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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 11 Nov 2014 13:49:41 +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/t1415713838mgl0vn5m0laqaoy.htm/, Retrieved Fri, 17 May 2024 06:22:28 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253613, Retrieved Fri, 17 May 2024 06:22:28 +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 depression al...] [2014-11-11 13:49:41] [8568a324fefbb8dbb43f697bfa8d1be6] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gertrude Mary Cox' @ cox.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 & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253613&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253613&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253613&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 27.8614 -0.0183402Connected[t] + 0.0105745Separate[t] -0.156991Learning[t] -0.0485941Software[t] -0.65751Happiness[t] -0.0361091Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  27.8614 -0.0183402Connected[t] +  0.0105745Separate[t] -0.156991Learning[t] -0.0485941Software[t] -0.65751Happiness[t] -0.0361091Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253613&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  27.8614 -0.0183402Connected[t] +  0.0105745Separate[t] -0.156991Learning[t] -0.0485941Software[t] -0.65751Happiness[t] -0.0361091Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253613&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253613&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
Depression[t] = + 27.8614 -0.0183402Connected[t] + 0.0105745Separate[t] -0.156991Learning[t] -0.0485941Software[t] -0.65751Happiness[t] -0.0361091Belonging[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)	27.8614	2.99993	9.287	1.35978e-16	6.79891e-17
Connected	-0.0183402	0.0682008	-0.2689	0.788353	0.394177
Separate	0.0105745	0.0638166	0.1657	0.868608	0.434304
Learning	-0.156991	0.114122	-1.376	0.170915	0.0854574
Software	-0.0485941	0.116512	-0.4171	0.677203	0.338601
Happiness	-0.65751	0.0955357	-6.882	1.37313e-10	6.86563e-11
Belonging	-0.0361091	0.0203739	-1.772	0.078306	0.039153

\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) & 27.8614 & 2.99993 & 9.287 & 1.35978e-16 & 6.79891e-17 \tabularnewline
Connected & -0.0183402 & 0.0682008 & -0.2689 & 0.788353 & 0.394177 \tabularnewline
Separate & 0.0105745 & 0.0638166 & 0.1657 & 0.868608 & 0.434304 \tabularnewline
Learning & -0.156991 & 0.114122 & -1.376 & 0.170915 & 0.0854574 \tabularnewline
Software & -0.0485941 & 0.116512 & -0.4171 & 0.677203 & 0.338601 \tabularnewline
Happiness & -0.65751 & 0.0955357 & -6.882 & 1.37313e-10 & 6.86563e-11 \tabularnewline
Belonging & -0.0361091 & 0.0203739 & -1.772 & 0.078306 & 0.039153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253613&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]27.8614[/C][C]2.99993[/C][C]9.287[/C][C]1.35978e-16[/C][C]6.79891e-17[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0183402[/C][C]0.0682008[/C][C]-0.2689[/C][C]0.788353[/C][C]0.394177[/C][/ROW]
[ROW][C]Separate[/C][C]0.0105745[/C][C]0.0638166[/C][C]0.1657[/C][C]0.868608[/C][C]0.434304[/C][/ROW]
[ROW][C]Learning[/C][C]-0.156991[/C][C]0.114122[/C][C]-1.376[/C][C]0.170915[/C][C]0.0854574[/C][/ROW]
[ROW][C]Software[/C][C]-0.0485941[/C][C]0.116512[/C][C]-0.4171[/C][C]0.677203[/C][C]0.338601[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.65751[/C][C]0.0955357[/C][C]-6.882[/C][C]1.37313e-10[/C][C]6.86563e-11[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.0361091[/C][C]0.0203739[/C][C]-1.772[/C][C]0.078306[/C][C]0.039153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253613&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253613&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)	27.8614	2.99993	9.287	1.35978e-16	6.79891e-17
Connected	-0.0183402	0.0682008	-0.2689	0.788353	0.394177
Separate	0.0105745	0.0638166	0.1657	0.868608	0.434304
Learning	-0.156991	0.114122	-1.376	0.170915	0.0854574
Software	-0.0485941	0.116512	-0.4171	0.677203	0.338601
Happiness	-0.65751	0.0955357	-6.882	1.37313e-10	6.86563e-11
Belonging	-0.0361091	0.0203739	-1.772	0.078306	0.039153

Multiple Linear Regression - Regression Statistics
Multiple R	0.574108
R-squared	0.3296
Adjusted R-squared	0.303649
F-TEST (value)	12.7009
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	1.24041e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.64209
Sum Squared Residuals	1082

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.574108 \tabularnewline
R-squared & 0.3296 \tabularnewline
Adjusted R-squared & 0.303649 \tabularnewline
F-TEST (value) & 12.7009 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 1.24041e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.64209 \tabularnewline
Sum Squared Residuals & 1082 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253613&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.574108[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3296[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.303649[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.7009[/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]1.24041e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.64209[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1082[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253613&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253613&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	0.574108
R-squared	0.3296
Adjusted R-squared	0.303649
F-TEST (value)	12.7009
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	1.24041e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.64209
Sum Squared Residuals	1082

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12	13.7683	-1.7683
2	11	9.49751	1.50249
3	14	14.3537	-0.3537
4	12	14.6859	-2.6859
5	21	11.535	9.46501
6	12	10.3476	1.65241
7	22	12.789	9.21099
8	11	12.4894	-1.48942
9	10	12.2555	-2.25549
10	13	12.3312	0.668776
11	10	10.4641	-0.464147
12	8	10.0231	-2.02307
13	15	15.4451	-0.445136
14	14	12.0315	1.96853
15	10	9.27295	0.72705
16	14	13.386	0.614038
17	14	12.7854	1.21464
18	11	9.48803	1.51197
19	10	12.6714	-2.67142
20	13	11.9395	1.06051
21	7	10.3587	-3.35865
22	14	14.8376	-0.8376
23	12	12.7783	-0.77833
24	14	14.6365	-0.636549
25	11	10.0952	0.90478
26	9	15.3409	-6.3409
27	11	11.309	-0.308963
28	15	12.9155	2.08447
29	14	11.8309	2.16906
30	13	15.251	-2.25101
31	9	11.242	-2.24201
32	15	14.9344	0.0656149
33	10	11.0853	-1.08534
34	11	11.6382	-0.638241
35	13	12.8968	0.103197
36	8	13.1004	-5.10042
37	20	17.6034	2.39664
38	12	12.3427	-0.342664
39	10	10.7375	-0.737536
40	10	13.5314	-3.53145
41	9	11.9886	-2.98863
42	14	11.5774	2.4226
43	8	11.164	-3.16402
44	14	15.1168	-1.1168
45	11	14.395	-3.39505
46	13	15.5548	-2.55481
47	9	12.5375	-3.53749
48	11	12.1554	-1.15543
49	15	10.7068	4.2932
50	11	13.466	-2.46599
51	10	11.3641	-1.36411
52	14	13.2834	0.716564
53	18	15.6489	2.35108
54	14	14.3672	-0.367152
55	11	15.792	-4.79197
56	12	12.4248	-0.424768
57	13	11.2184	1.7816
58	9	12.3121	-3.31207
59	10	14.148	-4.14796
60	15	14.1634	0.836577
61	20	18.2093	1.79068
62	12	13.1009	-1.10094
63	12	14.4027	-2.40271
64	14	13.8477	0.152309
65	13	12.6581	0.341932
66	11	14.8463	-3.8463
67	17	14.4492	2.55076
68	12	13.7102	-1.7102
69	13	12.9645	0.0354795
70	14	12.1615	1.83846
71	13	13.962	-0.961995
72	15	12.4922	2.50782
73	13	11.3565	1.64345
74	10	11.7376	-1.73764
75	11	12.8475	-1.84752
76	19	13.5783	5.42172
77	13	10.7251	2.27487
78	17	13.48	3.51996
79	13	12.4086	0.591427
80	9	13.8758	-4.87577
81	11	11.8266	-0.826552
82	10	12.2582	-2.25821
83	9	12.0893	-3.08932
84	12	11.4376	0.562396
85	12	12.2821	-0.282093
86	13	12.6006	0.399404
87	13	12.3708	0.629224
88	12	12.824	-0.824004
89	15	14.0754	0.924637
90	22	17.5369	4.46312
91	13	11.5386	1.46138
92	15	13.6442	1.35579
93	13	11.915	1.08504
94	15	12.4665	2.53353
95	10	13.5864	-3.58635
96	11	11.1201	-0.12008
97	16	14.0276	1.97236
98	11	12.5764	-1.5764
99	11	10.3448	0.655195
100	10	12.3644	-2.36439
101	10	10.431	-0.430971
102	16	13.9804	2.0196
103	12	10.1342	1.86581
104	11	15.0653	-4.06534
105	16	11.9069	4.09313
106	19	16.4691	2.53089
107	11	11.0181	-0.018103
108	16	12.0004	3.99957
109	15	15.9998	-0.999829
110	24	17.0567	6.94332
111	14	11.7521	2.2479
112	15	14.5449	0.455094
113	11	12.7794	-1.77941
114	15	14.4707	0.529279
115	12	10.593	1.40701
116	10	10.438	-0.438043
117	14	13.6945	0.305523
118	13	13.3414	-0.341361
119	9	13.3541	-4.35415
120	15	11.9952	3.00484
121	15	15.232	-0.231953
122	14	12.4853	1.51469
123	11	11.8765	-0.876457
124	8	11.7855	-3.78548
125	11	12.0987	-1.09873
126	11	13.0007	-2.00075
127	8	10.2162	-2.21617
128	10	10.4137	-0.413662
129	11	9.42707	1.57293
130	13	12.6875	0.312528
131	11	13.5142	-2.51425
132	20	17.281	2.71902
133	10	11.9753	-1.97534
134	15	13.0552	1.94477
135	12	12.2689	-0.268869
136	14	11.162	2.83797
137	23	15.9197	7.08027
138	14	13.2385	0.761521
139	16	16.2859	-0.285911
140	11	13.1327	-2.1327
141	12	14.9305	-2.93048
142	10	12.9208	-2.92085
143	14	11.3251	2.67486
144	12	11.973	0.0270293
145	12	11.8466	0.153423
146	11	11.763	-0.763022
147	12	10.838	1.16204
148	13	15.5586	-2.55862
149	11	13.909	-2.90899
150	19	16.526	2.47396
151	12	12.0773	-0.0773286
152	17	13.0955	3.90452
153	9	11.6065	-2.60651
154	12	14.3385	-2.33845
155	19	16.5832	2.41677
156	18	14.2611	3.73887
157	15	13.6442	1.35579
158	14	13.6366	0.363393
159	11	9.42707	1.57293
160	9	12.949	-3.94898
161	18	14.4486	3.55136
162	16	14.1497	1.85033

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.7683 & -1.7683 \tabularnewline
2 & 11 & 9.49751 & 1.50249 \tabularnewline
3 & 14 & 14.3537 & -0.3537 \tabularnewline
4 & 12 & 14.6859 & -2.6859 \tabularnewline
5 & 21 & 11.535 & 9.46501 \tabularnewline
6 & 12 & 10.3476 & 1.65241 \tabularnewline
7 & 22 & 12.789 & 9.21099 \tabularnewline
8 & 11 & 12.4894 & -1.48942 \tabularnewline
9 & 10 & 12.2555 & -2.25549 \tabularnewline
10 & 13 & 12.3312 & 0.668776 \tabularnewline
11 & 10 & 10.4641 & -0.464147 \tabularnewline
12 & 8 & 10.0231 & -2.02307 \tabularnewline
13 & 15 & 15.4451 & -0.445136 \tabularnewline
14 & 14 & 12.0315 & 1.96853 \tabularnewline
15 & 10 & 9.27295 & 0.72705 \tabularnewline
16 & 14 & 13.386 & 0.614038 \tabularnewline
17 & 14 & 12.7854 & 1.21464 \tabularnewline
18 & 11 & 9.48803 & 1.51197 \tabularnewline
19 & 10 & 12.6714 & -2.67142 \tabularnewline
20 & 13 & 11.9395 & 1.06051 \tabularnewline
21 & 7 & 10.3587 & -3.35865 \tabularnewline
22 & 14 & 14.8376 & -0.8376 \tabularnewline
23 & 12 & 12.7783 & -0.77833 \tabularnewline
24 & 14 & 14.6365 & -0.636549 \tabularnewline
25 & 11 & 10.0952 & 0.90478 \tabularnewline
26 & 9 & 15.3409 & -6.3409 \tabularnewline
27 & 11 & 11.309 & -0.308963 \tabularnewline
28 & 15 & 12.9155 & 2.08447 \tabularnewline
29 & 14 & 11.8309 & 2.16906 \tabularnewline
30 & 13 & 15.251 & -2.25101 \tabularnewline
31 & 9 & 11.242 & -2.24201 \tabularnewline
32 & 15 & 14.9344 & 0.0656149 \tabularnewline
33 & 10 & 11.0853 & -1.08534 \tabularnewline
34 & 11 & 11.6382 & -0.638241 \tabularnewline
35 & 13 & 12.8968 & 0.103197 \tabularnewline
36 & 8 & 13.1004 & -5.10042 \tabularnewline
37 & 20 & 17.6034 & 2.39664 \tabularnewline
38 & 12 & 12.3427 & -0.342664 \tabularnewline
39 & 10 & 10.7375 & -0.737536 \tabularnewline
40 & 10 & 13.5314 & -3.53145 \tabularnewline
41 & 9 & 11.9886 & -2.98863 \tabularnewline
42 & 14 & 11.5774 & 2.4226 \tabularnewline
43 & 8 & 11.164 & -3.16402 \tabularnewline
44 & 14 & 15.1168 & -1.1168 \tabularnewline
45 & 11 & 14.395 & -3.39505 \tabularnewline
46 & 13 & 15.5548 & -2.55481 \tabularnewline
47 & 9 & 12.5375 & -3.53749 \tabularnewline
48 & 11 & 12.1554 & -1.15543 \tabularnewline
49 & 15 & 10.7068 & 4.2932 \tabularnewline
50 & 11 & 13.466 & -2.46599 \tabularnewline
51 & 10 & 11.3641 & -1.36411 \tabularnewline
52 & 14 & 13.2834 & 0.716564 \tabularnewline
53 & 18 & 15.6489 & 2.35108 \tabularnewline
54 & 14 & 14.3672 & -0.367152 \tabularnewline
55 & 11 & 15.792 & -4.79197 \tabularnewline
56 & 12 & 12.4248 & -0.424768 \tabularnewline
57 & 13 & 11.2184 & 1.7816 \tabularnewline
58 & 9 & 12.3121 & -3.31207 \tabularnewline
59 & 10 & 14.148 & -4.14796 \tabularnewline
60 & 15 & 14.1634 & 0.836577 \tabularnewline
61 & 20 & 18.2093 & 1.79068 \tabularnewline
62 & 12 & 13.1009 & -1.10094 \tabularnewline
63 & 12 & 14.4027 & -2.40271 \tabularnewline
64 & 14 & 13.8477 & 0.152309 \tabularnewline
65 & 13 & 12.6581 & 0.341932 \tabularnewline
66 & 11 & 14.8463 & -3.8463 \tabularnewline
67 & 17 & 14.4492 & 2.55076 \tabularnewline
68 & 12 & 13.7102 & -1.7102 \tabularnewline
69 & 13 & 12.9645 & 0.0354795 \tabularnewline
70 & 14 & 12.1615 & 1.83846 \tabularnewline
71 & 13 & 13.962 & -0.961995 \tabularnewline
72 & 15 & 12.4922 & 2.50782 \tabularnewline
73 & 13 & 11.3565 & 1.64345 \tabularnewline
74 & 10 & 11.7376 & -1.73764 \tabularnewline
75 & 11 & 12.8475 & -1.84752 \tabularnewline
76 & 19 & 13.5783 & 5.42172 \tabularnewline
77 & 13 & 10.7251 & 2.27487 \tabularnewline
78 & 17 & 13.48 & 3.51996 \tabularnewline
79 & 13 & 12.4086 & 0.591427 \tabularnewline
80 & 9 & 13.8758 & -4.87577 \tabularnewline
81 & 11 & 11.8266 & -0.826552 \tabularnewline
82 & 10 & 12.2582 & -2.25821 \tabularnewline
83 & 9 & 12.0893 & -3.08932 \tabularnewline
84 & 12 & 11.4376 & 0.562396 \tabularnewline
85 & 12 & 12.2821 & -0.282093 \tabularnewline
86 & 13 & 12.6006 & 0.399404 \tabularnewline
87 & 13 & 12.3708 & 0.629224 \tabularnewline
88 & 12 & 12.824 & -0.824004 \tabularnewline
89 & 15 & 14.0754 & 0.924637 \tabularnewline
90 & 22 & 17.5369 & 4.46312 \tabularnewline
91 & 13 & 11.5386 & 1.46138 \tabularnewline
92 & 15 & 13.6442 & 1.35579 \tabularnewline
93 & 13 & 11.915 & 1.08504 \tabularnewline
94 & 15 & 12.4665 & 2.53353 \tabularnewline
95 & 10 & 13.5864 & -3.58635 \tabularnewline
96 & 11 & 11.1201 & -0.12008 \tabularnewline
97 & 16 & 14.0276 & 1.97236 \tabularnewline
98 & 11 & 12.5764 & -1.5764 \tabularnewline
99 & 11 & 10.3448 & 0.655195 \tabularnewline
100 & 10 & 12.3644 & -2.36439 \tabularnewline
101 & 10 & 10.431 & -0.430971 \tabularnewline
102 & 16 & 13.9804 & 2.0196 \tabularnewline
103 & 12 & 10.1342 & 1.86581 \tabularnewline
104 & 11 & 15.0653 & -4.06534 \tabularnewline
105 & 16 & 11.9069 & 4.09313 \tabularnewline
106 & 19 & 16.4691 & 2.53089 \tabularnewline
107 & 11 & 11.0181 & -0.018103 \tabularnewline
108 & 16 & 12.0004 & 3.99957 \tabularnewline
109 & 15 & 15.9998 & -0.999829 \tabularnewline
110 & 24 & 17.0567 & 6.94332 \tabularnewline
111 & 14 & 11.7521 & 2.2479 \tabularnewline
112 & 15 & 14.5449 & 0.455094 \tabularnewline
113 & 11 & 12.7794 & -1.77941 \tabularnewline
114 & 15 & 14.4707 & 0.529279 \tabularnewline
115 & 12 & 10.593 & 1.40701 \tabularnewline
116 & 10 & 10.438 & -0.438043 \tabularnewline
117 & 14 & 13.6945 & 0.305523 \tabularnewline
118 & 13 & 13.3414 & -0.341361 \tabularnewline
119 & 9 & 13.3541 & -4.35415 \tabularnewline
120 & 15 & 11.9952 & 3.00484 \tabularnewline
121 & 15 & 15.232 & -0.231953 \tabularnewline
122 & 14 & 12.4853 & 1.51469 \tabularnewline
123 & 11 & 11.8765 & -0.876457 \tabularnewline
124 & 8 & 11.7855 & -3.78548 \tabularnewline
125 & 11 & 12.0987 & -1.09873 \tabularnewline
126 & 11 & 13.0007 & -2.00075 \tabularnewline
127 & 8 & 10.2162 & -2.21617 \tabularnewline
128 & 10 & 10.4137 & -0.413662 \tabularnewline
129 & 11 & 9.42707 & 1.57293 \tabularnewline
130 & 13 & 12.6875 & 0.312528 \tabularnewline
131 & 11 & 13.5142 & -2.51425 \tabularnewline
132 & 20 & 17.281 & 2.71902 \tabularnewline
133 & 10 & 11.9753 & -1.97534 \tabularnewline
134 & 15 & 13.0552 & 1.94477 \tabularnewline
135 & 12 & 12.2689 & -0.268869 \tabularnewline
136 & 14 & 11.162 & 2.83797 \tabularnewline
137 & 23 & 15.9197 & 7.08027 \tabularnewline
138 & 14 & 13.2385 & 0.761521 \tabularnewline
139 & 16 & 16.2859 & -0.285911 \tabularnewline
140 & 11 & 13.1327 & -2.1327 \tabularnewline
141 & 12 & 14.9305 & -2.93048 \tabularnewline
142 & 10 & 12.9208 & -2.92085 \tabularnewline
143 & 14 & 11.3251 & 2.67486 \tabularnewline
144 & 12 & 11.973 & 0.0270293 \tabularnewline
145 & 12 & 11.8466 & 0.153423 \tabularnewline
146 & 11 & 11.763 & -0.763022 \tabularnewline
147 & 12 & 10.838 & 1.16204 \tabularnewline
148 & 13 & 15.5586 & -2.55862 \tabularnewline
149 & 11 & 13.909 & -2.90899 \tabularnewline
150 & 19 & 16.526 & 2.47396 \tabularnewline
151 & 12 & 12.0773 & -0.0773286 \tabularnewline
152 & 17 & 13.0955 & 3.90452 \tabularnewline
153 & 9 & 11.6065 & -2.60651 \tabularnewline
154 & 12 & 14.3385 & -2.33845 \tabularnewline
155 & 19 & 16.5832 & 2.41677 \tabularnewline
156 & 18 & 14.2611 & 3.73887 \tabularnewline
157 & 15 & 13.6442 & 1.35579 \tabularnewline
158 & 14 & 13.6366 & 0.363393 \tabularnewline
159 & 11 & 9.42707 & 1.57293 \tabularnewline
160 & 9 & 12.949 & -3.94898 \tabularnewline
161 & 18 & 14.4486 & 3.55136 \tabularnewline
162 & 16 & 14.1497 & 1.85033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253613&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]12[/C][C]13.7683[/C][C]-1.7683[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.49751[/C][C]1.50249[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]14.3537[/C][C]-0.3537[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.6859[/C][C]-2.6859[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.535[/C][C]9.46501[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.3476[/C][C]1.65241[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.789[/C][C]9.21099[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.4894[/C][C]-1.48942[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.2555[/C][C]-2.25549[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.3312[/C][C]0.668776[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.4641[/C][C]-0.464147[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]10.0231[/C][C]-2.02307[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.4451[/C][C]-0.445136[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.0315[/C][C]1.96853[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.27295[/C][C]0.72705[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.386[/C][C]0.614038[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.7854[/C][C]1.21464[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.48803[/C][C]1.51197[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.6714[/C][C]-2.67142[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.9395[/C][C]1.06051[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]10.3587[/C][C]-3.35865[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]14.8376[/C][C]-0.8376[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.7783[/C][C]-0.77833[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.6365[/C][C]-0.636549[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.0952[/C][C]0.90478[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.3409[/C][C]-6.3409[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.309[/C][C]-0.308963[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.9155[/C][C]2.08447[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]11.8309[/C][C]2.16906[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]15.251[/C][C]-2.25101[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.242[/C][C]-2.24201[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]14.9344[/C][C]0.0656149[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]11.0853[/C][C]-1.08534[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.6382[/C][C]-0.638241[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.8968[/C][C]0.103197[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]13.1004[/C][C]-5.10042[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.6034[/C][C]2.39664[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.3427[/C][C]-0.342664[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]10.7375[/C][C]-0.737536[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.5314[/C][C]-3.53145[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]11.9886[/C][C]-2.98863[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.5774[/C][C]2.4226[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]11.164[/C][C]-3.16402[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]15.1168[/C][C]-1.1168[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.395[/C][C]-3.39505[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.5548[/C][C]-2.55481[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.5375[/C][C]-3.53749[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.1554[/C][C]-1.15543[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.7068[/C][C]4.2932[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.466[/C][C]-2.46599[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.3641[/C][C]-1.36411[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.2834[/C][C]0.716564[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.6489[/C][C]2.35108[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.3672[/C][C]-0.367152[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.792[/C][C]-4.79197[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4248[/C][C]-0.424768[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.2184[/C][C]1.7816[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.3121[/C][C]-3.31207[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]14.148[/C][C]-4.14796[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.1634[/C][C]0.836577[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]18.2093[/C][C]1.79068[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.1009[/C][C]-1.10094[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.4027[/C][C]-2.40271[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.8477[/C][C]0.152309[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.6581[/C][C]0.341932[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]14.8463[/C][C]-3.8463[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]14.4492[/C][C]2.55076[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.7102[/C][C]-1.7102[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.9645[/C][C]0.0354795[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.1615[/C][C]1.83846[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.962[/C][C]-0.961995[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]12.4922[/C][C]2.50782[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.3565[/C][C]1.64345[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.7376[/C][C]-1.73764[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.8475[/C][C]-1.84752[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]13.5783[/C][C]5.42172[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.7251[/C][C]2.27487[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]13.48[/C][C]3.51996[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.4086[/C][C]0.591427[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]13.8758[/C][C]-4.87577[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.8266[/C][C]-0.826552[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]12.2582[/C][C]-2.25821[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.0893[/C][C]-3.08932[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.4376[/C][C]0.562396[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.2821[/C][C]-0.282093[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.6006[/C][C]0.399404[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.3708[/C][C]0.629224[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.824[/C][C]-0.824004[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.0754[/C][C]0.924637[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]17.5369[/C][C]4.46312[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]11.5386[/C][C]1.46138[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.6442[/C][C]1.35579[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]11.915[/C][C]1.08504[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.4665[/C][C]2.53353[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.5864[/C][C]-3.58635[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.1201[/C][C]-0.12008[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.0276[/C][C]1.97236[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.5764[/C][C]-1.5764[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.3448[/C][C]0.655195[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.3644[/C][C]-2.36439[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.431[/C][C]-0.430971[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.9804[/C][C]2.0196[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.1342[/C][C]1.86581[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]15.0653[/C][C]-4.06534[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]11.9069[/C][C]4.09313[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]16.4691[/C][C]2.53089[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.0181[/C][C]-0.018103[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.0004[/C][C]3.99957[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.9998[/C][C]-0.999829[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]17.0567[/C][C]6.94332[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]11.7521[/C][C]2.2479[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]14.5449[/C][C]0.455094[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.7794[/C][C]-1.77941[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]14.4707[/C][C]0.529279[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]10.593[/C][C]1.40701[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.438[/C][C]-0.438043[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.6945[/C][C]0.305523[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.3414[/C][C]-0.341361[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.3541[/C][C]-4.35415[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]11.9952[/C][C]3.00484[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]15.232[/C][C]-0.231953[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.4853[/C][C]1.51469[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.8765[/C][C]-0.876457[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]11.7855[/C][C]-3.78548[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.0987[/C][C]-1.09873[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.0007[/C][C]-2.00075[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.2162[/C][C]-2.21617[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.4137[/C][C]-0.413662[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.42707[/C][C]1.57293[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.6875[/C][C]0.312528[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.5142[/C][C]-2.51425[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]17.281[/C][C]2.71902[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.9753[/C][C]-1.97534[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.0552[/C][C]1.94477[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.2689[/C][C]-0.268869[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.162[/C][C]2.83797[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.9197[/C][C]7.08027[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.2385[/C][C]0.761521[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]16.2859[/C][C]-0.285911[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.1327[/C][C]-2.1327[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]14.9305[/C][C]-2.93048[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.9208[/C][C]-2.92085[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.3251[/C][C]2.67486[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.973[/C][C]0.0270293[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.8466[/C][C]0.153423[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.763[/C][C]-0.763022[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]10.838[/C][C]1.16204[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.5586[/C][C]-2.55862[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.909[/C][C]-2.90899[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]16.526[/C][C]2.47396[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.0773[/C][C]-0.0773286[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.0955[/C][C]3.90452[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.6065[/C][C]-2.60651[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.3385[/C][C]-2.33845[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]16.5832[/C][C]2.41677[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]14.2611[/C][C]3.73887[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.6442[/C][C]1.35579[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.6366[/C][C]0.363393[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.42707[/C][C]1.57293[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]12.949[/C][C]-3.94898[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]14.4486[/C][C]3.55136[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.1497[/C][C]1.85033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253613&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253613&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	12	13.7683	-1.7683
2	11	9.49751	1.50249
3	14	14.3537	-0.3537
4	12	14.6859	-2.6859
5	21	11.535	9.46501
6	12	10.3476	1.65241
7	22	12.789	9.21099
8	11	12.4894	-1.48942
9	10	12.2555	-2.25549
10	13	12.3312	0.668776
11	10	10.4641	-0.464147
12	8	10.0231	-2.02307
13	15	15.4451	-0.445136
14	14	12.0315	1.96853
15	10	9.27295	0.72705
16	14	13.386	0.614038
17	14	12.7854	1.21464
18	11	9.48803	1.51197
19	10	12.6714	-2.67142
20	13	11.9395	1.06051
21	7	10.3587	-3.35865
22	14	14.8376	-0.8376
23	12	12.7783	-0.77833
24	14	14.6365	-0.636549
25	11	10.0952	0.90478
26	9	15.3409	-6.3409
27	11	11.309	-0.308963
28	15	12.9155	2.08447
29	14	11.8309	2.16906
30	13	15.251	-2.25101
31	9	11.242	-2.24201
32	15	14.9344	0.0656149
33	10	11.0853	-1.08534
34	11	11.6382	-0.638241
35	13	12.8968	0.103197
36	8	13.1004	-5.10042
37	20	17.6034	2.39664
38	12	12.3427	-0.342664
39	10	10.7375	-0.737536
40	10	13.5314	-3.53145
41	9	11.9886	-2.98863
42	14	11.5774	2.4226
43	8	11.164	-3.16402
44	14	15.1168	-1.1168
45	11	14.395	-3.39505
46	13	15.5548	-2.55481
47	9	12.5375	-3.53749
48	11	12.1554	-1.15543
49	15	10.7068	4.2932
50	11	13.466	-2.46599
51	10	11.3641	-1.36411
52	14	13.2834	0.716564
53	18	15.6489	2.35108
54	14	14.3672	-0.367152
55	11	15.792	-4.79197
56	12	12.4248	-0.424768
57	13	11.2184	1.7816
58	9	12.3121	-3.31207
59	10	14.148	-4.14796
60	15	14.1634	0.836577
61	20	18.2093	1.79068
62	12	13.1009	-1.10094
63	12	14.4027	-2.40271
64	14	13.8477	0.152309
65	13	12.6581	0.341932
66	11	14.8463	-3.8463
67	17	14.4492	2.55076
68	12	13.7102	-1.7102
69	13	12.9645	0.0354795
70	14	12.1615	1.83846
71	13	13.962	-0.961995
72	15	12.4922	2.50782
73	13	11.3565	1.64345
74	10	11.7376	-1.73764
75	11	12.8475	-1.84752
76	19	13.5783	5.42172
77	13	10.7251	2.27487
78	17	13.48	3.51996
79	13	12.4086	0.591427
80	9	13.8758	-4.87577
81	11	11.8266	-0.826552
82	10	12.2582	-2.25821
83	9	12.0893	-3.08932
84	12	11.4376	0.562396
85	12	12.2821	-0.282093
86	13	12.6006	0.399404
87	13	12.3708	0.629224
88	12	12.824	-0.824004
89	15	14.0754	0.924637
90	22	17.5369	4.46312
91	13	11.5386	1.46138
92	15	13.6442	1.35579
93	13	11.915	1.08504
94	15	12.4665	2.53353
95	10	13.5864	-3.58635
96	11	11.1201	-0.12008
97	16	14.0276	1.97236
98	11	12.5764	-1.5764
99	11	10.3448	0.655195
100	10	12.3644	-2.36439
101	10	10.431	-0.430971
102	16	13.9804	2.0196
103	12	10.1342	1.86581
104	11	15.0653	-4.06534
105	16	11.9069	4.09313
106	19	16.4691	2.53089
107	11	11.0181	-0.018103
108	16	12.0004	3.99957
109	15	15.9998	-0.999829
110	24	17.0567	6.94332
111	14	11.7521	2.2479
112	15	14.5449	0.455094
113	11	12.7794	-1.77941
114	15	14.4707	0.529279
115	12	10.593	1.40701
116	10	10.438	-0.438043
117	14	13.6945	0.305523
118	13	13.3414	-0.341361
119	9	13.3541	-4.35415
120	15	11.9952	3.00484
121	15	15.232	-0.231953
122	14	12.4853	1.51469
123	11	11.8765	-0.876457
124	8	11.7855	-3.78548
125	11	12.0987	-1.09873
126	11	13.0007	-2.00075
127	8	10.2162	-2.21617
128	10	10.4137	-0.413662
129	11	9.42707	1.57293
130	13	12.6875	0.312528
131	11	13.5142	-2.51425
132	20	17.281	2.71902
133	10	11.9753	-1.97534
134	15	13.0552	1.94477
135	12	12.2689	-0.268869
136	14	11.162	2.83797
137	23	15.9197	7.08027
138	14	13.2385	0.761521
139	16	16.2859	-0.285911
140	11	13.1327	-2.1327
141	12	14.9305	-2.93048
142	10	12.9208	-2.92085
143	14	11.3251	2.67486
144	12	11.973	0.0270293
145	12	11.8466	0.153423
146	11	11.763	-0.763022
147	12	10.838	1.16204
148	13	15.5586	-2.55862
149	11	13.909	-2.90899
150	19	16.526	2.47396
151	12	12.0773	-0.0773286
152	17	13.0955	3.90452
153	9	11.6065	-2.60651
154	12	14.3385	-2.33845
155	19	16.5832	2.41677
156	18	14.2611	3.73887
157	15	13.6442	1.35579
158	14	13.6366	0.363393
159	11	9.42707	1.57293
160	9	12.949	-3.94898
161	18	14.4486	3.55136
162	16	14.1497	1.85033

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.866721	0.266559	0.133279
11	0.813602	0.372795	0.186398
12	0.999859	0.000281712	0.000140856
13	0.99967	0.000660866	0.000330433
14	0.999284	0.00143251	0.000716255
15	0.998703	0.00259429	0.00129715
16	0.997486	0.00502729	0.00251364
17	0.995485	0.0090291	0.00451455
18	0.992768	0.0144647	0.00723233
19	0.993714	0.0125723	0.00628614
20	0.98968	0.0206403	0.0103201
21	0.991871	0.0162577	0.00812883
22	0.98764	0.0247198	0.0123599
23	0.983213	0.033574	0.016787
24	0.97469	0.0506194	0.0253097
25	0.96381	0.07238	0.03619
26	0.991419	0.017162	0.00858102
27	0.98704	0.0259191	0.0129595
28	0.984035	0.0319291	0.0159645
29	0.980156	0.0396883	0.0198442
30	0.973897	0.0522069	0.0261035
31	0.972198	0.055604	0.027802
32	0.962128	0.0757434	0.0378717
33	0.951397	0.0972062	0.0486031
34	0.935806	0.128388	0.064194
35	0.916282	0.167436	0.0837182
36	0.932016	0.135969	0.0679844
37	0.95772	0.0845603	0.0422801
38	0.943644	0.112712	0.0563559
39	0.930407	0.139186	0.0695928
40	0.937266	0.125469	0.0627344
41	0.936784	0.126433	0.0632164
42	0.933209	0.133581	0.0667906
43	0.945942	0.108116	0.054058
44	0.931352	0.137297	0.0686484
45	0.929141	0.141717	0.0708586
46	0.919786	0.160428	0.0802141
47	0.92997	0.140061	0.0700305
48	0.914325	0.171351	0.0856753
49	0.935343	0.129314	0.0646572
50	0.92779	0.14442	0.0722098
51	0.918275	0.16345	0.0817248
52	0.901347	0.197305	0.0986526
53	0.907315	0.18537	0.0926852
54	0.885351	0.229298	0.114649
55	0.917571	0.164858	0.082429
56	0.899861	0.200278	0.100139
57	0.889229	0.221541	0.110771
58	0.904731	0.190539	0.0952694
59	0.924384	0.151233	0.0756165
60	0.910812	0.178376	0.089188
61	0.912136	0.175727	0.0878637
62	0.894868	0.210264	0.105132
63	0.887293	0.225415	0.112707
64	0.862836	0.274328	0.137164
65	0.835189	0.329622	0.164811
66	0.859779	0.280441	0.140221
67	0.861558	0.276885	0.138442
68	0.847504	0.304993	0.152496
69	0.819528	0.360944	0.180472
70	0.80369	0.392619	0.19631
71	0.778383	0.443234	0.221617
72	0.77469	0.45062	0.22531
73	0.755115	0.48977	0.244885
74	0.734616	0.530768	0.265384
75	0.718509	0.562983	0.281491
76	0.836865	0.326271	0.163135
77	0.831919	0.336163	0.168081
78	0.854007	0.291985	0.145993
79	0.828135	0.343731	0.171865
80	0.89887	0.20226	0.10113
81	0.878592	0.242816	0.121408
82	0.875848	0.248303	0.124152
83	0.884457	0.231086	0.115543
84	0.861232	0.277537	0.138768
85	0.833889	0.332221	0.166111
86	0.803488	0.393024	0.196512
87	0.771423	0.457153	0.228577
88	0.738499	0.523002	0.261501
89	0.703815	0.592371	0.296185
90	0.772958	0.454083	0.227042
91	0.749775	0.50045	0.250225
92	0.720245	0.55951	0.279755
93	0.689897	0.620207	0.310103
94	0.681954	0.636091	0.318046
95	0.720264	0.559473	0.279736
96	0.678924	0.642152	0.321076
97	0.657042	0.685917	0.342958
98	0.634721	0.730559	0.365279
99	0.591142	0.817716	0.408858
100	0.579311	0.841379	0.420689
101	0.532253	0.935493	0.467747
102	0.507293	0.985413	0.492707
103	0.497135	0.994271	0.502865
104	0.602295	0.79541	0.397705
105	0.702278	0.595444	0.297722
106	0.688635	0.622729	0.311365
107	0.643321	0.713359	0.356679
108	0.721061	0.557877	0.278939
109	0.692647	0.614705	0.307353
110	0.901945	0.196109	0.0980547
111	0.899232	0.201537	0.100768
112	0.874971	0.250059	0.125029
113	0.853945	0.29211	0.146055
114	0.823994	0.352012	0.176006
115	0.799948	0.400105	0.200052
116	0.760489	0.479022	0.239511
117	0.743513	0.512973	0.256487
118	0.703144	0.593711	0.296856
119	0.776268	0.447463	0.223732
120	0.807986	0.384029	0.192014
121	0.767585	0.46483	0.232415
122	0.730102	0.539797	0.269898
123	0.682772	0.634457	0.317228
124	0.686889	0.626221	0.313111
125	0.649172	0.701655	0.350828
126	0.635479	0.729043	0.364521
127	0.63777	0.724459	0.36223
128	0.596732	0.806535	0.403268
129	0.583188	0.833625	0.416812
130	0.522919	0.954163	0.477081
131	0.52781	0.944381	0.47219
132	0.484103	0.968207	0.515897
133	0.435144	0.870288	0.564856
134	0.3861	0.772199	0.6139
135	0.326787	0.653574	0.673213
136	0.300369	0.600738	0.699631
137	0.544619	0.910761	0.455381
138	0.491825	0.983649	0.508175
139	0.424311	0.848623	0.575689
140	0.372778	0.745555	0.627222
141	0.450284	0.900568	0.549716
142	0.413739	0.827478	0.586261
143	0.407446	0.814891	0.592554
144	0.325564	0.651129	0.674436
145	0.249615	0.499231	0.750385
146	0.402109	0.804219	0.597891
147	0.310215	0.62043	0.689785
148	0.474805	0.94961	0.525195
149	0.492402	0.984805	0.507598
150	0.371916	0.743831	0.628084
151	0.61946	0.761081	0.38054
152	0.465771	0.931541	0.534229

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.866721 & 0.266559 & 0.133279 \tabularnewline
11 & 0.813602 & 0.372795 & 0.186398 \tabularnewline
12 & 0.999859 & 0.000281712 & 0.000140856 \tabularnewline
13 & 0.99967 & 0.000660866 & 0.000330433 \tabularnewline
14 & 0.999284 & 0.00143251 & 0.000716255 \tabularnewline
15 & 0.998703 & 0.00259429 & 0.00129715 \tabularnewline
16 & 0.997486 & 0.00502729 & 0.00251364 \tabularnewline
17 & 0.995485 & 0.0090291 & 0.00451455 \tabularnewline
18 & 0.992768 & 0.0144647 & 0.00723233 \tabularnewline
19 & 0.993714 & 0.0125723 & 0.00628614 \tabularnewline
20 & 0.98968 & 0.0206403 & 0.0103201 \tabularnewline
21 & 0.991871 & 0.0162577 & 0.00812883 \tabularnewline
22 & 0.98764 & 0.0247198 & 0.0123599 \tabularnewline
23 & 0.983213 & 0.033574 & 0.016787 \tabularnewline
24 & 0.97469 & 0.0506194 & 0.0253097 \tabularnewline
25 & 0.96381 & 0.07238 & 0.03619 \tabularnewline
26 & 0.991419 & 0.017162 & 0.00858102 \tabularnewline
27 & 0.98704 & 0.0259191 & 0.0129595 \tabularnewline
28 & 0.984035 & 0.0319291 & 0.0159645 \tabularnewline
29 & 0.980156 & 0.0396883 & 0.0198442 \tabularnewline
30 & 0.973897 & 0.0522069 & 0.0261035 \tabularnewline
31 & 0.972198 & 0.055604 & 0.027802 \tabularnewline
32 & 0.962128 & 0.0757434 & 0.0378717 \tabularnewline
33 & 0.951397 & 0.0972062 & 0.0486031 \tabularnewline
34 & 0.935806 & 0.128388 & 0.064194 \tabularnewline
35 & 0.916282 & 0.167436 & 0.0837182 \tabularnewline
36 & 0.932016 & 0.135969 & 0.0679844 \tabularnewline
37 & 0.95772 & 0.0845603 & 0.0422801 \tabularnewline
38 & 0.943644 & 0.112712 & 0.0563559 \tabularnewline
39 & 0.930407 & 0.139186 & 0.0695928 \tabularnewline
40 & 0.937266 & 0.125469 & 0.0627344 \tabularnewline
41 & 0.936784 & 0.126433 & 0.0632164 \tabularnewline
42 & 0.933209 & 0.133581 & 0.0667906 \tabularnewline
43 & 0.945942 & 0.108116 & 0.054058 \tabularnewline
44 & 0.931352 & 0.137297 & 0.0686484 \tabularnewline
45 & 0.929141 & 0.141717 & 0.0708586 \tabularnewline
46 & 0.919786 & 0.160428 & 0.0802141 \tabularnewline
47 & 0.92997 & 0.140061 & 0.0700305 \tabularnewline
48 & 0.914325 & 0.171351 & 0.0856753 \tabularnewline
49 & 0.935343 & 0.129314 & 0.0646572 \tabularnewline
50 & 0.92779 & 0.14442 & 0.0722098 \tabularnewline
51 & 0.918275 & 0.16345 & 0.0817248 \tabularnewline
52 & 0.901347 & 0.197305 & 0.0986526 \tabularnewline
53 & 0.907315 & 0.18537 & 0.0926852 \tabularnewline
54 & 0.885351 & 0.229298 & 0.114649 \tabularnewline
55 & 0.917571 & 0.164858 & 0.082429 \tabularnewline
56 & 0.899861 & 0.200278 & 0.100139 \tabularnewline
57 & 0.889229 & 0.221541 & 0.110771 \tabularnewline
58 & 0.904731 & 0.190539 & 0.0952694 \tabularnewline
59 & 0.924384 & 0.151233 & 0.0756165 \tabularnewline
60 & 0.910812 & 0.178376 & 0.089188 \tabularnewline
61 & 0.912136 & 0.175727 & 0.0878637 \tabularnewline
62 & 0.894868 & 0.210264 & 0.105132 \tabularnewline
63 & 0.887293 & 0.225415 & 0.112707 \tabularnewline
64 & 0.862836 & 0.274328 & 0.137164 \tabularnewline
65 & 0.835189 & 0.329622 & 0.164811 \tabularnewline
66 & 0.859779 & 0.280441 & 0.140221 \tabularnewline
67 & 0.861558 & 0.276885 & 0.138442 \tabularnewline
68 & 0.847504 & 0.304993 & 0.152496 \tabularnewline
69 & 0.819528 & 0.360944 & 0.180472 \tabularnewline
70 & 0.80369 & 0.392619 & 0.19631 \tabularnewline
71 & 0.778383 & 0.443234 & 0.221617 \tabularnewline
72 & 0.77469 & 0.45062 & 0.22531 \tabularnewline
73 & 0.755115 & 0.48977 & 0.244885 \tabularnewline
74 & 0.734616 & 0.530768 & 0.265384 \tabularnewline
75 & 0.718509 & 0.562983 & 0.281491 \tabularnewline
76 & 0.836865 & 0.326271 & 0.163135 \tabularnewline
77 & 0.831919 & 0.336163 & 0.168081 \tabularnewline
78 & 0.854007 & 0.291985 & 0.145993 \tabularnewline
79 & 0.828135 & 0.343731 & 0.171865 \tabularnewline
80 & 0.89887 & 0.20226 & 0.10113 \tabularnewline
81 & 0.878592 & 0.242816 & 0.121408 \tabularnewline
82 & 0.875848 & 0.248303 & 0.124152 \tabularnewline
83 & 0.884457 & 0.231086 & 0.115543 \tabularnewline
84 & 0.861232 & 0.277537 & 0.138768 \tabularnewline
85 & 0.833889 & 0.332221 & 0.166111 \tabularnewline
86 & 0.803488 & 0.393024 & 0.196512 \tabularnewline
87 & 0.771423 & 0.457153 & 0.228577 \tabularnewline
88 & 0.738499 & 0.523002 & 0.261501 \tabularnewline
89 & 0.703815 & 0.592371 & 0.296185 \tabularnewline
90 & 0.772958 & 0.454083 & 0.227042 \tabularnewline
91 & 0.749775 & 0.50045 & 0.250225 \tabularnewline
92 & 0.720245 & 0.55951 & 0.279755 \tabularnewline
93 & 0.689897 & 0.620207 & 0.310103 \tabularnewline
94 & 0.681954 & 0.636091 & 0.318046 \tabularnewline
95 & 0.720264 & 0.559473 & 0.279736 \tabularnewline
96 & 0.678924 & 0.642152 & 0.321076 \tabularnewline
97 & 0.657042 & 0.685917 & 0.342958 \tabularnewline
98 & 0.634721 & 0.730559 & 0.365279 \tabularnewline
99 & 0.591142 & 0.817716 & 0.408858 \tabularnewline
100 & 0.579311 & 0.841379 & 0.420689 \tabularnewline
101 & 0.532253 & 0.935493 & 0.467747 \tabularnewline
102 & 0.507293 & 0.985413 & 0.492707 \tabularnewline
103 & 0.497135 & 0.994271 & 0.502865 \tabularnewline
104 & 0.602295 & 0.79541 & 0.397705 \tabularnewline
105 & 0.702278 & 0.595444 & 0.297722 \tabularnewline
106 & 0.688635 & 0.622729 & 0.311365 \tabularnewline
107 & 0.643321 & 0.713359 & 0.356679 \tabularnewline
108 & 0.721061 & 0.557877 & 0.278939 \tabularnewline
109 & 0.692647 & 0.614705 & 0.307353 \tabularnewline
110 & 0.901945 & 0.196109 & 0.0980547 \tabularnewline
111 & 0.899232 & 0.201537 & 0.100768 \tabularnewline
112 & 0.874971 & 0.250059 & 0.125029 \tabularnewline
113 & 0.853945 & 0.29211 & 0.146055 \tabularnewline
114 & 0.823994 & 0.352012 & 0.176006 \tabularnewline
115 & 0.799948 & 0.400105 & 0.200052 \tabularnewline
116 & 0.760489 & 0.479022 & 0.239511 \tabularnewline
117 & 0.743513 & 0.512973 & 0.256487 \tabularnewline
118 & 0.703144 & 0.593711 & 0.296856 \tabularnewline
119 & 0.776268 & 0.447463 & 0.223732 \tabularnewline
120 & 0.807986 & 0.384029 & 0.192014 \tabularnewline
121 & 0.767585 & 0.46483 & 0.232415 \tabularnewline
122 & 0.730102 & 0.539797 & 0.269898 \tabularnewline
123 & 0.682772 & 0.634457 & 0.317228 \tabularnewline
124 & 0.686889 & 0.626221 & 0.313111 \tabularnewline
125 & 0.649172 & 0.701655 & 0.350828 \tabularnewline
126 & 0.635479 & 0.729043 & 0.364521 \tabularnewline
127 & 0.63777 & 0.724459 & 0.36223 \tabularnewline
128 & 0.596732 & 0.806535 & 0.403268 \tabularnewline
129 & 0.583188 & 0.833625 & 0.416812 \tabularnewline
130 & 0.522919 & 0.954163 & 0.477081 \tabularnewline
131 & 0.52781 & 0.944381 & 0.47219 \tabularnewline
132 & 0.484103 & 0.968207 & 0.515897 \tabularnewline
133 & 0.435144 & 0.870288 & 0.564856 \tabularnewline
134 & 0.3861 & 0.772199 & 0.6139 \tabularnewline
135 & 0.326787 & 0.653574 & 0.673213 \tabularnewline
136 & 0.300369 & 0.600738 & 0.699631 \tabularnewline
137 & 0.544619 & 0.910761 & 0.455381 \tabularnewline
138 & 0.491825 & 0.983649 & 0.508175 \tabularnewline
139 & 0.424311 & 0.848623 & 0.575689 \tabularnewline
140 & 0.372778 & 0.745555 & 0.627222 \tabularnewline
141 & 0.450284 & 0.900568 & 0.549716 \tabularnewline
142 & 0.413739 & 0.827478 & 0.586261 \tabularnewline
143 & 0.407446 & 0.814891 & 0.592554 \tabularnewline
144 & 0.325564 & 0.651129 & 0.674436 \tabularnewline
145 & 0.249615 & 0.499231 & 0.750385 \tabularnewline
146 & 0.402109 & 0.804219 & 0.597891 \tabularnewline
147 & 0.310215 & 0.62043 & 0.689785 \tabularnewline
148 & 0.474805 & 0.94961 & 0.525195 \tabularnewline
149 & 0.492402 & 0.984805 & 0.507598 \tabularnewline
150 & 0.371916 & 0.743831 & 0.628084 \tabularnewline
151 & 0.61946 & 0.761081 & 0.38054 \tabularnewline
152 & 0.465771 & 0.931541 & 0.534229 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253613&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]0.866721[/C][C]0.266559[/C][C]0.133279[/C][/ROW]
[ROW][C]11[/C][C]0.813602[/C][C]0.372795[/C][C]0.186398[/C][/ROW]
[ROW][C]12[/C][C]0.999859[/C][C]0.000281712[/C][C]0.000140856[/C][/ROW]
[ROW][C]13[/C][C]0.99967[/C][C]0.000660866[/C][C]0.000330433[/C][/ROW]
[ROW][C]14[/C][C]0.999284[/C][C]0.00143251[/C][C]0.000716255[/C][/ROW]
[ROW][C]15[/C][C]0.998703[/C][C]0.00259429[/C][C]0.00129715[/C][/ROW]
[ROW][C]16[/C][C]0.997486[/C][C]0.00502729[/C][C]0.00251364[/C][/ROW]
[ROW][C]17[/C][C]0.995485[/C][C]0.0090291[/C][C]0.00451455[/C][/ROW]
[ROW][C]18[/C][C]0.992768[/C][C]0.0144647[/C][C]0.00723233[/C][/ROW]
[ROW][C]19[/C][C]0.993714[/C][C]0.0125723[/C][C]0.00628614[/C][/ROW]
[ROW][C]20[/C][C]0.98968[/C][C]0.0206403[/C][C]0.0103201[/C][/ROW]
[ROW][C]21[/C][C]0.991871[/C][C]0.0162577[/C][C]0.00812883[/C][/ROW]
[ROW][C]22[/C][C]0.98764[/C][C]0.0247198[/C][C]0.0123599[/C][/ROW]
[ROW][C]23[/C][C]0.983213[/C][C]0.033574[/C][C]0.016787[/C][/ROW]
[ROW][C]24[/C][C]0.97469[/C][C]0.0506194[/C][C]0.0253097[/C][/ROW]
[ROW][C]25[/C][C]0.96381[/C][C]0.07238[/C][C]0.03619[/C][/ROW]
[ROW][C]26[/C][C]0.991419[/C][C]0.017162[/C][C]0.00858102[/C][/ROW]
[ROW][C]27[/C][C]0.98704[/C][C]0.0259191[/C][C]0.0129595[/C][/ROW]
[ROW][C]28[/C][C]0.984035[/C][C]0.0319291[/C][C]0.0159645[/C][/ROW]
[ROW][C]29[/C][C]0.980156[/C][C]0.0396883[/C][C]0.0198442[/C][/ROW]
[ROW][C]30[/C][C]0.973897[/C][C]0.0522069[/C][C]0.0261035[/C][/ROW]
[ROW][C]31[/C][C]0.972198[/C][C]0.055604[/C][C]0.027802[/C][/ROW]
[ROW][C]32[/C][C]0.962128[/C][C]0.0757434[/C][C]0.0378717[/C][/ROW]
[ROW][C]33[/C][C]0.951397[/C][C]0.0972062[/C][C]0.0486031[/C][/ROW]
[ROW][C]34[/C][C]0.935806[/C][C]0.128388[/C][C]0.064194[/C][/ROW]
[ROW][C]35[/C][C]0.916282[/C][C]0.167436[/C][C]0.0837182[/C][/ROW]
[ROW][C]36[/C][C]0.932016[/C][C]0.135969[/C][C]0.0679844[/C][/ROW]
[ROW][C]37[/C][C]0.95772[/C][C]0.0845603[/C][C]0.0422801[/C][/ROW]
[ROW][C]38[/C][C]0.943644[/C][C]0.112712[/C][C]0.0563559[/C][/ROW]
[ROW][C]39[/C][C]0.930407[/C][C]0.139186[/C][C]0.0695928[/C][/ROW]
[ROW][C]40[/C][C]0.937266[/C][C]0.125469[/C][C]0.0627344[/C][/ROW]
[ROW][C]41[/C][C]0.936784[/C][C]0.126433[/C][C]0.0632164[/C][/ROW]
[ROW][C]42[/C][C]0.933209[/C][C]0.133581[/C][C]0.0667906[/C][/ROW]
[ROW][C]43[/C][C]0.945942[/C][C]0.108116[/C][C]0.054058[/C][/ROW]
[ROW][C]44[/C][C]0.931352[/C][C]0.137297[/C][C]0.0686484[/C][/ROW]
[ROW][C]45[/C][C]0.929141[/C][C]0.141717[/C][C]0.0708586[/C][/ROW]
[ROW][C]46[/C][C]0.919786[/C][C]0.160428[/C][C]0.0802141[/C][/ROW]
[ROW][C]47[/C][C]0.92997[/C][C]0.140061[/C][C]0.0700305[/C][/ROW]
[ROW][C]48[/C][C]0.914325[/C][C]0.171351[/C][C]0.0856753[/C][/ROW]
[ROW][C]49[/C][C]0.935343[/C][C]0.129314[/C][C]0.0646572[/C][/ROW]
[ROW][C]50[/C][C]0.92779[/C][C]0.14442[/C][C]0.0722098[/C][/ROW]
[ROW][C]51[/C][C]0.918275[/C][C]0.16345[/C][C]0.0817248[/C][/ROW]
[ROW][C]52[/C][C]0.901347[/C][C]0.197305[/C][C]0.0986526[/C][/ROW]
[ROW][C]53[/C][C]0.907315[/C][C]0.18537[/C][C]0.0926852[/C][/ROW]
[ROW][C]54[/C][C]0.885351[/C][C]0.229298[/C][C]0.114649[/C][/ROW]
[ROW][C]55[/C][C]0.917571[/C][C]0.164858[/C][C]0.082429[/C][/ROW]
[ROW][C]56[/C][C]0.899861[/C][C]0.200278[/C][C]0.100139[/C][/ROW]
[ROW][C]57[/C][C]0.889229[/C][C]0.221541[/C][C]0.110771[/C][/ROW]
[ROW][C]58[/C][C]0.904731[/C][C]0.190539[/C][C]0.0952694[/C][/ROW]
[ROW][C]59[/C][C]0.924384[/C][C]0.151233[/C][C]0.0756165[/C][/ROW]
[ROW][C]60[/C][C]0.910812[/C][C]0.178376[/C][C]0.089188[/C][/ROW]
[ROW][C]61[/C][C]0.912136[/C][C]0.175727[/C][C]0.0878637[/C][/ROW]
[ROW][C]62[/C][C]0.894868[/C][C]0.210264[/C][C]0.105132[/C][/ROW]
[ROW][C]63[/C][C]0.887293[/C][C]0.225415[/C][C]0.112707[/C][/ROW]
[ROW][C]64[/C][C]0.862836[/C][C]0.274328[/C][C]0.137164[/C][/ROW]
[ROW][C]65[/C][C]0.835189[/C][C]0.329622[/C][C]0.164811[/C][/ROW]
[ROW][C]66[/C][C]0.859779[/C][C]0.280441[/C][C]0.140221[/C][/ROW]
[ROW][C]67[/C][C]0.861558[/C][C]0.276885[/C][C]0.138442[/C][/ROW]
[ROW][C]68[/C][C]0.847504[/C][C]0.304993[/C][C]0.152496[/C][/ROW]
[ROW][C]69[/C][C]0.819528[/C][C]0.360944[/C][C]0.180472[/C][/ROW]
[ROW][C]70[/C][C]0.80369[/C][C]0.392619[/C][C]0.19631[/C][/ROW]
[ROW][C]71[/C][C]0.778383[/C][C]0.443234[/C][C]0.221617[/C][/ROW]
[ROW][C]72[/C][C]0.77469[/C][C]0.45062[/C][C]0.22531[/C][/ROW]
[ROW][C]73[/C][C]0.755115[/C][C]0.48977[/C][C]0.244885[/C][/ROW]
[ROW][C]74[/C][C]0.734616[/C][C]0.530768[/C][C]0.265384[/C][/ROW]
[ROW][C]75[/C][C]0.718509[/C][C]0.562983[/C][C]0.281491[/C][/ROW]
[ROW][C]76[/C][C]0.836865[/C][C]0.326271[/C][C]0.163135[/C][/ROW]
[ROW][C]77[/C][C]0.831919[/C][C]0.336163[/C][C]0.168081[/C][/ROW]
[ROW][C]78[/C][C]0.854007[/C][C]0.291985[/C][C]0.145993[/C][/ROW]
[ROW][C]79[/C][C]0.828135[/C][C]0.343731[/C][C]0.171865[/C][/ROW]
[ROW][C]80[/C][C]0.89887[/C][C]0.20226[/C][C]0.10113[/C][/ROW]
[ROW][C]81[/C][C]0.878592[/C][C]0.242816[/C][C]0.121408[/C][/ROW]
[ROW][C]82[/C][C]0.875848[/C][C]0.248303[/C][C]0.124152[/C][/ROW]
[ROW][C]83[/C][C]0.884457[/C][C]0.231086[/C][C]0.115543[/C][/ROW]
[ROW][C]84[/C][C]0.861232[/C][C]0.277537[/C][C]0.138768[/C][/ROW]
[ROW][C]85[/C][C]0.833889[/C][C]0.332221[/C][C]0.166111[/C][/ROW]
[ROW][C]86[/C][C]0.803488[/C][C]0.393024[/C][C]0.196512[/C][/ROW]
[ROW][C]87[/C][C]0.771423[/C][C]0.457153[/C][C]0.228577[/C][/ROW]
[ROW][C]88[/C][C]0.738499[/C][C]0.523002[/C][C]0.261501[/C][/ROW]
[ROW][C]89[/C][C]0.703815[/C][C]0.592371[/C][C]0.296185[/C][/ROW]
[ROW][C]90[/C][C]0.772958[/C][C]0.454083[/C][C]0.227042[/C][/ROW]
[ROW][C]91[/C][C]0.749775[/C][C]0.50045[/C][C]0.250225[/C][/ROW]
[ROW][C]92[/C][C]0.720245[/C][C]0.55951[/C][C]0.279755[/C][/ROW]
[ROW][C]93[/C][C]0.689897[/C][C]0.620207[/C][C]0.310103[/C][/ROW]
[ROW][C]94[/C][C]0.681954[/C][C]0.636091[/C][C]0.318046[/C][/ROW]
[ROW][C]95[/C][C]0.720264[/C][C]0.559473[/C][C]0.279736[/C][/ROW]
[ROW][C]96[/C][C]0.678924[/C][C]0.642152[/C][C]0.321076[/C][/ROW]
[ROW][C]97[/C][C]0.657042[/C][C]0.685917[/C][C]0.342958[/C][/ROW]
[ROW][C]98[/C][C]0.634721[/C][C]0.730559[/C][C]0.365279[/C][/ROW]
[ROW][C]99[/C][C]0.591142[/C][C]0.817716[/C][C]0.408858[/C][/ROW]
[ROW][C]100[/C][C]0.579311[/C][C]0.841379[/C][C]0.420689[/C][/ROW]
[ROW][C]101[/C][C]0.532253[/C][C]0.935493[/C][C]0.467747[/C][/ROW]
[ROW][C]102[/C][C]0.507293[/C][C]0.985413[/C][C]0.492707[/C][/ROW]
[ROW][C]103[/C][C]0.497135[/C][C]0.994271[/C][C]0.502865[/C][/ROW]
[ROW][C]104[/C][C]0.602295[/C][C]0.79541[/C][C]0.397705[/C][/ROW]
[ROW][C]105[/C][C]0.702278[/C][C]0.595444[/C][C]0.297722[/C][/ROW]
[ROW][C]106[/C][C]0.688635[/C][C]0.622729[/C][C]0.311365[/C][/ROW]
[ROW][C]107[/C][C]0.643321[/C][C]0.713359[/C][C]0.356679[/C][/ROW]
[ROW][C]108[/C][C]0.721061[/C][C]0.557877[/C][C]0.278939[/C][/ROW]
[ROW][C]109[/C][C]0.692647[/C][C]0.614705[/C][C]0.307353[/C][/ROW]
[ROW][C]110[/C][C]0.901945[/C][C]0.196109[/C][C]0.0980547[/C][/ROW]
[ROW][C]111[/C][C]0.899232[/C][C]0.201537[/C][C]0.100768[/C][/ROW]
[ROW][C]112[/C][C]0.874971[/C][C]0.250059[/C][C]0.125029[/C][/ROW]
[ROW][C]113[/C][C]0.853945[/C][C]0.29211[/C][C]0.146055[/C][/ROW]
[ROW][C]114[/C][C]0.823994[/C][C]0.352012[/C][C]0.176006[/C][/ROW]
[ROW][C]115[/C][C]0.799948[/C][C]0.400105[/C][C]0.200052[/C][/ROW]
[ROW][C]116[/C][C]0.760489[/C][C]0.479022[/C][C]0.239511[/C][/ROW]
[ROW][C]117[/C][C]0.743513[/C][C]0.512973[/C][C]0.256487[/C][/ROW]
[ROW][C]118[/C][C]0.703144[/C][C]0.593711[/C][C]0.296856[/C][/ROW]
[ROW][C]119[/C][C]0.776268[/C][C]0.447463[/C][C]0.223732[/C][/ROW]
[ROW][C]120[/C][C]0.807986[/C][C]0.384029[/C][C]0.192014[/C][/ROW]
[ROW][C]121[/C][C]0.767585[/C][C]0.46483[/C][C]0.232415[/C][/ROW]
[ROW][C]122[/C][C]0.730102[/C][C]0.539797[/C][C]0.269898[/C][/ROW]
[ROW][C]123[/C][C]0.682772[/C][C]0.634457[/C][C]0.317228[/C][/ROW]
[ROW][C]124[/C][C]0.686889[/C][C]0.626221[/C][C]0.313111[/C][/ROW]
[ROW][C]125[/C][C]0.649172[/C][C]0.701655[/C][C]0.350828[/C][/ROW]
[ROW][C]126[/C][C]0.635479[/C][C]0.729043[/C][C]0.364521[/C][/ROW]
[ROW][C]127[/C][C]0.63777[/C][C]0.724459[/C][C]0.36223[/C][/ROW]
[ROW][C]128[/C][C]0.596732[/C][C]0.806535[/C][C]0.403268[/C][/ROW]
[ROW][C]129[/C][C]0.583188[/C][C]0.833625[/C][C]0.416812[/C][/ROW]
[ROW][C]130[/C][C]0.522919[/C][C]0.954163[/C][C]0.477081[/C][/ROW]
[ROW][C]131[/C][C]0.52781[/C][C]0.944381[/C][C]0.47219[/C][/ROW]
[ROW][C]132[/C][C]0.484103[/C][C]0.968207[/C][C]0.515897[/C][/ROW]
[ROW][C]133[/C][C]0.435144[/C][C]0.870288[/C][C]0.564856[/C][/ROW]
[ROW][C]134[/C][C]0.3861[/C][C]0.772199[/C][C]0.6139[/C][/ROW]
[ROW][C]135[/C][C]0.326787[/C][C]0.653574[/C][C]0.673213[/C][/ROW]
[ROW][C]136[/C][C]0.300369[/C][C]0.600738[/C][C]0.699631[/C][/ROW]
[ROW][C]137[/C][C]0.544619[/C][C]0.910761[/C][C]0.455381[/C][/ROW]
[ROW][C]138[/C][C]0.491825[/C][C]0.983649[/C][C]0.508175[/C][/ROW]
[ROW][C]139[/C][C]0.424311[/C][C]0.848623[/C][C]0.575689[/C][/ROW]
[ROW][C]140[/C][C]0.372778[/C][C]0.745555[/C][C]0.627222[/C][/ROW]
[ROW][C]141[/C][C]0.450284[/C][C]0.900568[/C][C]0.549716[/C][/ROW]
[ROW][C]142[/C][C]0.413739[/C][C]0.827478[/C][C]0.586261[/C][/ROW]
[ROW][C]143[/C][C]0.407446[/C][C]0.814891[/C][C]0.592554[/C][/ROW]
[ROW][C]144[/C][C]0.325564[/C][C]0.651129[/C][C]0.674436[/C][/ROW]
[ROW][C]145[/C][C]0.249615[/C][C]0.499231[/C][C]0.750385[/C][/ROW]
[ROW][C]146[/C][C]0.402109[/C][C]0.804219[/C][C]0.597891[/C][/ROW]
[ROW][C]147[/C][C]0.310215[/C][C]0.62043[/C][C]0.689785[/C][/ROW]
[ROW][C]148[/C][C]0.474805[/C][C]0.94961[/C][C]0.525195[/C][/ROW]
[ROW][C]149[/C][C]0.492402[/C][C]0.984805[/C][C]0.507598[/C][/ROW]
[ROW][C]150[/C][C]0.371916[/C][C]0.743831[/C][C]0.628084[/C][/ROW]
[ROW][C]151[/C][C]0.61946[/C][C]0.761081[/C][C]0.38054[/C][/ROW]
[ROW][C]152[/C][C]0.465771[/C][C]0.931541[/C][C]0.534229[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253613&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253613&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	0.866721	0.266559	0.133279
11	0.813602	0.372795	0.186398
12	0.999859	0.000281712	0.000140856
13	0.99967	0.000660866	0.000330433
14	0.999284	0.00143251	0.000716255
15	0.998703	0.00259429	0.00129715
16	0.997486	0.00502729	0.00251364
17	0.995485	0.0090291	0.00451455
18	0.992768	0.0144647	0.00723233
19	0.993714	0.0125723	0.00628614
20	0.98968	0.0206403	0.0103201
21	0.991871	0.0162577	0.00812883
22	0.98764	0.0247198	0.0123599
23	0.983213	0.033574	0.016787
24	0.97469	0.0506194	0.0253097
25	0.96381	0.07238	0.03619
26	0.991419	0.017162	0.00858102
27	0.98704	0.0259191	0.0129595
28	0.984035	0.0319291	0.0159645
29	0.980156	0.0396883	0.0198442
30	0.973897	0.0522069	0.0261035
31	0.972198	0.055604	0.027802
32	0.962128	0.0757434	0.0378717
33	0.951397	0.0972062	0.0486031
34	0.935806	0.128388	0.064194
35	0.916282	0.167436	0.0837182
36	0.932016	0.135969	0.0679844
37	0.95772	0.0845603	0.0422801
38	0.943644	0.112712	0.0563559
39	0.930407	0.139186	0.0695928
40	0.937266	0.125469	0.0627344
41	0.936784	0.126433	0.0632164
42	0.933209	0.133581	0.0667906
43	0.945942	0.108116	0.054058
44	0.931352	0.137297	0.0686484
45	0.929141	0.141717	0.0708586
46	0.919786	0.160428	0.0802141
47	0.92997	0.140061	0.0700305
48	0.914325	0.171351	0.0856753
49	0.935343	0.129314	0.0646572
50	0.92779	0.14442	0.0722098
51	0.918275	0.16345	0.0817248
52	0.901347	0.197305	0.0986526
53	0.907315	0.18537	0.0926852
54	0.885351	0.229298	0.114649
55	0.917571	0.164858	0.082429
56	0.899861	0.200278	0.100139
57	0.889229	0.221541	0.110771
58	0.904731	0.190539	0.0952694
59	0.924384	0.151233	0.0756165
60	0.910812	0.178376	0.089188
61	0.912136	0.175727	0.0878637
62	0.894868	0.210264	0.105132
63	0.887293	0.225415	0.112707
64	0.862836	0.274328	0.137164
65	0.835189	0.329622	0.164811
66	0.859779	0.280441	0.140221
67	0.861558	0.276885	0.138442
68	0.847504	0.304993	0.152496
69	0.819528	0.360944	0.180472
70	0.80369	0.392619	0.19631
71	0.778383	0.443234	0.221617
72	0.77469	0.45062	0.22531
73	0.755115	0.48977	0.244885
74	0.734616	0.530768	0.265384
75	0.718509	0.562983	0.281491
76	0.836865	0.326271	0.163135
77	0.831919	0.336163	0.168081
78	0.854007	0.291985	0.145993
79	0.828135	0.343731	0.171865
80	0.89887	0.20226	0.10113
81	0.878592	0.242816	0.121408
82	0.875848	0.248303	0.124152
83	0.884457	0.231086	0.115543
84	0.861232	0.277537	0.138768
85	0.833889	0.332221	0.166111
86	0.803488	0.393024	0.196512
87	0.771423	0.457153	0.228577
88	0.738499	0.523002	0.261501
89	0.703815	0.592371	0.296185
90	0.772958	0.454083	0.227042
91	0.749775	0.50045	0.250225
92	0.720245	0.55951	0.279755
93	0.689897	0.620207	0.310103
94	0.681954	0.636091	0.318046
95	0.720264	0.559473	0.279736
96	0.678924	0.642152	0.321076
97	0.657042	0.685917	0.342958
98	0.634721	0.730559	0.365279
99	0.591142	0.817716	0.408858
100	0.579311	0.841379	0.420689
101	0.532253	0.935493	0.467747
102	0.507293	0.985413	0.492707
103	0.497135	0.994271	0.502865
104	0.602295	0.79541	0.397705
105	0.702278	0.595444	0.297722
106	0.688635	0.622729	0.311365
107	0.643321	0.713359	0.356679
108	0.721061	0.557877	0.278939
109	0.692647	0.614705	0.307353
110	0.901945	0.196109	0.0980547
111	0.899232	0.201537	0.100768
112	0.874971	0.250059	0.125029
113	0.853945	0.29211	0.146055
114	0.823994	0.352012	0.176006
115	0.799948	0.400105	0.200052
116	0.760489	0.479022	0.239511
117	0.743513	0.512973	0.256487
118	0.703144	0.593711	0.296856
119	0.776268	0.447463	0.223732
120	0.807986	0.384029	0.192014
121	0.767585	0.46483	0.232415
122	0.730102	0.539797	0.269898
123	0.682772	0.634457	0.317228
124	0.686889	0.626221	0.313111
125	0.649172	0.701655	0.350828
126	0.635479	0.729043	0.364521
127	0.63777	0.724459	0.36223
128	0.596732	0.806535	0.403268
129	0.583188	0.833625	0.416812
130	0.522919	0.954163	0.477081
131	0.52781	0.944381	0.47219
132	0.484103	0.968207	0.515897
133	0.435144	0.870288	0.564856
134	0.3861	0.772199	0.6139
135	0.326787	0.653574	0.673213
136	0.300369	0.600738	0.699631
137	0.544619	0.910761	0.455381
138	0.491825	0.983649	0.508175
139	0.424311	0.848623	0.575689
140	0.372778	0.745555	0.627222
141	0.450284	0.900568	0.549716
142	0.413739	0.827478	0.586261
143	0.407446	0.814891	0.592554
144	0.325564	0.651129	0.674436
145	0.249615	0.499231	0.750385
146	0.402109	0.804219	0.597891
147	0.310215	0.62043	0.689785
148	0.474805	0.94961	0.525195
149	0.492402	0.984805	0.507598
150	0.371916	0.743831	0.628084
151	0.61946	0.761081	0.38054
152	0.465771	0.931541	0.534229

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	6	0.041958	NOK
5% type I error level	16	0.111888	NOK
10% type I error level	23	0.160839	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 & 6 & 0.041958 & NOK \tabularnewline
5% type I error level & 16 & 0.111888 & NOK \tabularnewline
10% type I error level & 23 & 0.160839 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253613&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]6[/C][C]0.041958[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.111888[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.160839[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253613&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253613&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	6	0.041958	NOK
5% type I error level	16	0.111888	NOK
10% type I error level	23	0.160839	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):

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