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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 06 Dec 2016 11:02:01 +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/06/t1481018560rsma955os8k04pf.htm/, Retrieved Sat, 04 May 2024 10:50:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297766, Retrieved Sat, 04 May 2024 10:50:14 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

107

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

-       [Multiple Regression] [] [2016-12-06 10:02:01] [9b171b8beffcb53bb49a1e7c02b89c12] [Current]

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Dataseries X:

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Histogram

Boxplots

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297766&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&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	8 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
TVDC [t] = + 6.60213 + 0.586942SK1[t] + 1.15724SK2[t] + 0.0710539SK3[t] + 0.277997SK4[t] + 0.195335SK5[t] -0.0317354SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC
[t] =  +  6.60213 +  0.586942SK1[t] +  1.15724SK2[t] +  0.0710539SK3[t] +  0.277997SK4[t] +  0.195335SK5[t] -0.0317354SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC
[t] =  +  6.60213 +  0.586942SK1[t] +  1.15724SK2[t] +  0.0710539SK3[t] +  0.277997SK4[t] +  0.195335SK5[t] -0.0317354SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297766&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&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
TVDC [t] = + 6.60213 + 0.586942SK1[t] + 1.15724SK2[t] + 0.0710539SK3[t] + 0.277997SK4[t] + 0.195335SK5[t] -0.0317354SK6[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)	+6.602	1.537	+4.2960e+00	3.078e-05	1.539e-05
SK1	+0.5869	0.1625	+3.6120e+00	0.0004113	0.0002056
SK2	+1.157	0.198	+5.8450e+00	2.958e-08	1.479e-08
SK3	+0.07105	0.1451	+4.8960e-01	0.6251	0.3126
SK4	+0.278	0.1974	+1.4080e+00	0.1611	0.08055
SK5	+0.1953	0.1866	+1.0470e+00	0.2968	0.1484
SK6	-0.03173	0.1936	-1.6400e-01	0.87	0.435

\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) & +6.602 &  1.537 & +4.2960e+00 &  3.078e-05 &  1.539e-05 \tabularnewline
SK1 & +0.5869 &  0.1625 & +3.6120e+00 &  0.0004113 &  0.0002056 \tabularnewline
SK2 & +1.157 &  0.198 & +5.8450e+00 &  2.958e-08 &  1.479e-08 \tabularnewline
SK3 & +0.07105 &  0.1451 & +4.8960e-01 &  0.6251 &  0.3126 \tabularnewline
SK4 & +0.278 &  0.1974 & +1.4080e+00 &  0.1611 &  0.08055 \tabularnewline
SK5 & +0.1953 &  0.1866 & +1.0470e+00 &  0.2968 &  0.1484 \tabularnewline
SK6 & -0.03173 &  0.1936 & -1.6400e-01 &  0.87 &  0.435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&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]+6.602[/C][C] 1.537[/C][C]+4.2960e+00[/C][C] 3.078e-05[/C][C] 1.539e-05[/C][/ROW]
[ROW][C]SK1[/C][C]+0.5869[/C][C] 0.1625[/C][C]+3.6120e+00[/C][C] 0.0004113[/C][C] 0.0002056[/C][/ROW]
[ROW][C]SK2[/C][C]+1.157[/C][C] 0.198[/C][C]+5.8450e+00[/C][C] 2.958e-08[/C][C] 1.479e-08[/C][/ROW]
[ROW][C]SK3[/C][C]+0.07105[/C][C] 0.1451[/C][C]+4.8960e-01[/C][C] 0.6251[/C][C] 0.3126[/C][/ROW]
[ROW][C]SK4[/C][C]+0.278[/C][C] 0.1974[/C][C]+1.4080e+00[/C][C] 0.1611[/C][C] 0.08055[/C][/ROW]
[ROW][C]SK5[/C][C]+0.1953[/C][C] 0.1866[/C][C]+1.0470e+00[/C][C] 0.2968[/C][C] 0.1484[/C][/ROW]
[ROW][C]SK6[/C][C]-0.03173[/C][C] 0.1936[/C][C]-1.6400e-01[/C][C] 0.87[/C][C] 0.435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297766&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&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)	+6.602	1.537	+4.2960e+00	3.078e-05	1.539e-05
SK1	+0.5869	0.1625	+3.6120e+00	0.0004113	0.0002056
SK2	+1.157	0.198	+5.8450e+00	2.958e-08	1.479e-08
SK3	+0.07105	0.1451	+4.8960e-01	0.6251	0.3126
SK4	+0.278	0.1974	+1.4080e+00	0.1611	0.08055
SK5	+0.1953	0.1866	+1.0470e+00	0.2968	0.1484
SK6	-0.03173	0.1936	-1.6400e-01	0.87	0.435

Multiple Linear Regression - Regression Statistics
Multiple R	0.5761
R-squared	0.3319
Adjusted R-squared	0.3057
F-TEST (value)	12.67
F-TEST (DF numerator)	6
F-TEST (DF denominator)	153
p-value	1.406e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.426
Sum Squared Residuals	311.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5761 \tabularnewline
R-squared &  0.3319 \tabularnewline
Adjusted R-squared &  0.3057 \tabularnewline
F-TEST (value) &  12.67 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value &  1.406e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.426 \tabularnewline
Sum Squared Residuals &  311.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5761[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3319[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3057[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 12.67[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C] 1.406e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.426[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 311.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297766&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&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	0.5761
R-squared	0.3319
Adjusted R-squared	0.3057
F-TEST (value)	12.67
F-TEST (DF numerator)	6
F-TEST (DF denominator)	153
p-value	1.406e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.426
Sum Squared Residuals	311.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.23	-0.2323
2	16	15.18	0.8166
3	17	15.9	1.104
4	15	14.69	0.3065
5	16	15.9	0.1042
6	16	15.21	0.7939
7	18	14.57	3.431
8	16	15.11	0.8864
9	17	16.95	0.04971
10	17	17.02	-0.02134
11	17	15.68	1.317
12	15	15.62	-0.6178
13	16	15.32	0.6803
14	14	13.92	0.07541
15	16	15.28	0.7219
16	17	15.08	1.918
17	16	15.08	0.9182
18	15	17.1	-2.096
19	17	15.82	1.175
20	16	14.8	1.196
21	15	15.79	-0.7931
22	16	15.67	0.3312
23	15	15.7	-0.7005
24	17	15.67	1.331
25	14	15.04	-1.043
26	16	14.93	1.072
27	15	15.63	-0.6295
28	16	14.69	1.31
29	16	16.22	-0.2164
30	13	14.54	-1.543
31	15	17.02	-2.021
32	17	16.26	0.7443
33	13	13.56	-0.5647
34	17	16.64	0.3554
35	15	15.11	-0.1136
36	14	14.23	-0.2335
37	14	14.4	-0.4012
38	18	15.7	2.299
39	15	16.22	-1.216
40	17	17.02	-0.02134
41	13	13.89	-0.8853
42	16	17.33	-1.33
43	15	16.01	-1.009
44	15	15.32	-0.3197
45	16	15.63	0.3705
46	15	15.8	-0.7975
47	13	15.82	-2.825
48	17	16.86	0.1423
49	18	17.43	0.5731
50	18	17.44	0.5553
51	11	14.71	-3.707
52	14	14.16	-0.1625
53	13	15.7	-2.701
54	15	14.57	0.4308
55	17	15.07	1.926
56	16	15.55	0.4532
57	15	15.82	-0.8248
58	17	17.47	-0.4662
59	16	14.62	1.381
60	16	15.79	0.2069
61	16	14.65	1.348
62	15	15.86	-0.8641
63	12	13.31	-1.315
64	17	15.58	1.421
65	14	15.55	-1.547
66	14	15.78	-1.782
67	16	14.93	1.072
68	15	14.73	0.2672
69	15	17.33	-2.331
70	13	15.63	-2.629
71	18	16.1	1.897
72	15	14.97	0.02854
73	16	15.82	0.1752
74	14	15	-0.9992
75	15	13.92	1.075
76	17	14.54	2.457
77	16	15.67	0.3312
78	10	13.81	-3.814
79	16	15.82	0.1752
80	17	15.72	1.278
81	17	15.79	1.207
82	20	16.22	3.784
83	17	16.39	0.6084
84	18	15.86	2.136
85	15	15.08	-0.08183
86	14	12.97	1.034
87	15	15.73	-0.7322
88	17	15.67	1.331
89	16	15.82	0.1752
90	17	17.01	-0.01376
91	15	14.93	0.07189
92	16	15.67	0.3312
93	18	16.11	1.886
94	18	16.53	1.466
95	16	16.95	-0.9503
96	17	15.22	1.777
97	15	15.67	-0.6688
98	13	16.18	-3.185
99	17	16.66	0.342
100	16	15.31	0.6879
101	15	15.7	-0.7005
102	16	15.7	0.2995
103	16	15.34	0.6594
104	14	15.63	-1.629
105	15	15.32	-0.3197
106	12	13.77	-1.771
107	19	15.35	3.649
108	16	15.11	0.8864
109	16	15.51	0.4948
110	17	15.62	1.383
111	16	16.45	-0.451
112	14	15.66	-1.661
113	15	15.42	-0.4225
114	14	14.73	-0.7328
115	16	15.63	0.3705
116	15	15.82	-0.8248
117	17	16.72	0.2844
118	15	15.04	-0.04251
119	16	15.35	0.6485
120	16	15.6	0.4023
121	15	14.69	0.3065
122	15	15.38	-0.3832
123	11	12.17	-1.172
124	16	15.55	0.4532
125	18	16.13	1.866
126	13	13.95	-0.9505
127	11	13.89	-2.885
128	16	15.35	0.6485
129	18	17.57	0.431
130	15	16.86	-1.858
131	19	17.92	1.082
132	17	17.02	-0.02134
133	13	15.32	-2.32
134	14	15.31	-1.309
135	16	15.7	0.2995
136	13	15.49	-2.487
137	17	15.72	1.278
138	14	15.79	-1.793
139	19	16.2	2.795
140	14	14.54	-0.5433
141	16	15.7	0.2995
142	12	13.47	-1.465
143	16	16.89	-0.8895
144	16	15.35	0.6485
145	15	15.6	-0.5977
146	12	15	-2.999
147	15	15.67	-0.6688
148	17	16.48	0.5172
149	14	15.51	-1.505
150	15	13.37	1.631
151	18	15.6	2.402
152	15	14.36	0.6422
153	18	15.66	2.339
154	15	17.14	-2.136
155	15	16.15	-1.145
156	16	15.94	0.0616
157	13	13.84	-0.8357
158	16	15.6	0.4023
159	14	15.82	-1.825
160	16	13.77	2.228

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.23 & -0.2323 \tabularnewline
2 &  16 &  15.18 &  0.8166 \tabularnewline
3 &  17 &  15.9 &  1.104 \tabularnewline
4 &  15 &  14.69 &  0.3065 \tabularnewline
5 &  16 &  15.9 &  0.1042 \tabularnewline
6 &  16 &  15.21 &  0.7939 \tabularnewline
7 &  18 &  14.57 &  3.431 \tabularnewline
8 &  16 &  15.11 &  0.8864 \tabularnewline
9 &  17 &  16.95 &  0.04971 \tabularnewline
10 &  17 &  17.02 & -0.02134 \tabularnewline
11 &  17 &  15.68 &  1.317 \tabularnewline
12 &  15 &  15.62 & -0.6178 \tabularnewline
13 &  16 &  15.32 &  0.6803 \tabularnewline
14 &  14 &  13.92 &  0.07541 \tabularnewline
15 &  16 &  15.28 &  0.7219 \tabularnewline
16 &  17 &  15.08 &  1.918 \tabularnewline
17 &  16 &  15.08 &  0.9182 \tabularnewline
18 &  15 &  17.1 & -2.096 \tabularnewline
19 &  17 &  15.82 &  1.175 \tabularnewline
20 &  16 &  14.8 &  1.196 \tabularnewline
21 &  15 &  15.79 & -0.7931 \tabularnewline
22 &  16 &  15.67 &  0.3312 \tabularnewline
23 &  15 &  15.7 & -0.7005 \tabularnewline
24 &  17 &  15.67 &  1.331 \tabularnewline
25 &  14 &  15.04 & -1.043 \tabularnewline
26 &  16 &  14.93 &  1.072 \tabularnewline
27 &  15 &  15.63 & -0.6295 \tabularnewline
28 &  16 &  14.69 &  1.31 \tabularnewline
29 &  16 &  16.22 & -0.2164 \tabularnewline
30 &  13 &  14.54 & -1.543 \tabularnewline
31 &  15 &  17.02 & -2.021 \tabularnewline
32 &  17 &  16.26 &  0.7443 \tabularnewline
33 &  13 &  13.56 & -0.5647 \tabularnewline
34 &  17 &  16.64 &  0.3554 \tabularnewline
35 &  15 &  15.11 & -0.1136 \tabularnewline
36 &  14 &  14.23 & -0.2335 \tabularnewline
37 &  14 &  14.4 & -0.4012 \tabularnewline
38 &  18 &  15.7 &  2.299 \tabularnewline
39 &  15 &  16.22 & -1.216 \tabularnewline
40 &  17 &  17.02 & -0.02134 \tabularnewline
41 &  13 &  13.89 & -0.8853 \tabularnewline
42 &  16 &  17.33 & -1.33 \tabularnewline
43 &  15 &  16.01 & -1.009 \tabularnewline
44 &  15 &  15.32 & -0.3197 \tabularnewline
45 &  16 &  15.63 &  0.3705 \tabularnewline
46 &  15 &  15.8 & -0.7975 \tabularnewline
47 &  13 &  15.82 & -2.825 \tabularnewline
48 &  17 &  16.86 &  0.1423 \tabularnewline
49 &  18 &  17.43 &  0.5731 \tabularnewline
50 &  18 &  17.44 &  0.5553 \tabularnewline
51 &  11 &  14.71 & -3.707 \tabularnewline
52 &  14 &  14.16 & -0.1625 \tabularnewline
53 &  13 &  15.7 & -2.701 \tabularnewline
54 &  15 &  14.57 &  0.4308 \tabularnewline
55 &  17 &  15.07 &  1.926 \tabularnewline
56 &  16 &  15.55 &  0.4532 \tabularnewline
57 &  15 &  15.82 & -0.8248 \tabularnewline
58 &  17 &  17.47 & -0.4662 \tabularnewline
59 &  16 &  14.62 &  1.381 \tabularnewline
60 &  16 &  15.79 &  0.2069 \tabularnewline
61 &  16 &  14.65 &  1.348 \tabularnewline
62 &  15 &  15.86 & -0.8641 \tabularnewline
63 &  12 &  13.31 & -1.315 \tabularnewline
64 &  17 &  15.58 &  1.421 \tabularnewline
65 &  14 &  15.55 & -1.547 \tabularnewline
66 &  14 &  15.78 & -1.782 \tabularnewline
67 &  16 &  14.93 &  1.072 \tabularnewline
68 &  15 &  14.73 &  0.2672 \tabularnewline
69 &  15 &  17.33 & -2.331 \tabularnewline
70 &  13 &  15.63 & -2.629 \tabularnewline
71 &  18 &  16.1 &  1.897 \tabularnewline
72 &  15 &  14.97 &  0.02854 \tabularnewline
73 &  16 &  15.82 &  0.1752 \tabularnewline
74 &  14 &  15 & -0.9992 \tabularnewline
75 &  15 &  13.92 &  1.075 \tabularnewline
76 &  17 &  14.54 &  2.457 \tabularnewline
77 &  16 &  15.67 &  0.3312 \tabularnewline
78 &  10 &  13.81 & -3.814 \tabularnewline
79 &  16 &  15.82 &  0.1752 \tabularnewline
80 &  17 &  15.72 &  1.278 \tabularnewline
81 &  17 &  15.79 &  1.207 \tabularnewline
82 &  20 &  16.22 &  3.784 \tabularnewline
83 &  17 &  16.39 &  0.6084 \tabularnewline
84 &  18 &  15.86 &  2.136 \tabularnewline
85 &  15 &  15.08 & -0.08183 \tabularnewline
86 &  14 &  12.97 &  1.034 \tabularnewline
87 &  15 &  15.73 & -0.7322 \tabularnewline
88 &  17 &  15.67 &  1.331 \tabularnewline
89 &  16 &  15.82 &  0.1752 \tabularnewline
90 &  17 &  17.01 & -0.01376 \tabularnewline
91 &  15 &  14.93 &  0.07189 \tabularnewline
92 &  16 &  15.67 &  0.3312 \tabularnewline
93 &  18 &  16.11 &  1.886 \tabularnewline
94 &  18 &  16.53 &  1.466 \tabularnewline
95 &  16 &  16.95 & -0.9503 \tabularnewline
96 &  17 &  15.22 &  1.777 \tabularnewline
97 &  15 &  15.67 & -0.6688 \tabularnewline
98 &  13 &  16.18 & -3.185 \tabularnewline
99 &  17 &  16.66 &  0.342 \tabularnewline
100 &  16 &  15.31 &  0.6879 \tabularnewline
101 &  15 &  15.7 & -0.7005 \tabularnewline
102 &  16 &  15.7 &  0.2995 \tabularnewline
103 &  16 &  15.34 &  0.6594 \tabularnewline
104 &  14 &  15.63 & -1.629 \tabularnewline
105 &  15 &  15.32 & -0.3197 \tabularnewline
106 &  12 &  13.77 & -1.771 \tabularnewline
107 &  19 &  15.35 &  3.649 \tabularnewline
108 &  16 &  15.11 &  0.8864 \tabularnewline
109 &  16 &  15.51 &  0.4948 \tabularnewline
110 &  17 &  15.62 &  1.383 \tabularnewline
111 &  16 &  16.45 & -0.451 \tabularnewline
112 &  14 &  15.66 & -1.661 \tabularnewline
113 &  15 &  15.42 & -0.4225 \tabularnewline
114 &  14 &  14.73 & -0.7328 \tabularnewline
115 &  16 &  15.63 &  0.3705 \tabularnewline
116 &  15 &  15.82 & -0.8248 \tabularnewline
117 &  17 &  16.72 &  0.2844 \tabularnewline
118 &  15 &  15.04 & -0.04251 \tabularnewline
119 &  16 &  15.35 &  0.6485 \tabularnewline
120 &  16 &  15.6 &  0.4023 \tabularnewline
121 &  15 &  14.69 &  0.3065 \tabularnewline
122 &  15 &  15.38 & -0.3832 \tabularnewline
123 &  11 &  12.17 & -1.172 \tabularnewline
124 &  16 &  15.55 &  0.4532 \tabularnewline
125 &  18 &  16.13 &  1.866 \tabularnewline
126 &  13 &  13.95 & -0.9505 \tabularnewline
127 &  11 &  13.89 & -2.885 \tabularnewline
128 &  16 &  15.35 &  0.6485 \tabularnewline
129 &  18 &  17.57 &  0.431 \tabularnewline
130 &  15 &  16.86 & -1.858 \tabularnewline
131 &  19 &  17.92 &  1.082 \tabularnewline
132 &  17 &  17.02 & -0.02134 \tabularnewline
133 &  13 &  15.32 & -2.32 \tabularnewline
134 &  14 &  15.31 & -1.309 \tabularnewline
135 &  16 &  15.7 &  0.2995 \tabularnewline
136 &  13 &  15.49 & -2.487 \tabularnewline
137 &  17 &  15.72 &  1.278 \tabularnewline
138 &  14 &  15.79 & -1.793 \tabularnewline
139 &  19 &  16.2 &  2.795 \tabularnewline
140 &  14 &  14.54 & -0.5433 \tabularnewline
141 &  16 &  15.7 &  0.2995 \tabularnewline
142 &  12 &  13.47 & -1.465 \tabularnewline
143 &  16 &  16.89 & -0.8895 \tabularnewline
144 &  16 &  15.35 &  0.6485 \tabularnewline
145 &  15 &  15.6 & -0.5977 \tabularnewline
146 &  12 &  15 & -2.999 \tabularnewline
147 &  15 &  15.67 & -0.6688 \tabularnewline
148 &  17 &  16.48 &  0.5172 \tabularnewline
149 &  14 &  15.51 & -1.505 \tabularnewline
150 &  15 &  13.37 &  1.631 \tabularnewline
151 &  18 &  15.6 &  2.402 \tabularnewline
152 &  15 &  14.36 &  0.6422 \tabularnewline
153 &  18 &  15.66 &  2.339 \tabularnewline
154 &  15 &  17.14 & -2.136 \tabularnewline
155 &  15 &  16.15 & -1.145 \tabularnewline
156 &  16 &  15.94 &  0.0616 \tabularnewline
157 &  13 &  13.84 & -0.8357 \tabularnewline
158 &  16 &  15.6 &  0.4023 \tabularnewline
159 &  14 &  15.82 & -1.825 \tabularnewline
160 &  16 &  13.77 &  2.228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.23[/C][C]-0.2323[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.18[/C][C] 0.8166[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.9[/C][C] 1.104[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.69[/C][C] 0.3065[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.9[/C][C] 0.1042[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.21[/C][C] 0.7939[/C][/ROW]
[ROW][C]7[/C][C] 18[/C][C] 14.57[/C][C] 3.431[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.11[/C][C] 0.8864[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.95[/C][C] 0.04971[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 17.02[/C][C]-0.02134[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.68[/C][C] 1.317[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.62[/C][C]-0.6178[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.32[/C][C] 0.6803[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 13.92[/C][C] 0.07541[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.28[/C][C] 0.7219[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.08[/C][C] 1.918[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.08[/C][C] 0.9182[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 17.1[/C][C]-2.096[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.82[/C][C] 1.175[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.8[/C][C] 1.196[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.79[/C][C]-0.7931[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.67[/C][C] 0.3312[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.7[/C][C]-0.7005[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.67[/C][C] 1.331[/C][/ROW]
[ROW][C]25[/C][C] 14[/C][C] 15.04[/C][C]-1.043[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.93[/C][C] 1.072[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.63[/C][C]-0.6295[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.69[/C][C] 1.31[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.22[/C][C]-0.2164[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.54[/C][C]-1.543[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 17.02[/C][C]-2.021[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.26[/C][C] 0.7443[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 13.56[/C][C]-0.5647[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 16.64[/C][C] 0.3554[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 15.11[/C][C]-0.1136[/C][/ROW]
[ROW][C]36[/C][C] 14[/C][C] 14.23[/C][C]-0.2335[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.4[/C][C]-0.4012[/C][/ROW]
[ROW][C]38[/C][C] 18[/C][C] 15.7[/C][C] 2.299[/C][/ROW]
[ROW][C]39[/C][C] 15[/C][C] 16.22[/C][C]-1.216[/C][/ROW]
[ROW][C]40[/C][C] 17[/C][C] 17.02[/C][C]-0.02134[/C][/ROW]
[ROW][C]41[/C][C] 13[/C][C] 13.89[/C][C]-0.8853[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 17.33[/C][C]-1.33[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 16.01[/C][C]-1.009[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.32[/C][C]-0.3197[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 15.63[/C][C] 0.3705[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 15.8[/C][C]-0.7975[/C][/ROW]
[ROW][C]47[/C][C] 13[/C][C] 15.82[/C][C]-2.825[/C][/ROW]
[ROW][C]48[/C][C] 17[/C][C] 16.86[/C][C] 0.1423[/C][/ROW]
[ROW][C]49[/C][C] 18[/C][C] 17.43[/C][C] 0.5731[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 17.44[/C][C] 0.5553[/C][/ROW]
[ROW][C]51[/C][C] 11[/C][C] 14.71[/C][C]-3.707[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 14.16[/C][C]-0.1625[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 15.7[/C][C]-2.701[/C][/ROW]
[ROW][C]54[/C][C] 15[/C][C] 14.57[/C][C] 0.4308[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.07[/C][C] 1.926[/C][/ROW]
[ROW][C]56[/C][C] 16[/C][C] 15.55[/C][C] 0.4532[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 15.82[/C][C]-0.8248[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 17.47[/C][C]-0.4662[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 14.62[/C][C] 1.381[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 15.79[/C][C] 0.2069[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 14.65[/C][C] 1.348[/C][/ROW]
[ROW][C]62[/C][C] 15[/C][C] 15.86[/C][C]-0.8641[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 13.31[/C][C]-1.315[/C][/ROW]
[ROW][C]64[/C][C] 17[/C][C] 15.58[/C][C] 1.421[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 15.55[/C][C]-1.547[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.78[/C][C]-1.782[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 14.93[/C][C] 1.072[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 14.73[/C][C] 0.2672[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 17.33[/C][C]-2.331[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 15.63[/C][C]-2.629[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 16.1[/C][C] 1.897[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 14.97[/C][C] 0.02854[/C][/ROW]
[ROW][C]73[/C][C] 16[/C][C] 15.82[/C][C] 0.1752[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 15[/C][C]-0.9992[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 13.92[/C][C] 1.075[/C][/ROW]
[ROW][C]76[/C][C] 17[/C][C] 14.54[/C][C] 2.457[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 15.67[/C][C] 0.3312[/C][/ROW]
[ROW][C]78[/C][C] 10[/C][C] 13.81[/C][C]-3.814[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.82[/C][C] 0.1752[/C][/ROW]
[ROW][C]80[/C][C] 17[/C][C] 15.72[/C][C] 1.278[/C][/ROW]
[ROW][C]81[/C][C] 17[/C][C] 15.79[/C][C] 1.207[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 16.22[/C][C] 3.784[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 16.39[/C][C] 0.6084[/C][/ROW]
[ROW][C]84[/C][C] 18[/C][C] 15.86[/C][C] 2.136[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 15.08[/C][C]-0.08183[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 12.97[/C][C] 1.034[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.73[/C][C]-0.7322[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.67[/C][C] 1.331[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 15.82[/C][C] 0.1752[/C][/ROW]
[ROW][C]90[/C][C] 17[/C][C] 17.01[/C][C]-0.01376[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 14.93[/C][C] 0.07189[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.67[/C][C] 0.3312[/C][/ROW]
[ROW][C]93[/C][C] 18[/C][C] 16.11[/C][C] 1.886[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 16.53[/C][C] 1.466[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 16.95[/C][C]-0.9503[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 15.22[/C][C] 1.777[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 15.67[/C][C]-0.6688[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 16.18[/C][C]-3.185[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 16.66[/C][C] 0.342[/C][/ROW]
[ROW][C]100[/C][C] 16[/C][C] 15.31[/C][C] 0.6879[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 15.7[/C][C]-0.7005[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 15.7[/C][C] 0.2995[/C][/ROW]
[ROW][C]103[/C][C] 16[/C][C] 15.34[/C][C] 0.6594[/C][/ROW]
[ROW][C]104[/C][C] 14[/C][C] 15.63[/C][C]-1.629[/C][/ROW]
[ROW][C]105[/C][C] 15[/C][C] 15.32[/C][C]-0.3197[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 13.77[/C][C]-1.771[/C][/ROW]
[ROW][C]107[/C][C] 19[/C][C] 15.35[/C][C] 3.649[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.11[/C][C] 0.8864[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 15.51[/C][C] 0.4948[/C][/ROW]
[ROW][C]110[/C][C] 17[/C][C] 15.62[/C][C] 1.383[/C][/ROW]
[ROW][C]111[/C][C] 16[/C][C] 16.45[/C][C]-0.451[/C][/ROW]
[ROW][C]112[/C][C] 14[/C][C] 15.66[/C][C]-1.661[/C][/ROW]
[ROW][C]113[/C][C] 15[/C][C] 15.42[/C][C]-0.4225[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 14.73[/C][C]-0.7328[/C][/ROW]
[ROW][C]115[/C][C] 16[/C][C] 15.63[/C][C] 0.3705[/C][/ROW]
[ROW][C]116[/C][C] 15[/C][C] 15.82[/C][C]-0.8248[/C][/ROW]
[ROW][C]117[/C][C] 17[/C][C] 16.72[/C][C] 0.2844[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.04[/C][C]-0.04251[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 15.35[/C][C] 0.6485[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.6[/C][C] 0.4023[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 14.69[/C][C] 0.3065[/C][/ROW]
[ROW][C]122[/C][C] 15[/C][C] 15.38[/C][C]-0.3832[/C][/ROW]
[ROW][C]123[/C][C] 11[/C][C] 12.17[/C][C]-1.172[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.55[/C][C] 0.4532[/C][/ROW]
[ROW][C]125[/C][C] 18[/C][C] 16.13[/C][C] 1.866[/C][/ROW]
[ROW][C]126[/C][C] 13[/C][C] 13.95[/C][C]-0.9505[/C][/ROW]
[ROW][C]127[/C][C] 11[/C][C] 13.89[/C][C]-2.885[/C][/ROW]
[ROW][C]128[/C][C] 16[/C][C] 15.35[/C][C] 0.6485[/C][/ROW]
[ROW][C]129[/C][C] 18[/C][C] 17.57[/C][C] 0.431[/C][/ROW]
[ROW][C]130[/C][C] 15[/C][C] 16.86[/C][C]-1.858[/C][/ROW]
[ROW][C]131[/C][C] 19[/C][C] 17.92[/C][C] 1.082[/C][/ROW]
[ROW][C]132[/C][C] 17[/C][C] 17.02[/C][C]-0.02134[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 15.32[/C][C]-2.32[/C][/ROW]
[ROW][C]134[/C][C] 14[/C][C] 15.31[/C][C]-1.309[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 15.7[/C][C] 0.2995[/C][/ROW]
[ROW][C]136[/C][C] 13[/C][C] 15.49[/C][C]-2.487[/C][/ROW]
[ROW][C]137[/C][C] 17[/C][C] 15.72[/C][C] 1.278[/C][/ROW]
[ROW][C]138[/C][C] 14[/C][C] 15.79[/C][C]-1.793[/C][/ROW]
[ROW][C]139[/C][C] 19[/C][C] 16.2[/C][C] 2.795[/C][/ROW]
[ROW][C]140[/C][C] 14[/C][C] 14.54[/C][C]-0.5433[/C][/ROW]
[ROW][C]141[/C][C] 16[/C][C] 15.7[/C][C] 0.2995[/C][/ROW]
[ROW][C]142[/C][C] 12[/C][C] 13.47[/C][C]-1.465[/C][/ROW]
[ROW][C]143[/C][C] 16[/C][C] 16.89[/C][C]-0.8895[/C][/ROW]
[ROW][C]144[/C][C] 16[/C][C] 15.35[/C][C] 0.6485[/C][/ROW]
[ROW][C]145[/C][C] 15[/C][C] 15.6[/C][C]-0.5977[/C][/ROW]
[ROW][C]146[/C][C] 12[/C][C] 15[/C][C]-2.999[/C][/ROW]
[ROW][C]147[/C][C] 15[/C][C] 15.67[/C][C]-0.6688[/C][/ROW]
[ROW][C]148[/C][C] 17[/C][C] 16.48[/C][C] 0.5172[/C][/ROW]
[ROW][C]149[/C][C] 14[/C][C] 15.51[/C][C]-1.505[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 13.37[/C][C] 1.631[/C][/ROW]
[ROW][C]151[/C][C] 18[/C][C] 15.6[/C][C] 2.402[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 14.36[/C][C] 0.6422[/C][/ROW]
[ROW][C]153[/C][C] 18[/C][C] 15.66[/C][C] 2.339[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 17.14[/C][C]-2.136[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 16.15[/C][C]-1.145[/C][/ROW]
[ROW][C]156[/C][C] 16[/C][C] 15.94[/C][C] 0.0616[/C][/ROW]
[ROW][C]157[/C][C] 13[/C][C] 13.84[/C][C]-0.8357[/C][/ROW]
[ROW][C]158[/C][C] 16[/C][C] 15.6[/C][C] 0.4023[/C][/ROW]
[ROW][C]159[/C][C] 14[/C][C] 15.82[/C][C]-1.825[/C][/ROW]
[ROW][C]160[/C][C] 16[/C][C] 13.77[/C][C] 2.228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297766&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.23	-0.2323
2	16	15.18	0.8166
3	17	15.9	1.104
4	15	14.69	0.3065
5	16	15.9	0.1042
6	16	15.21	0.7939
7	18	14.57	3.431
8	16	15.11	0.8864
9	17	16.95	0.04971
10	17	17.02	-0.02134
11	17	15.68	1.317
12	15	15.62	-0.6178
13	16	15.32	0.6803
14	14	13.92	0.07541
15	16	15.28	0.7219
16	17	15.08	1.918
17	16	15.08	0.9182
18	15	17.1	-2.096
19	17	15.82	1.175
20	16	14.8	1.196
21	15	15.79	-0.7931
22	16	15.67	0.3312
23	15	15.7	-0.7005
24	17	15.67	1.331
25	14	15.04	-1.043
26	16	14.93	1.072
27	15	15.63	-0.6295
28	16	14.69	1.31
29	16	16.22	-0.2164
30	13	14.54	-1.543
31	15	17.02	-2.021
32	17	16.26	0.7443
33	13	13.56	-0.5647
34	17	16.64	0.3554
35	15	15.11	-0.1136
36	14	14.23	-0.2335
37	14	14.4	-0.4012
38	18	15.7	2.299
39	15	16.22	-1.216
40	17	17.02	-0.02134
41	13	13.89	-0.8853
42	16	17.33	-1.33
43	15	16.01	-1.009
44	15	15.32	-0.3197
45	16	15.63	0.3705
46	15	15.8	-0.7975
47	13	15.82	-2.825
48	17	16.86	0.1423
49	18	17.43	0.5731
50	18	17.44	0.5553
51	11	14.71	-3.707
52	14	14.16	-0.1625
53	13	15.7	-2.701
54	15	14.57	0.4308
55	17	15.07	1.926
56	16	15.55	0.4532
57	15	15.82	-0.8248
58	17	17.47	-0.4662
59	16	14.62	1.381
60	16	15.79	0.2069
61	16	14.65	1.348
62	15	15.86	-0.8641
63	12	13.31	-1.315
64	17	15.58	1.421
65	14	15.55	-1.547
66	14	15.78	-1.782
67	16	14.93	1.072
68	15	14.73	0.2672
69	15	17.33	-2.331
70	13	15.63	-2.629
71	18	16.1	1.897
72	15	14.97	0.02854
73	16	15.82	0.1752
74	14	15	-0.9992
75	15	13.92	1.075
76	17	14.54	2.457
77	16	15.67	0.3312
78	10	13.81	-3.814
79	16	15.82	0.1752
80	17	15.72	1.278
81	17	15.79	1.207
82	20	16.22	3.784
83	17	16.39	0.6084
84	18	15.86	2.136
85	15	15.08	-0.08183
86	14	12.97	1.034
87	15	15.73	-0.7322
88	17	15.67	1.331
89	16	15.82	0.1752
90	17	17.01	-0.01376
91	15	14.93	0.07189
92	16	15.67	0.3312
93	18	16.11	1.886
94	18	16.53	1.466
95	16	16.95	-0.9503
96	17	15.22	1.777
97	15	15.67	-0.6688
98	13	16.18	-3.185
99	17	16.66	0.342
100	16	15.31	0.6879
101	15	15.7	-0.7005
102	16	15.7	0.2995
103	16	15.34	0.6594
104	14	15.63	-1.629
105	15	15.32	-0.3197
106	12	13.77	-1.771
107	19	15.35	3.649
108	16	15.11	0.8864
109	16	15.51	0.4948
110	17	15.62	1.383
111	16	16.45	-0.451
112	14	15.66	-1.661
113	15	15.42	-0.4225
114	14	14.73	-0.7328
115	16	15.63	0.3705
116	15	15.82	-0.8248
117	17	16.72	0.2844
118	15	15.04	-0.04251
119	16	15.35	0.6485
120	16	15.6	0.4023
121	15	14.69	0.3065
122	15	15.38	-0.3832
123	11	12.17	-1.172
124	16	15.55	0.4532
125	18	16.13	1.866
126	13	13.95	-0.9505
127	11	13.89	-2.885
128	16	15.35	0.6485
129	18	17.57	0.431
130	15	16.86	-1.858
131	19	17.92	1.082
132	17	17.02	-0.02134
133	13	15.32	-2.32
134	14	15.31	-1.309
135	16	15.7	0.2995
136	13	15.49	-2.487
137	17	15.72	1.278
138	14	15.79	-1.793
139	19	16.2	2.795
140	14	14.54	-0.5433
141	16	15.7	0.2995
142	12	13.47	-1.465
143	16	16.89	-0.8895
144	16	15.35	0.6485
145	15	15.6	-0.5977
146	12	15	-2.999
147	15	15.67	-0.6688
148	17	16.48	0.5172
149	14	15.51	-1.505
150	15	13.37	1.631
151	18	15.6	2.402
152	15	14.36	0.6422
153	18	15.66	2.339
154	15	17.14	-2.136
155	15	16.15	-1.145
156	16	15.94	0.0616
157	13	13.84	-0.8357
158	16	15.6	0.4023
159	14	15.82	-1.825
160	16	13.77	2.228

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.2753	0.5506	0.7247
11	0.1987	0.3974	0.8013
12	0.105	0.21	0.895
13	0.05591	0.1118	0.9441
14	0.05236	0.1047	0.9476
15	0.07159	0.1432	0.9284
16	0.07889	0.1578	0.9211
17	0.04627	0.09253	0.9537
18	0.09839	0.1968	0.9016
19	0.06984	0.1397	0.9302
20	0.05433	0.1087	0.9457
21	0.045	0.09	0.955
22	0.02788	0.05577	0.9721
23	0.02898	0.05795	0.971
24	0.02844	0.05689	0.9716
25	0.08643	0.1729	0.9136
26	0.06303	0.1261	0.937
27	0.05415	0.1083	0.9458
28	0.0392	0.07841	0.9608
29	0.02625	0.05249	0.9738
30	0.03518	0.07036	0.9648
31	0.05187	0.1037	0.9481
32	0.04935	0.09871	0.9506
33	0.0476	0.09519	0.9524
34	0.03488	0.06976	0.9651
35	0.02461	0.04921	0.9754
36	0.01889	0.03777	0.9811
37	0.01589	0.03177	0.9841
38	0.04016	0.08033	0.9598
39	0.03504	0.07008	0.965
40	0.02494	0.04987	0.9751
41	0.02633	0.05265	0.9737
42	0.02251	0.04502	0.9775
43	0.01692	0.03384	0.9831
44	0.01302	0.02605	0.987
45	0.009101	0.0182	0.9909
46	0.008262	0.01652	0.9917
47	0.02431	0.04862	0.9757
48	0.01821	0.03642	0.9818
49	0.0146	0.0292	0.9854
50	0.0132	0.02639	0.9868
51	0.07631	0.1526	0.9237
52	0.05957	0.1191	0.9404
53	0.1072	0.2145	0.8928
54	0.08896	0.1779	0.911
55	0.1049	0.2099	0.8951
56	0.08928	0.1786	0.9107
57	0.07503	0.1501	0.925
58	0.06007	0.1201	0.9399
59	0.05475	0.1095	0.9453
60	0.04263	0.08525	0.9574
61	0.04177	0.08353	0.9582
62	0.03447	0.06894	0.9655
63	0.03378	0.06757	0.9662
64	0.03808	0.07616	0.9619
65	0.04109	0.08217	0.9589
66	0.04373	0.08747	0.9563
67	0.03853	0.07706	0.9615
68	0.03168	0.06335	0.9683
69	0.04692	0.09385	0.9531
70	0.08997	0.1799	0.91
71	0.1187	0.2374	0.8813
72	0.1071	0.2141	0.8929
73	0.08784	0.1757	0.9122
74	0.0795	0.159	0.9205
75	0.07247	0.1449	0.9275
76	0.1255	0.251	0.8745
77	0.105	0.2101	0.895
78	0.3521	0.7042	0.6479
79	0.3117	0.6235	0.6883
80	0.3049	0.6098	0.6951
81	0.2955	0.591	0.7045
82	0.58	0.8399	0.4199
83	0.541	0.918	0.459
84	0.6006	0.7989	0.3994
85	0.5603	0.8795	0.4397
86	0.5314	0.9371	0.4686
87	0.4981	0.9962	0.5019
88	0.4989	0.9978	0.5011
89	0.4526	0.9052	0.5474
90	0.4066	0.8132	0.5934
91	0.3654	0.7308	0.6346
92	0.3269	0.6539	0.6731
93	0.3645	0.729	0.6355
94	0.3754	0.7507	0.6246
95	0.3472	0.6943	0.6528
96	0.3704	0.7408	0.6296
97	0.3329	0.6657	0.6671
98	0.4973	0.9947	0.5027
99	0.4524	0.9048	0.5476
100	0.4135	0.827	0.5865
101	0.3758	0.7515	0.6242
102	0.333	0.666	0.667
103	0.2971	0.5942	0.7029
104	0.3051	0.6102	0.6949
105	0.2643	0.5285	0.7357
106	0.2814	0.5629	0.7186
107	0.5594	0.8812	0.4406
108	0.5411	0.9179	0.4589
109	0.5112	0.9775	0.4888
110	0.5012	0.9975	0.4988
111	0.4629	0.9258	0.5371
112	0.4806	0.9613	0.5194
113	0.431	0.8619	0.569
114	0.3901	0.7802	0.6099
115	0.3463	0.6926	0.6537
116	0.3212	0.6423	0.6788
117	0.2916	0.5832	0.7084
118	0.2527	0.5053	0.7473
119	0.2271	0.4542	0.7729
120	0.2018	0.4035	0.7982
121	0.1903	0.3806	0.8097
122	0.1554	0.3108	0.8446
123	0.1529	0.3059	0.8471
124	0.1233	0.2466	0.8767
125	0.1259	0.2519	0.8741
126	0.1308	0.2616	0.8692
127	0.2595	0.5189	0.7405
128	0.2486	0.4972	0.7514
129	0.2148	0.4297	0.7852
130	0.1907	0.3814	0.8093
131	0.1621	0.3241	0.8379
132	0.1514	0.3028	0.8486
133	0.1492	0.2985	0.8508
134	0.1368	0.2736	0.8632
135	0.1035	0.207	0.8965
136	0.1166	0.2332	0.8834
137	0.1398	0.2796	0.8602
138	0.1293	0.2586	0.8707
139	0.2304	0.4608	0.7696
140	0.3245	0.6489	0.6755
141	0.2539	0.5078	0.7461
142	0.28	0.5601	0.72
143	0.2183	0.4366	0.7817
144	0.2091	0.4182	0.7909
145	0.1467	0.2935	0.8532
146	0.1387	0.2775	0.8613
147	0.09406	0.1881	0.9059
148	0.05663	0.1133	0.9434
149	0.1536	0.3071	0.8464
150	0.5763	0.8473	0.4237

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.2753 &  0.5506 &  0.7247 \tabularnewline
11 &  0.1987 &  0.3974 &  0.8013 \tabularnewline
12 &  0.105 &  0.21 &  0.895 \tabularnewline
13 &  0.05591 &  0.1118 &  0.9441 \tabularnewline
14 &  0.05236 &  0.1047 &  0.9476 \tabularnewline
15 &  0.07159 &  0.1432 &  0.9284 \tabularnewline
16 &  0.07889 &  0.1578 &  0.9211 \tabularnewline
17 &  0.04627 &  0.09253 &  0.9537 \tabularnewline
18 &  0.09839 &  0.1968 &  0.9016 \tabularnewline
19 &  0.06984 &  0.1397 &  0.9302 \tabularnewline
20 &  0.05433 &  0.1087 &  0.9457 \tabularnewline
21 &  0.045 &  0.09 &  0.955 \tabularnewline
22 &  0.02788 &  0.05577 &  0.9721 \tabularnewline
23 &  0.02898 &  0.05795 &  0.971 \tabularnewline
24 &  0.02844 &  0.05689 &  0.9716 \tabularnewline
25 &  0.08643 &  0.1729 &  0.9136 \tabularnewline
26 &  0.06303 &  0.1261 &  0.937 \tabularnewline
27 &  0.05415 &  0.1083 &  0.9458 \tabularnewline
28 &  0.0392 &  0.07841 &  0.9608 \tabularnewline
29 &  0.02625 &  0.05249 &  0.9738 \tabularnewline
30 &  0.03518 &  0.07036 &  0.9648 \tabularnewline
31 &  0.05187 &  0.1037 &  0.9481 \tabularnewline
32 &  0.04935 &  0.09871 &  0.9506 \tabularnewline
33 &  0.0476 &  0.09519 &  0.9524 \tabularnewline
34 &  0.03488 &  0.06976 &  0.9651 \tabularnewline
35 &  0.02461 &  0.04921 &  0.9754 \tabularnewline
36 &  0.01889 &  0.03777 &  0.9811 \tabularnewline
37 &  0.01589 &  0.03177 &  0.9841 \tabularnewline
38 &  0.04016 &  0.08033 &  0.9598 \tabularnewline
39 &  0.03504 &  0.07008 &  0.965 \tabularnewline
40 &  0.02494 &  0.04987 &  0.9751 \tabularnewline
41 &  0.02633 &  0.05265 &  0.9737 \tabularnewline
42 &  0.02251 &  0.04502 &  0.9775 \tabularnewline
43 &  0.01692 &  0.03384 &  0.9831 \tabularnewline
44 &  0.01302 &  0.02605 &  0.987 \tabularnewline
45 &  0.009101 &  0.0182 &  0.9909 \tabularnewline
46 &  0.008262 &  0.01652 &  0.9917 \tabularnewline
47 &  0.02431 &  0.04862 &  0.9757 \tabularnewline
48 &  0.01821 &  0.03642 &  0.9818 \tabularnewline
49 &  0.0146 &  0.0292 &  0.9854 \tabularnewline
50 &  0.0132 &  0.02639 &  0.9868 \tabularnewline
51 &  0.07631 &  0.1526 &  0.9237 \tabularnewline
52 &  0.05957 &  0.1191 &  0.9404 \tabularnewline
53 &  0.1072 &  0.2145 &  0.8928 \tabularnewline
54 &  0.08896 &  0.1779 &  0.911 \tabularnewline
55 &  0.1049 &  0.2099 &  0.8951 \tabularnewline
56 &  0.08928 &  0.1786 &  0.9107 \tabularnewline
57 &  0.07503 &  0.1501 &  0.925 \tabularnewline
58 &  0.06007 &  0.1201 &  0.9399 \tabularnewline
59 &  0.05475 &  0.1095 &  0.9453 \tabularnewline
60 &  0.04263 &  0.08525 &  0.9574 \tabularnewline
61 &  0.04177 &  0.08353 &  0.9582 \tabularnewline
62 &  0.03447 &  0.06894 &  0.9655 \tabularnewline
63 &  0.03378 &  0.06757 &  0.9662 \tabularnewline
64 &  0.03808 &  0.07616 &  0.9619 \tabularnewline
65 &  0.04109 &  0.08217 &  0.9589 \tabularnewline
66 &  0.04373 &  0.08747 &  0.9563 \tabularnewline
67 &  0.03853 &  0.07706 &  0.9615 \tabularnewline
68 &  0.03168 &  0.06335 &  0.9683 \tabularnewline
69 &  0.04692 &  0.09385 &  0.9531 \tabularnewline
70 &  0.08997 &  0.1799 &  0.91 \tabularnewline
71 &  0.1187 &  0.2374 &  0.8813 \tabularnewline
72 &  0.1071 &  0.2141 &  0.8929 \tabularnewline
73 &  0.08784 &  0.1757 &  0.9122 \tabularnewline
74 &  0.0795 &  0.159 &  0.9205 \tabularnewline
75 &  0.07247 &  0.1449 &  0.9275 \tabularnewline
76 &  0.1255 &  0.251 &  0.8745 \tabularnewline
77 &  0.105 &  0.2101 &  0.895 \tabularnewline
78 &  0.3521 &  0.7042 &  0.6479 \tabularnewline
79 &  0.3117 &  0.6235 &  0.6883 \tabularnewline
80 &  0.3049 &  0.6098 &  0.6951 \tabularnewline
81 &  0.2955 &  0.591 &  0.7045 \tabularnewline
82 &  0.58 &  0.8399 &  0.4199 \tabularnewline
83 &  0.541 &  0.918 &  0.459 \tabularnewline
84 &  0.6006 &  0.7989 &  0.3994 \tabularnewline
85 &  0.5603 &  0.8795 &  0.4397 \tabularnewline
86 &  0.5314 &  0.9371 &  0.4686 \tabularnewline
87 &  0.4981 &  0.9962 &  0.5019 \tabularnewline
88 &  0.4989 &  0.9978 &  0.5011 \tabularnewline
89 &  0.4526 &  0.9052 &  0.5474 \tabularnewline
90 &  0.4066 &  0.8132 &  0.5934 \tabularnewline
91 &  0.3654 &  0.7308 &  0.6346 \tabularnewline
92 &  0.3269 &  0.6539 &  0.6731 \tabularnewline
93 &  0.3645 &  0.729 &  0.6355 \tabularnewline
94 &  0.3754 &  0.7507 &  0.6246 \tabularnewline
95 &  0.3472 &  0.6943 &  0.6528 \tabularnewline
96 &  0.3704 &  0.7408 &  0.6296 \tabularnewline
97 &  0.3329 &  0.6657 &  0.6671 \tabularnewline
98 &  0.4973 &  0.9947 &  0.5027 \tabularnewline
99 &  0.4524 &  0.9048 &  0.5476 \tabularnewline
100 &  0.4135 &  0.827 &  0.5865 \tabularnewline
101 &  0.3758 &  0.7515 &  0.6242 \tabularnewline
102 &  0.333 &  0.666 &  0.667 \tabularnewline
103 &  0.2971 &  0.5942 &  0.7029 \tabularnewline
104 &  0.3051 &  0.6102 &  0.6949 \tabularnewline
105 &  0.2643 &  0.5285 &  0.7357 \tabularnewline
106 &  0.2814 &  0.5629 &  0.7186 \tabularnewline
107 &  0.5594 &  0.8812 &  0.4406 \tabularnewline
108 &  0.5411 &  0.9179 &  0.4589 \tabularnewline
109 &  0.5112 &  0.9775 &  0.4888 \tabularnewline
110 &  0.5012 &  0.9975 &  0.4988 \tabularnewline
111 &  0.4629 &  0.9258 &  0.5371 \tabularnewline
112 &  0.4806 &  0.9613 &  0.5194 \tabularnewline
113 &  0.431 &  0.8619 &  0.569 \tabularnewline
114 &  0.3901 &  0.7802 &  0.6099 \tabularnewline
115 &  0.3463 &  0.6926 &  0.6537 \tabularnewline
116 &  0.3212 &  0.6423 &  0.6788 \tabularnewline
117 &  0.2916 &  0.5832 &  0.7084 \tabularnewline
118 &  0.2527 &  0.5053 &  0.7473 \tabularnewline
119 &  0.2271 &  0.4542 &  0.7729 \tabularnewline
120 &  0.2018 &  0.4035 &  0.7982 \tabularnewline
121 &  0.1903 &  0.3806 &  0.8097 \tabularnewline
122 &  0.1554 &  0.3108 &  0.8446 \tabularnewline
123 &  0.1529 &  0.3059 &  0.8471 \tabularnewline
124 &  0.1233 &  0.2466 &  0.8767 \tabularnewline
125 &  0.1259 &  0.2519 &  0.8741 \tabularnewline
126 &  0.1308 &  0.2616 &  0.8692 \tabularnewline
127 &  0.2595 &  0.5189 &  0.7405 \tabularnewline
128 &  0.2486 &  0.4972 &  0.7514 \tabularnewline
129 &  0.2148 &  0.4297 &  0.7852 \tabularnewline
130 &  0.1907 &  0.3814 &  0.8093 \tabularnewline
131 &  0.1621 &  0.3241 &  0.8379 \tabularnewline
132 &  0.1514 &  0.3028 &  0.8486 \tabularnewline
133 &  0.1492 &  0.2985 &  0.8508 \tabularnewline
134 &  0.1368 &  0.2736 &  0.8632 \tabularnewline
135 &  0.1035 &  0.207 &  0.8965 \tabularnewline
136 &  0.1166 &  0.2332 &  0.8834 \tabularnewline
137 &  0.1398 &  0.2796 &  0.8602 \tabularnewline
138 &  0.1293 &  0.2586 &  0.8707 \tabularnewline
139 &  0.2304 &  0.4608 &  0.7696 \tabularnewline
140 &  0.3245 &  0.6489 &  0.6755 \tabularnewline
141 &  0.2539 &  0.5078 &  0.7461 \tabularnewline
142 &  0.28 &  0.5601 &  0.72 \tabularnewline
143 &  0.2183 &  0.4366 &  0.7817 \tabularnewline
144 &  0.2091 &  0.4182 &  0.7909 \tabularnewline
145 &  0.1467 &  0.2935 &  0.8532 \tabularnewline
146 &  0.1387 &  0.2775 &  0.8613 \tabularnewline
147 &  0.09406 &  0.1881 &  0.9059 \tabularnewline
148 &  0.05663 &  0.1133 &  0.9434 \tabularnewline
149 &  0.1536 &  0.3071 &  0.8464 \tabularnewline
150 &  0.5763 &  0.8473 &  0.4237 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&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] 0.2753[/C][C] 0.5506[/C][C] 0.7247[/C][/ROW]
[ROW][C]11[/C][C] 0.1987[/C][C] 0.3974[/C][C] 0.8013[/C][/ROW]
[ROW][C]12[/C][C] 0.105[/C][C] 0.21[/C][C] 0.895[/C][/ROW]
[ROW][C]13[/C][C] 0.05591[/C][C] 0.1118[/C][C] 0.9441[/C][/ROW]
[ROW][C]14[/C][C] 0.05236[/C][C] 0.1047[/C][C] 0.9476[/C][/ROW]
[ROW][C]15[/C][C] 0.07159[/C][C] 0.1432[/C][C] 0.9284[/C][/ROW]
[ROW][C]16[/C][C] 0.07889[/C][C] 0.1578[/C][C] 0.9211[/C][/ROW]
[ROW][C]17[/C][C] 0.04627[/C][C] 0.09253[/C][C] 0.9537[/C][/ROW]
[ROW][C]18[/C][C] 0.09839[/C][C] 0.1968[/C][C] 0.9016[/C][/ROW]
[ROW][C]19[/C][C] 0.06984[/C][C] 0.1397[/C][C] 0.9302[/C][/ROW]
[ROW][C]20[/C][C] 0.05433[/C][C] 0.1087[/C][C] 0.9457[/C][/ROW]
[ROW][C]21[/C][C] 0.045[/C][C] 0.09[/C][C] 0.955[/C][/ROW]
[ROW][C]22[/C][C] 0.02788[/C][C] 0.05577[/C][C] 0.9721[/C][/ROW]
[ROW][C]23[/C][C] 0.02898[/C][C] 0.05795[/C][C] 0.971[/C][/ROW]
[ROW][C]24[/C][C] 0.02844[/C][C] 0.05689[/C][C] 0.9716[/C][/ROW]
[ROW][C]25[/C][C] 0.08643[/C][C] 0.1729[/C][C] 0.9136[/C][/ROW]
[ROW][C]26[/C][C] 0.06303[/C][C] 0.1261[/C][C] 0.937[/C][/ROW]
[ROW][C]27[/C][C] 0.05415[/C][C] 0.1083[/C][C] 0.9458[/C][/ROW]
[ROW][C]28[/C][C] 0.0392[/C][C] 0.07841[/C][C] 0.9608[/C][/ROW]
[ROW][C]29[/C][C] 0.02625[/C][C] 0.05249[/C][C] 0.9738[/C][/ROW]
[ROW][C]30[/C][C] 0.03518[/C][C] 0.07036[/C][C] 0.9648[/C][/ROW]
[ROW][C]31[/C][C] 0.05187[/C][C] 0.1037[/C][C] 0.9481[/C][/ROW]
[ROW][C]32[/C][C] 0.04935[/C][C] 0.09871[/C][C] 0.9506[/C][/ROW]
[ROW][C]33[/C][C] 0.0476[/C][C] 0.09519[/C][C] 0.9524[/C][/ROW]
[ROW][C]34[/C][C] 0.03488[/C][C] 0.06976[/C][C] 0.9651[/C][/ROW]
[ROW][C]35[/C][C] 0.02461[/C][C] 0.04921[/C][C] 0.9754[/C][/ROW]
[ROW][C]36[/C][C] 0.01889[/C][C] 0.03777[/C][C] 0.9811[/C][/ROW]
[ROW][C]37[/C][C] 0.01589[/C][C] 0.03177[/C][C] 0.9841[/C][/ROW]
[ROW][C]38[/C][C] 0.04016[/C][C] 0.08033[/C][C] 0.9598[/C][/ROW]
[ROW][C]39[/C][C] 0.03504[/C][C] 0.07008[/C][C] 0.965[/C][/ROW]
[ROW][C]40[/C][C] 0.02494[/C][C] 0.04987[/C][C] 0.9751[/C][/ROW]
[ROW][C]41[/C][C] 0.02633[/C][C] 0.05265[/C][C] 0.9737[/C][/ROW]
[ROW][C]42[/C][C] 0.02251[/C][C] 0.04502[/C][C] 0.9775[/C][/ROW]
[ROW][C]43[/C][C] 0.01692[/C][C] 0.03384[/C][C] 0.9831[/C][/ROW]
[ROW][C]44[/C][C] 0.01302[/C][C] 0.02605[/C][C] 0.987[/C][/ROW]
[ROW][C]45[/C][C] 0.009101[/C][C] 0.0182[/C][C] 0.9909[/C][/ROW]
[ROW][C]46[/C][C] 0.008262[/C][C] 0.01652[/C][C] 0.9917[/C][/ROW]
[ROW][C]47[/C][C] 0.02431[/C][C] 0.04862[/C][C] 0.9757[/C][/ROW]
[ROW][C]48[/C][C] 0.01821[/C][C] 0.03642[/C][C] 0.9818[/C][/ROW]
[ROW][C]49[/C][C] 0.0146[/C][C] 0.0292[/C][C] 0.9854[/C][/ROW]
[ROW][C]50[/C][C] 0.0132[/C][C] 0.02639[/C][C] 0.9868[/C][/ROW]
[ROW][C]51[/C][C] 0.07631[/C][C] 0.1526[/C][C] 0.9237[/C][/ROW]
[ROW][C]52[/C][C] 0.05957[/C][C] 0.1191[/C][C] 0.9404[/C][/ROW]
[ROW][C]53[/C][C] 0.1072[/C][C] 0.2145[/C][C] 0.8928[/C][/ROW]
[ROW][C]54[/C][C] 0.08896[/C][C] 0.1779[/C][C] 0.911[/C][/ROW]
[ROW][C]55[/C][C] 0.1049[/C][C] 0.2099[/C][C] 0.8951[/C][/ROW]
[ROW][C]56[/C][C] 0.08928[/C][C] 0.1786[/C][C] 0.9107[/C][/ROW]
[ROW][C]57[/C][C] 0.07503[/C][C] 0.1501[/C][C] 0.925[/C][/ROW]
[ROW][C]58[/C][C] 0.06007[/C][C] 0.1201[/C][C] 0.9399[/C][/ROW]
[ROW][C]59[/C][C] 0.05475[/C][C] 0.1095[/C][C] 0.9453[/C][/ROW]
[ROW][C]60[/C][C] 0.04263[/C][C] 0.08525[/C][C] 0.9574[/C][/ROW]
[ROW][C]61[/C][C] 0.04177[/C][C] 0.08353[/C][C] 0.9582[/C][/ROW]
[ROW][C]62[/C][C] 0.03447[/C][C] 0.06894[/C][C] 0.9655[/C][/ROW]
[ROW][C]63[/C][C] 0.03378[/C][C] 0.06757[/C][C] 0.9662[/C][/ROW]
[ROW][C]64[/C][C] 0.03808[/C][C] 0.07616[/C][C] 0.9619[/C][/ROW]
[ROW][C]65[/C][C] 0.04109[/C][C] 0.08217[/C][C] 0.9589[/C][/ROW]
[ROW][C]66[/C][C] 0.04373[/C][C] 0.08747[/C][C] 0.9563[/C][/ROW]
[ROW][C]67[/C][C] 0.03853[/C][C] 0.07706[/C][C] 0.9615[/C][/ROW]
[ROW][C]68[/C][C] 0.03168[/C][C] 0.06335[/C][C] 0.9683[/C][/ROW]
[ROW][C]69[/C][C] 0.04692[/C][C] 0.09385[/C][C] 0.9531[/C][/ROW]
[ROW][C]70[/C][C] 0.08997[/C][C] 0.1799[/C][C] 0.91[/C][/ROW]
[ROW][C]71[/C][C] 0.1187[/C][C] 0.2374[/C][C] 0.8813[/C][/ROW]
[ROW][C]72[/C][C] 0.1071[/C][C] 0.2141[/C][C] 0.8929[/C][/ROW]
[ROW][C]73[/C][C] 0.08784[/C][C] 0.1757[/C][C] 0.9122[/C][/ROW]
[ROW][C]74[/C][C] 0.0795[/C][C] 0.159[/C][C] 0.9205[/C][/ROW]
[ROW][C]75[/C][C] 0.07247[/C][C] 0.1449[/C][C] 0.9275[/C][/ROW]
[ROW][C]76[/C][C] 0.1255[/C][C] 0.251[/C][C] 0.8745[/C][/ROW]
[ROW][C]77[/C][C] 0.105[/C][C] 0.2101[/C][C] 0.895[/C][/ROW]
[ROW][C]78[/C][C] 0.3521[/C][C] 0.7042[/C][C] 0.6479[/C][/ROW]
[ROW][C]79[/C][C] 0.3117[/C][C] 0.6235[/C][C] 0.6883[/C][/ROW]
[ROW][C]80[/C][C] 0.3049[/C][C] 0.6098[/C][C] 0.6951[/C][/ROW]
[ROW][C]81[/C][C] 0.2955[/C][C] 0.591[/C][C] 0.7045[/C][/ROW]
[ROW][C]82[/C][C] 0.58[/C][C] 0.8399[/C][C] 0.4199[/C][/ROW]
[ROW][C]83[/C][C] 0.541[/C][C] 0.918[/C][C] 0.459[/C][/ROW]
[ROW][C]84[/C][C] 0.6006[/C][C] 0.7989[/C][C] 0.3994[/C][/ROW]
[ROW][C]85[/C][C] 0.5603[/C][C] 0.8795[/C][C] 0.4397[/C][/ROW]
[ROW][C]86[/C][C] 0.5314[/C][C] 0.9371[/C][C] 0.4686[/C][/ROW]
[ROW][C]87[/C][C] 0.4981[/C][C] 0.9962[/C][C] 0.5019[/C][/ROW]
[ROW][C]88[/C][C] 0.4989[/C][C] 0.9978[/C][C] 0.5011[/C][/ROW]
[ROW][C]89[/C][C] 0.4526[/C][C] 0.9052[/C][C] 0.5474[/C][/ROW]
[ROW][C]90[/C][C] 0.4066[/C][C] 0.8132[/C][C] 0.5934[/C][/ROW]
[ROW][C]91[/C][C] 0.3654[/C][C] 0.7308[/C][C] 0.6346[/C][/ROW]
[ROW][C]92[/C][C] 0.3269[/C][C] 0.6539[/C][C] 0.6731[/C][/ROW]
[ROW][C]93[/C][C] 0.3645[/C][C] 0.729[/C][C] 0.6355[/C][/ROW]
[ROW][C]94[/C][C] 0.3754[/C][C] 0.7507[/C][C] 0.6246[/C][/ROW]
[ROW][C]95[/C][C] 0.3472[/C][C] 0.6943[/C][C] 0.6528[/C][/ROW]
[ROW][C]96[/C][C] 0.3704[/C][C] 0.7408[/C][C] 0.6296[/C][/ROW]
[ROW][C]97[/C][C] 0.3329[/C][C] 0.6657[/C][C] 0.6671[/C][/ROW]
[ROW][C]98[/C][C] 0.4973[/C][C] 0.9947[/C][C] 0.5027[/C][/ROW]
[ROW][C]99[/C][C] 0.4524[/C][C] 0.9048[/C][C] 0.5476[/C][/ROW]
[ROW][C]100[/C][C] 0.4135[/C][C] 0.827[/C][C] 0.5865[/C][/ROW]
[ROW][C]101[/C][C] 0.3758[/C][C] 0.7515[/C][C] 0.6242[/C][/ROW]
[ROW][C]102[/C][C] 0.333[/C][C] 0.666[/C][C] 0.667[/C][/ROW]
[ROW][C]103[/C][C] 0.2971[/C][C] 0.5942[/C][C] 0.7029[/C][/ROW]
[ROW][C]104[/C][C] 0.3051[/C][C] 0.6102[/C][C] 0.6949[/C][/ROW]
[ROW][C]105[/C][C] 0.2643[/C][C] 0.5285[/C][C] 0.7357[/C][/ROW]
[ROW][C]106[/C][C] 0.2814[/C][C] 0.5629[/C][C] 0.7186[/C][/ROW]
[ROW][C]107[/C][C] 0.5594[/C][C] 0.8812[/C][C] 0.4406[/C][/ROW]
[ROW][C]108[/C][C] 0.5411[/C][C] 0.9179[/C][C] 0.4589[/C][/ROW]
[ROW][C]109[/C][C] 0.5112[/C][C] 0.9775[/C][C] 0.4888[/C][/ROW]
[ROW][C]110[/C][C] 0.5012[/C][C] 0.9975[/C][C] 0.4988[/C][/ROW]
[ROW][C]111[/C][C] 0.4629[/C][C] 0.9258[/C][C] 0.5371[/C][/ROW]
[ROW][C]112[/C][C] 0.4806[/C][C] 0.9613[/C][C] 0.5194[/C][/ROW]
[ROW][C]113[/C][C] 0.431[/C][C] 0.8619[/C][C] 0.569[/C][/ROW]
[ROW][C]114[/C][C] 0.3901[/C][C] 0.7802[/C][C] 0.6099[/C][/ROW]
[ROW][C]115[/C][C] 0.3463[/C][C] 0.6926[/C][C] 0.6537[/C][/ROW]
[ROW][C]116[/C][C] 0.3212[/C][C] 0.6423[/C][C] 0.6788[/C][/ROW]
[ROW][C]117[/C][C] 0.2916[/C][C] 0.5832[/C][C] 0.7084[/C][/ROW]
[ROW][C]118[/C][C] 0.2527[/C][C] 0.5053[/C][C] 0.7473[/C][/ROW]
[ROW][C]119[/C][C] 0.2271[/C][C] 0.4542[/C][C] 0.7729[/C][/ROW]
[ROW][C]120[/C][C] 0.2018[/C][C] 0.4035[/C][C] 0.7982[/C][/ROW]
[ROW][C]121[/C][C] 0.1903[/C][C] 0.3806[/C][C] 0.8097[/C][/ROW]
[ROW][C]122[/C][C] 0.1554[/C][C] 0.3108[/C][C] 0.8446[/C][/ROW]
[ROW][C]123[/C][C] 0.1529[/C][C] 0.3059[/C][C] 0.8471[/C][/ROW]
[ROW][C]124[/C][C] 0.1233[/C][C] 0.2466[/C][C] 0.8767[/C][/ROW]
[ROW][C]125[/C][C] 0.1259[/C][C] 0.2519[/C][C] 0.8741[/C][/ROW]
[ROW][C]126[/C][C] 0.1308[/C][C] 0.2616[/C][C] 0.8692[/C][/ROW]
[ROW][C]127[/C][C] 0.2595[/C][C] 0.5189[/C][C] 0.7405[/C][/ROW]
[ROW][C]128[/C][C] 0.2486[/C][C] 0.4972[/C][C] 0.7514[/C][/ROW]
[ROW][C]129[/C][C] 0.2148[/C][C] 0.4297[/C][C] 0.7852[/C][/ROW]
[ROW][C]130[/C][C] 0.1907[/C][C] 0.3814[/C][C] 0.8093[/C][/ROW]
[ROW][C]131[/C][C] 0.1621[/C][C] 0.3241[/C][C] 0.8379[/C][/ROW]
[ROW][C]132[/C][C] 0.1514[/C][C] 0.3028[/C][C] 0.8486[/C][/ROW]
[ROW][C]133[/C][C] 0.1492[/C][C] 0.2985[/C][C] 0.8508[/C][/ROW]
[ROW][C]134[/C][C] 0.1368[/C][C] 0.2736[/C][C] 0.8632[/C][/ROW]
[ROW][C]135[/C][C] 0.1035[/C][C] 0.207[/C][C] 0.8965[/C][/ROW]
[ROW][C]136[/C][C] 0.1166[/C][C] 0.2332[/C][C] 0.8834[/C][/ROW]
[ROW][C]137[/C][C] 0.1398[/C][C] 0.2796[/C][C] 0.8602[/C][/ROW]
[ROW][C]138[/C][C] 0.1293[/C][C] 0.2586[/C][C] 0.8707[/C][/ROW]
[ROW][C]139[/C][C] 0.2304[/C][C] 0.4608[/C][C] 0.7696[/C][/ROW]
[ROW][C]140[/C][C] 0.3245[/C][C] 0.6489[/C][C] 0.6755[/C][/ROW]
[ROW][C]141[/C][C] 0.2539[/C][C] 0.5078[/C][C] 0.7461[/C][/ROW]
[ROW][C]142[/C][C] 0.28[/C][C] 0.5601[/C][C] 0.72[/C][/ROW]
[ROW][C]143[/C][C] 0.2183[/C][C] 0.4366[/C][C] 0.7817[/C][/ROW]
[ROW][C]144[/C][C] 0.2091[/C][C] 0.4182[/C][C] 0.7909[/C][/ROW]
[ROW][C]145[/C][C] 0.1467[/C][C] 0.2935[/C][C] 0.8532[/C][/ROW]
[ROW][C]146[/C][C] 0.1387[/C][C] 0.2775[/C][C] 0.8613[/C][/ROW]
[ROW][C]147[/C][C] 0.09406[/C][C] 0.1881[/C][C] 0.9059[/C][/ROW]
[ROW][C]148[/C][C] 0.05663[/C][C] 0.1133[/C][C] 0.9434[/C][/ROW]
[ROW][C]149[/C][C] 0.1536[/C][C] 0.3071[/C][C] 0.8464[/C][/ROW]
[ROW][C]150[/C][C] 0.5763[/C][C] 0.8473[/C][C] 0.4237[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297766&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&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	0.2753	0.5506	0.7247
11	0.1987	0.3974	0.8013
12	0.105	0.21	0.895
13	0.05591	0.1118	0.9441
14	0.05236	0.1047	0.9476
15	0.07159	0.1432	0.9284
16	0.07889	0.1578	0.9211
17	0.04627	0.09253	0.9537
18	0.09839	0.1968	0.9016
19	0.06984	0.1397	0.9302
20	0.05433	0.1087	0.9457
21	0.045	0.09	0.955
22	0.02788	0.05577	0.9721
23	0.02898	0.05795	0.971
24	0.02844	0.05689	0.9716
25	0.08643	0.1729	0.9136
26	0.06303	0.1261	0.937
27	0.05415	0.1083	0.9458
28	0.0392	0.07841	0.9608
29	0.02625	0.05249	0.9738
30	0.03518	0.07036	0.9648
31	0.05187	0.1037	0.9481
32	0.04935	0.09871	0.9506
33	0.0476	0.09519	0.9524
34	0.03488	0.06976	0.9651
35	0.02461	0.04921	0.9754
36	0.01889	0.03777	0.9811
37	0.01589	0.03177	0.9841
38	0.04016	0.08033	0.9598
39	0.03504	0.07008	0.965
40	0.02494	0.04987	0.9751
41	0.02633	0.05265	0.9737
42	0.02251	0.04502	0.9775
43	0.01692	0.03384	0.9831
44	0.01302	0.02605	0.987
45	0.009101	0.0182	0.9909
46	0.008262	0.01652	0.9917
47	0.02431	0.04862	0.9757
48	0.01821	0.03642	0.9818
49	0.0146	0.0292	0.9854
50	0.0132	0.02639	0.9868
51	0.07631	0.1526	0.9237
52	0.05957	0.1191	0.9404
53	0.1072	0.2145	0.8928
54	0.08896	0.1779	0.911
55	0.1049	0.2099	0.8951
56	0.08928	0.1786	0.9107
57	0.07503	0.1501	0.925
58	0.06007	0.1201	0.9399
59	0.05475	0.1095	0.9453
60	0.04263	0.08525	0.9574
61	0.04177	0.08353	0.9582
62	0.03447	0.06894	0.9655
63	0.03378	0.06757	0.9662
64	0.03808	0.07616	0.9619
65	0.04109	0.08217	0.9589
66	0.04373	0.08747	0.9563
67	0.03853	0.07706	0.9615
68	0.03168	0.06335	0.9683
69	0.04692	0.09385	0.9531
70	0.08997	0.1799	0.91
71	0.1187	0.2374	0.8813
72	0.1071	0.2141	0.8929
73	0.08784	0.1757	0.9122
74	0.0795	0.159	0.9205
75	0.07247	0.1449	0.9275
76	0.1255	0.251	0.8745
77	0.105	0.2101	0.895
78	0.3521	0.7042	0.6479
79	0.3117	0.6235	0.6883
80	0.3049	0.6098	0.6951
81	0.2955	0.591	0.7045
82	0.58	0.8399	0.4199
83	0.541	0.918	0.459
84	0.6006	0.7989	0.3994
85	0.5603	0.8795	0.4397
86	0.5314	0.9371	0.4686
87	0.4981	0.9962	0.5019
88	0.4989	0.9978	0.5011
89	0.4526	0.9052	0.5474
90	0.4066	0.8132	0.5934
91	0.3654	0.7308	0.6346
92	0.3269	0.6539	0.6731
93	0.3645	0.729	0.6355
94	0.3754	0.7507	0.6246
95	0.3472	0.6943	0.6528
96	0.3704	0.7408	0.6296
97	0.3329	0.6657	0.6671
98	0.4973	0.9947	0.5027
99	0.4524	0.9048	0.5476
100	0.4135	0.827	0.5865
101	0.3758	0.7515	0.6242
102	0.333	0.666	0.667
103	0.2971	0.5942	0.7029
104	0.3051	0.6102	0.6949
105	0.2643	0.5285	0.7357
106	0.2814	0.5629	0.7186
107	0.5594	0.8812	0.4406
108	0.5411	0.9179	0.4589
109	0.5112	0.9775	0.4888
110	0.5012	0.9975	0.4988
111	0.4629	0.9258	0.5371
112	0.4806	0.9613	0.5194
113	0.431	0.8619	0.569
114	0.3901	0.7802	0.6099
115	0.3463	0.6926	0.6537
116	0.3212	0.6423	0.6788
117	0.2916	0.5832	0.7084
118	0.2527	0.5053	0.7473
119	0.2271	0.4542	0.7729
120	0.2018	0.4035	0.7982
121	0.1903	0.3806	0.8097
122	0.1554	0.3108	0.8446
123	0.1529	0.3059	0.8471
124	0.1233	0.2466	0.8767
125	0.1259	0.2519	0.8741
126	0.1308	0.2616	0.8692
127	0.2595	0.5189	0.7405
128	0.2486	0.4972	0.7514
129	0.2148	0.4297	0.7852
130	0.1907	0.3814	0.8093
131	0.1621	0.3241	0.8379
132	0.1514	0.3028	0.8486
133	0.1492	0.2985	0.8508
134	0.1368	0.2736	0.8632
135	0.1035	0.207	0.8965
136	0.1166	0.2332	0.8834
137	0.1398	0.2796	0.8602
138	0.1293	0.2586	0.8707
139	0.2304	0.4608	0.7696
140	0.3245	0.6489	0.6755
141	0.2539	0.5078	0.7461
142	0.28	0.5601	0.72
143	0.2183	0.4366	0.7817
144	0.2091	0.4182	0.7909
145	0.1467	0.2935	0.8532
146	0.1387	0.2775	0.8613
147	0.09406	0.1881	0.9059
148	0.05663	0.1133	0.9434
149	0.1536	0.3071	0.8464
150	0.5763	0.8473	0.4237

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	13	0.0921986	NOK
10% type I error level	37	0.262411	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 13 & 0.0921986 & NOK \tabularnewline
10% type I error level & 37 & 0.262411 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&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]13[/C][C]0.0921986[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.262411[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297766&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&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	13	0.0921986	NOK
10% type I error level	37	0.262411	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.632, df1 = 2, df2 = 151, p-value = 0.199
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.281, df1 = 12, df2 = 141, p-value = 0.2361
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.257, df1 = 2, df2 = 151, p-value = 0.2875

\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 = 1.632, df1 = 2, df2 = 151, p-value = 0.199
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.281, df1 = 12, df2 = 141, p-value = 0.2361
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.257, df1 = 2, df2 = 151, p-value = 0.2875
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&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 = 1.632, df1 = 2, df2 = 151, p-value = 0.199
[/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 = 1.281, df1 = 12, df2 = 141, p-value = 0.2361
[/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 = 1.257, df1 = 2, df2 = 151, p-value = 0.2875
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297766&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&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 = 1.632, df1 = 2, df2 = 151, p-value = 0.199
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.281, df1 = 12, df2 = 141, p-value = 0.2361
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.257, df1 = 2, df2 = 151, p-value = 0.2875

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.093130 1.130790 1.045070 1.043667 1.045488 1.032797

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.093130 1.130790 1.045070 1.043667 1.045488 1.032797 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297766&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.093130 1.130790 1.045070 1.043667 1.045488 1.032797 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297766&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297766&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 SK1 SK2 SK3 SK4 SK5 SK6 1.093130 1.130790 1.045070 1.043667 1.045488 1.032797

Figure 1

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Parameters (Session):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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