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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 22 Dec 2008 10:58:14 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/22/t1229969230fusp510in7ezgcv.htm/, Retrieved Sun, 12 May 2024 18:18:11 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=36155, Retrieved Sun, 12 May 2024 18:18:11 +0000

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Estimated Impact

147

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

-     [Multiple Regression] [Multiple..] [2008-12-09 17:33:14] [f77c9ab3b413812d7baee6b7ec69a15d]
-   PD    [Multiple Regression] [Multiple Linear R...] [2008-12-22 17:58:14] [d300b7a0882cee7d84584ad37a3d4ede] [Current]

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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	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36155&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36155&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36155&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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Suiker[t] = + 99.4132576769026 -2.44007113962896dummie[t] + 0.104017120867038t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Suiker[t] =  +  99.4132576769026 -2.44007113962896dummie[t] +  0.104017120867038t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36155&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Suiker[t] =  +  99.4132576769026 -2.44007113962896dummie[t] +  0.104017120867038t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36155&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36155&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
Suiker[t] = + 99.4132576769026 -2.44007113962896dummie[t] + 0.104017120867038t + 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)	99.4132576769026	0.148892	667.6885	0	0
dummie	-2.44007113962896	0.226128	-10.7907	0	0
t	0.104017120867038	0.003747	27.7604	0	0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 99.4132576769026 & 0.148892 & 667.6885 & 0 & 0 \tabularnewline
dummie & -2.44007113962896 & 0.226128 & -10.7907 & 0 & 0 \tabularnewline
t & 0.104017120867038 & 0.003747 & 27.7604 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36155&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]99.4132576769026[/C][C]0.148892[/C][C]667.6885[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]dummie[/C][C]-2.44007113962896[/C][C]0.226128[/C][C]-10.7907[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.104017120867038[/C][C]0.003747[/C][C]27.7604[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36155&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36155&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)	99.4132576769026	0.148892	667.6885	0	0
dummie	-2.44007113962896	0.226128	-10.7907	0	0
t	0.104017120867038	0.003747	27.7604	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.958334489981909
R-squared	0.918404994688885
Adjusted R-squared	0.916390303199722
F-TEST (value)	455.853910948063
F-TEST (DF numerator)	2
F-TEST (DF denominator)	81
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.597942876975073
Sum Squared Residuals	28.9603904141434

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.958334489981909 \tabularnewline
R-squared & 0.918404994688885 \tabularnewline
Adjusted R-squared & 0.916390303199722 \tabularnewline
F-TEST (value) & 455.853910948063 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 81 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.597942876975073 \tabularnewline
Sum Squared Residuals & 28.9603904141434 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36155&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.958334489981909[/C][/ROW]
[ROW][C]R-squared[/C][C]0.918404994688885[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.916390303199722[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]455.853910948063[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]81[/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]0.597942876975073[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]28.9603904141434[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36155&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36155&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	0.958334489981909
R-squared	0.918404994688885
Adjusted R-squared	0.916390303199722
F-TEST (value)	455.853910948063
F-TEST (DF numerator)	2
F-TEST (DF denominator)	81
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.597942876975073
Sum Squared Residuals	28.9603904141434

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.02	99.5172747977695	1.50272520223053
2	100.67	99.6212919186366	1.04870808136339
3	100.47	99.7253090395037	0.744690960496348
4	100.38	99.8293261603707	0.550673839629299
5	100.33	99.9333432812377	0.396656718762264
6	100.34	100.037360402105	0.302639597895230
7	100.37	100.141377522972	0.228622477028193
8	100.39	100.245394643839	0.144605356161151
9	100.21	100.349411764706	-0.139411764705895
10	100.21	100.453428885573	-0.243428885572933
11	100.22	100.55744600644	-0.337446006439966
12	100.28	100.661463127307	-0.381463127307002
13	100.25	100.765480248174	-0.515480248174042
14	100.25	100.869497369041	-0.61949736904108
15	100.21	100.973514489908	-0.763514489908125
16	100.16	101.077531610775	-0.91753161077516
17	100.18	101.181548731642	-1.00154873164219
18	100.1	98.8454947128803	1.25450528711972
19	99.96	98.9495118337473	1.01048816625269
20	99.88	99.0535289546144	0.82647104538565
21	99.88	99.1575460754814	0.722453924518611
22	99.86	99.2615631963484	0.598436803651576
23	99.84	99.3655803172155	0.474419682784542
24	99.8	99.4695974380825	0.330402561917497
25	99.82	99.5736145589495	0.246385441050455
26	99.81	99.6776316798166	0.132368320183426
27	99.92	99.7816488006836	0.138351199316387
28	100.03	99.8856659215507	0.144334078449348
29	99.99	99.9896830424177	0.000316957582303101
30	100.02	100.093700163285	-0.0737001632847341
31	100.01	100.197717284152	-0.187717284151763
32	100.13	100.301734405019	-0.171734405018812
33	100.33	100.405751525886	-0.075751525885847
34	100.13	100.509768646753	-0.379768646752888
35	99.96	100.61378576762	-0.653785767619928
36	100.05	100.717802888487	-0.667802888486963
37	99.83	100.821820009354	-0.991820009354
38	99.8	100.925837130221	-1.12583713022104
39	100.01	101.029854251088	-1.01985425108807
40	100.1	101.133871371955	-1.03387137195512
41	100.13	101.237888492822	-1.10788849282216
42	100.16	101.341905613689	-1.18190561368919
43	100.41	101.445922734556	-1.03592273455623
44	101.34	101.549939855423	-0.209939855423264
45	101.65	101.653956976290	-0.00395697629030009
46	101.85	101.757974097157	0.0920259028426502
47	102.07	101.861991218024	0.208008781975611
48	102.12	101.966008338891	0.153991661108584
49	102.14	102.070025459758	0.0699745402415413
50	102.21	102.174042580625	0.0359574193744960
51	102.28	102.278059701493	0.00194029850746505
52	102.19	102.382076822360	-0.192076822359577
53	102.33	102.486093943227	-0.156093943226615
54	102.54	102.590111064094	-0.050111064093645
55	102.44	102.694128184961	-0.254128184960692
56	102.78	102.798145305828	-0.0181453058277269
57	102.9	102.902162426695	-0.00216242669476072
58	103.08	103.006179547562	0.0738204524381934
59	102.77	103.110196668429	-0.340196668428847
60	102.65	103.214213789296	-0.564213789295876
61	102.71	103.318230910163	-0.608230910162926
62	103.29	103.42224803103	-0.132248031029952
63	102.86	103.526265151897	-0.666265151896997
64	103.45	103.630282272764	-0.180282272764033
65	103.72	103.734299393631	-0.0142993936310746
66	103.65	103.838316514498	-0.188316514498106
67	103.83	103.942333635365	-0.112333635365152
68	104.45	104.046350756232	0.403649243767814
69	105.14	104.150367877099	0.989632122900773
70	105.07	104.254384997966	0.815615002033727
71	105.31	104.358402118833	0.951597881166698
72	105.19	104.462419239700	0.727580760299655
73	105.3	104.566436360567	0.733563639432616
74	105.02	104.670453481434	0.349546518565576
75	105.17	104.774470602301	0.395529397698544
76	105.28	104.878487723168	0.401512276831505
77	105.45	104.982504844036	0.467495155964468
78	105.38	105.086521964903	0.293478035097423
79	105.8	105.190539085770	0.609460914230386
80	105.96	105.294556206637	0.665443793363344
81	105.08	105.398573327504	-0.318573327503690
82	105.11	105.502590448371	-0.392590448370727
83	105.61	105.606607569238	0.00339243076223475
84	105.5	105.710624690105	-0.210624690104803

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.02 & 99.5172747977695 & 1.50272520223053 \tabularnewline
2 & 100.67 & 99.6212919186366 & 1.04870808136339 \tabularnewline
3 & 100.47 & 99.7253090395037 & 0.744690960496348 \tabularnewline
4 & 100.38 & 99.8293261603707 & 0.550673839629299 \tabularnewline
5 & 100.33 & 99.9333432812377 & 0.396656718762264 \tabularnewline
6 & 100.34 & 100.037360402105 & 0.302639597895230 \tabularnewline
7 & 100.37 & 100.141377522972 & 0.228622477028193 \tabularnewline
8 & 100.39 & 100.245394643839 & 0.144605356161151 \tabularnewline
9 & 100.21 & 100.349411764706 & -0.139411764705895 \tabularnewline
10 & 100.21 & 100.453428885573 & -0.243428885572933 \tabularnewline
11 & 100.22 & 100.55744600644 & -0.337446006439966 \tabularnewline
12 & 100.28 & 100.661463127307 & -0.381463127307002 \tabularnewline
13 & 100.25 & 100.765480248174 & -0.515480248174042 \tabularnewline
14 & 100.25 & 100.869497369041 & -0.61949736904108 \tabularnewline
15 & 100.21 & 100.973514489908 & -0.763514489908125 \tabularnewline
16 & 100.16 & 101.077531610775 & -0.91753161077516 \tabularnewline
17 & 100.18 & 101.181548731642 & -1.00154873164219 \tabularnewline
18 & 100.1 & 98.8454947128803 & 1.25450528711972 \tabularnewline
19 & 99.96 & 98.9495118337473 & 1.01048816625269 \tabularnewline
20 & 99.88 & 99.0535289546144 & 0.82647104538565 \tabularnewline
21 & 99.88 & 99.1575460754814 & 0.722453924518611 \tabularnewline
22 & 99.86 & 99.2615631963484 & 0.598436803651576 \tabularnewline
23 & 99.84 & 99.3655803172155 & 0.474419682784542 \tabularnewline
24 & 99.8 & 99.4695974380825 & 0.330402561917497 \tabularnewline
25 & 99.82 & 99.5736145589495 & 0.246385441050455 \tabularnewline
26 & 99.81 & 99.6776316798166 & 0.132368320183426 \tabularnewline
27 & 99.92 & 99.7816488006836 & 0.138351199316387 \tabularnewline
28 & 100.03 & 99.8856659215507 & 0.144334078449348 \tabularnewline
29 & 99.99 & 99.9896830424177 & 0.000316957582303101 \tabularnewline
30 & 100.02 & 100.093700163285 & -0.0737001632847341 \tabularnewline
31 & 100.01 & 100.197717284152 & -0.187717284151763 \tabularnewline
32 & 100.13 & 100.301734405019 & -0.171734405018812 \tabularnewline
33 & 100.33 & 100.405751525886 & -0.075751525885847 \tabularnewline
34 & 100.13 & 100.509768646753 & -0.379768646752888 \tabularnewline
35 & 99.96 & 100.61378576762 & -0.653785767619928 \tabularnewline
36 & 100.05 & 100.717802888487 & -0.667802888486963 \tabularnewline
37 & 99.83 & 100.821820009354 & -0.991820009354 \tabularnewline
38 & 99.8 & 100.925837130221 & -1.12583713022104 \tabularnewline
39 & 100.01 & 101.029854251088 & -1.01985425108807 \tabularnewline
40 & 100.1 & 101.133871371955 & -1.03387137195512 \tabularnewline
41 & 100.13 & 101.237888492822 & -1.10788849282216 \tabularnewline
42 & 100.16 & 101.341905613689 & -1.18190561368919 \tabularnewline
43 & 100.41 & 101.445922734556 & -1.03592273455623 \tabularnewline
44 & 101.34 & 101.549939855423 & -0.209939855423264 \tabularnewline
45 & 101.65 & 101.653956976290 & -0.00395697629030009 \tabularnewline
46 & 101.85 & 101.757974097157 & 0.0920259028426502 \tabularnewline
47 & 102.07 & 101.861991218024 & 0.208008781975611 \tabularnewline
48 & 102.12 & 101.966008338891 & 0.153991661108584 \tabularnewline
49 & 102.14 & 102.070025459758 & 0.0699745402415413 \tabularnewline
50 & 102.21 & 102.174042580625 & 0.0359574193744960 \tabularnewline
51 & 102.28 & 102.278059701493 & 0.00194029850746505 \tabularnewline
52 & 102.19 & 102.382076822360 & -0.192076822359577 \tabularnewline
53 & 102.33 & 102.486093943227 & -0.156093943226615 \tabularnewline
54 & 102.54 & 102.590111064094 & -0.050111064093645 \tabularnewline
55 & 102.44 & 102.694128184961 & -0.254128184960692 \tabularnewline
56 & 102.78 & 102.798145305828 & -0.0181453058277269 \tabularnewline
57 & 102.9 & 102.902162426695 & -0.00216242669476072 \tabularnewline
58 & 103.08 & 103.006179547562 & 0.0738204524381934 \tabularnewline
59 & 102.77 & 103.110196668429 & -0.340196668428847 \tabularnewline
60 & 102.65 & 103.214213789296 & -0.564213789295876 \tabularnewline
61 & 102.71 & 103.318230910163 & -0.608230910162926 \tabularnewline
62 & 103.29 & 103.42224803103 & -0.132248031029952 \tabularnewline
63 & 102.86 & 103.526265151897 & -0.666265151896997 \tabularnewline
64 & 103.45 & 103.630282272764 & -0.180282272764033 \tabularnewline
65 & 103.72 & 103.734299393631 & -0.0142993936310746 \tabularnewline
66 & 103.65 & 103.838316514498 & -0.188316514498106 \tabularnewline
67 & 103.83 & 103.942333635365 & -0.112333635365152 \tabularnewline
68 & 104.45 & 104.046350756232 & 0.403649243767814 \tabularnewline
69 & 105.14 & 104.150367877099 & 0.989632122900773 \tabularnewline
70 & 105.07 & 104.254384997966 & 0.815615002033727 \tabularnewline
71 & 105.31 & 104.358402118833 & 0.951597881166698 \tabularnewline
72 & 105.19 & 104.462419239700 & 0.727580760299655 \tabularnewline
73 & 105.3 & 104.566436360567 & 0.733563639432616 \tabularnewline
74 & 105.02 & 104.670453481434 & 0.349546518565576 \tabularnewline
75 & 105.17 & 104.774470602301 & 0.395529397698544 \tabularnewline
76 & 105.28 & 104.878487723168 & 0.401512276831505 \tabularnewline
77 & 105.45 & 104.982504844036 & 0.467495155964468 \tabularnewline
78 & 105.38 & 105.086521964903 & 0.293478035097423 \tabularnewline
79 & 105.8 & 105.190539085770 & 0.609460914230386 \tabularnewline
80 & 105.96 & 105.294556206637 & 0.665443793363344 \tabularnewline
81 & 105.08 & 105.398573327504 & -0.318573327503690 \tabularnewline
82 & 105.11 & 105.502590448371 & -0.392590448370727 \tabularnewline
83 & 105.61 & 105.606607569238 & 0.00339243076223475 \tabularnewline
84 & 105.5 & 105.710624690105 & -0.210624690104803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36155&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]101.02[/C][C]99.5172747977695[/C][C]1.50272520223053[/C][/ROW]
[ROW][C]2[/C][C]100.67[/C][C]99.6212919186366[/C][C]1.04870808136339[/C][/ROW]
[ROW][C]3[/C][C]100.47[/C][C]99.7253090395037[/C][C]0.744690960496348[/C][/ROW]
[ROW][C]4[/C][C]100.38[/C][C]99.8293261603707[/C][C]0.550673839629299[/C][/ROW]
[ROW][C]5[/C][C]100.33[/C][C]99.9333432812377[/C][C]0.396656718762264[/C][/ROW]
[ROW][C]6[/C][C]100.34[/C][C]100.037360402105[/C][C]0.302639597895230[/C][/ROW]
[ROW][C]7[/C][C]100.37[/C][C]100.141377522972[/C][C]0.228622477028193[/C][/ROW]
[ROW][C]8[/C][C]100.39[/C][C]100.245394643839[/C][C]0.144605356161151[/C][/ROW]
[ROW][C]9[/C][C]100.21[/C][C]100.349411764706[/C][C]-0.139411764705895[/C][/ROW]
[ROW][C]10[/C][C]100.21[/C][C]100.453428885573[/C][C]-0.243428885572933[/C][/ROW]
[ROW][C]11[/C][C]100.22[/C][C]100.55744600644[/C][C]-0.337446006439966[/C][/ROW]
[ROW][C]12[/C][C]100.28[/C][C]100.661463127307[/C][C]-0.381463127307002[/C][/ROW]
[ROW][C]13[/C][C]100.25[/C][C]100.765480248174[/C][C]-0.515480248174042[/C][/ROW]
[ROW][C]14[/C][C]100.25[/C][C]100.869497369041[/C][C]-0.61949736904108[/C][/ROW]
[ROW][C]15[/C][C]100.21[/C][C]100.973514489908[/C][C]-0.763514489908125[/C][/ROW]
[ROW][C]16[/C][C]100.16[/C][C]101.077531610775[/C][C]-0.91753161077516[/C][/ROW]
[ROW][C]17[/C][C]100.18[/C][C]101.181548731642[/C][C]-1.00154873164219[/C][/ROW]
[ROW][C]18[/C][C]100.1[/C][C]98.8454947128803[/C][C]1.25450528711972[/C][/ROW]
[ROW][C]19[/C][C]99.96[/C][C]98.9495118337473[/C][C]1.01048816625269[/C][/ROW]
[ROW][C]20[/C][C]99.88[/C][C]99.0535289546144[/C][C]0.82647104538565[/C][/ROW]
[ROW][C]21[/C][C]99.88[/C][C]99.1575460754814[/C][C]0.722453924518611[/C][/ROW]
[ROW][C]22[/C][C]99.86[/C][C]99.2615631963484[/C][C]0.598436803651576[/C][/ROW]
[ROW][C]23[/C][C]99.84[/C][C]99.3655803172155[/C][C]0.474419682784542[/C][/ROW]
[ROW][C]24[/C][C]99.8[/C][C]99.4695974380825[/C][C]0.330402561917497[/C][/ROW]
[ROW][C]25[/C][C]99.82[/C][C]99.5736145589495[/C][C]0.246385441050455[/C][/ROW]
[ROW][C]26[/C][C]99.81[/C][C]99.6776316798166[/C][C]0.132368320183426[/C][/ROW]
[ROW][C]27[/C][C]99.92[/C][C]99.7816488006836[/C][C]0.138351199316387[/C][/ROW]
[ROW][C]28[/C][C]100.03[/C][C]99.8856659215507[/C][C]0.144334078449348[/C][/ROW]
[ROW][C]29[/C][C]99.99[/C][C]99.9896830424177[/C][C]0.000316957582303101[/C][/ROW]
[ROW][C]30[/C][C]100.02[/C][C]100.093700163285[/C][C]-0.0737001632847341[/C][/ROW]
[ROW][C]31[/C][C]100.01[/C][C]100.197717284152[/C][C]-0.187717284151763[/C][/ROW]
[ROW][C]32[/C][C]100.13[/C][C]100.301734405019[/C][C]-0.171734405018812[/C][/ROW]
[ROW][C]33[/C][C]100.33[/C][C]100.405751525886[/C][C]-0.075751525885847[/C][/ROW]
[ROW][C]34[/C][C]100.13[/C][C]100.509768646753[/C][C]-0.379768646752888[/C][/ROW]
[ROW][C]35[/C][C]99.96[/C][C]100.61378576762[/C][C]-0.653785767619928[/C][/ROW]
[ROW][C]36[/C][C]100.05[/C][C]100.717802888487[/C][C]-0.667802888486963[/C][/ROW]
[ROW][C]37[/C][C]99.83[/C][C]100.821820009354[/C][C]-0.991820009354[/C][/ROW]
[ROW][C]38[/C][C]99.8[/C][C]100.925837130221[/C][C]-1.12583713022104[/C][/ROW]
[ROW][C]39[/C][C]100.01[/C][C]101.029854251088[/C][C]-1.01985425108807[/C][/ROW]
[ROW][C]40[/C][C]100.1[/C][C]101.133871371955[/C][C]-1.03387137195512[/C][/ROW]
[ROW][C]41[/C][C]100.13[/C][C]101.237888492822[/C][C]-1.10788849282216[/C][/ROW]
[ROW][C]42[/C][C]100.16[/C][C]101.341905613689[/C][C]-1.18190561368919[/C][/ROW]
[ROW][C]43[/C][C]100.41[/C][C]101.445922734556[/C][C]-1.03592273455623[/C][/ROW]
[ROW][C]44[/C][C]101.34[/C][C]101.549939855423[/C][C]-0.209939855423264[/C][/ROW]
[ROW][C]45[/C][C]101.65[/C][C]101.653956976290[/C][C]-0.00395697629030009[/C][/ROW]
[ROW][C]46[/C][C]101.85[/C][C]101.757974097157[/C][C]0.0920259028426502[/C][/ROW]
[ROW][C]47[/C][C]102.07[/C][C]101.861991218024[/C][C]0.208008781975611[/C][/ROW]
[ROW][C]48[/C][C]102.12[/C][C]101.966008338891[/C][C]0.153991661108584[/C][/ROW]
[ROW][C]49[/C][C]102.14[/C][C]102.070025459758[/C][C]0.0699745402415413[/C][/ROW]
[ROW][C]50[/C][C]102.21[/C][C]102.174042580625[/C][C]0.0359574193744960[/C][/ROW]
[ROW][C]51[/C][C]102.28[/C][C]102.278059701493[/C][C]0.00194029850746505[/C][/ROW]
[ROW][C]52[/C][C]102.19[/C][C]102.382076822360[/C][C]-0.192076822359577[/C][/ROW]
[ROW][C]53[/C][C]102.33[/C][C]102.486093943227[/C][C]-0.156093943226615[/C][/ROW]
[ROW][C]54[/C][C]102.54[/C][C]102.590111064094[/C][C]-0.050111064093645[/C][/ROW]
[ROW][C]55[/C][C]102.44[/C][C]102.694128184961[/C][C]-0.254128184960692[/C][/ROW]
[ROW][C]56[/C][C]102.78[/C][C]102.798145305828[/C][C]-0.0181453058277269[/C][/ROW]
[ROW][C]57[/C][C]102.9[/C][C]102.902162426695[/C][C]-0.00216242669476072[/C][/ROW]
[ROW][C]58[/C][C]103.08[/C][C]103.006179547562[/C][C]0.0738204524381934[/C][/ROW]
[ROW][C]59[/C][C]102.77[/C][C]103.110196668429[/C][C]-0.340196668428847[/C][/ROW]
[ROW][C]60[/C][C]102.65[/C][C]103.214213789296[/C][C]-0.564213789295876[/C][/ROW]
[ROW][C]61[/C][C]102.71[/C][C]103.318230910163[/C][C]-0.608230910162926[/C][/ROW]
[ROW][C]62[/C][C]103.29[/C][C]103.42224803103[/C][C]-0.132248031029952[/C][/ROW]
[ROW][C]63[/C][C]102.86[/C][C]103.526265151897[/C][C]-0.666265151896997[/C][/ROW]
[ROW][C]64[/C][C]103.45[/C][C]103.630282272764[/C][C]-0.180282272764033[/C][/ROW]
[ROW][C]65[/C][C]103.72[/C][C]103.734299393631[/C][C]-0.0142993936310746[/C][/ROW]
[ROW][C]66[/C][C]103.65[/C][C]103.838316514498[/C][C]-0.188316514498106[/C][/ROW]
[ROW][C]67[/C][C]103.83[/C][C]103.942333635365[/C][C]-0.112333635365152[/C][/ROW]
[ROW][C]68[/C][C]104.45[/C][C]104.046350756232[/C][C]0.403649243767814[/C][/ROW]
[ROW][C]69[/C][C]105.14[/C][C]104.150367877099[/C][C]0.989632122900773[/C][/ROW]
[ROW][C]70[/C][C]105.07[/C][C]104.254384997966[/C][C]0.815615002033727[/C][/ROW]
[ROW][C]71[/C][C]105.31[/C][C]104.358402118833[/C][C]0.951597881166698[/C][/ROW]
[ROW][C]72[/C][C]105.19[/C][C]104.462419239700[/C][C]0.727580760299655[/C][/ROW]
[ROW][C]73[/C][C]105.3[/C][C]104.566436360567[/C][C]0.733563639432616[/C][/ROW]
[ROW][C]74[/C][C]105.02[/C][C]104.670453481434[/C][C]0.349546518565576[/C][/ROW]
[ROW][C]75[/C][C]105.17[/C][C]104.774470602301[/C][C]0.395529397698544[/C][/ROW]
[ROW][C]76[/C][C]105.28[/C][C]104.878487723168[/C][C]0.401512276831505[/C][/ROW]
[ROW][C]77[/C][C]105.45[/C][C]104.982504844036[/C][C]0.467495155964468[/C][/ROW]
[ROW][C]78[/C][C]105.38[/C][C]105.086521964903[/C][C]0.293478035097423[/C][/ROW]
[ROW][C]79[/C][C]105.8[/C][C]105.190539085770[/C][C]0.609460914230386[/C][/ROW]
[ROW][C]80[/C][C]105.96[/C][C]105.294556206637[/C][C]0.665443793363344[/C][/ROW]
[ROW][C]81[/C][C]105.08[/C][C]105.398573327504[/C][C]-0.318573327503690[/C][/ROW]
[ROW][C]82[/C][C]105.11[/C][C]105.502590448371[/C][C]-0.392590448370727[/C][/ROW]
[ROW][C]83[/C][C]105.61[/C][C]105.606607569238[/C][C]0.00339243076223475[/C][/ROW]
[ROW][C]84[/C][C]105.5[/C][C]105.710624690105[/C][C]-0.210624690104803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36155&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36155&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	101.02	99.5172747977695	1.50272520223053
2	100.67	99.6212919186366	1.04870808136339
3	100.47	99.7253090395037	0.744690960496348
4	100.38	99.8293261603707	0.550673839629299
5	100.33	99.9333432812377	0.396656718762264
6	100.34	100.037360402105	0.302639597895230
7	100.37	100.141377522972	0.228622477028193
8	100.39	100.245394643839	0.144605356161151
9	100.21	100.349411764706	-0.139411764705895
10	100.21	100.453428885573	-0.243428885572933
11	100.22	100.55744600644	-0.337446006439966
12	100.28	100.661463127307	-0.381463127307002
13	100.25	100.765480248174	-0.515480248174042
14	100.25	100.869497369041	-0.61949736904108
15	100.21	100.973514489908	-0.763514489908125
16	100.16	101.077531610775	-0.91753161077516
17	100.18	101.181548731642	-1.00154873164219
18	100.1	98.8454947128803	1.25450528711972
19	99.96	98.9495118337473	1.01048816625269
20	99.88	99.0535289546144	0.82647104538565
21	99.88	99.1575460754814	0.722453924518611
22	99.86	99.2615631963484	0.598436803651576
23	99.84	99.3655803172155	0.474419682784542
24	99.8	99.4695974380825	0.330402561917497
25	99.82	99.5736145589495	0.246385441050455
26	99.81	99.6776316798166	0.132368320183426
27	99.92	99.7816488006836	0.138351199316387
28	100.03	99.8856659215507	0.144334078449348
29	99.99	99.9896830424177	0.000316957582303101
30	100.02	100.093700163285	-0.0737001632847341
31	100.01	100.197717284152	-0.187717284151763
32	100.13	100.301734405019	-0.171734405018812
33	100.33	100.405751525886	-0.075751525885847
34	100.13	100.509768646753	-0.379768646752888
35	99.96	100.61378576762	-0.653785767619928
36	100.05	100.717802888487	-0.667802888486963
37	99.83	100.821820009354	-0.991820009354
38	99.8	100.925837130221	-1.12583713022104
39	100.01	101.029854251088	-1.01985425108807
40	100.1	101.133871371955	-1.03387137195512
41	100.13	101.237888492822	-1.10788849282216
42	100.16	101.341905613689	-1.18190561368919
43	100.41	101.445922734556	-1.03592273455623
44	101.34	101.549939855423	-0.209939855423264
45	101.65	101.653956976290	-0.00395697629030009
46	101.85	101.757974097157	0.0920259028426502
47	102.07	101.861991218024	0.208008781975611
48	102.12	101.966008338891	0.153991661108584
49	102.14	102.070025459758	0.0699745402415413
50	102.21	102.174042580625	0.0359574193744960
51	102.28	102.278059701493	0.00194029850746505
52	102.19	102.382076822360	-0.192076822359577
53	102.33	102.486093943227	-0.156093943226615
54	102.54	102.590111064094	-0.050111064093645
55	102.44	102.694128184961	-0.254128184960692
56	102.78	102.798145305828	-0.0181453058277269
57	102.9	102.902162426695	-0.00216242669476072
58	103.08	103.006179547562	0.0738204524381934
59	102.77	103.110196668429	-0.340196668428847
60	102.65	103.214213789296	-0.564213789295876
61	102.71	103.318230910163	-0.608230910162926
62	103.29	103.42224803103	-0.132248031029952
63	102.86	103.526265151897	-0.666265151896997
64	103.45	103.630282272764	-0.180282272764033
65	103.72	103.734299393631	-0.0142993936310746
66	103.65	103.838316514498	-0.188316514498106
67	103.83	103.942333635365	-0.112333635365152
68	104.45	104.046350756232	0.403649243767814
69	105.14	104.150367877099	0.989632122900773
70	105.07	104.254384997966	0.815615002033727
71	105.31	104.358402118833	0.951597881166698
72	105.19	104.462419239700	0.727580760299655
73	105.3	104.566436360567	0.733563639432616
74	105.02	104.670453481434	0.349546518565576
75	105.17	104.774470602301	0.395529397698544
76	105.28	104.878487723168	0.401512276831505
77	105.45	104.982504844036	0.467495155964468
78	105.38	105.086521964903	0.293478035097423
79	105.8	105.190539085770	0.609460914230386
80	105.96	105.294556206637	0.665443793363344
81	105.08	105.398573327504	-0.318573327503690
82	105.11	105.502590448371	-0.392590448370727
83	105.61	105.606607569238	0.00339243076223475
84	105.5	105.710624690105	-0.210624690104803

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.0287637850952882	0.0575275701905764	0.971236214904712
7	0.0166795319629144	0.0333590639258287	0.983320468037086
8	0.00949006321659528	0.0189801264331906	0.990509936783405
9	0.00281317335631635	0.00562634671263269	0.997186826643684
10	0.000873297255353614	0.00174659451070723	0.999126702744646
11	0.000305468498901160	0.000610936997802319	0.999694531501099
12	0.000158405036329251	0.000316810072658501	0.99984159496367
13	6.50095542355928e-05	0.000130019108471186	0.999934990445764
14	2.64655722464999e-05	5.29311444929998e-05	0.999973534427753
15	8.80187542900342e-06	1.76037508580068e-05	0.999991198124571
16	2.47318068398553e-06	4.94636136797106e-06	0.999997526819316
17	7.74513637983662e-07	1.54902727596732e-06	0.999999225486362
18	3.52265030854663e-07	7.04530061709326e-07	0.99999964773497
19	1.73666481155338e-07	3.47332962310675e-07	0.999999826333519
20	8.54368005517826e-08	1.70873601103565e-07	0.9999999145632
21	3.71205994572043e-08	7.42411989144086e-08	0.9999999628794
22	1.56305650478694e-08	3.12611300957388e-08	0.999999984369435
23	6.37572388801722e-09	1.27514477760344e-08	0.999999993624276
24	2.41752392539292e-09	4.83504785078584e-09	0.999999997582476
25	9.674021671726e-10	1.9348043343452e-09	0.999999999032598
26	3.83843495423785e-10	7.67686990847571e-10	0.999999999616156
27	4.57980842292158e-10	9.15961684584315e-10	0.99999999954202
28	2.33124801924041e-09	4.66249603848083e-09	0.999999997668752
29	4.09817397264892e-09	8.19634794529783e-09	0.999999995901826
30	7.75305006024299e-09	1.55061001204860e-08	0.99999999224695
31	9.61847391001265e-09	1.92369478200253e-08	0.999999990381526
32	3.19208078652028e-08	6.38416157304057e-08	0.999999968079192
33	5.65384121859292e-07	1.13076824371858e-06	0.999999434615878
34	5.22441373224131e-07	1.04488274644826e-06	0.999999477558627
35	2.12444803416276e-07	4.24889606832553e-07	0.999999787555197
36	1.03900684513796e-07	2.07801369027592e-07	0.999999896099315
37	4.9236337479875e-08	9.847267495975e-08	0.999999950763663
38	3.08506150731304e-08	6.17012301462607e-08	0.999999969149385
39	2.08173434928627e-08	4.16346869857254e-08	0.999999979182656
40	2.29461019837440e-08	4.58922039674879e-08	0.999999977053898
41	3.91790071806859e-08	7.83580143613717e-08	0.999999960820993
42	1.29199100701209e-07	2.58398201402418e-07	0.9999998708009
43	1.71722933850841e-06	3.43445867701683e-06	0.999998282770661
44	0.00436852076728915	0.00873704153457831	0.99563147923271
45	0.106842554939729	0.213685109879459	0.893157445060271
46	0.367558358093725	0.73511671618745	0.632441641906275
47	0.639973046174159	0.720053907651682	0.360026953825841
48	0.776040730984945	0.44791853803011	0.223959269015055
49	0.831812829637285	0.336374340725429	0.168187170362715
50	0.857820611502719	0.284358776994562	0.142179388497281
51	0.86711171863821	0.265776562723578	0.132888281361789
52	0.851567789651852	0.296864420696295	0.148432210348148
53	0.833986567110161	0.332026865779677	0.166013432889839
54	0.820484119009788	0.359031761980423	0.179515880990212
55	0.788843995450144	0.422312009099712	0.211156004549856
56	0.767671152351559	0.464657695296882	0.232328847648441
57	0.742041570797795	0.515916858404411	0.257958429202205
58	0.720062153426443	0.559875693147113	0.279937846573557
59	0.672220065700781	0.655559868598438	0.327779934299219
60	0.660116551571286	0.679766896857428	0.339883448428714
61	0.688846192989867	0.622307614020267	0.311153807010133
62	0.65863753933849	0.68272492132302	0.34136246066151
63	0.789270352164121	0.421459295671758	0.210729647835879
64	0.823506037040025	0.35298792591995	0.176493962959975
65	0.849425466537057	0.301149066925885	0.150574533462943
66	0.936612368103822	0.126775263792355	0.0633876318961777
67	0.993128931481532	0.0137421370369355	0.00687106851846774
68	0.9979323365699	0.00413532686019816	0.00206766343009908
69	0.997229221130378	0.00554155773924447	0.00277077886962223
70	0.995568321539522	0.00886335692095556	0.00443167846047778
71	0.992933847284498	0.0141323054310042	0.00706615271550211
72	0.986340819085302	0.0273183618293961	0.0136591809146981
73	0.974215373862854	0.0515692522742928	0.0257846261371464
74	0.959169105120563	0.0816617897588734	0.0408308948794367
75	0.931623035984738	0.136753928030524	0.0683769640152621
76	0.885163829449365	0.22967234110127	0.114836170550635
77	0.79401261492615	0.411974770147699	0.205987385073849
78	0.687059003146804	0.625881993706392	0.312940996853196

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.0287637850952882 & 0.0575275701905764 & 0.971236214904712 \tabularnewline
7 & 0.0166795319629144 & 0.0333590639258287 & 0.983320468037086 \tabularnewline
8 & 0.00949006321659528 & 0.0189801264331906 & 0.990509936783405 \tabularnewline
9 & 0.00281317335631635 & 0.00562634671263269 & 0.997186826643684 \tabularnewline
10 & 0.000873297255353614 & 0.00174659451070723 & 0.999126702744646 \tabularnewline
11 & 0.000305468498901160 & 0.000610936997802319 & 0.999694531501099 \tabularnewline
12 & 0.000158405036329251 & 0.000316810072658501 & 0.99984159496367 \tabularnewline
13 & 6.50095542355928e-05 & 0.000130019108471186 & 0.999934990445764 \tabularnewline
14 & 2.64655722464999e-05 & 5.29311444929998e-05 & 0.999973534427753 \tabularnewline
15 & 8.80187542900342e-06 & 1.76037508580068e-05 & 0.999991198124571 \tabularnewline
16 & 2.47318068398553e-06 & 4.94636136797106e-06 & 0.999997526819316 \tabularnewline
17 & 7.74513637983662e-07 & 1.54902727596732e-06 & 0.999999225486362 \tabularnewline
18 & 3.52265030854663e-07 & 7.04530061709326e-07 & 0.99999964773497 \tabularnewline
19 & 1.73666481155338e-07 & 3.47332962310675e-07 & 0.999999826333519 \tabularnewline
20 & 8.54368005517826e-08 & 1.70873601103565e-07 & 0.9999999145632 \tabularnewline
21 & 3.71205994572043e-08 & 7.42411989144086e-08 & 0.9999999628794 \tabularnewline
22 & 1.56305650478694e-08 & 3.12611300957388e-08 & 0.999999984369435 \tabularnewline
23 & 6.37572388801722e-09 & 1.27514477760344e-08 & 0.999999993624276 \tabularnewline
24 & 2.41752392539292e-09 & 4.83504785078584e-09 & 0.999999997582476 \tabularnewline
25 & 9.674021671726e-10 & 1.9348043343452e-09 & 0.999999999032598 \tabularnewline
26 & 3.83843495423785e-10 & 7.67686990847571e-10 & 0.999999999616156 \tabularnewline
27 & 4.57980842292158e-10 & 9.15961684584315e-10 & 0.99999999954202 \tabularnewline
28 & 2.33124801924041e-09 & 4.66249603848083e-09 & 0.999999997668752 \tabularnewline
29 & 4.09817397264892e-09 & 8.19634794529783e-09 & 0.999999995901826 \tabularnewline
30 & 7.75305006024299e-09 & 1.55061001204860e-08 & 0.99999999224695 \tabularnewline
31 & 9.61847391001265e-09 & 1.92369478200253e-08 & 0.999999990381526 \tabularnewline
32 & 3.19208078652028e-08 & 6.38416157304057e-08 & 0.999999968079192 \tabularnewline
33 & 5.65384121859292e-07 & 1.13076824371858e-06 & 0.999999434615878 \tabularnewline
34 & 5.22441373224131e-07 & 1.04488274644826e-06 & 0.999999477558627 \tabularnewline
35 & 2.12444803416276e-07 & 4.24889606832553e-07 & 0.999999787555197 \tabularnewline
36 & 1.03900684513796e-07 & 2.07801369027592e-07 & 0.999999896099315 \tabularnewline
37 & 4.9236337479875e-08 & 9.847267495975e-08 & 0.999999950763663 \tabularnewline
38 & 3.08506150731304e-08 & 6.17012301462607e-08 & 0.999999969149385 \tabularnewline
39 & 2.08173434928627e-08 & 4.16346869857254e-08 & 0.999999979182656 \tabularnewline
40 & 2.29461019837440e-08 & 4.58922039674879e-08 & 0.999999977053898 \tabularnewline
41 & 3.91790071806859e-08 & 7.83580143613717e-08 & 0.999999960820993 \tabularnewline
42 & 1.29199100701209e-07 & 2.58398201402418e-07 & 0.9999998708009 \tabularnewline
43 & 1.71722933850841e-06 & 3.43445867701683e-06 & 0.999998282770661 \tabularnewline
44 & 0.00436852076728915 & 0.00873704153457831 & 0.99563147923271 \tabularnewline
45 & 0.106842554939729 & 0.213685109879459 & 0.893157445060271 \tabularnewline
46 & 0.367558358093725 & 0.73511671618745 & 0.632441641906275 \tabularnewline
47 & 0.639973046174159 & 0.720053907651682 & 0.360026953825841 \tabularnewline
48 & 0.776040730984945 & 0.44791853803011 & 0.223959269015055 \tabularnewline
49 & 0.831812829637285 & 0.336374340725429 & 0.168187170362715 \tabularnewline
50 & 0.857820611502719 & 0.284358776994562 & 0.142179388497281 \tabularnewline
51 & 0.86711171863821 & 0.265776562723578 & 0.132888281361789 \tabularnewline
52 & 0.851567789651852 & 0.296864420696295 & 0.148432210348148 \tabularnewline
53 & 0.833986567110161 & 0.332026865779677 & 0.166013432889839 \tabularnewline
54 & 0.820484119009788 & 0.359031761980423 & 0.179515880990212 \tabularnewline
55 & 0.788843995450144 & 0.422312009099712 & 0.211156004549856 \tabularnewline
56 & 0.767671152351559 & 0.464657695296882 & 0.232328847648441 \tabularnewline
57 & 0.742041570797795 & 0.515916858404411 & 0.257958429202205 \tabularnewline
58 & 0.720062153426443 & 0.559875693147113 & 0.279937846573557 \tabularnewline
59 & 0.672220065700781 & 0.655559868598438 & 0.327779934299219 \tabularnewline
60 & 0.660116551571286 & 0.679766896857428 & 0.339883448428714 \tabularnewline
61 & 0.688846192989867 & 0.622307614020267 & 0.311153807010133 \tabularnewline
62 & 0.65863753933849 & 0.68272492132302 & 0.34136246066151 \tabularnewline
63 & 0.789270352164121 & 0.421459295671758 & 0.210729647835879 \tabularnewline
64 & 0.823506037040025 & 0.35298792591995 & 0.176493962959975 \tabularnewline
65 & 0.849425466537057 & 0.301149066925885 & 0.150574533462943 \tabularnewline
66 & 0.936612368103822 & 0.126775263792355 & 0.0633876318961777 \tabularnewline
67 & 0.993128931481532 & 0.0137421370369355 & 0.00687106851846774 \tabularnewline
68 & 0.9979323365699 & 0.00413532686019816 & 0.00206766343009908 \tabularnewline
69 & 0.997229221130378 & 0.00554155773924447 & 0.00277077886962223 \tabularnewline
70 & 0.995568321539522 & 0.00886335692095556 & 0.00443167846047778 \tabularnewline
71 & 0.992933847284498 & 0.0141323054310042 & 0.00706615271550211 \tabularnewline
72 & 0.986340819085302 & 0.0273183618293961 & 0.0136591809146981 \tabularnewline
73 & 0.974215373862854 & 0.0515692522742928 & 0.0257846261371464 \tabularnewline
74 & 0.959169105120563 & 0.0816617897588734 & 0.0408308948794367 \tabularnewline
75 & 0.931623035984738 & 0.136753928030524 & 0.0683769640152621 \tabularnewline
76 & 0.885163829449365 & 0.22967234110127 & 0.114836170550635 \tabularnewline
77 & 0.79401261492615 & 0.411974770147699 & 0.205987385073849 \tabularnewline
78 & 0.687059003146804 & 0.625881993706392 & 0.312940996853196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36155&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]6[/C][C]0.0287637850952882[/C][C]0.0575275701905764[/C][C]0.971236214904712[/C][/ROW]
[ROW][C]7[/C][C]0.0166795319629144[/C][C]0.0333590639258287[/C][C]0.983320468037086[/C][/ROW]
[ROW][C]8[/C][C]0.00949006321659528[/C][C]0.0189801264331906[/C][C]0.990509936783405[/C][/ROW]
[ROW][C]9[/C][C]0.00281317335631635[/C][C]0.00562634671263269[/C][C]0.997186826643684[/C][/ROW]
[ROW][C]10[/C][C]0.000873297255353614[/C][C]0.00174659451070723[/C][C]0.999126702744646[/C][/ROW]
[ROW][C]11[/C][C]0.000305468498901160[/C][C]0.000610936997802319[/C][C]0.999694531501099[/C][/ROW]
[ROW][C]12[/C][C]0.000158405036329251[/C][C]0.000316810072658501[/C][C]0.99984159496367[/C][/ROW]
[ROW][C]13[/C][C]6.50095542355928e-05[/C][C]0.000130019108471186[/C][C]0.999934990445764[/C][/ROW]
[ROW][C]14[/C][C]2.64655722464999e-05[/C][C]5.29311444929998e-05[/C][C]0.999973534427753[/C][/ROW]
[ROW][C]15[/C][C]8.80187542900342e-06[/C][C]1.76037508580068e-05[/C][C]0.999991198124571[/C][/ROW]
[ROW][C]16[/C][C]2.47318068398553e-06[/C][C]4.94636136797106e-06[/C][C]0.999997526819316[/C][/ROW]
[ROW][C]17[/C][C]7.74513637983662e-07[/C][C]1.54902727596732e-06[/C][C]0.999999225486362[/C][/ROW]
[ROW][C]18[/C][C]3.52265030854663e-07[/C][C]7.04530061709326e-07[/C][C]0.99999964773497[/C][/ROW]
[ROW][C]19[/C][C]1.73666481155338e-07[/C][C]3.47332962310675e-07[/C][C]0.999999826333519[/C][/ROW]
[ROW][C]20[/C][C]8.54368005517826e-08[/C][C]1.70873601103565e-07[/C][C]0.9999999145632[/C][/ROW]
[ROW][C]21[/C][C]3.71205994572043e-08[/C][C]7.42411989144086e-08[/C][C]0.9999999628794[/C][/ROW]
[ROW][C]22[/C][C]1.56305650478694e-08[/C][C]3.12611300957388e-08[/C][C]0.999999984369435[/C][/ROW]
[ROW][C]23[/C][C]6.37572388801722e-09[/C][C]1.27514477760344e-08[/C][C]0.999999993624276[/C][/ROW]
[ROW][C]24[/C][C]2.41752392539292e-09[/C][C]4.83504785078584e-09[/C][C]0.999999997582476[/C][/ROW]
[ROW][C]25[/C][C]9.674021671726e-10[/C][C]1.9348043343452e-09[/C][C]0.999999999032598[/C][/ROW]
[ROW][C]26[/C][C]3.83843495423785e-10[/C][C]7.67686990847571e-10[/C][C]0.999999999616156[/C][/ROW]
[ROW][C]27[/C][C]4.57980842292158e-10[/C][C]9.15961684584315e-10[/C][C]0.99999999954202[/C][/ROW]
[ROW][C]28[/C][C]2.33124801924041e-09[/C][C]4.66249603848083e-09[/C][C]0.999999997668752[/C][/ROW]
[ROW][C]29[/C][C]4.09817397264892e-09[/C][C]8.19634794529783e-09[/C][C]0.999999995901826[/C][/ROW]
[ROW][C]30[/C][C]7.75305006024299e-09[/C][C]1.55061001204860e-08[/C][C]0.99999999224695[/C][/ROW]
[ROW][C]31[/C][C]9.61847391001265e-09[/C][C]1.92369478200253e-08[/C][C]0.999999990381526[/C][/ROW]
[ROW][C]32[/C][C]3.19208078652028e-08[/C][C]6.38416157304057e-08[/C][C]0.999999968079192[/C][/ROW]
[ROW][C]33[/C][C]5.65384121859292e-07[/C][C]1.13076824371858e-06[/C][C]0.999999434615878[/C][/ROW]
[ROW][C]34[/C][C]5.22441373224131e-07[/C][C]1.04488274644826e-06[/C][C]0.999999477558627[/C][/ROW]
[ROW][C]35[/C][C]2.12444803416276e-07[/C][C]4.24889606832553e-07[/C][C]0.999999787555197[/C][/ROW]
[ROW][C]36[/C][C]1.03900684513796e-07[/C][C]2.07801369027592e-07[/C][C]0.999999896099315[/C][/ROW]
[ROW][C]37[/C][C]4.9236337479875e-08[/C][C]9.847267495975e-08[/C][C]0.999999950763663[/C][/ROW]
[ROW][C]38[/C][C]3.08506150731304e-08[/C][C]6.17012301462607e-08[/C][C]0.999999969149385[/C][/ROW]
[ROW][C]39[/C][C]2.08173434928627e-08[/C][C]4.16346869857254e-08[/C][C]0.999999979182656[/C][/ROW]
[ROW][C]40[/C][C]2.29461019837440e-08[/C][C]4.58922039674879e-08[/C][C]0.999999977053898[/C][/ROW]
[ROW][C]41[/C][C]3.91790071806859e-08[/C][C]7.83580143613717e-08[/C][C]0.999999960820993[/C][/ROW]
[ROW][C]42[/C][C]1.29199100701209e-07[/C][C]2.58398201402418e-07[/C][C]0.9999998708009[/C][/ROW]
[ROW][C]43[/C][C]1.71722933850841e-06[/C][C]3.43445867701683e-06[/C][C]0.999998282770661[/C][/ROW]
[ROW][C]44[/C][C]0.00436852076728915[/C][C]0.00873704153457831[/C][C]0.99563147923271[/C][/ROW]
[ROW][C]45[/C][C]0.106842554939729[/C][C]0.213685109879459[/C][C]0.893157445060271[/C][/ROW]
[ROW][C]46[/C][C]0.367558358093725[/C][C]0.73511671618745[/C][C]0.632441641906275[/C][/ROW]
[ROW][C]47[/C][C]0.639973046174159[/C][C]0.720053907651682[/C][C]0.360026953825841[/C][/ROW]
[ROW][C]48[/C][C]0.776040730984945[/C][C]0.44791853803011[/C][C]0.223959269015055[/C][/ROW]
[ROW][C]49[/C][C]0.831812829637285[/C][C]0.336374340725429[/C][C]0.168187170362715[/C][/ROW]
[ROW][C]50[/C][C]0.857820611502719[/C][C]0.284358776994562[/C][C]0.142179388497281[/C][/ROW]
[ROW][C]51[/C][C]0.86711171863821[/C][C]0.265776562723578[/C][C]0.132888281361789[/C][/ROW]
[ROW][C]52[/C][C]0.851567789651852[/C][C]0.296864420696295[/C][C]0.148432210348148[/C][/ROW]
[ROW][C]53[/C][C]0.833986567110161[/C][C]0.332026865779677[/C][C]0.166013432889839[/C][/ROW]
[ROW][C]54[/C][C]0.820484119009788[/C][C]0.359031761980423[/C][C]0.179515880990212[/C][/ROW]
[ROW][C]55[/C][C]0.788843995450144[/C][C]0.422312009099712[/C][C]0.211156004549856[/C][/ROW]
[ROW][C]56[/C][C]0.767671152351559[/C][C]0.464657695296882[/C][C]0.232328847648441[/C][/ROW]
[ROW][C]57[/C][C]0.742041570797795[/C][C]0.515916858404411[/C][C]0.257958429202205[/C][/ROW]
[ROW][C]58[/C][C]0.720062153426443[/C][C]0.559875693147113[/C][C]0.279937846573557[/C][/ROW]
[ROW][C]59[/C][C]0.672220065700781[/C][C]0.655559868598438[/C][C]0.327779934299219[/C][/ROW]
[ROW][C]60[/C][C]0.660116551571286[/C][C]0.679766896857428[/C][C]0.339883448428714[/C][/ROW]
[ROW][C]61[/C][C]0.688846192989867[/C][C]0.622307614020267[/C][C]0.311153807010133[/C][/ROW]
[ROW][C]62[/C][C]0.65863753933849[/C][C]0.68272492132302[/C][C]0.34136246066151[/C][/ROW]
[ROW][C]63[/C][C]0.789270352164121[/C][C]0.421459295671758[/C][C]0.210729647835879[/C][/ROW]
[ROW][C]64[/C][C]0.823506037040025[/C][C]0.35298792591995[/C][C]0.176493962959975[/C][/ROW]
[ROW][C]65[/C][C]0.849425466537057[/C][C]0.301149066925885[/C][C]0.150574533462943[/C][/ROW]
[ROW][C]66[/C][C]0.936612368103822[/C][C]0.126775263792355[/C][C]0.0633876318961777[/C][/ROW]
[ROW][C]67[/C][C]0.993128931481532[/C][C]0.0137421370369355[/C][C]0.00687106851846774[/C][/ROW]
[ROW][C]68[/C][C]0.9979323365699[/C][C]0.00413532686019816[/C][C]0.00206766343009908[/C][/ROW]
[ROW][C]69[/C][C]0.997229221130378[/C][C]0.00554155773924447[/C][C]0.00277077886962223[/C][/ROW]
[ROW][C]70[/C][C]0.995568321539522[/C][C]0.00886335692095556[/C][C]0.00443167846047778[/C][/ROW]
[ROW][C]71[/C][C]0.992933847284498[/C][C]0.0141323054310042[/C][C]0.00706615271550211[/C][/ROW]
[ROW][C]72[/C][C]0.986340819085302[/C][C]0.0273183618293961[/C][C]0.0136591809146981[/C][/ROW]
[ROW][C]73[/C][C]0.974215373862854[/C][C]0.0515692522742928[/C][C]0.0257846261371464[/C][/ROW]
[ROW][C]74[/C][C]0.959169105120563[/C][C]0.0816617897588734[/C][C]0.0408308948794367[/C][/ROW]
[ROW][C]75[/C][C]0.931623035984738[/C][C]0.136753928030524[/C][C]0.0683769640152621[/C][/ROW]
[ROW][C]76[/C][C]0.885163829449365[/C][C]0.22967234110127[/C][C]0.114836170550635[/C][/ROW]
[ROW][C]77[/C][C]0.79401261492615[/C][C]0.411974770147699[/C][C]0.205987385073849[/C][/ROW]
[ROW][C]78[/C][C]0.687059003146804[/C][C]0.625881993706392[/C][C]0.312940996853196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36155&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36155&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
6	0.0287637850952882	0.0575275701905764	0.971236214904712
7	0.0166795319629144	0.0333590639258287	0.983320468037086
8	0.00949006321659528	0.0189801264331906	0.990509936783405
9	0.00281317335631635	0.00562634671263269	0.997186826643684
10	0.000873297255353614	0.00174659451070723	0.999126702744646
11	0.000305468498901160	0.000610936997802319	0.999694531501099
12	0.000158405036329251	0.000316810072658501	0.99984159496367
13	6.50095542355928e-05	0.000130019108471186	0.999934990445764
14	2.64655722464999e-05	5.29311444929998e-05	0.999973534427753
15	8.80187542900342e-06	1.76037508580068e-05	0.999991198124571
16	2.47318068398553e-06	4.94636136797106e-06	0.999997526819316
17	7.74513637983662e-07	1.54902727596732e-06	0.999999225486362
18	3.52265030854663e-07	7.04530061709326e-07	0.99999964773497
19	1.73666481155338e-07	3.47332962310675e-07	0.999999826333519
20	8.54368005517826e-08	1.70873601103565e-07	0.9999999145632
21	3.71205994572043e-08	7.42411989144086e-08	0.9999999628794
22	1.56305650478694e-08	3.12611300957388e-08	0.999999984369435
23	6.37572388801722e-09	1.27514477760344e-08	0.999999993624276
24	2.41752392539292e-09	4.83504785078584e-09	0.999999997582476
25	9.674021671726e-10	1.9348043343452e-09	0.999999999032598
26	3.83843495423785e-10	7.67686990847571e-10	0.999999999616156
27	4.57980842292158e-10	9.15961684584315e-10	0.99999999954202
28	2.33124801924041e-09	4.66249603848083e-09	0.999999997668752
29	4.09817397264892e-09	8.19634794529783e-09	0.999999995901826
30	7.75305006024299e-09	1.55061001204860e-08	0.99999999224695
31	9.61847391001265e-09	1.92369478200253e-08	0.999999990381526
32	3.19208078652028e-08	6.38416157304057e-08	0.999999968079192
33	5.65384121859292e-07	1.13076824371858e-06	0.999999434615878
34	5.22441373224131e-07	1.04488274644826e-06	0.999999477558627
35	2.12444803416276e-07	4.24889606832553e-07	0.999999787555197
36	1.03900684513796e-07	2.07801369027592e-07	0.999999896099315
37	4.9236337479875e-08	9.847267495975e-08	0.999999950763663
38	3.08506150731304e-08	6.17012301462607e-08	0.999999969149385
39	2.08173434928627e-08	4.16346869857254e-08	0.999999979182656
40	2.29461019837440e-08	4.58922039674879e-08	0.999999977053898
41	3.91790071806859e-08	7.83580143613717e-08	0.999999960820993
42	1.29199100701209e-07	2.58398201402418e-07	0.9999998708009
43	1.71722933850841e-06	3.43445867701683e-06	0.999998282770661
44	0.00436852076728915	0.00873704153457831	0.99563147923271
45	0.106842554939729	0.213685109879459	0.893157445060271
46	0.367558358093725	0.73511671618745	0.632441641906275
47	0.639973046174159	0.720053907651682	0.360026953825841
48	0.776040730984945	0.44791853803011	0.223959269015055
49	0.831812829637285	0.336374340725429	0.168187170362715
50	0.857820611502719	0.284358776994562	0.142179388497281
51	0.86711171863821	0.265776562723578	0.132888281361789
52	0.851567789651852	0.296864420696295	0.148432210348148
53	0.833986567110161	0.332026865779677	0.166013432889839
54	0.820484119009788	0.359031761980423	0.179515880990212
55	0.788843995450144	0.422312009099712	0.211156004549856
56	0.767671152351559	0.464657695296882	0.232328847648441
57	0.742041570797795	0.515916858404411	0.257958429202205
58	0.720062153426443	0.559875693147113	0.279937846573557
59	0.672220065700781	0.655559868598438	0.327779934299219
60	0.660116551571286	0.679766896857428	0.339883448428714
61	0.688846192989867	0.622307614020267	0.311153807010133
62	0.65863753933849	0.68272492132302	0.34136246066151
63	0.789270352164121	0.421459295671758	0.210729647835879
64	0.823506037040025	0.35298792591995	0.176493962959975
65	0.849425466537057	0.301149066925885	0.150574533462943
66	0.936612368103822	0.126775263792355	0.0633876318961777
67	0.993128931481532	0.0137421370369355	0.00687106851846774
68	0.9979323365699	0.00413532686019816	0.00206766343009908
69	0.997229221130378	0.00554155773924447	0.00277077886962223
70	0.995568321539522	0.00886335692095556	0.00443167846047778
71	0.992933847284498	0.0141323054310042	0.00706615271550211
72	0.986340819085302	0.0273183618293961	0.0136591809146981
73	0.974215373862854	0.0515692522742928	0.0257846261371464
74	0.959169105120563	0.0816617897588734	0.0408308948794367
75	0.931623035984738	0.136753928030524	0.0683769640152621
76	0.885163829449365	0.22967234110127	0.114836170550635
77	0.79401261492615	0.411974770147699	0.205987385073849
78	0.687059003146804	0.625881993706392	0.312940996853196

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	39	0.534246575342466	NOK
5% type I error level	44	0.602739726027397	NOK
10% type I error level	47	0.643835616438356	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 39 & 0.534246575342466 & NOK \tabularnewline
5% type I error level & 44 & 0.602739726027397 & NOK \tabularnewline
10% type I error level & 47 & 0.643835616438356 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36155&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]39[/C][C]0.534246575342466[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.602739726027397[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.643835616438356[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36155&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36155&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	39	0.534246575342466	NOK
5% type I error level	44	0.602739726027397	NOK
10% type I error level	47	0.643835616438356	NOK

Figure 1

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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 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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