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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 16 Dec 2012 12:05:45 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t135567765462rb4us1w1jaiea.htm/, Retrieved Fri, 01 Nov 2024 00:19:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200490, Retrieved Fri, 01 Nov 2024 00:19:02 +0000

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No (this computation is public)

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Estimated Impact

152

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

-       [Multiple Regression] [] [2012-12-16 17:05:45] [c8e18a68d7e55abb9d5b5bbd2b98426e] [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	9 seconds
R Server	'Sir Maurice George Kendall' @ kendall.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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200490&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200490&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200490&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	9 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Used[t] = + 14.8998521934881 + 0.67241862676928Correctanal[t] -0.0951388356000594uselimit[t] + 0.159323996262626useful[t] + 0.0709581898357269T40[t] + 0.0693788122346552outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Used[t] =  +  14.8998521934881 +  0.67241862676928Correctanal[t] -0.0951388356000594uselimit[t] +  0.159323996262626useful[t] +  0.0709581898357269T40[t] +  0.0693788122346552outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200490&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Used[t] =  +  14.8998521934881 +  0.67241862676928Correctanal[t] -0.0951388356000594uselimit[t] +  0.159323996262626useful[t] +  0.0709581898357269T40[t] +  0.0693788122346552outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200490&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200490&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
Used[t] = + 14.8998521934881 + 0.67241862676928Correctanal[t] -0.0951388356000594uselimit[t] + 0.159323996262626useful[t] + 0.0709581898357269T40[t] + 0.0693788122346552outcome[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)	14.8998521934881	12.947976	1.1507	0.253264	0.126632
Correctanal	0.67241862676928	0.156001	4.3103	4.6e-05	2.3e-05
uselimit	-0.0951388356000594	0.10325	-0.9214	0.359592	0.179796
useful	0.159323996262626	0.097085	1.6411	0.104709	0.052354
T40	0.0709581898357269	0.109341	0.649	0.518223	0.259111
outcome	0.0693788122346552	0.090246	0.7688	0.444291	0.222146

\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) & 14.8998521934881 & 12.947976 & 1.1507 & 0.253264 & 0.126632 \tabularnewline
Correctanal & 0.67241862676928 & 0.156001 & 4.3103 & 4.6e-05 & 2.3e-05 \tabularnewline
uselimit & -0.0951388356000594 & 0.10325 & -0.9214 & 0.359592 & 0.179796 \tabularnewline
useful & 0.159323996262626 & 0.097085 & 1.6411 & 0.104709 & 0.052354 \tabularnewline
T40 & 0.0709581898357269 & 0.109341 & 0.649 & 0.518223 & 0.259111 \tabularnewline
outcome & 0.0693788122346552 & 0.090246 & 0.7688 & 0.444291 & 0.222146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200490&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]14.8998521934881[/C][C]12.947976[/C][C]1.1507[/C][C]0.253264[/C][C]0.126632[/C][/ROW]
[ROW][C]Correctanal[/C][C]0.67241862676928[/C][C]0.156001[/C][C]4.3103[/C][C]4.6e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]uselimit[/C][C]-0.0951388356000594[/C][C]0.10325[/C][C]-0.9214[/C][C]0.359592[/C][C]0.179796[/C][/ROW]
[ROW][C]useful[/C][C]0.159323996262626[/C][C]0.097085[/C][C]1.6411[/C][C]0.104709[/C][C]0.052354[/C][/ROW]
[ROW][C]T40[/C][C]0.0709581898357269[/C][C]0.109341[/C][C]0.649[/C][C]0.518223[/C][C]0.259111[/C][/ROW]
[ROW][C]outcome[/C][C]0.0693788122346552[/C][C]0.090246[/C][C]0.7688[/C][C]0.444291[/C][C]0.222146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200490&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200490&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)	14.8998521934881	12.947976	1.1507	0.253264	0.126632
Correctanal	0.67241862676928	0.156001	4.3103	4.6e-05	2.3e-05
uselimit	-0.0951388356000594	0.10325	-0.9214	0.359592	0.179796
useful	0.159323996262626	0.097085	1.6411	0.104709	0.052354
T40	0.0709581898357269	0.109341	0.649	0.518223	0.259111
outcome	0.0693788122346552	0.090246	0.7688	0.444291	0.222146

Multiple Linear Regression - Regression Statistics
Multiple R	0.533704990484674
R-squared	0.284841016868246
Adjusted R-squared	0.240143580422511
F-TEST (value)	6.3726477292285
F-TEST (DF numerator)	5
F-TEST (DF denominator)	80
p-value	4.9740959433775e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	20.5432947228294
Sum Squared Residuals	33762.1566455224

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.533704990484674 \tabularnewline
R-squared & 0.284841016868246 \tabularnewline
Adjusted R-squared & 0.240143580422511 \tabularnewline
F-TEST (value) & 6.3726477292285 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 80 \tabularnewline
p-value & 4.9740959433775e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 20.5432947228294 \tabularnewline
Sum Squared Residuals & 33762.1566455224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200490&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.533704990484674[/C][/ROW]
[ROW][C]R-squared[/C][C]0.284841016868246[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.240143580422511[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.3726477292285[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]80[/C][/ROW]
[ROW][C]p-value[/C][C]4.9740959433775e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]20.5432947228294[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33762.1566455224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200490&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200490&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	0.533704990484674
R-squared	0.284841016868246
Adjusted R-squared	0.240143580422511
F-TEST (value)	6.3726477292285
F-TEST (DF numerator)	5
F-TEST (DF denominator)	80
p-value	4.9740959433775e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	20.5432947228294
Sum Squared Residuals	33762.1566455224

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	50	61.0067999921157	-11.0067999921157
2	50	58.7468916685995	-8.74689166859952
3	50	58.7468916685995	-8.74689166859952
4	50	58.7468916685995	-8.74689166859954
5	50	58.7468916685995	-8.74689166859953
6	50	65.4250903134606	-15.4250903134606
7	50	58.7468916685995	-8.74689166859953
8	50	62.2948011603859	-12.2948011603859
9	50	62.2158322803323	-12.2158322803323
10	50	53.9899498885966	-3.98994988859656
11	50	57.5378593803829	-7.5378593803829
12	50	58.7468916685995	-8.74689166859953
13	100	66.7130914817308	33.2869085182692
14	50	57.5378593803829	-7.5378593803829
15	100	70.1820320934636	29.8179679065364
16	100	73.7299415852499	26.2700584147501
17	100	99.1249905319782	0.875009468021765
18	50	57.5378593803829	-7.5378593803829
19	50	62.2158322803323	-12.2158322803323
20	100	107.350872923714	-7.35087292371396
21	50	61.9561497017279	-11.9561497017279
22	100	65.4250903134606	34.5749096865394
23	50	70.1820320934636	-20.1820320934636
24	50	65.4250903134606	-15.4250903134606
25	100	65.7637417721186	34.2362582278814
26	100	66.7130914817308	33.2869085182692
27	50	57.4588905003293	-7.45889050032932
28	100	58.7468916685995	41.2531083314005
29	50	62.2158322803323	-12.2158322803323
30	50	66.7130914817308	-16.7130914817308
31	50	58.7468916685995	-8.74689166859953
32	50	53.9899498885966	-3.98994988859656
33	50	61.9561497017279	-11.9561497017279
34	50	65.7637417721186	-15.7637417721186
35	50	58.7468916685995	-8.74689166859953
36	50	58.7468916685995	-8.74689166859953
37	100	65.5040591935142	34.4959408064858
38	100	62.2158322803323	37.7841677196677
39	50	70.1820320934636	-20.1820320934636
40	50	70.2610009735172	-20.2610009735172
41	100	103.802963431928	-3.80296343192762
42	100	62.2158322803323	37.7841677196677
43	50	65.4250903134606	-15.4250903134606
44	50	57.5378593803829	-7.5378593803829
45	50	66.7130914817308	-16.7130914817308
46	50	70.1820320934636	-20.1820320934636
47	50	58.7468916685995	-8.74689166859953
48	50	62.2158322803323	-12.2158322803323
49	50	70.1820320934636	-20.1820320934636
50	50	58.7468916685995	-8.74689166859953
51	100	62.2948011603859	37.7051988396141
52	100	99.1249905319782	0.875009468021765
53	50	62.2158322803323	-12.2158322803323
54	100	92.3678230070635	7.63217699293645
55	50	58.7468916685995	-8.74689166859953
56	100	65.7637417721186	34.2362582278814
57	100	70.1820320934636	29.8179679065364
58	50	62.2158322803323	-12.2158322803323
59	50	62.2158322803323	-12.2158322803323
60	100	102.593931143711	-2.59393114371099
61	50	61.0067999921157	-11.0067999921157
62	100	66.7130914817308	33.2869085182692
63	50	58.7468916685995	-8.74689166859953
64	50	61.0067999921157	-11.0067999921157
65	50	58.7468916685995	-8.74689166859953
66	50	58.7468916685995	-8.74689166859953
67	100	103.881932311981	-3.8819323119812
68	50	53.9899498885966	-3.98994988859656
69	50	62.2158322803323	-12.2158322803323
70	100	58.7468916685995	41.2531083314005
71	50	58.7468916685995	-8.74689166859953
72	50	62.2158322803323	-12.2158322803323
73	100	62.2158322803323	37.7841677196677
74	100	53.9899498885966	46.0100501114034
75	50	62.2158322803323	-12.2158322803323
76	50	73.7299415852499	-23.72994158525
77	50	62.2158322803323	-12.2158322803323
78	100	70.1820320934636	29.8179679065364
79	100	99.3846731105827	0.615326889417349
80	50	70.2610009735172	-20.2610009735172
81	50	58.7468916685995	-8.74689166859953
82	100	57.4588905003293	42.5411094996707
83	50	58.7468916685995	-8.74689166859953
84	100	92.3678230070635	7.63217699293645
85	50	70.1820320934636	-20.1820320934636
86	50	53.9899498885966	-3.98994988859656

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 50 & 61.0067999921157 & -11.0067999921157 \tabularnewline
2 & 50 & 58.7468916685995 & -8.74689166859952 \tabularnewline
3 & 50 & 58.7468916685995 & -8.74689166859952 \tabularnewline
4 & 50 & 58.7468916685995 & -8.74689166859954 \tabularnewline
5 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
6 & 50 & 65.4250903134606 & -15.4250903134606 \tabularnewline
7 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
8 & 50 & 62.2948011603859 & -12.2948011603859 \tabularnewline
9 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
10 & 50 & 53.9899498885966 & -3.98994988859656 \tabularnewline
11 & 50 & 57.5378593803829 & -7.5378593803829 \tabularnewline
12 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
13 & 100 & 66.7130914817308 & 33.2869085182692 \tabularnewline
14 & 50 & 57.5378593803829 & -7.5378593803829 \tabularnewline
15 & 100 & 70.1820320934636 & 29.8179679065364 \tabularnewline
16 & 100 & 73.7299415852499 & 26.2700584147501 \tabularnewline
17 & 100 & 99.1249905319782 & 0.875009468021765 \tabularnewline
18 & 50 & 57.5378593803829 & -7.5378593803829 \tabularnewline
19 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
20 & 100 & 107.350872923714 & -7.35087292371396 \tabularnewline
21 & 50 & 61.9561497017279 & -11.9561497017279 \tabularnewline
22 & 100 & 65.4250903134606 & 34.5749096865394 \tabularnewline
23 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
24 & 50 & 65.4250903134606 & -15.4250903134606 \tabularnewline
25 & 100 & 65.7637417721186 & 34.2362582278814 \tabularnewline
26 & 100 & 66.7130914817308 & 33.2869085182692 \tabularnewline
27 & 50 & 57.4588905003293 & -7.45889050032932 \tabularnewline
28 & 100 & 58.7468916685995 & 41.2531083314005 \tabularnewline
29 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
30 & 50 & 66.7130914817308 & -16.7130914817308 \tabularnewline
31 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
32 & 50 & 53.9899498885966 & -3.98994988859656 \tabularnewline
33 & 50 & 61.9561497017279 & -11.9561497017279 \tabularnewline
34 & 50 & 65.7637417721186 & -15.7637417721186 \tabularnewline
35 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
36 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
37 & 100 & 65.5040591935142 & 34.4959408064858 \tabularnewline
38 & 100 & 62.2158322803323 & 37.7841677196677 \tabularnewline
39 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
40 & 50 & 70.2610009735172 & -20.2610009735172 \tabularnewline
41 & 100 & 103.802963431928 & -3.80296343192762 \tabularnewline
42 & 100 & 62.2158322803323 & 37.7841677196677 \tabularnewline
43 & 50 & 65.4250903134606 & -15.4250903134606 \tabularnewline
44 & 50 & 57.5378593803829 & -7.5378593803829 \tabularnewline
45 & 50 & 66.7130914817308 & -16.7130914817308 \tabularnewline
46 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
47 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
48 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
49 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
50 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
51 & 100 & 62.2948011603859 & 37.7051988396141 \tabularnewline
52 & 100 & 99.1249905319782 & 0.875009468021765 \tabularnewline
53 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
54 & 100 & 92.3678230070635 & 7.63217699293645 \tabularnewline
55 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
56 & 100 & 65.7637417721186 & 34.2362582278814 \tabularnewline
57 & 100 & 70.1820320934636 & 29.8179679065364 \tabularnewline
58 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
59 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
60 & 100 & 102.593931143711 & -2.59393114371099 \tabularnewline
61 & 50 & 61.0067999921157 & -11.0067999921157 \tabularnewline
62 & 100 & 66.7130914817308 & 33.2869085182692 \tabularnewline
63 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
64 & 50 & 61.0067999921157 & -11.0067999921157 \tabularnewline
65 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
66 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
67 & 100 & 103.881932311981 & -3.8819323119812 \tabularnewline
68 & 50 & 53.9899498885966 & -3.98994988859656 \tabularnewline
69 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
70 & 100 & 58.7468916685995 & 41.2531083314005 \tabularnewline
71 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
72 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
73 & 100 & 62.2158322803323 & 37.7841677196677 \tabularnewline
74 & 100 & 53.9899498885966 & 46.0100501114034 \tabularnewline
75 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
76 & 50 & 73.7299415852499 & -23.72994158525 \tabularnewline
77 & 50 & 62.2158322803323 & -12.2158322803323 \tabularnewline
78 & 100 & 70.1820320934636 & 29.8179679065364 \tabularnewline
79 & 100 & 99.3846731105827 & 0.615326889417349 \tabularnewline
80 & 50 & 70.2610009735172 & -20.2610009735172 \tabularnewline
81 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
82 & 100 & 57.4588905003293 & 42.5411094996707 \tabularnewline
83 & 50 & 58.7468916685995 & -8.74689166859953 \tabularnewline
84 & 100 & 92.3678230070635 & 7.63217699293645 \tabularnewline
85 & 50 & 70.1820320934636 & -20.1820320934636 \tabularnewline
86 & 50 & 53.9899498885966 & -3.98994988859656 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200490&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]50[/C][C]61.0067999921157[/C][C]-11.0067999921157[/C][/ROW]
[ROW][C]2[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859952[/C][/ROW]
[ROW][C]3[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859952[/C][/ROW]
[ROW][C]4[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859954[/C][/ROW]
[ROW][C]5[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]6[/C][C]50[/C][C]65.4250903134606[/C][C]-15.4250903134606[/C][/ROW]
[ROW][C]7[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]8[/C][C]50[/C][C]62.2948011603859[/C][C]-12.2948011603859[/C][/ROW]
[ROW][C]9[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]10[/C][C]50[/C][C]53.9899498885966[/C][C]-3.98994988859656[/C][/ROW]
[ROW][C]11[/C][C]50[/C][C]57.5378593803829[/C][C]-7.5378593803829[/C][/ROW]
[ROW][C]12[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]13[/C][C]100[/C][C]66.7130914817308[/C][C]33.2869085182692[/C][/ROW]
[ROW][C]14[/C][C]50[/C][C]57.5378593803829[/C][C]-7.5378593803829[/C][/ROW]
[ROW][C]15[/C][C]100[/C][C]70.1820320934636[/C][C]29.8179679065364[/C][/ROW]
[ROW][C]16[/C][C]100[/C][C]73.7299415852499[/C][C]26.2700584147501[/C][/ROW]
[ROW][C]17[/C][C]100[/C][C]99.1249905319782[/C][C]0.875009468021765[/C][/ROW]
[ROW][C]18[/C][C]50[/C][C]57.5378593803829[/C][C]-7.5378593803829[/C][/ROW]
[ROW][C]19[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]20[/C][C]100[/C][C]107.350872923714[/C][C]-7.35087292371396[/C][/ROW]
[ROW][C]21[/C][C]50[/C][C]61.9561497017279[/C][C]-11.9561497017279[/C][/ROW]
[ROW][C]22[/C][C]100[/C][C]65.4250903134606[/C][C]34.5749096865394[/C][/ROW]
[ROW][C]23[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]24[/C][C]50[/C][C]65.4250903134606[/C][C]-15.4250903134606[/C][/ROW]
[ROW][C]25[/C][C]100[/C][C]65.7637417721186[/C][C]34.2362582278814[/C][/ROW]
[ROW][C]26[/C][C]100[/C][C]66.7130914817308[/C][C]33.2869085182692[/C][/ROW]
[ROW][C]27[/C][C]50[/C][C]57.4588905003293[/C][C]-7.45889050032932[/C][/ROW]
[ROW][C]28[/C][C]100[/C][C]58.7468916685995[/C][C]41.2531083314005[/C][/ROW]
[ROW][C]29[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]30[/C][C]50[/C][C]66.7130914817308[/C][C]-16.7130914817308[/C][/ROW]
[ROW][C]31[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]32[/C][C]50[/C][C]53.9899498885966[/C][C]-3.98994988859656[/C][/ROW]
[ROW][C]33[/C][C]50[/C][C]61.9561497017279[/C][C]-11.9561497017279[/C][/ROW]
[ROW][C]34[/C][C]50[/C][C]65.7637417721186[/C][C]-15.7637417721186[/C][/ROW]
[ROW][C]35[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]36[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]37[/C][C]100[/C][C]65.5040591935142[/C][C]34.4959408064858[/C][/ROW]
[ROW][C]38[/C][C]100[/C][C]62.2158322803323[/C][C]37.7841677196677[/C][/ROW]
[ROW][C]39[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]40[/C][C]50[/C][C]70.2610009735172[/C][C]-20.2610009735172[/C][/ROW]
[ROW][C]41[/C][C]100[/C][C]103.802963431928[/C][C]-3.80296343192762[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]62.2158322803323[/C][C]37.7841677196677[/C][/ROW]
[ROW][C]43[/C][C]50[/C][C]65.4250903134606[/C][C]-15.4250903134606[/C][/ROW]
[ROW][C]44[/C][C]50[/C][C]57.5378593803829[/C][C]-7.5378593803829[/C][/ROW]
[ROW][C]45[/C][C]50[/C][C]66.7130914817308[/C][C]-16.7130914817308[/C][/ROW]
[ROW][C]46[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]47[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]48[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]49[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]51[/C][C]100[/C][C]62.2948011603859[/C][C]37.7051988396141[/C][/ROW]
[ROW][C]52[/C][C]100[/C][C]99.1249905319782[/C][C]0.875009468021765[/C][/ROW]
[ROW][C]53[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]54[/C][C]100[/C][C]92.3678230070635[/C][C]7.63217699293645[/C][/ROW]
[ROW][C]55[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]56[/C][C]100[/C][C]65.7637417721186[/C][C]34.2362582278814[/C][/ROW]
[ROW][C]57[/C][C]100[/C][C]70.1820320934636[/C][C]29.8179679065364[/C][/ROW]
[ROW][C]58[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]59[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]60[/C][C]100[/C][C]102.593931143711[/C][C]-2.59393114371099[/C][/ROW]
[ROW][C]61[/C][C]50[/C][C]61.0067999921157[/C][C]-11.0067999921157[/C][/ROW]
[ROW][C]62[/C][C]100[/C][C]66.7130914817308[/C][C]33.2869085182692[/C][/ROW]
[ROW][C]63[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]64[/C][C]50[/C][C]61.0067999921157[/C][C]-11.0067999921157[/C][/ROW]
[ROW][C]65[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]66[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]67[/C][C]100[/C][C]103.881932311981[/C][C]-3.8819323119812[/C][/ROW]
[ROW][C]68[/C][C]50[/C][C]53.9899498885966[/C][C]-3.98994988859656[/C][/ROW]
[ROW][C]69[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]70[/C][C]100[/C][C]58.7468916685995[/C][C]41.2531083314005[/C][/ROW]
[ROW][C]71[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]72[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]73[/C][C]100[/C][C]62.2158322803323[/C][C]37.7841677196677[/C][/ROW]
[ROW][C]74[/C][C]100[/C][C]53.9899498885966[/C][C]46.0100501114034[/C][/ROW]
[ROW][C]75[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]76[/C][C]50[/C][C]73.7299415852499[/C][C]-23.72994158525[/C][/ROW]
[ROW][C]77[/C][C]50[/C][C]62.2158322803323[/C][C]-12.2158322803323[/C][/ROW]
[ROW][C]78[/C][C]100[/C][C]70.1820320934636[/C][C]29.8179679065364[/C][/ROW]
[ROW][C]79[/C][C]100[/C][C]99.3846731105827[/C][C]0.615326889417349[/C][/ROW]
[ROW][C]80[/C][C]50[/C][C]70.2610009735172[/C][C]-20.2610009735172[/C][/ROW]
[ROW][C]81[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]82[/C][C]100[/C][C]57.4588905003293[/C][C]42.5411094996707[/C][/ROW]
[ROW][C]83[/C][C]50[/C][C]58.7468916685995[/C][C]-8.74689166859953[/C][/ROW]
[ROW][C]84[/C][C]100[/C][C]92.3678230070635[/C][C]7.63217699293645[/C][/ROW]
[ROW][C]85[/C][C]50[/C][C]70.1820320934636[/C][C]-20.1820320934636[/C][/ROW]
[ROW][C]86[/C][C]50[/C][C]53.9899498885966[/C][C]-3.98994988859656[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200490&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200490&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	50	61.0067999921157	-11.0067999921157
2	50	58.7468916685995	-8.74689166859952
3	50	58.7468916685995	-8.74689166859952
4	50	58.7468916685995	-8.74689166859954
5	50	58.7468916685995	-8.74689166859953
6	50	65.4250903134606	-15.4250903134606
7	50	58.7468916685995	-8.74689166859953
8	50	62.2948011603859	-12.2948011603859
9	50	62.2158322803323	-12.2158322803323
10	50	53.9899498885966	-3.98994988859656
11	50	57.5378593803829	-7.5378593803829
12	50	58.7468916685995	-8.74689166859953
13	100	66.7130914817308	33.2869085182692
14	50	57.5378593803829	-7.5378593803829
15	100	70.1820320934636	29.8179679065364
16	100	73.7299415852499	26.2700584147501
17	100	99.1249905319782	0.875009468021765
18	50	57.5378593803829	-7.5378593803829
19	50	62.2158322803323	-12.2158322803323
20	100	107.350872923714	-7.35087292371396
21	50	61.9561497017279	-11.9561497017279
22	100	65.4250903134606	34.5749096865394
23	50	70.1820320934636	-20.1820320934636
24	50	65.4250903134606	-15.4250903134606
25	100	65.7637417721186	34.2362582278814
26	100	66.7130914817308	33.2869085182692
27	50	57.4588905003293	-7.45889050032932
28	100	58.7468916685995	41.2531083314005
29	50	62.2158322803323	-12.2158322803323
30	50	66.7130914817308	-16.7130914817308
31	50	58.7468916685995	-8.74689166859953
32	50	53.9899498885966	-3.98994988859656
33	50	61.9561497017279	-11.9561497017279
34	50	65.7637417721186	-15.7637417721186
35	50	58.7468916685995	-8.74689166859953
36	50	58.7468916685995	-8.74689166859953
37	100	65.5040591935142	34.4959408064858
38	100	62.2158322803323	37.7841677196677
39	50	70.1820320934636	-20.1820320934636
40	50	70.2610009735172	-20.2610009735172
41	100	103.802963431928	-3.80296343192762
42	100	62.2158322803323	37.7841677196677
43	50	65.4250903134606	-15.4250903134606
44	50	57.5378593803829	-7.5378593803829
45	50	66.7130914817308	-16.7130914817308
46	50	70.1820320934636	-20.1820320934636
47	50	58.7468916685995	-8.74689166859953
48	50	62.2158322803323	-12.2158322803323
49	50	70.1820320934636	-20.1820320934636
50	50	58.7468916685995	-8.74689166859953
51	100	62.2948011603859	37.7051988396141
52	100	99.1249905319782	0.875009468021765
53	50	62.2158322803323	-12.2158322803323
54	100	92.3678230070635	7.63217699293645
55	50	58.7468916685995	-8.74689166859953
56	100	65.7637417721186	34.2362582278814
57	100	70.1820320934636	29.8179679065364
58	50	62.2158322803323	-12.2158322803323
59	50	62.2158322803323	-12.2158322803323
60	100	102.593931143711	-2.59393114371099
61	50	61.0067999921157	-11.0067999921157
62	100	66.7130914817308	33.2869085182692
63	50	58.7468916685995	-8.74689166859953
64	50	61.0067999921157	-11.0067999921157
65	50	58.7468916685995	-8.74689166859953
66	50	58.7468916685995	-8.74689166859953
67	100	103.881932311981	-3.8819323119812
68	50	53.9899498885966	-3.98994988859656
69	50	62.2158322803323	-12.2158322803323
70	100	58.7468916685995	41.2531083314005
71	50	58.7468916685995	-8.74689166859953
72	50	62.2158322803323	-12.2158322803323
73	100	62.2158322803323	37.7841677196677
74	100	53.9899498885966	46.0100501114034
75	50	62.2158322803323	-12.2158322803323
76	50	73.7299415852499	-23.72994158525
77	50	62.2158322803323	-12.2158322803323
78	100	70.1820320934636	29.8179679065364
79	100	99.3846731105827	0.615326889417349
80	50	70.2610009735172	-20.2610009735172
81	50	58.7468916685995	-8.74689166859953
82	100	57.4588905003293	42.5411094996707
83	50	58.7468916685995	-8.74689166859953
84	100	92.3678230070635	7.63217699293645
85	50	70.1820320934636	-20.1820320934636
86	50	53.9899498885966	-3.98994988859656

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	6.77054496660541e-95	1.35410899332108e-94	1
10	3.08728550853195e-64	6.17457101706389e-64	1
11	8.18422347986106e-79	1.63684469597221e-78	1
12	8.50530669564521e-95	1.70106133912904e-94	1
13	0.0275989634105373	0.0551979268210747	0.972401036589463
14	0.0121313241594421	0.0242626483188842	0.987868675840558
15	0.0203308703149165	0.040661740629833	0.979669129685083
16	0.010967226330513	0.021934452661026	0.989032773669487
17	0.0049618267998306	0.0099236535996612	0.995038173200169
18	0.00224537205391715	0.00449074410783429	0.997754627946083
19	0.000978557401249392	0.00195711480249878	0.999021442598751
20	0.000483983534986408	0.000967967069972816	0.999516016465014
21	0.00172986550182764	0.00345973100365527	0.998270134498172
22	0.00943291644879758	0.0188658328975952	0.990567083551202
23	0.0405715774372667	0.0811431548745333	0.959428422562733
24	0.0410277696947192	0.0820555393894385	0.958972230305281
25	0.104079083479489	0.208158166958979	0.895920916520511
26	0.121257028395937	0.242514056791875	0.878742971604063
27	0.102418322269846	0.204836644539691	0.897581677730154
28	0.308359810890539	0.616719621781077	0.691640189109461
29	0.257852633246682	0.515705266493364	0.742147366753318
30	0.310718600577265	0.62143720115453	0.689281399422735
31	0.256742673299858	0.513485346599716	0.743257326700142
32	0.215169858592315	0.43033971718463	0.784830141407685
33	0.188024614419472	0.376049228838945	0.811975385580528
34	0.178622012063318	0.357244024126636	0.821377987936682
35	0.141886976648576	0.283773953297152	0.858113023351424
36	0.110956051808052	0.221912103616103	0.889043948191948
37	0.14478691105992	0.289573822119839	0.85521308894008
38	0.297696336640315	0.59539267328063	0.702303663359685
39	0.323077048290445	0.64615409658089	0.676922951709555
40	0.362909709099341	0.725819418198683	0.637090290900659
41	0.302559836898828	0.605119673797656	0.697440163101172
42	0.445599696062006	0.891199392124012	0.554400303937994
43	0.419891215349581	0.839782430699162	0.580108784650419
44	0.370491069538351	0.740982139076702	0.629508930461649
45	0.352415865135056	0.704831730270111	0.647584134864944
46	0.352913674975562	0.705827349951123	0.647086325024438
47	0.303712242184424	0.607424484368847	0.696287757815576
48	0.264001414420616	0.528002828841233	0.735998585579384
49	0.2654138055225	0.530827611045	0.7345861944775
50	0.223448505975497	0.446897011950994	0.776551494024503
51	0.388653006256511	0.777306012513021	0.611346993743489
52	0.330603410895481	0.661206821790962	0.669396589104519
53	0.291276244818474	0.582552489636948	0.708723755181526
54	0.244418874094833	0.488837748189667	0.755581125905167
55	0.200833802970533	0.401667605941067	0.799166197029467
56	0.467096239295526	0.934192478591051	0.532903760704474
57	0.487800310522947	0.975600621045895	0.512199689477053
58	0.437003847666727	0.874007695333454	0.562996152333273
59	0.389170209032375	0.77834041806475	0.610829790967625
60	0.359142855587806	0.718285711175612	0.640857144412194
61	0.297962683503188	0.595925367006375	0.702037316496812
62	0.361447382746663	0.722894765493327	0.638552617253337
63	0.296733093891917	0.593466187783834	0.703266906108083
64	0.246272306570871	0.492544613141741	0.753727693429129
65	0.191734324413534	0.383468648827069	0.808265675586466
66	0.145203024254776	0.290406048509553	0.854796975745224
67	0.105726940126744	0.211453880253489	0.894273059873256
68	0.113429125059568	0.226858250119135	0.886570874940432
69	0.0902194620139476	0.180438924027895	0.909780537986052
70	0.299364449514298	0.598728899028595	0.700635550485702
71	0.221898997319769	0.443797994639538	0.778101002680231
72	0.180561804390421	0.361123608780842	0.819438195609579
73	0.404787339020733	0.809574678041466	0.595212660979267
74	0.520430790412391	0.959138419175218	0.479569209587609
75	0.411050716284932	0.822101432569863	0.588949283715068
76	0.322159066791025	0.64431813358205	0.677840933208975
77	0.253174043185819	0.506348086371637	0.746825956814181

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 6.77054496660541e-95 & 1.35410899332108e-94 & 1 \tabularnewline
10 & 3.08728550853195e-64 & 6.17457101706389e-64 & 1 \tabularnewline
11 & 8.18422347986106e-79 & 1.63684469597221e-78 & 1 \tabularnewline
12 & 8.50530669564521e-95 & 1.70106133912904e-94 & 1 \tabularnewline
13 & 0.0275989634105373 & 0.0551979268210747 & 0.972401036589463 \tabularnewline
14 & 0.0121313241594421 & 0.0242626483188842 & 0.987868675840558 \tabularnewline
15 & 0.0203308703149165 & 0.040661740629833 & 0.979669129685083 \tabularnewline
16 & 0.010967226330513 & 0.021934452661026 & 0.989032773669487 \tabularnewline
17 & 0.0049618267998306 & 0.0099236535996612 & 0.995038173200169 \tabularnewline
18 & 0.00224537205391715 & 0.00449074410783429 & 0.997754627946083 \tabularnewline
19 & 0.000978557401249392 & 0.00195711480249878 & 0.999021442598751 \tabularnewline
20 & 0.000483983534986408 & 0.000967967069972816 & 0.999516016465014 \tabularnewline
21 & 0.00172986550182764 & 0.00345973100365527 & 0.998270134498172 \tabularnewline
22 & 0.00943291644879758 & 0.0188658328975952 & 0.990567083551202 \tabularnewline
23 & 0.0405715774372667 & 0.0811431548745333 & 0.959428422562733 \tabularnewline
24 & 0.0410277696947192 & 0.0820555393894385 & 0.958972230305281 \tabularnewline
25 & 0.104079083479489 & 0.208158166958979 & 0.895920916520511 \tabularnewline
26 & 0.121257028395937 & 0.242514056791875 & 0.878742971604063 \tabularnewline
27 & 0.102418322269846 & 0.204836644539691 & 0.897581677730154 \tabularnewline
28 & 0.308359810890539 & 0.616719621781077 & 0.691640189109461 \tabularnewline
29 & 0.257852633246682 & 0.515705266493364 & 0.742147366753318 \tabularnewline
30 & 0.310718600577265 & 0.62143720115453 & 0.689281399422735 \tabularnewline
31 & 0.256742673299858 & 0.513485346599716 & 0.743257326700142 \tabularnewline
32 & 0.215169858592315 & 0.43033971718463 & 0.784830141407685 \tabularnewline
33 & 0.188024614419472 & 0.376049228838945 & 0.811975385580528 \tabularnewline
34 & 0.178622012063318 & 0.357244024126636 & 0.821377987936682 \tabularnewline
35 & 0.141886976648576 & 0.283773953297152 & 0.858113023351424 \tabularnewline
36 & 0.110956051808052 & 0.221912103616103 & 0.889043948191948 \tabularnewline
37 & 0.14478691105992 & 0.289573822119839 & 0.85521308894008 \tabularnewline
38 & 0.297696336640315 & 0.59539267328063 & 0.702303663359685 \tabularnewline
39 & 0.323077048290445 & 0.64615409658089 & 0.676922951709555 \tabularnewline
40 & 0.362909709099341 & 0.725819418198683 & 0.637090290900659 \tabularnewline
41 & 0.302559836898828 & 0.605119673797656 & 0.697440163101172 \tabularnewline
42 & 0.445599696062006 & 0.891199392124012 & 0.554400303937994 \tabularnewline
43 & 0.419891215349581 & 0.839782430699162 & 0.580108784650419 \tabularnewline
44 & 0.370491069538351 & 0.740982139076702 & 0.629508930461649 \tabularnewline
45 & 0.352415865135056 & 0.704831730270111 & 0.647584134864944 \tabularnewline
46 & 0.352913674975562 & 0.705827349951123 & 0.647086325024438 \tabularnewline
47 & 0.303712242184424 & 0.607424484368847 & 0.696287757815576 \tabularnewline
48 & 0.264001414420616 & 0.528002828841233 & 0.735998585579384 \tabularnewline
49 & 0.2654138055225 & 0.530827611045 & 0.7345861944775 \tabularnewline
50 & 0.223448505975497 & 0.446897011950994 & 0.776551494024503 \tabularnewline
51 & 0.388653006256511 & 0.777306012513021 & 0.611346993743489 \tabularnewline
52 & 0.330603410895481 & 0.661206821790962 & 0.669396589104519 \tabularnewline
53 & 0.291276244818474 & 0.582552489636948 & 0.708723755181526 \tabularnewline
54 & 0.244418874094833 & 0.488837748189667 & 0.755581125905167 \tabularnewline
55 & 0.200833802970533 & 0.401667605941067 & 0.799166197029467 \tabularnewline
56 & 0.467096239295526 & 0.934192478591051 & 0.532903760704474 \tabularnewline
57 & 0.487800310522947 & 0.975600621045895 & 0.512199689477053 \tabularnewline
58 & 0.437003847666727 & 0.874007695333454 & 0.562996152333273 \tabularnewline
59 & 0.389170209032375 & 0.77834041806475 & 0.610829790967625 \tabularnewline
60 & 0.359142855587806 & 0.718285711175612 & 0.640857144412194 \tabularnewline
61 & 0.297962683503188 & 0.595925367006375 & 0.702037316496812 \tabularnewline
62 & 0.361447382746663 & 0.722894765493327 & 0.638552617253337 \tabularnewline
63 & 0.296733093891917 & 0.593466187783834 & 0.703266906108083 \tabularnewline
64 & 0.246272306570871 & 0.492544613141741 & 0.753727693429129 \tabularnewline
65 & 0.191734324413534 & 0.383468648827069 & 0.808265675586466 \tabularnewline
66 & 0.145203024254776 & 0.290406048509553 & 0.854796975745224 \tabularnewline
67 & 0.105726940126744 & 0.211453880253489 & 0.894273059873256 \tabularnewline
68 & 0.113429125059568 & 0.226858250119135 & 0.886570874940432 \tabularnewline
69 & 0.0902194620139476 & 0.180438924027895 & 0.909780537986052 \tabularnewline
70 & 0.299364449514298 & 0.598728899028595 & 0.700635550485702 \tabularnewline
71 & 0.221898997319769 & 0.443797994639538 & 0.778101002680231 \tabularnewline
72 & 0.180561804390421 & 0.361123608780842 & 0.819438195609579 \tabularnewline
73 & 0.404787339020733 & 0.809574678041466 & 0.595212660979267 \tabularnewline
74 & 0.520430790412391 & 0.959138419175218 & 0.479569209587609 \tabularnewline
75 & 0.411050716284932 & 0.822101432569863 & 0.588949283715068 \tabularnewline
76 & 0.322159066791025 & 0.64431813358205 & 0.677840933208975 \tabularnewline
77 & 0.253174043185819 & 0.506348086371637 & 0.746825956814181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200490&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]9[/C][C]6.77054496660541e-95[/C][C]1.35410899332108e-94[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]3.08728550853195e-64[/C][C]6.17457101706389e-64[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]8.18422347986106e-79[/C][C]1.63684469597221e-78[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]8.50530669564521e-95[/C][C]1.70106133912904e-94[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0.0275989634105373[/C][C]0.0551979268210747[/C][C]0.972401036589463[/C][/ROW]
[ROW][C]14[/C][C]0.0121313241594421[/C][C]0.0242626483188842[/C][C]0.987868675840558[/C][/ROW]
[ROW][C]15[/C][C]0.0203308703149165[/C][C]0.040661740629833[/C][C]0.979669129685083[/C][/ROW]
[ROW][C]16[/C][C]0.010967226330513[/C][C]0.021934452661026[/C][C]0.989032773669487[/C][/ROW]
[ROW][C]17[/C][C]0.0049618267998306[/C][C]0.0099236535996612[/C][C]0.995038173200169[/C][/ROW]
[ROW][C]18[/C][C]0.00224537205391715[/C][C]0.00449074410783429[/C][C]0.997754627946083[/C][/ROW]
[ROW][C]19[/C][C]0.000978557401249392[/C][C]0.00195711480249878[/C][C]0.999021442598751[/C][/ROW]
[ROW][C]20[/C][C]0.000483983534986408[/C][C]0.000967967069972816[/C][C]0.999516016465014[/C][/ROW]
[ROW][C]21[/C][C]0.00172986550182764[/C][C]0.00345973100365527[/C][C]0.998270134498172[/C][/ROW]
[ROW][C]22[/C][C]0.00943291644879758[/C][C]0.0188658328975952[/C][C]0.990567083551202[/C][/ROW]
[ROW][C]23[/C][C]0.0405715774372667[/C][C]0.0811431548745333[/C][C]0.959428422562733[/C][/ROW]
[ROW][C]24[/C][C]0.0410277696947192[/C][C]0.0820555393894385[/C][C]0.958972230305281[/C][/ROW]
[ROW][C]25[/C][C]0.104079083479489[/C][C]0.208158166958979[/C][C]0.895920916520511[/C][/ROW]
[ROW][C]26[/C][C]0.121257028395937[/C][C]0.242514056791875[/C][C]0.878742971604063[/C][/ROW]
[ROW][C]27[/C][C]0.102418322269846[/C][C]0.204836644539691[/C][C]0.897581677730154[/C][/ROW]
[ROW][C]28[/C][C]0.308359810890539[/C][C]0.616719621781077[/C][C]0.691640189109461[/C][/ROW]
[ROW][C]29[/C][C]0.257852633246682[/C][C]0.515705266493364[/C][C]0.742147366753318[/C][/ROW]
[ROW][C]30[/C][C]0.310718600577265[/C][C]0.62143720115453[/C][C]0.689281399422735[/C][/ROW]
[ROW][C]31[/C][C]0.256742673299858[/C][C]0.513485346599716[/C][C]0.743257326700142[/C][/ROW]
[ROW][C]32[/C][C]0.215169858592315[/C][C]0.43033971718463[/C][C]0.784830141407685[/C][/ROW]
[ROW][C]33[/C][C]0.188024614419472[/C][C]0.376049228838945[/C][C]0.811975385580528[/C][/ROW]
[ROW][C]34[/C][C]0.178622012063318[/C][C]0.357244024126636[/C][C]0.821377987936682[/C][/ROW]
[ROW][C]35[/C][C]0.141886976648576[/C][C]0.283773953297152[/C][C]0.858113023351424[/C][/ROW]
[ROW][C]36[/C][C]0.110956051808052[/C][C]0.221912103616103[/C][C]0.889043948191948[/C][/ROW]
[ROW][C]37[/C][C]0.14478691105992[/C][C]0.289573822119839[/C][C]0.85521308894008[/C][/ROW]
[ROW][C]38[/C][C]0.297696336640315[/C][C]0.59539267328063[/C][C]0.702303663359685[/C][/ROW]
[ROW][C]39[/C][C]0.323077048290445[/C][C]0.64615409658089[/C][C]0.676922951709555[/C][/ROW]
[ROW][C]40[/C][C]0.362909709099341[/C][C]0.725819418198683[/C][C]0.637090290900659[/C][/ROW]
[ROW][C]41[/C][C]0.302559836898828[/C][C]0.605119673797656[/C][C]0.697440163101172[/C][/ROW]
[ROW][C]42[/C][C]0.445599696062006[/C][C]0.891199392124012[/C][C]0.554400303937994[/C][/ROW]
[ROW][C]43[/C][C]0.419891215349581[/C][C]0.839782430699162[/C][C]0.580108784650419[/C][/ROW]
[ROW][C]44[/C][C]0.370491069538351[/C][C]0.740982139076702[/C][C]0.629508930461649[/C][/ROW]
[ROW][C]45[/C][C]0.352415865135056[/C][C]0.704831730270111[/C][C]0.647584134864944[/C][/ROW]
[ROW][C]46[/C][C]0.352913674975562[/C][C]0.705827349951123[/C][C]0.647086325024438[/C][/ROW]
[ROW][C]47[/C][C]0.303712242184424[/C][C]0.607424484368847[/C][C]0.696287757815576[/C][/ROW]
[ROW][C]48[/C][C]0.264001414420616[/C][C]0.528002828841233[/C][C]0.735998585579384[/C][/ROW]
[ROW][C]49[/C][C]0.2654138055225[/C][C]0.530827611045[/C][C]0.7345861944775[/C][/ROW]
[ROW][C]50[/C][C]0.223448505975497[/C][C]0.446897011950994[/C][C]0.776551494024503[/C][/ROW]
[ROW][C]51[/C][C]0.388653006256511[/C][C]0.777306012513021[/C][C]0.611346993743489[/C][/ROW]
[ROW][C]52[/C][C]0.330603410895481[/C][C]0.661206821790962[/C][C]0.669396589104519[/C][/ROW]
[ROW][C]53[/C][C]0.291276244818474[/C][C]0.582552489636948[/C][C]0.708723755181526[/C][/ROW]
[ROW][C]54[/C][C]0.244418874094833[/C][C]0.488837748189667[/C][C]0.755581125905167[/C][/ROW]
[ROW][C]55[/C][C]0.200833802970533[/C][C]0.401667605941067[/C][C]0.799166197029467[/C][/ROW]
[ROW][C]56[/C][C]0.467096239295526[/C][C]0.934192478591051[/C][C]0.532903760704474[/C][/ROW]
[ROW][C]57[/C][C]0.487800310522947[/C][C]0.975600621045895[/C][C]0.512199689477053[/C][/ROW]
[ROW][C]58[/C][C]0.437003847666727[/C][C]0.874007695333454[/C][C]0.562996152333273[/C][/ROW]
[ROW][C]59[/C][C]0.389170209032375[/C][C]0.77834041806475[/C][C]0.610829790967625[/C][/ROW]
[ROW][C]60[/C][C]0.359142855587806[/C][C]0.718285711175612[/C][C]0.640857144412194[/C][/ROW]
[ROW][C]61[/C][C]0.297962683503188[/C][C]0.595925367006375[/C][C]0.702037316496812[/C][/ROW]
[ROW][C]62[/C][C]0.361447382746663[/C][C]0.722894765493327[/C][C]0.638552617253337[/C][/ROW]
[ROW][C]63[/C][C]0.296733093891917[/C][C]0.593466187783834[/C][C]0.703266906108083[/C][/ROW]
[ROW][C]64[/C][C]0.246272306570871[/C][C]0.492544613141741[/C][C]0.753727693429129[/C][/ROW]
[ROW][C]65[/C][C]0.191734324413534[/C][C]0.383468648827069[/C][C]0.808265675586466[/C][/ROW]
[ROW][C]66[/C][C]0.145203024254776[/C][C]0.290406048509553[/C][C]0.854796975745224[/C][/ROW]
[ROW][C]67[/C][C]0.105726940126744[/C][C]0.211453880253489[/C][C]0.894273059873256[/C][/ROW]
[ROW][C]68[/C][C]0.113429125059568[/C][C]0.226858250119135[/C][C]0.886570874940432[/C][/ROW]
[ROW][C]69[/C][C]0.0902194620139476[/C][C]0.180438924027895[/C][C]0.909780537986052[/C][/ROW]
[ROW][C]70[/C][C]0.299364449514298[/C][C]0.598728899028595[/C][C]0.700635550485702[/C][/ROW]
[ROW][C]71[/C][C]0.221898997319769[/C][C]0.443797994639538[/C][C]0.778101002680231[/C][/ROW]
[ROW][C]72[/C][C]0.180561804390421[/C][C]0.361123608780842[/C][C]0.819438195609579[/C][/ROW]
[ROW][C]73[/C][C]0.404787339020733[/C][C]0.809574678041466[/C][C]0.595212660979267[/C][/ROW]
[ROW][C]74[/C][C]0.520430790412391[/C][C]0.959138419175218[/C][C]0.479569209587609[/C][/ROW]
[ROW][C]75[/C][C]0.411050716284932[/C][C]0.822101432569863[/C][C]0.588949283715068[/C][/ROW]
[ROW][C]76[/C][C]0.322159066791025[/C][C]0.64431813358205[/C][C]0.677840933208975[/C][/ROW]
[ROW][C]77[/C][C]0.253174043185819[/C][C]0.506348086371637[/C][C]0.746825956814181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200490&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200490&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
9	6.77054496660541e-95	1.35410899332108e-94	1
10	3.08728550853195e-64	6.17457101706389e-64	1
11	8.18422347986106e-79	1.63684469597221e-78	1
12	8.50530669564521e-95	1.70106133912904e-94	1
13	0.0275989634105373	0.0551979268210747	0.972401036589463
14	0.0121313241594421	0.0242626483188842	0.987868675840558
15	0.0203308703149165	0.040661740629833	0.979669129685083
16	0.010967226330513	0.021934452661026	0.989032773669487
17	0.0049618267998306	0.0099236535996612	0.995038173200169
18	0.00224537205391715	0.00449074410783429	0.997754627946083
19	0.000978557401249392	0.00195711480249878	0.999021442598751
20	0.000483983534986408	0.000967967069972816	0.999516016465014
21	0.00172986550182764	0.00345973100365527	0.998270134498172
22	0.00943291644879758	0.0188658328975952	0.990567083551202
23	0.0405715774372667	0.0811431548745333	0.959428422562733
24	0.0410277696947192	0.0820555393894385	0.958972230305281
25	0.104079083479489	0.208158166958979	0.895920916520511
26	0.121257028395937	0.242514056791875	0.878742971604063
27	0.102418322269846	0.204836644539691	0.897581677730154
28	0.308359810890539	0.616719621781077	0.691640189109461
29	0.257852633246682	0.515705266493364	0.742147366753318
30	0.310718600577265	0.62143720115453	0.689281399422735
31	0.256742673299858	0.513485346599716	0.743257326700142
32	0.215169858592315	0.43033971718463	0.784830141407685
33	0.188024614419472	0.376049228838945	0.811975385580528
34	0.178622012063318	0.357244024126636	0.821377987936682
35	0.141886976648576	0.283773953297152	0.858113023351424
36	0.110956051808052	0.221912103616103	0.889043948191948
37	0.14478691105992	0.289573822119839	0.85521308894008
38	0.297696336640315	0.59539267328063	0.702303663359685
39	0.323077048290445	0.64615409658089	0.676922951709555
40	0.362909709099341	0.725819418198683	0.637090290900659
41	0.302559836898828	0.605119673797656	0.697440163101172
42	0.445599696062006	0.891199392124012	0.554400303937994
43	0.419891215349581	0.839782430699162	0.580108784650419
44	0.370491069538351	0.740982139076702	0.629508930461649
45	0.352415865135056	0.704831730270111	0.647584134864944
46	0.352913674975562	0.705827349951123	0.647086325024438
47	0.303712242184424	0.607424484368847	0.696287757815576
48	0.264001414420616	0.528002828841233	0.735998585579384
49	0.2654138055225	0.530827611045	0.7345861944775
50	0.223448505975497	0.446897011950994	0.776551494024503
51	0.388653006256511	0.777306012513021	0.611346993743489
52	0.330603410895481	0.661206821790962	0.669396589104519
53	0.291276244818474	0.582552489636948	0.708723755181526
54	0.244418874094833	0.488837748189667	0.755581125905167
55	0.200833802970533	0.401667605941067	0.799166197029467
56	0.467096239295526	0.934192478591051	0.532903760704474
57	0.487800310522947	0.975600621045895	0.512199689477053
58	0.437003847666727	0.874007695333454	0.562996152333273
59	0.389170209032375	0.77834041806475	0.610829790967625
60	0.359142855587806	0.718285711175612	0.640857144412194
61	0.297962683503188	0.595925367006375	0.702037316496812
62	0.361447382746663	0.722894765493327	0.638552617253337
63	0.296733093891917	0.593466187783834	0.703266906108083
64	0.246272306570871	0.492544613141741	0.753727693429129
65	0.191734324413534	0.383468648827069	0.808265675586466
66	0.145203024254776	0.290406048509553	0.854796975745224
67	0.105726940126744	0.211453880253489	0.894273059873256
68	0.113429125059568	0.226858250119135	0.886570874940432
69	0.0902194620139476	0.180438924027895	0.909780537986052
70	0.299364449514298	0.598728899028595	0.700635550485702
71	0.221898997319769	0.443797994639538	0.778101002680231
72	0.180561804390421	0.361123608780842	0.819438195609579
73	0.404787339020733	0.809574678041466	0.595212660979267
74	0.520430790412391	0.959138419175218	0.479569209587609
75	0.411050716284932	0.822101432569863	0.588949283715068
76	0.322159066791025	0.64431813358205	0.677840933208975
77	0.253174043185819	0.506348086371637	0.746825956814181

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.130434782608696	NOK
5% type I error level	13	0.188405797101449	NOK
10% type I error level	16	0.231884057971014	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 & 9 & 0.130434782608696 & NOK \tabularnewline
5% type I error level & 13 & 0.188405797101449 & NOK \tabularnewline
10% type I error level & 16 & 0.231884057971014 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200490&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]9[/C][C]0.130434782608696[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.188405797101449[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.231884057971014[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200490&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200490&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	9	0.130434782608696	NOK
5% type I error level	13	0.188405797101449	NOK
10% type I error level	16	0.231884057971014	NOK

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; 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