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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 07 Dec 2016 17:16:47 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/07/t1481127475g5rm0zvyce1ojtm.htm/, Retrieved Fri, 01 Nov 2024 03:48:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298236, Retrieved Fri, 01 Nov 2024 03:48:23 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Mulitple regression: SK1, SK2, SK3, SK4, SK5, SK6, ITHSUM

Estimated Impact

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

-     [Multiple Regression] [Multiple regressi...] [2016-12-07 16:06:08] [18cdc3a4292fc57d63398df4d659b9a6]
- R P     [Multiple Regression] [Multiple regressi...] [2016-12-07 16:16:47] [84a79156fb687334cf7dc390d7b82d5a] [Current]

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Dataseries X:

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Histogram

Boxplots

4	2	4	3	5	4	14
5	3	3	4	5	4	19
4	4	5	4	5	4	17
3	4	3	3	4	4	17
4	4	5	4	5	4	15
3	4	4	4	5	5	20
3	4	4	3	3	4	15
3	4	5	4	4	4	19
4	5	4	4	5	5	15
4	5	5	4	5	5	15
4	4	2	4	5	4	19
4	4	4	3	4	5	20
3	3	5	4	4	5	18
4	4	5	4	2	5	15
3	4	5	4	4	5	14
3	4	5	4	4	5	20
5	5	4	3	4	4	16
4	4	4	4	5	4	16
3	4	5	3	4	5	16
4	4	4	4	5	5	10
4	4	5	4	4	5	19
4	4	5	4	4	4	19
4	4	5	4	4	5	16
3	4	4	4	4	4	15
3	4	4	3	5	5	18
4	4	4	4	4	4	17
2	4	5	4	5	5	19
5	4	4	4	4	4	17
4	5	5	4	5	5	19
5	4	5	4	4	5	20
2	3	5	4	5	4	19
4	5	2	4	4	4	16
3	4	5	4	4	4	15
4	3	5	3	4	5	16
4	3	3	4	4	4	18
4	4	5	4	4	4	16
5	4	4	4	4	4	15
4	5	5	4	5	5	17
5	5	5	3	5	5	20
5	4	5	3	4	4	19
4	4	4	3	4	5	7
4	4	4	4	4	4	13
3	5	5	3	3	4	16
4	4	4	4	5	4	16
4	5	5	4	4	4	18
5	5	2	4	5	4	18
5	5	5	4	4	4	16
4	3	5	4	5	5	17
4	3	4	3	4	5	19
4	4	5	4	4	4	16
3	4	4	3	3	4	19
3	4	4	4	4	3	13
4	4	4	3	5	4	16
4	4	4	4	5	4	13
5	5	3	4	5	5	12
2	4	4	4	5	5	17
4	4	4	4	5	5	17
3	4	4	4	2	4	17
4	4	5	4	5	5	16
4	2	4	4	4	4	16
4	4	4	3	5	3	14
4	4	4	3	5	4	16
5	4	5	3	3	5	13
3	4	4	3	5	5	16
3	4	4	3	4	5	14
4	5	5	5	5	4	20
4	4	4	4	4	4	13
4	4	4	5	5	4	18
3	4	3	4	4	4	14
4	4	4	4	5	4	19
3	4	5	3	5	5	18
3	3	5	4	4	5	14
4	3	5	4	4	4	18
4	4	5	4	4	5	19
3	3	3	4	4	4	15
4	4	4	4	5	4	14
4	4	3	4	5	5	17
4	4	4	4	5	5	19
5	4	4	4	4	4	13
5	4	3	5	4	5	19
4	4	5	4	5	5	18
3	4	5	4	4	5	20
4	2	3	3	4	4	15
4	4	5	4	4	3	15
4	4	5	4	4	5	20
4	4	4	4	5	4	15
4	5	4	4	5	3	19
3	4	4	3	5	5	18
4	4	5	4	4	5	18
5	4	3	4	4	5	15
5	4	5	5	4	5	20
4	5	4	4	5	5	17
3	4	5	4	4	5	12
5	3	4	4	5	5	18
4	4	5	4	4	5	19
5	4	4	4	4	5	20
5	4	4	5	5	5	17
4	4	3	3	4	3	16
4	4	5	4	4	4	18
4	4	5	4	4	4	18
3	4	5	4	5	3	14
4	4	4	4	4	4	15
4	4	4	3	4	5	12
3	3	4	3	5	5	17
4	4	4	3	4	4	14
3	4	5	4	4	4	18
4	4	5	4	3	4	17
5	4	5	1	5	5	17
5	4	5	4	5	5	20
4	4	4	4	4	3	16
4	4	5	3	4	4	14
3	4	4	3	4	5	15
4	4	4	4	4	4	18
4	4	4	4	5	4	20
4	5	3	4	4	4	17
3	4	4	4	4	4	17
4	4	4	3	4	4	17
4	4	4	4	4	5	17
3	4	3	3	4	4	15
4	4	4	3	4	3	17
3	2	4	2	4	4	18
4	4	4	3	5	4	17
5	4	4	3	5	4	20
2	4	4	3	3	5	15
3	3	4	4	4	4	16
4	4	4	3	4	4	15
5	5	4	4	5	4	18
4	5	5	4	4	4	15
5	5	5	5	5	4	18
4	5	5	4	5	5	20
4	4	4	3	4	5	19
3	4	5	4	5	4	14
4	4	5	4	4	4	16
4	4	2	4	4	4	15
4	4	3	4	5	5	17
4	4	4	4	5	5	18
5	4	5	3	5	4	20
4	3	5	4	4	4	17
4	4	5	4	4	4	18
3	3	2	3	4	4	15
4	5	5	4	4	3	16
4	4	4	3	4	4	11
4	4	4	4	4	5	15
3	4	5	3	5	5	18
4	4	5	4	4	5	17
5	4	5	4	5	4	16
4	4	5	4	3	4	12
2	3	5	4	4	4	19
4	4	4	4	4	5	18
4	3	4	3	5	5	15
4	4	4	4	4	3	17
4	5	5	5	4	4	19
5	4	3	4	4	4	18
5	4	4	3	4	4	19
3	3	1	4	5	5	16
4	4	4	4	4	5	16
4	4	4	4	5	4	16
2	3	4	5	5	4	14

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	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298236&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&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	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
SK1,[t] = + 2.07979 + 0.316544`SK2,`[t] -0.0688338`SK3,`[t] + 0.0234453`SK4,`[t] + 0.0862048`SK5,`[t] -0.0354811`SK6,`[t] + 0.0311182ITHSUM[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SK1,[t] =  +  2.07979 +  0.316544`SK2,`[t] -0.0688338`SK3,`[t] +  0.0234453`SK4,`[t] +  0.0862048`SK5,`[t] -0.0354811`SK6,`[t] +  0.0311182ITHSUM[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SK1,[t] =  +  2.07979 +  0.316544`SK2,`[t] -0.0688338`SK3,`[t] +  0.0234453`SK4,`[t] +  0.0862048`SK5,`[t] -0.0354811`SK6,`[t] +  0.0311182ITHSUM[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298236&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&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
SK1,[t] = + 2.07979 + 0.316544`SK2,`[t] -0.0688338`SK3,`[t] + 0.0234453`SK4,`[t] + 0.0862048`SK5,`[t] -0.0354811`SK6,`[t] + 0.0311182ITHSUM[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+2.08	0.781	+2.6630e+00	0.008585	0.004293
`SK2,`	+0.3165	0.09717	+3.2580e+00	0.001388	0.000694
`SK3,`	-0.06883	0.07363	-9.3490e-01	0.3513	0.1757
`SK4,`	+0.02345	0.1	+2.3440e-01	0.815	0.4075
`SK5,`	+0.08621	0.09423	+9.1480e-01	0.3618	0.1809
`SK6,`	-0.03548	0.09717	-3.6510e-01	0.7155	0.3578
ITHSUM	+0.03112	0.02577	+1.2080e+00	0.2291	0.1146

\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) & +2.08 &  0.781 & +2.6630e+00 &  0.008585 &  0.004293 \tabularnewline
`SK2,` & +0.3165 &  0.09717 & +3.2580e+00 &  0.001388 &  0.000694 \tabularnewline
`SK3,` & -0.06883 &  0.07363 & -9.3490e-01 &  0.3513 &  0.1757 \tabularnewline
`SK4,` & +0.02345 &  0.1 & +2.3440e-01 &  0.815 &  0.4075 \tabularnewline
`SK5,` & +0.08621 &  0.09423 & +9.1480e-01 &  0.3618 &  0.1809 \tabularnewline
`SK6,` & -0.03548 &  0.09717 & -3.6510e-01 &  0.7155 &  0.3578 \tabularnewline
ITHSUM & +0.03112 &  0.02577 & +1.2080e+00 &  0.2291 &  0.1146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&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]+2.08[/C][C] 0.781[/C][C]+2.6630e+00[/C][C] 0.008585[/C][C] 0.004293[/C][/ROW]
[ROW][C]`SK2,`[/C][C]+0.3165[/C][C] 0.09717[/C][C]+3.2580e+00[/C][C] 0.001388[/C][C] 0.000694[/C][/ROW]
[ROW][C]`SK3,`[/C][C]-0.06883[/C][C] 0.07363[/C][C]-9.3490e-01[/C][C] 0.3513[/C][C] 0.1757[/C][/ROW]
[ROW][C]`SK4,`[/C][C]+0.02345[/C][C] 0.1[/C][C]+2.3440e-01[/C][C] 0.815[/C][C] 0.4075[/C][/ROW]
[ROW][C]`SK5,`[/C][C]+0.08621[/C][C] 0.09423[/C][C]+9.1480e-01[/C][C] 0.3618[/C][C] 0.1809[/C][/ROW]
[ROW][C]`SK6,`[/C][C]-0.03548[/C][C] 0.09717[/C][C]-3.6510e-01[/C][C] 0.7155[/C][C] 0.3578[/C][/ROW]
[ROW][C]ITHSUM[/C][C]+0.03112[/C][C] 0.02577[/C][C]+1.2080e+00[/C][C] 0.2291[/C][C] 0.1146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298236&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&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)	+2.08	0.781	+2.6630e+00	0.008585	0.004293
`SK2,`	+0.3165	0.09717	+3.2580e+00	0.001388	0.000694
`SK3,`	-0.06883	0.07363	-9.3490e-01	0.3513	0.1757
`SK4,`	+0.02345	0.1	+2.3440e-01	0.815	0.4075
`SK5,`	+0.08621	0.09423	+9.1480e-01	0.3618	0.1809
`SK6,`	-0.03548	0.09717	-3.6510e-01	0.7155	0.3578
ITHSUM	+0.03112	0.02577	+1.2080e+00	0.2291	0.1146

Multiple Linear Regression - Regression Statistics
Multiple R	0.3043
R-squared	0.09259
Adjusted R-squared	0.05654
F-TEST (value)	2.568
F-TEST (DF numerator)	6
F-TEST (DF denominator)	151
p-value	0.02134
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.7114
Sum Squared Residuals	76.41

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3043 \tabularnewline
R-squared &  0.09259 \tabularnewline
Adjusted R-squared &  0.05654 \tabularnewline
F-TEST (value) &  2.568 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value &  0.02134 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.7114 \tabularnewline
Sum Squared Residuals &  76.41 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3043[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.09259[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.05654[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.568[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C] 0.02134[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.7114[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 76.41[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298236&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&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	0.3043
R-squared	0.09259
Adjusted R-squared	0.05654
F-TEST (value)	2.568
F-TEST (DF numerator)	6
F-TEST (DF denominator)	151
p-value	0.02134
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.7114
Sum Squared Residuals	76.41

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4	3.233	0.7674
2	5	3.797	1.203
3	4	3.914	0.08631
4	3	3.942	-0.9417
5	4	3.851	0.1485
6	3	4.04	-1.04
7	3	3.724	-0.7244
8	3	3.89	-0.8897
9	4	4.201	-0.2014
10	4	4.133	-0.1325
11	4	4.182	-0.1824
12	4	3.931	0.06925
13	3	3.507	-0.5066
14	4	3.557	0.4426
15	3	3.699	-0.6986
16	3	3.885	-0.8854
17	5	4.158	0.8417
18	4	3.951	0.04859
19	3	3.737	-0.7374
20	4	3.729	0.2708
21	4	3.854	0.1458
22	4	3.89	0.1103
23	4	3.761	0.2391
24	3	3.834	-0.8341
25	3	3.955	-0.9547
26	4	3.896	0.1037
27	2	3.94	-1.94
28	5	3.896	1.104
29	4	4.257	-0.257
30	5	3.885	1.115
31	2	3.659	-1.659
32	4	4.319	-0.3194
33	3	3.765	-0.7652
34	4	3.421	0.5791
35	4	3.68	0.3203
36	4	3.796	0.2036
37	5	3.834	1.166
38	4	4.195	-0.1948
39	5	4.265	0.7353
40	5	3.866	1.134
41	4	3.526	0.4738
42	4	3.772	0.2282
43	3	4.003	-1.003
44	4	3.951	0.04859
45	4	4.175	-0.1751
46	5	4.468	0.5321
47	5	4.113	0.8871
48	4	3.562	0.4383
49	4	3.583	0.4169
50	4	3.796	0.2036
51	3	3.849	-0.8489
52	3	3.807	-0.8073
53	4	3.928	0.07204
54	4	3.858	0.1419
55	5	4.177	0.8232
56	2	3.947	-1.947
57	4	3.947	0.05296
58	3	3.724	-0.7239
59	4	3.847	0.1529
60	4	3.232	0.7679
61	4	3.901	0.0988
62	4	3.928	0.07204
63	5	3.558	1.442
64	3	3.892	-0.8925
65	3	3.744	-0.744
66	4	4.347	-0.347
67	4	3.772	0.2282
68	4	4.037	-0.03709
69	3	3.872	-0.8718
70	4	4.045	-0.04476
71	3	3.886	-0.8859
72	3	3.382	-0.3821
73	4	3.542	0.4579
74	4	3.854	0.1458
75	3	3.586	-0.5864
76	4	3.889	0.1108
77	4	4.016	-0.01588
78	4	4.009	-0.009279
79	5	3.772	1.228
80	5	4.015	0.9846
81	4	3.909	0.09067
82	3	3.885	-0.8854
83	4	3.246	0.7536
84	4	3.801	0.1993
85	4	3.885	0.1146
86	4	3.92	0.07971
87	4	4.397	-0.3968
88	3	3.955	-0.9547
89	4	3.823	0.1769
90	5	3.867	1.133
91	5	3.909	1.091
92	4	4.264	-0.2636
93	3	3.636	-0.6364
94	5	3.662	1.338
95	4	3.854	0.1458
96	5	3.954	1.046
97	5	3.97	1.03
98	4	3.946	0.05393
99	4	3.859	0.1414
100	4	3.859	0.1414
101	3	3.856	-0.8558
102	4	3.834	0.1659
103	4	3.682	0.3182
104	3	3.607	-0.6071
105	4	3.78	0.2205
106	3	3.859	-0.8586
107	4	3.741	0.2587
108	5	3.808	1.192
109	5	3.972	1.028
110	4	3.901	0.09932
111	4	3.711	0.2893
112	3	3.775	-0.7752
113	4	3.927	0.07256
114	4	4.076	-0.07588
115	4	4.282	-0.2817
116	3	3.896	-0.8963
117	4	3.873	0.1271
118	4	3.861	0.1392
119	3	3.879	-0.8795
120	4	3.908	0.09165
121	3	3.247	-0.2475
122	4	3.959	0.04092
123	5	4.052	0.9476
124	2	3.689	-1.689
125	3	3.549	-0.5487
126	4	3.811	0.1894
127	5	4.33	0.6698
128	4	4.082	-0.08179
129	5	4.285	0.7152
130	4	4.288	-0.2881
131	4	3.9	0.1004
132	3	3.82	-0.8203
133	4	3.796	0.2036
134	4	3.972	0.02825
135	4	4.016	-0.01588
136	4	3.978	0.02184
137	5	3.984	1.016
138	4	3.511	0.4891
139	4	3.859	0.1414
140	3	3.632	-0.6318
141	4	4.148	-0.1484
142	4	3.686	0.3138
143	4	3.799	0.2014
144	3	3.886	-0.8859
145	4	3.792	0.208
146	5	3.883	1.117
147	4	3.586	0.4143
148	2	3.573	-1.573
149	4	3.892	0.108
150	4	3.545	0.4552
151	4	3.932	0.0682
152	4	4.23	-0.2297
153	5	3.996	1.004
154	5	3.935	1.065
155	3	3.806	-0.8059
156	4	3.83	0.1703
157	4	3.951	0.04859
158	2	3.596	-1.596

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  4 &  3.233 &  0.7674 \tabularnewline
2 &  5 &  3.797 &  1.203 \tabularnewline
3 &  4 &  3.914 &  0.08631 \tabularnewline
4 &  3 &  3.942 & -0.9417 \tabularnewline
5 &  4 &  3.851 &  0.1485 \tabularnewline
6 &  3 &  4.04 & -1.04 \tabularnewline
7 &  3 &  3.724 & -0.7244 \tabularnewline
8 &  3 &  3.89 & -0.8897 \tabularnewline
9 &  4 &  4.201 & -0.2014 \tabularnewline
10 &  4 &  4.133 & -0.1325 \tabularnewline
11 &  4 &  4.182 & -0.1824 \tabularnewline
12 &  4 &  3.931 &  0.06925 \tabularnewline
13 &  3 &  3.507 & -0.5066 \tabularnewline
14 &  4 &  3.557 &  0.4426 \tabularnewline
15 &  3 &  3.699 & -0.6986 \tabularnewline
16 &  3 &  3.885 & -0.8854 \tabularnewline
17 &  5 &  4.158 &  0.8417 \tabularnewline
18 &  4 &  3.951 &  0.04859 \tabularnewline
19 &  3 &  3.737 & -0.7374 \tabularnewline
20 &  4 &  3.729 &  0.2708 \tabularnewline
21 &  4 &  3.854 &  0.1458 \tabularnewline
22 &  4 &  3.89 &  0.1103 \tabularnewline
23 &  4 &  3.761 &  0.2391 \tabularnewline
24 &  3 &  3.834 & -0.8341 \tabularnewline
25 &  3 &  3.955 & -0.9547 \tabularnewline
26 &  4 &  3.896 &  0.1037 \tabularnewline
27 &  2 &  3.94 & -1.94 \tabularnewline
28 &  5 &  3.896 &  1.104 \tabularnewline
29 &  4 &  4.257 & -0.257 \tabularnewline
30 &  5 &  3.885 &  1.115 \tabularnewline
31 &  2 &  3.659 & -1.659 \tabularnewline
32 &  4 &  4.319 & -0.3194 \tabularnewline
33 &  3 &  3.765 & -0.7652 \tabularnewline
34 &  4 &  3.421 &  0.5791 \tabularnewline
35 &  4 &  3.68 &  0.3203 \tabularnewline
36 &  4 &  3.796 &  0.2036 \tabularnewline
37 &  5 &  3.834 &  1.166 \tabularnewline
38 &  4 &  4.195 & -0.1948 \tabularnewline
39 &  5 &  4.265 &  0.7353 \tabularnewline
40 &  5 &  3.866 &  1.134 \tabularnewline
41 &  4 &  3.526 &  0.4738 \tabularnewline
42 &  4 &  3.772 &  0.2282 \tabularnewline
43 &  3 &  4.003 & -1.003 \tabularnewline
44 &  4 &  3.951 &  0.04859 \tabularnewline
45 &  4 &  4.175 & -0.1751 \tabularnewline
46 &  5 &  4.468 &  0.5321 \tabularnewline
47 &  5 &  4.113 &  0.8871 \tabularnewline
48 &  4 &  3.562 &  0.4383 \tabularnewline
49 &  4 &  3.583 &  0.4169 \tabularnewline
50 &  4 &  3.796 &  0.2036 \tabularnewline
51 &  3 &  3.849 & -0.8489 \tabularnewline
52 &  3 &  3.807 & -0.8073 \tabularnewline
53 &  4 &  3.928 &  0.07204 \tabularnewline
54 &  4 &  3.858 &  0.1419 \tabularnewline
55 &  5 &  4.177 &  0.8232 \tabularnewline
56 &  2 &  3.947 & -1.947 \tabularnewline
57 &  4 &  3.947 &  0.05296 \tabularnewline
58 &  3 &  3.724 & -0.7239 \tabularnewline
59 &  4 &  3.847 &  0.1529 \tabularnewline
60 &  4 &  3.232 &  0.7679 \tabularnewline
61 &  4 &  3.901 &  0.0988 \tabularnewline
62 &  4 &  3.928 &  0.07204 \tabularnewline
63 &  5 &  3.558 &  1.442 \tabularnewline
64 &  3 &  3.892 & -0.8925 \tabularnewline
65 &  3 &  3.744 & -0.744 \tabularnewline
66 &  4 &  4.347 & -0.347 \tabularnewline
67 &  4 &  3.772 &  0.2282 \tabularnewline
68 &  4 &  4.037 & -0.03709 \tabularnewline
69 &  3 &  3.872 & -0.8718 \tabularnewline
70 &  4 &  4.045 & -0.04476 \tabularnewline
71 &  3 &  3.886 & -0.8859 \tabularnewline
72 &  3 &  3.382 & -0.3821 \tabularnewline
73 &  4 &  3.542 &  0.4579 \tabularnewline
74 &  4 &  3.854 &  0.1458 \tabularnewline
75 &  3 &  3.586 & -0.5864 \tabularnewline
76 &  4 &  3.889 &  0.1108 \tabularnewline
77 &  4 &  4.016 & -0.01588 \tabularnewline
78 &  4 &  4.009 & -0.009279 \tabularnewline
79 &  5 &  3.772 &  1.228 \tabularnewline
80 &  5 &  4.015 &  0.9846 \tabularnewline
81 &  4 &  3.909 &  0.09067 \tabularnewline
82 &  3 &  3.885 & -0.8854 \tabularnewline
83 &  4 &  3.246 &  0.7536 \tabularnewline
84 &  4 &  3.801 &  0.1993 \tabularnewline
85 &  4 &  3.885 &  0.1146 \tabularnewline
86 &  4 &  3.92 &  0.07971 \tabularnewline
87 &  4 &  4.397 & -0.3968 \tabularnewline
88 &  3 &  3.955 & -0.9547 \tabularnewline
89 &  4 &  3.823 &  0.1769 \tabularnewline
90 &  5 &  3.867 &  1.133 \tabularnewline
91 &  5 &  3.909 &  1.091 \tabularnewline
92 &  4 &  4.264 & -0.2636 \tabularnewline
93 &  3 &  3.636 & -0.6364 \tabularnewline
94 &  5 &  3.662 &  1.338 \tabularnewline
95 &  4 &  3.854 &  0.1458 \tabularnewline
96 &  5 &  3.954 &  1.046 \tabularnewline
97 &  5 &  3.97 &  1.03 \tabularnewline
98 &  4 &  3.946 &  0.05393 \tabularnewline
99 &  4 &  3.859 &  0.1414 \tabularnewline
100 &  4 &  3.859 &  0.1414 \tabularnewline
101 &  3 &  3.856 & -0.8558 \tabularnewline
102 &  4 &  3.834 &  0.1659 \tabularnewline
103 &  4 &  3.682 &  0.3182 \tabularnewline
104 &  3 &  3.607 & -0.6071 \tabularnewline
105 &  4 &  3.78 &  0.2205 \tabularnewline
106 &  3 &  3.859 & -0.8586 \tabularnewline
107 &  4 &  3.741 &  0.2587 \tabularnewline
108 &  5 &  3.808 &  1.192 \tabularnewline
109 &  5 &  3.972 &  1.028 \tabularnewline
110 &  4 &  3.901 &  0.09932 \tabularnewline
111 &  4 &  3.711 &  0.2893 \tabularnewline
112 &  3 &  3.775 & -0.7752 \tabularnewline
113 &  4 &  3.927 &  0.07256 \tabularnewline
114 &  4 &  4.076 & -0.07588 \tabularnewline
115 &  4 &  4.282 & -0.2817 \tabularnewline
116 &  3 &  3.896 & -0.8963 \tabularnewline
117 &  4 &  3.873 &  0.1271 \tabularnewline
118 &  4 &  3.861 &  0.1392 \tabularnewline
119 &  3 &  3.879 & -0.8795 \tabularnewline
120 &  4 &  3.908 &  0.09165 \tabularnewline
121 &  3 &  3.247 & -0.2475 \tabularnewline
122 &  4 &  3.959 &  0.04092 \tabularnewline
123 &  5 &  4.052 &  0.9476 \tabularnewline
124 &  2 &  3.689 & -1.689 \tabularnewline
125 &  3 &  3.549 & -0.5487 \tabularnewline
126 &  4 &  3.811 &  0.1894 \tabularnewline
127 &  5 &  4.33 &  0.6698 \tabularnewline
128 &  4 &  4.082 & -0.08179 \tabularnewline
129 &  5 &  4.285 &  0.7152 \tabularnewline
130 &  4 &  4.288 & -0.2881 \tabularnewline
131 &  4 &  3.9 &  0.1004 \tabularnewline
132 &  3 &  3.82 & -0.8203 \tabularnewline
133 &  4 &  3.796 &  0.2036 \tabularnewline
134 &  4 &  3.972 &  0.02825 \tabularnewline
135 &  4 &  4.016 & -0.01588 \tabularnewline
136 &  4 &  3.978 &  0.02184 \tabularnewline
137 &  5 &  3.984 &  1.016 \tabularnewline
138 &  4 &  3.511 &  0.4891 \tabularnewline
139 &  4 &  3.859 &  0.1414 \tabularnewline
140 &  3 &  3.632 & -0.6318 \tabularnewline
141 &  4 &  4.148 & -0.1484 \tabularnewline
142 &  4 &  3.686 &  0.3138 \tabularnewline
143 &  4 &  3.799 &  0.2014 \tabularnewline
144 &  3 &  3.886 & -0.8859 \tabularnewline
145 &  4 &  3.792 &  0.208 \tabularnewline
146 &  5 &  3.883 &  1.117 \tabularnewline
147 &  4 &  3.586 &  0.4143 \tabularnewline
148 &  2 &  3.573 & -1.573 \tabularnewline
149 &  4 &  3.892 &  0.108 \tabularnewline
150 &  4 &  3.545 &  0.4552 \tabularnewline
151 &  4 &  3.932 &  0.0682 \tabularnewline
152 &  4 &  4.23 & -0.2297 \tabularnewline
153 &  5 &  3.996 &  1.004 \tabularnewline
154 &  5 &  3.935 &  1.065 \tabularnewline
155 &  3 &  3.806 & -0.8059 \tabularnewline
156 &  4 &  3.83 &  0.1703 \tabularnewline
157 &  4 &  3.951 &  0.04859 \tabularnewline
158 &  2 &  3.596 & -1.596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&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] 4[/C][C] 3.233[/C][C] 0.7674[/C][/ROW]
[ROW][C]2[/C][C] 5[/C][C] 3.797[/C][C] 1.203[/C][/ROW]
[ROW][C]3[/C][C] 4[/C][C] 3.914[/C][C] 0.08631[/C][/ROW]
[ROW][C]4[/C][C] 3[/C][C] 3.942[/C][C]-0.9417[/C][/ROW]
[ROW][C]5[/C][C] 4[/C][C] 3.851[/C][C] 0.1485[/C][/ROW]
[ROW][C]6[/C][C] 3[/C][C] 4.04[/C][C]-1.04[/C][/ROW]
[ROW][C]7[/C][C] 3[/C][C] 3.724[/C][C]-0.7244[/C][/ROW]
[ROW][C]8[/C][C] 3[/C][C] 3.89[/C][C]-0.8897[/C][/ROW]
[ROW][C]9[/C][C] 4[/C][C] 4.201[/C][C]-0.2014[/C][/ROW]
[ROW][C]10[/C][C] 4[/C][C] 4.133[/C][C]-0.1325[/C][/ROW]
[ROW][C]11[/C][C] 4[/C][C] 4.182[/C][C]-0.1824[/C][/ROW]
[ROW][C]12[/C][C] 4[/C][C] 3.931[/C][C] 0.06925[/C][/ROW]
[ROW][C]13[/C][C] 3[/C][C] 3.507[/C][C]-0.5066[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 3.557[/C][C] 0.4426[/C][/ROW]
[ROW][C]15[/C][C] 3[/C][C] 3.699[/C][C]-0.6986[/C][/ROW]
[ROW][C]16[/C][C] 3[/C][C] 3.885[/C][C]-0.8854[/C][/ROW]
[ROW][C]17[/C][C] 5[/C][C] 4.158[/C][C] 0.8417[/C][/ROW]
[ROW][C]18[/C][C] 4[/C][C] 3.951[/C][C] 0.04859[/C][/ROW]
[ROW][C]19[/C][C] 3[/C][C] 3.737[/C][C]-0.7374[/C][/ROW]
[ROW][C]20[/C][C] 4[/C][C] 3.729[/C][C] 0.2708[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 3.854[/C][C] 0.1458[/C][/ROW]
[ROW][C]22[/C][C] 4[/C][C] 3.89[/C][C] 0.1103[/C][/ROW]
[ROW][C]23[/C][C] 4[/C][C] 3.761[/C][C] 0.2391[/C][/ROW]
[ROW][C]24[/C][C] 3[/C][C] 3.834[/C][C]-0.8341[/C][/ROW]
[ROW][C]25[/C][C] 3[/C][C] 3.955[/C][C]-0.9547[/C][/ROW]
[ROW][C]26[/C][C] 4[/C][C] 3.896[/C][C] 0.1037[/C][/ROW]
[ROW][C]27[/C][C] 2[/C][C] 3.94[/C][C]-1.94[/C][/ROW]
[ROW][C]28[/C][C] 5[/C][C] 3.896[/C][C] 1.104[/C][/ROW]
[ROW][C]29[/C][C] 4[/C][C] 4.257[/C][C]-0.257[/C][/ROW]
[ROW][C]30[/C][C] 5[/C][C] 3.885[/C][C] 1.115[/C][/ROW]
[ROW][C]31[/C][C] 2[/C][C] 3.659[/C][C]-1.659[/C][/ROW]
[ROW][C]32[/C][C] 4[/C][C] 4.319[/C][C]-0.3194[/C][/ROW]
[ROW][C]33[/C][C] 3[/C][C] 3.765[/C][C]-0.7652[/C][/ROW]
[ROW][C]34[/C][C] 4[/C][C] 3.421[/C][C] 0.5791[/C][/ROW]
[ROW][C]35[/C][C] 4[/C][C] 3.68[/C][C] 0.3203[/C][/ROW]
[ROW][C]36[/C][C] 4[/C][C] 3.796[/C][C] 0.2036[/C][/ROW]
[ROW][C]37[/C][C] 5[/C][C] 3.834[/C][C] 1.166[/C][/ROW]
[ROW][C]38[/C][C] 4[/C][C] 4.195[/C][C]-0.1948[/C][/ROW]
[ROW][C]39[/C][C] 5[/C][C] 4.265[/C][C] 0.7353[/C][/ROW]
[ROW][C]40[/C][C] 5[/C][C] 3.866[/C][C] 1.134[/C][/ROW]
[ROW][C]41[/C][C] 4[/C][C] 3.526[/C][C] 0.4738[/C][/ROW]
[ROW][C]42[/C][C] 4[/C][C] 3.772[/C][C] 0.2282[/C][/ROW]
[ROW][C]43[/C][C] 3[/C][C] 4.003[/C][C]-1.003[/C][/ROW]
[ROW][C]44[/C][C] 4[/C][C] 3.951[/C][C] 0.04859[/C][/ROW]
[ROW][C]45[/C][C] 4[/C][C] 4.175[/C][C]-0.1751[/C][/ROW]
[ROW][C]46[/C][C] 5[/C][C] 4.468[/C][C] 0.5321[/C][/ROW]
[ROW][C]47[/C][C] 5[/C][C] 4.113[/C][C] 0.8871[/C][/ROW]
[ROW][C]48[/C][C] 4[/C][C] 3.562[/C][C] 0.4383[/C][/ROW]
[ROW][C]49[/C][C] 4[/C][C] 3.583[/C][C] 0.4169[/C][/ROW]
[ROW][C]50[/C][C] 4[/C][C] 3.796[/C][C] 0.2036[/C][/ROW]
[ROW][C]51[/C][C] 3[/C][C] 3.849[/C][C]-0.8489[/C][/ROW]
[ROW][C]52[/C][C] 3[/C][C] 3.807[/C][C]-0.8073[/C][/ROW]
[ROW][C]53[/C][C] 4[/C][C] 3.928[/C][C] 0.07204[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 3.858[/C][C] 0.1419[/C][/ROW]
[ROW][C]55[/C][C] 5[/C][C] 4.177[/C][C] 0.8232[/C][/ROW]
[ROW][C]56[/C][C] 2[/C][C] 3.947[/C][C]-1.947[/C][/ROW]
[ROW][C]57[/C][C] 4[/C][C] 3.947[/C][C] 0.05296[/C][/ROW]
[ROW][C]58[/C][C] 3[/C][C] 3.724[/C][C]-0.7239[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C] 3.847[/C][C] 0.1529[/C][/ROW]
[ROW][C]60[/C][C] 4[/C][C] 3.232[/C][C] 0.7679[/C][/ROW]
[ROW][C]61[/C][C] 4[/C][C] 3.901[/C][C] 0.0988[/C][/ROW]
[ROW][C]62[/C][C] 4[/C][C] 3.928[/C][C] 0.07204[/C][/ROW]
[ROW][C]63[/C][C] 5[/C][C] 3.558[/C][C] 1.442[/C][/ROW]
[ROW][C]64[/C][C] 3[/C][C] 3.892[/C][C]-0.8925[/C][/ROW]
[ROW][C]65[/C][C] 3[/C][C] 3.744[/C][C]-0.744[/C][/ROW]
[ROW][C]66[/C][C] 4[/C][C] 4.347[/C][C]-0.347[/C][/ROW]
[ROW][C]67[/C][C] 4[/C][C] 3.772[/C][C] 0.2282[/C][/ROW]
[ROW][C]68[/C][C] 4[/C][C] 4.037[/C][C]-0.03709[/C][/ROW]
[ROW][C]69[/C][C] 3[/C][C] 3.872[/C][C]-0.8718[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 4.045[/C][C]-0.04476[/C][/ROW]
[ROW][C]71[/C][C] 3[/C][C] 3.886[/C][C]-0.8859[/C][/ROW]
[ROW][C]72[/C][C] 3[/C][C] 3.382[/C][C]-0.3821[/C][/ROW]
[ROW][C]73[/C][C] 4[/C][C] 3.542[/C][C] 0.4579[/C][/ROW]
[ROW][C]74[/C][C] 4[/C][C] 3.854[/C][C] 0.1458[/C][/ROW]
[ROW][C]75[/C][C] 3[/C][C] 3.586[/C][C]-0.5864[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 3.889[/C][C] 0.1108[/C][/ROW]
[ROW][C]77[/C][C] 4[/C][C] 4.016[/C][C]-0.01588[/C][/ROW]
[ROW][C]78[/C][C] 4[/C][C] 4.009[/C][C]-0.009279[/C][/ROW]
[ROW][C]79[/C][C] 5[/C][C] 3.772[/C][C] 1.228[/C][/ROW]
[ROW][C]80[/C][C] 5[/C][C] 4.015[/C][C] 0.9846[/C][/ROW]
[ROW][C]81[/C][C] 4[/C][C] 3.909[/C][C] 0.09067[/C][/ROW]
[ROW][C]82[/C][C] 3[/C][C] 3.885[/C][C]-0.8854[/C][/ROW]
[ROW][C]83[/C][C] 4[/C][C] 3.246[/C][C] 0.7536[/C][/ROW]
[ROW][C]84[/C][C] 4[/C][C] 3.801[/C][C] 0.1993[/C][/ROW]
[ROW][C]85[/C][C] 4[/C][C] 3.885[/C][C] 0.1146[/C][/ROW]
[ROW][C]86[/C][C] 4[/C][C] 3.92[/C][C] 0.07971[/C][/ROW]
[ROW][C]87[/C][C] 4[/C][C] 4.397[/C][C]-0.3968[/C][/ROW]
[ROW][C]88[/C][C] 3[/C][C] 3.955[/C][C]-0.9547[/C][/ROW]
[ROW][C]89[/C][C] 4[/C][C] 3.823[/C][C] 0.1769[/C][/ROW]
[ROW][C]90[/C][C] 5[/C][C] 3.867[/C][C] 1.133[/C][/ROW]
[ROW][C]91[/C][C] 5[/C][C] 3.909[/C][C] 1.091[/C][/ROW]
[ROW][C]92[/C][C] 4[/C][C] 4.264[/C][C]-0.2636[/C][/ROW]
[ROW][C]93[/C][C] 3[/C][C] 3.636[/C][C]-0.6364[/C][/ROW]
[ROW][C]94[/C][C] 5[/C][C] 3.662[/C][C] 1.338[/C][/ROW]
[ROW][C]95[/C][C] 4[/C][C] 3.854[/C][C] 0.1458[/C][/ROW]
[ROW][C]96[/C][C] 5[/C][C] 3.954[/C][C] 1.046[/C][/ROW]
[ROW][C]97[/C][C] 5[/C][C] 3.97[/C][C] 1.03[/C][/ROW]
[ROW][C]98[/C][C] 4[/C][C] 3.946[/C][C] 0.05393[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 3.859[/C][C] 0.1414[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 3.859[/C][C] 0.1414[/C][/ROW]
[ROW][C]101[/C][C] 3[/C][C] 3.856[/C][C]-0.8558[/C][/ROW]
[ROW][C]102[/C][C] 4[/C][C] 3.834[/C][C] 0.1659[/C][/ROW]
[ROW][C]103[/C][C] 4[/C][C] 3.682[/C][C] 0.3182[/C][/ROW]
[ROW][C]104[/C][C] 3[/C][C] 3.607[/C][C]-0.6071[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 3.78[/C][C] 0.2205[/C][/ROW]
[ROW][C]106[/C][C] 3[/C][C] 3.859[/C][C]-0.8586[/C][/ROW]
[ROW][C]107[/C][C] 4[/C][C] 3.741[/C][C] 0.2587[/C][/ROW]
[ROW][C]108[/C][C] 5[/C][C] 3.808[/C][C] 1.192[/C][/ROW]
[ROW][C]109[/C][C] 5[/C][C] 3.972[/C][C] 1.028[/C][/ROW]
[ROW][C]110[/C][C] 4[/C][C] 3.901[/C][C] 0.09932[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 3.711[/C][C] 0.2893[/C][/ROW]
[ROW][C]112[/C][C] 3[/C][C] 3.775[/C][C]-0.7752[/C][/ROW]
[ROW][C]113[/C][C] 4[/C][C] 3.927[/C][C] 0.07256[/C][/ROW]
[ROW][C]114[/C][C] 4[/C][C] 4.076[/C][C]-0.07588[/C][/ROW]
[ROW][C]115[/C][C] 4[/C][C] 4.282[/C][C]-0.2817[/C][/ROW]
[ROW][C]116[/C][C] 3[/C][C] 3.896[/C][C]-0.8963[/C][/ROW]
[ROW][C]117[/C][C] 4[/C][C] 3.873[/C][C] 0.1271[/C][/ROW]
[ROW][C]118[/C][C] 4[/C][C] 3.861[/C][C] 0.1392[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 3.879[/C][C]-0.8795[/C][/ROW]
[ROW][C]120[/C][C] 4[/C][C] 3.908[/C][C] 0.09165[/C][/ROW]
[ROW][C]121[/C][C] 3[/C][C] 3.247[/C][C]-0.2475[/C][/ROW]
[ROW][C]122[/C][C] 4[/C][C] 3.959[/C][C] 0.04092[/C][/ROW]
[ROW][C]123[/C][C] 5[/C][C] 4.052[/C][C] 0.9476[/C][/ROW]
[ROW][C]124[/C][C] 2[/C][C] 3.689[/C][C]-1.689[/C][/ROW]
[ROW][C]125[/C][C] 3[/C][C] 3.549[/C][C]-0.5487[/C][/ROW]
[ROW][C]126[/C][C] 4[/C][C] 3.811[/C][C] 0.1894[/C][/ROW]
[ROW][C]127[/C][C] 5[/C][C] 4.33[/C][C] 0.6698[/C][/ROW]
[ROW][C]128[/C][C] 4[/C][C] 4.082[/C][C]-0.08179[/C][/ROW]
[ROW][C]129[/C][C] 5[/C][C] 4.285[/C][C] 0.7152[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 4.288[/C][C]-0.2881[/C][/ROW]
[ROW][C]131[/C][C] 4[/C][C] 3.9[/C][C] 0.1004[/C][/ROW]
[ROW][C]132[/C][C] 3[/C][C] 3.82[/C][C]-0.8203[/C][/ROW]
[ROW][C]133[/C][C] 4[/C][C] 3.796[/C][C] 0.2036[/C][/ROW]
[ROW][C]134[/C][C] 4[/C][C] 3.972[/C][C] 0.02825[/C][/ROW]
[ROW][C]135[/C][C] 4[/C][C] 4.016[/C][C]-0.01588[/C][/ROW]
[ROW][C]136[/C][C] 4[/C][C] 3.978[/C][C] 0.02184[/C][/ROW]
[ROW][C]137[/C][C] 5[/C][C] 3.984[/C][C] 1.016[/C][/ROW]
[ROW][C]138[/C][C] 4[/C][C] 3.511[/C][C] 0.4891[/C][/ROW]
[ROW][C]139[/C][C] 4[/C][C] 3.859[/C][C] 0.1414[/C][/ROW]
[ROW][C]140[/C][C] 3[/C][C] 3.632[/C][C]-0.6318[/C][/ROW]
[ROW][C]141[/C][C] 4[/C][C] 4.148[/C][C]-0.1484[/C][/ROW]
[ROW][C]142[/C][C] 4[/C][C] 3.686[/C][C] 0.3138[/C][/ROW]
[ROW][C]143[/C][C] 4[/C][C] 3.799[/C][C] 0.2014[/C][/ROW]
[ROW][C]144[/C][C] 3[/C][C] 3.886[/C][C]-0.8859[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 3.792[/C][C] 0.208[/C][/ROW]
[ROW][C]146[/C][C] 5[/C][C] 3.883[/C][C] 1.117[/C][/ROW]
[ROW][C]147[/C][C] 4[/C][C] 3.586[/C][C] 0.4143[/C][/ROW]
[ROW][C]148[/C][C] 2[/C][C] 3.573[/C][C]-1.573[/C][/ROW]
[ROW][C]149[/C][C] 4[/C][C] 3.892[/C][C] 0.108[/C][/ROW]
[ROW][C]150[/C][C] 4[/C][C] 3.545[/C][C] 0.4552[/C][/ROW]
[ROW][C]151[/C][C] 4[/C][C] 3.932[/C][C] 0.0682[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 4.23[/C][C]-0.2297[/C][/ROW]
[ROW][C]153[/C][C] 5[/C][C] 3.996[/C][C] 1.004[/C][/ROW]
[ROW][C]154[/C][C] 5[/C][C] 3.935[/C][C] 1.065[/C][/ROW]
[ROW][C]155[/C][C] 3[/C][C] 3.806[/C][C]-0.8059[/C][/ROW]
[ROW][C]156[/C][C] 4[/C][C] 3.83[/C][C] 0.1703[/C][/ROW]
[ROW][C]157[/C][C] 4[/C][C] 3.951[/C][C] 0.04859[/C][/ROW]
[ROW][C]158[/C][C] 2[/C][C] 3.596[/C][C]-1.596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298236&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&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	4	3.233	0.7674
2	5	3.797	1.203
3	4	3.914	0.08631
4	3	3.942	-0.9417
5	4	3.851	0.1485
6	3	4.04	-1.04
7	3	3.724	-0.7244
8	3	3.89	-0.8897
9	4	4.201	-0.2014
10	4	4.133	-0.1325
11	4	4.182	-0.1824
12	4	3.931	0.06925
13	3	3.507	-0.5066
14	4	3.557	0.4426
15	3	3.699	-0.6986
16	3	3.885	-0.8854
17	5	4.158	0.8417
18	4	3.951	0.04859
19	3	3.737	-0.7374
20	4	3.729	0.2708
21	4	3.854	0.1458
22	4	3.89	0.1103
23	4	3.761	0.2391
24	3	3.834	-0.8341
25	3	3.955	-0.9547
26	4	3.896	0.1037
27	2	3.94	-1.94
28	5	3.896	1.104
29	4	4.257	-0.257
30	5	3.885	1.115
31	2	3.659	-1.659
32	4	4.319	-0.3194
33	3	3.765	-0.7652
34	4	3.421	0.5791
35	4	3.68	0.3203
36	4	3.796	0.2036
37	5	3.834	1.166
38	4	4.195	-0.1948
39	5	4.265	0.7353
40	5	3.866	1.134
41	4	3.526	0.4738
42	4	3.772	0.2282
43	3	4.003	-1.003
44	4	3.951	0.04859
45	4	4.175	-0.1751
46	5	4.468	0.5321
47	5	4.113	0.8871
48	4	3.562	0.4383
49	4	3.583	0.4169
50	4	3.796	0.2036
51	3	3.849	-0.8489
52	3	3.807	-0.8073
53	4	3.928	0.07204
54	4	3.858	0.1419
55	5	4.177	0.8232
56	2	3.947	-1.947
57	4	3.947	0.05296
58	3	3.724	-0.7239
59	4	3.847	0.1529
60	4	3.232	0.7679
61	4	3.901	0.0988
62	4	3.928	0.07204
63	5	3.558	1.442
64	3	3.892	-0.8925
65	3	3.744	-0.744
66	4	4.347	-0.347
67	4	3.772	0.2282
68	4	4.037	-0.03709
69	3	3.872	-0.8718
70	4	4.045	-0.04476
71	3	3.886	-0.8859
72	3	3.382	-0.3821
73	4	3.542	0.4579
74	4	3.854	0.1458
75	3	3.586	-0.5864
76	4	3.889	0.1108
77	4	4.016	-0.01588
78	4	4.009	-0.009279
79	5	3.772	1.228
80	5	4.015	0.9846
81	4	3.909	0.09067
82	3	3.885	-0.8854
83	4	3.246	0.7536
84	4	3.801	0.1993
85	4	3.885	0.1146
86	4	3.92	0.07971
87	4	4.397	-0.3968
88	3	3.955	-0.9547
89	4	3.823	0.1769
90	5	3.867	1.133
91	5	3.909	1.091
92	4	4.264	-0.2636
93	3	3.636	-0.6364
94	5	3.662	1.338
95	4	3.854	0.1458
96	5	3.954	1.046
97	5	3.97	1.03
98	4	3.946	0.05393
99	4	3.859	0.1414
100	4	3.859	0.1414
101	3	3.856	-0.8558
102	4	3.834	0.1659
103	4	3.682	0.3182
104	3	3.607	-0.6071
105	4	3.78	0.2205
106	3	3.859	-0.8586
107	4	3.741	0.2587
108	5	3.808	1.192
109	5	3.972	1.028
110	4	3.901	0.09932
111	4	3.711	0.2893
112	3	3.775	-0.7752
113	4	3.927	0.07256
114	4	4.076	-0.07588
115	4	4.282	-0.2817
116	3	3.896	-0.8963
117	4	3.873	0.1271
118	4	3.861	0.1392
119	3	3.879	-0.8795
120	4	3.908	0.09165
121	3	3.247	-0.2475
122	4	3.959	0.04092
123	5	4.052	0.9476
124	2	3.689	-1.689
125	3	3.549	-0.5487
126	4	3.811	0.1894
127	5	4.33	0.6698
128	4	4.082	-0.08179
129	5	4.285	0.7152
130	4	4.288	-0.2881
131	4	3.9	0.1004
132	3	3.82	-0.8203
133	4	3.796	0.2036
134	4	3.972	0.02825
135	4	4.016	-0.01588
136	4	3.978	0.02184
137	5	3.984	1.016
138	4	3.511	0.4891
139	4	3.859	0.1414
140	3	3.632	-0.6318
141	4	4.148	-0.1484
142	4	3.686	0.3138
143	4	3.799	0.2014
144	3	3.886	-0.8859
145	4	3.792	0.208
146	5	3.883	1.117
147	4	3.586	0.4143
148	2	3.573	-1.573
149	4	3.892	0.108
150	4	3.545	0.4552
151	4	3.932	0.0682
152	4	4.23	-0.2297
153	5	3.996	1.004
154	5	3.935	1.065
155	3	3.806	-0.8059
156	4	3.83	0.1703
157	4	3.951	0.04859
158	2	3.596	-1.596

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1828	0.3656	0.8172
11	0.1999	0.3998	0.8001
12	0.5633	0.8735	0.4367
13	0.4573	0.9146	0.5427
14	0.5512	0.8976	0.4488
15	0.5713	0.8575	0.4287
16	0.4941	0.9882	0.5059
17	0.6798	0.6404	0.3202
18	0.5943	0.8114	0.4057
19	0.5372	0.9256	0.4628
20	0.4545	0.9089	0.5455
21	0.4411	0.8823	0.5589
22	0.3764	0.7528	0.6236
23	0.3329	0.6659	0.6671
24	0.3835	0.767	0.6165
25	0.3733	0.7466	0.6267
26	0.3086	0.6171	0.6914
27	0.5534	0.8932	0.4466
28	0.6264	0.7472	0.3736
29	0.5943	0.8114	0.4057
30	0.7748	0.4504	0.2252
31	0.8921	0.2159	0.1079
32	0.8819	0.2362	0.1181
33	0.8817	0.2366	0.1183
34	0.8793	0.2414	0.1207
35	0.8499	0.3002	0.1501
36	0.8196	0.3609	0.1804
37	0.8587	0.2827	0.1413
38	0.8318	0.3363	0.1682
39	0.8792	0.2416	0.1208
40	0.9087	0.1825	0.09127
41	0.8892	0.2217	0.1108
42	0.8629	0.2743	0.1371
43	0.8966	0.2068	0.1034
44	0.8708	0.2583	0.1292
45	0.8425	0.3151	0.1575
46	0.8261	0.3478	0.1739
47	0.8431	0.3137	0.1569
48	0.8292	0.3416	0.1708
49	0.8068	0.3865	0.1932
50	0.7723	0.4554	0.2277
51	0.7883	0.4233	0.2117
52	0.8064	0.3872	0.1936
53	0.7701	0.4598	0.2299
54	0.7317	0.5366	0.2683
55	0.7361	0.5279	0.2639
56	0.9132	0.1736	0.08679
57	0.8927	0.2146	0.1073
58	0.8927	0.2146	0.1073
59	0.8706	0.2587	0.1294
60	0.8718	0.2564	0.1282
61	0.8466	0.3068	0.1534
62	0.8166	0.3668	0.1834
63	0.8879	0.2242	0.1121
64	0.9013	0.1975	0.09875
65	0.9051	0.1899	0.09495
66	0.8902	0.2197	0.1098
67	0.8694	0.2612	0.1306
68	0.843	0.3141	0.157
69	0.8574	0.2852	0.1426
70	0.8303	0.3393	0.1697
71	0.8461	0.3077	0.1539
72	0.8246	0.3508	0.1754
73	0.8067	0.3865	0.1933
74	0.7775	0.4451	0.2225
75	0.7649	0.4701	0.2351
76	0.7283	0.5434	0.2717
77	0.6892	0.6216	0.3108
78	0.6503	0.6994	0.3497
79	0.7379	0.5243	0.2621
80	0.7697	0.4605	0.2303
81	0.7346	0.5308	0.2654
82	0.7679	0.4643	0.2321
83	0.7877	0.4245	0.2123
84	0.758	0.4839	0.242
85	0.7261	0.5478	0.2739
86	0.6871	0.6259	0.3129
87	0.6675	0.6651	0.3325
88	0.7256	0.5487	0.2744
89	0.6877	0.6245	0.3123
90	0.7613	0.4775	0.2387
91	0.7991	0.4017	0.2009
92	0.7828	0.4344	0.2172
93	0.7713	0.4574	0.2287
94	0.8673	0.2653	0.1327
95	0.8404	0.3193	0.1596
96	0.8685	0.263	0.1315
97	0.9096	0.1808	0.0904
98	0.888	0.2241	0.112
99	0.8637	0.2726	0.1363
100	0.836	0.328	0.164
101	0.8545	0.291	0.1455
102	0.8297	0.3407	0.1703
103	0.8105	0.379	0.1895
104	0.7962	0.4076	0.2038
105	0.7631	0.4739	0.2369
106	0.7888	0.4224	0.2112
107	0.7633	0.4733	0.2367
108	0.7983	0.4033	0.2017
109	0.8298	0.3403	0.1702
110	0.7946	0.4107	0.2054
111	0.762	0.476	0.238
112	0.763	0.4739	0.237
113	0.7203	0.5594	0.2797
114	0.6784	0.6432	0.3216
115	0.647	0.706	0.353
116	0.6742	0.6515	0.3258
117	0.623	0.754	0.377
118	0.5786	0.8427	0.4214
119	0.6275	0.7449	0.3725
120	0.5869	0.8261	0.4131
121	0.5325	0.935	0.4675
122	0.4893	0.9786	0.5107
123	0.4714	0.9427	0.5286
124	0.7793	0.4414	0.2207
125	0.7395	0.5211	0.2605
126	0.6909	0.6183	0.3091
127	0.6513	0.6973	0.3487
128	0.6224	0.7551	0.3776
129	0.644	0.7121	0.356
130	0.6221	0.7559	0.3779
131	0.5925	0.8149	0.4075
132	0.5923	0.8153	0.4077
133	0.5235	0.9529	0.4765
134	0.4505	0.9011	0.5495
135	0.3782	0.7563	0.6218
136	0.3085	0.6169	0.6915
137	0.2987	0.5974	0.7013
138	0.3761	0.7521	0.6239
139	0.308	0.616	0.692
140	0.3051	0.6102	0.6949
141	0.343	0.686	0.657
142	0.4216	0.8433	0.5784
143	0.3263	0.6526	0.6737
144	0.8537	0.2926	0.1463
145	0.7709	0.4582	0.2291
146	0.8587	0.2826	0.1413
147	0.7832	0.4336	0.2168
148	0.7109	0.5782	0.2891

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1828 &  0.3656 &  0.8172 \tabularnewline
11 &  0.1999 &  0.3998 &  0.8001 \tabularnewline
12 &  0.5633 &  0.8735 &  0.4367 \tabularnewline
13 &  0.4573 &  0.9146 &  0.5427 \tabularnewline
14 &  0.5512 &  0.8976 &  0.4488 \tabularnewline
15 &  0.5713 &  0.8575 &  0.4287 \tabularnewline
16 &  0.4941 &  0.9882 &  0.5059 \tabularnewline
17 &  0.6798 &  0.6404 &  0.3202 \tabularnewline
18 &  0.5943 &  0.8114 &  0.4057 \tabularnewline
19 &  0.5372 &  0.9256 &  0.4628 \tabularnewline
20 &  0.4545 &  0.9089 &  0.5455 \tabularnewline
21 &  0.4411 &  0.8823 &  0.5589 \tabularnewline
22 &  0.3764 &  0.7528 &  0.6236 \tabularnewline
23 &  0.3329 &  0.6659 &  0.6671 \tabularnewline
24 &  0.3835 &  0.767 &  0.6165 \tabularnewline
25 &  0.3733 &  0.7466 &  0.6267 \tabularnewline
26 &  0.3086 &  0.6171 &  0.6914 \tabularnewline
27 &  0.5534 &  0.8932 &  0.4466 \tabularnewline
28 &  0.6264 &  0.7472 &  0.3736 \tabularnewline
29 &  0.5943 &  0.8114 &  0.4057 \tabularnewline
30 &  0.7748 &  0.4504 &  0.2252 \tabularnewline
31 &  0.8921 &  0.2159 &  0.1079 \tabularnewline
32 &  0.8819 &  0.2362 &  0.1181 \tabularnewline
33 &  0.8817 &  0.2366 &  0.1183 \tabularnewline
34 &  0.8793 &  0.2414 &  0.1207 \tabularnewline
35 &  0.8499 &  0.3002 &  0.1501 \tabularnewline
36 &  0.8196 &  0.3609 &  0.1804 \tabularnewline
37 &  0.8587 &  0.2827 &  0.1413 \tabularnewline
38 &  0.8318 &  0.3363 &  0.1682 \tabularnewline
39 &  0.8792 &  0.2416 &  0.1208 \tabularnewline
40 &  0.9087 &  0.1825 &  0.09127 \tabularnewline
41 &  0.8892 &  0.2217 &  0.1108 \tabularnewline
42 &  0.8629 &  0.2743 &  0.1371 \tabularnewline
43 &  0.8966 &  0.2068 &  0.1034 \tabularnewline
44 &  0.8708 &  0.2583 &  0.1292 \tabularnewline
45 &  0.8425 &  0.3151 &  0.1575 \tabularnewline
46 &  0.8261 &  0.3478 &  0.1739 \tabularnewline
47 &  0.8431 &  0.3137 &  0.1569 \tabularnewline
48 &  0.8292 &  0.3416 &  0.1708 \tabularnewline
49 &  0.8068 &  0.3865 &  0.1932 \tabularnewline
50 &  0.7723 &  0.4554 &  0.2277 \tabularnewline
51 &  0.7883 &  0.4233 &  0.2117 \tabularnewline
52 &  0.8064 &  0.3872 &  0.1936 \tabularnewline
53 &  0.7701 &  0.4598 &  0.2299 \tabularnewline
54 &  0.7317 &  0.5366 &  0.2683 \tabularnewline
55 &  0.7361 &  0.5279 &  0.2639 \tabularnewline
56 &  0.9132 &  0.1736 &  0.08679 \tabularnewline
57 &  0.8927 &  0.2146 &  0.1073 \tabularnewline
58 &  0.8927 &  0.2146 &  0.1073 \tabularnewline
59 &  0.8706 &  0.2587 &  0.1294 \tabularnewline
60 &  0.8718 &  0.2564 &  0.1282 \tabularnewline
61 &  0.8466 &  0.3068 &  0.1534 \tabularnewline
62 &  0.8166 &  0.3668 &  0.1834 \tabularnewline
63 &  0.8879 &  0.2242 &  0.1121 \tabularnewline
64 &  0.9013 &  0.1975 &  0.09875 \tabularnewline
65 &  0.9051 &  0.1899 &  0.09495 \tabularnewline
66 &  0.8902 &  0.2197 &  0.1098 \tabularnewline
67 &  0.8694 &  0.2612 &  0.1306 \tabularnewline
68 &  0.843 &  0.3141 &  0.157 \tabularnewline
69 &  0.8574 &  0.2852 &  0.1426 \tabularnewline
70 &  0.8303 &  0.3393 &  0.1697 \tabularnewline
71 &  0.8461 &  0.3077 &  0.1539 \tabularnewline
72 &  0.8246 &  0.3508 &  0.1754 \tabularnewline
73 &  0.8067 &  0.3865 &  0.1933 \tabularnewline
74 &  0.7775 &  0.4451 &  0.2225 \tabularnewline
75 &  0.7649 &  0.4701 &  0.2351 \tabularnewline
76 &  0.7283 &  0.5434 &  0.2717 \tabularnewline
77 &  0.6892 &  0.6216 &  0.3108 \tabularnewline
78 &  0.6503 &  0.6994 &  0.3497 \tabularnewline
79 &  0.7379 &  0.5243 &  0.2621 \tabularnewline
80 &  0.7697 &  0.4605 &  0.2303 \tabularnewline
81 &  0.7346 &  0.5308 &  0.2654 \tabularnewline
82 &  0.7679 &  0.4643 &  0.2321 \tabularnewline
83 &  0.7877 &  0.4245 &  0.2123 \tabularnewline
84 &  0.758 &  0.4839 &  0.242 \tabularnewline
85 &  0.7261 &  0.5478 &  0.2739 \tabularnewline
86 &  0.6871 &  0.6259 &  0.3129 \tabularnewline
87 &  0.6675 &  0.6651 &  0.3325 \tabularnewline
88 &  0.7256 &  0.5487 &  0.2744 \tabularnewline
89 &  0.6877 &  0.6245 &  0.3123 \tabularnewline
90 &  0.7613 &  0.4775 &  0.2387 \tabularnewline
91 &  0.7991 &  0.4017 &  0.2009 \tabularnewline
92 &  0.7828 &  0.4344 &  0.2172 \tabularnewline
93 &  0.7713 &  0.4574 &  0.2287 \tabularnewline
94 &  0.8673 &  0.2653 &  0.1327 \tabularnewline
95 &  0.8404 &  0.3193 &  0.1596 \tabularnewline
96 &  0.8685 &  0.263 &  0.1315 \tabularnewline
97 &  0.9096 &  0.1808 &  0.0904 \tabularnewline
98 &  0.888 &  0.2241 &  0.112 \tabularnewline
99 &  0.8637 &  0.2726 &  0.1363 \tabularnewline
100 &  0.836 &  0.328 &  0.164 \tabularnewline
101 &  0.8545 &  0.291 &  0.1455 \tabularnewline
102 &  0.8297 &  0.3407 &  0.1703 \tabularnewline
103 &  0.8105 &  0.379 &  0.1895 \tabularnewline
104 &  0.7962 &  0.4076 &  0.2038 \tabularnewline
105 &  0.7631 &  0.4739 &  0.2369 \tabularnewline
106 &  0.7888 &  0.4224 &  0.2112 \tabularnewline
107 &  0.7633 &  0.4733 &  0.2367 \tabularnewline
108 &  0.7983 &  0.4033 &  0.2017 \tabularnewline
109 &  0.8298 &  0.3403 &  0.1702 \tabularnewline
110 &  0.7946 &  0.4107 &  0.2054 \tabularnewline
111 &  0.762 &  0.476 &  0.238 \tabularnewline
112 &  0.763 &  0.4739 &  0.237 \tabularnewline
113 &  0.7203 &  0.5594 &  0.2797 \tabularnewline
114 &  0.6784 &  0.6432 &  0.3216 \tabularnewline
115 &  0.647 &  0.706 &  0.353 \tabularnewline
116 &  0.6742 &  0.6515 &  0.3258 \tabularnewline
117 &  0.623 &  0.754 &  0.377 \tabularnewline
118 &  0.5786 &  0.8427 &  0.4214 \tabularnewline
119 &  0.6275 &  0.7449 &  0.3725 \tabularnewline
120 &  0.5869 &  0.8261 &  0.4131 \tabularnewline
121 &  0.5325 &  0.935 &  0.4675 \tabularnewline
122 &  0.4893 &  0.9786 &  0.5107 \tabularnewline
123 &  0.4714 &  0.9427 &  0.5286 \tabularnewline
124 &  0.7793 &  0.4414 &  0.2207 \tabularnewline
125 &  0.7395 &  0.5211 &  0.2605 \tabularnewline
126 &  0.6909 &  0.6183 &  0.3091 \tabularnewline
127 &  0.6513 &  0.6973 &  0.3487 \tabularnewline
128 &  0.6224 &  0.7551 &  0.3776 \tabularnewline
129 &  0.644 &  0.7121 &  0.356 \tabularnewline
130 &  0.6221 &  0.7559 &  0.3779 \tabularnewline
131 &  0.5925 &  0.8149 &  0.4075 \tabularnewline
132 &  0.5923 &  0.8153 &  0.4077 \tabularnewline
133 &  0.5235 &  0.9529 &  0.4765 \tabularnewline
134 &  0.4505 &  0.9011 &  0.5495 \tabularnewline
135 &  0.3782 &  0.7563 &  0.6218 \tabularnewline
136 &  0.3085 &  0.6169 &  0.6915 \tabularnewline
137 &  0.2987 &  0.5974 &  0.7013 \tabularnewline
138 &  0.3761 &  0.7521 &  0.6239 \tabularnewline
139 &  0.308 &  0.616 &  0.692 \tabularnewline
140 &  0.3051 &  0.6102 &  0.6949 \tabularnewline
141 &  0.343 &  0.686 &  0.657 \tabularnewline
142 &  0.4216 &  0.8433 &  0.5784 \tabularnewline
143 &  0.3263 &  0.6526 &  0.6737 \tabularnewline
144 &  0.8537 &  0.2926 &  0.1463 \tabularnewline
145 &  0.7709 &  0.4582 &  0.2291 \tabularnewline
146 &  0.8587 &  0.2826 &  0.1413 \tabularnewline
147 &  0.7832 &  0.4336 &  0.2168 \tabularnewline
148 &  0.7109 &  0.5782 &  0.2891 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&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] 0.1828[/C][C] 0.3656[/C][C] 0.8172[/C][/ROW]
[ROW][C]11[/C][C] 0.1999[/C][C] 0.3998[/C][C] 0.8001[/C][/ROW]
[ROW][C]12[/C][C] 0.5633[/C][C] 0.8735[/C][C] 0.4367[/C][/ROW]
[ROW][C]13[/C][C] 0.4573[/C][C] 0.9146[/C][C] 0.5427[/C][/ROW]
[ROW][C]14[/C][C] 0.5512[/C][C] 0.8976[/C][C] 0.4488[/C][/ROW]
[ROW][C]15[/C][C] 0.5713[/C][C] 0.8575[/C][C] 0.4287[/C][/ROW]
[ROW][C]16[/C][C] 0.4941[/C][C] 0.9882[/C][C] 0.5059[/C][/ROW]
[ROW][C]17[/C][C] 0.6798[/C][C] 0.6404[/C][C] 0.3202[/C][/ROW]
[ROW][C]18[/C][C] 0.5943[/C][C] 0.8114[/C][C] 0.4057[/C][/ROW]
[ROW][C]19[/C][C] 0.5372[/C][C] 0.9256[/C][C] 0.4628[/C][/ROW]
[ROW][C]20[/C][C] 0.4545[/C][C] 0.9089[/C][C] 0.5455[/C][/ROW]
[ROW][C]21[/C][C] 0.4411[/C][C] 0.8823[/C][C] 0.5589[/C][/ROW]
[ROW][C]22[/C][C] 0.3764[/C][C] 0.7528[/C][C] 0.6236[/C][/ROW]
[ROW][C]23[/C][C] 0.3329[/C][C] 0.6659[/C][C] 0.6671[/C][/ROW]
[ROW][C]24[/C][C] 0.3835[/C][C] 0.767[/C][C] 0.6165[/C][/ROW]
[ROW][C]25[/C][C] 0.3733[/C][C] 0.7466[/C][C] 0.6267[/C][/ROW]
[ROW][C]26[/C][C] 0.3086[/C][C] 0.6171[/C][C] 0.6914[/C][/ROW]
[ROW][C]27[/C][C] 0.5534[/C][C] 0.8932[/C][C] 0.4466[/C][/ROW]
[ROW][C]28[/C][C] 0.6264[/C][C] 0.7472[/C][C] 0.3736[/C][/ROW]
[ROW][C]29[/C][C] 0.5943[/C][C] 0.8114[/C][C] 0.4057[/C][/ROW]
[ROW][C]30[/C][C] 0.7748[/C][C] 0.4504[/C][C] 0.2252[/C][/ROW]
[ROW][C]31[/C][C] 0.8921[/C][C] 0.2159[/C][C] 0.1079[/C][/ROW]
[ROW][C]32[/C][C] 0.8819[/C][C] 0.2362[/C][C] 0.1181[/C][/ROW]
[ROW][C]33[/C][C] 0.8817[/C][C] 0.2366[/C][C] 0.1183[/C][/ROW]
[ROW][C]34[/C][C] 0.8793[/C][C] 0.2414[/C][C] 0.1207[/C][/ROW]
[ROW][C]35[/C][C] 0.8499[/C][C] 0.3002[/C][C] 0.1501[/C][/ROW]
[ROW][C]36[/C][C] 0.8196[/C][C] 0.3609[/C][C] 0.1804[/C][/ROW]
[ROW][C]37[/C][C] 0.8587[/C][C] 0.2827[/C][C] 0.1413[/C][/ROW]
[ROW][C]38[/C][C] 0.8318[/C][C] 0.3363[/C][C] 0.1682[/C][/ROW]
[ROW][C]39[/C][C] 0.8792[/C][C] 0.2416[/C][C] 0.1208[/C][/ROW]
[ROW][C]40[/C][C] 0.9087[/C][C] 0.1825[/C][C] 0.09127[/C][/ROW]
[ROW][C]41[/C][C] 0.8892[/C][C] 0.2217[/C][C] 0.1108[/C][/ROW]
[ROW][C]42[/C][C] 0.8629[/C][C] 0.2743[/C][C] 0.1371[/C][/ROW]
[ROW][C]43[/C][C] 0.8966[/C][C] 0.2068[/C][C] 0.1034[/C][/ROW]
[ROW][C]44[/C][C] 0.8708[/C][C] 0.2583[/C][C] 0.1292[/C][/ROW]
[ROW][C]45[/C][C] 0.8425[/C][C] 0.3151[/C][C] 0.1575[/C][/ROW]
[ROW][C]46[/C][C] 0.8261[/C][C] 0.3478[/C][C] 0.1739[/C][/ROW]
[ROW][C]47[/C][C] 0.8431[/C][C] 0.3137[/C][C] 0.1569[/C][/ROW]
[ROW][C]48[/C][C] 0.8292[/C][C] 0.3416[/C][C] 0.1708[/C][/ROW]
[ROW][C]49[/C][C] 0.8068[/C][C] 0.3865[/C][C] 0.1932[/C][/ROW]
[ROW][C]50[/C][C] 0.7723[/C][C] 0.4554[/C][C] 0.2277[/C][/ROW]
[ROW][C]51[/C][C] 0.7883[/C][C] 0.4233[/C][C] 0.2117[/C][/ROW]
[ROW][C]52[/C][C] 0.8064[/C][C] 0.3872[/C][C] 0.1936[/C][/ROW]
[ROW][C]53[/C][C] 0.7701[/C][C] 0.4598[/C][C] 0.2299[/C][/ROW]
[ROW][C]54[/C][C] 0.7317[/C][C] 0.5366[/C][C] 0.2683[/C][/ROW]
[ROW][C]55[/C][C] 0.7361[/C][C] 0.5279[/C][C] 0.2639[/C][/ROW]
[ROW][C]56[/C][C] 0.9132[/C][C] 0.1736[/C][C] 0.08679[/C][/ROW]
[ROW][C]57[/C][C] 0.8927[/C][C] 0.2146[/C][C] 0.1073[/C][/ROW]
[ROW][C]58[/C][C] 0.8927[/C][C] 0.2146[/C][C] 0.1073[/C][/ROW]
[ROW][C]59[/C][C] 0.8706[/C][C] 0.2587[/C][C] 0.1294[/C][/ROW]
[ROW][C]60[/C][C] 0.8718[/C][C] 0.2564[/C][C] 0.1282[/C][/ROW]
[ROW][C]61[/C][C] 0.8466[/C][C] 0.3068[/C][C] 0.1534[/C][/ROW]
[ROW][C]62[/C][C] 0.8166[/C][C] 0.3668[/C][C] 0.1834[/C][/ROW]
[ROW][C]63[/C][C] 0.8879[/C][C] 0.2242[/C][C] 0.1121[/C][/ROW]
[ROW][C]64[/C][C] 0.9013[/C][C] 0.1975[/C][C] 0.09875[/C][/ROW]
[ROW][C]65[/C][C] 0.9051[/C][C] 0.1899[/C][C] 0.09495[/C][/ROW]
[ROW][C]66[/C][C] 0.8902[/C][C] 0.2197[/C][C] 0.1098[/C][/ROW]
[ROW][C]67[/C][C] 0.8694[/C][C] 0.2612[/C][C] 0.1306[/C][/ROW]
[ROW][C]68[/C][C] 0.843[/C][C] 0.3141[/C][C] 0.157[/C][/ROW]
[ROW][C]69[/C][C] 0.8574[/C][C] 0.2852[/C][C] 0.1426[/C][/ROW]
[ROW][C]70[/C][C] 0.8303[/C][C] 0.3393[/C][C] 0.1697[/C][/ROW]
[ROW][C]71[/C][C] 0.8461[/C][C] 0.3077[/C][C] 0.1539[/C][/ROW]
[ROW][C]72[/C][C] 0.8246[/C][C] 0.3508[/C][C] 0.1754[/C][/ROW]
[ROW][C]73[/C][C] 0.8067[/C][C] 0.3865[/C][C] 0.1933[/C][/ROW]
[ROW][C]74[/C][C] 0.7775[/C][C] 0.4451[/C][C] 0.2225[/C][/ROW]
[ROW][C]75[/C][C] 0.7649[/C][C] 0.4701[/C][C] 0.2351[/C][/ROW]
[ROW][C]76[/C][C] 0.7283[/C][C] 0.5434[/C][C] 0.2717[/C][/ROW]
[ROW][C]77[/C][C] 0.6892[/C][C] 0.6216[/C][C] 0.3108[/C][/ROW]
[ROW][C]78[/C][C] 0.6503[/C][C] 0.6994[/C][C] 0.3497[/C][/ROW]
[ROW][C]79[/C][C] 0.7379[/C][C] 0.5243[/C][C] 0.2621[/C][/ROW]
[ROW][C]80[/C][C] 0.7697[/C][C] 0.4605[/C][C] 0.2303[/C][/ROW]
[ROW][C]81[/C][C] 0.7346[/C][C] 0.5308[/C][C] 0.2654[/C][/ROW]
[ROW][C]82[/C][C] 0.7679[/C][C] 0.4643[/C][C] 0.2321[/C][/ROW]
[ROW][C]83[/C][C] 0.7877[/C][C] 0.4245[/C][C] 0.2123[/C][/ROW]
[ROW][C]84[/C][C] 0.758[/C][C] 0.4839[/C][C] 0.242[/C][/ROW]
[ROW][C]85[/C][C] 0.7261[/C][C] 0.5478[/C][C] 0.2739[/C][/ROW]
[ROW][C]86[/C][C] 0.6871[/C][C] 0.6259[/C][C] 0.3129[/C][/ROW]
[ROW][C]87[/C][C] 0.6675[/C][C] 0.6651[/C][C] 0.3325[/C][/ROW]
[ROW][C]88[/C][C] 0.7256[/C][C] 0.5487[/C][C] 0.2744[/C][/ROW]
[ROW][C]89[/C][C] 0.6877[/C][C] 0.6245[/C][C] 0.3123[/C][/ROW]
[ROW][C]90[/C][C] 0.7613[/C][C] 0.4775[/C][C] 0.2387[/C][/ROW]
[ROW][C]91[/C][C] 0.7991[/C][C] 0.4017[/C][C] 0.2009[/C][/ROW]
[ROW][C]92[/C][C] 0.7828[/C][C] 0.4344[/C][C] 0.2172[/C][/ROW]
[ROW][C]93[/C][C] 0.7713[/C][C] 0.4574[/C][C] 0.2287[/C][/ROW]
[ROW][C]94[/C][C] 0.8673[/C][C] 0.2653[/C][C] 0.1327[/C][/ROW]
[ROW][C]95[/C][C] 0.8404[/C][C] 0.3193[/C][C] 0.1596[/C][/ROW]
[ROW][C]96[/C][C] 0.8685[/C][C] 0.263[/C][C] 0.1315[/C][/ROW]
[ROW][C]97[/C][C] 0.9096[/C][C] 0.1808[/C][C] 0.0904[/C][/ROW]
[ROW][C]98[/C][C] 0.888[/C][C] 0.2241[/C][C] 0.112[/C][/ROW]
[ROW][C]99[/C][C] 0.8637[/C][C] 0.2726[/C][C] 0.1363[/C][/ROW]
[ROW][C]100[/C][C] 0.836[/C][C] 0.328[/C][C] 0.164[/C][/ROW]
[ROW][C]101[/C][C] 0.8545[/C][C] 0.291[/C][C] 0.1455[/C][/ROW]
[ROW][C]102[/C][C] 0.8297[/C][C] 0.3407[/C][C] 0.1703[/C][/ROW]
[ROW][C]103[/C][C] 0.8105[/C][C] 0.379[/C][C] 0.1895[/C][/ROW]
[ROW][C]104[/C][C] 0.7962[/C][C] 0.4076[/C][C] 0.2038[/C][/ROW]
[ROW][C]105[/C][C] 0.7631[/C][C] 0.4739[/C][C] 0.2369[/C][/ROW]
[ROW][C]106[/C][C] 0.7888[/C][C] 0.4224[/C][C] 0.2112[/C][/ROW]
[ROW][C]107[/C][C] 0.7633[/C][C] 0.4733[/C][C] 0.2367[/C][/ROW]
[ROW][C]108[/C][C] 0.7983[/C][C] 0.4033[/C][C] 0.2017[/C][/ROW]
[ROW][C]109[/C][C] 0.8298[/C][C] 0.3403[/C][C] 0.1702[/C][/ROW]
[ROW][C]110[/C][C] 0.7946[/C][C] 0.4107[/C][C] 0.2054[/C][/ROW]
[ROW][C]111[/C][C] 0.762[/C][C] 0.476[/C][C] 0.238[/C][/ROW]
[ROW][C]112[/C][C] 0.763[/C][C] 0.4739[/C][C] 0.237[/C][/ROW]
[ROW][C]113[/C][C] 0.7203[/C][C] 0.5594[/C][C] 0.2797[/C][/ROW]
[ROW][C]114[/C][C] 0.6784[/C][C] 0.6432[/C][C] 0.3216[/C][/ROW]
[ROW][C]115[/C][C] 0.647[/C][C] 0.706[/C][C] 0.353[/C][/ROW]
[ROW][C]116[/C][C] 0.6742[/C][C] 0.6515[/C][C] 0.3258[/C][/ROW]
[ROW][C]117[/C][C] 0.623[/C][C] 0.754[/C][C] 0.377[/C][/ROW]
[ROW][C]118[/C][C] 0.5786[/C][C] 0.8427[/C][C] 0.4214[/C][/ROW]
[ROW][C]119[/C][C] 0.6275[/C][C] 0.7449[/C][C] 0.3725[/C][/ROW]
[ROW][C]120[/C][C] 0.5869[/C][C] 0.8261[/C][C] 0.4131[/C][/ROW]
[ROW][C]121[/C][C] 0.5325[/C][C] 0.935[/C][C] 0.4675[/C][/ROW]
[ROW][C]122[/C][C] 0.4893[/C][C] 0.9786[/C][C] 0.5107[/C][/ROW]
[ROW][C]123[/C][C] 0.4714[/C][C] 0.9427[/C][C] 0.5286[/C][/ROW]
[ROW][C]124[/C][C] 0.7793[/C][C] 0.4414[/C][C] 0.2207[/C][/ROW]
[ROW][C]125[/C][C] 0.7395[/C][C] 0.5211[/C][C] 0.2605[/C][/ROW]
[ROW][C]126[/C][C] 0.6909[/C][C] 0.6183[/C][C] 0.3091[/C][/ROW]
[ROW][C]127[/C][C] 0.6513[/C][C] 0.6973[/C][C] 0.3487[/C][/ROW]
[ROW][C]128[/C][C] 0.6224[/C][C] 0.7551[/C][C] 0.3776[/C][/ROW]
[ROW][C]129[/C][C] 0.644[/C][C] 0.7121[/C][C] 0.356[/C][/ROW]
[ROW][C]130[/C][C] 0.6221[/C][C] 0.7559[/C][C] 0.3779[/C][/ROW]
[ROW][C]131[/C][C] 0.5925[/C][C] 0.8149[/C][C] 0.4075[/C][/ROW]
[ROW][C]132[/C][C] 0.5923[/C][C] 0.8153[/C][C] 0.4077[/C][/ROW]
[ROW][C]133[/C][C] 0.5235[/C][C] 0.9529[/C][C] 0.4765[/C][/ROW]
[ROW][C]134[/C][C] 0.4505[/C][C] 0.9011[/C][C] 0.5495[/C][/ROW]
[ROW][C]135[/C][C] 0.3782[/C][C] 0.7563[/C][C] 0.6218[/C][/ROW]
[ROW][C]136[/C][C] 0.3085[/C][C] 0.6169[/C][C] 0.6915[/C][/ROW]
[ROW][C]137[/C][C] 0.2987[/C][C] 0.5974[/C][C] 0.7013[/C][/ROW]
[ROW][C]138[/C][C] 0.3761[/C][C] 0.7521[/C][C] 0.6239[/C][/ROW]
[ROW][C]139[/C][C] 0.308[/C][C] 0.616[/C][C] 0.692[/C][/ROW]
[ROW][C]140[/C][C] 0.3051[/C][C] 0.6102[/C][C] 0.6949[/C][/ROW]
[ROW][C]141[/C][C] 0.343[/C][C] 0.686[/C][C] 0.657[/C][/ROW]
[ROW][C]142[/C][C] 0.4216[/C][C] 0.8433[/C][C] 0.5784[/C][/ROW]
[ROW][C]143[/C][C] 0.3263[/C][C] 0.6526[/C][C] 0.6737[/C][/ROW]
[ROW][C]144[/C][C] 0.8537[/C][C] 0.2926[/C][C] 0.1463[/C][/ROW]
[ROW][C]145[/C][C] 0.7709[/C][C] 0.4582[/C][C] 0.2291[/C][/ROW]
[ROW][C]146[/C][C] 0.8587[/C][C] 0.2826[/C][C] 0.1413[/C][/ROW]
[ROW][C]147[/C][C] 0.7832[/C][C] 0.4336[/C][C] 0.2168[/C][/ROW]
[ROW][C]148[/C][C] 0.7109[/C][C] 0.5782[/C][C] 0.2891[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298236&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&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	0.1828	0.3656	0.8172
11	0.1999	0.3998	0.8001
12	0.5633	0.8735	0.4367
13	0.4573	0.9146	0.5427
14	0.5512	0.8976	0.4488
15	0.5713	0.8575	0.4287
16	0.4941	0.9882	0.5059
17	0.6798	0.6404	0.3202
18	0.5943	0.8114	0.4057
19	0.5372	0.9256	0.4628
20	0.4545	0.9089	0.5455
21	0.4411	0.8823	0.5589
22	0.3764	0.7528	0.6236
23	0.3329	0.6659	0.6671
24	0.3835	0.767	0.6165
25	0.3733	0.7466	0.6267
26	0.3086	0.6171	0.6914
27	0.5534	0.8932	0.4466
28	0.6264	0.7472	0.3736
29	0.5943	0.8114	0.4057
30	0.7748	0.4504	0.2252
31	0.8921	0.2159	0.1079
32	0.8819	0.2362	0.1181
33	0.8817	0.2366	0.1183
34	0.8793	0.2414	0.1207
35	0.8499	0.3002	0.1501
36	0.8196	0.3609	0.1804
37	0.8587	0.2827	0.1413
38	0.8318	0.3363	0.1682
39	0.8792	0.2416	0.1208
40	0.9087	0.1825	0.09127
41	0.8892	0.2217	0.1108
42	0.8629	0.2743	0.1371
43	0.8966	0.2068	0.1034
44	0.8708	0.2583	0.1292
45	0.8425	0.3151	0.1575
46	0.8261	0.3478	0.1739
47	0.8431	0.3137	0.1569
48	0.8292	0.3416	0.1708
49	0.8068	0.3865	0.1932
50	0.7723	0.4554	0.2277
51	0.7883	0.4233	0.2117
52	0.8064	0.3872	0.1936
53	0.7701	0.4598	0.2299
54	0.7317	0.5366	0.2683
55	0.7361	0.5279	0.2639
56	0.9132	0.1736	0.08679
57	0.8927	0.2146	0.1073
58	0.8927	0.2146	0.1073
59	0.8706	0.2587	0.1294
60	0.8718	0.2564	0.1282
61	0.8466	0.3068	0.1534
62	0.8166	0.3668	0.1834
63	0.8879	0.2242	0.1121
64	0.9013	0.1975	0.09875
65	0.9051	0.1899	0.09495
66	0.8902	0.2197	0.1098
67	0.8694	0.2612	0.1306
68	0.843	0.3141	0.157
69	0.8574	0.2852	0.1426
70	0.8303	0.3393	0.1697
71	0.8461	0.3077	0.1539
72	0.8246	0.3508	0.1754
73	0.8067	0.3865	0.1933
74	0.7775	0.4451	0.2225
75	0.7649	0.4701	0.2351
76	0.7283	0.5434	0.2717
77	0.6892	0.6216	0.3108
78	0.6503	0.6994	0.3497
79	0.7379	0.5243	0.2621
80	0.7697	0.4605	0.2303
81	0.7346	0.5308	0.2654
82	0.7679	0.4643	0.2321
83	0.7877	0.4245	0.2123
84	0.758	0.4839	0.242
85	0.7261	0.5478	0.2739
86	0.6871	0.6259	0.3129
87	0.6675	0.6651	0.3325
88	0.7256	0.5487	0.2744
89	0.6877	0.6245	0.3123
90	0.7613	0.4775	0.2387
91	0.7991	0.4017	0.2009
92	0.7828	0.4344	0.2172
93	0.7713	0.4574	0.2287
94	0.8673	0.2653	0.1327
95	0.8404	0.3193	0.1596
96	0.8685	0.263	0.1315
97	0.9096	0.1808	0.0904
98	0.888	0.2241	0.112
99	0.8637	0.2726	0.1363
100	0.836	0.328	0.164
101	0.8545	0.291	0.1455
102	0.8297	0.3407	0.1703
103	0.8105	0.379	0.1895
104	0.7962	0.4076	0.2038
105	0.7631	0.4739	0.2369
106	0.7888	0.4224	0.2112
107	0.7633	0.4733	0.2367
108	0.7983	0.4033	0.2017
109	0.8298	0.3403	0.1702
110	0.7946	0.4107	0.2054
111	0.762	0.476	0.238
112	0.763	0.4739	0.237
113	0.7203	0.5594	0.2797
114	0.6784	0.6432	0.3216
115	0.647	0.706	0.353
116	0.6742	0.6515	0.3258
117	0.623	0.754	0.377
118	0.5786	0.8427	0.4214
119	0.6275	0.7449	0.3725
120	0.5869	0.8261	0.4131
121	0.5325	0.935	0.4675
122	0.4893	0.9786	0.5107
123	0.4714	0.9427	0.5286
124	0.7793	0.4414	0.2207
125	0.7395	0.5211	0.2605
126	0.6909	0.6183	0.3091
127	0.6513	0.6973	0.3487
128	0.6224	0.7551	0.3776
129	0.644	0.7121	0.356
130	0.6221	0.7559	0.3779
131	0.5925	0.8149	0.4075
132	0.5923	0.8153	0.4077
133	0.5235	0.9529	0.4765
134	0.4505	0.9011	0.5495
135	0.3782	0.7563	0.6218
136	0.3085	0.6169	0.6915
137	0.2987	0.5974	0.7013
138	0.3761	0.7521	0.6239
139	0.308	0.616	0.692
140	0.3051	0.6102	0.6949
141	0.343	0.686	0.657
142	0.4216	0.8433	0.5784
143	0.3263	0.6526	0.6737
144	0.8537	0.2926	0.1463
145	0.7709	0.4582	0.2291
146	0.8587	0.2826	0.1413
147	0.7832	0.4336	0.2168
148	0.7109	0.5782	0.2891

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298236&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&T=6

As an alternative you can also use a QR Code:

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

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8155, df1 = 2, df2 = 149, p-value = 0.1663
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0859, df1 = 12, df2 = 139, p-value = 0.3766
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1349, df1 = 2, df2 = 149, p-value = 0.3242

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.8155, df1 = 2, df2 = 149, p-value = 0.1663
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0859, df1 = 12, df2 = 139, p-value = 0.3766
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1349, df1 = 2, df2 = 149, p-value = 0.3242
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.8155, df1 = 2, df2 = 149, p-value = 0.1663
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0859, df1 = 12, df2 = 139, p-value = 0.3766
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1349, df1 = 2, df2 = 149, p-value = 0.3242
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298236&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8155, df1 = 2, df2 = 149, p-value = 0.1663
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0859, df1 = 12, df2 = 139, p-value = 0.3766
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1349, df1 = 2, df2 = 149, p-value = 0.3242

Variance Inflation Factors (Multicollinearity)
> vif `SK2,` `SK3,` `SK4,` `SK5,` `SK6,` ITHSUM 1.060653 1.074865 1.065037 1.062323 1.047742 1.081199

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
  `SK2,`   `SK3,`   `SK4,`   `SK5,`   `SK6,`   ITHSUM 
1.060653 1.074865 1.065037 1.062323 1.047742 1.081199 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298236&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
  `SK2,`   `SK3,`   `SK4,`   `SK5,`   `SK6,`   ITHSUM 
1.060653 1.074865 1.065037 1.062323 1.047742 1.081199 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298236&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298236&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif `SK2,` `SK3,` `SK4,` `SK5,` `SK6,` ITHSUM 1.060653 1.074865 1.065037 1.062323 1.047742 1.081199

Figure 1

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Figure 10

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Parameters (Session):

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

Parameters (R input):

par1 = ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
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,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code