Free Statistics

of Irreproducible Research!

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, 26 Nov 2011 12:11:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/26/t1322327696fawh5vlsf0g92lr.htm/, Retrieved Sun, 05 Feb 2023 14:41:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147429, Retrieved Sun, 05 Feb 2023 14:41:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
- RMPD    [Multiple Regression] [WS8 - Multiple Li...] [2011-11-26 17:11:26] [82ceb5b481b3a9ad89a8151bb4a3670f] [Current]
Feedback Forum

Post a new message
Dataseries X:
37
30
47
35
30
43
82
40
47
19
52
136
80
42
54
66
81
63
137
72
107
58
36
52
79
77
54
84
48
96
83
66
61
53
30
74
69
59
42
65
70
100
63
105
82
81
75
102
121
98
76
77
63
37
35
23
40
29
37
51
20
28
13
22
25
13
16
13
16
17
9
17
25
14
8
7
10
7
10
3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147429&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147429&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147429&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
KIA[t] = + 99.3511904761905 -13.6846655328798M1[t] -24.890589569161M2[t] -31.953656462585M3[t] -22.4452947845805M4[t] -25.936933106576M5[t] -20.7142857142857M6[t] -10.4916383219955M7[t] -24.6975623582767M8[t] -15.1203231292517M9[t] -30.46910430839M10[t] -32.8178854875284M11[t] -0.651218820861678t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
KIA[t] =  +  99.3511904761905 -13.6846655328798M1[t] -24.890589569161M2[t] -31.953656462585M3[t] -22.4452947845805M4[t] -25.936933106576M5[t] -20.7142857142857M6[t] -10.4916383219955M7[t] -24.6975623582767M8[t] -15.1203231292517M9[t] -30.46910430839M10[t] -32.8178854875284M11[t] -0.651218820861678t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147429&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]KIA[t] =  +  99.3511904761905 -13.6846655328798M1[t] -24.890589569161M2[t] -31.953656462585M3[t] -22.4452947845805M4[t] -25.936933106576M5[t] -20.7142857142857M6[t] -10.4916383219955M7[t] -24.6975623582767M8[t] -15.1203231292517M9[t] -30.46910430839M10[t] -32.8178854875284M11[t] -0.651218820861678t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147429&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147429&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
KIA[t] = + 99.3511904761905 -13.6846655328798M1[t] -24.890589569161M2[t] -31.953656462585M3[t] -22.4452947845805M4[t] -25.936933106576M5[t] -20.7142857142857M6[t] -10.4916383219955M7[t] -24.6975623582767M8[t] -15.1203231292517M9[t] -30.46910430839M10[t] -32.8178854875284M11[t] -0.651218820861678t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)99.351190476190513.2047737.523900
M1-13.684665532879816.110628-0.84940.3986730.199337
M2-24.89058956916116.105105-1.54550.1269340.063467
M3-31.95365646258516.100808-1.98460.0512860.025643
M4-22.445294784580516.097738-1.39430.167830.083915
M5-25.93693310657616.095896-1.61140.1117940.055897
M6-20.714285714285716.095281-1.2870.202530.101265
M7-10.491638321995516.095896-0.65180.5167470.258373
M8-24.697562358276716.097738-1.53420.1296830.064842
M9-15.120323129251716.708188-0.9050.3687270.184363
M10-30.4691043083916.70523-1.82390.0726230.036312
M11-32.817885487528416.703455-1.96470.0535940.026797
t-0.6512188208616780.140604-4.63161.7e-059e-06

\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) & 99.3511904761905 & 13.204773 & 7.5239 & 0 & 0 \tabularnewline
M1 & -13.6846655328798 & 16.110628 & -0.8494 & 0.398673 & 0.199337 \tabularnewline
M2 & -24.890589569161 & 16.105105 & -1.5455 & 0.126934 & 0.063467 \tabularnewline
M3 & -31.953656462585 & 16.100808 & -1.9846 & 0.051286 & 0.025643 \tabularnewline
M4 & -22.4452947845805 & 16.097738 & -1.3943 & 0.16783 & 0.083915 \tabularnewline
M5 & -25.936933106576 & 16.095896 & -1.6114 & 0.111794 & 0.055897 \tabularnewline
M6 & -20.7142857142857 & 16.095281 & -1.287 & 0.20253 & 0.101265 \tabularnewline
M7 & -10.4916383219955 & 16.095896 & -0.6518 & 0.516747 & 0.258373 \tabularnewline
M8 & -24.6975623582767 & 16.097738 & -1.5342 & 0.129683 & 0.064842 \tabularnewline
M9 & -15.1203231292517 & 16.708188 & -0.905 & 0.368727 & 0.184363 \tabularnewline
M10 & -30.46910430839 & 16.70523 & -1.8239 & 0.072623 & 0.036312 \tabularnewline
M11 & -32.8178854875284 & 16.703455 & -1.9647 & 0.053594 & 0.026797 \tabularnewline
t & -0.651218820861678 & 0.140604 & -4.6316 & 1.7e-05 & 9e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147429&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]99.3511904761905[/C][C]13.204773[/C][C]7.5239[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-13.6846655328798[/C][C]16.110628[/C][C]-0.8494[/C][C]0.398673[/C][C]0.199337[/C][/ROW]
[ROW][C]M2[/C][C]-24.890589569161[/C][C]16.105105[/C][C]-1.5455[/C][C]0.126934[/C][C]0.063467[/C][/ROW]
[ROW][C]M3[/C][C]-31.953656462585[/C][C]16.100808[/C][C]-1.9846[/C][C]0.051286[/C][C]0.025643[/C][/ROW]
[ROW][C]M4[/C][C]-22.4452947845805[/C][C]16.097738[/C][C]-1.3943[/C][C]0.16783[/C][C]0.083915[/C][/ROW]
[ROW][C]M5[/C][C]-25.936933106576[/C][C]16.095896[/C][C]-1.6114[/C][C]0.111794[/C][C]0.055897[/C][/ROW]
[ROW][C]M6[/C][C]-20.7142857142857[/C][C]16.095281[/C][C]-1.287[/C][C]0.20253[/C][C]0.101265[/C][/ROW]
[ROW][C]M7[/C][C]-10.4916383219955[/C][C]16.095896[/C][C]-0.6518[/C][C]0.516747[/C][C]0.258373[/C][/ROW]
[ROW][C]M8[/C][C]-24.6975623582767[/C][C]16.097738[/C][C]-1.5342[/C][C]0.129683[/C][C]0.064842[/C][/ROW]
[ROW][C]M9[/C][C]-15.1203231292517[/C][C]16.708188[/C][C]-0.905[/C][C]0.368727[/C][C]0.184363[/C][/ROW]
[ROW][C]M10[/C][C]-30.46910430839[/C][C]16.70523[/C][C]-1.8239[/C][C]0.072623[/C][C]0.036312[/C][/ROW]
[ROW][C]M11[/C][C]-32.8178854875284[/C][C]16.703455[/C][C]-1.9647[/C][C]0.053594[/C][C]0.026797[/C][/ROW]
[ROW][C]t[/C][C]-0.651218820861678[/C][C]0.140604[/C][C]-4.6316[/C][C]1.7e-05[/C][C]9e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147429&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147429&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)99.351190476190513.2047737.523900
M1-13.684665532879816.110628-0.84940.3986730.199337
M2-24.89058956916116.105105-1.54550.1269340.063467
M3-31.95365646258516.100808-1.98460.0512860.025643
M4-22.445294784580516.097738-1.39430.167830.083915
M5-25.93693310657616.095896-1.61140.1117940.055897
M6-20.714285714285716.095281-1.2870.202530.101265
M7-10.491638321995516.095896-0.65180.5167470.258373
M8-24.697562358276716.097738-1.53420.1296830.064842
M9-15.120323129251716.708188-0.9050.3687270.184363
M10-30.4691043083916.70523-1.82390.0726230.036312
M11-32.817885487528416.703455-1.96470.0535940.026797
t-0.6512188208616780.140604-4.63161.7e-059e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.55071598803982
R-squared0.303288099482675
Adjusted R-squared0.178503878494497
F-TEST (value)2.43050040366409
F-TEST (DF numerator)12
F-TEST (DF denominator)67
p-value0.0109600910241202
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation28.930207162859
Sum Squared Residuals56076.1113945578

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.55071598803982 \tabularnewline
R-squared & 0.303288099482675 \tabularnewline
Adjusted R-squared & 0.178503878494497 \tabularnewline
F-TEST (value) & 2.43050040366409 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 0.0109600910241202 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 28.930207162859 \tabularnewline
Sum Squared Residuals & 56076.1113945578 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147429&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.55071598803982[/C][/ROW]
[ROW][C]R-squared[/C][C]0.303288099482675[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.178503878494497[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.43050040366409[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/C][/ROW]
[ROW][C]p-value[/C][C]0.0109600910241202[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]28.930207162859[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]56076.1113945578[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147429&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147429&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.55071598803982
R-squared0.303288099482675
Adjusted R-squared0.178503878494497
F-TEST (value)2.43050040366409
F-TEST (DF numerator)12
F-TEST (DF denominator)67
p-value0.0109600910241202
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation28.930207162859
Sum Squared Residuals56076.1113945578







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13785.015306122449-48.015306122449
23073.1581632653061-43.1581632653061
34765.4438775510204-18.4438775510204
43574.3010204081633-39.3010204081633
53070.1581632653061-40.1581632653061
64374.7295918367347-31.7295918367347
78284.3010204081633-2.30102040816325
84069.4438775510204-29.4438775510204
94778.3698979591837-31.3698979591837
101962.3698979591837-43.3698979591837
115259.3698979591837-7.36989795918366
1213691.536564625850344.4634353741497
138077.20068027210882.79931972789117
144265.343537414966-23.343537414966
155457.6292517006803-3.62925170068026
166666.4863945578231-0.486394557823124
178162.34353741496618.656462585034
186366.9149659863945-3.91496598639455
1913776.486394557823160.5136054421769
207261.629251700680310.3707482993197
2110770.555272108843536.4447278911565
225854.55527210884353.44472789115647
233651.5552721088435-15.5552721088435
245283.7219387755102-31.7219387755102
257969.38605442176879.61394557823133
267757.528911564625819.4710884353742
275449.81462585034014.18537414965987
288458.67176870748325.328231292517
294854.5289115646259-6.52891156462585
309659.100340136054436.8996598639456
318368.67176870748314.328231292517
326653.814625850340112.1853741496599
336162.7406462585034-1.7406462585034
345346.74064625850346.25935374149659
353043.7406462585034-13.7406462585034
367475.90731292517-1.90731292517006
376961.57142857142857.42857142857146
385949.71428571428579.2857142857143
3942426.98659879949659e-15
406550.857142857142914.1428571428571
417046.714285714285723.2857142857143
4210051.285714285714348.7142857142857
436360.85714285714292.14285714285714
441054659
458254.926020408163327.0739795918367
468138.926020408163342.0739795918367
477535.926020408163339.0739795918367
4810268.0926870748333.9073129251701
4912153.756802721088467.2431972789116
509841.899659863945656.1003401360544
517634.185374149659941.8146258503401
527743.042517006802733.9574829931973
536338.899659863945624.1003401360544
543743.4710884353741-6.47108843537415
553553.0425170068027-18.0425170068027
562338.1853741496599-15.1853741496599
574047.1113945578231-7.11139455782313
582931.1113945578231-2.11139455782313
593728.11139455782318.88860544217688
605160.2780612244898-9.2780612244898
612045.9421768707483-25.9421768707483
622834.0850340136054-6.08503401360544
631326.3707482993197-13.3707482993197
642235.2278911564626-13.2278911564626
652531.0850340136055-6.08503401360546
661335.656462585034-22.656462585034
671645.2278911564626-29.2278911564626
681330.3707482993197-17.3707482993197
691639.296768707483-23.296768707483
701723.296768707483-6.296768707483
71920.296768707483-11.296768707483
721752.4634353741497-35.4634353741497
732538.1275510204082-13.1275510204082
741426.2704081632653-12.2704081632653
75818.5561224489796-10.5561224489796
76727.4132653061225-20.4132653061225
771023.2704081632653-13.2704081632653
78727.8418367346939-20.8418367346939
791037.4132653061225-27.4132653061225
80322.5561224489796-19.5561224489796

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 37 & 85.015306122449 & -48.015306122449 \tabularnewline
2 & 30 & 73.1581632653061 & -43.1581632653061 \tabularnewline
3 & 47 & 65.4438775510204 & -18.4438775510204 \tabularnewline
4 & 35 & 74.3010204081633 & -39.3010204081633 \tabularnewline
5 & 30 & 70.1581632653061 & -40.1581632653061 \tabularnewline
6 & 43 & 74.7295918367347 & -31.7295918367347 \tabularnewline
7 & 82 & 84.3010204081633 & -2.30102040816325 \tabularnewline
8 & 40 & 69.4438775510204 & -29.4438775510204 \tabularnewline
9 & 47 & 78.3698979591837 & -31.3698979591837 \tabularnewline
10 & 19 & 62.3698979591837 & -43.3698979591837 \tabularnewline
11 & 52 & 59.3698979591837 & -7.36989795918366 \tabularnewline
12 & 136 & 91.5365646258503 & 44.4634353741497 \tabularnewline
13 & 80 & 77.2006802721088 & 2.79931972789117 \tabularnewline
14 & 42 & 65.343537414966 & -23.343537414966 \tabularnewline
15 & 54 & 57.6292517006803 & -3.62925170068026 \tabularnewline
16 & 66 & 66.4863945578231 & -0.486394557823124 \tabularnewline
17 & 81 & 62.343537414966 & 18.656462585034 \tabularnewline
18 & 63 & 66.9149659863945 & -3.91496598639455 \tabularnewline
19 & 137 & 76.4863945578231 & 60.5136054421769 \tabularnewline
20 & 72 & 61.6292517006803 & 10.3707482993197 \tabularnewline
21 & 107 & 70.5552721088435 & 36.4447278911565 \tabularnewline
22 & 58 & 54.5552721088435 & 3.44472789115647 \tabularnewline
23 & 36 & 51.5552721088435 & -15.5552721088435 \tabularnewline
24 & 52 & 83.7219387755102 & -31.7219387755102 \tabularnewline
25 & 79 & 69.3860544217687 & 9.61394557823133 \tabularnewline
26 & 77 & 57.5289115646258 & 19.4710884353742 \tabularnewline
27 & 54 & 49.8146258503401 & 4.18537414965987 \tabularnewline
28 & 84 & 58.671768707483 & 25.328231292517 \tabularnewline
29 & 48 & 54.5289115646259 & -6.52891156462585 \tabularnewline
30 & 96 & 59.1003401360544 & 36.8996598639456 \tabularnewline
31 & 83 & 68.671768707483 & 14.328231292517 \tabularnewline
32 & 66 & 53.8146258503401 & 12.1853741496599 \tabularnewline
33 & 61 & 62.7406462585034 & -1.7406462585034 \tabularnewline
34 & 53 & 46.7406462585034 & 6.25935374149659 \tabularnewline
35 & 30 & 43.7406462585034 & -13.7406462585034 \tabularnewline
36 & 74 & 75.90731292517 & -1.90731292517006 \tabularnewline
37 & 69 & 61.5714285714285 & 7.42857142857146 \tabularnewline
38 & 59 & 49.7142857142857 & 9.2857142857143 \tabularnewline
39 & 42 & 42 & 6.98659879949659e-15 \tabularnewline
40 & 65 & 50.8571428571429 & 14.1428571428571 \tabularnewline
41 & 70 & 46.7142857142857 & 23.2857142857143 \tabularnewline
42 & 100 & 51.2857142857143 & 48.7142857142857 \tabularnewline
43 & 63 & 60.8571428571429 & 2.14285714285714 \tabularnewline
44 & 105 & 46 & 59 \tabularnewline
45 & 82 & 54.9260204081633 & 27.0739795918367 \tabularnewline
46 & 81 & 38.9260204081633 & 42.0739795918367 \tabularnewline
47 & 75 & 35.9260204081633 & 39.0739795918367 \tabularnewline
48 & 102 & 68.09268707483 & 33.9073129251701 \tabularnewline
49 & 121 & 53.7568027210884 & 67.2431972789116 \tabularnewline
50 & 98 & 41.8996598639456 & 56.1003401360544 \tabularnewline
51 & 76 & 34.1853741496599 & 41.8146258503401 \tabularnewline
52 & 77 & 43.0425170068027 & 33.9574829931973 \tabularnewline
53 & 63 & 38.8996598639456 & 24.1003401360544 \tabularnewline
54 & 37 & 43.4710884353741 & -6.47108843537415 \tabularnewline
55 & 35 & 53.0425170068027 & -18.0425170068027 \tabularnewline
56 & 23 & 38.1853741496599 & -15.1853741496599 \tabularnewline
57 & 40 & 47.1113945578231 & -7.11139455782313 \tabularnewline
58 & 29 & 31.1113945578231 & -2.11139455782313 \tabularnewline
59 & 37 & 28.1113945578231 & 8.88860544217688 \tabularnewline
60 & 51 & 60.2780612244898 & -9.2780612244898 \tabularnewline
61 & 20 & 45.9421768707483 & -25.9421768707483 \tabularnewline
62 & 28 & 34.0850340136054 & -6.08503401360544 \tabularnewline
63 & 13 & 26.3707482993197 & -13.3707482993197 \tabularnewline
64 & 22 & 35.2278911564626 & -13.2278911564626 \tabularnewline
65 & 25 & 31.0850340136055 & -6.08503401360546 \tabularnewline
66 & 13 & 35.656462585034 & -22.656462585034 \tabularnewline
67 & 16 & 45.2278911564626 & -29.2278911564626 \tabularnewline
68 & 13 & 30.3707482993197 & -17.3707482993197 \tabularnewline
69 & 16 & 39.296768707483 & -23.296768707483 \tabularnewline
70 & 17 & 23.296768707483 & -6.296768707483 \tabularnewline
71 & 9 & 20.296768707483 & -11.296768707483 \tabularnewline
72 & 17 & 52.4634353741497 & -35.4634353741497 \tabularnewline
73 & 25 & 38.1275510204082 & -13.1275510204082 \tabularnewline
74 & 14 & 26.2704081632653 & -12.2704081632653 \tabularnewline
75 & 8 & 18.5561224489796 & -10.5561224489796 \tabularnewline
76 & 7 & 27.4132653061225 & -20.4132653061225 \tabularnewline
77 & 10 & 23.2704081632653 & -13.2704081632653 \tabularnewline
78 & 7 & 27.8418367346939 & -20.8418367346939 \tabularnewline
79 & 10 & 37.4132653061225 & -27.4132653061225 \tabularnewline
80 & 3 & 22.5561224489796 & -19.5561224489796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147429&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]37[/C][C]85.015306122449[/C][C]-48.015306122449[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]73.1581632653061[/C][C]-43.1581632653061[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]65.4438775510204[/C][C]-18.4438775510204[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]74.3010204081633[/C][C]-39.3010204081633[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]70.1581632653061[/C][C]-40.1581632653061[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]74.7295918367347[/C][C]-31.7295918367347[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]84.3010204081633[/C][C]-2.30102040816325[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]69.4438775510204[/C][C]-29.4438775510204[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]78.3698979591837[/C][C]-31.3698979591837[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]62.3698979591837[/C][C]-43.3698979591837[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]59.3698979591837[/C][C]-7.36989795918366[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]91.5365646258503[/C][C]44.4634353741497[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]77.2006802721088[/C][C]2.79931972789117[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]65.343537414966[/C][C]-23.343537414966[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]57.6292517006803[/C][C]-3.62925170068026[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]66.4863945578231[/C][C]-0.486394557823124[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]62.343537414966[/C][C]18.656462585034[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]66.9149659863945[/C][C]-3.91496598639455[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]76.4863945578231[/C][C]60.5136054421769[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]61.6292517006803[/C][C]10.3707482993197[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]70.5552721088435[/C][C]36.4447278911565[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]54.5552721088435[/C][C]3.44472789115647[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]51.5552721088435[/C][C]-15.5552721088435[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]83.7219387755102[/C][C]-31.7219387755102[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]69.3860544217687[/C][C]9.61394557823133[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]57.5289115646258[/C][C]19.4710884353742[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]49.8146258503401[/C][C]4.18537414965987[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]58.671768707483[/C][C]25.328231292517[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]54.5289115646259[/C][C]-6.52891156462585[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]59.1003401360544[/C][C]36.8996598639456[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]68.671768707483[/C][C]14.328231292517[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]53.8146258503401[/C][C]12.1853741496599[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]62.7406462585034[/C][C]-1.7406462585034[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]46.7406462585034[/C][C]6.25935374149659[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]43.7406462585034[/C][C]-13.7406462585034[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]75.90731292517[/C][C]-1.90731292517006[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]61.5714285714285[/C][C]7.42857142857146[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]49.7142857142857[/C][C]9.2857142857143[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]42[/C][C]6.98659879949659e-15[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]50.8571428571429[/C][C]14.1428571428571[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]46.7142857142857[/C][C]23.2857142857143[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]51.2857142857143[/C][C]48.7142857142857[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]60.8571428571429[/C][C]2.14285714285714[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]46[/C][C]59[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]54.9260204081633[/C][C]27.0739795918367[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]38.9260204081633[/C][C]42.0739795918367[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]35.9260204081633[/C][C]39.0739795918367[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]68.09268707483[/C][C]33.9073129251701[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]53.7568027210884[/C][C]67.2431972789116[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]41.8996598639456[/C][C]56.1003401360544[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]34.1853741496599[/C][C]41.8146258503401[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]43.0425170068027[/C][C]33.9574829931973[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]38.8996598639456[/C][C]24.1003401360544[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]43.4710884353741[/C][C]-6.47108843537415[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]53.0425170068027[/C][C]-18.0425170068027[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]38.1853741496599[/C][C]-15.1853741496599[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]47.1113945578231[/C][C]-7.11139455782313[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]31.1113945578231[/C][C]-2.11139455782313[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]28.1113945578231[/C][C]8.88860544217688[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]60.2780612244898[/C][C]-9.2780612244898[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]45.9421768707483[/C][C]-25.9421768707483[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]34.0850340136054[/C][C]-6.08503401360544[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]26.3707482993197[/C][C]-13.3707482993197[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]35.2278911564626[/C][C]-13.2278911564626[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]31.0850340136055[/C][C]-6.08503401360546[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]35.656462585034[/C][C]-22.656462585034[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]45.2278911564626[/C][C]-29.2278911564626[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]30.3707482993197[/C][C]-17.3707482993197[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]39.296768707483[/C][C]-23.296768707483[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]23.296768707483[/C][C]-6.296768707483[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]20.296768707483[/C][C]-11.296768707483[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]52.4634353741497[/C][C]-35.4634353741497[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]38.1275510204082[/C][C]-13.1275510204082[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]26.2704081632653[/C][C]-12.2704081632653[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]18.5561224489796[/C][C]-10.5561224489796[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]27.4132653061225[/C][C]-20.4132653061225[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]23.2704081632653[/C][C]-13.2704081632653[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]27.8418367346939[/C][C]-20.8418367346939[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]37.4132653061225[/C][C]-27.4132653061225[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]22.5561224489796[/C][C]-19.5561224489796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147429&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147429&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13785.015306122449-48.015306122449
23073.1581632653061-43.1581632653061
34765.4438775510204-18.4438775510204
43574.3010204081633-39.3010204081633
53070.1581632653061-40.1581632653061
64374.7295918367347-31.7295918367347
78284.3010204081633-2.30102040816325
84069.4438775510204-29.4438775510204
94778.3698979591837-31.3698979591837
101962.3698979591837-43.3698979591837
115259.3698979591837-7.36989795918366
1213691.536564625850344.4634353741497
138077.20068027210882.79931972789117
144265.343537414966-23.343537414966
155457.6292517006803-3.62925170068026
166666.4863945578231-0.486394557823124
178162.34353741496618.656462585034
186366.9149659863945-3.91496598639455
1913776.486394557823160.5136054421769
207261.629251700680310.3707482993197
2110770.555272108843536.4447278911565
225854.55527210884353.44472789115647
233651.5552721088435-15.5552721088435
245283.7219387755102-31.7219387755102
257969.38605442176879.61394557823133
267757.528911564625819.4710884353742
275449.81462585034014.18537414965987
288458.67176870748325.328231292517
294854.5289115646259-6.52891156462585
309659.100340136054436.8996598639456
318368.67176870748314.328231292517
326653.814625850340112.1853741496599
336162.7406462585034-1.7406462585034
345346.74064625850346.25935374149659
353043.7406462585034-13.7406462585034
367475.90731292517-1.90731292517006
376961.57142857142857.42857142857146
385949.71428571428579.2857142857143
3942426.98659879949659e-15
406550.857142857142914.1428571428571
417046.714285714285723.2857142857143
4210051.285714285714348.7142857142857
436360.85714285714292.14285714285714
441054659
458254.926020408163327.0739795918367
468138.926020408163342.0739795918367
477535.926020408163339.0739795918367
4810268.0926870748333.9073129251701
4912153.756802721088467.2431972789116
509841.899659863945656.1003401360544
517634.185374149659941.8146258503401
527743.042517006802733.9574829931973
536338.899659863945624.1003401360544
543743.4710884353741-6.47108843537415
553553.0425170068027-18.0425170068027
562338.1853741496599-15.1853741496599
574047.1113945578231-7.11139455782313
582931.1113945578231-2.11139455782313
593728.11139455782318.88860544217688
605160.2780612244898-9.2780612244898
612045.9421768707483-25.9421768707483
622834.0850340136054-6.08503401360544
631326.3707482993197-13.3707482993197
642235.2278911564626-13.2278911564626
652531.0850340136055-6.08503401360546
661335.656462585034-22.656462585034
671645.2278911564626-29.2278911564626
681330.3707482993197-17.3707482993197
691639.296768707483-23.296768707483
701723.296768707483-6.296768707483
71920.296768707483-11.296768707483
721752.4634353741497-35.4634353741497
732538.1275510204082-13.1275510204082
741426.2704081632653-12.2704081632653
75818.5561224489796-10.5561224489796
76727.4132653061225-20.4132653061225
771023.2704081632653-13.2704081632653
78727.8418367346939-20.8418367346939
791037.4132653061225-27.4132653061225
80322.5561224489796-19.5561224489796







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1361699461495910.2723398922991830.863830053850409
170.1327533838515350.2655067677030690.867246616148465
180.07087295178338320.1417459035667660.929127048216617
190.08484730967316560.1696946193463310.915152690326834
200.04272494057023140.08544988114046280.957275059429769
210.04882667668927610.09765335337855210.951173323310724
220.02676173589001250.0535234717800250.973238264109987
230.1152915400058170.2305830800116340.884708459994183
240.8597035797466270.2805928405067470.140296420253373
250.8195190345289430.3609619309421140.180480965471057
260.7694739909159240.4610520181681520.230526009084076
270.7573108096766890.4853783806466220.242689190323311
280.6879104633158950.624179073368210.312089536684105
290.7423592351157350.5152815297685310.257640764884265
300.6901873087854980.6196253824290050.309812691214502
310.7460227735729540.5079544528540920.253977226427046
320.6996864535938920.6006270928122160.300313546406108
330.7141839905123450.5716320189753090.285816009487655
340.6880943590683530.6238112818632940.311905640931647
350.7917106753317370.4165786493365260.208289324668263
360.8160765727786650.3678468544426710.183923427221336
370.8259705273138960.3480589453722070.174029472686104
380.8490690226296480.3018619547407030.150930977370352
390.9088518304534180.1822963390931650.0911481695465823
400.9105457657415320.1789084685169370.0894542342584683
410.9018044304381640.1963911391236710.0981955695618357
420.8885236612617460.2229526774765070.111476338738254
430.920199744189230.1596005116215410.0798002558107703
440.9389049690830320.1221900618339350.0610950309169676
450.9132536524351990.1734926951296020.0867463475648012
460.8958786085962940.2082427828074110.104121391403706
470.8667704160365570.2664591679268860.133229583963443
480.8551178451297050.2897643097405890.144882154870295
490.9771032219804450.04579355603910960.0228967780195548
500.9948990890614370.01020182187712640.00510091093856321
510.9986869405814360.002626118837126960.00131305941856348
520.9999249552708420.0001500894583156037.50447291578014e-05
530.9999837920787893.24158424226431e-051.62079212113215e-05
540.9999801099082873.97801834259794e-051.98900917129897e-05
550.999973507454015.29850919806909e-052.64925459903454e-05
560.9999502329671289.95340657445804e-054.97670328722902e-05
570.999908307764730.0001833844705385719.16922352692853e-05
580.9996994287787530.000601142442494770.000300571221247385
590.9995906033486140.0008187933027721130.000409396651386056
600.9999592322446928.15355106162252e-054.07677553081126e-05
610.9999891936307542.16127384916888e-051.08063692458444e-05
620.9999359924596070.000128015080785386.40075403926899e-05
630.999674078954070.0006518420918577950.000325921045928898
640.9983551871160250.00328962576795060.0016448128839753

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.136169946149591 & 0.272339892299183 & 0.863830053850409 \tabularnewline
17 & 0.132753383851535 & 0.265506767703069 & 0.867246616148465 \tabularnewline
18 & 0.0708729517833832 & 0.141745903566766 & 0.929127048216617 \tabularnewline
19 & 0.0848473096731656 & 0.169694619346331 & 0.915152690326834 \tabularnewline
20 & 0.0427249405702314 & 0.0854498811404628 & 0.957275059429769 \tabularnewline
21 & 0.0488266766892761 & 0.0976533533785521 & 0.951173323310724 \tabularnewline
22 & 0.0267617358900125 & 0.053523471780025 & 0.973238264109987 \tabularnewline
23 & 0.115291540005817 & 0.230583080011634 & 0.884708459994183 \tabularnewline
24 & 0.859703579746627 & 0.280592840506747 & 0.140296420253373 \tabularnewline
25 & 0.819519034528943 & 0.360961930942114 & 0.180480965471057 \tabularnewline
26 & 0.769473990915924 & 0.461052018168152 & 0.230526009084076 \tabularnewline
27 & 0.757310809676689 & 0.485378380646622 & 0.242689190323311 \tabularnewline
28 & 0.687910463315895 & 0.62417907336821 & 0.312089536684105 \tabularnewline
29 & 0.742359235115735 & 0.515281529768531 & 0.257640764884265 \tabularnewline
30 & 0.690187308785498 & 0.619625382429005 & 0.309812691214502 \tabularnewline
31 & 0.746022773572954 & 0.507954452854092 & 0.253977226427046 \tabularnewline
32 & 0.699686453593892 & 0.600627092812216 & 0.300313546406108 \tabularnewline
33 & 0.714183990512345 & 0.571632018975309 & 0.285816009487655 \tabularnewline
34 & 0.688094359068353 & 0.623811281863294 & 0.311905640931647 \tabularnewline
35 & 0.791710675331737 & 0.416578649336526 & 0.208289324668263 \tabularnewline
36 & 0.816076572778665 & 0.367846854442671 & 0.183923427221336 \tabularnewline
37 & 0.825970527313896 & 0.348058945372207 & 0.174029472686104 \tabularnewline
38 & 0.849069022629648 & 0.301861954740703 & 0.150930977370352 \tabularnewline
39 & 0.908851830453418 & 0.182296339093165 & 0.0911481695465823 \tabularnewline
40 & 0.910545765741532 & 0.178908468516937 & 0.0894542342584683 \tabularnewline
41 & 0.901804430438164 & 0.196391139123671 & 0.0981955695618357 \tabularnewline
42 & 0.888523661261746 & 0.222952677476507 & 0.111476338738254 \tabularnewline
43 & 0.92019974418923 & 0.159600511621541 & 0.0798002558107703 \tabularnewline
44 & 0.938904969083032 & 0.122190061833935 & 0.0610950309169676 \tabularnewline
45 & 0.913253652435199 & 0.173492695129602 & 0.0867463475648012 \tabularnewline
46 & 0.895878608596294 & 0.208242782807411 & 0.104121391403706 \tabularnewline
47 & 0.866770416036557 & 0.266459167926886 & 0.133229583963443 \tabularnewline
48 & 0.855117845129705 & 0.289764309740589 & 0.144882154870295 \tabularnewline
49 & 0.977103221980445 & 0.0457935560391096 & 0.0228967780195548 \tabularnewline
50 & 0.994899089061437 & 0.0102018218771264 & 0.00510091093856321 \tabularnewline
51 & 0.998686940581436 & 0.00262611883712696 & 0.00131305941856348 \tabularnewline
52 & 0.999924955270842 & 0.000150089458315603 & 7.50447291578014e-05 \tabularnewline
53 & 0.999983792078789 & 3.24158424226431e-05 & 1.62079212113215e-05 \tabularnewline
54 & 0.999980109908287 & 3.97801834259794e-05 & 1.98900917129897e-05 \tabularnewline
55 & 0.99997350745401 & 5.29850919806909e-05 & 2.64925459903454e-05 \tabularnewline
56 & 0.999950232967128 & 9.95340657445804e-05 & 4.97670328722902e-05 \tabularnewline
57 & 0.99990830776473 & 0.000183384470538571 & 9.16922352692853e-05 \tabularnewline
58 & 0.999699428778753 & 0.00060114244249477 & 0.000300571221247385 \tabularnewline
59 & 0.999590603348614 & 0.000818793302772113 & 0.000409396651386056 \tabularnewline
60 & 0.999959232244692 & 8.15355106162252e-05 & 4.07677553081126e-05 \tabularnewline
61 & 0.999989193630754 & 2.16127384916888e-05 & 1.08063692458444e-05 \tabularnewline
62 & 0.999935992459607 & 0.00012801508078538 & 6.40075403926899e-05 \tabularnewline
63 & 0.99967407895407 & 0.000651842091857795 & 0.000325921045928898 \tabularnewline
64 & 0.998355187116025 & 0.0032896257679506 & 0.0016448128839753 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147429&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.136169946149591[/C][C]0.272339892299183[/C][C]0.863830053850409[/C][/ROW]
[ROW][C]17[/C][C]0.132753383851535[/C][C]0.265506767703069[/C][C]0.867246616148465[/C][/ROW]
[ROW][C]18[/C][C]0.0708729517833832[/C][C]0.141745903566766[/C][C]0.929127048216617[/C][/ROW]
[ROW][C]19[/C][C]0.0848473096731656[/C][C]0.169694619346331[/C][C]0.915152690326834[/C][/ROW]
[ROW][C]20[/C][C]0.0427249405702314[/C][C]0.0854498811404628[/C][C]0.957275059429769[/C][/ROW]
[ROW][C]21[/C][C]0.0488266766892761[/C][C]0.0976533533785521[/C][C]0.951173323310724[/C][/ROW]
[ROW][C]22[/C][C]0.0267617358900125[/C][C]0.053523471780025[/C][C]0.973238264109987[/C][/ROW]
[ROW][C]23[/C][C]0.115291540005817[/C][C]0.230583080011634[/C][C]0.884708459994183[/C][/ROW]
[ROW][C]24[/C][C]0.859703579746627[/C][C]0.280592840506747[/C][C]0.140296420253373[/C][/ROW]
[ROW][C]25[/C][C]0.819519034528943[/C][C]0.360961930942114[/C][C]0.180480965471057[/C][/ROW]
[ROW][C]26[/C][C]0.769473990915924[/C][C]0.461052018168152[/C][C]0.230526009084076[/C][/ROW]
[ROW][C]27[/C][C]0.757310809676689[/C][C]0.485378380646622[/C][C]0.242689190323311[/C][/ROW]
[ROW][C]28[/C][C]0.687910463315895[/C][C]0.62417907336821[/C][C]0.312089536684105[/C][/ROW]
[ROW][C]29[/C][C]0.742359235115735[/C][C]0.515281529768531[/C][C]0.257640764884265[/C][/ROW]
[ROW][C]30[/C][C]0.690187308785498[/C][C]0.619625382429005[/C][C]0.309812691214502[/C][/ROW]
[ROW][C]31[/C][C]0.746022773572954[/C][C]0.507954452854092[/C][C]0.253977226427046[/C][/ROW]
[ROW][C]32[/C][C]0.699686453593892[/C][C]0.600627092812216[/C][C]0.300313546406108[/C][/ROW]
[ROW][C]33[/C][C]0.714183990512345[/C][C]0.571632018975309[/C][C]0.285816009487655[/C][/ROW]
[ROW][C]34[/C][C]0.688094359068353[/C][C]0.623811281863294[/C][C]0.311905640931647[/C][/ROW]
[ROW][C]35[/C][C]0.791710675331737[/C][C]0.416578649336526[/C][C]0.208289324668263[/C][/ROW]
[ROW][C]36[/C][C]0.816076572778665[/C][C]0.367846854442671[/C][C]0.183923427221336[/C][/ROW]
[ROW][C]37[/C][C]0.825970527313896[/C][C]0.348058945372207[/C][C]0.174029472686104[/C][/ROW]
[ROW][C]38[/C][C]0.849069022629648[/C][C]0.301861954740703[/C][C]0.150930977370352[/C][/ROW]
[ROW][C]39[/C][C]0.908851830453418[/C][C]0.182296339093165[/C][C]0.0911481695465823[/C][/ROW]
[ROW][C]40[/C][C]0.910545765741532[/C][C]0.178908468516937[/C][C]0.0894542342584683[/C][/ROW]
[ROW][C]41[/C][C]0.901804430438164[/C][C]0.196391139123671[/C][C]0.0981955695618357[/C][/ROW]
[ROW][C]42[/C][C]0.888523661261746[/C][C]0.222952677476507[/C][C]0.111476338738254[/C][/ROW]
[ROW][C]43[/C][C]0.92019974418923[/C][C]0.159600511621541[/C][C]0.0798002558107703[/C][/ROW]
[ROW][C]44[/C][C]0.938904969083032[/C][C]0.122190061833935[/C][C]0.0610950309169676[/C][/ROW]
[ROW][C]45[/C][C]0.913253652435199[/C][C]0.173492695129602[/C][C]0.0867463475648012[/C][/ROW]
[ROW][C]46[/C][C]0.895878608596294[/C][C]0.208242782807411[/C][C]0.104121391403706[/C][/ROW]
[ROW][C]47[/C][C]0.866770416036557[/C][C]0.266459167926886[/C][C]0.133229583963443[/C][/ROW]
[ROW][C]48[/C][C]0.855117845129705[/C][C]0.289764309740589[/C][C]0.144882154870295[/C][/ROW]
[ROW][C]49[/C][C]0.977103221980445[/C][C]0.0457935560391096[/C][C]0.0228967780195548[/C][/ROW]
[ROW][C]50[/C][C]0.994899089061437[/C][C]0.0102018218771264[/C][C]0.00510091093856321[/C][/ROW]
[ROW][C]51[/C][C]0.998686940581436[/C][C]0.00262611883712696[/C][C]0.00131305941856348[/C][/ROW]
[ROW][C]52[/C][C]0.999924955270842[/C][C]0.000150089458315603[/C][C]7.50447291578014e-05[/C][/ROW]
[ROW][C]53[/C][C]0.999983792078789[/C][C]3.24158424226431e-05[/C][C]1.62079212113215e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999980109908287[/C][C]3.97801834259794e-05[/C][C]1.98900917129897e-05[/C][/ROW]
[ROW][C]55[/C][C]0.99997350745401[/C][C]5.29850919806909e-05[/C][C]2.64925459903454e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999950232967128[/C][C]9.95340657445804e-05[/C][C]4.97670328722902e-05[/C][/ROW]
[ROW][C]57[/C][C]0.99990830776473[/C][C]0.000183384470538571[/C][C]9.16922352692853e-05[/C][/ROW]
[ROW][C]58[/C][C]0.999699428778753[/C][C]0.00060114244249477[/C][C]0.000300571221247385[/C][/ROW]
[ROW][C]59[/C][C]0.999590603348614[/C][C]0.000818793302772113[/C][C]0.000409396651386056[/C][/ROW]
[ROW][C]60[/C][C]0.999959232244692[/C][C]8.15355106162252e-05[/C][C]4.07677553081126e-05[/C][/ROW]
[ROW][C]61[/C][C]0.999989193630754[/C][C]2.16127384916888e-05[/C][C]1.08063692458444e-05[/C][/ROW]
[ROW][C]62[/C][C]0.999935992459607[/C][C]0.00012801508078538[/C][C]6.40075403926899e-05[/C][/ROW]
[ROW][C]63[/C][C]0.99967407895407[/C][C]0.000651842091857795[/C][C]0.000325921045928898[/C][/ROW]
[ROW][C]64[/C][C]0.998355187116025[/C][C]0.0032896257679506[/C][C]0.0016448128839753[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147429&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147429&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1361699461495910.2723398922991830.863830053850409
170.1327533838515350.2655067677030690.867246616148465
180.07087295178338320.1417459035667660.929127048216617
190.08484730967316560.1696946193463310.915152690326834
200.04272494057023140.08544988114046280.957275059429769
210.04882667668927610.09765335337855210.951173323310724
220.02676173589001250.0535234717800250.973238264109987
230.1152915400058170.2305830800116340.884708459994183
240.8597035797466270.2805928405067470.140296420253373
250.8195190345289430.3609619309421140.180480965471057
260.7694739909159240.4610520181681520.230526009084076
270.7573108096766890.4853783806466220.242689190323311
280.6879104633158950.624179073368210.312089536684105
290.7423592351157350.5152815297685310.257640764884265
300.6901873087854980.6196253824290050.309812691214502
310.7460227735729540.5079544528540920.253977226427046
320.6996864535938920.6006270928122160.300313546406108
330.7141839905123450.5716320189753090.285816009487655
340.6880943590683530.6238112818632940.311905640931647
350.7917106753317370.4165786493365260.208289324668263
360.8160765727786650.3678468544426710.183923427221336
370.8259705273138960.3480589453722070.174029472686104
380.8490690226296480.3018619547407030.150930977370352
390.9088518304534180.1822963390931650.0911481695465823
400.9105457657415320.1789084685169370.0894542342584683
410.9018044304381640.1963911391236710.0981955695618357
420.8885236612617460.2229526774765070.111476338738254
430.920199744189230.1596005116215410.0798002558107703
440.9389049690830320.1221900618339350.0610950309169676
450.9132536524351990.1734926951296020.0867463475648012
460.8958786085962940.2082427828074110.104121391403706
470.8667704160365570.2664591679268860.133229583963443
480.8551178451297050.2897643097405890.144882154870295
490.9771032219804450.04579355603910960.0228967780195548
500.9948990890614370.01020182187712640.00510091093856321
510.9986869405814360.002626118837126960.00131305941856348
520.9999249552708420.0001500894583156037.50447291578014e-05
530.9999837920787893.24158424226431e-051.62079212113215e-05
540.9999801099082873.97801834259794e-051.98900917129897e-05
550.999973507454015.29850919806909e-052.64925459903454e-05
560.9999502329671289.95340657445804e-054.97670328722902e-05
570.999908307764730.0001833844705385719.16922352692853e-05
580.9996994287787530.000601142442494770.000300571221247385
590.9995906033486140.0008187933027721130.000409396651386056
600.9999592322446928.15355106162252e-054.07677553081126e-05
610.9999891936307542.16127384916888e-051.08063692458444e-05
620.9999359924596070.000128015080785386.40075403926899e-05
630.999674078954070.0006518420918577950.000325921045928898
640.9983551871160250.00328962576795060.0016448128839753







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.285714285714286NOK
5% type I error level160.326530612244898NOK
10% type I error level190.387755102040816NOK

\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 & 14 & 0.285714285714286 & NOK \tabularnewline
5% type I error level & 16 & 0.326530612244898 & NOK \tabularnewline
10% type I error level & 19 & 0.387755102040816 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147429&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]14[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.326530612244898[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.387755102040816[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147429&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147429&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.285714285714286NOK
5% type I error level160.326530612244898NOK
10% type I error level190.387755102040816NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}