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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 06 Dec 2014 13:33:17 +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/06/t1417872833kkndfk3m33nzz3q.htm/, Retrieved Thu, 16 May 2024 14:31:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263619, Retrieved Thu, 16 May 2024 14:31:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-11-10 14:10:49] [e2644f4f6eaa29e5d324bb4bc112fe2e]
- R  D    [Multiple Regression] [Multiple Linear R...] [2014-12-06 13:33:17] [310e7528d8f6aa5642dc98f4186768d1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
48 41 23 12 34 4
50 146 16 45 61 13
150 182 33 37 70 18
154 192 32 37 69 18
109 263 37 108 145 17
68 35 14 10 23 13
194 439 52 68 120 17
158 214 75 72 147 19
159 341 72 143 215 16
67 58 15 9 24 13
147 292 29 55 84 18
39 85 13 17 30 15
100 200 40 37 77 11
111 158 19 27 46 13
138 199 24 37 61 16
101 297 121 58 178 14
131 227 93 66 160 19
101 108 36 21 57 14
114 86 23 19 42 15
165 302 85 78 163 16
114 148 41 35 75 15
111 178 46 48 94 15
75 120 18 27 45 12
82 207 35 43 78 13
121 157 17 30 47 17
32 128 4 25 29 9
150 296 28 69 97 18
117 323 44 72 116 16
71 79 10 23 32 12
165 70 38 13 50 18
154 146 57 61 118 15
126 246 23 43 66 18
138 145 26 22 48 16
149 196 36 51 86 16
145 199 22 67 89 18
120 127 40 36 76 14
138 91 18 21 39 12
109 153 31 44 75 14
132 299 11 45 57 15
172 228 38 34 72 18
169 190 24 36 60 10
114 180 37 72 109 16
156 212 37 39 76 18
172 269 22 43 65 17
68 130 15 25 40 15
89 179 2 56 58 14
167 243 43 80 123 19
113 190 31 40 71 16
115 299 29 73 102 15
78 121 45 34 80 12
118 137 25 72 97 18
87 305 4 42 46 16
173 157 31 61 93 17
2 96 -4 23 19 16
162 183 66 74 140 20
49 52 61 16 78 11
122 238 32 66 98 18
96 40 31 9 40 15
100 226 39 41 80 15
82 190 19 57 76 11
100 214 31 48 79 16
115 145 36 51 87 18
141 119 42 53 95 15
165 222 21 29 49 15
165 222 21 29 49 15
110 159 25 55 80 18
118 165 32 54 86 13
158 249 26 43 69 19
146 125 28 51 79 15
49 122 32 20 52 8
90 186 41 79 120 13
121 148 29 39 69 14
155 274 33 61 94 19
104 172 17 55 72 15
147 84 13 30 43 13
110 168 32 55 87 16
108 102 30 22 52 17
113 106 34 37 71 13
115 2 59 2 61 12
61 139 13 38 51 13
60 95 23 27 50 10
109 130 10 56 67 15
68 72 5 25 30 10
111 141 31 39 70 18
77 113 19 33 52 14
73 206 32 43 75 15
151 268 30 57 87 15
89 175 25 43 69 14
78 77 48 23 72 15
110 125 35 44 79 16
220 255 67 54 121 19
65 111 15 28 43 14
141 132 22 36 58 14
117 211 18 39 57 16
122 92 33 16 50 13
63 76 46 23 69 15
44 171 24 40 64 10
52 83 14 24 38 13
62 119 23 29 53 12
131 266 12 78 90 19
101 186 38 57 96 17
42 50 12 37 49 11
152 117 28 27 56 15
107 219 41 61 102 12
77 246 12 27 40 13
154 279 31 69 100 16
103 148 33 34 67 12
96 137 34 44 78 16
154 130 41 21 62 14
175 181 21 34 55 18
57 98 20 39 59 11
112 226 44 51 96 16
143 234 52 34 86 18
49 138 7 31 38 8
110 85 29 13 43 12
131 66 11 12 23 16
167 236 26 51 77 19
56 106 24 24 48 15
137 135 7 19 26 17
86 122 60 30 91 16
121 218 13 81 94 18
149 199 20 42 62 19
168 112 52 22 74 19
140 278 28 85 114 19
88 94 25 27 52 13
168 113 39 25 64 13
94 84 9 22 31 10
51 86 19 19 38 5
48 62 13 14 27 12
145 222 60 45 105 14
66 167 19 45 64 12
85 82 34 28 62 12
109 207 14 51 65 13
63 184 17 41 58 11
102 83 45 31 76 15
162 183 66 74 140 20
128 85 24 24 48 18
86 89 48 19 68 11
114 225 29 51 80 15
164 237 -2 73 71 18
119 102 51 24 76 14
126 221 2 61 63 16
132 128 24 23 46 13
142 91 40 14 53 12
83 198 20 54 74 12
94 204 19 51 70 15
81 158 16 62 78 15
166 138 20 36 56 13
110 226 40 59 100 17
64 44 27 24 51 8
93 196 25 26 52 8
104 83 49 54 102 13
105 79 39 39 78 8
49 52 61 16 78 11
88 105 19 36 55 12
95 116 67 31 98 10
102 83 45 31 76 15
99 196 30 42 73 17
63 153 8 39 47 13
76 157 19 25 45 14
109 75 52 31 83 16
117 106 22 38 60 17
57 58 17 31 48 11
120 75 33 17 50 15
73 74 34 22 56 12
91 185 22 55 77 15
108 265 30 62 91 18
105 131 25 51 76 15
117 139 38 30 68 15
119 196 26 49 74 17
31 78 13 16 29 8

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=263619&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=263619&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263619&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
TOT[t] = + 8.36043 + 0.0407567LFM[t] + 0.003387BBB[t] -0.38259PRH[t] -0.343614CH[t] + 0.376173HHH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.36043 +  0.0407567LFM[t] +  0.003387BBB[t] -0.38259PRH[t] -0.343614CH[t] +  0.376173HHH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263619&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.36043 +  0.0407567LFM[t] +  0.003387BBB[t] -0.38259PRH[t] -0.343614CH[t] +  0.376173HHH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263619&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263619&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
TOT[t] = + 8.36043 + 0.0407567LFM[t] + 0.003387BBB[t] -0.38259PRH[t] -0.343614CH[t] + 0.376173HHH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.360430.56196914.882.69326e-321.34663e-32
LFM0.04075670.005339547.6331.74683e-128.73413e-13
BBB0.0033870.003628280.93350.3519260.175963
PRH-0.382590.363965-1.0510.2947160.147358
CH-0.3436140.362904-0.94680.3451020.172551
HHH0.3761730.3627971.0370.3013150.150657

\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) & 8.36043 & 0.561969 & 14.88 & 2.69326e-32 & 1.34663e-32 \tabularnewline
LFM & 0.0407567 & 0.00533954 & 7.633 & 1.74683e-12 & 8.73413e-13 \tabularnewline
BBB & 0.003387 & 0.00362828 & 0.9335 & 0.351926 & 0.175963 \tabularnewline
PRH & -0.38259 & 0.363965 & -1.051 & 0.294716 & 0.147358 \tabularnewline
CH & -0.343614 & 0.362904 & -0.9468 & 0.345102 & 0.172551 \tabularnewline
HHH & 0.376173 & 0.362797 & 1.037 & 0.301315 & 0.150657 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263619&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]8.36043[/C][C]0.561969[/C][C]14.88[/C][C]2.69326e-32[/C][C]1.34663e-32[/C][/ROW]
[ROW][C]LFM[/C][C]0.0407567[/C][C]0.00533954[/C][C]7.633[/C][C]1.74683e-12[/C][C]8.73413e-13[/C][/ROW]
[ROW][C]BBB[/C][C]0.003387[/C][C]0.00362828[/C][C]0.9335[/C][C]0.351926[/C][C]0.175963[/C][/ROW]
[ROW][C]PRH[/C][C]-0.38259[/C][C]0.363965[/C][C]-1.051[/C][C]0.294716[/C][C]0.147358[/C][/ROW]
[ROW][C]CH[/C][C]-0.343614[/C][C]0.362904[/C][C]-0.9468[/C][C]0.345102[/C][C]0.172551[/C][/ROW]
[ROW][C]HHH[/C][C]0.376173[/C][C]0.362797[/C][C]1.037[/C][C]0.301315[/C][C]0.150657[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263619&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263619&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)8.360430.56196914.882.69326e-321.34663e-32
LFM0.04075670.005339547.6331.74683e-128.73413e-13
BBB0.0033870.003628280.93350.3519260.175963
PRH-0.382590.363965-1.0510.2947160.147358
CH-0.3436140.362904-0.94680.3451020.172551
HHH0.3761730.3627971.0370.3013150.150657






Multiple Linear Regression - Regression Statistics
Multiple R0.683168
R-squared0.466718
Adjusted R-squared0.450558
F-TEST (value)28.8809
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21822
Sum Squared Residuals811.88

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.683168 \tabularnewline
R-squared & 0.466718 \tabularnewline
Adjusted R-squared & 0.450558 \tabularnewline
F-TEST (value) & 28.8809 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.21822 \tabularnewline
Sum Squared Residuals & 811.88 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263619&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.683168[/C][/ROW]
[ROW][C]R-squared[/C][C]0.466718[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.450558[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28.8809[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]165[/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]2.21822[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]811.88[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263619&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263619&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 R0.683168
R-squared0.466718
Adjusted R-squared0.450558
F-TEST (value)28.8809
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21822
Sum Squared Residuals811.88






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1410.3225-6.32253
21312.25520.744796
31816.08321.91676
41816.28661.71345
51716.97250.0274749
61311.111.89001
71719.6344-2.63436
81917.38771.61234
91620.1895-4.18946
101311.48431.51567
111816.94531.05474
121510.70794.29211
131114.0614-3.06144
141314.1767-1.17671
151615.70950.290502
161414.2184-0.218436
171916.39662.60344
181413.29530.704666
191513.7691.23103
201618.1022-2.10219
211514.00820.991795
221514.75490.245114
231212.5872-0.587175
241313.579-0.578969
251714.69142.3086
26910.8865-1.88647
271817.54330.45669
281616.2848-0.284775
291211.83020.169781
301815.12562.87442
311516.7517-1.7517
321815.58142.41863
331615.02540.97461
341616.1503-0.150289
351816.98441.01562
361414.5968-0.596767
371214.8613-2.86128
381414.5547-0.554733
391516.5237-1.52373
401817.00590.994076
411016.9099-6.90992
421615.72310.27691
431816.46881.53117
441717.5405-0.540502
451512.28992.71011
461414.4045-0.40448
471918.31850.681464
481614.71281.28715
491516.2508-1.2508
501213.1436-1.14363
511815.81752.18251
521614.28111.71892
531718.1064-1.10638
54169.541616.45839
552017.56862.43143
561111.0392-0.0392449
571816.08231.91767
581512.50262.49738
591514.28620.713845
601114.0799-3.07989
611614.52481.47524
621814.9683.032
631515.9662-0.966218
641516.2704-1.27045
651516.2704-1.27045
661815.01252.98754
671315.2814-2.28135
681916.87652.1235
691516.2151-1.21506
70811.2165-3.21652
711314.9675-1.96749
721415.2531-1.2531
731917.381.61998
741514.86330.136706
751315.5295-2.52949
761614.9981.00198
771713.63143.36862
781314.3114-1.31141
791212.7407-0.740687
801312.47120.528835
811011.8591-1.85906
821515.3785-0.37848
831012.1576-2.15762
841814.43283.56719
851412.83391.16609
861513.2281.77197
871517.0857-2.0857
881414.1962-0.196234
891512.61722.38279
901614.4751.52502
911919.5187-0.518741
921412.20091.79906
931415.5851-1.58512
941614.99791.00213
951314.3297-1.32967
961511.63913.36087
971011.8812-1.8812
981311.45241.54755
991212.4632-0.46315
1001917.0631.93697
1011715.0951.90505
1021111.3692-0.369204
1031516.0273-1.02728
1041215.1861-3.18607
1051313.5101-0.510131
1061617.6295-1.6295
1071213.9548-1.95484
1081613.95142.04855
1091415.4979-1.49787
1101817.07810.92189
1111112.1569-1.1569
1121615.44490.555099
1131815.75442.24555
114811.7893-3.7893
1151213.7449-1.74488
1161614.24321.75679
1171917.45971.54026
1181511.62923.3708
1191714.9752.02497
1201613.24662.75343
1211816.58411.41585
1221916.34632.65372
1231915.96953.03055
1241917.97191.02812
1251312.9840.0159745
1261316.1539-3.15395
1271013.1346-3.13459
128511.227-6.22697
1291210.89911.10087
1301416.1021-2.10212
1311212.9592-0.959185
1321212.7959-0.795908
1331315.0746-2.07464
1341112.7771-1.7771
1351513.51921.48076
1362017.56862.43143
1371814.49263.50745
1381112.8537-1.85367
1391515.2431-0.243124
1401818.2368-0.236843
1411414.3862-0.386212
1421616.2175-0.217524
1431314.3925-1.39249
1441214.279-2.27903
1451214.0437-2.04365
1461514.4210.578958
1471514.11280.887203
1481316.6372-3.63719
1491715.64951.35047
150811.726-3.726
151813.8769-5.8769
1521313.9477-0.947747
153813.9269-5.92693
1541111.0392-0.0392449
1551213.3528-1.35281
1561013.2045-3.20452
1571513.51921.48076
1581714.61032.38972
1591312.66470.335256
1601413.05790.942111
1611613.73252.26749
1621714.5842.41599
1631111.7802-0.780211
1641513.8471.15304
1651212.0844-0.0843818
1661514.34540.654602
1671815.10962.89039
1681514.58360.416392
1691514.33260.667373
1701714.92662.07335
171810.3256-2.32557

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 10.3225 & -6.32253 \tabularnewline
2 & 13 & 12.2552 & 0.744796 \tabularnewline
3 & 18 & 16.0832 & 1.91676 \tabularnewline
4 & 18 & 16.2866 & 1.71345 \tabularnewline
5 & 17 & 16.9725 & 0.0274749 \tabularnewline
6 & 13 & 11.11 & 1.89001 \tabularnewline
7 & 17 & 19.6344 & -2.63436 \tabularnewline
8 & 19 & 17.3877 & 1.61234 \tabularnewline
9 & 16 & 20.1895 & -4.18946 \tabularnewline
10 & 13 & 11.4843 & 1.51567 \tabularnewline
11 & 18 & 16.9453 & 1.05474 \tabularnewline
12 & 15 & 10.7079 & 4.29211 \tabularnewline
13 & 11 & 14.0614 & -3.06144 \tabularnewline
14 & 13 & 14.1767 & -1.17671 \tabularnewline
15 & 16 & 15.7095 & 0.290502 \tabularnewline
16 & 14 & 14.2184 & -0.218436 \tabularnewline
17 & 19 & 16.3966 & 2.60344 \tabularnewline
18 & 14 & 13.2953 & 0.704666 \tabularnewline
19 & 15 & 13.769 & 1.23103 \tabularnewline
20 & 16 & 18.1022 & -2.10219 \tabularnewline
21 & 15 & 14.0082 & 0.991795 \tabularnewline
22 & 15 & 14.7549 & 0.245114 \tabularnewline
23 & 12 & 12.5872 & -0.587175 \tabularnewline
24 & 13 & 13.579 & -0.578969 \tabularnewline
25 & 17 & 14.6914 & 2.3086 \tabularnewline
26 & 9 & 10.8865 & -1.88647 \tabularnewline
27 & 18 & 17.5433 & 0.45669 \tabularnewline
28 & 16 & 16.2848 & -0.284775 \tabularnewline
29 & 12 & 11.8302 & 0.169781 \tabularnewline
30 & 18 & 15.1256 & 2.87442 \tabularnewline
31 & 15 & 16.7517 & -1.7517 \tabularnewline
32 & 18 & 15.5814 & 2.41863 \tabularnewline
33 & 16 & 15.0254 & 0.97461 \tabularnewline
34 & 16 & 16.1503 & -0.150289 \tabularnewline
35 & 18 & 16.9844 & 1.01562 \tabularnewline
36 & 14 & 14.5968 & -0.596767 \tabularnewline
37 & 12 & 14.8613 & -2.86128 \tabularnewline
38 & 14 & 14.5547 & -0.554733 \tabularnewline
39 & 15 & 16.5237 & -1.52373 \tabularnewline
40 & 18 & 17.0059 & 0.994076 \tabularnewline
41 & 10 & 16.9099 & -6.90992 \tabularnewline
42 & 16 & 15.7231 & 0.27691 \tabularnewline
43 & 18 & 16.4688 & 1.53117 \tabularnewline
44 & 17 & 17.5405 & -0.540502 \tabularnewline
45 & 15 & 12.2899 & 2.71011 \tabularnewline
46 & 14 & 14.4045 & -0.40448 \tabularnewline
47 & 19 & 18.3185 & 0.681464 \tabularnewline
48 & 16 & 14.7128 & 1.28715 \tabularnewline
49 & 15 & 16.2508 & -1.2508 \tabularnewline
50 & 12 & 13.1436 & -1.14363 \tabularnewline
51 & 18 & 15.8175 & 2.18251 \tabularnewline
52 & 16 & 14.2811 & 1.71892 \tabularnewline
53 & 17 & 18.1064 & -1.10638 \tabularnewline
54 & 16 & 9.54161 & 6.45839 \tabularnewline
55 & 20 & 17.5686 & 2.43143 \tabularnewline
56 & 11 & 11.0392 & -0.0392449 \tabularnewline
57 & 18 & 16.0823 & 1.91767 \tabularnewline
58 & 15 & 12.5026 & 2.49738 \tabularnewline
59 & 15 & 14.2862 & 0.713845 \tabularnewline
60 & 11 & 14.0799 & -3.07989 \tabularnewline
61 & 16 & 14.5248 & 1.47524 \tabularnewline
62 & 18 & 14.968 & 3.032 \tabularnewline
63 & 15 & 15.9662 & -0.966218 \tabularnewline
64 & 15 & 16.2704 & -1.27045 \tabularnewline
65 & 15 & 16.2704 & -1.27045 \tabularnewline
66 & 18 & 15.0125 & 2.98754 \tabularnewline
67 & 13 & 15.2814 & -2.28135 \tabularnewline
68 & 19 & 16.8765 & 2.1235 \tabularnewline
69 & 15 & 16.2151 & -1.21506 \tabularnewline
70 & 8 & 11.2165 & -3.21652 \tabularnewline
71 & 13 & 14.9675 & -1.96749 \tabularnewline
72 & 14 & 15.2531 & -1.2531 \tabularnewline
73 & 19 & 17.38 & 1.61998 \tabularnewline
74 & 15 & 14.8633 & 0.136706 \tabularnewline
75 & 13 & 15.5295 & -2.52949 \tabularnewline
76 & 16 & 14.998 & 1.00198 \tabularnewline
77 & 17 & 13.6314 & 3.36862 \tabularnewline
78 & 13 & 14.3114 & -1.31141 \tabularnewline
79 & 12 & 12.7407 & -0.740687 \tabularnewline
80 & 13 & 12.4712 & 0.528835 \tabularnewline
81 & 10 & 11.8591 & -1.85906 \tabularnewline
82 & 15 & 15.3785 & -0.37848 \tabularnewline
83 & 10 & 12.1576 & -2.15762 \tabularnewline
84 & 18 & 14.4328 & 3.56719 \tabularnewline
85 & 14 & 12.8339 & 1.16609 \tabularnewline
86 & 15 & 13.228 & 1.77197 \tabularnewline
87 & 15 & 17.0857 & -2.0857 \tabularnewline
88 & 14 & 14.1962 & -0.196234 \tabularnewline
89 & 15 & 12.6172 & 2.38279 \tabularnewline
90 & 16 & 14.475 & 1.52502 \tabularnewline
91 & 19 & 19.5187 & -0.518741 \tabularnewline
92 & 14 & 12.2009 & 1.79906 \tabularnewline
93 & 14 & 15.5851 & -1.58512 \tabularnewline
94 & 16 & 14.9979 & 1.00213 \tabularnewline
95 & 13 & 14.3297 & -1.32967 \tabularnewline
96 & 15 & 11.6391 & 3.36087 \tabularnewline
97 & 10 & 11.8812 & -1.8812 \tabularnewline
98 & 13 & 11.4524 & 1.54755 \tabularnewline
99 & 12 & 12.4632 & -0.46315 \tabularnewline
100 & 19 & 17.063 & 1.93697 \tabularnewline
101 & 17 & 15.095 & 1.90505 \tabularnewline
102 & 11 & 11.3692 & -0.369204 \tabularnewline
103 & 15 & 16.0273 & -1.02728 \tabularnewline
104 & 12 & 15.1861 & -3.18607 \tabularnewline
105 & 13 & 13.5101 & -0.510131 \tabularnewline
106 & 16 & 17.6295 & -1.6295 \tabularnewline
107 & 12 & 13.9548 & -1.95484 \tabularnewline
108 & 16 & 13.9514 & 2.04855 \tabularnewline
109 & 14 & 15.4979 & -1.49787 \tabularnewline
110 & 18 & 17.0781 & 0.92189 \tabularnewline
111 & 11 & 12.1569 & -1.1569 \tabularnewline
112 & 16 & 15.4449 & 0.555099 \tabularnewline
113 & 18 & 15.7544 & 2.24555 \tabularnewline
114 & 8 & 11.7893 & -3.7893 \tabularnewline
115 & 12 & 13.7449 & -1.74488 \tabularnewline
116 & 16 & 14.2432 & 1.75679 \tabularnewline
117 & 19 & 17.4597 & 1.54026 \tabularnewline
118 & 15 & 11.6292 & 3.3708 \tabularnewline
119 & 17 & 14.975 & 2.02497 \tabularnewline
120 & 16 & 13.2466 & 2.75343 \tabularnewline
121 & 18 & 16.5841 & 1.41585 \tabularnewline
122 & 19 & 16.3463 & 2.65372 \tabularnewline
123 & 19 & 15.9695 & 3.03055 \tabularnewline
124 & 19 & 17.9719 & 1.02812 \tabularnewline
125 & 13 & 12.984 & 0.0159745 \tabularnewline
126 & 13 & 16.1539 & -3.15395 \tabularnewline
127 & 10 & 13.1346 & -3.13459 \tabularnewline
128 & 5 & 11.227 & -6.22697 \tabularnewline
129 & 12 & 10.8991 & 1.10087 \tabularnewline
130 & 14 & 16.1021 & -2.10212 \tabularnewline
131 & 12 & 12.9592 & -0.959185 \tabularnewline
132 & 12 & 12.7959 & -0.795908 \tabularnewline
133 & 13 & 15.0746 & -2.07464 \tabularnewline
134 & 11 & 12.7771 & -1.7771 \tabularnewline
135 & 15 & 13.5192 & 1.48076 \tabularnewline
136 & 20 & 17.5686 & 2.43143 \tabularnewline
137 & 18 & 14.4926 & 3.50745 \tabularnewline
138 & 11 & 12.8537 & -1.85367 \tabularnewline
139 & 15 & 15.2431 & -0.243124 \tabularnewline
140 & 18 & 18.2368 & -0.236843 \tabularnewline
141 & 14 & 14.3862 & -0.386212 \tabularnewline
142 & 16 & 16.2175 & -0.217524 \tabularnewline
143 & 13 & 14.3925 & -1.39249 \tabularnewline
144 & 12 & 14.279 & -2.27903 \tabularnewline
145 & 12 & 14.0437 & -2.04365 \tabularnewline
146 & 15 & 14.421 & 0.578958 \tabularnewline
147 & 15 & 14.1128 & 0.887203 \tabularnewline
148 & 13 & 16.6372 & -3.63719 \tabularnewline
149 & 17 & 15.6495 & 1.35047 \tabularnewline
150 & 8 & 11.726 & -3.726 \tabularnewline
151 & 8 & 13.8769 & -5.8769 \tabularnewline
152 & 13 & 13.9477 & -0.947747 \tabularnewline
153 & 8 & 13.9269 & -5.92693 \tabularnewline
154 & 11 & 11.0392 & -0.0392449 \tabularnewline
155 & 12 & 13.3528 & -1.35281 \tabularnewline
156 & 10 & 13.2045 & -3.20452 \tabularnewline
157 & 15 & 13.5192 & 1.48076 \tabularnewline
158 & 17 & 14.6103 & 2.38972 \tabularnewline
159 & 13 & 12.6647 & 0.335256 \tabularnewline
160 & 14 & 13.0579 & 0.942111 \tabularnewline
161 & 16 & 13.7325 & 2.26749 \tabularnewline
162 & 17 & 14.584 & 2.41599 \tabularnewline
163 & 11 & 11.7802 & -0.780211 \tabularnewline
164 & 15 & 13.847 & 1.15304 \tabularnewline
165 & 12 & 12.0844 & -0.0843818 \tabularnewline
166 & 15 & 14.3454 & 0.654602 \tabularnewline
167 & 18 & 15.1096 & 2.89039 \tabularnewline
168 & 15 & 14.5836 & 0.416392 \tabularnewline
169 & 15 & 14.3326 & 0.667373 \tabularnewline
170 & 17 & 14.9266 & 2.07335 \tabularnewline
171 & 8 & 10.3256 & -2.32557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263619&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]4[/C][C]10.3225[/C][C]-6.32253[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]12.2552[/C][C]0.744796[/C][/ROW]
[ROW][C]3[/C][C]18[/C][C]16.0832[/C][C]1.91676[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]16.2866[/C][C]1.71345[/C][/ROW]
[ROW][C]5[/C][C]17[/C][C]16.9725[/C][C]0.0274749[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]11.11[/C][C]1.89001[/C][/ROW]
[ROW][C]7[/C][C]17[/C][C]19.6344[/C][C]-2.63436[/C][/ROW]
[ROW][C]8[/C][C]19[/C][C]17.3877[/C][C]1.61234[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]20.1895[/C][C]-4.18946[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]11.4843[/C][C]1.51567[/C][/ROW]
[ROW][C]11[/C][C]18[/C][C]16.9453[/C][C]1.05474[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]10.7079[/C][C]4.29211[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]14.0614[/C][C]-3.06144[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]14.1767[/C][C]-1.17671[/C][/ROW]
[ROW][C]15[/C][C]16[/C][C]15.7095[/C][C]0.290502[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.2184[/C][C]-0.218436[/C][/ROW]
[ROW][C]17[/C][C]19[/C][C]16.3966[/C][C]2.60344[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]13.2953[/C][C]0.704666[/C][/ROW]
[ROW][C]19[/C][C]15[/C][C]13.769[/C][C]1.23103[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]18.1022[/C][C]-2.10219[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]14.0082[/C][C]0.991795[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.7549[/C][C]0.245114[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.5872[/C][C]-0.587175[/C][/ROW]
[ROW][C]24[/C][C]13[/C][C]13.579[/C][C]-0.578969[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.6914[/C][C]2.3086[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]10.8865[/C][C]-1.88647[/C][/ROW]
[ROW][C]27[/C][C]18[/C][C]17.5433[/C][C]0.45669[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]16.2848[/C][C]-0.284775[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.8302[/C][C]0.169781[/C][/ROW]
[ROW][C]30[/C][C]18[/C][C]15.1256[/C][C]2.87442[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]16.7517[/C][C]-1.7517[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]15.5814[/C][C]2.41863[/C][/ROW]
[ROW][C]33[/C][C]16[/C][C]15.0254[/C][C]0.97461[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]16.1503[/C][C]-0.150289[/C][/ROW]
[ROW][C]35[/C][C]18[/C][C]16.9844[/C][C]1.01562[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]14.5968[/C][C]-0.596767[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]14.8613[/C][C]-2.86128[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]14.5547[/C][C]-0.554733[/C][/ROW]
[ROW][C]39[/C][C]15[/C][C]16.5237[/C][C]-1.52373[/C][/ROW]
[ROW][C]40[/C][C]18[/C][C]17.0059[/C][C]0.994076[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]16.9099[/C][C]-6.90992[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]15.7231[/C][C]0.27691[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]16.4688[/C][C]1.53117[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]17.5405[/C][C]-0.540502[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]12.2899[/C][C]2.71011[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]14.4045[/C][C]-0.40448[/C][/ROW]
[ROW][C]47[/C][C]19[/C][C]18.3185[/C][C]0.681464[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]14.7128[/C][C]1.28715[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]16.2508[/C][C]-1.2508[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]13.1436[/C][C]-1.14363[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]15.8175[/C][C]2.18251[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]14.2811[/C][C]1.71892[/C][/ROW]
[ROW][C]53[/C][C]17[/C][C]18.1064[/C][C]-1.10638[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]9.54161[/C][C]6.45839[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]17.5686[/C][C]2.43143[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]11.0392[/C][C]-0.0392449[/C][/ROW]
[ROW][C]57[/C][C]18[/C][C]16.0823[/C][C]1.91767[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]12.5026[/C][C]2.49738[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.2862[/C][C]0.713845[/C][/ROW]
[ROW][C]60[/C][C]11[/C][C]14.0799[/C][C]-3.07989[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]14.5248[/C][C]1.47524[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]14.968[/C][C]3.032[/C][/ROW]
[ROW][C]63[/C][C]15[/C][C]15.9662[/C][C]-0.966218[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]16.2704[/C][C]-1.27045[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]16.2704[/C][C]-1.27045[/C][/ROW]
[ROW][C]66[/C][C]18[/C][C]15.0125[/C][C]2.98754[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]15.2814[/C][C]-2.28135[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]16.8765[/C][C]2.1235[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]16.2151[/C][C]-1.21506[/C][/ROW]
[ROW][C]70[/C][C]8[/C][C]11.2165[/C][C]-3.21652[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]14.9675[/C][C]-1.96749[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]15.2531[/C][C]-1.2531[/C][/ROW]
[ROW][C]73[/C][C]19[/C][C]17.38[/C][C]1.61998[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.8633[/C][C]0.136706[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.5295[/C][C]-2.52949[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]14.998[/C][C]1.00198[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]13.6314[/C][C]3.36862[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]14.3114[/C][C]-1.31141[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]12.7407[/C][C]-0.740687[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.4712[/C][C]0.528835[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]11.8591[/C][C]-1.85906[/C][/ROW]
[ROW][C]82[/C][C]15[/C][C]15.3785[/C][C]-0.37848[/C][/ROW]
[ROW][C]83[/C][C]10[/C][C]12.1576[/C][C]-2.15762[/C][/ROW]
[ROW][C]84[/C][C]18[/C][C]14.4328[/C][C]3.56719[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]12.8339[/C][C]1.16609[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]13.228[/C][C]1.77197[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]17.0857[/C][C]-2.0857[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.1962[/C][C]-0.196234[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]12.6172[/C][C]2.38279[/C][/ROW]
[ROW][C]90[/C][C]16[/C][C]14.475[/C][C]1.52502[/C][/ROW]
[ROW][C]91[/C][C]19[/C][C]19.5187[/C][C]-0.518741[/C][/ROW]
[ROW][C]92[/C][C]14[/C][C]12.2009[/C][C]1.79906[/C][/ROW]
[ROW][C]93[/C][C]14[/C][C]15.5851[/C][C]-1.58512[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.9979[/C][C]1.00213[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.3297[/C][C]-1.32967[/C][/ROW]
[ROW][C]96[/C][C]15[/C][C]11.6391[/C][C]3.36087[/C][/ROW]
[ROW][C]97[/C][C]10[/C][C]11.8812[/C][C]-1.8812[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]11.4524[/C][C]1.54755[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]12.4632[/C][C]-0.46315[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]17.063[/C][C]1.93697[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.095[/C][C]1.90505[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.3692[/C][C]-0.369204[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]16.0273[/C][C]-1.02728[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]15.1861[/C][C]-3.18607[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]13.5101[/C][C]-0.510131[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]17.6295[/C][C]-1.6295[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]13.9548[/C][C]-1.95484[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]13.9514[/C][C]2.04855[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]15.4979[/C][C]-1.49787[/C][/ROW]
[ROW][C]110[/C][C]18[/C][C]17.0781[/C][C]0.92189[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.1569[/C][C]-1.1569[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.4449[/C][C]0.555099[/C][/ROW]
[ROW][C]113[/C][C]18[/C][C]15.7544[/C][C]2.24555[/C][/ROW]
[ROW][C]114[/C][C]8[/C][C]11.7893[/C][C]-3.7893[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.7449[/C][C]-1.74488[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]14.2432[/C][C]1.75679[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]17.4597[/C][C]1.54026[/C][/ROW]
[ROW][C]118[/C][C]15[/C][C]11.6292[/C][C]3.3708[/C][/ROW]
[ROW][C]119[/C][C]17[/C][C]14.975[/C][C]2.02497[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]13.2466[/C][C]2.75343[/C][/ROW]
[ROW][C]121[/C][C]18[/C][C]16.5841[/C][C]1.41585[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]16.3463[/C][C]2.65372[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]15.9695[/C][C]3.03055[/C][/ROW]
[ROW][C]124[/C][C]19[/C][C]17.9719[/C][C]1.02812[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]12.984[/C][C]0.0159745[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]16.1539[/C][C]-3.15395[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]13.1346[/C][C]-3.13459[/C][/ROW]
[ROW][C]128[/C][C]5[/C][C]11.227[/C][C]-6.22697[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]10.8991[/C][C]1.10087[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]16.1021[/C][C]-2.10212[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]12.9592[/C][C]-0.959185[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]12.7959[/C][C]-0.795908[/C][/ROW]
[ROW][C]133[/C][C]13[/C][C]15.0746[/C][C]-2.07464[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]12.7771[/C][C]-1.7771[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.5192[/C][C]1.48076[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]17.5686[/C][C]2.43143[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]14.4926[/C][C]3.50745[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]12.8537[/C][C]-1.85367[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]15.2431[/C][C]-0.243124[/C][/ROW]
[ROW][C]140[/C][C]18[/C][C]18.2368[/C][C]-0.236843[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]14.3862[/C][C]-0.386212[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]16.2175[/C][C]-0.217524[/C][/ROW]
[ROW][C]143[/C][C]13[/C][C]14.3925[/C][C]-1.39249[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]14.279[/C][C]-2.27903[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]14.0437[/C][C]-2.04365[/C][/ROW]
[ROW][C]146[/C][C]15[/C][C]14.421[/C][C]0.578958[/C][/ROW]
[ROW][C]147[/C][C]15[/C][C]14.1128[/C][C]0.887203[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]16.6372[/C][C]-3.63719[/C][/ROW]
[ROW][C]149[/C][C]17[/C][C]15.6495[/C][C]1.35047[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]11.726[/C][C]-3.726[/C][/ROW]
[ROW][C]151[/C][C]8[/C][C]13.8769[/C][C]-5.8769[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]13.9477[/C][C]-0.947747[/C][/ROW]
[ROW][C]153[/C][C]8[/C][C]13.9269[/C][C]-5.92693[/C][/ROW]
[ROW][C]154[/C][C]11[/C][C]11.0392[/C][C]-0.0392449[/C][/ROW]
[ROW][C]155[/C][C]12[/C][C]13.3528[/C][C]-1.35281[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]13.2045[/C][C]-3.20452[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.5192[/C][C]1.48076[/C][/ROW]
[ROW][C]158[/C][C]17[/C][C]14.6103[/C][C]2.38972[/C][/ROW]
[ROW][C]159[/C][C]13[/C][C]12.6647[/C][C]0.335256[/C][/ROW]
[ROW][C]160[/C][C]14[/C][C]13.0579[/C][C]0.942111[/C][/ROW]
[ROW][C]161[/C][C]16[/C][C]13.7325[/C][C]2.26749[/C][/ROW]
[ROW][C]162[/C][C]17[/C][C]14.584[/C][C]2.41599[/C][/ROW]
[ROW][C]163[/C][C]11[/C][C]11.7802[/C][C]-0.780211[/C][/ROW]
[ROW][C]164[/C][C]15[/C][C]13.847[/C][C]1.15304[/C][/ROW]
[ROW][C]165[/C][C]12[/C][C]12.0844[/C][C]-0.0843818[/C][/ROW]
[ROW][C]166[/C][C]15[/C][C]14.3454[/C][C]0.654602[/C][/ROW]
[ROW][C]167[/C][C]18[/C][C]15.1096[/C][C]2.89039[/C][/ROW]
[ROW][C]168[/C][C]15[/C][C]14.5836[/C][C]0.416392[/C][/ROW]
[ROW][C]169[/C][C]15[/C][C]14.3326[/C][C]0.667373[/C][/ROW]
[ROW][C]170[/C][C]17[/C][C]14.9266[/C][C]2.07335[/C][/ROW]
[ROW][C]171[/C][C]8[/C][C]10.3256[/C][C]-2.32557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263619&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263619&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
1410.3225-6.32253
21312.25520.744796
31816.08321.91676
41816.28661.71345
51716.97250.0274749
61311.111.89001
71719.6344-2.63436
81917.38771.61234
91620.1895-4.18946
101311.48431.51567
111816.94531.05474
121510.70794.29211
131114.0614-3.06144
141314.1767-1.17671
151615.70950.290502
161414.2184-0.218436
171916.39662.60344
181413.29530.704666
191513.7691.23103
201618.1022-2.10219
211514.00820.991795
221514.75490.245114
231212.5872-0.587175
241313.579-0.578969
251714.69142.3086
26910.8865-1.88647
271817.54330.45669
281616.2848-0.284775
291211.83020.169781
301815.12562.87442
311516.7517-1.7517
321815.58142.41863
331615.02540.97461
341616.1503-0.150289
351816.98441.01562
361414.5968-0.596767
371214.8613-2.86128
381414.5547-0.554733
391516.5237-1.52373
401817.00590.994076
411016.9099-6.90992
421615.72310.27691
431816.46881.53117
441717.5405-0.540502
451512.28992.71011
461414.4045-0.40448
471918.31850.681464
481614.71281.28715
491516.2508-1.2508
501213.1436-1.14363
511815.81752.18251
521614.28111.71892
531718.1064-1.10638
54169.541616.45839
552017.56862.43143
561111.0392-0.0392449
571816.08231.91767
581512.50262.49738
591514.28620.713845
601114.0799-3.07989
611614.52481.47524
621814.9683.032
631515.9662-0.966218
641516.2704-1.27045
651516.2704-1.27045
661815.01252.98754
671315.2814-2.28135
681916.87652.1235
691516.2151-1.21506
70811.2165-3.21652
711314.9675-1.96749
721415.2531-1.2531
731917.381.61998
741514.86330.136706
751315.5295-2.52949
761614.9981.00198
771713.63143.36862
781314.3114-1.31141
791212.7407-0.740687
801312.47120.528835
811011.8591-1.85906
821515.3785-0.37848
831012.1576-2.15762
841814.43283.56719
851412.83391.16609
861513.2281.77197
871517.0857-2.0857
881414.1962-0.196234
891512.61722.38279
901614.4751.52502
911919.5187-0.518741
921412.20091.79906
931415.5851-1.58512
941614.99791.00213
951314.3297-1.32967
961511.63913.36087
971011.8812-1.8812
981311.45241.54755
991212.4632-0.46315
1001917.0631.93697
1011715.0951.90505
1021111.3692-0.369204
1031516.0273-1.02728
1041215.1861-3.18607
1051313.5101-0.510131
1061617.6295-1.6295
1071213.9548-1.95484
1081613.95142.04855
1091415.4979-1.49787
1101817.07810.92189
1111112.1569-1.1569
1121615.44490.555099
1131815.75442.24555
114811.7893-3.7893
1151213.7449-1.74488
1161614.24321.75679
1171917.45971.54026
1181511.62923.3708
1191714.9752.02497
1201613.24662.75343
1211816.58411.41585
1221916.34632.65372
1231915.96953.03055
1241917.97191.02812
1251312.9840.0159745
1261316.1539-3.15395
1271013.1346-3.13459
128511.227-6.22697
1291210.89911.10087
1301416.1021-2.10212
1311212.9592-0.959185
1321212.7959-0.795908
1331315.0746-2.07464
1341112.7771-1.7771
1351513.51921.48076
1362017.56862.43143
1371814.49263.50745
1381112.8537-1.85367
1391515.2431-0.243124
1401818.2368-0.236843
1411414.3862-0.386212
1421616.2175-0.217524
1431314.3925-1.39249
1441214.279-2.27903
1451214.0437-2.04365
1461514.4210.578958
1471514.11280.887203
1481316.6372-3.63719
1491715.64951.35047
150811.726-3.726
151813.8769-5.8769
1521313.9477-0.947747
153813.9269-5.92693
1541111.0392-0.0392449
1551213.3528-1.35281
1561013.2045-3.20452
1571513.51921.48076
1581714.61032.38972
1591312.66470.335256
1601413.05790.942111
1611613.73252.26749
1621714.5842.41599
1631111.7802-0.780211
1641513.8471.15304
1651212.0844-0.0843818
1661514.34540.654602
1671815.10962.89039
1681514.58360.416392
1691514.33260.667373
1701714.92662.07335
171810.3256-2.32557






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9008720.1982560.0991281
100.8440160.3119680.155984
110.7612290.4775420.238771
120.802470.3950590.19753
130.8200670.3598660.179933
140.8747620.2504770.125238
150.8362560.3274880.163744
160.9182750.1634490.0817246
170.8983040.2033920.101696
180.8611380.2777240.138862
190.8138460.3723070.186154
200.7928040.4143930.207196
210.7940820.4118370.205918
220.7373810.5252380.262619
230.6939730.6120530.306027
240.631660.736680.36834
250.6061750.787650.393825
260.5777220.8445560.422278
270.532740.934520.46726
280.4828140.9656280.517186
290.4305230.8610460.569477
300.408030.816060.59197
310.4501560.9003110.549844
320.4569520.9139040.543048
330.4043310.8086620.595669
340.3496560.6993130.650344
350.3039350.607870.696065
360.2768610.5537230.723139
370.409150.81830.59085
380.360710.7214190.63929
390.3528750.7057490.647125
400.3053960.6107920.694604
410.7708820.4582350.229118
420.7321220.5357560.267878
430.7063340.5873320.293666
440.6605370.6789260.339463
450.6729440.6541130.327056
460.6253990.7492020.374601
470.5886270.8227470.411373
480.5524590.8950830.447541
490.5126130.9747740.487387
500.5006510.9986990.499349
510.4999670.9999350.500033
520.4911540.9823080.508846
530.4582750.9165510.541725
540.7469110.5061770.253089
550.7553240.4893530.244676
560.7259730.5480550.274027
570.7121580.5756830.287842
580.7125850.5748290.287415
590.6730370.6539270.326963
600.7219740.5560520.278026
610.6955460.6089070.304454
620.7182880.5634240.281712
630.6898550.6202890.310145
640.656880.6862390.34312
650.6224720.7550570.377528
660.6475230.7049540.352477
670.6583040.6833920.341696
680.655480.689040.34452
690.6287360.7425290.371264
700.6942550.6114910.305745
710.6965610.6068780.303439
720.6755380.6489240.324462
730.6551330.6897340.344867
740.6123410.7753190.387659
750.6257390.7485220.374261
760.5882070.8235870.411793
770.6406770.7186460.359323
780.6166780.7666440.383322
790.5810720.8378550.418928
800.5405270.9189460.459473
810.5318320.9363360.468168
820.4901280.9802570.509872
830.4878790.9757570.512121
840.5531680.8936640.446832
850.5217280.9565440.478272
860.505140.9897190.49486
870.5013780.9972450.498622
880.4580150.916030.541985
890.457820.9156390.54218
900.4310050.862010.568995
910.3983960.7967930.601604
920.3915870.7831730.608413
930.371210.742420.62879
940.339110.678220.66089
950.3155210.6310430.684479
960.3768240.7536480.623176
970.3645150.7290290.635485
980.3593920.7187850.640608
990.3227710.6455410.677229
1000.3077840.6155670.692216
1010.2903960.5807920.709604
1020.2578390.5156780.742161
1030.2321740.4643490.767826
1040.2812830.5625660.718717
1050.2473430.4946850.752657
1060.2481440.4962880.751856
1070.2392820.4785630.760718
1080.2331460.4662910.766854
1090.2173630.4347270.782637
1100.1882630.3765270.811737
1110.1644470.3288940.835553
1120.1373980.2747960.862602
1130.1310190.2620380.868981
1140.1628010.3256030.837199
1150.1486390.2972780.851361
1160.1460970.2921930.853903
1170.1279570.2559130.872043
1180.1917040.3834090.808296
1190.2064420.4128840.793558
1200.2263570.4527150.773643
1210.1977930.3955850.802207
1220.217710.435420.78229
1230.270190.5403790.72981
1240.2347150.4694310.765285
1250.2036730.4073460.796327
1260.220210.4404210.77979
1270.2259990.4519970.774001
1280.4486990.8973980.551301
1290.4475230.8950450.552477
1300.4540330.9080650.545967
1310.4050890.8101790.594911
1320.3551130.7102260.644887
1330.3449750.689950.655025
1340.3146760.6293520.685324
1350.2954880.5909770.704512
1360.2646950.5293890.735305
1370.4196020.8392040.580398
1380.3778770.7557540.622123
1390.3271490.6542980.672851
1400.2783120.5566240.721688
1410.2326460.4652930.767354
1420.1906640.3813280.809336
1430.1542340.3084690.845766
1440.131790.2635810.86821
1450.1387390.2774780.861261
1460.1057740.2115490.894226
1470.07890130.1578030.921099
1480.1297870.2595730.870213
1490.09850750.1970150.901493
1500.1068190.2136390.893181
1510.5945560.8108880.405444
1520.526680.9466390.47332
1530.9599540.08009180.0400459
1540.9687160.0625680.031284
1550.9783670.0432660.021633
1560.9990550.001889980.000944992
1570.9973420.005315690.00265785
1580.9933270.01334570.00667284
1590.9852470.02950590.014753
1600.9872190.02556180.0127809
1610.9712550.05749070.0287454
1620.981190.03761960.0188098

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.900872 & 0.198256 & 0.0991281 \tabularnewline
10 & 0.844016 & 0.311968 & 0.155984 \tabularnewline
11 & 0.761229 & 0.477542 & 0.238771 \tabularnewline
12 & 0.80247 & 0.395059 & 0.19753 \tabularnewline
13 & 0.820067 & 0.359866 & 0.179933 \tabularnewline
14 & 0.874762 & 0.250477 & 0.125238 \tabularnewline
15 & 0.836256 & 0.327488 & 0.163744 \tabularnewline
16 & 0.918275 & 0.163449 & 0.0817246 \tabularnewline
17 & 0.898304 & 0.203392 & 0.101696 \tabularnewline
18 & 0.861138 & 0.277724 & 0.138862 \tabularnewline
19 & 0.813846 & 0.372307 & 0.186154 \tabularnewline
20 & 0.792804 & 0.414393 & 0.207196 \tabularnewline
21 & 0.794082 & 0.411837 & 0.205918 \tabularnewline
22 & 0.737381 & 0.525238 & 0.262619 \tabularnewline
23 & 0.693973 & 0.612053 & 0.306027 \tabularnewline
24 & 0.63166 & 0.73668 & 0.36834 \tabularnewline
25 & 0.606175 & 0.78765 & 0.393825 \tabularnewline
26 & 0.577722 & 0.844556 & 0.422278 \tabularnewline
27 & 0.53274 & 0.93452 & 0.46726 \tabularnewline
28 & 0.482814 & 0.965628 & 0.517186 \tabularnewline
29 & 0.430523 & 0.861046 & 0.569477 \tabularnewline
30 & 0.40803 & 0.81606 & 0.59197 \tabularnewline
31 & 0.450156 & 0.900311 & 0.549844 \tabularnewline
32 & 0.456952 & 0.913904 & 0.543048 \tabularnewline
33 & 0.404331 & 0.808662 & 0.595669 \tabularnewline
34 & 0.349656 & 0.699313 & 0.650344 \tabularnewline
35 & 0.303935 & 0.60787 & 0.696065 \tabularnewline
36 & 0.276861 & 0.553723 & 0.723139 \tabularnewline
37 & 0.40915 & 0.8183 & 0.59085 \tabularnewline
38 & 0.36071 & 0.721419 & 0.63929 \tabularnewline
39 & 0.352875 & 0.705749 & 0.647125 \tabularnewline
40 & 0.305396 & 0.610792 & 0.694604 \tabularnewline
41 & 0.770882 & 0.458235 & 0.229118 \tabularnewline
42 & 0.732122 & 0.535756 & 0.267878 \tabularnewline
43 & 0.706334 & 0.587332 & 0.293666 \tabularnewline
44 & 0.660537 & 0.678926 & 0.339463 \tabularnewline
45 & 0.672944 & 0.654113 & 0.327056 \tabularnewline
46 & 0.625399 & 0.749202 & 0.374601 \tabularnewline
47 & 0.588627 & 0.822747 & 0.411373 \tabularnewline
48 & 0.552459 & 0.895083 & 0.447541 \tabularnewline
49 & 0.512613 & 0.974774 & 0.487387 \tabularnewline
50 & 0.500651 & 0.998699 & 0.499349 \tabularnewline
51 & 0.499967 & 0.999935 & 0.500033 \tabularnewline
52 & 0.491154 & 0.982308 & 0.508846 \tabularnewline
53 & 0.458275 & 0.916551 & 0.541725 \tabularnewline
54 & 0.746911 & 0.506177 & 0.253089 \tabularnewline
55 & 0.755324 & 0.489353 & 0.244676 \tabularnewline
56 & 0.725973 & 0.548055 & 0.274027 \tabularnewline
57 & 0.712158 & 0.575683 & 0.287842 \tabularnewline
58 & 0.712585 & 0.574829 & 0.287415 \tabularnewline
59 & 0.673037 & 0.653927 & 0.326963 \tabularnewline
60 & 0.721974 & 0.556052 & 0.278026 \tabularnewline
61 & 0.695546 & 0.608907 & 0.304454 \tabularnewline
62 & 0.718288 & 0.563424 & 0.281712 \tabularnewline
63 & 0.689855 & 0.620289 & 0.310145 \tabularnewline
64 & 0.65688 & 0.686239 & 0.34312 \tabularnewline
65 & 0.622472 & 0.755057 & 0.377528 \tabularnewline
66 & 0.647523 & 0.704954 & 0.352477 \tabularnewline
67 & 0.658304 & 0.683392 & 0.341696 \tabularnewline
68 & 0.65548 & 0.68904 & 0.34452 \tabularnewline
69 & 0.628736 & 0.742529 & 0.371264 \tabularnewline
70 & 0.694255 & 0.611491 & 0.305745 \tabularnewline
71 & 0.696561 & 0.606878 & 0.303439 \tabularnewline
72 & 0.675538 & 0.648924 & 0.324462 \tabularnewline
73 & 0.655133 & 0.689734 & 0.344867 \tabularnewline
74 & 0.612341 & 0.775319 & 0.387659 \tabularnewline
75 & 0.625739 & 0.748522 & 0.374261 \tabularnewline
76 & 0.588207 & 0.823587 & 0.411793 \tabularnewline
77 & 0.640677 & 0.718646 & 0.359323 \tabularnewline
78 & 0.616678 & 0.766644 & 0.383322 \tabularnewline
79 & 0.581072 & 0.837855 & 0.418928 \tabularnewline
80 & 0.540527 & 0.918946 & 0.459473 \tabularnewline
81 & 0.531832 & 0.936336 & 0.468168 \tabularnewline
82 & 0.490128 & 0.980257 & 0.509872 \tabularnewline
83 & 0.487879 & 0.975757 & 0.512121 \tabularnewline
84 & 0.553168 & 0.893664 & 0.446832 \tabularnewline
85 & 0.521728 & 0.956544 & 0.478272 \tabularnewline
86 & 0.50514 & 0.989719 & 0.49486 \tabularnewline
87 & 0.501378 & 0.997245 & 0.498622 \tabularnewline
88 & 0.458015 & 0.91603 & 0.541985 \tabularnewline
89 & 0.45782 & 0.915639 & 0.54218 \tabularnewline
90 & 0.431005 & 0.86201 & 0.568995 \tabularnewline
91 & 0.398396 & 0.796793 & 0.601604 \tabularnewline
92 & 0.391587 & 0.783173 & 0.608413 \tabularnewline
93 & 0.37121 & 0.74242 & 0.62879 \tabularnewline
94 & 0.33911 & 0.67822 & 0.66089 \tabularnewline
95 & 0.315521 & 0.631043 & 0.684479 \tabularnewline
96 & 0.376824 & 0.753648 & 0.623176 \tabularnewline
97 & 0.364515 & 0.729029 & 0.635485 \tabularnewline
98 & 0.359392 & 0.718785 & 0.640608 \tabularnewline
99 & 0.322771 & 0.645541 & 0.677229 \tabularnewline
100 & 0.307784 & 0.615567 & 0.692216 \tabularnewline
101 & 0.290396 & 0.580792 & 0.709604 \tabularnewline
102 & 0.257839 & 0.515678 & 0.742161 \tabularnewline
103 & 0.232174 & 0.464349 & 0.767826 \tabularnewline
104 & 0.281283 & 0.562566 & 0.718717 \tabularnewline
105 & 0.247343 & 0.494685 & 0.752657 \tabularnewline
106 & 0.248144 & 0.496288 & 0.751856 \tabularnewline
107 & 0.239282 & 0.478563 & 0.760718 \tabularnewline
108 & 0.233146 & 0.466291 & 0.766854 \tabularnewline
109 & 0.217363 & 0.434727 & 0.782637 \tabularnewline
110 & 0.188263 & 0.376527 & 0.811737 \tabularnewline
111 & 0.164447 & 0.328894 & 0.835553 \tabularnewline
112 & 0.137398 & 0.274796 & 0.862602 \tabularnewline
113 & 0.131019 & 0.262038 & 0.868981 \tabularnewline
114 & 0.162801 & 0.325603 & 0.837199 \tabularnewline
115 & 0.148639 & 0.297278 & 0.851361 \tabularnewline
116 & 0.146097 & 0.292193 & 0.853903 \tabularnewline
117 & 0.127957 & 0.255913 & 0.872043 \tabularnewline
118 & 0.191704 & 0.383409 & 0.808296 \tabularnewline
119 & 0.206442 & 0.412884 & 0.793558 \tabularnewline
120 & 0.226357 & 0.452715 & 0.773643 \tabularnewline
121 & 0.197793 & 0.395585 & 0.802207 \tabularnewline
122 & 0.21771 & 0.43542 & 0.78229 \tabularnewline
123 & 0.27019 & 0.540379 & 0.72981 \tabularnewline
124 & 0.234715 & 0.469431 & 0.765285 \tabularnewline
125 & 0.203673 & 0.407346 & 0.796327 \tabularnewline
126 & 0.22021 & 0.440421 & 0.77979 \tabularnewline
127 & 0.225999 & 0.451997 & 0.774001 \tabularnewline
128 & 0.448699 & 0.897398 & 0.551301 \tabularnewline
129 & 0.447523 & 0.895045 & 0.552477 \tabularnewline
130 & 0.454033 & 0.908065 & 0.545967 \tabularnewline
131 & 0.405089 & 0.810179 & 0.594911 \tabularnewline
132 & 0.355113 & 0.710226 & 0.644887 \tabularnewline
133 & 0.344975 & 0.68995 & 0.655025 \tabularnewline
134 & 0.314676 & 0.629352 & 0.685324 \tabularnewline
135 & 0.295488 & 0.590977 & 0.704512 \tabularnewline
136 & 0.264695 & 0.529389 & 0.735305 \tabularnewline
137 & 0.419602 & 0.839204 & 0.580398 \tabularnewline
138 & 0.377877 & 0.755754 & 0.622123 \tabularnewline
139 & 0.327149 & 0.654298 & 0.672851 \tabularnewline
140 & 0.278312 & 0.556624 & 0.721688 \tabularnewline
141 & 0.232646 & 0.465293 & 0.767354 \tabularnewline
142 & 0.190664 & 0.381328 & 0.809336 \tabularnewline
143 & 0.154234 & 0.308469 & 0.845766 \tabularnewline
144 & 0.13179 & 0.263581 & 0.86821 \tabularnewline
145 & 0.138739 & 0.277478 & 0.861261 \tabularnewline
146 & 0.105774 & 0.211549 & 0.894226 \tabularnewline
147 & 0.0789013 & 0.157803 & 0.921099 \tabularnewline
148 & 0.129787 & 0.259573 & 0.870213 \tabularnewline
149 & 0.0985075 & 0.197015 & 0.901493 \tabularnewline
150 & 0.106819 & 0.213639 & 0.893181 \tabularnewline
151 & 0.594556 & 0.810888 & 0.405444 \tabularnewline
152 & 0.52668 & 0.946639 & 0.47332 \tabularnewline
153 & 0.959954 & 0.0800918 & 0.0400459 \tabularnewline
154 & 0.968716 & 0.062568 & 0.031284 \tabularnewline
155 & 0.978367 & 0.043266 & 0.021633 \tabularnewline
156 & 0.999055 & 0.00188998 & 0.000944992 \tabularnewline
157 & 0.997342 & 0.00531569 & 0.00265785 \tabularnewline
158 & 0.993327 & 0.0133457 & 0.00667284 \tabularnewline
159 & 0.985247 & 0.0295059 & 0.014753 \tabularnewline
160 & 0.987219 & 0.0255618 & 0.0127809 \tabularnewline
161 & 0.971255 & 0.0574907 & 0.0287454 \tabularnewline
162 & 0.98119 & 0.0376196 & 0.0188098 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263619&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]9[/C][C]0.900872[/C][C]0.198256[/C][C]0.0991281[/C][/ROW]
[ROW][C]10[/C][C]0.844016[/C][C]0.311968[/C][C]0.155984[/C][/ROW]
[ROW][C]11[/C][C]0.761229[/C][C]0.477542[/C][C]0.238771[/C][/ROW]
[ROW][C]12[/C][C]0.80247[/C][C]0.395059[/C][C]0.19753[/C][/ROW]
[ROW][C]13[/C][C]0.820067[/C][C]0.359866[/C][C]0.179933[/C][/ROW]
[ROW][C]14[/C][C]0.874762[/C][C]0.250477[/C][C]0.125238[/C][/ROW]
[ROW][C]15[/C][C]0.836256[/C][C]0.327488[/C][C]0.163744[/C][/ROW]
[ROW][C]16[/C][C]0.918275[/C][C]0.163449[/C][C]0.0817246[/C][/ROW]
[ROW][C]17[/C][C]0.898304[/C][C]0.203392[/C][C]0.101696[/C][/ROW]
[ROW][C]18[/C][C]0.861138[/C][C]0.277724[/C][C]0.138862[/C][/ROW]
[ROW][C]19[/C][C]0.813846[/C][C]0.372307[/C][C]0.186154[/C][/ROW]
[ROW][C]20[/C][C]0.792804[/C][C]0.414393[/C][C]0.207196[/C][/ROW]
[ROW][C]21[/C][C]0.794082[/C][C]0.411837[/C][C]0.205918[/C][/ROW]
[ROW][C]22[/C][C]0.737381[/C][C]0.525238[/C][C]0.262619[/C][/ROW]
[ROW][C]23[/C][C]0.693973[/C][C]0.612053[/C][C]0.306027[/C][/ROW]
[ROW][C]24[/C][C]0.63166[/C][C]0.73668[/C][C]0.36834[/C][/ROW]
[ROW][C]25[/C][C]0.606175[/C][C]0.78765[/C][C]0.393825[/C][/ROW]
[ROW][C]26[/C][C]0.577722[/C][C]0.844556[/C][C]0.422278[/C][/ROW]
[ROW][C]27[/C][C]0.53274[/C][C]0.93452[/C][C]0.46726[/C][/ROW]
[ROW][C]28[/C][C]0.482814[/C][C]0.965628[/C][C]0.517186[/C][/ROW]
[ROW][C]29[/C][C]0.430523[/C][C]0.861046[/C][C]0.569477[/C][/ROW]
[ROW][C]30[/C][C]0.40803[/C][C]0.81606[/C][C]0.59197[/C][/ROW]
[ROW][C]31[/C][C]0.450156[/C][C]0.900311[/C][C]0.549844[/C][/ROW]
[ROW][C]32[/C][C]0.456952[/C][C]0.913904[/C][C]0.543048[/C][/ROW]
[ROW][C]33[/C][C]0.404331[/C][C]0.808662[/C][C]0.595669[/C][/ROW]
[ROW][C]34[/C][C]0.349656[/C][C]0.699313[/C][C]0.650344[/C][/ROW]
[ROW][C]35[/C][C]0.303935[/C][C]0.60787[/C][C]0.696065[/C][/ROW]
[ROW][C]36[/C][C]0.276861[/C][C]0.553723[/C][C]0.723139[/C][/ROW]
[ROW][C]37[/C][C]0.40915[/C][C]0.8183[/C][C]0.59085[/C][/ROW]
[ROW][C]38[/C][C]0.36071[/C][C]0.721419[/C][C]0.63929[/C][/ROW]
[ROW][C]39[/C][C]0.352875[/C][C]0.705749[/C][C]0.647125[/C][/ROW]
[ROW][C]40[/C][C]0.305396[/C][C]0.610792[/C][C]0.694604[/C][/ROW]
[ROW][C]41[/C][C]0.770882[/C][C]0.458235[/C][C]0.229118[/C][/ROW]
[ROW][C]42[/C][C]0.732122[/C][C]0.535756[/C][C]0.267878[/C][/ROW]
[ROW][C]43[/C][C]0.706334[/C][C]0.587332[/C][C]0.293666[/C][/ROW]
[ROW][C]44[/C][C]0.660537[/C][C]0.678926[/C][C]0.339463[/C][/ROW]
[ROW][C]45[/C][C]0.672944[/C][C]0.654113[/C][C]0.327056[/C][/ROW]
[ROW][C]46[/C][C]0.625399[/C][C]0.749202[/C][C]0.374601[/C][/ROW]
[ROW][C]47[/C][C]0.588627[/C][C]0.822747[/C][C]0.411373[/C][/ROW]
[ROW][C]48[/C][C]0.552459[/C][C]0.895083[/C][C]0.447541[/C][/ROW]
[ROW][C]49[/C][C]0.512613[/C][C]0.974774[/C][C]0.487387[/C][/ROW]
[ROW][C]50[/C][C]0.500651[/C][C]0.998699[/C][C]0.499349[/C][/ROW]
[ROW][C]51[/C][C]0.499967[/C][C]0.999935[/C][C]0.500033[/C][/ROW]
[ROW][C]52[/C][C]0.491154[/C][C]0.982308[/C][C]0.508846[/C][/ROW]
[ROW][C]53[/C][C]0.458275[/C][C]0.916551[/C][C]0.541725[/C][/ROW]
[ROW][C]54[/C][C]0.746911[/C][C]0.506177[/C][C]0.253089[/C][/ROW]
[ROW][C]55[/C][C]0.755324[/C][C]0.489353[/C][C]0.244676[/C][/ROW]
[ROW][C]56[/C][C]0.725973[/C][C]0.548055[/C][C]0.274027[/C][/ROW]
[ROW][C]57[/C][C]0.712158[/C][C]0.575683[/C][C]0.287842[/C][/ROW]
[ROW][C]58[/C][C]0.712585[/C][C]0.574829[/C][C]0.287415[/C][/ROW]
[ROW][C]59[/C][C]0.673037[/C][C]0.653927[/C][C]0.326963[/C][/ROW]
[ROW][C]60[/C][C]0.721974[/C][C]0.556052[/C][C]0.278026[/C][/ROW]
[ROW][C]61[/C][C]0.695546[/C][C]0.608907[/C][C]0.304454[/C][/ROW]
[ROW][C]62[/C][C]0.718288[/C][C]0.563424[/C][C]0.281712[/C][/ROW]
[ROW][C]63[/C][C]0.689855[/C][C]0.620289[/C][C]0.310145[/C][/ROW]
[ROW][C]64[/C][C]0.65688[/C][C]0.686239[/C][C]0.34312[/C][/ROW]
[ROW][C]65[/C][C]0.622472[/C][C]0.755057[/C][C]0.377528[/C][/ROW]
[ROW][C]66[/C][C]0.647523[/C][C]0.704954[/C][C]0.352477[/C][/ROW]
[ROW][C]67[/C][C]0.658304[/C][C]0.683392[/C][C]0.341696[/C][/ROW]
[ROW][C]68[/C][C]0.65548[/C][C]0.68904[/C][C]0.34452[/C][/ROW]
[ROW][C]69[/C][C]0.628736[/C][C]0.742529[/C][C]0.371264[/C][/ROW]
[ROW][C]70[/C][C]0.694255[/C][C]0.611491[/C][C]0.305745[/C][/ROW]
[ROW][C]71[/C][C]0.696561[/C][C]0.606878[/C][C]0.303439[/C][/ROW]
[ROW][C]72[/C][C]0.675538[/C][C]0.648924[/C][C]0.324462[/C][/ROW]
[ROW][C]73[/C][C]0.655133[/C][C]0.689734[/C][C]0.344867[/C][/ROW]
[ROW][C]74[/C][C]0.612341[/C][C]0.775319[/C][C]0.387659[/C][/ROW]
[ROW][C]75[/C][C]0.625739[/C][C]0.748522[/C][C]0.374261[/C][/ROW]
[ROW][C]76[/C][C]0.588207[/C][C]0.823587[/C][C]0.411793[/C][/ROW]
[ROW][C]77[/C][C]0.640677[/C][C]0.718646[/C][C]0.359323[/C][/ROW]
[ROW][C]78[/C][C]0.616678[/C][C]0.766644[/C][C]0.383322[/C][/ROW]
[ROW][C]79[/C][C]0.581072[/C][C]0.837855[/C][C]0.418928[/C][/ROW]
[ROW][C]80[/C][C]0.540527[/C][C]0.918946[/C][C]0.459473[/C][/ROW]
[ROW][C]81[/C][C]0.531832[/C][C]0.936336[/C][C]0.468168[/C][/ROW]
[ROW][C]82[/C][C]0.490128[/C][C]0.980257[/C][C]0.509872[/C][/ROW]
[ROW][C]83[/C][C]0.487879[/C][C]0.975757[/C][C]0.512121[/C][/ROW]
[ROW][C]84[/C][C]0.553168[/C][C]0.893664[/C][C]0.446832[/C][/ROW]
[ROW][C]85[/C][C]0.521728[/C][C]0.956544[/C][C]0.478272[/C][/ROW]
[ROW][C]86[/C][C]0.50514[/C][C]0.989719[/C][C]0.49486[/C][/ROW]
[ROW][C]87[/C][C]0.501378[/C][C]0.997245[/C][C]0.498622[/C][/ROW]
[ROW][C]88[/C][C]0.458015[/C][C]0.91603[/C][C]0.541985[/C][/ROW]
[ROW][C]89[/C][C]0.45782[/C][C]0.915639[/C][C]0.54218[/C][/ROW]
[ROW][C]90[/C][C]0.431005[/C][C]0.86201[/C][C]0.568995[/C][/ROW]
[ROW][C]91[/C][C]0.398396[/C][C]0.796793[/C][C]0.601604[/C][/ROW]
[ROW][C]92[/C][C]0.391587[/C][C]0.783173[/C][C]0.608413[/C][/ROW]
[ROW][C]93[/C][C]0.37121[/C][C]0.74242[/C][C]0.62879[/C][/ROW]
[ROW][C]94[/C][C]0.33911[/C][C]0.67822[/C][C]0.66089[/C][/ROW]
[ROW][C]95[/C][C]0.315521[/C][C]0.631043[/C][C]0.684479[/C][/ROW]
[ROW][C]96[/C][C]0.376824[/C][C]0.753648[/C][C]0.623176[/C][/ROW]
[ROW][C]97[/C][C]0.364515[/C][C]0.729029[/C][C]0.635485[/C][/ROW]
[ROW][C]98[/C][C]0.359392[/C][C]0.718785[/C][C]0.640608[/C][/ROW]
[ROW][C]99[/C][C]0.322771[/C][C]0.645541[/C][C]0.677229[/C][/ROW]
[ROW][C]100[/C][C]0.307784[/C][C]0.615567[/C][C]0.692216[/C][/ROW]
[ROW][C]101[/C][C]0.290396[/C][C]0.580792[/C][C]0.709604[/C][/ROW]
[ROW][C]102[/C][C]0.257839[/C][C]0.515678[/C][C]0.742161[/C][/ROW]
[ROW][C]103[/C][C]0.232174[/C][C]0.464349[/C][C]0.767826[/C][/ROW]
[ROW][C]104[/C][C]0.281283[/C][C]0.562566[/C][C]0.718717[/C][/ROW]
[ROW][C]105[/C][C]0.247343[/C][C]0.494685[/C][C]0.752657[/C][/ROW]
[ROW][C]106[/C][C]0.248144[/C][C]0.496288[/C][C]0.751856[/C][/ROW]
[ROW][C]107[/C][C]0.239282[/C][C]0.478563[/C][C]0.760718[/C][/ROW]
[ROW][C]108[/C][C]0.233146[/C][C]0.466291[/C][C]0.766854[/C][/ROW]
[ROW][C]109[/C][C]0.217363[/C][C]0.434727[/C][C]0.782637[/C][/ROW]
[ROW][C]110[/C][C]0.188263[/C][C]0.376527[/C][C]0.811737[/C][/ROW]
[ROW][C]111[/C][C]0.164447[/C][C]0.328894[/C][C]0.835553[/C][/ROW]
[ROW][C]112[/C][C]0.137398[/C][C]0.274796[/C][C]0.862602[/C][/ROW]
[ROW][C]113[/C][C]0.131019[/C][C]0.262038[/C][C]0.868981[/C][/ROW]
[ROW][C]114[/C][C]0.162801[/C][C]0.325603[/C][C]0.837199[/C][/ROW]
[ROW][C]115[/C][C]0.148639[/C][C]0.297278[/C][C]0.851361[/C][/ROW]
[ROW][C]116[/C][C]0.146097[/C][C]0.292193[/C][C]0.853903[/C][/ROW]
[ROW][C]117[/C][C]0.127957[/C][C]0.255913[/C][C]0.872043[/C][/ROW]
[ROW][C]118[/C][C]0.191704[/C][C]0.383409[/C][C]0.808296[/C][/ROW]
[ROW][C]119[/C][C]0.206442[/C][C]0.412884[/C][C]0.793558[/C][/ROW]
[ROW][C]120[/C][C]0.226357[/C][C]0.452715[/C][C]0.773643[/C][/ROW]
[ROW][C]121[/C][C]0.197793[/C][C]0.395585[/C][C]0.802207[/C][/ROW]
[ROW][C]122[/C][C]0.21771[/C][C]0.43542[/C][C]0.78229[/C][/ROW]
[ROW][C]123[/C][C]0.27019[/C][C]0.540379[/C][C]0.72981[/C][/ROW]
[ROW][C]124[/C][C]0.234715[/C][C]0.469431[/C][C]0.765285[/C][/ROW]
[ROW][C]125[/C][C]0.203673[/C][C]0.407346[/C][C]0.796327[/C][/ROW]
[ROW][C]126[/C][C]0.22021[/C][C]0.440421[/C][C]0.77979[/C][/ROW]
[ROW][C]127[/C][C]0.225999[/C][C]0.451997[/C][C]0.774001[/C][/ROW]
[ROW][C]128[/C][C]0.448699[/C][C]0.897398[/C][C]0.551301[/C][/ROW]
[ROW][C]129[/C][C]0.447523[/C][C]0.895045[/C][C]0.552477[/C][/ROW]
[ROW][C]130[/C][C]0.454033[/C][C]0.908065[/C][C]0.545967[/C][/ROW]
[ROW][C]131[/C][C]0.405089[/C][C]0.810179[/C][C]0.594911[/C][/ROW]
[ROW][C]132[/C][C]0.355113[/C][C]0.710226[/C][C]0.644887[/C][/ROW]
[ROW][C]133[/C][C]0.344975[/C][C]0.68995[/C][C]0.655025[/C][/ROW]
[ROW][C]134[/C][C]0.314676[/C][C]0.629352[/C][C]0.685324[/C][/ROW]
[ROW][C]135[/C][C]0.295488[/C][C]0.590977[/C][C]0.704512[/C][/ROW]
[ROW][C]136[/C][C]0.264695[/C][C]0.529389[/C][C]0.735305[/C][/ROW]
[ROW][C]137[/C][C]0.419602[/C][C]0.839204[/C][C]0.580398[/C][/ROW]
[ROW][C]138[/C][C]0.377877[/C][C]0.755754[/C][C]0.622123[/C][/ROW]
[ROW][C]139[/C][C]0.327149[/C][C]0.654298[/C][C]0.672851[/C][/ROW]
[ROW][C]140[/C][C]0.278312[/C][C]0.556624[/C][C]0.721688[/C][/ROW]
[ROW][C]141[/C][C]0.232646[/C][C]0.465293[/C][C]0.767354[/C][/ROW]
[ROW][C]142[/C][C]0.190664[/C][C]0.381328[/C][C]0.809336[/C][/ROW]
[ROW][C]143[/C][C]0.154234[/C][C]0.308469[/C][C]0.845766[/C][/ROW]
[ROW][C]144[/C][C]0.13179[/C][C]0.263581[/C][C]0.86821[/C][/ROW]
[ROW][C]145[/C][C]0.138739[/C][C]0.277478[/C][C]0.861261[/C][/ROW]
[ROW][C]146[/C][C]0.105774[/C][C]0.211549[/C][C]0.894226[/C][/ROW]
[ROW][C]147[/C][C]0.0789013[/C][C]0.157803[/C][C]0.921099[/C][/ROW]
[ROW][C]148[/C][C]0.129787[/C][C]0.259573[/C][C]0.870213[/C][/ROW]
[ROW][C]149[/C][C]0.0985075[/C][C]0.197015[/C][C]0.901493[/C][/ROW]
[ROW][C]150[/C][C]0.106819[/C][C]0.213639[/C][C]0.893181[/C][/ROW]
[ROW][C]151[/C][C]0.594556[/C][C]0.810888[/C][C]0.405444[/C][/ROW]
[ROW][C]152[/C][C]0.52668[/C][C]0.946639[/C][C]0.47332[/C][/ROW]
[ROW][C]153[/C][C]0.959954[/C][C]0.0800918[/C][C]0.0400459[/C][/ROW]
[ROW][C]154[/C][C]0.968716[/C][C]0.062568[/C][C]0.031284[/C][/ROW]
[ROW][C]155[/C][C]0.978367[/C][C]0.043266[/C][C]0.021633[/C][/ROW]
[ROW][C]156[/C][C]0.999055[/C][C]0.00188998[/C][C]0.000944992[/C][/ROW]
[ROW][C]157[/C][C]0.997342[/C][C]0.00531569[/C][C]0.00265785[/C][/ROW]
[ROW][C]158[/C][C]0.993327[/C][C]0.0133457[/C][C]0.00667284[/C][/ROW]
[ROW][C]159[/C][C]0.985247[/C][C]0.0295059[/C][C]0.014753[/C][/ROW]
[ROW][C]160[/C][C]0.987219[/C][C]0.0255618[/C][C]0.0127809[/C][/ROW]
[ROW][C]161[/C][C]0.971255[/C][C]0.0574907[/C][C]0.0287454[/C][/ROW]
[ROW][C]162[/C][C]0.98119[/C][C]0.0376196[/C][C]0.0188098[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263619&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263619&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9008720.1982560.0991281
100.8440160.3119680.155984
110.7612290.4775420.238771
120.802470.3950590.19753
130.8200670.3598660.179933
140.8747620.2504770.125238
150.8362560.3274880.163744
160.9182750.1634490.0817246
170.8983040.2033920.101696
180.8611380.2777240.138862
190.8138460.3723070.186154
200.7928040.4143930.207196
210.7940820.4118370.205918
220.7373810.5252380.262619
230.6939730.6120530.306027
240.631660.736680.36834
250.6061750.787650.393825
260.5777220.8445560.422278
270.532740.934520.46726
280.4828140.9656280.517186
290.4305230.8610460.569477
300.408030.816060.59197
310.4501560.9003110.549844
320.4569520.9139040.543048
330.4043310.8086620.595669
340.3496560.6993130.650344
350.3039350.607870.696065
360.2768610.5537230.723139
370.409150.81830.59085
380.360710.7214190.63929
390.3528750.7057490.647125
400.3053960.6107920.694604
410.7708820.4582350.229118
420.7321220.5357560.267878
430.7063340.5873320.293666
440.6605370.6789260.339463
450.6729440.6541130.327056
460.6253990.7492020.374601
470.5886270.8227470.411373
480.5524590.8950830.447541
490.5126130.9747740.487387
500.5006510.9986990.499349
510.4999670.9999350.500033
520.4911540.9823080.508846
530.4582750.9165510.541725
540.7469110.5061770.253089
550.7553240.4893530.244676
560.7259730.5480550.274027
570.7121580.5756830.287842
580.7125850.5748290.287415
590.6730370.6539270.326963
600.7219740.5560520.278026
610.6955460.6089070.304454
620.7182880.5634240.281712
630.6898550.6202890.310145
640.656880.6862390.34312
650.6224720.7550570.377528
660.6475230.7049540.352477
670.6583040.6833920.341696
680.655480.689040.34452
690.6287360.7425290.371264
700.6942550.6114910.305745
710.6965610.6068780.303439
720.6755380.6489240.324462
730.6551330.6897340.344867
740.6123410.7753190.387659
750.6257390.7485220.374261
760.5882070.8235870.411793
770.6406770.7186460.359323
780.6166780.7666440.383322
790.5810720.8378550.418928
800.5405270.9189460.459473
810.5318320.9363360.468168
820.4901280.9802570.509872
830.4878790.9757570.512121
840.5531680.8936640.446832
850.5217280.9565440.478272
860.505140.9897190.49486
870.5013780.9972450.498622
880.4580150.916030.541985
890.457820.9156390.54218
900.4310050.862010.568995
910.3983960.7967930.601604
920.3915870.7831730.608413
930.371210.742420.62879
940.339110.678220.66089
950.3155210.6310430.684479
960.3768240.7536480.623176
970.3645150.7290290.635485
980.3593920.7187850.640608
990.3227710.6455410.677229
1000.3077840.6155670.692216
1010.2903960.5807920.709604
1020.2578390.5156780.742161
1030.2321740.4643490.767826
1040.2812830.5625660.718717
1050.2473430.4946850.752657
1060.2481440.4962880.751856
1070.2392820.4785630.760718
1080.2331460.4662910.766854
1090.2173630.4347270.782637
1100.1882630.3765270.811737
1110.1644470.3288940.835553
1120.1373980.2747960.862602
1130.1310190.2620380.868981
1140.1628010.3256030.837199
1150.1486390.2972780.851361
1160.1460970.2921930.853903
1170.1279570.2559130.872043
1180.1917040.3834090.808296
1190.2064420.4128840.793558
1200.2263570.4527150.773643
1210.1977930.3955850.802207
1220.217710.435420.78229
1230.270190.5403790.72981
1240.2347150.4694310.765285
1250.2036730.4073460.796327
1260.220210.4404210.77979
1270.2259990.4519970.774001
1280.4486990.8973980.551301
1290.4475230.8950450.552477
1300.4540330.9080650.545967
1310.4050890.8101790.594911
1320.3551130.7102260.644887
1330.3449750.689950.655025
1340.3146760.6293520.685324
1350.2954880.5909770.704512
1360.2646950.5293890.735305
1370.4196020.8392040.580398
1380.3778770.7557540.622123
1390.3271490.6542980.672851
1400.2783120.5566240.721688
1410.2326460.4652930.767354
1420.1906640.3813280.809336
1430.1542340.3084690.845766
1440.131790.2635810.86821
1450.1387390.2774780.861261
1460.1057740.2115490.894226
1470.07890130.1578030.921099
1480.1297870.2595730.870213
1490.09850750.1970150.901493
1500.1068190.2136390.893181
1510.5945560.8108880.405444
1520.526680.9466390.47332
1530.9599540.08009180.0400459
1540.9687160.0625680.031284
1550.9783670.0432660.021633
1560.9990550.001889980.000944992
1570.9973420.005315690.00265785
1580.9933270.01334570.00667284
1590.9852470.02950590.014753
1600.9872190.02556180.0127809
1610.9712550.05749070.0287454
1620.981190.03761960.0188098






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.012987NOK
5% type I error level70.0454545OK
10% type I error level100.0649351OK

\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 & 2 & 0.012987 & NOK \tabularnewline
5% type I error level & 7 & 0.0454545 & OK \tabularnewline
10% type I error level & 10 & 0.0649351 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263619&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]2[/C][C]0.012987[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.0454545[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0649351[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263619&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263619&T=6

As an alternative you can also use a QR Code:  
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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 level20.012987NOK
5% type I error level70.0454545OK
10% type I error level100.0649351OK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '6'
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')
}