727820116633206
22	1.9	1.09903504466157	0.800964955338435
23	1.3	2.38588398848226	-1.08588398848226
24	2	2.34689427846869	-0.346894278468690
25	5.6	4.07705994506629	1.52294005493371
26	3.1	3.52000870112606	-0.420008701126056
27	1.8	1.96501302730277	-0.165013027302765
28	0.9	0.791323507027615	0.108676492972385
29	1.8	2.20780797183028	-0.407807971830278
30	1.9	1.24426858124898	0.65573141875102
31	0.9	0.997944429777654	-0.0979444297776535
32	2.6	1.45621239200535	1.14378760799465
33	2.4	2.92865473841119	-0.528654738411193
34	1.2	2.03422815680023	-0.83422815680023
35	0.9	1.60581372360984	-0.705813723609843
36	0.5	0.787594042033552	-0.287594042033552
37	0.6	0.491177477450044	0.108822522549956
38	2.3	2.10277088381891	0.197229116181091
39	0.5	0.527886940280407	-0.0278869402804072
40	2.6	3.46160046703619	-0.861600467036191
41	0.6	0.155280042770744	0.444719957229256
42	6.6	4.09559170120545	2.50440829879455

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.867792319701568	0.264415360596863	0.132207680298432
12	0.87435411602056	0.251291767958879	0.125645883979439
13	0.913669484051028	0.172661031897945	0.0863305159489724
14	0.851153651604207	0.297692696791587	0.148846348395793
15	0.78635314205445	0.427293715891101	0.213646857945551
16	0.71159120203052	0.57681759593896	0.28840879796948
17	0.640943346499543	0.718113307000913	0.359056653500457
18	0.647327813597236	0.705344372805529	0.352672186402764
19	0.573130128334434	0.853739743331132	0.426869871665566
20	0.49102338337574	0.98204676675148	0.50897661662426
21	0.422814367987739	0.845628735975478	0.577185632012261
22	0.438615803212461	0.877231606424922	0.561384196787539
23	0.509296993154059	0.981406013691882	0.490703006845941
24	0.563843008462008	0.872313983075983	0.436156991537992
25	0.638903524800384	0.722192950399231	0.361096475199616
26	0.592490286809026	0.815019426381948	0.407509713190974
27	0.481925069706886	0.963850139413772	0.518074930293114
28	0.367235106157113	0.734470212314227	0.632764893842887
29	0.314846356572056	0.629692713144112	0.685153643427944
30	0.346589670255331	0.693179340510661	0.65341032974467
31	0.224940433304570	0.449880866609139	0.77505956669543

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.867792319701568 & 0.264415360596863 & 0.132207680298432 \tabularnewline
12 & 0.87435411602056 & 0.251291767958879 & 0.125645883979439 \tabularnewline
13 & 0.913669484051028 & 0.172661031897945 & 0.0863305159489724 \tabularnewline
14 & 0.851153651604207 & 0.297692696791587 & 0.148846348395793 \tabularnewline
15 & 0.78635314205445 & 0.427293715891101 & 0.213646857945551 \tabularnewline
16 & 0.71159120203052 & 0.57681759593896 & 0.28840879796948 \tabularnewline
17 & 0.640943346499543 & 0.718113307000913 & 0.359056653500457 \tabularnewline
18 & 0.647327813597236 & 0.705344372805529 & 0.352672186402764 \tabularnewline
19 & 0.573130128334434 & 0.853739743331132 & 0.426869871665566 \tabularnewline
20 & 0.49102338337574 & 0.98204676675148 & 0.50897661662426 \tabularnewline
21 & 0.422814367987739 & 0.845628735975478 & 0.577185632012261 \tabularnewline
22 & 0.438615803212461 & 0.877231606424922 & 0.561384196787539 \tabularnewline
23 & 0.509296993154059 & 0.981406013691882 & 0.490703006845941 \tabularnewline
24 & 0.563843008462008 & 0.872313983075983 & 0.436156991537992 \tabularnewline
25 & 0.638903524800384 & 0.722192950399231 & 0.361096475199616 \tabularnewline
26 & 0.592490286809026 & 0.815019426381948 & 0.407509713190974 \tabularnewline
27 & 0.481925069706886 & 0.963850139413772 & 0.518074930293114 \tabularnewline
28 & 0.367235106157113 & 0.734470212314227 & 0.632764893842887 \tabularnewline
29 & 0.314846356572056 & 0.629692713144112 & 0.685153643427944 \tabularnewline
30 & 0.346589670255331 & 0.693179340510661 & 0.65341032974467 \tabularnewline
31 & 0.224940433304570 & 0.449880866609139 & 0.77505956669543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110273&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]11[/C][C]0.867792319701568[/C][C]0.264415360596863[/C][C]0.132207680298432[/C][/ROW]
[ROW][C]12[/C][C]0.87435411602056[/C][C]0.251291767958879[/C][C]0.125645883979439[/C][/ROW]
[ROW][C]13[/C][C]0.913669484051028[/C][C]0.172661031897945[/C][C]0.0863305159489724[/C][/ROW]
[ROW][C]14[/C][C]0.851153651604207[/C][C]0.297692696791587[/C][C]0.148846348395793[/C][/ROW]
[ROW][C]15[/C][C]0.78635314205445[/C][C]0.427293715891101[/C][C]0.213646857945551[/C][/ROW]
[ROW][C]16[/C][C]0.71159120203052[/C][C]0.57681759593896[/C][C]0.28840879796948[/C][/ROW]
[ROW][C]17[/C][C]0.640943346499543[/C][C]0.718113307000913[/C][C]0.359056653500457[/C][/ROW]
[ROW][C]18[/C][C]0.647327813597236[/C][C]0.705344372805529[/C][C]0.352672186402764[/C][/ROW]
[ROW][C]19[/C][C]0.573130128334434[/C][C]0.853739743331132[/C][C]0.426869871665566[/C][/ROW]
[ROW][C]20[/C][C]0.49102338337574[/C][C]0.98204676675148[/C][C]0.50897661662426[/C][/ROW]
[ROW][C]21[/C][C]0.422814367987739[/C][C]0.845628735975478[/C][C]0.577185632012261[/C][/ROW]
[ROW][C]22[/C][C]0.438615803212461[/C][C]0.877231606424922[/C][C]0.561384196787539[/C][/ROW]
[ROW][C]23[/C][C]0.509296993154059[/C][C]0.981406013691882[/C][C]0.490703006845941[/C][/ROW]
[ROW][C]24[/C][C]0.563843008462008[/C][C]0.872313983075983[/C][C]0.436156991537992[/C][/ROW]
[ROW][C]25[/C][C]0.638903524800384[/C][C]0.722192950399231[/C][C]0.361096475199616[/C][/ROW]
[ROW][C]26[/C][C]0.592490286809026[/C][C]0.815019426381948[/C][C]0.407509713190974[/C][/ROW]
[ROW][C]27[/C][C]0.481925069706886[/C][C]0.963850139413772[/C][C]0.518074930293114[/C][/ROW]
[ROW][C]28[/C][C]0.367235106157113[/C][C]0.734470212314227[/C][C]0.632764893842887[/C][/ROW]
[ROW][C]29[/C][C]0.314846356572056[/C][C]0.629692713144112[/C][C]0.685153643427944[/C][/ROW]
[ROW][C]30[/C][C]0.346589670255331[/C][C]0.693179340510661[/C][C]0.65341032974467[/C][/ROW]
[ROW][C]31[/C][C]0.224940433304570[/C][C]0.449880866609139[/C][C]0.77505956669543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110273&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110273&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
11	0.867792319701568	0.264415360596863	0.132207680298432
12	0.87435411602056	0.251291767958879	0.125645883979439
13	0.913669484051028	0.172661031897945	0.0863305159489724
14	0.851153651604207	0.297692696791587	0.148846348395793
15	0.78635314205445	0.427293715891101	0.213646857945551
16	0.71159120203052	0.57681759593896	0.28840879796948
17	0.640943346499543	0.718113307000913	0.359056653500457
18	0.647327813597236	0.705344372805529	0.352672186402764
19	0.573130128334434	0.853739743331132	0.426869871665566
20	0.49102338337574	0.98204676675148	0.50897661662426
21	0.422814367987739	0.845628735975478	0.577185632012261
22	0.438615803212461	0.877231606424922	0.561384196787539
23	0.509296993154059	0.981406013691882	0.490703006845941
24	0.563843008462008	0.872313983075983	0.436156991537992
25	0.638903524800384	0.722192950399231	0.361096475199616
26	0.592490286809026	0.815019426381948	0.407509713190974
27	0.481925069706886	0.963850139413772	0.518074930293114
28	0.367235106157113	0.734470212314227	0.632764893842887
29	0.314846356572056	0.629692713144112	0.685153643427944
30	0.346589670255331	0.693179340510661	0.65341032974467
31	0.224940433304570	0.449880866609139	0.77505956669543

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	0	0	OK
10% type I error level	0	0	OK

\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=110273&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=110273&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110273&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	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = pearson ;

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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code