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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 07 Dec 2016 15:07:10 +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/t1481122336yvf2qbx0vwzryrm.htm/, Retrieved Tue, 07 May 2024 20:35:28 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Tue, 07 May 2024 20:35:28 +0200

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Estimated Impact

Dataseries X:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\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]

8 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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.79398 + 0.562836SK1[t] + 1.10586SK2[t] + 0.0919962SK3[t] + 0.287161SK4[t] + 0.202138SK5[t] -0.039412SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.79398 +  0.562836SK1[t] +  1.10586SK2[t] +  0.0919962SK3[t] +  0.287161SK4[t] +  0.202138SK5[t] -0.039412SK6[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]TVDC[t] =  +  6.79398 +  0.562836SK1[t] +  1.10586SK2[t] +  0.0919962SK3[t] +  0.287161SK4[t] +  0.202138SK5[t] -0.039412SK6[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
TVDC[t] = + 6.79398 + 0.562836SK1[t] + 1.10586SK2[t] + 0.0919962SK3[t] + 0.287161SK4[t] + 0.202138SK5[t] -0.039412SK6[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.794	1.515	+4.4850e+00	1.395e-05	6.976e-06
SK1	+0.5628	0.1618	+3.4790e+00	0.0006498	0.0003249
SK2	+1.106	0.195	+5.6720e+00	6.546e-08	3.273e-08
SK3	+0.092	0.1434	+6.4170e-01	0.522	0.261
SK4	+0.2872	0.1952	+1.4710e+00	0.1432	0.07158
SK5	+0.2021	0.1855	+1.0900e+00	0.2774	0.1387
SK6	-0.03941	0.193	-2.0420e-01	0.8385	0.4192

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +6.794 &  1.515 & +4.4850e+00 &  1.395e-05 &  6.976e-06 \tabularnewline
SK1 & +0.5628 &  0.1618 & +3.4790e+00 &  0.0006498 &  0.0003249 \tabularnewline
SK2 & +1.106 &  0.195 & +5.6720e+00 &  6.546e-08 &  3.273e-08 \tabularnewline
SK3 & +0.092 &  0.1434 & +6.4170e-01 &  0.522 &  0.261 \tabularnewline
SK4 & +0.2872 &  0.1952 & +1.4710e+00 &  0.1432 &  0.07158 \tabularnewline
SK5 & +0.2021 &  0.1855 & +1.0900e+00 &  0.2774 &  0.1387 \tabularnewline
SK6 & -0.03941 &  0.193 & -2.0420e-01 &  0.8385 &  0.4192 \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]+6.794[/C][C] 1.515[/C][C]+4.4850e+00[/C][C] 1.395e-05[/C][C] 6.976e-06[/C][/ROW]
[ROW][C]SK1[/C][C]+0.5628[/C][C] 0.1618[/C][C]+3.4790e+00[/C][C] 0.0006498[/C][C] 0.0003249[/C][/ROW]
[ROW][C]SK2[/C][C]+1.106[/C][C] 0.195[/C][C]+5.6720e+00[/C][C] 6.546e-08[/C][C] 3.273e-08[/C][/ROW]
[ROW][C]SK3[/C][C]+0.092[/C][C] 0.1434[/C][C]+6.4170e-01[/C][C] 0.522[/C][C] 0.261[/C][/ROW]
[ROW][C]SK4[/C][C]+0.2872[/C][C] 0.1952[/C][C]+1.4710e+00[/C][C] 0.1432[/C][C] 0.07158[/C][/ROW]
[ROW][C]SK5[/C][C]+0.2021[/C][C] 0.1855[/C][C]+1.0900e+00[/C][C] 0.2774[/C][C] 0.1387[/C][/ROW]
[ROW][C]SK6[/C][C]-0.03941[/C][C] 0.193[/C][C]-2.0420e-01[/C][C] 0.8385[/C][C] 0.4192[/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)	+6.794	1.515	+4.4850e+00	1.395e-05	6.976e-06
SK1	+0.5628	0.1618	+3.4790e+00	0.0006498	0.0003249
SK2	+1.106	0.195	+5.6720e+00	6.546e-08	3.273e-08
SK3	+0.092	0.1434	+6.4170e-01	0.522	0.261
SK4	+0.2872	0.1952	+1.4710e+00	0.1432	0.07158
SK5	+0.2021	0.1855	+1.0900e+00	0.2774	0.1387
SK6	-0.03941	0.193	-2.0420e-01	0.8385	0.4192

Multiple Linear Regression - Regression Statistics
Multiple R	0.56
R-squared	0.3136
Adjusted R-squared	0.2875
F-TEST (value)	12.03
F-TEST (DF numerator)	6
F-TEST (DF denominator)	158
p-value	4.134e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.431
Sum Squared Residuals	323.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.56 \tabularnewline
R-squared &  0.3136 \tabularnewline
Adjusted R-squared &  0.2875 \tabularnewline
F-TEST (value) &  12.03 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value &  4.134e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.431 \tabularnewline
Sum Squared Residuals &  323.5 \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] 0.56[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3136[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2875[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 12.03[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C] 4.134e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.431[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 323.5[/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	0.56
R-squared	0.3136
Adjusted R-squared	0.2875
F-TEST (value)	12.03
F-TEST (DF numerator)	6
F-TEST (DF denominator)	158
p-value	4.134e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.431
Sum Squared Residuals	323.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.34	-0.3396
2	16	15.2	0.7966
3	17	15.93	1.07
4	15	14.69	0.3057
5	16	15.93	0.06957
6	16	15.24	0.7638
7	18	14.58	3.416
8	16	15.17	0.8345
9	17	16.9	0.09512
10	17	17	0.003121
11	17	15.65	1.346
12	15	15.64	-0.6433
13	16	15.31	0.6903
14	14	14.02	-0.02019
15	16	15.28	0.7154
16	17	15.13	1.874
17	16	15.13	0.874
18	15	17.02	-2.018
19	17	15.84	1.162
20	16	14.84	1.161
21	15	15.8	-0.799
22	16	15.69	0.3111
23	15	15.73	-0.7283
24	17	15.69	1.311
25	14	15.07	-1.073
26	16	14.95	1.051
27	15	15.64	-0.6363
28	16	14.77	1.235
29	16	16.2	-0.1991
30	13	14.62	-1.622
31	15	17	-1.997
32	17	16.25	0.7483
33	15	14.79	0.2148
34	13	13.7	-0.6989
35	17	16.56	0.4418
36	15	15.17	-0.1655
37	14	14.3	-0.2959
38	14	14.44	-0.4384
39	18	15.73	2.272
40	15	16.2	-1.199
41	17	17	0.003121
42	13	13.97	-0.9676
43	16	17.27	-1.273
44	15	16	-1.004
45	15	15.31	-0.3097
46	16	15.64	0.3637
47	15	15.78	-0.782
48	13	15.84	-2.838
49	17	16.83	0.1658
50	18	17.32	0.6769
51	18	17.4	0.603
52	11	14.79	-3.785
53	14	14.2	-0.2039
54	13	15.73	-2.728
55	15	14.58	0.4158
56	17	15.11	1.887
57	16	15.55	0.4487
58	15	15.84	-0.8384
59	17	17.38	-0.3757
60	16	14.67	1.327
61	16	15.8	0.201
62	16	14.67	1.331
63	15	15.89	-0.891
64	12	13.42	-1.425
65	17	15.59	1.409
66	14	15.55	-1.551
67	14	15.76	-1.762
68	16	14.95	1.051
69	15	14.75	0.2531
70	15	17.32	-2.323
71	14	15.54	-1.544
72	13	15.64	-2.636
73	18	16.13	1.874
74	15	14.98	0.01853
75	16	15.84	0.1616
76	14	15.04	-1.041
77	15	14.02	0.9798
78	17	14.62	2.378
79	16	15.69	0.3111
80	10	13.88	-3.876
81	16	15.84	0.1616
82	17	15.71	1.293
83	17	15.8	1.201
84	20	16.2	3.801
85	17	16.35	0.6451
86	18	15.89	2.109
87	15	15.13	-0.126
88	17	13.97	3.032
89	14	13.05	0.9546
90	15	15.77	-0.7677
91	17	15.69	1.311
92	16	15.84	0.1616
93	17	16.98	0.01629
94	15	14.95	0.05097
95	16	15.69	0.3111
96	18	16.07	1.932
97	18	16.54	1.461
98	16	16.9	-0.9049
99	17	15.26	1.744
100	15	15.69	-0.6889
101	13	16.16	-3.16
102	15	14.79	0.2137
103	17	16.65	0.351
104	16	15.6	0.3961
105	16	15.3	0.7034
106	15	15.73	-0.7283
107	16	15.73	0.2717
108	16	15.41	0.593
109	14	15.64	-1.636
110	15	15.31	-0.3097
111	12	13.84	-1.843
112	19	15.35	3.651
113	16	15.17	0.8345
114	16	15.53	0.4738
115	17	15.59	1.408
116	16	16.45	-0.4539
117	14	15.68	-1.676
118	15	15.44	-0.4411
119	14	14.75	-0.7469
120	16	15.64	0.3637
121	15	15.84	-0.8384
122	17	16.65	0.3498
123	15	15.07	-0.07346
124	16	15.35	0.6509
125	16	15.6	0.4031
126	15	14.69	0.3057
127	15	15.39	-0.3885
128	11	12.29	-1.287
129	16	15.55	0.4487
130	18	16.11	1.886
131	13	13.98	-0.9819
132	11	13.97	-2.968
133	16	15.35	0.6509
134	18	17.51	0.4929
135	15	16.83	-1.834
136	19	17.89	1.114
137	17	17	0.003121
138	13	15.31	-2.31
139	14	15.37	-1.368
140	16	15.73	0.2717
141	13	15.45	-2.452
142	17	15.71	1.293
143	14	15.8	-1.799
144	19	16.21	2.794
145	14	14.62	-0.6224
146	16	15.73	0.2717
147	12	13.5	-1.496
148	16	16.87	-0.8736
149	16	15.35	0.6509
150	15	15.6	-0.5969
151	12	15.04	-3.041
152	15	15.69	-0.6889
153	17	16.49	0.5067
154	14	15.53	-1.526
155	15	13.5	1.503
156	18	15.6	2.403
157	15	14.41	0.594
158	18	15.68	2.324
159	15	17.12	-2.121
160	15	16.11	-1.107
161	16	15.91	0.08803
162	13	13.85	-0.8543
163	16	15.6	0.4031
164	14	15.84	-1.838
165	16	13.89	2.106

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.34 & -0.3396 \tabularnewline
2 &  16 &  15.2 &  0.7966 \tabularnewline
3 &  17 &  15.93 &  1.07 \tabularnewline
4 &  15 &  14.69 &  0.3057 \tabularnewline
5 &  16 &  15.93 &  0.06957 \tabularnewline
6 &  16 &  15.24 &  0.7638 \tabularnewline
7 &  18 &  14.58 &  3.416 \tabularnewline
8 &  16 &  15.17 &  0.8345 \tabularnewline
9 &  17 &  16.9 &  0.09512 \tabularnewline
10 &  17 &  17 &  0.003121 \tabularnewline
11 &  17 &  15.65 &  1.346 \tabularnewline
12 &  15 &  15.64 & -0.6433 \tabularnewline
13 &  16 &  15.31 &  0.6903 \tabularnewline
14 &  14 &  14.02 & -0.02019 \tabularnewline
15 &  16 &  15.28 &  0.7154 \tabularnewline
16 &  17 &  15.13 &  1.874 \tabularnewline
17 &  16 &  15.13 &  0.874 \tabularnewline
18 &  15 &  17.02 & -2.018 \tabularnewline
19 &  17 &  15.84 &  1.162 \tabularnewline
20 &  16 &  14.84 &  1.161 \tabularnewline
21 &  15 &  15.8 & -0.799 \tabularnewline
22 &  16 &  15.69 &  0.3111 \tabularnewline
23 &  15 &  15.73 & -0.7283 \tabularnewline
24 &  17 &  15.69 &  1.311 \tabularnewline
25 &  14 &  15.07 & -1.073 \tabularnewline
26 &  16 &  14.95 &  1.051 \tabularnewline
27 &  15 &  15.64 & -0.6363 \tabularnewline
28 &  16 &  14.77 &  1.235 \tabularnewline
29 &  16 &  16.2 & -0.1991 \tabularnewline
30 &  13 &  14.62 & -1.622 \tabularnewline
31 &  15 &  17 & -1.997 \tabularnewline
32 &  17 &  16.25 &  0.7483 \tabularnewline
33 &  15 &  14.79 &  0.2148 \tabularnewline
34 &  13 &  13.7 & -0.6989 \tabularnewline
35 &  17 &  16.56 &  0.4418 \tabularnewline
36 &  15 &  15.17 & -0.1655 \tabularnewline
37 &  14 &  14.3 & -0.2959 \tabularnewline
38 &  14 &  14.44 & -0.4384 \tabularnewline
39 &  18 &  15.73 &  2.272 \tabularnewline
40 &  15 &  16.2 & -1.199 \tabularnewline
41 &  17 &  17 &  0.003121 \tabularnewline
42 &  13 &  13.97 & -0.9676 \tabularnewline
43 &  16 &  17.27 & -1.273 \tabularnewline
44 &  15 &  16 & -1.004 \tabularnewline
45 &  15 &  15.31 & -0.3097 \tabularnewline
46 &  16 &  15.64 &  0.3637 \tabularnewline
47 &  15 &  15.78 & -0.782 \tabularnewline
48 &  13 &  15.84 & -2.838 \tabularnewline
49 &  17 &  16.83 &  0.1658 \tabularnewline
50 &  18 &  17.32 &  0.6769 \tabularnewline
51 &  18 &  17.4 &  0.603 \tabularnewline
52 &  11 &  14.79 & -3.785 \tabularnewline
53 &  14 &  14.2 & -0.2039 \tabularnewline
54 &  13 &  15.73 & -2.728 \tabularnewline
55 &  15 &  14.58 &  0.4158 \tabularnewline
56 &  17 &  15.11 &  1.887 \tabularnewline
57 &  16 &  15.55 &  0.4487 \tabularnewline
58 &  15 &  15.84 & -0.8384 \tabularnewline
59 &  17 &  17.38 & -0.3757 \tabularnewline
60 &  16 &  14.67 &  1.327 \tabularnewline
61 &  16 &  15.8 &  0.201 \tabularnewline
62 &  16 &  14.67 &  1.331 \tabularnewline
63 &  15 &  15.89 & -0.891 \tabularnewline
64 &  12 &  13.42 & -1.425 \tabularnewline
65 &  17 &  15.59 &  1.409 \tabularnewline
66 &  14 &  15.55 & -1.551 \tabularnewline
67 &  14 &  15.76 & -1.762 \tabularnewline
68 &  16 &  14.95 &  1.051 \tabularnewline
69 &  15 &  14.75 &  0.2531 \tabularnewline
70 &  15 &  17.32 & -2.323 \tabularnewline
71 &  14 &  15.54 & -1.544 \tabularnewline
72 &  13 &  15.64 & -2.636 \tabularnewline
73 &  18 &  16.13 &  1.874 \tabularnewline
74 &  15 &  14.98 &  0.01853 \tabularnewline
75 &  16 &  15.84 &  0.1616 \tabularnewline
76 &  14 &  15.04 & -1.041 \tabularnewline
77 &  15 &  14.02 &  0.9798 \tabularnewline
78 &  17 &  14.62 &  2.378 \tabularnewline
79 &  16 &  15.69 &  0.3111 \tabularnewline
80 &  10 &  13.88 & -3.876 \tabularnewline
81 &  16 &  15.84 &  0.1616 \tabularnewline
82 &  17 &  15.71 &  1.293 \tabularnewline
83 &  17 &  15.8 &  1.201 \tabularnewline
84 &  20 &  16.2 &  3.801 \tabularnewline
85 &  17 &  16.35 &  0.6451 \tabularnewline
86 &  18 &  15.89 &  2.109 \tabularnewline
87 &  15 &  15.13 & -0.126 \tabularnewline
88 &  17 &  13.97 &  3.032 \tabularnewline
89 &  14 &  13.05 &  0.9546 \tabularnewline
90 &  15 &  15.77 & -0.7677 \tabularnewline
91 &  17 &  15.69 &  1.311 \tabularnewline
92 &  16 &  15.84 &  0.1616 \tabularnewline
93 &  17 &  16.98 &  0.01629 \tabularnewline
94 &  15 &  14.95 &  0.05097 \tabularnewline
95 &  16 &  15.69 &  0.3111 \tabularnewline
96 &  18 &  16.07 &  1.932 \tabularnewline
97 &  18 &  16.54 &  1.461 \tabularnewline
98 &  16 &  16.9 & -0.9049 \tabularnewline
99 &  17 &  15.26 &  1.744 \tabularnewline
100 &  15 &  15.69 & -0.6889 \tabularnewline
101 &  13 &  16.16 & -3.16 \tabularnewline
102 &  15 &  14.79 &  0.2137 \tabularnewline
103 &  17 &  16.65 &  0.351 \tabularnewline
104 &  16 &  15.6 &  0.3961 \tabularnewline
105 &  16 &  15.3 &  0.7034 \tabularnewline
106 &  15 &  15.73 & -0.7283 \tabularnewline
107 &  16 &  15.73 &  0.2717 \tabularnewline
108 &  16 &  15.41 &  0.593 \tabularnewline
109 &  14 &  15.64 & -1.636 \tabularnewline
110 &  15 &  15.31 & -0.3097 \tabularnewline
111 &  12 &  13.84 & -1.843 \tabularnewline
112 &  19 &  15.35 &  3.651 \tabularnewline
113 &  16 &  15.17 &  0.8345 \tabularnewline
114 &  16 &  15.53 &  0.4738 \tabularnewline
115 &  17 &  15.59 &  1.408 \tabularnewline
116 &  16 &  16.45 & -0.4539 \tabularnewline
117 &  14 &  15.68 & -1.676 \tabularnewline
118 &  15 &  15.44 & -0.4411 \tabularnewline
119 &  14 &  14.75 & -0.7469 \tabularnewline
120 &  16 &  15.64 &  0.3637 \tabularnewline
121 &  15 &  15.84 & -0.8384 \tabularnewline
122 &  17 &  16.65 &  0.3498 \tabularnewline
123 &  15 &  15.07 & -0.07346 \tabularnewline
124 &  16 &  15.35 &  0.6509 \tabularnewline
125 &  16 &  15.6 &  0.4031 \tabularnewline
126 &  15 &  14.69 &  0.3057 \tabularnewline
127 &  15 &  15.39 & -0.3885 \tabularnewline
128 &  11 &  12.29 & -1.287 \tabularnewline
129 &  16 &  15.55 &  0.4487 \tabularnewline
130 &  18 &  16.11 &  1.886 \tabularnewline
131 &  13 &  13.98 & -0.9819 \tabularnewline
132 &  11 &  13.97 & -2.968 \tabularnewline
133 &  16 &  15.35 &  0.6509 \tabularnewline
134 &  18 &  17.51 &  0.4929 \tabularnewline
135 &  15 &  16.83 & -1.834 \tabularnewline
136 &  19 &  17.89 &  1.114 \tabularnewline
137 &  17 &  17 &  0.003121 \tabularnewline
138 &  13 &  15.31 & -2.31 \tabularnewline
139 &  14 &  15.37 & -1.368 \tabularnewline
140 &  16 &  15.73 &  0.2717 \tabularnewline
141 &  13 &  15.45 & -2.452 \tabularnewline
142 &  17 &  15.71 &  1.293 \tabularnewline
143 &  14 &  15.8 & -1.799 \tabularnewline
144 &  19 &  16.21 &  2.794 \tabularnewline
145 &  14 &  14.62 & -0.6224 \tabularnewline
146 &  16 &  15.73 &  0.2717 \tabularnewline
147 &  12 &  13.5 & -1.496 \tabularnewline
148 &  16 &  16.87 & -0.8736 \tabularnewline
149 &  16 &  15.35 &  0.6509 \tabularnewline
150 &  15 &  15.6 & -0.5969 \tabularnewline
151 &  12 &  15.04 & -3.041 \tabularnewline
152 &  15 &  15.69 & -0.6889 \tabularnewline
153 &  17 &  16.49 &  0.5067 \tabularnewline
154 &  14 &  15.53 & -1.526 \tabularnewline
155 &  15 &  13.5 &  1.503 \tabularnewline
156 &  18 &  15.6 &  2.403 \tabularnewline
157 &  15 &  14.41 &  0.594 \tabularnewline
158 &  18 &  15.68 &  2.324 \tabularnewline
159 &  15 &  17.12 & -2.121 \tabularnewline
160 &  15 &  16.11 & -1.107 \tabularnewline
161 &  16 &  15.91 &  0.08803 \tabularnewline
162 &  13 &  13.85 & -0.8543 \tabularnewline
163 &  16 &  15.6 &  0.4031 \tabularnewline
164 &  14 &  15.84 & -1.838 \tabularnewline
165 &  16 &  13.89 &  2.106 \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] 13[/C][C] 13.34[/C][C]-0.3396[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.2[/C][C] 0.7966[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.93[/C][C] 1.07[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.69[/C][C] 0.3057[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.93[/C][C] 0.06957[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.24[/C][C] 0.7638[/C][/ROW]
[ROW][C]7[/C][C] 18[/C][C] 14.58[/C][C] 3.416[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.17[/C][C] 0.8345[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.9[/C][C] 0.09512[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 17[/C][C] 0.003121[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.65[/C][C] 1.346[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.64[/C][C]-0.6433[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.31[/C][C] 0.6903[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.02[/C][C]-0.02019[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.28[/C][C] 0.7154[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.13[/C][C] 1.874[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.13[/C][C] 0.874[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 17.02[/C][C]-2.018[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.84[/C][C] 1.162[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.84[/C][C] 1.161[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.8[/C][C]-0.799[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.69[/C][C] 0.3111[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.73[/C][C]-0.7283[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.69[/C][C] 1.311[/C][/ROW]
[ROW][C]25[/C][C] 14[/C][C] 15.07[/C][C]-1.073[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.95[/C][C] 1.051[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.64[/C][C]-0.6363[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.77[/C][C] 1.235[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.2[/C][C]-0.1991[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.62[/C][C]-1.622[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 17[/C][C]-1.997[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.25[/C][C] 0.7483[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.79[/C][C] 0.2148[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.7[/C][C]-0.6989[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.56[/C][C] 0.4418[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.17[/C][C]-0.1655[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.3[/C][C]-0.2959[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.44[/C][C]-0.4384[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.73[/C][C] 2.272[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.2[/C][C]-1.199[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 17[/C][C] 0.003121[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.97[/C][C]-0.9676[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 17.27[/C][C]-1.273[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 16[/C][C]-1.004[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.31[/C][C]-0.3097[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.64[/C][C] 0.3637[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.78[/C][C]-0.782[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.84[/C][C]-2.838[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 16.83[/C][C] 0.1658[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 17.32[/C][C] 0.6769[/C][/ROW]
[ROW][C]51[/C][C] 18[/C][C] 17.4[/C][C] 0.603[/C][/ROW]
[ROW][C]52[/C][C] 11[/C][C] 14.79[/C][C]-3.785[/C][/ROW]
[ROW][C]53[/C][C] 14[/C][C] 14.2[/C][C]-0.2039[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 15.73[/C][C]-2.728[/C][/ROW]
[ROW][C]55[/C][C] 15[/C][C] 14.58[/C][C] 0.4158[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 15.11[/C][C] 1.887[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 15.55[/C][C] 0.4487[/C][/ROW]
[ROW][C]58[/C][C] 15[/C][C] 15.84[/C][C]-0.8384[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 17.38[/C][C]-0.3757[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 14.67[/C][C] 1.327[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.8[/C][C] 0.201[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 14.67[/C][C] 1.331[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.89[/C][C]-0.891[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.42[/C][C]-1.425[/C][/ROW]
[ROW][C]65[/C][C] 17[/C][C] 15.59[/C][C] 1.409[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.55[/C][C]-1.551[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.76[/C][C]-1.762[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 14.95[/C][C] 1.051[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.75[/C][C] 0.2531[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 17.32[/C][C]-2.323[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 15.54[/C][C]-1.544[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 15.64[/C][C]-2.636[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 16.13[/C][C] 1.874[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 14.98[/C][C] 0.01853[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.84[/C][C] 0.1616[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 15.04[/C][C]-1.041[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 14.02[/C][C] 0.9798[/C][/ROW]
[ROW][C]78[/C][C] 17[/C][C] 14.62[/C][C] 2.378[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.69[/C][C] 0.3111[/C][/ROW]
[ROW][C]80[/C][C] 10[/C][C] 13.88[/C][C]-3.876[/C][/ROW]
[ROW][C]81[/C][C] 16[/C][C] 15.84[/C][C] 0.1616[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 15.71[/C][C] 1.293[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.8[/C][C] 1.201[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 16.2[/C][C] 3.801[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.35[/C][C] 0.6451[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 15.89[/C][C] 2.109[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.13[/C][C]-0.126[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 13.97[/C][C] 3.032[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 13.05[/C][C] 0.9546[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 15.77[/C][C]-0.7677[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 15.69[/C][C] 1.311[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.84[/C][C] 0.1616[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.98[/C][C] 0.01629[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.95[/C][C] 0.05097[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 15.69[/C][C] 0.3111[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 16.07[/C][C] 1.932[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.54[/C][C] 1.461[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.9[/C][C]-0.9049[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 15.26[/C][C] 1.744[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 15.69[/C][C]-0.6889[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 16.16[/C][C]-3.16[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.79[/C][C] 0.2137[/C][/ROW]
[ROW][C]103[/C][C] 17[/C][C] 16.65[/C][C] 0.351[/C][/ROW]
[ROW][C]104[/C][C] 16[/C][C] 15.6[/C][C] 0.3961[/C][/ROW]
[ROW][C]105[/C][C] 16[/C][C] 15.3[/C][C] 0.7034[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 15.73[/C][C]-0.7283[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.73[/C][C] 0.2717[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.41[/C][C] 0.593[/C][/ROW]
[ROW][C]109[/C][C] 14[/C][C] 15.64[/C][C]-1.636[/C][/ROW]
[ROW][C]110[/C][C] 15[/C][C] 15.31[/C][C]-0.3097[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 13.84[/C][C]-1.843[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 15.35[/C][C] 3.651[/C][/ROW]
[ROW][C]113[/C][C] 16[/C][C] 15.17[/C][C] 0.8345[/C][/ROW]
[ROW][C]114[/C][C] 16[/C][C] 15.53[/C][C] 0.4738[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 15.59[/C][C] 1.408[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 16.45[/C][C]-0.4539[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 15.68[/C][C]-1.676[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.44[/C][C]-0.4411[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 14.75[/C][C]-0.7469[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.64[/C][C] 0.3637[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 15.84[/C][C]-0.8384[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.65[/C][C] 0.3498[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.07[/C][C]-0.07346[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.35[/C][C] 0.6509[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 15.6[/C][C] 0.4031[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 14.69[/C][C] 0.3057[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 15.39[/C][C]-0.3885[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 12.29[/C][C]-1.287[/C][/ROW]
[ROW][C]129[/C][C] 16[/C][C] 15.55[/C][C] 0.4487[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 16.11[/C][C] 1.886[/C][/ROW]
[ROW][C]131[/C][C] 13[/C][C] 13.98[/C][C]-0.9819[/C][/ROW]
[ROW][C]132[/C][C] 11[/C][C] 13.97[/C][C]-2.968[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 15.35[/C][C] 0.6509[/C][/ROW]
[ROW][C]134[/C][C] 18[/C][C] 17.51[/C][C] 0.4929[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 16.83[/C][C]-1.834[/C][/ROW]
[ROW][C]136[/C][C] 19[/C][C] 17.89[/C][C] 1.114[/C][/ROW]
[ROW][C]137[/C][C] 17[/C][C] 17[/C][C] 0.003121[/C][/ROW]
[ROW][C]138[/C][C] 13[/C][C] 15.31[/C][C]-2.31[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 15.37[/C][C]-1.368[/C][/ROW]
[ROW][C]140[/C][C] 16[/C][C] 15.73[/C][C] 0.2717[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 15.45[/C][C]-2.452[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 15.71[/C][C] 1.293[/C][/ROW]
[ROW][C]143[/C][C] 14[/C][C] 15.8[/C][C]-1.799[/C][/ROW]
[ROW][C]144[/C][C] 19[/C][C] 16.21[/C][C] 2.794[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 14.62[/C][C]-0.6224[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 15.73[/C][C] 0.2717[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 13.5[/C][C]-1.496[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 16.87[/C][C]-0.8736[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 15.35[/C][C] 0.6509[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 15.6[/C][C]-0.5969[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 15.04[/C][C]-3.041[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 15.69[/C][C]-0.6889[/C][/ROW]
[ROW][C]153[/C][C] 17[/C][C] 16.49[/C][C] 0.5067[/C][/ROW]
[ROW][C]154[/C][C] 14[/C][C] 15.53[/C][C]-1.526[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.5[/C][C] 1.503[/C][/ROW]
[ROW][C]156[/C][C] 18[/C][C] 15.6[/C][C] 2.403[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 14.41[/C][C] 0.594[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 15.68[/C][C] 2.324[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 17.12[/C][C]-2.121[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 16.11[/C][C]-1.107[/C][/ROW]
[ROW][C]161[/C][C] 16[/C][C] 15.91[/C][C] 0.08803[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 13.85[/C][C]-0.8543[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.6[/C][C] 0.4031[/C][/ROW]
[ROW][C]164[/C][C] 14[/C][C] 15.84[/C][C]-1.838[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 13.89[/C][C] 2.106[/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	13	13.34	-0.3396
2	16	15.2	0.7966
3	17	15.93	1.07
4	15	14.69	0.3057
5	16	15.93	0.06957
6	16	15.24	0.7638
7	18	14.58	3.416
8	16	15.17	0.8345
9	17	16.9	0.09512
10	17	17	0.003121
11	17	15.65	1.346
12	15	15.64	-0.6433
13	16	15.31	0.6903
14	14	14.02	-0.02019
15	16	15.28	0.7154
16	17	15.13	1.874
17	16	15.13	0.874
18	15	17.02	-2.018
19	17	15.84	1.162
20	16	14.84	1.161
21	15	15.8	-0.799
22	16	15.69	0.3111
23	15	15.73	-0.7283
24	17	15.69	1.311
25	14	15.07	-1.073
26	16	14.95	1.051
27	15	15.64	-0.6363
28	16	14.77	1.235
29	16	16.2	-0.1991
30	13	14.62	-1.622
31	15	17	-1.997
32	17	16.25	0.7483
33	15	14.79	0.2148
34	13	13.7	-0.6989
35	17	16.56	0.4418
36	15	15.17	-0.1655
37	14	14.3	-0.2959
38	14	14.44	-0.4384
39	18	15.73	2.272
40	15	16.2	-1.199
41	17	17	0.003121
42	13	13.97	-0.9676
43	16	17.27	-1.273
44	15	16	-1.004
45	15	15.31	-0.3097
46	16	15.64	0.3637
47	15	15.78	-0.782
48	13	15.84	-2.838
49	17	16.83	0.1658
50	18	17.32	0.6769
51	18	17.4	0.603
52	11	14.79	-3.785
53	14	14.2	-0.2039
54	13	15.73	-2.728
55	15	14.58	0.4158
56	17	15.11	1.887
57	16	15.55	0.4487
58	15	15.84	-0.8384
59	17	17.38	-0.3757
60	16	14.67	1.327
61	16	15.8	0.201
62	16	14.67	1.331
63	15	15.89	-0.891
64	12	13.42	-1.425
65	17	15.59	1.409
66	14	15.55	-1.551
67	14	15.76	-1.762
68	16	14.95	1.051
69	15	14.75	0.2531
70	15	17.32	-2.323
71	14	15.54	-1.544
72	13	15.64	-2.636
73	18	16.13	1.874
74	15	14.98	0.01853
75	16	15.84	0.1616
76	14	15.04	-1.041
77	15	14.02	0.9798
78	17	14.62	2.378
79	16	15.69	0.3111
80	10	13.88	-3.876
81	16	15.84	0.1616
82	17	15.71	1.293
83	17	15.8	1.201
84	20	16.2	3.801
85	17	16.35	0.6451
86	18	15.89	2.109
87	15	15.13	-0.126
88	17	13.97	3.032
89	14	13.05	0.9546
90	15	15.77	-0.7677
91	17	15.69	1.311
92	16	15.84	0.1616
93	17	16.98	0.01629
94	15	14.95	0.05097
95	16	15.69	0.3111
96	18	16.07	1.932
97	18	16.54	1.461
98	16	16.9	-0.9049
99	17	15.26	1.744
100	15	15.69	-0.6889
101	13	16.16	-3.16
102	15	14.79	0.2137
103	17	16.65	0.351
104	16	15.6	0.3961
105	16	15.3	0.7034
106	15	15.73	-0.7283
107	16	15.73	0.2717
108	16	15.41	0.593
109	14	15.64	-1.636
110	15	15.31	-0.3097
111	12	13.84	-1.843
112	19	15.35	3.651
113	16	15.17	0.8345
114	16	15.53	0.4738
115	17	15.59	1.408
116	16	16.45	-0.4539
117	14	15.68	-1.676
118	15	15.44	-0.4411
119	14	14.75	-0.7469
120	16	15.64	0.3637
121	15	15.84	-0.8384
122	17	16.65	0.3498
123	15	15.07	-0.07346
124	16	15.35	0.6509
125	16	15.6	0.4031
126	15	14.69	0.3057
127	15	15.39	-0.3885
128	11	12.29	-1.287
129	16	15.55	0.4487
130	18	16.11	1.886
131	13	13.98	-0.9819
132	11	13.97	-2.968
133	16	15.35	0.6509
134	18	17.51	0.4929
135	15	16.83	-1.834
136	19	17.89	1.114
137	17	17	0.003121
138	13	15.31	-2.31
139	14	15.37	-1.368
140	16	15.73	0.2717
141	13	15.45	-2.452
142	17	15.71	1.293
143	14	15.8	-1.799
144	19	16.21	2.794
145	14	14.62	-0.6224
146	16	15.73	0.2717
147	12	13.5	-1.496
148	16	16.87	-0.8736
149	16	15.35	0.6509
150	15	15.6	-0.5969
151	12	15.04	-3.041
152	15	15.69	-0.6889
153	17	16.49	0.5067
154	14	15.53	-1.526
155	15	13.5	1.503
156	18	15.6	2.403
157	15	14.41	0.594
158	18	15.68	2.324
159	15	17.12	-2.121
160	15	16.11	-1.107
161	16	15.91	0.08803
162	13	13.85	-0.8543
163	16	15.6	0.4031
164	14	15.84	-1.838
165	16	13.89	2.106

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.2732	0.5463	0.7268
11	0.1967	0.3934	0.8033
12	0.1037	0.2073	0.8963
13	0.05505	0.1101	0.9449
14	0.05148	0.103	0.9485
15	0.07033	0.1407	0.9297
16	0.07718	0.1544	0.9228
17	0.04505	0.09009	0.955
18	0.09545	0.1909	0.9045
19	0.06746	0.1349	0.9325
20	0.0522	0.1044	0.9478
21	0.04314	0.08627	0.9569
22	0.02661	0.05322	0.9734
23	0.02765	0.05531	0.9723
24	0.02704	0.05407	0.973
25	0.08292	0.1658	0.9171
26	0.06013	0.1203	0.9399
27	0.05158	0.1032	0.9484
28	0.03686	0.07373	0.9631
29	0.02457	0.04914	0.9754
30	0.03317	0.06634	0.9668
31	0.04886	0.09771	0.9511
32	0.04645	0.09291	0.9535
33	0.03251	0.06502	0.9675
34	0.03171	0.06341	0.9683
35	0.02278	0.04556	0.9772
36	0.01574	0.03148	0.9843
37	0.01202	0.02403	0.988
38	0.01012	0.02025	0.9899
39	0.02718	0.05437	0.9728
40	0.02354	0.04708	0.9765
41	0.01644	0.03288	0.9836
42	0.01769	0.03537	0.9823
43	0.01494	0.02988	0.9851
44	0.01104	0.02208	0.989
45	0.00842	0.01684	0.9916
46	0.005789	0.01158	0.9942
47	0.005148	0.0103	0.9949
48	0.01621	0.03243	0.9838
49	0.012	0.02401	0.988
50	0.009623	0.01925	0.9904
51	0.008735	0.01747	0.9913
52	0.06073	0.1215	0.9393
53	0.04713	0.09426	0.9529
54	0.08786	0.1757	0.9121
55	0.0718	0.1436	0.9282
56	0.08544	0.1709	0.9146
57	0.07222	0.1444	0.9278
58	0.06045	0.1209	0.9395
59	0.0478	0.09559	0.9522
60	0.04248	0.08495	0.9575
61	0.03265	0.0653	0.9674
62	0.0311	0.0622	0.9689
63	0.02562	0.05124	0.9744
64	0.02633	0.05267	0.9737
65	0.03012	0.06024	0.9699
66	0.03244	0.06488	0.9676
67	0.03437	0.06873	0.9656
68	0.02987	0.05974	0.9701
69	0.02424	0.04849	0.9758
70	0.03614	0.07229	0.9639
71	0.04234	0.08469	0.9577
72	0.07898	0.158	0.921
73	0.1045	0.2089	0.8955
74	0.09141	0.1828	0.9086
75	0.0747	0.1494	0.9253
76	0.06854	0.1371	0.9315
77	0.06056	0.1211	0.9394
78	0.1012	0.2024	0.8988
79	0.08373	0.1675	0.9163
80	0.3019	0.6037	0.6981
81	0.2651	0.5301	0.7349
82	0.2596	0.5192	0.7404
83	0.2503	0.5007	0.7497
84	0.5226	0.9549	0.4774
85	0.4843	0.9686	0.5157
86	0.5387	0.9226	0.4613
87	0.4972	0.9944	0.5028
88	0.6548	0.6904	0.3452
89	0.6258	0.7485	0.3742
90	0.595	0.8101	0.405
91	0.595	0.8101	0.405
92	0.5499	0.9001	0.4501
93	0.5043	0.9913	0.4957
94	0.4614	0.9229	0.5386
95	0.4207	0.8414	0.5793
96	0.4649	0.9299	0.5351
97	0.478	0.956	0.522
98	0.4469	0.8939	0.5531
99	0.4714	0.9427	0.5286
100	0.4319	0.8639	0.5681
101	0.5928	0.8144	0.4072
102	0.5504	0.8992	0.4496
103	0.506	0.9879	0.494
104	0.4635	0.9269	0.5365
105	0.4252	0.8504	0.5748
106	0.3879	0.7757	0.6121
107	0.3448	0.6896	0.6552
108	0.3074	0.6149	0.6926
109	0.3147	0.6294	0.6853
110	0.2735	0.5469	0.7265
111	0.2934	0.5868	0.7066
112	0.5703	0.8593	0.4297
113	0.5519	0.8962	0.4481
114	0.5226	0.9549	0.4774
115	0.5116	0.9768	0.4884
116	0.4736	0.9473	0.5264
117	0.491	0.982	0.509
118	0.4415	0.8831	0.5585
119	0.4005	0.8009	0.5995
120	0.3566	0.7132	0.6434
121	0.3315	0.6629	0.6685
122	0.3019	0.6037	0.6981
123	0.2625	0.525	0.7375
124	0.2367	0.4734	0.7633
125	0.211	0.4219	0.789
126	0.1995	0.3989	0.8005
127	0.1635	0.3271	0.8365
128	0.1624	0.3248	0.8376
129	0.1315	0.2631	0.8685
130	0.1341	0.2683	0.8659
131	0.1393	0.2786	0.8607
132	0.2735	0.547	0.7265
133	0.2625	0.525	0.7375
134	0.228	0.4561	0.772
135	0.2019	0.4038	0.7981
136	0.1723	0.3445	0.8277
137	0.1613	0.3225	0.8387
138	0.1578	0.3156	0.8422
139	0.1453	0.2907	0.8547
140	0.1105	0.221	0.8895
141	0.1228	0.2456	0.8772
142	0.1468	0.2935	0.8532
143	0.1356	0.2713	0.8644
144	0.2384	0.4767	0.7616
145	0.334	0.6681	0.666
146	0.2624	0.5248	0.7376
147	0.2889	0.5778	0.7111
148	0.2259	0.4517	0.7741
149	0.2163	0.4326	0.7837
150	0.1525	0.3049	0.8475
151	0.1439	0.2878	0.8561
152	0.09794	0.1959	0.9021
153	0.05921	0.1184	0.9408
154	0.1581	0.3163	0.8419
155	0.5804	0.8391	0.4196

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.2732 &  0.5463 &  0.7268 \tabularnewline
11 &  0.1967 &  0.3934 &  0.8033 \tabularnewline
12 &  0.1037 &  0.2073 &  0.8963 \tabularnewline
13 &  0.05505 &  0.1101 &  0.9449 \tabularnewline
14 &  0.05148 &  0.103 &  0.9485 \tabularnewline
15 &  0.07033 &  0.1407 &  0.9297 \tabularnewline
16 &  0.07718 &  0.1544 &  0.9228 \tabularnewline
17 &  0.04505 &  0.09009 &  0.955 \tabularnewline
18 &  0.09545 &  0.1909 &  0.9045 \tabularnewline
19 &  0.06746 &  0.1349 &  0.9325 \tabularnewline
20 &  0.0522 &  0.1044 &  0.9478 \tabularnewline
21 &  0.04314 &  0.08627 &  0.9569 \tabularnewline
22 &  0.02661 &  0.05322 &  0.9734 \tabularnewline
23 &  0.02765 &  0.05531 &  0.9723 \tabularnewline
24 &  0.02704 &  0.05407 &  0.973 \tabularnewline
25 &  0.08292 &  0.1658 &  0.9171 \tabularnewline
26 &  0.06013 &  0.1203 &  0.9399 \tabularnewline
27 &  0.05158 &  0.1032 &  0.9484 \tabularnewline
28 &  0.03686 &  0.07373 &  0.9631 \tabularnewline
29 &  0.02457 &  0.04914 &  0.9754 \tabularnewline
30 &  0.03317 &  0.06634 &  0.9668 \tabularnewline
31 &  0.04886 &  0.09771 &  0.9511 \tabularnewline
32 &  0.04645 &  0.09291 &  0.9535 \tabularnewline
33 &  0.03251 &  0.06502 &  0.9675 \tabularnewline
34 &  0.03171 &  0.06341 &  0.9683 \tabularnewline
35 &  0.02278 &  0.04556 &  0.9772 \tabularnewline
36 &  0.01574 &  0.03148 &  0.9843 \tabularnewline
37 &  0.01202 &  0.02403 &  0.988 \tabularnewline
38 &  0.01012 &  0.02025 &  0.9899 \tabularnewline
39 &  0.02718 &  0.05437 &  0.9728 \tabularnewline
40 &  0.02354 &  0.04708 &  0.9765 \tabularnewline
41 &  0.01644 &  0.03288 &  0.9836 \tabularnewline
42 &  0.01769 &  0.03537 &  0.9823 \tabularnewline
43 &  0.01494 &  0.02988 &  0.9851 \tabularnewline
44 &  0.01104 &  0.02208 &  0.989 \tabularnewline
45 &  0.00842 &  0.01684 &  0.9916 \tabularnewline
46 &  0.005789 &  0.01158 &  0.9942 \tabularnewline
47 &  0.005148 &  0.0103 &  0.9949 \tabularnewline
48 &  0.01621 &  0.03243 &  0.9838 \tabularnewline
49 &  0.012 &  0.02401 &  0.988 \tabularnewline
50 &  0.009623 &  0.01925 &  0.9904 \tabularnewline
51 &  0.008735 &  0.01747 &  0.9913 \tabularnewline
52 &  0.06073 &  0.1215 &  0.9393 \tabularnewline
53 &  0.04713 &  0.09426 &  0.9529 \tabularnewline
54 &  0.08786 &  0.1757 &  0.9121 \tabularnewline
55 &  0.0718 &  0.1436 &  0.9282 \tabularnewline
56 &  0.08544 &  0.1709 &  0.9146 \tabularnewline
57 &  0.07222 &  0.1444 &  0.9278 \tabularnewline
58 &  0.06045 &  0.1209 &  0.9395 \tabularnewline
59 &  0.0478 &  0.09559 &  0.9522 \tabularnewline
60 &  0.04248 &  0.08495 &  0.9575 \tabularnewline
61 &  0.03265 &  0.0653 &  0.9674 \tabularnewline
62 &  0.0311 &  0.0622 &  0.9689 \tabularnewline
63 &  0.02562 &  0.05124 &  0.9744 \tabularnewline
64 &  0.02633 &  0.05267 &  0.9737 \tabularnewline
65 &  0.03012 &  0.06024 &  0.9699 \tabularnewline
66 &  0.03244 &  0.06488 &  0.9676 \tabularnewline
67 &  0.03437 &  0.06873 &  0.9656 \tabularnewline
68 &  0.02987 &  0.05974 &  0.9701 \tabularnewline
69 &  0.02424 &  0.04849 &  0.9758 \tabularnewline
70 &  0.03614 &  0.07229 &  0.9639 \tabularnewline
71 &  0.04234 &  0.08469 &  0.9577 \tabularnewline
72 &  0.07898 &  0.158 &  0.921 \tabularnewline
73 &  0.1045 &  0.2089 &  0.8955 \tabularnewline
74 &  0.09141 &  0.1828 &  0.9086 \tabularnewline
75 &  0.0747 &  0.1494 &  0.9253 \tabularnewline
76 &  0.06854 &  0.1371 &  0.9315 \tabularnewline
77 &  0.06056 &  0.1211 &  0.9394 \tabularnewline
78 &  0.1012 &  0.2024 &  0.8988 \tabularnewline
79 &  0.08373 &  0.1675 &  0.9163 \tabularnewline
80 &  0.3019 &  0.6037 &  0.6981 \tabularnewline
81 &  0.2651 &  0.5301 &  0.7349 \tabularnewline
82 &  0.2596 &  0.5192 &  0.7404 \tabularnewline
83 &  0.2503 &  0.5007 &  0.7497 \tabularnewline
84 &  0.5226 &  0.9549 &  0.4774 \tabularnewline
85 &  0.4843 &  0.9686 &  0.5157 \tabularnewline
86 &  0.5387 &  0.9226 &  0.4613 \tabularnewline
87 &  0.4972 &  0.9944 &  0.5028 \tabularnewline
88 &  0.6548 &  0.6904 &  0.3452 \tabularnewline
89 &  0.6258 &  0.7485 &  0.3742 \tabularnewline
90 &  0.595 &  0.8101 &  0.405 \tabularnewline
91 &  0.595 &  0.8101 &  0.405 \tabularnewline
92 &  0.5499 &  0.9001 &  0.4501 \tabularnewline
93 &  0.5043 &  0.9913 &  0.4957 \tabularnewline
94 &  0.4614 &  0.9229 &  0.5386 \tabularnewline
95 &  0.4207 &  0.8414 &  0.5793 \tabularnewline
96 &  0.4649 &  0.9299 &  0.5351 \tabularnewline
97 &  0.478 &  0.956 &  0.522 \tabularnewline
98 &  0.4469 &  0.8939 &  0.5531 \tabularnewline
99 &  0.4714 &  0.9427 &  0.5286 \tabularnewline
100 &  0.4319 &  0.8639 &  0.5681 \tabularnewline
101 &  0.5928 &  0.8144 &  0.4072 \tabularnewline
102 &  0.5504 &  0.8992 &  0.4496 \tabularnewline
103 &  0.506 &  0.9879 &  0.494 \tabularnewline
104 &  0.4635 &  0.9269 &  0.5365 \tabularnewline
105 &  0.4252 &  0.8504 &  0.5748 \tabularnewline
106 &  0.3879 &  0.7757 &  0.6121 \tabularnewline
107 &  0.3448 &  0.6896 &  0.6552 \tabularnewline
108 &  0.3074 &  0.6149 &  0.6926 \tabularnewline
109 &  0.3147 &  0.6294 &  0.6853 \tabularnewline
110 &  0.2735 &  0.5469 &  0.7265 \tabularnewline
111 &  0.2934 &  0.5868 &  0.7066 \tabularnewline
112 &  0.5703 &  0.8593 &  0.4297 \tabularnewline
113 &  0.5519 &  0.8962 &  0.4481 \tabularnewline
114 &  0.5226 &  0.9549 &  0.4774 \tabularnewline
115 &  0.5116 &  0.9768 &  0.4884 \tabularnewline
116 &  0.4736 &  0.9473 &  0.5264 \tabularnewline
117 &  0.491 &  0.982 &  0.509 \tabularnewline
118 &  0.4415 &  0.8831 &  0.5585 \tabularnewline
119 &  0.4005 &  0.8009 &  0.5995 \tabularnewline
120 &  0.3566 &  0.7132 &  0.6434 \tabularnewline
121 &  0.3315 &  0.6629 &  0.6685 \tabularnewline
122 &  0.3019 &  0.6037 &  0.6981 \tabularnewline
123 &  0.2625 &  0.525 &  0.7375 \tabularnewline
124 &  0.2367 &  0.4734 &  0.7633 \tabularnewline
125 &  0.211 &  0.4219 &  0.789 \tabularnewline
126 &  0.1995 &  0.3989 &  0.8005 \tabularnewline
127 &  0.1635 &  0.3271 &  0.8365 \tabularnewline
128 &  0.1624 &  0.3248 &  0.8376 \tabularnewline
129 &  0.1315 &  0.2631 &  0.8685 \tabularnewline
130 &  0.1341 &  0.2683 &  0.8659 \tabularnewline
131 &  0.1393 &  0.2786 &  0.8607 \tabularnewline
132 &  0.2735 &  0.547 &  0.7265 \tabularnewline
133 &  0.2625 &  0.525 &  0.7375 \tabularnewline
134 &  0.228 &  0.4561 &  0.772 \tabularnewline
135 &  0.2019 &  0.4038 &  0.7981 \tabularnewline
136 &  0.1723 &  0.3445 &  0.8277 \tabularnewline
137 &  0.1613 &  0.3225 &  0.8387 \tabularnewline
138 &  0.1578 &  0.3156 &  0.8422 \tabularnewline
139 &  0.1453 &  0.2907 &  0.8547 \tabularnewline
140 &  0.1105 &  0.221 &  0.8895 \tabularnewline
141 &  0.1228 &  0.2456 &  0.8772 \tabularnewline
142 &  0.1468 &  0.2935 &  0.8532 \tabularnewline
143 &  0.1356 &  0.2713 &  0.8644 \tabularnewline
144 &  0.2384 &  0.4767 &  0.7616 \tabularnewline
145 &  0.334 &  0.6681 &  0.666 \tabularnewline
146 &  0.2624 &  0.5248 &  0.7376 \tabularnewline
147 &  0.2889 &  0.5778 &  0.7111 \tabularnewline
148 &  0.2259 &  0.4517 &  0.7741 \tabularnewline
149 &  0.2163 &  0.4326 &  0.7837 \tabularnewline
150 &  0.1525 &  0.3049 &  0.8475 \tabularnewline
151 &  0.1439 &  0.2878 &  0.8561 \tabularnewline
152 &  0.09794 &  0.1959 &  0.9021 \tabularnewline
153 &  0.05921 &  0.1184 &  0.9408 \tabularnewline
154 &  0.1581 &  0.3163 &  0.8419 \tabularnewline
155 &  0.5804 &  0.8391 &  0.4196 \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]10[/C][C] 0.2732[/C][C] 0.5463[/C][C] 0.7268[/C][/ROW]
[ROW][C]11[/C][C] 0.1967[/C][C] 0.3934[/C][C] 0.8033[/C][/ROW]
[ROW][C]12[/C][C] 0.1037[/C][C] 0.2073[/C][C] 0.8963[/C][/ROW]
[ROW][C]13[/C][C] 0.05505[/C][C] 0.1101[/C][C] 0.9449[/C][/ROW]
[ROW][C]14[/C][C] 0.05148[/C][C] 0.103[/C][C] 0.9485[/C][/ROW]
[ROW][C]15[/C][C] 0.07033[/C][C] 0.1407[/C][C] 0.9297[/C][/ROW]
[ROW][C]16[/C][C] 0.07718[/C][C] 0.1544[/C][C] 0.9228[/C][/ROW]
[ROW][C]17[/C][C] 0.04505[/C][C] 0.09009[/C][C] 0.955[/C][/ROW]
[ROW][C]18[/C][C] 0.09545[/C][C] 0.1909[/C][C] 0.9045[/C][/ROW]
[ROW][C]19[/C][C] 0.06746[/C][C] 0.1349[/C][C] 0.9325[/C][/ROW]
[ROW][C]20[/C][C] 0.0522[/C][C] 0.1044[/C][C] 0.9478[/C][/ROW]
[ROW][C]21[/C][C] 0.04314[/C][C] 0.08627[/C][C] 0.9569[/C][/ROW]
[ROW][C]22[/C][C] 0.02661[/C][C] 0.05322[/C][C] 0.9734[/C][/ROW]
[ROW][C]23[/C][C] 0.02765[/C][C] 0.05531[/C][C] 0.9723[/C][/ROW]
[ROW][C]24[/C][C] 0.02704[/C][C] 0.05407[/C][C] 0.973[/C][/ROW]
[ROW][C]25[/C][C] 0.08292[/C][C] 0.1658[/C][C] 0.9171[/C][/ROW]
[ROW][C]26[/C][C] 0.06013[/C][C] 0.1203[/C][C] 0.9399[/C][/ROW]
[ROW][C]27[/C][C] 0.05158[/C][C] 0.1032[/C][C] 0.9484[/C][/ROW]
[ROW][C]28[/C][C] 0.03686[/C][C] 0.07373[/C][C] 0.9631[/C][/ROW]
[ROW][C]29[/C][C] 0.02457[/C][C] 0.04914[/C][C] 0.9754[/C][/ROW]
[ROW][C]30[/C][C] 0.03317[/C][C] 0.06634[/C][C] 0.9668[/C][/ROW]
[ROW][C]31[/C][C] 0.04886[/C][C] 0.09771[/C][C] 0.9511[/C][/ROW]
[ROW][C]32[/C][C] 0.04645[/C][C] 0.09291[/C][C] 0.9535[/C][/ROW]
[ROW][C]33[/C][C] 0.03251[/C][C] 0.06502[/C][C] 0.9675[/C][/ROW]
[ROW][C]34[/C][C] 0.03171[/C][C] 0.06341[/C][C] 0.9683[/C][/ROW]
[ROW][C]35[/C][C] 0.02278[/C][C] 0.04556[/C][C] 0.9772[/C][/ROW]
[ROW][C]36[/C][C] 0.01574[/C][C] 0.03148[/C][C] 0.9843[/C][/ROW]
[ROW][C]37[/C][C] 0.01202[/C][C] 0.02403[/C][C] 0.988[/C][/ROW]
[ROW][C]38[/C][C] 0.01012[/C][C] 0.02025[/C][C] 0.9899[/C][/ROW]
[ROW][C]39[/C][C] 0.02718[/C][C] 0.05437[/C][C] 0.9728[/C][/ROW]
[ROW][C]40[/C][C] 0.02354[/C][C] 0.04708[/C][C] 0.9765[/C][/ROW]
[ROW][C]41[/C][C] 0.01644[/C][C] 0.03288[/C][C] 0.9836[/C][/ROW]
[ROW][C]42[/C][C] 0.01769[/C][C] 0.03537[/C][C] 0.9823[/C][/ROW]
[ROW][C]43[/C][C] 0.01494[/C][C] 0.02988[/C][C] 0.9851[/C][/ROW]
[ROW][C]44[/C][C] 0.01104[/C][C] 0.02208[/C][C] 0.989[/C][/ROW]
[ROW][C]45[/C][C] 0.00842[/C][C] 0.01684[/C][C] 0.9916[/C][/ROW]
[ROW][C]46[/C][C] 0.005789[/C][C] 0.01158[/C][C] 0.9942[/C][/ROW]
[ROW][C]47[/C][C] 0.005148[/C][C] 0.0103[/C][C] 0.9949[/C][/ROW]
[ROW][C]48[/C][C] 0.01621[/C][C] 0.03243[/C][C] 0.9838[/C][/ROW]
[ROW][C]49[/C][C] 0.012[/C][C] 0.02401[/C][C] 0.988[/C][/ROW]
[ROW][C]50[/C][C] 0.009623[/C][C] 0.01925[/C][C] 0.9904[/C][/ROW]
[ROW][C]51[/C][C] 0.008735[/C][C] 0.01747[/C][C] 0.9913[/C][/ROW]
[ROW][C]52[/C][C] 0.06073[/C][C] 0.1215[/C][C] 0.9393[/C][/ROW]
[ROW][C]53[/C][C] 0.04713[/C][C] 0.09426[/C][C] 0.9529[/C][/ROW]
[ROW][C]54[/C][C] 0.08786[/C][C] 0.1757[/C][C] 0.9121[/C][/ROW]
[ROW][C]55[/C][C] 0.0718[/C][C] 0.1436[/C][C] 0.9282[/C][/ROW]
[ROW][C]56[/C][C] 0.08544[/C][C] 0.1709[/C][C] 0.9146[/C][/ROW]
[ROW][C]57[/C][C] 0.07222[/C][C] 0.1444[/C][C] 0.9278[/C][/ROW]
[ROW][C]58[/C][C] 0.06045[/C][C] 0.1209[/C][C] 0.9395[/C][/ROW]
[ROW][C]59[/C][C] 0.0478[/C][C] 0.09559[/C][C] 0.9522[/C][/ROW]
[ROW][C]60[/C][C] 0.04248[/C][C] 0.08495[/C][C] 0.9575[/C][/ROW]
[ROW][C]61[/C][C] 0.03265[/C][C] 0.0653[/C][C] 0.9674[/C][/ROW]
[ROW][C]62[/C][C] 0.0311[/C][C] 0.0622[/C][C] 0.9689[/C][/ROW]
[ROW][C]63[/C][C] 0.02562[/C][C] 0.05124[/C][C] 0.9744[/C][/ROW]
[ROW][C]64[/C][C] 0.02633[/C][C] 0.05267[/C][C] 0.9737[/C][/ROW]
[ROW][C]65[/C][C] 0.03012[/C][C] 0.06024[/C][C] 0.9699[/C][/ROW]
[ROW][C]66[/C][C] 0.03244[/C][C] 0.06488[/C][C] 0.9676[/C][/ROW]
[ROW][C]67[/C][C] 0.03437[/C][C] 0.06873[/C][C] 0.9656[/C][/ROW]
[ROW][C]68[/C][C] 0.02987[/C][C] 0.05974[/C][C] 0.9701[/C][/ROW]
[ROW][C]69[/C][C] 0.02424[/C][C] 0.04849[/C][C] 0.9758[/C][/ROW]
[ROW][C]70[/C][C] 0.03614[/C][C] 0.07229[/C][C] 0.9639[/C][/ROW]
[ROW][C]71[/C][C] 0.04234[/C][C] 0.08469[/C][C] 0.9577[/C][/ROW]
[ROW][C]72[/C][C] 0.07898[/C][C] 0.158[/C][C] 0.921[/C][/ROW]
[ROW][C]73[/C][C] 0.1045[/C][C] 0.2089[/C][C] 0.8955[/C][/ROW]
[ROW][C]74[/C][C] 0.09141[/C][C] 0.1828[/C][C] 0.9086[/C][/ROW]
[ROW][C]75[/C][C] 0.0747[/C][C] 0.1494[/C][C] 0.9253[/C][/ROW]
[ROW][C]76[/C][C] 0.06854[/C][C] 0.1371[/C][C] 0.9315[/C][/ROW]
[ROW][C]77[/C][C] 0.06056[/C][C] 0.1211[/C][C] 0.9394[/C][/ROW]
[ROW][C]78[/C][C] 0.1012[/C][C] 0.2024[/C][C] 0.8988[/C][/ROW]
[ROW][C]79[/C][C] 0.08373[/C][C] 0.1675[/C][C] 0.9163[/C][/ROW]
[ROW][C]80[/C][C] 0.3019[/C][C] 0.6037[/C][C] 0.6981[/C][/ROW]
[ROW][C]81[/C][C] 0.2651[/C][C] 0.5301[/C][C] 0.7349[/C][/ROW]
[ROW][C]82[/C][C] 0.2596[/C][C] 0.5192[/C][C] 0.7404[/C][/ROW]
[ROW][C]83[/C][C] 0.2503[/C][C] 0.5007[/C][C] 0.7497[/C][/ROW]
[ROW][C]84[/C][C] 0.5226[/C][C] 0.9549[/C][C] 0.4774[/C][/ROW]
[ROW][C]85[/C][C] 0.4843[/C][C] 0.9686[/C][C] 0.5157[/C][/ROW]
[ROW][C]86[/C][C] 0.5387[/C][C] 0.9226[/C][C] 0.4613[/C][/ROW]
[ROW][C]87[/C][C] 0.4972[/C][C] 0.9944[/C][C] 0.5028[/C][/ROW]
[ROW][C]88[/C][C] 0.6548[/C][C] 0.6904[/C][C] 0.3452[/C][/ROW]
[ROW][C]89[/C][C] 0.6258[/C][C] 0.7485[/C][C] 0.3742[/C][/ROW]
[ROW][C]90[/C][C] 0.595[/C][C] 0.8101[/C][C] 0.405[/C][/ROW]
[ROW][C]91[/C][C] 0.595[/C][C] 0.8101[/C][C] 0.405[/C][/ROW]
[ROW][C]92[/C][C] 0.5499[/C][C] 0.9001[/C][C] 0.4501[/C][/ROW]
[ROW][C]93[/C][C] 0.5043[/C][C] 0.9913[/C][C] 0.4957[/C][/ROW]
[ROW][C]94[/C][C] 0.4614[/C][C] 0.9229[/C][C] 0.5386[/C][/ROW]
[ROW][C]95[/C][C] 0.4207[/C][C] 0.8414[/C][C] 0.5793[/C][/ROW]
[ROW][C]96[/C][C] 0.4649[/C][C] 0.9299[/C][C] 0.5351[/C][/ROW]
[ROW][C]97[/C][C] 0.478[/C][C] 0.956[/C][C] 0.522[/C][/ROW]
[ROW][C]98[/C][C] 0.4469[/C][C] 0.8939[/C][C] 0.5531[/C][/ROW]
[ROW][C]99[/C][C] 0.4714[/C][C] 0.9427[/C][C] 0.5286[/C][/ROW]
[ROW][C]100[/C][C] 0.4319[/C][C] 0.8639[/C][C] 0.5681[/C][/ROW]
[ROW][C]101[/C][C] 0.5928[/C][C] 0.8144[/C][C] 0.4072[/C][/ROW]
[ROW][C]102[/C][C] 0.5504[/C][C] 0.8992[/C][C] 0.4496[/C][/ROW]
[ROW][C]103[/C][C] 0.506[/C][C] 0.9879[/C][C] 0.494[/C][/ROW]
[ROW][C]104[/C][C] 0.4635[/C][C] 0.9269[/C][C] 0.5365[/C][/ROW]
[ROW][C]105[/C][C] 0.4252[/C][C] 0.8504[/C][C] 0.5748[/C][/ROW]
[ROW][C]106[/C][C] 0.3879[/C][C] 0.7757[/C][C] 0.6121[/C][/ROW]
[ROW][C]107[/C][C] 0.3448[/C][C] 0.6896[/C][C] 0.6552[/C][/ROW]
[ROW][C]108[/C][C] 0.3074[/C][C] 0.6149[/C][C] 0.6926[/C][/ROW]
[ROW][C]109[/C][C] 0.3147[/C][C] 0.6294[/C][C] 0.6853[/C][/ROW]
[ROW][C]110[/C][C] 0.2735[/C][C] 0.5469[/C][C] 0.7265[/C][/ROW]
[ROW][C]111[/C][C] 0.2934[/C][C] 0.5868[/C][C] 0.7066[/C][/ROW]
[ROW][C]112[/C][C] 0.5703[/C][C] 0.8593[/C][C] 0.4297[/C][/ROW]
[ROW][C]113[/C][C] 0.5519[/C][C] 0.8962[/C][C] 0.4481[/C][/ROW]
[ROW][C]114[/C][C] 0.5226[/C][C] 0.9549[/C][C] 0.4774[/C][/ROW]
[ROW][C]115[/C][C] 0.5116[/C][C] 0.9768[/C][C] 0.4884[/C][/ROW]
[ROW][C]116[/C][C] 0.4736[/C][C] 0.9473[/C][C] 0.5264[/C][/ROW]
[ROW][C]117[/C][C] 0.491[/C][C] 0.982[/C][C] 0.509[/C][/ROW]
[ROW][C]118[/C][C] 0.4415[/C][C] 0.8831[/C][C] 0.5585[/C][/ROW]
[ROW][C]119[/C][C] 0.4005[/C][C] 0.8009[/C][C] 0.5995[/C][/ROW]
[ROW][C]120[/C][C] 0.3566[/C][C] 0.7132[/C][C] 0.6434[/C][/ROW]
[ROW][C]121[/C][C] 0.3315[/C][C] 0.6629[/C][C] 0.6685[/C][/ROW]
[ROW][C]122[/C][C] 0.3019[/C][C] 0.6037[/C][C] 0.6981[/C][/ROW]
[ROW][C]123[/C][C] 0.2625[/C][C] 0.525[/C][C] 0.7375[/C][/ROW]
[ROW][C]124[/C][C] 0.2367[/C][C] 0.4734[/C][C] 0.7633[/C][/ROW]
[ROW][C]125[/C][C] 0.211[/C][C] 0.4219[/C][C] 0.789[/C][/ROW]
[ROW][C]126[/C][C] 0.1995[/C][C] 0.3989[/C][C] 0.8005[/C][/ROW]
[ROW][C]127[/C][C] 0.1635[/C][C] 0.3271[/C][C] 0.8365[/C][/ROW]
[ROW][C]128[/C][C] 0.1624[/C][C] 0.3248[/C][C] 0.8376[/C][/ROW]
[ROW][C]129[/C][C] 0.1315[/C][C] 0.2631[/C][C] 0.8685[/C][/ROW]
[ROW][C]130[/C][C] 0.1341[/C][C] 0.2683[/C][C] 0.8659[/C][/ROW]
[ROW][C]131[/C][C] 0.1393[/C][C] 0.2786[/C][C] 0.8607[/C][/ROW]
[ROW][C]132[/C][C] 0.2735[/C][C] 0.547[/C][C] 0.7265[/C][/ROW]
[ROW][C]133[/C][C] 0.2625[/C][C] 0.525[/C][C] 0.7375[/C][/ROW]
[ROW][C]134[/C][C] 0.228[/C][C] 0.4561[/C][C] 0.772[/C][/ROW]
[ROW][C]135[/C][C] 0.2019[/C][C] 0.4038[/C][C] 0.7981[/C][/ROW]
[ROW][C]136[/C][C] 0.1723[/C][C] 0.3445[/C][C] 0.8277[/C][/ROW]
[ROW][C]137[/C][C] 0.1613[/C][C] 0.3225[/C][C] 0.8387[/C][/ROW]
[ROW][C]138[/C][C] 0.1578[/C][C] 0.3156[/C][C] 0.8422[/C][/ROW]
[ROW][C]139[/C][C] 0.1453[/C][C] 0.2907[/C][C] 0.8547[/C][/ROW]
[ROW][C]140[/C][C] 0.1105[/C][C] 0.221[/C][C] 0.8895[/C][/ROW]
[ROW][C]141[/C][C] 0.1228[/C][C] 0.2456[/C][C] 0.8772[/C][/ROW]
[ROW][C]142[/C][C] 0.1468[/C][C] 0.2935[/C][C] 0.8532[/C][/ROW]
[ROW][C]143[/C][C] 0.1356[/C][C] 0.2713[/C][C] 0.8644[/C][/ROW]
[ROW][C]144[/C][C] 0.2384[/C][C] 0.4767[/C][C] 0.7616[/C][/ROW]
[ROW][C]145[/C][C] 0.334[/C][C] 0.6681[/C][C] 0.666[/C][/ROW]
[ROW][C]146[/C][C] 0.2624[/C][C] 0.5248[/C][C] 0.7376[/C][/ROW]
[ROW][C]147[/C][C] 0.2889[/C][C] 0.5778[/C][C] 0.7111[/C][/ROW]
[ROW][C]148[/C][C] 0.2259[/C][C] 0.4517[/C][C] 0.7741[/C][/ROW]
[ROW][C]149[/C][C] 0.2163[/C][C] 0.4326[/C][C] 0.7837[/C][/ROW]
[ROW][C]150[/C][C] 0.1525[/C][C] 0.3049[/C][C] 0.8475[/C][/ROW]
[ROW][C]151[/C][C] 0.1439[/C][C] 0.2878[/C][C] 0.8561[/C][/ROW]
[ROW][C]152[/C][C] 0.09794[/C][C] 0.1959[/C][C] 0.9021[/C][/ROW]
[ROW][C]153[/C][C] 0.05921[/C][C] 0.1184[/C][C] 0.9408[/C][/ROW]
[ROW][C]154[/C][C] 0.1581[/C][C] 0.3163[/C][C] 0.8419[/C][/ROW]
[ROW][C]155[/C][C] 0.5804[/C][C] 0.8391[/C][C] 0.4196[/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
10	0.2732	0.5463	0.7268
11	0.1967	0.3934	0.8033
12	0.1037	0.2073	0.8963
13	0.05505	0.1101	0.9449
14	0.05148	0.103	0.9485
15	0.07033	0.1407	0.9297
16	0.07718	0.1544	0.9228
17	0.04505	0.09009	0.955
18	0.09545	0.1909	0.9045
19	0.06746	0.1349	0.9325
20	0.0522	0.1044	0.9478
21	0.04314	0.08627	0.9569
22	0.02661	0.05322	0.9734
23	0.02765	0.05531	0.9723
24	0.02704	0.05407	0.973
25	0.08292	0.1658	0.9171
26	0.06013	0.1203	0.9399
27	0.05158	0.1032	0.9484
28	0.03686	0.07373	0.9631
29	0.02457	0.04914	0.9754
30	0.03317	0.06634	0.9668
31	0.04886	0.09771	0.9511
32	0.04645	0.09291	0.9535
33	0.03251	0.06502	0.9675
34	0.03171	0.06341	0.9683
35	0.02278	0.04556	0.9772
36	0.01574	0.03148	0.9843
37	0.01202	0.02403	0.988
38	0.01012	0.02025	0.9899
39	0.02718	0.05437	0.9728
40	0.02354	0.04708	0.9765
41	0.01644	0.03288	0.9836
42	0.01769	0.03537	0.9823
43	0.01494	0.02988	0.9851
44	0.01104	0.02208	0.989
45	0.00842	0.01684	0.9916
46	0.005789	0.01158	0.9942
47	0.005148	0.0103	0.9949
48	0.01621	0.03243	0.9838
49	0.012	0.02401	0.988
50	0.009623	0.01925	0.9904
51	0.008735	0.01747	0.9913
52	0.06073	0.1215	0.9393
53	0.04713	0.09426	0.9529
54	0.08786	0.1757	0.9121
55	0.0718	0.1436	0.9282
56	0.08544	0.1709	0.9146
57	0.07222	0.1444	0.9278
58	0.06045	0.1209	0.9395
59	0.0478	0.09559	0.9522
60	0.04248	0.08495	0.9575
61	0.03265	0.0653	0.9674
62	0.0311	0.0622	0.9689
63	0.02562	0.05124	0.9744
64	0.02633	0.05267	0.9737
65	0.03012	0.06024	0.9699
66	0.03244	0.06488	0.9676
67	0.03437	0.06873	0.9656
68	0.02987	0.05974	0.9701
69	0.02424	0.04849	0.9758
70	0.03614	0.07229	0.9639
71	0.04234	0.08469	0.9577
72	0.07898	0.158	0.921
73	0.1045	0.2089	0.8955
74	0.09141	0.1828	0.9086
75	0.0747	0.1494	0.9253
76	0.06854	0.1371	0.9315
77	0.06056	0.1211	0.9394
78	0.1012	0.2024	0.8988
79	0.08373	0.1675	0.9163
80	0.3019	0.6037	0.6981
81	0.2651	0.5301	0.7349
82	0.2596	0.5192	0.7404
83	0.2503	0.5007	0.7497
84	0.5226	0.9549	0.4774
85	0.4843	0.9686	0.5157
86	0.5387	0.9226	0.4613
87	0.4972	0.9944	0.5028
88	0.6548	0.6904	0.3452
89	0.6258	0.7485	0.3742
90	0.595	0.8101	0.405
91	0.595	0.8101	0.405
92	0.5499	0.9001	0.4501
93	0.5043	0.9913	0.4957
94	0.4614	0.9229	0.5386
95	0.4207	0.8414	0.5793
96	0.4649	0.9299	0.5351
97	0.478	0.956	0.522
98	0.4469	0.8939	0.5531
99	0.4714	0.9427	0.5286
100	0.4319	0.8639	0.5681
101	0.5928	0.8144	0.4072
102	0.5504	0.8992	0.4496
103	0.506	0.9879	0.494
104	0.4635	0.9269	0.5365
105	0.4252	0.8504	0.5748
106	0.3879	0.7757	0.6121
107	0.3448	0.6896	0.6552
108	0.3074	0.6149	0.6926
109	0.3147	0.6294	0.6853
110	0.2735	0.5469	0.7265
111	0.2934	0.5868	0.7066
112	0.5703	0.8593	0.4297
113	0.5519	0.8962	0.4481
114	0.5226	0.9549	0.4774
115	0.5116	0.9768	0.4884
116	0.4736	0.9473	0.5264
117	0.491	0.982	0.509
118	0.4415	0.8831	0.5585
119	0.4005	0.8009	0.5995
120	0.3566	0.7132	0.6434
121	0.3315	0.6629	0.6685
122	0.3019	0.6037	0.6981
123	0.2625	0.525	0.7375
124	0.2367	0.4734	0.7633
125	0.211	0.4219	0.789
126	0.1995	0.3989	0.8005
127	0.1635	0.3271	0.8365
128	0.1624	0.3248	0.8376
129	0.1315	0.2631	0.8685
130	0.1341	0.2683	0.8659
131	0.1393	0.2786	0.8607
132	0.2735	0.547	0.7265
133	0.2625	0.525	0.7375
134	0.228	0.4561	0.772
135	0.2019	0.4038	0.7981
136	0.1723	0.3445	0.8277
137	0.1613	0.3225	0.8387
138	0.1578	0.3156	0.8422
139	0.1453	0.2907	0.8547
140	0.1105	0.221	0.8895
141	0.1228	0.2456	0.8772
142	0.1468	0.2935	0.8532
143	0.1356	0.2713	0.8644
144	0.2384	0.4767	0.7616
145	0.334	0.6681	0.666
146	0.2624	0.5248	0.7376
147	0.2889	0.5778	0.7111
148	0.2259	0.4517	0.7741
149	0.2163	0.4326	0.7837
150	0.1525	0.3049	0.8475
151	0.1439	0.2878	0.8561
152	0.09794	0.1959	0.9021
153	0.05921	0.1184	0.9408
154	0.1581	0.3163	0.8419
155	0.5804	0.8391	0.4196

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	18	0.123288	NOK
10% type I error level	43	0.294521	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 18 & 0.123288 & NOK \tabularnewline
10% type I error level & 43 & 0.294521 & 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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.123288[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]43[/C][C]0.294521[/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	0	0	OK
5% type I error level	18	0.123288	NOK
10% type I error level	43	0.294521	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.4823, df1 = 2, df2 = 156, p-value = 0.2303
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0889, df1 = 12, df2 = 146, p-value = 0.3737
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.3856, df1 = 2, df2 = 156, p-value = 0.2532

\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 = 1.4823, df1 = 2, df2 = 156, p-value = 0.2303
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0889, df1 = 12, df2 = 146, p-value = 0.3737
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3856, df1 = 2, df2 = 156, p-value = 0.2532
 \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 = 1.4823, df1 = 2, df2 = 156, p-value = 0.2303
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0889, df1 = 12, df2 = 146, p-value = 0.3737
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3856, df1 = 2, df2 = 156, p-value = 0.2532
[/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 = 1.4823, df1 = 2, df2 = 156, p-value = 0.2303
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0889, df1 = 12, df2 = 146, p-value = 0.3737
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.3856, df1 = 2, df2 = 156, p-value = 0.2532

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.096654 1.123377 1.041969 1.038007 1.046991 1.042807

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.096654 1.123377 1.041969 1.038007 1.046991 1.042807 
 \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
     SK1      SK2      SK3      SK4      SK5      SK6 
1.096654 1.123377 1.041969 1.038007 1.046991 1.042807 
[/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 SK1 SK2 SK3 SK4 SK5 SK6 1.096654 1.123377 1.041969 1.038007 1.046991 1.042807

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 ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

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

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

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '7'
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