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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 07 Dec 2016 14:56:36 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/07/t14811190113o3tcjjw3qc3w8h.htm/, Retrieved Tue, 07 May 2024 13:34:51 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Tue, 07 May 2024 13:34:51 +0200
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0

Tree of Dependent Computations

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	13
2	16
3	17
2	NA
2	NA
3	16
3	NA
2	NA
2	NA
4	17
2	17
2	15
3	16
2	14
3	16
2	17
2	NA
NA	NA
3	NA
2	NA
2	16
3	NA
1	16
2	NA
3	NA
2	NA
2	16
3	15
5	16
2	16
5	13
2	15
2	17
4	NA
1	13
2	17
2	NA
3	14
2	14
3	18
2	NA
3	17
4	13
4	16
3	15
2	15
2	NA
1	15
4	13
4	NA
3	17
2	NA
2	NA
2	11
2	14
3	13
2	NA
2	17
3	16
3	NA
2	17
2	16
4	16
4	16
3	15
4	12
4	17
4	14
4	14
5	16
3	NA
4	NA
4	NA
2	NA
2	NA
3	15
4	16
2	14
5	15
1	17
3	NA
3	10
2	NA
2	17
1	NA
2	20
1	17
2	18
2	NA
2	17
2	14
3	NA
2	17
1	NA
1	17
3	NA
2	16
3	18
1	18
2	16
2	NA
3	NA
2	15
2	13
3	NA
1	NA
4	NA
3	NA
2	NA
3	16
3	NA
3	NA
4	NA
3	12
2	NA
3	16
2	16
1	NA
1	16
2	14
4	15
3	14
2	NA
2	15
3	NA
3	15
3	16
2	NA
2	NA
2	NA
3	11
4	NA
2	18
2	NA
1	11
3	NA
3	18
4	NA
3	15
1	19
1	17
2	NA
1	14
5	NA
3	13
2	17
2	14
4	19
2	14
4	NA
4	NA
3	16
4	16
3	15
4	12
3	NA
4	17
2	NA
4	NA
1	18
4	15
3	18
2	15
2	NA
2	NA
4	NA
3	16
3	NA
2	16

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted


\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 time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & 
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Engine error message[/C][C]
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=0

As an alternative you can also use a QR Code:  
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 Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted






Multiple Linear Regression - Estimated Regression Equation
TVDSUM[t] = + 16.2633 -0.299347EP3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDSUM[t] =  +  16.2633 -0.299347EP3[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDSUM[t] =  +  16.2633 -0.299347EP3[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
TVDSUM[t] = + 16.2633 -0.299347EP3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+16.26 0.5002+3.2510e+01 2.612e-55 1.306e-55
EP3-0.2994 0.177-1.6920e+00 0.0938 0.0469

\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) & +16.26 &  0.5002 & +3.2510e+01 &  2.612e-55 &  1.306e-55 \tabularnewline
EP3 & -0.2994 &  0.177 & -1.6920e+00 &  0.0938 &  0.0469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+16.26[/C][C] 0.5002[/C][C]+3.2510e+01[/C][C] 2.612e-55[/C][C] 1.306e-55[/C][/ROW]
[ROW][C]EP3[/C][C]-0.2994[/C][C] 0.177[/C][C]-1.6920e+00[/C][C] 0.0938[/C][C] 0.0469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+16.26 0.5002+3.2510e+01 2.612e-55 1.306e-55
EP3-0.2994 0.177-1.6920e+00 0.0938 0.0469






Multiple Linear Regression - Regression Statistics
Multiple R 0.166
R-squared 0.02755
Adjusted R-squared 0.01792
F-TEST (value) 2.862
F-TEST (DF numerator)1
F-TEST (DF denominator)101
p-value 0.0938
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.856
Sum Squared Residuals 347.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.166 \tabularnewline
R-squared &  0.02755 \tabularnewline
Adjusted R-squared &  0.01792 \tabularnewline
F-TEST (value) &  2.862 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 101 \tabularnewline
p-value &  0.0938 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.856 \tabularnewline
Sum Squared Residuals &  347.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.166[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.02755[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01792[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.862[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]101[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0938[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.856[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 347.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R 0.166
R-squared 0.02755
Adjusted R-squared 0.01792
F-TEST (value) 2.862
F-TEST (DF numerator)1
F-TEST (DF denominator)101
p-value 0.0938
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.856
Sum Squared Residuals 347.8






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 13 15.07-2.066
2 16 15.66 0.3354
3 17 15.37 1.635
4 16 15.37 0.6347
5 17 15.07 1.934
6 17 15.66 1.335
7 15 15.66-0.6646
8 16 15.37 0.6347
9 14 15.66-1.665
10 16 15.37 0.6347
11 17 15.66 1.335
12 16 15.66 0.3354
13 16 15.96 0.03602
14 16 15.66 0.3354
15 15 15.37-0.3653
16 16 14.77 1.233
17 16 15.66 0.3354
18 13 14.77-1.767
19 15 15.66-0.6646
20 17 15.66 1.335
21 13 15.96-2.964
22 17 15.66 1.335
23 14 15.37-1.365
24 14 15.66-1.665
25 18 15.37 2.635
26 17 15.37 1.635
27 13 15.07-2.066
28 16 15.07 0.9341
29 15 15.37-0.3653
30 15 15.66-0.6646
31 15 15.96-0.964
32 13 15.07-2.066
33 17 15.37 1.635
34 11 15.66-4.665
35 14 15.66-1.665
36 13 15.37-2.365
37 17 15.66 1.335
38 16 15.37 0.6347
39 17 15.66 1.335
40 16 15.66 0.3354
41 16 15.07 0.9341
42 16 15.07 0.9341
43 15 15.37-0.3653
44 12 15.07-3.066
45 17 15.07 1.934
46 14 15.07-1.066
47 14 15.07-1.066
48 16 14.77 1.233
49 15 15.37-0.3653
50 16 15.07 0.9341
51 14 15.66-1.665
52 15 14.77 0.2334
53 17 15.96 1.036
54 10 15.37-5.365
55 17 15.66 1.335
56 20 15.66 4.335
57 17 15.96 1.036
58 18 15.66 2.335
59 17 15.66 1.335
60 14 15.66-1.665
61 17 15.66 1.335
62 17 15.96 1.036
63 16 15.66 0.3354
64 18 15.37 2.635
65 18 15.96 2.036
66 16 15.66 0.3354
67 15 15.66-0.6646
68 13 15.66-2.665
69 16 15.37 0.6347
70 12 15.37-3.365
71 16 15.37 0.6347
72 16 15.66 0.3354
73 16 15.96 0.03602
74 14 15.66-1.665
75 15 15.07-0.06594
76 14 15.37-1.365
77 15 15.66-0.6646
78 15 15.37-0.3653
79 16 15.37 0.6347
80 11 15.37-4.365
81 18 15.66 2.335
82 11 15.96-4.964
83 18 15.37 2.635
84 15 15.37-0.3653
85 19 15.96 3.036
86 17 15.96 1.036
87 14 15.96-1.964
88 13 15.37-2.365
89 17 15.66 1.335
90 14 15.66-1.665
91 19 15.07 3.934
92 14 15.66-1.665
93 16 15.37 0.6347
94 16 15.07 0.9341
95 15 15.37-0.3653
96 12 15.07-3.066
97 17 15.07 1.934
98 18 15.96 2.036
99 15 15.07-0.06594
100 18 15.37 2.635
101 15 15.66-0.6646
102 16 15.37 0.6347
103 16 15.66 0.3354

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  15.07 & -2.066 \tabularnewline
2 &  16 &  15.66 &  0.3354 \tabularnewline
3 &  17 &  15.37 &  1.635 \tabularnewline
4 &  16 &  15.37 &  0.6347 \tabularnewline
5 &  17 &  15.07 &  1.934 \tabularnewline
6 &  17 &  15.66 &  1.335 \tabularnewline
7 &  15 &  15.66 & -0.6646 \tabularnewline
8 &  16 &  15.37 &  0.6347 \tabularnewline
9 &  14 &  15.66 & -1.665 \tabularnewline
10 &  16 &  15.37 &  0.6347 \tabularnewline
11 &  17 &  15.66 &  1.335 \tabularnewline
12 &  16 &  15.66 &  0.3354 \tabularnewline
13 &  16 &  15.96 &  0.03602 \tabularnewline
14 &  16 &  15.66 &  0.3354 \tabularnewline
15 &  15 &  15.37 & -0.3653 \tabularnewline
16 &  16 &  14.77 &  1.233 \tabularnewline
17 &  16 &  15.66 &  0.3354 \tabularnewline
18 &  13 &  14.77 & -1.767 \tabularnewline
19 &  15 &  15.66 & -0.6646 \tabularnewline
20 &  17 &  15.66 &  1.335 \tabularnewline
21 &  13 &  15.96 & -2.964 \tabularnewline
22 &  17 &  15.66 &  1.335 \tabularnewline
23 &  14 &  15.37 & -1.365 \tabularnewline
24 &  14 &  15.66 & -1.665 \tabularnewline
25 &  18 &  15.37 &  2.635 \tabularnewline
26 &  17 &  15.37 &  1.635 \tabularnewline
27 &  13 &  15.07 & -2.066 \tabularnewline
28 &  16 &  15.07 &  0.9341 \tabularnewline
29 &  15 &  15.37 & -0.3653 \tabularnewline
30 &  15 &  15.66 & -0.6646 \tabularnewline
31 &  15 &  15.96 & -0.964 \tabularnewline
32 &  13 &  15.07 & -2.066 \tabularnewline
33 &  17 &  15.37 &  1.635 \tabularnewline
34 &  11 &  15.66 & -4.665 \tabularnewline
35 &  14 &  15.66 & -1.665 \tabularnewline
36 &  13 &  15.37 & -2.365 \tabularnewline
37 &  17 &  15.66 &  1.335 \tabularnewline
38 &  16 &  15.37 &  0.6347 \tabularnewline
39 &  17 &  15.66 &  1.335 \tabularnewline
40 &  16 &  15.66 &  0.3354 \tabularnewline
41 &  16 &  15.07 &  0.9341 \tabularnewline
42 &  16 &  15.07 &  0.9341 \tabularnewline
43 &  15 &  15.37 & -0.3653 \tabularnewline
44 &  12 &  15.07 & -3.066 \tabularnewline
45 &  17 &  15.07 &  1.934 \tabularnewline
46 &  14 &  15.07 & -1.066 \tabularnewline
47 &  14 &  15.07 & -1.066 \tabularnewline
48 &  16 &  14.77 &  1.233 \tabularnewline
49 &  15 &  15.37 & -0.3653 \tabularnewline
50 &  16 &  15.07 &  0.9341 \tabularnewline
51 &  14 &  15.66 & -1.665 \tabularnewline
52 &  15 &  14.77 &  0.2334 \tabularnewline
53 &  17 &  15.96 &  1.036 \tabularnewline
54 &  10 &  15.37 & -5.365 \tabularnewline
55 &  17 &  15.66 &  1.335 \tabularnewline
56 &  20 &  15.66 &  4.335 \tabularnewline
57 &  17 &  15.96 &  1.036 \tabularnewline
58 &  18 &  15.66 &  2.335 \tabularnewline
59 &  17 &  15.66 &  1.335 \tabularnewline
60 &  14 &  15.66 & -1.665 \tabularnewline
61 &  17 &  15.66 &  1.335 \tabularnewline
62 &  17 &  15.96 &  1.036 \tabularnewline
63 &  16 &  15.66 &  0.3354 \tabularnewline
64 &  18 &  15.37 &  2.635 \tabularnewline
65 &  18 &  15.96 &  2.036 \tabularnewline
66 &  16 &  15.66 &  0.3354 \tabularnewline
67 &  15 &  15.66 & -0.6646 \tabularnewline
68 &  13 &  15.66 & -2.665 \tabularnewline
69 &  16 &  15.37 &  0.6347 \tabularnewline
70 &  12 &  15.37 & -3.365 \tabularnewline
71 &  16 &  15.37 &  0.6347 \tabularnewline
72 &  16 &  15.66 &  0.3354 \tabularnewline
73 &  16 &  15.96 &  0.03602 \tabularnewline
74 &  14 &  15.66 & -1.665 \tabularnewline
75 &  15 &  15.07 & -0.06594 \tabularnewline
76 &  14 &  15.37 & -1.365 \tabularnewline
77 &  15 &  15.66 & -0.6646 \tabularnewline
78 &  15 &  15.37 & -0.3653 \tabularnewline
79 &  16 &  15.37 &  0.6347 \tabularnewline
80 &  11 &  15.37 & -4.365 \tabularnewline
81 &  18 &  15.66 &  2.335 \tabularnewline
82 &  11 &  15.96 & -4.964 \tabularnewline
83 &  18 &  15.37 &  2.635 \tabularnewline
84 &  15 &  15.37 & -0.3653 \tabularnewline
85 &  19 &  15.96 &  3.036 \tabularnewline
86 &  17 &  15.96 &  1.036 \tabularnewline
87 &  14 &  15.96 & -1.964 \tabularnewline
88 &  13 &  15.37 & -2.365 \tabularnewline
89 &  17 &  15.66 &  1.335 \tabularnewline
90 &  14 &  15.66 & -1.665 \tabularnewline
91 &  19 &  15.07 &  3.934 \tabularnewline
92 &  14 &  15.66 & -1.665 \tabularnewline
93 &  16 &  15.37 &  0.6347 \tabularnewline
94 &  16 &  15.07 &  0.9341 \tabularnewline
95 &  15 &  15.37 & -0.3653 \tabularnewline
96 &  12 &  15.07 & -3.066 \tabularnewline
97 &  17 &  15.07 &  1.934 \tabularnewline
98 &  18 &  15.96 &  2.036 \tabularnewline
99 &  15 &  15.07 & -0.06594 \tabularnewline
100 &  18 &  15.37 &  2.635 \tabularnewline
101 &  15 &  15.66 & -0.6646 \tabularnewline
102 &  16 &  15.37 &  0.6347 \tabularnewline
103 &  16 &  15.66 &  0.3354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 15.07[/C][C]-2.066[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.37[/C][C] 1.635[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 15.07[/C][C] 1.934[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.66[/C][C]-0.6646[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 15.66[/C][C]-1.665[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.96[/C][C] 0.03602[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.37[/C][C]-0.3653[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 14.77[/C][C] 1.233[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.77[/C][C]-1.767[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 15.66[/C][C]-0.6646[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 15.96[/C][C]-2.964[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 15.37[/C][C]-1.365[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 15.66[/C][C]-1.665[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 15.37[/C][C] 2.635[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 15.37[/C][C] 1.635[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 15.07[/C][C]-2.066[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 15.07[/C][C] 0.9341[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.37[/C][C]-0.3653[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 15.66[/C][C]-0.6646[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 15.96[/C][C]-0.964[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.07[/C][C]-2.066[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 15.37[/C][C] 1.635[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 15.66[/C][C]-4.665[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 15.66[/C][C]-1.665[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 15.37[/C][C]-2.365[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.07[/C][C] 0.9341[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 15.07[/C][C] 0.9341[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.37[/C][C]-0.3653[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 15.07[/C][C]-3.066[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 15.07[/C][C] 1.934[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.07[/C][C]-1.066[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 15.07[/C][C]-1.066[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.77[/C][C] 1.233[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.37[/C][C]-0.3653[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 15.07[/C][C] 0.9341[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 15.66[/C][C]-1.665[/C][/ROW]
[ROW][C]52[/C][C] 15[/C][C] 14.77[/C][C] 0.2334[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 15.96[/C][C] 1.036[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 15.37[/C][C]-5.365[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 15.66[/C][C] 4.335[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.96[/C][C] 1.036[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 15.66[/C][C] 2.335[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]60[/C][C] 14[/C][C] 15.66[/C][C]-1.665[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 15.96[/C][C] 1.036[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 15.37[/C][C] 2.635[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 15.96[/C][C] 2.036[/C][/ROW]
[ROW][C]66[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[ROW][C]67[/C][C] 15[/C][C] 15.66[/C][C]-0.6646[/C][/ROW]
[ROW][C]68[/C][C] 13[/C][C] 15.66[/C][C]-2.665[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 15.37[/C][C]-3.365[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[ROW][C]73[/C][C] 16[/C][C] 15.96[/C][C] 0.03602[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 15.66[/C][C]-1.665[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 15.07[/C][C]-0.06594[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 15.37[/C][C]-1.365[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 15.66[/C][C]-0.6646[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 15.37[/C][C]-0.3653[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]80[/C][C] 11[/C][C] 15.37[/C][C]-4.365[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 15.66[/C][C] 2.335[/C][/ROW]
[ROW][C]82[/C][C] 11[/C][C] 15.96[/C][C]-4.964[/C][/ROW]
[ROW][C]83[/C][C] 18[/C][C] 15.37[/C][C] 2.635[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 15.37[/C][C]-0.3653[/C][/ROW]
[ROW][C]85[/C][C] 19[/C][C] 15.96[/C][C] 3.036[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 15.96[/C][C] 1.036[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 15.96[/C][C]-1.964[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 15.37[/C][C]-2.365[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 15.66[/C][C] 1.335[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 15.66[/C][C]-1.665[/C][/ROW]
[ROW][C]91[/C][C] 19[/C][C] 15.07[/C][C] 3.934[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 15.66[/C][C]-1.665[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]94[/C][C] 16[/C][C] 15.07[/C][C] 0.9341[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 15.37[/C][C]-0.3653[/C][/ROW]
[ROW][C]96[/C][C] 12[/C][C] 15.07[/C][C]-3.066[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 15.07[/C][C] 1.934[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 15.96[/C][C] 2.036[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 15.07[/C][C]-0.06594[/C][/ROW]
[ROW][C]100[/C][C] 18[/C][C] 15.37[/C][C] 2.635[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 15.66[/C][C]-0.6646[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 15.37[/C][C] 0.6347[/C][/ROW]
[ROW][C]103[/C][C] 16[/C][C] 15.66[/C][C] 0.3354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 13 15.07-2.066
2 16 15.66 0.3354
3 17 15.37 1.635
4 16 15.37 0.6347
5 17 15.07 1.934
6 17 15.66 1.335
7 15 15.66-0.6646
8 16 15.37 0.6347
9 14 15.66-1.665
10 16 15.37 0.6347
11 17 15.66 1.335
12 16 15.66 0.3354
13 16 15.96 0.03602
14 16 15.66 0.3354
15 15 15.37-0.3653
16 16 14.77 1.233
17 16 15.66 0.3354
18 13 14.77-1.767
19 15 15.66-0.6646
20 17 15.66 1.335
21 13 15.96-2.964
22 17 15.66 1.335
23 14 15.37-1.365
24 14 15.66-1.665
25 18 15.37 2.635
26 17 15.37 1.635
27 13 15.07-2.066
28 16 15.07 0.9341
29 15 15.37-0.3653
30 15 15.66-0.6646
31 15 15.96-0.964
32 13 15.07-2.066
33 17 15.37 1.635
34 11 15.66-4.665
35 14 15.66-1.665
36 13 15.37-2.365
37 17 15.66 1.335
38 16 15.37 0.6347
39 17 15.66 1.335
40 16 15.66 0.3354
41 16 15.07 0.9341
42 16 15.07 0.9341
43 15 15.37-0.3653
44 12 15.07-3.066
45 17 15.07 1.934
46 14 15.07-1.066
47 14 15.07-1.066
48 16 14.77 1.233
49 15 15.37-0.3653
50 16 15.07 0.9341
51 14 15.66-1.665
52 15 14.77 0.2334
53 17 15.96 1.036
54 10 15.37-5.365
55 17 15.66 1.335
56 20 15.66 4.335
57 17 15.96 1.036
58 18 15.66 2.335
59 17 15.66 1.335
60 14 15.66-1.665
61 17 15.66 1.335
62 17 15.96 1.036
63 16 15.66 0.3354
64 18 15.37 2.635
65 18 15.96 2.036
66 16 15.66 0.3354
67 15 15.66-0.6646
68 13 15.66-2.665
69 16 15.37 0.6347
70 12 15.37-3.365
71 16 15.37 0.6347
72 16 15.66 0.3354
73 16 15.96 0.03602
74 14 15.66-1.665
75 15 15.07-0.06594
76 14 15.37-1.365
77 15 15.66-0.6646
78 15 15.37-0.3653
79 16 15.37 0.6347
80 11 15.37-4.365
81 18 15.66 2.335
82 11 15.96-4.964
83 18 15.37 2.635
84 15 15.37-0.3653
85 19 15.96 3.036
86 17 15.96 1.036
87 14 15.96-1.964
88 13 15.37-2.365
89 17 15.66 1.335
90 14 15.66-1.665
91 19 15.07 3.934
92 14 15.66-1.665
93 16 15.37 0.6347
94 16 15.07 0.9341
95 15 15.37-0.3653
96 12 15.07-3.066
97 17 15.07 1.934
98 18 15.96 2.036
99 15 15.07-0.06594
100 18 15.37 2.635
101 15 15.66-0.6646
102 16 15.37 0.6347
103 16 15.66 0.3354






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.5641 0.8717 0.4359
6 0.4006 0.8012 0.5994
7 0.3527 0.7055 0.6473
8 0.2349 0.4697 0.7651
9 0.2715 0.5431 0.7285
10 0.1855 0.3709 0.8145
11 0.1467 0.2935 0.8533
12 0.09297 0.1859 0.907
13 0.05683 0.1137 0.9432
14 0.03316 0.06633 0.9668
15 0.02117 0.04233 0.9788
16 0.01318 0.02635 0.9868
17 0.007077 0.01415 0.9929
18 0.01293 0.02586 0.9871
19 0.008974 0.01795 0.991
20 0.006772 0.01354 0.9932
21 0.02996 0.05992 0.97
22 0.02527 0.05055 0.9747
23 0.02349 0.04697 0.9765
24 0.02373 0.04747 0.9763
25 0.04095 0.0819 0.959
26 0.03726 0.07452 0.9627
27 0.05038 0.1008 0.9496
28 0.03772 0.07544 0.9623
29 0.02643 0.05286 0.9736
30 0.01886 0.03771 0.9811
31 0.01383 0.02766 0.9862
32 0.01807 0.03613 0.9819
33 0.01734 0.03469 0.9827
34 0.1179 0.2358 0.8821
35 0.1092 0.2183 0.8908
36 0.128 0.256 0.872
37 0.1187 0.2374 0.8813
38 0.09492 0.1898 0.9051
39 0.08638 0.1728 0.9136
40 0.06616 0.1323 0.9338
41 0.05278 0.1056 0.9472
42 0.04152 0.08303 0.9585
43 0.0303 0.0606 0.9697
44 0.05595 0.1119 0.944
45 0.05795 0.1159 0.9421
46 0.04778 0.09555 0.9522
47 0.03896 0.07792 0.961
48 0.03241 0.06481 0.9676
49 0.02346 0.04692 0.9765
50 0.0181 0.03619 0.9819
51 0.01655 0.03311 0.9834
52 0.01154 0.02307 0.9885
53 0.009296 0.01859 0.9907
54 0.09755 0.1951 0.9024
55 0.08726 0.1745 0.9127
56 0.2329 0.4659 0.7671
57 0.2039 0.4078 0.7961
58 0.2259 0.4517 0.7741
59 0.2047 0.4094 0.7953
60 0.1954 0.3909 0.8046
61 0.1759 0.3519 0.8241
62 0.1519 0.3037 0.8481
63 0.1213 0.2425 0.8787
64 0.15 0.3 0.85
65 0.1604 0.3209 0.8396
66 0.129 0.2581 0.871
67 0.1031 0.2063 0.8969
68 0.1268 0.2537 0.8732
69 0.1012 0.2024 0.8988
70 0.1687 0.3374 0.8313
71 0.1363 0.2725 0.8637
72 0.1068 0.2136 0.8932
73 0.08179 0.1636 0.9182
74 0.07418 0.1484 0.9258
75 0.05493 0.1099 0.9451
76 0.04722 0.09444 0.9528
77 0.03469 0.06937 0.9653
78 0.02455 0.0491 0.9754
79 0.01706 0.03411 0.9829
80 0.07789 0.1558 0.9221
81 0.08711 0.1742 0.9129
82 0.3429 0.6857 0.6571
83 0.3782 0.7564 0.6218
84 0.317 0.6339 0.683
85 0.4195 0.839 0.5805
86 0.3802 0.7604 0.6198
87 0.3651 0.7303 0.6349
88 0.4457 0.8915 0.5543
89 0.3922 0.7844 0.6078
90 0.3908 0.7817 0.6092
91 0.6434 0.7131 0.3566
92 0.6997 0.6006 0.3003
93 0.6011 0.7977 0.3989
94 0.522 0.9559 0.478
95 0.4191 0.8382 0.5809
96 0.7637 0.4725 0.2363
97 0.7032 0.5937 0.2968
98 0.6394 0.7212 0.3606

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.5641 &  0.8717 &  0.4359 \tabularnewline
6 &  0.4006 &  0.8012 &  0.5994 \tabularnewline
7 &  0.3527 &  0.7055 &  0.6473 \tabularnewline
8 &  0.2349 &  0.4697 &  0.7651 \tabularnewline
9 &  0.2715 &  0.5431 &  0.7285 \tabularnewline
10 &  0.1855 &  0.3709 &  0.8145 \tabularnewline
11 &  0.1467 &  0.2935 &  0.8533 \tabularnewline
12 &  0.09297 &  0.1859 &  0.907 \tabularnewline
13 &  0.05683 &  0.1137 &  0.9432 \tabularnewline
14 &  0.03316 &  0.06633 &  0.9668 \tabularnewline
15 &  0.02117 &  0.04233 &  0.9788 \tabularnewline
16 &  0.01318 &  0.02635 &  0.9868 \tabularnewline
17 &  0.007077 &  0.01415 &  0.9929 \tabularnewline
18 &  0.01293 &  0.02586 &  0.9871 \tabularnewline
19 &  0.008974 &  0.01795 &  0.991 \tabularnewline
20 &  0.006772 &  0.01354 &  0.9932 \tabularnewline
21 &  0.02996 &  0.05992 &  0.97 \tabularnewline
22 &  0.02527 &  0.05055 &  0.9747 \tabularnewline
23 &  0.02349 &  0.04697 &  0.9765 \tabularnewline
24 &  0.02373 &  0.04747 &  0.9763 \tabularnewline
25 &  0.04095 &  0.0819 &  0.959 \tabularnewline
26 &  0.03726 &  0.07452 &  0.9627 \tabularnewline
27 &  0.05038 &  0.1008 &  0.9496 \tabularnewline
28 &  0.03772 &  0.07544 &  0.9623 \tabularnewline
29 &  0.02643 &  0.05286 &  0.9736 \tabularnewline
30 &  0.01886 &  0.03771 &  0.9811 \tabularnewline
31 &  0.01383 &  0.02766 &  0.9862 \tabularnewline
32 &  0.01807 &  0.03613 &  0.9819 \tabularnewline
33 &  0.01734 &  0.03469 &  0.9827 \tabularnewline
34 &  0.1179 &  0.2358 &  0.8821 \tabularnewline
35 &  0.1092 &  0.2183 &  0.8908 \tabularnewline
36 &  0.128 &  0.256 &  0.872 \tabularnewline
37 &  0.1187 &  0.2374 &  0.8813 \tabularnewline
38 &  0.09492 &  0.1898 &  0.9051 \tabularnewline
39 &  0.08638 &  0.1728 &  0.9136 \tabularnewline
40 &  0.06616 &  0.1323 &  0.9338 \tabularnewline
41 &  0.05278 &  0.1056 &  0.9472 \tabularnewline
42 &  0.04152 &  0.08303 &  0.9585 \tabularnewline
43 &  0.0303 &  0.0606 &  0.9697 \tabularnewline
44 &  0.05595 &  0.1119 &  0.944 \tabularnewline
45 &  0.05795 &  0.1159 &  0.9421 \tabularnewline
46 &  0.04778 &  0.09555 &  0.9522 \tabularnewline
47 &  0.03896 &  0.07792 &  0.961 \tabularnewline
48 &  0.03241 &  0.06481 &  0.9676 \tabularnewline
49 &  0.02346 &  0.04692 &  0.9765 \tabularnewline
50 &  0.0181 &  0.03619 &  0.9819 \tabularnewline
51 &  0.01655 &  0.03311 &  0.9834 \tabularnewline
52 &  0.01154 &  0.02307 &  0.9885 \tabularnewline
53 &  0.009296 &  0.01859 &  0.9907 \tabularnewline
54 &  0.09755 &  0.1951 &  0.9024 \tabularnewline
55 &  0.08726 &  0.1745 &  0.9127 \tabularnewline
56 &  0.2329 &  0.4659 &  0.7671 \tabularnewline
57 &  0.2039 &  0.4078 &  0.7961 \tabularnewline
58 &  0.2259 &  0.4517 &  0.7741 \tabularnewline
59 &  0.2047 &  0.4094 &  0.7953 \tabularnewline
60 &  0.1954 &  0.3909 &  0.8046 \tabularnewline
61 &  0.1759 &  0.3519 &  0.8241 \tabularnewline
62 &  0.1519 &  0.3037 &  0.8481 \tabularnewline
63 &  0.1213 &  0.2425 &  0.8787 \tabularnewline
64 &  0.15 &  0.3 &  0.85 \tabularnewline
65 &  0.1604 &  0.3209 &  0.8396 \tabularnewline
66 &  0.129 &  0.2581 &  0.871 \tabularnewline
67 &  0.1031 &  0.2063 &  0.8969 \tabularnewline
68 &  0.1268 &  0.2537 &  0.8732 \tabularnewline
69 &  0.1012 &  0.2024 &  0.8988 \tabularnewline
70 &  0.1687 &  0.3374 &  0.8313 \tabularnewline
71 &  0.1363 &  0.2725 &  0.8637 \tabularnewline
72 &  0.1068 &  0.2136 &  0.8932 \tabularnewline
73 &  0.08179 &  0.1636 &  0.9182 \tabularnewline
74 &  0.07418 &  0.1484 &  0.9258 \tabularnewline
75 &  0.05493 &  0.1099 &  0.9451 \tabularnewline
76 &  0.04722 &  0.09444 &  0.9528 \tabularnewline
77 &  0.03469 &  0.06937 &  0.9653 \tabularnewline
78 &  0.02455 &  0.0491 &  0.9754 \tabularnewline
79 &  0.01706 &  0.03411 &  0.9829 \tabularnewline
80 &  0.07789 &  0.1558 &  0.9221 \tabularnewline
81 &  0.08711 &  0.1742 &  0.9129 \tabularnewline
82 &  0.3429 &  0.6857 &  0.6571 \tabularnewline
83 &  0.3782 &  0.7564 &  0.6218 \tabularnewline
84 &  0.317 &  0.6339 &  0.683 \tabularnewline
85 &  0.4195 &  0.839 &  0.5805 \tabularnewline
86 &  0.3802 &  0.7604 &  0.6198 \tabularnewline
87 &  0.3651 &  0.7303 &  0.6349 \tabularnewline
88 &  0.4457 &  0.8915 &  0.5543 \tabularnewline
89 &  0.3922 &  0.7844 &  0.6078 \tabularnewline
90 &  0.3908 &  0.7817 &  0.6092 \tabularnewline
91 &  0.6434 &  0.7131 &  0.3566 \tabularnewline
92 &  0.6997 &  0.6006 &  0.3003 \tabularnewline
93 &  0.6011 &  0.7977 &  0.3989 \tabularnewline
94 &  0.522 &  0.9559 &  0.478 \tabularnewline
95 &  0.4191 &  0.8382 &  0.5809 \tabularnewline
96 &  0.7637 &  0.4725 &  0.2363 \tabularnewline
97 &  0.7032 &  0.5937 &  0.2968 \tabularnewline
98 &  0.6394 &  0.7212 &  0.3606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C] 0.5641[/C][C] 0.8717[/C][C] 0.4359[/C][/ROW]
[ROW][C]6[/C][C] 0.4006[/C][C] 0.8012[/C][C] 0.5994[/C][/ROW]
[ROW][C]7[/C][C] 0.3527[/C][C] 0.7055[/C][C] 0.6473[/C][/ROW]
[ROW][C]8[/C][C] 0.2349[/C][C] 0.4697[/C][C] 0.7651[/C][/ROW]
[ROW][C]9[/C][C] 0.2715[/C][C] 0.5431[/C][C] 0.7285[/C][/ROW]
[ROW][C]10[/C][C] 0.1855[/C][C] 0.3709[/C][C] 0.8145[/C][/ROW]
[ROW][C]11[/C][C] 0.1467[/C][C] 0.2935[/C][C] 0.8533[/C][/ROW]
[ROW][C]12[/C][C] 0.09297[/C][C] 0.1859[/C][C] 0.907[/C][/ROW]
[ROW][C]13[/C][C] 0.05683[/C][C] 0.1137[/C][C] 0.9432[/C][/ROW]
[ROW][C]14[/C][C] 0.03316[/C][C] 0.06633[/C][C] 0.9668[/C][/ROW]
[ROW][C]15[/C][C] 0.02117[/C][C] 0.04233[/C][C] 0.9788[/C][/ROW]
[ROW][C]16[/C][C] 0.01318[/C][C] 0.02635[/C][C] 0.9868[/C][/ROW]
[ROW][C]17[/C][C] 0.007077[/C][C] 0.01415[/C][C] 0.9929[/C][/ROW]
[ROW][C]18[/C][C] 0.01293[/C][C] 0.02586[/C][C] 0.9871[/C][/ROW]
[ROW][C]19[/C][C] 0.008974[/C][C] 0.01795[/C][C] 0.991[/C][/ROW]
[ROW][C]20[/C][C] 0.006772[/C][C] 0.01354[/C][C] 0.9932[/C][/ROW]
[ROW][C]21[/C][C] 0.02996[/C][C] 0.05992[/C][C] 0.97[/C][/ROW]
[ROW][C]22[/C][C] 0.02527[/C][C] 0.05055[/C][C] 0.9747[/C][/ROW]
[ROW][C]23[/C][C] 0.02349[/C][C] 0.04697[/C][C] 0.9765[/C][/ROW]
[ROW][C]24[/C][C] 0.02373[/C][C] 0.04747[/C][C] 0.9763[/C][/ROW]
[ROW][C]25[/C][C] 0.04095[/C][C] 0.0819[/C][C] 0.959[/C][/ROW]
[ROW][C]26[/C][C] 0.03726[/C][C] 0.07452[/C][C] 0.9627[/C][/ROW]
[ROW][C]27[/C][C] 0.05038[/C][C] 0.1008[/C][C] 0.9496[/C][/ROW]
[ROW][C]28[/C][C] 0.03772[/C][C] 0.07544[/C][C] 0.9623[/C][/ROW]
[ROW][C]29[/C][C] 0.02643[/C][C] 0.05286[/C][C] 0.9736[/C][/ROW]
[ROW][C]30[/C][C] 0.01886[/C][C] 0.03771[/C][C] 0.9811[/C][/ROW]
[ROW][C]31[/C][C] 0.01383[/C][C] 0.02766[/C][C] 0.9862[/C][/ROW]
[ROW][C]32[/C][C] 0.01807[/C][C] 0.03613[/C][C] 0.9819[/C][/ROW]
[ROW][C]33[/C][C] 0.01734[/C][C] 0.03469[/C][C] 0.9827[/C][/ROW]
[ROW][C]34[/C][C] 0.1179[/C][C] 0.2358[/C][C] 0.8821[/C][/ROW]
[ROW][C]35[/C][C] 0.1092[/C][C] 0.2183[/C][C] 0.8908[/C][/ROW]
[ROW][C]36[/C][C] 0.128[/C][C] 0.256[/C][C] 0.872[/C][/ROW]
[ROW][C]37[/C][C] 0.1187[/C][C] 0.2374[/C][C] 0.8813[/C][/ROW]
[ROW][C]38[/C][C] 0.09492[/C][C] 0.1898[/C][C] 0.9051[/C][/ROW]
[ROW][C]39[/C][C] 0.08638[/C][C] 0.1728[/C][C] 0.9136[/C][/ROW]
[ROW][C]40[/C][C] 0.06616[/C][C] 0.1323[/C][C] 0.9338[/C][/ROW]
[ROW][C]41[/C][C] 0.05278[/C][C] 0.1056[/C][C] 0.9472[/C][/ROW]
[ROW][C]42[/C][C] 0.04152[/C][C] 0.08303[/C][C] 0.9585[/C][/ROW]
[ROW][C]43[/C][C] 0.0303[/C][C] 0.0606[/C][C] 0.9697[/C][/ROW]
[ROW][C]44[/C][C] 0.05595[/C][C] 0.1119[/C][C] 0.944[/C][/ROW]
[ROW][C]45[/C][C] 0.05795[/C][C] 0.1159[/C][C] 0.9421[/C][/ROW]
[ROW][C]46[/C][C] 0.04778[/C][C] 0.09555[/C][C] 0.9522[/C][/ROW]
[ROW][C]47[/C][C] 0.03896[/C][C] 0.07792[/C][C] 0.961[/C][/ROW]
[ROW][C]48[/C][C] 0.03241[/C][C] 0.06481[/C][C] 0.9676[/C][/ROW]
[ROW][C]49[/C][C] 0.02346[/C][C] 0.04692[/C][C] 0.9765[/C][/ROW]
[ROW][C]50[/C][C] 0.0181[/C][C] 0.03619[/C][C] 0.9819[/C][/ROW]
[ROW][C]51[/C][C] 0.01655[/C][C] 0.03311[/C][C] 0.9834[/C][/ROW]
[ROW][C]52[/C][C] 0.01154[/C][C] 0.02307[/C][C] 0.9885[/C][/ROW]
[ROW][C]53[/C][C] 0.009296[/C][C] 0.01859[/C][C] 0.9907[/C][/ROW]
[ROW][C]54[/C][C] 0.09755[/C][C] 0.1951[/C][C] 0.9024[/C][/ROW]
[ROW][C]55[/C][C] 0.08726[/C][C] 0.1745[/C][C] 0.9127[/C][/ROW]
[ROW][C]56[/C][C] 0.2329[/C][C] 0.4659[/C][C] 0.7671[/C][/ROW]
[ROW][C]57[/C][C] 0.2039[/C][C] 0.4078[/C][C] 0.7961[/C][/ROW]
[ROW][C]58[/C][C] 0.2259[/C][C] 0.4517[/C][C] 0.7741[/C][/ROW]
[ROW][C]59[/C][C] 0.2047[/C][C] 0.4094[/C][C] 0.7953[/C][/ROW]
[ROW][C]60[/C][C] 0.1954[/C][C] 0.3909[/C][C] 0.8046[/C][/ROW]
[ROW][C]61[/C][C] 0.1759[/C][C] 0.3519[/C][C] 0.8241[/C][/ROW]
[ROW][C]62[/C][C] 0.1519[/C][C] 0.3037[/C][C] 0.8481[/C][/ROW]
[ROW][C]63[/C][C] 0.1213[/C][C] 0.2425[/C][C] 0.8787[/C][/ROW]
[ROW][C]64[/C][C] 0.15[/C][C] 0.3[/C][C] 0.85[/C][/ROW]
[ROW][C]65[/C][C] 0.1604[/C][C] 0.3209[/C][C] 0.8396[/C][/ROW]
[ROW][C]66[/C][C] 0.129[/C][C] 0.2581[/C][C] 0.871[/C][/ROW]
[ROW][C]67[/C][C] 0.1031[/C][C] 0.2063[/C][C] 0.8969[/C][/ROW]
[ROW][C]68[/C][C] 0.1268[/C][C] 0.2537[/C][C] 0.8732[/C][/ROW]
[ROW][C]69[/C][C] 0.1012[/C][C] 0.2024[/C][C] 0.8988[/C][/ROW]
[ROW][C]70[/C][C] 0.1687[/C][C] 0.3374[/C][C] 0.8313[/C][/ROW]
[ROW][C]71[/C][C] 0.1363[/C][C] 0.2725[/C][C] 0.8637[/C][/ROW]
[ROW][C]72[/C][C] 0.1068[/C][C] 0.2136[/C][C] 0.8932[/C][/ROW]
[ROW][C]73[/C][C] 0.08179[/C][C] 0.1636[/C][C] 0.9182[/C][/ROW]
[ROW][C]74[/C][C] 0.07418[/C][C] 0.1484[/C][C] 0.9258[/C][/ROW]
[ROW][C]75[/C][C] 0.05493[/C][C] 0.1099[/C][C] 0.9451[/C][/ROW]
[ROW][C]76[/C][C] 0.04722[/C][C] 0.09444[/C][C] 0.9528[/C][/ROW]
[ROW][C]77[/C][C] 0.03469[/C][C] 0.06937[/C][C] 0.9653[/C][/ROW]
[ROW][C]78[/C][C] 0.02455[/C][C] 0.0491[/C][C] 0.9754[/C][/ROW]
[ROW][C]79[/C][C] 0.01706[/C][C] 0.03411[/C][C] 0.9829[/C][/ROW]
[ROW][C]80[/C][C] 0.07789[/C][C] 0.1558[/C][C] 0.9221[/C][/ROW]
[ROW][C]81[/C][C] 0.08711[/C][C] 0.1742[/C][C] 0.9129[/C][/ROW]
[ROW][C]82[/C][C] 0.3429[/C][C] 0.6857[/C][C] 0.6571[/C][/ROW]
[ROW][C]83[/C][C] 0.3782[/C][C] 0.7564[/C][C] 0.6218[/C][/ROW]
[ROW][C]84[/C][C] 0.317[/C][C] 0.6339[/C][C] 0.683[/C][/ROW]
[ROW][C]85[/C][C] 0.4195[/C][C] 0.839[/C][C] 0.5805[/C][/ROW]
[ROW][C]86[/C][C] 0.3802[/C][C] 0.7604[/C][C] 0.6198[/C][/ROW]
[ROW][C]87[/C][C] 0.3651[/C][C] 0.7303[/C][C] 0.6349[/C][/ROW]
[ROW][C]88[/C][C] 0.4457[/C][C] 0.8915[/C][C] 0.5543[/C][/ROW]
[ROW][C]89[/C][C] 0.3922[/C][C] 0.7844[/C][C] 0.6078[/C][/ROW]
[ROW][C]90[/C][C] 0.3908[/C][C] 0.7817[/C][C] 0.6092[/C][/ROW]
[ROW][C]91[/C][C] 0.6434[/C][C] 0.7131[/C][C] 0.3566[/C][/ROW]
[ROW][C]92[/C][C] 0.6997[/C][C] 0.6006[/C][C] 0.3003[/C][/ROW]
[ROW][C]93[/C][C] 0.6011[/C][C] 0.7977[/C][C] 0.3989[/C][/ROW]
[ROW][C]94[/C][C] 0.522[/C][C] 0.9559[/C][C] 0.478[/C][/ROW]
[ROW][C]95[/C][C] 0.4191[/C][C] 0.8382[/C][C] 0.5809[/C][/ROW]
[ROW][C]96[/C][C] 0.7637[/C][C] 0.4725[/C][C] 0.2363[/C][/ROW]
[ROW][C]97[/C][C] 0.7032[/C][C] 0.5937[/C][C] 0.2968[/C][/ROW]
[ROW][C]98[/C][C] 0.6394[/C][C] 0.7212[/C][C] 0.3606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

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

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.5641 0.8717 0.4359
6 0.4006 0.8012 0.5994
7 0.3527 0.7055 0.6473
8 0.2349 0.4697 0.7651
9 0.2715 0.5431 0.7285
10 0.1855 0.3709 0.8145
11 0.1467 0.2935 0.8533
12 0.09297 0.1859 0.907
13 0.05683 0.1137 0.9432
14 0.03316 0.06633 0.9668
15 0.02117 0.04233 0.9788
16 0.01318 0.02635 0.9868
17 0.007077 0.01415 0.9929
18 0.01293 0.02586 0.9871
19 0.008974 0.01795 0.991
20 0.006772 0.01354 0.9932
21 0.02996 0.05992 0.97
22 0.02527 0.05055 0.9747
23 0.02349 0.04697 0.9765
24 0.02373 0.04747 0.9763
25 0.04095 0.0819 0.959
26 0.03726 0.07452 0.9627
27 0.05038 0.1008 0.9496
28 0.03772 0.07544 0.9623
29 0.02643 0.05286 0.9736
30 0.01886 0.03771 0.9811
31 0.01383 0.02766 0.9862
32 0.01807 0.03613 0.9819
33 0.01734 0.03469 0.9827
34 0.1179 0.2358 0.8821
35 0.1092 0.2183 0.8908
36 0.128 0.256 0.872
37 0.1187 0.2374 0.8813
38 0.09492 0.1898 0.9051
39 0.08638 0.1728 0.9136
40 0.06616 0.1323 0.9338
41 0.05278 0.1056 0.9472
42 0.04152 0.08303 0.9585
43 0.0303 0.0606 0.9697
44 0.05595 0.1119 0.944
45 0.05795 0.1159 0.9421
46 0.04778 0.09555 0.9522
47 0.03896 0.07792 0.961
48 0.03241 0.06481 0.9676
49 0.02346 0.04692 0.9765
50 0.0181 0.03619 0.9819
51 0.01655 0.03311 0.9834
52 0.01154 0.02307 0.9885
53 0.009296 0.01859 0.9907
54 0.09755 0.1951 0.9024
55 0.08726 0.1745 0.9127
56 0.2329 0.4659 0.7671
57 0.2039 0.4078 0.7961
58 0.2259 0.4517 0.7741
59 0.2047 0.4094 0.7953
60 0.1954 0.3909 0.8046
61 0.1759 0.3519 0.8241
62 0.1519 0.3037 0.8481
63 0.1213 0.2425 0.8787
64 0.15 0.3 0.85
65 0.1604 0.3209 0.8396
66 0.129 0.2581 0.871
67 0.1031 0.2063 0.8969
68 0.1268 0.2537 0.8732
69 0.1012 0.2024 0.8988
70 0.1687 0.3374 0.8313
71 0.1363 0.2725 0.8637
72 0.1068 0.2136 0.8932
73 0.08179 0.1636 0.9182
74 0.07418 0.1484 0.9258
75 0.05493 0.1099 0.9451
76 0.04722 0.09444 0.9528
77 0.03469 0.06937 0.9653
78 0.02455 0.0491 0.9754
79 0.01706 0.03411 0.9829
80 0.07789 0.1558 0.9221
81 0.08711 0.1742 0.9129
82 0.3429 0.6857 0.6571
83 0.3782 0.7564 0.6218
84 0.317 0.6339 0.683
85 0.4195 0.839 0.5805
86 0.3802 0.7604 0.6198
87 0.3651 0.7303 0.6349
88 0.4457 0.8915 0.5543
89 0.3922 0.7844 0.6078
90 0.3908 0.7817 0.6092
91 0.6434 0.7131 0.3566
92 0.6997 0.6006 0.3003
93 0.6011 0.7977 0.3989
94 0.522 0.9559 0.478
95 0.4191 0.8382 0.5809
96 0.7637 0.4725 0.2363
97 0.7032 0.5937 0.2968
98 0.6394 0.7212 0.3606






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level190.202128NOK
10% type I error level330.351064NOK

\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 & 19 & 0.202128 & NOK \tabularnewline
10% type I error level & 33 & 0.351064 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.202128[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.351064[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level0 0OK
5% type I error level190.202128NOK
10% type I error level330.351064NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486


\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 = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486

[/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 = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

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

As an alternative you can also use a QR Code:  
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 = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486


Figures (Output of Computation)

Figure 1
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Figure 2
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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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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;
Parameters (R input):
par1 = 2 ; 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')