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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 11:16:23 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/23/t1485166623ic7kxdg8287yoec.htm/, Retrieved Wed, 15 May 2024 17:29:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304834, Retrieved Wed, 15 May 2024 17:29:38 +0000

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

-       [Multiple Regression] [] [2017-01-23 10:16:23] [2e11ca31a00cf8de75c33c1af2d59434] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

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

R Framework error message[/C][C]

Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304834&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&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	10 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 9.64227 + 0.299468SKEOU1[t] + 0.478358SKEOU4[t] + 0.440942SKEOU5[t] + 0.48118SKEOU6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  9.64227 +  0.299468SKEOU1[t] +  0.478358SKEOU4[t] +  0.440942SKEOU5[t] +  0.48118SKEOU6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  9.64227 +  0.299468SKEOU1[t] +  0.478358SKEOU4[t] +  0.440942SKEOU5[t] +  0.48118SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304834&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&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
ITHSUM[t] = + 9.64227 + 0.299468SKEOU1[t] + 0.478358SKEOU4[t] + 0.440942SKEOU5[t] + 0.48118SKEOU6[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)	+9.642	2.171	+4.4410e+00	1.701e-05	8.505e-06
SKEOU1	+0.2995	0.2455	+1.2200e+00	0.2245	0.1122
SKEOU4	+0.4784	0.3075	+1.5560e+00	0.1218	0.06091
SKEOU5	+0.4409	0.2929	+1.5050e+00	0.1343	0.06716
SKEOU6	+0.4812	0.3015	+1.5960e+00	0.1126	0.05628

\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) & +9.642 &  2.171 & +4.4410e+00 &  1.701e-05 &  8.505e-06 \tabularnewline
SKEOU1 & +0.2995 &  0.2455 & +1.2200e+00 &  0.2245 &  0.1122 \tabularnewline
SKEOU4 & +0.4784 &  0.3075 & +1.5560e+00 &  0.1218 &  0.06091 \tabularnewline
SKEOU5 & +0.4409 &  0.2929 & +1.5050e+00 &  0.1343 &  0.06716 \tabularnewline
SKEOU6 & +0.4812 &  0.3015 & +1.5960e+00 &  0.1126 &  0.05628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&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]+9.642[/C][C] 2.171[/C][C]+4.4410e+00[/C][C] 1.701e-05[/C][C] 8.505e-06[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.2995[/C][C] 0.2455[/C][C]+1.2200e+00[/C][C] 0.2245[/C][C] 0.1122[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.4784[/C][C] 0.3075[/C][C]+1.5560e+00[/C][C] 0.1218[/C][C] 0.06091[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.4409[/C][C] 0.2929[/C][C]+1.5050e+00[/C][C] 0.1343[/C][C] 0.06716[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.4812[/C][C] 0.3015[/C][C]+1.5960e+00[/C][C] 0.1126[/C][C] 0.05628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304834&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&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)	+9.642	2.171	+4.4410e+00	1.701e-05	8.505e-06
SKEOU1	+0.2995	0.2455	+1.2200e+00	0.2245	0.1122
SKEOU4	+0.4784	0.3075	+1.5560e+00	0.1218	0.06091
SKEOU5	+0.4409	0.2929	+1.5050e+00	0.1343	0.06716
SKEOU6	+0.4812	0.3015	+1.5960e+00	0.1126	0.05628

Multiple Linear Regression - Regression Statistics
Multiple R	0.2515
R-squared	0.06323
Adjusted R-squared	0.0389
F-TEST (value)	2.599
F-TEST (DF numerator)	4
F-TEST (DF denominator)	154
p-value	0.03838
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.242
Sum Squared Residuals	774.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2515 \tabularnewline
R-squared &  0.06323 \tabularnewline
Adjusted R-squared &  0.0389 \tabularnewline
F-TEST (value) &  2.599 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value &  0.03838 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.242 \tabularnewline
Sum Squared Residuals &  774.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2515[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.06323[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.0389[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.599[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C] 0.03838[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.242[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 774.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304834&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&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	0.2515
R-squared	0.06323
Adjusted R-squared	0.0389
F-TEST (value)	2.599
F-TEST (DF numerator)	4
F-TEST (DF denominator)	154
p-value	0.03838
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.242
Sum Squared Residuals	774.2

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.4	-2.405
2	19	17.18	1.818
3	17	16.88	0.117
4	17	15.66	1.336
5	15	16.88	-1.883
6	20	17.06	2.935
7	15	15.22	-0.2233
8	19	16.14	2.857
9	15	17.36	-2.364
10	15	17.36	-2.364
11	19	16.88	2.117
12	20	16.44	3.555
13	18	16.62	1.376
14	15	16.04	-1.041
15	14	16.62	-2.624
16	20	16.62	3.376
17	16	16.26	-0.2632
18	16	16.88	-0.883
19	16	16.15	-0.1454
20	10	17.36	-7.364
21	19	16.92	2.077
22	19	16.44	2.558
23	16	16.92	-0.9232
24	15	16.14	-1.143
25	18	16.59	1.414
26	17	16.44	0.5579
27	19	16.77	2.235
28	17	16.74	0.2585
29	19	17.36	1.636
30	20	17.22	2.777
31	19	16.28	2.716
32	16	16.44	-0.4421
33	15	16.14	-1.143
34	16	16.44	-0.4449
35	18	16.44	1.558
36	16	16.44	-0.4421
37	15	16.74	-1.742
38	17	17.36	-0.3642
39	20	17.19	2.815
40	19	16.26	2.737
41	7	16.44	-9.445
42	13	16.44	-3.442
43	16	15.22	0.7767
44	16	16.88	-0.883
45	18	16.44	1.558
46	18	17.18	0.8175
47	16	16.74	-0.7415
48	17	17.36	-0.3642
49	19	16.44	2.555
50	16	16.44	-0.4421
51	19	15.22	3.777
52	13	15.66	-2.661
53	16	16.4	-0.4047
54	13	16.88	-3.883
55	12	17.66	-5.664
56	17	16.77	0.2347
57	17	17.36	-0.3642
58	17	15.26	1.739
59	16	17.36	-1.364
60	16	16.44	-0.4421
61	14	15.92	-1.923
62	16	16.4	-0.4047
63	13	16.3	-3.303
64	16	16.59	-0.5864
65	14	16.15	-2.145
66	20	17.36	2.639
67	13	16.44	-3.442
68	18	17.36	0.6386
69	14	16.14	-2.143
70	19	16.88	2.117
71	18	16.59	1.414
72	14	16.62	-2.624
73	18	16.44	1.558
74	19	16.92	2.077
75	15	16.14	-1.143
76	14	16.88	-2.883
77	17	17.36	-0.3642
78	19	17.36	1.636
79	13	16.74	-3.742
80	19	17.7	1.299
81	18	17.36	0.6358
82	20	16.62	3.376
83	15	16.14	-1.143
84	15	15.96	-0.9637
85	15	15.96	-0.9609
86	20	16.92	3.077
87	15	16.88	-1.883
88	19	16.4	2.598
89	18	16.59	1.414
90	18	16.92	1.077
91	15	17.22	-2.223
92	20	17.7	2.299
93	17	17.36	-0.3642
94	12	16.62	-4.624
95	18	17.66	0.3363
96	19	16.92	2.077
97	20	17.22	2.777
98	17	18.14	-1.142
99	16	15.48	0.5175
100	18	16.44	1.558
101	18	16.44	1.558
102	14	16.1	-2.102
103	15	16.44	-1.442
104	12	16.44	-4.445
105	17	16.59	0.4136
106	14	15.96	-1.964
107	18	16.14	1.857
108	17	16	0.9989
109	17	16.23	0.7714
110	20	17.66	2.336
111	16	15.96	0.03911
112	14	15.96	-1.964
113	15	16.15	-1.145
114	18	16.44	1.558
115	20	16.88	3.117
116	17	16.44	0.5579
117	17	16.14	0.8574
118	17	15.96	1.036
119	17	16.92	0.07675
120	15	15.66	-0.6642
121	17	15.48	1.517
122	18	15.19	2.814
123	17	16.4	0.5954
124	20	16.7	3.296
125	15	15.4	-0.405
126	16	16.14	-0.1426
127	15	15.96	-0.9637
128	18	17.18	0.8175
129	15	16.44	-1.442
130	18	17.66	0.3392
131	20	17.36	2.636
132	19	16.44	2.555
133	14	16.58	-2.584
134	16	16.44	-0.4421
135	15	16.44	-1.442
136	17	17.36	-0.3642
137	18	17.36	0.6358
138	20	16.7	3.296
139	17	16.44	0.5579
140	18	16.44	1.558
141	15	15.66	-0.6642
142	16	15.96	0.03911
143	11	15.96	-4.964
144	15	16.92	-1.923
145	18	16.59	1.414
146	17	16.92	0.07675
147	16	17.18	-1.182
148	12	16	-4.001
149	19	15.84	3.157
150	18	16.92	1.077
151	15	16.89	-1.886
152	17	15.96	1.039
153	19	16.92	2.08
154	18	16.74	1.258
155	19	16.26	2.737
156	16	17.06	-1.065
157	16	16.92	-0.9232
158	16	16.88	-0.883
159	14	16.76	-2.762

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.4 & -2.405 \tabularnewline
2 &  19 &  17.18 &  1.818 \tabularnewline
3 &  17 &  16.88 &  0.117 \tabularnewline
4 &  17 &  15.66 &  1.336 \tabularnewline
5 &  15 &  16.88 & -1.883 \tabularnewline
6 &  20 &  17.06 &  2.935 \tabularnewline
7 &  15 &  15.22 & -0.2233 \tabularnewline
8 &  19 &  16.14 &  2.857 \tabularnewline
9 &  15 &  17.36 & -2.364 \tabularnewline
10 &  15 &  17.36 & -2.364 \tabularnewline
11 &  19 &  16.88 &  2.117 \tabularnewline
12 &  20 &  16.44 &  3.555 \tabularnewline
13 &  18 &  16.62 &  1.376 \tabularnewline
14 &  15 &  16.04 & -1.041 \tabularnewline
15 &  14 &  16.62 & -2.624 \tabularnewline
16 &  20 &  16.62 &  3.376 \tabularnewline
17 &  16 &  16.26 & -0.2632 \tabularnewline
18 &  16 &  16.88 & -0.883 \tabularnewline
19 &  16 &  16.15 & -0.1454 \tabularnewline
20 &  10 &  17.36 & -7.364 \tabularnewline
21 &  19 &  16.92 &  2.077 \tabularnewline
22 &  19 &  16.44 &  2.558 \tabularnewline
23 &  16 &  16.92 & -0.9232 \tabularnewline
24 &  15 &  16.14 & -1.143 \tabularnewline
25 &  18 &  16.59 &  1.414 \tabularnewline
26 &  17 &  16.44 &  0.5579 \tabularnewline
27 &  19 &  16.77 &  2.235 \tabularnewline
28 &  17 &  16.74 &  0.2585 \tabularnewline
29 &  19 &  17.36 &  1.636 \tabularnewline
30 &  20 &  17.22 &  2.777 \tabularnewline
31 &  19 &  16.28 &  2.716 \tabularnewline
32 &  16 &  16.44 & -0.4421 \tabularnewline
33 &  15 &  16.14 & -1.143 \tabularnewline
34 &  16 &  16.44 & -0.4449 \tabularnewline
35 &  18 &  16.44 &  1.558 \tabularnewline
36 &  16 &  16.44 & -0.4421 \tabularnewline
37 &  15 &  16.74 & -1.742 \tabularnewline
38 &  17 &  17.36 & -0.3642 \tabularnewline
39 &  20 &  17.19 &  2.815 \tabularnewline
40 &  19 &  16.26 &  2.737 \tabularnewline
41 &  7 &  16.44 & -9.445 \tabularnewline
42 &  13 &  16.44 & -3.442 \tabularnewline
43 &  16 &  15.22 &  0.7767 \tabularnewline
44 &  16 &  16.88 & -0.883 \tabularnewline
45 &  18 &  16.44 &  1.558 \tabularnewline
46 &  18 &  17.18 &  0.8175 \tabularnewline
47 &  16 &  16.74 & -0.7415 \tabularnewline
48 &  17 &  17.36 & -0.3642 \tabularnewline
49 &  19 &  16.44 &  2.555 \tabularnewline
50 &  16 &  16.44 & -0.4421 \tabularnewline
51 &  19 &  15.22 &  3.777 \tabularnewline
52 &  13 &  15.66 & -2.661 \tabularnewline
53 &  16 &  16.4 & -0.4047 \tabularnewline
54 &  13 &  16.88 & -3.883 \tabularnewline
55 &  12 &  17.66 & -5.664 \tabularnewline
56 &  17 &  16.77 &  0.2347 \tabularnewline
57 &  17 &  17.36 & -0.3642 \tabularnewline
58 &  17 &  15.26 &  1.739 \tabularnewline
59 &  16 &  17.36 & -1.364 \tabularnewline
60 &  16 &  16.44 & -0.4421 \tabularnewline
61 &  14 &  15.92 & -1.923 \tabularnewline
62 &  16 &  16.4 & -0.4047 \tabularnewline
63 &  13 &  16.3 & -3.303 \tabularnewline
64 &  16 &  16.59 & -0.5864 \tabularnewline
65 &  14 &  16.15 & -2.145 \tabularnewline
66 &  20 &  17.36 &  2.639 \tabularnewline
67 &  13 &  16.44 & -3.442 \tabularnewline
68 &  18 &  17.36 &  0.6386 \tabularnewline
69 &  14 &  16.14 & -2.143 \tabularnewline
70 &  19 &  16.88 &  2.117 \tabularnewline
71 &  18 &  16.59 &  1.414 \tabularnewline
72 &  14 &  16.62 & -2.624 \tabularnewline
73 &  18 &  16.44 &  1.558 \tabularnewline
74 &  19 &  16.92 &  2.077 \tabularnewline
75 &  15 &  16.14 & -1.143 \tabularnewline
76 &  14 &  16.88 & -2.883 \tabularnewline
77 &  17 &  17.36 & -0.3642 \tabularnewline
78 &  19 &  17.36 &  1.636 \tabularnewline
79 &  13 &  16.74 & -3.742 \tabularnewline
80 &  19 &  17.7 &  1.299 \tabularnewline
81 &  18 &  17.36 &  0.6358 \tabularnewline
82 &  20 &  16.62 &  3.376 \tabularnewline
83 &  15 &  16.14 & -1.143 \tabularnewline
84 &  15 &  15.96 & -0.9637 \tabularnewline
85 &  15 &  15.96 & -0.9609 \tabularnewline
86 &  20 &  16.92 &  3.077 \tabularnewline
87 &  15 &  16.88 & -1.883 \tabularnewline
88 &  19 &  16.4 &  2.598 \tabularnewline
89 &  18 &  16.59 &  1.414 \tabularnewline
90 &  18 &  16.92 &  1.077 \tabularnewline
91 &  15 &  17.22 & -2.223 \tabularnewline
92 &  20 &  17.7 &  2.299 \tabularnewline
93 &  17 &  17.36 & -0.3642 \tabularnewline
94 &  12 &  16.62 & -4.624 \tabularnewline
95 &  18 &  17.66 &  0.3363 \tabularnewline
96 &  19 &  16.92 &  2.077 \tabularnewline
97 &  20 &  17.22 &  2.777 \tabularnewline
98 &  17 &  18.14 & -1.142 \tabularnewline
99 &  16 &  15.48 &  0.5175 \tabularnewline
100 &  18 &  16.44 &  1.558 \tabularnewline
101 &  18 &  16.44 &  1.558 \tabularnewline
102 &  14 &  16.1 & -2.102 \tabularnewline
103 &  15 &  16.44 & -1.442 \tabularnewline
104 &  12 &  16.44 & -4.445 \tabularnewline
105 &  17 &  16.59 &  0.4136 \tabularnewline
106 &  14 &  15.96 & -1.964 \tabularnewline
107 &  18 &  16.14 &  1.857 \tabularnewline
108 &  17 &  16 &  0.9989 \tabularnewline
109 &  17 &  16.23 &  0.7714 \tabularnewline
110 &  20 &  17.66 &  2.336 \tabularnewline
111 &  16 &  15.96 &  0.03911 \tabularnewline
112 &  14 &  15.96 & -1.964 \tabularnewline
113 &  15 &  16.15 & -1.145 \tabularnewline
114 &  18 &  16.44 &  1.558 \tabularnewline
115 &  20 &  16.88 &  3.117 \tabularnewline
116 &  17 &  16.44 &  0.5579 \tabularnewline
117 &  17 &  16.14 &  0.8574 \tabularnewline
118 &  17 &  15.96 &  1.036 \tabularnewline
119 &  17 &  16.92 &  0.07675 \tabularnewline
120 &  15 &  15.66 & -0.6642 \tabularnewline
121 &  17 &  15.48 &  1.517 \tabularnewline
122 &  18 &  15.19 &  2.814 \tabularnewline
123 &  17 &  16.4 &  0.5954 \tabularnewline
124 &  20 &  16.7 &  3.296 \tabularnewline
125 &  15 &  15.4 & -0.405 \tabularnewline
126 &  16 &  16.14 & -0.1426 \tabularnewline
127 &  15 &  15.96 & -0.9637 \tabularnewline
128 &  18 &  17.18 &  0.8175 \tabularnewline
129 &  15 &  16.44 & -1.442 \tabularnewline
130 &  18 &  17.66 &  0.3392 \tabularnewline
131 &  20 &  17.36 &  2.636 \tabularnewline
132 &  19 &  16.44 &  2.555 \tabularnewline
133 &  14 &  16.58 & -2.584 \tabularnewline
134 &  16 &  16.44 & -0.4421 \tabularnewline
135 &  15 &  16.44 & -1.442 \tabularnewline
136 &  17 &  17.36 & -0.3642 \tabularnewline
137 &  18 &  17.36 &  0.6358 \tabularnewline
138 &  20 &  16.7 &  3.296 \tabularnewline
139 &  17 &  16.44 &  0.5579 \tabularnewline
140 &  18 &  16.44 &  1.558 \tabularnewline
141 &  15 &  15.66 & -0.6642 \tabularnewline
142 &  16 &  15.96 &  0.03911 \tabularnewline
143 &  11 &  15.96 & -4.964 \tabularnewline
144 &  15 &  16.92 & -1.923 \tabularnewline
145 &  18 &  16.59 &  1.414 \tabularnewline
146 &  17 &  16.92 &  0.07675 \tabularnewline
147 &  16 &  17.18 & -1.182 \tabularnewline
148 &  12 &  16 & -4.001 \tabularnewline
149 &  19 &  15.84 &  3.157 \tabularnewline
150 &  18 &  16.92 &  1.077 \tabularnewline
151 &  15 &  16.89 & -1.886 \tabularnewline
152 &  17 &  15.96 &  1.039 \tabularnewline
153 &  19 &  16.92 &  2.08 \tabularnewline
154 &  18 &  16.74 &  1.258 \tabularnewline
155 &  19 &  16.26 &  2.737 \tabularnewline
156 &  16 &  17.06 & -1.065 \tabularnewline
157 &  16 &  16.92 & -0.9232 \tabularnewline
158 &  16 &  16.88 & -0.883 \tabularnewline
159 &  14 &  16.76 & -2.762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 14[/C][C] 16.4[/C][C]-2.405[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 17.18[/C][C] 1.818[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.88[/C][C] 0.117[/C][/ROW]
[ROW][C]4[/C][C] 17[/C][C] 15.66[/C][C] 1.336[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 16.88[/C][C]-1.883[/C][/ROW]
[ROW][C]6[/C][C] 20[/C][C] 17.06[/C][C] 2.935[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.22[/C][C]-0.2233[/C][/ROW]
[ROW][C]8[/C][C] 19[/C][C] 16.14[/C][C] 2.857[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 17.36[/C][C]-2.364[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 17.36[/C][C]-2.364[/C][/ROW]
[ROW][C]11[/C][C] 19[/C][C] 16.88[/C][C] 2.117[/C][/ROW]
[ROW][C]12[/C][C] 20[/C][C] 16.44[/C][C] 3.555[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.62[/C][C] 1.376[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 16.04[/C][C]-1.041[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 16.62[/C][C]-2.624[/C][/ROW]
[ROW][C]16[/C][C] 20[/C][C] 16.62[/C][C] 3.376[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.26[/C][C]-0.2632[/C][/ROW]
[ROW][C]18[/C][C] 16[/C][C] 16.88[/C][C]-0.883[/C][/ROW]
[ROW][C]19[/C][C] 16[/C][C] 16.15[/C][C]-0.1454[/C][/ROW]
[ROW][C]20[/C][C] 10[/C][C] 17.36[/C][C]-7.364[/C][/ROW]
[ROW][C]21[/C][C] 19[/C][C] 16.92[/C][C] 2.077[/C][/ROW]
[ROW][C]22[/C][C] 19[/C][C] 16.44[/C][C] 2.558[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 16.92[/C][C]-0.9232[/C][/ROW]
[ROW][C]24[/C][C] 15[/C][C] 16.14[/C][C]-1.143[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 16.59[/C][C] 1.414[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.44[/C][C] 0.5579[/C][/ROW]
[ROW][C]27[/C][C] 19[/C][C] 16.77[/C][C] 2.235[/C][/ROW]
[ROW][C]28[/C][C] 17[/C][C] 16.74[/C][C] 0.2585[/C][/ROW]
[ROW][C]29[/C][C] 19[/C][C] 17.36[/C][C] 1.636[/C][/ROW]
[ROW][C]30[/C][C] 20[/C][C] 17.22[/C][C] 2.777[/C][/ROW]
[ROW][C]31[/C][C] 19[/C][C] 16.28[/C][C] 2.716[/C][/ROW]
[ROW][C]32[/C][C] 16[/C][C] 16.44[/C][C]-0.4421[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 16.14[/C][C]-1.143[/C][/ROW]
[ROW][C]34[/C][C] 16[/C][C] 16.44[/C][C]-0.4449[/C][/ROW]
[ROW][C]35[/C][C] 18[/C][C] 16.44[/C][C] 1.558[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 16.44[/C][C]-0.4421[/C][/ROW]
[ROW][C]37[/C][C] 15[/C][C] 16.74[/C][C]-1.742[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.36[/C][C]-0.3642[/C][/ROW]
[ROW][C]39[/C][C] 20[/C][C] 17.19[/C][C] 2.815[/C][/ROW]
[ROW][C]40[/C][C] 19[/C][C] 16.26[/C][C] 2.737[/C][/ROW]
[ROW][C]41[/C][C] 7[/C][C] 16.44[/C][C]-9.445[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 16.44[/C][C]-3.442[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 15.22[/C][C] 0.7767[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 16.88[/C][C]-0.883[/C][/ROW]
[ROW][C]45[/C][C] 18[/C][C] 16.44[/C][C] 1.558[/C][/ROW]
[ROW][C]46[/C][C] 18[/C][C] 17.18[/C][C] 0.8175[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 16.74[/C][C]-0.7415[/C][/ROW]
[ROW][C]48[/C][C] 17[/C][C] 17.36[/C][C]-0.3642[/C][/ROW]
[ROW][C]49[/C][C] 19[/C][C] 16.44[/C][C] 2.555[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 16.44[/C][C]-0.4421[/C][/ROW]
[ROW][C]51[/C][C] 19[/C][C] 15.22[/C][C] 3.777[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 15.66[/C][C]-2.661[/C][/ROW]
[ROW][C]53[/C][C] 16[/C][C] 16.4[/C][C]-0.4047[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 16.88[/C][C]-3.883[/C][/ROW]
[ROW][C]55[/C][C] 12[/C][C] 17.66[/C][C]-5.664[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 16.77[/C][C] 0.2347[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 17.36[/C][C]-0.3642[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 15.26[/C][C] 1.739[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 17.36[/C][C]-1.364[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 16.44[/C][C]-0.4421[/C][/ROW]
[ROW][C]61[/C][C] 14[/C][C] 15.92[/C][C]-1.923[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.4[/C][C]-0.4047[/C][/ROW]
[ROW][C]63[/C][C] 13[/C][C] 16.3[/C][C]-3.303[/C][/ROW]
[ROW][C]64[/C][C] 16[/C][C] 16.59[/C][C]-0.5864[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 16.15[/C][C]-2.145[/C][/ROW]
[ROW][C]66[/C][C] 20[/C][C] 17.36[/C][C] 2.639[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.44[/C][C]-3.442[/C][/ROW]
[ROW][C]68[/C][C] 18[/C][C] 17.36[/C][C] 0.6386[/C][/ROW]
[ROW][C]69[/C][C] 14[/C][C] 16.14[/C][C]-2.143[/C][/ROW]
[ROW][C]70[/C][C] 19[/C][C] 16.88[/C][C] 2.117[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 16.59[/C][C] 1.414[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 16.62[/C][C]-2.624[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 16.44[/C][C] 1.558[/C][/ROW]
[ROW][C]74[/C][C] 19[/C][C] 16.92[/C][C] 2.077[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 16.14[/C][C]-1.143[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 16.88[/C][C]-2.883[/C][/ROW]
[ROW][C]77[/C][C] 17[/C][C] 17.36[/C][C]-0.3642[/C][/ROW]
[ROW][C]78[/C][C] 19[/C][C] 17.36[/C][C] 1.636[/C][/ROW]
[ROW][C]79[/C][C] 13[/C][C] 16.74[/C][C]-3.742[/C][/ROW]
[ROW][C]80[/C][C] 19[/C][C] 17.7[/C][C] 1.299[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 17.36[/C][C] 0.6358[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 16.62[/C][C] 3.376[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 16.14[/C][C]-1.143[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 15.96[/C][C]-0.9637[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 15.96[/C][C]-0.9609[/C][/ROW]
[ROW][C]86[/C][C] 20[/C][C] 16.92[/C][C] 3.077[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 16.88[/C][C]-1.883[/C][/ROW]
[ROW][C]88[/C][C] 19[/C][C] 16.4[/C][C] 2.598[/C][/ROW]
[ROW][C]89[/C][C] 18[/C][C] 16.59[/C][C] 1.414[/C][/ROW]
[ROW][C]90[/C][C] 18[/C][C] 16.92[/C][C] 1.077[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 17.22[/C][C]-2.223[/C][/ROW]
[ROW][C]92[/C][C] 20[/C][C] 17.7[/C][C] 2.299[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 17.36[/C][C]-0.3642[/C][/ROW]
[ROW][C]94[/C][C] 12[/C][C] 16.62[/C][C]-4.624[/C][/ROW]
[ROW][C]95[/C][C] 18[/C][C] 17.66[/C][C] 0.3363[/C][/ROW]
[ROW][C]96[/C][C] 19[/C][C] 16.92[/C][C] 2.077[/C][/ROW]
[ROW][C]97[/C][C] 20[/C][C] 17.22[/C][C] 2.777[/C][/ROW]
[ROW][C]98[/C][C] 17[/C][C] 18.14[/C][C]-1.142[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 15.48[/C][C] 0.5175[/C][/ROW]
[ROW][C]100[/C][C] 18[/C][C] 16.44[/C][C] 1.558[/C][/ROW]
[ROW][C]101[/C][C] 18[/C][C] 16.44[/C][C] 1.558[/C][/ROW]
[ROW][C]102[/C][C] 14[/C][C] 16.1[/C][C]-2.102[/C][/ROW]
[ROW][C]103[/C][C] 15[/C][C] 16.44[/C][C]-1.442[/C][/ROW]
[ROW][C]104[/C][C] 12[/C][C] 16.44[/C][C]-4.445[/C][/ROW]
[ROW][C]105[/C][C] 17[/C][C] 16.59[/C][C] 0.4136[/C][/ROW]
[ROW][C]106[/C][C] 14[/C][C] 15.96[/C][C]-1.964[/C][/ROW]
[ROW][C]107[/C][C] 18[/C][C] 16.14[/C][C] 1.857[/C][/ROW]
[ROW][C]108[/C][C] 17[/C][C] 16[/C][C] 0.9989[/C][/ROW]
[ROW][C]109[/C][C] 17[/C][C] 16.23[/C][C] 0.7714[/C][/ROW]
[ROW][C]110[/C][C] 20[/C][C] 17.66[/C][C] 2.336[/C][/ROW]
[ROW][C]111[/C][C] 16[/C][C] 15.96[/C][C] 0.03911[/C][/ROW]
[ROW][C]112[/C][C] 14[/C][C] 15.96[/C][C]-1.964[/C][/ROW]
[ROW][C]113[/C][C] 15[/C][C] 16.15[/C][C]-1.145[/C][/ROW]
[ROW][C]114[/C][C] 18[/C][C] 16.44[/C][C] 1.558[/C][/ROW]
[ROW][C]115[/C][C] 20[/C][C] 16.88[/C][C] 3.117[/C][/ROW]
[ROW][C]116[/C][C] 17[/C][C] 16.44[/C][C] 0.5579[/C][/ROW]
[ROW][C]117[/C][C] 17[/C][C] 16.14[/C][C] 0.8574[/C][/ROW]
[ROW][C]118[/C][C] 17[/C][C] 15.96[/C][C] 1.036[/C][/ROW]
[ROW][C]119[/C][C] 17[/C][C] 16.92[/C][C] 0.07675[/C][/ROW]
[ROW][C]120[/C][C] 15[/C][C] 15.66[/C][C]-0.6642[/C][/ROW]
[ROW][C]121[/C][C] 17[/C][C] 15.48[/C][C] 1.517[/C][/ROW]
[ROW][C]122[/C][C] 18[/C][C] 15.19[/C][C] 2.814[/C][/ROW]
[ROW][C]123[/C][C] 17[/C][C] 16.4[/C][C] 0.5954[/C][/ROW]
[ROW][C]124[/C][C] 20[/C][C] 16.7[/C][C] 3.296[/C][/ROW]
[ROW][C]125[/C][C] 15[/C][C] 15.4[/C][C]-0.405[/C][/ROW]
[ROW][C]126[/C][C] 16[/C][C] 16.14[/C][C]-0.1426[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 15.96[/C][C]-0.9637[/C][/ROW]
[ROW][C]128[/C][C] 18[/C][C] 17.18[/C][C] 0.8175[/C][/ROW]
[ROW][C]129[/C][C] 15[/C][C] 16.44[/C][C]-1.442[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 17.66[/C][C] 0.3392[/C][/ROW]
[ROW][C]131[/C][C] 20[/C][C] 17.36[/C][C] 2.636[/C][/ROW]
[ROW][C]132[/C][C] 19[/C][C] 16.44[/C][C] 2.555[/C][/ROW]
[ROW][C]133[/C][C] 14[/C][C] 16.58[/C][C]-2.584[/C][/ROW]
[ROW][C]134[/C][C] 16[/C][C] 16.44[/C][C]-0.4421[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 16.44[/C][C]-1.442[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 17.36[/C][C]-0.3642[/C][/ROW]
[ROW][C]137[/C][C] 18[/C][C] 17.36[/C][C] 0.6358[/C][/ROW]
[ROW][C]138[/C][C] 20[/C][C] 16.7[/C][C] 3.296[/C][/ROW]
[ROW][C]139[/C][C] 17[/C][C] 16.44[/C][C] 0.5579[/C][/ROW]
[ROW][C]140[/C][C] 18[/C][C] 16.44[/C][C] 1.558[/C][/ROW]
[ROW][C]141[/C][C] 15[/C][C] 15.66[/C][C]-0.6642[/C][/ROW]
[ROW][C]142[/C][C] 16[/C][C] 15.96[/C][C] 0.03911[/C][/ROW]
[ROW][C]143[/C][C] 11[/C][C] 15.96[/C][C]-4.964[/C][/ROW]
[ROW][C]144[/C][C] 15[/C][C] 16.92[/C][C]-1.923[/C][/ROW]
[ROW][C]145[/C][C] 18[/C][C] 16.59[/C][C] 1.414[/C][/ROW]
[ROW][C]146[/C][C] 17[/C][C] 16.92[/C][C] 0.07675[/C][/ROW]
[ROW][C]147[/C][C] 16[/C][C] 17.18[/C][C]-1.182[/C][/ROW]
[ROW][C]148[/C][C] 12[/C][C] 16[/C][C]-4.001[/C][/ROW]
[ROW][C]149[/C][C] 19[/C][C] 15.84[/C][C] 3.157[/C][/ROW]
[ROW][C]150[/C][C] 18[/C][C] 16.92[/C][C] 1.077[/C][/ROW]
[ROW][C]151[/C][C] 15[/C][C] 16.89[/C][C]-1.886[/C][/ROW]
[ROW][C]152[/C][C] 17[/C][C] 15.96[/C][C] 1.039[/C][/ROW]
[ROW][C]153[/C][C] 19[/C][C] 16.92[/C][C] 2.08[/C][/ROW]
[ROW][C]154[/C][C] 18[/C][C] 16.74[/C][C] 1.258[/C][/ROW]
[ROW][C]155[/C][C] 19[/C][C] 16.26[/C][C] 2.737[/C][/ROW]
[ROW][C]156[/C][C] 16[/C][C] 17.06[/C][C]-1.065[/C][/ROW]
[ROW][C]157[/C][C] 16[/C][C] 16.92[/C][C]-0.9232[/C][/ROW]
[ROW][C]158[/C][C] 16[/C][C] 16.88[/C][C]-0.883[/C][/ROW]
[ROW][C]159[/C][C] 14[/C][C] 16.76[/C][C]-2.762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304834&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.4	-2.405
2	19	17.18	1.818
3	17	16.88	0.117
4	17	15.66	1.336
5	15	16.88	-1.883
6	20	17.06	2.935
7	15	15.22	-0.2233
8	19	16.14	2.857
9	15	17.36	-2.364
10	15	17.36	-2.364
11	19	16.88	2.117
12	20	16.44	3.555
13	18	16.62	1.376
14	15	16.04	-1.041
15	14	16.62	-2.624
16	20	16.62	3.376
17	16	16.26	-0.2632
18	16	16.88	-0.883
19	16	16.15	-0.1454
20	10	17.36	-7.364
21	19	16.92	2.077
22	19	16.44	2.558
23	16	16.92	-0.9232
24	15	16.14	-1.143
25	18	16.59	1.414
26	17	16.44	0.5579
27	19	16.77	2.235
28	17	16.74	0.2585
29	19	17.36	1.636
30	20	17.22	2.777
31	19	16.28	2.716
32	16	16.44	-0.4421
33	15	16.14	-1.143
34	16	16.44	-0.4449
35	18	16.44	1.558
36	16	16.44	-0.4421
37	15	16.74	-1.742
38	17	17.36	-0.3642
39	20	17.19	2.815
40	19	16.26	2.737
41	7	16.44	-9.445
42	13	16.44	-3.442
43	16	15.22	0.7767
44	16	16.88	-0.883
45	18	16.44	1.558
46	18	17.18	0.8175
47	16	16.74	-0.7415
48	17	17.36	-0.3642
49	19	16.44	2.555
50	16	16.44	-0.4421
51	19	15.22	3.777
52	13	15.66	-2.661
53	16	16.4	-0.4047
54	13	16.88	-3.883
55	12	17.66	-5.664
56	17	16.77	0.2347
57	17	17.36	-0.3642
58	17	15.26	1.739
59	16	17.36	-1.364
60	16	16.44	-0.4421
61	14	15.92	-1.923
62	16	16.4	-0.4047
63	13	16.3	-3.303
64	16	16.59	-0.5864
65	14	16.15	-2.145
66	20	17.36	2.639
67	13	16.44	-3.442
68	18	17.36	0.6386
69	14	16.14	-2.143
70	19	16.88	2.117
71	18	16.59	1.414
72	14	16.62	-2.624
73	18	16.44	1.558
74	19	16.92	2.077
75	15	16.14	-1.143
76	14	16.88	-2.883
77	17	17.36	-0.3642
78	19	17.36	1.636
79	13	16.74	-3.742
80	19	17.7	1.299
81	18	17.36	0.6358
82	20	16.62	3.376
83	15	16.14	-1.143
84	15	15.96	-0.9637
85	15	15.96	-0.9609
86	20	16.92	3.077
87	15	16.88	-1.883
88	19	16.4	2.598
89	18	16.59	1.414
90	18	16.92	1.077
91	15	17.22	-2.223
92	20	17.7	2.299
93	17	17.36	-0.3642
94	12	16.62	-4.624
95	18	17.66	0.3363
96	19	16.92	2.077
97	20	17.22	2.777
98	17	18.14	-1.142
99	16	15.48	0.5175
100	18	16.44	1.558
101	18	16.44	1.558
102	14	16.1	-2.102
103	15	16.44	-1.442
104	12	16.44	-4.445
105	17	16.59	0.4136
106	14	15.96	-1.964
107	18	16.14	1.857
108	17	16	0.9989
109	17	16.23	0.7714
110	20	17.66	2.336
111	16	15.96	0.03911
112	14	15.96	-1.964
113	15	16.15	-1.145
114	18	16.44	1.558
115	20	16.88	3.117
116	17	16.44	0.5579
117	17	16.14	0.8574
118	17	15.96	1.036
119	17	16.92	0.07675
120	15	15.66	-0.6642
121	17	15.48	1.517
122	18	15.19	2.814
123	17	16.4	0.5954
124	20	16.7	3.296
125	15	15.4	-0.405
126	16	16.14	-0.1426
127	15	15.96	-0.9637
128	18	17.18	0.8175
129	15	16.44	-1.442
130	18	17.66	0.3392
131	20	17.36	2.636
132	19	16.44	2.555
133	14	16.58	-2.584
134	16	16.44	-0.4421
135	15	16.44	-1.442
136	17	17.36	-0.3642
137	18	17.36	0.6358
138	20	16.7	3.296
139	17	16.44	0.5579
140	18	16.44	1.558
141	15	15.66	-0.6642
142	16	15.96	0.03911
143	11	15.96	-4.964
144	15	16.92	-1.923
145	18	16.59	1.414
146	17	16.92	0.07675
147	16	17.18	-1.182
148	12	16	-4.001
149	19	15.84	3.157
150	18	16.92	1.077
151	15	16.89	-1.886
152	17	15.96	1.039
153	19	16.92	2.08
154	18	16.74	1.258
155	19	16.26	2.737
156	16	17.06	-1.065
157	16	16.92	-0.9232
158	16	16.88	-0.883
159	14	16.76	-2.762

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.5394	0.9213	0.4606
9	0.6677	0.6646	0.3323
10	0.5883	0.8233	0.4117
11	0.5016	0.9967	0.4984
12	0.7904	0.4192	0.2096
13	0.7124	0.5752	0.2876
14	0.7046	0.5907	0.2954
15	0.7616	0.4769	0.2384
16	0.7892	0.4216	0.2108
17	0.7233	0.5533	0.2767
18	0.6646	0.6707	0.3354
19	0.5977	0.8047	0.4023
20	0.9352	0.1296	0.06479
21	0.9388	0.1223	0.06115
22	0.9312	0.1377	0.06883
23	0.9067	0.1866	0.09328
24	0.9051	0.1899	0.09493
25	0.8875	0.225	0.1125
26	0.8538	0.2924	0.1462
27	0.8322	0.3355	0.1678
28	0.794	0.4119	0.206
29	0.7871	0.4259	0.2129
30	0.8251	0.3498	0.1749
31	0.8115	0.3769	0.1885
32	0.7745	0.451	0.2255
33	0.7589	0.4823	0.2411
34	0.7135	0.5729	0.2865
35	0.6791	0.6419	0.3209
36	0.6317	0.7367	0.3684
37	0.6023	0.7954	0.3977
38	0.5476	0.9048	0.4524
39	0.5909	0.8181	0.4091
40	0.5991	0.8019	0.4009
41	0.9896	0.02085	0.01042
42	0.9933	0.01339	0.006694
43	0.9907	0.01865	0.009323
44	0.9877	0.02463	0.01231
45	0.9849	0.03013	0.01506
46	0.9802	0.0395	0.01975
47	0.9741	0.05187	0.02594
48	0.9656	0.06871	0.03435
49	0.9681	0.06371	0.03186
50	0.9588	0.08249	0.04125
51	0.9699	0.06017	0.03008
52	0.9753	0.0494	0.0247
53	0.9679	0.06426	0.03213
54	0.9801	0.03983	0.01992
55	0.9954	0.009257	0.004628
56	0.9935	0.01291	0.006456
57	0.9911	0.0177	0.00885
58	0.9905	0.01903	0.009517
59	0.9884	0.02327	0.01163
60	0.9843	0.03131	0.01565
61	0.9836	0.03279	0.0164
62	0.9786	0.04287	0.02143
63	0.9838	0.03233	0.01616
64	0.979	0.042	0.021
65	0.9787	0.04265	0.02133
66	0.9808	0.03844	0.01922
67	0.987	0.02591	0.01295
68	0.983	0.03407	0.01704
69	0.9828	0.0345	0.01725
70	0.9822	0.03563	0.01781
71	0.9789	0.04213	0.02107
72	0.9808	0.0384	0.0192
73	0.9778	0.04432	0.02216
74	0.9773	0.04541	0.02271
75	0.9723	0.05535	0.02768
76	0.9774	0.04513	0.02257
77	0.971	0.05802	0.02901
78	0.9671	0.06574	0.03287
79	0.981	0.03795	0.01897
80	0.9772	0.04567	0.02284
81	0.9707	0.05867	0.02934
82	0.9809	0.03829	0.01915
83	0.9763	0.04746	0.02373
84	0.9707	0.05868	0.02934
85	0.9642	0.07166	0.03583
86	0.9727	0.0546	0.0273
87	0.9721	0.05583	0.02791
88	0.9737	0.05259	0.0263
89	0.9699	0.06012	0.03006
90	0.9636	0.07281	0.0364
91	0.9662	0.06756	0.03378
92	0.9657	0.06861	0.03431
93	0.9559	0.08815	0.04408
94	0.9815	0.03704	0.01852
95	0.9758	0.04848	0.02424
96	0.9751	0.04983	0.02491
97	0.9784	0.04326	0.02163
98	0.9747	0.05056	0.02528
99	0.9671	0.0658	0.0329
100	0.9622	0.07563	0.03782
101	0.9568	0.08637	0.04319
102	0.9595	0.08109	0.04054
103	0.9533	0.09333	0.04667
104	0.9817	0.0366	0.0183
105	0.9754	0.04929	0.02465
106	0.976	0.04802	0.02401
107	0.9754	0.0491	0.02455
108	0.9701	0.05974	0.02987
109	0.9656	0.06885	0.03442
110	0.9631	0.07379	0.0369
111	0.9512	0.0976	0.0488
112	0.9546	0.09071	0.04536
113	0.9447	0.1106	0.05532
114	0.9372	0.1255	0.06277
115	0.9497	0.1006	0.05032
116	0.935	0.1299	0.06497
117	0.9229	0.1542	0.07709
118	0.9031	0.1937	0.09687
119	0.8771	0.2459	0.1229
120	0.8503	0.2994	0.1497
121	0.8237	0.3526	0.1763
122	0.8336	0.3327	0.1664
123	0.7954	0.4092	0.2046
124	0.8136	0.3728	0.1864
125	0.7745	0.451	0.2255
126	0.7292	0.5416	0.2708
127	0.6863	0.6275	0.3137
128	0.6312	0.7376	0.3688
129	0.5919	0.8162	0.4081
130	0.5294	0.9412	0.4706
131	0.5491	0.9018	0.4509
132	0.5756	0.8489	0.4244
133	0.5872	0.8255	0.4128
134	0.5208	0.9585	0.4792
135	0.4762	0.9524	0.5238
136	0.4076	0.8151	0.5924
137	0.3451	0.6902	0.6549
138	0.3952	0.7903	0.6048
139	0.3296	0.6592	0.6704
140	0.2981	0.5961	0.7019
141	0.2334	0.4667	0.7666
142	0.1754	0.3508	0.8246
143	0.4647	0.9294	0.5353
144	0.4093	0.8185	0.5907
145	0.3524	0.7049	0.6476
146	0.2687	0.5373	0.7313
147	0.2055	0.4109	0.7945
148	0.9167	0.1667	0.08334
149	0.9408	0.1184	0.05918
150	0.8869	0.2262	0.1131
151	0.7876	0.4248	0.2124

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.5394 &  0.9213 &  0.4606 \tabularnewline
9 &  0.6677 &  0.6646 &  0.3323 \tabularnewline
10 &  0.5883 &  0.8233 &  0.4117 \tabularnewline
11 &  0.5016 &  0.9967 &  0.4984 \tabularnewline
12 &  0.7904 &  0.4192 &  0.2096 \tabularnewline
13 &  0.7124 &  0.5752 &  0.2876 \tabularnewline
14 &  0.7046 &  0.5907 &  0.2954 \tabularnewline
15 &  0.7616 &  0.4769 &  0.2384 \tabularnewline
16 &  0.7892 &  0.4216 &  0.2108 \tabularnewline
17 &  0.7233 &  0.5533 &  0.2767 \tabularnewline
18 &  0.6646 &  0.6707 &  0.3354 \tabularnewline
19 &  0.5977 &  0.8047 &  0.4023 \tabularnewline
20 &  0.9352 &  0.1296 &  0.06479 \tabularnewline
21 &  0.9388 &  0.1223 &  0.06115 \tabularnewline
22 &  0.9312 &  0.1377 &  0.06883 \tabularnewline
23 &  0.9067 &  0.1866 &  0.09328 \tabularnewline
24 &  0.9051 &  0.1899 &  0.09493 \tabularnewline
25 &  0.8875 &  0.225 &  0.1125 \tabularnewline
26 &  0.8538 &  0.2924 &  0.1462 \tabularnewline
27 &  0.8322 &  0.3355 &  0.1678 \tabularnewline
28 &  0.794 &  0.4119 &  0.206 \tabularnewline
29 &  0.7871 &  0.4259 &  0.2129 \tabularnewline
30 &  0.8251 &  0.3498 &  0.1749 \tabularnewline
31 &  0.8115 &  0.3769 &  0.1885 \tabularnewline
32 &  0.7745 &  0.451 &  0.2255 \tabularnewline
33 &  0.7589 &  0.4823 &  0.2411 \tabularnewline
34 &  0.7135 &  0.5729 &  0.2865 \tabularnewline
35 &  0.6791 &  0.6419 &  0.3209 \tabularnewline
36 &  0.6317 &  0.7367 &  0.3684 \tabularnewline
37 &  0.6023 &  0.7954 &  0.3977 \tabularnewline
38 &  0.5476 &  0.9048 &  0.4524 \tabularnewline
39 &  0.5909 &  0.8181 &  0.4091 \tabularnewline
40 &  0.5991 &  0.8019 &  0.4009 \tabularnewline
41 &  0.9896 &  0.02085 &  0.01042 \tabularnewline
42 &  0.9933 &  0.01339 &  0.006694 \tabularnewline
43 &  0.9907 &  0.01865 &  0.009323 \tabularnewline
44 &  0.9877 &  0.02463 &  0.01231 \tabularnewline
45 &  0.9849 &  0.03013 &  0.01506 \tabularnewline
46 &  0.9802 &  0.0395 &  0.01975 \tabularnewline
47 &  0.9741 &  0.05187 &  0.02594 \tabularnewline
48 &  0.9656 &  0.06871 &  0.03435 \tabularnewline
49 &  0.9681 &  0.06371 &  0.03186 \tabularnewline
50 &  0.9588 &  0.08249 &  0.04125 \tabularnewline
51 &  0.9699 &  0.06017 &  0.03008 \tabularnewline
52 &  0.9753 &  0.0494 &  0.0247 \tabularnewline
53 &  0.9679 &  0.06426 &  0.03213 \tabularnewline
54 &  0.9801 &  0.03983 &  0.01992 \tabularnewline
55 &  0.9954 &  0.009257 &  0.004628 \tabularnewline
56 &  0.9935 &  0.01291 &  0.006456 \tabularnewline
57 &  0.9911 &  0.0177 &  0.00885 \tabularnewline
58 &  0.9905 &  0.01903 &  0.009517 \tabularnewline
59 &  0.9884 &  0.02327 &  0.01163 \tabularnewline
60 &  0.9843 &  0.03131 &  0.01565 \tabularnewline
61 &  0.9836 &  0.03279 &  0.0164 \tabularnewline
62 &  0.9786 &  0.04287 &  0.02143 \tabularnewline
63 &  0.9838 &  0.03233 &  0.01616 \tabularnewline
64 &  0.979 &  0.042 &  0.021 \tabularnewline
65 &  0.9787 &  0.04265 &  0.02133 \tabularnewline
66 &  0.9808 &  0.03844 &  0.01922 \tabularnewline
67 &  0.987 &  0.02591 &  0.01295 \tabularnewline
68 &  0.983 &  0.03407 &  0.01704 \tabularnewline
69 &  0.9828 &  0.0345 &  0.01725 \tabularnewline
70 &  0.9822 &  0.03563 &  0.01781 \tabularnewline
71 &  0.9789 &  0.04213 &  0.02107 \tabularnewline
72 &  0.9808 &  0.0384 &  0.0192 \tabularnewline
73 &  0.9778 &  0.04432 &  0.02216 \tabularnewline
74 &  0.9773 &  0.04541 &  0.02271 \tabularnewline
75 &  0.9723 &  0.05535 &  0.02768 \tabularnewline
76 &  0.9774 &  0.04513 &  0.02257 \tabularnewline
77 &  0.971 &  0.05802 &  0.02901 \tabularnewline
78 &  0.9671 &  0.06574 &  0.03287 \tabularnewline
79 &  0.981 &  0.03795 &  0.01897 \tabularnewline
80 &  0.9772 &  0.04567 &  0.02284 \tabularnewline
81 &  0.9707 &  0.05867 &  0.02934 \tabularnewline
82 &  0.9809 &  0.03829 &  0.01915 \tabularnewline
83 &  0.9763 &  0.04746 &  0.02373 \tabularnewline
84 &  0.9707 &  0.05868 &  0.02934 \tabularnewline
85 &  0.9642 &  0.07166 &  0.03583 \tabularnewline
86 &  0.9727 &  0.0546 &  0.0273 \tabularnewline
87 &  0.9721 &  0.05583 &  0.02791 \tabularnewline
88 &  0.9737 &  0.05259 &  0.0263 \tabularnewline
89 &  0.9699 &  0.06012 &  0.03006 \tabularnewline
90 &  0.9636 &  0.07281 &  0.0364 \tabularnewline
91 &  0.9662 &  0.06756 &  0.03378 \tabularnewline
92 &  0.9657 &  0.06861 &  0.03431 \tabularnewline
93 &  0.9559 &  0.08815 &  0.04408 \tabularnewline
94 &  0.9815 &  0.03704 &  0.01852 \tabularnewline
95 &  0.9758 &  0.04848 &  0.02424 \tabularnewline
96 &  0.9751 &  0.04983 &  0.02491 \tabularnewline
97 &  0.9784 &  0.04326 &  0.02163 \tabularnewline
98 &  0.9747 &  0.05056 &  0.02528 \tabularnewline
99 &  0.9671 &  0.0658 &  0.0329 \tabularnewline
100 &  0.9622 &  0.07563 &  0.03782 \tabularnewline
101 &  0.9568 &  0.08637 &  0.04319 \tabularnewline
102 &  0.9595 &  0.08109 &  0.04054 \tabularnewline
103 &  0.9533 &  0.09333 &  0.04667 \tabularnewline
104 &  0.9817 &  0.0366 &  0.0183 \tabularnewline
105 &  0.9754 &  0.04929 &  0.02465 \tabularnewline
106 &  0.976 &  0.04802 &  0.02401 \tabularnewline
107 &  0.9754 &  0.0491 &  0.02455 \tabularnewline
108 &  0.9701 &  0.05974 &  0.02987 \tabularnewline
109 &  0.9656 &  0.06885 &  0.03442 \tabularnewline
110 &  0.9631 &  0.07379 &  0.0369 \tabularnewline
111 &  0.9512 &  0.0976 &  0.0488 \tabularnewline
112 &  0.9546 &  0.09071 &  0.04536 \tabularnewline
113 &  0.9447 &  0.1106 &  0.05532 \tabularnewline
114 &  0.9372 &  0.1255 &  0.06277 \tabularnewline
115 &  0.9497 &  0.1006 &  0.05032 \tabularnewline
116 &  0.935 &  0.1299 &  0.06497 \tabularnewline
117 &  0.9229 &  0.1542 &  0.07709 \tabularnewline
118 &  0.9031 &  0.1937 &  0.09687 \tabularnewline
119 &  0.8771 &  0.2459 &  0.1229 \tabularnewline
120 &  0.8503 &  0.2994 &  0.1497 \tabularnewline
121 &  0.8237 &  0.3526 &  0.1763 \tabularnewline
122 &  0.8336 &  0.3327 &  0.1664 \tabularnewline
123 &  0.7954 &  0.4092 &  0.2046 \tabularnewline
124 &  0.8136 &  0.3728 &  0.1864 \tabularnewline
125 &  0.7745 &  0.451 &  0.2255 \tabularnewline
126 &  0.7292 &  0.5416 &  0.2708 \tabularnewline
127 &  0.6863 &  0.6275 &  0.3137 \tabularnewline
128 &  0.6312 &  0.7376 &  0.3688 \tabularnewline
129 &  0.5919 &  0.8162 &  0.4081 \tabularnewline
130 &  0.5294 &  0.9412 &  0.4706 \tabularnewline
131 &  0.5491 &  0.9018 &  0.4509 \tabularnewline
132 &  0.5756 &  0.8489 &  0.4244 \tabularnewline
133 &  0.5872 &  0.8255 &  0.4128 \tabularnewline
134 &  0.5208 &  0.9585 &  0.4792 \tabularnewline
135 &  0.4762 &  0.9524 &  0.5238 \tabularnewline
136 &  0.4076 &  0.8151 &  0.5924 \tabularnewline
137 &  0.3451 &  0.6902 &  0.6549 \tabularnewline
138 &  0.3952 &  0.7903 &  0.6048 \tabularnewline
139 &  0.3296 &  0.6592 &  0.6704 \tabularnewline
140 &  0.2981 &  0.5961 &  0.7019 \tabularnewline
141 &  0.2334 &  0.4667 &  0.7666 \tabularnewline
142 &  0.1754 &  0.3508 &  0.8246 \tabularnewline
143 &  0.4647 &  0.9294 &  0.5353 \tabularnewline
144 &  0.4093 &  0.8185 &  0.5907 \tabularnewline
145 &  0.3524 &  0.7049 &  0.6476 \tabularnewline
146 &  0.2687 &  0.5373 &  0.7313 \tabularnewline
147 &  0.2055 &  0.4109 &  0.7945 \tabularnewline
148 &  0.9167 &  0.1667 &  0.08334 \tabularnewline
149 &  0.9408 &  0.1184 &  0.05918 \tabularnewline
150 &  0.8869 &  0.2262 &  0.1131 \tabularnewline
151 &  0.7876 &  0.4248 &  0.2124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&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]8[/C][C] 0.5394[/C][C] 0.9213[/C][C] 0.4606[/C][/ROW]
[ROW][C]9[/C][C] 0.6677[/C][C] 0.6646[/C][C] 0.3323[/C][/ROW]
[ROW][C]10[/C][C] 0.5883[/C][C] 0.8233[/C][C] 0.4117[/C][/ROW]
[ROW][C]11[/C][C] 0.5016[/C][C] 0.9967[/C][C] 0.4984[/C][/ROW]
[ROW][C]12[/C][C] 0.7904[/C][C] 0.4192[/C][C] 0.2096[/C][/ROW]
[ROW][C]13[/C][C] 0.7124[/C][C] 0.5752[/C][C] 0.2876[/C][/ROW]
[ROW][C]14[/C][C] 0.7046[/C][C] 0.5907[/C][C] 0.2954[/C][/ROW]
[ROW][C]15[/C][C] 0.7616[/C][C] 0.4769[/C][C] 0.2384[/C][/ROW]
[ROW][C]16[/C][C] 0.7892[/C][C] 0.4216[/C][C] 0.2108[/C][/ROW]
[ROW][C]17[/C][C] 0.7233[/C][C] 0.5533[/C][C] 0.2767[/C][/ROW]
[ROW][C]18[/C][C] 0.6646[/C][C] 0.6707[/C][C] 0.3354[/C][/ROW]
[ROW][C]19[/C][C] 0.5977[/C][C] 0.8047[/C][C] 0.4023[/C][/ROW]
[ROW][C]20[/C][C] 0.9352[/C][C] 0.1296[/C][C] 0.06479[/C][/ROW]
[ROW][C]21[/C][C] 0.9388[/C][C] 0.1223[/C][C] 0.06115[/C][/ROW]
[ROW][C]22[/C][C] 0.9312[/C][C] 0.1377[/C][C] 0.06883[/C][/ROW]
[ROW][C]23[/C][C] 0.9067[/C][C] 0.1866[/C][C] 0.09328[/C][/ROW]
[ROW][C]24[/C][C] 0.9051[/C][C] 0.1899[/C][C] 0.09493[/C][/ROW]
[ROW][C]25[/C][C] 0.8875[/C][C] 0.225[/C][C] 0.1125[/C][/ROW]
[ROW][C]26[/C][C] 0.8538[/C][C] 0.2924[/C][C] 0.1462[/C][/ROW]
[ROW][C]27[/C][C] 0.8322[/C][C] 0.3355[/C][C] 0.1678[/C][/ROW]
[ROW][C]28[/C][C] 0.794[/C][C] 0.4119[/C][C] 0.206[/C][/ROW]
[ROW][C]29[/C][C] 0.7871[/C][C] 0.4259[/C][C] 0.2129[/C][/ROW]
[ROW][C]30[/C][C] 0.8251[/C][C] 0.3498[/C][C] 0.1749[/C][/ROW]
[ROW][C]31[/C][C] 0.8115[/C][C] 0.3769[/C][C] 0.1885[/C][/ROW]
[ROW][C]32[/C][C] 0.7745[/C][C] 0.451[/C][C] 0.2255[/C][/ROW]
[ROW][C]33[/C][C] 0.7589[/C][C] 0.4823[/C][C] 0.2411[/C][/ROW]
[ROW][C]34[/C][C] 0.7135[/C][C] 0.5729[/C][C] 0.2865[/C][/ROW]
[ROW][C]35[/C][C] 0.6791[/C][C] 0.6419[/C][C] 0.3209[/C][/ROW]
[ROW][C]36[/C][C] 0.6317[/C][C] 0.7367[/C][C] 0.3684[/C][/ROW]
[ROW][C]37[/C][C] 0.6023[/C][C] 0.7954[/C][C] 0.3977[/C][/ROW]
[ROW][C]38[/C][C] 0.5476[/C][C] 0.9048[/C][C] 0.4524[/C][/ROW]
[ROW][C]39[/C][C] 0.5909[/C][C] 0.8181[/C][C] 0.4091[/C][/ROW]
[ROW][C]40[/C][C] 0.5991[/C][C] 0.8019[/C][C] 0.4009[/C][/ROW]
[ROW][C]41[/C][C] 0.9896[/C][C] 0.02085[/C][C] 0.01042[/C][/ROW]
[ROW][C]42[/C][C] 0.9933[/C][C] 0.01339[/C][C] 0.006694[/C][/ROW]
[ROW][C]43[/C][C] 0.9907[/C][C] 0.01865[/C][C] 0.009323[/C][/ROW]
[ROW][C]44[/C][C] 0.9877[/C][C] 0.02463[/C][C] 0.01231[/C][/ROW]
[ROW][C]45[/C][C] 0.9849[/C][C] 0.03013[/C][C] 0.01506[/C][/ROW]
[ROW][C]46[/C][C] 0.9802[/C][C] 0.0395[/C][C] 0.01975[/C][/ROW]
[ROW][C]47[/C][C] 0.9741[/C][C] 0.05187[/C][C] 0.02594[/C][/ROW]
[ROW][C]48[/C][C] 0.9656[/C][C] 0.06871[/C][C] 0.03435[/C][/ROW]
[ROW][C]49[/C][C] 0.9681[/C][C] 0.06371[/C][C] 0.03186[/C][/ROW]
[ROW][C]50[/C][C] 0.9588[/C][C] 0.08249[/C][C] 0.04125[/C][/ROW]
[ROW][C]51[/C][C] 0.9699[/C][C] 0.06017[/C][C] 0.03008[/C][/ROW]
[ROW][C]52[/C][C] 0.9753[/C][C] 0.0494[/C][C] 0.0247[/C][/ROW]
[ROW][C]53[/C][C] 0.9679[/C][C] 0.06426[/C][C] 0.03213[/C][/ROW]
[ROW][C]54[/C][C] 0.9801[/C][C] 0.03983[/C][C] 0.01992[/C][/ROW]
[ROW][C]55[/C][C] 0.9954[/C][C] 0.009257[/C][C] 0.004628[/C][/ROW]
[ROW][C]56[/C][C] 0.9935[/C][C] 0.01291[/C][C] 0.006456[/C][/ROW]
[ROW][C]57[/C][C] 0.9911[/C][C] 0.0177[/C][C] 0.00885[/C][/ROW]
[ROW][C]58[/C][C] 0.9905[/C][C] 0.01903[/C][C] 0.009517[/C][/ROW]
[ROW][C]59[/C][C] 0.9884[/C][C] 0.02327[/C][C] 0.01163[/C][/ROW]
[ROW][C]60[/C][C] 0.9843[/C][C] 0.03131[/C][C] 0.01565[/C][/ROW]
[ROW][C]61[/C][C] 0.9836[/C][C] 0.03279[/C][C] 0.0164[/C][/ROW]
[ROW][C]62[/C][C] 0.9786[/C][C] 0.04287[/C][C] 0.02143[/C][/ROW]
[ROW][C]63[/C][C] 0.9838[/C][C] 0.03233[/C][C] 0.01616[/C][/ROW]
[ROW][C]64[/C][C] 0.979[/C][C] 0.042[/C][C] 0.021[/C][/ROW]
[ROW][C]65[/C][C] 0.9787[/C][C] 0.04265[/C][C] 0.02133[/C][/ROW]
[ROW][C]66[/C][C] 0.9808[/C][C] 0.03844[/C][C] 0.01922[/C][/ROW]
[ROW][C]67[/C][C] 0.987[/C][C] 0.02591[/C][C] 0.01295[/C][/ROW]
[ROW][C]68[/C][C] 0.983[/C][C] 0.03407[/C][C] 0.01704[/C][/ROW]
[ROW][C]69[/C][C] 0.9828[/C][C] 0.0345[/C][C] 0.01725[/C][/ROW]
[ROW][C]70[/C][C] 0.9822[/C][C] 0.03563[/C][C] 0.01781[/C][/ROW]
[ROW][C]71[/C][C] 0.9789[/C][C] 0.04213[/C][C] 0.02107[/C][/ROW]
[ROW][C]72[/C][C] 0.9808[/C][C] 0.0384[/C][C] 0.0192[/C][/ROW]
[ROW][C]73[/C][C] 0.9778[/C][C] 0.04432[/C][C] 0.02216[/C][/ROW]
[ROW][C]74[/C][C] 0.9773[/C][C] 0.04541[/C][C] 0.02271[/C][/ROW]
[ROW][C]75[/C][C] 0.9723[/C][C] 0.05535[/C][C] 0.02768[/C][/ROW]
[ROW][C]76[/C][C] 0.9774[/C][C] 0.04513[/C][C] 0.02257[/C][/ROW]
[ROW][C]77[/C][C] 0.971[/C][C] 0.05802[/C][C] 0.02901[/C][/ROW]
[ROW][C]78[/C][C] 0.9671[/C][C] 0.06574[/C][C] 0.03287[/C][/ROW]
[ROW][C]79[/C][C] 0.981[/C][C] 0.03795[/C][C] 0.01897[/C][/ROW]
[ROW][C]80[/C][C] 0.9772[/C][C] 0.04567[/C][C] 0.02284[/C][/ROW]
[ROW][C]81[/C][C] 0.9707[/C][C] 0.05867[/C][C] 0.02934[/C][/ROW]
[ROW][C]82[/C][C] 0.9809[/C][C] 0.03829[/C][C] 0.01915[/C][/ROW]
[ROW][C]83[/C][C] 0.9763[/C][C] 0.04746[/C][C] 0.02373[/C][/ROW]
[ROW][C]84[/C][C] 0.9707[/C][C] 0.05868[/C][C] 0.02934[/C][/ROW]
[ROW][C]85[/C][C] 0.9642[/C][C] 0.07166[/C][C] 0.03583[/C][/ROW]
[ROW][C]86[/C][C] 0.9727[/C][C] 0.0546[/C][C] 0.0273[/C][/ROW]
[ROW][C]87[/C][C] 0.9721[/C][C] 0.05583[/C][C] 0.02791[/C][/ROW]
[ROW][C]88[/C][C] 0.9737[/C][C] 0.05259[/C][C] 0.0263[/C][/ROW]
[ROW][C]89[/C][C] 0.9699[/C][C] 0.06012[/C][C] 0.03006[/C][/ROW]
[ROW][C]90[/C][C] 0.9636[/C][C] 0.07281[/C][C] 0.0364[/C][/ROW]
[ROW][C]91[/C][C] 0.9662[/C][C] 0.06756[/C][C] 0.03378[/C][/ROW]
[ROW][C]92[/C][C] 0.9657[/C][C] 0.06861[/C][C] 0.03431[/C][/ROW]
[ROW][C]93[/C][C] 0.9559[/C][C] 0.08815[/C][C] 0.04408[/C][/ROW]
[ROW][C]94[/C][C] 0.9815[/C][C] 0.03704[/C][C] 0.01852[/C][/ROW]
[ROW][C]95[/C][C] 0.9758[/C][C] 0.04848[/C][C] 0.02424[/C][/ROW]
[ROW][C]96[/C][C] 0.9751[/C][C] 0.04983[/C][C] 0.02491[/C][/ROW]
[ROW][C]97[/C][C] 0.9784[/C][C] 0.04326[/C][C] 0.02163[/C][/ROW]
[ROW][C]98[/C][C] 0.9747[/C][C] 0.05056[/C][C] 0.02528[/C][/ROW]
[ROW][C]99[/C][C] 0.9671[/C][C] 0.0658[/C][C] 0.0329[/C][/ROW]
[ROW][C]100[/C][C] 0.9622[/C][C] 0.07563[/C][C] 0.03782[/C][/ROW]
[ROW][C]101[/C][C] 0.9568[/C][C] 0.08637[/C][C] 0.04319[/C][/ROW]
[ROW][C]102[/C][C] 0.9595[/C][C] 0.08109[/C][C] 0.04054[/C][/ROW]
[ROW][C]103[/C][C] 0.9533[/C][C] 0.09333[/C][C] 0.04667[/C][/ROW]
[ROW][C]104[/C][C] 0.9817[/C][C] 0.0366[/C][C] 0.0183[/C][/ROW]
[ROW][C]105[/C][C] 0.9754[/C][C] 0.04929[/C][C] 0.02465[/C][/ROW]
[ROW][C]106[/C][C] 0.976[/C][C] 0.04802[/C][C] 0.02401[/C][/ROW]
[ROW][C]107[/C][C] 0.9754[/C][C] 0.0491[/C][C] 0.02455[/C][/ROW]
[ROW][C]108[/C][C] 0.9701[/C][C] 0.05974[/C][C] 0.02987[/C][/ROW]
[ROW][C]109[/C][C] 0.9656[/C][C] 0.06885[/C][C] 0.03442[/C][/ROW]
[ROW][C]110[/C][C] 0.9631[/C][C] 0.07379[/C][C] 0.0369[/C][/ROW]
[ROW][C]111[/C][C] 0.9512[/C][C] 0.0976[/C][C] 0.0488[/C][/ROW]
[ROW][C]112[/C][C] 0.9546[/C][C] 0.09071[/C][C] 0.04536[/C][/ROW]
[ROW][C]113[/C][C] 0.9447[/C][C] 0.1106[/C][C] 0.05532[/C][/ROW]
[ROW][C]114[/C][C] 0.9372[/C][C] 0.1255[/C][C] 0.06277[/C][/ROW]
[ROW][C]115[/C][C] 0.9497[/C][C] 0.1006[/C][C] 0.05032[/C][/ROW]
[ROW][C]116[/C][C] 0.935[/C][C] 0.1299[/C][C] 0.06497[/C][/ROW]
[ROW][C]117[/C][C] 0.9229[/C][C] 0.1542[/C][C] 0.07709[/C][/ROW]
[ROW][C]118[/C][C] 0.9031[/C][C] 0.1937[/C][C] 0.09687[/C][/ROW]
[ROW][C]119[/C][C] 0.8771[/C][C] 0.2459[/C][C] 0.1229[/C][/ROW]
[ROW][C]120[/C][C] 0.8503[/C][C] 0.2994[/C][C] 0.1497[/C][/ROW]
[ROW][C]121[/C][C] 0.8237[/C][C] 0.3526[/C][C] 0.1763[/C][/ROW]
[ROW][C]122[/C][C] 0.8336[/C][C] 0.3327[/C][C] 0.1664[/C][/ROW]
[ROW][C]123[/C][C] 0.7954[/C][C] 0.4092[/C][C] 0.2046[/C][/ROW]
[ROW][C]124[/C][C] 0.8136[/C][C] 0.3728[/C][C] 0.1864[/C][/ROW]
[ROW][C]125[/C][C] 0.7745[/C][C] 0.451[/C][C] 0.2255[/C][/ROW]
[ROW][C]126[/C][C] 0.7292[/C][C] 0.5416[/C][C] 0.2708[/C][/ROW]
[ROW][C]127[/C][C] 0.6863[/C][C] 0.6275[/C][C] 0.3137[/C][/ROW]
[ROW][C]128[/C][C] 0.6312[/C][C] 0.7376[/C][C] 0.3688[/C][/ROW]
[ROW][C]129[/C][C] 0.5919[/C][C] 0.8162[/C][C] 0.4081[/C][/ROW]
[ROW][C]130[/C][C] 0.5294[/C][C] 0.9412[/C][C] 0.4706[/C][/ROW]
[ROW][C]131[/C][C] 0.5491[/C][C] 0.9018[/C][C] 0.4509[/C][/ROW]
[ROW][C]132[/C][C] 0.5756[/C][C] 0.8489[/C][C] 0.4244[/C][/ROW]
[ROW][C]133[/C][C] 0.5872[/C][C] 0.8255[/C][C] 0.4128[/C][/ROW]
[ROW][C]134[/C][C] 0.5208[/C][C] 0.9585[/C][C] 0.4792[/C][/ROW]
[ROW][C]135[/C][C] 0.4762[/C][C] 0.9524[/C][C] 0.5238[/C][/ROW]
[ROW][C]136[/C][C] 0.4076[/C][C] 0.8151[/C][C] 0.5924[/C][/ROW]
[ROW][C]137[/C][C] 0.3451[/C][C] 0.6902[/C][C] 0.6549[/C][/ROW]
[ROW][C]138[/C][C] 0.3952[/C][C] 0.7903[/C][C] 0.6048[/C][/ROW]
[ROW][C]139[/C][C] 0.3296[/C][C] 0.6592[/C][C] 0.6704[/C][/ROW]
[ROW][C]140[/C][C] 0.2981[/C][C] 0.5961[/C][C] 0.7019[/C][/ROW]
[ROW][C]141[/C][C] 0.2334[/C][C] 0.4667[/C][C] 0.7666[/C][/ROW]
[ROW][C]142[/C][C] 0.1754[/C][C] 0.3508[/C][C] 0.8246[/C][/ROW]
[ROW][C]143[/C][C] 0.4647[/C][C] 0.9294[/C][C] 0.5353[/C][/ROW]
[ROW][C]144[/C][C] 0.4093[/C][C] 0.8185[/C][C] 0.5907[/C][/ROW]
[ROW][C]145[/C][C] 0.3524[/C][C] 0.7049[/C][C] 0.6476[/C][/ROW]
[ROW][C]146[/C][C] 0.2687[/C][C] 0.5373[/C][C] 0.7313[/C][/ROW]
[ROW][C]147[/C][C] 0.2055[/C][C] 0.4109[/C][C] 0.7945[/C][/ROW]
[ROW][C]148[/C][C] 0.9167[/C][C] 0.1667[/C][C] 0.08334[/C][/ROW]
[ROW][C]149[/C][C] 0.9408[/C][C] 0.1184[/C][C] 0.05918[/C][/ROW]
[ROW][C]150[/C][C] 0.8869[/C][C] 0.2262[/C][C] 0.1131[/C][/ROW]
[ROW][C]151[/C][C] 0.7876[/C][C] 0.4248[/C][C] 0.2124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304834&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&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
8	0.5394	0.9213	0.4606
9	0.6677	0.6646	0.3323
10	0.5883	0.8233	0.4117
11	0.5016	0.9967	0.4984
12	0.7904	0.4192	0.2096
13	0.7124	0.5752	0.2876
14	0.7046	0.5907	0.2954
15	0.7616	0.4769	0.2384
16	0.7892	0.4216	0.2108
17	0.7233	0.5533	0.2767
18	0.6646	0.6707	0.3354
19	0.5977	0.8047	0.4023
20	0.9352	0.1296	0.06479
21	0.9388	0.1223	0.06115
22	0.9312	0.1377	0.06883
23	0.9067	0.1866	0.09328
24	0.9051	0.1899	0.09493
25	0.8875	0.225	0.1125
26	0.8538	0.2924	0.1462
27	0.8322	0.3355	0.1678
28	0.794	0.4119	0.206
29	0.7871	0.4259	0.2129
30	0.8251	0.3498	0.1749
31	0.8115	0.3769	0.1885
32	0.7745	0.451	0.2255
33	0.7589	0.4823	0.2411
34	0.7135	0.5729	0.2865
35	0.6791	0.6419	0.3209
36	0.6317	0.7367	0.3684
37	0.6023	0.7954	0.3977
38	0.5476	0.9048	0.4524
39	0.5909	0.8181	0.4091
40	0.5991	0.8019	0.4009
41	0.9896	0.02085	0.01042
42	0.9933	0.01339	0.006694
43	0.9907	0.01865	0.009323
44	0.9877	0.02463	0.01231
45	0.9849	0.03013	0.01506
46	0.9802	0.0395	0.01975
47	0.9741	0.05187	0.02594
48	0.9656	0.06871	0.03435
49	0.9681	0.06371	0.03186
50	0.9588	0.08249	0.04125
51	0.9699	0.06017	0.03008
52	0.9753	0.0494	0.0247
53	0.9679	0.06426	0.03213
54	0.9801	0.03983	0.01992
55	0.9954	0.009257	0.004628
56	0.9935	0.01291	0.006456
57	0.9911	0.0177	0.00885
58	0.9905	0.01903	0.009517
59	0.9884	0.02327	0.01163
60	0.9843	0.03131	0.01565
61	0.9836	0.03279	0.0164
62	0.9786	0.04287	0.02143
63	0.9838	0.03233	0.01616
64	0.979	0.042	0.021
65	0.9787	0.04265	0.02133
66	0.9808	0.03844	0.01922
67	0.987	0.02591	0.01295
68	0.983	0.03407	0.01704
69	0.9828	0.0345	0.01725
70	0.9822	0.03563	0.01781
71	0.9789	0.04213	0.02107
72	0.9808	0.0384	0.0192
73	0.9778	0.04432	0.02216
74	0.9773	0.04541	0.02271
75	0.9723	0.05535	0.02768
76	0.9774	0.04513	0.02257
77	0.971	0.05802	0.02901
78	0.9671	0.06574	0.03287
79	0.981	0.03795	0.01897
80	0.9772	0.04567	0.02284
81	0.9707	0.05867	0.02934
82	0.9809	0.03829	0.01915
83	0.9763	0.04746	0.02373
84	0.9707	0.05868	0.02934
85	0.9642	0.07166	0.03583
86	0.9727	0.0546	0.0273
87	0.9721	0.05583	0.02791
88	0.9737	0.05259	0.0263
89	0.9699	0.06012	0.03006
90	0.9636	0.07281	0.0364
91	0.9662	0.06756	0.03378
92	0.9657	0.06861	0.03431
93	0.9559	0.08815	0.04408
94	0.9815	0.03704	0.01852
95	0.9758	0.04848	0.02424
96	0.9751	0.04983	0.02491
97	0.9784	0.04326	0.02163
98	0.9747	0.05056	0.02528
99	0.9671	0.0658	0.0329
100	0.9622	0.07563	0.03782
101	0.9568	0.08637	0.04319
102	0.9595	0.08109	0.04054
103	0.9533	0.09333	0.04667
104	0.9817	0.0366	0.0183
105	0.9754	0.04929	0.02465
106	0.976	0.04802	0.02401
107	0.9754	0.0491	0.02455
108	0.9701	0.05974	0.02987
109	0.9656	0.06885	0.03442
110	0.9631	0.07379	0.0369
111	0.9512	0.0976	0.0488
112	0.9546	0.09071	0.04536
113	0.9447	0.1106	0.05532
114	0.9372	0.1255	0.06277
115	0.9497	0.1006	0.05032
116	0.935	0.1299	0.06497
117	0.9229	0.1542	0.07709
118	0.9031	0.1937	0.09687
119	0.8771	0.2459	0.1229
120	0.8503	0.2994	0.1497
121	0.8237	0.3526	0.1763
122	0.8336	0.3327	0.1664
123	0.7954	0.4092	0.2046
124	0.8136	0.3728	0.1864
125	0.7745	0.451	0.2255
126	0.7292	0.5416	0.2708
127	0.6863	0.6275	0.3137
128	0.6312	0.7376	0.3688
129	0.5919	0.8162	0.4081
130	0.5294	0.9412	0.4706
131	0.5491	0.9018	0.4509
132	0.5756	0.8489	0.4244
133	0.5872	0.8255	0.4128
134	0.5208	0.9585	0.4792
135	0.4762	0.9524	0.5238
136	0.4076	0.8151	0.5924
137	0.3451	0.6902	0.6549
138	0.3952	0.7903	0.6048
139	0.3296	0.6592	0.6704
140	0.2981	0.5961	0.7019
141	0.2334	0.4667	0.7666
142	0.1754	0.3508	0.8246
143	0.4647	0.9294	0.5353
144	0.4093	0.8185	0.5907
145	0.3524	0.7049	0.6476
146	0.2687	0.5373	0.7313
147	0.2055	0.4109	0.7945
148	0.9167	0.1667	0.08334
149	0.9408	0.1184	0.05918
150	0.8869	0.2262	0.1131
151	0.7876	0.4248	0.2124

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.006944	OK
5% type I error level	41	0.284722	NOK
10% type I error level	72	0.5	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 & 1 &  0.006944 & OK \tabularnewline
5% type I error level & 41 & 0.284722 & NOK \tabularnewline
10% type I error level & 72 & 0.5 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&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]1[/C][C] 0.006944[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]41[/C][C]0.284722[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]72[/C][C]0.5[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304834&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&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	1	0.006944	OK
5% type I error level	41	0.284722	NOK
10% type I error level	72	0.5	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.4679, df1 = 2, df2 = 152, p-value = 0.08815
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.53672, df1 = 8, df2 = 146, p-value = 0.8273
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.83, df1 = 2, df2 = 152, p-value = 0.1639

\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 = 2.4679, df1 = 2, df2 = 152, p-value = 0.08815
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.53672, df1 = 8, df2 = 146, p-value = 0.8273
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.83, df1 = 2, df2 = 152, p-value = 0.1639
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&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 = 2.4679, df1 = 2, df2 = 152, p-value = 0.08815
[/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 = 0.53672, df1 = 8, df2 = 146, p-value = 0.8273
[/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 = 1.83, df1 = 2, df2 = 152, p-value = 0.1639
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304834&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&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 = 2.4679, df1 = 2, df2 = 152, p-value = 0.08815
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.53672, df1 = 8, df2 = 146, p-value = 0.8273
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.83, df1 = 2, df2 = 152, p-value = 0.1639

Variance Inflation Factors (Multicollinearity)
> vif SKEOU1 SKEOU4 SKEOU5 SKEOU6 1.018884 1.014321 1.035170 1.017670

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
  SKEOU1   SKEOU4   SKEOU5   SKEOU6 
1.018884 1.014321 1.035170 1.017670 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304834&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
  SKEOU1   SKEOU4   SKEOU5   SKEOU6 
1.018884 1.014321 1.035170 1.017670 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304834&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304834&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 SKEOU1 SKEOU4 SKEOU5 SKEOU6 1.018884 1.014321 1.035170 1.017670

Figure 1

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Parameters (Session):

Parameters (R input):

par1 = 1 ; 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