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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Tue, 20 Nov 2012 15:48:38 -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/2012/Nov/20/t1353444546os136ikdpja823e.htm/, Retrieved Mon, 29 Apr 2024 23:46:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191284, Retrieved Mon, 29 Apr 2024 23:46:17 +0000

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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] [consumer goods] [2012-11-20 20:48:38] [a1c9ee8128156b02a669e54abb47d426] [Current]
-   PD      [Multiple Regression] [energy production] [2012-11-20 21:12:48] [0f86cfddc502cf698caf54991235c44d] 

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104.8	117.6	112.3	103.9	100.4
111.4	118.6	125.7	110.9	108.6
106.9	110.1	117.5	106.6	109.9
103.9	113.1	105.8	103.2	109.5
107.6	 91.2	115.1	108.7	109.2
104.7	106.2	119.3	104.6	100.9
109.1	115.5	119.0	108.7	98.4
109.3	106.2	126.8	109.5	94.2
102.2	 95.9	116.3	102.6	94.7
109.8	113.2	127.1	109.5	95.2
106.2	 78.3	106.5	108.1	100.3
 95.1	79.8 	95.5 	96.1 	100.9
118.7	121.2	121.3	118.5	97.9
116.9	125.6	118.3	116.3	106.9
105.3	 97.2	 95.6	105.9	100.8
119.5	102.8	90.1 	120.7	106.6
 96.5	 88.8	82.6 	 97.0	108.2
 99.3	 95.3	 86.9	 99.6	98.4
113.8	107.6	 95.9	114.2	102.0
102.7	 95.0	 88.8	103.3	95.7
 98.8	 87.5	 93.2	 99.6	100.8
109.9	106.7	 98.9	110.1	98.8
103.6	 75.8	 79.6	105.7	99.6
 96.6	 80.0	 80.7	 97.8	106.1
111.6	117.2	102.9	111.1	106.3
111.6	106.6	101.7	112.0	105.7
107.0	104.7	 95.9	107.2	103.7
111.5	 95.2	 87.1	112.7	111.2
102.0	 94.0	 87.5	102.5	114.8
113.5	 95.7	 93.6	114.9	103.6
125.5	112.6	109.6	126.4	107.0
106.7	 99.1	103.2	107.3	104.8
102.9	 91.6	 98.9	103.8	104.7
123.6	111.5	113.7	124.5	102.0
107.7	 76.6	 87.9	110.1	103.4
105.5	 83.4	 89.6	107.1	107.0
117.1	113.5	114.4	117.3	104.0
113.3	106.4	108.8	113.8	105.4
118.0	104.1	104.3	119.0	107.9
118.4	108.4	 97.0	119.1	110.1
105.8	 91.0	100.4	106.9	111.0
114.6	108.3	105.3	115.0	98.5
140.3	121.0	122.3	141.7	101.9
113.8	 95.4	105.6	115.2	103.4
117.4	109.9	116.9	117.9	102.9
115.4	101.4	110.5	116.5	101.0
105.9	 86.0	 88.6	107.4	103.4
120.4	 96.5	 94.5	122.2	107.2
126.9	124.6	115.7	126.9	104.5
117.1	109.3	107.8	117.7	104.7
113.8	104.5	106.6	114.4	107.0
112.8	101.8	100.7	113.6	110.3
106.7	101.5	100.4	107.0	107.9
107.3	103.4	101.7	107.5	97.1
121.8	125.9	115.2	121.4	98.6
101.1	 96.8	100.9	101.3	95.3
103.1	104.4	105.3	102.9	101.7
110.4	121.1	109.8	109.5	96.3
108.3	 83.7	 92.1	110.2	99.0
116.3	 91.5	 92.5	118.2	104.0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191284&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191284&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	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
consumer_goods[t] = -0.0429446894771792 + 0.0694911177070696durable_consumer_goods[t] + 0.000787201827295588intermediate_and_capital_goods[t] + 0.929810783418774`non-durable_consumer_goods`[t] + 0.000637625069844741energy[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
consumer_goods[t] =  -0.0429446894771792 +  0.0694911177070696durable_consumer_goods[t] +  0.000787201827295588intermediate_and_capital_goods[t] +  0.929810783418774`non-durable_consumer_goods`[t] +  0.000637625069844741energy[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191284&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]consumer_goods[t] =  -0.0429446894771792 +  0.0694911177070696durable_consumer_goods[t] +  0.000787201827295588intermediate_and_capital_goods[t] +  0.929810783418774`non-durable_consumer_goods`[t] +  0.000637625069844741energy[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191284&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
consumer_goods[t] = -0.0429446894771792 + 0.0694911177070696durable_consumer_goods[t] + 0.000787201827295588intermediate_and_capital_goods[t] + 0.929810783418774`non-durable_consumer_goods`[t] + 0.000637625069844741energy[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)	-0.0429446894771792	0.206983	-0.2075	0.836402	0.418201
durable_consumer_goods	0.0694911177070696	0.000956	72.7265	0	0
intermediate_and_capital_goods	0.000787201827295588	0.000967	0.8143	0.419005	0.209503
`non-durable_consumer_goods`	0.929810783418774	0.001118	831.5637	0	0
energy	0.000637625069844741	0.001729	0.3687	0.713765	0.356882

\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) & -0.0429446894771792 & 0.206983 & -0.2075 & 0.836402 & 0.418201 \tabularnewline
durable_consumer_goods & 0.0694911177070696 & 0.000956 & 72.7265 & 0 & 0 \tabularnewline
intermediate_and_capital_goods & 0.000787201827295588 & 0.000967 & 0.8143 & 0.419005 & 0.209503 \tabularnewline
`non-durable_consumer_goods` & 0.929810783418774 & 0.001118 & 831.5637 & 0 & 0 \tabularnewline
energy & 0.000637625069844741 & 0.001729 & 0.3687 & 0.713765 & 0.356882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191284&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]-0.0429446894771792[/C][C]0.206983[/C][C]-0.2075[/C][C]0.836402[/C][C]0.418201[/C][/ROW]
[ROW][C]durable_consumer_goods[/C][C]0.0694911177070696[/C][C]0.000956[/C][C]72.7265[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]intermediate_and_capital_goods[/C][C]0.000787201827295588[/C][C]0.000967[/C][C]0.8143[/C][C]0.419005[/C][C]0.209503[/C][/ROW]
[ROW][C]`non-durable_consumer_goods`[/C][C]0.929810783418774[/C][C]0.001118[/C][C]831.5637[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]energy[/C][C]0.000637625069844741[/C][C]0.001729[/C][C]0.3687[/C][C]0.713765[/C][C]0.356882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191284&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191284&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)	-0.0429446894771792	0.206983	-0.2075	0.836402	0.418201
durable_consumer_goods	0.0694911177070696	0.000956	72.7265	0	0
intermediate_and_capital_goods	0.000787201827295588	0.000967	0.8143	0.419005	0.209503
`non-durable_consumer_goods`	0.929810783418774	0.001118	831.5637	0	0
energy	0.000637625069844741	0.001729	0.3687	0.713765	0.356882

Multiple Linear Regression - Regression Statistics
Multiple R	0.99997552446912
R-squared	0.999951049537292
Adjusted R-squared	0.999947489503641
F-TEST (value)	280882.471188093
F-TEST (DF numerator)	4
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0597228360617345
Sum Squared Residuals	0.196174943099125

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99997552446912 \tabularnewline
R-squared & 0.999951049537292 \tabularnewline
Adjusted R-squared & 0.999947489503641 \tabularnewline
F-TEST (value) & 280882.471188093 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0597228360617345 \tabularnewline
Sum Squared Residuals & 0.196174943099125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191284&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99997552446912[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999951049537292[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999947489503641[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]280882.471188093[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/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]0.0597228360617345[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.196174943099125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191284&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191284&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	0.99997552446912
R-squared	0.999951049537292
Adjusted R-squared	0.999947489503641
F-TEST (value)	280882.471188093
F-TEST (DF numerator)	4
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0597228360617345
Sum Squared Residuals	0.196174943099125

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	104.8	104.888971472303	-0.088971472302516
2	111.4	111.482915104	-0.0829151039995374
3	106.9	106.888428092396	0.0115719076043193
4	103.9	103.926079470486	-0.0260794704857729
5	107.6	107.525312990977	0.0746870090228907
6	104.7	104.753469504161	-0.0534695041610923
7	109.1	109.210130887631	-0.11013088763103
8	109.3	109.31117426865	-0.0111742686498548
9	102.2	102.171774544026	0.0282254559741971
10	109.8	109.798485878217	0.00151412178262483
11	106.2	106.058546303668	0.141453696331729
12	95.1	94.9967769341452	0.103223065854754
13	118.7	118.719867687733	-0.0198676877331609
14	116.9	116.98342190227	-0.0834219022696744
15	105.3	105.318083017428	-0.0180830174279993
16	119.5	119.46780148654	0.0321985134595846
17	96.5	96.4535264580235	0.046473541976477
18	99.3	99.3198630021812	-0.0198630021811765
19	113.8	113.759221454589	0.0407785454106511
20	102.7	102.739089661302	-0.0390896613018
21	98.8	98.7843219557456	0.0156780442543754
22	109.9	109.884776441894	0.015223558105621
23	103.6	103.631650562492	-0.0316505624924117
24	96.6	96.5830185528178	0.0169814471822037
25	111.6	111.55217495657	0.0478250434295866
26	111.6	111.651071596718	-0.0510715967177158
27	107	107.050105691926	-0.0501056919261603
28	111.5	111.501754194456	-0.00175419445589264
29	102	101.936905193318	0.0630948066817323
30	113.5	113.582354338177	-0.082354338177334
31	125.5	125.464341391217	0.035658608783084
32	106.7	106.760384472025	-0.0603844720245266
33	102.9	102.981414616891	-0.0814146168914362
34	123.6	123.621300075386	-0.0213000753861574
35	107.7	107.787367654133	-0.0873676541326206
36	105.5	105.474108597642	0.0258914023577819
37	117.1	117.067470961604	0.0325290383960834
38	113.3	113.316230628783	-0.0162306287829366
39	118	117.989468786286	0.0105312137139159
40	118.4	118.376917872583	0.0230821274172496
41	105.8	105.827331215546	-0.0273312155463768
42	114.6	114.556881873151	0.0431181268485605
43	140.3	140.280917341614	0.0190826583860364
44	113.8	113.849769134804	-0.049769134804425
45	117.4	117.376456024901	0.0235439750988632
46	115.4	115.477796848277	-0.0777968482773577
47	105.9	105.930646086628	-0.0306460866274992
48	120.4	120.428569883196	-0.0285698831960411
49	126.9	126.766348063883	0.133651936116977
50	117.1	117.14278338609	-0.0427833860904782
51	113.8	113.741372331282	0.0586276687175225
52	112.8	112.807357358688	-0.00735735868780357
53	106.7	106.647992392096	0.0520076079040447
54	107.3	107.23906791907	0.0609320809300585
55	121.8	121.738571619273	0.0614283807267671
56	101.1	101.013822198419	0.0861778015806762
57	103.1	103.03719643495	0.0628035650497927
58	110.4	110.334548504068	0.0654514959321681
59	108.3	108.374236365562	-0.0742363655620307
60	116.3	116.358256357108	-0.0582563571075098

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 104.8 & 104.888971472303 & -0.088971472302516 \tabularnewline
2 & 111.4 & 111.482915104 & -0.0829151039995374 \tabularnewline
3 & 106.9 & 106.888428092396 & 0.0115719076043193 \tabularnewline
4 & 103.9 & 103.926079470486 & -0.0260794704857729 \tabularnewline
5 & 107.6 & 107.525312990977 & 0.0746870090228907 \tabularnewline
6 & 104.7 & 104.753469504161 & -0.0534695041610923 \tabularnewline
7 & 109.1 & 109.210130887631 & -0.11013088763103 \tabularnewline
8 & 109.3 & 109.31117426865 & -0.0111742686498548 \tabularnewline
9 & 102.2 & 102.171774544026 & 0.0282254559741971 \tabularnewline
10 & 109.8 & 109.798485878217 & 0.00151412178262483 \tabularnewline
11 & 106.2 & 106.058546303668 & 0.141453696331729 \tabularnewline
12 & 95.1 & 94.9967769341452 & 0.103223065854754 \tabularnewline
13 & 118.7 & 118.719867687733 & -0.0198676877331609 \tabularnewline
14 & 116.9 & 116.98342190227 & -0.0834219022696744 \tabularnewline
15 & 105.3 & 105.318083017428 & -0.0180830174279993 \tabularnewline
16 & 119.5 & 119.46780148654 & 0.0321985134595846 \tabularnewline
17 & 96.5 & 96.4535264580235 & 0.046473541976477 \tabularnewline
18 & 99.3 & 99.3198630021812 & -0.0198630021811765 \tabularnewline
19 & 113.8 & 113.759221454589 & 0.0407785454106511 \tabularnewline
20 & 102.7 & 102.739089661302 & -0.0390896613018 \tabularnewline
21 & 98.8 & 98.7843219557456 & 0.0156780442543754 \tabularnewline
22 & 109.9 & 109.884776441894 & 0.015223558105621 \tabularnewline
23 & 103.6 & 103.631650562492 & -0.0316505624924117 \tabularnewline
24 & 96.6 & 96.5830185528178 & 0.0169814471822037 \tabularnewline
25 & 111.6 & 111.55217495657 & 0.0478250434295866 \tabularnewline
26 & 111.6 & 111.651071596718 & -0.0510715967177158 \tabularnewline
27 & 107 & 107.050105691926 & -0.0501056919261603 \tabularnewline
28 & 111.5 & 111.501754194456 & -0.00175419445589264 \tabularnewline
29 & 102 & 101.936905193318 & 0.0630948066817323 \tabularnewline
30 & 113.5 & 113.582354338177 & -0.082354338177334 \tabularnewline
31 & 125.5 & 125.464341391217 & 0.035658608783084 \tabularnewline
32 & 106.7 & 106.760384472025 & -0.0603844720245266 \tabularnewline
33 & 102.9 & 102.981414616891 & -0.0814146168914362 \tabularnewline
34 & 123.6 & 123.621300075386 & -0.0213000753861574 \tabularnewline
35 & 107.7 & 107.787367654133 & -0.0873676541326206 \tabularnewline
36 & 105.5 & 105.474108597642 & 0.0258914023577819 \tabularnewline
37 & 117.1 & 117.067470961604 & 0.0325290383960834 \tabularnewline
38 & 113.3 & 113.316230628783 & -0.0162306287829366 \tabularnewline
39 & 118 & 117.989468786286 & 0.0105312137139159 \tabularnewline
40 & 118.4 & 118.376917872583 & 0.0230821274172496 \tabularnewline
41 & 105.8 & 105.827331215546 & -0.0273312155463768 \tabularnewline
42 & 114.6 & 114.556881873151 & 0.0431181268485605 \tabularnewline
43 & 140.3 & 140.280917341614 & 0.0190826583860364 \tabularnewline
44 & 113.8 & 113.849769134804 & -0.049769134804425 \tabularnewline
45 & 117.4 & 117.376456024901 & 0.0235439750988632 \tabularnewline
46 & 115.4 & 115.477796848277 & -0.0777968482773577 \tabularnewline
47 & 105.9 & 105.930646086628 & -0.0306460866274992 \tabularnewline
48 & 120.4 & 120.428569883196 & -0.0285698831960411 \tabularnewline
49 & 126.9 & 126.766348063883 & 0.133651936116977 \tabularnewline
50 & 117.1 & 117.14278338609 & -0.0427833860904782 \tabularnewline
51 & 113.8 & 113.741372331282 & 0.0586276687175225 \tabularnewline
52 & 112.8 & 112.807357358688 & -0.00735735868780357 \tabularnewline
53 & 106.7 & 106.647992392096 & 0.0520076079040447 \tabularnewline
54 & 107.3 & 107.23906791907 & 0.0609320809300585 \tabularnewline
55 & 121.8 & 121.738571619273 & 0.0614283807267671 \tabularnewline
56 & 101.1 & 101.013822198419 & 0.0861778015806762 \tabularnewline
57 & 103.1 & 103.03719643495 & 0.0628035650497927 \tabularnewline
58 & 110.4 & 110.334548504068 & 0.0654514959321681 \tabularnewline
59 & 108.3 & 108.374236365562 & -0.0742363655620307 \tabularnewline
60 & 116.3 & 116.358256357108 & -0.0582563571075098 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191284&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]104.8[/C][C]104.888971472303[/C][C]-0.088971472302516[/C][/ROW]
[ROW][C]2[/C][C]111.4[/C][C]111.482915104[/C][C]-0.0829151039995374[/C][/ROW]
[ROW][C]3[/C][C]106.9[/C][C]106.888428092396[/C][C]0.0115719076043193[/C][/ROW]
[ROW][C]4[/C][C]103.9[/C][C]103.926079470486[/C][C]-0.0260794704857729[/C][/ROW]
[ROW][C]5[/C][C]107.6[/C][C]107.525312990977[/C][C]0.0746870090228907[/C][/ROW]
[ROW][C]6[/C][C]104.7[/C][C]104.753469504161[/C][C]-0.0534695041610923[/C][/ROW]
[ROW][C]7[/C][C]109.1[/C][C]109.210130887631[/C][C]-0.11013088763103[/C][/ROW]
[ROW][C]8[/C][C]109.3[/C][C]109.31117426865[/C][C]-0.0111742686498548[/C][/ROW]
[ROW][C]9[/C][C]102.2[/C][C]102.171774544026[/C][C]0.0282254559741971[/C][/ROW]
[ROW][C]10[/C][C]109.8[/C][C]109.798485878217[/C][C]0.00151412178262483[/C][/ROW]
[ROW][C]11[/C][C]106.2[/C][C]106.058546303668[/C][C]0.141453696331729[/C][/ROW]
[ROW][C]12[/C][C]95.1[/C][C]94.9967769341452[/C][C]0.103223065854754[/C][/ROW]
[ROW][C]13[/C][C]118.7[/C][C]118.719867687733[/C][C]-0.0198676877331609[/C][/ROW]
[ROW][C]14[/C][C]116.9[/C][C]116.98342190227[/C][C]-0.0834219022696744[/C][/ROW]
[ROW][C]15[/C][C]105.3[/C][C]105.318083017428[/C][C]-0.0180830174279993[/C][/ROW]
[ROW][C]16[/C][C]119.5[/C][C]119.46780148654[/C][C]0.0321985134595846[/C][/ROW]
[ROW][C]17[/C][C]96.5[/C][C]96.4535264580235[/C][C]0.046473541976477[/C][/ROW]
[ROW][C]18[/C][C]99.3[/C][C]99.3198630021812[/C][C]-0.0198630021811765[/C][/ROW]
[ROW][C]19[/C][C]113.8[/C][C]113.759221454589[/C][C]0.0407785454106511[/C][/ROW]
[ROW][C]20[/C][C]102.7[/C][C]102.739089661302[/C][C]-0.0390896613018[/C][/ROW]
[ROW][C]21[/C][C]98.8[/C][C]98.7843219557456[/C][C]0.0156780442543754[/C][/ROW]
[ROW][C]22[/C][C]109.9[/C][C]109.884776441894[/C][C]0.015223558105621[/C][/ROW]
[ROW][C]23[/C][C]103.6[/C][C]103.631650562492[/C][C]-0.0316505624924117[/C][/ROW]
[ROW][C]24[/C][C]96.6[/C][C]96.5830185528178[/C][C]0.0169814471822037[/C][/ROW]
[ROW][C]25[/C][C]111.6[/C][C]111.55217495657[/C][C]0.0478250434295866[/C][/ROW]
[ROW][C]26[/C][C]111.6[/C][C]111.651071596718[/C][C]-0.0510715967177158[/C][/ROW]
[ROW][C]27[/C][C]107[/C][C]107.050105691926[/C][C]-0.0501056919261603[/C][/ROW]
[ROW][C]28[/C][C]111.5[/C][C]111.501754194456[/C][C]-0.00175419445589264[/C][/ROW]
[ROW][C]29[/C][C]102[/C][C]101.936905193318[/C][C]0.0630948066817323[/C][/ROW]
[ROW][C]30[/C][C]113.5[/C][C]113.582354338177[/C][C]-0.082354338177334[/C][/ROW]
[ROW][C]31[/C][C]125.5[/C][C]125.464341391217[/C][C]0.035658608783084[/C][/ROW]
[ROW][C]32[/C][C]106.7[/C][C]106.760384472025[/C][C]-0.0603844720245266[/C][/ROW]
[ROW][C]33[/C][C]102.9[/C][C]102.981414616891[/C][C]-0.0814146168914362[/C][/ROW]
[ROW][C]34[/C][C]123.6[/C][C]123.621300075386[/C][C]-0.0213000753861574[/C][/ROW]
[ROW][C]35[/C][C]107.7[/C][C]107.787367654133[/C][C]-0.0873676541326206[/C][/ROW]
[ROW][C]36[/C][C]105.5[/C][C]105.474108597642[/C][C]0.0258914023577819[/C][/ROW]
[ROW][C]37[/C][C]117.1[/C][C]117.067470961604[/C][C]0.0325290383960834[/C][/ROW]
[ROW][C]38[/C][C]113.3[/C][C]113.316230628783[/C][C]-0.0162306287829366[/C][/ROW]
[ROW][C]39[/C][C]118[/C][C]117.989468786286[/C][C]0.0105312137139159[/C][/ROW]
[ROW][C]40[/C][C]118.4[/C][C]118.376917872583[/C][C]0.0230821274172496[/C][/ROW]
[ROW][C]41[/C][C]105.8[/C][C]105.827331215546[/C][C]-0.0273312155463768[/C][/ROW]
[ROW][C]42[/C][C]114.6[/C][C]114.556881873151[/C][C]0.0431181268485605[/C][/ROW]
[ROW][C]43[/C][C]140.3[/C][C]140.280917341614[/C][C]0.0190826583860364[/C][/ROW]
[ROW][C]44[/C][C]113.8[/C][C]113.849769134804[/C][C]-0.049769134804425[/C][/ROW]
[ROW][C]45[/C][C]117.4[/C][C]117.376456024901[/C][C]0.0235439750988632[/C][/ROW]
[ROW][C]46[/C][C]115.4[/C][C]115.477796848277[/C][C]-0.0777968482773577[/C][/ROW]
[ROW][C]47[/C][C]105.9[/C][C]105.930646086628[/C][C]-0.0306460866274992[/C][/ROW]
[ROW][C]48[/C][C]120.4[/C][C]120.428569883196[/C][C]-0.0285698831960411[/C][/ROW]
[ROW][C]49[/C][C]126.9[/C][C]126.766348063883[/C][C]0.133651936116977[/C][/ROW]
[ROW][C]50[/C][C]117.1[/C][C]117.14278338609[/C][C]-0.0427833860904782[/C][/ROW]
[ROW][C]51[/C][C]113.8[/C][C]113.741372331282[/C][C]0.0586276687175225[/C][/ROW]
[ROW][C]52[/C][C]112.8[/C][C]112.807357358688[/C][C]-0.00735735868780357[/C][/ROW]
[ROW][C]53[/C][C]106.7[/C][C]106.647992392096[/C][C]0.0520076079040447[/C][/ROW]
[ROW][C]54[/C][C]107.3[/C][C]107.23906791907[/C][C]0.0609320809300585[/C][/ROW]
[ROW][C]55[/C][C]121.8[/C][C]121.738571619273[/C][C]0.0614283807267671[/C][/ROW]
[ROW][C]56[/C][C]101.1[/C][C]101.013822198419[/C][C]0.0861778015806762[/C][/ROW]
[ROW][C]57[/C][C]103.1[/C][C]103.03719643495[/C][C]0.0628035650497927[/C][/ROW]
[ROW][C]58[/C][C]110.4[/C][C]110.334548504068[/C][C]0.0654514959321681[/C][/ROW]
[ROW][C]59[/C][C]108.3[/C][C]108.374236365562[/C][C]-0.0742363655620307[/C][/ROW]
[ROW][C]60[/C][C]116.3[/C][C]116.358256357108[/C][C]-0.0582563571075098[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191284&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191284&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	104.8	104.888971472303	-0.088971472302516
2	111.4	111.482915104	-0.0829151039995374
3	106.9	106.888428092396	0.0115719076043193
4	103.9	103.926079470486	-0.0260794704857729
5	107.6	107.525312990977	0.0746870090228907
6	104.7	104.753469504161	-0.0534695041610923
7	109.1	109.210130887631	-0.11013088763103
8	109.3	109.31117426865	-0.0111742686498548
9	102.2	102.171774544026	0.0282254559741971
10	109.8	109.798485878217	0.00151412178262483
11	106.2	106.058546303668	0.141453696331729
12	95.1	94.9967769341452	0.103223065854754
13	118.7	118.719867687733	-0.0198676877331609
14	116.9	116.98342190227	-0.0834219022696744
15	105.3	105.318083017428	-0.0180830174279993
16	119.5	119.46780148654	0.0321985134595846
17	96.5	96.4535264580235	0.046473541976477
18	99.3	99.3198630021812	-0.0198630021811765
19	113.8	113.759221454589	0.0407785454106511
20	102.7	102.739089661302	-0.0390896613018
21	98.8	98.7843219557456	0.0156780442543754
22	109.9	109.884776441894	0.015223558105621
23	103.6	103.631650562492	-0.0316505624924117
24	96.6	96.5830185528178	0.0169814471822037
25	111.6	111.55217495657	0.0478250434295866
26	111.6	111.651071596718	-0.0510715967177158
27	107	107.050105691926	-0.0501056919261603
28	111.5	111.501754194456	-0.00175419445589264
29	102	101.936905193318	0.0630948066817323
30	113.5	113.582354338177	-0.082354338177334
31	125.5	125.464341391217	0.035658608783084
32	106.7	106.760384472025	-0.0603844720245266
33	102.9	102.981414616891	-0.0814146168914362
34	123.6	123.621300075386	-0.0213000753861574
35	107.7	107.787367654133	-0.0873676541326206
36	105.5	105.474108597642	0.0258914023577819
37	117.1	117.067470961604	0.0325290383960834
38	113.3	113.316230628783	-0.0162306287829366
39	118	117.989468786286	0.0105312137139159
40	118.4	118.376917872583	0.0230821274172496
41	105.8	105.827331215546	-0.0273312155463768
42	114.6	114.556881873151	0.0431181268485605
43	140.3	140.280917341614	0.0190826583860364
44	113.8	113.849769134804	-0.049769134804425
45	117.4	117.376456024901	0.0235439750988632
46	115.4	115.477796848277	-0.0777968482773577
47	105.9	105.930646086628	-0.0306460866274992
48	120.4	120.428569883196	-0.0285698831960411
49	126.9	126.766348063883	0.133651936116977
50	117.1	117.14278338609	-0.0427833860904782
51	113.8	113.741372331282	0.0586276687175225
52	112.8	112.807357358688	-0.00735735868780357
53	106.7	106.647992392096	0.0520076079040447
54	107.3	107.23906791907	0.0609320809300585
55	121.8	121.738571619273	0.0614283807267671
56	101.1	101.013822198419	0.0861778015806762
57	103.1	103.03719643495	0.0628035650497927
58	110.4	110.334548504068	0.0654514959321681
59	108.3	108.374236365562	-0.0742363655620307
60	116.3	116.358256357108	-0.0582563571075098

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.259291111409659	0.518582222819317	0.740708888590341
9	0.130271659658021	0.260543319316041	0.86972834034198
10	0.245751051983896	0.491502103967793	0.754248948016104
11	0.225123856486763	0.450247712973525	0.774876143513237
12	0.198546325304212	0.397092650608423	0.801453674695788
13	0.169386128625961	0.338772257251922	0.830613871374039
14	0.210719059590411	0.421438119180822	0.789280940409589
15	0.245047692478776	0.490095384957552	0.754952307521224
16	0.174747740264294	0.349495480528588	0.825252259735706
17	0.125732297678656	0.251464595357313	0.874267702321344
18	0.0934493167159645	0.186898633431929	0.906550683284036
19	0.111332576036209	0.222665152072419	0.888667423963791
20	0.130815572881405	0.261631145762811	0.869184427118595
21	0.10630372477011	0.212607449540221	0.89369627522989
22	0.0960954396093792	0.192190879218758	0.903904560390621
23	0.447238122379049	0.894476244758098	0.552761877620951
24	0.423543618473257	0.847087236946514	0.576456381526743
25	0.560462718851988	0.879074562296025	0.439537281148012
26	0.630431540515533	0.739136918968934	0.369568459484467
27	0.72681905573844	0.546361888523121	0.27318094426156
28	0.682274477278753	0.635451045442494	0.317725522721247
29	0.658794333675468	0.682411332649064	0.341205666324532
30	0.827075067292991	0.345849865414019	0.172924932707009
31	0.794145355423937	0.411709289152126	0.205854644576063
32	0.852702970787752	0.294594058424496	0.147297029212248
33	0.937072814526947	0.125854370946105	0.0629271854730526
34	0.915089898567028	0.169820202865945	0.0849101014329723
35	0.950045134763118	0.099909730473764	0.049954865236882
36	0.963896859577557	0.072206280844887	0.0361031404224435
37	0.949746801417547	0.100506397164906	0.0502531985824532
38	0.938537351312439	0.122925297375122	0.0614626486875609
39	0.90735697749086	0.185286045018281	0.0926430225091403
40	0.869650237225643	0.260699525548714	0.130349762774357
41	0.828996497769782	0.342007004460437	0.171003502230218
42	0.788223245177958	0.423553509644083	0.211776754822042
43	0.764671250174808	0.470657499650385	0.235328749825192
44	0.707257799525129	0.585484400949742	0.292742200474871
45	0.630552780117009	0.738894439765983	0.369447219882991
46	0.727937110300246	0.544125779399508	0.272062889699754
47	0.639820231847753	0.720359536304493	0.360179768152246
48	0.568263986737175	0.863472026525649	0.431736013262825
49	0.868133985499376	0.263732029001248	0.131866014500624
50	0.935534992620885	0.128930014758231	0.0644650073791154
51	0.894979084532413	0.210041830935175	0.105020915467587
52	0.78561580272991	0.42876839454018	0.21438419727009

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.259291111409659 & 0.518582222819317 & 0.740708888590341 \tabularnewline
9 & 0.130271659658021 & 0.260543319316041 & 0.86972834034198 \tabularnewline
10 & 0.245751051983896 & 0.491502103967793 & 0.754248948016104 \tabularnewline
11 & 0.225123856486763 & 0.450247712973525 & 0.774876143513237 \tabularnewline
12 & 0.198546325304212 & 0.397092650608423 & 0.801453674695788 \tabularnewline
13 & 0.169386128625961 & 0.338772257251922 & 0.830613871374039 \tabularnewline
14 & 0.210719059590411 & 0.421438119180822 & 0.789280940409589 \tabularnewline
15 & 0.245047692478776 & 0.490095384957552 & 0.754952307521224 \tabularnewline
16 & 0.174747740264294 & 0.349495480528588 & 0.825252259735706 \tabularnewline
17 & 0.125732297678656 & 0.251464595357313 & 0.874267702321344 \tabularnewline
18 & 0.0934493167159645 & 0.186898633431929 & 0.906550683284036 \tabularnewline
19 & 0.111332576036209 & 0.222665152072419 & 0.888667423963791 \tabularnewline
20 & 0.130815572881405 & 0.261631145762811 & 0.869184427118595 \tabularnewline
21 & 0.10630372477011 & 0.212607449540221 & 0.89369627522989 \tabularnewline
22 & 0.0960954396093792 & 0.192190879218758 & 0.903904560390621 \tabularnewline
23 & 0.447238122379049 & 0.894476244758098 & 0.552761877620951 \tabularnewline
24 & 0.423543618473257 & 0.847087236946514 & 0.576456381526743 \tabularnewline
25 & 0.560462718851988 & 0.879074562296025 & 0.439537281148012 \tabularnewline
26 & 0.630431540515533 & 0.739136918968934 & 0.369568459484467 \tabularnewline
27 & 0.72681905573844 & 0.546361888523121 & 0.27318094426156 \tabularnewline
28 & 0.682274477278753 & 0.635451045442494 & 0.317725522721247 \tabularnewline
29 & 0.658794333675468 & 0.682411332649064 & 0.341205666324532 \tabularnewline
30 & 0.827075067292991 & 0.345849865414019 & 0.172924932707009 \tabularnewline
31 & 0.794145355423937 & 0.411709289152126 & 0.205854644576063 \tabularnewline
32 & 0.852702970787752 & 0.294594058424496 & 0.147297029212248 \tabularnewline
33 & 0.937072814526947 & 0.125854370946105 & 0.0629271854730526 \tabularnewline
34 & 0.915089898567028 & 0.169820202865945 & 0.0849101014329723 \tabularnewline
35 & 0.950045134763118 & 0.099909730473764 & 0.049954865236882 \tabularnewline
36 & 0.963896859577557 & 0.072206280844887 & 0.0361031404224435 \tabularnewline
37 & 0.949746801417547 & 0.100506397164906 & 0.0502531985824532 \tabularnewline
38 & 0.938537351312439 & 0.122925297375122 & 0.0614626486875609 \tabularnewline
39 & 0.90735697749086 & 0.185286045018281 & 0.0926430225091403 \tabularnewline
40 & 0.869650237225643 & 0.260699525548714 & 0.130349762774357 \tabularnewline
41 & 0.828996497769782 & 0.342007004460437 & 0.171003502230218 \tabularnewline
42 & 0.788223245177958 & 0.423553509644083 & 0.211776754822042 \tabularnewline
43 & 0.764671250174808 & 0.470657499650385 & 0.235328749825192 \tabularnewline
44 & 0.707257799525129 & 0.585484400949742 & 0.292742200474871 \tabularnewline
45 & 0.630552780117009 & 0.738894439765983 & 0.369447219882991 \tabularnewline
46 & 0.727937110300246 & 0.544125779399508 & 0.272062889699754 \tabularnewline
47 & 0.639820231847753 & 0.720359536304493 & 0.360179768152246 \tabularnewline
48 & 0.568263986737175 & 0.863472026525649 & 0.431736013262825 \tabularnewline
49 & 0.868133985499376 & 0.263732029001248 & 0.131866014500624 \tabularnewline
50 & 0.935534992620885 & 0.128930014758231 & 0.0644650073791154 \tabularnewline
51 & 0.894979084532413 & 0.210041830935175 & 0.105020915467587 \tabularnewline
52 & 0.78561580272991 & 0.42876839454018 & 0.21438419727009 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191284&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]0.259291111409659[/C][C]0.518582222819317[/C][C]0.740708888590341[/C][/ROW]
[ROW][C]9[/C][C]0.130271659658021[/C][C]0.260543319316041[/C][C]0.86972834034198[/C][/ROW]
[ROW][C]10[/C][C]0.245751051983896[/C][C]0.491502103967793[/C][C]0.754248948016104[/C][/ROW]
[ROW][C]11[/C][C]0.225123856486763[/C][C]0.450247712973525[/C][C]0.774876143513237[/C][/ROW]
[ROW][C]12[/C][C]0.198546325304212[/C][C]0.397092650608423[/C][C]0.801453674695788[/C][/ROW]
[ROW][C]13[/C][C]0.169386128625961[/C][C]0.338772257251922[/C][C]0.830613871374039[/C][/ROW]
[ROW][C]14[/C][C]0.210719059590411[/C][C]0.421438119180822[/C][C]0.789280940409589[/C][/ROW]
[ROW][C]15[/C][C]0.245047692478776[/C][C]0.490095384957552[/C][C]0.754952307521224[/C][/ROW]
[ROW][C]16[/C][C]0.174747740264294[/C][C]0.349495480528588[/C][C]0.825252259735706[/C][/ROW]
[ROW][C]17[/C][C]0.125732297678656[/C][C]0.251464595357313[/C][C]0.874267702321344[/C][/ROW]
[ROW][C]18[/C][C]0.0934493167159645[/C][C]0.186898633431929[/C][C]0.906550683284036[/C][/ROW]
[ROW][C]19[/C][C]0.111332576036209[/C][C]0.222665152072419[/C][C]0.888667423963791[/C][/ROW]
[ROW][C]20[/C][C]0.130815572881405[/C][C]0.261631145762811[/C][C]0.869184427118595[/C][/ROW]
[ROW][C]21[/C][C]0.10630372477011[/C][C]0.212607449540221[/C][C]0.89369627522989[/C][/ROW]
[ROW][C]22[/C][C]0.0960954396093792[/C][C]0.192190879218758[/C][C]0.903904560390621[/C][/ROW]
[ROW][C]23[/C][C]0.447238122379049[/C][C]0.894476244758098[/C][C]0.552761877620951[/C][/ROW]
[ROW][C]24[/C][C]0.423543618473257[/C][C]0.847087236946514[/C][C]0.576456381526743[/C][/ROW]
[ROW][C]25[/C][C]0.560462718851988[/C][C]0.879074562296025[/C][C]0.439537281148012[/C][/ROW]
[ROW][C]26[/C][C]0.630431540515533[/C][C]0.739136918968934[/C][C]0.369568459484467[/C][/ROW]
[ROW][C]27[/C][C]0.72681905573844[/C][C]0.546361888523121[/C][C]0.27318094426156[/C][/ROW]
[ROW][C]28[/C][C]0.682274477278753[/C][C]0.635451045442494[/C][C]0.317725522721247[/C][/ROW]
[ROW][C]29[/C][C]0.658794333675468[/C][C]0.682411332649064[/C][C]0.341205666324532[/C][/ROW]
[ROW][C]30[/C][C]0.827075067292991[/C][C]0.345849865414019[/C][C]0.172924932707009[/C][/ROW]
[ROW][C]31[/C][C]0.794145355423937[/C][C]0.411709289152126[/C][C]0.205854644576063[/C][/ROW]
[ROW][C]32[/C][C]0.852702970787752[/C][C]0.294594058424496[/C][C]0.147297029212248[/C][/ROW]
[ROW][C]33[/C][C]0.937072814526947[/C][C]0.125854370946105[/C][C]0.0629271854730526[/C][/ROW]
[ROW][C]34[/C][C]0.915089898567028[/C][C]0.169820202865945[/C][C]0.0849101014329723[/C][/ROW]
[ROW][C]35[/C][C]0.950045134763118[/C][C]0.099909730473764[/C][C]0.049954865236882[/C][/ROW]
[ROW][C]36[/C][C]0.963896859577557[/C][C]0.072206280844887[/C][C]0.0361031404224435[/C][/ROW]
[ROW][C]37[/C][C]0.949746801417547[/C][C]0.100506397164906[/C][C]0.0502531985824532[/C][/ROW]
[ROW][C]38[/C][C]0.938537351312439[/C][C]0.122925297375122[/C][C]0.0614626486875609[/C][/ROW]
[ROW][C]39[/C][C]0.90735697749086[/C][C]0.185286045018281[/C][C]0.0926430225091403[/C][/ROW]
[ROW][C]40[/C][C]0.869650237225643[/C][C]0.260699525548714[/C][C]0.130349762774357[/C][/ROW]
[ROW][C]41[/C][C]0.828996497769782[/C][C]0.342007004460437[/C][C]0.171003502230218[/C][/ROW]
[ROW][C]42[/C][C]0.788223245177958[/C][C]0.423553509644083[/C][C]0.211776754822042[/C][/ROW]
[ROW][C]43[/C][C]0.764671250174808[/C][C]0.470657499650385[/C][C]0.235328749825192[/C][/ROW]
[ROW][C]44[/C][C]0.707257799525129[/C][C]0.585484400949742[/C][C]0.292742200474871[/C][/ROW]
[ROW][C]45[/C][C]0.630552780117009[/C][C]0.738894439765983[/C][C]0.369447219882991[/C][/ROW]
[ROW][C]46[/C][C]0.727937110300246[/C][C]0.544125779399508[/C][C]0.272062889699754[/C][/ROW]
[ROW][C]47[/C][C]0.639820231847753[/C][C]0.720359536304493[/C][C]0.360179768152246[/C][/ROW]
[ROW][C]48[/C][C]0.568263986737175[/C][C]0.863472026525649[/C][C]0.431736013262825[/C][/ROW]
[ROW][C]49[/C][C]0.868133985499376[/C][C]0.263732029001248[/C][C]0.131866014500624[/C][/ROW]
[ROW][C]50[/C][C]0.935534992620885[/C][C]0.128930014758231[/C][C]0.0644650073791154[/C][/ROW]
[ROW][C]51[/C][C]0.894979084532413[/C][C]0.210041830935175[/C][C]0.105020915467587[/C][/ROW]
[ROW][C]52[/C][C]0.78561580272991[/C][C]0.42876839454018[/C][C]0.21438419727009[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191284&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191284&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
8	0.259291111409659	0.518582222819317	0.740708888590341
9	0.130271659658021	0.260543319316041	0.86972834034198
10	0.245751051983896	0.491502103967793	0.754248948016104
11	0.225123856486763	0.450247712973525	0.774876143513237
12	0.198546325304212	0.397092650608423	0.801453674695788
13	0.169386128625961	0.338772257251922	0.830613871374039
14	0.210719059590411	0.421438119180822	0.789280940409589
15	0.245047692478776	0.490095384957552	0.754952307521224
16	0.174747740264294	0.349495480528588	0.825252259735706
17	0.125732297678656	0.251464595357313	0.874267702321344
18	0.0934493167159645	0.186898633431929	0.906550683284036
19	0.111332576036209	0.222665152072419	0.888667423963791
20	0.130815572881405	0.261631145762811	0.869184427118595
21	0.10630372477011	0.212607449540221	0.89369627522989
22	0.0960954396093792	0.192190879218758	0.903904560390621
23	0.447238122379049	0.894476244758098	0.552761877620951
24	0.423543618473257	0.847087236946514	0.576456381526743
25	0.560462718851988	0.879074562296025	0.439537281148012
26	0.630431540515533	0.739136918968934	0.369568459484467
27	0.72681905573844	0.546361888523121	0.27318094426156
28	0.682274477278753	0.635451045442494	0.317725522721247
29	0.658794333675468	0.682411332649064	0.341205666324532
30	0.827075067292991	0.345849865414019	0.172924932707009
31	0.794145355423937	0.411709289152126	0.205854644576063
32	0.852702970787752	0.294594058424496	0.147297029212248
33	0.937072814526947	0.125854370946105	0.0629271854730526
34	0.915089898567028	0.169820202865945	0.0849101014329723
35	0.950045134763118	0.099909730473764	0.049954865236882
36	0.963896859577557	0.072206280844887	0.0361031404224435
37	0.949746801417547	0.100506397164906	0.0502531985824532
38	0.938537351312439	0.122925297375122	0.0614626486875609
39	0.90735697749086	0.185286045018281	0.0926430225091403
40	0.869650237225643	0.260699525548714	0.130349762774357
41	0.828996497769782	0.342007004460437	0.171003502230218
42	0.788223245177958	0.423553509644083	0.211776754822042
43	0.764671250174808	0.470657499650385	0.235328749825192
44	0.707257799525129	0.585484400949742	0.292742200474871
45	0.630552780117009	0.738894439765983	0.369447219882991
46	0.727937110300246	0.544125779399508	0.272062889699754
47	0.639820231847753	0.720359536304493	0.360179768152246
48	0.568263986737175	0.863472026525649	0.431736013262825
49	0.868133985499376	0.263732029001248	0.131866014500624
50	0.935534992620885	0.128930014758231	0.0644650073791154
51	0.894979084532413	0.210041830935175	0.105020915467587
52	0.78561580272991	0.42876839454018	0.21438419727009

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	2	0.0444444444444444	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 & 2 & 0.0444444444444444 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191284&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]2[/C][C]0.0444444444444444[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191284&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191284&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	2	0.0444444444444444	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 = 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code