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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 computationThu, 18 Dec 2014 11:18:05 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t1418901574k8d8d4h1z795rs6.htm/, Retrieved Sun, 19 May 2024 21:18:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270809, Retrieved Sun, 19 May 2024 21:18:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-18 11:18:05] [4621f922aed0297f88122271e88ec2ef] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
89 149 96 18 68 7.5 1.5 12.9
99 139 70 31 39 6 2.1 12.2
113 148 88 39 32 6.5 2.1 12.8
93 158 114 46 62 1 1.9 7.4
96 128 69 31 33 1 1.6 6.7
100 224 176 67 52 5.5 2.1 12.6
120 159 114 35 62 8.5 2.1 14.8
85 105 121 52 77 6.5 2.2 13.3
100 159 110 77 76 4.5 1.5 11.1
101 167 158 37 41 2 1.9 8.2
73 165 116 32 48 5 2.2 11.4
97 159 181 36 63 0.5 1.6 6.4
91 119 77 38 30 5 1.5 10.6
95 176 141 69 78 5 1.9 12
115 54 35 21 19 2.5 0.1 6.3
128 91 80 26 31 5 2.2 11.3
92 163 152 54 66 5.5 1.8 11.9
102 124 97 36 35 3.5 1.6 9.3
104 137 99 42 42 3 2.2 9.6
107 121 84 23 45 4 2.1 10
102 148 101 112 25 6.5 1.6 13.8
106 221 107 35 44 4.5 1.9 10.8
113 149 112 47 54 5.5 1.8 11.7
121 244 171 37 74 4 2.4 10.9
128 148 137 109 80 7.5 2.4 16.1
88 92 77 24 42 7 2.5 13.4
99 150 66 20 61 4 1.9 9.9
86 153 93 22 41 5.5 2.1 11.5
66 94 105 23 46 2.5 1.9 8.3
101 156 131 32 39 5.5 2.1 11.7
88 132 102 30 34 3.5 1.5 9
118 161 161 92 51 2.5 1.9 9.7
101 105 120 43 42 4.5 2.1 10.8
88 97 127 55 31 4.5 1.5 10.3
103 151 77 16 39 4.5 2.1 10.4
98 131 108 49 20 6 2.1 12.7
105 166 85 71 49 2.5 1.8 9.3
105 157 168 43 53 5 2.4 11.8
66 111 48 29 31 0 2.1 5.9
92 145 152 56 39 5 1.9 11.4
123 162 75 46 54 6.5 2.1 13
65 163 107 19 49 5 1.9 10.8
102 59 62 23 34 6 2.4 12.3
103 187 121 59 46 4.5 2.1 11.3
84 109 124 30 55 5.5 2.2 11.8
91 90 72 61 42 1 2.2 7.9
152 105 40 7 50 7.5 1.8 12.7
114 83 58 38 13 6 2.1 12.3
88 116 97 32 37 5 2.4 11.6
99 42 88 16 25 1 2.2 6.7
61 148 126 19 30 5 2.1 10.9
105 155 104 22 28 6.5 1.5 12.1
81 125 148 48 45 7 1.9 13.3
113 116 146 23 35 4.5 1.8 10.1
107 128 80 26 28 0 1.8 5.7
89 138 97 33 41 8.5 1.6 14.3
85 49 25 9 6 3.5 1.2 8
99 96 99 24 45 7.5 1.8 13.3
87 164 118 34 73 3.5 1.5 9.3
101 162 58 48 17 6 2.1 12.5
129 99 63 18 40 1.5 2.4 7.6
73 202 139 43 64 9 2.4 15.9
107 186 50 33 37 3.5 1.5 9.2
73 66 60 28 25 3.5 1.8 9.1
104 183 152 71 65 4 2.1 11.1
111 214 142 26 100 6.5 2.2 13
83 188 94 67 28 7.5 2.1 14.5
61 104 66 34 35 6 1.9 12.2
112 177 127 80 56 5 2.1 12.3
103 126 67 29 29 5.5 1.9 11.4
92 99 75 59 59 7.5 2.4 14.6
80 139 128 32 50 6.5 1.9 12.6
95 78 41 47 3 NA 1.9 NA
98 162 146 43 59 6.5 2.1 13
107 108 69 38 27 6.5 1.8 12.6
93 159 186 29 61 7 2.1 13.2
80 74 81 36 28 3.5 2.4 9.9
74 110 85 32 51 1.5 2.1 7.7
90 96 54 35 35 4 2.2 10.5
76 116 46 21 29 7.5 2.1 13.4
100 87 106 29 48 4.5 2.2 10.9
86 97 34 12 25 0 1.6 4.3
107 127 60 37 44 3.5 2.4 10.3
91 106 95 37 64 5.5 2.1 11.8
81 80 57 47 32 5 1.9 11.2
129 74 62 51 20 4.5 2.4 11.4
86 91 36 32 28 2.5 2.1 8.6
99 133 56 21 34 7.5 1.8 13.2
87 74 54 13 31 7 2.1 12.6
115 114 64 14 26 0 1.8 5.6
96 140 76 -2 58 4.5 1.9 9.9
78 95 98 20 23 3 1.9 8.8
86 98 88 24 21 1.5 2.4 7.7
89 121 35 11 21 3.5 1.8 9
92 126 102 23 33 2.5 1.8 7.3
83 98 61 24 16 5.5 2.1 11.4
97 95 80 14 20 8 2.1 13.6
123 110 49 52 37 1 2.4 7.9
109 70 78 15 35 5 1.9 10.7
84 102 90 23 33 4.5 1.8 10.3
105 130 55 35 41 3 2.2 9.6
89 96 96 24 40 8 2.4 14.2
107 102 43 39 35 2.5 1.8 8.5
101 100 52 29 28 7 2.4 13.5
103 52 54 8 22 1 1.9 6.4
77 98 51 18 44 3.5 2.4 9.6
102 118 51 24 27 5.5 2.1 11.6
100 99 38 19 17 5.5 1.9 11.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270809&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270809&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270809&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
PERFTOT[t] = + 58.0982 + 0.0341736LFM[t] -0.0921547Blog[t] -0.085324PRH[t] + 0.062194CH[t] -11.3754Ex[t] -11.934PA[t] + 11.1065TOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PERFTOT[t] =  +  58.0982 +  0.0341736LFM[t] -0.0921547Blog[t] -0.085324PRH[t] +  0.062194CH[t] -11.3754Ex[t] -11.934PA[t] +  11.1065TOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270809&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PERFTOT[t] =  +  58.0982 +  0.0341736LFM[t] -0.0921547Blog[t] -0.085324PRH[t] +  0.062194CH[t] -11.3754Ex[t] -11.934PA[t] +  11.1065TOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270809&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270809&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
PERFTOT[t] = + 58.0982 + 0.0341736LFM[t] -0.0921547Blog[t] -0.085324PRH[t] + 0.062194CH[t] -11.3754Ex[t] -11.934PA[t] + 11.1065TOT[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)58.098231.34451.8540.06678270.0333914
LFM0.03417360.05430640.62930.5306190.26531
Blog-0.09215470.0572348-1.610.1105560.0552782
PRH-0.0853240.235093-0.36290.7174260.358713
CH0.0621940.1260740.49330.6228830.311441
Ex-11.375410.2427-1.1110.2694380.134719
PA-11.93411.8364-1.0080.3157940.157897
TOT11.106510.08781.1010.273570.136785

\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) & 58.0982 & 31.3445 & 1.854 & 0.0667827 & 0.0333914 \tabularnewline
LFM & 0.0341736 & 0.0543064 & 0.6293 & 0.530619 & 0.26531 \tabularnewline
Blog & -0.0921547 & 0.0572348 & -1.61 & 0.110556 & 0.0552782 \tabularnewline
PRH & -0.085324 & 0.235093 & -0.3629 & 0.717426 & 0.358713 \tabularnewline
CH & 0.062194 & 0.126074 & 0.4933 & 0.622883 & 0.311441 \tabularnewline
Ex & -11.3754 & 10.2427 & -1.111 & 0.269438 & 0.134719 \tabularnewline
PA & -11.934 & 11.8364 & -1.008 & 0.315794 & 0.157897 \tabularnewline
TOT & 11.1065 & 10.0878 & 1.101 & 0.27357 & 0.136785 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270809&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]58.0982[/C][C]31.3445[/C][C]1.854[/C][C]0.0667827[/C][C]0.0333914[/C][/ROW]
[ROW][C]LFM[/C][C]0.0341736[/C][C]0.0543064[/C][C]0.6293[/C][C]0.530619[/C][C]0.26531[/C][/ROW]
[ROW][C]Blog[/C][C]-0.0921547[/C][C]0.0572348[/C][C]-1.61[/C][C]0.110556[/C][C]0.0552782[/C][/ROW]
[ROW][C]PRH[/C][C]-0.085324[/C][C]0.235093[/C][C]-0.3629[/C][C]0.717426[/C][C]0.358713[/C][/ROW]
[ROW][C]CH[/C][C]0.062194[/C][C]0.126074[/C][C]0.4933[/C][C]0.622883[/C][C]0.311441[/C][/ROW]
[ROW][C]Ex[/C][C]-11.3754[/C][C]10.2427[/C][C]-1.111[/C][C]0.269438[/C][C]0.134719[/C][/ROW]
[ROW][C]PA[/C][C]-11.934[/C][C]11.8364[/C][C]-1.008[/C][C]0.315794[/C][C]0.157897[/C][/ROW]
[ROW][C]TOT[/C][C]11.1065[/C][C]10.0878[/C][C]1.101[/C][C]0.27357[/C][C]0.136785[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270809&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270809&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)58.098231.34451.8540.06678270.0333914
LFM0.03417360.05430640.62930.5306190.26531
Blog-0.09215470.0572348-1.610.1105560.0552782
PRH-0.0853240.235093-0.36290.7174260.358713
CH0.0621940.1260740.49330.6228830.311441
Ex-11.375410.2427-1.1110.2694380.134719
PA-11.93411.8364-1.0080.3157940.157897
TOT11.106510.08781.1010.273570.136785






Multiple Linear Regression - Regression Statistics
Multiple R0.271652
R-squared0.0737946
Adjusted R-squared0.00830537
F-TEST (value)1.12682
F-TEST (DF numerator)7
F-TEST (DF denominator)99
p-value0.352551
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.6692
Sum Squared Residuals24306.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.271652 \tabularnewline
R-squared & 0.0737946 \tabularnewline
Adjusted R-squared & 0.00830537 \tabularnewline
F-TEST (value) & 1.12682 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 99 \tabularnewline
p-value & 0.352551 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 15.6692 \tabularnewline
Sum Squared Residuals & 24306.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270809&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.271652[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0737946[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00830537[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.12682[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]99[/C][/ROW]
[ROW][C]p-value[/C][C]0.352551[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]15.6692[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]24306.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270809&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270809&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 R0.271652
R-squared0.0737946
Adjusted R-squared0.00830537
F-TEST (value)1.12682
F-TEST (DF numerator)7
F-TEST (DF denominator)99
p-value0.352551
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.6692
Sum Squared Residuals24306.8






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18997.0946-8.09463
29998.36420.63581
311396.871316.1287
493101.062-8.0616
59699.4652-3.4652
610099.36770.632287
712096.520623.4794
88598.4101-13.4101
9100105.744-5.74401
1010194.2866.71395
117396.7848-23.7848
129793.99823.00181
139196.6439-5.64392
1495103.81-8.80975
1511596.447418.5526
1612895.917532.0825
179297.2804-5.28038
1810296.88455.11551
1910498.92715.07288
2010795.83111.169
21102106.083-4.08276
22106101.6274.37291
2311398.117814.8822
2412199.041721.9583
25128111.06416.9357
268894.0757-6.07567
2799101.008-2.00807
288695.5285-9.52848
296693.604-27.604
3010193.37287.6272
318895.0083-7.00829
32118100.70617.2942
3310193.27127.72884
348892.2518-4.25178
3510396.48046.51965
369897.42470.575326
37105106.299-1.29861
3810593.147611.8524
396697.3889-31.3889
409293.7564-1.75639
41123101.5421.46
426595.6335-30.6335
4310294.26967.73038
44103100.4182.58189
458493.4947-9.49473
4691102.058-11.0576
4715294.769257.2308
4811496.452717.5473
498896.0116-8.01158
509988.397310.6027
516190.9121-29.9121
5210596.22358.77647
538192.849-11.849
5411388.327824.6722
5510796.449210.5508
568996.6478-7.64784
578591.7918-6.79182
589993.9275.07302
5987100.044-13.0436
60101100.7690.230773
6112995.332733.6673
627398.0773-25.0773
63107103.7983.20237
647393.7647-20.7647
65104101.0492.9512
66111100.51710.4833
6783102.553-19.5532
686199.4188-38.4188
69112103.7728.22757
7010396.93446.06564
7192101.404-9.40371
728094.7597-14.7597
739592.5642.43604
749889.38858.61152
75107103.6283.37221
7693106.332-13.3317
7780103.862-23.8616
787484.4554-10.4554
7990110.286-20.286
807671.43124.56884
81100101.475-1.47453
828681.43054.56948
83107113.221-6.22057
8491108.401-17.4011
858150.589630.4104
86129141.918-12.918
878684.61521.38476
8899103.723-4.72289
898769.234117.7659
90115114.7480.25236
9196110.974-14.9744
927884.4117-6.41167
938695.0392-9.03919
948981.25247.7476
9592102.762-10.7617
968375.00627.99379
979776.930820.0692
98123107.48815.5123
99109120.107-11.1069
1008482.27781.72221
101105107.04-2.04029
1028982.95646.04359
103107103.663.34035
10410190.616510.3835
105103122.115-19.1155
1067773.27223.72781
10710297.45914.54089
108100NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 89 & 97.0946 & -8.09463 \tabularnewline
2 & 99 & 98.3642 & 0.63581 \tabularnewline
3 & 113 & 96.8713 & 16.1287 \tabularnewline
4 & 93 & 101.062 & -8.0616 \tabularnewline
5 & 96 & 99.4652 & -3.4652 \tabularnewline
6 & 100 & 99.3677 & 0.632287 \tabularnewline
7 & 120 & 96.5206 & 23.4794 \tabularnewline
8 & 85 & 98.4101 & -13.4101 \tabularnewline
9 & 100 & 105.744 & -5.74401 \tabularnewline
10 & 101 & 94.286 & 6.71395 \tabularnewline
11 & 73 & 96.7848 & -23.7848 \tabularnewline
12 & 97 & 93.9982 & 3.00181 \tabularnewline
13 & 91 & 96.6439 & -5.64392 \tabularnewline
14 & 95 & 103.81 & -8.80975 \tabularnewline
15 & 115 & 96.4474 & 18.5526 \tabularnewline
16 & 128 & 95.9175 & 32.0825 \tabularnewline
17 & 92 & 97.2804 & -5.28038 \tabularnewline
18 & 102 & 96.8845 & 5.11551 \tabularnewline
19 & 104 & 98.9271 & 5.07288 \tabularnewline
20 & 107 & 95.831 & 11.169 \tabularnewline
21 & 102 & 106.083 & -4.08276 \tabularnewline
22 & 106 & 101.627 & 4.37291 \tabularnewline
23 & 113 & 98.1178 & 14.8822 \tabularnewline
24 & 121 & 99.0417 & 21.9583 \tabularnewline
25 & 128 & 111.064 & 16.9357 \tabularnewline
26 & 88 & 94.0757 & -6.07567 \tabularnewline
27 & 99 & 101.008 & -2.00807 \tabularnewline
28 & 86 & 95.5285 & -9.52848 \tabularnewline
29 & 66 & 93.604 & -27.604 \tabularnewline
30 & 101 & 93.3728 & 7.6272 \tabularnewline
31 & 88 & 95.0083 & -7.00829 \tabularnewline
32 & 118 & 100.706 & 17.2942 \tabularnewline
33 & 101 & 93.2712 & 7.72884 \tabularnewline
34 & 88 & 92.2518 & -4.25178 \tabularnewline
35 & 103 & 96.4804 & 6.51965 \tabularnewline
36 & 98 & 97.4247 & 0.575326 \tabularnewline
37 & 105 & 106.299 & -1.29861 \tabularnewline
38 & 105 & 93.1476 & 11.8524 \tabularnewline
39 & 66 & 97.3889 & -31.3889 \tabularnewline
40 & 92 & 93.7564 & -1.75639 \tabularnewline
41 & 123 & 101.54 & 21.46 \tabularnewline
42 & 65 & 95.6335 & -30.6335 \tabularnewline
43 & 102 & 94.2696 & 7.73038 \tabularnewline
44 & 103 & 100.418 & 2.58189 \tabularnewline
45 & 84 & 93.4947 & -9.49473 \tabularnewline
46 & 91 & 102.058 & -11.0576 \tabularnewline
47 & 152 & 94.7692 & 57.2308 \tabularnewline
48 & 114 & 96.4527 & 17.5473 \tabularnewline
49 & 88 & 96.0116 & -8.01158 \tabularnewline
50 & 99 & 88.3973 & 10.6027 \tabularnewline
51 & 61 & 90.9121 & -29.9121 \tabularnewline
52 & 105 & 96.2235 & 8.77647 \tabularnewline
53 & 81 & 92.849 & -11.849 \tabularnewline
54 & 113 & 88.3278 & 24.6722 \tabularnewline
55 & 107 & 96.4492 & 10.5508 \tabularnewline
56 & 89 & 96.6478 & -7.64784 \tabularnewline
57 & 85 & 91.7918 & -6.79182 \tabularnewline
58 & 99 & 93.927 & 5.07302 \tabularnewline
59 & 87 & 100.044 & -13.0436 \tabularnewline
60 & 101 & 100.769 & 0.230773 \tabularnewline
61 & 129 & 95.3327 & 33.6673 \tabularnewline
62 & 73 & 98.0773 & -25.0773 \tabularnewline
63 & 107 & 103.798 & 3.20237 \tabularnewline
64 & 73 & 93.7647 & -20.7647 \tabularnewline
65 & 104 & 101.049 & 2.9512 \tabularnewline
66 & 111 & 100.517 & 10.4833 \tabularnewline
67 & 83 & 102.553 & -19.5532 \tabularnewline
68 & 61 & 99.4188 & -38.4188 \tabularnewline
69 & 112 & 103.772 & 8.22757 \tabularnewline
70 & 103 & 96.9344 & 6.06564 \tabularnewline
71 & 92 & 101.404 & -9.40371 \tabularnewline
72 & 80 & 94.7597 & -14.7597 \tabularnewline
73 & 95 & 92.564 & 2.43604 \tabularnewline
74 & 98 & 89.3885 & 8.61152 \tabularnewline
75 & 107 & 103.628 & 3.37221 \tabularnewline
76 & 93 & 106.332 & -13.3317 \tabularnewline
77 & 80 & 103.862 & -23.8616 \tabularnewline
78 & 74 & 84.4554 & -10.4554 \tabularnewline
79 & 90 & 110.286 & -20.286 \tabularnewline
80 & 76 & 71.4312 & 4.56884 \tabularnewline
81 & 100 & 101.475 & -1.47453 \tabularnewline
82 & 86 & 81.4305 & 4.56948 \tabularnewline
83 & 107 & 113.221 & -6.22057 \tabularnewline
84 & 91 & 108.401 & -17.4011 \tabularnewline
85 & 81 & 50.5896 & 30.4104 \tabularnewline
86 & 129 & 141.918 & -12.918 \tabularnewline
87 & 86 & 84.6152 & 1.38476 \tabularnewline
88 & 99 & 103.723 & -4.72289 \tabularnewline
89 & 87 & 69.2341 & 17.7659 \tabularnewline
90 & 115 & 114.748 & 0.25236 \tabularnewline
91 & 96 & 110.974 & -14.9744 \tabularnewline
92 & 78 & 84.4117 & -6.41167 \tabularnewline
93 & 86 & 95.0392 & -9.03919 \tabularnewline
94 & 89 & 81.2524 & 7.7476 \tabularnewline
95 & 92 & 102.762 & -10.7617 \tabularnewline
96 & 83 & 75.0062 & 7.99379 \tabularnewline
97 & 97 & 76.9308 & 20.0692 \tabularnewline
98 & 123 & 107.488 & 15.5123 \tabularnewline
99 & 109 & 120.107 & -11.1069 \tabularnewline
100 & 84 & 82.2778 & 1.72221 \tabularnewline
101 & 105 & 107.04 & -2.04029 \tabularnewline
102 & 89 & 82.9564 & 6.04359 \tabularnewline
103 & 107 & 103.66 & 3.34035 \tabularnewline
104 & 101 & 90.6165 & 10.3835 \tabularnewline
105 & 103 & 122.115 & -19.1155 \tabularnewline
106 & 77 & 73.2722 & 3.72781 \tabularnewline
107 & 102 & 97.4591 & 4.54089 \tabularnewline
108 & 100 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270809&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]89[/C][C]97.0946[/C][C]-8.09463[/C][/ROW]
[ROW][C]2[/C][C]99[/C][C]98.3642[/C][C]0.63581[/C][/ROW]
[ROW][C]3[/C][C]113[/C][C]96.8713[/C][C]16.1287[/C][/ROW]
[ROW][C]4[/C][C]93[/C][C]101.062[/C][C]-8.0616[/C][/ROW]
[ROW][C]5[/C][C]96[/C][C]99.4652[/C][C]-3.4652[/C][/ROW]
[ROW][C]6[/C][C]100[/C][C]99.3677[/C][C]0.632287[/C][/ROW]
[ROW][C]7[/C][C]120[/C][C]96.5206[/C][C]23.4794[/C][/ROW]
[ROW][C]8[/C][C]85[/C][C]98.4101[/C][C]-13.4101[/C][/ROW]
[ROW][C]9[/C][C]100[/C][C]105.744[/C][C]-5.74401[/C][/ROW]
[ROW][C]10[/C][C]101[/C][C]94.286[/C][C]6.71395[/C][/ROW]
[ROW][C]11[/C][C]73[/C][C]96.7848[/C][C]-23.7848[/C][/ROW]
[ROW][C]12[/C][C]97[/C][C]93.9982[/C][C]3.00181[/C][/ROW]
[ROW][C]13[/C][C]91[/C][C]96.6439[/C][C]-5.64392[/C][/ROW]
[ROW][C]14[/C][C]95[/C][C]103.81[/C][C]-8.80975[/C][/ROW]
[ROW][C]15[/C][C]115[/C][C]96.4474[/C][C]18.5526[/C][/ROW]
[ROW][C]16[/C][C]128[/C][C]95.9175[/C][C]32.0825[/C][/ROW]
[ROW][C]17[/C][C]92[/C][C]97.2804[/C][C]-5.28038[/C][/ROW]
[ROW][C]18[/C][C]102[/C][C]96.8845[/C][C]5.11551[/C][/ROW]
[ROW][C]19[/C][C]104[/C][C]98.9271[/C][C]5.07288[/C][/ROW]
[ROW][C]20[/C][C]107[/C][C]95.831[/C][C]11.169[/C][/ROW]
[ROW][C]21[/C][C]102[/C][C]106.083[/C][C]-4.08276[/C][/ROW]
[ROW][C]22[/C][C]106[/C][C]101.627[/C][C]4.37291[/C][/ROW]
[ROW][C]23[/C][C]113[/C][C]98.1178[/C][C]14.8822[/C][/ROW]
[ROW][C]24[/C][C]121[/C][C]99.0417[/C][C]21.9583[/C][/ROW]
[ROW][C]25[/C][C]128[/C][C]111.064[/C][C]16.9357[/C][/ROW]
[ROW][C]26[/C][C]88[/C][C]94.0757[/C][C]-6.07567[/C][/ROW]
[ROW][C]27[/C][C]99[/C][C]101.008[/C][C]-2.00807[/C][/ROW]
[ROW][C]28[/C][C]86[/C][C]95.5285[/C][C]-9.52848[/C][/ROW]
[ROW][C]29[/C][C]66[/C][C]93.604[/C][C]-27.604[/C][/ROW]
[ROW][C]30[/C][C]101[/C][C]93.3728[/C][C]7.6272[/C][/ROW]
[ROW][C]31[/C][C]88[/C][C]95.0083[/C][C]-7.00829[/C][/ROW]
[ROW][C]32[/C][C]118[/C][C]100.706[/C][C]17.2942[/C][/ROW]
[ROW][C]33[/C][C]101[/C][C]93.2712[/C][C]7.72884[/C][/ROW]
[ROW][C]34[/C][C]88[/C][C]92.2518[/C][C]-4.25178[/C][/ROW]
[ROW][C]35[/C][C]103[/C][C]96.4804[/C][C]6.51965[/C][/ROW]
[ROW][C]36[/C][C]98[/C][C]97.4247[/C][C]0.575326[/C][/ROW]
[ROW][C]37[/C][C]105[/C][C]106.299[/C][C]-1.29861[/C][/ROW]
[ROW][C]38[/C][C]105[/C][C]93.1476[/C][C]11.8524[/C][/ROW]
[ROW][C]39[/C][C]66[/C][C]97.3889[/C][C]-31.3889[/C][/ROW]
[ROW][C]40[/C][C]92[/C][C]93.7564[/C][C]-1.75639[/C][/ROW]
[ROW][C]41[/C][C]123[/C][C]101.54[/C][C]21.46[/C][/ROW]
[ROW][C]42[/C][C]65[/C][C]95.6335[/C][C]-30.6335[/C][/ROW]
[ROW][C]43[/C][C]102[/C][C]94.2696[/C][C]7.73038[/C][/ROW]
[ROW][C]44[/C][C]103[/C][C]100.418[/C][C]2.58189[/C][/ROW]
[ROW][C]45[/C][C]84[/C][C]93.4947[/C][C]-9.49473[/C][/ROW]
[ROW][C]46[/C][C]91[/C][C]102.058[/C][C]-11.0576[/C][/ROW]
[ROW][C]47[/C][C]152[/C][C]94.7692[/C][C]57.2308[/C][/ROW]
[ROW][C]48[/C][C]114[/C][C]96.4527[/C][C]17.5473[/C][/ROW]
[ROW][C]49[/C][C]88[/C][C]96.0116[/C][C]-8.01158[/C][/ROW]
[ROW][C]50[/C][C]99[/C][C]88.3973[/C][C]10.6027[/C][/ROW]
[ROW][C]51[/C][C]61[/C][C]90.9121[/C][C]-29.9121[/C][/ROW]
[ROW][C]52[/C][C]105[/C][C]96.2235[/C][C]8.77647[/C][/ROW]
[ROW][C]53[/C][C]81[/C][C]92.849[/C][C]-11.849[/C][/ROW]
[ROW][C]54[/C][C]113[/C][C]88.3278[/C][C]24.6722[/C][/ROW]
[ROW][C]55[/C][C]107[/C][C]96.4492[/C][C]10.5508[/C][/ROW]
[ROW][C]56[/C][C]89[/C][C]96.6478[/C][C]-7.64784[/C][/ROW]
[ROW][C]57[/C][C]85[/C][C]91.7918[/C][C]-6.79182[/C][/ROW]
[ROW][C]58[/C][C]99[/C][C]93.927[/C][C]5.07302[/C][/ROW]
[ROW][C]59[/C][C]87[/C][C]100.044[/C][C]-13.0436[/C][/ROW]
[ROW][C]60[/C][C]101[/C][C]100.769[/C][C]0.230773[/C][/ROW]
[ROW][C]61[/C][C]129[/C][C]95.3327[/C][C]33.6673[/C][/ROW]
[ROW][C]62[/C][C]73[/C][C]98.0773[/C][C]-25.0773[/C][/ROW]
[ROW][C]63[/C][C]107[/C][C]103.798[/C][C]3.20237[/C][/ROW]
[ROW][C]64[/C][C]73[/C][C]93.7647[/C][C]-20.7647[/C][/ROW]
[ROW][C]65[/C][C]104[/C][C]101.049[/C][C]2.9512[/C][/ROW]
[ROW][C]66[/C][C]111[/C][C]100.517[/C][C]10.4833[/C][/ROW]
[ROW][C]67[/C][C]83[/C][C]102.553[/C][C]-19.5532[/C][/ROW]
[ROW][C]68[/C][C]61[/C][C]99.4188[/C][C]-38.4188[/C][/ROW]
[ROW][C]69[/C][C]112[/C][C]103.772[/C][C]8.22757[/C][/ROW]
[ROW][C]70[/C][C]103[/C][C]96.9344[/C][C]6.06564[/C][/ROW]
[ROW][C]71[/C][C]92[/C][C]101.404[/C][C]-9.40371[/C][/ROW]
[ROW][C]72[/C][C]80[/C][C]94.7597[/C][C]-14.7597[/C][/ROW]
[ROW][C]73[/C][C]95[/C][C]92.564[/C][C]2.43604[/C][/ROW]
[ROW][C]74[/C][C]98[/C][C]89.3885[/C][C]8.61152[/C][/ROW]
[ROW][C]75[/C][C]107[/C][C]103.628[/C][C]3.37221[/C][/ROW]
[ROW][C]76[/C][C]93[/C][C]106.332[/C][C]-13.3317[/C][/ROW]
[ROW][C]77[/C][C]80[/C][C]103.862[/C][C]-23.8616[/C][/ROW]
[ROW][C]78[/C][C]74[/C][C]84.4554[/C][C]-10.4554[/C][/ROW]
[ROW][C]79[/C][C]90[/C][C]110.286[/C][C]-20.286[/C][/ROW]
[ROW][C]80[/C][C]76[/C][C]71.4312[/C][C]4.56884[/C][/ROW]
[ROW][C]81[/C][C]100[/C][C]101.475[/C][C]-1.47453[/C][/ROW]
[ROW][C]82[/C][C]86[/C][C]81.4305[/C][C]4.56948[/C][/ROW]
[ROW][C]83[/C][C]107[/C][C]113.221[/C][C]-6.22057[/C][/ROW]
[ROW][C]84[/C][C]91[/C][C]108.401[/C][C]-17.4011[/C][/ROW]
[ROW][C]85[/C][C]81[/C][C]50.5896[/C][C]30.4104[/C][/ROW]
[ROW][C]86[/C][C]129[/C][C]141.918[/C][C]-12.918[/C][/ROW]
[ROW][C]87[/C][C]86[/C][C]84.6152[/C][C]1.38476[/C][/ROW]
[ROW][C]88[/C][C]99[/C][C]103.723[/C][C]-4.72289[/C][/ROW]
[ROW][C]89[/C][C]87[/C][C]69.2341[/C][C]17.7659[/C][/ROW]
[ROW][C]90[/C][C]115[/C][C]114.748[/C][C]0.25236[/C][/ROW]
[ROW][C]91[/C][C]96[/C][C]110.974[/C][C]-14.9744[/C][/ROW]
[ROW][C]92[/C][C]78[/C][C]84.4117[/C][C]-6.41167[/C][/ROW]
[ROW][C]93[/C][C]86[/C][C]95.0392[/C][C]-9.03919[/C][/ROW]
[ROW][C]94[/C][C]89[/C][C]81.2524[/C][C]7.7476[/C][/ROW]
[ROW][C]95[/C][C]92[/C][C]102.762[/C][C]-10.7617[/C][/ROW]
[ROW][C]96[/C][C]83[/C][C]75.0062[/C][C]7.99379[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]76.9308[/C][C]20.0692[/C][/ROW]
[ROW][C]98[/C][C]123[/C][C]107.488[/C][C]15.5123[/C][/ROW]
[ROW][C]99[/C][C]109[/C][C]120.107[/C][C]-11.1069[/C][/ROW]
[ROW][C]100[/C][C]84[/C][C]82.2778[/C][C]1.72221[/C][/ROW]
[ROW][C]101[/C][C]105[/C][C]107.04[/C][C]-2.04029[/C][/ROW]
[ROW][C]102[/C][C]89[/C][C]82.9564[/C][C]6.04359[/C][/ROW]
[ROW][C]103[/C][C]107[/C][C]103.66[/C][C]3.34035[/C][/ROW]
[ROW][C]104[/C][C]101[/C][C]90.6165[/C][C]10.3835[/C][/ROW]
[ROW][C]105[/C][C]103[/C][C]122.115[/C][C]-19.1155[/C][/ROW]
[ROW][C]106[/C][C]77[/C][C]73.2722[/C][C]3.72781[/C][/ROW]
[ROW][C]107[/C][C]102[/C][C]97.4591[/C][C]4.54089[/C][/ROW]
[ROW][C]108[/C][C]100[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270809&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270809&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
18997.0946-8.09463
29998.36420.63581
311396.871316.1287
493101.062-8.0616
59699.4652-3.4652
610099.36770.632287
712096.520623.4794
88598.4101-13.4101
9100105.744-5.74401
1010194.2866.71395
117396.7848-23.7848
129793.99823.00181
139196.6439-5.64392
1495103.81-8.80975
1511596.447418.5526
1612895.917532.0825
179297.2804-5.28038
1810296.88455.11551
1910498.92715.07288
2010795.83111.169
21102106.083-4.08276
22106101.6274.37291
2311398.117814.8822
2412199.041721.9583
25128111.06416.9357
268894.0757-6.07567
2799101.008-2.00807
288695.5285-9.52848
296693.604-27.604
3010193.37287.6272
318895.0083-7.00829
32118100.70617.2942
3310193.27127.72884
348892.2518-4.25178
3510396.48046.51965
369897.42470.575326
37105106.299-1.29861
3810593.147611.8524
396697.3889-31.3889
409293.7564-1.75639
41123101.5421.46
426595.6335-30.6335
4310294.26967.73038
44103100.4182.58189
458493.4947-9.49473
4691102.058-11.0576
4715294.769257.2308
4811496.452717.5473
498896.0116-8.01158
509988.397310.6027
516190.9121-29.9121
5210596.22358.77647
538192.849-11.849
5411388.327824.6722
5510796.449210.5508
568996.6478-7.64784
578591.7918-6.79182
589993.9275.07302
5987100.044-13.0436
60101100.7690.230773
6112995.332733.6673
627398.0773-25.0773
63107103.7983.20237
647393.7647-20.7647
65104101.0492.9512
66111100.51710.4833
6783102.553-19.5532
686199.4188-38.4188
69112103.7728.22757
7010396.93446.06564
7192101.404-9.40371
728094.7597-14.7597
739592.5642.43604
749889.38858.61152
75107103.6283.37221
7693106.332-13.3317
7780103.862-23.8616
787484.4554-10.4554
7990110.286-20.286
807671.43124.56884
81100101.475-1.47453
828681.43054.56948
83107113.221-6.22057
8491108.401-17.4011
858150.589630.4104
86129141.918-12.918
878684.61521.38476
8899103.723-4.72289
898769.234117.7659
90115114.7480.25236
9196110.974-14.9744
927884.4117-6.41167
938695.0392-9.03919
948981.25247.7476
9592102.762-10.7617
968375.00627.99379
979776.930820.0692
98123107.48815.5123
99109120.107-11.1069
1008482.27781.72221
101105107.04-2.04029
1028982.95646.04359
103107103.663.34035
10410190.616510.3835
105103122.115-19.1155
1067773.27223.72781
10710297.45914.54089
108100NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4159970.8319940.584003
120.2729080.5458170.727092
130.4739660.9479330.526034
140.3932660.7865320.606734
150.3688050.737610.631195
160.5278090.9443820.472191
170.4275330.8550650.572467
180.3364790.6729590.663521
190.2553180.5106350.744682
200.2245240.4490490.775476
210.1806340.3612680.819366
220.1314690.2629380.868531
230.1420750.284150.857925
240.2339570.4679150.766043
250.2011710.4023410.798829
260.182220.364440.81778
270.1365750.273150.863425
280.1245110.2490210.875489
290.2256660.4513320.774334
300.1796790.3593580.820321
310.1488090.2976180.851191
320.2209130.4418270.779087
330.1903840.3807670.809616
340.1497030.2994070.850297
350.1183750.2367510.881625
360.1114080.2228170.888592
370.08318740.1663750.916813
380.07031060.1406210.929689
390.09325090.1865020.906749
400.07160290.1432060.928397
410.1084990.2169980.891501
420.2435820.4871640.756418
430.2048470.4096930.795153
440.1671550.334310.832845
450.1452790.2905580.854721
460.1182560.2365130.881744
470.7548210.4903570.245179
480.7572410.4855180.242759
490.7277530.5444930.272247
500.7169910.5660180.283009
510.865230.269540.13477
520.8403460.3193070.159654
530.8284830.3430340.171517
540.8735430.2529140.126457
550.8576470.2847060.142353
560.841910.3161810.15809
570.8111540.3776920.188846
580.7924920.4150150.207508
590.7653580.4692840.234642
600.7202220.5595570.279778
610.8467470.3065060.153253
620.8959540.2080920.104046
630.8676250.2647490.132375
640.8833180.2333650.116682
650.8507550.2984910.149245
660.8426580.3146850.157342
670.8733520.2532970.126648
680.974070.05185910.0259296
690.963660.07267970.0363398
700.9505150.09896970.0494848
710.9349660.1300680.0650341
720.9332510.1334980.0667489
730.9082780.1834430.0917215
740.8835240.2329530.116476
750.8496430.3007150.150357
760.8487830.3024340.151217
770.9043760.1912480.0956239
780.8909660.2180690.109034
790.9011990.1976020.0988012
800.8666190.2667610.133381
810.8204790.3590420.179521
820.7666450.4667090.233355
830.7071030.5857940.292897
840.8189290.3621410.181071
850.8927550.214490.107245
860.9132260.1735480.086774
870.8696870.2606270.130313
880.8235620.3528760.176438
890.8839530.2320940.116047
900.8926190.2147630.107381
910.8729680.2540630.127032
920.8102520.3794960.189748
930.712410.5751790.28759
940.6905930.6188150.309407
950.7827730.4344540.217227
960.7006980.5986050.299302
970.5653360.8693290.434664

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.415997 & 0.831994 & 0.584003 \tabularnewline
12 & 0.272908 & 0.545817 & 0.727092 \tabularnewline
13 & 0.473966 & 0.947933 & 0.526034 \tabularnewline
14 & 0.393266 & 0.786532 & 0.606734 \tabularnewline
15 & 0.368805 & 0.73761 & 0.631195 \tabularnewline
16 & 0.527809 & 0.944382 & 0.472191 \tabularnewline
17 & 0.427533 & 0.855065 & 0.572467 \tabularnewline
18 & 0.336479 & 0.672959 & 0.663521 \tabularnewline
19 & 0.255318 & 0.510635 & 0.744682 \tabularnewline
20 & 0.224524 & 0.449049 & 0.775476 \tabularnewline
21 & 0.180634 & 0.361268 & 0.819366 \tabularnewline
22 & 0.131469 & 0.262938 & 0.868531 \tabularnewline
23 & 0.142075 & 0.28415 & 0.857925 \tabularnewline
24 & 0.233957 & 0.467915 & 0.766043 \tabularnewline
25 & 0.201171 & 0.402341 & 0.798829 \tabularnewline
26 & 0.18222 & 0.36444 & 0.81778 \tabularnewline
27 & 0.136575 & 0.27315 & 0.863425 \tabularnewline
28 & 0.124511 & 0.249021 & 0.875489 \tabularnewline
29 & 0.225666 & 0.451332 & 0.774334 \tabularnewline
30 & 0.179679 & 0.359358 & 0.820321 \tabularnewline
31 & 0.148809 & 0.297618 & 0.851191 \tabularnewline
32 & 0.220913 & 0.441827 & 0.779087 \tabularnewline
33 & 0.190384 & 0.380767 & 0.809616 \tabularnewline
34 & 0.149703 & 0.299407 & 0.850297 \tabularnewline
35 & 0.118375 & 0.236751 & 0.881625 \tabularnewline
36 & 0.111408 & 0.222817 & 0.888592 \tabularnewline
37 & 0.0831874 & 0.166375 & 0.916813 \tabularnewline
38 & 0.0703106 & 0.140621 & 0.929689 \tabularnewline
39 & 0.0932509 & 0.186502 & 0.906749 \tabularnewline
40 & 0.0716029 & 0.143206 & 0.928397 \tabularnewline
41 & 0.108499 & 0.216998 & 0.891501 \tabularnewline
42 & 0.243582 & 0.487164 & 0.756418 \tabularnewline
43 & 0.204847 & 0.409693 & 0.795153 \tabularnewline
44 & 0.167155 & 0.33431 & 0.832845 \tabularnewline
45 & 0.145279 & 0.290558 & 0.854721 \tabularnewline
46 & 0.118256 & 0.236513 & 0.881744 \tabularnewline
47 & 0.754821 & 0.490357 & 0.245179 \tabularnewline
48 & 0.757241 & 0.485518 & 0.242759 \tabularnewline
49 & 0.727753 & 0.544493 & 0.272247 \tabularnewline
50 & 0.716991 & 0.566018 & 0.283009 \tabularnewline
51 & 0.86523 & 0.26954 & 0.13477 \tabularnewline
52 & 0.840346 & 0.319307 & 0.159654 \tabularnewline
53 & 0.828483 & 0.343034 & 0.171517 \tabularnewline
54 & 0.873543 & 0.252914 & 0.126457 \tabularnewline
55 & 0.857647 & 0.284706 & 0.142353 \tabularnewline
56 & 0.84191 & 0.316181 & 0.15809 \tabularnewline
57 & 0.811154 & 0.377692 & 0.188846 \tabularnewline
58 & 0.792492 & 0.415015 & 0.207508 \tabularnewline
59 & 0.765358 & 0.469284 & 0.234642 \tabularnewline
60 & 0.720222 & 0.559557 & 0.279778 \tabularnewline
61 & 0.846747 & 0.306506 & 0.153253 \tabularnewline
62 & 0.895954 & 0.208092 & 0.104046 \tabularnewline
63 & 0.867625 & 0.264749 & 0.132375 \tabularnewline
64 & 0.883318 & 0.233365 & 0.116682 \tabularnewline
65 & 0.850755 & 0.298491 & 0.149245 \tabularnewline
66 & 0.842658 & 0.314685 & 0.157342 \tabularnewline
67 & 0.873352 & 0.253297 & 0.126648 \tabularnewline
68 & 0.97407 & 0.0518591 & 0.0259296 \tabularnewline
69 & 0.96366 & 0.0726797 & 0.0363398 \tabularnewline
70 & 0.950515 & 0.0989697 & 0.0494848 \tabularnewline
71 & 0.934966 & 0.130068 & 0.0650341 \tabularnewline
72 & 0.933251 & 0.133498 & 0.0667489 \tabularnewline
73 & 0.908278 & 0.183443 & 0.0917215 \tabularnewline
74 & 0.883524 & 0.232953 & 0.116476 \tabularnewline
75 & 0.849643 & 0.300715 & 0.150357 \tabularnewline
76 & 0.848783 & 0.302434 & 0.151217 \tabularnewline
77 & 0.904376 & 0.191248 & 0.0956239 \tabularnewline
78 & 0.890966 & 0.218069 & 0.109034 \tabularnewline
79 & 0.901199 & 0.197602 & 0.0988012 \tabularnewline
80 & 0.866619 & 0.266761 & 0.133381 \tabularnewline
81 & 0.820479 & 0.359042 & 0.179521 \tabularnewline
82 & 0.766645 & 0.466709 & 0.233355 \tabularnewline
83 & 0.707103 & 0.585794 & 0.292897 \tabularnewline
84 & 0.818929 & 0.362141 & 0.181071 \tabularnewline
85 & 0.892755 & 0.21449 & 0.107245 \tabularnewline
86 & 0.913226 & 0.173548 & 0.086774 \tabularnewline
87 & 0.869687 & 0.260627 & 0.130313 \tabularnewline
88 & 0.823562 & 0.352876 & 0.176438 \tabularnewline
89 & 0.883953 & 0.232094 & 0.116047 \tabularnewline
90 & 0.892619 & 0.214763 & 0.107381 \tabularnewline
91 & 0.872968 & 0.254063 & 0.127032 \tabularnewline
92 & 0.810252 & 0.379496 & 0.189748 \tabularnewline
93 & 0.71241 & 0.575179 & 0.28759 \tabularnewline
94 & 0.690593 & 0.618815 & 0.309407 \tabularnewline
95 & 0.782773 & 0.434454 & 0.217227 \tabularnewline
96 & 0.700698 & 0.598605 & 0.299302 \tabularnewline
97 & 0.565336 & 0.869329 & 0.434664 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270809&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.415997[/C][C]0.831994[/C][C]0.584003[/C][/ROW]
[ROW][C]12[/C][C]0.272908[/C][C]0.545817[/C][C]0.727092[/C][/ROW]
[ROW][C]13[/C][C]0.473966[/C][C]0.947933[/C][C]0.526034[/C][/ROW]
[ROW][C]14[/C][C]0.393266[/C][C]0.786532[/C][C]0.606734[/C][/ROW]
[ROW][C]15[/C][C]0.368805[/C][C]0.73761[/C][C]0.631195[/C][/ROW]
[ROW][C]16[/C][C]0.527809[/C][C]0.944382[/C][C]0.472191[/C][/ROW]
[ROW][C]17[/C][C]0.427533[/C][C]0.855065[/C][C]0.572467[/C][/ROW]
[ROW][C]18[/C][C]0.336479[/C][C]0.672959[/C][C]0.663521[/C][/ROW]
[ROW][C]19[/C][C]0.255318[/C][C]0.510635[/C][C]0.744682[/C][/ROW]
[ROW][C]20[/C][C]0.224524[/C][C]0.449049[/C][C]0.775476[/C][/ROW]
[ROW][C]21[/C][C]0.180634[/C][C]0.361268[/C][C]0.819366[/C][/ROW]
[ROW][C]22[/C][C]0.131469[/C][C]0.262938[/C][C]0.868531[/C][/ROW]
[ROW][C]23[/C][C]0.142075[/C][C]0.28415[/C][C]0.857925[/C][/ROW]
[ROW][C]24[/C][C]0.233957[/C][C]0.467915[/C][C]0.766043[/C][/ROW]
[ROW][C]25[/C][C]0.201171[/C][C]0.402341[/C][C]0.798829[/C][/ROW]
[ROW][C]26[/C][C]0.18222[/C][C]0.36444[/C][C]0.81778[/C][/ROW]
[ROW][C]27[/C][C]0.136575[/C][C]0.27315[/C][C]0.863425[/C][/ROW]
[ROW][C]28[/C][C]0.124511[/C][C]0.249021[/C][C]0.875489[/C][/ROW]
[ROW][C]29[/C][C]0.225666[/C][C]0.451332[/C][C]0.774334[/C][/ROW]
[ROW][C]30[/C][C]0.179679[/C][C]0.359358[/C][C]0.820321[/C][/ROW]
[ROW][C]31[/C][C]0.148809[/C][C]0.297618[/C][C]0.851191[/C][/ROW]
[ROW][C]32[/C][C]0.220913[/C][C]0.441827[/C][C]0.779087[/C][/ROW]
[ROW][C]33[/C][C]0.190384[/C][C]0.380767[/C][C]0.809616[/C][/ROW]
[ROW][C]34[/C][C]0.149703[/C][C]0.299407[/C][C]0.850297[/C][/ROW]
[ROW][C]35[/C][C]0.118375[/C][C]0.236751[/C][C]0.881625[/C][/ROW]
[ROW][C]36[/C][C]0.111408[/C][C]0.222817[/C][C]0.888592[/C][/ROW]
[ROW][C]37[/C][C]0.0831874[/C][C]0.166375[/C][C]0.916813[/C][/ROW]
[ROW][C]38[/C][C]0.0703106[/C][C]0.140621[/C][C]0.929689[/C][/ROW]
[ROW][C]39[/C][C]0.0932509[/C][C]0.186502[/C][C]0.906749[/C][/ROW]
[ROW][C]40[/C][C]0.0716029[/C][C]0.143206[/C][C]0.928397[/C][/ROW]
[ROW][C]41[/C][C]0.108499[/C][C]0.216998[/C][C]0.891501[/C][/ROW]
[ROW][C]42[/C][C]0.243582[/C][C]0.487164[/C][C]0.756418[/C][/ROW]
[ROW][C]43[/C][C]0.204847[/C][C]0.409693[/C][C]0.795153[/C][/ROW]
[ROW][C]44[/C][C]0.167155[/C][C]0.33431[/C][C]0.832845[/C][/ROW]
[ROW][C]45[/C][C]0.145279[/C][C]0.290558[/C][C]0.854721[/C][/ROW]
[ROW][C]46[/C][C]0.118256[/C][C]0.236513[/C][C]0.881744[/C][/ROW]
[ROW][C]47[/C][C]0.754821[/C][C]0.490357[/C][C]0.245179[/C][/ROW]
[ROW][C]48[/C][C]0.757241[/C][C]0.485518[/C][C]0.242759[/C][/ROW]
[ROW][C]49[/C][C]0.727753[/C][C]0.544493[/C][C]0.272247[/C][/ROW]
[ROW][C]50[/C][C]0.716991[/C][C]0.566018[/C][C]0.283009[/C][/ROW]
[ROW][C]51[/C][C]0.86523[/C][C]0.26954[/C][C]0.13477[/C][/ROW]
[ROW][C]52[/C][C]0.840346[/C][C]0.319307[/C][C]0.159654[/C][/ROW]
[ROW][C]53[/C][C]0.828483[/C][C]0.343034[/C][C]0.171517[/C][/ROW]
[ROW][C]54[/C][C]0.873543[/C][C]0.252914[/C][C]0.126457[/C][/ROW]
[ROW][C]55[/C][C]0.857647[/C][C]0.284706[/C][C]0.142353[/C][/ROW]
[ROW][C]56[/C][C]0.84191[/C][C]0.316181[/C][C]0.15809[/C][/ROW]
[ROW][C]57[/C][C]0.811154[/C][C]0.377692[/C][C]0.188846[/C][/ROW]
[ROW][C]58[/C][C]0.792492[/C][C]0.415015[/C][C]0.207508[/C][/ROW]
[ROW][C]59[/C][C]0.765358[/C][C]0.469284[/C][C]0.234642[/C][/ROW]
[ROW][C]60[/C][C]0.720222[/C][C]0.559557[/C][C]0.279778[/C][/ROW]
[ROW][C]61[/C][C]0.846747[/C][C]0.306506[/C][C]0.153253[/C][/ROW]
[ROW][C]62[/C][C]0.895954[/C][C]0.208092[/C][C]0.104046[/C][/ROW]
[ROW][C]63[/C][C]0.867625[/C][C]0.264749[/C][C]0.132375[/C][/ROW]
[ROW][C]64[/C][C]0.883318[/C][C]0.233365[/C][C]0.116682[/C][/ROW]
[ROW][C]65[/C][C]0.850755[/C][C]0.298491[/C][C]0.149245[/C][/ROW]
[ROW][C]66[/C][C]0.842658[/C][C]0.314685[/C][C]0.157342[/C][/ROW]
[ROW][C]67[/C][C]0.873352[/C][C]0.253297[/C][C]0.126648[/C][/ROW]
[ROW][C]68[/C][C]0.97407[/C][C]0.0518591[/C][C]0.0259296[/C][/ROW]
[ROW][C]69[/C][C]0.96366[/C][C]0.0726797[/C][C]0.0363398[/C][/ROW]
[ROW][C]70[/C][C]0.950515[/C][C]0.0989697[/C][C]0.0494848[/C][/ROW]
[ROW][C]71[/C][C]0.934966[/C][C]0.130068[/C][C]0.0650341[/C][/ROW]
[ROW][C]72[/C][C]0.933251[/C][C]0.133498[/C][C]0.0667489[/C][/ROW]
[ROW][C]73[/C][C]0.908278[/C][C]0.183443[/C][C]0.0917215[/C][/ROW]
[ROW][C]74[/C][C]0.883524[/C][C]0.232953[/C][C]0.116476[/C][/ROW]
[ROW][C]75[/C][C]0.849643[/C][C]0.300715[/C][C]0.150357[/C][/ROW]
[ROW][C]76[/C][C]0.848783[/C][C]0.302434[/C][C]0.151217[/C][/ROW]
[ROW][C]77[/C][C]0.904376[/C][C]0.191248[/C][C]0.0956239[/C][/ROW]
[ROW][C]78[/C][C]0.890966[/C][C]0.218069[/C][C]0.109034[/C][/ROW]
[ROW][C]79[/C][C]0.901199[/C][C]0.197602[/C][C]0.0988012[/C][/ROW]
[ROW][C]80[/C][C]0.866619[/C][C]0.266761[/C][C]0.133381[/C][/ROW]
[ROW][C]81[/C][C]0.820479[/C][C]0.359042[/C][C]0.179521[/C][/ROW]
[ROW][C]82[/C][C]0.766645[/C][C]0.466709[/C][C]0.233355[/C][/ROW]
[ROW][C]83[/C][C]0.707103[/C][C]0.585794[/C][C]0.292897[/C][/ROW]
[ROW][C]84[/C][C]0.818929[/C][C]0.362141[/C][C]0.181071[/C][/ROW]
[ROW][C]85[/C][C]0.892755[/C][C]0.21449[/C][C]0.107245[/C][/ROW]
[ROW][C]86[/C][C]0.913226[/C][C]0.173548[/C][C]0.086774[/C][/ROW]
[ROW][C]87[/C][C]0.869687[/C][C]0.260627[/C][C]0.130313[/C][/ROW]
[ROW][C]88[/C][C]0.823562[/C][C]0.352876[/C][C]0.176438[/C][/ROW]
[ROW][C]89[/C][C]0.883953[/C][C]0.232094[/C][C]0.116047[/C][/ROW]
[ROW][C]90[/C][C]0.892619[/C][C]0.214763[/C][C]0.107381[/C][/ROW]
[ROW][C]91[/C][C]0.872968[/C][C]0.254063[/C][C]0.127032[/C][/ROW]
[ROW][C]92[/C][C]0.810252[/C][C]0.379496[/C][C]0.189748[/C][/ROW]
[ROW][C]93[/C][C]0.71241[/C][C]0.575179[/C][C]0.28759[/C][/ROW]
[ROW][C]94[/C][C]0.690593[/C][C]0.618815[/C][C]0.309407[/C][/ROW]
[ROW][C]95[/C][C]0.782773[/C][C]0.434454[/C][C]0.217227[/C][/ROW]
[ROW][C]96[/C][C]0.700698[/C][C]0.598605[/C][C]0.299302[/C][/ROW]
[ROW][C]97[/C][C]0.565336[/C][C]0.869329[/C][C]0.434664[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270809&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270809&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
110.4159970.8319940.584003
120.2729080.5458170.727092
130.4739660.9479330.526034
140.3932660.7865320.606734
150.3688050.737610.631195
160.5278090.9443820.472191
170.4275330.8550650.572467
180.3364790.6729590.663521
190.2553180.5106350.744682
200.2245240.4490490.775476
210.1806340.3612680.819366
220.1314690.2629380.868531
230.1420750.284150.857925
240.2339570.4679150.766043
250.2011710.4023410.798829
260.182220.364440.81778
270.1365750.273150.863425
280.1245110.2490210.875489
290.2256660.4513320.774334
300.1796790.3593580.820321
310.1488090.2976180.851191
320.2209130.4418270.779087
330.1903840.3807670.809616
340.1497030.2994070.850297
350.1183750.2367510.881625
360.1114080.2228170.888592
370.08318740.1663750.916813
380.07031060.1406210.929689
390.09325090.1865020.906749
400.07160290.1432060.928397
410.1084990.2169980.891501
420.2435820.4871640.756418
430.2048470.4096930.795153
440.1671550.334310.832845
450.1452790.2905580.854721
460.1182560.2365130.881744
470.7548210.4903570.245179
480.7572410.4855180.242759
490.7277530.5444930.272247
500.7169910.5660180.283009
510.865230.269540.13477
520.8403460.3193070.159654
530.8284830.3430340.171517
540.8735430.2529140.126457
550.8576470.2847060.142353
560.841910.3161810.15809
570.8111540.3776920.188846
580.7924920.4150150.207508
590.7653580.4692840.234642
600.7202220.5595570.279778
610.8467470.3065060.153253
620.8959540.2080920.104046
630.8676250.2647490.132375
640.8833180.2333650.116682
650.8507550.2984910.149245
660.8426580.3146850.157342
670.8733520.2532970.126648
680.974070.05185910.0259296
690.963660.07267970.0363398
700.9505150.09896970.0494848
710.9349660.1300680.0650341
720.9332510.1334980.0667489
730.9082780.1834430.0917215
740.8835240.2329530.116476
750.8496430.3007150.150357
760.8487830.3024340.151217
770.9043760.1912480.0956239
780.8909660.2180690.109034
790.9011990.1976020.0988012
800.8666190.2667610.133381
810.8204790.3590420.179521
820.7666450.4667090.233355
830.7071030.5857940.292897
840.8189290.3621410.181071
850.8927550.214490.107245
860.9132260.1735480.086774
870.8696870.2606270.130313
880.8235620.3528760.176438
890.8839530.2320940.116047
900.8926190.2147630.107381
910.8729680.2540630.127032
920.8102520.3794960.189748
930.712410.5751790.28759
940.6905930.6188150.309407
950.7827730.4344540.217227
960.7006980.5986050.299302
970.5653360.8693290.434664






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.0344828OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.0344828 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270809&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0344828[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270809&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270809&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 level00OK
5% type I error level00OK
10% type I error level30.0344828OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}