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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 17 Dec 2016 10:32:30 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/17/t1481967170e539qoxc9k6foe6.htm/, Retrieved Thu, 02 May 2024 05:34:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300636, Retrieved Thu, 02 May 2024 05:34:35 +0000

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Estimated Impact

105

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

-     [ML Fitting and QQ Plot- Normal Distribution] [Normal distribution] [2016-12-15 09:27:42] [061bcad4f8cbfaa4a6cadfe6faec1e5a]
- RMPD  [Multiple Regression] [Multiple regressi...] [2016-12-15 11:24:47] [061bcad4f8cbfaa4a6cadfe6faec1e5a]
- R P       [Multiple Regression] [] [2016-12-17 09:32:30] [63a9f0ea7bb98050796b649e85481845] [Current]

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Dataseries X:

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4	3	3	3	2	2	3	4
5	4	4	3	4	2	1	4
4	5	5	3	4	2	5	4
4	4	4	3	4	3	4	4
4	4	4	4	3	4	3	3
5	3	5	3	4	3	2	5
5	3	5	5	1	4	4	4
4	4	5	3	4	2	5	4
4	4	5	4	3	4	5	2
5	4	5	3	4	4	3	4
5	4	5	3	2	2	2	4
4	4	4	3	4	2	2	3
4	4	4	4	4	5	4	3
4	3	4	3	5	4	4	4
4	4	4	4	4	2	4	4
5	4	5	3	1	3	5	4
4	4	4	4	2	1	2	5
3	4	4	4	4	3	2	4
4	4	5	4	5	4	4	4
5	4	4	3	5	5	4	4
4	4	4	3	4	5	4	4
5	4	4	3	1	1	5	4
4	4	4	3	4	4	3	4
4	4	5	4	2	2	4	4
3	3	5	3	4	4	3	4
4	4	4	4	5	4	3	3
4	4	4	3	3	3	3	3
4	4	5	3	5	4	5	5
4	4	5	3	3	2	4	4
3	4	3	3	5	2	4	4
4	3	5	3	2	4	3	4
5	4	4	4	1	2	3	4
4	4	5	2	4	4	5	1
4	2	4	3	4	2	3	3
5	4	5	3	4	4	3	4
4	4	4	3	3	3	3	4
3	3	4	4	5	3	5	5
2	4	4	4	4	4	3	4
5	4	5	4	2	2	3	4
4	4	4	3	4	3	3	4
5	4	5	3	2	2	4	3
4	3	3	3	3	4	3	4
4	4	5	3	1	2	1	5
4	4	4	3	3	2	4	4
3	4	5	3	3	3	4	3
4	4	5	3	3	3	3	3
4	4	4	3	4	4	4	5
3	4	3	3	4	4	4	4
3	3	3	3	4	5	5	1
5	4	5	3	4	4	4	4
5	5	5	3	4	4	4	4
5	5	4	4	2	4	3	4
2	3	3	3	5	2	2	4
3	4	4	3	3	2	4	3
2	4	4	3	3	1	3	4
4	4	4	3	4	3	3	3
5	5	4	3	4	4	3	4
4	4	4	4	4	3	4	2
4	4	4	3	3	3	4	4
5	4	5	3	4	2	3	4
5	4	4	3	4	3	4	4
4	5	4	3	4	2	5	3
5	4	4	3	4	4	2	4
4	4	4	3	4	3	3	3
4	2	4	2	2	2	3	4
5	4	5	3	4	4	3	3
3	4	4	3	4	5	4	4
2	4	4	4	4	4	3	4
5	4	4	3	4	3	4	4
4	4	4	3	4	2	3	4
4	4	4	3	5	3	1	3
4	4	3	3	3	4	4	3
3	3	4	3	2	4	3	2
5	5	4	4	4	4	2	4
4	4	4	3	5	5	3	5
5	3	5	3	4	4	3	4
3	4	4	3	5	4	4	5
2	4	4	5	5	4	5	2
5	4	5	3	2	3	3	4
4	4	5	3	4	2	4	4
1	3	3	3	4	4	2	4
4	4	5	3	4	4	2	4
5	4	4	4	3	4	2	5
4	4	5	4	4	2	3	4
5	5	5	5	2	2	4	4
4	4	5	4	5	1	3	4
5	4	5	4	3	3	5	4
4	4	4	3	4	4	4	1
5	4	4	4	2	4	4	4
5	4	2	3	4	4	3	4
4	4	4	3	3	3	4	3
4	5	5	3	3	4	3	4
4	4	5	3	4	4	5	4
4	5	5	3	4	4	4	3
4	4	4	3	4	2	4	3
4	4	4	4	3	4	3	4
4	5	4	5	4	4	4	5
5	4	5	4	3	1	1	3
5	4	4	3	3	4	4	4
4	4	4	4	1	2	4	3
4	4	5	4	4	3	4	4
4	4	4	3	3	3	4	5
2	4	4	3	3	4	4	3
4	4	4	3	5	3	3	4
4	4	5	4	5	4	5	4
4	4	4	4	4	4	3	3
4	4	5	3	5	4	5	5
4	4	4	3	4	4	4	4
4	4	4	4	4	5	4	4
4	4	4	4	4	5	4	5
4	4	3	3	4	2	4	3
4	4	4	3	3	1	3	3
3	3	3	3	4	3	4	3
5	4	5	5	3	3	3	4
4	4	4	4	4	1	3	4
5	4	4	3	2	4	3	4
4	4	5	4	1	4	3	4
5	4	4	3	5	2	2	4
3	4	4	3	4	4	4	4
4	4	4	3	3	3	3	3
3	4	4	3	4	4	2	4
4	4	4	4	4	4	4	5
4	4	4	3	4	2	4	4
4	4	5	4	4	2	3	3
4	4	4	3	2	4	4	4
5	4	4	3	4	4	5	4
4	4	5	3	4	2	4	3
4	4	4	3	4	2	2	3
4	4	4	3	4	2	4	4
2	3	3	3	3	2	4	2
4	4	4	4	4	5	4	4
4	5	4	5	5	2	5	3
3	3	4	3	2	2	2	4
2	3	3	3	5	2	4	4
4	4	4	4	4	4	4	4
4	4	5	5	3	5	5	4
3	3	3	3	4	4	4	3
4	4	4	3	2	4	4	2
5	5	5	4	2	3	5	5
4	5	5	3	2	3	2	3
3	3	4	3	4	1	4	4
3	4	4	3	4	4	5	4
4	4	4	4	5	5	3	4
3	4	3	3	3	4	4	5
4	5	5	3	3	4	4	4
2	4	4	4	4	5	3	4
5	5	5	4	4	4	5	3
4	3	4	3	4	5	5	1
4	4	4	4	4	5	3	4
3	3	3	3	4	3	2	5
4	4	4	4	4	5	4	4
5	4	4	3	4	1	5	4
4	4	4	3	2	3	3	4
2	4	3	3	5	2	3	5
4	4	4	3	4	2	4	4
5	4	5	3	4	4	3	4
4	4	3	3	4	4	2	4
4	4	4	3	4	2	3	4
5	4	5	4	4	5	3	4
4	4	4	3	2	4	4	3
5	5	5	3	3	5	1	5
3	4	4	4	3	3	4	3
4	4	4	3	4	2	3	4
4	4	4	4	4	4	3	4
3	3	4	3	4	2	2	5
4	4	4	4	4	3	3	4
4	4	3	3	3	3	3	4
3	4	4	5	3	2	5	2

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=300636&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=300636&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&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
TVCD1[t] = + 1.27856 + 0.3767TCVD2[t] + 0.439956TCVD3[t] -0.110832TVCD4[t] -0.149874IVHB1[t] + 0.0348379IVHB2[t] -0.0477697IVHB3[t] + 0.0860749IVHB4[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVCD1[t] =  +  1.27856 +  0.3767TCVD2[t] +  0.439956TCVD3[t] -0.110832TVCD4[t] -0.149874IVHB1[t] +  0.0348379IVHB2[t] -0.0477697IVHB3[t] +  0.0860749IVHB4[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300636&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVCD1[t] =  +  1.27856 +  0.3767TCVD2[t] +  0.439956TCVD3[t] -0.110832TVCD4[t] -0.149874IVHB1[t] +  0.0348379IVHB2[t] -0.0477697IVHB3[t] +  0.0860749IVHB4[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300636&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&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
TVCD1[t] = + 1.27856 + 0.3767TCVD2[t] + 0.439956TCVD3[t] -0.110832TVCD4[t] -0.149874IVHB1[t] + 0.0348379IVHB2[t] -0.0477697IVHB3[t] + 0.0860749IVHB4[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+1.279	0.6388	+2.0010e+00	0.04703	0.02351
TCVD2	+0.3767	0.1143	+3.2970e+00	0.001204	0.0006022
TCVD3	+0.44	0.09294	+4.7340e+00	4.818e-06	2.409e-06
TVCD4	-0.1108	0.09752	-1.1370e+00	0.2574	0.1287
IVHB1	-0.1499	0.05716	-2.6220e+00	0.009583	0.004791
IVHB2	+0.03484	0.05125	+6.7980e-01	0.4976	0.2488
IVHB3	-0.04777	0.05941	-8.0410e-01	0.4226	0.2113
IVHB4	+0.08607	0.07035	+1.2240e+00	0.2229	0.1115

\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) & +1.279 &  0.6388 & +2.0010e+00 &  0.04703 &  0.02351 \tabularnewline
TCVD2 & +0.3767 &  0.1143 & +3.2970e+00 &  0.001204 &  0.0006022 \tabularnewline
TCVD3 & +0.44 &  0.09294 & +4.7340e+00 &  4.818e-06 &  2.409e-06 \tabularnewline
TVCD4 & -0.1108 &  0.09752 & -1.1370e+00 &  0.2574 &  0.1287 \tabularnewline
IVHB1 & -0.1499 &  0.05716 & -2.6220e+00 &  0.009583 &  0.004791 \tabularnewline
IVHB2 & +0.03484 &  0.05125 & +6.7980e-01 &  0.4976 &  0.2488 \tabularnewline
IVHB3 & -0.04777 &  0.05941 & -8.0410e-01 &  0.4226 &  0.2113 \tabularnewline
IVHB4 & +0.08607 &  0.07035 & +1.2240e+00 &  0.2229 &  0.1115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300636&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]+1.279[/C][C] 0.6388[/C][C]+2.0010e+00[/C][C] 0.04703[/C][C] 0.02351[/C][/ROW]
[ROW][C]TCVD2[/C][C]+0.3767[/C][C] 0.1143[/C][C]+3.2970e+00[/C][C] 0.001204[/C][C] 0.0006022[/C][/ROW]
[ROW][C]TCVD3[/C][C]+0.44[/C][C] 0.09294[/C][C]+4.7340e+00[/C][C] 4.818e-06[/C][C] 2.409e-06[/C][/ROW]
[ROW][C]TVCD4[/C][C]-0.1108[/C][C] 0.09752[/C][C]-1.1370e+00[/C][C] 0.2574[/C][C] 0.1287[/C][/ROW]
[ROW][C]IVHB1[/C][C]-0.1499[/C][C] 0.05716[/C][C]-2.6220e+00[/C][C] 0.009583[/C][C] 0.004791[/C][/ROW]
[ROW][C]IVHB2[/C][C]+0.03484[/C][C] 0.05125[/C][C]+6.7980e-01[/C][C] 0.4976[/C][C] 0.2488[/C][/ROW]
[ROW][C]IVHB3[/C][C]-0.04777[/C][C] 0.05941[/C][C]-8.0410e-01[/C][C] 0.4226[/C][C] 0.2113[/C][/ROW]
[ROW][C]IVHB4[/C][C]+0.08607[/C][C] 0.07035[/C][C]+1.2240e+00[/C][C] 0.2229[/C][C] 0.1115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300636&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&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)	+1.279	0.6388	+2.0010e+00	0.04703	0.02351
TCVD2	+0.3767	0.1143	+3.2970e+00	0.001204	0.0006022
TCVD3	+0.44	0.09294	+4.7340e+00	4.818e-06	2.409e-06
TVCD4	-0.1108	0.09752	-1.1370e+00	0.2574	0.1287
IVHB1	-0.1499	0.05716	-2.6220e+00	0.009583	0.004791
IVHB2	+0.03484	0.05125	+6.7980e-01	0.4976	0.2488
IVHB3	-0.04777	0.05941	-8.0410e-01	0.4226	0.2113
IVHB4	+0.08607	0.07035	+1.2240e+00	0.2229	0.1115

Multiple Linear Regression - Regression Statistics
Multiple R	0.5291
R-squared	0.28
Adjusted R-squared	0.2485
F-TEST (value)	8.887
F-TEST (DF numerator)	7
F-TEST (DF denominator)	160
p-value	3.165e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.7126
Sum Squared Residuals	81.26

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5291 \tabularnewline
R-squared &  0.28 \tabularnewline
Adjusted R-squared &  0.2485 \tabularnewline
F-TEST (value) &  8.887 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value &  3.165e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.7126 \tabularnewline
Sum Squared Residuals &  81.26 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300636&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5291[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.28[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2485[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 8.887[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C] 3.165e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.7126[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 81.26[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300636&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&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	0.5291
R-squared	0.28
Adjusted R-squared	0.2485
F-TEST (value)	8.887
F-TEST (DF numerator)	7
F-TEST (DF denominator)	160
p-value	3.165e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.7126
Sum Squared Residuals	81.26

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4	3.367	0.6331
2	5	3.979	1.021
3	4	4.605	-0.605
4	4	3.871	0.1291
5	4	3.906	0.0935
6	5	4.116	0.8842
7	5	4.197	0.803
8	4	4.228	-0.2283
9	4	4.165	-0.1648
10	5	4.393	0.6065
11	5	4.671	0.3287
12	4	3.846	0.1544
13	4	3.744	0.2563
14	4	3.379	0.6208
15	4	3.725	0.2747
16	5	4.713	0.2873
17	4	4.172	-0.1718
18	3	3.856	-0.8556
19	4	4.085	-0.08502
20	5	3.791	1.209
21	4	3.941	0.0594
22	5	4.203	0.7969
23	4	3.954	0.04647
24	4	4.465	-0.465
25	3	4.017	-1.017
26	4	3.607	0.3932
27	4	3.982	0.0175
28	4	4.234	-0.2342
29	4	4.426	-0.4259
30	3	3.246	-0.2463
31	4	4.317	-0.3165
32	5	4.223	0.7774
33	4	4.151	-0.1506
34	4	3.044	0.9556
35	5	4.393	0.6065
36	4	4.069	-0.06857
37	3	3.272	-0.2718
38	2	3.843	-1.843
39	5	4.513	0.4873
40	4	3.919	0.0813
41	5	4.49	0.5103
42	4	3.287	0.7132
43	4	4.955	-0.955
44	4	3.986	0.01404
45	3	4.375	-1.375
46	4	4.422	-0.4225
47	4	3.992	0.00816
48	3	3.466	-0.4658
49	3	2.818	0.182
50	5	4.346	0.6543
51	5	4.722	0.2776
52	5	4.519	0.4808
53	2	2.965	-0.9651
54	3	3.9	-0.8999
55	2	3.999	-1.999
56	4	3.833	0.1674
57	5	4.33	0.6698
58	4	3.588	0.4121
59	4	4.021	-0.0208
60	5	4.324	0.6762
61	5	3.871	1.129
62	4	4.079	-0.07894
63	5	4.001	0.9987
64	4	3.833	0.1674
65	4	3.541	0.459
66	5	4.307	0.6926
67	3	3.941	-0.9406
68	2	3.843	-1.843
69	5	3.871	1.129
70	4	3.884	0.1161
71	4	3.778	0.2217
72	4	3.53	0.4704
73	3	3.704	-0.7044
74	5	4.267	0.7328
75	4	3.925	0.07543
76	5	4.017	0.9832
77	3	3.842	-0.842
78	2	3.314	-1.314
79	5	4.658	0.3416
80	4	4.276	-0.276
81	1	3.185	-2.185
82	4	4.441	-0.4413
83	5	4.126	0.8736
84	4	4.213	-0.213
85	5	4.731	0.2692
86	4	4.028	-0.02827
87	5	4.302	0.6978
88	4	3.648	0.3525
89	5	4.095	0.9053
90	5	3.074	1.926
91	4	3.935	0.06527
92	4	4.92	-0.9201
93	4	4.298	-0.298
94	4	4.636	-0.6363
95	4	3.75	0.25
96	4	3.993	0.007424
97	4	4.147	-0.1469
98	5	4.337	0.6625
99	5	4.056	0.9444
100	4	4.089	-0.0888
101	4	4.2	-0.2001
102	4	4.107	-0.1069
103	2	3.97	-1.97
104	4	3.769	0.2312
105	4	4.037	-0.03725
106	4	3.757	0.2434
107	4	4.234	-0.2342
108	4	3.906	0.09423
109	4	3.83	0.1702
110	4	3.916	0.08415
111	4	3.31	0.6899
112	4	3.913	0.08718
113	3	2.968	0.0318
114	5	4.287	0.7131
115	4	3.738	0.2618
116	5	4.253	0.7467
117	4	4.732	-0.7323
118	5	3.782	1.218
119	3	3.906	-0.9058
120	4	3.982	0.0175
121	3	4.001	-1.001
122	4	3.881	0.119
123	4	3.836	0.1639
124	4	4.127	-0.1269
125	4	4.206	-0.2055
126	5	3.858	1.142
127	4	4.19	-0.19
128	4	3.846	0.1544
129	4	3.836	0.1639
130	2	2.997	-0.9972
131	4	3.83	0.1702
132	4	3.707	0.2926
133	3	3.855	-0.8547
134	2	2.87	-0.8696
135	4	3.795	0.2051
136	4	4.261	-0.261
137	3	3.003	-0.003034
138	4	4.033	-0.03336
139	5	4.915	0.0852
140	4	4.997	-0.9968
141	3	3.425	-0.4246
142	3	3.858	-0.858
143	4	3.728	0.2723
144	3	3.702	-0.7018
145	4	4.872	-0.8723
146	2	3.878	-1.878
147	5	4.478	0.5223
148	4	3.258	0.7421
149	4	3.878	0.1225
150	3	3.236	-0.2359
151	4	3.83	0.1702
152	5	3.753	1.247
153	4	4.218	-0.2184
154	2	3.38	-1.38
155	4	3.836	0.1639
156	5	4.393	0.6065
157	4	3.561	0.4387
158	4	3.884	0.1161
159	5	4.317	0.6825
160	4	4.119	-0.1194
161	5	5.137	-0.1365
162	3	3.824	-0.8239
163	4	3.884	0.1161
164	4	3.843	0.1573
165	3	3.641	-0.641
166	4	3.808	0.1921
167	4	3.629	0.3714
168	3	3.544	-0.5444

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  4 &  3.367 &  0.6331 \tabularnewline
2 &  5 &  3.979 &  1.021 \tabularnewline
3 &  4 &  4.605 & -0.605 \tabularnewline
4 &  4 &  3.871 &  0.1291 \tabularnewline
5 &  4 &  3.906 &  0.0935 \tabularnewline
6 &  5 &  4.116 &  0.8842 \tabularnewline
7 &  5 &  4.197 &  0.803 \tabularnewline
8 &  4 &  4.228 & -0.2283 \tabularnewline
9 &  4 &  4.165 & -0.1648 \tabularnewline
10 &  5 &  4.393 &  0.6065 \tabularnewline
11 &  5 &  4.671 &  0.3287 \tabularnewline
12 &  4 &  3.846 &  0.1544 \tabularnewline
13 &  4 &  3.744 &  0.2563 \tabularnewline
14 &  4 &  3.379 &  0.6208 \tabularnewline
15 &  4 &  3.725 &  0.2747 \tabularnewline
16 &  5 &  4.713 &  0.2873 \tabularnewline
17 &  4 &  4.172 & -0.1718 \tabularnewline
18 &  3 &  3.856 & -0.8556 \tabularnewline
19 &  4 &  4.085 & -0.08502 \tabularnewline
20 &  5 &  3.791 &  1.209 \tabularnewline
21 &  4 &  3.941 &  0.0594 \tabularnewline
22 &  5 &  4.203 &  0.7969 \tabularnewline
23 &  4 &  3.954 &  0.04647 \tabularnewline
24 &  4 &  4.465 & -0.465 \tabularnewline
25 &  3 &  4.017 & -1.017 \tabularnewline
26 &  4 &  3.607 &  0.3932 \tabularnewline
27 &  4 &  3.982 &  0.0175 \tabularnewline
28 &  4 &  4.234 & -0.2342 \tabularnewline
29 &  4 &  4.426 & -0.4259 \tabularnewline
30 &  3 &  3.246 & -0.2463 \tabularnewline
31 &  4 &  4.317 & -0.3165 \tabularnewline
32 &  5 &  4.223 &  0.7774 \tabularnewline
33 &  4 &  4.151 & -0.1506 \tabularnewline
34 &  4 &  3.044 &  0.9556 \tabularnewline
35 &  5 &  4.393 &  0.6065 \tabularnewline
36 &  4 &  4.069 & -0.06857 \tabularnewline
37 &  3 &  3.272 & -0.2718 \tabularnewline
38 &  2 &  3.843 & -1.843 \tabularnewline
39 &  5 &  4.513 &  0.4873 \tabularnewline
40 &  4 &  3.919 &  0.0813 \tabularnewline
41 &  5 &  4.49 &  0.5103 \tabularnewline
42 &  4 &  3.287 &  0.7132 \tabularnewline
43 &  4 &  4.955 & -0.955 \tabularnewline
44 &  4 &  3.986 &  0.01404 \tabularnewline
45 &  3 &  4.375 & -1.375 \tabularnewline
46 &  4 &  4.422 & -0.4225 \tabularnewline
47 &  4 &  3.992 &  0.00816 \tabularnewline
48 &  3 &  3.466 & -0.4658 \tabularnewline
49 &  3 &  2.818 &  0.182 \tabularnewline
50 &  5 &  4.346 &  0.6543 \tabularnewline
51 &  5 &  4.722 &  0.2776 \tabularnewline
52 &  5 &  4.519 &  0.4808 \tabularnewline
53 &  2 &  2.965 & -0.9651 \tabularnewline
54 &  3 &  3.9 & -0.8999 \tabularnewline
55 &  2 &  3.999 & -1.999 \tabularnewline
56 &  4 &  3.833 &  0.1674 \tabularnewline
57 &  5 &  4.33 &  0.6698 \tabularnewline
58 &  4 &  3.588 &  0.4121 \tabularnewline
59 &  4 &  4.021 & -0.0208 \tabularnewline
60 &  5 &  4.324 &  0.6762 \tabularnewline
61 &  5 &  3.871 &  1.129 \tabularnewline
62 &  4 &  4.079 & -0.07894 \tabularnewline
63 &  5 &  4.001 &  0.9987 \tabularnewline
64 &  4 &  3.833 &  0.1674 \tabularnewline
65 &  4 &  3.541 &  0.459 \tabularnewline
66 &  5 &  4.307 &  0.6926 \tabularnewline
67 &  3 &  3.941 & -0.9406 \tabularnewline
68 &  2 &  3.843 & -1.843 \tabularnewline
69 &  5 &  3.871 &  1.129 \tabularnewline
70 &  4 &  3.884 &  0.1161 \tabularnewline
71 &  4 &  3.778 &  0.2217 \tabularnewline
72 &  4 &  3.53 &  0.4704 \tabularnewline
73 &  3 &  3.704 & -0.7044 \tabularnewline
74 &  5 &  4.267 &  0.7328 \tabularnewline
75 &  4 &  3.925 &  0.07543 \tabularnewline
76 &  5 &  4.017 &  0.9832 \tabularnewline
77 &  3 &  3.842 & -0.842 \tabularnewline
78 &  2 &  3.314 & -1.314 \tabularnewline
79 &  5 &  4.658 &  0.3416 \tabularnewline
80 &  4 &  4.276 & -0.276 \tabularnewline
81 &  1 &  3.185 & -2.185 \tabularnewline
82 &  4 &  4.441 & -0.4413 \tabularnewline
83 &  5 &  4.126 &  0.8736 \tabularnewline
84 &  4 &  4.213 & -0.213 \tabularnewline
85 &  5 &  4.731 &  0.2692 \tabularnewline
86 &  4 &  4.028 & -0.02827 \tabularnewline
87 &  5 &  4.302 &  0.6978 \tabularnewline
88 &  4 &  3.648 &  0.3525 \tabularnewline
89 &  5 &  4.095 &  0.9053 \tabularnewline
90 &  5 &  3.074 &  1.926 \tabularnewline
91 &  4 &  3.935 &  0.06527 \tabularnewline
92 &  4 &  4.92 & -0.9201 \tabularnewline
93 &  4 &  4.298 & -0.298 \tabularnewline
94 &  4 &  4.636 & -0.6363 \tabularnewline
95 &  4 &  3.75 &  0.25 \tabularnewline
96 &  4 &  3.993 &  0.007424 \tabularnewline
97 &  4 &  4.147 & -0.1469 \tabularnewline
98 &  5 &  4.337 &  0.6625 \tabularnewline
99 &  5 &  4.056 &  0.9444 \tabularnewline
100 &  4 &  4.089 & -0.0888 \tabularnewline
101 &  4 &  4.2 & -0.2001 \tabularnewline
102 &  4 &  4.107 & -0.1069 \tabularnewline
103 &  2 &  3.97 & -1.97 \tabularnewline
104 &  4 &  3.769 &  0.2312 \tabularnewline
105 &  4 &  4.037 & -0.03725 \tabularnewline
106 &  4 &  3.757 &  0.2434 \tabularnewline
107 &  4 &  4.234 & -0.2342 \tabularnewline
108 &  4 &  3.906 &  0.09423 \tabularnewline
109 &  4 &  3.83 &  0.1702 \tabularnewline
110 &  4 &  3.916 &  0.08415 \tabularnewline
111 &  4 &  3.31 &  0.6899 \tabularnewline
112 &  4 &  3.913 &  0.08718 \tabularnewline
113 &  3 &  2.968 &  0.0318 \tabularnewline
114 &  5 &  4.287 &  0.7131 \tabularnewline
115 &  4 &  3.738 &  0.2618 \tabularnewline
116 &  5 &  4.253 &  0.7467 \tabularnewline
117 &  4 &  4.732 & -0.7323 \tabularnewline
118 &  5 &  3.782 &  1.218 \tabularnewline
119 &  3 &  3.906 & -0.9058 \tabularnewline
120 &  4 &  3.982 &  0.0175 \tabularnewline
121 &  3 &  4.001 & -1.001 \tabularnewline
122 &  4 &  3.881 &  0.119 \tabularnewline
123 &  4 &  3.836 &  0.1639 \tabularnewline
124 &  4 &  4.127 & -0.1269 \tabularnewline
125 &  4 &  4.206 & -0.2055 \tabularnewline
126 &  5 &  3.858 &  1.142 \tabularnewline
127 &  4 &  4.19 & -0.19 \tabularnewline
128 &  4 &  3.846 &  0.1544 \tabularnewline
129 &  4 &  3.836 &  0.1639 \tabularnewline
130 &  2 &  2.997 & -0.9972 \tabularnewline
131 &  4 &  3.83 &  0.1702 \tabularnewline
132 &  4 &  3.707 &  0.2926 \tabularnewline
133 &  3 &  3.855 & -0.8547 \tabularnewline
134 &  2 &  2.87 & -0.8696 \tabularnewline
135 &  4 &  3.795 &  0.2051 \tabularnewline
136 &  4 &  4.261 & -0.261 \tabularnewline
137 &  3 &  3.003 & -0.003034 \tabularnewline
138 &  4 &  4.033 & -0.03336 \tabularnewline
139 &  5 &  4.915 &  0.0852 \tabularnewline
140 &  4 &  4.997 & -0.9968 \tabularnewline
141 &  3 &  3.425 & -0.4246 \tabularnewline
142 &  3 &  3.858 & -0.858 \tabularnewline
143 &  4 &  3.728 &  0.2723 \tabularnewline
144 &  3 &  3.702 & -0.7018 \tabularnewline
145 &  4 &  4.872 & -0.8723 \tabularnewline
146 &  2 &  3.878 & -1.878 \tabularnewline
147 &  5 &  4.478 &  0.5223 \tabularnewline
148 &  4 &  3.258 &  0.7421 \tabularnewline
149 &  4 &  3.878 &  0.1225 \tabularnewline
150 &  3 &  3.236 & -0.2359 \tabularnewline
151 &  4 &  3.83 &  0.1702 \tabularnewline
152 &  5 &  3.753 &  1.247 \tabularnewline
153 &  4 &  4.218 & -0.2184 \tabularnewline
154 &  2 &  3.38 & -1.38 \tabularnewline
155 &  4 &  3.836 &  0.1639 \tabularnewline
156 &  5 &  4.393 &  0.6065 \tabularnewline
157 &  4 &  3.561 &  0.4387 \tabularnewline
158 &  4 &  3.884 &  0.1161 \tabularnewline
159 &  5 &  4.317 &  0.6825 \tabularnewline
160 &  4 &  4.119 & -0.1194 \tabularnewline
161 &  5 &  5.137 & -0.1365 \tabularnewline
162 &  3 &  3.824 & -0.8239 \tabularnewline
163 &  4 &  3.884 &  0.1161 \tabularnewline
164 &  4 &  3.843 &  0.1573 \tabularnewline
165 &  3 &  3.641 & -0.641 \tabularnewline
166 &  4 &  3.808 &  0.1921 \tabularnewline
167 &  4 &  3.629 &  0.3714 \tabularnewline
168 &  3 &  3.544 & -0.5444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300636&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] 3.367[/C][C] 0.6331[/C][/ROW]
[ROW][C]2[/C][C] 5[/C][C] 3.979[/C][C] 1.021[/C][/ROW]
[ROW][C]3[/C][C] 4[/C][C] 4.605[/C][C]-0.605[/C][/ROW]
[ROW][C]4[/C][C] 4[/C][C] 3.871[/C][C] 0.1291[/C][/ROW]
[ROW][C]5[/C][C] 4[/C][C] 3.906[/C][C] 0.0935[/C][/ROW]
[ROW][C]6[/C][C] 5[/C][C] 4.116[/C][C] 0.8842[/C][/ROW]
[ROW][C]7[/C][C] 5[/C][C] 4.197[/C][C] 0.803[/C][/ROW]
[ROW][C]8[/C][C] 4[/C][C] 4.228[/C][C]-0.2283[/C][/ROW]
[ROW][C]9[/C][C] 4[/C][C] 4.165[/C][C]-0.1648[/C][/ROW]
[ROW][C]10[/C][C] 5[/C][C] 4.393[/C][C] 0.6065[/C][/ROW]
[ROW][C]11[/C][C] 5[/C][C] 4.671[/C][C] 0.3287[/C][/ROW]
[ROW][C]12[/C][C] 4[/C][C] 3.846[/C][C] 0.1544[/C][/ROW]
[ROW][C]13[/C][C] 4[/C][C] 3.744[/C][C] 0.2563[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 3.379[/C][C] 0.6208[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C] 3.725[/C][C] 0.2747[/C][/ROW]
[ROW][C]16[/C][C] 5[/C][C] 4.713[/C][C] 0.2873[/C][/ROW]
[ROW][C]17[/C][C] 4[/C][C] 4.172[/C][C]-0.1718[/C][/ROW]
[ROW][C]18[/C][C] 3[/C][C] 3.856[/C][C]-0.8556[/C][/ROW]
[ROW][C]19[/C][C] 4[/C][C] 4.085[/C][C]-0.08502[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 3.791[/C][C] 1.209[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 3.941[/C][C] 0.0594[/C][/ROW]
[ROW][C]22[/C][C] 5[/C][C] 4.203[/C][C] 0.7969[/C][/ROW]
[ROW][C]23[/C][C] 4[/C][C] 3.954[/C][C] 0.04647[/C][/ROW]
[ROW][C]24[/C][C] 4[/C][C] 4.465[/C][C]-0.465[/C][/ROW]
[ROW][C]25[/C][C] 3[/C][C] 4.017[/C][C]-1.017[/C][/ROW]
[ROW][C]26[/C][C] 4[/C][C] 3.607[/C][C] 0.3932[/C][/ROW]
[ROW][C]27[/C][C] 4[/C][C] 3.982[/C][C] 0.0175[/C][/ROW]
[ROW][C]28[/C][C] 4[/C][C] 4.234[/C][C]-0.2342[/C][/ROW]
[ROW][C]29[/C][C] 4[/C][C] 4.426[/C][C]-0.4259[/C][/ROW]
[ROW][C]30[/C][C] 3[/C][C] 3.246[/C][C]-0.2463[/C][/ROW]
[ROW][C]31[/C][C] 4[/C][C] 4.317[/C][C]-0.3165[/C][/ROW]
[ROW][C]32[/C][C] 5[/C][C] 4.223[/C][C] 0.7774[/C][/ROW]
[ROW][C]33[/C][C] 4[/C][C] 4.151[/C][C]-0.1506[/C][/ROW]
[ROW][C]34[/C][C] 4[/C][C] 3.044[/C][C] 0.9556[/C][/ROW]
[ROW][C]35[/C][C] 5[/C][C] 4.393[/C][C] 0.6065[/C][/ROW]
[ROW][C]36[/C][C] 4[/C][C] 4.069[/C][C]-0.06857[/C][/ROW]
[ROW][C]37[/C][C] 3[/C][C] 3.272[/C][C]-0.2718[/C][/ROW]
[ROW][C]38[/C][C] 2[/C][C] 3.843[/C][C]-1.843[/C][/ROW]
[ROW][C]39[/C][C] 5[/C][C] 4.513[/C][C] 0.4873[/C][/ROW]
[ROW][C]40[/C][C] 4[/C][C] 3.919[/C][C] 0.0813[/C][/ROW]
[ROW][C]41[/C][C] 5[/C][C] 4.49[/C][C] 0.5103[/C][/ROW]
[ROW][C]42[/C][C] 4[/C][C] 3.287[/C][C] 0.7132[/C][/ROW]
[ROW][C]43[/C][C] 4[/C][C] 4.955[/C][C]-0.955[/C][/ROW]
[ROW][C]44[/C][C] 4[/C][C] 3.986[/C][C] 0.01404[/C][/ROW]
[ROW][C]45[/C][C] 3[/C][C] 4.375[/C][C]-1.375[/C][/ROW]
[ROW][C]46[/C][C] 4[/C][C] 4.422[/C][C]-0.4225[/C][/ROW]
[ROW][C]47[/C][C] 4[/C][C] 3.992[/C][C] 0.00816[/C][/ROW]
[ROW][C]48[/C][C] 3[/C][C] 3.466[/C][C]-0.4658[/C][/ROW]
[ROW][C]49[/C][C] 3[/C][C] 2.818[/C][C] 0.182[/C][/ROW]
[ROW][C]50[/C][C] 5[/C][C] 4.346[/C][C] 0.6543[/C][/ROW]
[ROW][C]51[/C][C] 5[/C][C] 4.722[/C][C] 0.2776[/C][/ROW]
[ROW][C]52[/C][C] 5[/C][C] 4.519[/C][C] 0.4808[/C][/ROW]
[ROW][C]53[/C][C] 2[/C][C] 2.965[/C][C]-0.9651[/C][/ROW]
[ROW][C]54[/C][C] 3[/C][C] 3.9[/C][C]-0.8999[/C][/ROW]
[ROW][C]55[/C][C] 2[/C][C] 3.999[/C][C]-1.999[/C][/ROW]
[ROW][C]56[/C][C] 4[/C][C] 3.833[/C][C] 0.1674[/C][/ROW]
[ROW][C]57[/C][C] 5[/C][C] 4.33[/C][C] 0.6698[/C][/ROW]
[ROW][C]58[/C][C] 4[/C][C] 3.588[/C][C] 0.4121[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C] 4.021[/C][C]-0.0208[/C][/ROW]
[ROW][C]60[/C][C] 5[/C][C] 4.324[/C][C] 0.6762[/C][/ROW]
[ROW][C]61[/C][C] 5[/C][C] 3.871[/C][C] 1.129[/C][/ROW]
[ROW][C]62[/C][C] 4[/C][C] 4.079[/C][C]-0.07894[/C][/ROW]
[ROW][C]63[/C][C] 5[/C][C] 4.001[/C][C] 0.9987[/C][/ROW]
[ROW][C]64[/C][C] 4[/C][C] 3.833[/C][C] 0.1674[/C][/ROW]
[ROW][C]65[/C][C] 4[/C][C] 3.541[/C][C] 0.459[/C][/ROW]
[ROW][C]66[/C][C] 5[/C][C] 4.307[/C][C] 0.6926[/C][/ROW]
[ROW][C]67[/C][C] 3[/C][C] 3.941[/C][C]-0.9406[/C][/ROW]
[ROW][C]68[/C][C] 2[/C][C] 3.843[/C][C]-1.843[/C][/ROW]
[ROW][C]69[/C][C] 5[/C][C] 3.871[/C][C] 1.129[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 3.884[/C][C] 0.1161[/C][/ROW]
[ROW][C]71[/C][C] 4[/C][C] 3.778[/C][C] 0.2217[/C][/ROW]
[ROW][C]72[/C][C] 4[/C][C] 3.53[/C][C] 0.4704[/C][/ROW]
[ROW][C]73[/C][C] 3[/C][C] 3.704[/C][C]-0.7044[/C][/ROW]
[ROW][C]74[/C][C] 5[/C][C] 4.267[/C][C] 0.7328[/C][/ROW]
[ROW][C]75[/C][C] 4[/C][C] 3.925[/C][C] 0.07543[/C][/ROW]
[ROW][C]76[/C][C] 5[/C][C] 4.017[/C][C] 0.9832[/C][/ROW]
[ROW][C]77[/C][C] 3[/C][C] 3.842[/C][C]-0.842[/C][/ROW]
[ROW][C]78[/C][C] 2[/C][C] 3.314[/C][C]-1.314[/C][/ROW]
[ROW][C]79[/C][C] 5[/C][C] 4.658[/C][C] 0.3416[/C][/ROW]
[ROW][C]80[/C][C] 4[/C][C] 4.276[/C][C]-0.276[/C][/ROW]
[ROW][C]81[/C][C] 1[/C][C] 3.185[/C][C]-2.185[/C][/ROW]
[ROW][C]82[/C][C] 4[/C][C] 4.441[/C][C]-0.4413[/C][/ROW]
[ROW][C]83[/C][C] 5[/C][C] 4.126[/C][C] 0.8736[/C][/ROW]
[ROW][C]84[/C][C] 4[/C][C] 4.213[/C][C]-0.213[/C][/ROW]
[ROW][C]85[/C][C] 5[/C][C] 4.731[/C][C] 0.2692[/C][/ROW]
[ROW][C]86[/C][C] 4[/C][C] 4.028[/C][C]-0.02827[/C][/ROW]
[ROW][C]87[/C][C] 5[/C][C] 4.302[/C][C] 0.6978[/C][/ROW]
[ROW][C]88[/C][C] 4[/C][C] 3.648[/C][C] 0.3525[/C][/ROW]
[ROW][C]89[/C][C] 5[/C][C] 4.095[/C][C] 0.9053[/C][/ROW]
[ROW][C]90[/C][C] 5[/C][C] 3.074[/C][C] 1.926[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 3.935[/C][C] 0.06527[/C][/ROW]
[ROW][C]92[/C][C] 4[/C][C] 4.92[/C][C]-0.9201[/C][/ROW]
[ROW][C]93[/C][C] 4[/C][C] 4.298[/C][C]-0.298[/C][/ROW]
[ROW][C]94[/C][C] 4[/C][C] 4.636[/C][C]-0.6363[/C][/ROW]
[ROW][C]95[/C][C] 4[/C][C] 3.75[/C][C] 0.25[/C][/ROW]
[ROW][C]96[/C][C] 4[/C][C] 3.993[/C][C] 0.007424[/C][/ROW]
[ROW][C]97[/C][C] 4[/C][C] 4.147[/C][C]-0.1469[/C][/ROW]
[ROW][C]98[/C][C] 5[/C][C] 4.337[/C][C] 0.6625[/C][/ROW]
[ROW][C]99[/C][C] 5[/C][C] 4.056[/C][C] 0.9444[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 4.089[/C][C]-0.0888[/C][/ROW]
[ROW][C]101[/C][C] 4[/C][C] 4.2[/C][C]-0.2001[/C][/ROW]
[ROW][C]102[/C][C] 4[/C][C] 4.107[/C][C]-0.1069[/C][/ROW]
[ROW][C]103[/C][C] 2[/C][C] 3.97[/C][C]-1.97[/C][/ROW]
[ROW][C]104[/C][C] 4[/C][C] 3.769[/C][C] 0.2312[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 4.037[/C][C]-0.03725[/C][/ROW]
[ROW][C]106[/C][C] 4[/C][C] 3.757[/C][C] 0.2434[/C][/ROW]
[ROW][C]107[/C][C] 4[/C][C] 4.234[/C][C]-0.2342[/C][/ROW]
[ROW][C]108[/C][C] 4[/C][C] 3.906[/C][C] 0.09423[/C][/ROW]
[ROW][C]109[/C][C] 4[/C][C] 3.83[/C][C] 0.1702[/C][/ROW]
[ROW][C]110[/C][C] 4[/C][C] 3.916[/C][C] 0.08415[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 3.31[/C][C] 0.6899[/C][/ROW]
[ROW][C]112[/C][C] 4[/C][C] 3.913[/C][C] 0.08718[/C][/ROW]
[ROW][C]113[/C][C] 3[/C][C] 2.968[/C][C] 0.0318[/C][/ROW]
[ROW][C]114[/C][C] 5[/C][C] 4.287[/C][C] 0.7131[/C][/ROW]
[ROW][C]115[/C][C] 4[/C][C] 3.738[/C][C] 0.2618[/C][/ROW]
[ROW][C]116[/C][C] 5[/C][C] 4.253[/C][C] 0.7467[/C][/ROW]
[ROW][C]117[/C][C] 4[/C][C] 4.732[/C][C]-0.7323[/C][/ROW]
[ROW][C]118[/C][C] 5[/C][C] 3.782[/C][C] 1.218[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 3.906[/C][C]-0.9058[/C][/ROW]
[ROW][C]120[/C][C] 4[/C][C] 3.982[/C][C] 0.0175[/C][/ROW]
[ROW][C]121[/C][C] 3[/C][C] 4.001[/C][C]-1.001[/C][/ROW]
[ROW][C]122[/C][C] 4[/C][C] 3.881[/C][C] 0.119[/C][/ROW]
[ROW][C]123[/C][C] 4[/C][C] 3.836[/C][C] 0.1639[/C][/ROW]
[ROW][C]124[/C][C] 4[/C][C] 4.127[/C][C]-0.1269[/C][/ROW]
[ROW][C]125[/C][C] 4[/C][C] 4.206[/C][C]-0.2055[/C][/ROW]
[ROW][C]126[/C][C] 5[/C][C] 3.858[/C][C] 1.142[/C][/ROW]
[ROW][C]127[/C][C] 4[/C][C] 4.19[/C][C]-0.19[/C][/ROW]
[ROW][C]128[/C][C] 4[/C][C] 3.846[/C][C] 0.1544[/C][/ROW]
[ROW][C]129[/C][C] 4[/C][C] 3.836[/C][C] 0.1639[/C][/ROW]
[ROW][C]130[/C][C] 2[/C][C] 2.997[/C][C]-0.9972[/C][/ROW]
[ROW][C]131[/C][C] 4[/C][C] 3.83[/C][C] 0.1702[/C][/ROW]
[ROW][C]132[/C][C] 4[/C][C] 3.707[/C][C] 0.2926[/C][/ROW]
[ROW][C]133[/C][C] 3[/C][C] 3.855[/C][C]-0.8547[/C][/ROW]
[ROW][C]134[/C][C] 2[/C][C] 2.87[/C][C]-0.8696[/C][/ROW]
[ROW][C]135[/C][C] 4[/C][C] 3.795[/C][C] 0.2051[/C][/ROW]
[ROW][C]136[/C][C] 4[/C][C] 4.261[/C][C]-0.261[/C][/ROW]
[ROW][C]137[/C][C] 3[/C][C] 3.003[/C][C]-0.003034[/C][/ROW]
[ROW][C]138[/C][C] 4[/C][C] 4.033[/C][C]-0.03336[/C][/ROW]
[ROW][C]139[/C][C] 5[/C][C] 4.915[/C][C] 0.0852[/C][/ROW]
[ROW][C]140[/C][C] 4[/C][C] 4.997[/C][C]-0.9968[/C][/ROW]
[ROW][C]141[/C][C] 3[/C][C] 3.425[/C][C]-0.4246[/C][/ROW]
[ROW][C]142[/C][C] 3[/C][C] 3.858[/C][C]-0.858[/C][/ROW]
[ROW][C]143[/C][C] 4[/C][C] 3.728[/C][C] 0.2723[/C][/ROW]
[ROW][C]144[/C][C] 3[/C][C] 3.702[/C][C]-0.7018[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 4.872[/C][C]-0.8723[/C][/ROW]
[ROW][C]146[/C][C] 2[/C][C] 3.878[/C][C]-1.878[/C][/ROW]
[ROW][C]147[/C][C] 5[/C][C] 4.478[/C][C] 0.5223[/C][/ROW]
[ROW][C]148[/C][C] 4[/C][C] 3.258[/C][C] 0.7421[/C][/ROW]
[ROW][C]149[/C][C] 4[/C][C] 3.878[/C][C] 0.1225[/C][/ROW]
[ROW][C]150[/C][C] 3[/C][C] 3.236[/C][C]-0.2359[/C][/ROW]
[ROW][C]151[/C][C] 4[/C][C] 3.83[/C][C] 0.1702[/C][/ROW]
[ROW][C]152[/C][C] 5[/C][C] 3.753[/C][C] 1.247[/C][/ROW]
[ROW][C]153[/C][C] 4[/C][C] 4.218[/C][C]-0.2184[/C][/ROW]
[ROW][C]154[/C][C] 2[/C][C] 3.38[/C][C]-1.38[/C][/ROW]
[ROW][C]155[/C][C] 4[/C][C] 3.836[/C][C] 0.1639[/C][/ROW]
[ROW][C]156[/C][C] 5[/C][C] 4.393[/C][C] 0.6065[/C][/ROW]
[ROW][C]157[/C][C] 4[/C][C] 3.561[/C][C] 0.4387[/C][/ROW]
[ROW][C]158[/C][C] 4[/C][C] 3.884[/C][C] 0.1161[/C][/ROW]
[ROW][C]159[/C][C] 5[/C][C] 4.317[/C][C] 0.6825[/C][/ROW]
[ROW][C]160[/C][C] 4[/C][C] 4.119[/C][C]-0.1194[/C][/ROW]
[ROW][C]161[/C][C] 5[/C][C] 5.137[/C][C]-0.1365[/C][/ROW]
[ROW][C]162[/C][C] 3[/C][C] 3.824[/C][C]-0.8239[/C][/ROW]
[ROW][C]163[/C][C] 4[/C][C] 3.884[/C][C] 0.1161[/C][/ROW]
[ROW][C]164[/C][C] 4[/C][C] 3.843[/C][C] 0.1573[/C][/ROW]
[ROW][C]165[/C][C] 3[/C][C] 3.641[/C][C]-0.641[/C][/ROW]
[ROW][C]166[/C][C] 4[/C][C] 3.808[/C][C] 0.1921[/C][/ROW]
[ROW][C]167[/C][C] 4[/C][C] 3.629[/C][C] 0.3714[/C][/ROW]
[ROW][C]168[/C][C] 3[/C][C] 3.544[/C][C]-0.5444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300636&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&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	3.367	0.6331
2	5	3.979	1.021
3	4	4.605	-0.605
4	4	3.871	0.1291
5	4	3.906	0.0935
6	5	4.116	0.8842
7	5	4.197	0.803
8	4	4.228	-0.2283
9	4	4.165	-0.1648
10	5	4.393	0.6065
11	5	4.671	0.3287
12	4	3.846	0.1544
13	4	3.744	0.2563
14	4	3.379	0.6208
15	4	3.725	0.2747
16	5	4.713	0.2873
17	4	4.172	-0.1718
18	3	3.856	-0.8556
19	4	4.085	-0.08502
20	5	3.791	1.209
21	4	3.941	0.0594
22	5	4.203	0.7969
23	4	3.954	0.04647
24	4	4.465	-0.465
25	3	4.017	-1.017
26	4	3.607	0.3932
27	4	3.982	0.0175
28	4	4.234	-0.2342
29	4	4.426	-0.4259
30	3	3.246	-0.2463
31	4	4.317	-0.3165
32	5	4.223	0.7774
33	4	4.151	-0.1506
34	4	3.044	0.9556
35	5	4.393	0.6065
36	4	4.069	-0.06857
37	3	3.272	-0.2718
38	2	3.843	-1.843
39	5	4.513	0.4873
40	4	3.919	0.0813
41	5	4.49	0.5103
42	4	3.287	0.7132
43	4	4.955	-0.955
44	4	3.986	0.01404
45	3	4.375	-1.375
46	4	4.422	-0.4225
47	4	3.992	0.00816
48	3	3.466	-0.4658
49	3	2.818	0.182
50	5	4.346	0.6543
51	5	4.722	0.2776
52	5	4.519	0.4808
53	2	2.965	-0.9651
54	3	3.9	-0.8999
55	2	3.999	-1.999
56	4	3.833	0.1674
57	5	4.33	0.6698
58	4	3.588	0.4121
59	4	4.021	-0.0208
60	5	4.324	0.6762
61	5	3.871	1.129
62	4	4.079	-0.07894
63	5	4.001	0.9987
64	4	3.833	0.1674
65	4	3.541	0.459
66	5	4.307	0.6926
67	3	3.941	-0.9406
68	2	3.843	-1.843
69	5	3.871	1.129
70	4	3.884	0.1161
71	4	3.778	0.2217
72	4	3.53	0.4704
73	3	3.704	-0.7044
74	5	4.267	0.7328
75	4	3.925	0.07543
76	5	4.017	0.9832
77	3	3.842	-0.842
78	2	3.314	-1.314
79	5	4.658	0.3416
80	4	4.276	-0.276
81	1	3.185	-2.185
82	4	4.441	-0.4413
83	5	4.126	0.8736
84	4	4.213	-0.213
85	5	4.731	0.2692
86	4	4.028	-0.02827
87	5	4.302	0.6978
88	4	3.648	0.3525
89	5	4.095	0.9053
90	5	3.074	1.926
91	4	3.935	0.06527
92	4	4.92	-0.9201
93	4	4.298	-0.298
94	4	4.636	-0.6363
95	4	3.75	0.25
96	4	3.993	0.007424
97	4	4.147	-0.1469
98	5	4.337	0.6625
99	5	4.056	0.9444
100	4	4.089	-0.0888
101	4	4.2	-0.2001
102	4	4.107	-0.1069
103	2	3.97	-1.97
104	4	3.769	0.2312
105	4	4.037	-0.03725
106	4	3.757	0.2434
107	4	4.234	-0.2342
108	4	3.906	0.09423
109	4	3.83	0.1702
110	4	3.916	0.08415
111	4	3.31	0.6899
112	4	3.913	0.08718
113	3	2.968	0.0318
114	5	4.287	0.7131
115	4	3.738	0.2618
116	5	4.253	0.7467
117	4	4.732	-0.7323
118	5	3.782	1.218
119	3	3.906	-0.9058
120	4	3.982	0.0175
121	3	4.001	-1.001
122	4	3.881	0.119
123	4	3.836	0.1639
124	4	4.127	-0.1269
125	4	4.206	-0.2055
126	5	3.858	1.142
127	4	4.19	-0.19
128	4	3.846	0.1544
129	4	3.836	0.1639
130	2	2.997	-0.9972
131	4	3.83	0.1702
132	4	3.707	0.2926
133	3	3.855	-0.8547
134	2	2.87	-0.8696
135	4	3.795	0.2051
136	4	4.261	-0.261
137	3	3.003	-0.003034
138	4	4.033	-0.03336
139	5	4.915	0.0852
140	4	4.997	-0.9968
141	3	3.425	-0.4246
142	3	3.858	-0.858
143	4	3.728	0.2723
144	3	3.702	-0.7018
145	4	4.872	-0.8723
146	2	3.878	-1.878
147	5	4.478	0.5223
148	4	3.258	0.7421
149	4	3.878	0.1225
150	3	3.236	-0.2359
151	4	3.83	0.1702
152	5	3.753	1.247
153	4	4.218	-0.2184
154	2	3.38	-1.38
155	4	3.836	0.1639
156	5	4.393	0.6065
157	4	3.561	0.4387
158	4	3.884	0.1161
159	5	4.317	0.6825
160	4	4.119	-0.1194
161	5	5.137	-0.1365
162	3	3.824	-0.8239
163	4	3.884	0.1161
164	4	3.843	0.1573
165	3	3.641	-0.641
166	4	3.808	0.1921
167	4	3.629	0.3714
168	3	3.544	-0.5444

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.08158	0.1632	0.9184
12	0.03465	0.06931	0.9653
13	0.01101	0.02202	0.989
14	0.003299	0.006598	0.9967
15	0.0009706	0.001941	0.999
16	0.000766	0.001532	0.9992
17	0.002384	0.004767	0.9976
18	0.03698	0.07396	0.963
19	0.01997	0.03993	0.98
20	0.02954	0.05908	0.9705
21	0.0298	0.0596	0.9702
22	0.03553	0.07106	0.9645
23	0.02621	0.05242	0.9738
24	0.02016	0.04033	0.9798
25	0.1669	0.3339	0.8331
26	0.1378	0.2755	0.8622
27	0.1057	0.2115	0.8943
28	0.08094	0.1619	0.9191
29	0.06401	0.128	0.936
30	0.05306	0.1061	0.9469
31	0.05899	0.118	0.941
32	0.05075	0.1015	0.9493
33	0.0359	0.07179	0.9641
34	0.03515	0.07029	0.9649
35	0.03328	0.06657	0.9667
36	0.02583	0.05165	0.9742
37	0.02071	0.04141	0.9793
38	0.1813	0.3625	0.8187
39	0.1652	0.3304	0.8348
40	0.1314	0.2629	0.8686
41	0.1123	0.2245	0.8877
42	0.09421	0.1884	0.9058
43	0.1378	0.2757	0.8622
44	0.1102	0.2204	0.8898
45	0.2097	0.4194	0.7903
46	0.1826	0.3652	0.8174
47	0.1489	0.2979	0.8511
48	0.1467	0.2934	0.8533
49	0.1248	0.2497	0.8752
50	0.1292	0.2583	0.8708
51	0.1192	0.2384	0.8808
52	0.1094	0.2189	0.8906
53	0.1474	0.2948	0.8526
54	0.1662	0.3323	0.8338
55	0.4171	0.8343	0.5829
56	0.3748	0.7497	0.6252
57	0.3688	0.7375	0.6312
58	0.3462	0.6923	0.6538
59	0.3023	0.6046	0.6977
60	0.3159	0.6317	0.6841
61	0.3814	0.7629	0.6186
62	0.339	0.6781	0.661
63	0.3652	0.7304	0.6348
64	0.3229	0.6459	0.6771
65	0.2983	0.5966	0.7017
66	0.2926	0.5851	0.7074
67	0.3498	0.6997	0.6502
68	0.6091	0.7818	0.3909
69	0.6717	0.6566	0.3283
70	0.63	0.7399	0.37
71	0.5891	0.8218	0.4109
72	0.5568	0.8863	0.4432
73	0.5682	0.8637	0.4318
74	0.5648	0.8703	0.4352
75	0.5212	0.9577	0.4788
76	0.5729	0.8543	0.4271
77	0.5916	0.8168	0.4084
78	0.6806	0.6387	0.3194
79	0.657	0.686	0.343
80	0.6162	0.7675	0.3838
81	0.8944	0.2112	0.1056
82	0.8803	0.2393	0.1197
83	0.8923	0.2155	0.1077
84	0.8703	0.2595	0.1297
85	0.8491	0.3018	0.1509
86	0.8222	0.3556	0.1778
87	0.8278	0.3445	0.1722
88	0.8027	0.3945	0.1973
89	0.8281	0.3438	0.1719
90	0.9485	0.103	0.05151
91	0.9358	0.1284	0.06422
92	0.9453	0.1094	0.05469
93	0.9337	0.1325	0.06625
94	0.9352	0.1296	0.06481
95	0.9207	0.1585	0.07927
96	0.9029	0.1942	0.09712
97	0.8827	0.2347	0.1173
98	0.8828	0.2343	0.1172
99	0.9076	0.1848	0.09241
100	0.8904	0.2192	0.1096
101	0.869	0.2619	0.131
102	0.8442	0.3117	0.1558
103	0.9652	0.06962	0.03481
104	0.9553	0.08939	0.04469
105	0.9446	0.1108	0.0554
106	0.9309	0.1381	0.06905
107	0.921	0.158	0.07899
108	0.9012	0.1975	0.09877
109	0.8792	0.2416	0.1208
110	0.8536	0.2929	0.1464
111	0.8553	0.2895	0.1447
112	0.8278	0.3444	0.1722
113	0.7965	0.407	0.2035
114	0.8165	0.3671	0.1835
115	0.7956	0.4088	0.2044
116	0.8429	0.3142	0.1571
117	0.8233	0.3534	0.1767
118	0.8862	0.2275	0.1138
119	0.9124	0.1752	0.08761
120	0.8923	0.2155	0.1077
121	0.911	0.1779	0.08897
122	0.8891	0.2218	0.1109
123	0.8625	0.2751	0.1375
124	0.8299	0.3403	0.1701
125	0.7943	0.4113	0.2057
126	0.8363	0.3274	0.1637
127	0.8124	0.3751	0.1876
128	0.7758	0.4484	0.2242
129	0.7339	0.5321	0.2661
130	0.7435	0.513	0.2565
131	0.6984	0.6032	0.3016
132	0.6497	0.7007	0.3503
133	0.6197	0.7607	0.3803
134	0.646	0.708	0.354
135	0.5972	0.8057	0.4028
136	0.5369	0.9262	0.4631
137	0.4727	0.9455	0.5273
138	0.409	0.8179	0.591
139	0.3936	0.7871	0.6064
140	0.4646	0.9291	0.5354
141	0.4218	0.8436	0.5782
142	0.4504	0.9008	0.5496
143	0.3917	0.7834	0.6083
144	0.334	0.6679	0.666
145	0.4639	0.9278	0.5361
146	0.8542	0.2915	0.1458
147	0.8061	0.3878	0.1939
148	0.7713	0.4575	0.2287
149	0.6951	0.6098	0.3049
150	0.6192	0.7617	0.3808
151	0.5274	0.9453	0.4726
152	0.7829	0.4342	0.2171
153	0.7104	0.5792	0.2896
154	0.9742	0.05158	0.02579
155	0.9635	0.073	0.0365
156	0.9132	0.1736	0.08679
157	0.8104	0.3793	0.1896

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.08158 &  0.1632 &  0.9184 \tabularnewline
12 &  0.03465 &  0.06931 &  0.9653 \tabularnewline
13 &  0.01101 &  0.02202 &  0.989 \tabularnewline
14 &  0.003299 &  0.006598 &  0.9967 \tabularnewline
15 &  0.0009706 &  0.001941 &  0.999 \tabularnewline
16 &  0.000766 &  0.001532 &  0.9992 \tabularnewline
17 &  0.002384 &  0.004767 &  0.9976 \tabularnewline
18 &  0.03698 &  0.07396 &  0.963 \tabularnewline
19 &  0.01997 &  0.03993 &  0.98 \tabularnewline
20 &  0.02954 &  0.05908 &  0.9705 \tabularnewline
21 &  0.0298 &  0.0596 &  0.9702 \tabularnewline
22 &  0.03553 &  0.07106 &  0.9645 \tabularnewline
23 &  0.02621 &  0.05242 &  0.9738 \tabularnewline
24 &  0.02016 &  0.04033 &  0.9798 \tabularnewline
25 &  0.1669 &  0.3339 &  0.8331 \tabularnewline
26 &  0.1378 &  0.2755 &  0.8622 \tabularnewline
27 &  0.1057 &  0.2115 &  0.8943 \tabularnewline
28 &  0.08094 &  0.1619 &  0.9191 \tabularnewline
29 &  0.06401 &  0.128 &  0.936 \tabularnewline
30 &  0.05306 &  0.1061 &  0.9469 \tabularnewline
31 &  0.05899 &  0.118 &  0.941 \tabularnewline
32 &  0.05075 &  0.1015 &  0.9493 \tabularnewline
33 &  0.0359 &  0.07179 &  0.9641 \tabularnewline
34 &  0.03515 &  0.07029 &  0.9649 \tabularnewline
35 &  0.03328 &  0.06657 &  0.9667 \tabularnewline
36 &  0.02583 &  0.05165 &  0.9742 \tabularnewline
37 &  0.02071 &  0.04141 &  0.9793 \tabularnewline
38 &  0.1813 &  0.3625 &  0.8187 \tabularnewline
39 &  0.1652 &  0.3304 &  0.8348 \tabularnewline
40 &  0.1314 &  0.2629 &  0.8686 \tabularnewline
41 &  0.1123 &  0.2245 &  0.8877 \tabularnewline
42 &  0.09421 &  0.1884 &  0.9058 \tabularnewline
43 &  0.1378 &  0.2757 &  0.8622 \tabularnewline
44 &  0.1102 &  0.2204 &  0.8898 \tabularnewline
45 &  0.2097 &  0.4194 &  0.7903 \tabularnewline
46 &  0.1826 &  0.3652 &  0.8174 \tabularnewline
47 &  0.1489 &  0.2979 &  0.8511 \tabularnewline
48 &  0.1467 &  0.2934 &  0.8533 \tabularnewline
49 &  0.1248 &  0.2497 &  0.8752 \tabularnewline
50 &  0.1292 &  0.2583 &  0.8708 \tabularnewline
51 &  0.1192 &  0.2384 &  0.8808 \tabularnewline
52 &  0.1094 &  0.2189 &  0.8906 \tabularnewline
53 &  0.1474 &  0.2948 &  0.8526 \tabularnewline
54 &  0.1662 &  0.3323 &  0.8338 \tabularnewline
55 &  0.4171 &  0.8343 &  0.5829 \tabularnewline
56 &  0.3748 &  0.7497 &  0.6252 \tabularnewline
57 &  0.3688 &  0.7375 &  0.6312 \tabularnewline
58 &  0.3462 &  0.6923 &  0.6538 \tabularnewline
59 &  0.3023 &  0.6046 &  0.6977 \tabularnewline
60 &  0.3159 &  0.6317 &  0.6841 \tabularnewline
61 &  0.3814 &  0.7629 &  0.6186 \tabularnewline
62 &  0.339 &  0.6781 &  0.661 \tabularnewline
63 &  0.3652 &  0.7304 &  0.6348 \tabularnewline
64 &  0.3229 &  0.6459 &  0.6771 \tabularnewline
65 &  0.2983 &  0.5966 &  0.7017 \tabularnewline
66 &  0.2926 &  0.5851 &  0.7074 \tabularnewline
67 &  0.3498 &  0.6997 &  0.6502 \tabularnewline
68 &  0.6091 &  0.7818 &  0.3909 \tabularnewline
69 &  0.6717 &  0.6566 &  0.3283 \tabularnewline
70 &  0.63 &  0.7399 &  0.37 \tabularnewline
71 &  0.5891 &  0.8218 &  0.4109 \tabularnewline
72 &  0.5568 &  0.8863 &  0.4432 \tabularnewline
73 &  0.5682 &  0.8637 &  0.4318 \tabularnewline
74 &  0.5648 &  0.8703 &  0.4352 \tabularnewline
75 &  0.5212 &  0.9577 &  0.4788 \tabularnewline
76 &  0.5729 &  0.8543 &  0.4271 \tabularnewline
77 &  0.5916 &  0.8168 &  0.4084 \tabularnewline
78 &  0.6806 &  0.6387 &  0.3194 \tabularnewline
79 &  0.657 &  0.686 &  0.343 \tabularnewline
80 &  0.6162 &  0.7675 &  0.3838 \tabularnewline
81 &  0.8944 &  0.2112 &  0.1056 \tabularnewline
82 &  0.8803 &  0.2393 &  0.1197 \tabularnewline
83 &  0.8923 &  0.2155 &  0.1077 \tabularnewline
84 &  0.8703 &  0.2595 &  0.1297 \tabularnewline
85 &  0.8491 &  0.3018 &  0.1509 \tabularnewline
86 &  0.8222 &  0.3556 &  0.1778 \tabularnewline
87 &  0.8278 &  0.3445 &  0.1722 \tabularnewline
88 &  0.8027 &  0.3945 &  0.1973 \tabularnewline
89 &  0.8281 &  0.3438 &  0.1719 \tabularnewline
90 &  0.9485 &  0.103 &  0.05151 \tabularnewline
91 &  0.9358 &  0.1284 &  0.06422 \tabularnewline
92 &  0.9453 &  0.1094 &  0.05469 \tabularnewline
93 &  0.9337 &  0.1325 &  0.06625 \tabularnewline
94 &  0.9352 &  0.1296 &  0.06481 \tabularnewline
95 &  0.9207 &  0.1585 &  0.07927 \tabularnewline
96 &  0.9029 &  0.1942 &  0.09712 \tabularnewline
97 &  0.8827 &  0.2347 &  0.1173 \tabularnewline
98 &  0.8828 &  0.2343 &  0.1172 \tabularnewline
99 &  0.9076 &  0.1848 &  0.09241 \tabularnewline
100 &  0.8904 &  0.2192 &  0.1096 \tabularnewline
101 &  0.869 &  0.2619 &  0.131 \tabularnewline
102 &  0.8442 &  0.3117 &  0.1558 \tabularnewline
103 &  0.9652 &  0.06962 &  0.03481 \tabularnewline
104 &  0.9553 &  0.08939 &  0.04469 \tabularnewline
105 &  0.9446 &  0.1108 &  0.0554 \tabularnewline
106 &  0.9309 &  0.1381 &  0.06905 \tabularnewline
107 &  0.921 &  0.158 &  0.07899 \tabularnewline
108 &  0.9012 &  0.1975 &  0.09877 \tabularnewline
109 &  0.8792 &  0.2416 &  0.1208 \tabularnewline
110 &  0.8536 &  0.2929 &  0.1464 \tabularnewline
111 &  0.8553 &  0.2895 &  0.1447 \tabularnewline
112 &  0.8278 &  0.3444 &  0.1722 \tabularnewline
113 &  0.7965 &  0.407 &  0.2035 \tabularnewline
114 &  0.8165 &  0.3671 &  0.1835 \tabularnewline
115 &  0.7956 &  0.4088 &  0.2044 \tabularnewline
116 &  0.8429 &  0.3142 &  0.1571 \tabularnewline
117 &  0.8233 &  0.3534 &  0.1767 \tabularnewline
118 &  0.8862 &  0.2275 &  0.1138 \tabularnewline
119 &  0.9124 &  0.1752 &  0.08761 \tabularnewline
120 &  0.8923 &  0.2155 &  0.1077 \tabularnewline
121 &  0.911 &  0.1779 &  0.08897 \tabularnewline
122 &  0.8891 &  0.2218 &  0.1109 \tabularnewline
123 &  0.8625 &  0.2751 &  0.1375 \tabularnewline
124 &  0.8299 &  0.3403 &  0.1701 \tabularnewline
125 &  0.7943 &  0.4113 &  0.2057 \tabularnewline
126 &  0.8363 &  0.3274 &  0.1637 \tabularnewline
127 &  0.8124 &  0.3751 &  0.1876 \tabularnewline
128 &  0.7758 &  0.4484 &  0.2242 \tabularnewline
129 &  0.7339 &  0.5321 &  0.2661 \tabularnewline
130 &  0.7435 &  0.513 &  0.2565 \tabularnewline
131 &  0.6984 &  0.6032 &  0.3016 \tabularnewline
132 &  0.6497 &  0.7007 &  0.3503 \tabularnewline
133 &  0.6197 &  0.7607 &  0.3803 \tabularnewline
134 &  0.646 &  0.708 &  0.354 \tabularnewline
135 &  0.5972 &  0.8057 &  0.4028 \tabularnewline
136 &  0.5369 &  0.9262 &  0.4631 \tabularnewline
137 &  0.4727 &  0.9455 &  0.5273 \tabularnewline
138 &  0.409 &  0.8179 &  0.591 \tabularnewline
139 &  0.3936 &  0.7871 &  0.6064 \tabularnewline
140 &  0.4646 &  0.9291 &  0.5354 \tabularnewline
141 &  0.4218 &  0.8436 &  0.5782 \tabularnewline
142 &  0.4504 &  0.9008 &  0.5496 \tabularnewline
143 &  0.3917 &  0.7834 &  0.6083 \tabularnewline
144 &  0.334 &  0.6679 &  0.666 \tabularnewline
145 &  0.4639 &  0.9278 &  0.5361 \tabularnewline
146 &  0.8542 &  0.2915 &  0.1458 \tabularnewline
147 &  0.8061 &  0.3878 &  0.1939 \tabularnewline
148 &  0.7713 &  0.4575 &  0.2287 \tabularnewline
149 &  0.6951 &  0.6098 &  0.3049 \tabularnewline
150 &  0.6192 &  0.7617 &  0.3808 \tabularnewline
151 &  0.5274 &  0.9453 &  0.4726 \tabularnewline
152 &  0.7829 &  0.4342 &  0.2171 \tabularnewline
153 &  0.7104 &  0.5792 &  0.2896 \tabularnewline
154 &  0.9742 &  0.05158 &  0.02579 \tabularnewline
155 &  0.9635 &  0.073 &  0.0365 \tabularnewline
156 &  0.9132 &  0.1736 &  0.08679 \tabularnewline
157 &  0.8104 &  0.3793 &  0.1896 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300636&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C] 0.08158[/C][C] 0.1632[/C][C] 0.9184[/C][/ROW]
[ROW][C]12[/C][C] 0.03465[/C][C] 0.06931[/C][C] 0.9653[/C][/ROW]
[ROW][C]13[/C][C] 0.01101[/C][C] 0.02202[/C][C] 0.989[/C][/ROW]
[ROW][C]14[/C][C] 0.003299[/C][C] 0.006598[/C][C] 0.9967[/C][/ROW]
[ROW][C]15[/C][C] 0.0009706[/C][C] 0.001941[/C][C] 0.999[/C][/ROW]
[ROW][C]16[/C][C] 0.000766[/C][C] 0.001532[/C][C] 0.9992[/C][/ROW]
[ROW][C]17[/C][C] 0.002384[/C][C] 0.004767[/C][C] 0.9976[/C][/ROW]
[ROW][C]18[/C][C] 0.03698[/C][C] 0.07396[/C][C] 0.963[/C][/ROW]
[ROW][C]19[/C][C] 0.01997[/C][C] 0.03993[/C][C] 0.98[/C][/ROW]
[ROW][C]20[/C][C] 0.02954[/C][C] 0.05908[/C][C] 0.9705[/C][/ROW]
[ROW][C]21[/C][C] 0.0298[/C][C] 0.0596[/C][C] 0.9702[/C][/ROW]
[ROW][C]22[/C][C] 0.03553[/C][C] 0.07106[/C][C] 0.9645[/C][/ROW]
[ROW][C]23[/C][C] 0.02621[/C][C] 0.05242[/C][C] 0.9738[/C][/ROW]
[ROW][C]24[/C][C] 0.02016[/C][C] 0.04033[/C][C] 0.9798[/C][/ROW]
[ROW][C]25[/C][C] 0.1669[/C][C] 0.3339[/C][C] 0.8331[/C][/ROW]
[ROW][C]26[/C][C] 0.1378[/C][C] 0.2755[/C][C] 0.8622[/C][/ROW]
[ROW][C]27[/C][C] 0.1057[/C][C] 0.2115[/C][C] 0.8943[/C][/ROW]
[ROW][C]28[/C][C] 0.08094[/C][C] 0.1619[/C][C] 0.9191[/C][/ROW]
[ROW][C]29[/C][C] 0.06401[/C][C] 0.128[/C][C] 0.936[/C][/ROW]
[ROW][C]30[/C][C] 0.05306[/C][C] 0.1061[/C][C] 0.9469[/C][/ROW]
[ROW][C]31[/C][C] 0.05899[/C][C] 0.118[/C][C] 0.941[/C][/ROW]
[ROW][C]32[/C][C] 0.05075[/C][C] 0.1015[/C][C] 0.9493[/C][/ROW]
[ROW][C]33[/C][C] 0.0359[/C][C] 0.07179[/C][C] 0.9641[/C][/ROW]
[ROW][C]34[/C][C] 0.03515[/C][C] 0.07029[/C][C] 0.9649[/C][/ROW]
[ROW][C]35[/C][C] 0.03328[/C][C] 0.06657[/C][C] 0.9667[/C][/ROW]
[ROW][C]36[/C][C] 0.02583[/C][C] 0.05165[/C][C] 0.9742[/C][/ROW]
[ROW][C]37[/C][C] 0.02071[/C][C] 0.04141[/C][C] 0.9793[/C][/ROW]
[ROW][C]38[/C][C] 0.1813[/C][C] 0.3625[/C][C] 0.8187[/C][/ROW]
[ROW][C]39[/C][C] 0.1652[/C][C] 0.3304[/C][C] 0.8348[/C][/ROW]
[ROW][C]40[/C][C] 0.1314[/C][C] 0.2629[/C][C] 0.8686[/C][/ROW]
[ROW][C]41[/C][C] 0.1123[/C][C] 0.2245[/C][C] 0.8877[/C][/ROW]
[ROW][C]42[/C][C] 0.09421[/C][C] 0.1884[/C][C] 0.9058[/C][/ROW]
[ROW][C]43[/C][C] 0.1378[/C][C] 0.2757[/C][C] 0.8622[/C][/ROW]
[ROW][C]44[/C][C] 0.1102[/C][C] 0.2204[/C][C] 0.8898[/C][/ROW]
[ROW][C]45[/C][C] 0.2097[/C][C] 0.4194[/C][C] 0.7903[/C][/ROW]
[ROW][C]46[/C][C] 0.1826[/C][C] 0.3652[/C][C] 0.8174[/C][/ROW]
[ROW][C]47[/C][C] 0.1489[/C][C] 0.2979[/C][C] 0.8511[/C][/ROW]
[ROW][C]48[/C][C] 0.1467[/C][C] 0.2934[/C][C] 0.8533[/C][/ROW]
[ROW][C]49[/C][C] 0.1248[/C][C] 0.2497[/C][C] 0.8752[/C][/ROW]
[ROW][C]50[/C][C] 0.1292[/C][C] 0.2583[/C][C] 0.8708[/C][/ROW]
[ROW][C]51[/C][C] 0.1192[/C][C] 0.2384[/C][C] 0.8808[/C][/ROW]
[ROW][C]52[/C][C] 0.1094[/C][C] 0.2189[/C][C] 0.8906[/C][/ROW]
[ROW][C]53[/C][C] 0.1474[/C][C] 0.2948[/C][C] 0.8526[/C][/ROW]
[ROW][C]54[/C][C] 0.1662[/C][C] 0.3323[/C][C] 0.8338[/C][/ROW]
[ROW][C]55[/C][C] 0.4171[/C][C] 0.8343[/C][C] 0.5829[/C][/ROW]
[ROW][C]56[/C][C] 0.3748[/C][C] 0.7497[/C][C] 0.6252[/C][/ROW]
[ROW][C]57[/C][C] 0.3688[/C][C] 0.7375[/C][C] 0.6312[/C][/ROW]
[ROW][C]58[/C][C] 0.3462[/C][C] 0.6923[/C][C] 0.6538[/C][/ROW]
[ROW][C]59[/C][C] 0.3023[/C][C] 0.6046[/C][C] 0.6977[/C][/ROW]
[ROW][C]60[/C][C] 0.3159[/C][C] 0.6317[/C][C] 0.6841[/C][/ROW]
[ROW][C]61[/C][C] 0.3814[/C][C] 0.7629[/C][C] 0.6186[/C][/ROW]
[ROW][C]62[/C][C] 0.339[/C][C] 0.6781[/C][C] 0.661[/C][/ROW]
[ROW][C]63[/C][C] 0.3652[/C][C] 0.7304[/C][C] 0.6348[/C][/ROW]
[ROW][C]64[/C][C] 0.3229[/C][C] 0.6459[/C][C] 0.6771[/C][/ROW]
[ROW][C]65[/C][C] 0.2983[/C][C] 0.5966[/C][C] 0.7017[/C][/ROW]
[ROW][C]66[/C][C] 0.2926[/C][C] 0.5851[/C][C] 0.7074[/C][/ROW]
[ROW][C]67[/C][C] 0.3498[/C][C] 0.6997[/C][C] 0.6502[/C][/ROW]
[ROW][C]68[/C][C] 0.6091[/C][C] 0.7818[/C][C] 0.3909[/C][/ROW]
[ROW][C]69[/C][C] 0.6717[/C][C] 0.6566[/C][C] 0.3283[/C][/ROW]
[ROW][C]70[/C][C] 0.63[/C][C] 0.7399[/C][C] 0.37[/C][/ROW]
[ROW][C]71[/C][C] 0.5891[/C][C] 0.8218[/C][C] 0.4109[/C][/ROW]
[ROW][C]72[/C][C] 0.5568[/C][C] 0.8863[/C][C] 0.4432[/C][/ROW]
[ROW][C]73[/C][C] 0.5682[/C][C] 0.8637[/C][C] 0.4318[/C][/ROW]
[ROW][C]74[/C][C] 0.5648[/C][C] 0.8703[/C][C] 0.4352[/C][/ROW]
[ROW][C]75[/C][C] 0.5212[/C][C] 0.9577[/C][C] 0.4788[/C][/ROW]
[ROW][C]76[/C][C] 0.5729[/C][C] 0.8543[/C][C] 0.4271[/C][/ROW]
[ROW][C]77[/C][C] 0.5916[/C][C] 0.8168[/C][C] 0.4084[/C][/ROW]
[ROW][C]78[/C][C] 0.6806[/C][C] 0.6387[/C][C] 0.3194[/C][/ROW]
[ROW][C]79[/C][C] 0.657[/C][C] 0.686[/C][C] 0.343[/C][/ROW]
[ROW][C]80[/C][C] 0.6162[/C][C] 0.7675[/C][C] 0.3838[/C][/ROW]
[ROW][C]81[/C][C] 0.8944[/C][C] 0.2112[/C][C] 0.1056[/C][/ROW]
[ROW][C]82[/C][C] 0.8803[/C][C] 0.2393[/C][C] 0.1197[/C][/ROW]
[ROW][C]83[/C][C] 0.8923[/C][C] 0.2155[/C][C] 0.1077[/C][/ROW]
[ROW][C]84[/C][C] 0.8703[/C][C] 0.2595[/C][C] 0.1297[/C][/ROW]
[ROW][C]85[/C][C] 0.8491[/C][C] 0.3018[/C][C] 0.1509[/C][/ROW]
[ROW][C]86[/C][C] 0.8222[/C][C] 0.3556[/C][C] 0.1778[/C][/ROW]
[ROW][C]87[/C][C] 0.8278[/C][C] 0.3445[/C][C] 0.1722[/C][/ROW]
[ROW][C]88[/C][C] 0.8027[/C][C] 0.3945[/C][C] 0.1973[/C][/ROW]
[ROW][C]89[/C][C] 0.8281[/C][C] 0.3438[/C][C] 0.1719[/C][/ROW]
[ROW][C]90[/C][C] 0.9485[/C][C] 0.103[/C][C] 0.05151[/C][/ROW]
[ROW][C]91[/C][C] 0.9358[/C][C] 0.1284[/C][C] 0.06422[/C][/ROW]
[ROW][C]92[/C][C] 0.9453[/C][C] 0.1094[/C][C] 0.05469[/C][/ROW]
[ROW][C]93[/C][C] 0.9337[/C][C] 0.1325[/C][C] 0.06625[/C][/ROW]
[ROW][C]94[/C][C] 0.9352[/C][C] 0.1296[/C][C] 0.06481[/C][/ROW]
[ROW][C]95[/C][C] 0.9207[/C][C] 0.1585[/C][C] 0.07927[/C][/ROW]
[ROW][C]96[/C][C] 0.9029[/C][C] 0.1942[/C][C] 0.09712[/C][/ROW]
[ROW][C]97[/C][C] 0.8827[/C][C] 0.2347[/C][C] 0.1173[/C][/ROW]
[ROW][C]98[/C][C] 0.8828[/C][C] 0.2343[/C][C] 0.1172[/C][/ROW]
[ROW][C]99[/C][C] 0.9076[/C][C] 0.1848[/C][C] 0.09241[/C][/ROW]
[ROW][C]100[/C][C] 0.8904[/C][C] 0.2192[/C][C] 0.1096[/C][/ROW]
[ROW][C]101[/C][C] 0.869[/C][C] 0.2619[/C][C] 0.131[/C][/ROW]
[ROW][C]102[/C][C] 0.8442[/C][C] 0.3117[/C][C] 0.1558[/C][/ROW]
[ROW][C]103[/C][C] 0.9652[/C][C] 0.06962[/C][C] 0.03481[/C][/ROW]
[ROW][C]104[/C][C] 0.9553[/C][C] 0.08939[/C][C] 0.04469[/C][/ROW]
[ROW][C]105[/C][C] 0.9446[/C][C] 0.1108[/C][C] 0.0554[/C][/ROW]
[ROW][C]106[/C][C] 0.9309[/C][C] 0.1381[/C][C] 0.06905[/C][/ROW]
[ROW][C]107[/C][C] 0.921[/C][C] 0.158[/C][C] 0.07899[/C][/ROW]
[ROW][C]108[/C][C] 0.9012[/C][C] 0.1975[/C][C] 0.09877[/C][/ROW]
[ROW][C]109[/C][C] 0.8792[/C][C] 0.2416[/C][C] 0.1208[/C][/ROW]
[ROW][C]110[/C][C] 0.8536[/C][C] 0.2929[/C][C] 0.1464[/C][/ROW]
[ROW][C]111[/C][C] 0.8553[/C][C] 0.2895[/C][C] 0.1447[/C][/ROW]
[ROW][C]112[/C][C] 0.8278[/C][C] 0.3444[/C][C] 0.1722[/C][/ROW]
[ROW][C]113[/C][C] 0.7965[/C][C] 0.407[/C][C] 0.2035[/C][/ROW]
[ROW][C]114[/C][C] 0.8165[/C][C] 0.3671[/C][C] 0.1835[/C][/ROW]
[ROW][C]115[/C][C] 0.7956[/C][C] 0.4088[/C][C] 0.2044[/C][/ROW]
[ROW][C]116[/C][C] 0.8429[/C][C] 0.3142[/C][C] 0.1571[/C][/ROW]
[ROW][C]117[/C][C] 0.8233[/C][C] 0.3534[/C][C] 0.1767[/C][/ROW]
[ROW][C]118[/C][C] 0.8862[/C][C] 0.2275[/C][C] 0.1138[/C][/ROW]
[ROW][C]119[/C][C] 0.9124[/C][C] 0.1752[/C][C] 0.08761[/C][/ROW]
[ROW][C]120[/C][C] 0.8923[/C][C] 0.2155[/C][C] 0.1077[/C][/ROW]
[ROW][C]121[/C][C] 0.911[/C][C] 0.1779[/C][C] 0.08897[/C][/ROW]
[ROW][C]122[/C][C] 0.8891[/C][C] 0.2218[/C][C] 0.1109[/C][/ROW]
[ROW][C]123[/C][C] 0.8625[/C][C] 0.2751[/C][C] 0.1375[/C][/ROW]
[ROW][C]124[/C][C] 0.8299[/C][C] 0.3403[/C][C] 0.1701[/C][/ROW]
[ROW][C]125[/C][C] 0.7943[/C][C] 0.4113[/C][C] 0.2057[/C][/ROW]
[ROW][C]126[/C][C] 0.8363[/C][C] 0.3274[/C][C] 0.1637[/C][/ROW]
[ROW][C]127[/C][C] 0.8124[/C][C] 0.3751[/C][C] 0.1876[/C][/ROW]
[ROW][C]128[/C][C] 0.7758[/C][C] 0.4484[/C][C] 0.2242[/C][/ROW]
[ROW][C]129[/C][C] 0.7339[/C][C] 0.5321[/C][C] 0.2661[/C][/ROW]
[ROW][C]130[/C][C] 0.7435[/C][C] 0.513[/C][C] 0.2565[/C][/ROW]
[ROW][C]131[/C][C] 0.6984[/C][C] 0.6032[/C][C] 0.3016[/C][/ROW]
[ROW][C]132[/C][C] 0.6497[/C][C] 0.7007[/C][C] 0.3503[/C][/ROW]
[ROW][C]133[/C][C] 0.6197[/C][C] 0.7607[/C][C] 0.3803[/C][/ROW]
[ROW][C]134[/C][C] 0.646[/C][C] 0.708[/C][C] 0.354[/C][/ROW]
[ROW][C]135[/C][C] 0.5972[/C][C] 0.8057[/C][C] 0.4028[/C][/ROW]
[ROW][C]136[/C][C] 0.5369[/C][C] 0.9262[/C][C] 0.4631[/C][/ROW]
[ROW][C]137[/C][C] 0.4727[/C][C] 0.9455[/C][C] 0.5273[/C][/ROW]
[ROW][C]138[/C][C] 0.409[/C][C] 0.8179[/C][C] 0.591[/C][/ROW]
[ROW][C]139[/C][C] 0.3936[/C][C] 0.7871[/C][C] 0.6064[/C][/ROW]
[ROW][C]140[/C][C] 0.4646[/C][C] 0.9291[/C][C] 0.5354[/C][/ROW]
[ROW][C]141[/C][C] 0.4218[/C][C] 0.8436[/C][C] 0.5782[/C][/ROW]
[ROW][C]142[/C][C] 0.4504[/C][C] 0.9008[/C][C] 0.5496[/C][/ROW]
[ROW][C]143[/C][C] 0.3917[/C][C] 0.7834[/C][C] 0.6083[/C][/ROW]
[ROW][C]144[/C][C] 0.334[/C][C] 0.6679[/C][C] 0.666[/C][/ROW]
[ROW][C]145[/C][C] 0.4639[/C][C] 0.9278[/C][C] 0.5361[/C][/ROW]
[ROW][C]146[/C][C] 0.8542[/C][C] 0.2915[/C][C] 0.1458[/C][/ROW]
[ROW][C]147[/C][C] 0.8061[/C][C] 0.3878[/C][C] 0.1939[/C][/ROW]
[ROW][C]148[/C][C] 0.7713[/C][C] 0.4575[/C][C] 0.2287[/C][/ROW]
[ROW][C]149[/C][C] 0.6951[/C][C] 0.6098[/C][C] 0.3049[/C][/ROW]
[ROW][C]150[/C][C] 0.6192[/C][C] 0.7617[/C][C] 0.3808[/C][/ROW]
[ROW][C]151[/C][C] 0.5274[/C][C] 0.9453[/C][C] 0.4726[/C][/ROW]
[ROW][C]152[/C][C] 0.7829[/C][C] 0.4342[/C][C] 0.2171[/C][/ROW]
[ROW][C]153[/C][C] 0.7104[/C][C] 0.5792[/C][C] 0.2896[/C][/ROW]
[ROW][C]154[/C][C] 0.9742[/C][C] 0.05158[/C][C] 0.02579[/C][/ROW]
[ROW][C]155[/C][C] 0.9635[/C][C] 0.073[/C][C] 0.0365[/C][/ROW]
[ROW][C]156[/C][C] 0.9132[/C][C] 0.1736[/C][C] 0.08679[/C][/ROW]
[ROW][C]157[/C][C] 0.8104[/C][C] 0.3793[/C][C] 0.1896[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300636&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.08158	0.1632	0.9184
12	0.03465	0.06931	0.9653
13	0.01101	0.02202	0.989
14	0.003299	0.006598	0.9967
15	0.0009706	0.001941	0.999
16	0.000766	0.001532	0.9992
17	0.002384	0.004767	0.9976
18	0.03698	0.07396	0.963
19	0.01997	0.03993	0.98
20	0.02954	0.05908	0.9705
21	0.0298	0.0596	0.9702
22	0.03553	0.07106	0.9645
23	0.02621	0.05242	0.9738
24	0.02016	0.04033	0.9798
25	0.1669	0.3339	0.8331
26	0.1378	0.2755	0.8622
27	0.1057	0.2115	0.8943
28	0.08094	0.1619	0.9191
29	0.06401	0.128	0.936
30	0.05306	0.1061	0.9469
31	0.05899	0.118	0.941
32	0.05075	0.1015	0.9493
33	0.0359	0.07179	0.9641
34	0.03515	0.07029	0.9649
35	0.03328	0.06657	0.9667
36	0.02583	0.05165	0.9742
37	0.02071	0.04141	0.9793
38	0.1813	0.3625	0.8187
39	0.1652	0.3304	0.8348
40	0.1314	0.2629	0.8686
41	0.1123	0.2245	0.8877
42	0.09421	0.1884	0.9058
43	0.1378	0.2757	0.8622
44	0.1102	0.2204	0.8898
45	0.2097	0.4194	0.7903
46	0.1826	0.3652	0.8174
47	0.1489	0.2979	0.8511
48	0.1467	0.2934	0.8533
49	0.1248	0.2497	0.8752
50	0.1292	0.2583	0.8708
51	0.1192	0.2384	0.8808
52	0.1094	0.2189	0.8906
53	0.1474	0.2948	0.8526
54	0.1662	0.3323	0.8338
55	0.4171	0.8343	0.5829
56	0.3748	0.7497	0.6252
57	0.3688	0.7375	0.6312
58	0.3462	0.6923	0.6538
59	0.3023	0.6046	0.6977
60	0.3159	0.6317	0.6841
61	0.3814	0.7629	0.6186
62	0.339	0.6781	0.661
63	0.3652	0.7304	0.6348
64	0.3229	0.6459	0.6771
65	0.2983	0.5966	0.7017
66	0.2926	0.5851	0.7074
67	0.3498	0.6997	0.6502
68	0.6091	0.7818	0.3909
69	0.6717	0.6566	0.3283
70	0.63	0.7399	0.37
71	0.5891	0.8218	0.4109
72	0.5568	0.8863	0.4432
73	0.5682	0.8637	0.4318
74	0.5648	0.8703	0.4352
75	0.5212	0.9577	0.4788
76	0.5729	0.8543	0.4271
77	0.5916	0.8168	0.4084
78	0.6806	0.6387	0.3194
79	0.657	0.686	0.343
80	0.6162	0.7675	0.3838
81	0.8944	0.2112	0.1056
82	0.8803	0.2393	0.1197
83	0.8923	0.2155	0.1077
84	0.8703	0.2595	0.1297
85	0.8491	0.3018	0.1509
86	0.8222	0.3556	0.1778
87	0.8278	0.3445	0.1722
88	0.8027	0.3945	0.1973
89	0.8281	0.3438	0.1719
90	0.9485	0.103	0.05151
91	0.9358	0.1284	0.06422
92	0.9453	0.1094	0.05469
93	0.9337	0.1325	0.06625
94	0.9352	0.1296	0.06481
95	0.9207	0.1585	0.07927
96	0.9029	0.1942	0.09712
97	0.8827	0.2347	0.1173
98	0.8828	0.2343	0.1172
99	0.9076	0.1848	0.09241
100	0.8904	0.2192	0.1096
101	0.869	0.2619	0.131
102	0.8442	0.3117	0.1558
103	0.9652	0.06962	0.03481
104	0.9553	0.08939	0.04469
105	0.9446	0.1108	0.0554
106	0.9309	0.1381	0.06905
107	0.921	0.158	0.07899
108	0.9012	0.1975	0.09877
109	0.8792	0.2416	0.1208
110	0.8536	0.2929	0.1464
111	0.8553	0.2895	0.1447
112	0.8278	0.3444	0.1722
113	0.7965	0.407	0.2035
114	0.8165	0.3671	0.1835
115	0.7956	0.4088	0.2044
116	0.8429	0.3142	0.1571
117	0.8233	0.3534	0.1767
118	0.8862	0.2275	0.1138
119	0.9124	0.1752	0.08761
120	0.8923	0.2155	0.1077
121	0.911	0.1779	0.08897
122	0.8891	0.2218	0.1109
123	0.8625	0.2751	0.1375
124	0.8299	0.3403	0.1701
125	0.7943	0.4113	0.2057
126	0.8363	0.3274	0.1637
127	0.8124	0.3751	0.1876
128	0.7758	0.4484	0.2242
129	0.7339	0.5321	0.2661
130	0.7435	0.513	0.2565
131	0.6984	0.6032	0.3016
132	0.6497	0.7007	0.3503
133	0.6197	0.7607	0.3803
134	0.646	0.708	0.354
135	0.5972	0.8057	0.4028
136	0.5369	0.9262	0.4631
137	0.4727	0.9455	0.5273
138	0.409	0.8179	0.591
139	0.3936	0.7871	0.6064
140	0.4646	0.9291	0.5354
141	0.4218	0.8436	0.5782
142	0.4504	0.9008	0.5496
143	0.3917	0.7834	0.6083
144	0.334	0.6679	0.666
145	0.4639	0.9278	0.5361
146	0.8542	0.2915	0.1458
147	0.8061	0.3878	0.1939
148	0.7713	0.4575	0.2287
149	0.6951	0.6098	0.3049
150	0.6192	0.7617	0.3808
151	0.5274	0.9453	0.4726
152	0.7829	0.4342	0.2171
153	0.7104	0.5792	0.2896
154	0.9742	0.05158	0.02579
155	0.9635	0.073	0.0365
156	0.9132	0.1736	0.08679
157	0.8104	0.3793	0.1896

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.02721	NOK
5% type I error level	8	0.0544218	NOK
10% type I error level	22	0.14966	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 & 4 &  0.02721 & NOK \tabularnewline
5% type I error level & 8 & 0.0544218 & NOK \tabularnewline
10% type I error level & 22 & 0.14966 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300636&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]4[/C][C] 0.02721[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0544218[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.14966[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300636&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&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	4	0.02721	NOK
5% type I error level	8	0.0544218	NOK
10% type I error level	22	0.14966	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.1731, df1 = 2, df2 = 158, p-value = 0.1172
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3606, df1 = 14, df2 = 146, p-value = 0.1798
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.44301, df1 = 2, df2 = 158, p-value = 0.6429

\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 = 2.1731, df1 = 2, df2 = 158, p-value = 0.1172
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3606, df1 = 14, df2 = 146, p-value = 0.1798
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.44301, df1 = 2, df2 = 158, p-value = 0.6429
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300636&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 = 2.1731, df1 = 2, df2 = 158, p-value = 0.1172
[/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 = 1.3606, df1 = 14, df2 = 146, p-value = 0.1798
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.44301, df1 = 2, df2 = 158, p-value = 0.6429
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300636&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&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 = 2.1731, df1 = 2, df2 = 158, p-value = 0.1172
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3606, df1 = 14, df2 = 146, p-value = 0.1798
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.44301, df1 = 2, df2 = 158, p-value = 0.6429

Variance Inflation Factors (Multicollinearity)
> vif TCVD2 TCVD3 TVCD4 IVHB1 IVHB2 IVHB3 IVHB4 1.167209 1.154887 1.096735 1.077645 1.059283 1.097967 1.067053

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   TCVD2    TCVD3    TVCD4    IVHB1    IVHB2    IVHB3    IVHB4 
1.167209 1.154887 1.096735 1.077645 1.059283 1.097967 1.067053 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300636&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   TCVD2    TCVD3    TVCD4    IVHB1    IVHB2    IVHB3    IVHB4 
1.167209 1.154887 1.096735 1.077645 1.059283 1.097967 1.067053 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300636&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300636&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 TCVD2 TCVD3 TVCD4 IVHB1 IVHB2 IVHB3 IVHB4 1.167209 1.154887 1.096735 1.077645 1.059283 1.097967 1.067053

Figure 1

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Figure 3

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

Parameters (R input):

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

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 <- 'FALSE'
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