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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 11 Nov 2014 13:21:13 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/11/t1415712386dt0fmyz4orr9uwp.htm/, Retrieved Fri, 17 May 2024 06:37:25 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253600, Retrieved Fri, 17 May 2024 06:37:25 +0000

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Estimated Impact

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

-       [Multiple Regression] [WS7 ] [2014-11-11 13:21:13] [8568a324fefbb8dbb43f697bfa8d1be6] [Current]

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14	41	38	13	12	12	53
18	39	32	16	11	11	86
11	30	35	19	15	14	66
12	31	33	15	6	12	67
16	34	37	14	13	21	76
18	35	29	13	10	12	78
14	39	31	19	12	22	53
14	34	36	15	14	11	80
15	36	35	14	12	10	74
15	37	38	15	6	13	76
17	38	31	16	10	10	79
19	36	34	16	12	8	54
10	38	35	16	12	15	67
16	39	38	16	11	14	54
18	33	37	17	15	10	87
14	32	33	15	12	14	58
14	36	32	15	10	14	75
17	38	38	20	12	11	88
14	39	38	18	11	10	64
16	32	32	16	12	13	57
18	32	33	16	11	7	66
11	31	31	16	12	14	68
14	39	38	19	13	12	54
12	37	39	16	11	14	56
17	39	32	17	9	11	86
9	41	32	17	13	9	80
16	36	35	16	10	11	76
14	33	37	15	14	15	69
15	33	33	16	12	14	78
11	34	33	14	10	13	67
16	31	28	15	12	9	80
13	27	32	12	8	15	54
17	37	31	14	10	10	71
15	34	37	16	12	11	84
14	34	30	14	12	13	74
16	32	33	7	7	8	71
9	29	31	10	6	20	63
15	36	33	14	12	12	71
17	29	31	16	10	10	76
13	35	33	16	10	10	69
15	37	32	16	10	9	74
16	34	33	14	12	14	75
16	38	32	20	15	8	54
12	35	33	14	10	14	52
12	38	28	14	10	11	69
11	37	35	11	12	13	68
15	38	39	14	13	9	65
15	33	34	15	11	11	75
17	36	38	16	11	15	74
13	38	32	14	12	11	75
16	32	38	16	14	10	72
14	32	30	14	10	14	67
11	32	33	12	12	18	63
12	34	38	16	13	14	62
12	32	32	9	5	11	63
15	37	32	14	6	12	76
16	39	34	16	12	13	74
15	29	34	16	12	9	67
12	37	36	15	11	10	73
12	35	34	16	10	15	70
8	30	28	12	7	20	53
13	38	34	16	12	12	77
11	34	35	16	14	12	77
14	31	35	14	11	14	52
15	34	31	16	12	13	54
10	35	37	17	13	11	80
11	36	35	18	14	17	66
12	30	27	18	11	12	73
15	39	40	12	12	13	63
15	35	37	16	12	14	69
14	38	36	10	8	13	67
16	31	38	14	11	15	54
15	34	39	18	14	13	81
15	38	41	18	14	10	69
13	34	27	16	12	11	84
12	39	30	17	9	19	80
17	37	37	16	13	13	70
13	34	31	16	11	17	69
15	28	31	13	12	13	77
13	37	27	16	12	9	54
15	33	36	16	12	11	79
16	37	38	20	12	10	30
15	35	37	16	12	9	71
16	37	33	15	12	12	73
15	32	34	15	11	12	72
14	33	31	16	10	13	77
15	38	39	14	9	13	75
14	33	34	16	12	12	69
13	29	32	16	12	15	54
7	33	33	15	12	22	70
17	31	36	12	9	13	73
13	36	32	17	15	15	54
15	35	41	16	12	13	77
14	32	28	15	12	15	82
13	29	30	13	12	10	80
16	39	36	16	10	11	80
12	37	35	16	13	16	69
14	35	31	16	9	11	78
17	37	34	16	12	11	81
15	32	36	14	10	10	76
17	38	36	16	14	10	76
12	37	35	16	11	16	73
16	36	37	20	15	12	85
11	32	28	15	11	11	66
15	33	39	16	11	16	79
9	40	32	13	12	19	68
16	38	35	17	12	11	76
15	41	39	16	12	16	71
10	36	35	16	11	15	54
10	43	42	12	7	24	46
15	30	34	16	12	14	82
11	31	33	16	14	15	74
13	32	41	17	11	11	88
14	32	33	13	11	15	38
18	37	34	12	10	12	76
16	37	32	18	13	10	86
14	33	40	14	13	14	54
14	34	40	14	8	13	70
14	33	35	13	11	9	69
14	38	36	16	12	15	90
12	33	37	13	11	15	54
14	31	27	16	13	14	76
15	38	39	13	12	11	89
15	37	38	16	14	8	76
15	33	31	15	13	11	73
13	31	33	16	15	11	79
17	39	32	15	10	8	90
17	44	39	17	11	10	74
19	33	36	15	9	11	81
15	35	33	12	11	13	72
13	32	33	16	10	11	71
9	28	32	10	11	20	66
15	40	37	16	8	10	77
15	27	30	12	11	15	65
15	37	38	14	12	12	74
16	32	29	15	12	14	82
11	28	22	13	9	23	54
14	34	35	15	11	14	63
11	30	35	11	10	16	54
15	35	34	12	8	11	64
13	31	35	8	9	12	69
15	32	34	16	8	10	54
16	30	34	15	9	14	84
14	30	35	17	15	12	86
15	31	23	16	11	12	77
16	40	31	10	8	11	89
16	32	27	18	13	12	76
11	36	36	13	12	13	60
12	32	31	16	12	11	75
9	35	32	13	9	19	73
16	38	39	10	7	12	85
13	42	37	15	13	17	79
16	34	38	16	9	9	71
12	35	39	16	6	12	72
9	35	34	14	8	19	69
13	33	31	10	8	18	78
13	36	32	17	15	15	54
14	32	37	13	6	14	69
19	33	36	15	9	11	81
13	34	32	16	11	9	84
12	32	35	12	8	18	84
13	34	36	13	8	16	69

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253600&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253600&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253600&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	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 12.8462 + 0.0143385Connected[t] + 0.0718533Separate[t] + 0.0695935Learning[t] -0.0459178Software[t] -0.355985Depression[t] + 0.0326856Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  12.8462 +  0.0143385Connected[t] +  0.0718533Separate[t] +  0.0695935Learning[t] -0.0459178Software[t] -0.355985Depression[t] +  0.0326856Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253600&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  12.8462 +  0.0143385Connected[t] +  0.0718533Separate[t] +  0.0695935Learning[t] -0.0459178Software[t] -0.355985Depression[t] +  0.0326856Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253600&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253600&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] = + 12.8462 + 0.0143385Connected[t] + 0.0718533Separate[t] + 0.0695935Learning[t] -0.0459178Software[t] -0.355985Depression[t] + 0.0326856Belonging[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)	12.8462	2.55329	5.031	1.33492e-06	6.6746e-07
Connected	0.0143385	0.0501812	0.2857	0.775464	0.387732
Separate	0.0718533	0.046605	1.542	0.125173	0.0625864
Learning	0.0695935	0.0842976	0.8256	0.410317	0.205159
Software	-0.0459178	0.0856996	-0.5358	0.592866	0.296433
Depression	-0.355985	0.0517244	-6.882	1.37313e-10	6.86563e-11
Belonging	0.0326856	0.0149131	2.192	0.0298909	0.0149455

\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) & 12.8462 & 2.55329 & 5.031 & 1.33492e-06 & 6.6746e-07 \tabularnewline
Connected & 0.0143385 & 0.0501812 & 0.2857 & 0.775464 & 0.387732 \tabularnewline
Separate & 0.0718533 & 0.046605 & 1.542 & 0.125173 & 0.0625864 \tabularnewline
Learning & 0.0695935 & 0.0842976 & 0.8256 & 0.410317 & 0.205159 \tabularnewline
Software & -0.0459178 & 0.0856996 & -0.5358 & 0.592866 & 0.296433 \tabularnewline
Depression & -0.355985 & 0.0517244 & -6.882 & 1.37313e-10 & 6.86563e-11 \tabularnewline
Belonging & 0.0326856 & 0.0149131 & 2.192 & 0.0298909 & 0.0149455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253600&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]12.8462[/C][C]2.55329[/C][C]5.031[/C][C]1.33492e-06[/C][C]6.6746e-07[/C][/ROW]
[ROW][C]Connected[/C][C]0.0143385[/C][C]0.0501812[/C][C]0.2857[/C][C]0.775464[/C][C]0.387732[/C][/ROW]
[ROW][C]Separate[/C][C]0.0718533[/C][C]0.046605[/C][C]1.542[/C][C]0.125173[/C][C]0.0625864[/C][/ROW]
[ROW][C]Learning[/C][C]0.0695935[/C][C]0.0842976[/C][C]0.8256[/C][C]0.410317[/C][C]0.205159[/C][/ROW]
[ROW][C]Software[/C][C]-0.0459178[/C][C]0.0856996[/C][C]-0.5358[/C][C]0.592866[/C][C]0.296433[/C][/ROW]
[ROW][C]Depression[/C][C]-0.355985[/C][C]0.0517244[/C][C]-6.882[/C][C]1.37313e-10[/C][C]6.86563e-11[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0326856[/C][C]0.0149131[/C][C]2.192[/C][C]0.0298909[/C][C]0.0149455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253600&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253600&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)	12.8462	2.55329	5.031	1.33492e-06	6.6746e-07
Connected	0.0143385	0.0501812	0.2857	0.775464	0.387732
Separate	0.0718533	0.046605	1.542	0.125173	0.0625864
Learning	0.0695935	0.0842976	0.8256	0.410317	0.205159
Software	-0.0459178	0.0856996	-0.5358	0.592866	0.296433
Depression	-0.355985	0.0517244	-6.882	1.37313e-10	6.86563e-11
Belonging	0.0326856	0.0149131	2.192	0.0298909	0.0149455

Multiple Linear Regression - Regression Statistics
Multiple R	0.578049
R-squared	0.334141
Adjusted R-squared	0.308365
F-TEST (value)	12.9637
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	7.52043e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.94407
Sum Squared Residuals	585.808

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.578049 \tabularnewline
R-squared & 0.334141 \tabularnewline
Adjusted R-squared & 0.308365 \tabularnewline
F-TEST (value) & 12.9637 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 7.52043e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.94407 \tabularnewline
Sum Squared Residuals & 585.808 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253600&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.578049[/C][/ROW]
[ROW][C]R-squared[/C][C]0.334141[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.308365[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.9637[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]7.52043e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.94407[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]585.808[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253600&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253600&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	0.578049
R-squared	0.334141
Adjusted R-squared	0.308365
F-TEST (value)	12.9637
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	7.52043e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.94407
Sum Squared Residuals	585.808

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	13.9787	0.0213288
2	18	15.2082	2.79182
3	11	13.5981	-2.59814
4	12	14.3483	-2.34831
5	16	11.378	4.62197
6	18	14.1549	3.84506
7	14	10.3047	3.69527
8	14	15.0204	-1.02044
9	15	15.1594	-0.159379
10	15	14.7318	0.268206
11	17	15.2951	1.70491
12	19	15.285	3.71503
13	10	13.3185	-3.31852
14	16	13.5254	2.47459
15	18	15.756	2.24399
16	14	13.081	0.918997
17	14	13.714	0.286006
18	17	15.9228	1.07721
19	14	15.4154	-1.41539
20	16	13.402	2.59796
21	18	15.9499	2.05011
22	11	13.3194	-2.31941
23	14	14.3543	-0.354323
24	12	13.634	-1.63396
25	17	15.3696	1.63039
26	9	15.7305	-6.73047
27	16	15.0998	0.900212
28	14	13.2945	0.705525
29	15	13.8186	1.18135
30	11	13.7821	-2.78208
31	16	15.2064	0.793595
32	13	12.4256	0.574379
33	17	14.8801	2.11992
34	15	15.3845	-0.384466
35	14	13.7035	0.296519
36	16	15.3147	0.685333
37	9	10.8493	-1.84934
38	15	14.2056	0.794354
39	17	15.068	1.93201
40	13	15.0689	-2.06893
41	15	15.5452	-0.545165
42	16	13.5957	2.40426
43	16	15.3106	0.689438
44	12	12.9501	-0.950147
45	12	14.2575	-2.25751
46	11	13.7009	-2.70087
47	15	15.4914	-0.491367
48	15	14.8367	0.163278
49	17	13.7801	3.21988
50	13	14.6492	-1.6492
51	16	15.2996	0.700434
52	14	13.1819	0.818145
53	11	11.6117	-0.61171
54	12	13.6234	-1.62336
55	12	14.1444	-2.1444
56	15	14.5871	0.412935
57	16	14.2018	1.79823
58	15	15.2535	-0.25353
59	12	15.3284	-3.3284
60	12	13.3935	-1.39354
61	8	10.4145	-2.41453
62	13	14.6415	-1.64148
63	11	14.5641	-3.56414
64	14	12.9906	1.00942
65	15	13.2608	1.73919
66	10	15.2917	-5.29174
67	11	12.5925	-1.59254
68	12	14.0782	-2.07816
69	15	13.995	1.00502
70	15	13.8406	1.15943
71	14	13.8685	0.131547
72	16	12.9155	3.08447
73	15	14.7655	0.234502
74	15	15.6423	-0.642287
75	13	14.6659	-1.66593
76	12	12.1819	-0.18191
77	17	14.212	2.788
78	13	12.3731	0.626929
79	15	13.7178	1.28223
80	13	14.4404	-1.44035
81	15	15.1348	-0.134847
82	16	14.3687	1.63133
83	15	15.6859	-0.685863
84	16	14.3549	1.64505
85	15	14.3683	0.631658
86	14	14.0901	-0.0900745
87	15	14.578	0.422047
88	14	14.3083	-0.3083
89	13	12.549	0.450999
90	7	10.6397	-3.63969
91	17	14.0575	2.94253
92	13	12.5812	0.41879
93	15	14.7454	0.254551
94	14	13.1502	0.849795
95	13	14.8263	-1.82626
96	16	15.3454	0.654601
97	12	12.9676	-0.967649
98	14	14.9093	-0.909325
99	17	15.1139	1.88613
100	15	15.3311	-0.331085
101	17	15.3726	1.62737
102	12	13.1902	-1.19023
103	16	15.2305	0.769536
104	11	14.0971	-3.09709
105	15	13.6164	1.3836
106	9	11.5316	-2.5316
107	16	15.1062	0.893777
108	15	13.4237	1.5763
109	10	12.9108	-2.91085
110	10	9.95414	0.0458625
111	15	13.9782	1.02177
112	11	13.2114	-2.21141
113	13	15.8895	-2.88946
114	14	11.978	2.02196
115	18	14.4079	3.59209
116	16	15.5828	0.41716
117	14	13.3521	0.647939
118	14	14.4749	-0.474943
119	14	15.2852	-1.28525
120	14	14.1421	-0.14214
121	12	12.8028	-0.802758
122	14	13.2476	0.75244
123	15	15.5402	-0.540174
124	15	16.214	-1.21397
125	15	14.464	0.536044
126	13	14.7529	-1.75286
127	17	16.3832	0.616797
128	17	15.8162	1.1838
129	19	15.2684	3.73162
130	15	13.7747	1.22526
131	13	14.7353	-1.7353
132	9	10.7753	-1.77532
133	15	15.7814	-0.781354
134	15	12.5037	2.4963
135	15	14.6773	0.322692
136	16	13.578	2.42196
137	11	8.89722	2.10278
138	14	13.4627	0.537268
139	11	12.1668	-1.16678
140	15	14.4348	0.565169
141	13	13.9325	-0.932482
142	15	14.6993	0.300681
143	16	14.1118	1.88824
144	14	14.8246	-0.824632
145	15	13.7966	1.20336
146	16	14.9689	1.03108
147	16	14.1131	1.88694
148	11	13.6361	-2.63609
149	12	14.6305	-2.6305
150	9	11.7611	-2.76109
151	16	15.0743	0.925744
152	13	13.0843	-0.0843249
153	16	15.8811	0.118869
154	12	15.0698	-3.06981
155	9	11.8896	-2.88957
156	13	12.0171	0.98289
157	13	12.5812	0.41879
158	14	13.8643	0.135723
159	19	15.2684	3.73162
160	13	15.7831	-2.78309
161	12	12.6255	-0.625485
162	13	13.0173	-0.0172952

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.9787 & 0.0213288 \tabularnewline
2 & 18 & 15.2082 & 2.79182 \tabularnewline
3 & 11 & 13.5981 & -2.59814 \tabularnewline
4 & 12 & 14.3483 & -2.34831 \tabularnewline
5 & 16 & 11.378 & 4.62197 \tabularnewline
6 & 18 & 14.1549 & 3.84506 \tabularnewline
7 & 14 & 10.3047 & 3.69527 \tabularnewline
8 & 14 & 15.0204 & -1.02044 \tabularnewline
9 & 15 & 15.1594 & -0.159379 \tabularnewline
10 & 15 & 14.7318 & 0.268206 \tabularnewline
11 & 17 & 15.2951 & 1.70491 \tabularnewline
12 & 19 & 15.285 & 3.71503 \tabularnewline
13 & 10 & 13.3185 & -3.31852 \tabularnewline
14 & 16 & 13.5254 & 2.47459 \tabularnewline
15 & 18 & 15.756 & 2.24399 \tabularnewline
16 & 14 & 13.081 & 0.918997 \tabularnewline
17 & 14 & 13.714 & 0.286006 \tabularnewline
18 & 17 & 15.9228 & 1.07721 \tabularnewline
19 & 14 & 15.4154 & -1.41539 \tabularnewline
20 & 16 & 13.402 & 2.59796 \tabularnewline
21 & 18 & 15.9499 & 2.05011 \tabularnewline
22 & 11 & 13.3194 & -2.31941 \tabularnewline
23 & 14 & 14.3543 & -0.354323 \tabularnewline
24 & 12 & 13.634 & -1.63396 \tabularnewline
25 & 17 & 15.3696 & 1.63039 \tabularnewline
26 & 9 & 15.7305 & -6.73047 \tabularnewline
27 & 16 & 15.0998 & 0.900212 \tabularnewline
28 & 14 & 13.2945 & 0.705525 \tabularnewline
29 & 15 & 13.8186 & 1.18135 \tabularnewline
30 & 11 & 13.7821 & -2.78208 \tabularnewline
31 & 16 & 15.2064 & 0.793595 \tabularnewline
32 & 13 & 12.4256 & 0.574379 \tabularnewline
33 & 17 & 14.8801 & 2.11992 \tabularnewline
34 & 15 & 15.3845 & -0.384466 \tabularnewline
35 & 14 & 13.7035 & 0.296519 \tabularnewline
36 & 16 & 15.3147 & 0.685333 \tabularnewline
37 & 9 & 10.8493 & -1.84934 \tabularnewline
38 & 15 & 14.2056 & 0.794354 \tabularnewline
39 & 17 & 15.068 & 1.93201 \tabularnewline
40 & 13 & 15.0689 & -2.06893 \tabularnewline
41 & 15 & 15.5452 & -0.545165 \tabularnewline
42 & 16 & 13.5957 & 2.40426 \tabularnewline
43 & 16 & 15.3106 & 0.689438 \tabularnewline
44 & 12 & 12.9501 & -0.950147 \tabularnewline
45 & 12 & 14.2575 & -2.25751 \tabularnewline
46 & 11 & 13.7009 & -2.70087 \tabularnewline
47 & 15 & 15.4914 & -0.491367 \tabularnewline
48 & 15 & 14.8367 & 0.163278 \tabularnewline
49 & 17 & 13.7801 & 3.21988 \tabularnewline
50 & 13 & 14.6492 & -1.6492 \tabularnewline
51 & 16 & 15.2996 & 0.700434 \tabularnewline
52 & 14 & 13.1819 & 0.818145 \tabularnewline
53 & 11 & 11.6117 & -0.61171 \tabularnewline
54 & 12 & 13.6234 & -1.62336 \tabularnewline
55 & 12 & 14.1444 & -2.1444 \tabularnewline
56 & 15 & 14.5871 & 0.412935 \tabularnewline
57 & 16 & 14.2018 & 1.79823 \tabularnewline
58 & 15 & 15.2535 & -0.25353 \tabularnewline
59 & 12 & 15.3284 & -3.3284 \tabularnewline
60 & 12 & 13.3935 & -1.39354 \tabularnewline
61 & 8 & 10.4145 & -2.41453 \tabularnewline
62 & 13 & 14.6415 & -1.64148 \tabularnewline
63 & 11 & 14.5641 & -3.56414 \tabularnewline
64 & 14 & 12.9906 & 1.00942 \tabularnewline
65 & 15 & 13.2608 & 1.73919 \tabularnewline
66 & 10 & 15.2917 & -5.29174 \tabularnewline
67 & 11 & 12.5925 & -1.59254 \tabularnewline
68 & 12 & 14.0782 & -2.07816 \tabularnewline
69 & 15 & 13.995 & 1.00502 \tabularnewline
70 & 15 & 13.8406 & 1.15943 \tabularnewline
71 & 14 & 13.8685 & 0.131547 \tabularnewline
72 & 16 & 12.9155 & 3.08447 \tabularnewline
73 & 15 & 14.7655 & 0.234502 \tabularnewline
74 & 15 & 15.6423 & -0.642287 \tabularnewline
75 & 13 & 14.6659 & -1.66593 \tabularnewline
76 & 12 & 12.1819 & -0.18191 \tabularnewline
77 & 17 & 14.212 & 2.788 \tabularnewline
78 & 13 & 12.3731 & 0.626929 \tabularnewline
79 & 15 & 13.7178 & 1.28223 \tabularnewline
80 & 13 & 14.4404 & -1.44035 \tabularnewline
81 & 15 & 15.1348 & -0.134847 \tabularnewline
82 & 16 & 14.3687 & 1.63133 \tabularnewline
83 & 15 & 15.6859 & -0.685863 \tabularnewline
84 & 16 & 14.3549 & 1.64505 \tabularnewline
85 & 15 & 14.3683 & 0.631658 \tabularnewline
86 & 14 & 14.0901 & -0.0900745 \tabularnewline
87 & 15 & 14.578 & 0.422047 \tabularnewline
88 & 14 & 14.3083 & -0.3083 \tabularnewline
89 & 13 & 12.549 & 0.450999 \tabularnewline
90 & 7 & 10.6397 & -3.63969 \tabularnewline
91 & 17 & 14.0575 & 2.94253 \tabularnewline
92 & 13 & 12.5812 & 0.41879 \tabularnewline
93 & 15 & 14.7454 & 0.254551 \tabularnewline
94 & 14 & 13.1502 & 0.849795 \tabularnewline
95 & 13 & 14.8263 & -1.82626 \tabularnewline
96 & 16 & 15.3454 & 0.654601 \tabularnewline
97 & 12 & 12.9676 & -0.967649 \tabularnewline
98 & 14 & 14.9093 & -0.909325 \tabularnewline
99 & 17 & 15.1139 & 1.88613 \tabularnewline
100 & 15 & 15.3311 & -0.331085 \tabularnewline
101 & 17 & 15.3726 & 1.62737 \tabularnewline
102 & 12 & 13.1902 & -1.19023 \tabularnewline
103 & 16 & 15.2305 & 0.769536 \tabularnewline
104 & 11 & 14.0971 & -3.09709 \tabularnewline
105 & 15 & 13.6164 & 1.3836 \tabularnewline
106 & 9 & 11.5316 & -2.5316 \tabularnewline
107 & 16 & 15.1062 & 0.893777 \tabularnewline
108 & 15 & 13.4237 & 1.5763 \tabularnewline
109 & 10 & 12.9108 & -2.91085 \tabularnewline
110 & 10 & 9.95414 & 0.0458625 \tabularnewline
111 & 15 & 13.9782 & 1.02177 \tabularnewline
112 & 11 & 13.2114 & -2.21141 \tabularnewline
113 & 13 & 15.8895 & -2.88946 \tabularnewline
114 & 14 & 11.978 & 2.02196 \tabularnewline
115 & 18 & 14.4079 & 3.59209 \tabularnewline
116 & 16 & 15.5828 & 0.41716 \tabularnewline
117 & 14 & 13.3521 & 0.647939 \tabularnewline
118 & 14 & 14.4749 & -0.474943 \tabularnewline
119 & 14 & 15.2852 & -1.28525 \tabularnewline
120 & 14 & 14.1421 & -0.14214 \tabularnewline
121 & 12 & 12.8028 & -0.802758 \tabularnewline
122 & 14 & 13.2476 & 0.75244 \tabularnewline
123 & 15 & 15.5402 & -0.540174 \tabularnewline
124 & 15 & 16.214 & -1.21397 \tabularnewline
125 & 15 & 14.464 & 0.536044 \tabularnewline
126 & 13 & 14.7529 & -1.75286 \tabularnewline
127 & 17 & 16.3832 & 0.616797 \tabularnewline
128 & 17 & 15.8162 & 1.1838 \tabularnewline
129 & 19 & 15.2684 & 3.73162 \tabularnewline
130 & 15 & 13.7747 & 1.22526 \tabularnewline
131 & 13 & 14.7353 & -1.7353 \tabularnewline
132 & 9 & 10.7753 & -1.77532 \tabularnewline
133 & 15 & 15.7814 & -0.781354 \tabularnewline
134 & 15 & 12.5037 & 2.4963 \tabularnewline
135 & 15 & 14.6773 & 0.322692 \tabularnewline
136 & 16 & 13.578 & 2.42196 \tabularnewline
137 & 11 & 8.89722 & 2.10278 \tabularnewline
138 & 14 & 13.4627 & 0.537268 \tabularnewline
139 & 11 & 12.1668 & -1.16678 \tabularnewline
140 & 15 & 14.4348 & 0.565169 \tabularnewline
141 & 13 & 13.9325 & -0.932482 \tabularnewline
142 & 15 & 14.6993 & 0.300681 \tabularnewline
143 & 16 & 14.1118 & 1.88824 \tabularnewline
144 & 14 & 14.8246 & -0.824632 \tabularnewline
145 & 15 & 13.7966 & 1.20336 \tabularnewline
146 & 16 & 14.9689 & 1.03108 \tabularnewline
147 & 16 & 14.1131 & 1.88694 \tabularnewline
148 & 11 & 13.6361 & -2.63609 \tabularnewline
149 & 12 & 14.6305 & -2.6305 \tabularnewline
150 & 9 & 11.7611 & -2.76109 \tabularnewline
151 & 16 & 15.0743 & 0.925744 \tabularnewline
152 & 13 & 13.0843 & -0.0843249 \tabularnewline
153 & 16 & 15.8811 & 0.118869 \tabularnewline
154 & 12 & 15.0698 & -3.06981 \tabularnewline
155 & 9 & 11.8896 & -2.88957 \tabularnewline
156 & 13 & 12.0171 & 0.98289 \tabularnewline
157 & 13 & 12.5812 & 0.41879 \tabularnewline
158 & 14 & 13.8643 & 0.135723 \tabularnewline
159 & 19 & 15.2684 & 3.73162 \tabularnewline
160 & 13 & 15.7831 & -2.78309 \tabularnewline
161 & 12 & 12.6255 & -0.625485 \tabularnewline
162 & 13 & 13.0173 & -0.0172952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253600&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]13.9787[/C][C]0.0213288[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.2082[/C][C]2.79182[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.5981[/C][C]-2.59814[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.3483[/C][C]-2.34831[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.378[/C][C]4.62197[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.1549[/C][C]3.84506[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.3047[/C][C]3.69527[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.0204[/C][C]-1.02044[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.1594[/C][C]-0.159379[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.7318[/C][C]0.268206[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.2951[/C][C]1.70491[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.285[/C][C]3.71503[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.3185[/C][C]-3.31852[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.5254[/C][C]2.47459[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.756[/C][C]2.24399[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.081[/C][C]0.918997[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.714[/C][C]0.286006[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.9228[/C][C]1.07721[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.4154[/C][C]-1.41539[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.402[/C][C]2.59796[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.9499[/C][C]2.05011[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.3194[/C][C]-2.31941[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.3543[/C][C]-0.354323[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.634[/C][C]-1.63396[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.3696[/C][C]1.63039[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.7305[/C][C]-6.73047[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0998[/C][C]0.900212[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.2945[/C][C]0.705525[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.8186[/C][C]1.18135[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.7821[/C][C]-2.78208[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.2064[/C][C]0.793595[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.4256[/C][C]0.574379[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.8801[/C][C]2.11992[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.3845[/C][C]-0.384466[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.7035[/C][C]0.296519[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.3147[/C][C]0.685333[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.8493[/C][C]-1.84934[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.2056[/C][C]0.794354[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.068[/C][C]1.93201[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]15.0689[/C][C]-2.06893[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.5452[/C][C]-0.545165[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.5957[/C][C]2.40426[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.3106[/C][C]0.689438[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.9501[/C][C]-0.950147[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.2575[/C][C]-2.25751[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.7009[/C][C]-2.70087[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.4914[/C][C]-0.491367[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.8367[/C][C]0.163278[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.7801[/C][C]3.21988[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6492[/C][C]-1.6492[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.2996[/C][C]0.700434[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.1819[/C][C]0.818145[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.6117[/C][C]-0.61171[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.6234[/C][C]-1.62336[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.1444[/C][C]-2.1444[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.5871[/C][C]0.412935[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.2018[/C][C]1.79823[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.2535[/C][C]-0.25353[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.3284[/C][C]-3.3284[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.3935[/C][C]-1.39354[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.4145[/C][C]-2.41453[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.6415[/C][C]-1.64148[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.5641[/C][C]-3.56414[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.9906[/C][C]1.00942[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.2608[/C][C]1.73919[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.2917[/C][C]-5.29174[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.5925[/C][C]-1.59254[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.0782[/C][C]-2.07816[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.995[/C][C]1.00502[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.8406[/C][C]1.15943[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.8685[/C][C]0.131547[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]12.9155[/C][C]3.08447[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.7655[/C][C]0.234502[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.6423[/C][C]-0.642287[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.6659[/C][C]-1.66593[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.1819[/C][C]-0.18191[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.212[/C][C]2.788[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.3731[/C][C]0.626929[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.7178[/C][C]1.28223[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.4404[/C][C]-1.44035[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.1348[/C][C]-0.134847[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.3687[/C][C]1.63133[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6859[/C][C]-0.685863[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.3549[/C][C]1.64505[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.3683[/C][C]0.631658[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0901[/C][C]-0.0900745[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.578[/C][C]0.422047[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.3083[/C][C]-0.3083[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]12.549[/C][C]0.450999[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.6397[/C][C]-3.63969[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.0575[/C][C]2.94253[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.5812[/C][C]0.41879[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.7454[/C][C]0.254551[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.1502[/C][C]0.849795[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.8263[/C][C]-1.82626[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.3454[/C][C]0.654601[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.9676[/C][C]-0.967649[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.9093[/C][C]-0.909325[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]15.1139[/C][C]1.88613[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]15.3311[/C][C]-0.331085[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.3726[/C][C]1.62737[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]13.1902[/C][C]-1.19023[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.2305[/C][C]0.769536[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.0971[/C][C]-3.09709[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.6164[/C][C]1.3836[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.5316[/C][C]-2.5316[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.1062[/C][C]0.893777[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.4237[/C][C]1.5763[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.9108[/C][C]-2.91085[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.95414[/C][C]0.0458625[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]13.9782[/C][C]1.02177[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.2114[/C][C]-2.21141[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.8895[/C][C]-2.88946[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.978[/C][C]2.02196[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.4079[/C][C]3.59209[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.5828[/C][C]0.41716[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.3521[/C][C]0.647939[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.4749[/C][C]-0.474943[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.2852[/C][C]-1.28525[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.1421[/C][C]-0.14214[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.8028[/C][C]-0.802758[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.2476[/C][C]0.75244[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.5402[/C][C]-0.540174[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.214[/C][C]-1.21397[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.464[/C][C]0.536044[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.7529[/C][C]-1.75286[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]16.3832[/C][C]0.616797[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.8162[/C][C]1.1838[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.2684[/C][C]3.73162[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.7747[/C][C]1.22526[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.7353[/C][C]-1.7353[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.7753[/C][C]-1.77532[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.7814[/C][C]-0.781354[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.5037[/C][C]2.4963[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.6773[/C][C]0.322692[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.578[/C][C]2.42196[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]8.89722[/C][C]2.10278[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.4627[/C][C]0.537268[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.1668[/C][C]-1.16678[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.4348[/C][C]0.565169[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.9325[/C][C]-0.932482[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.6993[/C][C]0.300681[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]14.1118[/C][C]1.88824[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.8246[/C][C]-0.824632[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.7966[/C][C]1.20336[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.9689[/C][C]1.03108[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.1131[/C][C]1.88694[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.6361[/C][C]-2.63609[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.6305[/C][C]-2.6305[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.7611[/C][C]-2.76109[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.0743[/C][C]0.925744[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]13.0843[/C][C]-0.0843249[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.8811[/C][C]0.118869[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]15.0698[/C][C]-3.06981[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.8896[/C][C]-2.88957[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.0171[/C][C]0.98289[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]12.5812[/C][C]0.41879[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.8643[/C][C]0.135723[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.2684[/C][C]3.73162[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.7831[/C][C]-2.78309[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.6255[/C][C]-0.625485[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.0173[/C][C]-0.0172952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253600&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253600&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	13.9787	0.0213288
2	18	15.2082	2.79182
3	11	13.5981	-2.59814
4	12	14.3483	-2.34831
5	16	11.378	4.62197
6	18	14.1549	3.84506
7	14	10.3047	3.69527
8	14	15.0204	-1.02044
9	15	15.1594	-0.159379
10	15	14.7318	0.268206
11	17	15.2951	1.70491
12	19	15.285	3.71503
13	10	13.3185	-3.31852
14	16	13.5254	2.47459
15	18	15.756	2.24399
16	14	13.081	0.918997
17	14	13.714	0.286006
18	17	15.9228	1.07721
19	14	15.4154	-1.41539
20	16	13.402	2.59796
21	18	15.9499	2.05011
22	11	13.3194	-2.31941
23	14	14.3543	-0.354323
24	12	13.634	-1.63396
25	17	15.3696	1.63039
26	9	15.7305	-6.73047
27	16	15.0998	0.900212
28	14	13.2945	0.705525
29	15	13.8186	1.18135
30	11	13.7821	-2.78208
31	16	15.2064	0.793595
32	13	12.4256	0.574379
33	17	14.8801	2.11992
34	15	15.3845	-0.384466
35	14	13.7035	0.296519
36	16	15.3147	0.685333
37	9	10.8493	-1.84934
38	15	14.2056	0.794354
39	17	15.068	1.93201
40	13	15.0689	-2.06893
41	15	15.5452	-0.545165
42	16	13.5957	2.40426
43	16	15.3106	0.689438
44	12	12.9501	-0.950147
45	12	14.2575	-2.25751
46	11	13.7009	-2.70087
47	15	15.4914	-0.491367
48	15	14.8367	0.163278
49	17	13.7801	3.21988
50	13	14.6492	-1.6492
51	16	15.2996	0.700434
52	14	13.1819	0.818145
53	11	11.6117	-0.61171
54	12	13.6234	-1.62336
55	12	14.1444	-2.1444
56	15	14.5871	0.412935
57	16	14.2018	1.79823
58	15	15.2535	-0.25353
59	12	15.3284	-3.3284
60	12	13.3935	-1.39354
61	8	10.4145	-2.41453
62	13	14.6415	-1.64148
63	11	14.5641	-3.56414
64	14	12.9906	1.00942
65	15	13.2608	1.73919
66	10	15.2917	-5.29174
67	11	12.5925	-1.59254
68	12	14.0782	-2.07816
69	15	13.995	1.00502
70	15	13.8406	1.15943
71	14	13.8685	0.131547
72	16	12.9155	3.08447
73	15	14.7655	0.234502
74	15	15.6423	-0.642287
75	13	14.6659	-1.66593
76	12	12.1819	-0.18191
77	17	14.212	2.788
78	13	12.3731	0.626929
79	15	13.7178	1.28223
80	13	14.4404	-1.44035
81	15	15.1348	-0.134847
82	16	14.3687	1.63133
83	15	15.6859	-0.685863
84	16	14.3549	1.64505
85	15	14.3683	0.631658
86	14	14.0901	-0.0900745
87	15	14.578	0.422047
88	14	14.3083	-0.3083
89	13	12.549	0.450999
90	7	10.6397	-3.63969
91	17	14.0575	2.94253
92	13	12.5812	0.41879
93	15	14.7454	0.254551
94	14	13.1502	0.849795
95	13	14.8263	-1.82626
96	16	15.3454	0.654601
97	12	12.9676	-0.967649
98	14	14.9093	-0.909325
99	17	15.1139	1.88613
100	15	15.3311	-0.331085
101	17	15.3726	1.62737
102	12	13.1902	-1.19023
103	16	15.2305	0.769536
104	11	14.0971	-3.09709
105	15	13.6164	1.3836
106	9	11.5316	-2.5316
107	16	15.1062	0.893777
108	15	13.4237	1.5763
109	10	12.9108	-2.91085
110	10	9.95414	0.0458625
111	15	13.9782	1.02177
112	11	13.2114	-2.21141
113	13	15.8895	-2.88946
114	14	11.978	2.02196
115	18	14.4079	3.59209
116	16	15.5828	0.41716
117	14	13.3521	0.647939
118	14	14.4749	-0.474943
119	14	15.2852	-1.28525
120	14	14.1421	-0.14214
121	12	12.8028	-0.802758
122	14	13.2476	0.75244
123	15	15.5402	-0.540174
124	15	16.214	-1.21397
125	15	14.464	0.536044
126	13	14.7529	-1.75286
127	17	16.3832	0.616797
128	17	15.8162	1.1838
129	19	15.2684	3.73162
130	15	13.7747	1.22526
131	13	14.7353	-1.7353
132	9	10.7753	-1.77532
133	15	15.7814	-0.781354
134	15	12.5037	2.4963
135	15	14.6773	0.322692
136	16	13.578	2.42196
137	11	8.89722	2.10278
138	14	13.4627	0.537268
139	11	12.1668	-1.16678
140	15	14.4348	0.565169
141	13	13.9325	-0.932482
142	15	14.6993	0.300681
143	16	14.1118	1.88824
144	14	14.8246	-0.824632
145	15	13.7966	1.20336
146	16	14.9689	1.03108
147	16	14.1131	1.88694
148	11	13.6361	-2.63609
149	12	14.6305	-2.6305
150	9	11.7611	-2.76109
151	16	15.0743	0.925744
152	13	13.0843	-0.0843249
153	16	15.8811	0.118869
154	12	15.0698	-3.06981
155	9	11.8896	-2.88957
156	13	12.0171	0.98289
157	13	12.5812	0.41879
158	14	13.8643	0.135723
159	19	15.2684	3.73162
160	13	15.7831	-2.78309
161	12	12.6255	-0.625485
162	13	13.0173	-0.0172952

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.0591747	0.118349	0.940825
11	0.0189494	0.0378988	0.981051
12	0.885614	0.228772	0.114386
13	0.983366	0.0332689	0.0166344
14	0.98384	0.0323193	0.0161597
15	0.989058	0.0218838	0.0109419
16	0.981026	0.0379489	0.0189744
17	0.974189	0.051623	0.0258115
18	0.962118	0.075763	0.0378815
19	0.949558	0.100884	0.0504421
20	0.949668	0.100664	0.050332
21	0.948976	0.102048	0.0510238
22	0.970132	0.0597355	0.0298678
23	0.956842	0.0863158	0.0431579
24	0.946844	0.106312	0.0531561
25	0.929212	0.141575	0.0707876
26	0.999545	0.000910443	0.000455222
27	0.99926	0.00147925	0.000739623
28	0.998786	0.00242791	0.00121396
29	0.998122	0.00375578	0.00187789
30	0.999106	0.00178884	0.000894419
31	0.99858	0.00284079	0.00142039
32	0.997804	0.00439233	0.00219617
33	0.997433	0.00513449	0.00256725
34	0.996108	0.00778463	0.00389232
35	0.994467	0.0110665	0.00553327
36	0.991939	0.0161223	0.00806113
37	0.994075	0.0118506	0.00592531
38	0.991568	0.0168636	0.00843181
39	0.990883	0.0182332	0.00911661
40	0.991051	0.0178986	0.00894932
41	0.987708	0.0245837	0.0122919
42	0.987351	0.0252983	0.0126491
43	0.982943	0.0341138	0.0170569
44	0.978817	0.0423662	0.0211831
45	0.982336	0.0353271	0.0176636
46	0.987029	0.0259419	0.012971
47	0.982304	0.0353927	0.0176963
48	0.976021	0.0479583	0.0239791
49	0.983287	0.0334254	0.0167127
50	0.981799	0.036403	0.0182015
51	0.976041	0.0479183	0.0239591
52	0.969026	0.061948	0.030974
53	0.961823	0.076354	0.038177
54	0.959249	0.0815011	0.0407505
55	0.959251	0.0814989	0.0407495
56	0.947792	0.104415	0.0522077
57	0.943658	0.112684	0.0563419
58	0.928901	0.142199	0.0710993
59	0.953138	0.0937241	0.0468621
60	0.94884	0.102319	0.0511596
61	0.95611	0.0877797	0.0438899
62	0.95377	0.0924604	0.0462302
63	0.975625	0.0487492	0.0243746
64	0.970429	0.0591429	0.0295714
65	0.968042	0.0639163	0.0319581
66	0.994899	0.0102014	0.00510072
67	0.994361	0.011278	0.00563898
68	0.994821	0.0103573	0.00517867
69	0.993405	0.0131899	0.00659494
70	0.991862	0.0162754	0.00813768
71	0.988905	0.0221904	0.0110952
72	0.993019	0.0139629	0.00698146
73	0.990491	0.0190182	0.00950909
74	0.987418	0.0251648	0.0125824
75	0.986577	0.026846	0.013423
76	0.982067	0.0358667	0.0179334
77	0.986904	0.0261928	0.0130964
78	0.982984	0.0340324	0.0170162
79	0.97997	0.040059	0.0200295
80	0.978845	0.04231	0.021155
81	0.972155	0.0556905	0.0278452
82	0.970098	0.0598032	0.0299016
83	0.962359	0.0752815	0.0376407
84	0.959049	0.0819017	0.0409508
85	0.948971	0.102059	0.0510294
86	0.935482	0.129036	0.0645182
87	0.920025	0.159951	0.0799754
88	0.90138	0.19724	0.0986201
89	0.882392	0.235216	0.117608
90	0.927071	0.145858	0.0729288
91	0.946734	0.106532	0.0532659
92	0.933946	0.132108	0.0660542
93	0.918919	0.162162	0.0810812
94	0.902946	0.194109	0.0970543
95	0.902504	0.194991	0.0974957
96	0.882337	0.235325	0.117663
97	0.861962	0.276077	0.138038
98	0.843233	0.313534	0.156767
99	0.839851	0.320298	0.160149
100	0.809296	0.381408	0.190704
101	0.802206	0.395588	0.197794
102	0.77863	0.442741	0.22137
103	0.753094	0.493813	0.246906
104	0.823913	0.352174	0.176087
105	0.825723	0.348554	0.174277
106	0.85647	0.28706	0.14353
107	0.83292	0.334159	0.16708
108	0.837865	0.32427	0.162135
109	0.870845	0.258311	0.129155
110	0.847439	0.305121	0.152561
111	0.834951	0.330099	0.165049
112	0.832412	0.335176	0.167588
113	0.844237	0.311525	0.155763
114	0.852593	0.294814	0.147407
115	0.912703	0.174594	0.0872972
116	0.889673	0.220654	0.110327
117	0.889325	0.221351	0.110675
118	0.862549	0.274901	0.137451
119	0.8441	0.311801	0.1559
120	0.808248	0.383503	0.191752
121	0.770437	0.459125	0.229563
122	0.728045	0.543911	0.271955
123	0.67973	0.64054	0.32027
124	0.636948	0.726103	0.363052
125	0.582646	0.834709	0.417354
126	0.579487	0.841026	0.420513
127	0.52578	0.948441	0.47422
128	0.519855	0.960289	0.480145
129	0.674444	0.651112	0.325556
130	0.637361	0.725279	0.362639
131	0.632037	0.735927	0.367963
132	0.629425	0.741149	0.370575
133	0.568148	0.863703	0.431852
134	0.566552	0.866896	0.433448
135	0.519205	0.96159	0.480795
136	0.51678	0.96644	0.48322
137	0.510394	0.979211	0.489606
138	0.468946	0.937892	0.531054
139	0.39916	0.798319	0.60084
140	0.338878	0.677756	0.661122
141	0.290581	0.581162	0.709419
142	0.241769	0.483538	0.758231
143	0.2334	0.4668	0.7666
144	0.191083	0.382166	0.808917
145	0.152069	0.304138	0.847931
146	0.121	0.242	0.879
147	0.207669	0.415338	0.792331
148	0.507039	0.985923	0.492961
149	0.551034	0.897932	0.448966
150	0.434464	0.868927	0.565536
151	0.353364	0.706729	0.646636
152	0.219134	0.438269	0.780866

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0591747 & 0.118349 & 0.940825 \tabularnewline
11 & 0.0189494 & 0.0378988 & 0.981051 \tabularnewline
12 & 0.885614 & 0.228772 & 0.114386 \tabularnewline
13 & 0.983366 & 0.0332689 & 0.0166344 \tabularnewline
14 & 0.98384 & 0.0323193 & 0.0161597 \tabularnewline
15 & 0.989058 & 0.0218838 & 0.0109419 \tabularnewline
16 & 0.981026 & 0.0379489 & 0.0189744 \tabularnewline
17 & 0.974189 & 0.051623 & 0.0258115 \tabularnewline
18 & 0.962118 & 0.075763 & 0.0378815 \tabularnewline
19 & 0.949558 & 0.100884 & 0.0504421 \tabularnewline
20 & 0.949668 & 0.100664 & 0.050332 \tabularnewline
21 & 0.948976 & 0.102048 & 0.0510238 \tabularnewline
22 & 0.970132 & 0.0597355 & 0.0298678 \tabularnewline
23 & 0.956842 & 0.0863158 & 0.0431579 \tabularnewline
24 & 0.946844 & 0.106312 & 0.0531561 \tabularnewline
25 & 0.929212 & 0.141575 & 0.0707876 \tabularnewline
26 & 0.999545 & 0.000910443 & 0.000455222 \tabularnewline
27 & 0.99926 & 0.00147925 & 0.000739623 \tabularnewline
28 & 0.998786 & 0.00242791 & 0.00121396 \tabularnewline
29 & 0.998122 & 0.00375578 & 0.00187789 \tabularnewline
30 & 0.999106 & 0.00178884 & 0.000894419 \tabularnewline
31 & 0.99858 & 0.00284079 & 0.00142039 \tabularnewline
32 & 0.997804 & 0.00439233 & 0.00219617 \tabularnewline
33 & 0.997433 & 0.00513449 & 0.00256725 \tabularnewline
34 & 0.996108 & 0.00778463 & 0.00389232 \tabularnewline
35 & 0.994467 & 0.0110665 & 0.00553327 \tabularnewline
36 & 0.991939 & 0.0161223 & 0.00806113 \tabularnewline
37 & 0.994075 & 0.0118506 & 0.00592531 \tabularnewline
38 & 0.991568 & 0.0168636 & 0.00843181 \tabularnewline
39 & 0.990883 & 0.0182332 & 0.00911661 \tabularnewline
40 & 0.991051 & 0.0178986 & 0.00894932 \tabularnewline
41 & 0.987708 & 0.0245837 & 0.0122919 \tabularnewline
42 & 0.987351 & 0.0252983 & 0.0126491 \tabularnewline
43 & 0.982943 & 0.0341138 & 0.0170569 \tabularnewline
44 & 0.978817 & 0.0423662 & 0.0211831 \tabularnewline
45 & 0.982336 & 0.0353271 & 0.0176636 \tabularnewline
46 & 0.987029 & 0.0259419 & 0.012971 \tabularnewline
47 & 0.982304 & 0.0353927 & 0.0176963 \tabularnewline
48 & 0.976021 & 0.0479583 & 0.0239791 \tabularnewline
49 & 0.983287 & 0.0334254 & 0.0167127 \tabularnewline
50 & 0.981799 & 0.036403 & 0.0182015 \tabularnewline
51 & 0.976041 & 0.0479183 & 0.0239591 \tabularnewline
52 & 0.969026 & 0.061948 & 0.030974 \tabularnewline
53 & 0.961823 & 0.076354 & 0.038177 \tabularnewline
54 & 0.959249 & 0.0815011 & 0.0407505 \tabularnewline
55 & 0.959251 & 0.0814989 & 0.0407495 \tabularnewline
56 & 0.947792 & 0.104415 & 0.0522077 \tabularnewline
57 & 0.943658 & 0.112684 & 0.0563419 \tabularnewline
58 & 0.928901 & 0.142199 & 0.0710993 \tabularnewline
59 & 0.953138 & 0.0937241 & 0.0468621 \tabularnewline
60 & 0.94884 & 0.102319 & 0.0511596 \tabularnewline
61 & 0.95611 & 0.0877797 & 0.0438899 \tabularnewline
62 & 0.95377 & 0.0924604 & 0.0462302 \tabularnewline
63 & 0.975625 & 0.0487492 & 0.0243746 \tabularnewline
64 & 0.970429 & 0.0591429 & 0.0295714 \tabularnewline
65 & 0.968042 & 0.0639163 & 0.0319581 \tabularnewline
66 & 0.994899 & 0.0102014 & 0.00510072 \tabularnewline
67 & 0.994361 & 0.011278 & 0.00563898 \tabularnewline
68 & 0.994821 & 0.0103573 & 0.00517867 \tabularnewline
69 & 0.993405 & 0.0131899 & 0.00659494 \tabularnewline
70 & 0.991862 & 0.0162754 & 0.00813768 \tabularnewline
71 & 0.988905 & 0.0221904 & 0.0110952 \tabularnewline
72 & 0.993019 & 0.0139629 & 0.00698146 \tabularnewline
73 & 0.990491 & 0.0190182 & 0.00950909 \tabularnewline
74 & 0.987418 & 0.0251648 & 0.0125824 \tabularnewline
75 & 0.986577 & 0.026846 & 0.013423 \tabularnewline
76 & 0.982067 & 0.0358667 & 0.0179334 \tabularnewline
77 & 0.986904 & 0.0261928 & 0.0130964 \tabularnewline
78 & 0.982984 & 0.0340324 & 0.0170162 \tabularnewline
79 & 0.97997 & 0.040059 & 0.0200295 \tabularnewline
80 & 0.978845 & 0.04231 & 0.021155 \tabularnewline
81 & 0.972155 & 0.0556905 & 0.0278452 \tabularnewline
82 & 0.970098 & 0.0598032 & 0.0299016 \tabularnewline
83 & 0.962359 & 0.0752815 & 0.0376407 \tabularnewline
84 & 0.959049 & 0.0819017 & 0.0409508 \tabularnewline
85 & 0.948971 & 0.102059 & 0.0510294 \tabularnewline
86 & 0.935482 & 0.129036 & 0.0645182 \tabularnewline
87 & 0.920025 & 0.159951 & 0.0799754 \tabularnewline
88 & 0.90138 & 0.19724 & 0.0986201 \tabularnewline
89 & 0.882392 & 0.235216 & 0.117608 \tabularnewline
90 & 0.927071 & 0.145858 & 0.0729288 \tabularnewline
91 & 0.946734 & 0.106532 & 0.0532659 \tabularnewline
92 & 0.933946 & 0.132108 & 0.0660542 \tabularnewline
93 & 0.918919 & 0.162162 & 0.0810812 \tabularnewline
94 & 0.902946 & 0.194109 & 0.0970543 \tabularnewline
95 & 0.902504 & 0.194991 & 0.0974957 \tabularnewline
96 & 0.882337 & 0.235325 & 0.117663 \tabularnewline
97 & 0.861962 & 0.276077 & 0.138038 \tabularnewline
98 & 0.843233 & 0.313534 & 0.156767 \tabularnewline
99 & 0.839851 & 0.320298 & 0.160149 \tabularnewline
100 & 0.809296 & 0.381408 & 0.190704 \tabularnewline
101 & 0.802206 & 0.395588 & 0.197794 \tabularnewline
102 & 0.77863 & 0.442741 & 0.22137 \tabularnewline
103 & 0.753094 & 0.493813 & 0.246906 \tabularnewline
104 & 0.823913 & 0.352174 & 0.176087 \tabularnewline
105 & 0.825723 & 0.348554 & 0.174277 \tabularnewline
106 & 0.85647 & 0.28706 & 0.14353 \tabularnewline
107 & 0.83292 & 0.334159 & 0.16708 \tabularnewline
108 & 0.837865 & 0.32427 & 0.162135 \tabularnewline
109 & 0.870845 & 0.258311 & 0.129155 \tabularnewline
110 & 0.847439 & 0.305121 & 0.152561 \tabularnewline
111 & 0.834951 & 0.330099 & 0.165049 \tabularnewline
112 & 0.832412 & 0.335176 & 0.167588 \tabularnewline
113 & 0.844237 & 0.311525 & 0.155763 \tabularnewline
114 & 0.852593 & 0.294814 & 0.147407 \tabularnewline
115 & 0.912703 & 0.174594 & 0.0872972 \tabularnewline
116 & 0.889673 & 0.220654 & 0.110327 \tabularnewline
117 & 0.889325 & 0.221351 & 0.110675 \tabularnewline
118 & 0.862549 & 0.274901 & 0.137451 \tabularnewline
119 & 0.8441 & 0.311801 & 0.1559 \tabularnewline
120 & 0.808248 & 0.383503 & 0.191752 \tabularnewline
121 & 0.770437 & 0.459125 & 0.229563 \tabularnewline
122 & 0.728045 & 0.543911 & 0.271955 \tabularnewline
123 & 0.67973 & 0.64054 & 0.32027 \tabularnewline
124 & 0.636948 & 0.726103 & 0.363052 \tabularnewline
125 & 0.582646 & 0.834709 & 0.417354 \tabularnewline
126 & 0.579487 & 0.841026 & 0.420513 \tabularnewline
127 & 0.52578 & 0.948441 & 0.47422 \tabularnewline
128 & 0.519855 & 0.960289 & 0.480145 \tabularnewline
129 & 0.674444 & 0.651112 & 0.325556 \tabularnewline
130 & 0.637361 & 0.725279 & 0.362639 \tabularnewline
131 & 0.632037 & 0.735927 & 0.367963 \tabularnewline
132 & 0.629425 & 0.741149 & 0.370575 \tabularnewline
133 & 0.568148 & 0.863703 & 0.431852 \tabularnewline
134 & 0.566552 & 0.866896 & 0.433448 \tabularnewline
135 & 0.519205 & 0.96159 & 0.480795 \tabularnewline
136 & 0.51678 & 0.96644 & 0.48322 \tabularnewline
137 & 0.510394 & 0.979211 & 0.489606 \tabularnewline
138 & 0.468946 & 0.937892 & 0.531054 \tabularnewline
139 & 0.39916 & 0.798319 & 0.60084 \tabularnewline
140 & 0.338878 & 0.677756 & 0.661122 \tabularnewline
141 & 0.290581 & 0.581162 & 0.709419 \tabularnewline
142 & 0.241769 & 0.483538 & 0.758231 \tabularnewline
143 & 0.2334 & 0.4668 & 0.7666 \tabularnewline
144 & 0.191083 & 0.382166 & 0.808917 \tabularnewline
145 & 0.152069 & 0.304138 & 0.847931 \tabularnewline
146 & 0.121 & 0.242 & 0.879 \tabularnewline
147 & 0.207669 & 0.415338 & 0.792331 \tabularnewline
148 & 0.507039 & 0.985923 & 0.492961 \tabularnewline
149 & 0.551034 & 0.897932 & 0.448966 \tabularnewline
150 & 0.434464 & 0.868927 & 0.565536 \tabularnewline
151 & 0.353364 & 0.706729 & 0.646636 \tabularnewline
152 & 0.219134 & 0.438269 & 0.780866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253600&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]10[/C][C]0.0591747[/C][C]0.118349[/C][C]0.940825[/C][/ROW]
[ROW][C]11[/C][C]0.0189494[/C][C]0.0378988[/C][C]0.981051[/C][/ROW]
[ROW][C]12[/C][C]0.885614[/C][C]0.228772[/C][C]0.114386[/C][/ROW]
[ROW][C]13[/C][C]0.983366[/C][C]0.0332689[/C][C]0.0166344[/C][/ROW]
[ROW][C]14[/C][C]0.98384[/C][C]0.0323193[/C][C]0.0161597[/C][/ROW]
[ROW][C]15[/C][C]0.989058[/C][C]0.0218838[/C][C]0.0109419[/C][/ROW]
[ROW][C]16[/C][C]0.981026[/C][C]0.0379489[/C][C]0.0189744[/C][/ROW]
[ROW][C]17[/C][C]0.974189[/C][C]0.051623[/C][C]0.0258115[/C][/ROW]
[ROW][C]18[/C][C]0.962118[/C][C]0.075763[/C][C]0.0378815[/C][/ROW]
[ROW][C]19[/C][C]0.949558[/C][C]0.100884[/C][C]0.0504421[/C][/ROW]
[ROW][C]20[/C][C]0.949668[/C][C]0.100664[/C][C]0.050332[/C][/ROW]
[ROW][C]21[/C][C]0.948976[/C][C]0.102048[/C][C]0.0510238[/C][/ROW]
[ROW][C]22[/C][C]0.970132[/C][C]0.0597355[/C][C]0.0298678[/C][/ROW]
[ROW][C]23[/C][C]0.956842[/C][C]0.0863158[/C][C]0.0431579[/C][/ROW]
[ROW][C]24[/C][C]0.946844[/C][C]0.106312[/C][C]0.0531561[/C][/ROW]
[ROW][C]25[/C][C]0.929212[/C][C]0.141575[/C][C]0.0707876[/C][/ROW]
[ROW][C]26[/C][C]0.999545[/C][C]0.000910443[/C][C]0.000455222[/C][/ROW]
[ROW][C]27[/C][C]0.99926[/C][C]0.00147925[/C][C]0.000739623[/C][/ROW]
[ROW][C]28[/C][C]0.998786[/C][C]0.00242791[/C][C]0.00121396[/C][/ROW]
[ROW][C]29[/C][C]0.998122[/C][C]0.00375578[/C][C]0.00187789[/C][/ROW]
[ROW][C]30[/C][C]0.999106[/C][C]0.00178884[/C][C]0.000894419[/C][/ROW]
[ROW][C]31[/C][C]0.99858[/C][C]0.00284079[/C][C]0.00142039[/C][/ROW]
[ROW][C]32[/C][C]0.997804[/C][C]0.00439233[/C][C]0.00219617[/C][/ROW]
[ROW][C]33[/C][C]0.997433[/C][C]0.00513449[/C][C]0.00256725[/C][/ROW]
[ROW][C]34[/C][C]0.996108[/C][C]0.00778463[/C][C]0.00389232[/C][/ROW]
[ROW][C]35[/C][C]0.994467[/C][C]0.0110665[/C][C]0.00553327[/C][/ROW]
[ROW][C]36[/C][C]0.991939[/C][C]0.0161223[/C][C]0.00806113[/C][/ROW]
[ROW][C]37[/C][C]0.994075[/C][C]0.0118506[/C][C]0.00592531[/C][/ROW]
[ROW][C]38[/C][C]0.991568[/C][C]0.0168636[/C][C]0.00843181[/C][/ROW]
[ROW][C]39[/C][C]0.990883[/C][C]0.0182332[/C][C]0.00911661[/C][/ROW]
[ROW][C]40[/C][C]0.991051[/C][C]0.0178986[/C][C]0.00894932[/C][/ROW]
[ROW][C]41[/C][C]0.987708[/C][C]0.0245837[/C][C]0.0122919[/C][/ROW]
[ROW][C]42[/C][C]0.987351[/C][C]0.0252983[/C][C]0.0126491[/C][/ROW]
[ROW][C]43[/C][C]0.982943[/C][C]0.0341138[/C][C]0.0170569[/C][/ROW]
[ROW][C]44[/C][C]0.978817[/C][C]0.0423662[/C][C]0.0211831[/C][/ROW]
[ROW][C]45[/C][C]0.982336[/C][C]0.0353271[/C][C]0.0176636[/C][/ROW]
[ROW][C]46[/C][C]0.987029[/C][C]0.0259419[/C][C]0.012971[/C][/ROW]
[ROW][C]47[/C][C]0.982304[/C][C]0.0353927[/C][C]0.0176963[/C][/ROW]
[ROW][C]48[/C][C]0.976021[/C][C]0.0479583[/C][C]0.0239791[/C][/ROW]
[ROW][C]49[/C][C]0.983287[/C][C]0.0334254[/C][C]0.0167127[/C][/ROW]
[ROW][C]50[/C][C]0.981799[/C][C]0.036403[/C][C]0.0182015[/C][/ROW]
[ROW][C]51[/C][C]0.976041[/C][C]0.0479183[/C][C]0.0239591[/C][/ROW]
[ROW][C]52[/C][C]0.969026[/C][C]0.061948[/C][C]0.030974[/C][/ROW]
[ROW][C]53[/C][C]0.961823[/C][C]0.076354[/C][C]0.038177[/C][/ROW]
[ROW][C]54[/C][C]0.959249[/C][C]0.0815011[/C][C]0.0407505[/C][/ROW]
[ROW][C]55[/C][C]0.959251[/C][C]0.0814989[/C][C]0.0407495[/C][/ROW]
[ROW][C]56[/C][C]0.947792[/C][C]0.104415[/C][C]0.0522077[/C][/ROW]
[ROW][C]57[/C][C]0.943658[/C][C]0.112684[/C][C]0.0563419[/C][/ROW]
[ROW][C]58[/C][C]0.928901[/C][C]0.142199[/C][C]0.0710993[/C][/ROW]
[ROW][C]59[/C][C]0.953138[/C][C]0.0937241[/C][C]0.0468621[/C][/ROW]
[ROW][C]60[/C][C]0.94884[/C][C]0.102319[/C][C]0.0511596[/C][/ROW]
[ROW][C]61[/C][C]0.95611[/C][C]0.0877797[/C][C]0.0438899[/C][/ROW]
[ROW][C]62[/C][C]0.95377[/C][C]0.0924604[/C][C]0.0462302[/C][/ROW]
[ROW][C]63[/C][C]0.975625[/C][C]0.0487492[/C][C]0.0243746[/C][/ROW]
[ROW][C]64[/C][C]0.970429[/C][C]0.0591429[/C][C]0.0295714[/C][/ROW]
[ROW][C]65[/C][C]0.968042[/C][C]0.0639163[/C][C]0.0319581[/C][/ROW]
[ROW][C]66[/C][C]0.994899[/C][C]0.0102014[/C][C]0.00510072[/C][/ROW]
[ROW][C]67[/C][C]0.994361[/C][C]0.011278[/C][C]0.00563898[/C][/ROW]
[ROW][C]68[/C][C]0.994821[/C][C]0.0103573[/C][C]0.00517867[/C][/ROW]
[ROW][C]69[/C][C]0.993405[/C][C]0.0131899[/C][C]0.00659494[/C][/ROW]
[ROW][C]70[/C][C]0.991862[/C][C]0.0162754[/C][C]0.00813768[/C][/ROW]
[ROW][C]71[/C][C]0.988905[/C][C]0.0221904[/C][C]0.0110952[/C][/ROW]
[ROW][C]72[/C][C]0.993019[/C][C]0.0139629[/C][C]0.00698146[/C][/ROW]
[ROW][C]73[/C][C]0.990491[/C][C]0.0190182[/C][C]0.00950909[/C][/ROW]
[ROW][C]74[/C][C]0.987418[/C][C]0.0251648[/C][C]0.0125824[/C][/ROW]
[ROW][C]75[/C][C]0.986577[/C][C]0.026846[/C][C]0.013423[/C][/ROW]
[ROW][C]76[/C][C]0.982067[/C][C]0.0358667[/C][C]0.0179334[/C][/ROW]
[ROW][C]77[/C][C]0.986904[/C][C]0.0261928[/C][C]0.0130964[/C][/ROW]
[ROW][C]78[/C][C]0.982984[/C][C]0.0340324[/C][C]0.0170162[/C][/ROW]
[ROW][C]79[/C][C]0.97997[/C][C]0.040059[/C][C]0.0200295[/C][/ROW]
[ROW][C]80[/C][C]0.978845[/C][C]0.04231[/C][C]0.021155[/C][/ROW]
[ROW][C]81[/C][C]0.972155[/C][C]0.0556905[/C][C]0.0278452[/C][/ROW]
[ROW][C]82[/C][C]0.970098[/C][C]0.0598032[/C][C]0.0299016[/C][/ROW]
[ROW][C]83[/C][C]0.962359[/C][C]0.0752815[/C][C]0.0376407[/C][/ROW]
[ROW][C]84[/C][C]0.959049[/C][C]0.0819017[/C][C]0.0409508[/C][/ROW]
[ROW][C]85[/C][C]0.948971[/C][C]0.102059[/C][C]0.0510294[/C][/ROW]
[ROW][C]86[/C][C]0.935482[/C][C]0.129036[/C][C]0.0645182[/C][/ROW]
[ROW][C]87[/C][C]0.920025[/C][C]0.159951[/C][C]0.0799754[/C][/ROW]
[ROW][C]88[/C][C]0.90138[/C][C]0.19724[/C][C]0.0986201[/C][/ROW]
[ROW][C]89[/C][C]0.882392[/C][C]0.235216[/C][C]0.117608[/C][/ROW]
[ROW][C]90[/C][C]0.927071[/C][C]0.145858[/C][C]0.0729288[/C][/ROW]
[ROW][C]91[/C][C]0.946734[/C][C]0.106532[/C][C]0.0532659[/C][/ROW]
[ROW][C]92[/C][C]0.933946[/C][C]0.132108[/C][C]0.0660542[/C][/ROW]
[ROW][C]93[/C][C]0.918919[/C][C]0.162162[/C][C]0.0810812[/C][/ROW]
[ROW][C]94[/C][C]0.902946[/C][C]0.194109[/C][C]0.0970543[/C][/ROW]
[ROW][C]95[/C][C]0.902504[/C][C]0.194991[/C][C]0.0974957[/C][/ROW]
[ROW][C]96[/C][C]0.882337[/C][C]0.235325[/C][C]0.117663[/C][/ROW]
[ROW][C]97[/C][C]0.861962[/C][C]0.276077[/C][C]0.138038[/C][/ROW]
[ROW][C]98[/C][C]0.843233[/C][C]0.313534[/C][C]0.156767[/C][/ROW]
[ROW][C]99[/C][C]0.839851[/C][C]0.320298[/C][C]0.160149[/C][/ROW]
[ROW][C]100[/C][C]0.809296[/C][C]0.381408[/C][C]0.190704[/C][/ROW]
[ROW][C]101[/C][C]0.802206[/C][C]0.395588[/C][C]0.197794[/C][/ROW]
[ROW][C]102[/C][C]0.77863[/C][C]0.442741[/C][C]0.22137[/C][/ROW]
[ROW][C]103[/C][C]0.753094[/C][C]0.493813[/C][C]0.246906[/C][/ROW]
[ROW][C]104[/C][C]0.823913[/C][C]0.352174[/C][C]0.176087[/C][/ROW]
[ROW][C]105[/C][C]0.825723[/C][C]0.348554[/C][C]0.174277[/C][/ROW]
[ROW][C]106[/C][C]0.85647[/C][C]0.28706[/C][C]0.14353[/C][/ROW]
[ROW][C]107[/C][C]0.83292[/C][C]0.334159[/C][C]0.16708[/C][/ROW]
[ROW][C]108[/C][C]0.837865[/C][C]0.32427[/C][C]0.162135[/C][/ROW]
[ROW][C]109[/C][C]0.870845[/C][C]0.258311[/C][C]0.129155[/C][/ROW]
[ROW][C]110[/C][C]0.847439[/C][C]0.305121[/C][C]0.152561[/C][/ROW]
[ROW][C]111[/C][C]0.834951[/C][C]0.330099[/C][C]0.165049[/C][/ROW]
[ROW][C]112[/C][C]0.832412[/C][C]0.335176[/C][C]0.167588[/C][/ROW]
[ROW][C]113[/C][C]0.844237[/C][C]0.311525[/C][C]0.155763[/C][/ROW]
[ROW][C]114[/C][C]0.852593[/C][C]0.294814[/C][C]0.147407[/C][/ROW]
[ROW][C]115[/C][C]0.912703[/C][C]0.174594[/C][C]0.0872972[/C][/ROW]
[ROW][C]116[/C][C]0.889673[/C][C]0.220654[/C][C]0.110327[/C][/ROW]
[ROW][C]117[/C][C]0.889325[/C][C]0.221351[/C][C]0.110675[/C][/ROW]
[ROW][C]118[/C][C]0.862549[/C][C]0.274901[/C][C]0.137451[/C][/ROW]
[ROW][C]119[/C][C]0.8441[/C][C]0.311801[/C][C]0.1559[/C][/ROW]
[ROW][C]120[/C][C]0.808248[/C][C]0.383503[/C][C]0.191752[/C][/ROW]
[ROW][C]121[/C][C]0.770437[/C][C]0.459125[/C][C]0.229563[/C][/ROW]
[ROW][C]122[/C][C]0.728045[/C][C]0.543911[/C][C]0.271955[/C][/ROW]
[ROW][C]123[/C][C]0.67973[/C][C]0.64054[/C][C]0.32027[/C][/ROW]
[ROW][C]124[/C][C]0.636948[/C][C]0.726103[/C][C]0.363052[/C][/ROW]
[ROW][C]125[/C][C]0.582646[/C][C]0.834709[/C][C]0.417354[/C][/ROW]
[ROW][C]126[/C][C]0.579487[/C][C]0.841026[/C][C]0.420513[/C][/ROW]
[ROW][C]127[/C][C]0.52578[/C][C]0.948441[/C][C]0.47422[/C][/ROW]
[ROW][C]128[/C][C]0.519855[/C][C]0.960289[/C][C]0.480145[/C][/ROW]
[ROW][C]129[/C][C]0.674444[/C][C]0.651112[/C][C]0.325556[/C][/ROW]
[ROW][C]130[/C][C]0.637361[/C][C]0.725279[/C][C]0.362639[/C][/ROW]
[ROW][C]131[/C][C]0.632037[/C][C]0.735927[/C][C]0.367963[/C][/ROW]
[ROW][C]132[/C][C]0.629425[/C][C]0.741149[/C][C]0.370575[/C][/ROW]
[ROW][C]133[/C][C]0.568148[/C][C]0.863703[/C][C]0.431852[/C][/ROW]
[ROW][C]134[/C][C]0.566552[/C][C]0.866896[/C][C]0.433448[/C][/ROW]
[ROW][C]135[/C][C]0.519205[/C][C]0.96159[/C][C]0.480795[/C][/ROW]
[ROW][C]136[/C][C]0.51678[/C][C]0.96644[/C][C]0.48322[/C][/ROW]
[ROW][C]137[/C][C]0.510394[/C][C]0.979211[/C][C]0.489606[/C][/ROW]
[ROW][C]138[/C][C]0.468946[/C][C]0.937892[/C][C]0.531054[/C][/ROW]
[ROW][C]139[/C][C]0.39916[/C][C]0.798319[/C][C]0.60084[/C][/ROW]
[ROW][C]140[/C][C]0.338878[/C][C]0.677756[/C][C]0.661122[/C][/ROW]
[ROW][C]141[/C][C]0.290581[/C][C]0.581162[/C][C]0.709419[/C][/ROW]
[ROW][C]142[/C][C]0.241769[/C][C]0.483538[/C][C]0.758231[/C][/ROW]
[ROW][C]143[/C][C]0.2334[/C][C]0.4668[/C][C]0.7666[/C][/ROW]
[ROW][C]144[/C][C]0.191083[/C][C]0.382166[/C][C]0.808917[/C][/ROW]
[ROW][C]145[/C][C]0.152069[/C][C]0.304138[/C][C]0.847931[/C][/ROW]
[ROW][C]146[/C][C]0.121[/C][C]0.242[/C][C]0.879[/C][/ROW]
[ROW][C]147[/C][C]0.207669[/C][C]0.415338[/C][C]0.792331[/C][/ROW]
[ROW][C]148[/C][C]0.507039[/C][C]0.985923[/C][C]0.492961[/C][/ROW]
[ROW][C]149[/C][C]0.551034[/C][C]0.897932[/C][C]0.448966[/C][/ROW]
[ROW][C]150[/C][C]0.434464[/C][C]0.868927[/C][C]0.565536[/C][/ROW]
[ROW][C]151[/C][C]0.353364[/C][C]0.706729[/C][C]0.646636[/C][/ROW]
[ROW][C]152[/C][C]0.219134[/C][C]0.438269[/C][C]0.780866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253600&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253600&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
10	0.0591747	0.118349	0.940825
11	0.0189494	0.0378988	0.981051
12	0.885614	0.228772	0.114386
13	0.983366	0.0332689	0.0166344
14	0.98384	0.0323193	0.0161597
15	0.989058	0.0218838	0.0109419
16	0.981026	0.0379489	0.0189744
17	0.974189	0.051623	0.0258115
18	0.962118	0.075763	0.0378815
19	0.949558	0.100884	0.0504421
20	0.949668	0.100664	0.050332
21	0.948976	0.102048	0.0510238
22	0.970132	0.0597355	0.0298678
23	0.956842	0.0863158	0.0431579
24	0.946844	0.106312	0.0531561
25	0.929212	0.141575	0.0707876
26	0.999545	0.000910443	0.000455222
27	0.99926	0.00147925	0.000739623
28	0.998786	0.00242791	0.00121396
29	0.998122	0.00375578	0.00187789
30	0.999106	0.00178884	0.000894419
31	0.99858	0.00284079	0.00142039
32	0.997804	0.00439233	0.00219617
33	0.997433	0.00513449	0.00256725
34	0.996108	0.00778463	0.00389232
35	0.994467	0.0110665	0.00553327
36	0.991939	0.0161223	0.00806113
37	0.994075	0.0118506	0.00592531
38	0.991568	0.0168636	0.00843181
39	0.990883	0.0182332	0.00911661
40	0.991051	0.0178986	0.00894932
41	0.987708	0.0245837	0.0122919
42	0.987351	0.0252983	0.0126491
43	0.982943	0.0341138	0.0170569
44	0.978817	0.0423662	0.0211831
45	0.982336	0.0353271	0.0176636
46	0.987029	0.0259419	0.012971
47	0.982304	0.0353927	0.0176963
48	0.976021	0.0479583	0.0239791
49	0.983287	0.0334254	0.0167127
50	0.981799	0.036403	0.0182015
51	0.976041	0.0479183	0.0239591
52	0.969026	0.061948	0.030974
53	0.961823	0.076354	0.038177
54	0.959249	0.0815011	0.0407505
55	0.959251	0.0814989	0.0407495
56	0.947792	0.104415	0.0522077
57	0.943658	0.112684	0.0563419
58	0.928901	0.142199	0.0710993
59	0.953138	0.0937241	0.0468621
60	0.94884	0.102319	0.0511596
61	0.95611	0.0877797	0.0438899
62	0.95377	0.0924604	0.0462302
63	0.975625	0.0487492	0.0243746
64	0.970429	0.0591429	0.0295714
65	0.968042	0.0639163	0.0319581
66	0.994899	0.0102014	0.00510072
67	0.994361	0.011278	0.00563898
68	0.994821	0.0103573	0.00517867
69	0.993405	0.0131899	0.00659494
70	0.991862	0.0162754	0.00813768
71	0.988905	0.0221904	0.0110952
72	0.993019	0.0139629	0.00698146
73	0.990491	0.0190182	0.00950909
74	0.987418	0.0251648	0.0125824
75	0.986577	0.026846	0.013423
76	0.982067	0.0358667	0.0179334
77	0.986904	0.0261928	0.0130964
78	0.982984	0.0340324	0.0170162
79	0.97997	0.040059	0.0200295
80	0.978845	0.04231	0.021155
81	0.972155	0.0556905	0.0278452
82	0.970098	0.0598032	0.0299016
83	0.962359	0.0752815	0.0376407
84	0.959049	0.0819017	0.0409508
85	0.948971	0.102059	0.0510294
86	0.935482	0.129036	0.0645182
87	0.920025	0.159951	0.0799754
88	0.90138	0.19724	0.0986201
89	0.882392	0.235216	0.117608
90	0.927071	0.145858	0.0729288
91	0.946734	0.106532	0.0532659
92	0.933946	0.132108	0.0660542
93	0.918919	0.162162	0.0810812
94	0.902946	0.194109	0.0970543
95	0.902504	0.194991	0.0974957
96	0.882337	0.235325	0.117663
97	0.861962	0.276077	0.138038
98	0.843233	0.313534	0.156767
99	0.839851	0.320298	0.160149
100	0.809296	0.381408	0.190704
101	0.802206	0.395588	0.197794
102	0.77863	0.442741	0.22137
103	0.753094	0.493813	0.246906
104	0.823913	0.352174	0.176087
105	0.825723	0.348554	0.174277
106	0.85647	0.28706	0.14353
107	0.83292	0.334159	0.16708
108	0.837865	0.32427	0.162135
109	0.870845	0.258311	0.129155
110	0.847439	0.305121	0.152561
111	0.834951	0.330099	0.165049
112	0.832412	0.335176	0.167588
113	0.844237	0.311525	0.155763
114	0.852593	0.294814	0.147407
115	0.912703	0.174594	0.0872972
116	0.889673	0.220654	0.110327
117	0.889325	0.221351	0.110675
118	0.862549	0.274901	0.137451
119	0.8441	0.311801	0.1559
120	0.808248	0.383503	0.191752
121	0.770437	0.459125	0.229563
122	0.728045	0.543911	0.271955
123	0.67973	0.64054	0.32027
124	0.636948	0.726103	0.363052
125	0.582646	0.834709	0.417354
126	0.579487	0.841026	0.420513
127	0.52578	0.948441	0.47422
128	0.519855	0.960289	0.480145
129	0.674444	0.651112	0.325556
130	0.637361	0.725279	0.362639
131	0.632037	0.735927	0.367963
132	0.629425	0.741149	0.370575
133	0.568148	0.863703	0.431852
134	0.566552	0.866896	0.433448
135	0.519205	0.96159	0.480795
136	0.51678	0.96644	0.48322
137	0.510394	0.979211	0.489606
138	0.468946	0.937892	0.531054
139	0.39916	0.798319	0.60084
140	0.338878	0.677756	0.661122
141	0.290581	0.581162	0.709419
142	0.241769	0.483538	0.758231
143	0.2334	0.4668	0.7666
144	0.191083	0.382166	0.808917
145	0.152069	0.304138	0.847931
146	0.121	0.242	0.879
147	0.207669	0.415338	0.792331
148	0.507039	0.985923	0.492961
149	0.551034	0.897932	0.448966
150	0.434464	0.868927	0.565536
151	0.353364	0.706729	0.646636
152	0.219134	0.438269	0.780866

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.0629371	NOK
5% type I error level	47	0.328671	NOK
10% type I error level	64	0.447552	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 9 & 0.0629371 & NOK \tabularnewline
5% type I error level & 47 & 0.328671 & NOK \tabularnewline
10% type I error level & 64 & 0.447552 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253600&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]9[/C][C]0.0629371[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]47[/C][C]0.328671[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]64[/C][C]0.447552[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253600&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253600&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.0629371	NOK
5% type I error level	47	0.328671	NOK
10% type I error level	64	0.447552	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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