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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 computationSat, 03 Dec 2016 20:08:36 +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/03/t1480792394xoq8l2nid9hvuvi.htm/, Retrieved Sun, 05 May 2024 18:35:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297651, Retrieved Sun, 05 May 2024 18:35:30 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100

Tree of Dependent Computations

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

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

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297651&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]7 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297651&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&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 time7 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
TVDCsum[t] = + 13.3914 + 0.115366ITH1[t] + 0.190647ITH2[t] + 0.192445ITH3[t] -0.0110018ITH4[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
TVDCsum[t] =  +  13.3914 +  0.115366ITH1[t] +  0.190647ITH2[t] +  0.192445ITH3[t] -0.0110018ITH4[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=297651&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCsum[t] =  +  13.3914 +  0.115366ITH1[t] +  0.190647ITH2[t] +  0.192445ITH3[t] -0.0110018ITH4[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=297651&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&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
TVDCsum[t] = + 13.3914 + 0.115366ITH1[t] + 0.190647ITH2[t] + 0.192445ITH3[t] -0.0110018ITH4[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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+13.39 0.9339+1.4340e+01 3.705e-30 1.853e-30
ITH1+0.1154 0.2096+5.5030e-01 0.5829 0.2914
ITH2+0.1906 0.2313+8.2410e-01 0.4111 0.2056
ITH3+0.1924 0.1951+9.8640e-01 0.3255 0.1627
ITH4-0.011 0.1571-7.0030e-02 0.9443 0.4721

\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) & +13.39 &  0.9339 & +1.4340e+01 &  3.705e-30 &  1.853e-30 \tabularnewline
ITH1 & +0.1154 &  0.2096 & +5.5030e-01 &  0.5829 &  0.2914 \tabularnewline
ITH2 & +0.1906 &  0.2313 & +8.2410e-01 &  0.4111 &  0.2056 \tabularnewline
ITH3 & +0.1924 &  0.1951 & +9.8640e-01 &  0.3255 &  0.1627 \tabularnewline
ITH4 & -0.011 &  0.1571 & -7.0030e-02 &  0.9443 &  0.4721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297651&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]+13.39[/C][C] 0.9339[/C][C]+1.4340e+01[/C][C] 3.705e-30[/C][C] 1.853e-30[/C][/ROW]
[ROW][C]ITH1[/C][C]+0.1154[/C][C] 0.2096[/C][C]+5.5030e-01[/C][C] 0.5829[/C][C] 0.2914[/C][/ROW]
[ROW][C]ITH2[/C][C]+0.1906[/C][C] 0.2313[/C][C]+8.2410e-01[/C][C] 0.4111[/C][C] 0.2056[/C][/ROW]
[ROW][C]ITH3[/C][C]+0.1924[/C][C] 0.1951[/C][C]+9.8640e-01[/C][C] 0.3255[/C][C] 0.1627[/C][/ROW]
[ROW][C]ITH4[/C][C]-0.011[/C][C] 0.1571[/C][C]-7.0030e-02[/C][C] 0.9443[/C][C] 0.4721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297651&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&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)+13.39 0.9339+1.4340e+01 3.705e-30 1.853e-30
ITH1+0.1154 0.2096+5.5030e-01 0.5829 0.2914
ITH2+0.1906 0.2313+8.2410e-01 0.4111 0.2056
ITH3+0.1924 0.1951+9.8640e-01 0.3255 0.1627
ITH4-0.011 0.1571-7.0030e-02 0.9443 0.4721






Multiple Linear Regression - Regression Statistics
Multiple R 0.1883
R-squared 0.03546
Adjusted R-squared 0.01041
F-TEST (value) 1.415
F-TEST (DF numerator)4
F-TEST (DF denominator)154
p-value 0.2314
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.655
Sum Squared Residuals 422

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1883 \tabularnewline
R-squared &  0.03546 \tabularnewline
Adjusted R-squared &  0.01041 \tabularnewline
F-TEST (value) &  1.415 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value &  0.2314 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.655 \tabularnewline
Sum Squared Residuals &  422 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297651&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1883[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.03546[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01041[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.415[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C] 0.2314[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.655[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 422[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297651&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&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 0.1883
R-squared 0.03546
Adjusted R-squared 0.01041
F-TEST (value) 1.415
F-TEST (DF numerator)4
F-TEST (DF denominator)154
p-value 0.2314
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.655
Sum Squared Residuals 422






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 13 15.03-2.033
2 16 15.84 0.1603
3 17 15.46 1.543
4 15 15.46-0.4566
5 16 15.15 0.8512
6 16 15.83 0.1713
7 16 15.84 0.1603
8 17 15.68 1.32
9 17 15.28 1.725
10 17 15.84 1.16
11 15 15.54-0.5446
12 16 15.83 0.1713
13 14 15.65-1.647
14 16 15.15 0.8512
15 17 15.24 1.763
16 16 15.83 0.1713
17 17 15.06 1.938
18 16 15.34 0.6588
19 15 14.75 0.2542
20 16 15.64 0.3638
21 15 15.64-0.6362
22 17 15.67 1.331
23 14 15.35-1.352
24 16 15.72 0.2757
25 15 15.53-0.5319
26 16 15.64 0.3638
27 16 15.66 0.3418
28 13 15.36-2.363
29 15 15.64-0.6362
30 17 15.83 1.171
31 15 13.87 1.132
32 13 15.64-2.636
33 17 15.54 1.457
34 15 15.35-0.3522
35 14 15.34-1.341
36 14 15.65-1.647
37 18 15.54 2.455
38 15 15.35-0.3522
39 17 15.46 1.543
40 13 15.04-2.035
41 16 15.83 0.1713
42 15 15.84-0.8397
43 15 14.17 0.8262
44 16 14.84 1.157
45 15 15.14-0.1378
46 13 15.34-2.339
47 17 15.65 1.353
48 18 15.85 2.149
49 18 15.34 2.659
50 11 15.45-4.455
51 14 15.84-1.84
52 13 15.34-2.341
53 15 15.64-0.6362
54 17 15.37 1.628
55 16 15.34 0.6588
56 15 15.04-0.04439
57 17 15.18 1.818
58 16 15.53 0.4682
59 16 15.46 0.5434
60 16 15.53 0.4682
61 15 15.54-0.5429
62 12 15.34-3.341
63 17 14.96 2.042
64 14 15.34-1.341
65 14 15.12-1.121
66 16 15.54 0.4571
67 15 15.16-0.1598
68 15 15.83-0.8287
69 14 14.85-0.8537
70 13 15.04-2.044
71 18 15.65 2.351
72 15 14.96 0.04189
73 16 15.84 0.1603
74 14 15.52-1.521
75 15 14.96 0.04189
76 17 15.44 1.556
77 16 15.84 0.1603
78 10 15.28-5.275
79 16 15.16 0.8402
80 17 15.46 1.543
81 17 15.84 1.16
82 20 15.32 4.677
83 17 15.64 1.362
84 18 15.65 2.353
85 15 15.83-0.8287
86 17 15.48 1.521
87 14 15.35-1.352
88 15 15.35-0.3522
89 17 15.83 1.171
90 16 15.35 0.6478
91 17 15.84 1.16
92 15 15.65-0.6472
93 16 15.65 0.351
94 18 15.35 2.648
95 18 15.83 2.171
96 16 15.86 0.1383
97 17 15.65 1.351
98 15 15.84-0.8397
99 13 15.83-2.829
100 15 14.97 0.03089
101 17 15.53 1.466
102 16 15.35 0.6478
103 16 15.34 0.6588
104 15 15.85-0.8507
105 16 15.65 0.3528
106 16 14.96 1.044
107 14 15.23-1.226
108 15 15.06-0.0554
109 12 15.53-3.534
110 16 15.65 0.3528
111 16 15.46 0.5434
112 17 15.53 1.466
113 16 15.83 0.1713
114 14 15.47-1.468
115 15 15.16-0.1598
116 14 15.15-1.149
117 16 15.65 0.3528
118 15 15.83-0.8287
119 17 15.45 1.545
120 15 15.45-0.4548
121 16 15.53 0.4682
122 16 15.46 0.5434
123 15 15.23-0.2258
124 15 15.66-0.6582
125 13 15.65-2.649
126 18 15.83 2.171
127 13 15.35-2.352
128 11 15.34-4.341
129 18 15.52 2.477
130 15 15.35-0.3522
131 19 15.65 3.351
132 17 15.83 1.171
133 13 15.84-2.84
134 14 15.36-1.363
135 13 15.48-2.479
136 17 15.46 1.543
137 14 15.65-1.649
138 19 15.83 3.171
139 14 15.46-1.458
140 16 15.65 0.351
141 12 15.35-3.352
142 16 15.47 0.5324
143 16 14.86 1.135
144 15 15.23-0.2258
145 12 15.52-3.521
146 15 15.53-0.5319
147 17 15.21 1.787
148 14 15.06-1.055
149 15 15.84-0.8397
150 18 15.65 2.353
151 15 15.48-0.4786
152 18 15.46 2.543
153 15 15.84-0.8397
154 15 15.65-0.649
155 16 15.84 0.1603
156 13 15.67-2.671
157 16 15.34 0.6588
158 14 15.54-1.545
159 16 15.31 0.6861

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  15.03 & -2.033 \tabularnewline
2 &  16 &  15.84 &  0.1603 \tabularnewline
3 &  17 &  15.46 &  1.543 \tabularnewline
4 &  15 &  15.46 & -0.4566 \tabularnewline
5 &  16 &  15.15 &  0.8512 \tabularnewline
6 &  16 &  15.83 &  0.1713 \tabularnewline
7 &  16 &  15.84 &  0.1603 \tabularnewline
8 &  17 &  15.68 &  1.32 \tabularnewline
9 &  17 &  15.28 &  1.725 \tabularnewline
10 &  17 &  15.84 &  1.16 \tabularnewline
11 &  15 &  15.54 & -0.5446 \tabularnewline
12 &  16 &  15.83 &  0.1713 \tabularnewline
13 &  14 &  15.65 & -1.647 \tabularnewline
14 &  16 &  15.15 &  0.8512 \tabularnewline
15 &  17 &  15.24 &  1.763 \tabularnewline
16 &  16 &  15.83 &  0.1713 \tabularnewline
17 &  17 &  15.06 &  1.938 \tabularnewline
18 &  16 &  15.34 &  0.6588 \tabularnewline
19 &  15 &  14.75 &  0.2542 \tabularnewline
20 &  16 &  15.64 &  0.3638 \tabularnewline
21 &  15 &  15.64 & -0.6362 \tabularnewline
22 &  17 &  15.67 &  1.331 \tabularnewline
23 &  14 &  15.35 & -1.352 \tabularnewline
24 &  16 &  15.72 &  0.2757 \tabularnewline
25 &  15 &  15.53 & -0.5319 \tabularnewline
26 &  16 &  15.64 &  0.3638 \tabularnewline
27 &  16 &  15.66 &  0.3418 \tabularnewline
28 &  13 &  15.36 & -2.363 \tabularnewline
29 &  15 &  15.64 & -0.6362 \tabularnewline
30 &  17 &  15.83 &  1.171 \tabularnewline
31 &  15 &  13.87 &  1.132 \tabularnewline
32 &  13 &  15.64 & -2.636 \tabularnewline
33 &  17 &  15.54 &  1.457 \tabularnewline
34 &  15 &  15.35 & -0.3522 \tabularnewline
35 &  14 &  15.34 & -1.341 \tabularnewline
36 &  14 &  15.65 & -1.647 \tabularnewline
37 &  18 &  15.54 &  2.455 \tabularnewline
38 &  15 &  15.35 & -0.3522 \tabularnewline
39 &  17 &  15.46 &  1.543 \tabularnewline
40 &  13 &  15.04 & -2.035 \tabularnewline
41 &  16 &  15.83 &  0.1713 \tabularnewline
42 &  15 &  15.84 & -0.8397 \tabularnewline
43 &  15 &  14.17 &  0.8262 \tabularnewline
44 &  16 &  14.84 &  1.157 \tabularnewline
45 &  15 &  15.14 & -0.1378 \tabularnewline
46 &  13 &  15.34 & -2.339 \tabularnewline
47 &  17 &  15.65 &  1.353 \tabularnewline
48 &  18 &  15.85 &  2.149 \tabularnewline
49 &  18 &  15.34 &  2.659 \tabularnewline
50 &  11 &  15.45 & -4.455 \tabularnewline
51 &  14 &  15.84 & -1.84 \tabularnewline
52 &  13 &  15.34 & -2.341 \tabularnewline
53 &  15 &  15.64 & -0.6362 \tabularnewline
54 &  17 &  15.37 &  1.628 \tabularnewline
55 &  16 &  15.34 &  0.6588 \tabularnewline
56 &  15 &  15.04 & -0.04439 \tabularnewline
57 &  17 &  15.18 &  1.818 \tabularnewline
58 &  16 &  15.53 &  0.4682 \tabularnewline
59 &  16 &  15.46 &  0.5434 \tabularnewline
60 &  16 &  15.53 &  0.4682 \tabularnewline
61 &  15 &  15.54 & -0.5429 \tabularnewline
62 &  12 &  15.34 & -3.341 \tabularnewline
63 &  17 &  14.96 &  2.042 \tabularnewline
64 &  14 &  15.34 & -1.341 \tabularnewline
65 &  14 &  15.12 & -1.121 \tabularnewline
66 &  16 &  15.54 &  0.4571 \tabularnewline
67 &  15 &  15.16 & -0.1598 \tabularnewline
68 &  15 &  15.83 & -0.8287 \tabularnewline
69 &  14 &  14.85 & -0.8537 \tabularnewline
70 &  13 &  15.04 & -2.044 \tabularnewline
71 &  18 &  15.65 &  2.351 \tabularnewline
72 &  15 &  14.96 &  0.04189 \tabularnewline
73 &  16 &  15.84 &  0.1603 \tabularnewline
74 &  14 &  15.52 & -1.521 \tabularnewline
75 &  15 &  14.96 &  0.04189 \tabularnewline
76 &  17 &  15.44 &  1.556 \tabularnewline
77 &  16 &  15.84 &  0.1603 \tabularnewline
78 &  10 &  15.28 & -5.275 \tabularnewline
79 &  16 &  15.16 &  0.8402 \tabularnewline
80 &  17 &  15.46 &  1.543 \tabularnewline
81 &  17 &  15.84 &  1.16 \tabularnewline
82 &  20 &  15.32 &  4.677 \tabularnewline
83 &  17 &  15.64 &  1.362 \tabularnewline
84 &  18 &  15.65 &  2.353 \tabularnewline
85 &  15 &  15.83 & -0.8287 \tabularnewline
86 &  17 &  15.48 &  1.521 \tabularnewline
87 &  14 &  15.35 & -1.352 \tabularnewline
88 &  15 &  15.35 & -0.3522 \tabularnewline
89 &  17 &  15.83 &  1.171 \tabularnewline
90 &  16 &  15.35 &  0.6478 \tabularnewline
91 &  17 &  15.84 &  1.16 \tabularnewline
92 &  15 &  15.65 & -0.6472 \tabularnewline
93 &  16 &  15.65 &  0.351 \tabularnewline
94 &  18 &  15.35 &  2.648 \tabularnewline
95 &  18 &  15.83 &  2.171 \tabularnewline
96 &  16 &  15.86 &  0.1383 \tabularnewline
97 &  17 &  15.65 &  1.351 \tabularnewline
98 &  15 &  15.84 & -0.8397 \tabularnewline
99 &  13 &  15.83 & -2.829 \tabularnewline
100 &  15 &  14.97 &  0.03089 \tabularnewline
101 &  17 &  15.53 &  1.466 \tabularnewline
102 &  16 &  15.35 &  0.6478 \tabularnewline
103 &  16 &  15.34 &  0.6588 \tabularnewline
104 &  15 &  15.85 & -0.8507 \tabularnewline
105 &  16 &  15.65 &  0.3528 \tabularnewline
106 &  16 &  14.96 &  1.044 \tabularnewline
107 &  14 &  15.23 & -1.226 \tabularnewline
108 &  15 &  15.06 & -0.0554 \tabularnewline
109 &  12 &  15.53 & -3.534 \tabularnewline
110 &  16 &  15.65 &  0.3528 \tabularnewline
111 &  16 &  15.46 &  0.5434 \tabularnewline
112 &  17 &  15.53 &  1.466 \tabularnewline
113 &  16 &  15.83 &  0.1713 \tabularnewline
114 &  14 &  15.47 & -1.468 \tabularnewline
115 &  15 &  15.16 & -0.1598 \tabularnewline
116 &  14 &  15.15 & -1.149 \tabularnewline
117 &  16 &  15.65 &  0.3528 \tabularnewline
118 &  15 &  15.83 & -0.8287 \tabularnewline
119 &  17 &  15.45 &  1.545 \tabularnewline
120 &  15 &  15.45 & -0.4548 \tabularnewline
121 &  16 &  15.53 &  0.4682 \tabularnewline
122 &  16 &  15.46 &  0.5434 \tabularnewline
123 &  15 &  15.23 & -0.2258 \tabularnewline
124 &  15 &  15.66 & -0.6582 \tabularnewline
125 &  13 &  15.65 & -2.649 \tabularnewline
126 &  18 &  15.83 &  2.171 \tabularnewline
127 &  13 &  15.35 & -2.352 \tabularnewline
128 &  11 &  15.34 & -4.341 \tabularnewline
129 &  18 &  15.52 &  2.477 \tabularnewline
130 &  15 &  15.35 & -0.3522 \tabularnewline
131 &  19 &  15.65 &  3.351 \tabularnewline
132 &  17 &  15.83 &  1.171 \tabularnewline
133 &  13 &  15.84 & -2.84 \tabularnewline
134 &  14 &  15.36 & -1.363 \tabularnewline
135 &  13 &  15.48 & -2.479 \tabularnewline
136 &  17 &  15.46 &  1.543 \tabularnewline
137 &  14 &  15.65 & -1.649 \tabularnewline
138 &  19 &  15.83 &  3.171 \tabularnewline
139 &  14 &  15.46 & -1.458 \tabularnewline
140 &  16 &  15.65 &  0.351 \tabularnewline
141 &  12 &  15.35 & -3.352 \tabularnewline
142 &  16 &  15.47 &  0.5324 \tabularnewline
143 &  16 &  14.86 &  1.135 \tabularnewline
144 &  15 &  15.23 & -0.2258 \tabularnewline
145 &  12 &  15.52 & -3.521 \tabularnewline
146 &  15 &  15.53 & -0.5319 \tabularnewline
147 &  17 &  15.21 &  1.787 \tabularnewline
148 &  14 &  15.06 & -1.055 \tabularnewline
149 &  15 &  15.84 & -0.8397 \tabularnewline
150 &  18 &  15.65 &  2.353 \tabularnewline
151 &  15 &  15.48 & -0.4786 \tabularnewline
152 &  18 &  15.46 &  2.543 \tabularnewline
153 &  15 &  15.84 & -0.8397 \tabularnewline
154 &  15 &  15.65 & -0.649 \tabularnewline
155 &  16 &  15.84 &  0.1603 \tabularnewline
156 &  13 &  15.67 & -2.671 \tabularnewline
157 &  16 &  15.34 &  0.6588 \tabularnewline
158 &  14 &  15.54 & -1.545 \tabularnewline
159 &  16 &  15.31 &  0.6861 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297651&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] 15.03[/C][C]-2.033[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.84[/C][C] 0.1603[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.46[/C][C] 1.543[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 15.46[/C][C]-0.4566[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.15[/C][C] 0.8512[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.83[/C][C] 0.1713[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 15.84[/C][C] 0.1603[/C][/ROW]
[ROW][C]8[/C][C] 17[/C][C] 15.68[/C][C] 1.32[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 15.28[/C][C] 1.725[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.84[/C][C] 1.16[/C][/ROW]
[ROW][C]11[/C][C] 15[/C][C] 15.54[/C][C]-0.5446[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.83[/C][C] 0.1713[/C][/ROW]
[ROW][C]13[/C][C] 14[/C][C] 15.65[/C][C]-1.647[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 15.15[/C][C] 0.8512[/C][/ROW]
[ROW][C]15[/C][C] 17[/C][C] 15.24[/C][C] 1.763[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 15.83[/C][C] 0.1713[/C][/ROW]
[ROW][C]17[/C][C] 17[/C][C] 15.06[/C][C] 1.938[/C][/ROW]
[ROW][C]18[/C][C] 16[/C][C] 15.34[/C][C] 0.6588[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 14.75[/C][C] 0.2542[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 15.64[/C][C] 0.3638[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.64[/C][C]-0.6362[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 15.67[/C][C] 1.331[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 15.35[/C][C]-1.352[/C][/ROW]
[ROW][C]24[/C][C] 16[/C][C] 15.72[/C][C] 0.2757[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 15.53[/C][C]-0.5319[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 15.64[/C][C] 0.3638[/C][/ROW]
[ROW][C]27[/C][C] 16[/C][C] 15.66[/C][C] 0.3418[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 15.36[/C][C]-2.363[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.64[/C][C]-0.6362[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 15.83[/C][C] 1.171[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 13.87[/C][C] 1.132[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.64[/C][C]-2.636[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 15.54[/C][C] 1.457[/C][/ROW]
[ROW][C]34[/C][C] 15[/C][C] 15.35[/C][C]-0.3522[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 15.34[/C][C]-1.341[/C][/ROW]
[ROW][C]36[/C][C] 14[/C][C] 15.65[/C][C]-1.647[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 15.54[/C][C] 2.455[/C][/ROW]
[ROW][C]38[/C][C] 15[/C][C] 15.35[/C][C]-0.3522[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 15.46[/C][C] 1.543[/C][/ROW]
[ROW][C]40[/C][C] 13[/C][C] 15.04[/C][C]-2.035[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.83[/C][C] 0.1713[/C][/ROW]
[ROW][C]42[/C][C] 15[/C][C] 15.84[/C][C]-0.8397[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 14.17[/C][C] 0.8262[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 14.84[/C][C] 1.157[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.14[/C][C]-0.1378[/C][/ROW]
[ROW][C]46[/C][C] 13[/C][C] 15.34[/C][C]-2.339[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 15.65[/C][C] 1.353[/C][/ROW]
[ROW][C]48[/C][C] 18[/C][C] 15.85[/C][C] 2.149[/C][/ROW]
[ROW][C]49[/C][C] 18[/C][C] 15.34[/C][C] 2.659[/C][/ROW]
[ROW][C]50[/C][C] 11[/C][C] 15.45[/C][C]-4.455[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 15.84[/C][C]-1.84[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 15.34[/C][C]-2.341[/C][/ROW]
[ROW][C]53[/C][C] 15[/C][C] 15.64[/C][C]-0.6362[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 15.37[/C][C] 1.628[/C][/ROW]
[ROW][C]55[/C][C] 16[/C][C] 15.34[/C][C] 0.6588[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 15.04[/C][C]-0.04439[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.18[/C][C] 1.818[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 15.53[/C][C] 0.4682[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 15.46[/C][C] 0.5434[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 15.53[/C][C] 0.4682[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 15.54[/C][C]-0.5429[/C][/ROW]
[ROW][C]62[/C][C] 12[/C][C] 15.34[/C][C]-3.341[/C][/ROW]
[ROW][C]63[/C][C] 17[/C][C] 14.96[/C][C] 2.042[/C][/ROW]
[ROW][C]64[/C][C] 14[/C][C] 15.34[/C][C]-1.341[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 15.12[/C][C]-1.121[/C][/ROW]
[ROW][C]66[/C][C] 16[/C][C] 15.54[/C][C] 0.4571[/C][/ROW]
[ROW][C]67[/C][C] 15[/C][C] 15.16[/C][C]-0.1598[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 15.83[/C][C]-0.8287[/C][/ROW]
[ROW][C]69[/C][C] 14[/C][C] 14.85[/C][C]-0.8537[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 15.04[/C][C]-2.044[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 15.65[/C][C] 2.351[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 14.96[/C][C] 0.04189[/C][/ROW]
[ROW][C]73[/C][C] 16[/C][C] 15.84[/C][C] 0.1603[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 15.52[/C][C]-1.521[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 14.96[/C][C] 0.04189[/C][/ROW]
[ROW][C]76[/C][C] 17[/C][C] 15.44[/C][C] 1.556[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 15.84[/C][C] 0.1603[/C][/ROW]
[ROW][C]78[/C][C] 10[/C][C] 15.28[/C][C]-5.275[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.16[/C][C] 0.8402[/C][/ROW]
[ROW][C]80[/C][C] 17[/C][C] 15.46[/C][C] 1.543[/C][/ROW]
[ROW][C]81[/C][C] 17[/C][C] 15.84[/C][C] 1.16[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 15.32[/C][C] 4.677[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.64[/C][C] 1.362[/C][/ROW]
[ROW][C]84[/C][C] 18[/C][C] 15.65[/C][C] 2.353[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 15.83[/C][C]-0.8287[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 15.48[/C][C] 1.521[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 15.35[/C][C]-1.352[/C][/ROW]
[ROW][C]88[/C][C] 15[/C][C] 15.35[/C][C]-0.3522[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 15.83[/C][C] 1.171[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 15.35[/C][C] 0.6478[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 15.84[/C][C] 1.16[/C][/ROW]
[ROW][C]92[/C][C] 15[/C][C] 15.65[/C][C]-0.6472[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.65[/C][C] 0.351[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 15.35[/C][C] 2.648[/C][/ROW]
[ROW][C]95[/C][C] 18[/C][C] 15.83[/C][C] 2.171[/C][/ROW]
[ROW][C]96[/C][C] 16[/C][C] 15.86[/C][C] 0.1383[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 15.65[/C][C] 1.351[/C][/ROW]
[ROW][C]98[/C][C] 15[/C][C] 15.84[/C][C]-0.8397[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 15.83[/C][C]-2.829[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 14.97[/C][C] 0.03089[/C][/ROW]
[ROW][C]101[/C][C] 17[/C][C] 15.53[/C][C] 1.466[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 15.35[/C][C] 0.6478[/C][/ROW]
[ROW][C]103[/C][C] 16[/C][C] 15.34[/C][C] 0.6588[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 15.85[/C][C]-0.8507[/C][/ROW]
[ROW][C]105[/C][C] 16[/C][C] 15.65[/C][C] 0.3528[/C][/ROW]
[ROW][C]106[/C][C] 16[/C][C] 14.96[/C][C] 1.044[/C][/ROW]
[ROW][C]107[/C][C] 14[/C][C] 15.23[/C][C]-1.226[/C][/ROW]
[ROW][C]108[/C][C] 15[/C][C] 15.06[/C][C]-0.0554[/C][/ROW]
[ROW][C]109[/C][C] 12[/C][C] 15.53[/C][C]-3.534[/C][/ROW]
[ROW][C]110[/C][C] 16[/C][C] 15.65[/C][C] 0.3528[/C][/ROW]
[ROW][C]111[/C][C] 16[/C][C] 15.46[/C][C] 0.5434[/C][/ROW]
[ROW][C]112[/C][C] 17[/C][C] 15.53[/C][C] 1.466[/C][/ROW]
[ROW][C]113[/C][C] 16[/C][C] 15.83[/C][C] 0.1713[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 15.47[/C][C]-1.468[/C][/ROW]
[ROW][C]115[/C][C] 15[/C][C] 15.16[/C][C]-0.1598[/C][/ROW]
[ROW][C]116[/C][C] 14[/C][C] 15.15[/C][C]-1.149[/C][/ROW]
[ROW][C]117[/C][C] 16[/C][C] 15.65[/C][C] 0.3528[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.83[/C][C]-0.8287[/C][/ROW]
[ROW][C]119[/C][C] 17[/C][C] 15.45[/C][C] 1.545[/C][/ROW]
[ROW][C]120[/C][C] 15[/C][C] 15.45[/C][C]-0.4548[/C][/ROW]
[ROW][C]121[/C][C] 16[/C][C] 15.53[/C][C] 0.4682[/C][/ROW]
[ROW][C]122[/C][C] 16[/C][C] 15.46[/C][C] 0.5434[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.23[/C][C]-0.2258[/C][/ROW]
[ROW][C]124[/C][C] 15[/C][C] 15.66[/C][C]-0.6582[/C][/ROW]
[ROW][C]125[/C][C] 13[/C][C] 15.65[/C][C]-2.649[/C][/ROW]
[ROW][C]126[/C][C] 18[/C][C] 15.83[/C][C] 2.171[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 15.35[/C][C]-2.352[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 15.34[/C][C]-4.341[/C][/ROW]
[ROW][C]129[/C][C] 18[/C][C] 15.52[/C][C] 2.477[/C][/ROW]
[ROW][C]130[/C][C] 15[/C][C] 15.35[/C][C]-0.3522[/C][/ROW]
[ROW][C]131[/C][C] 19[/C][C] 15.65[/C][C] 3.351[/C][/ROW]
[ROW][C]132[/C][C] 17[/C][C] 15.83[/C][C] 1.171[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 15.84[/C][C]-2.84[/C][/ROW]
[ROW][C]134[/C][C] 14[/C][C] 15.36[/C][C]-1.363[/C][/ROW]
[ROW][C]135[/C][C] 13[/C][C] 15.48[/C][C]-2.479[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 15.46[/C][C] 1.543[/C][/ROW]
[ROW][C]137[/C][C] 14[/C][C] 15.65[/C][C]-1.649[/C][/ROW]
[ROW][C]138[/C][C] 19[/C][C] 15.83[/C][C] 3.171[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 15.46[/C][C]-1.458[/C][/ROW]
[ROW][C]140[/C][C] 16[/C][C] 15.65[/C][C] 0.351[/C][/ROW]
[ROW][C]141[/C][C] 12[/C][C] 15.35[/C][C]-3.352[/C][/ROW]
[ROW][C]142[/C][C] 16[/C][C] 15.47[/C][C] 0.5324[/C][/ROW]
[ROW][C]143[/C][C] 16[/C][C] 14.86[/C][C] 1.135[/C][/ROW]
[ROW][C]144[/C][C] 15[/C][C] 15.23[/C][C]-0.2258[/C][/ROW]
[ROW][C]145[/C][C] 12[/C][C] 15.52[/C][C]-3.521[/C][/ROW]
[ROW][C]146[/C][C] 15[/C][C] 15.53[/C][C]-0.5319[/C][/ROW]
[ROW][C]147[/C][C] 17[/C][C] 15.21[/C][C] 1.787[/C][/ROW]
[ROW][C]148[/C][C] 14[/C][C] 15.06[/C][C]-1.055[/C][/ROW]
[ROW][C]149[/C][C] 15[/C][C] 15.84[/C][C]-0.8397[/C][/ROW]
[ROW][C]150[/C][C] 18[/C][C] 15.65[/C][C] 2.353[/C][/ROW]
[ROW][C]151[/C][C] 15[/C][C] 15.48[/C][C]-0.4786[/C][/ROW]
[ROW][C]152[/C][C] 18[/C][C] 15.46[/C][C] 2.543[/C][/ROW]
[ROW][C]153[/C][C] 15[/C][C] 15.84[/C][C]-0.8397[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 15.65[/C][C]-0.649[/C][/ROW]
[ROW][C]155[/C][C] 16[/C][C] 15.84[/C][C] 0.1603[/C][/ROW]
[ROW][C]156[/C][C] 13[/C][C] 15.67[/C][C]-2.671[/C][/ROW]
[ROW][C]157[/C][C] 16[/C][C] 15.34[/C][C] 0.6588[/C][/ROW]
[ROW][C]158[/C][C] 14[/C][C] 15.54[/C][C]-1.545[/C][/ROW]
[ROW][C]159[/C][C] 16[/C][C] 15.31[/C][C] 0.6861[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297651&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&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 15.03-2.033
2 16 15.84 0.1603
3 17 15.46 1.543
4 15 15.46-0.4566
5 16 15.15 0.8512
6 16 15.83 0.1713
7 16 15.84 0.1603
8 17 15.68 1.32
9 17 15.28 1.725
10 17 15.84 1.16
11 15 15.54-0.5446
12 16 15.83 0.1713
13 14 15.65-1.647
14 16 15.15 0.8512
15 17 15.24 1.763
16 16 15.83 0.1713
17 17 15.06 1.938
18 16 15.34 0.6588
19 15 14.75 0.2542
20 16 15.64 0.3638
21 15 15.64-0.6362
22 17 15.67 1.331
23 14 15.35-1.352
24 16 15.72 0.2757
25 15 15.53-0.5319
26 16 15.64 0.3638
27 16 15.66 0.3418
28 13 15.36-2.363
29 15 15.64-0.6362
30 17 15.83 1.171
31 15 13.87 1.132
32 13 15.64-2.636
33 17 15.54 1.457
34 15 15.35-0.3522
35 14 15.34-1.341
36 14 15.65-1.647
37 18 15.54 2.455
38 15 15.35-0.3522
39 17 15.46 1.543
40 13 15.04-2.035
41 16 15.83 0.1713
42 15 15.84-0.8397
43 15 14.17 0.8262
44 16 14.84 1.157
45 15 15.14-0.1378
46 13 15.34-2.339
47 17 15.65 1.353
48 18 15.85 2.149
49 18 15.34 2.659
50 11 15.45-4.455
51 14 15.84-1.84
52 13 15.34-2.341
53 15 15.64-0.6362
54 17 15.37 1.628
55 16 15.34 0.6588
56 15 15.04-0.04439
57 17 15.18 1.818
58 16 15.53 0.4682
59 16 15.46 0.5434
60 16 15.53 0.4682
61 15 15.54-0.5429
62 12 15.34-3.341
63 17 14.96 2.042
64 14 15.34-1.341
65 14 15.12-1.121
66 16 15.54 0.4571
67 15 15.16-0.1598
68 15 15.83-0.8287
69 14 14.85-0.8537
70 13 15.04-2.044
71 18 15.65 2.351
72 15 14.96 0.04189
73 16 15.84 0.1603
74 14 15.52-1.521
75 15 14.96 0.04189
76 17 15.44 1.556
77 16 15.84 0.1603
78 10 15.28-5.275
79 16 15.16 0.8402
80 17 15.46 1.543
81 17 15.84 1.16
82 20 15.32 4.677
83 17 15.64 1.362
84 18 15.65 2.353
85 15 15.83-0.8287
86 17 15.48 1.521
87 14 15.35-1.352
88 15 15.35-0.3522
89 17 15.83 1.171
90 16 15.35 0.6478
91 17 15.84 1.16
92 15 15.65-0.6472
93 16 15.65 0.351
94 18 15.35 2.648
95 18 15.83 2.171
96 16 15.86 0.1383
97 17 15.65 1.351
98 15 15.84-0.8397
99 13 15.83-2.829
100 15 14.97 0.03089
101 17 15.53 1.466
102 16 15.35 0.6478
103 16 15.34 0.6588
104 15 15.85-0.8507
105 16 15.65 0.3528
106 16 14.96 1.044
107 14 15.23-1.226
108 15 15.06-0.0554
109 12 15.53-3.534
110 16 15.65 0.3528
111 16 15.46 0.5434
112 17 15.53 1.466
113 16 15.83 0.1713
114 14 15.47-1.468
115 15 15.16-0.1598
116 14 15.15-1.149
117 16 15.65 0.3528
118 15 15.83-0.8287
119 17 15.45 1.545
120 15 15.45-0.4548
121 16 15.53 0.4682
122 16 15.46 0.5434
123 15 15.23-0.2258
124 15 15.66-0.6582
125 13 15.65-2.649
126 18 15.83 2.171
127 13 15.35-2.352
128 11 15.34-4.341
129 18 15.52 2.477
130 15 15.35-0.3522
131 19 15.65 3.351
132 17 15.83 1.171
133 13 15.84-2.84
134 14 15.36-1.363
135 13 15.48-2.479
136 17 15.46 1.543
137 14 15.65-1.649
138 19 15.83 3.171
139 14 15.46-1.458
140 16 15.65 0.351
141 12 15.35-3.352
142 16 15.47 0.5324
143 16 14.86 1.135
144 15 15.23-0.2258
145 12 15.52-3.521
146 15 15.53-0.5319
147 17 15.21 1.787
148 14 15.06-1.055
149 15 15.84-0.8397
150 18 15.65 2.353
151 15 15.48-0.4786
152 18 15.46 2.543
153 15 15.84-0.8397
154 15 15.65-0.649
155 16 15.84 0.1603
156 13 15.67-2.671
157 16 15.34 0.6588
158 14 15.54-1.545
159 16 15.31 0.6861






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.1432 0.2863 0.8568
9 0.07795 0.1559 0.9221
10 0.05684 0.1137 0.9432
11 0.03926 0.07853 0.9607
12 0.01691 0.03381 0.9831
13 0.07438 0.1488 0.9256
14 0.05879 0.1176 0.9412
15 0.1226 0.2453 0.8774
16 0.08035 0.1607 0.9197
17 0.05557 0.1111 0.9444
18 0.03416 0.06833 0.9658
19 0.02577 0.05155 0.9742
20 0.01558 0.03116 0.9844
21 0.009924 0.01985 0.9901
22 0.005863 0.01173 0.9941
23 0.01154 0.02309 0.9885
24 0.007721 0.01544 0.9923
25 0.004586 0.009172 0.9954
26 0.002643 0.005286 0.9974
27 0.001494 0.002987 0.9985
28 0.009212 0.01842 0.9908
29 0.006915 0.01383 0.9931
30 0.005986 0.01197 0.994
31 0.004205 0.008409 0.9958
32 0.01443 0.02886 0.9856
33 0.01488 0.02976 0.9851
34 0.01071 0.02143 0.9893
35 0.01053 0.02105 0.9895
36 0.01261 0.02521 0.9874
37 0.01969 0.03939 0.9803
38 0.01478 0.02957 0.9852
39 0.01261 0.02523 0.9874
40 0.01798 0.03597 0.982
41 0.01273 0.02547 0.9873
42 0.009956 0.01991 0.99
43 0.007002 0.014 0.993
44 0.005917 0.01183 0.9941
45 0.003933 0.007866 0.9961
46 0.005754 0.01151 0.9942
47 0.005214 0.01043 0.9948
48 0.006304 0.01261 0.9937
49 0.01249 0.02497 0.9875
50 0.08993 0.1799 0.9101
51 0.1016 0.2033 0.8984
52 0.1347 0.2694 0.8653
53 0.1115 0.223 0.8885
54 0.1057 0.2114 0.8943
55 0.08721 0.1744 0.9128
56 0.06891 0.1378 0.9311
57 0.06532 0.1306 0.9347
58 0.05449 0.109 0.9455
59 0.04256 0.08511 0.9574
60 0.03472 0.06944 0.9653
61 0.02712 0.05424 0.9729
62 0.06908 0.1382 0.9309
63 0.07401 0.148 0.926
64 0.06969 0.1394 0.9303
65 0.05869 0.1174 0.9413
66 0.04721 0.09443 0.9528
67 0.03721 0.07441 0.9628
68 0.03044 0.06088 0.9696
69 0.02618 0.05235 0.9738
70 0.02954 0.05908 0.9705
71 0.03505 0.0701 0.965
72 0.02716 0.05431 0.9728
73 0.02055 0.0411 0.9794
74 0.01933 0.03866 0.9807
75 0.0146 0.0292 0.9854
76 0.01608 0.03215 0.9839
77 0.01191 0.02383 0.9881
78 0.1656 0.3312 0.8344
79 0.1438 0.2876 0.8562
80 0.1368 0.2736 0.8632
81 0.1228 0.2455 0.8772
82 0.4051 0.8102 0.5949
83 0.3808 0.7616 0.6192
84 0.4263 0.8527 0.5737
85 0.3964 0.7928 0.6036
86 0.4023 0.8046 0.5977
87 0.3915 0.7829 0.6085
88 0.3523 0.7045 0.6477
89 0.3275 0.6551 0.6725
90 0.2962 0.5924 0.7038
91 0.2774 0.5549 0.7226
92 0.2455 0.4909 0.7545
93 0.2118 0.4235 0.7882
94 0.2804 0.5608 0.7196
95 0.3037 0.6075 0.6963
96 0.2974 0.5947 0.7026
97 0.2839 0.5678 0.7161
98 0.2539 0.5078 0.7461
99 0.3523 0.7046 0.6477
100 0.3098 0.6195 0.6902
101 0.3067 0.6135 0.6933
102 0.2828 0.5657 0.7172
103 0.2485 0.4969 0.7515
104 0.223 0.446 0.777
105 0.1899 0.3797 0.8101
106 0.1689 0.3379 0.8311
107 0.1547 0.3095 0.8453
108 0.1354 0.2708 0.8646
109 0.251 0.5019 0.749
110 0.2146 0.4292 0.7854
111 0.1812 0.3623 0.8188
112 0.1777 0.3553 0.8223
113 0.1472 0.2944 0.8528
114 0.1365 0.273 0.8635
115 0.1106 0.2213 0.8894
116 0.1065 0.213 0.8935
117 0.08521 0.1704 0.9148
118 0.07459 0.1492 0.9254
119 0.06803 0.1361 0.932
120 0.0551 0.1102 0.9449
121 0.04289 0.08577 0.9571
122 0.03212 0.06425 0.9679
123 0.02365 0.04729 0.9764
124 0.01757 0.03513 0.9824
125 0.02853 0.05706 0.9715
126 0.03003 0.06007 0.97
127 0.03345 0.06689 0.9666
128 0.1905 0.3811 0.8095
129 0.1933 0.3866 0.8067
130 0.1549 0.3099 0.8451
131 0.2935 0.587 0.7065
132 0.2622 0.5244 0.7378
133 0.3132 0.6264 0.6868
134 0.2664 0.5329 0.7336
135 0.2725 0.5449 0.7275
136 0.2421 0.4842 0.7579
137 0.2248 0.4496 0.7752
138 0.377 0.754 0.623
139 0.3739 0.7478 0.6261
140 0.306 0.6119 0.694
141 0.5127 0.9746 0.4873
142 0.4316 0.8632 0.5684
143 0.3551 0.7102 0.6449
144 0.2836 0.5672 0.7164
145 0.8567 0.2866 0.1433
146 0.8241 0.3517 0.1759
147 0.8231 0.3538 0.1769
148 0.9459 0.1083 0.05413
149 0.9035 0.1929 0.09645
150 0.8285 0.343 0.1715
151 0.6851 0.6298 0.3149

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.1432 &  0.2863 &  0.8568 \tabularnewline
9 &  0.07795 &  0.1559 &  0.9221 \tabularnewline
10 &  0.05684 &  0.1137 &  0.9432 \tabularnewline
11 &  0.03926 &  0.07853 &  0.9607 \tabularnewline
12 &  0.01691 &  0.03381 &  0.9831 \tabularnewline
13 &  0.07438 &  0.1488 &  0.9256 \tabularnewline
14 &  0.05879 &  0.1176 &  0.9412 \tabularnewline
15 &  0.1226 &  0.2453 &  0.8774 \tabularnewline
16 &  0.08035 &  0.1607 &  0.9197 \tabularnewline
17 &  0.05557 &  0.1111 &  0.9444 \tabularnewline
18 &  0.03416 &  0.06833 &  0.9658 \tabularnewline
19 &  0.02577 &  0.05155 &  0.9742 \tabularnewline
20 &  0.01558 &  0.03116 &  0.9844 \tabularnewline
21 &  0.009924 &  0.01985 &  0.9901 \tabularnewline
22 &  0.005863 &  0.01173 &  0.9941 \tabularnewline
23 &  0.01154 &  0.02309 &  0.9885 \tabularnewline
24 &  0.007721 &  0.01544 &  0.9923 \tabularnewline
25 &  0.004586 &  0.009172 &  0.9954 \tabularnewline
26 &  0.002643 &  0.005286 &  0.9974 \tabularnewline
27 &  0.001494 &  0.002987 &  0.9985 \tabularnewline
28 &  0.009212 &  0.01842 &  0.9908 \tabularnewline
29 &  0.006915 &  0.01383 &  0.9931 \tabularnewline
30 &  0.005986 &  0.01197 &  0.994 \tabularnewline
31 &  0.004205 &  0.008409 &  0.9958 \tabularnewline
32 &  0.01443 &  0.02886 &  0.9856 \tabularnewline
33 &  0.01488 &  0.02976 &  0.9851 \tabularnewline
34 &  0.01071 &  0.02143 &  0.9893 \tabularnewline
35 &  0.01053 &  0.02105 &  0.9895 \tabularnewline
36 &  0.01261 &  0.02521 &  0.9874 \tabularnewline
37 &  0.01969 &  0.03939 &  0.9803 \tabularnewline
38 &  0.01478 &  0.02957 &  0.9852 \tabularnewline
39 &  0.01261 &  0.02523 &  0.9874 \tabularnewline
40 &  0.01798 &  0.03597 &  0.982 \tabularnewline
41 &  0.01273 &  0.02547 &  0.9873 \tabularnewline
42 &  0.009956 &  0.01991 &  0.99 \tabularnewline
43 &  0.007002 &  0.014 &  0.993 \tabularnewline
44 &  0.005917 &  0.01183 &  0.9941 \tabularnewline
45 &  0.003933 &  0.007866 &  0.9961 \tabularnewline
46 &  0.005754 &  0.01151 &  0.9942 \tabularnewline
47 &  0.005214 &  0.01043 &  0.9948 \tabularnewline
48 &  0.006304 &  0.01261 &  0.9937 \tabularnewline
49 &  0.01249 &  0.02497 &  0.9875 \tabularnewline
50 &  0.08993 &  0.1799 &  0.9101 \tabularnewline
51 &  0.1016 &  0.2033 &  0.8984 \tabularnewline
52 &  0.1347 &  0.2694 &  0.8653 \tabularnewline
53 &  0.1115 &  0.223 &  0.8885 \tabularnewline
54 &  0.1057 &  0.2114 &  0.8943 \tabularnewline
55 &  0.08721 &  0.1744 &  0.9128 \tabularnewline
56 &  0.06891 &  0.1378 &  0.9311 \tabularnewline
57 &  0.06532 &  0.1306 &  0.9347 \tabularnewline
58 &  0.05449 &  0.109 &  0.9455 \tabularnewline
59 &  0.04256 &  0.08511 &  0.9574 \tabularnewline
60 &  0.03472 &  0.06944 &  0.9653 \tabularnewline
61 &  0.02712 &  0.05424 &  0.9729 \tabularnewline
62 &  0.06908 &  0.1382 &  0.9309 \tabularnewline
63 &  0.07401 &  0.148 &  0.926 \tabularnewline
64 &  0.06969 &  0.1394 &  0.9303 \tabularnewline
65 &  0.05869 &  0.1174 &  0.9413 \tabularnewline
66 &  0.04721 &  0.09443 &  0.9528 \tabularnewline
67 &  0.03721 &  0.07441 &  0.9628 \tabularnewline
68 &  0.03044 &  0.06088 &  0.9696 \tabularnewline
69 &  0.02618 &  0.05235 &  0.9738 \tabularnewline
70 &  0.02954 &  0.05908 &  0.9705 \tabularnewline
71 &  0.03505 &  0.0701 &  0.965 \tabularnewline
72 &  0.02716 &  0.05431 &  0.9728 \tabularnewline
73 &  0.02055 &  0.0411 &  0.9794 \tabularnewline
74 &  0.01933 &  0.03866 &  0.9807 \tabularnewline
75 &  0.0146 &  0.0292 &  0.9854 \tabularnewline
76 &  0.01608 &  0.03215 &  0.9839 \tabularnewline
77 &  0.01191 &  0.02383 &  0.9881 \tabularnewline
78 &  0.1656 &  0.3312 &  0.8344 \tabularnewline
79 &  0.1438 &  0.2876 &  0.8562 \tabularnewline
80 &  0.1368 &  0.2736 &  0.8632 \tabularnewline
81 &  0.1228 &  0.2455 &  0.8772 \tabularnewline
82 &  0.4051 &  0.8102 &  0.5949 \tabularnewline
83 &  0.3808 &  0.7616 &  0.6192 \tabularnewline
84 &  0.4263 &  0.8527 &  0.5737 \tabularnewline
85 &  0.3964 &  0.7928 &  0.6036 \tabularnewline
86 &  0.4023 &  0.8046 &  0.5977 \tabularnewline
87 &  0.3915 &  0.7829 &  0.6085 \tabularnewline
88 &  0.3523 &  0.7045 &  0.6477 \tabularnewline
89 &  0.3275 &  0.6551 &  0.6725 \tabularnewline
90 &  0.2962 &  0.5924 &  0.7038 \tabularnewline
91 &  0.2774 &  0.5549 &  0.7226 \tabularnewline
92 &  0.2455 &  0.4909 &  0.7545 \tabularnewline
93 &  0.2118 &  0.4235 &  0.7882 \tabularnewline
94 &  0.2804 &  0.5608 &  0.7196 \tabularnewline
95 &  0.3037 &  0.6075 &  0.6963 \tabularnewline
96 &  0.2974 &  0.5947 &  0.7026 \tabularnewline
97 &  0.2839 &  0.5678 &  0.7161 \tabularnewline
98 &  0.2539 &  0.5078 &  0.7461 \tabularnewline
99 &  0.3523 &  0.7046 &  0.6477 \tabularnewline
100 &  0.3098 &  0.6195 &  0.6902 \tabularnewline
101 &  0.3067 &  0.6135 &  0.6933 \tabularnewline
102 &  0.2828 &  0.5657 &  0.7172 \tabularnewline
103 &  0.2485 &  0.4969 &  0.7515 \tabularnewline
104 &  0.223 &  0.446 &  0.777 \tabularnewline
105 &  0.1899 &  0.3797 &  0.8101 \tabularnewline
106 &  0.1689 &  0.3379 &  0.8311 \tabularnewline
107 &  0.1547 &  0.3095 &  0.8453 \tabularnewline
108 &  0.1354 &  0.2708 &  0.8646 \tabularnewline
109 &  0.251 &  0.5019 &  0.749 \tabularnewline
110 &  0.2146 &  0.4292 &  0.7854 \tabularnewline
111 &  0.1812 &  0.3623 &  0.8188 \tabularnewline
112 &  0.1777 &  0.3553 &  0.8223 \tabularnewline
113 &  0.1472 &  0.2944 &  0.8528 \tabularnewline
114 &  0.1365 &  0.273 &  0.8635 \tabularnewline
115 &  0.1106 &  0.2213 &  0.8894 \tabularnewline
116 &  0.1065 &  0.213 &  0.8935 \tabularnewline
117 &  0.08521 &  0.1704 &  0.9148 \tabularnewline
118 &  0.07459 &  0.1492 &  0.9254 \tabularnewline
119 &  0.06803 &  0.1361 &  0.932 \tabularnewline
120 &  0.0551 &  0.1102 &  0.9449 \tabularnewline
121 &  0.04289 &  0.08577 &  0.9571 \tabularnewline
122 &  0.03212 &  0.06425 &  0.9679 \tabularnewline
123 &  0.02365 &  0.04729 &  0.9764 \tabularnewline
124 &  0.01757 &  0.03513 &  0.9824 \tabularnewline
125 &  0.02853 &  0.05706 &  0.9715 \tabularnewline
126 &  0.03003 &  0.06007 &  0.97 \tabularnewline
127 &  0.03345 &  0.06689 &  0.9666 \tabularnewline
128 &  0.1905 &  0.3811 &  0.8095 \tabularnewline
129 &  0.1933 &  0.3866 &  0.8067 \tabularnewline
130 &  0.1549 &  0.3099 &  0.8451 \tabularnewline
131 &  0.2935 &  0.587 &  0.7065 \tabularnewline
132 &  0.2622 &  0.5244 &  0.7378 \tabularnewline
133 &  0.3132 &  0.6264 &  0.6868 \tabularnewline
134 &  0.2664 &  0.5329 &  0.7336 \tabularnewline
135 &  0.2725 &  0.5449 &  0.7275 \tabularnewline
136 &  0.2421 &  0.4842 &  0.7579 \tabularnewline
137 &  0.2248 &  0.4496 &  0.7752 \tabularnewline
138 &  0.377 &  0.754 &  0.623 \tabularnewline
139 &  0.3739 &  0.7478 &  0.6261 \tabularnewline
140 &  0.306 &  0.6119 &  0.694 \tabularnewline
141 &  0.5127 &  0.9746 &  0.4873 \tabularnewline
142 &  0.4316 &  0.8632 &  0.5684 \tabularnewline
143 &  0.3551 &  0.7102 &  0.6449 \tabularnewline
144 &  0.2836 &  0.5672 &  0.7164 \tabularnewline
145 &  0.8567 &  0.2866 &  0.1433 \tabularnewline
146 &  0.8241 &  0.3517 &  0.1759 \tabularnewline
147 &  0.8231 &  0.3538 &  0.1769 \tabularnewline
148 &  0.9459 &  0.1083 &  0.05413 \tabularnewline
149 &  0.9035 &  0.1929 &  0.09645 \tabularnewline
150 &  0.8285 &  0.343 &  0.1715 \tabularnewline
151 &  0.6851 &  0.6298 &  0.3149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297651&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]8[/C][C] 0.1432[/C][C] 0.2863[/C][C] 0.8568[/C][/ROW]
[ROW][C]9[/C][C] 0.07795[/C][C] 0.1559[/C][C] 0.9221[/C][/ROW]
[ROW][C]10[/C][C] 0.05684[/C][C] 0.1137[/C][C] 0.9432[/C][/ROW]
[ROW][C]11[/C][C] 0.03926[/C][C] 0.07853[/C][C] 0.9607[/C][/ROW]
[ROW][C]12[/C][C] 0.01691[/C][C] 0.03381[/C][C] 0.9831[/C][/ROW]
[ROW][C]13[/C][C] 0.07438[/C][C] 0.1488[/C][C] 0.9256[/C][/ROW]
[ROW][C]14[/C][C] 0.05879[/C][C] 0.1176[/C][C] 0.9412[/C][/ROW]
[ROW][C]15[/C][C] 0.1226[/C][C] 0.2453[/C][C] 0.8774[/C][/ROW]
[ROW][C]16[/C][C] 0.08035[/C][C] 0.1607[/C][C] 0.9197[/C][/ROW]
[ROW][C]17[/C][C] 0.05557[/C][C] 0.1111[/C][C] 0.9444[/C][/ROW]
[ROW][C]18[/C][C] 0.03416[/C][C] 0.06833[/C][C] 0.9658[/C][/ROW]
[ROW][C]19[/C][C] 0.02577[/C][C] 0.05155[/C][C] 0.9742[/C][/ROW]
[ROW][C]20[/C][C] 0.01558[/C][C] 0.03116[/C][C] 0.9844[/C][/ROW]
[ROW][C]21[/C][C] 0.009924[/C][C] 0.01985[/C][C] 0.9901[/C][/ROW]
[ROW][C]22[/C][C] 0.005863[/C][C] 0.01173[/C][C] 0.9941[/C][/ROW]
[ROW][C]23[/C][C] 0.01154[/C][C] 0.02309[/C][C] 0.9885[/C][/ROW]
[ROW][C]24[/C][C] 0.007721[/C][C] 0.01544[/C][C] 0.9923[/C][/ROW]
[ROW][C]25[/C][C] 0.004586[/C][C] 0.009172[/C][C] 0.9954[/C][/ROW]
[ROW][C]26[/C][C] 0.002643[/C][C] 0.005286[/C][C] 0.9974[/C][/ROW]
[ROW][C]27[/C][C] 0.001494[/C][C] 0.002987[/C][C] 0.9985[/C][/ROW]
[ROW][C]28[/C][C] 0.009212[/C][C] 0.01842[/C][C] 0.9908[/C][/ROW]
[ROW][C]29[/C][C] 0.006915[/C][C] 0.01383[/C][C] 0.9931[/C][/ROW]
[ROW][C]30[/C][C] 0.005986[/C][C] 0.01197[/C][C] 0.994[/C][/ROW]
[ROW][C]31[/C][C] 0.004205[/C][C] 0.008409[/C][C] 0.9958[/C][/ROW]
[ROW][C]32[/C][C] 0.01443[/C][C] 0.02886[/C][C] 0.9856[/C][/ROW]
[ROW][C]33[/C][C] 0.01488[/C][C] 0.02976[/C][C] 0.9851[/C][/ROW]
[ROW][C]34[/C][C] 0.01071[/C][C] 0.02143[/C][C] 0.9893[/C][/ROW]
[ROW][C]35[/C][C] 0.01053[/C][C] 0.02105[/C][C] 0.9895[/C][/ROW]
[ROW][C]36[/C][C] 0.01261[/C][C] 0.02521[/C][C] 0.9874[/C][/ROW]
[ROW][C]37[/C][C] 0.01969[/C][C] 0.03939[/C][C] 0.9803[/C][/ROW]
[ROW][C]38[/C][C] 0.01478[/C][C] 0.02957[/C][C] 0.9852[/C][/ROW]
[ROW][C]39[/C][C] 0.01261[/C][C] 0.02523[/C][C] 0.9874[/C][/ROW]
[ROW][C]40[/C][C] 0.01798[/C][C] 0.03597[/C][C] 0.982[/C][/ROW]
[ROW][C]41[/C][C] 0.01273[/C][C] 0.02547[/C][C] 0.9873[/C][/ROW]
[ROW][C]42[/C][C] 0.009956[/C][C] 0.01991[/C][C] 0.99[/C][/ROW]
[ROW][C]43[/C][C] 0.007002[/C][C] 0.014[/C][C] 0.993[/C][/ROW]
[ROW][C]44[/C][C] 0.005917[/C][C] 0.01183[/C][C] 0.9941[/C][/ROW]
[ROW][C]45[/C][C] 0.003933[/C][C] 0.007866[/C][C] 0.9961[/C][/ROW]
[ROW][C]46[/C][C] 0.005754[/C][C] 0.01151[/C][C] 0.9942[/C][/ROW]
[ROW][C]47[/C][C] 0.005214[/C][C] 0.01043[/C][C] 0.9948[/C][/ROW]
[ROW][C]48[/C][C] 0.006304[/C][C] 0.01261[/C][C] 0.9937[/C][/ROW]
[ROW][C]49[/C][C] 0.01249[/C][C] 0.02497[/C][C] 0.9875[/C][/ROW]
[ROW][C]50[/C][C] 0.08993[/C][C] 0.1799[/C][C] 0.9101[/C][/ROW]
[ROW][C]51[/C][C] 0.1016[/C][C] 0.2033[/C][C] 0.8984[/C][/ROW]
[ROW][C]52[/C][C] 0.1347[/C][C] 0.2694[/C][C] 0.8653[/C][/ROW]
[ROW][C]53[/C][C] 0.1115[/C][C] 0.223[/C][C] 0.8885[/C][/ROW]
[ROW][C]54[/C][C] 0.1057[/C][C] 0.2114[/C][C] 0.8943[/C][/ROW]
[ROW][C]55[/C][C] 0.08721[/C][C] 0.1744[/C][C] 0.9128[/C][/ROW]
[ROW][C]56[/C][C] 0.06891[/C][C] 0.1378[/C][C] 0.9311[/C][/ROW]
[ROW][C]57[/C][C] 0.06532[/C][C] 0.1306[/C][C] 0.9347[/C][/ROW]
[ROW][C]58[/C][C] 0.05449[/C][C] 0.109[/C][C] 0.9455[/C][/ROW]
[ROW][C]59[/C][C] 0.04256[/C][C] 0.08511[/C][C] 0.9574[/C][/ROW]
[ROW][C]60[/C][C] 0.03472[/C][C] 0.06944[/C][C] 0.9653[/C][/ROW]
[ROW][C]61[/C][C] 0.02712[/C][C] 0.05424[/C][C] 0.9729[/C][/ROW]
[ROW][C]62[/C][C] 0.06908[/C][C] 0.1382[/C][C] 0.9309[/C][/ROW]
[ROW][C]63[/C][C] 0.07401[/C][C] 0.148[/C][C] 0.926[/C][/ROW]
[ROW][C]64[/C][C] 0.06969[/C][C] 0.1394[/C][C] 0.9303[/C][/ROW]
[ROW][C]65[/C][C] 0.05869[/C][C] 0.1174[/C][C] 0.9413[/C][/ROW]
[ROW][C]66[/C][C] 0.04721[/C][C] 0.09443[/C][C] 0.9528[/C][/ROW]
[ROW][C]67[/C][C] 0.03721[/C][C] 0.07441[/C][C] 0.9628[/C][/ROW]
[ROW][C]68[/C][C] 0.03044[/C][C] 0.06088[/C][C] 0.9696[/C][/ROW]
[ROW][C]69[/C][C] 0.02618[/C][C] 0.05235[/C][C] 0.9738[/C][/ROW]
[ROW][C]70[/C][C] 0.02954[/C][C] 0.05908[/C][C] 0.9705[/C][/ROW]
[ROW][C]71[/C][C] 0.03505[/C][C] 0.0701[/C][C] 0.965[/C][/ROW]
[ROW][C]72[/C][C] 0.02716[/C][C] 0.05431[/C][C] 0.9728[/C][/ROW]
[ROW][C]73[/C][C] 0.02055[/C][C] 0.0411[/C][C] 0.9794[/C][/ROW]
[ROW][C]74[/C][C] 0.01933[/C][C] 0.03866[/C][C] 0.9807[/C][/ROW]
[ROW][C]75[/C][C] 0.0146[/C][C] 0.0292[/C][C] 0.9854[/C][/ROW]
[ROW][C]76[/C][C] 0.01608[/C][C] 0.03215[/C][C] 0.9839[/C][/ROW]
[ROW][C]77[/C][C] 0.01191[/C][C] 0.02383[/C][C] 0.9881[/C][/ROW]
[ROW][C]78[/C][C] 0.1656[/C][C] 0.3312[/C][C] 0.8344[/C][/ROW]
[ROW][C]79[/C][C] 0.1438[/C][C] 0.2876[/C][C] 0.8562[/C][/ROW]
[ROW][C]80[/C][C] 0.1368[/C][C] 0.2736[/C][C] 0.8632[/C][/ROW]
[ROW][C]81[/C][C] 0.1228[/C][C] 0.2455[/C][C] 0.8772[/C][/ROW]
[ROW][C]82[/C][C] 0.4051[/C][C] 0.8102[/C][C] 0.5949[/C][/ROW]
[ROW][C]83[/C][C] 0.3808[/C][C] 0.7616[/C][C] 0.6192[/C][/ROW]
[ROW][C]84[/C][C] 0.4263[/C][C] 0.8527[/C][C] 0.5737[/C][/ROW]
[ROW][C]85[/C][C] 0.3964[/C][C] 0.7928[/C][C] 0.6036[/C][/ROW]
[ROW][C]86[/C][C] 0.4023[/C][C] 0.8046[/C][C] 0.5977[/C][/ROW]
[ROW][C]87[/C][C] 0.3915[/C][C] 0.7829[/C][C] 0.6085[/C][/ROW]
[ROW][C]88[/C][C] 0.3523[/C][C] 0.7045[/C][C] 0.6477[/C][/ROW]
[ROW][C]89[/C][C] 0.3275[/C][C] 0.6551[/C][C] 0.6725[/C][/ROW]
[ROW][C]90[/C][C] 0.2962[/C][C] 0.5924[/C][C] 0.7038[/C][/ROW]
[ROW][C]91[/C][C] 0.2774[/C][C] 0.5549[/C][C] 0.7226[/C][/ROW]
[ROW][C]92[/C][C] 0.2455[/C][C] 0.4909[/C][C] 0.7545[/C][/ROW]
[ROW][C]93[/C][C] 0.2118[/C][C] 0.4235[/C][C] 0.7882[/C][/ROW]
[ROW][C]94[/C][C] 0.2804[/C][C] 0.5608[/C][C] 0.7196[/C][/ROW]
[ROW][C]95[/C][C] 0.3037[/C][C] 0.6075[/C][C] 0.6963[/C][/ROW]
[ROW][C]96[/C][C] 0.2974[/C][C] 0.5947[/C][C] 0.7026[/C][/ROW]
[ROW][C]97[/C][C] 0.2839[/C][C] 0.5678[/C][C] 0.7161[/C][/ROW]
[ROW][C]98[/C][C] 0.2539[/C][C] 0.5078[/C][C] 0.7461[/C][/ROW]
[ROW][C]99[/C][C] 0.3523[/C][C] 0.7046[/C][C] 0.6477[/C][/ROW]
[ROW][C]100[/C][C] 0.3098[/C][C] 0.6195[/C][C] 0.6902[/C][/ROW]
[ROW][C]101[/C][C] 0.3067[/C][C] 0.6135[/C][C] 0.6933[/C][/ROW]
[ROW][C]102[/C][C] 0.2828[/C][C] 0.5657[/C][C] 0.7172[/C][/ROW]
[ROW][C]103[/C][C] 0.2485[/C][C] 0.4969[/C][C] 0.7515[/C][/ROW]
[ROW][C]104[/C][C] 0.223[/C][C] 0.446[/C][C] 0.777[/C][/ROW]
[ROW][C]105[/C][C] 0.1899[/C][C] 0.3797[/C][C] 0.8101[/C][/ROW]
[ROW][C]106[/C][C] 0.1689[/C][C] 0.3379[/C][C] 0.8311[/C][/ROW]
[ROW][C]107[/C][C] 0.1547[/C][C] 0.3095[/C][C] 0.8453[/C][/ROW]
[ROW][C]108[/C][C] 0.1354[/C][C] 0.2708[/C][C] 0.8646[/C][/ROW]
[ROW][C]109[/C][C] 0.251[/C][C] 0.5019[/C][C] 0.749[/C][/ROW]
[ROW][C]110[/C][C] 0.2146[/C][C] 0.4292[/C][C] 0.7854[/C][/ROW]
[ROW][C]111[/C][C] 0.1812[/C][C] 0.3623[/C][C] 0.8188[/C][/ROW]
[ROW][C]112[/C][C] 0.1777[/C][C] 0.3553[/C][C] 0.8223[/C][/ROW]
[ROW][C]113[/C][C] 0.1472[/C][C] 0.2944[/C][C] 0.8528[/C][/ROW]
[ROW][C]114[/C][C] 0.1365[/C][C] 0.273[/C][C] 0.8635[/C][/ROW]
[ROW][C]115[/C][C] 0.1106[/C][C] 0.2213[/C][C] 0.8894[/C][/ROW]
[ROW][C]116[/C][C] 0.1065[/C][C] 0.213[/C][C] 0.8935[/C][/ROW]
[ROW][C]117[/C][C] 0.08521[/C][C] 0.1704[/C][C] 0.9148[/C][/ROW]
[ROW][C]118[/C][C] 0.07459[/C][C] 0.1492[/C][C] 0.9254[/C][/ROW]
[ROW][C]119[/C][C] 0.06803[/C][C] 0.1361[/C][C] 0.932[/C][/ROW]
[ROW][C]120[/C][C] 0.0551[/C][C] 0.1102[/C][C] 0.9449[/C][/ROW]
[ROW][C]121[/C][C] 0.04289[/C][C] 0.08577[/C][C] 0.9571[/C][/ROW]
[ROW][C]122[/C][C] 0.03212[/C][C] 0.06425[/C][C] 0.9679[/C][/ROW]
[ROW][C]123[/C][C] 0.02365[/C][C] 0.04729[/C][C] 0.9764[/C][/ROW]
[ROW][C]124[/C][C] 0.01757[/C][C] 0.03513[/C][C] 0.9824[/C][/ROW]
[ROW][C]125[/C][C] 0.02853[/C][C] 0.05706[/C][C] 0.9715[/C][/ROW]
[ROW][C]126[/C][C] 0.03003[/C][C] 0.06007[/C][C] 0.97[/C][/ROW]
[ROW][C]127[/C][C] 0.03345[/C][C] 0.06689[/C][C] 0.9666[/C][/ROW]
[ROW][C]128[/C][C] 0.1905[/C][C] 0.3811[/C][C] 0.8095[/C][/ROW]
[ROW][C]129[/C][C] 0.1933[/C][C] 0.3866[/C][C] 0.8067[/C][/ROW]
[ROW][C]130[/C][C] 0.1549[/C][C] 0.3099[/C][C] 0.8451[/C][/ROW]
[ROW][C]131[/C][C] 0.2935[/C][C] 0.587[/C][C] 0.7065[/C][/ROW]
[ROW][C]132[/C][C] 0.2622[/C][C] 0.5244[/C][C] 0.7378[/C][/ROW]
[ROW][C]133[/C][C] 0.3132[/C][C] 0.6264[/C][C] 0.6868[/C][/ROW]
[ROW][C]134[/C][C] 0.2664[/C][C] 0.5329[/C][C] 0.7336[/C][/ROW]
[ROW][C]135[/C][C] 0.2725[/C][C] 0.5449[/C][C] 0.7275[/C][/ROW]
[ROW][C]136[/C][C] 0.2421[/C][C] 0.4842[/C][C] 0.7579[/C][/ROW]
[ROW][C]137[/C][C] 0.2248[/C][C] 0.4496[/C][C] 0.7752[/C][/ROW]
[ROW][C]138[/C][C] 0.377[/C][C] 0.754[/C][C] 0.623[/C][/ROW]
[ROW][C]139[/C][C] 0.3739[/C][C] 0.7478[/C][C] 0.6261[/C][/ROW]
[ROW][C]140[/C][C] 0.306[/C][C] 0.6119[/C][C] 0.694[/C][/ROW]
[ROW][C]141[/C][C] 0.5127[/C][C] 0.9746[/C][C] 0.4873[/C][/ROW]
[ROW][C]142[/C][C] 0.4316[/C][C] 0.8632[/C][C] 0.5684[/C][/ROW]
[ROW][C]143[/C][C] 0.3551[/C][C] 0.7102[/C][C] 0.6449[/C][/ROW]
[ROW][C]144[/C][C] 0.2836[/C][C] 0.5672[/C][C] 0.7164[/C][/ROW]
[ROW][C]145[/C][C] 0.8567[/C][C] 0.2866[/C][C] 0.1433[/C][/ROW]
[ROW][C]146[/C][C] 0.8241[/C][C] 0.3517[/C][C] 0.1759[/C][/ROW]
[ROW][C]147[/C][C] 0.8231[/C][C] 0.3538[/C][C] 0.1769[/C][/ROW]
[ROW][C]148[/C][C] 0.9459[/C][C] 0.1083[/C][C] 0.05413[/C][/ROW]
[ROW][C]149[/C][C] 0.9035[/C][C] 0.1929[/C][C] 0.09645[/C][/ROW]
[ROW][C]150[/C][C] 0.8285[/C][C] 0.343[/C][C] 0.1715[/C][/ROW]
[ROW][C]151[/C][C] 0.6851[/C][C] 0.6298[/C][C] 0.3149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297651&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&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
8 0.1432 0.2863 0.8568
9 0.07795 0.1559 0.9221
10 0.05684 0.1137 0.9432
11 0.03926 0.07853 0.9607
12 0.01691 0.03381 0.9831
13 0.07438 0.1488 0.9256
14 0.05879 0.1176 0.9412
15 0.1226 0.2453 0.8774
16 0.08035 0.1607 0.9197
17 0.05557 0.1111 0.9444
18 0.03416 0.06833 0.9658
19 0.02577 0.05155 0.9742
20 0.01558 0.03116 0.9844
21 0.009924 0.01985 0.9901
22 0.005863 0.01173 0.9941
23 0.01154 0.02309 0.9885
24 0.007721 0.01544 0.9923
25 0.004586 0.009172 0.9954
26 0.002643 0.005286 0.9974
27 0.001494 0.002987 0.9985
28 0.009212 0.01842 0.9908
29 0.006915 0.01383 0.9931
30 0.005986 0.01197 0.994
31 0.004205 0.008409 0.9958
32 0.01443 0.02886 0.9856
33 0.01488 0.02976 0.9851
34 0.01071 0.02143 0.9893
35 0.01053 0.02105 0.9895
36 0.01261 0.02521 0.9874
37 0.01969 0.03939 0.9803
38 0.01478 0.02957 0.9852
39 0.01261 0.02523 0.9874
40 0.01798 0.03597 0.982
41 0.01273 0.02547 0.9873
42 0.009956 0.01991 0.99
43 0.007002 0.014 0.993
44 0.005917 0.01183 0.9941
45 0.003933 0.007866 0.9961
46 0.005754 0.01151 0.9942
47 0.005214 0.01043 0.9948
48 0.006304 0.01261 0.9937
49 0.01249 0.02497 0.9875
50 0.08993 0.1799 0.9101
51 0.1016 0.2033 0.8984
52 0.1347 0.2694 0.8653
53 0.1115 0.223 0.8885
54 0.1057 0.2114 0.8943
55 0.08721 0.1744 0.9128
56 0.06891 0.1378 0.9311
57 0.06532 0.1306 0.9347
58 0.05449 0.109 0.9455
59 0.04256 0.08511 0.9574
60 0.03472 0.06944 0.9653
61 0.02712 0.05424 0.9729
62 0.06908 0.1382 0.9309
63 0.07401 0.148 0.926
64 0.06969 0.1394 0.9303
65 0.05869 0.1174 0.9413
66 0.04721 0.09443 0.9528
67 0.03721 0.07441 0.9628
68 0.03044 0.06088 0.9696
69 0.02618 0.05235 0.9738
70 0.02954 0.05908 0.9705
71 0.03505 0.0701 0.965
72 0.02716 0.05431 0.9728
73 0.02055 0.0411 0.9794
74 0.01933 0.03866 0.9807
75 0.0146 0.0292 0.9854
76 0.01608 0.03215 0.9839
77 0.01191 0.02383 0.9881
78 0.1656 0.3312 0.8344
79 0.1438 0.2876 0.8562
80 0.1368 0.2736 0.8632
81 0.1228 0.2455 0.8772
82 0.4051 0.8102 0.5949
83 0.3808 0.7616 0.6192
84 0.4263 0.8527 0.5737
85 0.3964 0.7928 0.6036
86 0.4023 0.8046 0.5977
87 0.3915 0.7829 0.6085
88 0.3523 0.7045 0.6477
89 0.3275 0.6551 0.6725
90 0.2962 0.5924 0.7038
91 0.2774 0.5549 0.7226
92 0.2455 0.4909 0.7545
93 0.2118 0.4235 0.7882
94 0.2804 0.5608 0.7196
95 0.3037 0.6075 0.6963
96 0.2974 0.5947 0.7026
97 0.2839 0.5678 0.7161
98 0.2539 0.5078 0.7461
99 0.3523 0.7046 0.6477
100 0.3098 0.6195 0.6902
101 0.3067 0.6135 0.6933
102 0.2828 0.5657 0.7172
103 0.2485 0.4969 0.7515
104 0.223 0.446 0.777
105 0.1899 0.3797 0.8101
106 0.1689 0.3379 0.8311
107 0.1547 0.3095 0.8453
108 0.1354 0.2708 0.8646
109 0.251 0.5019 0.749
110 0.2146 0.4292 0.7854
111 0.1812 0.3623 0.8188
112 0.1777 0.3553 0.8223
113 0.1472 0.2944 0.8528
114 0.1365 0.273 0.8635
115 0.1106 0.2213 0.8894
116 0.1065 0.213 0.8935
117 0.08521 0.1704 0.9148
118 0.07459 0.1492 0.9254
119 0.06803 0.1361 0.932
120 0.0551 0.1102 0.9449
121 0.04289 0.08577 0.9571
122 0.03212 0.06425 0.9679
123 0.02365 0.04729 0.9764
124 0.01757 0.03513 0.9824
125 0.02853 0.05706 0.9715
126 0.03003 0.06007 0.97
127 0.03345 0.06689 0.9666
128 0.1905 0.3811 0.8095
129 0.1933 0.3866 0.8067
130 0.1549 0.3099 0.8451
131 0.2935 0.587 0.7065
132 0.2622 0.5244 0.7378
133 0.3132 0.6264 0.6868
134 0.2664 0.5329 0.7336
135 0.2725 0.5449 0.7275
136 0.2421 0.4842 0.7579
137 0.2248 0.4496 0.7752
138 0.377 0.754 0.623
139 0.3739 0.7478 0.6261
140 0.306 0.6119 0.694
141 0.5127 0.9746 0.4873
142 0.4316 0.8632 0.5684
143 0.3551 0.7102 0.6449
144 0.2836 0.5672 0.7164
145 0.8567 0.2866 0.1433
146 0.8241 0.3517 0.1759
147 0.8231 0.3538 0.1769
148 0.9459 0.1083 0.05413
149 0.9035 0.1929 0.09645
150 0.8285 0.343 0.1715
151 0.6851 0.6298 0.3149






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level5 0.03472NOK
5% type I error level380.263889NOK
10% type I error level560.388889NOK

\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 & 5 &  0.03472 & NOK \tabularnewline
5% type I error level & 38 & 0.263889 & NOK \tabularnewline
10% type I error level & 56 & 0.388889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297651&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]5[/C][C] 0.03472[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]38[/C][C]0.263889[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]56[/C][C]0.388889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297651&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&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 level5 0.03472NOK
5% type I error level380.263889NOK
10% type I error level560.388889NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.84959, df1 = 2, df2 = 152, p-value = 0.4296
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.167, df1 = 8, df2 = 146, p-value = 0.323
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3688, df1 = 2, df2 = 152, p-value = 0.2575


\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 = 0.84959, df1 = 2, df2 = 152, p-value = 0.4296

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.167, df1 = 8, df2 = 146, p-value = 0.323

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3688, df1 = 2, df2 = 152, p-value = 0.2575

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297651&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 = 0.84959, df1 = 2, df2 = 152, p-value = 0.4296

[/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 = 1.167, df1 = 8, df2 = 146, p-value = 0.323

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3688, df1 = 2, df2 = 152, p-value = 0.2575

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297651&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&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 = 0.84959, df1 = 2, df2 = 152, p-value = 0.4296
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.167, df1 = 8, df2 = 146, p-value = 0.323
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3688, df1 = 2, df2 = 152, p-value = 0.2575






Variance Inflation Factors (Multicollinearity)
> vif
    ITH1     ITH2     ITH3     ITH4 
1.704240 1.450420 1.558983 1.245195 


\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline

> vif
    ITH1     ITH2     ITH3     ITH4 
1.704240 1.450420 1.558983 1.245195 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297651&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
    ITH1     ITH2     ITH3     ITH4 
1.704240 1.450420 1.558983 1.245195 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297651&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297651&T=8

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


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

Variance Inflation Factors (Multicollinearity)
> vif
    ITH1     ITH2     ITH3     ITH4 
1.704240 1.450420 1.558983 1.245195


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 9
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Figure 10
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Input Parameters & R Code

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