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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 10:48:15 +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/t148516538190hdd2l72sx4du9.htm/, Retrieved Wed, 15 May 2024 12:39:55 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 15 May 2024 12:39:55 +0200

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Dataseries X:

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14 22 4 2 4 3 5 4
19 24 5 3 3 4 5 4
17 21 4 4 5 4 5 4
17 21 3 4 3 3 4 4
15 24 4 4 5 4 5 4
20 20 3 4 4 4 5 5
15 22 3 4 4 3 3 4
19 20 3 4 5 4 4 4
15 19 4 5 4 4 5 5
15 23 4 5 5 4 5 5
19 21 4 4 2 4 5 4
NA 19 4 4 5 3 5 4
20 19 4 4 4 3 4 5
18 21 3 3 5 4 4 5
15 21 4 4 5 4 2 5
14 22 3 4 5 4 4 5
20 22 3 4 5 4 4 5
16 21 5 5 4 3 4 4
16 21 4 4 4 4 5 4
16 21 3 4 5 3 4 5
10 20 4 4 4 4 5 5
19 22 4 4 5 4 4 5
19 22 4 4 5 4 4 4
16 24 4 4 5 4 4 5
15 21 3 4 4 4 4 4
18 19 3 4 4 3 5 5
17 19 4 4 4 4 4 4
19 23 2 4 5 4 5 5
17 21 5 4 4 4 4 4
NA 21 4 3 5 4 4 4
19 19 4 5 5 4 5 5
20 21 5 4 5 4 4 5
5 19 4 3 5 4 NA 5
19 21 2 3 5 4 5 4
16 21 4 5 2 4 4 4
15 23 3 4 5 4 4 4
16 19 4 3 5 3 4 5
18 19 4 3 3 4 4 4
16 19 4 4 5 4 4 4
15 18 5 4 4 4 4 4
17 22 4 5 5 4 5 5
NA 18 3 3 4 4 4 4
20 22 5 5 5 3 5 5
19 18 5 4 5 3 4 4
7 22 4 4 4 3 4 5
13 22 4 4 4 4 4 4
16 19 3 5 5 3 3 4
16 22 4 4 4 4 5 4
18 19 4 5 5 4 4 4
18 19 5 5 2 4 5 4
16 19 5 5 5 4 4 4
17 19 4 3 5 4 5 5
19 21 4 3 4 3 4 5
16 21 4 4 5 4 4 4
19 20 3 4 4 3 3 4
13 19 3 4 4 4 4 3
16 19 4 4 4 3 5 4
13 22 4 4 4 4 5 4
12 26 5 5 3 4 5 5
17 19 2 4 4 4 5 5
17 21 4 4 4 4 5 5
17 21 3 4 4 4 2 4
16 20 4 4 5 4 5 5
16 23 4 2 4 4 4 4
14 22 4 4 4 3 5 3
16 22 4 4 4 3 5 4
13 22 5 4 5 3 3 5
16 21 3 4 4 3 5 5
14 21 3 4 4 3 4 5
20 22 4 5 5 5 5 4
13 18 4 4 4 4 4 4
18 24 4 4 4 5 5 4
14 22 3 4 3 4 4 4
19 21 4 4 4 4 5 4
18 21 3 4 5 3 5 5
14 21 3 3 5 4 4 5
18 23 4 3 5 4 4 4
19 21 4 4 5 4 4 5
15 23 3 3 3 4 4 4
14 21 4 4 4 4 5 4
17 19 4 4 3 4 5 5
19 21 4 4 4 4 5 5
13 21 5 4 4 4 4 4
19 21 5 4 3 5 4 5
18 23 4 4 5 4 5 5
20 23 3 4 5 4 4 5
15 20 4 2 3 3 4 4
15 19 4 4 5 4 4 3
20 23 4 4 5 4 4 5
15 22 4 4 4 4 5 4
19 19 4 5 4 4 5 3
18 23 3 4 4 3 5 5
18 22 4 4 5 4 4 5
15 22 5 4 3 4 4 5
20 21 5 4 5 5 4 5
17 21 4 5 4 4 5 5
12 21 3 4 5 4 4 5
18 21 5 3 4 4 5 5
19 22 4 4 5 4 4 5
20 25 5 4 4 4 4 5
17 23 5 4 4 5 5 5
16 22 4 4 3 3 4 3
18 20 4 4 5 4 4 4
18 21 4 4 5 4 4 4
14 25 3 4 5 4 5 3
15 21 4 4 4 4 4 4
12 19 4 4 4 3 4 5
17 23 3 3 4 3 5 5
14 22 4 4 4 3 4 4
18 21 3 4 5 4 4 4
17 24 4 4 5 4 3 4
17 21 5 4 5 1 5 5
20 19 5 4 5 4 5 5
16 18 4 4 4 4 4 3
14 19 4 4 5 3 4 4
15 20 3 4 4 3 4 5
18 19 4 4 4 4 4 4
20 22 4 4 4 4 5 4
17 21 4 5 3 4 4 4
17 22 3 4 4 4 4 4
17 24 4 4 4 3 4 4
17 28 4 4 4 4 4 5
15 19 3 4 3 3 4 4
17 18 4 4 4 3 4 3
18 23 3 2 4 2 4 4
17 19 4 4 4 3 5 4
20 23 5 4 4 3 5 4
15 19 2 4 4 3 3 5
16 22 3 3 4 4 4 4
15 21 4 4 4 3 4 4
18 19 5 5 4 4 5 4
15 21 4 5 5 4 4 4
18 23 5 5 5 5 5 4
20 22 4 5 5 4 5 5
19 19 4 4 4 3 4 5
14 19 3 4 5 4 5 4
16 21 4 4 5 4 4 4
15 22 4 4 2 4 4 4
17 21 4 4 3 4 5 5
18 20 4 4 4 4 5 5
20 23 5 4 5 3 5 4
17 22 4 3 5 4 4 4
18 23 4 4 5 4 4 4
15 22 3 3 2 3 4 4
16 21 4 5 5 4 4 3
11 20 4 4 4 3 4 4
15 18 4 4 4 4 4 5
18 18 3 4 5 3 5 5
17 20 4 4 5 4 4 5
16 19 5 4 5 4 5 4
12 21 4 4 5 4 3 4
19 24 2 3 5 4 4 4
18 19 4 4 4 4 4 5
15 20 4 3 4 3 5 5
17 19 4 4 4 4 4 3
19 23 4 5 5 5 4 4
18 22 5 4 3 4 4 4
19 21 5 4 4 3 4 4
16 24 3 3 1 4 5 5
16 21 4 4 4 4 4 5
16 21 4 4 4 4 5 4
14 22 2 3 4 5 5 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

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

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 7.66755 + 0.0269725Bevr_Leeftijd[t] + 0.306787SKEOU1[t] -0.0238402SKEOU2[t] + 0.435938SKEOU3[t] + 0.423284SKEOU4[t] + 0.495697SKEOU5[t] + 0.39003SKEOU6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  7.66755 +  0.0269725Bevr_Leeftijd[t] +  0.306787SKEOU1[t] -0.0238402SKEOU2[t] +  0.435938SKEOU3[t] +  0.423284SKEOU4[t] +  0.495697SKEOU5[t] +  0.39003SKEOU6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  7.66755 +  0.0269725Bevr_Leeftijd[t] +  0.306787SKEOU1[t] -0.0238402SKEOU2[t] +  0.435938SKEOU3[t] +  0.423284SKEOU4[t] +  0.495697SKEOU5[t] +  0.39003SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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] = + 7.66755 + 0.0269725Bevr_Leeftijd[t] + 0.306787SKEOU1[t] -0.0238402SKEOU2[t] + 0.435938SKEOU3[t] + 0.423284SKEOU4[t] + 0.495697SKEOU5[t] + 0.39003SKEOU6[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)	+7.668	3.171	+2.4180e+00	0.01679	0.008397
Bevr_Leeftijd	+0.02697	0.1051	+2.5660e-01	0.7979	0.3989
SKEOU1	+0.3068	0.2553	+1.2020e+00	0.2314	0.1157
SKEOU2	-0.02384	0.3208	-7.4320e-02	0.9409	0.4704
SKEOU3	+0.4359	0.2301	+1.8950e+00	0.06005	0.03003
SKEOU4	+0.4233	0.319	+1.3270e+00	0.1866	0.09329
SKEOU5	+0.4957	0.2954	+1.6780e+00	0.09538	0.04769
SKEOU6	+0.39	0.3057	+1.2760e+00	0.2039	0.102

\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) & +7.668 &  3.171 & +2.4180e+00 &  0.01679 &  0.008397 \tabularnewline
Bevr_Leeftijd & +0.02697 &  0.1051 & +2.5660e-01 &  0.7979 &  0.3989 \tabularnewline
SKEOU1 & +0.3068 &  0.2553 & +1.2020e+00 &  0.2314 &  0.1157 \tabularnewline
SKEOU2 & -0.02384 &  0.3208 & -7.4320e-02 &  0.9409 &  0.4704 \tabularnewline
SKEOU3 & +0.4359 &  0.2301 & +1.8950e+00 &  0.06005 &  0.03003 \tabularnewline
SKEOU4 & +0.4233 &  0.319 & +1.3270e+00 &  0.1866 &  0.09329 \tabularnewline
SKEOU5 & +0.4957 &  0.2954 & +1.6780e+00 &  0.09538 &  0.04769 \tabularnewline
SKEOU6 & +0.39 &  0.3057 & +1.2760e+00 &  0.2039 &  0.102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]+7.668[/C][C] 3.171[/C][C]+2.4180e+00[/C][C] 0.01679[/C][C] 0.008397[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.02697[/C][C] 0.1051[/C][C]+2.5660e-01[/C][C] 0.7979[/C][C] 0.3989[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.3068[/C][C] 0.2553[/C][C]+1.2020e+00[/C][C] 0.2314[/C][C] 0.1157[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.02384[/C][C] 0.3208[/C][C]-7.4320e-02[/C][C] 0.9409[/C][C] 0.4704[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+0.4359[/C][C] 0.2301[/C][C]+1.8950e+00[/C][C] 0.06005[/C][C] 0.03003[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.4233[/C][C] 0.319[/C][C]+1.3270e+00[/C][C] 0.1866[/C][C] 0.09329[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.4957[/C][C] 0.2954[/C][C]+1.6780e+00[/C][C] 0.09538[/C][C] 0.04769[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.39[/C][C] 0.3057[/C][C]+1.2760e+00[/C][C] 0.2039[/C][C] 0.102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	+7.668	3.171	+2.4180e+00	0.01679	0.008397
Bevr_Leeftijd	+0.02697	0.1051	+2.5660e-01	0.7979	0.3989
SKEOU1	+0.3068	0.2553	+1.2020e+00	0.2314	0.1157
SKEOU2	-0.02384	0.3208	-7.4320e-02	0.9409	0.4704
SKEOU3	+0.4359	0.2301	+1.8950e+00	0.06005	0.03003
SKEOU4	+0.4233	0.319	+1.3270e+00	0.1866	0.09329
SKEOU5	+0.4957	0.2954	+1.6780e+00	0.09538	0.04769
SKEOU6	+0.39	0.3057	+1.2760e+00	0.2039	0.102

Multiple Linear Regression - Regression Statistics
Multiple R	0.2904
R-squared	0.08435
Adjusted R-squared	0.04162
F-TEST (value)	1.974
F-TEST (DF numerator)	7
F-TEST (DF denominator)	150
p-value	0.06218
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.243
Sum Squared Residuals	754.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2904 \tabularnewline
R-squared &  0.08435 \tabularnewline
Adjusted R-squared &  0.04162 \tabularnewline
F-TEST (value) &  1.974 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value &  0.06218 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.243 \tabularnewline
Sum Squared Residuals &  754.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2904[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.08435[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04162[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.974[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C] 0.06218[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.243[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 754.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.2904
R-squared	0.08435
Adjusted R-squared	0.04162
F-TEST (value)	1.974
F-TEST (DF numerator)	7
F-TEST (DF denominator)	150
p-value	0.06218
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.243
Sum Squared Residuals	754.4

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.49	-2.493
2	19	16.82	2.183
3	17	17.28	-0.2772
4	17	15.18	1.82
5	15	17.36	-2.358
6	20	16.9	3.102
7	15	15.15	-0.1468
8	19	16.45	2.552
9	15	17.15	-2.154
10	15	17.7	-2.697
11	19	15.97	3.031
12	20	16.26	3.742
13	18	16.89	1.111
14	15	16.18	-1.18
15	14	16.89	-2.892
16	20	16.89	3.108
17	16	16.21	-0.2052
18	16	16.84	-0.8413
19	16	16.44	-0.4415
20	10	17.2	-7.204
21	19	17.2	1.802
22	19	16.81	2.192
23	16	17.25	-1.252
24	15	16.04	-1.039
25	18	16.45	1.553
26	17	16.29	0.7084
27	19	17.11	1.892
28	17	16.65	0.3477
29	19	17.59	1.411
30	20	17.48	2.522
31	19	16.69	2.313
32	16	15.45	0.5502
33	15	16.53	-1.529
34	16	16.72	-0.7181
35	18	15.88	2.12
36	16	16.73	-0.7276
37	15	16.57	-1.571
38	17	17.67	-0.6704
39	20	17.55	2.446
40	19	16.58	2.416
41	7	16.34	-9.339
42	13	16.37	-3.373
43	16	15.48	0.5221
44	16	16.87	-0.8682
45	18	16.7	1.296
46	18	16.2	1.802
47	16	17.01	-1.01
48	17	17.64	-0.6371
49	19	16.34	2.664
50	16	16.78	-0.7815
51	19	15.09	3.907
52	13	15.59	-2.595
53	16	16.36	-0.364
54	13	16.87	-3.868
55	12	17.21	-5.213
56	17	16.56	0.4362
57	17	17.23	-0.2313
58	17	15.05	1.953
59	16	17.64	-1.64
60	16	16.45	-0.4472
61	14	16.05	-2.055
62	16	16.44	-0.4449
63	13	16.59	-3.586
64	16	16.5	-0.5012
65	14	16.01	-2.006
66	20	17.7	2.296
67	13	16.26	-3.265
68	18	17.35	0.6545
69	14	15.63	-1.63
70	19	16.84	2.159
71	18	16.94	1.063
72	14	16.89	-2.889
73	18	16.86	1.141
74	19	17.17	1.828
75	15	15.68	-0.6806
76	14	16.84	-2.841
77	17	16.74	0.2586
78	19	17.23	1.769
79	13	16.65	-3.652
80	19	17.03	1.97
81	18	17.72	0.2788
82	20	16.92	3.081
83	15	15.51	-0.507
84	15	16.34	-1.338
85	20	17.23	2.775
86	15	16.87	-1.868
87	19	16.37	2.627
88	18	16.56	1.445
89	18	17.2	0.8015
90	15	16.63	-1.633
91	20	17.9	2.098
92	17	17.21	-0.2074
93	12	16.86	-4.865
94	18	17.56	0.4381
95	19	17.2	1.802
96	20	17.15	2.85
97	17	18.02	-1.015
98	16	15.12	0.8767
99	18	16.75	1.245
100	18	16.78	1.218
101	14	16.69	-2.688
102	15	16.35	-1.346
103	12	16.26	-4.258
104	17	16.58	0.421
105	14	15.95	-1.949
106	18	16.47	1.525
107	17	16.37	0.6333
108	17	16.7	0.2958
109	20	17.92	2.08
110	16	15.87	0.1254
111	14	16.3	-2.304
112	15	15.98	-0.9785
113	18	16.29	1.708
114	20	16.87	3.132
115	17	15.89	1.114
116	17	16.07	0.9343
117	17	16	0.9968
118	17	16.92	0.0756
119	15	15.13	-0.1256
120	17	15.45	1.549
121	18	15.29	2.706
122	17	16.36	0.636
123	20	16.78	3.221
124	15	15.15	-0.1491
125	16	16.09	-0.08958
126	15	15.92	-0.9223
127	18	17.07	0.9297
128	15	16.76	-1.758
129	18	18.04	-0.03737
130	20	17.67	2.33
131	19	16.26	2.742
132	14	16.92	-2.916
133	16	16.78	-0.7815
134	15	15.5	-0.5007
135	17	16.8	0.2047
136	18	17.2	0.7957
137	20	17.21	2.785
138	17	16.83	0.1677
139	18	16.84	1.165
140	15	14.79	0.2056
141	16	16.37	-0.3676
142	11	15.9	-4.895
143	15	16.65	-1.655
144	18	16.86	1.144
145	17	17.14	-0.1446
146	16	17.53	-1.53
147	12	16.29	-4.286
148	19	16.27	2.727
149	18	16.68	1.318
150	15	16.8	-1.805
151	17	15.9	1.098
152	19	17.23	1.765
153	18	16.24	1.757
154	19	16.23	2.771
155	16	15.72	0.2786
156	16	16.74	-0.7356
157	16	16.84	-0.8413
158	14	16.7	-2.702

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.49 & -2.493 \tabularnewline
2 &  19 &  16.82 &  2.183 \tabularnewline
3 &  17 &  17.28 & -0.2772 \tabularnewline
4 &  17 &  15.18 &  1.82 \tabularnewline
5 &  15 &  17.36 & -2.358 \tabularnewline
6 &  20 &  16.9 &  3.102 \tabularnewline
7 &  15 &  15.15 & -0.1468 \tabularnewline
8 &  19 &  16.45 &  2.552 \tabularnewline
9 &  15 &  17.15 & -2.154 \tabularnewline
10 &  15 &  17.7 & -2.697 \tabularnewline
11 &  19 &  15.97 &  3.031 \tabularnewline
12 &  20 &  16.26 &  3.742 \tabularnewline
13 &  18 &  16.89 &  1.111 \tabularnewline
14 &  15 &  16.18 & -1.18 \tabularnewline
15 &  14 &  16.89 & -2.892 \tabularnewline
16 &  20 &  16.89 &  3.108 \tabularnewline
17 &  16 &  16.21 & -0.2052 \tabularnewline
18 &  16 &  16.84 & -0.8413 \tabularnewline
19 &  16 &  16.44 & -0.4415 \tabularnewline
20 &  10 &  17.2 & -7.204 \tabularnewline
21 &  19 &  17.2 &  1.802 \tabularnewline
22 &  19 &  16.81 &  2.192 \tabularnewline
23 &  16 &  17.25 & -1.252 \tabularnewline
24 &  15 &  16.04 & -1.039 \tabularnewline
25 &  18 &  16.45 &  1.553 \tabularnewline
26 &  17 &  16.29 &  0.7084 \tabularnewline
27 &  19 &  17.11 &  1.892 \tabularnewline
28 &  17 &  16.65 &  0.3477 \tabularnewline
29 &  19 &  17.59 &  1.411 \tabularnewline
30 &  20 &  17.48 &  2.522 \tabularnewline
31 &  19 &  16.69 &  2.313 \tabularnewline
32 &  16 &  15.45 &  0.5502 \tabularnewline
33 &  15 &  16.53 & -1.529 \tabularnewline
34 &  16 &  16.72 & -0.7181 \tabularnewline
35 &  18 &  15.88 &  2.12 \tabularnewline
36 &  16 &  16.73 & -0.7276 \tabularnewline
37 &  15 &  16.57 & -1.571 \tabularnewline
38 &  17 &  17.67 & -0.6704 \tabularnewline
39 &  20 &  17.55 &  2.446 \tabularnewline
40 &  19 &  16.58 &  2.416 \tabularnewline
41 &  7 &  16.34 & -9.339 \tabularnewline
42 &  13 &  16.37 & -3.373 \tabularnewline
43 &  16 &  15.48 &  0.5221 \tabularnewline
44 &  16 &  16.87 & -0.8682 \tabularnewline
45 &  18 &  16.7 &  1.296 \tabularnewline
46 &  18 &  16.2 &  1.802 \tabularnewline
47 &  16 &  17.01 & -1.01 \tabularnewline
48 &  17 &  17.64 & -0.6371 \tabularnewline
49 &  19 &  16.34 &  2.664 \tabularnewline
50 &  16 &  16.78 & -0.7815 \tabularnewline
51 &  19 &  15.09 &  3.907 \tabularnewline
52 &  13 &  15.59 & -2.595 \tabularnewline
53 &  16 &  16.36 & -0.364 \tabularnewline
54 &  13 &  16.87 & -3.868 \tabularnewline
55 &  12 &  17.21 & -5.213 \tabularnewline
56 &  17 &  16.56 &  0.4362 \tabularnewline
57 &  17 &  17.23 & -0.2313 \tabularnewline
58 &  17 &  15.05 &  1.953 \tabularnewline
59 &  16 &  17.64 & -1.64 \tabularnewline
60 &  16 &  16.45 & -0.4472 \tabularnewline
61 &  14 &  16.05 & -2.055 \tabularnewline
62 &  16 &  16.44 & -0.4449 \tabularnewline
63 &  13 &  16.59 & -3.586 \tabularnewline
64 &  16 &  16.5 & -0.5012 \tabularnewline
65 &  14 &  16.01 & -2.006 \tabularnewline
66 &  20 &  17.7 &  2.296 \tabularnewline
67 &  13 &  16.26 & -3.265 \tabularnewline
68 &  18 &  17.35 &  0.6545 \tabularnewline
69 &  14 &  15.63 & -1.63 \tabularnewline
70 &  19 &  16.84 &  2.159 \tabularnewline
71 &  18 &  16.94 &  1.063 \tabularnewline
72 &  14 &  16.89 & -2.889 \tabularnewline
73 &  18 &  16.86 &  1.141 \tabularnewline
74 &  19 &  17.17 &  1.828 \tabularnewline
75 &  15 &  15.68 & -0.6806 \tabularnewline
76 &  14 &  16.84 & -2.841 \tabularnewline
77 &  17 &  16.74 &  0.2586 \tabularnewline
78 &  19 &  17.23 &  1.769 \tabularnewline
79 &  13 &  16.65 & -3.652 \tabularnewline
80 &  19 &  17.03 &  1.97 \tabularnewline
81 &  18 &  17.72 &  0.2788 \tabularnewline
82 &  20 &  16.92 &  3.081 \tabularnewline
83 &  15 &  15.51 & -0.507 \tabularnewline
84 &  15 &  16.34 & -1.338 \tabularnewline
85 &  20 &  17.23 &  2.775 \tabularnewline
86 &  15 &  16.87 & -1.868 \tabularnewline
87 &  19 &  16.37 &  2.627 \tabularnewline
88 &  18 &  16.56 &  1.445 \tabularnewline
89 &  18 &  17.2 &  0.8015 \tabularnewline
90 &  15 &  16.63 & -1.633 \tabularnewline
91 &  20 &  17.9 &  2.098 \tabularnewline
92 &  17 &  17.21 & -0.2074 \tabularnewline
93 &  12 &  16.86 & -4.865 \tabularnewline
94 &  18 &  17.56 &  0.4381 \tabularnewline
95 &  19 &  17.2 &  1.802 \tabularnewline
96 &  20 &  17.15 &  2.85 \tabularnewline
97 &  17 &  18.02 & -1.015 \tabularnewline
98 &  16 &  15.12 &  0.8767 \tabularnewline
99 &  18 &  16.75 &  1.245 \tabularnewline
100 &  18 &  16.78 &  1.218 \tabularnewline
101 &  14 &  16.69 & -2.688 \tabularnewline
102 &  15 &  16.35 & -1.346 \tabularnewline
103 &  12 &  16.26 & -4.258 \tabularnewline
104 &  17 &  16.58 &  0.421 \tabularnewline
105 &  14 &  15.95 & -1.949 \tabularnewline
106 &  18 &  16.47 &  1.525 \tabularnewline
107 &  17 &  16.37 &  0.6333 \tabularnewline
108 &  17 &  16.7 &  0.2958 \tabularnewline
109 &  20 &  17.92 &  2.08 \tabularnewline
110 &  16 &  15.87 &  0.1254 \tabularnewline
111 &  14 &  16.3 & -2.304 \tabularnewline
112 &  15 &  15.98 & -0.9785 \tabularnewline
113 &  18 &  16.29 &  1.708 \tabularnewline
114 &  20 &  16.87 &  3.132 \tabularnewline
115 &  17 &  15.89 &  1.114 \tabularnewline
116 &  17 &  16.07 &  0.9343 \tabularnewline
117 &  17 &  16 &  0.9968 \tabularnewline
118 &  17 &  16.92 &  0.0756 \tabularnewline
119 &  15 &  15.13 & -0.1256 \tabularnewline
120 &  17 &  15.45 &  1.549 \tabularnewline
121 &  18 &  15.29 &  2.706 \tabularnewline
122 &  17 &  16.36 &  0.636 \tabularnewline
123 &  20 &  16.78 &  3.221 \tabularnewline
124 &  15 &  15.15 & -0.1491 \tabularnewline
125 &  16 &  16.09 & -0.08958 \tabularnewline
126 &  15 &  15.92 & -0.9223 \tabularnewline
127 &  18 &  17.07 &  0.9297 \tabularnewline
128 &  15 &  16.76 & -1.758 \tabularnewline
129 &  18 &  18.04 & -0.03737 \tabularnewline
130 &  20 &  17.67 &  2.33 \tabularnewline
131 &  19 &  16.26 &  2.742 \tabularnewline
132 &  14 &  16.92 & -2.916 \tabularnewline
133 &  16 &  16.78 & -0.7815 \tabularnewline
134 &  15 &  15.5 & -0.5007 \tabularnewline
135 &  17 &  16.8 &  0.2047 \tabularnewline
136 &  18 &  17.2 &  0.7957 \tabularnewline
137 &  20 &  17.21 &  2.785 \tabularnewline
138 &  17 &  16.83 &  0.1677 \tabularnewline
139 &  18 &  16.84 &  1.165 \tabularnewline
140 &  15 &  14.79 &  0.2056 \tabularnewline
141 &  16 &  16.37 & -0.3676 \tabularnewline
142 &  11 &  15.9 & -4.895 \tabularnewline
143 &  15 &  16.65 & -1.655 \tabularnewline
144 &  18 &  16.86 &  1.144 \tabularnewline
145 &  17 &  17.14 & -0.1446 \tabularnewline
146 &  16 &  17.53 & -1.53 \tabularnewline
147 &  12 &  16.29 & -4.286 \tabularnewline
148 &  19 &  16.27 &  2.727 \tabularnewline
149 &  18 &  16.68 &  1.318 \tabularnewline
150 &  15 &  16.8 & -1.805 \tabularnewline
151 &  17 &  15.9 &  1.098 \tabularnewline
152 &  19 &  17.23 &  1.765 \tabularnewline
153 &  18 &  16.24 &  1.757 \tabularnewline
154 &  19 &  16.23 &  2.771 \tabularnewline
155 &  16 &  15.72 &  0.2786 \tabularnewline
156 &  16 &  16.74 & -0.7356 \tabularnewline
157 &  16 &  16.84 & -0.8413 \tabularnewline
158 &  14 &  16.7 & -2.702 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 16.49[/C][C]-2.493[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 16.82[/C][C] 2.183[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.28[/C][C]-0.2772[/C][/ROW]
[ROW][C]4[/C][C] 17[/C][C] 15.18[/C][C] 1.82[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 17.36[/C][C]-2.358[/C][/ROW]
[ROW][C]6[/C][C] 20[/C][C] 16.9[/C][C] 3.102[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.15[/C][C]-0.1468[/C][/ROW]
[ROW][C]8[/C][C] 19[/C][C] 16.45[/C][C] 2.552[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 17.15[/C][C]-2.154[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 17.7[/C][C]-2.697[/C][/ROW]
[ROW][C]11[/C][C] 19[/C][C] 15.97[/C][C] 3.031[/C][/ROW]
[ROW][C]12[/C][C] 20[/C][C] 16.26[/C][C] 3.742[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.89[/C][C] 1.111[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 16.18[/C][C]-1.18[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 16.89[/C][C]-2.892[/C][/ROW]
[ROW][C]16[/C][C] 20[/C][C] 16.89[/C][C] 3.108[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.21[/C][C]-0.2052[/C][/ROW]
[ROW][C]18[/C][C] 16[/C][C] 16.84[/C][C]-0.8413[/C][/ROW]
[ROW][C]19[/C][C] 16[/C][C] 16.44[/C][C]-0.4415[/C][/ROW]
[ROW][C]20[/C][C] 10[/C][C] 17.2[/C][C]-7.204[/C][/ROW]
[ROW][C]21[/C][C] 19[/C][C] 17.2[/C][C] 1.802[/C][/ROW]
[ROW][C]22[/C][C] 19[/C][C] 16.81[/C][C] 2.192[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 17.25[/C][C]-1.252[/C][/ROW]
[ROW][C]24[/C][C] 15[/C][C] 16.04[/C][C]-1.039[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 16.45[/C][C] 1.553[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.29[/C][C] 0.7084[/C][/ROW]
[ROW][C]27[/C][C] 19[/C][C] 17.11[/C][C] 1.892[/C][/ROW]
[ROW][C]28[/C][C] 17[/C][C] 16.65[/C][C] 0.3477[/C][/ROW]
[ROW][C]29[/C][C] 19[/C][C] 17.59[/C][C] 1.411[/C][/ROW]
[ROW][C]30[/C][C] 20[/C][C] 17.48[/C][C] 2.522[/C][/ROW]
[ROW][C]31[/C][C] 19[/C][C] 16.69[/C][C] 2.313[/C][/ROW]
[ROW][C]32[/C][C] 16[/C][C] 15.45[/C][C] 0.5502[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 16.53[/C][C]-1.529[/C][/ROW]
[ROW][C]34[/C][C] 16[/C][C] 16.72[/C][C]-0.7181[/C][/ROW]
[ROW][C]35[/C][C] 18[/C][C] 15.88[/C][C] 2.12[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 16.73[/C][C]-0.7276[/C][/ROW]
[ROW][C]37[/C][C] 15[/C][C] 16.57[/C][C]-1.571[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.67[/C][C]-0.6704[/C][/ROW]
[ROW][C]39[/C][C] 20[/C][C] 17.55[/C][C] 2.446[/C][/ROW]
[ROW][C]40[/C][C] 19[/C][C] 16.58[/C][C] 2.416[/C][/ROW]
[ROW][C]41[/C][C] 7[/C][C] 16.34[/C][C]-9.339[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 16.37[/C][C]-3.373[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 15.48[/C][C] 0.5221[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 16.87[/C][C]-0.8682[/C][/ROW]
[ROW][C]45[/C][C] 18[/C][C] 16.7[/C][C] 1.296[/C][/ROW]
[ROW][C]46[/C][C] 18[/C][C] 16.2[/C][C] 1.802[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 17.01[/C][C]-1.01[/C][/ROW]
[ROW][C]48[/C][C] 17[/C][C] 17.64[/C][C]-0.6371[/C][/ROW]
[ROW][C]49[/C][C] 19[/C][C] 16.34[/C][C] 2.664[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 16.78[/C][C]-0.7815[/C][/ROW]
[ROW][C]51[/C][C] 19[/C][C] 15.09[/C][C] 3.907[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 15.59[/C][C]-2.595[/C][/ROW]
[ROW][C]53[/C][C] 16[/C][C] 16.36[/C][C]-0.364[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 16.87[/C][C]-3.868[/C][/ROW]
[ROW][C]55[/C][C] 12[/C][C] 17.21[/C][C]-5.213[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 16.56[/C][C] 0.4362[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 17.23[/C][C]-0.2313[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 15.05[/C][C] 1.953[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 17.64[/C][C]-1.64[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 16.45[/C][C]-0.4472[/C][/ROW]
[ROW][C]61[/C][C] 14[/C][C] 16.05[/C][C]-2.055[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.44[/C][C]-0.4449[/C][/ROW]
[ROW][C]63[/C][C] 13[/C][C] 16.59[/C][C]-3.586[/C][/ROW]
[ROW][C]64[/C][C] 16[/C][C] 16.5[/C][C]-0.5012[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 16.01[/C][C]-2.006[/C][/ROW]
[ROW][C]66[/C][C] 20[/C][C] 17.7[/C][C] 2.296[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.26[/C][C]-3.265[/C][/ROW]
[ROW][C]68[/C][C] 18[/C][C] 17.35[/C][C] 0.6545[/C][/ROW]
[ROW][C]69[/C][C] 14[/C][C] 15.63[/C][C]-1.63[/C][/ROW]
[ROW][C]70[/C][C] 19[/C][C] 16.84[/C][C] 2.159[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 16.94[/C][C] 1.063[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 16.89[/C][C]-2.889[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 16.86[/C][C] 1.141[/C][/ROW]
[ROW][C]74[/C][C] 19[/C][C] 17.17[/C][C] 1.828[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 15.68[/C][C]-0.6806[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 16.84[/C][C]-2.841[/C][/ROW]
[ROW][C]77[/C][C] 17[/C][C] 16.74[/C][C] 0.2586[/C][/ROW]
[ROW][C]78[/C][C] 19[/C][C] 17.23[/C][C] 1.769[/C][/ROW]
[ROW][C]79[/C][C] 13[/C][C] 16.65[/C][C]-3.652[/C][/ROW]
[ROW][C]80[/C][C] 19[/C][C] 17.03[/C][C] 1.97[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 17.72[/C][C] 0.2788[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 16.92[/C][C] 3.081[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 15.51[/C][C]-0.507[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 16.34[/C][C]-1.338[/C][/ROW]
[ROW][C]85[/C][C] 20[/C][C] 17.23[/C][C] 2.775[/C][/ROW]
[ROW][C]86[/C][C] 15[/C][C] 16.87[/C][C]-1.868[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 16.37[/C][C] 2.627[/C][/ROW]
[ROW][C]88[/C][C] 18[/C][C] 16.56[/C][C] 1.445[/C][/ROW]
[ROW][C]89[/C][C] 18[/C][C] 17.2[/C][C] 0.8015[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 16.63[/C][C]-1.633[/C][/ROW]
[ROW][C]91[/C][C] 20[/C][C] 17.9[/C][C] 2.098[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 17.21[/C][C]-0.2074[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 16.86[/C][C]-4.865[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 17.56[/C][C] 0.4381[/C][/ROW]
[ROW][C]95[/C][C] 19[/C][C] 17.2[/C][C] 1.802[/C][/ROW]
[ROW][C]96[/C][C] 20[/C][C] 17.15[/C][C] 2.85[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 18.02[/C][C]-1.015[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.12[/C][C] 0.8767[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 16.75[/C][C] 1.245[/C][/ROW]
[ROW][C]100[/C][C] 18[/C][C] 16.78[/C][C] 1.218[/C][/ROW]
[ROW][C]101[/C][C] 14[/C][C] 16.69[/C][C]-2.688[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 16.35[/C][C]-1.346[/C][/ROW]
[ROW][C]103[/C][C] 12[/C][C] 16.26[/C][C]-4.258[/C][/ROW]
[ROW][C]104[/C][C] 17[/C][C] 16.58[/C][C] 0.421[/C][/ROW]
[ROW][C]105[/C][C] 14[/C][C] 15.95[/C][C]-1.949[/C][/ROW]
[ROW][C]106[/C][C] 18[/C][C] 16.47[/C][C] 1.525[/C][/ROW]
[ROW][C]107[/C][C] 17[/C][C] 16.37[/C][C] 0.6333[/C][/ROW]
[ROW][C]108[/C][C] 17[/C][C] 16.7[/C][C] 0.2958[/C][/ROW]
[ROW][C]109[/C][C] 20[/C][C] 17.92[/C][C] 2.08[/C][/ROW]
[ROW][C]110[/C][C] 16[/C][C] 15.87[/C][C] 0.1254[/C][/ROW]
[ROW][C]111[/C][C] 14[/C][C] 16.3[/C][C]-2.304[/C][/ROW]
[ROW][C]112[/C][C] 15[/C][C] 15.98[/C][C]-0.9785[/C][/ROW]
[ROW][C]113[/C][C] 18[/C][C] 16.29[/C][C] 1.708[/C][/ROW]
[ROW][C]114[/C][C] 20[/C][C] 16.87[/C][C] 3.132[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 15.89[/C][C] 1.114[/C][/ROW]
[ROW][C]116[/C][C] 17[/C][C] 16.07[/C][C] 0.9343[/C][/ROW]
[ROW][C]117[/C][C] 17[/C][C] 16[/C][C] 0.9968[/C][/ROW]
[ROW][C]118[/C][C] 17[/C][C] 16.92[/C][C] 0.0756[/C][/ROW]
[ROW][C]119[/C][C] 15[/C][C] 15.13[/C][C]-0.1256[/C][/ROW]
[ROW][C]120[/C][C] 17[/C][C] 15.45[/C][C] 1.549[/C][/ROW]
[ROW][C]121[/C][C] 18[/C][C] 15.29[/C][C] 2.706[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.36[/C][C] 0.636[/C][/ROW]
[ROW][C]123[/C][C] 20[/C][C] 16.78[/C][C] 3.221[/C][/ROW]
[ROW][C]124[/C][C] 15[/C][C] 15.15[/C][C]-0.1491[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 16.09[/C][C]-0.08958[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 15.92[/C][C]-0.9223[/C][/ROW]
[ROW][C]127[/C][C] 18[/C][C] 17.07[/C][C] 0.9297[/C][/ROW]
[ROW][C]128[/C][C] 15[/C][C] 16.76[/C][C]-1.758[/C][/ROW]
[ROW][C]129[/C][C] 18[/C][C] 18.04[/C][C]-0.03737[/C][/ROW]
[ROW][C]130[/C][C] 20[/C][C] 17.67[/C][C] 2.33[/C][/ROW]
[ROW][C]131[/C][C] 19[/C][C] 16.26[/C][C] 2.742[/C][/ROW]
[ROW][C]132[/C][C] 14[/C][C] 16.92[/C][C]-2.916[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 16.78[/C][C]-0.7815[/C][/ROW]
[ROW][C]134[/C][C] 15[/C][C] 15.5[/C][C]-0.5007[/C][/ROW]
[ROW][C]135[/C][C] 17[/C][C] 16.8[/C][C] 0.2047[/C][/ROW]
[ROW][C]136[/C][C] 18[/C][C] 17.2[/C][C] 0.7957[/C][/ROW]
[ROW][C]137[/C][C] 20[/C][C] 17.21[/C][C] 2.785[/C][/ROW]
[ROW][C]138[/C][C] 17[/C][C] 16.83[/C][C] 0.1677[/C][/ROW]
[ROW][C]139[/C][C] 18[/C][C] 16.84[/C][C] 1.165[/C][/ROW]
[ROW][C]140[/C][C] 15[/C][C] 14.79[/C][C] 0.2056[/C][/ROW]
[ROW][C]141[/C][C] 16[/C][C] 16.37[/C][C]-0.3676[/C][/ROW]
[ROW][C]142[/C][C] 11[/C][C] 15.9[/C][C]-4.895[/C][/ROW]
[ROW][C]143[/C][C] 15[/C][C] 16.65[/C][C]-1.655[/C][/ROW]
[ROW][C]144[/C][C] 18[/C][C] 16.86[/C][C] 1.144[/C][/ROW]
[ROW][C]145[/C][C] 17[/C][C] 17.14[/C][C]-0.1446[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 17.53[/C][C]-1.53[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 16.29[/C][C]-4.286[/C][/ROW]
[ROW][C]148[/C][C] 19[/C][C] 16.27[/C][C] 2.727[/C][/ROW]
[ROW][C]149[/C][C] 18[/C][C] 16.68[/C][C] 1.318[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 16.8[/C][C]-1.805[/C][/ROW]
[ROW][C]151[/C][C] 17[/C][C] 15.9[/C][C] 1.098[/C][/ROW]
[ROW][C]152[/C][C] 19[/C][C] 17.23[/C][C] 1.765[/C][/ROW]
[ROW][C]153[/C][C] 18[/C][C] 16.24[/C][C] 1.757[/C][/ROW]
[ROW][C]154[/C][C] 19[/C][C] 16.23[/C][C] 2.771[/C][/ROW]
[ROW][C]155[/C][C] 16[/C][C] 15.72[/C][C] 0.2786[/C][/ROW]
[ROW][C]156[/C][C] 16[/C][C] 16.74[/C][C]-0.7356[/C][/ROW]
[ROW][C]157[/C][C] 16[/C][C] 16.84[/C][C]-0.8413[/C][/ROW]
[ROW][C]158[/C][C] 14[/C][C] 16.7[/C][C]-2.702[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	16.49	-2.493
2	19	16.82	2.183
3	17	17.28	-0.2772
4	17	15.18	1.82
5	15	17.36	-2.358
6	20	16.9	3.102
7	15	15.15	-0.1468
8	19	16.45	2.552
9	15	17.15	-2.154
10	15	17.7	-2.697
11	19	15.97	3.031
12	20	16.26	3.742
13	18	16.89	1.111
14	15	16.18	-1.18
15	14	16.89	-2.892
16	20	16.89	3.108
17	16	16.21	-0.2052
18	16	16.84	-0.8413
19	16	16.44	-0.4415
20	10	17.2	-7.204
21	19	17.2	1.802
22	19	16.81	2.192
23	16	17.25	-1.252
24	15	16.04	-1.039
25	18	16.45	1.553
26	17	16.29	0.7084
27	19	17.11	1.892
28	17	16.65	0.3477
29	19	17.59	1.411
30	20	17.48	2.522
31	19	16.69	2.313
32	16	15.45	0.5502
33	15	16.53	-1.529
34	16	16.72	-0.7181
35	18	15.88	2.12
36	16	16.73	-0.7276
37	15	16.57	-1.571
38	17	17.67	-0.6704
39	20	17.55	2.446
40	19	16.58	2.416
41	7	16.34	-9.339
42	13	16.37	-3.373
43	16	15.48	0.5221
44	16	16.87	-0.8682
45	18	16.7	1.296
46	18	16.2	1.802
47	16	17.01	-1.01
48	17	17.64	-0.6371
49	19	16.34	2.664
50	16	16.78	-0.7815
51	19	15.09	3.907
52	13	15.59	-2.595
53	16	16.36	-0.364
54	13	16.87	-3.868
55	12	17.21	-5.213
56	17	16.56	0.4362
57	17	17.23	-0.2313
58	17	15.05	1.953
59	16	17.64	-1.64
60	16	16.45	-0.4472
61	14	16.05	-2.055
62	16	16.44	-0.4449
63	13	16.59	-3.586
64	16	16.5	-0.5012
65	14	16.01	-2.006
66	20	17.7	2.296
67	13	16.26	-3.265
68	18	17.35	0.6545
69	14	15.63	-1.63
70	19	16.84	2.159
71	18	16.94	1.063
72	14	16.89	-2.889
73	18	16.86	1.141
74	19	17.17	1.828
75	15	15.68	-0.6806
76	14	16.84	-2.841
77	17	16.74	0.2586
78	19	17.23	1.769
79	13	16.65	-3.652
80	19	17.03	1.97
81	18	17.72	0.2788
82	20	16.92	3.081
83	15	15.51	-0.507
84	15	16.34	-1.338
85	20	17.23	2.775
86	15	16.87	-1.868
87	19	16.37	2.627
88	18	16.56	1.445
89	18	17.2	0.8015
90	15	16.63	-1.633
91	20	17.9	2.098
92	17	17.21	-0.2074
93	12	16.86	-4.865
94	18	17.56	0.4381
95	19	17.2	1.802
96	20	17.15	2.85
97	17	18.02	-1.015
98	16	15.12	0.8767
99	18	16.75	1.245
100	18	16.78	1.218
101	14	16.69	-2.688
102	15	16.35	-1.346
103	12	16.26	-4.258
104	17	16.58	0.421
105	14	15.95	-1.949
106	18	16.47	1.525
107	17	16.37	0.6333
108	17	16.7	0.2958
109	20	17.92	2.08
110	16	15.87	0.1254
111	14	16.3	-2.304
112	15	15.98	-0.9785
113	18	16.29	1.708
114	20	16.87	3.132
115	17	15.89	1.114
116	17	16.07	0.9343
117	17	16	0.9968
118	17	16.92	0.0756
119	15	15.13	-0.1256
120	17	15.45	1.549
121	18	15.29	2.706
122	17	16.36	0.636
123	20	16.78	3.221
124	15	15.15	-0.1491
125	16	16.09	-0.08958
126	15	15.92	-0.9223
127	18	17.07	0.9297
128	15	16.76	-1.758
129	18	18.04	-0.03737
130	20	17.67	2.33
131	19	16.26	2.742
132	14	16.92	-2.916
133	16	16.78	-0.7815
134	15	15.5	-0.5007
135	17	16.8	0.2047
136	18	17.2	0.7957
137	20	17.21	2.785
138	17	16.83	0.1677
139	18	16.84	1.165
140	15	14.79	0.2056
141	16	16.37	-0.3676
142	11	15.9	-4.895
143	15	16.65	-1.655
144	18	16.86	1.144
145	17	17.14	-0.1446
146	16	17.53	-1.53
147	12	16.29	-4.286
148	19	16.27	2.727
149	18	16.68	1.318
150	15	16.8	-1.805
151	17	15.9	1.098
152	19	17.23	1.765
153	18	16.24	1.757
154	19	16.23	2.771
155	16	15.72	0.2786
156	16	16.74	-0.7356
157	16	16.84	-0.8413
158	14	16.7	-2.702

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.2814	0.5628	0.7186
12	0.6808	0.6383	0.3192
13	0.6501	0.6997	0.3499
14	0.6941	0.6117	0.3059
15	0.6831	0.6338	0.3169
16	0.788	0.4239	0.212
17	0.7309	0.5382	0.2691
18	0.6804	0.6393	0.3196
19	0.595	0.8101	0.405
20	0.9694	0.0612	0.0306
21	0.9733	0.05348	0.02674
22	0.9707	0.0586	0.0293
23	0.957	0.08605	0.04302
24	0.9573	0.0854	0.0427
25	0.9461	0.1078	0.05391
26	0.9252	0.1496	0.07481
27	0.9122	0.1756	0.0878
28	0.8852	0.2295	0.1148
29	0.8786	0.2428	0.1214
30	0.9005	0.199	0.09951
31	0.8842	0.2316	0.1158
32	0.8533	0.2934	0.1467
33	0.8338	0.3324	0.1662
34	0.8035	0.3929	0.1965
35	0.7709	0.4582	0.2291
36	0.7348	0.5305	0.2652
37	0.7153	0.5695	0.2847
38	0.6659	0.6682	0.3341
39	0.7138	0.5723	0.2862
40	0.7083	0.5834	0.2917
41	0.9931	0.01381	0.006906
42	0.9952	0.009678	0.004839
43	0.993	0.01396	0.00698
44	0.9905	0.0191	0.009549
45	0.9872	0.02555	0.01277
46	0.9846	0.03072	0.01536
47	0.9806	0.03873	0.01936
48	0.9747	0.0505	0.02525
49	0.9781	0.04383	0.02191
50	0.9714	0.0572	0.0286
51	0.9803	0.03935	0.01967
52	0.9853	0.02934	0.01467
53	0.9806	0.03871	0.01935
54	0.9878	0.02437	0.01218
55	0.9963	0.007485	0.003743
56	0.995	0.01006	0.00503
57	0.9929	0.01414	0.007071
58	0.9928	0.01442	0.00721
59	0.9915	0.01696	0.008479
60	0.9883	0.02338	0.01169
61	0.9878	0.02449	0.01225
62	0.9841	0.03182	0.01591
63	0.9895	0.02102	0.01051
64	0.986	0.02807	0.01404
65	0.9854	0.02919	0.0146
66	0.9864	0.0271	0.01355
67	0.9914	0.01725	0.008627
68	0.9889	0.02225	0.01112
69	0.9871	0.02578	0.01289
70	0.9869	0.02625	0.01312
71	0.9835	0.03299	0.0165
72	0.986	0.02799	0.01399
73	0.9831	0.03386	0.01693
74	0.9817	0.0366	0.0183
75	0.976	0.04794	0.02397
76	0.9806	0.03888	0.01944
77	0.9743	0.0515	0.02575
78	0.9716	0.0569	0.02845
79	0.9835	0.03297	0.01648
80	0.9824	0.03527	0.01764
81	0.9773	0.04542	0.02271
82	0.9835	0.03298	0.01649
83	0.9782	0.0437	0.02185
84	0.9737	0.0526	0.0263
85	0.9781	0.04389	0.02195
86	0.9777	0.04454	0.02227
87	0.9802	0.03957	0.01978
88	0.9771	0.04584	0.02292
89	0.9708	0.05842	0.02921
90	0.9704	0.05927	0.02964
91	0.9685	0.06305	0.03152
92	0.9588	0.08247	0.04123
93	0.9855	0.0291	0.01455
94	0.9806	0.03887	0.01944
95	0.9783	0.04332	0.02166
96	0.9805	0.03907	0.01954
97	0.9776	0.04486	0.02243
98	0.9712	0.05752	0.02876
99	0.9658	0.0684	0.0342
100	0.959	0.082	0.041
101	0.9726	0.05484	0.02742
102	0.9673	0.06534	0.03267
103	0.9855	0.02904	0.01452
104	0.9803	0.03936	0.01968
105	0.9822	0.03552	0.01776
106	0.9803	0.03931	0.01965
107	0.9736	0.05281	0.0264
108	0.9727	0.05456	0.02728
109	0.972	0.05593	0.02797
110	0.9636	0.07283	0.03642
111	0.9669	0.06629	0.03314
112	0.9592	0.08154	0.04077
113	0.9618	0.07644	0.03822
114	0.9694	0.06114	0.03057
115	0.9608	0.07835	0.03918
116	0.951	0.0979	0.04895
117	0.9382	0.1235	0.06176
118	0.9406	0.1187	0.05936
119	0.9207	0.1586	0.07932
120	0.9288	0.1423	0.07117
121	0.9202	0.1595	0.07977
122	0.8979	0.2042	0.1021
123	0.8884	0.2232	0.1116
124	0.8573	0.2854	0.1427
125	0.8218	0.3564	0.1782
126	0.7942	0.4117	0.2058
127	0.7605	0.4791	0.2395
128	0.7593	0.4813	0.2407
129	0.7188	0.5624	0.2812
130	0.6667	0.6666	0.3333
131	0.7137	0.5725	0.2863
132	0.7013	0.5974	0.2987
133	0.6392	0.7216	0.3608
134	0.5688	0.8624	0.4312
135	0.4925	0.985	0.5075
136	0.4221	0.8442	0.5779
137	0.3613	0.7226	0.6387
138	0.294	0.588	0.706
139	0.2285	0.4569	0.7715
140	0.1702	0.3404	0.8298
141	0.1274	0.2548	0.8726
142	0.5373	0.9253	0.4627
143	0.4302	0.8605	0.5698
144	0.3401	0.6803	0.6599
145	0.2511	0.5021	0.7489
146	0.1649	0.3297	0.8351
147	0.8303	0.3394	0.1697

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.2814 &  0.5628 &  0.7186 \tabularnewline
12 &  0.6808 &  0.6383 &  0.3192 \tabularnewline
13 &  0.6501 &  0.6997 &  0.3499 \tabularnewline
14 &  0.6941 &  0.6117 &  0.3059 \tabularnewline
15 &  0.6831 &  0.6338 &  0.3169 \tabularnewline
16 &  0.788 &  0.4239 &  0.212 \tabularnewline
17 &  0.7309 &  0.5382 &  0.2691 \tabularnewline
18 &  0.6804 &  0.6393 &  0.3196 \tabularnewline
19 &  0.595 &  0.8101 &  0.405 \tabularnewline
20 &  0.9694 &  0.0612 &  0.0306 \tabularnewline
21 &  0.9733 &  0.05348 &  0.02674 \tabularnewline
22 &  0.9707 &  0.0586 &  0.0293 \tabularnewline
23 &  0.957 &  0.08605 &  0.04302 \tabularnewline
24 &  0.9573 &  0.0854 &  0.0427 \tabularnewline
25 &  0.9461 &  0.1078 &  0.05391 \tabularnewline
26 &  0.9252 &  0.1496 &  0.07481 \tabularnewline
27 &  0.9122 &  0.1756 &  0.0878 \tabularnewline
28 &  0.8852 &  0.2295 &  0.1148 \tabularnewline
29 &  0.8786 &  0.2428 &  0.1214 \tabularnewline
30 &  0.9005 &  0.199 &  0.09951 \tabularnewline
31 &  0.8842 &  0.2316 &  0.1158 \tabularnewline
32 &  0.8533 &  0.2934 &  0.1467 \tabularnewline
33 &  0.8338 &  0.3324 &  0.1662 \tabularnewline
34 &  0.8035 &  0.3929 &  0.1965 \tabularnewline
35 &  0.7709 &  0.4582 &  0.2291 \tabularnewline
36 &  0.7348 &  0.5305 &  0.2652 \tabularnewline
37 &  0.7153 &  0.5695 &  0.2847 \tabularnewline
38 &  0.6659 &  0.6682 &  0.3341 \tabularnewline
39 &  0.7138 &  0.5723 &  0.2862 \tabularnewline
40 &  0.7083 &  0.5834 &  0.2917 \tabularnewline
41 &  0.9931 &  0.01381 &  0.006906 \tabularnewline
42 &  0.9952 &  0.009678 &  0.004839 \tabularnewline
43 &  0.993 &  0.01396 &  0.00698 \tabularnewline
44 &  0.9905 &  0.0191 &  0.009549 \tabularnewline
45 &  0.9872 &  0.02555 &  0.01277 \tabularnewline
46 &  0.9846 &  0.03072 &  0.01536 \tabularnewline
47 &  0.9806 &  0.03873 &  0.01936 \tabularnewline
48 &  0.9747 &  0.0505 &  0.02525 \tabularnewline
49 &  0.9781 &  0.04383 &  0.02191 \tabularnewline
50 &  0.9714 &  0.0572 &  0.0286 \tabularnewline
51 &  0.9803 &  0.03935 &  0.01967 \tabularnewline
52 &  0.9853 &  0.02934 &  0.01467 \tabularnewline
53 &  0.9806 &  0.03871 &  0.01935 \tabularnewline
54 &  0.9878 &  0.02437 &  0.01218 \tabularnewline
55 &  0.9963 &  0.007485 &  0.003743 \tabularnewline
56 &  0.995 &  0.01006 &  0.00503 \tabularnewline
57 &  0.9929 &  0.01414 &  0.007071 \tabularnewline
58 &  0.9928 &  0.01442 &  0.00721 \tabularnewline
59 &  0.9915 &  0.01696 &  0.008479 \tabularnewline
60 &  0.9883 &  0.02338 &  0.01169 \tabularnewline
61 &  0.9878 &  0.02449 &  0.01225 \tabularnewline
62 &  0.9841 &  0.03182 &  0.01591 \tabularnewline
63 &  0.9895 &  0.02102 &  0.01051 \tabularnewline
64 &  0.986 &  0.02807 &  0.01404 \tabularnewline
65 &  0.9854 &  0.02919 &  0.0146 \tabularnewline
66 &  0.9864 &  0.0271 &  0.01355 \tabularnewline
67 &  0.9914 &  0.01725 &  0.008627 \tabularnewline
68 &  0.9889 &  0.02225 &  0.01112 \tabularnewline
69 &  0.9871 &  0.02578 &  0.01289 \tabularnewline
70 &  0.9869 &  0.02625 &  0.01312 \tabularnewline
71 &  0.9835 &  0.03299 &  0.0165 \tabularnewline
72 &  0.986 &  0.02799 &  0.01399 \tabularnewline
73 &  0.9831 &  0.03386 &  0.01693 \tabularnewline
74 &  0.9817 &  0.0366 &  0.0183 \tabularnewline
75 &  0.976 &  0.04794 &  0.02397 \tabularnewline
76 &  0.9806 &  0.03888 &  0.01944 \tabularnewline
77 &  0.9743 &  0.0515 &  0.02575 \tabularnewline
78 &  0.9716 &  0.0569 &  0.02845 \tabularnewline
79 &  0.9835 &  0.03297 &  0.01648 \tabularnewline
80 &  0.9824 &  0.03527 &  0.01764 \tabularnewline
81 &  0.9773 &  0.04542 &  0.02271 \tabularnewline
82 &  0.9835 &  0.03298 &  0.01649 \tabularnewline
83 &  0.9782 &  0.0437 &  0.02185 \tabularnewline
84 &  0.9737 &  0.0526 &  0.0263 \tabularnewline
85 &  0.9781 &  0.04389 &  0.02195 \tabularnewline
86 &  0.9777 &  0.04454 &  0.02227 \tabularnewline
87 &  0.9802 &  0.03957 &  0.01978 \tabularnewline
88 &  0.9771 &  0.04584 &  0.02292 \tabularnewline
89 &  0.9708 &  0.05842 &  0.02921 \tabularnewline
90 &  0.9704 &  0.05927 &  0.02964 \tabularnewline
91 &  0.9685 &  0.06305 &  0.03152 \tabularnewline
92 &  0.9588 &  0.08247 &  0.04123 \tabularnewline
93 &  0.9855 &  0.0291 &  0.01455 \tabularnewline
94 &  0.9806 &  0.03887 &  0.01944 \tabularnewline
95 &  0.9783 &  0.04332 &  0.02166 \tabularnewline
96 &  0.9805 &  0.03907 &  0.01954 \tabularnewline
97 &  0.9776 &  0.04486 &  0.02243 \tabularnewline
98 &  0.9712 &  0.05752 &  0.02876 \tabularnewline
99 &  0.9658 &  0.0684 &  0.0342 \tabularnewline
100 &  0.959 &  0.082 &  0.041 \tabularnewline
101 &  0.9726 &  0.05484 &  0.02742 \tabularnewline
102 &  0.9673 &  0.06534 &  0.03267 \tabularnewline
103 &  0.9855 &  0.02904 &  0.01452 \tabularnewline
104 &  0.9803 &  0.03936 &  0.01968 \tabularnewline
105 &  0.9822 &  0.03552 &  0.01776 \tabularnewline
106 &  0.9803 &  0.03931 &  0.01965 \tabularnewline
107 &  0.9736 &  0.05281 &  0.0264 \tabularnewline
108 &  0.9727 &  0.05456 &  0.02728 \tabularnewline
109 &  0.972 &  0.05593 &  0.02797 \tabularnewline
110 &  0.9636 &  0.07283 &  0.03642 \tabularnewline
111 &  0.9669 &  0.06629 &  0.03314 \tabularnewline
112 &  0.9592 &  0.08154 &  0.04077 \tabularnewline
113 &  0.9618 &  0.07644 &  0.03822 \tabularnewline
114 &  0.9694 &  0.06114 &  0.03057 \tabularnewline
115 &  0.9608 &  0.07835 &  0.03918 \tabularnewline
116 &  0.951 &  0.0979 &  0.04895 \tabularnewline
117 &  0.9382 &  0.1235 &  0.06176 \tabularnewline
118 &  0.9406 &  0.1187 &  0.05936 \tabularnewline
119 &  0.9207 &  0.1586 &  0.07932 \tabularnewline
120 &  0.9288 &  0.1423 &  0.07117 \tabularnewline
121 &  0.9202 &  0.1595 &  0.07977 \tabularnewline
122 &  0.8979 &  0.2042 &  0.1021 \tabularnewline
123 &  0.8884 &  0.2232 &  0.1116 \tabularnewline
124 &  0.8573 &  0.2854 &  0.1427 \tabularnewline
125 &  0.8218 &  0.3564 &  0.1782 \tabularnewline
126 &  0.7942 &  0.4117 &  0.2058 \tabularnewline
127 &  0.7605 &  0.4791 &  0.2395 \tabularnewline
128 &  0.7593 &  0.4813 &  0.2407 \tabularnewline
129 &  0.7188 &  0.5624 &  0.2812 \tabularnewline
130 &  0.6667 &  0.6666 &  0.3333 \tabularnewline
131 &  0.7137 &  0.5725 &  0.2863 \tabularnewline
132 &  0.7013 &  0.5974 &  0.2987 \tabularnewline
133 &  0.6392 &  0.7216 &  0.3608 \tabularnewline
134 &  0.5688 &  0.8624 &  0.4312 \tabularnewline
135 &  0.4925 &  0.985 &  0.5075 \tabularnewline
136 &  0.4221 &  0.8442 &  0.5779 \tabularnewline
137 &  0.3613 &  0.7226 &  0.6387 \tabularnewline
138 &  0.294 &  0.588 &  0.706 \tabularnewline
139 &  0.2285 &  0.4569 &  0.7715 \tabularnewline
140 &  0.1702 &  0.3404 &  0.8298 \tabularnewline
141 &  0.1274 &  0.2548 &  0.8726 \tabularnewline
142 &  0.5373 &  0.9253 &  0.4627 \tabularnewline
143 &  0.4302 &  0.8605 &  0.5698 \tabularnewline
144 &  0.3401 &  0.6803 &  0.6599 \tabularnewline
145 &  0.2511 &  0.5021 &  0.7489 \tabularnewline
146 &  0.1649 &  0.3297 &  0.8351 \tabularnewline
147 &  0.8303 &  0.3394 &  0.1697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]11[/C][C] 0.2814[/C][C] 0.5628[/C][C] 0.7186[/C][/ROW]
[ROW][C]12[/C][C] 0.6808[/C][C] 0.6383[/C][C] 0.3192[/C][/ROW]
[ROW][C]13[/C][C] 0.6501[/C][C] 0.6997[/C][C] 0.3499[/C][/ROW]
[ROW][C]14[/C][C] 0.6941[/C][C] 0.6117[/C][C] 0.3059[/C][/ROW]
[ROW][C]15[/C][C] 0.6831[/C][C] 0.6338[/C][C] 0.3169[/C][/ROW]
[ROW][C]16[/C][C] 0.788[/C][C] 0.4239[/C][C] 0.212[/C][/ROW]
[ROW][C]17[/C][C] 0.7309[/C][C] 0.5382[/C][C] 0.2691[/C][/ROW]
[ROW][C]18[/C][C] 0.6804[/C][C] 0.6393[/C][C] 0.3196[/C][/ROW]
[ROW][C]19[/C][C] 0.595[/C][C] 0.8101[/C][C] 0.405[/C][/ROW]
[ROW][C]20[/C][C] 0.9694[/C][C] 0.0612[/C][C] 0.0306[/C][/ROW]
[ROW][C]21[/C][C] 0.9733[/C][C] 0.05348[/C][C] 0.02674[/C][/ROW]
[ROW][C]22[/C][C] 0.9707[/C][C] 0.0586[/C][C] 0.0293[/C][/ROW]
[ROW][C]23[/C][C] 0.957[/C][C] 0.08605[/C][C] 0.04302[/C][/ROW]
[ROW][C]24[/C][C] 0.9573[/C][C] 0.0854[/C][C] 0.0427[/C][/ROW]
[ROW][C]25[/C][C] 0.9461[/C][C] 0.1078[/C][C] 0.05391[/C][/ROW]
[ROW][C]26[/C][C] 0.9252[/C][C] 0.1496[/C][C] 0.07481[/C][/ROW]
[ROW][C]27[/C][C] 0.9122[/C][C] 0.1756[/C][C] 0.0878[/C][/ROW]
[ROW][C]28[/C][C] 0.8852[/C][C] 0.2295[/C][C] 0.1148[/C][/ROW]
[ROW][C]29[/C][C] 0.8786[/C][C] 0.2428[/C][C] 0.1214[/C][/ROW]
[ROW][C]30[/C][C] 0.9005[/C][C] 0.199[/C][C] 0.09951[/C][/ROW]
[ROW][C]31[/C][C] 0.8842[/C][C] 0.2316[/C][C] 0.1158[/C][/ROW]
[ROW][C]32[/C][C] 0.8533[/C][C] 0.2934[/C][C] 0.1467[/C][/ROW]
[ROW][C]33[/C][C] 0.8338[/C][C] 0.3324[/C][C] 0.1662[/C][/ROW]
[ROW][C]34[/C][C] 0.8035[/C][C] 0.3929[/C][C] 0.1965[/C][/ROW]
[ROW][C]35[/C][C] 0.7709[/C][C] 0.4582[/C][C] 0.2291[/C][/ROW]
[ROW][C]36[/C][C] 0.7348[/C][C] 0.5305[/C][C] 0.2652[/C][/ROW]
[ROW][C]37[/C][C] 0.7153[/C][C] 0.5695[/C][C] 0.2847[/C][/ROW]
[ROW][C]38[/C][C] 0.6659[/C][C] 0.6682[/C][C] 0.3341[/C][/ROW]
[ROW][C]39[/C][C] 0.7138[/C][C] 0.5723[/C][C] 0.2862[/C][/ROW]
[ROW][C]40[/C][C] 0.7083[/C][C] 0.5834[/C][C] 0.2917[/C][/ROW]
[ROW][C]41[/C][C] 0.9931[/C][C] 0.01381[/C][C] 0.006906[/C][/ROW]
[ROW][C]42[/C][C] 0.9952[/C][C] 0.009678[/C][C] 0.004839[/C][/ROW]
[ROW][C]43[/C][C] 0.993[/C][C] 0.01396[/C][C] 0.00698[/C][/ROW]
[ROW][C]44[/C][C] 0.9905[/C][C] 0.0191[/C][C] 0.009549[/C][/ROW]
[ROW][C]45[/C][C] 0.9872[/C][C] 0.02555[/C][C] 0.01277[/C][/ROW]
[ROW][C]46[/C][C] 0.9846[/C][C] 0.03072[/C][C] 0.01536[/C][/ROW]
[ROW][C]47[/C][C] 0.9806[/C][C] 0.03873[/C][C] 0.01936[/C][/ROW]
[ROW][C]48[/C][C] 0.9747[/C][C] 0.0505[/C][C] 0.02525[/C][/ROW]
[ROW][C]49[/C][C] 0.9781[/C][C] 0.04383[/C][C] 0.02191[/C][/ROW]
[ROW][C]50[/C][C] 0.9714[/C][C] 0.0572[/C][C] 0.0286[/C][/ROW]
[ROW][C]51[/C][C] 0.9803[/C][C] 0.03935[/C][C] 0.01967[/C][/ROW]
[ROW][C]52[/C][C] 0.9853[/C][C] 0.02934[/C][C] 0.01467[/C][/ROW]
[ROW][C]53[/C][C] 0.9806[/C][C] 0.03871[/C][C] 0.01935[/C][/ROW]
[ROW][C]54[/C][C] 0.9878[/C][C] 0.02437[/C][C] 0.01218[/C][/ROW]
[ROW][C]55[/C][C] 0.9963[/C][C] 0.007485[/C][C] 0.003743[/C][/ROW]
[ROW][C]56[/C][C] 0.995[/C][C] 0.01006[/C][C] 0.00503[/C][/ROW]
[ROW][C]57[/C][C] 0.9929[/C][C] 0.01414[/C][C] 0.007071[/C][/ROW]
[ROW][C]58[/C][C] 0.9928[/C][C] 0.01442[/C][C] 0.00721[/C][/ROW]
[ROW][C]59[/C][C] 0.9915[/C][C] 0.01696[/C][C] 0.008479[/C][/ROW]
[ROW][C]60[/C][C] 0.9883[/C][C] 0.02338[/C][C] 0.01169[/C][/ROW]
[ROW][C]61[/C][C] 0.9878[/C][C] 0.02449[/C][C] 0.01225[/C][/ROW]
[ROW][C]62[/C][C] 0.9841[/C][C] 0.03182[/C][C] 0.01591[/C][/ROW]
[ROW][C]63[/C][C] 0.9895[/C][C] 0.02102[/C][C] 0.01051[/C][/ROW]
[ROW][C]64[/C][C] 0.986[/C][C] 0.02807[/C][C] 0.01404[/C][/ROW]
[ROW][C]65[/C][C] 0.9854[/C][C] 0.02919[/C][C] 0.0146[/C][/ROW]
[ROW][C]66[/C][C] 0.9864[/C][C] 0.0271[/C][C] 0.01355[/C][/ROW]
[ROW][C]67[/C][C] 0.9914[/C][C] 0.01725[/C][C] 0.008627[/C][/ROW]
[ROW][C]68[/C][C] 0.9889[/C][C] 0.02225[/C][C] 0.01112[/C][/ROW]
[ROW][C]69[/C][C] 0.9871[/C][C] 0.02578[/C][C] 0.01289[/C][/ROW]
[ROW][C]70[/C][C] 0.9869[/C][C] 0.02625[/C][C] 0.01312[/C][/ROW]
[ROW][C]71[/C][C] 0.9835[/C][C] 0.03299[/C][C] 0.0165[/C][/ROW]
[ROW][C]72[/C][C] 0.986[/C][C] 0.02799[/C][C] 0.01399[/C][/ROW]
[ROW][C]73[/C][C] 0.9831[/C][C] 0.03386[/C][C] 0.01693[/C][/ROW]
[ROW][C]74[/C][C] 0.9817[/C][C] 0.0366[/C][C] 0.0183[/C][/ROW]
[ROW][C]75[/C][C] 0.976[/C][C] 0.04794[/C][C] 0.02397[/C][/ROW]
[ROW][C]76[/C][C] 0.9806[/C][C] 0.03888[/C][C] 0.01944[/C][/ROW]
[ROW][C]77[/C][C] 0.9743[/C][C] 0.0515[/C][C] 0.02575[/C][/ROW]
[ROW][C]78[/C][C] 0.9716[/C][C] 0.0569[/C][C] 0.02845[/C][/ROW]
[ROW][C]79[/C][C] 0.9835[/C][C] 0.03297[/C][C] 0.01648[/C][/ROW]
[ROW][C]80[/C][C] 0.9824[/C][C] 0.03527[/C][C] 0.01764[/C][/ROW]
[ROW][C]81[/C][C] 0.9773[/C][C] 0.04542[/C][C] 0.02271[/C][/ROW]
[ROW][C]82[/C][C] 0.9835[/C][C] 0.03298[/C][C] 0.01649[/C][/ROW]
[ROW][C]83[/C][C] 0.9782[/C][C] 0.0437[/C][C] 0.02185[/C][/ROW]
[ROW][C]84[/C][C] 0.9737[/C][C] 0.0526[/C][C] 0.0263[/C][/ROW]
[ROW][C]85[/C][C] 0.9781[/C][C] 0.04389[/C][C] 0.02195[/C][/ROW]
[ROW][C]86[/C][C] 0.9777[/C][C] 0.04454[/C][C] 0.02227[/C][/ROW]
[ROW][C]87[/C][C] 0.9802[/C][C] 0.03957[/C][C] 0.01978[/C][/ROW]
[ROW][C]88[/C][C] 0.9771[/C][C] 0.04584[/C][C] 0.02292[/C][/ROW]
[ROW][C]89[/C][C] 0.9708[/C][C] 0.05842[/C][C] 0.02921[/C][/ROW]
[ROW][C]90[/C][C] 0.9704[/C][C] 0.05927[/C][C] 0.02964[/C][/ROW]
[ROW][C]91[/C][C] 0.9685[/C][C] 0.06305[/C][C] 0.03152[/C][/ROW]
[ROW][C]92[/C][C] 0.9588[/C][C] 0.08247[/C][C] 0.04123[/C][/ROW]
[ROW][C]93[/C][C] 0.9855[/C][C] 0.0291[/C][C] 0.01455[/C][/ROW]
[ROW][C]94[/C][C] 0.9806[/C][C] 0.03887[/C][C] 0.01944[/C][/ROW]
[ROW][C]95[/C][C] 0.9783[/C][C] 0.04332[/C][C] 0.02166[/C][/ROW]
[ROW][C]96[/C][C] 0.9805[/C][C] 0.03907[/C][C] 0.01954[/C][/ROW]
[ROW][C]97[/C][C] 0.9776[/C][C] 0.04486[/C][C] 0.02243[/C][/ROW]
[ROW][C]98[/C][C] 0.9712[/C][C] 0.05752[/C][C] 0.02876[/C][/ROW]
[ROW][C]99[/C][C] 0.9658[/C][C] 0.0684[/C][C] 0.0342[/C][/ROW]
[ROW][C]100[/C][C] 0.959[/C][C] 0.082[/C][C] 0.041[/C][/ROW]
[ROW][C]101[/C][C] 0.9726[/C][C] 0.05484[/C][C] 0.02742[/C][/ROW]
[ROW][C]102[/C][C] 0.9673[/C][C] 0.06534[/C][C] 0.03267[/C][/ROW]
[ROW][C]103[/C][C] 0.9855[/C][C] 0.02904[/C][C] 0.01452[/C][/ROW]
[ROW][C]104[/C][C] 0.9803[/C][C] 0.03936[/C][C] 0.01968[/C][/ROW]
[ROW][C]105[/C][C] 0.9822[/C][C] 0.03552[/C][C] 0.01776[/C][/ROW]
[ROW][C]106[/C][C] 0.9803[/C][C] 0.03931[/C][C] 0.01965[/C][/ROW]
[ROW][C]107[/C][C] 0.9736[/C][C] 0.05281[/C][C] 0.0264[/C][/ROW]
[ROW][C]108[/C][C] 0.9727[/C][C] 0.05456[/C][C] 0.02728[/C][/ROW]
[ROW][C]109[/C][C] 0.972[/C][C] 0.05593[/C][C] 0.02797[/C][/ROW]
[ROW][C]110[/C][C] 0.9636[/C][C] 0.07283[/C][C] 0.03642[/C][/ROW]
[ROW][C]111[/C][C] 0.9669[/C][C] 0.06629[/C][C] 0.03314[/C][/ROW]
[ROW][C]112[/C][C] 0.9592[/C][C] 0.08154[/C][C] 0.04077[/C][/ROW]
[ROW][C]113[/C][C] 0.9618[/C][C] 0.07644[/C][C] 0.03822[/C][/ROW]
[ROW][C]114[/C][C] 0.9694[/C][C] 0.06114[/C][C] 0.03057[/C][/ROW]
[ROW][C]115[/C][C] 0.9608[/C][C] 0.07835[/C][C] 0.03918[/C][/ROW]
[ROW][C]116[/C][C] 0.951[/C][C] 0.0979[/C][C] 0.04895[/C][/ROW]
[ROW][C]117[/C][C] 0.9382[/C][C] 0.1235[/C][C] 0.06176[/C][/ROW]
[ROW][C]118[/C][C] 0.9406[/C][C] 0.1187[/C][C] 0.05936[/C][/ROW]
[ROW][C]119[/C][C] 0.9207[/C][C] 0.1586[/C][C] 0.07932[/C][/ROW]
[ROW][C]120[/C][C] 0.9288[/C][C] 0.1423[/C][C] 0.07117[/C][/ROW]
[ROW][C]121[/C][C] 0.9202[/C][C] 0.1595[/C][C] 0.07977[/C][/ROW]
[ROW][C]122[/C][C] 0.8979[/C][C] 0.2042[/C][C] 0.1021[/C][/ROW]
[ROW][C]123[/C][C] 0.8884[/C][C] 0.2232[/C][C] 0.1116[/C][/ROW]
[ROW][C]124[/C][C] 0.8573[/C][C] 0.2854[/C][C] 0.1427[/C][/ROW]
[ROW][C]125[/C][C] 0.8218[/C][C] 0.3564[/C][C] 0.1782[/C][/ROW]
[ROW][C]126[/C][C] 0.7942[/C][C] 0.4117[/C][C] 0.2058[/C][/ROW]
[ROW][C]127[/C][C] 0.7605[/C][C] 0.4791[/C][C] 0.2395[/C][/ROW]
[ROW][C]128[/C][C] 0.7593[/C][C] 0.4813[/C][C] 0.2407[/C][/ROW]
[ROW][C]129[/C][C] 0.7188[/C][C] 0.5624[/C][C] 0.2812[/C][/ROW]
[ROW][C]130[/C][C] 0.6667[/C][C] 0.6666[/C][C] 0.3333[/C][/ROW]
[ROW][C]131[/C][C] 0.7137[/C][C] 0.5725[/C][C] 0.2863[/C][/ROW]
[ROW][C]132[/C][C] 0.7013[/C][C] 0.5974[/C][C] 0.2987[/C][/ROW]
[ROW][C]133[/C][C] 0.6392[/C][C] 0.7216[/C][C] 0.3608[/C][/ROW]
[ROW][C]134[/C][C] 0.5688[/C][C] 0.8624[/C][C] 0.4312[/C][/ROW]
[ROW][C]135[/C][C] 0.4925[/C][C] 0.985[/C][C] 0.5075[/C][/ROW]
[ROW][C]136[/C][C] 0.4221[/C][C] 0.8442[/C][C] 0.5779[/C][/ROW]
[ROW][C]137[/C][C] 0.3613[/C][C] 0.7226[/C][C] 0.6387[/C][/ROW]
[ROW][C]138[/C][C] 0.294[/C][C] 0.588[/C][C] 0.706[/C][/ROW]
[ROW][C]139[/C][C] 0.2285[/C][C] 0.4569[/C][C] 0.7715[/C][/ROW]
[ROW][C]140[/C][C] 0.1702[/C][C] 0.3404[/C][C] 0.8298[/C][/ROW]
[ROW][C]141[/C][C] 0.1274[/C][C] 0.2548[/C][C] 0.8726[/C][/ROW]
[ROW][C]142[/C][C] 0.5373[/C][C] 0.9253[/C][C] 0.4627[/C][/ROW]
[ROW][C]143[/C][C] 0.4302[/C][C] 0.8605[/C][C] 0.5698[/C][/ROW]
[ROW][C]144[/C][C] 0.3401[/C][C] 0.6803[/C][C] 0.6599[/C][/ROW]
[ROW][C]145[/C][C] 0.2511[/C][C] 0.5021[/C][C] 0.7489[/C][/ROW]
[ROW][C]146[/C][C] 0.1649[/C][C] 0.3297[/C][C] 0.8351[/C][/ROW]
[ROW][C]147[/C][C] 0.8303[/C][C] 0.3394[/C][C] 0.1697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
11	0.2814	0.5628	0.7186
12	0.6808	0.6383	0.3192
13	0.6501	0.6997	0.3499
14	0.6941	0.6117	0.3059
15	0.6831	0.6338	0.3169
16	0.788	0.4239	0.212
17	0.7309	0.5382	0.2691
18	0.6804	0.6393	0.3196
19	0.595	0.8101	0.405
20	0.9694	0.0612	0.0306
21	0.9733	0.05348	0.02674
22	0.9707	0.0586	0.0293
23	0.957	0.08605	0.04302
24	0.9573	0.0854	0.0427
25	0.9461	0.1078	0.05391
26	0.9252	0.1496	0.07481
27	0.9122	0.1756	0.0878
28	0.8852	0.2295	0.1148
29	0.8786	0.2428	0.1214
30	0.9005	0.199	0.09951
31	0.8842	0.2316	0.1158
32	0.8533	0.2934	0.1467
33	0.8338	0.3324	0.1662
34	0.8035	0.3929	0.1965
35	0.7709	0.4582	0.2291
36	0.7348	0.5305	0.2652
37	0.7153	0.5695	0.2847
38	0.6659	0.6682	0.3341
39	0.7138	0.5723	0.2862
40	0.7083	0.5834	0.2917
41	0.9931	0.01381	0.006906
42	0.9952	0.009678	0.004839
43	0.993	0.01396	0.00698
44	0.9905	0.0191	0.009549
45	0.9872	0.02555	0.01277
46	0.9846	0.03072	0.01536
47	0.9806	0.03873	0.01936
48	0.9747	0.0505	0.02525
49	0.9781	0.04383	0.02191
50	0.9714	0.0572	0.0286
51	0.9803	0.03935	0.01967
52	0.9853	0.02934	0.01467
53	0.9806	0.03871	0.01935
54	0.9878	0.02437	0.01218
55	0.9963	0.007485	0.003743
56	0.995	0.01006	0.00503
57	0.9929	0.01414	0.007071
58	0.9928	0.01442	0.00721
59	0.9915	0.01696	0.008479
60	0.9883	0.02338	0.01169
61	0.9878	0.02449	0.01225
62	0.9841	0.03182	0.01591
63	0.9895	0.02102	0.01051
64	0.986	0.02807	0.01404
65	0.9854	0.02919	0.0146
66	0.9864	0.0271	0.01355
67	0.9914	0.01725	0.008627
68	0.9889	0.02225	0.01112
69	0.9871	0.02578	0.01289
70	0.9869	0.02625	0.01312
71	0.9835	0.03299	0.0165
72	0.986	0.02799	0.01399
73	0.9831	0.03386	0.01693
74	0.9817	0.0366	0.0183
75	0.976	0.04794	0.02397
76	0.9806	0.03888	0.01944
77	0.9743	0.0515	0.02575
78	0.9716	0.0569	0.02845
79	0.9835	0.03297	0.01648
80	0.9824	0.03527	0.01764
81	0.9773	0.04542	0.02271
82	0.9835	0.03298	0.01649
83	0.9782	0.0437	0.02185
84	0.9737	0.0526	0.0263
85	0.9781	0.04389	0.02195
86	0.9777	0.04454	0.02227
87	0.9802	0.03957	0.01978
88	0.9771	0.04584	0.02292
89	0.9708	0.05842	0.02921
90	0.9704	0.05927	0.02964
91	0.9685	0.06305	0.03152
92	0.9588	0.08247	0.04123
93	0.9855	0.0291	0.01455
94	0.9806	0.03887	0.01944
95	0.9783	0.04332	0.02166
96	0.9805	0.03907	0.01954
97	0.9776	0.04486	0.02243
98	0.9712	0.05752	0.02876
99	0.9658	0.0684	0.0342
100	0.959	0.082	0.041
101	0.9726	0.05484	0.02742
102	0.9673	0.06534	0.03267
103	0.9855	0.02904	0.01452
104	0.9803	0.03936	0.01968
105	0.9822	0.03552	0.01776
106	0.9803	0.03931	0.01965
107	0.9736	0.05281	0.0264
108	0.9727	0.05456	0.02728
109	0.972	0.05593	0.02797
110	0.9636	0.07283	0.03642
111	0.9669	0.06629	0.03314
112	0.9592	0.08154	0.04077
113	0.9618	0.07644	0.03822
114	0.9694	0.06114	0.03057
115	0.9608	0.07835	0.03918
116	0.951	0.0979	0.04895
117	0.9382	0.1235	0.06176
118	0.9406	0.1187	0.05936
119	0.9207	0.1586	0.07932
120	0.9288	0.1423	0.07117
121	0.9202	0.1595	0.07977
122	0.8979	0.2042	0.1021
123	0.8884	0.2232	0.1116
124	0.8573	0.2854	0.1427
125	0.8218	0.3564	0.1782
126	0.7942	0.4117	0.2058
127	0.7605	0.4791	0.2395
128	0.7593	0.4813	0.2407
129	0.7188	0.5624	0.2812
130	0.6667	0.6666	0.3333
131	0.7137	0.5725	0.2863
132	0.7013	0.5974	0.2987
133	0.6392	0.7216	0.3608
134	0.5688	0.8624	0.4312
135	0.4925	0.985	0.5075
136	0.4221	0.8442	0.5779
137	0.3613	0.7226	0.6387
138	0.294	0.588	0.706
139	0.2285	0.4569	0.7715
140	0.1702	0.3404	0.8298
141	0.1274	0.2548	0.8726
142	0.5373	0.9253	0.4627
143	0.4302	0.8605	0.5698
144	0.3401	0.6803	0.6599
145	0.2511	0.5021	0.7489
146	0.1649	0.3297	0.8351
147	0.8303	0.3394	0.1697

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0146	NOK
5% type I error level	52	0.379562	NOK
10% type I error level	81	0.591241	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 & 2 &  0.0146 & NOK \tabularnewline
5% type I error level & 52 & 0.379562 & NOK \tabularnewline
10% type I error level & 81 & 0.591241 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]2[/C][C] 0.0146[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]52[/C][C]0.379562[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]81[/C][C]0.591241[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	2	0.0146	NOK
5% type I error level	52	0.379562	NOK
10% type I error level	81	0.591241	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8727, df1 = 2, df2 = 148, p-value = 0.1573
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.54204, df1 = 14, df2 = 136, p-value = 0.9041
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.30152, df1 = 2, df2 = 148, p-value = 0.7401

\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 = 1.8727, df1 = 2, df2 = 148, p-value = 0.1573
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.54204, df1 = 14, df2 = 136, p-value = 0.9041
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.30152, df1 = 2, df2 = 148, p-value = 0.7401
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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 = 1.8727, df1 = 2, df2 = 148, p-value = 0.1573
[/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 = 0.54204, df1 = 14, df2 = 136, p-value = 0.9041
[/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 = 0.30152, df1 = 2, df2 = 148, p-value = 0.7401
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 = 1.8727, df1 = 2, df2 = 148, p-value = 0.1573
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.54204, df1 = 14, df2 = 136, p-value = 0.9041
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.30152, df1 = 2, df2 = 148, p-value = 0.7401

Variance Inflation Factors (Multicollinearity)
> vif Bevr_Leeftijd SKEOU1 SKEOU2 SKEOU3 SKEOU4 1.062668 1.091582 1.163054 1.056092 1.090233 SKEOU5 SKEOU6 1.050102 1.043220

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
Bevr_Leeftijd        SKEOU1        SKEOU2        SKEOU3        SKEOU4 
     1.062668      1.091582      1.163054      1.056092      1.090233 
       SKEOU5        SKEOU6 
     1.050102      1.043220 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
Bevr_Leeftijd        SKEOU1        SKEOU2        SKEOU3        SKEOU4 
     1.062668      1.091582      1.163054      1.056092      1.090233 
       SKEOU5        SKEOU6 
     1.050102      1.043220 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Bevr_Leeftijd SKEOU1 SKEOU2 SKEOU3 SKEOU4 1.062668 1.091582 1.163054 1.056092 1.090233 SKEOU5 SKEOU6 1.050102 1.043220

Figure 1

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1111111 ; par2 = 2Do not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal Dummies ; par3 = TRUENo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear Trend ; par4 = 0 ;

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