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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Dec 2013 10:59:24 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/20/t1387555198qdpab1m6wbg5jq8.htm/, Retrieved Thu, 28 Mar 2024 23:32:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232460, Retrieved Thu, 28 Mar 2024 23:32:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact190
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2013-12-20 15:59:24] [9e6a405f514733ea23d87e4507d39d29] [Current]
-   P     [Multiple Regression] [test] [2013-12-26 12:37:46] [fae8dcf58bca4d76df8979d2f4741e9c]
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Dataseries X:
56
55
54
52
72
71
56
46
47
47
48
50
44
38
33
33
52
54
39
22
31
31
38
42
41
31
36
34
51
47
31
19
30
33
36
40
32
25
28
29
55
55
40
38
44
41
49
59
61
47
43
39
66
68
63
68
67
59
68
78
82
70
62
68
94
102
100
104
103
93
110
114
120
102
95
103
122
139
135
135
137
130
148
148
145
128
131
133
146
163
151
157
152
149
172
167
160
150
160
165
171
179
171
176
170
169
194
196
188
174
186
191
197
206
197
204
201
190
213
213




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 15 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232460&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232460&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232460&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Omzet[t] = + 8.36944 -0.744907M1[t] -13.1954M2[t] -13.9458M3[t] -13.5963M4[t] + 2.75324M5[t] + 7.00278M6[t] -4.64769M7[t] -7.59815M8[t] -7.84861M9[t] -13.3991M10[t] -1.54954M11[t] + 1.55046t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Omzet[t] =  +  8.36944 -0.744907M1[t] -13.1954M2[t] -13.9458M3[t] -13.5963M4[t] +  2.75324M5[t] +  7.00278M6[t] -4.64769M7[t] -7.59815M8[t] -7.84861M9[t] -13.3991M10[t] -1.54954M11[t] +  1.55046t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232460&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Omzet[t] =  +  8.36944 -0.744907M1[t] -13.1954M2[t] -13.9458M3[t] -13.5963M4[t] +  2.75324M5[t] +  7.00278M6[t] -4.64769M7[t] -7.59815M8[t] -7.84861M9[t] -13.3991M10[t] -1.54954M11[t] +  1.55046t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232460&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232460&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Omzet[t] = + 8.36944 -0.744907M1[t] -13.1954M2[t] -13.9458M3[t] -13.5963M4[t] + 2.75324M5[t] + 7.00278M6[t] -4.64769M7[t] -7.59815M8[t] -7.84861M9[t] -13.3991M10[t] -1.54954M11[t] + 1.55046t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.369448.263951.0130.3134570.156728
M1-0.74490710.25-0.072670.9422010.471101
M2-13.195410.2463-1.2880.2005860.100293
M3-13.945810.2429-1.3620.176210.088105
M4-13.596310.2398-1.3280.1870740.0935369
M52.7532410.23710.26890.7884890.394244
M67.0027810.23480.68420.495320.24766
M7-4.6476910.2328-0.45420.6506090.325305
M8-7.5981510.2312-0.74260.4593240.229662
M9-7.8486110.2299-0.76720.4446410.22232
M10-13.399110.2291-1.310.1930340.0965169
M11-1.5495410.2285-0.15150.8798730.439936
t1.550460.060574725.62.00128e-471.00064e-47

\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) & 8.36944 & 8.26395 & 1.013 & 0.313457 & 0.156728 \tabularnewline
M1 & -0.744907 & 10.25 & -0.07267 & 0.942201 & 0.471101 \tabularnewline
M2 & -13.1954 & 10.2463 & -1.288 & 0.200586 & 0.100293 \tabularnewline
M3 & -13.9458 & 10.2429 & -1.362 & 0.17621 & 0.088105 \tabularnewline
M4 & -13.5963 & 10.2398 & -1.328 & 0.187074 & 0.0935369 \tabularnewline
M5 & 2.75324 & 10.2371 & 0.2689 & 0.788489 & 0.394244 \tabularnewline
M6 & 7.00278 & 10.2348 & 0.6842 & 0.49532 & 0.24766 \tabularnewline
M7 & -4.64769 & 10.2328 & -0.4542 & 0.650609 & 0.325305 \tabularnewline
M8 & -7.59815 & 10.2312 & -0.7426 & 0.459324 & 0.229662 \tabularnewline
M9 & -7.84861 & 10.2299 & -0.7672 & 0.444641 & 0.22232 \tabularnewline
M10 & -13.3991 & 10.2291 & -1.31 & 0.193034 & 0.0965169 \tabularnewline
M11 & -1.54954 & 10.2285 & -0.1515 & 0.879873 & 0.439936 \tabularnewline
t & 1.55046 & 0.0605747 & 25.6 & 2.00128e-47 & 1.00064e-47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232460&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]8.36944[/C][C]8.26395[/C][C]1.013[/C][C]0.313457[/C][C]0.156728[/C][/ROW]
[ROW][C]M1[/C][C]-0.744907[/C][C]10.25[/C][C]-0.07267[/C][C]0.942201[/C][C]0.471101[/C][/ROW]
[ROW][C]M2[/C][C]-13.1954[/C][C]10.2463[/C][C]-1.288[/C][C]0.200586[/C][C]0.100293[/C][/ROW]
[ROW][C]M3[/C][C]-13.9458[/C][C]10.2429[/C][C]-1.362[/C][C]0.17621[/C][C]0.088105[/C][/ROW]
[ROW][C]M4[/C][C]-13.5963[/C][C]10.2398[/C][C]-1.328[/C][C]0.187074[/C][C]0.0935369[/C][/ROW]
[ROW][C]M5[/C][C]2.75324[/C][C]10.2371[/C][C]0.2689[/C][C]0.788489[/C][C]0.394244[/C][/ROW]
[ROW][C]M6[/C][C]7.00278[/C][C]10.2348[/C][C]0.6842[/C][C]0.49532[/C][C]0.24766[/C][/ROW]
[ROW][C]M7[/C][C]-4.64769[/C][C]10.2328[/C][C]-0.4542[/C][C]0.650609[/C][C]0.325305[/C][/ROW]
[ROW][C]M8[/C][C]-7.59815[/C][C]10.2312[/C][C]-0.7426[/C][C]0.459324[/C][C]0.229662[/C][/ROW]
[ROW][C]M9[/C][C]-7.84861[/C][C]10.2299[/C][C]-0.7672[/C][C]0.444641[/C][C]0.22232[/C][/ROW]
[ROW][C]M10[/C][C]-13.3991[/C][C]10.2291[/C][C]-1.31[/C][C]0.193034[/C][C]0.0965169[/C][/ROW]
[ROW][C]M11[/C][C]-1.54954[/C][C]10.2285[/C][C]-0.1515[/C][C]0.879873[/C][C]0.439936[/C][/ROW]
[ROW][C]t[/C][C]1.55046[/C][C]0.0605747[/C][C]25.6[/C][C]2.00128e-47[/C][C]1.00064e-47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232460&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232460&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.369448.263951.0130.3134570.156728
M1-0.74490710.25-0.072670.9422010.471101
M2-13.195410.2463-1.2880.2005860.100293
M3-13.945810.2429-1.3620.176210.088105
M4-13.596310.2398-1.3280.1870740.0935369
M52.7532410.23710.26890.7884890.394244
M67.0027810.23480.68420.495320.24766
M7-4.6476910.2328-0.45420.6506090.325305
M8-7.5981510.2312-0.74260.4593240.229662
M9-7.8486110.2299-0.76720.4446410.22232
M10-13.399110.2291-1.310.1930340.0965169
M11-1.5495410.2285-0.15150.8798730.439936
t1.550460.060574725.62.00128e-471.00064e-47







Multiple Linear Regression - Regression Statistics
Multiple R0.929119
R-squared0.863261
Adjusted R-squared0.847926
F-TEST (value)56.2929
F-TEST (DF numerator)12
F-TEST (DF denominator)107
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.8713
Sum Squared Residuals55971.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.929119 \tabularnewline
R-squared & 0.863261 \tabularnewline
Adjusted R-squared & 0.847926 \tabularnewline
F-TEST (value) & 56.2929 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 22.8713 \tabularnewline
Sum Squared Residuals & 55971.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232460&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.929119[/C][/ROW]
[ROW][C]R-squared[/C][C]0.863261[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.847926[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]56.2929[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]22.8713[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]55971.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232460&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232460&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.929119
R-squared0.863261
Adjusted R-squared0.847926
F-TEST (value)56.2929
F-TEST (DF numerator)12
F-TEST (DF denominator)107
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.8713
Sum Squared Residuals55971.1







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1569.17546.825
255-1.72556.725
354-0.92554.925
4520.97551.025
57218.87553.125
67124.67546.325
75614.57541.425
84613.17532.825
94714.47532.525
104710.47536.525
114823.87524.125
125026.97523.025
134427.780616.2194
143816.880621.1194
153317.680615.3194
163319.580613.4194
175237.480614.5194
185443.280610.7194
193933.18065.81944
202231.7806-9.78056
213133.0806-2.08056
223129.08061.91944
233842.4806-4.48056
244245.5806-3.58056
254146.3861-5.38611
263135.4861-4.48611
273636.2861-0.286111
283438.1861-4.18611
295156.0861-5.08611
304761.8861-14.8861
313151.7861-20.7861
321950.3861-31.3861
333051.6861-21.6861
343347.6861-14.6861
353661.0861-25.0861
364064.1861-24.1861
373264.9917-32.9917
382554.0917-29.0917
392854.8917-26.8917
402956.7917-27.7917
415574.6917-19.6917
425580.4917-25.4917
434070.3917-30.3917
443868.9917-30.9917
454470.2917-26.2917
464166.2917-25.2917
474979.6917-30.6917
485982.7917-23.7917
496183.5972-22.5972
504772.6972-25.6972
514373.4972-30.4972
523975.3972-36.3972
536693.2972-27.2972
546899.0972-31.0972
556388.9972-25.9972
566887.5972-19.5972
576788.8972-21.8972
585984.8972-25.8972
596898.2972-30.2972
6078101.397-23.3972
6182102.203-20.2028
627091.3028-21.3028
636292.1028-30.1028
646894.0028-26.0028
6594111.903-17.9028
66102117.703-15.7028
67100107.603-7.60278
68104106.203-2.20278
69103107.503-4.50278
7093103.503-10.5028
71110116.903-6.90278
72114120.003-6.00278
73120120.808-0.808333
74102109.908-7.90833
7595110.708-15.7083
76103112.608-9.60833
77122130.508-8.50833
78139136.3082.69167
79135126.2088.79167
80135124.80810.1917
81137126.10810.8917
82130122.1087.89167
83148135.50812.4917
84148138.6089.39167
85145139.4145.58611
86128128.514-0.513889
87131129.3141.68611
88133131.2141.78611
89146149.114-3.11389
90163154.9148.08611
91151144.8146.18611
92157143.41413.5861
93152144.7147.28611
94149140.7148.28611
95172154.11417.8861
96167157.2149.78611
97160158.0191.98056
98150147.1192.88056
99160147.91912.0806
100165149.81915.1806
101171167.7193.28056
102179173.5195.48056
103171163.4197.58056
104176162.01913.9806
105170163.3196.68056
106169159.3199.68056
107194172.71921.2806
108196175.81920.1806
109188176.62511.375
110174165.7258.275
111186166.52519.475
112191168.42522.575
113197186.32510.675
114206192.12513.875
115197182.02514.975
116204180.62523.375
117201181.92519.075
118190177.92512.075
119213191.32521.675
120213194.42518.575

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 56 & 9.175 & 46.825 \tabularnewline
2 & 55 & -1.725 & 56.725 \tabularnewline
3 & 54 & -0.925 & 54.925 \tabularnewline
4 & 52 & 0.975 & 51.025 \tabularnewline
5 & 72 & 18.875 & 53.125 \tabularnewline
6 & 71 & 24.675 & 46.325 \tabularnewline
7 & 56 & 14.575 & 41.425 \tabularnewline
8 & 46 & 13.175 & 32.825 \tabularnewline
9 & 47 & 14.475 & 32.525 \tabularnewline
10 & 47 & 10.475 & 36.525 \tabularnewline
11 & 48 & 23.875 & 24.125 \tabularnewline
12 & 50 & 26.975 & 23.025 \tabularnewline
13 & 44 & 27.7806 & 16.2194 \tabularnewline
14 & 38 & 16.8806 & 21.1194 \tabularnewline
15 & 33 & 17.6806 & 15.3194 \tabularnewline
16 & 33 & 19.5806 & 13.4194 \tabularnewline
17 & 52 & 37.4806 & 14.5194 \tabularnewline
18 & 54 & 43.2806 & 10.7194 \tabularnewline
19 & 39 & 33.1806 & 5.81944 \tabularnewline
20 & 22 & 31.7806 & -9.78056 \tabularnewline
21 & 31 & 33.0806 & -2.08056 \tabularnewline
22 & 31 & 29.0806 & 1.91944 \tabularnewline
23 & 38 & 42.4806 & -4.48056 \tabularnewline
24 & 42 & 45.5806 & -3.58056 \tabularnewline
25 & 41 & 46.3861 & -5.38611 \tabularnewline
26 & 31 & 35.4861 & -4.48611 \tabularnewline
27 & 36 & 36.2861 & -0.286111 \tabularnewline
28 & 34 & 38.1861 & -4.18611 \tabularnewline
29 & 51 & 56.0861 & -5.08611 \tabularnewline
30 & 47 & 61.8861 & -14.8861 \tabularnewline
31 & 31 & 51.7861 & -20.7861 \tabularnewline
32 & 19 & 50.3861 & -31.3861 \tabularnewline
33 & 30 & 51.6861 & -21.6861 \tabularnewline
34 & 33 & 47.6861 & -14.6861 \tabularnewline
35 & 36 & 61.0861 & -25.0861 \tabularnewline
36 & 40 & 64.1861 & -24.1861 \tabularnewline
37 & 32 & 64.9917 & -32.9917 \tabularnewline
38 & 25 & 54.0917 & -29.0917 \tabularnewline
39 & 28 & 54.8917 & -26.8917 \tabularnewline
40 & 29 & 56.7917 & -27.7917 \tabularnewline
41 & 55 & 74.6917 & -19.6917 \tabularnewline
42 & 55 & 80.4917 & -25.4917 \tabularnewline
43 & 40 & 70.3917 & -30.3917 \tabularnewline
44 & 38 & 68.9917 & -30.9917 \tabularnewline
45 & 44 & 70.2917 & -26.2917 \tabularnewline
46 & 41 & 66.2917 & -25.2917 \tabularnewline
47 & 49 & 79.6917 & -30.6917 \tabularnewline
48 & 59 & 82.7917 & -23.7917 \tabularnewline
49 & 61 & 83.5972 & -22.5972 \tabularnewline
50 & 47 & 72.6972 & -25.6972 \tabularnewline
51 & 43 & 73.4972 & -30.4972 \tabularnewline
52 & 39 & 75.3972 & -36.3972 \tabularnewline
53 & 66 & 93.2972 & -27.2972 \tabularnewline
54 & 68 & 99.0972 & -31.0972 \tabularnewline
55 & 63 & 88.9972 & -25.9972 \tabularnewline
56 & 68 & 87.5972 & -19.5972 \tabularnewline
57 & 67 & 88.8972 & -21.8972 \tabularnewline
58 & 59 & 84.8972 & -25.8972 \tabularnewline
59 & 68 & 98.2972 & -30.2972 \tabularnewline
60 & 78 & 101.397 & -23.3972 \tabularnewline
61 & 82 & 102.203 & -20.2028 \tabularnewline
62 & 70 & 91.3028 & -21.3028 \tabularnewline
63 & 62 & 92.1028 & -30.1028 \tabularnewline
64 & 68 & 94.0028 & -26.0028 \tabularnewline
65 & 94 & 111.903 & -17.9028 \tabularnewline
66 & 102 & 117.703 & -15.7028 \tabularnewline
67 & 100 & 107.603 & -7.60278 \tabularnewline
68 & 104 & 106.203 & -2.20278 \tabularnewline
69 & 103 & 107.503 & -4.50278 \tabularnewline
70 & 93 & 103.503 & -10.5028 \tabularnewline
71 & 110 & 116.903 & -6.90278 \tabularnewline
72 & 114 & 120.003 & -6.00278 \tabularnewline
73 & 120 & 120.808 & -0.808333 \tabularnewline
74 & 102 & 109.908 & -7.90833 \tabularnewline
75 & 95 & 110.708 & -15.7083 \tabularnewline
76 & 103 & 112.608 & -9.60833 \tabularnewline
77 & 122 & 130.508 & -8.50833 \tabularnewline
78 & 139 & 136.308 & 2.69167 \tabularnewline
79 & 135 & 126.208 & 8.79167 \tabularnewline
80 & 135 & 124.808 & 10.1917 \tabularnewline
81 & 137 & 126.108 & 10.8917 \tabularnewline
82 & 130 & 122.108 & 7.89167 \tabularnewline
83 & 148 & 135.508 & 12.4917 \tabularnewline
84 & 148 & 138.608 & 9.39167 \tabularnewline
85 & 145 & 139.414 & 5.58611 \tabularnewline
86 & 128 & 128.514 & -0.513889 \tabularnewline
87 & 131 & 129.314 & 1.68611 \tabularnewline
88 & 133 & 131.214 & 1.78611 \tabularnewline
89 & 146 & 149.114 & -3.11389 \tabularnewline
90 & 163 & 154.914 & 8.08611 \tabularnewline
91 & 151 & 144.814 & 6.18611 \tabularnewline
92 & 157 & 143.414 & 13.5861 \tabularnewline
93 & 152 & 144.714 & 7.28611 \tabularnewline
94 & 149 & 140.714 & 8.28611 \tabularnewline
95 & 172 & 154.114 & 17.8861 \tabularnewline
96 & 167 & 157.214 & 9.78611 \tabularnewline
97 & 160 & 158.019 & 1.98056 \tabularnewline
98 & 150 & 147.119 & 2.88056 \tabularnewline
99 & 160 & 147.919 & 12.0806 \tabularnewline
100 & 165 & 149.819 & 15.1806 \tabularnewline
101 & 171 & 167.719 & 3.28056 \tabularnewline
102 & 179 & 173.519 & 5.48056 \tabularnewline
103 & 171 & 163.419 & 7.58056 \tabularnewline
104 & 176 & 162.019 & 13.9806 \tabularnewline
105 & 170 & 163.319 & 6.68056 \tabularnewline
106 & 169 & 159.319 & 9.68056 \tabularnewline
107 & 194 & 172.719 & 21.2806 \tabularnewline
108 & 196 & 175.819 & 20.1806 \tabularnewline
109 & 188 & 176.625 & 11.375 \tabularnewline
110 & 174 & 165.725 & 8.275 \tabularnewline
111 & 186 & 166.525 & 19.475 \tabularnewline
112 & 191 & 168.425 & 22.575 \tabularnewline
113 & 197 & 186.325 & 10.675 \tabularnewline
114 & 206 & 192.125 & 13.875 \tabularnewline
115 & 197 & 182.025 & 14.975 \tabularnewline
116 & 204 & 180.625 & 23.375 \tabularnewline
117 & 201 & 181.925 & 19.075 \tabularnewline
118 & 190 & 177.925 & 12.075 \tabularnewline
119 & 213 & 191.325 & 21.675 \tabularnewline
120 & 213 & 194.425 & 18.575 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232460&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]56[/C][C]9.175[/C][C]46.825[/C][/ROW]
[ROW][C]2[/C][C]55[/C][C]-1.725[/C][C]56.725[/C][/ROW]
[ROW][C]3[/C][C]54[/C][C]-0.925[/C][C]54.925[/C][/ROW]
[ROW][C]4[/C][C]52[/C][C]0.975[/C][C]51.025[/C][/ROW]
[ROW][C]5[/C][C]72[/C][C]18.875[/C][C]53.125[/C][/ROW]
[ROW][C]6[/C][C]71[/C][C]24.675[/C][C]46.325[/C][/ROW]
[ROW][C]7[/C][C]56[/C][C]14.575[/C][C]41.425[/C][/ROW]
[ROW][C]8[/C][C]46[/C][C]13.175[/C][C]32.825[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]14.475[/C][C]32.525[/C][/ROW]
[ROW][C]10[/C][C]47[/C][C]10.475[/C][C]36.525[/C][/ROW]
[ROW][C]11[/C][C]48[/C][C]23.875[/C][C]24.125[/C][/ROW]
[ROW][C]12[/C][C]50[/C][C]26.975[/C][C]23.025[/C][/ROW]
[ROW][C]13[/C][C]44[/C][C]27.7806[/C][C]16.2194[/C][/ROW]
[ROW][C]14[/C][C]38[/C][C]16.8806[/C][C]21.1194[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]17.6806[/C][C]15.3194[/C][/ROW]
[ROW][C]16[/C][C]33[/C][C]19.5806[/C][C]13.4194[/C][/ROW]
[ROW][C]17[/C][C]52[/C][C]37.4806[/C][C]14.5194[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]43.2806[/C][C]10.7194[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]33.1806[/C][C]5.81944[/C][/ROW]
[ROW][C]20[/C][C]22[/C][C]31.7806[/C][C]-9.78056[/C][/ROW]
[ROW][C]21[/C][C]31[/C][C]33.0806[/C][C]-2.08056[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]29.0806[/C][C]1.91944[/C][/ROW]
[ROW][C]23[/C][C]38[/C][C]42.4806[/C][C]-4.48056[/C][/ROW]
[ROW][C]24[/C][C]42[/C][C]45.5806[/C][C]-3.58056[/C][/ROW]
[ROW][C]25[/C][C]41[/C][C]46.3861[/C][C]-5.38611[/C][/ROW]
[ROW][C]26[/C][C]31[/C][C]35.4861[/C][C]-4.48611[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]36.2861[/C][C]-0.286111[/C][/ROW]
[ROW][C]28[/C][C]34[/C][C]38.1861[/C][C]-4.18611[/C][/ROW]
[ROW][C]29[/C][C]51[/C][C]56.0861[/C][C]-5.08611[/C][/ROW]
[ROW][C]30[/C][C]47[/C][C]61.8861[/C][C]-14.8861[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]51.7861[/C][C]-20.7861[/C][/ROW]
[ROW][C]32[/C][C]19[/C][C]50.3861[/C][C]-31.3861[/C][/ROW]
[ROW][C]33[/C][C]30[/C][C]51.6861[/C][C]-21.6861[/C][/ROW]
[ROW][C]34[/C][C]33[/C][C]47.6861[/C][C]-14.6861[/C][/ROW]
[ROW][C]35[/C][C]36[/C][C]61.0861[/C][C]-25.0861[/C][/ROW]
[ROW][C]36[/C][C]40[/C][C]64.1861[/C][C]-24.1861[/C][/ROW]
[ROW][C]37[/C][C]32[/C][C]64.9917[/C][C]-32.9917[/C][/ROW]
[ROW][C]38[/C][C]25[/C][C]54.0917[/C][C]-29.0917[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]54.8917[/C][C]-26.8917[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]56.7917[/C][C]-27.7917[/C][/ROW]
[ROW][C]41[/C][C]55[/C][C]74.6917[/C][C]-19.6917[/C][/ROW]
[ROW][C]42[/C][C]55[/C][C]80.4917[/C][C]-25.4917[/C][/ROW]
[ROW][C]43[/C][C]40[/C][C]70.3917[/C][C]-30.3917[/C][/ROW]
[ROW][C]44[/C][C]38[/C][C]68.9917[/C][C]-30.9917[/C][/ROW]
[ROW][C]45[/C][C]44[/C][C]70.2917[/C][C]-26.2917[/C][/ROW]
[ROW][C]46[/C][C]41[/C][C]66.2917[/C][C]-25.2917[/C][/ROW]
[ROW][C]47[/C][C]49[/C][C]79.6917[/C][C]-30.6917[/C][/ROW]
[ROW][C]48[/C][C]59[/C][C]82.7917[/C][C]-23.7917[/C][/ROW]
[ROW][C]49[/C][C]61[/C][C]83.5972[/C][C]-22.5972[/C][/ROW]
[ROW][C]50[/C][C]47[/C][C]72.6972[/C][C]-25.6972[/C][/ROW]
[ROW][C]51[/C][C]43[/C][C]73.4972[/C][C]-30.4972[/C][/ROW]
[ROW][C]52[/C][C]39[/C][C]75.3972[/C][C]-36.3972[/C][/ROW]
[ROW][C]53[/C][C]66[/C][C]93.2972[/C][C]-27.2972[/C][/ROW]
[ROW][C]54[/C][C]68[/C][C]99.0972[/C][C]-31.0972[/C][/ROW]
[ROW][C]55[/C][C]63[/C][C]88.9972[/C][C]-25.9972[/C][/ROW]
[ROW][C]56[/C][C]68[/C][C]87.5972[/C][C]-19.5972[/C][/ROW]
[ROW][C]57[/C][C]67[/C][C]88.8972[/C][C]-21.8972[/C][/ROW]
[ROW][C]58[/C][C]59[/C][C]84.8972[/C][C]-25.8972[/C][/ROW]
[ROW][C]59[/C][C]68[/C][C]98.2972[/C][C]-30.2972[/C][/ROW]
[ROW][C]60[/C][C]78[/C][C]101.397[/C][C]-23.3972[/C][/ROW]
[ROW][C]61[/C][C]82[/C][C]102.203[/C][C]-20.2028[/C][/ROW]
[ROW][C]62[/C][C]70[/C][C]91.3028[/C][C]-21.3028[/C][/ROW]
[ROW][C]63[/C][C]62[/C][C]92.1028[/C][C]-30.1028[/C][/ROW]
[ROW][C]64[/C][C]68[/C][C]94.0028[/C][C]-26.0028[/C][/ROW]
[ROW][C]65[/C][C]94[/C][C]111.903[/C][C]-17.9028[/C][/ROW]
[ROW][C]66[/C][C]102[/C][C]117.703[/C][C]-15.7028[/C][/ROW]
[ROW][C]67[/C][C]100[/C][C]107.603[/C][C]-7.60278[/C][/ROW]
[ROW][C]68[/C][C]104[/C][C]106.203[/C][C]-2.20278[/C][/ROW]
[ROW][C]69[/C][C]103[/C][C]107.503[/C][C]-4.50278[/C][/ROW]
[ROW][C]70[/C][C]93[/C][C]103.503[/C][C]-10.5028[/C][/ROW]
[ROW][C]71[/C][C]110[/C][C]116.903[/C][C]-6.90278[/C][/ROW]
[ROW][C]72[/C][C]114[/C][C]120.003[/C][C]-6.00278[/C][/ROW]
[ROW][C]73[/C][C]120[/C][C]120.808[/C][C]-0.808333[/C][/ROW]
[ROW][C]74[/C][C]102[/C][C]109.908[/C][C]-7.90833[/C][/ROW]
[ROW][C]75[/C][C]95[/C][C]110.708[/C][C]-15.7083[/C][/ROW]
[ROW][C]76[/C][C]103[/C][C]112.608[/C][C]-9.60833[/C][/ROW]
[ROW][C]77[/C][C]122[/C][C]130.508[/C][C]-8.50833[/C][/ROW]
[ROW][C]78[/C][C]139[/C][C]136.308[/C][C]2.69167[/C][/ROW]
[ROW][C]79[/C][C]135[/C][C]126.208[/C][C]8.79167[/C][/ROW]
[ROW][C]80[/C][C]135[/C][C]124.808[/C][C]10.1917[/C][/ROW]
[ROW][C]81[/C][C]137[/C][C]126.108[/C][C]10.8917[/C][/ROW]
[ROW][C]82[/C][C]130[/C][C]122.108[/C][C]7.89167[/C][/ROW]
[ROW][C]83[/C][C]148[/C][C]135.508[/C][C]12.4917[/C][/ROW]
[ROW][C]84[/C][C]148[/C][C]138.608[/C][C]9.39167[/C][/ROW]
[ROW][C]85[/C][C]145[/C][C]139.414[/C][C]5.58611[/C][/ROW]
[ROW][C]86[/C][C]128[/C][C]128.514[/C][C]-0.513889[/C][/ROW]
[ROW][C]87[/C][C]131[/C][C]129.314[/C][C]1.68611[/C][/ROW]
[ROW][C]88[/C][C]133[/C][C]131.214[/C][C]1.78611[/C][/ROW]
[ROW][C]89[/C][C]146[/C][C]149.114[/C][C]-3.11389[/C][/ROW]
[ROW][C]90[/C][C]163[/C][C]154.914[/C][C]8.08611[/C][/ROW]
[ROW][C]91[/C][C]151[/C][C]144.814[/C][C]6.18611[/C][/ROW]
[ROW][C]92[/C][C]157[/C][C]143.414[/C][C]13.5861[/C][/ROW]
[ROW][C]93[/C][C]152[/C][C]144.714[/C][C]7.28611[/C][/ROW]
[ROW][C]94[/C][C]149[/C][C]140.714[/C][C]8.28611[/C][/ROW]
[ROW][C]95[/C][C]172[/C][C]154.114[/C][C]17.8861[/C][/ROW]
[ROW][C]96[/C][C]167[/C][C]157.214[/C][C]9.78611[/C][/ROW]
[ROW][C]97[/C][C]160[/C][C]158.019[/C][C]1.98056[/C][/ROW]
[ROW][C]98[/C][C]150[/C][C]147.119[/C][C]2.88056[/C][/ROW]
[ROW][C]99[/C][C]160[/C][C]147.919[/C][C]12.0806[/C][/ROW]
[ROW][C]100[/C][C]165[/C][C]149.819[/C][C]15.1806[/C][/ROW]
[ROW][C]101[/C][C]171[/C][C]167.719[/C][C]3.28056[/C][/ROW]
[ROW][C]102[/C][C]179[/C][C]173.519[/C][C]5.48056[/C][/ROW]
[ROW][C]103[/C][C]171[/C][C]163.419[/C][C]7.58056[/C][/ROW]
[ROW][C]104[/C][C]176[/C][C]162.019[/C][C]13.9806[/C][/ROW]
[ROW][C]105[/C][C]170[/C][C]163.319[/C][C]6.68056[/C][/ROW]
[ROW][C]106[/C][C]169[/C][C]159.319[/C][C]9.68056[/C][/ROW]
[ROW][C]107[/C][C]194[/C][C]172.719[/C][C]21.2806[/C][/ROW]
[ROW][C]108[/C][C]196[/C][C]175.819[/C][C]20.1806[/C][/ROW]
[ROW][C]109[/C][C]188[/C][C]176.625[/C][C]11.375[/C][/ROW]
[ROW][C]110[/C][C]174[/C][C]165.725[/C][C]8.275[/C][/ROW]
[ROW][C]111[/C][C]186[/C][C]166.525[/C][C]19.475[/C][/ROW]
[ROW][C]112[/C][C]191[/C][C]168.425[/C][C]22.575[/C][/ROW]
[ROW][C]113[/C][C]197[/C][C]186.325[/C][C]10.675[/C][/ROW]
[ROW][C]114[/C][C]206[/C][C]192.125[/C][C]13.875[/C][/ROW]
[ROW][C]115[/C][C]197[/C][C]182.025[/C][C]14.975[/C][/ROW]
[ROW][C]116[/C][C]204[/C][C]180.625[/C][C]23.375[/C][/ROW]
[ROW][C]117[/C][C]201[/C][C]181.925[/C][C]19.075[/C][/ROW]
[ROW][C]118[/C][C]190[/C][C]177.925[/C][C]12.075[/C][/ROW]
[ROW][C]119[/C][C]213[/C][C]191.325[/C][C]21.675[/C][/ROW]
[ROW][C]120[/C][C]213[/C][C]194.425[/C][C]18.575[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232460&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232460&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1569.17546.825
255-1.72556.725
354-0.92554.925
4520.97551.025
57218.87553.125
67124.67546.325
75614.57541.425
84613.17532.825
94714.47532.525
104710.47536.525
114823.87524.125
125026.97523.025
134427.780616.2194
143816.880621.1194
153317.680615.3194
163319.580613.4194
175237.480614.5194
185443.280610.7194
193933.18065.81944
202231.7806-9.78056
213133.0806-2.08056
223129.08061.91944
233842.4806-4.48056
244245.5806-3.58056
254146.3861-5.38611
263135.4861-4.48611
273636.2861-0.286111
283438.1861-4.18611
295156.0861-5.08611
304761.8861-14.8861
313151.7861-20.7861
321950.3861-31.3861
333051.6861-21.6861
343347.6861-14.6861
353661.0861-25.0861
364064.1861-24.1861
373264.9917-32.9917
382554.0917-29.0917
392854.8917-26.8917
402956.7917-27.7917
415574.6917-19.6917
425580.4917-25.4917
434070.3917-30.3917
443868.9917-30.9917
454470.2917-26.2917
464166.2917-25.2917
474979.6917-30.6917
485982.7917-23.7917
496183.5972-22.5972
504772.6972-25.6972
514373.4972-30.4972
523975.3972-36.3972
536693.2972-27.2972
546899.0972-31.0972
556388.9972-25.9972
566887.5972-19.5972
576788.8972-21.8972
585984.8972-25.8972
596898.2972-30.2972
6078101.397-23.3972
6182102.203-20.2028
627091.3028-21.3028
636292.1028-30.1028
646894.0028-26.0028
6594111.903-17.9028
66102117.703-15.7028
67100107.603-7.60278
68104106.203-2.20278
69103107.503-4.50278
7093103.503-10.5028
71110116.903-6.90278
72114120.003-6.00278
73120120.808-0.808333
74102109.908-7.90833
7595110.708-15.7083
76103112.608-9.60833
77122130.508-8.50833
78139136.3082.69167
79135126.2088.79167
80135124.80810.1917
81137126.10810.8917
82130122.1087.89167
83148135.50812.4917
84148138.6089.39167
85145139.4145.58611
86128128.514-0.513889
87131129.3141.68611
88133131.2141.78611
89146149.114-3.11389
90163154.9148.08611
91151144.8146.18611
92157143.41413.5861
93152144.7147.28611
94149140.7148.28611
95172154.11417.8861
96167157.2149.78611
97160158.0191.98056
98150147.1192.88056
99160147.91912.0806
100165149.81915.1806
101171167.7193.28056
102179173.5195.48056
103171163.4197.58056
104176162.01913.9806
105170163.3196.68056
106169159.3199.68056
107194172.71921.2806
108196175.81920.1806
109188176.62511.375
110174165.7258.275
111186166.52519.475
112191168.42522.575
113197186.32510.675
114206192.12513.875
115197182.02514.975
116204180.62523.375
117201181.92519.075
118190177.92512.075
119213191.32521.675
120213194.42518.575







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.01177050.02354090.98823
170.003114970.006229930.996885
180.0006933720.001386740.999307
190.0001494230.0002988470.999851
200.0001543090.0003086180.999846
214.59735e-059.1947e-050.999954
221.56719e-053.13439e-050.999984
233.55256e-057.10512e-050.999964
249.5297e-050.0001905940.999905
250.002136750.004273510.997863
260.002447880.004895760.997552
270.01352270.02704540.986477
280.03403030.06806060.96597
290.06655610.1331120.933444
300.06725550.1345110.932745
310.0540750.108150.945925
320.03612550.07225090.963875
330.03906820.07813640.960932
340.07928470.1585690.920715
350.08345880.1669180.916541
360.09250070.1850010.907499
370.07158760.1431750.928412
380.05503960.1100790.94496
390.05069750.1013950.949302
400.05055550.1011110.949444
410.1407810.2815620.859219
420.2244940.4489890.775506
430.2891910.5783810.710809
440.6199060.7601880.380094
450.7794630.4410740.220537
460.82650.3469990.1735
470.8969330.2061340.103067
480.9577950.08440950.0422048
490.9885950.02281050.0114053
500.9915250.01694910.00847455
510.9907940.01841190.00920595
520.9922970.01540640.0077032
530.9921320.0157350.00786751
540.9944930.01101490.00550743
550.9978850.004230510.00211525
560.9998140.0003723990.0001862
570.9999340.0001313816.56904e-05
580.9999549.10093e-054.55046e-05
590.9999975.38353e-062.69176e-06
600.9999991.13216e-065.66081e-07
6113.9931e-071.99655e-07
6213.19866e-071.59933e-07
6313.51767e-081.75884e-08
6411.54939e-097.74696e-10
6511.54227e-097.71134e-10
6614.73938e-102.36969e-10
6711.69097e-108.45484e-11
6813.28299e-111.6415e-11
6912.05842e-111.02921e-11
7011.76052e-118.80259e-12
7112.14453e-121.07226e-12
7211.03544e-125.1772e-13
7311.2296e-126.148e-13
7412.68443e-121.34222e-12
7515.89844e-142.94922e-14
7611.64329e-158.21644e-16
7713.56513e-151.78256e-15
7818.30361e-154.15181e-15
7915.2396e-152.6198e-15
8011.04966e-145.24828e-15
8114.59132e-152.29566e-15
8213.98627e-151.99313e-15
8311.11334e-145.5667e-15
8413.37822e-141.68911e-14
8515.22989e-142.61495e-14
8612.73762e-131.36881e-13
8715.07745e-132.53872e-13
8811.22535e-136.12673e-14
8915.67669e-132.83835e-13
9011.35493e-126.77466e-13
9118.17034e-124.08517e-12
9214.30241e-112.1512e-11
9312.63215e-101.31608e-10
9416.1977e-103.09885e-10
9511.28728e-096.43641e-10
9611.01108e-085.0554e-09
9716.08502e-083.04251e-08
9814.78445e-072.39222e-07
990.9999983.3378e-061.6689e-06
1000.9999892.20497e-051.10249e-05
1010.9999280.000144977.24852e-05
1020.9995940.0008118650.000405933
1030.9977050.00458950.00229475
1040.9905980.01880330.00940166

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0117705 & 0.0235409 & 0.98823 \tabularnewline
17 & 0.00311497 & 0.00622993 & 0.996885 \tabularnewline
18 & 0.000693372 & 0.00138674 & 0.999307 \tabularnewline
19 & 0.000149423 & 0.000298847 & 0.999851 \tabularnewline
20 & 0.000154309 & 0.000308618 & 0.999846 \tabularnewline
21 & 4.59735e-05 & 9.1947e-05 & 0.999954 \tabularnewline
22 & 1.56719e-05 & 3.13439e-05 & 0.999984 \tabularnewline
23 & 3.55256e-05 & 7.10512e-05 & 0.999964 \tabularnewline
24 & 9.5297e-05 & 0.000190594 & 0.999905 \tabularnewline
25 & 0.00213675 & 0.00427351 & 0.997863 \tabularnewline
26 & 0.00244788 & 0.00489576 & 0.997552 \tabularnewline
27 & 0.0135227 & 0.0270454 & 0.986477 \tabularnewline
28 & 0.0340303 & 0.0680606 & 0.96597 \tabularnewline
29 & 0.0665561 & 0.133112 & 0.933444 \tabularnewline
30 & 0.0672555 & 0.134511 & 0.932745 \tabularnewline
31 & 0.054075 & 0.10815 & 0.945925 \tabularnewline
32 & 0.0361255 & 0.0722509 & 0.963875 \tabularnewline
33 & 0.0390682 & 0.0781364 & 0.960932 \tabularnewline
34 & 0.0792847 & 0.158569 & 0.920715 \tabularnewline
35 & 0.0834588 & 0.166918 & 0.916541 \tabularnewline
36 & 0.0925007 & 0.185001 & 0.907499 \tabularnewline
37 & 0.0715876 & 0.143175 & 0.928412 \tabularnewline
38 & 0.0550396 & 0.110079 & 0.94496 \tabularnewline
39 & 0.0506975 & 0.101395 & 0.949302 \tabularnewline
40 & 0.0505555 & 0.101111 & 0.949444 \tabularnewline
41 & 0.140781 & 0.281562 & 0.859219 \tabularnewline
42 & 0.224494 & 0.448989 & 0.775506 \tabularnewline
43 & 0.289191 & 0.578381 & 0.710809 \tabularnewline
44 & 0.619906 & 0.760188 & 0.380094 \tabularnewline
45 & 0.779463 & 0.441074 & 0.220537 \tabularnewline
46 & 0.8265 & 0.346999 & 0.1735 \tabularnewline
47 & 0.896933 & 0.206134 & 0.103067 \tabularnewline
48 & 0.957795 & 0.0844095 & 0.0422048 \tabularnewline
49 & 0.988595 & 0.0228105 & 0.0114053 \tabularnewline
50 & 0.991525 & 0.0169491 & 0.00847455 \tabularnewline
51 & 0.990794 & 0.0184119 & 0.00920595 \tabularnewline
52 & 0.992297 & 0.0154064 & 0.0077032 \tabularnewline
53 & 0.992132 & 0.015735 & 0.00786751 \tabularnewline
54 & 0.994493 & 0.0110149 & 0.00550743 \tabularnewline
55 & 0.997885 & 0.00423051 & 0.00211525 \tabularnewline
56 & 0.999814 & 0.000372399 & 0.0001862 \tabularnewline
57 & 0.999934 & 0.000131381 & 6.56904e-05 \tabularnewline
58 & 0.999954 & 9.10093e-05 & 4.55046e-05 \tabularnewline
59 & 0.999997 & 5.38353e-06 & 2.69176e-06 \tabularnewline
60 & 0.999999 & 1.13216e-06 & 5.66081e-07 \tabularnewline
61 & 1 & 3.9931e-07 & 1.99655e-07 \tabularnewline
62 & 1 & 3.19866e-07 & 1.59933e-07 \tabularnewline
63 & 1 & 3.51767e-08 & 1.75884e-08 \tabularnewline
64 & 1 & 1.54939e-09 & 7.74696e-10 \tabularnewline
65 & 1 & 1.54227e-09 & 7.71134e-10 \tabularnewline
66 & 1 & 4.73938e-10 & 2.36969e-10 \tabularnewline
67 & 1 & 1.69097e-10 & 8.45484e-11 \tabularnewline
68 & 1 & 3.28299e-11 & 1.6415e-11 \tabularnewline
69 & 1 & 2.05842e-11 & 1.02921e-11 \tabularnewline
70 & 1 & 1.76052e-11 & 8.80259e-12 \tabularnewline
71 & 1 & 2.14453e-12 & 1.07226e-12 \tabularnewline
72 & 1 & 1.03544e-12 & 5.1772e-13 \tabularnewline
73 & 1 & 1.2296e-12 & 6.148e-13 \tabularnewline
74 & 1 & 2.68443e-12 & 1.34222e-12 \tabularnewline
75 & 1 & 5.89844e-14 & 2.94922e-14 \tabularnewline
76 & 1 & 1.64329e-15 & 8.21644e-16 \tabularnewline
77 & 1 & 3.56513e-15 & 1.78256e-15 \tabularnewline
78 & 1 & 8.30361e-15 & 4.15181e-15 \tabularnewline
79 & 1 & 5.2396e-15 & 2.6198e-15 \tabularnewline
80 & 1 & 1.04966e-14 & 5.24828e-15 \tabularnewline
81 & 1 & 4.59132e-15 & 2.29566e-15 \tabularnewline
82 & 1 & 3.98627e-15 & 1.99313e-15 \tabularnewline
83 & 1 & 1.11334e-14 & 5.5667e-15 \tabularnewline
84 & 1 & 3.37822e-14 & 1.68911e-14 \tabularnewline
85 & 1 & 5.22989e-14 & 2.61495e-14 \tabularnewline
86 & 1 & 2.73762e-13 & 1.36881e-13 \tabularnewline
87 & 1 & 5.07745e-13 & 2.53872e-13 \tabularnewline
88 & 1 & 1.22535e-13 & 6.12673e-14 \tabularnewline
89 & 1 & 5.67669e-13 & 2.83835e-13 \tabularnewline
90 & 1 & 1.35493e-12 & 6.77466e-13 \tabularnewline
91 & 1 & 8.17034e-12 & 4.08517e-12 \tabularnewline
92 & 1 & 4.30241e-11 & 2.1512e-11 \tabularnewline
93 & 1 & 2.63215e-10 & 1.31608e-10 \tabularnewline
94 & 1 & 6.1977e-10 & 3.09885e-10 \tabularnewline
95 & 1 & 1.28728e-09 & 6.43641e-10 \tabularnewline
96 & 1 & 1.01108e-08 & 5.0554e-09 \tabularnewline
97 & 1 & 6.08502e-08 & 3.04251e-08 \tabularnewline
98 & 1 & 4.78445e-07 & 2.39222e-07 \tabularnewline
99 & 0.999998 & 3.3378e-06 & 1.6689e-06 \tabularnewline
100 & 0.999989 & 2.20497e-05 & 1.10249e-05 \tabularnewline
101 & 0.999928 & 0.00014497 & 7.24852e-05 \tabularnewline
102 & 0.999594 & 0.000811865 & 0.000405933 \tabularnewline
103 & 0.997705 & 0.0045895 & 0.00229475 \tabularnewline
104 & 0.990598 & 0.0188033 & 0.00940166 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232460&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]16[/C][C]0.0117705[/C][C]0.0235409[/C][C]0.98823[/C][/ROW]
[ROW][C]17[/C][C]0.00311497[/C][C]0.00622993[/C][C]0.996885[/C][/ROW]
[ROW][C]18[/C][C]0.000693372[/C][C]0.00138674[/C][C]0.999307[/C][/ROW]
[ROW][C]19[/C][C]0.000149423[/C][C]0.000298847[/C][C]0.999851[/C][/ROW]
[ROW][C]20[/C][C]0.000154309[/C][C]0.000308618[/C][C]0.999846[/C][/ROW]
[ROW][C]21[/C][C]4.59735e-05[/C][C]9.1947e-05[/C][C]0.999954[/C][/ROW]
[ROW][C]22[/C][C]1.56719e-05[/C][C]3.13439e-05[/C][C]0.999984[/C][/ROW]
[ROW][C]23[/C][C]3.55256e-05[/C][C]7.10512e-05[/C][C]0.999964[/C][/ROW]
[ROW][C]24[/C][C]9.5297e-05[/C][C]0.000190594[/C][C]0.999905[/C][/ROW]
[ROW][C]25[/C][C]0.00213675[/C][C]0.00427351[/C][C]0.997863[/C][/ROW]
[ROW][C]26[/C][C]0.00244788[/C][C]0.00489576[/C][C]0.997552[/C][/ROW]
[ROW][C]27[/C][C]0.0135227[/C][C]0.0270454[/C][C]0.986477[/C][/ROW]
[ROW][C]28[/C][C]0.0340303[/C][C]0.0680606[/C][C]0.96597[/C][/ROW]
[ROW][C]29[/C][C]0.0665561[/C][C]0.133112[/C][C]0.933444[/C][/ROW]
[ROW][C]30[/C][C]0.0672555[/C][C]0.134511[/C][C]0.932745[/C][/ROW]
[ROW][C]31[/C][C]0.054075[/C][C]0.10815[/C][C]0.945925[/C][/ROW]
[ROW][C]32[/C][C]0.0361255[/C][C]0.0722509[/C][C]0.963875[/C][/ROW]
[ROW][C]33[/C][C]0.0390682[/C][C]0.0781364[/C][C]0.960932[/C][/ROW]
[ROW][C]34[/C][C]0.0792847[/C][C]0.158569[/C][C]0.920715[/C][/ROW]
[ROW][C]35[/C][C]0.0834588[/C][C]0.166918[/C][C]0.916541[/C][/ROW]
[ROW][C]36[/C][C]0.0925007[/C][C]0.185001[/C][C]0.907499[/C][/ROW]
[ROW][C]37[/C][C]0.0715876[/C][C]0.143175[/C][C]0.928412[/C][/ROW]
[ROW][C]38[/C][C]0.0550396[/C][C]0.110079[/C][C]0.94496[/C][/ROW]
[ROW][C]39[/C][C]0.0506975[/C][C]0.101395[/C][C]0.949302[/C][/ROW]
[ROW][C]40[/C][C]0.0505555[/C][C]0.101111[/C][C]0.949444[/C][/ROW]
[ROW][C]41[/C][C]0.140781[/C][C]0.281562[/C][C]0.859219[/C][/ROW]
[ROW][C]42[/C][C]0.224494[/C][C]0.448989[/C][C]0.775506[/C][/ROW]
[ROW][C]43[/C][C]0.289191[/C][C]0.578381[/C][C]0.710809[/C][/ROW]
[ROW][C]44[/C][C]0.619906[/C][C]0.760188[/C][C]0.380094[/C][/ROW]
[ROW][C]45[/C][C]0.779463[/C][C]0.441074[/C][C]0.220537[/C][/ROW]
[ROW][C]46[/C][C]0.8265[/C][C]0.346999[/C][C]0.1735[/C][/ROW]
[ROW][C]47[/C][C]0.896933[/C][C]0.206134[/C][C]0.103067[/C][/ROW]
[ROW][C]48[/C][C]0.957795[/C][C]0.0844095[/C][C]0.0422048[/C][/ROW]
[ROW][C]49[/C][C]0.988595[/C][C]0.0228105[/C][C]0.0114053[/C][/ROW]
[ROW][C]50[/C][C]0.991525[/C][C]0.0169491[/C][C]0.00847455[/C][/ROW]
[ROW][C]51[/C][C]0.990794[/C][C]0.0184119[/C][C]0.00920595[/C][/ROW]
[ROW][C]52[/C][C]0.992297[/C][C]0.0154064[/C][C]0.0077032[/C][/ROW]
[ROW][C]53[/C][C]0.992132[/C][C]0.015735[/C][C]0.00786751[/C][/ROW]
[ROW][C]54[/C][C]0.994493[/C][C]0.0110149[/C][C]0.00550743[/C][/ROW]
[ROW][C]55[/C][C]0.997885[/C][C]0.00423051[/C][C]0.00211525[/C][/ROW]
[ROW][C]56[/C][C]0.999814[/C][C]0.000372399[/C][C]0.0001862[/C][/ROW]
[ROW][C]57[/C][C]0.999934[/C][C]0.000131381[/C][C]6.56904e-05[/C][/ROW]
[ROW][C]58[/C][C]0.999954[/C][C]9.10093e-05[/C][C]4.55046e-05[/C][/ROW]
[ROW][C]59[/C][C]0.999997[/C][C]5.38353e-06[/C][C]2.69176e-06[/C][/ROW]
[ROW][C]60[/C][C]0.999999[/C][C]1.13216e-06[/C][C]5.66081e-07[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]3.9931e-07[/C][C]1.99655e-07[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]3.19866e-07[/C][C]1.59933e-07[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]3.51767e-08[/C][C]1.75884e-08[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]1.54939e-09[/C][C]7.74696e-10[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]1.54227e-09[/C][C]7.71134e-10[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]4.73938e-10[/C][C]2.36969e-10[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]1.69097e-10[/C][C]8.45484e-11[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]3.28299e-11[/C][C]1.6415e-11[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]2.05842e-11[/C][C]1.02921e-11[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.76052e-11[/C][C]8.80259e-12[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]2.14453e-12[/C][C]1.07226e-12[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]1.03544e-12[/C][C]5.1772e-13[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]1.2296e-12[/C][C]6.148e-13[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]2.68443e-12[/C][C]1.34222e-12[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]5.89844e-14[/C][C]2.94922e-14[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.64329e-15[/C][C]8.21644e-16[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]3.56513e-15[/C][C]1.78256e-15[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]8.30361e-15[/C][C]4.15181e-15[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]5.2396e-15[/C][C]2.6198e-15[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.04966e-14[/C][C]5.24828e-15[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]4.59132e-15[/C][C]2.29566e-15[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]3.98627e-15[/C][C]1.99313e-15[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.11334e-14[/C][C]5.5667e-15[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]3.37822e-14[/C][C]1.68911e-14[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]5.22989e-14[/C][C]2.61495e-14[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]2.73762e-13[/C][C]1.36881e-13[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]5.07745e-13[/C][C]2.53872e-13[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.22535e-13[/C][C]6.12673e-14[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]5.67669e-13[/C][C]2.83835e-13[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.35493e-12[/C][C]6.77466e-13[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]8.17034e-12[/C][C]4.08517e-12[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]4.30241e-11[/C][C]2.1512e-11[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]2.63215e-10[/C][C]1.31608e-10[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]6.1977e-10[/C][C]3.09885e-10[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.28728e-09[/C][C]6.43641e-10[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.01108e-08[/C][C]5.0554e-09[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]6.08502e-08[/C][C]3.04251e-08[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]4.78445e-07[/C][C]2.39222e-07[/C][/ROW]
[ROW][C]99[/C][C]0.999998[/C][C]3.3378e-06[/C][C]1.6689e-06[/C][/ROW]
[ROW][C]100[/C][C]0.999989[/C][C]2.20497e-05[/C][C]1.10249e-05[/C][/ROW]
[ROW][C]101[/C][C]0.999928[/C][C]0.00014497[/C][C]7.24852e-05[/C][/ROW]
[ROW][C]102[/C][C]0.999594[/C][C]0.000811865[/C][C]0.000405933[/C][/ROW]
[ROW][C]103[/C][C]0.997705[/C][C]0.0045895[/C][C]0.00229475[/C][/ROW]
[ROW][C]104[/C][C]0.990598[/C][C]0.0188033[/C][C]0.00940166[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232460&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232460&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.01177050.02354090.98823
170.003114970.006229930.996885
180.0006933720.001386740.999307
190.0001494230.0002988470.999851
200.0001543090.0003086180.999846
214.59735e-059.1947e-050.999954
221.56719e-053.13439e-050.999984
233.55256e-057.10512e-050.999964
249.5297e-050.0001905940.999905
250.002136750.004273510.997863
260.002447880.004895760.997552
270.01352270.02704540.986477
280.03403030.06806060.96597
290.06655610.1331120.933444
300.06725550.1345110.932745
310.0540750.108150.945925
320.03612550.07225090.963875
330.03906820.07813640.960932
340.07928470.1585690.920715
350.08345880.1669180.916541
360.09250070.1850010.907499
370.07158760.1431750.928412
380.05503960.1100790.94496
390.05069750.1013950.949302
400.05055550.1011110.949444
410.1407810.2815620.859219
420.2244940.4489890.775506
430.2891910.5783810.710809
440.6199060.7601880.380094
450.7794630.4410740.220537
460.82650.3469990.1735
470.8969330.2061340.103067
480.9577950.08440950.0422048
490.9885950.02281050.0114053
500.9915250.01694910.00847455
510.9907940.01841190.00920595
520.9922970.01540640.0077032
530.9921320.0157350.00786751
540.9944930.01101490.00550743
550.9978850.004230510.00211525
560.9998140.0003723990.0001862
570.9999340.0001313816.56904e-05
580.9999549.10093e-054.55046e-05
590.9999975.38353e-062.69176e-06
600.9999991.13216e-065.66081e-07
6113.9931e-071.99655e-07
6213.19866e-071.59933e-07
6313.51767e-081.75884e-08
6411.54939e-097.74696e-10
6511.54227e-097.71134e-10
6614.73938e-102.36969e-10
6711.69097e-108.45484e-11
6813.28299e-111.6415e-11
6912.05842e-111.02921e-11
7011.76052e-118.80259e-12
7112.14453e-121.07226e-12
7211.03544e-125.1772e-13
7311.2296e-126.148e-13
7412.68443e-121.34222e-12
7515.89844e-142.94922e-14
7611.64329e-158.21644e-16
7713.56513e-151.78256e-15
7818.30361e-154.15181e-15
7915.2396e-152.6198e-15
8011.04966e-145.24828e-15
8114.59132e-152.29566e-15
8213.98627e-151.99313e-15
8311.11334e-145.5667e-15
8413.37822e-141.68911e-14
8515.22989e-142.61495e-14
8612.73762e-131.36881e-13
8715.07745e-132.53872e-13
8811.22535e-136.12673e-14
8915.67669e-132.83835e-13
9011.35493e-126.77466e-13
9118.17034e-124.08517e-12
9214.30241e-112.1512e-11
9312.63215e-101.31608e-10
9416.1977e-103.09885e-10
9511.28728e-096.43641e-10
9611.01108e-085.0554e-09
9716.08502e-083.04251e-08
9814.78445e-072.39222e-07
990.9999983.3378e-061.6689e-06
1000.9999892.20497e-051.10249e-05
1010.9999280.000144977.24852e-05
1020.9995940.0008118650.000405933
1030.9977050.00458950.00229475
1040.9905980.01880330.00940166







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level590.662921NOK
5% type I error level680.764045NOK
10% type I error level720.808989NOK

\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 & 59 & 0.662921 & NOK \tabularnewline
5% type I error level & 68 & 0.764045 & NOK \tabularnewline
10% type I error level & 72 & 0.808989 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232460&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]59[/C][C]0.662921[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]68[/C][C]0.764045[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]72[/C][C]0.808989[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232460&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232460&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level590.662921NOK
5% type I error level680.764045NOK
10% type I error level720.808989NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}