Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

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

Tue, 22 Nov 2011 12:17:07 -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/2011/Nov/22/t13219822460s2fo4c07q4zgir.htm/, Retrieved Wed, 19 Nov 2025 22:32:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146325, Retrieved Wed, 19 Nov 2025 22:32:43 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

428

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

-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 12:52:07] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D    [Multiple Regression] [WS7 comp 3] [2010-11-23 09:24:05] [dc30d19c3bc2be07fe595ad36c2cf923]
-           [Multiple Regression] [] [2010-12-02 15:15:07] [2e1e44f0ae3cb9513dc28781dfdb387b]
- R  D          [Multiple Regression] [ws7] [2011-11-22 17:17:07] [13dfa60174f50d862e8699db2153bfc5] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

9	13	2528	80	15.3
9	12	3333	83	19.4
9	54	19611	96	19.5
9	19	3570	56	17
9	37	1722	43	19.3
9	2	583	51	12.9
9	72	4790	91	16.7
9	164	35971	81	13.8
9	18	25440	120	13.7
9	1	2217	46	14.3
9	53	1971	56	22.2
9	16	12620	37	16.8
9	32	19046	120	18
10	21	8612	103	15
10	23	3896	105	18.4
10	18	6298	42	18.2
10	112	27350	65	24.1
10	25	4145	51	23
10	5	1175	57	21.8
10	26	8297	60	19.1
10	8	7814	160	22.6
10	15	1745	48	14.3
10	11	5046	109	19
10	11	18943	50	18.5
10	87	8624	78	21.3
10	33	2225	41	20.5
11	22	12659	65	18
11	98	1967	50	16.8
11	1	1172	73	20.5
11	5	639	26	20.1
11	1	7056	60	24.5
11	38	1934	85	12
11	30	6260	133	21
11	12	424	62	20.2
11	24	3488	44	24
11	6	3330	67	14.9
11	15	2227	54	24
11	38	8115	110	20.5
11	84	1600	56	19.5
12	3	15305	85	17.5
12	18	7121	58	16
12	63	5794	34	17.5
12	239	8636	150	18.1
12	234	4803	93	24.3
12	6	1097	53	13.1
12	76	9765	130	16.9
12	25	4266	68	17
12	8	1507	51	21
12	23	3836	121	18.5
12	16	17419	48	18
12	6	8735	63	20.8
12	100	22550	107	23
13	80	9961	79	21.8
13	28	4706	61	19.7
13	48	4011	52	18.9
13	18	6949	100	18.6
13	36	11405	70	23.6
13	19	904	39	19.2
13	32	3332	73	17.7
13	3	575	33	18
13	106	29708	73	21.4
13	62	2511	60	17.7
13	23	18422	45	20.1
13	2	6311	46	18.5
13	26	1450	60	18.6
14	20	4106	96	15.4
14	38	10274	90	15
14	19	510	82	26.5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'AstonUniversity' @ aston.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 & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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	5 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Fish[t] = -63.6423894141477 + 2.31247145920237Month[t] + 0.00179280767392933Acre[t] + 0.374300156306726Depth[t] + 1.90718013651869Temp[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Fish[t] =  -63.6423894141477 +  2.31247145920237Month[t] +  0.00179280767392933Acre[t] +  0.374300156306726Depth[t] +  1.90718013651869Temp[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Fish[t] =  -63.6423894141477 +  2.31247145920237Month[t] +  0.00179280767392933Acre[t] +  0.374300156306726Depth[t] +  1.90718013651869Temp[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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
Fish[t] = -63.6423894141477 + 2.31247145920237Month[t] + 0.00179280767392933Acre[t] + 0.374300156306726Depth[t] + 1.90718013651869Temp[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)	-63.6423894141477	49.437503	-1.2873	0.202689	0.101344
Month	2.31247145920237	3.582327	0.6455	0.520933	0.260467
Acre	0.00179280767392933	0.000718	2.4977	0.015125	0.007563
Depth	0.374300156306726	0.188868	1.9818	0.051868	0.025934
Temp	1.90718013651869	1.727579	1.104	0.273809	0.136904

\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) & -63.6423894141477 & 49.437503 & -1.2873 & 0.202689 & 0.101344 \tabularnewline
Month & 2.31247145920237 & 3.582327 & 0.6455 & 0.520933 & 0.260467 \tabularnewline
Acre & 0.00179280767392933 & 0.000718 & 2.4977 & 0.015125 & 0.007563 \tabularnewline
Depth & 0.374300156306726 & 0.188868 & 1.9818 & 0.051868 & 0.025934 \tabularnewline
Temp & 1.90718013651869 & 1.727579 & 1.104 & 0.273809 & 0.136904 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&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]-63.6423894141477[/C][C]49.437503[/C][C]-1.2873[/C][C]0.202689[/C][C]0.101344[/C][/ROW]
[ROW][C]Month[/C][C]2.31247145920237[/C][C]3.582327[/C][C]0.6455[/C][C]0.520933[/C][C]0.260467[/C][/ROW]
[ROW][C]Acre[/C][C]0.00179280767392933[/C][C]0.000718[/C][C]2.4977[/C][C]0.015125[/C][C]0.007563[/C][/ROW]
[ROW][C]Depth[/C][C]0.374300156306726[/C][C]0.188868[/C][C]1.9818[/C][C]0.051868[/C][C]0.025934[/C][/ROW]
[ROW][C]Temp[/C][C]1.90718013651869[/C][C]1.727579[/C][C]1.104[/C][C]0.273809[/C][C]0.136904[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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)	-63.6423894141477	49.437503	-1.2873	0.202689	0.101344
Month	2.31247145920237	3.582327	0.6455	0.520933	0.260467
Acre	0.00179280767392933	0.000718	2.4977	0.015125	0.007563
Depth	0.374300156306726	0.188868	1.9818	0.051868	0.025934
Temp	1.90718013651869	1.727579	1.104	0.273809	0.136904

Multiple Linear Regression - Regression Statistics
Multiple R	0.44124252320546
R-squared	0.194694964284721
Adjusted R-squared	0.143564485826608
F-TEST (value)	3.80780642301673
F-TEST (DF numerator)	4
F-TEST (DF denominator)	63
p-value	0.00778070011843357
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	43.7633005626207
Sum Squared Residuals	120659.267996459

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.44124252320546 \tabularnewline
R-squared & 0.194694964284721 \tabularnewline
Adjusted R-squared & 0.143564485826608 \tabularnewline
F-TEST (value) & 3.80780642301673 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 0.00778070011843357 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 43.7633005626207 \tabularnewline
Sum Squared Residuals & 120659.267996459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.44124252320546[/C][/ROW]
[ROW][C]R-squared[/C][C]0.194694964284721[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.143564485826608[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.80780642301673[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/C][/ROW]
[ROW][C]p-value[/C][C]0.00778070011843357[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]43.7633005626207[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]120659.267996459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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	0.44124252320546
R-squared	0.194694964284721
Adjusted R-squared	0.143564485826608
F-TEST (value)	3.80780642301673
F-TEST (DF numerator)	4
F-TEST (DF denominator)	63
p-value	0.00778070011843357
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	43.7633005626207
Sum Squared Residuals	120659.267996459

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	20.8259401116409	-7.82594011164095
2	12	31.2114893178009	-19.2114893178009
3	54	65.4514326796618	-11.4514326796618
4	19	16.9530481885957	2.04695181140428
5	37	13.1605518891799	23.8394481108201
6	2	1.90699232530855	0.0930076746914495
7	72	31.6686249805693	40.3313750194307
8	164	78.2963371023882	85.7036628976118
9	18	73.8232675705489	-55.8232675705489
10	1	5.63499147410161	-4.63499147410161
11	53	24.0036854278799	28.9963145721201
12	16	25.6848186405246	-9.68481864052458
13	32	70.5609298904751	-38.5609298904751
14	21	42.0826030131285	-21.0826030131285
15	23	40.8607347996548	-17.8607347996548
16	18	21.2047129578055	-3.20471295780554
17	112	78.8081665098807	33.1918334901193
18	25	29.8679640978859	-4.86796409788595
19	5	24.5005100803338	-19.5005100803338
20	26	33.2424004343782	-7.24240043437816
21	8	76.4816204363583	-68.4816204363583
22	15	7.84985802382278	7.15014197617722
23	11	45.5639723318116	-34.5639723318116
24	11	47.4413212860513	-36.4413212860513
25	87	44.7618476576152	42.2381523423848
26	33	17.9148214595776	15.0851785404224
27	22	43.1487015986233	-21.1487015986233
28	98	16.0768834405476	81.9231165594524
29	1	30.3170714399477	-29.3170714399477
30	5	11.0065255487197	-6.00652554871973
31	1	43.6287703074351	-42.6287703074351
32	38	19.9637616027537	18.0362383972463
33	30	62.850476331563	-32.850476331563
34	12	24.2865955395189	-12.2865955395189
35	24	30.2896399576883	-6.28963995768835
36	6	21.2599406979421	-15.2599406979421
37	15	31.7719110439307	-16.7719110439307
38	38	56.6136409033879	-18.6136409033879
39	84	22.8141103306564	61.1858896693436
40	3	56.7373552209179	-53.7373552209179
41	18	29.0981427924206	-11.0981427924206
42	63	20.596653462533	42.403346537467
43	239	70.2549390853316	168.745060914668
44	234	53.872515208093	180.127484791907
45	6	10.8959461872325	-4.89594618723251
46	76	62.5043996592409	13.4956003407591
47	25	29.6298585829383	-4.62985858293833
48	8	25.9491200994277	-17.9491200994277
49	23	51.5576297721832	-28.5576297721832
50	16	47.631834928515	-31.6318349285149
51	6	43.0176998149659	-37.0176998149659
52	100	88.4503410081366	11.5496589918634
53	80	55.4241361198319	24.5758638801681
54	28	35.260450693123	-7.26045069312298
55	48	29.1200038437666	18.8799961562334
56	18	51.7815262515382	-33.7815262515382
57	36	58.077173239959	-22.077173239959
58	19	19.2560024098364	-0.256002409836356
59	32	33.4743745517874	-1.47437455178742
60	3	14.1317515834508	-11.1317515834508
61	106	87.8180362644665	18.1819637355335
62	62	27.136577419504	34.863422580496
63	23	54.6246703024375	-31.6246703024375
64	2	30.2347885013562	-28.2347885013562
65	26	26.9508706003318	-0.95087060033181
66	20	41.3968684316728	-21.3968684316728
67	38	49.4462331720211	-11.4462331720211
68	19	50.8794293632862	-31.8794293632862

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 20.8259401116409 & -7.82594011164095 \tabularnewline
2 & 12 & 31.2114893178009 & -19.2114893178009 \tabularnewline
3 & 54 & 65.4514326796618 & -11.4514326796618 \tabularnewline
4 & 19 & 16.9530481885957 & 2.04695181140428 \tabularnewline
5 & 37 & 13.1605518891799 & 23.8394481108201 \tabularnewline
6 & 2 & 1.90699232530855 & 0.0930076746914495 \tabularnewline
7 & 72 & 31.6686249805693 & 40.3313750194307 \tabularnewline
8 & 164 & 78.2963371023882 & 85.7036628976118 \tabularnewline
9 & 18 & 73.8232675705489 & -55.8232675705489 \tabularnewline
10 & 1 & 5.63499147410161 & -4.63499147410161 \tabularnewline
11 & 53 & 24.0036854278799 & 28.9963145721201 \tabularnewline
12 & 16 & 25.6848186405246 & -9.68481864052458 \tabularnewline
13 & 32 & 70.5609298904751 & -38.5609298904751 \tabularnewline
14 & 21 & 42.0826030131285 & -21.0826030131285 \tabularnewline
15 & 23 & 40.8607347996548 & -17.8607347996548 \tabularnewline
16 & 18 & 21.2047129578055 & -3.20471295780554 \tabularnewline
17 & 112 & 78.8081665098807 & 33.1918334901193 \tabularnewline
18 & 25 & 29.8679640978859 & -4.86796409788595 \tabularnewline
19 & 5 & 24.5005100803338 & -19.5005100803338 \tabularnewline
20 & 26 & 33.2424004343782 & -7.24240043437816 \tabularnewline
21 & 8 & 76.4816204363583 & -68.4816204363583 \tabularnewline
22 & 15 & 7.84985802382278 & 7.15014197617722 \tabularnewline
23 & 11 & 45.5639723318116 & -34.5639723318116 \tabularnewline
24 & 11 & 47.4413212860513 & -36.4413212860513 \tabularnewline
25 & 87 & 44.7618476576152 & 42.2381523423848 \tabularnewline
26 & 33 & 17.9148214595776 & 15.0851785404224 \tabularnewline
27 & 22 & 43.1487015986233 & -21.1487015986233 \tabularnewline
28 & 98 & 16.0768834405476 & 81.9231165594524 \tabularnewline
29 & 1 & 30.3170714399477 & -29.3170714399477 \tabularnewline
30 & 5 & 11.0065255487197 & -6.00652554871973 \tabularnewline
31 & 1 & 43.6287703074351 & -42.6287703074351 \tabularnewline
32 & 38 & 19.9637616027537 & 18.0362383972463 \tabularnewline
33 & 30 & 62.850476331563 & -32.850476331563 \tabularnewline
34 & 12 & 24.2865955395189 & -12.2865955395189 \tabularnewline
35 & 24 & 30.2896399576883 & -6.28963995768835 \tabularnewline
36 & 6 & 21.2599406979421 & -15.2599406979421 \tabularnewline
37 & 15 & 31.7719110439307 & -16.7719110439307 \tabularnewline
38 & 38 & 56.6136409033879 & -18.6136409033879 \tabularnewline
39 & 84 & 22.8141103306564 & 61.1858896693436 \tabularnewline
40 & 3 & 56.7373552209179 & -53.7373552209179 \tabularnewline
41 & 18 & 29.0981427924206 & -11.0981427924206 \tabularnewline
42 & 63 & 20.596653462533 & 42.403346537467 \tabularnewline
43 & 239 & 70.2549390853316 & 168.745060914668 \tabularnewline
44 & 234 & 53.872515208093 & 180.127484791907 \tabularnewline
45 & 6 & 10.8959461872325 & -4.89594618723251 \tabularnewline
46 & 76 & 62.5043996592409 & 13.4956003407591 \tabularnewline
47 & 25 & 29.6298585829383 & -4.62985858293833 \tabularnewline
48 & 8 & 25.9491200994277 & -17.9491200994277 \tabularnewline
49 & 23 & 51.5576297721832 & -28.5576297721832 \tabularnewline
50 & 16 & 47.631834928515 & -31.6318349285149 \tabularnewline
51 & 6 & 43.0176998149659 & -37.0176998149659 \tabularnewline
52 & 100 & 88.4503410081366 & 11.5496589918634 \tabularnewline
53 & 80 & 55.4241361198319 & 24.5758638801681 \tabularnewline
54 & 28 & 35.260450693123 & -7.26045069312298 \tabularnewline
55 & 48 & 29.1200038437666 & 18.8799961562334 \tabularnewline
56 & 18 & 51.7815262515382 & -33.7815262515382 \tabularnewline
57 & 36 & 58.077173239959 & -22.077173239959 \tabularnewline
58 & 19 & 19.2560024098364 & -0.256002409836356 \tabularnewline
59 & 32 & 33.4743745517874 & -1.47437455178742 \tabularnewline
60 & 3 & 14.1317515834508 & -11.1317515834508 \tabularnewline
61 & 106 & 87.8180362644665 & 18.1819637355335 \tabularnewline
62 & 62 & 27.136577419504 & 34.863422580496 \tabularnewline
63 & 23 & 54.6246703024375 & -31.6246703024375 \tabularnewline
64 & 2 & 30.2347885013562 & -28.2347885013562 \tabularnewline
65 & 26 & 26.9508706003318 & -0.95087060033181 \tabularnewline
66 & 20 & 41.3968684316728 & -21.3968684316728 \tabularnewline
67 & 38 & 49.4462331720211 & -11.4462331720211 \tabularnewline
68 & 19 & 50.8794293632862 & -31.8794293632862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&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]13[/C][C]20.8259401116409[/C][C]-7.82594011164095[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]31.2114893178009[/C][C]-19.2114893178009[/C][/ROW]
[ROW][C]3[/C][C]54[/C][C]65.4514326796618[/C][C]-11.4514326796618[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]16.9530481885957[/C][C]2.04695181140428[/C][/ROW]
[ROW][C]5[/C][C]37[/C][C]13.1605518891799[/C][C]23.8394481108201[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.90699232530855[/C][C]0.0930076746914495[/C][/ROW]
[ROW][C]7[/C][C]72[/C][C]31.6686249805693[/C][C]40.3313750194307[/C][/ROW]
[ROW][C]8[/C][C]164[/C][C]78.2963371023882[/C][C]85.7036628976118[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]73.8232675705489[/C][C]-55.8232675705489[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]5.63499147410161[/C][C]-4.63499147410161[/C][/ROW]
[ROW][C]11[/C][C]53[/C][C]24.0036854278799[/C][C]28.9963145721201[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]25.6848186405246[/C][C]-9.68481864052458[/C][/ROW]
[ROW][C]13[/C][C]32[/C][C]70.5609298904751[/C][C]-38.5609298904751[/C][/ROW]
[ROW][C]14[/C][C]21[/C][C]42.0826030131285[/C][C]-21.0826030131285[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]40.8607347996548[/C][C]-17.8607347996548[/C][/ROW]
[ROW][C]16[/C][C]18[/C][C]21.2047129578055[/C][C]-3.20471295780554[/C][/ROW]
[ROW][C]17[/C][C]112[/C][C]78.8081665098807[/C][C]33.1918334901193[/C][/ROW]
[ROW][C]18[/C][C]25[/C][C]29.8679640978859[/C][C]-4.86796409788595[/C][/ROW]
[ROW][C]19[/C][C]5[/C][C]24.5005100803338[/C][C]-19.5005100803338[/C][/ROW]
[ROW][C]20[/C][C]26[/C][C]33.2424004343782[/C][C]-7.24240043437816[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]76.4816204363583[/C][C]-68.4816204363583[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]7.84985802382278[/C][C]7.15014197617722[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]45.5639723318116[/C][C]-34.5639723318116[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]47.4413212860513[/C][C]-36.4413212860513[/C][/ROW]
[ROW][C]25[/C][C]87[/C][C]44.7618476576152[/C][C]42.2381523423848[/C][/ROW]
[ROW][C]26[/C][C]33[/C][C]17.9148214595776[/C][C]15.0851785404224[/C][/ROW]
[ROW][C]27[/C][C]22[/C][C]43.1487015986233[/C][C]-21.1487015986233[/C][/ROW]
[ROW][C]28[/C][C]98[/C][C]16.0768834405476[/C][C]81.9231165594524[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]30.3170714399477[/C][C]-29.3170714399477[/C][/ROW]
[ROW][C]30[/C][C]5[/C][C]11.0065255487197[/C][C]-6.00652554871973[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]43.6287703074351[/C][C]-42.6287703074351[/C][/ROW]
[ROW][C]32[/C][C]38[/C][C]19.9637616027537[/C][C]18.0362383972463[/C][/ROW]
[ROW][C]33[/C][C]30[/C][C]62.850476331563[/C][C]-32.850476331563[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]24.2865955395189[/C][C]-12.2865955395189[/C][/ROW]
[ROW][C]35[/C][C]24[/C][C]30.2896399576883[/C][C]-6.28963995768835[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]21.2599406979421[/C][C]-15.2599406979421[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]31.7719110439307[/C][C]-16.7719110439307[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]56.6136409033879[/C][C]-18.6136409033879[/C][/ROW]
[ROW][C]39[/C][C]84[/C][C]22.8141103306564[/C][C]61.1858896693436[/C][/ROW]
[ROW][C]40[/C][C]3[/C][C]56.7373552209179[/C][C]-53.7373552209179[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]29.0981427924206[/C][C]-11.0981427924206[/C][/ROW]
[ROW][C]42[/C][C]63[/C][C]20.596653462533[/C][C]42.403346537467[/C][/ROW]
[ROW][C]43[/C][C]239[/C][C]70.2549390853316[/C][C]168.745060914668[/C][/ROW]
[ROW][C]44[/C][C]234[/C][C]53.872515208093[/C][C]180.127484791907[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]10.8959461872325[/C][C]-4.89594618723251[/C][/ROW]
[ROW][C]46[/C][C]76[/C][C]62.5043996592409[/C][C]13.4956003407591[/C][/ROW]
[ROW][C]47[/C][C]25[/C][C]29.6298585829383[/C][C]-4.62985858293833[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]25.9491200994277[/C][C]-17.9491200994277[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]51.5576297721832[/C][C]-28.5576297721832[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]47.631834928515[/C][C]-31.6318349285149[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]43.0176998149659[/C][C]-37.0176998149659[/C][/ROW]
[ROW][C]52[/C][C]100[/C][C]88.4503410081366[/C][C]11.5496589918634[/C][/ROW]
[ROW][C]53[/C][C]80[/C][C]55.4241361198319[/C][C]24.5758638801681[/C][/ROW]
[ROW][C]54[/C][C]28[/C][C]35.260450693123[/C][C]-7.26045069312298[/C][/ROW]
[ROW][C]55[/C][C]48[/C][C]29.1200038437666[/C][C]18.8799961562334[/C][/ROW]
[ROW][C]56[/C][C]18[/C][C]51.7815262515382[/C][C]-33.7815262515382[/C][/ROW]
[ROW][C]57[/C][C]36[/C][C]58.077173239959[/C][C]-22.077173239959[/C][/ROW]
[ROW][C]58[/C][C]19[/C][C]19.2560024098364[/C][C]-0.256002409836356[/C][/ROW]
[ROW][C]59[/C][C]32[/C][C]33.4743745517874[/C][C]-1.47437455178742[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]14.1317515834508[/C][C]-11.1317515834508[/C][/ROW]
[ROW][C]61[/C][C]106[/C][C]87.8180362644665[/C][C]18.1819637355335[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]27.136577419504[/C][C]34.863422580496[/C][/ROW]
[ROW][C]63[/C][C]23[/C][C]54.6246703024375[/C][C]-31.6246703024375[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]30.2347885013562[/C][C]-28.2347885013562[/C][/ROW]
[ROW][C]65[/C][C]26[/C][C]26.9508706003318[/C][C]-0.95087060033181[/C][/ROW]
[ROW][C]66[/C][C]20[/C][C]41.3968684316728[/C][C]-21.3968684316728[/C][/ROW]
[ROW][C]67[/C][C]38[/C][C]49.4462331720211[/C][C]-11.4462331720211[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]50.8794293632862[/C][C]-31.8794293632862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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	13	20.8259401116409	-7.82594011164095
2	12	31.2114893178009	-19.2114893178009
3	54	65.4514326796618	-11.4514326796618
4	19	16.9530481885957	2.04695181140428
5	37	13.1605518891799	23.8394481108201
6	2	1.90699232530855	0.0930076746914495
7	72	31.6686249805693	40.3313750194307
8	164	78.2963371023882	85.7036628976118
9	18	73.8232675705489	-55.8232675705489
10	1	5.63499147410161	-4.63499147410161
11	53	24.0036854278799	28.9963145721201
12	16	25.6848186405246	-9.68481864052458
13	32	70.5609298904751	-38.5609298904751
14	21	42.0826030131285	-21.0826030131285
15	23	40.8607347996548	-17.8607347996548
16	18	21.2047129578055	-3.20471295780554
17	112	78.8081665098807	33.1918334901193
18	25	29.8679640978859	-4.86796409788595
19	5	24.5005100803338	-19.5005100803338
20	26	33.2424004343782	-7.24240043437816
21	8	76.4816204363583	-68.4816204363583
22	15	7.84985802382278	7.15014197617722
23	11	45.5639723318116	-34.5639723318116
24	11	47.4413212860513	-36.4413212860513
25	87	44.7618476576152	42.2381523423848
26	33	17.9148214595776	15.0851785404224
27	22	43.1487015986233	-21.1487015986233
28	98	16.0768834405476	81.9231165594524
29	1	30.3170714399477	-29.3170714399477
30	5	11.0065255487197	-6.00652554871973
31	1	43.6287703074351	-42.6287703074351
32	38	19.9637616027537	18.0362383972463
33	30	62.850476331563	-32.850476331563
34	12	24.2865955395189	-12.2865955395189
35	24	30.2896399576883	-6.28963995768835
36	6	21.2599406979421	-15.2599406979421
37	15	31.7719110439307	-16.7719110439307
38	38	56.6136409033879	-18.6136409033879
39	84	22.8141103306564	61.1858896693436
40	3	56.7373552209179	-53.7373552209179
41	18	29.0981427924206	-11.0981427924206
42	63	20.596653462533	42.403346537467
43	239	70.2549390853316	168.745060914668
44	234	53.872515208093	180.127484791907
45	6	10.8959461872325	-4.89594618723251
46	76	62.5043996592409	13.4956003407591
47	25	29.6298585829383	-4.62985858293833
48	8	25.9491200994277	-17.9491200994277
49	23	51.5576297721832	-28.5576297721832
50	16	47.631834928515	-31.6318349285149
51	6	43.0176998149659	-37.0176998149659
52	100	88.4503410081366	11.5496589918634
53	80	55.4241361198319	24.5758638801681
54	28	35.260450693123	-7.26045069312298
55	48	29.1200038437666	18.8799961562334
56	18	51.7815262515382	-33.7815262515382
57	36	58.077173239959	-22.077173239959
58	19	19.2560024098364	-0.256002409836356
59	32	33.4743745517874	-1.47437455178742
60	3	14.1317515834508	-11.1317515834508
61	106	87.8180362644665	18.1819637355335
62	62	27.136577419504	34.863422580496
63	23	54.6246703024375	-31.6246703024375
64	2	30.2347885013562	-28.2347885013562
65	26	26.9508706003318	-0.95087060033181
66	20	41.3968684316728	-21.3968684316728
67	38	49.4462331720211	-11.4462331720211
68	19	50.8794293632862	-31.8794293632862

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.402228187916562	0.804456375833124	0.597771812083438
9	0.551718555319181	0.896562889361637	0.448281444680819
10	0.448029000249077	0.896058000498153	0.551970999750923
11	0.325185344682083	0.650370689364166	0.674814655317917
12	0.371001073833402	0.742002147666803	0.628998926166598
13	0.297462159627115	0.594924319254231	0.702537840372885
14	0.208701399039136	0.417402798078272	0.791298600960864
15	0.141063522161644	0.282127044323288	0.858936477838356
16	0.104708029031369	0.209416058062737	0.895291970968631
17	0.0704872727809237	0.140974545561847	0.929512727219076
18	0.0448454588018341	0.0896909176036682	0.955154541198166
19	0.0286794695012955	0.057358939002591	0.971320530498704
20	0.0164240287606038	0.0328480575212076	0.983575971239396
21	0.0140954698589086	0.0281909397178172	0.985904530141091
22	0.00810429473443583	0.0162085894688717	0.991895705265564
23	0.00533940855715554	0.0106788171143111	0.994660591442844
24	0.00915074415703196	0.0183014883140639	0.990849255842968
25	0.0151518539314304	0.0303037078628609	0.98484814606857
26	0.00918919125638745	0.0183783825127749	0.990810808743613
27	0.00539269531405936	0.0107853906281187	0.99460730468594
28	0.0377790322198479	0.0755580644396959	0.962220967780152
29	0.0293471866011496	0.0586943732022991	0.97065281339885
30	0.0206499639426898	0.0412999278853796	0.97935003605731
31	0.0204576441573471	0.0409152883146941	0.979542355842653
32	0.0152953626463168	0.0305907252926335	0.984704637353683
33	0.016348743442466	0.032697486884932	0.983651256557534
34	0.0108520217021469	0.0217040434042938	0.989147978297853
35	0.00677136659574009	0.0135427331914802	0.99322863340426
36	0.00446015592734237	0.00892031185468473	0.995539844072658
37	0.00344469890556663	0.00688939781113327	0.996555301094433
38	0.00448810582227451	0.00897621164454901	0.995511894177725
39	0.00718677071679281	0.0143735414335856	0.992813229283207
40	0.0117007998291886	0.0234015996583772	0.988299200170811
41	0.00734507256854033	0.0146901451370807	0.99265492743146
42	0.00786706648910035	0.0157341329782007	0.9921329335109
43	0.511693106242601	0.976613787514798	0.488306893757399
44	0.999827549207465	0.000344901585069963	0.000172450792534981
45	0.999621800998252	0.000756398003495009	0.000378199001747504
46	0.999391861514584	0.00121627697083288	0.000608138485416441
47	0.998746497077928	0.00250700584414426	0.00125350292207213
48	0.99749028691504	0.00501942616991894	0.00250971308495947
49	0.995777739781598	0.00844452043680312	0.00422226021840156
50	0.99548606186148	0.00902787627704063	0.00451393813852031
51	0.997358006398727	0.00528398720254653	0.00264199360127326
52	0.993989135625012	0.0120217287499755	0.00601086437498773
53	0.99379756537127	0.0124048692574619	0.00620243462873094
54	0.986118393334615	0.0277632133307701	0.013881606665385
55	0.980091637178545	0.0398167256429096	0.0199083628214548
56	0.986422539375865	0.0271549212482709	0.0135774606241355
57	0.980154128214362	0.0396917435712765	0.0198458717856383
58	0.959501101227072	0.0809977975458569	0.0404988987729284
59	0.942990397687858	0.114019204624284	0.057009602312142
60	0.90775708452388	0.184485830952239	0.0922429154761197

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.402228187916562 & 0.804456375833124 & 0.597771812083438 \tabularnewline
9 & 0.551718555319181 & 0.896562889361637 & 0.448281444680819 \tabularnewline
10 & 0.448029000249077 & 0.896058000498153 & 0.551970999750923 \tabularnewline
11 & 0.325185344682083 & 0.650370689364166 & 0.674814655317917 \tabularnewline
12 & 0.371001073833402 & 0.742002147666803 & 0.628998926166598 \tabularnewline
13 & 0.297462159627115 & 0.594924319254231 & 0.702537840372885 \tabularnewline
14 & 0.208701399039136 & 0.417402798078272 & 0.791298600960864 \tabularnewline
15 & 0.141063522161644 & 0.282127044323288 & 0.858936477838356 \tabularnewline
16 & 0.104708029031369 & 0.209416058062737 & 0.895291970968631 \tabularnewline
17 & 0.0704872727809237 & 0.140974545561847 & 0.929512727219076 \tabularnewline
18 & 0.0448454588018341 & 0.0896909176036682 & 0.955154541198166 \tabularnewline
19 & 0.0286794695012955 & 0.057358939002591 & 0.971320530498704 \tabularnewline
20 & 0.0164240287606038 & 0.0328480575212076 & 0.983575971239396 \tabularnewline
21 & 0.0140954698589086 & 0.0281909397178172 & 0.985904530141091 \tabularnewline
22 & 0.00810429473443583 & 0.0162085894688717 & 0.991895705265564 \tabularnewline
23 & 0.00533940855715554 & 0.0106788171143111 & 0.994660591442844 \tabularnewline
24 & 0.00915074415703196 & 0.0183014883140639 & 0.990849255842968 \tabularnewline
25 & 0.0151518539314304 & 0.0303037078628609 & 0.98484814606857 \tabularnewline
26 & 0.00918919125638745 & 0.0183783825127749 & 0.990810808743613 \tabularnewline
27 & 0.00539269531405936 & 0.0107853906281187 & 0.99460730468594 \tabularnewline
28 & 0.0377790322198479 & 0.0755580644396959 & 0.962220967780152 \tabularnewline
29 & 0.0293471866011496 & 0.0586943732022991 & 0.97065281339885 \tabularnewline
30 & 0.0206499639426898 & 0.0412999278853796 & 0.97935003605731 \tabularnewline
31 & 0.0204576441573471 & 0.0409152883146941 & 0.979542355842653 \tabularnewline
32 & 0.0152953626463168 & 0.0305907252926335 & 0.984704637353683 \tabularnewline
33 & 0.016348743442466 & 0.032697486884932 & 0.983651256557534 \tabularnewline
34 & 0.0108520217021469 & 0.0217040434042938 & 0.989147978297853 \tabularnewline
35 & 0.00677136659574009 & 0.0135427331914802 & 0.99322863340426 \tabularnewline
36 & 0.00446015592734237 & 0.00892031185468473 & 0.995539844072658 \tabularnewline
37 & 0.00344469890556663 & 0.00688939781113327 & 0.996555301094433 \tabularnewline
38 & 0.00448810582227451 & 0.00897621164454901 & 0.995511894177725 \tabularnewline
39 & 0.00718677071679281 & 0.0143735414335856 & 0.992813229283207 \tabularnewline
40 & 0.0117007998291886 & 0.0234015996583772 & 0.988299200170811 \tabularnewline
41 & 0.00734507256854033 & 0.0146901451370807 & 0.99265492743146 \tabularnewline
42 & 0.00786706648910035 & 0.0157341329782007 & 0.9921329335109 \tabularnewline
43 & 0.511693106242601 & 0.976613787514798 & 0.488306893757399 \tabularnewline
44 & 0.999827549207465 & 0.000344901585069963 & 0.000172450792534981 \tabularnewline
45 & 0.999621800998252 & 0.000756398003495009 & 0.000378199001747504 \tabularnewline
46 & 0.999391861514584 & 0.00121627697083288 & 0.000608138485416441 \tabularnewline
47 & 0.998746497077928 & 0.00250700584414426 & 0.00125350292207213 \tabularnewline
48 & 0.99749028691504 & 0.00501942616991894 & 0.00250971308495947 \tabularnewline
49 & 0.995777739781598 & 0.00844452043680312 & 0.00422226021840156 \tabularnewline
50 & 0.99548606186148 & 0.00902787627704063 & 0.00451393813852031 \tabularnewline
51 & 0.997358006398727 & 0.00528398720254653 & 0.00264199360127326 \tabularnewline
52 & 0.993989135625012 & 0.0120217287499755 & 0.00601086437498773 \tabularnewline
53 & 0.99379756537127 & 0.0124048692574619 & 0.00620243462873094 \tabularnewline
54 & 0.986118393334615 & 0.0277632133307701 & 0.013881606665385 \tabularnewline
55 & 0.980091637178545 & 0.0398167256429096 & 0.0199083628214548 \tabularnewline
56 & 0.986422539375865 & 0.0271549212482709 & 0.0135774606241355 \tabularnewline
57 & 0.980154128214362 & 0.0396917435712765 & 0.0198458717856383 \tabularnewline
58 & 0.959501101227072 & 0.0809977975458569 & 0.0404988987729284 \tabularnewline
59 & 0.942990397687858 & 0.114019204624284 & 0.057009602312142 \tabularnewline
60 & 0.90775708452388 & 0.184485830952239 & 0.0922429154761197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.402228187916562[/C][C]0.804456375833124[/C][C]0.597771812083438[/C][/ROW]
[ROW][C]9[/C][C]0.551718555319181[/C][C]0.896562889361637[/C][C]0.448281444680819[/C][/ROW]
[ROW][C]10[/C][C]0.448029000249077[/C][C]0.896058000498153[/C][C]0.551970999750923[/C][/ROW]
[ROW][C]11[/C][C]0.325185344682083[/C][C]0.650370689364166[/C][C]0.674814655317917[/C][/ROW]
[ROW][C]12[/C][C]0.371001073833402[/C][C]0.742002147666803[/C][C]0.628998926166598[/C][/ROW]
[ROW][C]13[/C][C]0.297462159627115[/C][C]0.594924319254231[/C][C]0.702537840372885[/C][/ROW]
[ROW][C]14[/C][C]0.208701399039136[/C][C]0.417402798078272[/C][C]0.791298600960864[/C][/ROW]
[ROW][C]15[/C][C]0.141063522161644[/C][C]0.282127044323288[/C][C]0.858936477838356[/C][/ROW]
[ROW][C]16[/C][C]0.104708029031369[/C][C]0.209416058062737[/C][C]0.895291970968631[/C][/ROW]
[ROW][C]17[/C][C]0.0704872727809237[/C][C]0.140974545561847[/C][C]0.929512727219076[/C][/ROW]
[ROW][C]18[/C][C]0.0448454588018341[/C][C]0.0896909176036682[/C][C]0.955154541198166[/C][/ROW]
[ROW][C]19[/C][C]0.0286794695012955[/C][C]0.057358939002591[/C][C]0.971320530498704[/C][/ROW]
[ROW][C]20[/C][C]0.0164240287606038[/C][C]0.0328480575212076[/C][C]0.983575971239396[/C][/ROW]
[ROW][C]21[/C][C]0.0140954698589086[/C][C]0.0281909397178172[/C][C]0.985904530141091[/C][/ROW]
[ROW][C]22[/C][C]0.00810429473443583[/C][C]0.0162085894688717[/C][C]0.991895705265564[/C][/ROW]
[ROW][C]23[/C][C]0.00533940855715554[/C][C]0.0106788171143111[/C][C]0.994660591442844[/C][/ROW]
[ROW][C]24[/C][C]0.00915074415703196[/C][C]0.0183014883140639[/C][C]0.990849255842968[/C][/ROW]
[ROW][C]25[/C][C]0.0151518539314304[/C][C]0.0303037078628609[/C][C]0.98484814606857[/C][/ROW]
[ROW][C]26[/C][C]0.00918919125638745[/C][C]0.0183783825127749[/C][C]0.990810808743613[/C][/ROW]
[ROW][C]27[/C][C]0.00539269531405936[/C][C]0.0107853906281187[/C][C]0.99460730468594[/C][/ROW]
[ROW][C]28[/C][C]0.0377790322198479[/C][C]0.0755580644396959[/C][C]0.962220967780152[/C][/ROW]
[ROW][C]29[/C][C]0.0293471866011496[/C][C]0.0586943732022991[/C][C]0.97065281339885[/C][/ROW]
[ROW][C]30[/C][C]0.0206499639426898[/C][C]0.0412999278853796[/C][C]0.97935003605731[/C][/ROW]
[ROW][C]31[/C][C]0.0204576441573471[/C][C]0.0409152883146941[/C][C]0.979542355842653[/C][/ROW]
[ROW][C]32[/C][C]0.0152953626463168[/C][C]0.0305907252926335[/C][C]0.984704637353683[/C][/ROW]
[ROW][C]33[/C][C]0.016348743442466[/C][C]0.032697486884932[/C][C]0.983651256557534[/C][/ROW]
[ROW][C]34[/C][C]0.0108520217021469[/C][C]0.0217040434042938[/C][C]0.989147978297853[/C][/ROW]
[ROW][C]35[/C][C]0.00677136659574009[/C][C]0.0135427331914802[/C][C]0.99322863340426[/C][/ROW]
[ROW][C]36[/C][C]0.00446015592734237[/C][C]0.00892031185468473[/C][C]0.995539844072658[/C][/ROW]
[ROW][C]37[/C][C]0.00344469890556663[/C][C]0.00688939781113327[/C][C]0.996555301094433[/C][/ROW]
[ROW][C]38[/C][C]0.00448810582227451[/C][C]0.00897621164454901[/C][C]0.995511894177725[/C][/ROW]
[ROW][C]39[/C][C]0.00718677071679281[/C][C]0.0143735414335856[/C][C]0.992813229283207[/C][/ROW]
[ROW][C]40[/C][C]0.0117007998291886[/C][C]0.0234015996583772[/C][C]0.988299200170811[/C][/ROW]
[ROW][C]41[/C][C]0.00734507256854033[/C][C]0.0146901451370807[/C][C]0.99265492743146[/C][/ROW]
[ROW][C]42[/C][C]0.00786706648910035[/C][C]0.0157341329782007[/C][C]0.9921329335109[/C][/ROW]
[ROW][C]43[/C][C]0.511693106242601[/C][C]0.976613787514798[/C][C]0.488306893757399[/C][/ROW]
[ROW][C]44[/C][C]0.999827549207465[/C][C]0.000344901585069963[/C][C]0.000172450792534981[/C][/ROW]
[ROW][C]45[/C][C]0.999621800998252[/C][C]0.000756398003495009[/C][C]0.000378199001747504[/C][/ROW]
[ROW][C]46[/C][C]0.999391861514584[/C][C]0.00121627697083288[/C][C]0.000608138485416441[/C][/ROW]
[ROW][C]47[/C][C]0.998746497077928[/C][C]0.00250700584414426[/C][C]0.00125350292207213[/C][/ROW]
[ROW][C]48[/C][C]0.99749028691504[/C][C]0.00501942616991894[/C][C]0.00250971308495947[/C][/ROW]
[ROW][C]49[/C][C]0.995777739781598[/C][C]0.00844452043680312[/C][C]0.00422226021840156[/C][/ROW]
[ROW][C]50[/C][C]0.99548606186148[/C][C]0.00902787627704063[/C][C]0.00451393813852031[/C][/ROW]
[ROW][C]51[/C][C]0.997358006398727[/C][C]0.00528398720254653[/C][C]0.00264199360127326[/C][/ROW]
[ROW][C]52[/C][C]0.993989135625012[/C][C]0.0120217287499755[/C][C]0.00601086437498773[/C][/ROW]
[ROW][C]53[/C][C]0.99379756537127[/C][C]0.0124048692574619[/C][C]0.00620243462873094[/C][/ROW]
[ROW][C]54[/C][C]0.986118393334615[/C][C]0.0277632133307701[/C][C]0.013881606665385[/C][/ROW]
[ROW][C]55[/C][C]0.980091637178545[/C][C]0.0398167256429096[/C][C]0.0199083628214548[/C][/ROW]
[ROW][C]56[/C][C]0.986422539375865[/C][C]0.0271549212482709[/C][C]0.0135774606241355[/C][/ROW]
[ROW][C]57[/C][C]0.980154128214362[/C][C]0.0396917435712765[/C][C]0.0198458717856383[/C][/ROW]
[ROW][C]58[/C][C]0.959501101227072[/C][C]0.0809977975458569[/C][C]0.0404988987729284[/C][/ROW]
[ROW][C]59[/C][C]0.942990397687858[/C][C]0.114019204624284[/C][C]0.057009602312142[/C][/ROW]
[ROW][C]60[/C][C]0.90775708452388[/C][C]0.184485830952239[/C][C]0.0922429154761197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.402228187916562	0.804456375833124	0.597771812083438
9	0.551718555319181	0.896562889361637	0.448281444680819
10	0.448029000249077	0.896058000498153	0.551970999750923
11	0.325185344682083	0.650370689364166	0.674814655317917
12	0.371001073833402	0.742002147666803	0.628998926166598
13	0.297462159627115	0.594924319254231	0.702537840372885
14	0.208701399039136	0.417402798078272	0.791298600960864
15	0.141063522161644	0.282127044323288	0.858936477838356
16	0.104708029031369	0.209416058062737	0.895291970968631
17	0.0704872727809237	0.140974545561847	0.929512727219076
18	0.0448454588018341	0.0896909176036682	0.955154541198166
19	0.0286794695012955	0.057358939002591	0.971320530498704
20	0.0164240287606038	0.0328480575212076	0.983575971239396
21	0.0140954698589086	0.0281909397178172	0.985904530141091
22	0.00810429473443583	0.0162085894688717	0.991895705265564
23	0.00533940855715554	0.0106788171143111	0.994660591442844
24	0.00915074415703196	0.0183014883140639	0.990849255842968
25	0.0151518539314304	0.0303037078628609	0.98484814606857
26	0.00918919125638745	0.0183783825127749	0.990810808743613
27	0.00539269531405936	0.0107853906281187	0.99460730468594
28	0.0377790322198479	0.0755580644396959	0.962220967780152
29	0.0293471866011496	0.0586943732022991	0.97065281339885
30	0.0206499639426898	0.0412999278853796	0.97935003605731
31	0.0204576441573471	0.0409152883146941	0.979542355842653
32	0.0152953626463168	0.0305907252926335	0.984704637353683
33	0.016348743442466	0.032697486884932	0.983651256557534
34	0.0108520217021469	0.0217040434042938	0.989147978297853
35	0.00677136659574009	0.0135427331914802	0.99322863340426
36	0.00446015592734237	0.00892031185468473	0.995539844072658
37	0.00344469890556663	0.00688939781113327	0.996555301094433
38	0.00448810582227451	0.00897621164454901	0.995511894177725
39	0.00718677071679281	0.0143735414335856	0.992813229283207
40	0.0117007998291886	0.0234015996583772	0.988299200170811
41	0.00734507256854033	0.0146901451370807	0.99265492743146
42	0.00786706648910035	0.0157341329782007	0.9921329335109
43	0.511693106242601	0.976613787514798	0.488306893757399
44	0.999827549207465	0.000344901585069963	0.000172450792534981
45	0.999621800998252	0.000756398003495009	0.000378199001747504
46	0.999391861514584	0.00121627697083288	0.000608138485416441
47	0.998746497077928	0.00250700584414426	0.00125350292207213
48	0.99749028691504	0.00501942616991894	0.00250971308495947
49	0.995777739781598	0.00844452043680312	0.00422226021840156
50	0.99548606186148	0.00902787627704063	0.00451393813852031
51	0.997358006398727	0.00528398720254653	0.00264199360127326
52	0.993989135625012	0.0120217287499755	0.00601086437498773
53	0.99379756537127	0.0124048692574619	0.00620243462873094
54	0.986118393334615	0.0277632133307701	0.013881606665385
55	0.980091637178545	0.0398167256429096	0.0199083628214548
56	0.986422539375865	0.0271549212482709	0.0135774606241355
57	0.980154128214362	0.0396917435712765	0.0198458717856383
58	0.959501101227072	0.0809977975458569	0.0404988987729284
59	0.942990397687858	0.114019204624284	0.057009602312142
60	0.90775708452388	0.184485830952239	0.0922429154761197

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	11	0.207547169811321	NOK
5% type I error level	35	0.660377358490566	NOK
10% type I error level	40	0.754716981132076	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 & 11 & 0.207547169811321 & NOK \tabularnewline
5% type I error level & 35 & 0.660377358490566 & NOK \tabularnewline
10% type I error level & 40 & 0.754716981132076 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&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]11[/C][C]0.207547169811321[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.660377358490566[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]40[/C][C]0.754716981132076[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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	11	0.207547169811321	NOK
5% type I error level	35	0.660377358490566	NOK
10% type I error level	40	0.754716981132076	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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