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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 07 Dec 2016 17:06:08 +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/2016/Dec/07/t1481127029wcigxt4g9gjsh6v.htm/, Retrieved Tue, 07 May 2024 18:42:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298227, Retrieved Tue, 07 May 2024 18:42:50 +0000

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Estimated Impact

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

-       [Multiple Regression] [Multiple regressi...] [2016-12-07 16:06:08] [84a79156fb687334cf7dc390d7b82d5a] [Current]
- R P     [Multiple Regression] [Multiple regressi...] [2016-12-07 16:16:47] [18cdc3a4292fc57d63398df4d659b9a6] 
- R       [Multiple Regression] [Multiple regressi...] [2016-12-07 16:30:16] [18cdc3a4292fc57d63398df4d659b9a6] 
- RMP     [Kendall tau Correlation Matrix] [Correlation matri...] [2016-12-07 16:51:33] [18cdc3a4292fc57d63398df4d659b9a6] 

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Dataseries X:

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Histogram

Boxplots

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&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]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298227&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 8.19045 + 0.307354`SK1,`[t] -0.0366025`SK2,`[t] + 0.435165`SK3,`[t] + 0.438674`SK4,`[t] + 0.498882`SK5,`[t] + 0.396089`SK6,`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  8.19045 +  0.307354`SK1,`[t] -0.0366025`SK2,`[t] +  0.435165`SK3,`[t] +  0.438674`SK4,`[t] +  0.498882`SK5,`[t] +  0.396089`SK6,`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  8.19045 +  0.307354`SK1,`[t] -0.0366025`SK2,`[t] +  0.435165`SK3,`[t] +  0.438674`SK4,`[t] +  0.498882`SK5,`[t] +  0.396089`SK6,`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298227&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&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] = + 8.19045 + 0.307354`SK1,`[t] -0.0366025`SK2,`[t] + 0.435165`SK3,`[t] + 0.438674`SK4,`[t] + 0.498882`SK5,`[t] + 0.396089`SK6,`[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)	+8.19	2.421	+3.3820e+00	0.000915	0.0004575
`SK1,`	+0.3074	0.2545	+1.2080e+00	0.2291	0.1146
`SK2,`	-0.0366	0.3159	-1.1590e-01	0.9079	0.454
`SK3,`	+0.4352	0.2293	+1.8970e+00	0.05968	0.02984
`SK4,`	+0.4387	0.3124	+1.4040e+00	0.1623	0.08113
`SK5,`	+0.4989	0.2942	+1.6960e+00	0.09198	0.04599
`SK6,`	+0.3961	0.3038	+1.3040e+00	0.1943	0.09716

\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) & +8.19 &  2.421 & +3.3820e+00 &  0.000915 &  0.0004575 \tabularnewline
`SK1,` & +0.3074 &  0.2545 & +1.2080e+00 &  0.2291 &  0.1146 \tabularnewline
`SK2,` & -0.0366 &  0.3159 & -1.1590e-01 &  0.9079 &  0.454 \tabularnewline
`SK3,` & +0.4352 &  0.2293 & +1.8970e+00 &  0.05968 &  0.02984 \tabularnewline
`SK4,` & +0.4387 &  0.3124 & +1.4040e+00 &  0.1623 &  0.08113 \tabularnewline
`SK5,` & +0.4989 &  0.2942 & +1.6960e+00 &  0.09198 &  0.04599 \tabularnewline
`SK6,` & +0.3961 &  0.3038 & +1.3040e+00 &  0.1943 &  0.09716 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&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]+8.19[/C][C] 2.421[/C][C]+3.3820e+00[/C][C] 0.000915[/C][C] 0.0004575[/C][/ROW]
[ROW][C]`SK1,`[/C][C]+0.3074[/C][C] 0.2545[/C][C]+1.2080e+00[/C][C] 0.2291[/C][C] 0.1146[/C][/ROW]
[ROW][C]`SK2,`[/C][C]-0.0366[/C][C] 0.3159[/C][C]-1.1590e-01[/C][C] 0.9079[/C][C] 0.454[/C][/ROW]
[ROW][C]`SK3,`[/C][C]+0.4352[/C][C] 0.2293[/C][C]+1.8970e+00[/C][C] 0.05968[/C][C] 0.02984[/C][/ROW]
[ROW][C]`SK4,`[/C][C]+0.4387[/C][C] 0.3124[/C][C]+1.4040e+00[/C][C] 0.1623[/C][C] 0.08113[/C][/ROW]
[ROW][C]`SK5,`[/C][C]+0.4989[/C][C] 0.2942[/C][C]+1.6960e+00[/C][C] 0.09198[/C][C] 0.04599[/C][/ROW]
[ROW][C]`SK6,`[/C][C]+0.3961[/C][C] 0.3038[/C][C]+1.3040e+00[/C][C] 0.1943[/C][C] 0.09716[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298227&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&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)	+8.19	2.421	+3.3820e+00	0.000915	0.0004575
`SK1,`	+0.3074	0.2545	+1.2080e+00	0.2291	0.1146
`SK2,`	-0.0366	0.3159	-1.1590e-01	0.9079	0.454
`SK3,`	+0.4352	0.2293	+1.8970e+00	0.05968	0.02984
`SK4,`	+0.4387	0.3124	+1.4040e+00	0.1623	0.08113
`SK5,`	+0.4989	0.2942	+1.6960e+00	0.09198	0.04599
`SK6,`	+0.3961	0.3038	+1.3040e+00	0.1943	0.09716

Multiple Linear Regression - Regression Statistics
Multiple R	0.2897
R-squared	0.08395
Adjusted R-squared	0.04755
F-TEST (value)	2.306
F-TEST (DF numerator)	6
F-TEST (DF denominator)	151
p-value	0.03692
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.236
Sum Squared Residuals	754.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2897 \tabularnewline
R-squared &  0.08395 \tabularnewline
Adjusted R-squared &  0.04755 \tabularnewline
F-TEST (value) &  2.306 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value &  0.03692 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.236 \tabularnewline
Sum Squared Residuals &  754.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2897[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.08395[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04755[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.306[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C] 0.03692[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.236[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 754.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298227&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&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	0.2897
R-squared	0.08395
Adjusted R-squared	0.04755
F-TEST (value)	2.306
F-TEST (DF numerator)	6
F-TEST (DF denominator)	151
p-value	0.03692
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.236
Sum Squared Residuals	754.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.48	-2.482
2	19	16.76	2.244
3	17	17.28	-0.2827
4	17	15.17	1.833
5	15	17.28	-2.283
6	20	16.94	3.064
7	15	15.1	-0.1038
8	19	16.48	2.523
9	15	17.21	-2.207
10	15	17.64	-2.642
11	19	15.98	3.023
12	20	16.31	3.694
13	18	16.91	1.091
14	15	16.18	-1.182
15	14	16.87	-2.873
16	20	16.87	3.127
17	16	16.18	-0.1808
18	16	16.85	-0.8476
19	16	16.43	-0.4339
20	10	17.24	-7.244
21	19	17.18	1.82
22	19	16.78	2.216
23	16	17.18	-1.18
24	15	16.04	-1.041
25	18	16.5	1.502
26	17	16.35	0.6513
27	19	17.06	1.936
28	17	16.66	0.344
29	19	17.64	1.358
30	20	17.49	2.513
31	19	16.7	2.295
32	16	15.44	0.5582
33	15	16.48	-1.476
34	16	16.78	-0.7779
35	18	15.95	2.05
36	16	16.78	-0.7839
37	15	16.66	-1.656
38	17	17.64	-0.6422
39	20	17.51	2.489
40	19	16.65	2.347
41	7	16.31	-9.306
42	13	16.35	-3.349
43	16	15.5	0.4977
44	16	16.85	-0.8476
45	18	16.75	1.253
46	18	16.25	1.752
47	16	17.05	-1.055
48	17	17.72	-0.7154
49	19	16.34	2.657
50	16	16.78	-0.7839
51	19	15.1	3.896
52	13	15.65	-2.645
53	16	16.41	-0.4089
54	13	16.85	-3.848
55	12	17.08	-5.079
56	17	16.63	0.371
57	17	17.24	-0.2437
58	17	15.04	1.956
59	16	17.68	-1.679
60	16	16.42	-0.4219
61	14	16.01	-2.013
62	16	16.41	-0.4089
63	13	16.55	-3.55
64	16	16.5	-0.4976
65	14	16	-1.999
66	20	17.68	2.315
67	13	16.35	-3.349
68	18	17.29	0.7138
69	14	15.61	-1.606
70	19	16.85	2.152
71	18	16.93	1.067
72	14	16.91	-2.909
73	18	16.82	1.18
74	19	17.18	1.82
75	15	15.64	-0.6428
76	14	16.85	-2.848
77	17	16.81	0.1915
78	19	17.24	1.756
79	13	16.66	-3.656
80	19	17.06	1.944
81	18	17.68	0.3212
82	20	16.87	3.127
83	15	15.55	-0.5481
84	15	16.39	-1.388
85	20	17.18	2.82
86	15	16.85	-1.848
87	19	16.41	2.585
88	18	16.5	1.502
89	18	17.18	0.8201
90	15	16.62	-1.617
91	20	17.93	2.074
92	17	17.21	-0.2071
93	12	16.87	-4.873
94	18	17.59	0.4124
95	19	17.18	1.82
96	20	17.05	2.948
97	17	17.99	-0.9897
98	16	15.08	0.9212
99	18	16.78	1.216
100	18	16.78	1.216
101	14	16.58	-2.579
102	15	16.35	-1.349
103	12	16.31	-4.306
104	17	16.53	0.4658
105	14	15.91	-1.91
106	18	16.48	1.524
107	17	16.29	0.715
108	17	16.67	0.3298
109	20	17.99	2.014
110	16	15.95	0.0474
111	14	16.35	-2.345
112	15	16	-0.9988
113	18	16.35	1.651
114	20	16.85	3.152
115	17	15.88	1.123
116	17	16.04	0.9587
117	17	15.91	1.09
118	17	16.74	0.2552
119	15	15.17	-0.1675
120	17	15.51	1.486
121	18	15.24	2.763
122	17	16.41	0.5911
123	20	16.72	3.284
124	15	15.19	-0.1925
125	16	16.08	-0.07794
126	15	15.91	-0.91
127	18	17.12	0.8817
128	15	16.75	-1.747
129	18	17.99	0.007835
130	20	17.64	2.358
131	19	16.31	2.694
132	14	16.98	-2.975
133	16	16.78	-0.7839
134	15	15.48	-0.4784
135	17	16.81	0.1915
136	18	17.24	0.7563
137	20	17.15	2.849
138	17	16.82	0.1795
139	18	16.78	1.216
140	15	14.77	0.2311
141	16	16.35	-0.3512
142	11	15.91	-4.91
143	15	16.74	-1.745
144	18	16.93	1.067
145	17	17.18	-0.1799
146	16	17.59	-1.59
147	12	16.29	-4.285
148	19	16.21	2.794
149	18	16.74	1.255
150	15	16.84	-1.842
151	17	15.95	1.047
152	19	17.19	1.814
153	18	16.22	1.779
154	19	16.22	2.783
155	16	15.67	0.3326
156	16	16.74	-0.7448
157	16	16.85	-0.8476
158	14	16.71	-2.708

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.48 & -2.482 \tabularnewline
2 &  19 &  16.76 &  2.244 \tabularnewline
3 &  17 &  17.28 & -0.2827 \tabularnewline
4 &  17 &  15.17 &  1.833 \tabularnewline
5 &  15 &  17.28 & -2.283 \tabularnewline
6 &  20 &  16.94 &  3.064 \tabularnewline
7 &  15 &  15.1 & -0.1038 \tabularnewline
8 &  19 &  16.48 &  2.523 \tabularnewline
9 &  15 &  17.21 & -2.207 \tabularnewline
10 &  15 &  17.64 & -2.642 \tabularnewline
11 &  19 &  15.98 &  3.023 \tabularnewline
12 &  20 &  16.31 &  3.694 \tabularnewline
13 &  18 &  16.91 &  1.091 \tabularnewline
14 &  15 &  16.18 & -1.182 \tabularnewline
15 &  14 &  16.87 & -2.873 \tabularnewline
16 &  20 &  16.87 &  3.127 \tabularnewline
17 &  16 &  16.18 & -0.1808 \tabularnewline
18 &  16 &  16.85 & -0.8476 \tabularnewline
19 &  16 &  16.43 & -0.4339 \tabularnewline
20 &  10 &  17.24 & -7.244 \tabularnewline
21 &  19 &  17.18 &  1.82 \tabularnewline
22 &  19 &  16.78 &  2.216 \tabularnewline
23 &  16 &  17.18 & -1.18 \tabularnewline
24 &  15 &  16.04 & -1.041 \tabularnewline
25 &  18 &  16.5 &  1.502 \tabularnewline
26 &  17 &  16.35 &  0.6513 \tabularnewline
27 &  19 &  17.06 &  1.936 \tabularnewline
28 &  17 &  16.66 &  0.344 \tabularnewline
29 &  19 &  17.64 &  1.358 \tabularnewline
30 &  20 &  17.49 &  2.513 \tabularnewline
31 &  19 &  16.7 &  2.295 \tabularnewline
32 &  16 &  15.44 &  0.5582 \tabularnewline
33 &  15 &  16.48 & -1.476 \tabularnewline
34 &  16 &  16.78 & -0.7779 \tabularnewline
35 &  18 &  15.95 &  2.05 \tabularnewline
36 &  16 &  16.78 & -0.7839 \tabularnewline
37 &  15 &  16.66 & -1.656 \tabularnewline
38 &  17 &  17.64 & -0.6422 \tabularnewline
39 &  20 &  17.51 &  2.489 \tabularnewline
40 &  19 &  16.65 &  2.347 \tabularnewline
41 &  7 &  16.31 & -9.306 \tabularnewline
42 &  13 &  16.35 & -3.349 \tabularnewline
43 &  16 &  15.5 &  0.4977 \tabularnewline
44 &  16 &  16.85 & -0.8476 \tabularnewline
45 &  18 &  16.75 &  1.253 \tabularnewline
46 &  18 &  16.25 &  1.752 \tabularnewline
47 &  16 &  17.05 & -1.055 \tabularnewline
48 &  17 &  17.72 & -0.7154 \tabularnewline
49 &  19 &  16.34 &  2.657 \tabularnewline
50 &  16 &  16.78 & -0.7839 \tabularnewline
51 &  19 &  15.1 &  3.896 \tabularnewline
52 &  13 &  15.65 & -2.645 \tabularnewline
53 &  16 &  16.41 & -0.4089 \tabularnewline
54 &  13 &  16.85 & -3.848 \tabularnewline
55 &  12 &  17.08 & -5.079 \tabularnewline
56 &  17 &  16.63 &  0.371 \tabularnewline
57 &  17 &  17.24 & -0.2437 \tabularnewline
58 &  17 &  15.04 &  1.956 \tabularnewline
59 &  16 &  17.68 & -1.679 \tabularnewline
60 &  16 &  16.42 & -0.4219 \tabularnewline
61 &  14 &  16.01 & -2.013 \tabularnewline
62 &  16 &  16.41 & -0.4089 \tabularnewline
63 &  13 &  16.55 & -3.55 \tabularnewline
64 &  16 &  16.5 & -0.4976 \tabularnewline
65 &  14 &  16 & -1.999 \tabularnewline
66 &  20 &  17.68 &  2.315 \tabularnewline
67 &  13 &  16.35 & -3.349 \tabularnewline
68 &  18 &  17.29 &  0.7138 \tabularnewline
69 &  14 &  15.61 & -1.606 \tabularnewline
70 &  19 &  16.85 &  2.152 \tabularnewline
71 &  18 &  16.93 &  1.067 \tabularnewline
72 &  14 &  16.91 & -2.909 \tabularnewline
73 &  18 &  16.82 &  1.18 \tabularnewline
74 &  19 &  17.18 &  1.82 \tabularnewline
75 &  15 &  15.64 & -0.6428 \tabularnewline
76 &  14 &  16.85 & -2.848 \tabularnewline
77 &  17 &  16.81 &  0.1915 \tabularnewline
78 &  19 &  17.24 &  1.756 \tabularnewline
79 &  13 &  16.66 & -3.656 \tabularnewline
80 &  19 &  17.06 &  1.944 \tabularnewline
81 &  18 &  17.68 &  0.3212 \tabularnewline
82 &  20 &  16.87 &  3.127 \tabularnewline
83 &  15 &  15.55 & -0.5481 \tabularnewline
84 &  15 &  16.39 & -1.388 \tabularnewline
85 &  20 &  17.18 &  2.82 \tabularnewline
86 &  15 &  16.85 & -1.848 \tabularnewline
87 &  19 &  16.41 &  2.585 \tabularnewline
88 &  18 &  16.5 &  1.502 \tabularnewline
89 &  18 &  17.18 &  0.8201 \tabularnewline
90 &  15 &  16.62 & -1.617 \tabularnewline
91 &  20 &  17.93 &  2.074 \tabularnewline
92 &  17 &  17.21 & -0.2071 \tabularnewline
93 &  12 &  16.87 & -4.873 \tabularnewline
94 &  18 &  17.59 &  0.4124 \tabularnewline
95 &  19 &  17.18 &  1.82 \tabularnewline
96 &  20 &  17.05 &  2.948 \tabularnewline
97 &  17 &  17.99 & -0.9897 \tabularnewline
98 &  16 &  15.08 &  0.9212 \tabularnewline
99 &  18 &  16.78 &  1.216 \tabularnewline
100 &  18 &  16.78 &  1.216 \tabularnewline
101 &  14 &  16.58 & -2.579 \tabularnewline
102 &  15 &  16.35 & -1.349 \tabularnewline
103 &  12 &  16.31 & -4.306 \tabularnewline
104 &  17 &  16.53 &  0.4658 \tabularnewline
105 &  14 &  15.91 & -1.91 \tabularnewline
106 &  18 &  16.48 &  1.524 \tabularnewline
107 &  17 &  16.29 &  0.715 \tabularnewline
108 &  17 &  16.67 &  0.3298 \tabularnewline
109 &  20 &  17.99 &  2.014 \tabularnewline
110 &  16 &  15.95 &  0.0474 \tabularnewline
111 &  14 &  16.35 & -2.345 \tabularnewline
112 &  15 &  16 & -0.9988 \tabularnewline
113 &  18 &  16.35 &  1.651 \tabularnewline
114 &  20 &  16.85 &  3.152 \tabularnewline
115 &  17 &  15.88 &  1.123 \tabularnewline
116 &  17 &  16.04 &  0.9587 \tabularnewline
117 &  17 &  15.91 &  1.09 \tabularnewline
118 &  17 &  16.74 &  0.2552 \tabularnewline
119 &  15 &  15.17 & -0.1675 \tabularnewline
120 &  17 &  15.51 &  1.486 \tabularnewline
121 &  18 &  15.24 &  2.763 \tabularnewline
122 &  17 &  16.41 &  0.5911 \tabularnewline
123 &  20 &  16.72 &  3.284 \tabularnewline
124 &  15 &  15.19 & -0.1925 \tabularnewline
125 &  16 &  16.08 & -0.07794 \tabularnewline
126 &  15 &  15.91 & -0.91 \tabularnewline
127 &  18 &  17.12 &  0.8817 \tabularnewline
128 &  15 &  16.75 & -1.747 \tabularnewline
129 &  18 &  17.99 &  0.007835 \tabularnewline
130 &  20 &  17.64 &  2.358 \tabularnewline
131 &  19 &  16.31 &  2.694 \tabularnewline
132 &  14 &  16.98 & -2.975 \tabularnewline
133 &  16 &  16.78 & -0.7839 \tabularnewline
134 &  15 &  15.48 & -0.4784 \tabularnewline
135 &  17 &  16.81 &  0.1915 \tabularnewline
136 &  18 &  17.24 &  0.7563 \tabularnewline
137 &  20 &  17.15 &  2.849 \tabularnewline
138 &  17 &  16.82 &  0.1795 \tabularnewline
139 &  18 &  16.78 &  1.216 \tabularnewline
140 &  15 &  14.77 &  0.2311 \tabularnewline
141 &  16 &  16.35 & -0.3512 \tabularnewline
142 &  11 &  15.91 & -4.91 \tabularnewline
143 &  15 &  16.74 & -1.745 \tabularnewline
144 &  18 &  16.93 &  1.067 \tabularnewline
145 &  17 &  17.18 & -0.1799 \tabularnewline
146 &  16 &  17.59 & -1.59 \tabularnewline
147 &  12 &  16.29 & -4.285 \tabularnewline
148 &  19 &  16.21 &  2.794 \tabularnewline
149 &  18 &  16.74 &  1.255 \tabularnewline
150 &  15 &  16.84 & -1.842 \tabularnewline
151 &  17 &  15.95 &  1.047 \tabularnewline
152 &  19 &  17.19 &  1.814 \tabularnewline
153 &  18 &  16.22 &  1.779 \tabularnewline
154 &  19 &  16.22 &  2.783 \tabularnewline
155 &  16 &  15.67 &  0.3326 \tabularnewline
156 &  16 &  16.74 & -0.7448 \tabularnewline
157 &  16 &  16.85 & -0.8476 \tabularnewline
158 &  14 &  16.71 & -2.708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&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] 16.48[/C][C]-2.482[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 16.76[/C][C] 2.244[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.28[/C][C]-0.2827[/C][/ROW]
[ROW][C]4[/C][C] 17[/C][C] 15.17[/C][C] 1.833[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 17.28[/C][C]-2.283[/C][/ROW]
[ROW][C]6[/C][C] 20[/C][C] 16.94[/C][C] 3.064[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.1[/C][C]-0.1038[/C][/ROW]
[ROW][C]8[/C][C] 19[/C][C] 16.48[/C][C] 2.523[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 17.21[/C][C]-2.207[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 17.64[/C][C]-2.642[/C][/ROW]
[ROW][C]11[/C][C] 19[/C][C] 15.98[/C][C] 3.023[/C][/ROW]
[ROW][C]12[/C][C] 20[/C][C] 16.31[/C][C] 3.694[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.91[/C][C] 1.091[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 16.18[/C][C]-1.182[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 16.87[/C][C]-2.873[/C][/ROW]
[ROW][C]16[/C][C] 20[/C][C] 16.87[/C][C] 3.127[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.18[/C][C]-0.1808[/C][/ROW]
[ROW][C]18[/C][C] 16[/C][C] 16.85[/C][C]-0.8476[/C][/ROW]
[ROW][C]19[/C][C] 16[/C][C] 16.43[/C][C]-0.4339[/C][/ROW]
[ROW][C]20[/C][C] 10[/C][C] 17.24[/C][C]-7.244[/C][/ROW]
[ROW][C]21[/C][C] 19[/C][C] 17.18[/C][C] 1.82[/C][/ROW]
[ROW][C]22[/C][C] 19[/C][C] 16.78[/C][C] 2.216[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 17.18[/C][C]-1.18[/C][/ROW]
[ROW][C]24[/C][C] 15[/C][C] 16.04[/C][C]-1.041[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 16.5[/C][C] 1.502[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.35[/C][C] 0.6513[/C][/ROW]
[ROW][C]27[/C][C] 19[/C][C] 17.06[/C][C] 1.936[/C][/ROW]
[ROW][C]28[/C][C] 17[/C][C] 16.66[/C][C] 0.344[/C][/ROW]
[ROW][C]29[/C][C] 19[/C][C] 17.64[/C][C] 1.358[/C][/ROW]
[ROW][C]30[/C][C] 20[/C][C] 17.49[/C][C] 2.513[/C][/ROW]
[ROW][C]31[/C][C] 19[/C][C] 16.7[/C][C] 2.295[/C][/ROW]
[ROW][C]32[/C][C] 16[/C][C] 15.44[/C][C] 0.5582[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 16.48[/C][C]-1.476[/C][/ROW]
[ROW][C]34[/C][C] 16[/C][C] 16.78[/C][C]-0.7779[/C][/ROW]
[ROW][C]35[/C][C] 18[/C][C] 15.95[/C][C] 2.05[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 16.78[/C][C]-0.7839[/C][/ROW]
[ROW][C]37[/C][C] 15[/C][C] 16.66[/C][C]-1.656[/C][/ROW]
[ROW][C]38[/C][C] 17[/C][C] 17.64[/C][C]-0.6422[/C][/ROW]
[ROW][C]39[/C][C] 20[/C][C] 17.51[/C][C] 2.489[/C][/ROW]
[ROW][C]40[/C][C] 19[/C][C] 16.65[/C][C] 2.347[/C][/ROW]
[ROW][C]41[/C][C] 7[/C][C] 16.31[/C][C]-9.306[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 16.35[/C][C]-3.349[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 15.5[/C][C] 0.4977[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 16.85[/C][C]-0.8476[/C][/ROW]
[ROW][C]45[/C][C] 18[/C][C] 16.75[/C][C] 1.253[/C][/ROW]
[ROW][C]46[/C][C] 18[/C][C] 16.25[/C][C] 1.752[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 17.05[/C][C]-1.055[/C][/ROW]
[ROW][C]48[/C][C] 17[/C][C] 17.72[/C][C]-0.7154[/C][/ROW]
[ROW][C]49[/C][C] 19[/C][C] 16.34[/C][C] 2.657[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 16.78[/C][C]-0.7839[/C][/ROW]
[ROW][C]51[/C][C] 19[/C][C] 15.1[/C][C] 3.896[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 15.65[/C][C]-2.645[/C][/ROW]
[ROW][C]53[/C][C] 16[/C][C] 16.41[/C][C]-0.4089[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 16.85[/C][C]-3.848[/C][/ROW]
[ROW][C]55[/C][C] 12[/C][C] 17.08[/C][C]-5.079[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 16.63[/C][C] 0.371[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 17.24[/C][C]-0.2437[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 15.04[/C][C] 1.956[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 17.68[/C][C]-1.679[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 16.42[/C][C]-0.4219[/C][/ROW]
[ROW][C]61[/C][C] 14[/C][C] 16.01[/C][C]-2.013[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 16.41[/C][C]-0.4089[/C][/ROW]
[ROW][C]63[/C][C] 13[/C][C] 16.55[/C][C]-3.55[/C][/ROW]
[ROW][C]64[/C][C] 16[/C][C] 16.5[/C][C]-0.4976[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 16[/C][C]-1.999[/C][/ROW]
[ROW][C]66[/C][C] 20[/C][C] 17.68[/C][C] 2.315[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.35[/C][C]-3.349[/C][/ROW]
[ROW][C]68[/C][C] 18[/C][C] 17.29[/C][C] 0.7138[/C][/ROW]
[ROW][C]69[/C][C] 14[/C][C] 15.61[/C][C]-1.606[/C][/ROW]
[ROW][C]70[/C][C] 19[/C][C] 16.85[/C][C] 2.152[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 16.93[/C][C] 1.067[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 16.91[/C][C]-2.909[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 16.82[/C][C] 1.18[/C][/ROW]
[ROW][C]74[/C][C] 19[/C][C] 17.18[/C][C] 1.82[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 15.64[/C][C]-0.6428[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 16.85[/C][C]-2.848[/C][/ROW]
[ROW][C]77[/C][C] 17[/C][C] 16.81[/C][C] 0.1915[/C][/ROW]
[ROW][C]78[/C][C] 19[/C][C] 17.24[/C][C] 1.756[/C][/ROW]
[ROW][C]79[/C][C] 13[/C][C] 16.66[/C][C]-3.656[/C][/ROW]
[ROW][C]80[/C][C] 19[/C][C] 17.06[/C][C] 1.944[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 17.68[/C][C] 0.3212[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 16.87[/C][C] 3.127[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 15.55[/C][C]-0.5481[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 16.39[/C][C]-1.388[/C][/ROW]
[ROW][C]85[/C][C] 20[/C][C] 17.18[/C][C] 2.82[/C][/ROW]
[ROW][C]86[/C][C] 15[/C][C] 16.85[/C][C]-1.848[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 16.41[/C][C] 2.585[/C][/ROW]
[ROW][C]88[/C][C] 18[/C][C] 16.5[/C][C] 1.502[/C][/ROW]
[ROW][C]89[/C][C] 18[/C][C] 17.18[/C][C] 0.8201[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 16.62[/C][C]-1.617[/C][/ROW]
[ROW][C]91[/C][C] 20[/C][C] 17.93[/C][C] 2.074[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 17.21[/C][C]-0.2071[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 16.87[/C][C]-4.873[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 17.59[/C][C] 0.4124[/C][/ROW]
[ROW][C]95[/C][C] 19[/C][C] 17.18[/C][C] 1.82[/C][/ROW]
[ROW][C]96[/C][C] 20[/C][C] 17.05[/C][C] 2.948[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 17.99[/C][C]-0.9897[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.08[/C][C] 0.9212[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 16.78[/C][C] 1.216[/C][/ROW]
[ROW][C]100[/C][C] 18[/C][C] 16.78[/C][C] 1.216[/C][/ROW]
[ROW][C]101[/C][C] 14[/C][C] 16.58[/C][C]-2.579[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 16.35[/C][C]-1.349[/C][/ROW]
[ROW][C]103[/C][C] 12[/C][C] 16.31[/C][C]-4.306[/C][/ROW]
[ROW][C]104[/C][C] 17[/C][C] 16.53[/C][C] 0.4658[/C][/ROW]
[ROW][C]105[/C][C] 14[/C][C] 15.91[/C][C]-1.91[/C][/ROW]
[ROW][C]106[/C][C] 18[/C][C] 16.48[/C][C] 1.524[/C][/ROW]
[ROW][C]107[/C][C] 17[/C][C] 16.29[/C][C] 0.715[/C][/ROW]
[ROW][C]108[/C][C] 17[/C][C] 16.67[/C][C] 0.3298[/C][/ROW]
[ROW][C]109[/C][C] 20[/C][C] 17.99[/C][C] 2.014[/C][/ROW]
[ROW][C]110[/C][C] 16[/C][C] 15.95[/C][C] 0.0474[/C][/ROW]
[ROW][C]111[/C][C] 14[/C][C] 16.35[/C][C]-2.345[/C][/ROW]
[ROW][C]112[/C][C] 15[/C][C] 16[/C][C]-0.9988[/C][/ROW]
[ROW][C]113[/C][C] 18[/C][C] 16.35[/C][C] 1.651[/C][/ROW]
[ROW][C]114[/C][C] 20[/C][C] 16.85[/C][C] 3.152[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 15.88[/C][C] 1.123[/C][/ROW]
[ROW][C]116[/C][C] 17[/C][C] 16.04[/C][C] 0.9587[/C][/ROW]
[ROW][C]117[/C][C] 17[/C][C] 15.91[/C][C] 1.09[/C][/ROW]
[ROW][C]118[/C][C] 17[/C][C] 16.74[/C][C] 0.2552[/C][/ROW]
[ROW][C]119[/C][C] 15[/C][C] 15.17[/C][C]-0.1675[/C][/ROW]
[ROW][C]120[/C][C] 17[/C][C] 15.51[/C][C] 1.486[/C][/ROW]
[ROW][C]121[/C][C] 18[/C][C] 15.24[/C][C] 2.763[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.41[/C][C] 0.5911[/C][/ROW]
[ROW][C]123[/C][C] 20[/C][C] 16.72[/C][C] 3.284[/C][/ROW]
[ROW][C]124[/C][C] 15[/C][C] 15.19[/C][C]-0.1925[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 16.08[/C][C]-0.07794[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 15.91[/C][C]-0.91[/C][/ROW]
[ROW][C]127[/C][C] 18[/C][C] 17.12[/C][C] 0.8817[/C][/ROW]
[ROW][C]128[/C][C] 15[/C][C] 16.75[/C][C]-1.747[/C][/ROW]
[ROW][C]129[/C][C] 18[/C][C] 17.99[/C][C] 0.007835[/C][/ROW]
[ROW][C]130[/C][C] 20[/C][C] 17.64[/C][C] 2.358[/C][/ROW]
[ROW][C]131[/C][C] 19[/C][C] 16.31[/C][C] 2.694[/C][/ROW]
[ROW][C]132[/C][C] 14[/C][C] 16.98[/C][C]-2.975[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 16.78[/C][C]-0.7839[/C][/ROW]
[ROW][C]134[/C][C] 15[/C][C] 15.48[/C][C]-0.4784[/C][/ROW]
[ROW][C]135[/C][C] 17[/C][C] 16.81[/C][C] 0.1915[/C][/ROW]
[ROW][C]136[/C][C] 18[/C][C] 17.24[/C][C] 0.7563[/C][/ROW]
[ROW][C]137[/C][C] 20[/C][C] 17.15[/C][C] 2.849[/C][/ROW]
[ROW][C]138[/C][C] 17[/C][C] 16.82[/C][C] 0.1795[/C][/ROW]
[ROW][C]139[/C][C] 18[/C][C] 16.78[/C][C] 1.216[/C][/ROW]
[ROW][C]140[/C][C] 15[/C][C] 14.77[/C][C] 0.2311[/C][/ROW]
[ROW][C]141[/C][C] 16[/C][C] 16.35[/C][C]-0.3512[/C][/ROW]
[ROW][C]142[/C][C] 11[/C][C] 15.91[/C][C]-4.91[/C][/ROW]
[ROW][C]143[/C][C] 15[/C][C] 16.74[/C][C]-1.745[/C][/ROW]
[ROW][C]144[/C][C] 18[/C][C] 16.93[/C][C] 1.067[/C][/ROW]
[ROW][C]145[/C][C] 17[/C][C] 17.18[/C][C]-0.1799[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 17.59[/C][C]-1.59[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 16.29[/C][C]-4.285[/C][/ROW]
[ROW][C]148[/C][C] 19[/C][C] 16.21[/C][C] 2.794[/C][/ROW]
[ROW][C]149[/C][C] 18[/C][C] 16.74[/C][C] 1.255[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 16.84[/C][C]-1.842[/C][/ROW]
[ROW][C]151[/C][C] 17[/C][C] 15.95[/C][C] 1.047[/C][/ROW]
[ROW][C]152[/C][C] 19[/C][C] 17.19[/C][C] 1.814[/C][/ROW]
[ROW][C]153[/C][C] 18[/C][C] 16.22[/C][C] 1.779[/C][/ROW]
[ROW][C]154[/C][C] 19[/C][C] 16.22[/C][C] 2.783[/C][/ROW]
[ROW][C]155[/C][C] 16[/C][C] 15.67[/C][C] 0.3326[/C][/ROW]
[ROW][C]156[/C][C] 16[/C][C] 16.74[/C][C]-0.7448[/C][/ROW]
[ROW][C]157[/C][C] 16[/C][C] 16.85[/C][C]-0.8476[/C][/ROW]
[ROW][C]158[/C][C] 14[/C][C] 16.71[/C][C]-2.708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298227&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&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	16.48	-2.482
2	19	16.76	2.244
3	17	17.28	-0.2827
4	17	15.17	1.833
5	15	17.28	-2.283
6	20	16.94	3.064
7	15	15.1	-0.1038
8	19	16.48	2.523
9	15	17.21	-2.207
10	15	17.64	-2.642
11	19	15.98	3.023
12	20	16.31	3.694
13	18	16.91	1.091
14	15	16.18	-1.182
15	14	16.87	-2.873
16	20	16.87	3.127
17	16	16.18	-0.1808
18	16	16.85	-0.8476
19	16	16.43	-0.4339
20	10	17.24	-7.244
21	19	17.18	1.82
22	19	16.78	2.216
23	16	17.18	-1.18
24	15	16.04	-1.041
25	18	16.5	1.502
26	17	16.35	0.6513
27	19	17.06	1.936
28	17	16.66	0.344
29	19	17.64	1.358
30	20	17.49	2.513
31	19	16.7	2.295
32	16	15.44	0.5582
33	15	16.48	-1.476
34	16	16.78	-0.7779
35	18	15.95	2.05
36	16	16.78	-0.7839
37	15	16.66	-1.656
38	17	17.64	-0.6422
39	20	17.51	2.489
40	19	16.65	2.347
41	7	16.31	-9.306
42	13	16.35	-3.349
43	16	15.5	0.4977
44	16	16.85	-0.8476
45	18	16.75	1.253
46	18	16.25	1.752
47	16	17.05	-1.055
48	17	17.72	-0.7154
49	19	16.34	2.657
50	16	16.78	-0.7839
51	19	15.1	3.896
52	13	15.65	-2.645
53	16	16.41	-0.4089
54	13	16.85	-3.848
55	12	17.08	-5.079
56	17	16.63	0.371
57	17	17.24	-0.2437
58	17	15.04	1.956
59	16	17.68	-1.679
60	16	16.42	-0.4219
61	14	16.01	-2.013
62	16	16.41	-0.4089
63	13	16.55	-3.55
64	16	16.5	-0.4976
65	14	16	-1.999
66	20	17.68	2.315
67	13	16.35	-3.349
68	18	17.29	0.7138
69	14	15.61	-1.606
70	19	16.85	2.152
71	18	16.93	1.067
72	14	16.91	-2.909
73	18	16.82	1.18
74	19	17.18	1.82
75	15	15.64	-0.6428
76	14	16.85	-2.848
77	17	16.81	0.1915
78	19	17.24	1.756
79	13	16.66	-3.656
80	19	17.06	1.944
81	18	17.68	0.3212
82	20	16.87	3.127
83	15	15.55	-0.5481
84	15	16.39	-1.388
85	20	17.18	2.82
86	15	16.85	-1.848
87	19	16.41	2.585
88	18	16.5	1.502
89	18	17.18	0.8201
90	15	16.62	-1.617
91	20	17.93	2.074
92	17	17.21	-0.2071
93	12	16.87	-4.873
94	18	17.59	0.4124
95	19	17.18	1.82
96	20	17.05	2.948
97	17	17.99	-0.9897
98	16	15.08	0.9212
99	18	16.78	1.216
100	18	16.78	1.216
101	14	16.58	-2.579
102	15	16.35	-1.349
103	12	16.31	-4.306
104	17	16.53	0.4658
105	14	15.91	-1.91
106	18	16.48	1.524
107	17	16.29	0.715
108	17	16.67	0.3298
109	20	17.99	2.014
110	16	15.95	0.0474
111	14	16.35	-2.345
112	15	16	-0.9988
113	18	16.35	1.651
114	20	16.85	3.152
115	17	15.88	1.123
116	17	16.04	0.9587
117	17	15.91	1.09
118	17	16.74	0.2552
119	15	15.17	-0.1675
120	17	15.51	1.486
121	18	15.24	2.763
122	17	16.41	0.5911
123	20	16.72	3.284
124	15	15.19	-0.1925
125	16	16.08	-0.07794
126	15	15.91	-0.91
127	18	17.12	0.8817
128	15	16.75	-1.747
129	18	17.99	0.007835
130	20	17.64	2.358
131	19	16.31	2.694
132	14	16.98	-2.975
133	16	16.78	-0.7839
134	15	15.48	-0.4784
135	17	16.81	0.1915
136	18	17.24	0.7563
137	20	17.15	2.849
138	17	16.82	0.1795
139	18	16.78	1.216
140	15	14.77	0.2311
141	16	16.35	-0.3512
142	11	15.91	-4.91
143	15	16.74	-1.745
144	18	16.93	1.067
145	17	17.18	-0.1799
146	16	17.59	-1.59
147	12	16.29	-4.285
148	19	16.21	2.794
149	18	16.74	1.255
150	15	16.84	-1.842
151	17	15.95	1.047
152	19	17.19	1.814
153	18	16.22	1.779
154	19	16.22	2.783
155	16	15.67	0.3326
156	16	16.74	-0.7448
157	16	16.85	-0.8476
158	14	16.71	-2.708

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1618	0.3236	0.8382
11	0.1508	0.3016	0.8492
12	0.6534	0.6932	0.3466
13	0.6674	0.6653	0.3326
14	0.6879	0.6241	0.3121
15	0.7552	0.4896	0.2448
16	0.787	0.426	0.213
17	0.7409	0.5182	0.2591
18	0.6769	0.6463	0.3231
19	0.5952	0.8096	0.4048
20	0.9589	0.08216	0.04108
21	0.9635	0.07304	0.03652
22	0.9612	0.07754	0.03877
23	0.9447	0.1105	0.05525
24	0.9453	0.1095	0.05473
25	0.933	0.134	0.06698
26	0.9086	0.1827	0.09135
27	0.892	0.2159	0.108
28	0.8619	0.2761	0.1381
29	0.8602	0.2796	0.1398
30	0.888	0.2241	0.112
31	0.8741	0.2517	0.1259
32	0.8437	0.3126	0.1563
33	0.8296	0.3408	0.1704
34	0.7936	0.4128	0.2064
35	0.7627	0.4746	0.2373
36	0.7191	0.5618	0.2809
37	0.6902	0.6196	0.3098
38	0.6392	0.7215	0.3608
39	0.6857	0.6285	0.3143
40	0.686	0.628	0.314
41	0.9935	0.01302	0.006512
42	0.9958	0.008468	0.004234
43	0.9939	0.0123	0.006149
44	0.9917	0.0166	0.0083
45	0.9891	0.02178	0.01089
46	0.9871	0.02582	0.01291
47	0.9832	0.03356	0.01678
48	0.9775	0.04492	0.02246
49	0.9799	0.04019	0.0201
50	0.9738	0.05237	0.02619
51	0.9821	0.03589	0.01794
52	0.9854	0.0292	0.0146
53	0.9805	0.039	0.0195
54	0.9883	0.0235	0.01175
55	0.9965	0.007023	0.003511
56	0.995	0.009978	0.004989
57	0.993	0.01391	0.006956
58	0.9927	0.01465	0.007323
59	0.9914	0.0171	0.008551
60	0.9884	0.02325	0.01163
61	0.9879	0.02412	0.01206
62	0.984	0.03208	0.01604
63	0.9891	0.0217	0.01085
64	0.9856	0.02888	0.01444
65	0.9849	0.03011	0.01506
66	0.9854	0.02929	0.01464
67	0.9899	0.02012	0.01006
68	0.9867	0.02667	0.01334
69	0.9848	0.03038	0.01519
70	0.9844	0.03121	0.01561
71	0.9804	0.03915	0.01958
72	0.9837	0.03261	0.01631
73	0.9799	0.04019	0.0201
74	0.9781	0.04381	0.0219
75	0.9718	0.05649	0.02825
76	0.9767	0.04667	0.02334
77	0.9698	0.06043	0.03021
78	0.9665	0.06708	0.03354
79	0.9799	0.04026	0.02013
80	0.9781	0.04374	0.02187
81	0.9714	0.05717	0.02858
82	0.9792	0.04166	0.02083
83	0.9734	0.05318	0.02659
84	0.9689	0.06222	0.03111
85	0.9743	0.0514	0.0257
86	0.9734	0.05319	0.0266
87	0.9755	0.0491	0.02455
88	0.9726	0.05483	0.02741
89	0.9655	0.0689	0.03445
90	0.9645	0.07094	0.03547
91	0.9616	0.07686	0.03843
92	0.9502	0.09952	0.04976
93	0.981	0.03802	0.01901
94	0.9755	0.04908	0.02454
95	0.973	0.05393	0.02697
96	0.9769	0.04611	0.02305
97	0.9729	0.05425	0.02713
98	0.9654	0.0691	0.03455
99	0.9582	0.08358	0.04179
100	0.9499	0.1001	0.05005
101	0.9564	0.08721	0.0436
102	0.9492	0.1016	0.05082
103	0.9787	0.04251	0.02125
104	0.9714	0.05713	0.02856
105	0.9715	0.05706	0.02853
106	0.9688	0.0625	0.03125
107	0.9608	0.07838	0.03919
108	0.955	0.08995	0.04498
109	0.9489	0.1021	0.05106
110	0.9332	0.1337	0.06683
111	0.9425	0.115	0.05748
112	0.9306	0.1388	0.06942
113	0.9227	0.1547	0.07734
114	0.9384	0.1233	0.06163
115	0.9246	0.1509	0.07543
116	0.9116	0.1769	0.08843
117	0.8894	0.2212	0.1106
118	0.8603	0.2794	0.1397
119	0.8263	0.3473	0.1737
120	0.7977	0.4046	0.2023
121	0.8058	0.3884	0.1942
122	0.7627	0.4745	0.2373
123	0.7841	0.4319	0.2159
124	0.7367	0.5267	0.2633
125	0.6886	0.6228	0.3114
126	0.6423	0.7155	0.3577
127	0.5833	0.8335	0.4167
128	0.5646	0.8707	0.4354
129	0.5	0.9999	0.5
130	0.4781	0.9562	0.5219
131	0.4971	0.9942	0.5029
132	0.5386	0.9227	0.4614
133	0.4728	0.9456	0.5272
134	0.4035	0.8071	0.5965
135	0.3336	0.6671	0.6664
136	0.2721	0.5442	0.7279
137	0.2912	0.5823	0.7088
138	0.2376	0.4751	0.7624
139	0.2064	0.4127	0.7936
140	0.1522	0.3043	0.8478
141	0.1113	0.2226	0.8887
142	0.4792	0.9584	0.5208
143	0.4197	0.8395	0.5803
144	0.3788	0.7576	0.6212
145	0.285	0.5699	0.715
146	0.2022	0.4044	0.7978
147	0.8194	0.3612	0.1806
148	0.8516	0.2967	0.1484

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1618 &  0.3236 &  0.8382 \tabularnewline
11 &  0.1508 &  0.3016 &  0.8492 \tabularnewline
12 &  0.6534 &  0.6932 &  0.3466 \tabularnewline
13 &  0.6674 &  0.6653 &  0.3326 \tabularnewline
14 &  0.6879 &  0.6241 &  0.3121 \tabularnewline
15 &  0.7552 &  0.4896 &  0.2448 \tabularnewline
16 &  0.787 &  0.426 &  0.213 \tabularnewline
17 &  0.7409 &  0.5182 &  0.2591 \tabularnewline
18 &  0.6769 &  0.6463 &  0.3231 \tabularnewline
19 &  0.5952 &  0.8096 &  0.4048 \tabularnewline
20 &  0.9589 &  0.08216 &  0.04108 \tabularnewline
21 &  0.9635 &  0.07304 &  0.03652 \tabularnewline
22 &  0.9612 &  0.07754 &  0.03877 \tabularnewline
23 &  0.9447 &  0.1105 &  0.05525 \tabularnewline
24 &  0.9453 &  0.1095 &  0.05473 \tabularnewline
25 &  0.933 &  0.134 &  0.06698 \tabularnewline
26 &  0.9086 &  0.1827 &  0.09135 \tabularnewline
27 &  0.892 &  0.2159 &  0.108 \tabularnewline
28 &  0.8619 &  0.2761 &  0.1381 \tabularnewline
29 &  0.8602 &  0.2796 &  0.1398 \tabularnewline
30 &  0.888 &  0.2241 &  0.112 \tabularnewline
31 &  0.8741 &  0.2517 &  0.1259 \tabularnewline
32 &  0.8437 &  0.3126 &  0.1563 \tabularnewline
33 &  0.8296 &  0.3408 &  0.1704 \tabularnewline
34 &  0.7936 &  0.4128 &  0.2064 \tabularnewline
35 &  0.7627 &  0.4746 &  0.2373 \tabularnewline
36 &  0.7191 &  0.5618 &  0.2809 \tabularnewline
37 &  0.6902 &  0.6196 &  0.3098 \tabularnewline
38 &  0.6392 &  0.7215 &  0.3608 \tabularnewline
39 &  0.6857 &  0.6285 &  0.3143 \tabularnewline
40 &  0.686 &  0.628 &  0.314 \tabularnewline
41 &  0.9935 &  0.01302 &  0.006512 \tabularnewline
42 &  0.9958 &  0.008468 &  0.004234 \tabularnewline
43 &  0.9939 &  0.0123 &  0.006149 \tabularnewline
44 &  0.9917 &  0.0166 &  0.0083 \tabularnewline
45 &  0.9891 &  0.02178 &  0.01089 \tabularnewline
46 &  0.9871 &  0.02582 &  0.01291 \tabularnewline
47 &  0.9832 &  0.03356 &  0.01678 \tabularnewline
48 &  0.9775 &  0.04492 &  0.02246 \tabularnewline
49 &  0.9799 &  0.04019 &  0.0201 \tabularnewline
50 &  0.9738 &  0.05237 &  0.02619 \tabularnewline
51 &  0.9821 &  0.03589 &  0.01794 \tabularnewline
52 &  0.9854 &  0.0292 &  0.0146 \tabularnewline
53 &  0.9805 &  0.039 &  0.0195 \tabularnewline
54 &  0.9883 &  0.0235 &  0.01175 \tabularnewline
55 &  0.9965 &  0.007023 &  0.003511 \tabularnewline
56 &  0.995 &  0.009978 &  0.004989 \tabularnewline
57 &  0.993 &  0.01391 &  0.006956 \tabularnewline
58 &  0.9927 &  0.01465 &  0.007323 \tabularnewline
59 &  0.9914 &  0.0171 &  0.008551 \tabularnewline
60 &  0.9884 &  0.02325 &  0.01163 \tabularnewline
61 &  0.9879 &  0.02412 &  0.01206 \tabularnewline
62 &  0.984 &  0.03208 &  0.01604 \tabularnewline
63 &  0.9891 &  0.0217 &  0.01085 \tabularnewline
64 &  0.9856 &  0.02888 &  0.01444 \tabularnewline
65 &  0.9849 &  0.03011 &  0.01506 \tabularnewline
66 &  0.9854 &  0.02929 &  0.01464 \tabularnewline
67 &  0.9899 &  0.02012 &  0.01006 \tabularnewline
68 &  0.9867 &  0.02667 &  0.01334 \tabularnewline
69 &  0.9848 &  0.03038 &  0.01519 \tabularnewline
70 &  0.9844 &  0.03121 &  0.01561 \tabularnewline
71 &  0.9804 &  0.03915 &  0.01958 \tabularnewline
72 &  0.9837 &  0.03261 &  0.01631 \tabularnewline
73 &  0.9799 &  0.04019 &  0.0201 \tabularnewline
74 &  0.9781 &  0.04381 &  0.0219 \tabularnewline
75 &  0.9718 &  0.05649 &  0.02825 \tabularnewline
76 &  0.9767 &  0.04667 &  0.02334 \tabularnewline
77 &  0.9698 &  0.06043 &  0.03021 \tabularnewline
78 &  0.9665 &  0.06708 &  0.03354 \tabularnewline
79 &  0.9799 &  0.04026 &  0.02013 \tabularnewline
80 &  0.9781 &  0.04374 &  0.02187 \tabularnewline
81 &  0.9714 &  0.05717 &  0.02858 \tabularnewline
82 &  0.9792 &  0.04166 &  0.02083 \tabularnewline
83 &  0.9734 &  0.05318 &  0.02659 \tabularnewline
84 &  0.9689 &  0.06222 &  0.03111 \tabularnewline
85 &  0.9743 &  0.0514 &  0.0257 \tabularnewline
86 &  0.9734 &  0.05319 &  0.0266 \tabularnewline
87 &  0.9755 &  0.0491 &  0.02455 \tabularnewline
88 &  0.9726 &  0.05483 &  0.02741 \tabularnewline
89 &  0.9655 &  0.0689 &  0.03445 \tabularnewline
90 &  0.9645 &  0.07094 &  0.03547 \tabularnewline
91 &  0.9616 &  0.07686 &  0.03843 \tabularnewline
92 &  0.9502 &  0.09952 &  0.04976 \tabularnewline
93 &  0.981 &  0.03802 &  0.01901 \tabularnewline
94 &  0.9755 &  0.04908 &  0.02454 \tabularnewline
95 &  0.973 &  0.05393 &  0.02697 \tabularnewline
96 &  0.9769 &  0.04611 &  0.02305 \tabularnewline
97 &  0.9729 &  0.05425 &  0.02713 \tabularnewline
98 &  0.9654 &  0.0691 &  0.03455 \tabularnewline
99 &  0.9582 &  0.08358 &  0.04179 \tabularnewline
100 &  0.9499 &  0.1001 &  0.05005 \tabularnewline
101 &  0.9564 &  0.08721 &  0.0436 \tabularnewline
102 &  0.9492 &  0.1016 &  0.05082 \tabularnewline
103 &  0.9787 &  0.04251 &  0.02125 \tabularnewline
104 &  0.9714 &  0.05713 &  0.02856 \tabularnewline
105 &  0.9715 &  0.05706 &  0.02853 \tabularnewline
106 &  0.9688 &  0.0625 &  0.03125 \tabularnewline
107 &  0.9608 &  0.07838 &  0.03919 \tabularnewline
108 &  0.955 &  0.08995 &  0.04498 \tabularnewline
109 &  0.9489 &  0.1021 &  0.05106 \tabularnewline
110 &  0.9332 &  0.1337 &  0.06683 \tabularnewline
111 &  0.9425 &  0.115 &  0.05748 \tabularnewline
112 &  0.9306 &  0.1388 &  0.06942 \tabularnewline
113 &  0.9227 &  0.1547 &  0.07734 \tabularnewline
114 &  0.9384 &  0.1233 &  0.06163 \tabularnewline
115 &  0.9246 &  0.1509 &  0.07543 \tabularnewline
116 &  0.9116 &  0.1769 &  0.08843 \tabularnewline
117 &  0.8894 &  0.2212 &  0.1106 \tabularnewline
118 &  0.8603 &  0.2794 &  0.1397 \tabularnewline
119 &  0.8263 &  0.3473 &  0.1737 \tabularnewline
120 &  0.7977 &  0.4046 &  0.2023 \tabularnewline
121 &  0.8058 &  0.3884 &  0.1942 \tabularnewline
122 &  0.7627 &  0.4745 &  0.2373 \tabularnewline
123 &  0.7841 &  0.4319 &  0.2159 \tabularnewline
124 &  0.7367 &  0.5267 &  0.2633 \tabularnewline
125 &  0.6886 &  0.6228 &  0.3114 \tabularnewline
126 &  0.6423 &  0.7155 &  0.3577 \tabularnewline
127 &  0.5833 &  0.8335 &  0.4167 \tabularnewline
128 &  0.5646 &  0.8707 &  0.4354 \tabularnewline
129 &  0.5 &  0.9999 &  0.5 \tabularnewline
130 &  0.4781 &  0.9562 &  0.5219 \tabularnewline
131 &  0.4971 &  0.9942 &  0.5029 \tabularnewline
132 &  0.5386 &  0.9227 &  0.4614 \tabularnewline
133 &  0.4728 &  0.9456 &  0.5272 \tabularnewline
134 &  0.4035 &  0.8071 &  0.5965 \tabularnewline
135 &  0.3336 &  0.6671 &  0.6664 \tabularnewline
136 &  0.2721 &  0.5442 &  0.7279 \tabularnewline
137 &  0.2912 &  0.5823 &  0.7088 \tabularnewline
138 &  0.2376 &  0.4751 &  0.7624 \tabularnewline
139 &  0.2064 &  0.4127 &  0.7936 \tabularnewline
140 &  0.1522 &  0.3043 &  0.8478 \tabularnewline
141 &  0.1113 &  0.2226 &  0.8887 \tabularnewline
142 &  0.4792 &  0.9584 &  0.5208 \tabularnewline
143 &  0.4197 &  0.8395 &  0.5803 \tabularnewline
144 &  0.3788 &  0.7576 &  0.6212 \tabularnewline
145 &  0.285 &  0.5699 &  0.715 \tabularnewline
146 &  0.2022 &  0.4044 &  0.7978 \tabularnewline
147 &  0.8194 &  0.3612 &  0.1806 \tabularnewline
148 &  0.8516 &  0.2967 &  0.1484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C] 0.1618[/C][C] 0.3236[/C][C] 0.8382[/C][/ROW]
[ROW][C]11[/C][C] 0.1508[/C][C] 0.3016[/C][C] 0.8492[/C][/ROW]
[ROW][C]12[/C][C] 0.6534[/C][C] 0.6932[/C][C] 0.3466[/C][/ROW]
[ROW][C]13[/C][C] 0.6674[/C][C] 0.6653[/C][C] 0.3326[/C][/ROW]
[ROW][C]14[/C][C] 0.6879[/C][C] 0.6241[/C][C] 0.3121[/C][/ROW]
[ROW][C]15[/C][C] 0.7552[/C][C] 0.4896[/C][C] 0.2448[/C][/ROW]
[ROW][C]16[/C][C] 0.787[/C][C] 0.426[/C][C] 0.213[/C][/ROW]
[ROW][C]17[/C][C] 0.7409[/C][C] 0.5182[/C][C] 0.2591[/C][/ROW]
[ROW][C]18[/C][C] 0.6769[/C][C] 0.6463[/C][C] 0.3231[/C][/ROW]
[ROW][C]19[/C][C] 0.5952[/C][C] 0.8096[/C][C] 0.4048[/C][/ROW]
[ROW][C]20[/C][C] 0.9589[/C][C] 0.08216[/C][C] 0.04108[/C][/ROW]
[ROW][C]21[/C][C] 0.9635[/C][C] 0.07304[/C][C] 0.03652[/C][/ROW]
[ROW][C]22[/C][C] 0.9612[/C][C] 0.07754[/C][C] 0.03877[/C][/ROW]
[ROW][C]23[/C][C] 0.9447[/C][C] 0.1105[/C][C] 0.05525[/C][/ROW]
[ROW][C]24[/C][C] 0.9453[/C][C] 0.1095[/C][C] 0.05473[/C][/ROW]
[ROW][C]25[/C][C] 0.933[/C][C] 0.134[/C][C] 0.06698[/C][/ROW]
[ROW][C]26[/C][C] 0.9086[/C][C] 0.1827[/C][C] 0.09135[/C][/ROW]
[ROW][C]27[/C][C] 0.892[/C][C] 0.2159[/C][C] 0.108[/C][/ROW]
[ROW][C]28[/C][C] 0.8619[/C][C] 0.2761[/C][C] 0.1381[/C][/ROW]
[ROW][C]29[/C][C] 0.8602[/C][C] 0.2796[/C][C] 0.1398[/C][/ROW]
[ROW][C]30[/C][C] 0.888[/C][C] 0.2241[/C][C] 0.112[/C][/ROW]
[ROW][C]31[/C][C] 0.8741[/C][C] 0.2517[/C][C] 0.1259[/C][/ROW]
[ROW][C]32[/C][C] 0.8437[/C][C] 0.3126[/C][C] 0.1563[/C][/ROW]
[ROW][C]33[/C][C] 0.8296[/C][C] 0.3408[/C][C] 0.1704[/C][/ROW]
[ROW][C]34[/C][C] 0.7936[/C][C] 0.4128[/C][C] 0.2064[/C][/ROW]
[ROW][C]35[/C][C] 0.7627[/C][C] 0.4746[/C][C] 0.2373[/C][/ROW]
[ROW][C]36[/C][C] 0.7191[/C][C] 0.5618[/C][C] 0.2809[/C][/ROW]
[ROW][C]37[/C][C] 0.6902[/C][C] 0.6196[/C][C] 0.3098[/C][/ROW]
[ROW][C]38[/C][C] 0.6392[/C][C] 0.7215[/C][C] 0.3608[/C][/ROW]
[ROW][C]39[/C][C] 0.6857[/C][C] 0.6285[/C][C] 0.3143[/C][/ROW]
[ROW][C]40[/C][C] 0.686[/C][C] 0.628[/C][C] 0.314[/C][/ROW]
[ROW][C]41[/C][C] 0.9935[/C][C] 0.01302[/C][C] 0.006512[/C][/ROW]
[ROW][C]42[/C][C] 0.9958[/C][C] 0.008468[/C][C] 0.004234[/C][/ROW]
[ROW][C]43[/C][C] 0.9939[/C][C] 0.0123[/C][C] 0.006149[/C][/ROW]
[ROW][C]44[/C][C] 0.9917[/C][C] 0.0166[/C][C] 0.0083[/C][/ROW]
[ROW][C]45[/C][C] 0.9891[/C][C] 0.02178[/C][C] 0.01089[/C][/ROW]
[ROW][C]46[/C][C] 0.9871[/C][C] 0.02582[/C][C] 0.01291[/C][/ROW]
[ROW][C]47[/C][C] 0.9832[/C][C] 0.03356[/C][C] 0.01678[/C][/ROW]
[ROW][C]48[/C][C] 0.9775[/C][C] 0.04492[/C][C] 0.02246[/C][/ROW]
[ROW][C]49[/C][C] 0.9799[/C][C] 0.04019[/C][C] 0.0201[/C][/ROW]
[ROW][C]50[/C][C] 0.9738[/C][C] 0.05237[/C][C] 0.02619[/C][/ROW]
[ROW][C]51[/C][C] 0.9821[/C][C] 0.03589[/C][C] 0.01794[/C][/ROW]
[ROW][C]52[/C][C] 0.9854[/C][C] 0.0292[/C][C] 0.0146[/C][/ROW]
[ROW][C]53[/C][C] 0.9805[/C][C] 0.039[/C][C] 0.0195[/C][/ROW]
[ROW][C]54[/C][C] 0.9883[/C][C] 0.0235[/C][C] 0.01175[/C][/ROW]
[ROW][C]55[/C][C] 0.9965[/C][C] 0.007023[/C][C] 0.003511[/C][/ROW]
[ROW][C]56[/C][C] 0.995[/C][C] 0.009978[/C][C] 0.004989[/C][/ROW]
[ROW][C]57[/C][C] 0.993[/C][C] 0.01391[/C][C] 0.006956[/C][/ROW]
[ROW][C]58[/C][C] 0.9927[/C][C] 0.01465[/C][C] 0.007323[/C][/ROW]
[ROW][C]59[/C][C] 0.9914[/C][C] 0.0171[/C][C] 0.008551[/C][/ROW]
[ROW][C]60[/C][C] 0.9884[/C][C] 0.02325[/C][C] 0.01163[/C][/ROW]
[ROW][C]61[/C][C] 0.9879[/C][C] 0.02412[/C][C] 0.01206[/C][/ROW]
[ROW][C]62[/C][C] 0.984[/C][C] 0.03208[/C][C] 0.01604[/C][/ROW]
[ROW][C]63[/C][C] 0.9891[/C][C] 0.0217[/C][C] 0.01085[/C][/ROW]
[ROW][C]64[/C][C] 0.9856[/C][C] 0.02888[/C][C] 0.01444[/C][/ROW]
[ROW][C]65[/C][C] 0.9849[/C][C] 0.03011[/C][C] 0.01506[/C][/ROW]
[ROW][C]66[/C][C] 0.9854[/C][C] 0.02929[/C][C] 0.01464[/C][/ROW]
[ROW][C]67[/C][C] 0.9899[/C][C] 0.02012[/C][C] 0.01006[/C][/ROW]
[ROW][C]68[/C][C] 0.9867[/C][C] 0.02667[/C][C] 0.01334[/C][/ROW]
[ROW][C]69[/C][C] 0.9848[/C][C] 0.03038[/C][C] 0.01519[/C][/ROW]
[ROW][C]70[/C][C] 0.9844[/C][C] 0.03121[/C][C] 0.01561[/C][/ROW]
[ROW][C]71[/C][C] 0.9804[/C][C] 0.03915[/C][C] 0.01958[/C][/ROW]
[ROW][C]72[/C][C] 0.9837[/C][C] 0.03261[/C][C] 0.01631[/C][/ROW]
[ROW][C]73[/C][C] 0.9799[/C][C] 0.04019[/C][C] 0.0201[/C][/ROW]
[ROW][C]74[/C][C] 0.9781[/C][C] 0.04381[/C][C] 0.0219[/C][/ROW]
[ROW][C]75[/C][C] 0.9718[/C][C] 0.05649[/C][C] 0.02825[/C][/ROW]
[ROW][C]76[/C][C] 0.9767[/C][C] 0.04667[/C][C] 0.02334[/C][/ROW]
[ROW][C]77[/C][C] 0.9698[/C][C] 0.06043[/C][C] 0.03021[/C][/ROW]
[ROW][C]78[/C][C] 0.9665[/C][C] 0.06708[/C][C] 0.03354[/C][/ROW]
[ROW][C]79[/C][C] 0.9799[/C][C] 0.04026[/C][C] 0.02013[/C][/ROW]
[ROW][C]80[/C][C] 0.9781[/C][C] 0.04374[/C][C] 0.02187[/C][/ROW]
[ROW][C]81[/C][C] 0.9714[/C][C] 0.05717[/C][C] 0.02858[/C][/ROW]
[ROW][C]82[/C][C] 0.9792[/C][C] 0.04166[/C][C] 0.02083[/C][/ROW]
[ROW][C]83[/C][C] 0.9734[/C][C] 0.05318[/C][C] 0.02659[/C][/ROW]
[ROW][C]84[/C][C] 0.9689[/C][C] 0.06222[/C][C] 0.03111[/C][/ROW]
[ROW][C]85[/C][C] 0.9743[/C][C] 0.0514[/C][C] 0.0257[/C][/ROW]
[ROW][C]86[/C][C] 0.9734[/C][C] 0.05319[/C][C] 0.0266[/C][/ROW]
[ROW][C]87[/C][C] 0.9755[/C][C] 0.0491[/C][C] 0.02455[/C][/ROW]
[ROW][C]88[/C][C] 0.9726[/C][C] 0.05483[/C][C] 0.02741[/C][/ROW]
[ROW][C]89[/C][C] 0.9655[/C][C] 0.0689[/C][C] 0.03445[/C][/ROW]
[ROW][C]90[/C][C] 0.9645[/C][C] 0.07094[/C][C] 0.03547[/C][/ROW]
[ROW][C]91[/C][C] 0.9616[/C][C] 0.07686[/C][C] 0.03843[/C][/ROW]
[ROW][C]92[/C][C] 0.9502[/C][C] 0.09952[/C][C] 0.04976[/C][/ROW]
[ROW][C]93[/C][C] 0.981[/C][C] 0.03802[/C][C] 0.01901[/C][/ROW]
[ROW][C]94[/C][C] 0.9755[/C][C] 0.04908[/C][C] 0.02454[/C][/ROW]
[ROW][C]95[/C][C] 0.973[/C][C] 0.05393[/C][C] 0.02697[/C][/ROW]
[ROW][C]96[/C][C] 0.9769[/C][C] 0.04611[/C][C] 0.02305[/C][/ROW]
[ROW][C]97[/C][C] 0.9729[/C][C] 0.05425[/C][C] 0.02713[/C][/ROW]
[ROW][C]98[/C][C] 0.9654[/C][C] 0.0691[/C][C] 0.03455[/C][/ROW]
[ROW][C]99[/C][C] 0.9582[/C][C] 0.08358[/C][C] 0.04179[/C][/ROW]
[ROW][C]100[/C][C] 0.9499[/C][C] 0.1001[/C][C] 0.05005[/C][/ROW]
[ROW][C]101[/C][C] 0.9564[/C][C] 0.08721[/C][C] 0.0436[/C][/ROW]
[ROW][C]102[/C][C] 0.9492[/C][C] 0.1016[/C][C] 0.05082[/C][/ROW]
[ROW][C]103[/C][C] 0.9787[/C][C] 0.04251[/C][C] 0.02125[/C][/ROW]
[ROW][C]104[/C][C] 0.9714[/C][C] 0.05713[/C][C] 0.02856[/C][/ROW]
[ROW][C]105[/C][C] 0.9715[/C][C] 0.05706[/C][C] 0.02853[/C][/ROW]
[ROW][C]106[/C][C] 0.9688[/C][C] 0.0625[/C][C] 0.03125[/C][/ROW]
[ROW][C]107[/C][C] 0.9608[/C][C] 0.07838[/C][C] 0.03919[/C][/ROW]
[ROW][C]108[/C][C] 0.955[/C][C] 0.08995[/C][C] 0.04498[/C][/ROW]
[ROW][C]109[/C][C] 0.9489[/C][C] 0.1021[/C][C] 0.05106[/C][/ROW]
[ROW][C]110[/C][C] 0.9332[/C][C] 0.1337[/C][C] 0.06683[/C][/ROW]
[ROW][C]111[/C][C] 0.9425[/C][C] 0.115[/C][C] 0.05748[/C][/ROW]
[ROW][C]112[/C][C] 0.9306[/C][C] 0.1388[/C][C] 0.06942[/C][/ROW]
[ROW][C]113[/C][C] 0.9227[/C][C] 0.1547[/C][C] 0.07734[/C][/ROW]
[ROW][C]114[/C][C] 0.9384[/C][C] 0.1233[/C][C] 0.06163[/C][/ROW]
[ROW][C]115[/C][C] 0.9246[/C][C] 0.1509[/C][C] 0.07543[/C][/ROW]
[ROW][C]116[/C][C] 0.9116[/C][C] 0.1769[/C][C] 0.08843[/C][/ROW]
[ROW][C]117[/C][C] 0.8894[/C][C] 0.2212[/C][C] 0.1106[/C][/ROW]
[ROW][C]118[/C][C] 0.8603[/C][C] 0.2794[/C][C] 0.1397[/C][/ROW]
[ROW][C]119[/C][C] 0.8263[/C][C] 0.3473[/C][C] 0.1737[/C][/ROW]
[ROW][C]120[/C][C] 0.7977[/C][C] 0.4046[/C][C] 0.2023[/C][/ROW]
[ROW][C]121[/C][C] 0.8058[/C][C] 0.3884[/C][C] 0.1942[/C][/ROW]
[ROW][C]122[/C][C] 0.7627[/C][C] 0.4745[/C][C] 0.2373[/C][/ROW]
[ROW][C]123[/C][C] 0.7841[/C][C] 0.4319[/C][C] 0.2159[/C][/ROW]
[ROW][C]124[/C][C] 0.7367[/C][C] 0.5267[/C][C] 0.2633[/C][/ROW]
[ROW][C]125[/C][C] 0.6886[/C][C] 0.6228[/C][C] 0.3114[/C][/ROW]
[ROW][C]126[/C][C] 0.6423[/C][C] 0.7155[/C][C] 0.3577[/C][/ROW]
[ROW][C]127[/C][C] 0.5833[/C][C] 0.8335[/C][C] 0.4167[/C][/ROW]
[ROW][C]128[/C][C] 0.5646[/C][C] 0.8707[/C][C] 0.4354[/C][/ROW]
[ROW][C]129[/C][C] 0.5[/C][C] 0.9999[/C][C] 0.5[/C][/ROW]
[ROW][C]130[/C][C] 0.4781[/C][C] 0.9562[/C][C] 0.5219[/C][/ROW]
[ROW][C]131[/C][C] 0.4971[/C][C] 0.9942[/C][C] 0.5029[/C][/ROW]
[ROW][C]132[/C][C] 0.5386[/C][C] 0.9227[/C][C] 0.4614[/C][/ROW]
[ROW][C]133[/C][C] 0.4728[/C][C] 0.9456[/C][C] 0.5272[/C][/ROW]
[ROW][C]134[/C][C] 0.4035[/C][C] 0.8071[/C][C] 0.5965[/C][/ROW]
[ROW][C]135[/C][C] 0.3336[/C][C] 0.6671[/C][C] 0.6664[/C][/ROW]
[ROW][C]136[/C][C] 0.2721[/C][C] 0.5442[/C][C] 0.7279[/C][/ROW]
[ROW][C]137[/C][C] 0.2912[/C][C] 0.5823[/C][C] 0.7088[/C][/ROW]
[ROW][C]138[/C][C] 0.2376[/C][C] 0.4751[/C][C] 0.7624[/C][/ROW]
[ROW][C]139[/C][C] 0.2064[/C][C] 0.4127[/C][C] 0.7936[/C][/ROW]
[ROW][C]140[/C][C] 0.1522[/C][C] 0.3043[/C][C] 0.8478[/C][/ROW]
[ROW][C]141[/C][C] 0.1113[/C][C] 0.2226[/C][C] 0.8887[/C][/ROW]
[ROW][C]142[/C][C] 0.4792[/C][C] 0.9584[/C][C] 0.5208[/C][/ROW]
[ROW][C]143[/C][C] 0.4197[/C][C] 0.8395[/C][C] 0.5803[/C][/ROW]
[ROW][C]144[/C][C] 0.3788[/C][C] 0.7576[/C][C] 0.6212[/C][/ROW]
[ROW][C]145[/C][C] 0.285[/C][C] 0.5699[/C][C] 0.715[/C][/ROW]
[ROW][C]146[/C][C] 0.2022[/C][C] 0.4044[/C][C] 0.7978[/C][/ROW]
[ROW][C]147[/C][C] 0.8194[/C][C] 0.3612[/C][C] 0.1806[/C][/ROW]
[ROW][C]148[/C][C] 0.8516[/C][C] 0.2967[/C][C] 0.1484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298227&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1618	0.3236	0.8382
11	0.1508	0.3016	0.8492
12	0.6534	0.6932	0.3466
13	0.6674	0.6653	0.3326
14	0.6879	0.6241	0.3121
15	0.7552	0.4896	0.2448
16	0.787	0.426	0.213
17	0.7409	0.5182	0.2591
18	0.6769	0.6463	0.3231
19	0.5952	0.8096	0.4048
20	0.9589	0.08216	0.04108
21	0.9635	0.07304	0.03652
22	0.9612	0.07754	0.03877
23	0.9447	0.1105	0.05525
24	0.9453	0.1095	0.05473
25	0.933	0.134	0.06698
26	0.9086	0.1827	0.09135
27	0.892	0.2159	0.108
28	0.8619	0.2761	0.1381
29	0.8602	0.2796	0.1398
30	0.888	0.2241	0.112
31	0.8741	0.2517	0.1259
32	0.8437	0.3126	0.1563
33	0.8296	0.3408	0.1704
34	0.7936	0.4128	0.2064
35	0.7627	0.4746	0.2373
36	0.7191	0.5618	0.2809
37	0.6902	0.6196	0.3098
38	0.6392	0.7215	0.3608
39	0.6857	0.6285	0.3143
40	0.686	0.628	0.314
41	0.9935	0.01302	0.006512
42	0.9958	0.008468	0.004234
43	0.9939	0.0123	0.006149
44	0.9917	0.0166	0.0083
45	0.9891	0.02178	0.01089
46	0.9871	0.02582	0.01291
47	0.9832	0.03356	0.01678
48	0.9775	0.04492	0.02246
49	0.9799	0.04019	0.0201
50	0.9738	0.05237	0.02619
51	0.9821	0.03589	0.01794
52	0.9854	0.0292	0.0146
53	0.9805	0.039	0.0195
54	0.9883	0.0235	0.01175
55	0.9965	0.007023	0.003511
56	0.995	0.009978	0.004989
57	0.993	0.01391	0.006956
58	0.9927	0.01465	0.007323
59	0.9914	0.0171	0.008551
60	0.9884	0.02325	0.01163
61	0.9879	0.02412	0.01206
62	0.984	0.03208	0.01604
63	0.9891	0.0217	0.01085
64	0.9856	0.02888	0.01444
65	0.9849	0.03011	0.01506
66	0.9854	0.02929	0.01464
67	0.9899	0.02012	0.01006
68	0.9867	0.02667	0.01334
69	0.9848	0.03038	0.01519
70	0.9844	0.03121	0.01561
71	0.9804	0.03915	0.01958
72	0.9837	0.03261	0.01631
73	0.9799	0.04019	0.0201
74	0.9781	0.04381	0.0219
75	0.9718	0.05649	0.02825
76	0.9767	0.04667	0.02334
77	0.9698	0.06043	0.03021
78	0.9665	0.06708	0.03354
79	0.9799	0.04026	0.02013
80	0.9781	0.04374	0.02187
81	0.9714	0.05717	0.02858
82	0.9792	0.04166	0.02083
83	0.9734	0.05318	0.02659
84	0.9689	0.06222	0.03111
85	0.9743	0.0514	0.0257
86	0.9734	0.05319	0.0266
87	0.9755	0.0491	0.02455
88	0.9726	0.05483	0.02741
89	0.9655	0.0689	0.03445
90	0.9645	0.07094	0.03547
91	0.9616	0.07686	0.03843
92	0.9502	0.09952	0.04976
93	0.981	0.03802	0.01901
94	0.9755	0.04908	0.02454
95	0.973	0.05393	0.02697
96	0.9769	0.04611	0.02305
97	0.9729	0.05425	0.02713
98	0.9654	0.0691	0.03455
99	0.9582	0.08358	0.04179
100	0.9499	0.1001	0.05005
101	0.9564	0.08721	0.0436
102	0.9492	0.1016	0.05082
103	0.9787	0.04251	0.02125
104	0.9714	0.05713	0.02856
105	0.9715	0.05706	0.02853
106	0.9688	0.0625	0.03125
107	0.9608	0.07838	0.03919
108	0.955	0.08995	0.04498
109	0.9489	0.1021	0.05106
110	0.9332	0.1337	0.06683
111	0.9425	0.115	0.05748
112	0.9306	0.1388	0.06942
113	0.9227	0.1547	0.07734
114	0.9384	0.1233	0.06163
115	0.9246	0.1509	0.07543
116	0.9116	0.1769	0.08843
117	0.8894	0.2212	0.1106
118	0.8603	0.2794	0.1397
119	0.8263	0.3473	0.1737
120	0.7977	0.4046	0.2023
121	0.8058	0.3884	0.1942
122	0.7627	0.4745	0.2373
123	0.7841	0.4319	0.2159
124	0.7367	0.5267	0.2633
125	0.6886	0.6228	0.3114
126	0.6423	0.7155	0.3577
127	0.5833	0.8335	0.4167
128	0.5646	0.8707	0.4354
129	0.5	0.9999	0.5
130	0.4781	0.9562	0.5219
131	0.4971	0.9942	0.5029
132	0.5386	0.9227	0.4614
133	0.4728	0.9456	0.5272
134	0.4035	0.8071	0.5965
135	0.3336	0.6671	0.6664
136	0.2721	0.5442	0.7279
137	0.2912	0.5823	0.7088
138	0.2376	0.4751	0.7624
139	0.2064	0.4127	0.7936
140	0.1522	0.3043	0.8478
141	0.1113	0.2226	0.8887
142	0.4792	0.9584	0.5208
143	0.4197	0.8395	0.5803
144	0.3788	0.7576	0.6212
145	0.285	0.5699	0.715
146	0.2022	0.4044	0.7978
147	0.8194	0.3612	0.1806
148	0.8516	0.2967	0.1484

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.02158	NOK
5% type I error level	42	0.302158	NOK
10% type I error level	69	0.496403	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 & 3 &  0.02158 & NOK \tabularnewline
5% type I error level & 42 & 0.302158 & NOK \tabularnewline
10% type I error level & 69 & 0.496403 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&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]3[/C][C] 0.02158[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.302158[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]69[/C][C]0.496403[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298227&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&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	3	0.02158	NOK
5% type I error level	42	0.302158	NOK
10% type I error level	69	0.496403	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8532, df1 = 2, df2 = 149, p-value = 0.1603
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.64199, df1 = 12, df2 = 139, p-value = 0.8033
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.1527, df1 = 2, df2 = 149, p-value = 0.1198

\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 = 1.8532, df1 = 2, df2 = 149, p-value = 0.1603
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.64199, df1 = 12, df2 = 139, p-value = 0.8033
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.1527, df1 = 2, df2 = 149, p-value = 0.1198
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&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 = 1.8532, df1 = 2, df2 = 149, p-value = 0.1603
[/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 = 0.64199, df1 = 12, df2 = 139, p-value = 0.8033
[/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 = 2.1527, df1 = 2, df2 = 149, p-value = 0.1198
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298227&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&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 = 1.8532, df1 = 2, df2 = 149, p-value = 0.1603
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.64199, df1 = 12, df2 = 139, p-value = 0.8033
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.1527, df1 = 2, df2 = 149, p-value = 0.1198

Variance Inflation Factors (Multicollinearity)
> vif `SK1,` `SK2,` `SK3,` `SK4,` `SK5,` `SK6,` 1.091500 1.135089 1.055911 1.051688 1.048247 1.036994

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
  `SK1,`   `SK2,`   `SK3,`   `SK4,`   `SK5,`   `SK6,` 
1.091500 1.135089 1.055911 1.051688 1.048247 1.036994 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298227&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
  `SK1,`   `SK2,`   `SK3,`   `SK4,`   `SK5,`   `SK6,` 
1.091500 1.135089 1.055911 1.051688 1.048247 1.036994 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298227&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298227&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif `SK1,` `SK2,` `SK3,` `SK4,` `SK5,` `SK6,` 1.091500 1.135089 1.055911 1.051688 1.048247 1.036994

Figure 1

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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

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

par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '5'
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