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

Sat, 15 Dec 2012 09:37:20 -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/15/t1355582330wf4ghmifd3yr1q4.htm/, Retrieved Tue, 30 Apr 2024 09:51:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199973, Retrieved Tue, 30 Apr 2024 09:51:03 +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

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2012-11-04 15:50:35] [7a4d181f6fb11b759aaa32fc654b70bc]
- R         [Multiple Regression] [multiple regression] [2012-12-15 14:37:20] [2d2e1459719df4dac57978867f50cb50] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49

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 Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199973&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199973&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199973&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 Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 12.3751128387548 -0.00276337145016942Connected[t] + 0.0188561700784349Separate[t] + 0.182173267297123Learning[t] -0.0247186129204436Software[t] -0.283783772489723Depression[t] + 0.0797578146324659Belonging[t] -0.0774055262087711Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  12.3751128387548 -0.00276337145016942Connected[t] +  0.0188561700784349Separate[t] +  0.182173267297123Learning[t] -0.0247186129204436Software[t] -0.283783772489723Depression[t] +  0.0797578146324659Belonging[t] -0.0774055262087711Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199973&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  12.3751128387548 -0.00276337145016942Connected[t] +  0.0188561700784349Separate[t] +  0.182173267297123Learning[t] -0.0247186129204436Software[t] -0.283783772489723Depression[t] +  0.0797578146324659Belonging[t] -0.0774055262087711Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199973&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199973&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
Happiness[t] = + 12.3751128387548 -0.00276337145016942Connected[t] + 0.0188561700784349Separate[t] + 0.182173267297123Learning[t] -0.0247186129204436Software[t] -0.283783772489723Depression[t] + 0.0797578146324659Belonging[t] -0.0774055262087711Belonging_Final[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)	12.3751128387548	5.125311	2.4145	0.018932	0.009466
Connected	-0.00276337145016942	0.099714	-0.0277	0.977986	0.488993
Separate	0.0188561700784349	0.113144	0.1667	0.86822	0.43411
Learning	0.182173267297123	0.165518	1.1006	0.275608	0.137804
Software	-0.0247186129204436	0.162056	-0.1525	0.879298	0.439649
Depression	-0.283783772489723	0.101878	-2.7855	0.007208	0.003604
Belonging	0.0797578146324659	0.101817	0.7833	0.436613	0.218306
Belonging_Final	-0.0774055262087711	0.173014	-0.4474	0.656257	0.328129

\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) & 12.3751128387548 & 5.125311 & 2.4145 & 0.018932 & 0.009466 \tabularnewline
Connected & -0.00276337145016942 & 0.099714 & -0.0277 & 0.977986 & 0.488993 \tabularnewline
Separate & 0.0188561700784349 & 0.113144 & 0.1667 & 0.86822 & 0.43411 \tabularnewline
Learning & 0.182173267297123 & 0.165518 & 1.1006 & 0.275608 & 0.137804 \tabularnewline
Software & -0.0247186129204436 & 0.162056 & -0.1525 & 0.879298 & 0.439649 \tabularnewline
Depression & -0.283783772489723 & 0.101878 & -2.7855 & 0.007208 & 0.003604 \tabularnewline
Belonging & 0.0797578146324659 & 0.101817 & 0.7833 & 0.436613 & 0.218306 \tabularnewline
Belonging_Final & -0.0774055262087711 & 0.173014 & -0.4474 & 0.656257 & 0.328129 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199973&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]12.3751128387548[/C][C]5.125311[/C][C]2.4145[/C][C]0.018932[/C][C]0.009466[/C][/ROW]
[ROW][C]Connected[/C][C]-0.00276337145016942[/C][C]0.099714[/C][C]-0.0277[/C][C]0.977986[/C][C]0.488993[/C][/ROW]
[ROW][C]Separate[/C][C]0.0188561700784349[/C][C]0.113144[/C][C]0.1667[/C][C]0.86822[/C][C]0.43411[/C][/ROW]
[ROW][C]Learning[/C][C]0.182173267297123[/C][C]0.165518[/C][C]1.1006[/C][C]0.275608[/C][C]0.137804[/C][/ROW]
[ROW][C]Software[/C][C]-0.0247186129204436[/C][C]0.162056[/C][C]-0.1525[/C][C]0.879298[/C][C]0.439649[/C][/ROW]
[ROW][C]Depression[/C][C]-0.283783772489723[/C][C]0.101878[/C][C]-2.7855[/C][C]0.007208[/C][C]0.003604[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0797578146324659[/C][C]0.101817[/C][C]0.7833[/C][C]0.436613[/C][C]0.218306[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0774055262087711[/C][C]0.173014[/C][C]-0.4474[/C][C]0.656257[/C][C]0.328129[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199973&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199973&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)	12.3751128387548	5.125311	2.4145	0.018932	0.009466
Connected	-0.00276337145016942	0.099714	-0.0277	0.977986	0.488993
Separate	0.0188561700784349	0.113144	0.1667	0.86822	0.43411
Learning	0.182173267297123	0.165518	1.1006	0.275608	0.137804
Software	-0.0247186129204436	0.162056	-0.1525	0.879298	0.439649
Depression	-0.283783772489723	0.101878	-2.7855	0.007208	0.003604
Belonging	0.0797578146324659	0.101817	0.7833	0.436613	0.218306
Belonging_Final	-0.0774055262087711	0.173014	-0.4474	0.656257	0.328129

Multiple Linear Regression - Regression Statistics
Multiple R	0.466750254948674
R-squared	0.217855800494652
Adjusted R-squared	0.123459086761248
F-TEST (value)	2.30787483884155
F-TEST (DF numerator)	7
F-TEST (DF denominator)	58
p-value	0.0380297583234155
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.3255089370339
Sum Squared Residuals	313.663525341024

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.466750254948674 \tabularnewline
R-squared & 0.217855800494652 \tabularnewline
Adjusted R-squared & 0.123459086761248 \tabularnewline
F-TEST (value) & 2.30787483884155 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.0380297583234155 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.3255089370339 \tabularnewline
Sum Squared Residuals & 313.663525341024 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199973&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.466750254948674[/C][/ROW]
[ROW][C]R-squared[/C][C]0.217855800494652[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.123459086761248[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.30787483884155[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.0380297583234155[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.3255089370339[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]313.663525341024[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199973&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199973&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	0.466750254948674
R-squared	0.217855800494652
Adjusted R-squared	0.123459086761248
F-TEST (value)	2.30787483884155
F-TEST (DF numerator)	7
F-TEST (DF denominator)	58
p-value	0.0380297583234155
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.3255089370339
Sum Squared Residuals	313.663525341024

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	13.394760259059	0.605239740940966
2	18	15.303475053695	2.69652494630498
3	11	14.0827013829519	-3.08270138295189
4	12	14.260731004261	-2.26073100426104
5	16	11.7494007607249	4.25059923927513
6	18	14.1239346555751	3.8760653444249
7	14	11.136468059252	2.86353194074803
8	14	14.8126516498239	-0.812651649823897
9	15	14.7703916849189	0.229608315081099
10	15	14.308035027902	0.691964972097961
11	17	15.1923909904672	1.8076090095328
12	19	15.0171596162701	3.98284038372991
13	10	13.3842044903632	-3.38420449036324
14	16	13.4063101602154	2.59368983978457
15	18	15.7063654831082	2.2936345168918
16	14	13.2112957096604	0.788704290339575
17	14	13.8900563924952	0.109943607504821
18	17	16.0506489525225	0.949351047477537
19	14	15.0841257214231	-1.08412572142306
20	16	13.5786387647364	2.42136123526363
21	18	15.6557088833219	2.34429111667807
22	11	13.5368539449053	-2.53685394490534
23	14	14.4709602812454	-0.470960281245358
24	12	13.2805865976241	-1.28058659762405
25	17	15.4576800206243	1.54231997937574
26	9	15.8293271142706	-6.82932711427065
27	16	15.2147183545468	0.785281645453216
28	14	13.2088277714803	0.79117222851966
29	15	14.2892121622778	0.710787837722245
30	11	13.4553928198156	-2.4553928198156
31	16	15.2870971743673	0.712902825632739
32	13	12.38801259423	0.611987405770031
33	17	14.657178467516	2.34282153248398
34	15	14.9951219405263	0.00487805947364989
35	14	14.0625028385842	-0.062502838584209
36	16	13.9978126513759	2.00218734862411
37	9	11.0379997369063	-2.03799973690633
38	15	14.0806494083027	0.919350591697271
39	17	15.2102044682476	1.78979553175236
40	13	14.9826539821113	-1.98265398211132
41	15	15.4086273361583	-0.408627336158283
42	16	13.7602343385447	2.23976566145529
43	16	15.62845776364	0.371542236359953
44	12	12.9787502971027	-0.978750297102741
45	12	14.2319527102847	-2.23195271028467
46	11	13.2782379373573	-2.27823793735725
47	15	14.5363455026269	0.46365449737307
48	15	14.6852860253425	0.314713974657488
49	17	14.0293230588466	2.97067694115341
50	13	14.504270473926	-1.504270473926
51	16	15.0708128866521	0.92918711334792
52	14	13.430189384826	0.569810615174007
53	11	11.8510243647637	-0.851024364763722
54	12	13.0024804679711	-1.00248046797109
55	12	13.5225706168739	-1.52257061687386
56	15	14.296508512624	0.70349148737597
57	16	14.256240617615	1.74375938238497
58	15	15.0929212982747	-0.0929212982746733
59	12	15.0684296015498	-3.06842960154981
60	12	13.5849435781648	-1.58494357816483
61	8	10.9077452616139	-2.90774526161385
62	13	14.7820612054523	-1.78206120545232
63	11	14.7625336354905	-3.76253363549055
64	14	13.0027975101398	0.997202489860154
65	15	13.5471989864865	1.45280101351349
66	10	15.2178095959669	-5.21780959596685

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.394760259059 & 0.605239740940966 \tabularnewline
2 & 18 & 15.303475053695 & 2.69652494630498 \tabularnewline
3 & 11 & 14.0827013829519 & -3.08270138295189 \tabularnewline
4 & 12 & 14.260731004261 & -2.26073100426104 \tabularnewline
5 & 16 & 11.7494007607249 & 4.25059923927513 \tabularnewline
6 & 18 & 14.1239346555751 & 3.8760653444249 \tabularnewline
7 & 14 & 11.136468059252 & 2.86353194074803 \tabularnewline
8 & 14 & 14.8126516498239 & -0.812651649823897 \tabularnewline
9 & 15 & 14.7703916849189 & 0.229608315081099 \tabularnewline
10 & 15 & 14.308035027902 & 0.691964972097961 \tabularnewline
11 & 17 & 15.1923909904672 & 1.8076090095328 \tabularnewline
12 & 19 & 15.0171596162701 & 3.98284038372991 \tabularnewline
13 & 10 & 13.3842044903632 & -3.38420449036324 \tabularnewline
14 & 16 & 13.4063101602154 & 2.59368983978457 \tabularnewline
15 & 18 & 15.7063654831082 & 2.2936345168918 \tabularnewline
16 & 14 & 13.2112957096604 & 0.788704290339575 \tabularnewline
17 & 14 & 13.8900563924952 & 0.109943607504821 \tabularnewline
18 & 17 & 16.0506489525225 & 0.949351047477537 \tabularnewline
19 & 14 & 15.0841257214231 & -1.08412572142306 \tabularnewline
20 & 16 & 13.5786387647364 & 2.42136123526363 \tabularnewline
21 & 18 & 15.6557088833219 & 2.34429111667807 \tabularnewline
22 & 11 & 13.5368539449053 & -2.53685394490534 \tabularnewline
23 & 14 & 14.4709602812454 & -0.470960281245358 \tabularnewline
24 & 12 & 13.2805865976241 & -1.28058659762405 \tabularnewline
25 & 17 & 15.4576800206243 & 1.54231997937574 \tabularnewline
26 & 9 & 15.8293271142706 & -6.82932711427065 \tabularnewline
27 & 16 & 15.2147183545468 & 0.785281645453216 \tabularnewline
28 & 14 & 13.2088277714803 & 0.79117222851966 \tabularnewline
29 & 15 & 14.2892121622778 & 0.710787837722245 \tabularnewline
30 & 11 & 13.4553928198156 & -2.4553928198156 \tabularnewline
31 & 16 & 15.2870971743673 & 0.712902825632739 \tabularnewline
32 & 13 & 12.38801259423 & 0.611987405770031 \tabularnewline
33 & 17 & 14.657178467516 & 2.34282153248398 \tabularnewline
34 & 15 & 14.9951219405263 & 0.00487805947364989 \tabularnewline
35 & 14 & 14.0625028385842 & -0.062502838584209 \tabularnewline
36 & 16 & 13.9978126513759 & 2.00218734862411 \tabularnewline
37 & 9 & 11.0379997369063 & -2.03799973690633 \tabularnewline
38 & 15 & 14.0806494083027 & 0.919350591697271 \tabularnewline
39 & 17 & 15.2102044682476 & 1.78979553175236 \tabularnewline
40 & 13 & 14.9826539821113 & -1.98265398211132 \tabularnewline
41 & 15 & 15.4086273361583 & -0.408627336158283 \tabularnewline
42 & 16 & 13.7602343385447 & 2.23976566145529 \tabularnewline
43 & 16 & 15.62845776364 & 0.371542236359953 \tabularnewline
44 & 12 & 12.9787502971027 & -0.978750297102741 \tabularnewline
45 & 12 & 14.2319527102847 & -2.23195271028467 \tabularnewline
46 & 11 & 13.2782379373573 & -2.27823793735725 \tabularnewline
47 & 15 & 14.5363455026269 & 0.46365449737307 \tabularnewline
48 & 15 & 14.6852860253425 & 0.314713974657488 \tabularnewline
49 & 17 & 14.0293230588466 & 2.97067694115341 \tabularnewline
50 & 13 & 14.504270473926 & -1.504270473926 \tabularnewline
51 & 16 & 15.0708128866521 & 0.92918711334792 \tabularnewline
52 & 14 & 13.430189384826 & 0.569810615174007 \tabularnewline
53 & 11 & 11.8510243647637 & -0.851024364763722 \tabularnewline
54 & 12 & 13.0024804679711 & -1.00248046797109 \tabularnewline
55 & 12 & 13.5225706168739 & -1.52257061687386 \tabularnewline
56 & 15 & 14.296508512624 & 0.70349148737597 \tabularnewline
57 & 16 & 14.256240617615 & 1.74375938238497 \tabularnewline
58 & 15 & 15.0929212982747 & -0.0929212982746733 \tabularnewline
59 & 12 & 15.0684296015498 & -3.06842960154981 \tabularnewline
60 & 12 & 13.5849435781648 & -1.58494357816483 \tabularnewline
61 & 8 & 10.9077452616139 & -2.90774526161385 \tabularnewline
62 & 13 & 14.7820612054523 & -1.78206120545232 \tabularnewline
63 & 11 & 14.7625336354905 & -3.76253363549055 \tabularnewline
64 & 14 & 13.0027975101398 & 0.997202489860154 \tabularnewline
65 & 15 & 13.5471989864865 & 1.45280101351349 \tabularnewline
66 & 10 & 15.2178095959669 & -5.21780959596685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199973&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]14[/C][C]13.394760259059[/C][C]0.605239740940966[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.303475053695[/C][C]2.69652494630498[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.0827013829519[/C][C]-3.08270138295189[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.260731004261[/C][C]-2.26073100426104[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.7494007607249[/C][C]4.25059923927513[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.1239346555751[/C][C]3.8760653444249[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]11.136468059252[/C][C]2.86353194074803[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.8126516498239[/C][C]-0.812651649823897[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.7703916849189[/C][C]0.229608315081099[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.308035027902[/C][C]0.691964972097961[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.1923909904672[/C][C]1.8076090095328[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.0171596162701[/C][C]3.98284038372991[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.3842044903632[/C][C]-3.38420449036324[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.4063101602154[/C][C]2.59368983978457[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.7063654831082[/C][C]2.2936345168918[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.2112957096604[/C][C]0.788704290339575[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.8900563924952[/C][C]0.109943607504821[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]16.0506489525225[/C][C]0.949351047477537[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.0841257214231[/C][C]-1.08412572142306[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.5786387647364[/C][C]2.42136123526363[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.6557088833219[/C][C]2.34429111667807[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.5368539449053[/C][C]-2.53685394490534[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.4709602812454[/C][C]-0.470960281245358[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.2805865976241[/C][C]-1.28058659762405[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.4576800206243[/C][C]1.54231997937574[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.8293271142706[/C][C]-6.82932711427065[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.2147183545468[/C][C]0.785281645453216[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.2088277714803[/C][C]0.79117222851966[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.2892121622778[/C][C]0.710787837722245[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.4553928198156[/C][C]-2.4553928198156[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.2870971743673[/C][C]0.712902825632739[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.38801259423[/C][C]0.611987405770031[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.657178467516[/C][C]2.34282153248398[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.9951219405263[/C][C]0.00487805947364989[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.0625028385842[/C][C]-0.062502838584209[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.9978126513759[/C][C]2.00218734862411[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]11.0379997369063[/C][C]-2.03799973690633[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.0806494083027[/C][C]0.919350591697271[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.2102044682476[/C][C]1.78979553175236[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.9826539821113[/C][C]-1.98265398211132[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.4086273361583[/C][C]-0.408627336158283[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.7602343385447[/C][C]2.23976566145529[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.62845776364[/C][C]0.371542236359953[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.9787502971027[/C][C]-0.978750297102741[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.2319527102847[/C][C]-2.23195271028467[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.2782379373573[/C][C]-2.27823793735725[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.5363455026269[/C][C]0.46365449737307[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.6852860253425[/C][C]0.314713974657488[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]14.0293230588466[/C][C]2.97067694115341[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.504270473926[/C][C]-1.504270473926[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.0708128866521[/C][C]0.92918711334792[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.430189384826[/C][C]0.569810615174007[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.8510243647637[/C][C]-0.851024364763722[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.0024804679711[/C][C]-1.00248046797109[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.5225706168739[/C][C]-1.52257061687386[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.296508512624[/C][C]0.70349148737597[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.256240617615[/C][C]1.74375938238497[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.0929212982747[/C][C]-0.0929212982746733[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.0684296015498[/C][C]-3.06842960154981[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.5849435781648[/C][C]-1.58494357816483[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.9077452616139[/C][C]-2.90774526161385[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.7820612054523[/C][C]-1.78206120545232[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.7625336354905[/C][C]-3.76253363549055[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.0027975101398[/C][C]0.997202489860154[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.5471989864865[/C][C]1.45280101351349[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.2178095959669[/C][C]-5.21780959596685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199973&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199973&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	14	13.394760259059	0.605239740940966
2	18	15.303475053695	2.69652494630498
3	11	14.0827013829519	-3.08270138295189
4	12	14.260731004261	-2.26073100426104
5	16	11.7494007607249	4.25059923927513
6	18	14.1239346555751	3.8760653444249
7	14	11.136468059252	2.86353194074803
8	14	14.8126516498239	-0.812651649823897
9	15	14.7703916849189	0.229608315081099
10	15	14.308035027902	0.691964972097961
11	17	15.1923909904672	1.8076090095328
12	19	15.0171596162701	3.98284038372991
13	10	13.3842044903632	-3.38420449036324
14	16	13.4063101602154	2.59368983978457
15	18	15.7063654831082	2.2936345168918
16	14	13.2112957096604	0.788704290339575
17	14	13.8900563924952	0.109943607504821
18	17	16.0506489525225	0.949351047477537
19	14	15.0841257214231	-1.08412572142306
20	16	13.5786387647364	2.42136123526363
21	18	15.6557088833219	2.34429111667807
22	11	13.5368539449053	-2.53685394490534
23	14	14.4709602812454	-0.470960281245358
24	12	13.2805865976241	-1.28058659762405
25	17	15.4576800206243	1.54231997937574
26	9	15.8293271142706	-6.82932711427065
27	16	15.2147183545468	0.785281645453216
28	14	13.2088277714803	0.79117222851966
29	15	14.2892121622778	0.710787837722245
30	11	13.4553928198156	-2.4553928198156
31	16	15.2870971743673	0.712902825632739
32	13	12.38801259423	0.611987405770031
33	17	14.657178467516	2.34282153248398
34	15	14.9951219405263	0.00487805947364989
35	14	14.0625028385842	-0.062502838584209
36	16	13.9978126513759	2.00218734862411
37	9	11.0379997369063	-2.03799973690633
38	15	14.0806494083027	0.919350591697271
39	17	15.2102044682476	1.78979553175236
40	13	14.9826539821113	-1.98265398211132
41	15	15.4086273361583	-0.408627336158283
42	16	13.7602343385447	2.23976566145529
43	16	15.62845776364	0.371542236359953
44	12	12.9787502971027	-0.978750297102741
45	12	14.2319527102847	-2.23195271028467
46	11	13.2782379373573	-2.27823793735725
47	15	14.5363455026269	0.46365449737307
48	15	14.6852860253425	0.314713974657488
49	17	14.0293230588466	2.97067694115341
50	13	14.504270473926	-1.504270473926
51	16	15.0708128866521	0.92918711334792
52	14	13.430189384826	0.569810615174007
53	11	11.8510243647637	-0.851024364763722
54	12	13.0024804679711	-1.00248046797109
55	12	13.5225706168739	-1.52257061687386
56	15	14.296508512624	0.70349148737597
57	16	14.256240617615	1.74375938238497
58	15	15.0929212982747	-0.0929212982746733
59	12	15.0684296015498	-3.06842960154981
60	12	13.5849435781648	-1.58494357816483
61	8	10.9077452616139	-2.90774526161385
62	13	14.7820612054523	-1.78206120545232
63	11	14.7625336354905	-3.76253363549055
64	14	13.0027975101398	0.997202489860154
65	15	13.5471989864865	1.45280101351349
66	10	15.2178095959669	-5.21780959596685

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.0418563792485857	0.0837127584971715	0.958143620751414
12	0.776516243765631	0.446967512468738	0.223483756234369
13	0.933256133569989	0.133487732860022	0.0667438664300109
14	0.909959191351857	0.180081617296287	0.0900408086481433
15	0.931421470020418	0.137157059959163	0.0685785299795817
16	0.890213425562454	0.219573148875091	0.109786574437546
17	0.881235400456271	0.237529199087458	0.118764599543729
18	0.83097906449497	0.338041871010061	0.16902093550503
19	0.766544710619546	0.466910578760909	0.233455289380454
20	0.759932527623397	0.480134944753205	0.240067472376603
21	0.75686593170654	0.486268136586919	0.243134068293459
22	0.750016594541377	0.499966810917246	0.249983405458623
23	0.71444142033502	0.571117159329959	0.28555857966498
24	0.638089967752309	0.723820064495381	0.361910032247691
25	0.599576158000434	0.800847683999131	0.400423841999566
26	0.980437706186719	0.0391245876265622	0.0195622938132811
27	0.969624675208647	0.0607506495827063	0.0303753247913531
28	0.95665243584781	0.0866951283043796	0.0433475641521898
29	0.938177078719816	0.123645842560368	0.0618229212801838
30	0.939390583647211	0.121218832705577	0.0606094163527887
31	0.912060194676763	0.175879610646474	0.0879398053232371
32	0.879187565982503	0.241624868034995	0.120812434017497
33	0.877457618871471	0.245084762257059	0.122542381128529
34	0.833509277652101	0.332981444695797	0.166490722347899
35	0.791114137842882	0.417771724314237	0.208885862157118
36	0.782221659719452	0.435556680561096	0.217778340280548
37	0.778497563876775	0.44300487224645	0.221502436123225
38	0.744337738430207	0.511324523139587	0.255662261569793
39	0.737957533030783	0.524084933938434	0.262042466969217
40	0.708029174701222	0.583941650597555	0.291970825298778
41	0.633931999105446	0.732136001789108	0.366068000894554
42	0.713567732027746	0.572864535944509	0.286432267972254
43	0.639692783655785	0.72061443268843	0.360307216344215
44	0.612208902514197	0.775582194971605	0.387791097485802
45	0.562037735269096	0.875924529461809	0.437962264730904
46	0.510607023673213	0.978785952653575	0.489392976326787
47	0.416214414135433	0.832428828270866	0.583785585864567
48	0.374267091273544	0.748534182547087	0.625732908726456
49	0.532053192221464	0.935893615557071	0.467946807778536
50	0.46925233786993	0.938504675739861	0.53074766213007
51	0.569839973079854	0.860320053840291	0.430160026920146
52	0.480420144225095	0.960840288450189	0.519579855774905
53	0.534957888797496	0.930084222405007	0.465042111202504
54	0.478588505743649	0.957177011487298	0.521411494256351
55	0.325051508214841	0.650103016429682	0.674948491785159

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0418563792485857 & 0.0837127584971715 & 0.958143620751414 \tabularnewline
12 & 0.776516243765631 & 0.446967512468738 & 0.223483756234369 \tabularnewline
13 & 0.933256133569989 & 0.133487732860022 & 0.0667438664300109 \tabularnewline
14 & 0.909959191351857 & 0.180081617296287 & 0.0900408086481433 \tabularnewline
15 & 0.931421470020418 & 0.137157059959163 & 0.0685785299795817 \tabularnewline
16 & 0.890213425562454 & 0.219573148875091 & 0.109786574437546 \tabularnewline
17 & 0.881235400456271 & 0.237529199087458 & 0.118764599543729 \tabularnewline
18 & 0.83097906449497 & 0.338041871010061 & 0.16902093550503 \tabularnewline
19 & 0.766544710619546 & 0.466910578760909 & 0.233455289380454 \tabularnewline
20 & 0.759932527623397 & 0.480134944753205 & 0.240067472376603 \tabularnewline
21 & 0.75686593170654 & 0.486268136586919 & 0.243134068293459 \tabularnewline
22 & 0.750016594541377 & 0.499966810917246 & 0.249983405458623 \tabularnewline
23 & 0.71444142033502 & 0.571117159329959 & 0.28555857966498 \tabularnewline
24 & 0.638089967752309 & 0.723820064495381 & 0.361910032247691 \tabularnewline
25 & 0.599576158000434 & 0.800847683999131 & 0.400423841999566 \tabularnewline
26 & 0.980437706186719 & 0.0391245876265622 & 0.0195622938132811 \tabularnewline
27 & 0.969624675208647 & 0.0607506495827063 & 0.0303753247913531 \tabularnewline
28 & 0.95665243584781 & 0.0866951283043796 & 0.0433475641521898 \tabularnewline
29 & 0.938177078719816 & 0.123645842560368 & 0.0618229212801838 \tabularnewline
30 & 0.939390583647211 & 0.121218832705577 & 0.0606094163527887 \tabularnewline
31 & 0.912060194676763 & 0.175879610646474 & 0.0879398053232371 \tabularnewline
32 & 0.879187565982503 & 0.241624868034995 & 0.120812434017497 \tabularnewline
33 & 0.877457618871471 & 0.245084762257059 & 0.122542381128529 \tabularnewline
34 & 0.833509277652101 & 0.332981444695797 & 0.166490722347899 \tabularnewline
35 & 0.791114137842882 & 0.417771724314237 & 0.208885862157118 \tabularnewline
36 & 0.782221659719452 & 0.435556680561096 & 0.217778340280548 \tabularnewline
37 & 0.778497563876775 & 0.44300487224645 & 0.221502436123225 \tabularnewline
38 & 0.744337738430207 & 0.511324523139587 & 0.255662261569793 \tabularnewline
39 & 0.737957533030783 & 0.524084933938434 & 0.262042466969217 \tabularnewline
40 & 0.708029174701222 & 0.583941650597555 & 0.291970825298778 \tabularnewline
41 & 0.633931999105446 & 0.732136001789108 & 0.366068000894554 \tabularnewline
42 & 0.713567732027746 & 0.572864535944509 & 0.286432267972254 \tabularnewline
43 & 0.639692783655785 & 0.72061443268843 & 0.360307216344215 \tabularnewline
44 & 0.612208902514197 & 0.775582194971605 & 0.387791097485802 \tabularnewline
45 & 0.562037735269096 & 0.875924529461809 & 0.437962264730904 \tabularnewline
46 & 0.510607023673213 & 0.978785952653575 & 0.489392976326787 \tabularnewline
47 & 0.416214414135433 & 0.832428828270866 & 0.583785585864567 \tabularnewline
48 & 0.374267091273544 & 0.748534182547087 & 0.625732908726456 \tabularnewline
49 & 0.532053192221464 & 0.935893615557071 & 0.467946807778536 \tabularnewline
50 & 0.46925233786993 & 0.938504675739861 & 0.53074766213007 \tabularnewline
51 & 0.569839973079854 & 0.860320053840291 & 0.430160026920146 \tabularnewline
52 & 0.480420144225095 & 0.960840288450189 & 0.519579855774905 \tabularnewline
53 & 0.534957888797496 & 0.930084222405007 & 0.465042111202504 \tabularnewline
54 & 0.478588505743649 & 0.957177011487298 & 0.521411494256351 \tabularnewline
55 & 0.325051508214841 & 0.650103016429682 & 0.674948491785159 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199973&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.0418563792485857[/C][C]0.0837127584971715[/C][C]0.958143620751414[/C][/ROW]
[ROW][C]12[/C][C]0.776516243765631[/C][C]0.446967512468738[/C][C]0.223483756234369[/C][/ROW]
[ROW][C]13[/C][C]0.933256133569989[/C][C]0.133487732860022[/C][C]0.0667438664300109[/C][/ROW]
[ROW][C]14[/C][C]0.909959191351857[/C][C]0.180081617296287[/C][C]0.0900408086481433[/C][/ROW]
[ROW][C]15[/C][C]0.931421470020418[/C][C]0.137157059959163[/C][C]0.0685785299795817[/C][/ROW]
[ROW][C]16[/C][C]0.890213425562454[/C][C]0.219573148875091[/C][C]0.109786574437546[/C][/ROW]
[ROW][C]17[/C][C]0.881235400456271[/C][C]0.237529199087458[/C][C]0.118764599543729[/C][/ROW]
[ROW][C]18[/C][C]0.83097906449497[/C][C]0.338041871010061[/C][C]0.16902093550503[/C][/ROW]
[ROW][C]19[/C][C]0.766544710619546[/C][C]0.466910578760909[/C][C]0.233455289380454[/C][/ROW]
[ROW][C]20[/C][C]0.759932527623397[/C][C]0.480134944753205[/C][C]0.240067472376603[/C][/ROW]
[ROW][C]21[/C][C]0.75686593170654[/C][C]0.486268136586919[/C][C]0.243134068293459[/C][/ROW]
[ROW][C]22[/C][C]0.750016594541377[/C][C]0.499966810917246[/C][C]0.249983405458623[/C][/ROW]
[ROW][C]23[/C][C]0.71444142033502[/C][C]0.571117159329959[/C][C]0.28555857966498[/C][/ROW]
[ROW][C]24[/C][C]0.638089967752309[/C][C]0.723820064495381[/C][C]0.361910032247691[/C][/ROW]
[ROW][C]25[/C][C]0.599576158000434[/C][C]0.800847683999131[/C][C]0.400423841999566[/C][/ROW]
[ROW][C]26[/C][C]0.980437706186719[/C][C]0.0391245876265622[/C][C]0.0195622938132811[/C][/ROW]
[ROW][C]27[/C][C]0.969624675208647[/C][C]0.0607506495827063[/C][C]0.0303753247913531[/C][/ROW]
[ROW][C]28[/C][C]0.95665243584781[/C][C]0.0866951283043796[/C][C]0.0433475641521898[/C][/ROW]
[ROW][C]29[/C][C]0.938177078719816[/C][C]0.123645842560368[/C][C]0.0618229212801838[/C][/ROW]
[ROW][C]30[/C][C]0.939390583647211[/C][C]0.121218832705577[/C][C]0.0606094163527887[/C][/ROW]
[ROW][C]31[/C][C]0.912060194676763[/C][C]0.175879610646474[/C][C]0.0879398053232371[/C][/ROW]
[ROW][C]32[/C][C]0.879187565982503[/C][C]0.241624868034995[/C][C]0.120812434017497[/C][/ROW]
[ROW][C]33[/C][C]0.877457618871471[/C][C]0.245084762257059[/C][C]0.122542381128529[/C][/ROW]
[ROW][C]34[/C][C]0.833509277652101[/C][C]0.332981444695797[/C][C]0.166490722347899[/C][/ROW]
[ROW][C]35[/C][C]0.791114137842882[/C][C]0.417771724314237[/C][C]0.208885862157118[/C][/ROW]
[ROW][C]36[/C][C]0.782221659719452[/C][C]0.435556680561096[/C][C]0.217778340280548[/C][/ROW]
[ROW][C]37[/C][C]0.778497563876775[/C][C]0.44300487224645[/C][C]0.221502436123225[/C][/ROW]
[ROW][C]38[/C][C]0.744337738430207[/C][C]0.511324523139587[/C][C]0.255662261569793[/C][/ROW]
[ROW][C]39[/C][C]0.737957533030783[/C][C]0.524084933938434[/C][C]0.262042466969217[/C][/ROW]
[ROW][C]40[/C][C]0.708029174701222[/C][C]0.583941650597555[/C][C]0.291970825298778[/C][/ROW]
[ROW][C]41[/C][C]0.633931999105446[/C][C]0.732136001789108[/C][C]0.366068000894554[/C][/ROW]
[ROW][C]42[/C][C]0.713567732027746[/C][C]0.572864535944509[/C][C]0.286432267972254[/C][/ROW]
[ROW][C]43[/C][C]0.639692783655785[/C][C]0.72061443268843[/C][C]0.360307216344215[/C][/ROW]
[ROW][C]44[/C][C]0.612208902514197[/C][C]0.775582194971605[/C][C]0.387791097485802[/C][/ROW]
[ROW][C]45[/C][C]0.562037735269096[/C][C]0.875924529461809[/C][C]0.437962264730904[/C][/ROW]
[ROW][C]46[/C][C]0.510607023673213[/C][C]0.978785952653575[/C][C]0.489392976326787[/C][/ROW]
[ROW][C]47[/C][C]0.416214414135433[/C][C]0.832428828270866[/C][C]0.583785585864567[/C][/ROW]
[ROW][C]48[/C][C]0.374267091273544[/C][C]0.748534182547087[/C][C]0.625732908726456[/C][/ROW]
[ROW][C]49[/C][C]0.532053192221464[/C][C]0.935893615557071[/C][C]0.467946807778536[/C][/ROW]
[ROW][C]50[/C][C]0.46925233786993[/C][C]0.938504675739861[/C][C]0.53074766213007[/C][/ROW]
[ROW][C]51[/C][C]0.569839973079854[/C][C]0.860320053840291[/C][C]0.430160026920146[/C][/ROW]
[ROW][C]52[/C][C]0.480420144225095[/C][C]0.960840288450189[/C][C]0.519579855774905[/C][/ROW]
[ROW][C]53[/C][C]0.534957888797496[/C][C]0.930084222405007[/C][C]0.465042111202504[/C][/ROW]
[ROW][C]54[/C][C]0.478588505743649[/C][C]0.957177011487298[/C][C]0.521411494256351[/C][/ROW]
[ROW][C]55[/C][C]0.325051508214841[/C][C]0.650103016429682[/C][C]0.674948491785159[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199973&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199973&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.0418563792485857	0.0837127584971715	0.958143620751414
12	0.776516243765631	0.446967512468738	0.223483756234369
13	0.933256133569989	0.133487732860022	0.0667438664300109
14	0.909959191351857	0.180081617296287	0.0900408086481433
15	0.931421470020418	0.137157059959163	0.0685785299795817
16	0.890213425562454	0.219573148875091	0.109786574437546
17	0.881235400456271	0.237529199087458	0.118764599543729
18	0.83097906449497	0.338041871010061	0.16902093550503
19	0.766544710619546	0.466910578760909	0.233455289380454
20	0.759932527623397	0.480134944753205	0.240067472376603
21	0.75686593170654	0.486268136586919	0.243134068293459
22	0.750016594541377	0.499966810917246	0.249983405458623
23	0.71444142033502	0.571117159329959	0.28555857966498
24	0.638089967752309	0.723820064495381	0.361910032247691
25	0.599576158000434	0.800847683999131	0.400423841999566
26	0.980437706186719	0.0391245876265622	0.0195622938132811
27	0.969624675208647	0.0607506495827063	0.0303753247913531
28	0.95665243584781	0.0866951283043796	0.0433475641521898
29	0.938177078719816	0.123645842560368	0.0618229212801838
30	0.939390583647211	0.121218832705577	0.0606094163527887
31	0.912060194676763	0.175879610646474	0.0879398053232371
32	0.879187565982503	0.241624868034995	0.120812434017497
33	0.877457618871471	0.245084762257059	0.122542381128529
34	0.833509277652101	0.332981444695797	0.166490722347899
35	0.791114137842882	0.417771724314237	0.208885862157118
36	0.782221659719452	0.435556680561096	0.217778340280548
37	0.778497563876775	0.44300487224645	0.221502436123225
38	0.744337738430207	0.511324523139587	0.255662261569793
39	0.737957533030783	0.524084933938434	0.262042466969217
40	0.708029174701222	0.583941650597555	0.291970825298778
41	0.633931999105446	0.732136001789108	0.366068000894554
42	0.713567732027746	0.572864535944509	0.286432267972254
43	0.639692783655785	0.72061443268843	0.360307216344215
44	0.612208902514197	0.775582194971605	0.387791097485802
45	0.562037735269096	0.875924529461809	0.437962264730904
46	0.510607023673213	0.978785952653575	0.489392976326787
47	0.416214414135433	0.832428828270866	0.583785585864567
48	0.374267091273544	0.748534182547087	0.625732908726456
49	0.532053192221464	0.935893615557071	0.467946807778536
50	0.46925233786993	0.938504675739861	0.53074766213007
51	0.569839973079854	0.860320053840291	0.430160026920146
52	0.480420144225095	0.960840288450189	0.519579855774905
53	0.534957888797496	0.930084222405007	0.465042111202504
54	0.478588505743649	0.957177011487298	0.521411494256351
55	0.325051508214841	0.650103016429682	0.674948491785159

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0222222222222222	OK
10% type I error level	4	0.0888888888888889	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0222222222222222 & OK \tabularnewline
10% type I error level & 4 & 0.0888888888888889 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199973&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0222222222222222[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0888888888888889[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199973&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199973&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0222222222222222	OK
10% type I error level	4	0.0888888888888889	OK

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 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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