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*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 21 Dec 2016 12:19:48 +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/21/t148231928098sourcjkq0hp7c.htm/, Retrieved Fri, 01 Nov 2024 03:34:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302179, Retrieved Fri, 01 Nov 2024 03:34:07 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

132

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

-       [Multiple Regression] [MULTIPLE REGRESSION] [2016-12-21 11:19:48] [1e1af2256d87dfd5401e4c69cd3b64ca] [Current]

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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	15
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	4	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	17
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	14
4	4	4	5	5	4	17
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	4	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	4	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	14
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	14
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	13
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	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=302179&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=302179&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&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
SK/EOU1[t] = + 1.2123 + 0.143001`SK/EOU2`[t] -0.0453352`SK/EOU3`[t] + 0.00503123`SK/EOU4`[t] + 0.0765239`SK/EOU5`[t] -0.00259751`SK/EOU6`[t] + 0.125273TVDCSUM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SK/EOU1[t] =  +  1.2123 +  0.143001`SK/EOU2`[t] -0.0453352`SK/EOU3`[t] +  0.00503123`SK/EOU4`[t] +  0.0765239`SK/EOU5`[t] -0.00259751`SK/EOU6`[t] +  0.125273TVDCSUM[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302179&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SK/EOU1[t] =  +  1.2123 +  0.143001`SK/EOU2`[t] -0.0453352`SK/EOU3`[t] +  0.00503123`SK/EOU4`[t] +  0.0765239`SK/EOU5`[t] -0.00259751`SK/EOU6`[t] +  0.125273TVDCSUM[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302179&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&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
SK/EOU1[t] = + 1.2123 + 0.143001`SK/EOU2`[t] -0.0453352`SK/EOU3`[t] + 0.00503123`SK/EOU4`[t] + 0.0765239`SK/EOU5`[t] -0.00259751`SK/EOU6`[t] + 0.125273TVDCSUM[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)	+1.212	0.7672	+1.5800e+00	0.1161	0.05803
`SK/EOU2`	+0.143	0.1034	+1.3830e+00	0.1685	0.08424
`SK/EOU3`	-0.04534	0.06855	-6.6130e-01	0.5094	0.2547
`SK/EOU4`	+0.005031	0.09414	+5.3450e-02	0.9574	0.4787
`SK/EOU5`	+0.07652	0.08905	+8.5930e-01	0.3915	0.1957
`SK/EOU6`	-0.002597	0.09194	-2.8250e-02	0.9775	0.4887
TVDCSUM	+0.1253	0.03807	+3.2900e+00	0.001234	0.000617

\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) & +1.212 &  0.7672 & +1.5800e+00 &  0.1161 &  0.05803 \tabularnewline
`SK/EOU2` & +0.143 &  0.1034 & +1.3830e+00 &  0.1685 &  0.08424 \tabularnewline
`SK/EOU3` & -0.04534 &  0.06855 & -6.6130e-01 &  0.5094 &  0.2547 \tabularnewline
`SK/EOU4` & +0.005031 &  0.09414 & +5.3450e-02 &  0.9574 &  0.4787 \tabularnewline
`SK/EOU5` & +0.07652 &  0.08905 & +8.5930e-01 &  0.3915 &  0.1957 \tabularnewline
`SK/EOU6` & -0.002597 &  0.09194 & -2.8250e-02 &  0.9775 &  0.4887 \tabularnewline
TVDCSUM & +0.1253 &  0.03807 & +3.2900e+00 &  0.001234 &  0.000617 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302179&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]+1.212[/C][C] 0.7672[/C][C]+1.5800e+00[/C][C] 0.1161[/C][C] 0.05803[/C][/ROW]
[ROW][C]`SK/EOU2`[/C][C]+0.143[/C][C] 0.1034[/C][C]+1.3830e+00[/C][C] 0.1685[/C][C] 0.08424[/C][/ROW]
[ROW][C]`SK/EOU3`[/C][C]-0.04534[/C][C] 0.06855[/C][C]-6.6130e-01[/C][C] 0.5094[/C][C] 0.2547[/C][/ROW]
[ROW][C]`SK/EOU4`[/C][C]+0.005031[/C][C] 0.09414[/C][C]+5.3450e-02[/C][C] 0.9574[/C][C] 0.4787[/C][/ROW]
[ROW][C]`SK/EOU5`[/C][C]+0.07652[/C][C] 0.08905[/C][C]+8.5930e-01[/C][C] 0.3915[/C][C] 0.1957[/C][/ROW]
[ROW][C]`SK/EOU6`[/C][C]-0.002597[/C][C] 0.09194[/C][C]-2.8250e-02[/C][C] 0.9775[/C][C] 0.4887[/C][/ROW]
[ROW][C]TVDCSUM[/C][C]+0.1253[/C][C] 0.03807[/C][C]+3.2900e+00[/C][C] 0.001234[/C][C] 0.000617[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302179&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&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)	+1.212	0.7672	+1.5800e+00	0.1161	0.05803
`SK/EOU2`	+0.143	0.1034	+1.3830e+00	0.1685	0.08424
`SK/EOU3`	-0.04534	0.06855	-6.6130e-01	0.5094	0.2547
`SK/EOU4`	+0.005031	0.09414	+5.3450e-02	0.9574	0.4787
`SK/EOU5`	+0.07652	0.08905	+8.5930e-01	0.3915	0.1957
`SK/EOU6`	-0.002597	0.09194	-2.8250e-02	0.9775	0.4887
TVDCSUM	+0.1253	0.03807	+3.2900e+00	0.001234	0.000617

Multiple Linear Regression - Regression Statistics
Multiple R	0.3747
R-squared	0.1404
Adjusted R-squared	0.1077
F-TEST (value)	4.3
F-TEST (DF numerator)	6
F-TEST (DF denominator)	158
p-value	0.0004805
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.6832
Sum Squared Residuals	73.75

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3747 \tabularnewline
R-squared &  0.1404 \tabularnewline
Adjusted R-squared &  0.1077 \tabularnewline
F-TEST (value) &  4.3 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value &  0.0004805 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.6832 \tabularnewline
Sum Squared Residuals &  73.75 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302179&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3747[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1404[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1077[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4.3[/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] 0.0004805[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.6832[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 73.75[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302179&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&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	0.3747
R-squared	0.1404
Adjusted R-squared	0.1077
F-TEST (value)	4.3
F-TEST (DF numerator)	6
F-TEST (DF denominator)	158
p-value	0.0004805
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.6832
Sum Squared Residuals	73.75

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4	3.333	0.6672
2	5	3.902	1.098
3	4	4.08	-0.07962
4	3	3.838	-0.8382
5	4	3.954	0.04565
6	3	3.997	-0.9971
7	3	4.092	-1.092
8	3	3.878	-0.8778
9	4	4.265	-0.2654
10	4	4.22	-0.22
11	4	4.216	-0.2156
12	4	3.824	0.176
13	4	3.916	0.08447
14	3	3.482	-0.4817
15	4	3.722	0.2778
16	3	4	-1
17	3	3.875	-0.8752
18	5	3.936	1.064
19	4	4.125	-0.125
20	3	3.87	-0.8702
21	4	3.872	0.1282
22	4	3.875	0.1248
23	4	3.753	0.2474
24	4	4	-0.0004994
25	3	3.798	-0.7979
26	3	3.992	-0.9921
27	4	3.798	0.2021
28	2	3.952	-1.952
29	5	3.923	1.077
30	4	3.359	0.641
31	4	3.969	0.03052
32	5	4	0.9995
33	4	3.607	0.393
34	2	3.436	-1.436
35	4	4.282	-0.2821
36	3	3.753	-0.7526
37	4	3.477	0.5234
38	4	3.575	0.4251
39	4	4.128	-0.1284
40	5	3.798	1.202
41	4	4.22	-0.22
42	3	3.404	-0.4043
43	5	4.09	0.9103
44	5	3.748	1.252
45	4	3.79	0.2097
46	4	3.923	0.07684
47	3	3.814	-0.814
48	4	3.624	0.3761
49	4	4.146	-0.1461
50	5	4.484	0.5161
51	5	4.146	0.8539
52	4	3.182	0.8176
53	4	3.522	0.478
54	4	3.502	0.498
55	3	3.716	-0.7163
56	3	4.051	-1.051
57	4	3.995	0.005348
58	4	3.874	0.1256
59	5	4.311	0.6893
60	2	3.997	-1.997
61	4	3.997	0.002914
62	3	3.77	-0.7701
63	4	3.826	0.1735
64	4	3.136	0.8639
65	4	4.123	-0.1225
66	4	3.744	0.2559
67	5	3.543	1.457
68	3	3.992	-0.9921
69	3	3.79	-0.7903
70	4	3.977	0.02289
71	4	3.718	0.2821
72	4	3.673	0.3274
73	4	4.13	-0.13
74	3	3.843	-0.8432
75	4	4	0.000317
76	3	3.696	-0.6962
77	3	3.607	-0.607
78	4	3.86	0.1399
79	4	3.875	0.1248
80	3	3.074	-0.07386
81	4	4	0.000317
82	4	4.168	-0.1677
83	4	4.122	-0.1224
84	5	4.424	0.5757
85	5	4.096	0.9038
86	4	4.202	-0.2023
87	3	3.75	-0.75
88	3	4.048	-1.048
89	4	3.427	0.5731
90	4	3.755	0.2449
91	4	4	-0.0004994
92	4	4	0.000317
93	4	4.271	-0.2706
94	3	3.867	-0.8668
95	4	3.875	0.1248
96	5	4.216	0.7836
97	5	4.131	0.8692
98	4	4.14	-0.1401
99	5	3.979	1.021
100	4	3.75	0.25
101	5	3.545	1.455
102	3	3.793	-0.7929
103	5	4.127	0.8726
104	4	3.87	0.1298
105	4	3.966	0.03394
106	4	3.753	0.2474
107	4	3.878	0.1222
108	3	3.957	-0.9569
109	4	3.673	0.3274
110	4	3.79	0.2097
111	3	3.348	-0.348
112	4	3.668	0.3324
113	3	3.878	-0.8778
114	4	3.801	0.1987
115	5	4.062	0.9381
116	5	3.952	1.048
117	4	3.675	0.3248
118	4	3.748	0.2525
119	3	3.665	-0.665
120	4	3.923	0.07684
121	4	3.874	0.1256
122	4	4.237	-0.2368
123	3	3.798	-0.7979
124	4	3.918	0.08187
125	4	3.921	0.07944
126	3	3.838	-0.8382
127	4	3.795	0.2045
128	3	3.001	-0.0007299
129	4	3.995	0.005348
130	5	4.245	0.7548
131	2	3.588	-1.588
132	3	3.154	-0.1538
133	4	3.918	0.08187
134	5	4.393	0.6068
135	4	3.896	0.1044
136	5	4.478	0.5218
137	4	4.22	-0.22
138	4	3.54	0.4603
139	3	3.704	-0.7038
140	4	3.878	0.1222
141	4	3.638	0.362
142	4	4.168	-0.1677
143	4	3.747	0.2535
144	5	4.325	0.6749
145	4	3.484	0.5157
146	4	3.878	0.1222
147	3	3.365	-0.3647
148	4	4.023	-0.02342
149	4	3.918	0.08187
150	4	3.795	0.2047
151	3	3.446	-0.4456
152	4	3.75	0.25
153	5	4.08	0.9204
154	4	3.551	0.4492
155	2	3.61	-1.61
156	4	4.171	-0.1711
157	4	3.724	0.2762
158	4	4.176	-0.1763
159	4	3.901	0.09942
160	5	3.843	1.157
161	5	3.918	1.082
162	3	3.614	-0.6143
163	4	3.921	0.07944
164	4	3.624	0.3761
165	2	3.862	-1.862

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  4 &  3.333 &  0.6672 \tabularnewline
2 &  5 &  3.902 &  1.098 \tabularnewline
3 &  4 &  4.08 & -0.07962 \tabularnewline
4 &  3 &  3.838 & -0.8382 \tabularnewline
5 &  4 &  3.954 &  0.04565 \tabularnewline
6 &  3 &  3.997 & -0.9971 \tabularnewline
7 &  3 &  4.092 & -1.092 \tabularnewline
8 &  3 &  3.878 & -0.8778 \tabularnewline
9 &  4 &  4.265 & -0.2654 \tabularnewline
10 &  4 &  4.22 & -0.22 \tabularnewline
11 &  4 &  4.216 & -0.2156 \tabularnewline
12 &  4 &  3.824 &  0.176 \tabularnewline
13 &  4 &  3.916 &  0.08447 \tabularnewline
14 &  3 &  3.482 & -0.4817 \tabularnewline
15 &  4 &  3.722 &  0.2778 \tabularnewline
16 &  3 &  4 & -1 \tabularnewline
17 &  3 &  3.875 & -0.8752 \tabularnewline
18 &  5 &  3.936 &  1.064 \tabularnewline
19 &  4 &  4.125 & -0.125 \tabularnewline
20 &  3 &  3.87 & -0.8702 \tabularnewline
21 &  4 &  3.872 &  0.1282 \tabularnewline
22 &  4 &  3.875 &  0.1248 \tabularnewline
23 &  4 &  3.753 &  0.2474 \tabularnewline
24 &  4 &  4 & -0.0004994 \tabularnewline
25 &  3 &  3.798 & -0.7979 \tabularnewline
26 &  3 &  3.992 & -0.9921 \tabularnewline
27 &  4 &  3.798 &  0.2021 \tabularnewline
28 &  2 &  3.952 & -1.952 \tabularnewline
29 &  5 &  3.923 &  1.077 \tabularnewline
30 &  4 &  3.359 &  0.641 \tabularnewline
31 &  4 &  3.969 &  0.03052 \tabularnewline
32 &  5 &  4 &  0.9995 \tabularnewline
33 &  4 &  3.607 &  0.393 \tabularnewline
34 &  2 &  3.436 & -1.436 \tabularnewline
35 &  4 &  4.282 & -0.2821 \tabularnewline
36 &  3 &  3.753 & -0.7526 \tabularnewline
37 &  4 &  3.477 &  0.5234 \tabularnewline
38 &  4 &  3.575 &  0.4251 \tabularnewline
39 &  4 &  4.128 & -0.1284 \tabularnewline
40 &  5 &  3.798 &  1.202 \tabularnewline
41 &  4 &  4.22 & -0.22 \tabularnewline
42 &  3 &  3.404 & -0.4043 \tabularnewline
43 &  5 &  4.09 &  0.9103 \tabularnewline
44 &  5 &  3.748 &  1.252 \tabularnewline
45 &  4 &  3.79 &  0.2097 \tabularnewline
46 &  4 &  3.923 &  0.07684 \tabularnewline
47 &  3 &  3.814 & -0.814 \tabularnewline
48 &  4 &  3.624 &  0.3761 \tabularnewline
49 &  4 &  4.146 & -0.1461 \tabularnewline
50 &  5 &  4.484 &  0.5161 \tabularnewline
51 &  5 &  4.146 &  0.8539 \tabularnewline
52 &  4 &  3.182 &  0.8176 \tabularnewline
53 &  4 &  3.522 &  0.478 \tabularnewline
54 &  4 &  3.502 &  0.498 \tabularnewline
55 &  3 &  3.716 & -0.7163 \tabularnewline
56 &  3 &  4.051 & -1.051 \tabularnewline
57 &  4 &  3.995 &  0.005348 \tabularnewline
58 &  4 &  3.874 &  0.1256 \tabularnewline
59 &  5 &  4.311 &  0.6893 \tabularnewline
60 &  2 &  3.997 & -1.997 \tabularnewline
61 &  4 &  3.997 &  0.002914 \tabularnewline
62 &  3 &  3.77 & -0.7701 \tabularnewline
63 &  4 &  3.826 &  0.1735 \tabularnewline
64 &  4 &  3.136 &  0.8639 \tabularnewline
65 &  4 &  4.123 & -0.1225 \tabularnewline
66 &  4 &  3.744 &  0.2559 \tabularnewline
67 &  5 &  3.543 &  1.457 \tabularnewline
68 &  3 &  3.992 & -0.9921 \tabularnewline
69 &  3 &  3.79 & -0.7903 \tabularnewline
70 &  4 &  3.977 &  0.02289 \tabularnewline
71 &  4 &  3.718 &  0.2821 \tabularnewline
72 &  4 &  3.673 &  0.3274 \tabularnewline
73 &  4 &  4.13 & -0.13 \tabularnewline
74 &  3 &  3.843 & -0.8432 \tabularnewline
75 &  4 &  4 &  0.000317 \tabularnewline
76 &  3 &  3.696 & -0.6962 \tabularnewline
77 &  3 &  3.607 & -0.607 \tabularnewline
78 &  4 &  3.86 &  0.1399 \tabularnewline
79 &  4 &  3.875 &  0.1248 \tabularnewline
80 &  3 &  3.074 & -0.07386 \tabularnewline
81 &  4 &  4 &  0.000317 \tabularnewline
82 &  4 &  4.168 & -0.1677 \tabularnewline
83 &  4 &  4.122 & -0.1224 \tabularnewline
84 &  5 &  4.424 &  0.5757 \tabularnewline
85 &  5 &  4.096 &  0.9038 \tabularnewline
86 &  4 &  4.202 & -0.2023 \tabularnewline
87 &  3 &  3.75 & -0.75 \tabularnewline
88 &  3 &  4.048 & -1.048 \tabularnewline
89 &  4 &  3.427 &  0.5731 \tabularnewline
90 &  4 &  3.755 &  0.2449 \tabularnewline
91 &  4 &  4 & -0.0004994 \tabularnewline
92 &  4 &  4 &  0.000317 \tabularnewline
93 &  4 &  4.271 & -0.2706 \tabularnewline
94 &  3 &  3.867 & -0.8668 \tabularnewline
95 &  4 &  3.875 &  0.1248 \tabularnewline
96 &  5 &  4.216 &  0.7836 \tabularnewline
97 &  5 &  4.131 &  0.8692 \tabularnewline
98 &  4 &  4.14 & -0.1401 \tabularnewline
99 &  5 &  3.979 &  1.021 \tabularnewline
100 &  4 &  3.75 &  0.25 \tabularnewline
101 &  5 &  3.545 &  1.455 \tabularnewline
102 &  3 &  3.793 & -0.7929 \tabularnewline
103 &  5 &  4.127 &  0.8726 \tabularnewline
104 &  4 &  3.87 &  0.1298 \tabularnewline
105 &  4 &  3.966 &  0.03394 \tabularnewline
106 &  4 &  3.753 &  0.2474 \tabularnewline
107 &  4 &  3.878 &  0.1222 \tabularnewline
108 &  3 &  3.957 & -0.9569 \tabularnewline
109 &  4 &  3.673 &  0.3274 \tabularnewline
110 &  4 &  3.79 &  0.2097 \tabularnewline
111 &  3 &  3.348 & -0.348 \tabularnewline
112 &  4 &  3.668 &  0.3324 \tabularnewline
113 &  3 &  3.878 & -0.8778 \tabularnewline
114 &  4 &  3.801 &  0.1987 \tabularnewline
115 &  5 &  4.062 &  0.9381 \tabularnewline
116 &  5 &  3.952 &  1.048 \tabularnewline
117 &  4 &  3.675 &  0.3248 \tabularnewline
118 &  4 &  3.748 &  0.2525 \tabularnewline
119 &  3 &  3.665 & -0.665 \tabularnewline
120 &  4 &  3.923 &  0.07684 \tabularnewline
121 &  4 &  3.874 &  0.1256 \tabularnewline
122 &  4 &  4.237 & -0.2368 \tabularnewline
123 &  3 &  3.798 & -0.7979 \tabularnewline
124 &  4 &  3.918 &  0.08187 \tabularnewline
125 &  4 &  3.921 &  0.07944 \tabularnewline
126 &  3 &  3.838 & -0.8382 \tabularnewline
127 &  4 &  3.795 &  0.2045 \tabularnewline
128 &  3 &  3.001 & -0.0007299 \tabularnewline
129 &  4 &  3.995 &  0.005348 \tabularnewline
130 &  5 &  4.245 &  0.7548 \tabularnewline
131 &  2 &  3.588 & -1.588 \tabularnewline
132 &  3 &  3.154 & -0.1538 \tabularnewline
133 &  4 &  3.918 &  0.08187 \tabularnewline
134 &  5 &  4.393 &  0.6068 \tabularnewline
135 &  4 &  3.896 &  0.1044 \tabularnewline
136 &  5 &  4.478 &  0.5218 \tabularnewline
137 &  4 &  4.22 & -0.22 \tabularnewline
138 &  4 &  3.54 &  0.4603 \tabularnewline
139 &  3 &  3.704 & -0.7038 \tabularnewline
140 &  4 &  3.878 &  0.1222 \tabularnewline
141 &  4 &  3.638 &  0.362 \tabularnewline
142 &  4 &  4.168 & -0.1677 \tabularnewline
143 &  4 &  3.747 &  0.2535 \tabularnewline
144 &  5 &  4.325 &  0.6749 \tabularnewline
145 &  4 &  3.484 &  0.5157 \tabularnewline
146 &  4 &  3.878 &  0.1222 \tabularnewline
147 &  3 &  3.365 & -0.3647 \tabularnewline
148 &  4 &  4.023 & -0.02342 \tabularnewline
149 &  4 &  3.918 &  0.08187 \tabularnewline
150 &  4 &  3.795 &  0.2047 \tabularnewline
151 &  3 &  3.446 & -0.4456 \tabularnewline
152 &  4 &  3.75 &  0.25 \tabularnewline
153 &  5 &  4.08 &  0.9204 \tabularnewline
154 &  4 &  3.551 &  0.4492 \tabularnewline
155 &  2 &  3.61 & -1.61 \tabularnewline
156 &  4 &  4.171 & -0.1711 \tabularnewline
157 &  4 &  3.724 &  0.2762 \tabularnewline
158 &  4 &  4.176 & -0.1763 \tabularnewline
159 &  4 &  3.901 &  0.09942 \tabularnewline
160 &  5 &  3.843 &  1.157 \tabularnewline
161 &  5 &  3.918 &  1.082 \tabularnewline
162 &  3 &  3.614 & -0.6143 \tabularnewline
163 &  4 &  3.921 &  0.07944 \tabularnewline
164 &  4 &  3.624 &  0.3761 \tabularnewline
165 &  2 &  3.862 & -1.862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302179&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] 4[/C][C] 3.333[/C][C] 0.6672[/C][/ROW]
[ROW][C]2[/C][C] 5[/C][C] 3.902[/C][C] 1.098[/C][/ROW]
[ROW][C]3[/C][C] 4[/C][C] 4.08[/C][C]-0.07962[/C][/ROW]
[ROW][C]4[/C][C] 3[/C][C] 3.838[/C][C]-0.8382[/C][/ROW]
[ROW][C]5[/C][C] 4[/C][C] 3.954[/C][C] 0.04565[/C][/ROW]
[ROW][C]6[/C][C] 3[/C][C] 3.997[/C][C]-0.9971[/C][/ROW]
[ROW][C]7[/C][C] 3[/C][C] 4.092[/C][C]-1.092[/C][/ROW]
[ROW][C]8[/C][C] 3[/C][C] 3.878[/C][C]-0.8778[/C][/ROW]
[ROW][C]9[/C][C] 4[/C][C] 4.265[/C][C]-0.2654[/C][/ROW]
[ROW][C]10[/C][C] 4[/C][C] 4.22[/C][C]-0.22[/C][/ROW]
[ROW][C]11[/C][C] 4[/C][C] 4.216[/C][C]-0.2156[/C][/ROW]
[ROW][C]12[/C][C] 4[/C][C] 3.824[/C][C] 0.176[/C][/ROW]
[ROW][C]13[/C][C] 4[/C][C] 3.916[/C][C] 0.08447[/C][/ROW]
[ROW][C]14[/C][C] 3[/C][C] 3.482[/C][C]-0.4817[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C] 3.722[/C][C] 0.2778[/C][/ROW]
[ROW][C]16[/C][C] 3[/C][C] 4[/C][C]-1[/C][/ROW]
[ROW][C]17[/C][C] 3[/C][C] 3.875[/C][C]-0.8752[/C][/ROW]
[ROW][C]18[/C][C] 5[/C][C] 3.936[/C][C] 1.064[/C][/ROW]
[ROW][C]19[/C][C] 4[/C][C] 4.125[/C][C]-0.125[/C][/ROW]
[ROW][C]20[/C][C] 3[/C][C] 3.87[/C][C]-0.8702[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 3.872[/C][C] 0.1282[/C][/ROW]
[ROW][C]22[/C][C] 4[/C][C] 3.875[/C][C] 0.1248[/C][/ROW]
[ROW][C]23[/C][C] 4[/C][C] 3.753[/C][C] 0.2474[/C][/ROW]
[ROW][C]24[/C][C] 4[/C][C] 4[/C][C]-0.0004994[/C][/ROW]
[ROW][C]25[/C][C] 3[/C][C] 3.798[/C][C]-0.7979[/C][/ROW]
[ROW][C]26[/C][C] 3[/C][C] 3.992[/C][C]-0.9921[/C][/ROW]
[ROW][C]27[/C][C] 4[/C][C] 3.798[/C][C] 0.2021[/C][/ROW]
[ROW][C]28[/C][C] 2[/C][C] 3.952[/C][C]-1.952[/C][/ROW]
[ROW][C]29[/C][C] 5[/C][C] 3.923[/C][C] 1.077[/C][/ROW]
[ROW][C]30[/C][C] 4[/C][C] 3.359[/C][C] 0.641[/C][/ROW]
[ROW][C]31[/C][C] 4[/C][C] 3.969[/C][C] 0.03052[/C][/ROW]
[ROW][C]32[/C][C] 5[/C][C] 4[/C][C] 0.9995[/C][/ROW]
[ROW][C]33[/C][C] 4[/C][C] 3.607[/C][C] 0.393[/C][/ROW]
[ROW][C]34[/C][C] 2[/C][C] 3.436[/C][C]-1.436[/C][/ROW]
[ROW][C]35[/C][C] 4[/C][C] 4.282[/C][C]-0.2821[/C][/ROW]
[ROW][C]36[/C][C] 3[/C][C] 3.753[/C][C]-0.7526[/C][/ROW]
[ROW][C]37[/C][C] 4[/C][C] 3.477[/C][C] 0.5234[/C][/ROW]
[ROW][C]38[/C][C] 4[/C][C] 3.575[/C][C] 0.4251[/C][/ROW]
[ROW][C]39[/C][C] 4[/C][C] 4.128[/C][C]-0.1284[/C][/ROW]
[ROW][C]40[/C][C] 5[/C][C] 3.798[/C][C] 1.202[/C][/ROW]
[ROW][C]41[/C][C] 4[/C][C] 4.22[/C][C]-0.22[/C][/ROW]
[ROW][C]42[/C][C] 3[/C][C] 3.404[/C][C]-0.4043[/C][/ROW]
[ROW][C]43[/C][C] 5[/C][C] 4.09[/C][C] 0.9103[/C][/ROW]
[ROW][C]44[/C][C] 5[/C][C] 3.748[/C][C] 1.252[/C][/ROW]
[ROW][C]45[/C][C] 4[/C][C] 3.79[/C][C] 0.2097[/C][/ROW]
[ROW][C]46[/C][C] 4[/C][C] 3.923[/C][C] 0.07684[/C][/ROW]
[ROW][C]47[/C][C] 3[/C][C] 3.814[/C][C]-0.814[/C][/ROW]
[ROW][C]48[/C][C] 4[/C][C] 3.624[/C][C] 0.3761[/C][/ROW]
[ROW][C]49[/C][C] 4[/C][C] 4.146[/C][C]-0.1461[/C][/ROW]
[ROW][C]50[/C][C] 5[/C][C] 4.484[/C][C] 0.5161[/C][/ROW]
[ROW][C]51[/C][C] 5[/C][C] 4.146[/C][C] 0.8539[/C][/ROW]
[ROW][C]52[/C][C] 4[/C][C] 3.182[/C][C] 0.8176[/C][/ROW]
[ROW][C]53[/C][C] 4[/C][C] 3.522[/C][C] 0.478[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 3.502[/C][C] 0.498[/C][/ROW]
[ROW][C]55[/C][C] 3[/C][C] 3.716[/C][C]-0.7163[/C][/ROW]
[ROW][C]56[/C][C] 3[/C][C] 4.051[/C][C]-1.051[/C][/ROW]
[ROW][C]57[/C][C] 4[/C][C] 3.995[/C][C] 0.005348[/C][/ROW]
[ROW][C]58[/C][C] 4[/C][C] 3.874[/C][C] 0.1256[/C][/ROW]
[ROW][C]59[/C][C] 5[/C][C] 4.311[/C][C] 0.6893[/C][/ROW]
[ROW][C]60[/C][C] 2[/C][C] 3.997[/C][C]-1.997[/C][/ROW]
[ROW][C]61[/C][C] 4[/C][C] 3.997[/C][C] 0.002914[/C][/ROW]
[ROW][C]62[/C][C] 3[/C][C] 3.77[/C][C]-0.7701[/C][/ROW]
[ROW][C]63[/C][C] 4[/C][C] 3.826[/C][C] 0.1735[/C][/ROW]
[ROW][C]64[/C][C] 4[/C][C] 3.136[/C][C] 0.8639[/C][/ROW]
[ROW][C]65[/C][C] 4[/C][C] 4.123[/C][C]-0.1225[/C][/ROW]
[ROW][C]66[/C][C] 4[/C][C] 3.744[/C][C] 0.2559[/C][/ROW]
[ROW][C]67[/C][C] 5[/C][C] 3.543[/C][C] 1.457[/C][/ROW]
[ROW][C]68[/C][C] 3[/C][C] 3.992[/C][C]-0.9921[/C][/ROW]
[ROW][C]69[/C][C] 3[/C][C] 3.79[/C][C]-0.7903[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 3.977[/C][C] 0.02289[/C][/ROW]
[ROW][C]71[/C][C] 4[/C][C] 3.718[/C][C] 0.2821[/C][/ROW]
[ROW][C]72[/C][C] 4[/C][C] 3.673[/C][C] 0.3274[/C][/ROW]
[ROW][C]73[/C][C] 4[/C][C] 4.13[/C][C]-0.13[/C][/ROW]
[ROW][C]74[/C][C] 3[/C][C] 3.843[/C][C]-0.8432[/C][/ROW]
[ROW][C]75[/C][C] 4[/C][C] 4[/C][C] 0.000317[/C][/ROW]
[ROW][C]76[/C][C] 3[/C][C] 3.696[/C][C]-0.6962[/C][/ROW]
[ROW][C]77[/C][C] 3[/C][C] 3.607[/C][C]-0.607[/C][/ROW]
[ROW][C]78[/C][C] 4[/C][C] 3.86[/C][C] 0.1399[/C][/ROW]
[ROW][C]79[/C][C] 4[/C][C] 3.875[/C][C] 0.1248[/C][/ROW]
[ROW][C]80[/C][C] 3[/C][C] 3.074[/C][C]-0.07386[/C][/ROW]
[ROW][C]81[/C][C] 4[/C][C] 4[/C][C] 0.000317[/C][/ROW]
[ROW][C]82[/C][C] 4[/C][C] 4.168[/C][C]-0.1677[/C][/ROW]
[ROW][C]83[/C][C] 4[/C][C] 4.122[/C][C]-0.1224[/C][/ROW]
[ROW][C]84[/C][C] 5[/C][C] 4.424[/C][C] 0.5757[/C][/ROW]
[ROW][C]85[/C][C] 5[/C][C] 4.096[/C][C] 0.9038[/C][/ROW]
[ROW][C]86[/C][C] 4[/C][C] 4.202[/C][C]-0.2023[/C][/ROW]
[ROW][C]87[/C][C] 3[/C][C] 3.75[/C][C]-0.75[/C][/ROW]
[ROW][C]88[/C][C] 3[/C][C] 4.048[/C][C]-1.048[/C][/ROW]
[ROW][C]89[/C][C] 4[/C][C] 3.427[/C][C] 0.5731[/C][/ROW]
[ROW][C]90[/C][C] 4[/C][C] 3.755[/C][C] 0.2449[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 4[/C][C]-0.0004994[/C][/ROW]
[ROW][C]92[/C][C] 4[/C][C] 4[/C][C] 0.000317[/C][/ROW]
[ROW][C]93[/C][C] 4[/C][C] 4.271[/C][C]-0.2706[/C][/ROW]
[ROW][C]94[/C][C] 3[/C][C] 3.867[/C][C]-0.8668[/C][/ROW]
[ROW][C]95[/C][C] 4[/C][C] 3.875[/C][C] 0.1248[/C][/ROW]
[ROW][C]96[/C][C] 5[/C][C] 4.216[/C][C] 0.7836[/C][/ROW]
[ROW][C]97[/C][C] 5[/C][C] 4.131[/C][C] 0.8692[/C][/ROW]
[ROW][C]98[/C][C] 4[/C][C] 4.14[/C][C]-0.1401[/C][/ROW]
[ROW][C]99[/C][C] 5[/C][C] 3.979[/C][C] 1.021[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 3.75[/C][C] 0.25[/C][/ROW]
[ROW][C]101[/C][C] 5[/C][C] 3.545[/C][C] 1.455[/C][/ROW]
[ROW][C]102[/C][C] 3[/C][C] 3.793[/C][C]-0.7929[/C][/ROW]
[ROW][C]103[/C][C] 5[/C][C] 4.127[/C][C] 0.8726[/C][/ROW]
[ROW][C]104[/C][C] 4[/C][C] 3.87[/C][C] 0.1298[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 3.966[/C][C] 0.03394[/C][/ROW]
[ROW][C]106[/C][C] 4[/C][C] 3.753[/C][C] 0.2474[/C][/ROW]
[ROW][C]107[/C][C] 4[/C][C] 3.878[/C][C] 0.1222[/C][/ROW]
[ROW][C]108[/C][C] 3[/C][C] 3.957[/C][C]-0.9569[/C][/ROW]
[ROW][C]109[/C][C] 4[/C][C] 3.673[/C][C] 0.3274[/C][/ROW]
[ROW][C]110[/C][C] 4[/C][C] 3.79[/C][C] 0.2097[/C][/ROW]
[ROW][C]111[/C][C] 3[/C][C] 3.348[/C][C]-0.348[/C][/ROW]
[ROW][C]112[/C][C] 4[/C][C] 3.668[/C][C] 0.3324[/C][/ROW]
[ROW][C]113[/C][C] 3[/C][C] 3.878[/C][C]-0.8778[/C][/ROW]
[ROW][C]114[/C][C] 4[/C][C] 3.801[/C][C] 0.1987[/C][/ROW]
[ROW][C]115[/C][C] 5[/C][C] 4.062[/C][C] 0.9381[/C][/ROW]
[ROW][C]116[/C][C] 5[/C][C] 3.952[/C][C] 1.048[/C][/ROW]
[ROW][C]117[/C][C] 4[/C][C] 3.675[/C][C] 0.3248[/C][/ROW]
[ROW][C]118[/C][C] 4[/C][C] 3.748[/C][C] 0.2525[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 3.665[/C][C]-0.665[/C][/ROW]
[ROW][C]120[/C][C] 4[/C][C] 3.923[/C][C] 0.07684[/C][/ROW]
[ROW][C]121[/C][C] 4[/C][C] 3.874[/C][C] 0.1256[/C][/ROW]
[ROW][C]122[/C][C] 4[/C][C] 4.237[/C][C]-0.2368[/C][/ROW]
[ROW][C]123[/C][C] 3[/C][C] 3.798[/C][C]-0.7979[/C][/ROW]
[ROW][C]124[/C][C] 4[/C][C] 3.918[/C][C] 0.08187[/C][/ROW]
[ROW][C]125[/C][C] 4[/C][C] 3.921[/C][C] 0.07944[/C][/ROW]
[ROW][C]126[/C][C] 3[/C][C] 3.838[/C][C]-0.8382[/C][/ROW]
[ROW][C]127[/C][C] 4[/C][C] 3.795[/C][C] 0.2045[/C][/ROW]
[ROW][C]128[/C][C] 3[/C][C] 3.001[/C][C]-0.0007299[/C][/ROW]
[ROW][C]129[/C][C] 4[/C][C] 3.995[/C][C] 0.005348[/C][/ROW]
[ROW][C]130[/C][C] 5[/C][C] 4.245[/C][C] 0.7548[/C][/ROW]
[ROW][C]131[/C][C] 2[/C][C] 3.588[/C][C]-1.588[/C][/ROW]
[ROW][C]132[/C][C] 3[/C][C] 3.154[/C][C]-0.1538[/C][/ROW]
[ROW][C]133[/C][C] 4[/C][C] 3.918[/C][C] 0.08187[/C][/ROW]
[ROW][C]134[/C][C] 5[/C][C] 4.393[/C][C] 0.6068[/C][/ROW]
[ROW][C]135[/C][C] 4[/C][C] 3.896[/C][C] 0.1044[/C][/ROW]
[ROW][C]136[/C][C] 5[/C][C] 4.478[/C][C] 0.5218[/C][/ROW]
[ROW][C]137[/C][C] 4[/C][C] 4.22[/C][C]-0.22[/C][/ROW]
[ROW][C]138[/C][C] 4[/C][C] 3.54[/C][C] 0.4603[/C][/ROW]
[ROW][C]139[/C][C] 3[/C][C] 3.704[/C][C]-0.7038[/C][/ROW]
[ROW][C]140[/C][C] 4[/C][C] 3.878[/C][C] 0.1222[/C][/ROW]
[ROW][C]141[/C][C] 4[/C][C] 3.638[/C][C] 0.362[/C][/ROW]
[ROW][C]142[/C][C] 4[/C][C] 4.168[/C][C]-0.1677[/C][/ROW]
[ROW][C]143[/C][C] 4[/C][C] 3.747[/C][C] 0.2535[/C][/ROW]
[ROW][C]144[/C][C] 5[/C][C] 4.325[/C][C] 0.6749[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 3.484[/C][C] 0.5157[/C][/ROW]
[ROW][C]146[/C][C] 4[/C][C] 3.878[/C][C] 0.1222[/C][/ROW]
[ROW][C]147[/C][C] 3[/C][C] 3.365[/C][C]-0.3647[/C][/ROW]
[ROW][C]148[/C][C] 4[/C][C] 4.023[/C][C]-0.02342[/C][/ROW]
[ROW][C]149[/C][C] 4[/C][C] 3.918[/C][C] 0.08187[/C][/ROW]
[ROW][C]150[/C][C] 4[/C][C] 3.795[/C][C] 0.2047[/C][/ROW]
[ROW][C]151[/C][C] 3[/C][C] 3.446[/C][C]-0.4456[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 3.75[/C][C] 0.25[/C][/ROW]
[ROW][C]153[/C][C] 5[/C][C] 4.08[/C][C] 0.9204[/C][/ROW]
[ROW][C]154[/C][C] 4[/C][C] 3.551[/C][C] 0.4492[/C][/ROW]
[ROW][C]155[/C][C] 2[/C][C] 3.61[/C][C]-1.61[/C][/ROW]
[ROW][C]156[/C][C] 4[/C][C] 4.171[/C][C]-0.1711[/C][/ROW]
[ROW][C]157[/C][C] 4[/C][C] 3.724[/C][C] 0.2762[/C][/ROW]
[ROW][C]158[/C][C] 4[/C][C] 4.176[/C][C]-0.1763[/C][/ROW]
[ROW][C]159[/C][C] 4[/C][C] 3.901[/C][C] 0.09942[/C][/ROW]
[ROW][C]160[/C][C] 5[/C][C] 3.843[/C][C] 1.157[/C][/ROW]
[ROW][C]161[/C][C] 5[/C][C] 3.918[/C][C] 1.082[/C][/ROW]
[ROW][C]162[/C][C] 3[/C][C] 3.614[/C][C]-0.6143[/C][/ROW]
[ROW][C]163[/C][C] 4[/C][C] 3.921[/C][C] 0.07944[/C][/ROW]
[ROW][C]164[/C][C] 4[/C][C] 3.624[/C][C] 0.3761[/C][/ROW]
[ROW][C]165[/C][C] 2[/C][C] 3.862[/C][C]-1.862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302179&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&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	4	3.333	0.6672
2	5	3.902	1.098
3	4	4.08	-0.07962
4	3	3.838	-0.8382
5	4	3.954	0.04565
6	3	3.997	-0.9971
7	3	4.092	-1.092
8	3	3.878	-0.8778
9	4	4.265	-0.2654
10	4	4.22	-0.22
11	4	4.216	-0.2156
12	4	3.824	0.176
13	4	3.916	0.08447
14	3	3.482	-0.4817
15	4	3.722	0.2778
16	3	4	-1
17	3	3.875	-0.8752
18	5	3.936	1.064
19	4	4.125	-0.125
20	3	3.87	-0.8702
21	4	3.872	0.1282
22	4	3.875	0.1248
23	4	3.753	0.2474
24	4	4	-0.0004994
25	3	3.798	-0.7979
26	3	3.992	-0.9921
27	4	3.798	0.2021
28	2	3.952	-1.952
29	5	3.923	1.077
30	4	3.359	0.641
31	4	3.969	0.03052
32	5	4	0.9995
33	4	3.607	0.393
34	2	3.436	-1.436
35	4	4.282	-0.2821
36	3	3.753	-0.7526
37	4	3.477	0.5234
38	4	3.575	0.4251
39	4	4.128	-0.1284
40	5	3.798	1.202
41	4	4.22	-0.22
42	3	3.404	-0.4043
43	5	4.09	0.9103
44	5	3.748	1.252
45	4	3.79	0.2097
46	4	3.923	0.07684
47	3	3.814	-0.814
48	4	3.624	0.3761
49	4	4.146	-0.1461
50	5	4.484	0.5161
51	5	4.146	0.8539
52	4	3.182	0.8176
53	4	3.522	0.478
54	4	3.502	0.498
55	3	3.716	-0.7163
56	3	4.051	-1.051
57	4	3.995	0.005348
58	4	3.874	0.1256
59	5	4.311	0.6893
60	2	3.997	-1.997
61	4	3.997	0.002914
62	3	3.77	-0.7701
63	4	3.826	0.1735
64	4	3.136	0.8639
65	4	4.123	-0.1225
66	4	3.744	0.2559
67	5	3.543	1.457
68	3	3.992	-0.9921
69	3	3.79	-0.7903
70	4	3.977	0.02289
71	4	3.718	0.2821
72	4	3.673	0.3274
73	4	4.13	-0.13
74	3	3.843	-0.8432
75	4	4	0.000317
76	3	3.696	-0.6962
77	3	3.607	-0.607
78	4	3.86	0.1399
79	4	3.875	0.1248
80	3	3.074	-0.07386
81	4	4	0.000317
82	4	4.168	-0.1677
83	4	4.122	-0.1224
84	5	4.424	0.5757
85	5	4.096	0.9038
86	4	4.202	-0.2023
87	3	3.75	-0.75
88	3	4.048	-1.048
89	4	3.427	0.5731
90	4	3.755	0.2449
91	4	4	-0.0004994
92	4	4	0.000317
93	4	4.271	-0.2706
94	3	3.867	-0.8668
95	4	3.875	0.1248
96	5	4.216	0.7836
97	5	4.131	0.8692
98	4	4.14	-0.1401
99	5	3.979	1.021
100	4	3.75	0.25
101	5	3.545	1.455
102	3	3.793	-0.7929
103	5	4.127	0.8726
104	4	3.87	0.1298
105	4	3.966	0.03394
106	4	3.753	0.2474
107	4	3.878	0.1222
108	3	3.957	-0.9569
109	4	3.673	0.3274
110	4	3.79	0.2097
111	3	3.348	-0.348
112	4	3.668	0.3324
113	3	3.878	-0.8778
114	4	3.801	0.1987
115	5	4.062	0.9381
116	5	3.952	1.048
117	4	3.675	0.3248
118	4	3.748	0.2525
119	3	3.665	-0.665
120	4	3.923	0.07684
121	4	3.874	0.1256
122	4	4.237	-0.2368
123	3	3.798	-0.7979
124	4	3.918	0.08187
125	4	3.921	0.07944
126	3	3.838	-0.8382
127	4	3.795	0.2045
128	3	3.001	-0.0007299
129	4	3.995	0.005348
130	5	4.245	0.7548
131	2	3.588	-1.588
132	3	3.154	-0.1538
133	4	3.918	0.08187
134	5	4.393	0.6068
135	4	3.896	0.1044
136	5	4.478	0.5218
137	4	4.22	-0.22
138	4	3.54	0.4603
139	3	3.704	-0.7038
140	4	3.878	0.1222
141	4	3.638	0.362
142	4	4.168	-0.1677
143	4	3.747	0.2535
144	5	4.325	0.6749
145	4	3.484	0.5157
146	4	3.878	0.1222
147	3	3.365	-0.3647
148	4	4.023	-0.02342
149	4	3.918	0.08187
150	4	3.795	0.2047
151	3	3.446	-0.4456
152	4	3.75	0.25
153	5	4.08	0.9204
154	4	3.551	0.4492
155	2	3.61	-1.61
156	4	4.171	-0.1711
157	4	3.724	0.2762
158	4	4.176	-0.1763
159	4	3.901	0.09942
160	5	3.843	1.157
161	5	3.918	1.082
162	3	3.614	-0.6143
163	4	3.921	0.07944
164	4	3.624	0.3761
165	2	3.862	-1.862

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.4541	0.9082	0.5459
11	0.4216	0.8431	0.5784
12	0.2799	0.5599	0.7201
13	0.315	0.63	0.685
14	0.2207	0.4415	0.7793
15	0.596	0.808	0.404
16	0.607	0.7861	0.393
17	0.5683	0.8634	0.4317
18	0.6837	0.6325	0.3163
19	0.604	0.7921	0.396
20	0.5456	0.9088	0.4544
21	0.4663	0.9325	0.5337
22	0.4352	0.8704	0.5648
23	0.3602	0.7204	0.6398
24	0.3502	0.7003	0.6498
25	0.4288	0.8575	0.5712
26	0.4328	0.8656	0.5672
27	0.3655	0.731	0.6345
28	0.6722	0.6556	0.3278
29	0.7403	0.5195	0.2597
30	0.6956	0.6087	0.3044
31	0.6428	0.7144	0.3572
32	0.8171	0.3658	0.1829
33	0.7965	0.407	0.2035
34	0.9257	0.1485	0.07426
35	0.9112	0.1775	0.08877
36	0.9138	0.1724	0.08618
37	0.9087	0.1827	0.09133
38	0.8873	0.2254	0.1127
39	0.8605	0.2789	0.1395
40	0.9016	0.1969	0.09845
41	0.8825	0.2349	0.1175
42	0.8767	0.2467	0.1233
43	0.911	0.1779	0.08897
44	0.939	0.1219	0.06095
45	0.9227	0.1547	0.07734
46	0.9024	0.1953	0.09763
47	0.9229	0.1541	0.07706
48	0.9065	0.1869	0.09347
49	0.8845	0.231	0.1155
50	0.8742	0.2516	0.1258
51	0.888	0.224	0.112
52	0.8904	0.2191	0.1096
53	0.8756	0.2487	0.1244
54	0.8566	0.2869	0.1434
55	0.8662	0.2676	0.1338
56	0.8916	0.2167	0.1084
57	0.8673	0.2653	0.1327
58	0.8404	0.3192	0.1596
59	0.8374	0.3251	0.1626
60	0.9644	0.07119	0.03559
61	0.9543	0.09133	0.04566
62	0.9578	0.08433	0.04216
63	0.9471	0.1058	0.05288
64	0.9528	0.09432	0.04716
65	0.9402	0.1196	0.05979
66	0.9274	0.1452	0.07262
67	0.9626	0.07479	0.03739
68	0.9719	0.05623	0.02811
69	0.9759	0.04811	0.02405
70	0.9687	0.06252	0.03126
71	0.9609	0.07819	0.03909
72	0.9523	0.09533	0.04767
73	0.9404	0.1192	0.0596
74	0.9504	0.0992	0.0496
75	0.9375	0.125	0.06249
76	0.94	0.1201	0.06005
77	0.9351	0.1298	0.06492
78	0.9236	0.1528	0.0764
79	0.9077	0.1847	0.09233
80	0.8984	0.2032	0.1016
81	0.8765	0.247	0.1235
82	0.8562	0.2876	0.1438
83	0.8332	0.3335	0.1668
84	0.8341	0.3319	0.1659
85	0.8526	0.2948	0.1474
86	0.8328	0.3345	0.1672
87	0.8426	0.3148	0.1574
88	0.8827	0.2347	0.1173
89	0.8809	0.2382	0.1191
90	0.8614	0.2772	0.1386
91	0.8383	0.3234	0.1617
92	0.8081	0.3838	0.1919
93	0.7817	0.4365	0.2183
94	0.8168	0.3664	0.1832
95	0.7864	0.4273	0.2136
96	0.7889	0.4221	0.2111
97	0.8025	0.395	0.1975
98	0.7825	0.4349	0.2175
99	0.8295	0.3409	0.1705
100	0.8007	0.3987	0.1993
101	0.9002	0.1996	0.09981
102	0.9124	0.1752	0.0876
103	0.9292	0.1416	0.07079
104	0.9127	0.1746	0.08732
105	0.8926	0.2149	0.1074
106	0.8729	0.2543	0.1271
107	0.8469	0.3061	0.1531
108	0.8866	0.2268	0.1134
109	0.8698	0.2604	0.1302
110	0.8436	0.3129	0.1564
111	0.8181	0.3638	0.1819
112	0.7887	0.4226	0.2113
113	0.8149	0.3703	0.1851
114	0.7878	0.4245	0.2122
115	0.7943	0.4114	0.2057
116	0.8442	0.3116	0.1558
117	0.8175	0.365	0.1825
118	0.7831	0.4338	0.2169
119	0.7861	0.4279	0.2139
120	0.7462	0.5076	0.2538
121	0.7015	0.5969	0.2985
122	0.6745	0.651	0.3255
123	0.6891	0.6218	0.3109
124	0.6405	0.7191	0.3595
125	0.5907	0.8187	0.4093
126	0.67	0.6599	0.33
127	0.6254	0.7492	0.3746
128	0.5786	0.8428	0.4214
129	0.5385	0.923	0.4615
130	0.5059	0.9882	0.4941
131	0.8546	0.2907	0.1454
132	0.8295	0.341	0.1705
133	0.8069	0.3863	0.1931
134	0.7671	0.4658	0.2329
135	0.7361	0.5279	0.2639
136	0.7186	0.5627	0.2814
137	0.7146	0.5708	0.2854
138	0.6608	0.6783	0.3392
139	0.6443	0.7114	0.3557
140	0.5755	0.8489	0.4245
141	0.5115	0.9769	0.4885
142	0.4498	0.8996	0.5502
143	0.3836	0.7671	0.6164
144	0.3229	0.6457	0.6771
145	0.526	0.948	0.474
146	0.449	0.8979	0.551
147	0.3766	0.7532	0.6234
148	0.4243	0.8485	0.5757
149	0.4771	0.9542	0.5229
150	0.3781	0.7562	0.6219
151	0.7542	0.4915	0.2458
152	0.6496	0.7008	0.3504
153	0.7678	0.4643	0.2322
154	0.6886	0.6227	0.3114
155	0.7845	0.4309	0.2155

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.4541 &  0.9082 &  0.5459 \tabularnewline
11 &  0.4216 &  0.8431 &  0.5784 \tabularnewline
12 &  0.2799 &  0.5599 &  0.7201 \tabularnewline
13 &  0.315 &  0.63 &  0.685 \tabularnewline
14 &  0.2207 &  0.4415 &  0.7793 \tabularnewline
15 &  0.596 &  0.808 &  0.404 \tabularnewline
16 &  0.607 &  0.7861 &  0.393 \tabularnewline
17 &  0.5683 &  0.8634 &  0.4317 \tabularnewline
18 &  0.6837 &  0.6325 &  0.3163 \tabularnewline
19 &  0.604 &  0.7921 &  0.396 \tabularnewline
20 &  0.5456 &  0.9088 &  0.4544 \tabularnewline
21 &  0.4663 &  0.9325 &  0.5337 \tabularnewline
22 &  0.4352 &  0.8704 &  0.5648 \tabularnewline
23 &  0.3602 &  0.7204 &  0.6398 \tabularnewline
24 &  0.3502 &  0.7003 &  0.6498 \tabularnewline
25 &  0.4288 &  0.8575 &  0.5712 \tabularnewline
26 &  0.4328 &  0.8656 &  0.5672 \tabularnewline
27 &  0.3655 &  0.731 &  0.6345 \tabularnewline
28 &  0.6722 &  0.6556 &  0.3278 \tabularnewline
29 &  0.7403 &  0.5195 &  0.2597 \tabularnewline
30 &  0.6956 &  0.6087 &  0.3044 \tabularnewline
31 &  0.6428 &  0.7144 &  0.3572 \tabularnewline
32 &  0.8171 &  0.3658 &  0.1829 \tabularnewline
33 &  0.7965 &  0.407 &  0.2035 \tabularnewline
34 &  0.9257 &  0.1485 &  0.07426 \tabularnewline
35 &  0.9112 &  0.1775 &  0.08877 \tabularnewline
36 &  0.9138 &  0.1724 &  0.08618 \tabularnewline
37 &  0.9087 &  0.1827 &  0.09133 \tabularnewline
38 &  0.8873 &  0.2254 &  0.1127 \tabularnewline
39 &  0.8605 &  0.2789 &  0.1395 \tabularnewline
40 &  0.9016 &  0.1969 &  0.09845 \tabularnewline
41 &  0.8825 &  0.2349 &  0.1175 \tabularnewline
42 &  0.8767 &  0.2467 &  0.1233 \tabularnewline
43 &  0.911 &  0.1779 &  0.08897 \tabularnewline
44 &  0.939 &  0.1219 &  0.06095 \tabularnewline
45 &  0.9227 &  0.1547 &  0.07734 \tabularnewline
46 &  0.9024 &  0.1953 &  0.09763 \tabularnewline
47 &  0.9229 &  0.1541 &  0.07706 \tabularnewline
48 &  0.9065 &  0.1869 &  0.09347 \tabularnewline
49 &  0.8845 &  0.231 &  0.1155 \tabularnewline
50 &  0.8742 &  0.2516 &  0.1258 \tabularnewline
51 &  0.888 &  0.224 &  0.112 \tabularnewline
52 &  0.8904 &  0.2191 &  0.1096 \tabularnewline
53 &  0.8756 &  0.2487 &  0.1244 \tabularnewline
54 &  0.8566 &  0.2869 &  0.1434 \tabularnewline
55 &  0.8662 &  0.2676 &  0.1338 \tabularnewline
56 &  0.8916 &  0.2167 &  0.1084 \tabularnewline
57 &  0.8673 &  0.2653 &  0.1327 \tabularnewline
58 &  0.8404 &  0.3192 &  0.1596 \tabularnewline
59 &  0.8374 &  0.3251 &  0.1626 \tabularnewline
60 &  0.9644 &  0.07119 &  0.03559 \tabularnewline
61 &  0.9543 &  0.09133 &  0.04566 \tabularnewline
62 &  0.9578 &  0.08433 &  0.04216 \tabularnewline
63 &  0.9471 &  0.1058 &  0.05288 \tabularnewline
64 &  0.9528 &  0.09432 &  0.04716 \tabularnewline
65 &  0.9402 &  0.1196 &  0.05979 \tabularnewline
66 &  0.9274 &  0.1452 &  0.07262 \tabularnewline
67 &  0.9626 &  0.07479 &  0.03739 \tabularnewline
68 &  0.9719 &  0.05623 &  0.02811 \tabularnewline
69 &  0.9759 &  0.04811 &  0.02405 \tabularnewline
70 &  0.9687 &  0.06252 &  0.03126 \tabularnewline
71 &  0.9609 &  0.07819 &  0.03909 \tabularnewline
72 &  0.9523 &  0.09533 &  0.04767 \tabularnewline
73 &  0.9404 &  0.1192 &  0.0596 \tabularnewline
74 &  0.9504 &  0.0992 &  0.0496 \tabularnewline
75 &  0.9375 &  0.125 &  0.06249 \tabularnewline
76 &  0.94 &  0.1201 &  0.06005 \tabularnewline
77 &  0.9351 &  0.1298 &  0.06492 \tabularnewline
78 &  0.9236 &  0.1528 &  0.0764 \tabularnewline
79 &  0.9077 &  0.1847 &  0.09233 \tabularnewline
80 &  0.8984 &  0.2032 &  0.1016 \tabularnewline
81 &  0.8765 &  0.247 &  0.1235 \tabularnewline
82 &  0.8562 &  0.2876 &  0.1438 \tabularnewline
83 &  0.8332 &  0.3335 &  0.1668 \tabularnewline
84 &  0.8341 &  0.3319 &  0.1659 \tabularnewline
85 &  0.8526 &  0.2948 &  0.1474 \tabularnewline
86 &  0.8328 &  0.3345 &  0.1672 \tabularnewline
87 &  0.8426 &  0.3148 &  0.1574 \tabularnewline
88 &  0.8827 &  0.2347 &  0.1173 \tabularnewline
89 &  0.8809 &  0.2382 &  0.1191 \tabularnewline
90 &  0.8614 &  0.2772 &  0.1386 \tabularnewline
91 &  0.8383 &  0.3234 &  0.1617 \tabularnewline
92 &  0.8081 &  0.3838 &  0.1919 \tabularnewline
93 &  0.7817 &  0.4365 &  0.2183 \tabularnewline
94 &  0.8168 &  0.3664 &  0.1832 \tabularnewline
95 &  0.7864 &  0.4273 &  0.2136 \tabularnewline
96 &  0.7889 &  0.4221 &  0.2111 \tabularnewline
97 &  0.8025 &  0.395 &  0.1975 \tabularnewline
98 &  0.7825 &  0.4349 &  0.2175 \tabularnewline
99 &  0.8295 &  0.3409 &  0.1705 \tabularnewline
100 &  0.8007 &  0.3987 &  0.1993 \tabularnewline
101 &  0.9002 &  0.1996 &  0.09981 \tabularnewline
102 &  0.9124 &  0.1752 &  0.0876 \tabularnewline
103 &  0.9292 &  0.1416 &  0.07079 \tabularnewline
104 &  0.9127 &  0.1746 &  0.08732 \tabularnewline
105 &  0.8926 &  0.2149 &  0.1074 \tabularnewline
106 &  0.8729 &  0.2543 &  0.1271 \tabularnewline
107 &  0.8469 &  0.3061 &  0.1531 \tabularnewline
108 &  0.8866 &  0.2268 &  0.1134 \tabularnewline
109 &  0.8698 &  0.2604 &  0.1302 \tabularnewline
110 &  0.8436 &  0.3129 &  0.1564 \tabularnewline
111 &  0.8181 &  0.3638 &  0.1819 \tabularnewline
112 &  0.7887 &  0.4226 &  0.2113 \tabularnewline
113 &  0.8149 &  0.3703 &  0.1851 \tabularnewline
114 &  0.7878 &  0.4245 &  0.2122 \tabularnewline
115 &  0.7943 &  0.4114 &  0.2057 \tabularnewline
116 &  0.8442 &  0.3116 &  0.1558 \tabularnewline
117 &  0.8175 &  0.365 &  0.1825 \tabularnewline
118 &  0.7831 &  0.4338 &  0.2169 \tabularnewline
119 &  0.7861 &  0.4279 &  0.2139 \tabularnewline
120 &  0.7462 &  0.5076 &  0.2538 \tabularnewline
121 &  0.7015 &  0.5969 &  0.2985 \tabularnewline
122 &  0.6745 &  0.651 &  0.3255 \tabularnewline
123 &  0.6891 &  0.6218 &  0.3109 \tabularnewline
124 &  0.6405 &  0.7191 &  0.3595 \tabularnewline
125 &  0.5907 &  0.8187 &  0.4093 \tabularnewline
126 &  0.67 &  0.6599 &  0.33 \tabularnewline
127 &  0.6254 &  0.7492 &  0.3746 \tabularnewline
128 &  0.5786 &  0.8428 &  0.4214 \tabularnewline
129 &  0.5385 &  0.923 &  0.4615 \tabularnewline
130 &  0.5059 &  0.9882 &  0.4941 \tabularnewline
131 &  0.8546 &  0.2907 &  0.1454 \tabularnewline
132 &  0.8295 &  0.341 &  0.1705 \tabularnewline
133 &  0.8069 &  0.3863 &  0.1931 \tabularnewline
134 &  0.7671 &  0.4658 &  0.2329 \tabularnewline
135 &  0.7361 &  0.5279 &  0.2639 \tabularnewline
136 &  0.7186 &  0.5627 &  0.2814 \tabularnewline
137 &  0.7146 &  0.5708 &  0.2854 \tabularnewline
138 &  0.6608 &  0.6783 &  0.3392 \tabularnewline
139 &  0.6443 &  0.7114 &  0.3557 \tabularnewline
140 &  0.5755 &  0.8489 &  0.4245 \tabularnewline
141 &  0.5115 &  0.9769 &  0.4885 \tabularnewline
142 &  0.4498 &  0.8996 &  0.5502 \tabularnewline
143 &  0.3836 &  0.7671 &  0.6164 \tabularnewline
144 &  0.3229 &  0.6457 &  0.6771 \tabularnewline
145 &  0.526 &  0.948 &  0.474 \tabularnewline
146 &  0.449 &  0.8979 &  0.551 \tabularnewline
147 &  0.3766 &  0.7532 &  0.6234 \tabularnewline
148 &  0.4243 &  0.8485 &  0.5757 \tabularnewline
149 &  0.4771 &  0.9542 &  0.5229 \tabularnewline
150 &  0.3781 &  0.7562 &  0.6219 \tabularnewline
151 &  0.7542 &  0.4915 &  0.2458 \tabularnewline
152 &  0.6496 &  0.7008 &  0.3504 \tabularnewline
153 &  0.7678 &  0.4643 &  0.2322 \tabularnewline
154 &  0.6886 &  0.6227 &  0.3114 \tabularnewline
155 &  0.7845 &  0.4309 &  0.2155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302179&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] 0.4541[/C][C] 0.9082[/C][C] 0.5459[/C][/ROW]
[ROW][C]11[/C][C] 0.4216[/C][C] 0.8431[/C][C] 0.5784[/C][/ROW]
[ROW][C]12[/C][C] 0.2799[/C][C] 0.5599[/C][C] 0.7201[/C][/ROW]
[ROW][C]13[/C][C] 0.315[/C][C] 0.63[/C][C] 0.685[/C][/ROW]
[ROW][C]14[/C][C] 0.2207[/C][C] 0.4415[/C][C] 0.7793[/C][/ROW]
[ROW][C]15[/C][C] 0.596[/C][C] 0.808[/C][C] 0.404[/C][/ROW]
[ROW][C]16[/C][C] 0.607[/C][C] 0.7861[/C][C] 0.393[/C][/ROW]
[ROW][C]17[/C][C] 0.5683[/C][C] 0.8634[/C][C] 0.4317[/C][/ROW]
[ROW][C]18[/C][C] 0.6837[/C][C] 0.6325[/C][C] 0.3163[/C][/ROW]
[ROW][C]19[/C][C] 0.604[/C][C] 0.7921[/C][C] 0.396[/C][/ROW]
[ROW][C]20[/C][C] 0.5456[/C][C] 0.9088[/C][C] 0.4544[/C][/ROW]
[ROW][C]21[/C][C] 0.4663[/C][C] 0.9325[/C][C] 0.5337[/C][/ROW]
[ROW][C]22[/C][C] 0.4352[/C][C] 0.8704[/C][C] 0.5648[/C][/ROW]
[ROW][C]23[/C][C] 0.3602[/C][C] 0.7204[/C][C] 0.6398[/C][/ROW]
[ROW][C]24[/C][C] 0.3502[/C][C] 0.7003[/C][C] 0.6498[/C][/ROW]
[ROW][C]25[/C][C] 0.4288[/C][C] 0.8575[/C][C] 0.5712[/C][/ROW]
[ROW][C]26[/C][C] 0.4328[/C][C] 0.8656[/C][C] 0.5672[/C][/ROW]
[ROW][C]27[/C][C] 0.3655[/C][C] 0.731[/C][C] 0.6345[/C][/ROW]
[ROW][C]28[/C][C] 0.6722[/C][C] 0.6556[/C][C] 0.3278[/C][/ROW]
[ROW][C]29[/C][C] 0.7403[/C][C] 0.5195[/C][C] 0.2597[/C][/ROW]
[ROW][C]30[/C][C] 0.6956[/C][C] 0.6087[/C][C] 0.3044[/C][/ROW]
[ROW][C]31[/C][C] 0.6428[/C][C] 0.7144[/C][C] 0.3572[/C][/ROW]
[ROW][C]32[/C][C] 0.8171[/C][C] 0.3658[/C][C] 0.1829[/C][/ROW]
[ROW][C]33[/C][C] 0.7965[/C][C] 0.407[/C][C] 0.2035[/C][/ROW]
[ROW][C]34[/C][C] 0.9257[/C][C] 0.1485[/C][C] 0.07426[/C][/ROW]
[ROW][C]35[/C][C] 0.9112[/C][C] 0.1775[/C][C] 0.08877[/C][/ROW]
[ROW][C]36[/C][C] 0.9138[/C][C] 0.1724[/C][C] 0.08618[/C][/ROW]
[ROW][C]37[/C][C] 0.9087[/C][C] 0.1827[/C][C] 0.09133[/C][/ROW]
[ROW][C]38[/C][C] 0.8873[/C][C] 0.2254[/C][C] 0.1127[/C][/ROW]
[ROW][C]39[/C][C] 0.8605[/C][C] 0.2789[/C][C] 0.1395[/C][/ROW]
[ROW][C]40[/C][C] 0.9016[/C][C] 0.1969[/C][C] 0.09845[/C][/ROW]
[ROW][C]41[/C][C] 0.8825[/C][C] 0.2349[/C][C] 0.1175[/C][/ROW]
[ROW][C]42[/C][C] 0.8767[/C][C] 0.2467[/C][C] 0.1233[/C][/ROW]
[ROW][C]43[/C][C] 0.911[/C][C] 0.1779[/C][C] 0.08897[/C][/ROW]
[ROW][C]44[/C][C] 0.939[/C][C] 0.1219[/C][C] 0.06095[/C][/ROW]
[ROW][C]45[/C][C] 0.9227[/C][C] 0.1547[/C][C] 0.07734[/C][/ROW]
[ROW][C]46[/C][C] 0.9024[/C][C] 0.1953[/C][C] 0.09763[/C][/ROW]
[ROW][C]47[/C][C] 0.9229[/C][C] 0.1541[/C][C] 0.07706[/C][/ROW]
[ROW][C]48[/C][C] 0.9065[/C][C] 0.1869[/C][C] 0.09347[/C][/ROW]
[ROW][C]49[/C][C] 0.8845[/C][C] 0.231[/C][C] 0.1155[/C][/ROW]
[ROW][C]50[/C][C] 0.8742[/C][C] 0.2516[/C][C] 0.1258[/C][/ROW]
[ROW][C]51[/C][C] 0.888[/C][C] 0.224[/C][C] 0.112[/C][/ROW]
[ROW][C]52[/C][C] 0.8904[/C][C] 0.2191[/C][C] 0.1096[/C][/ROW]
[ROW][C]53[/C][C] 0.8756[/C][C] 0.2487[/C][C] 0.1244[/C][/ROW]
[ROW][C]54[/C][C] 0.8566[/C][C] 0.2869[/C][C] 0.1434[/C][/ROW]
[ROW][C]55[/C][C] 0.8662[/C][C] 0.2676[/C][C] 0.1338[/C][/ROW]
[ROW][C]56[/C][C] 0.8916[/C][C] 0.2167[/C][C] 0.1084[/C][/ROW]
[ROW][C]57[/C][C] 0.8673[/C][C] 0.2653[/C][C] 0.1327[/C][/ROW]
[ROW][C]58[/C][C] 0.8404[/C][C] 0.3192[/C][C] 0.1596[/C][/ROW]
[ROW][C]59[/C][C] 0.8374[/C][C] 0.3251[/C][C] 0.1626[/C][/ROW]
[ROW][C]60[/C][C] 0.9644[/C][C] 0.07119[/C][C] 0.03559[/C][/ROW]
[ROW][C]61[/C][C] 0.9543[/C][C] 0.09133[/C][C] 0.04566[/C][/ROW]
[ROW][C]62[/C][C] 0.9578[/C][C] 0.08433[/C][C] 0.04216[/C][/ROW]
[ROW][C]63[/C][C] 0.9471[/C][C] 0.1058[/C][C] 0.05288[/C][/ROW]
[ROW][C]64[/C][C] 0.9528[/C][C] 0.09432[/C][C] 0.04716[/C][/ROW]
[ROW][C]65[/C][C] 0.9402[/C][C] 0.1196[/C][C] 0.05979[/C][/ROW]
[ROW][C]66[/C][C] 0.9274[/C][C] 0.1452[/C][C] 0.07262[/C][/ROW]
[ROW][C]67[/C][C] 0.9626[/C][C] 0.07479[/C][C] 0.03739[/C][/ROW]
[ROW][C]68[/C][C] 0.9719[/C][C] 0.05623[/C][C] 0.02811[/C][/ROW]
[ROW][C]69[/C][C] 0.9759[/C][C] 0.04811[/C][C] 0.02405[/C][/ROW]
[ROW][C]70[/C][C] 0.9687[/C][C] 0.06252[/C][C] 0.03126[/C][/ROW]
[ROW][C]71[/C][C] 0.9609[/C][C] 0.07819[/C][C] 0.03909[/C][/ROW]
[ROW][C]72[/C][C] 0.9523[/C][C] 0.09533[/C][C] 0.04767[/C][/ROW]
[ROW][C]73[/C][C] 0.9404[/C][C] 0.1192[/C][C] 0.0596[/C][/ROW]
[ROW][C]74[/C][C] 0.9504[/C][C] 0.0992[/C][C] 0.0496[/C][/ROW]
[ROW][C]75[/C][C] 0.9375[/C][C] 0.125[/C][C] 0.06249[/C][/ROW]
[ROW][C]76[/C][C] 0.94[/C][C] 0.1201[/C][C] 0.06005[/C][/ROW]
[ROW][C]77[/C][C] 0.9351[/C][C] 0.1298[/C][C] 0.06492[/C][/ROW]
[ROW][C]78[/C][C] 0.9236[/C][C] 0.1528[/C][C] 0.0764[/C][/ROW]
[ROW][C]79[/C][C] 0.9077[/C][C] 0.1847[/C][C] 0.09233[/C][/ROW]
[ROW][C]80[/C][C] 0.8984[/C][C] 0.2032[/C][C] 0.1016[/C][/ROW]
[ROW][C]81[/C][C] 0.8765[/C][C] 0.247[/C][C] 0.1235[/C][/ROW]
[ROW][C]82[/C][C] 0.8562[/C][C] 0.2876[/C][C] 0.1438[/C][/ROW]
[ROW][C]83[/C][C] 0.8332[/C][C] 0.3335[/C][C] 0.1668[/C][/ROW]
[ROW][C]84[/C][C] 0.8341[/C][C] 0.3319[/C][C] 0.1659[/C][/ROW]
[ROW][C]85[/C][C] 0.8526[/C][C] 0.2948[/C][C] 0.1474[/C][/ROW]
[ROW][C]86[/C][C] 0.8328[/C][C] 0.3345[/C][C] 0.1672[/C][/ROW]
[ROW][C]87[/C][C] 0.8426[/C][C] 0.3148[/C][C] 0.1574[/C][/ROW]
[ROW][C]88[/C][C] 0.8827[/C][C] 0.2347[/C][C] 0.1173[/C][/ROW]
[ROW][C]89[/C][C] 0.8809[/C][C] 0.2382[/C][C] 0.1191[/C][/ROW]
[ROW][C]90[/C][C] 0.8614[/C][C] 0.2772[/C][C] 0.1386[/C][/ROW]
[ROW][C]91[/C][C] 0.8383[/C][C] 0.3234[/C][C] 0.1617[/C][/ROW]
[ROW][C]92[/C][C] 0.8081[/C][C] 0.3838[/C][C] 0.1919[/C][/ROW]
[ROW][C]93[/C][C] 0.7817[/C][C] 0.4365[/C][C] 0.2183[/C][/ROW]
[ROW][C]94[/C][C] 0.8168[/C][C] 0.3664[/C][C] 0.1832[/C][/ROW]
[ROW][C]95[/C][C] 0.7864[/C][C] 0.4273[/C][C] 0.2136[/C][/ROW]
[ROW][C]96[/C][C] 0.7889[/C][C] 0.4221[/C][C] 0.2111[/C][/ROW]
[ROW][C]97[/C][C] 0.8025[/C][C] 0.395[/C][C] 0.1975[/C][/ROW]
[ROW][C]98[/C][C] 0.7825[/C][C] 0.4349[/C][C] 0.2175[/C][/ROW]
[ROW][C]99[/C][C] 0.8295[/C][C] 0.3409[/C][C] 0.1705[/C][/ROW]
[ROW][C]100[/C][C] 0.8007[/C][C] 0.3987[/C][C] 0.1993[/C][/ROW]
[ROW][C]101[/C][C] 0.9002[/C][C] 0.1996[/C][C] 0.09981[/C][/ROW]
[ROW][C]102[/C][C] 0.9124[/C][C] 0.1752[/C][C] 0.0876[/C][/ROW]
[ROW][C]103[/C][C] 0.9292[/C][C] 0.1416[/C][C] 0.07079[/C][/ROW]
[ROW][C]104[/C][C] 0.9127[/C][C] 0.1746[/C][C] 0.08732[/C][/ROW]
[ROW][C]105[/C][C] 0.8926[/C][C] 0.2149[/C][C] 0.1074[/C][/ROW]
[ROW][C]106[/C][C] 0.8729[/C][C] 0.2543[/C][C] 0.1271[/C][/ROW]
[ROW][C]107[/C][C] 0.8469[/C][C] 0.3061[/C][C] 0.1531[/C][/ROW]
[ROW][C]108[/C][C] 0.8866[/C][C] 0.2268[/C][C] 0.1134[/C][/ROW]
[ROW][C]109[/C][C] 0.8698[/C][C] 0.2604[/C][C] 0.1302[/C][/ROW]
[ROW][C]110[/C][C] 0.8436[/C][C] 0.3129[/C][C] 0.1564[/C][/ROW]
[ROW][C]111[/C][C] 0.8181[/C][C] 0.3638[/C][C] 0.1819[/C][/ROW]
[ROW][C]112[/C][C] 0.7887[/C][C] 0.4226[/C][C] 0.2113[/C][/ROW]
[ROW][C]113[/C][C] 0.8149[/C][C] 0.3703[/C][C] 0.1851[/C][/ROW]
[ROW][C]114[/C][C] 0.7878[/C][C] 0.4245[/C][C] 0.2122[/C][/ROW]
[ROW][C]115[/C][C] 0.7943[/C][C] 0.4114[/C][C] 0.2057[/C][/ROW]
[ROW][C]116[/C][C] 0.8442[/C][C] 0.3116[/C][C] 0.1558[/C][/ROW]
[ROW][C]117[/C][C] 0.8175[/C][C] 0.365[/C][C] 0.1825[/C][/ROW]
[ROW][C]118[/C][C] 0.7831[/C][C] 0.4338[/C][C] 0.2169[/C][/ROW]
[ROW][C]119[/C][C] 0.7861[/C][C] 0.4279[/C][C] 0.2139[/C][/ROW]
[ROW][C]120[/C][C] 0.7462[/C][C] 0.5076[/C][C] 0.2538[/C][/ROW]
[ROW][C]121[/C][C] 0.7015[/C][C] 0.5969[/C][C] 0.2985[/C][/ROW]
[ROW][C]122[/C][C] 0.6745[/C][C] 0.651[/C][C] 0.3255[/C][/ROW]
[ROW][C]123[/C][C] 0.6891[/C][C] 0.6218[/C][C] 0.3109[/C][/ROW]
[ROW][C]124[/C][C] 0.6405[/C][C] 0.7191[/C][C] 0.3595[/C][/ROW]
[ROW][C]125[/C][C] 0.5907[/C][C] 0.8187[/C][C] 0.4093[/C][/ROW]
[ROW][C]126[/C][C] 0.67[/C][C] 0.6599[/C][C] 0.33[/C][/ROW]
[ROW][C]127[/C][C] 0.6254[/C][C] 0.7492[/C][C] 0.3746[/C][/ROW]
[ROW][C]128[/C][C] 0.5786[/C][C] 0.8428[/C][C] 0.4214[/C][/ROW]
[ROW][C]129[/C][C] 0.5385[/C][C] 0.923[/C][C] 0.4615[/C][/ROW]
[ROW][C]130[/C][C] 0.5059[/C][C] 0.9882[/C][C] 0.4941[/C][/ROW]
[ROW][C]131[/C][C] 0.8546[/C][C] 0.2907[/C][C] 0.1454[/C][/ROW]
[ROW][C]132[/C][C] 0.8295[/C][C] 0.341[/C][C] 0.1705[/C][/ROW]
[ROW][C]133[/C][C] 0.8069[/C][C] 0.3863[/C][C] 0.1931[/C][/ROW]
[ROW][C]134[/C][C] 0.7671[/C][C] 0.4658[/C][C] 0.2329[/C][/ROW]
[ROW][C]135[/C][C] 0.7361[/C][C] 0.5279[/C][C] 0.2639[/C][/ROW]
[ROW][C]136[/C][C] 0.7186[/C][C] 0.5627[/C][C] 0.2814[/C][/ROW]
[ROW][C]137[/C][C] 0.7146[/C][C] 0.5708[/C][C] 0.2854[/C][/ROW]
[ROW][C]138[/C][C] 0.6608[/C][C] 0.6783[/C][C] 0.3392[/C][/ROW]
[ROW][C]139[/C][C] 0.6443[/C][C] 0.7114[/C][C] 0.3557[/C][/ROW]
[ROW][C]140[/C][C] 0.5755[/C][C] 0.8489[/C][C] 0.4245[/C][/ROW]
[ROW][C]141[/C][C] 0.5115[/C][C] 0.9769[/C][C] 0.4885[/C][/ROW]
[ROW][C]142[/C][C] 0.4498[/C][C] 0.8996[/C][C] 0.5502[/C][/ROW]
[ROW][C]143[/C][C] 0.3836[/C][C] 0.7671[/C][C] 0.6164[/C][/ROW]
[ROW][C]144[/C][C] 0.3229[/C][C] 0.6457[/C][C] 0.6771[/C][/ROW]
[ROW][C]145[/C][C] 0.526[/C][C] 0.948[/C][C] 0.474[/C][/ROW]
[ROW][C]146[/C][C] 0.449[/C][C] 0.8979[/C][C] 0.551[/C][/ROW]
[ROW][C]147[/C][C] 0.3766[/C][C] 0.7532[/C][C] 0.6234[/C][/ROW]
[ROW][C]148[/C][C] 0.4243[/C][C] 0.8485[/C][C] 0.5757[/C][/ROW]
[ROW][C]149[/C][C] 0.4771[/C][C] 0.9542[/C][C] 0.5229[/C][/ROW]
[ROW][C]150[/C][C] 0.3781[/C][C] 0.7562[/C][C] 0.6219[/C][/ROW]
[ROW][C]151[/C][C] 0.7542[/C][C] 0.4915[/C][C] 0.2458[/C][/ROW]
[ROW][C]152[/C][C] 0.6496[/C][C] 0.7008[/C][C] 0.3504[/C][/ROW]
[ROW][C]153[/C][C] 0.7678[/C][C] 0.4643[/C][C] 0.2322[/C][/ROW]
[ROW][C]154[/C][C] 0.6886[/C][C] 0.6227[/C][C] 0.3114[/C][/ROW]
[ROW][C]155[/C][C] 0.7845[/C][C] 0.4309[/C][C] 0.2155[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302179&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&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	0.4541	0.9082	0.5459
11	0.4216	0.8431	0.5784
12	0.2799	0.5599	0.7201
13	0.315	0.63	0.685
14	0.2207	0.4415	0.7793
15	0.596	0.808	0.404
16	0.607	0.7861	0.393
17	0.5683	0.8634	0.4317
18	0.6837	0.6325	0.3163
19	0.604	0.7921	0.396
20	0.5456	0.9088	0.4544
21	0.4663	0.9325	0.5337
22	0.4352	0.8704	0.5648
23	0.3602	0.7204	0.6398
24	0.3502	0.7003	0.6498
25	0.4288	0.8575	0.5712
26	0.4328	0.8656	0.5672
27	0.3655	0.731	0.6345
28	0.6722	0.6556	0.3278
29	0.7403	0.5195	0.2597
30	0.6956	0.6087	0.3044
31	0.6428	0.7144	0.3572
32	0.8171	0.3658	0.1829
33	0.7965	0.407	0.2035
34	0.9257	0.1485	0.07426
35	0.9112	0.1775	0.08877
36	0.9138	0.1724	0.08618
37	0.9087	0.1827	0.09133
38	0.8873	0.2254	0.1127
39	0.8605	0.2789	0.1395
40	0.9016	0.1969	0.09845
41	0.8825	0.2349	0.1175
42	0.8767	0.2467	0.1233
43	0.911	0.1779	0.08897
44	0.939	0.1219	0.06095
45	0.9227	0.1547	0.07734
46	0.9024	0.1953	0.09763
47	0.9229	0.1541	0.07706
48	0.9065	0.1869	0.09347
49	0.8845	0.231	0.1155
50	0.8742	0.2516	0.1258
51	0.888	0.224	0.112
52	0.8904	0.2191	0.1096
53	0.8756	0.2487	0.1244
54	0.8566	0.2869	0.1434
55	0.8662	0.2676	0.1338
56	0.8916	0.2167	0.1084
57	0.8673	0.2653	0.1327
58	0.8404	0.3192	0.1596
59	0.8374	0.3251	0.1626
60	0.9644	0.07119	0.03559
61	0.9543	0.09133	0.04566
62	0.9578	0.08433	0.04216
63	0.9471	0.1058	0.05288
64	0.9528	0.09432	0.04716
65	0.9402	0.1196	0.05979
66	0.9274	0.1452	0.07262
67	0.9626	0.07479	0.03739
68	0.9719	0.05623	0.02811
69	0.9759	0.04811	0.02405
70	0.9687	0.06252	0.03126
71	0.9609	0.07819	0.03909
72	0.9523	0.09533	0.04767
73	0.9404	0.1192	0.0596
74	0.9504	0.0992	0.0496
75	0.9375	0.125	0.06249
76	0.94	0.1201	0.06005
77	0.9351	0.1298	0.06492
78	0.9236	0.1528	0.0764
79	0.9077	0.1847	0.09233
80	0.8984	0.2032	0.1016
81	0.8765	0.247	0.1235
82	0.8562	0.2876	0.1438
83	0.8332	0.3335	0.1668
84	0.8341	0.3319	0.1659
85	0.8526	0.2948	0.1474
86	0.8328	0.3345	0.1672
87	0.8426	0.3148	0.1574
88	0.8827	0.2347	0.1173
89	0.8809	0.2382	0.1191
90	0.8614	0.2772	0.1386
91	0.8383	0.3234	0.1617
92	0.8081	0.3838	0.1919
93	0.7817	0.4365	0.2183
94	0.8168	0.3664	0.1832
95	0.7864	0.4273	0.2136
96	0.7889	0.4221	0.2111
97	0.8025	0.395	0.1975
98	0.7825	0.4349	0.2175
99	0.8295	0.3409	0.1705
100	0.8007	0.3987	0.1993
101	0.9002	0.1996	0.09981
102	0.9124	0.1752	0.0876
103	0.9292	0.1416	0.07079
104	0.9127	0.1746	0.08732
105	0.8926	0.2149	0.1074
106	0.8729	0.2543	0.1271
107	0.8469	0.3061	0.1531
108	0.8866	0.2268	0.1134
109	0.8698	0.2604	0.1302
110	0.8436	0.3129	0.1564
111	0.8181	0.3638	0.1819
112	0.7887	0.4226	0.2113
113	0.8149	0.3703	0.1851
114	0.7878	0.4245	0.2122
115	0.7943	0.4114	0.2057
116	0.8442	0.3116	0.1558
117	0.8175	0.365	0.1825
118	0.7831	0.4338	0.2169
119	0.7861	0.4279	0.2139
120	0.7462	0.5076	0.2538
121	0.7015	0.5969	0.2985
122	0.6745	0.651	0.3255
123	0.6891	0.6218	0.3109
124	0.6405	0.7191	0.3595
125	0.5907	0.8187	0.4093
126	0.67	0.6599	0.33
127	0.6254	0.7492	0.3746
128	0.5786	0.8428	0.4214
129	0.5385	0.923	0.4615
130	0.5059	0.9882	0.4941
131	0.8546	0.2907	0.1454
132	0.8295	0.341	0.1705
133	0.8069	0.3863	0.1931
134	0.7671	0.4658	0.2329
135	0.7361	0.5279	0.2639
136	0.7186	0.5627	0.2814
137	0.7146	0.5708	0.2854
138	0.6608	0.6783	0.3392
139	0.6443	0.7114	0.3557
140	0.5755	0.8489	0.4245
141	0.5115	0.9769	0.4885
142	0.4498	0.8996	0.5502
143	0.3836	0.7671	0.6164
144	0.3229	0.6457	0.6771
145	0.526	0.948	0.474
146	0.449	0.8979	0.551
147	0.3766	0.7532	0.6234
148	0.4243	0.8485	0.5757
149	0.4771	0.9542	0.5229
150	0.3781	0.7562	0.6219
151	0.7542	0.4915	0.2458
152	0.6496	0.7008	0.3504
153	0.7678	0.4643	0.2322
154	0.6886	0.6227	0.3114
155	0.7845	0.4309	0.2155

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	1	0.00684932	OK
10% type I error level	11	0.0753425	OK

\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 & 1 & 0.00684932 & OK \tabularnewline
10% type I error level & 11 & 0.0753425 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302179&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]1[/C][C]0.00684932[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.0753425[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302179&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&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	1	0.00684932	OK
10% type I error level	11	0.0753425	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.9519, df1 = 2, df2 = 156, p-value = 0.05516
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3388, df1 = 12, df2 = 146, p-value = 0.2027
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 3.0459, df1 = 2, df2 = 156, p-value = 0.05039

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.9519, df1 = 2, df2 = 156, p-value = 0.05516
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3388, df1 = 12, df2 = 146, p-value = 0.2027
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.0459, df1 = 2, df2 = 156, p-value = 0.05039
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302179&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.9519, df1 = 2, df2 = 156, p-value = 0.05516
[/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 = 1.3388, df1 = 12, df2 = 146, p-value = 0.2027
[/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 = 3.0459, df1 = 2, df2 = 156, p-value = 0.05039
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302179&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.9519, df1 = 2, df2 = 156, p-value = 0.05516
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3388, df1 = 12, df2 = 146, p-value = 0.2027
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 3.0459, df1 = 2, df2 = 156, p-value = 0.05039

Variance Inflation Factors (Multicollinearity)
> vif `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` TVDCSUM 1.364497 1.044804 1.059245 1.046818 1.037801 1.388335

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
`SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6`   TVDCSUM 
 1.364497  1.044804  1.059245  1.046818  1.037801  1.388335 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302179&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
`SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6`   TVDCSUM 
 1.364497  1.044804  1.059245  1.046818  1.037801  1.388335 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302179&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302179&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 `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` TVDCSUM 1.364497 1.044804 1.059245 1.046818 1.037801 1.388335

Figure 1

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Figure 3

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Figure 4

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = pearson ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code