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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 01:17:58 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/24/t1259051070aws60ia75mm6lkl.htm/, Retrieved Sun, 03 Dec 2023 07:21:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58957, Retrieved Sun, 03 Dec 2023 07:21:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [] [2009-11-24 08:17:58] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
267413	294912
267366	293488
264777	290555
258863	284736
254844	281818
254868	287854
277267	316263
285351	325412
286602	326011
283042	328282
276687	317480
277915	317539
277128	313737
277103	312276
275037	309391
270150	302950
267140	300316
264993	304035
287259	333476
291186	337698
292300	335932
288186	323931
281477	313927
282656	314485
280190	313218
280408	309664
276836	302963
275216	298989
274352	298423
271311	301631
289802	329765
290726	335083
292300	327616
278506	309119
269826	295916
265861	291413
269034	291542
264176	284678
255198	276475
253353	272566
246057	264981
235372	263290
258556	296806
260993	303598
254663	286994
250643	276427
243422	266424
247105	267153
248541	268381
245039	262522
237080	255542
237085	253158
225554	243803
226839	250741
247934	280445
248333	285257
246969	270976
245098	261076
246263	255603
255765	260376
264319	263903
268347	264291
273046	263276
273963	262572
267430	256167
271993	264221
292710	293860




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58957&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58957&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58957&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 100661.943324773 + 0.56639088912659X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  100661.943324773 +  0.56639088912659X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58957&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  100661.943324773 +  0.56639088912659X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58957&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58957&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
Y[t] = + 100661.943324773 + 0.56639088912659X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)100661.94332477313014.6465057.734500
X0.566390889126590.04444412.743900

\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) & 100661.943324773 & 13014.646505 & 7.7345 & 0 & 0 \tabularnewline
X & 0.56639088912659 & 0.044444 & 12.7439 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58957&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]100661.943324773[/C][C]13014.646505[/C][C]7.7345[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.56639088912659[/C][C]0.044444[/C][C]12.7439[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58957&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58957&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)100661.94332477313014.6465057.734500
X0.566390889126590.04444412.743900







Multiple Linear Regression - Regression Statistics
Multiple R0.845084893761692
R-squared0.714168477664211
Adjusted R-squared0.709771069628276
F-TEST (value)162.406688628413
F-TEST (DF numerator)1
F-TEST (DF denominator)65
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9138.03088849535
Sum Squared Residuals5427734553.74118

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.845084893761692 \tabularnewline
R-squared & 0.714168477664211 \tabularnewline
Adjusted R-squared & 0.709771069628276 \tabularnewline
F-TEST (value) & 162.406688628413 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 65 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9138.03088849535 \tabularnewline
Sum Squared Residuals & 5427734553.74118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58957&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.845084893761692[/C][/ROW]
[ROW][C]R-squared[/C][C]0.714168477664211[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.709771069628276[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]162.406688628413[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]65[/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]9138.03088849535[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5427734553.74118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58957&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58957&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 R0.845084893761692
R-squared0.714168477664211
Adjusted R-squared0.709771069628276
F-TEST (value)162.406688628413
F-TEST (DF numerator)1
F-TEST (DF denominator)65
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9138.03088849535
Sum Squared Residuals5427734553.74118







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1267413267697.413218874-284.413218873647
2267366266890.872592757475.127407242538
3264777265229.648114949-452.648114949221
4258863261933.819531122-3070.81953112159
5254844260281.09091665-5437.09091665021
6254868263699.826323418-8831.8263234183
7277267279790.425092616-2523.42509261559
8285351284972.335337235378.664662765244
9286602285311.6034798221290.39652017842
10283042286597.877189028-3555.87718902807
11276687280479.722804683-3792.72280468265
12277915280513.139867141-2598.13986714112
13277128278359.721706682-1231.72170668182
14277103277532.224617668-429.224617667875
15275037275898.186902538-861.186902537663
16270150272250.063185673-2100.0631856733
17267140270758.189583714-3618.18958371386
18264993272864.597300376-7871.59730037565
19287259289539.711467152-2280.71146715158
20291186291931.013801044-745.01380104404
21292300290930.7674908461369.23250915352
22288186284133.5104304384052.48956956172
23281477278467.3359756163009.66402438412
24282656278783.3820917493872.61790825149
25280190278065.7648352252124.23516477488
26280408276052.8116152694355.18838473078
27276836272257.4262672324578.57373276806
28275216270006.5888738435209.41112615712
29274352269686.0116305974665.98836940277
30271311271502.993602915-191.993602915328
31289802287437.8348776032364.16512239720
32290726290449.901625978276.098374021991
33292300286220.660856876079.33914313024
34278506275744.1285806952761.87141930477
35269826268266.0696715571559.93032844313
36265861265715.61149782145.388502180165
37269034265788.6759225173245.32407748284
38264176261900.9688595522275.03114044775
39255198257254.864396047-2056.86439604684
40253353255040.842410451-1687.842410451
41246057250744.767516426-4687.76751642582
42235372249787.000522913-14415.0005229128
43258556268770.157562880-10214.1575628795
44260993272617.084481827-11624.0844818273
45254663263212.730158769-8549.73015876943
46250643257227.677633369-6584.67763336876
47243422251562.069569435-8140.06956943548
48247105251974.968527609-4869.96852760877
49248541252670.496539456-4129.49653945622
50245039249352.012320064-4313.01232006353
51237080245398.60391396-8318.60391395993
52237085244048.328034282-6963.32803428215
53225554238749.741266503-13195.7412665029
54226839242679.361255263-15840.3612552632
55247934259503.436225879-11569.4362258794
56248333262228.909184357-13895.9091843565
57246969254140.28089674-7171.28089673972
58245098248533.011094386-3435.01109438648
59246263245433.153758197829.84624180334
60255765248136.5374719987628.46252800213
61264319250134.19813794714184.8018620526
62268347250353.95780292817993.0421970715
63273046249779.07105046523266.928949535
64273963249380.3318645224582.6681354801
65267430245752.59821966421677.4017803359
66271993250314.31044069021678.6895593104
67292710267101.57000351325608.4299964874

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 267413 & 267697.413218874 & -284.413218873647 \tabularnewline
2 & 267366 & 266890.872592757 & 475.127407242538 \tabularnewline
3 & 264777 & 265229.648114949 & -452.648114949221 \tabularnewline
4 & 258863 & 261933.819531122 & -3070.81953112159 \tabularnewline
5 & 254844 & 260281.09091665 & -5437.09091665021 \tabularnewline
6 & 254868 & 263699.826323418 & -8831.8263234183 \tabularnewline
7 & 277267 & 279790.425092616 & -2523.42509261559 \tabularnewline
8 & 285351 & 284972.335337235 & 378.664662765244 \tabularnewline
9 & 286602 & 285311.603479822 & 1290.39652017842 \tabularnewline
10 & 283042 & 286597.877189028 & -3555.87718902807 \tabularnewline
11 & 276687 & 280479.722804683 & -3792.72280468265 \tabularnewline
12 & 277915 & 280513.139867141 & -2598.13986714112 \tabularnewline
13 & 277128 & 278359.721706682 & -1231.72170668182 \tabularnewline
14 & 277103 & 277532.224617668 & -429.224617667875 \tabularnewline
15 & 275037 & 275898.186902538 & -861.186902537663 \tabularnewline
16 & 270150 & 272250.063185673 & -2100.0631856733 \tabularnewline
17 & 267140 & 270758.189583714 & -3618.18958371386 \tabularnewline
18 & 264993 & 272864.597300376 & -7871.59730037565 \tabularnewline
19 & 287259 & 289539.711467152 & -2280.71146715158 \tabularnewline
20 & 291186 & 291931.013801044 & -745.01380104404 \tabularnewline
21 & 292300 & 290930.767490846 & 1369.23250915352 \tabularnewline
22 & 288186 & 284133.510430438 & 4052.48956956172 \tabularnewline
23 & 281477 & 278467.335975616 & 3009.66402438412 \tabularnewline
24 & 282656 & 278783.382091749 & 3872.61790825149 \tabularnewline
25 & 280190 & 278065.764835225 & 2124.23516477488 \tabularnewline
26 & 280408 & 276052.811615269 & 4355.18838473078 \tabularnewline
27 & 276836 & 272257.426267232 & 4578.57373276806 \tabularnewline
28 & 275216 & 270006.588873843 & 5209.41112615712 \tabularnewline
29 & 274352 & 269686.011630597 & 4665.98836940277 \tabularnewline
30 & 271311 & 271502.993602915 & -191.993602915328 \tabularnewline
31 & 289802 & 287437.834877603 & 2364.16512239720 \tabularnewline
32 & 290726 & 290449.901625978 & 276.098374021991 \tabularnewline
33 & 292300 & 286220.66085687 & 6079.33914313024 \tabularnewline
34 & 278506 & 275744.128580695 & 2761.87141930477 \tabularnewline
35 & 269826 & 268266.069671557 & 1559.93032844313 \tabularnewline
36 & 265861 & 265715.61149782 & 145.388502180165 \tabularnewline
37 & 269034 & 265788.675922517 & 3245.32407748284 \tabularnewline
38 & 264176 & 261900.968859552 & 2275.03114044775 \tabularnewline
39 & 255198 & 257254.864396047 & -2056.86439604684 \tabularnewline
40 & 253353 & 255040.842410451 & -1687.842410451 \tabularnewline
41 & 246057 & 250744.767516426 & -4687.76751642582 \tabularnewline
42 & 235372 & 249787.000522913 & -14415.0005229128 \tabularnewline
43 & 258556 & 268770.157562880 & -10214.1575628795 \tabularnewline
44 & 260993 & 272617.084481827 & -11624.0844818273 \tabularnewline
45 & 254663 & 263212.730158769 & -8549.73015876943 \tabularnewline
46 & 250643 & 257227.677633369 & -6584.67763336876 \tabularnewline
47 & 243422 & 251562.069569435 & -8140.06956943548 \tabularnewline
48 & 247105 & 251974.968527609 & -4869.96852760877 \tabularnewline
49 & 248541 & 252670.496539456 & -4129.49653945622 \tabularnewline
50 & 245039 & 249352.012320064 & -4313.01232006353 \tabularnewline
51 & 237080 & 245398.60391396 & -8318.60391395993 \tabularnewline
52 & 237085 & 244048.328034282 & -6963.32803428215 \tabularnewline
53 & 225554 & 238749.741266503 & -13195.7412665029 \tabularnewline
54 & 226839 & 242679.361255263 & -15840.3612552632 \tabularnewline
55 & 247934 & 259503.436225879 & -11569.4362258794 \tabularnewline
56 & 248333 & 262228.909184357 & -13895.9091843565 \tabularnewline
57 & 246969 & 254140.28089674 & -7171.28089673972 \tabularnewline
58 & 245098 & 248533.011094386 & -3435.01109438648 \tabularnewline
59 & 246263 & 245433.153758197 & 829.84624180334 \tabularnewline
60 & 255765 & 248136.537471998 & 7628.46252800213 \tabularnewline
61 & 264319 & 250134.198137947 & 14184.8018620526 \tabularnewline
62 & 268347 & 250353.957802928 & 17993.0421970715 \tabularnewline
63 & 273046 & 249779.071050465 & 23266.928949535 \tabularnewline
64 & 273963 & 249380.33186452 & 24582.6681354801 \tabularnewline
65 & 267430 & 245752.598219664 & 21677.4017803359 \tabularnewline
66 & 271993 & 250314.310440690 & 21678.6895593104 \tabularnewline
67 & 292710 & 267101.570003513 & 25608.4299964874 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58957&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]267413[/C][C]267697.413218874[/C][C]-284.413218873647[/C][/ROW]
[ROW][C]2[/C][C]267366[/C][C]266890.872592757[/C][C]475.127407242538[/C][/ROW]
[ROW][C]3[/C][C]264777[/C][C]265229.648114949[/C][C]-452.648114949221[/C][/ROW]
[ROW][C]4[/C][C]258863[/C][C]261933.819531122[/C][C]-3070.81953112159[/C][/ROW]
[ROW][C]5[/C][C]254844[/C][C]260281.09091665[/C][C]-5437.09091665021[/C][/ROW]
[ROW][C]6[/C][C]254868[/C][C]263699.826323418[/C][C]-8831.8263234183[/C][/ROW]
[ROW][C]7[/C][C]277267[/C][C]279790.425092616[/C][C]-2523.42509261559[/C][/ROW]
[ROW][C]8[/C][C]285351[/C][C]284972.335337235[/C][C]378.664662765244[/C][/ROW]
[ROW][C]9[/C][C]286602[/C][C]285311.603479822[/C][C]1290.39652017842[/C][/ROW]
[ROW][C]10[/C][C]283042[/C][C]286597.877189028[/C][C]-3555.87718902807[/C][/ROW]
[ROW][C]11[/C][C]276687[/C][C]280479.722804683[/C][C]-3792.72280468265[/C][/ROW]
[ROW][C]12[/C][C]277915[/C][C]280513.139867141[/C][C]-2598.13986714112[/C][/ROW]
[ROW][C]13[/C][C]277128[/C][C]278359.721706682[/C][C]-1231.72170668182[/C][/ROW]
[ROW][C]14[/C][C]277103[/C][C]277532.224617668[/C][C]-429.224617667875[/C][/ROW]
[ROW][C]15[/C][C]275037[/C][C]275898.186902538[/C][C]-861.186902537663[/C][/ROW]
[ROW][C]16[/C][C]270150[/C][C]272250.063185673[/C][C]-2100.0631856733[/C][/ROW]
[ROW][C]17[/C][C]267140[/C][C]270758.189583714[/C][C]-3618.18958371386[/C][/ROW]
[ROW][C]18[/C][C]264993[/C][C]272864.597300376[/C][C]-7871.59730037565[/C][/ROW]
[ROW][C]19[/C][C]287259[/C][C]289539.711467152[/C][C]-2280.71146715158[/C][/ROW]
[ROW][C]20[/C][C]291186[/C][C]291931.013801044[/C][C]-745.01380104404[/C][/ROW]
[ROW][C]21[/C][C]292300[/C][C]290930.767490846[/C][C]1369.23250915352[/C][/ROW]
[ROW][C]22[/C][C]288186[/C][C]284133.510430438[/C][C]4052.48956956172[/C][/ROW]
[ROW][C]23[/C][C]281477[/C][C]278467.335975616[/C][C]3009.66402438412[/C][/ROW]
[ROW][C]24[/C][C]282656[/C][C]278783.382091749[/C][C]3872.61790825149[/C][/ROW]
[ROW][C]25[/C][C]280190[/C][C]278065.764835225[/C][C]2124.23516477488[/C][/ROW]
[ROW][C]26[/C][C]280408[/C][C]276052.811615269[/C][C]4355.18838473078[/C][/ROW]
[ROW][C]27[/C][C]276836[/C][C]272257.426267232[/C][C]4578.57373276806[/C][/ROW]
[ROW][C]28[/C][C]275216[/C][C]270006.588873843[/C][C]5209.41112615712[/C][/ROW]
[ROW][C]29[/C][C]274352[/C][C]269686.011630597[/C][C]4665.98836940277[/C][/ROW]
[ROW][C]30[/C][C]271311[/C][C]271502.993602915[/C][C]-191.993602915328[/C][/ROW]
[ROW][C]31[/C][C]289802[/C][C]287437.834877603[/C][C]2364.16512239720[/C][/ROW]
[ROW][C]32[/C][C]290726[/C][C]290449.901625978[/C][C]276.098374021991[/C][/ROW]
[ROW][C]33[/C][C]292300[/C][C]286220.66085687[/C][C]6079.33914313024[/C][/ROW]
[ROW][C]34[/C][C]278506[/C][C]275744.128580695[/C][C]2761.87141930477[/C][/ROW]
[ROW][C]35[/C][C]269826[/C][C]268266.069671557[/C][C]1559.93032844313[/C][/ROW]
[ROW][C]36[/C][C]265861[/C][C]265715.61149782[/C][C]145.388502180165[/C][/ROW]
[ROW][C]37[/C][C]269034[/C][C]265788.675922517[/C][C]3245.32407748284[/C][/ROW]
[ROW][C]38[/C][C]264176[/C][C]261900.968859552[/C][C]2275.03114044775[/C][/ROW]
[ROW][C]39[/C][C]255198[/C][C]257254.864396047[/C][C]-2056.86439604684[/C][/ROW]
[ROW][C]40[/C][C]253353[/C][C]255040.842410451[/C][C]-1687.842410451[/C][/ROW]
[ROW][C]41[/C][C]246057[/C][C]250744.767516426[/C][C]-4687.76751642582[/C][/ROW]
[ROW][C]42[/C][C]235372[/C][C]249787.000522913[/C][C]-14415.0005229128[/C][/ROW]
[ROW][C]43[/C][C]258556[/C][C]268770.157562880[/C][C]-10214.1575628795[/C][/ROW]
[ROW][C]44[/C][C]260993[/C][C]272617.084481827[/C][C]-11624.0844818273[/C][/ROW]
[ROW][C]45[/C][C]254663[/C][C]263212.730158769[/C][C]-8549.73015876943[/C][/ROW]
[ROW][C]46[/C][C]250643[/C][C]257227.677633369[/C][C]-6584.67763336876[/C][/ROW]
[ROW][C]47[/C][C]243422[/C][C]251562.069569435[/C][C]-8140.06956943548[/C][/ROW]
[ROW][C]48[/C][C]247105[/C][C]251974.968527609[/C][C]-4869.96852760877[/C][/ROW]
[ROW][C]49[/C][C]248541[/C][C]252670.496539456[/C][C]-4129.49653945622[/C][/ROW]
[ROW][C]50[/C][C]245039[/C][C]249352.012320064[/C][C]-4313.01232006353[/C][/ROW]
[ROW][C]51[/C][C]237080[/C][C]245398.60391396[/C][C]-8318.60391395993[/C][/ROW]
[ROW][C]52[/C][C]237085[/C][C]244048.328034282[/C][C]-6963.32803428215[/C][/ROW]
[ROW][C]53[/C][C]225554[/C][C]238749.741266503[/C][C]-13195.7412665029[/C][/ROW]
[ROW][C]54[/C][C]226839[/C][C]242679.361255263[/C][C]-15840.3612552632[/C][/ROW]
[ROW][C]55[/C][C]247934[/C][C]259503.436225879[/C][C]-11569.4362258794[/C][/ROW]
[ROW][C]56[/C][C]248333[/C][C]262228.909184357[/C][C]-13895.9091843565[/C][/ROW]
[ROW][C]57[/C][C]246969[/C][C]254140.28089674[/C][C]-7171.28089673972[/C][/ROW]
[ROW][C]58[/C][C]245098[/C][C]248533.011094386[/C][C]-3435.01109438648[/C][/ROW]
[ROW][C]59[/C][C]246263[/C][C]245433.153758197[/C][C]829.84624180334[/C][/ROW]
[ROW][C]60[/C][C]255765[/C][C]248136.537471998[/C][C]7628.46252800213[/C][/ROW]
[ROW][C]61[/C][C]264319[/C][C]250134.198137947[/C][C]14184.8018620526[/C][/ROW]
[ROW][C]62[/C][C]268347[/C][C]250353.957802928[/C][C]17993.0421970715[/C][/ROW]
[ROW][C]63[/C][C]273046[/C][C]249779.071050465[/C][C]23266.928949535[/C][/ROW]
[ROW][C]64[/C][C]273963[/C][C]249380.33186452[/C][C]24582.6681354801[/C][/ROW]
[ROW][C]65[/C][C]267430[/C][C]245752.598219664[/C][C]21677.4017803359[/C][/ROW]
[ROW][C]66[/C][C]271993[/C][C]250314.310440690[/C][C]21678.6895593104[/C][/ROW]
[ROW][C]67[/C][C]292710[/C][C]267101.570003513[/C][C]25608.4299964874[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58957&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58957&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
1267413267697.413218874-284.413218873647
2267366266890.872592757475.127407242538
3264777265229.648114949-452.648114949221
4258863261933.819531122-3070.81953112159
5254844260281.09091665-5437.09091665021
6254868263699.826323418-8831.8263234183
7277267279790.425092616-2523.42509261559
8285351284972.335337235378.664662765244
9286602285311.6034798221290.39652017842
10283042286597.877189028-3555.87718902807
11276687280479.722804683-3792.72280468265
12277915280513.139867141-2598.13986714112
13277128278359.721706682-1231.72170668182
14277103277532.224617668-429.224617667875
15275037275898.186902538-861.186902537663
16270150272250.063185673-2100.0631856733
17267140270758.189583714-3618.18958371386
18264993272864.597300376-7871.59730037565
19287259289539.711467152-2280.71146715158
20291186291931.013801044-745.01380104404
21292300290930.7674908461369.23250915352
22288186284133.5104304384052.48956956172
23281477278467.3359756163009.66402438412
24282656278783.3820917493872.61790825149
25280190278065.7648352252124.23516477488
26280408276052.8116152694355.18838473078
27276836272257.4262672324578.57373276806
28275216270006.5888738435209.41112615712
29274352269686.0116305974665.98836940277
30271311271502.993602915-191.993602915328
31289802287437.8348776032364.16512239720
32290726290449.901625978276.098374021991
33292300286220.660856876079.33914313024
34278506275744.1285806952761.87141930477
35269826268266.0696715571559.93032844313
36265861265715.61149782145.388502180165
37269034265788.6759225173245.32407748284
38264176261900.9688595522275.03114044775
39255198257254.864396047-2056.86439604684
40253353255040.842410451-1687.842410451
41246057250744.767516426-4687.76751642582
42235372249787.000522913-14415.0005229128
43258556268770.157562880-10214.1575628795
44260993272617.084481827-11624.0844818273
45254663263212.730158769-8549.73015876943
46250643257227.677633369-6584.67763336876
47243422251562.069569435-8140.06956943548
48247105251974.968527609-4869.96852760877
49248541252670.496539456-4129.49653945622
50245039249352.012320064-4313.01232006353
51237080245398.60391396-8318.60391395993
52237085244048.328034282-6963.32803428215
53225554238749.741266503-13195.7412665029
54226839242679.361255263-15840.3612552632
55247934259503.436225879-11569.4362258794
56248333262228.909184357-13895.9091843565
57246969254140.28089674-7171.28089673972
58245098248533.011094386-3435.01109438648
59246263245433.153758197829.84624180334
60255765248136.5374719987628.46252800213
61264319250134.19813794714184.8018620526
62268347250353.95780292817993.0421970715
63273046249779.07105046523266.928949535
64273963249380.3318645224582.6681354801
65267430245752.59821966421677.4017803359
66271993250314.31044069021678.6895593104
67292710267101.57000351325608.4299964874







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.001040599665622890.002081199331245790.998959400334377
60.01962889923989750.03925779847979510.980371100760102
70.01524936223076520.03049872446153050.984750637769235
80.004711795197296310.009423590394592620.995288204802704
90.001389705477073860.002779410954147710.998610294522926
100.0008136700505813580.001627340101162720.999186329949419
110.0003148230173787980.0006296460347575960.99968517698262
129.18930629443465e-050.0001837861258886930.999908106937056
132.47106487751642e-054.94212975503284e-050.999975289351225
147.07656147277191e-061.41531229455438e-050.999992923438527
151.85432410759590e-063.70864821519179e-060.999998145675892
164.34760639019229e-078.69521278038459e-070.99999956523936
171.10406242674004e-072.20812485348008e-070.999999889593757
182.40585492371650e-074.81170984743301e-070.999999759414508
196.83934173886577e-081.36786834777315e-070.999999931606583
201.62846223681723e-083.25692447363447e-080.999999983715378
215.03093332144809e-091.00618666428962e-080.999999994969067
226.96324192289415e-091.39264838457883e-080.999999993036758
235.89022359943381e-091.17804471988676e-080.999999994109776
246.29746946809062e-091.25949389361812e-080.99999999370253
252.84534478016425e-095.69068956032851e-090.999999997154655
263.49567558664325e-096.9913511732865e-090.999999996504324
274.90647524734652e-099.81295049469304e-090.999999995093525
287.97585378841928e-091.59517075768386e-080.999999992024146
297.809777220857e-091.5619554441714e-080.999999992190223
302.3231610064275e-094.646322012855e-090.99999999767684
317.38897032803957e-101.47779406560791e-090.999999999261103
322.06882451630177e-104.13764903260354e-100.999999999793117
331.75128942818334e-103.50257885636668e-100.99999999982487
346.86504671135594e-111.37300934227119e-100.99999999993135
352.35075430045574e-114.70150860091147e-110.999999999976492
366.51215297246021e-121.30243059449204e-110.999999999993488
373.3320703752254e-126.6641407504508e-120.999999999996668
381.26817395071544e-122.53634790143088e-120.999999999998732
393.21086264875726e-136.42172529751452e-130.999999999999679
407.67833682532071e-141.53566736506414e-130.999999999999923
412.55917850321551e-145.11835700643103e-140.999999999999974
429.74484560012574e-131.94896912002515e-120.999999999999025
432.94769181198869e-125.89538362397737e-120.999999999997052
442.22322743825744e-114.44645487651487e-110.999999999977768
452.49186815864726e-114.98373631729452e-110.999999999975081
461.34962952069873e-112.69925904139746e-110.999999999986504
477.87604645213027e-121.57520929042605e-110.999999999992124
483.04170971944714e-126.08341943889428e-120.999999999996958
491.18215998599462e-122.36431997198923e-120.999999999998818
504.31934692960309e-138.63869385920619e-130.999999999999568
512.22110175816047e-134.44220351632094e-130.999999999999778
529.66742863667377e-141.93348572733475e-130.999999999999903
533.06730803002057e-136.13461606004114e-130.999999999999693
543.42639894093472e-116.85279788186943e-110.999999999965736
553.64310520151234e-107.28621040302467e-100.99999999963569
564.22703230456607e-078.45406460913214e-070.99999957729677
570.0001000306123606490.0002000612247212980.99989996938764
580.005736431372617940.01147286274523590.994263568627382
590.1172143271663180.2344286543326360.882785672833682
600.6408758884032880.7182482231934230.359124111596712
610.9252955127481170.1494089745037660.0747044872518831
620.986546131898880.02690773620223810.0134538681011190

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.00104059966562289 & 0.00208119933124579 & 0.998959400334377 \tabularnewline
6 & 0.0196288992398975 & 0.0392577984797951 & 0.980371100760102 \tabularnewline
7 & 0.0152493622307652 & 0.0304987244615305 & 0.984750637769235 \tabularnewline
8 & 0.00471179519729631 & 0.00942359039459262 & 0.995288204802704 \tabularnewline
9 & 0.00138970547707386 & 0.00277941095414771 & 0.998610294522926 \tabularnewline
10 & 0.000813670050581358 & 0.00162734010116272 & 0.999186329949419 \tabularnewline
11 & 0.000314823017378798 & 0.000629646034757596 & 0.99968517698262 \tabularnewline
12 & 9.18930629443465e-05 & 0.000183786125888693 & 0.999908106937056 \tabularnewline
13 & 2.47106487751642e-05 & 4.94212975503284e-05 & 0.999975289351225 \tabularnewline
14 & 7.07656147277191e-06 & 1.41531229455438e-05 & 0.999992923438527 \tabularnewline
15 & 1.85432410759590e-06 & 3.70864821519179e-06 & 0.999998145675892 \tabularnewline
16 & 4.34760639019229e-07 & 8.69521278038459e-07 & 0.99999956523936 \tabularnewline
17 & 1.10406242674004e-07 & 2.20812485348008e-07 & 0.999999889593757 \tabularnewline
18 & 2.40585492371650e-07 & 4.81170984743301e-07 & 0.999999759414508 \tabularnewline
19 & 6.83934173886577e-08 & 1.36786834777315e-07 & 0.999999931606583 \tabularnewline
20 & 1.62846223681723e-08 & 3.25692447363447e-08 & 0.999999983715378 \tabularnewline
21 & 5.03093332144809e-09 & 1.00618666428962e-08 & 0.999999994969067 \tabularnewline
22 & 6.96324192289415e-09 & 1.39264838457883e-08 & 0.999999993036758 \tabularnewline
23 & 5.89022359943381e-09 & 1.17804471988676e-08 & 0.999999994109776 \tabularnewline
24 & 6.29746946809062e-09 & 1.25949389361812e-08 & 0.99999999370253 \tabularnewline
25 & 2.84534478016425e-09 & 5.69068956032851e-09 & 0.999999997154655 \tabularnewline
26 & 3.49567558664325e-09 & 6.9913511732865e-09 & 0.999999996504324 \tabularnewline
27 & 4.90647524734652e-09 & 9.81295049469304e-09 & 0.999999995093525 \tabularnewline
28 & 7.97585378841928e-09 & 1.59517075768386e-08 & 0.999999992024146 \tabularnewline
29 & 7.809777220857e-09 & 1.5619554441714e-08 & 0.999999992190223 \tabularnewline
30 & 2.3231610064275e-09 & 4.646322012855e-09 & 0.99999999767684 \tabularnewline
31 & 7.38897032803957e-10 & 1.47779406560791e-09 & 0.999999999261103 \tabularnewline
32 & 2.06882451630177e-10 & 4.13764903260354e-10 & 0.999999999793117 \tabularnewline
33 & 1.75128942818334e-10 & 3.50257885636668e-10 & 0.99999999982487 \tabularnewline
34 & 6.86504671135594e-11 & 1.37300934227119e-10 & 0.99999999993135 \tabularnewline
35 & 2.35075430045574e-11 & 4.70150860091147e-11 & 0.999999999976492 \tabularnewline
36 & 6.51215297246021e-12 & 1.30243059449204e-11 & 0.999999999993488 \tabularnewline
37 & 3.3320703752254e-12 & 6.6641407504508e-12 & 0.999999999996668 \tabularnewline
38 & 1.26817395071544e-12 & 2.53634790143088e-12 & 0.999999999998732 \tabularnewline
39 & 3.21086264875726e-13 & 6.42172529751452e-13 & 0.999999999999679 \tabularnewline
40 & 7.67833682532071e-14 & 1.53566736506414e-13 & 0.999999999999923 \tabularnewline
41 & 2.55917850321551e-14 & 5.11835700643103e-14 & 0.999999999999974 \tabularnewline
42 & 9.74484560012574e-13 & 1.94896912002515e-12 & 0.999999999999025 \tabularnewline
43 & 2.94769181198869e-12 & 5.89538362397737e-12 & 0.999999999997052 \tabularnewline
44 & 2.22322743825744e-11 & 4.44645487651487e-11 & 0.999999999977768 \tabularnewline
45 & 2.49186815864726e-11 & 4.98373631729452e-11 & 0.999999999975081 \tabularnewline
46 & 1.34962952069873e-11 & 2.69925904139746e-11 & 0.999999999986504 \tabularnewline
47 & 7.87604645213027e-12 & 1.57520929042605e-11 & 0.999999999992124 \tabularnewline
48 & 3.04170971944714e-12 & 6.08341943889428e-12 & 0.999999999996958 \tabularnewline
49 & 1.18215998599462e-12 & 2.36431997198923e-12 & 0.999999999998818 \tabularnewline
50 & 4.31934692960309e-13 & 8.63869385920619e-13 & 0.999999999999568 \tabularnewline
51 & 2.22110175816047e-13 & 4.44220351632094e-13 & 0.999999999999778 \tabularnewline
52 & 9.66742863667377e-14 & 1.93348572733475e-13 & 0.999999999999903 \tabularnewline
53 & 3.06730803002057e-13 & 6.13461606004114e-13 & 0.999999999999693 \tabularnewline
54 & 3.42639894093472e-11 & 6.85279788186943e-11 & 0.999999999965736 \tabularnewline
55 & 3.64310520151234e-10 & 7.28621040302467e-10 & 0.99999999963569 \tabularnewline
56 & 4.22703230456607e-07 & 8.45406460913214e-07 & 0.99999957729677 \tabularnewline
57 & 0.000100030612360649 & 0.000200061224721298 & 0.99989996938764 \tabularnewline
58 & 0.00573643137261794 & 0.0114728627452359 & 0.994263568627382 \tabularnewline
59 & 0.117214327166318 & 0.234428654332636 & 0.882785672833682 \tabularnewline
60 & 0.640875888403288 & 0.718248223193423 & 0.359124111596712 \tabularnewline
61 & 0.925295512748117 & 0.149408974503766 & 0.0747044872518831 \tabularnewline
62 & 0.98654613189888 & 0.0269077362022381 & 0.0134538681011190 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58957&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]5[/C][C]0.00104059966562289[/C][C]0.00208119933124579[/C][C]0.998959400334377[/C][/ROW]
[ROW][C]6[/C][C]0.0196288992398975[/C][C]0.0392577984797951[/C][C]0.980371100760102[/C][/ROW]
[ROW][C]7[/C][C]0.0152493622307652[/C][C]0.0304987244615305[/C][C]0.984750637769235[/C][/ROW]
[ROW][C]8[/C][C]0.00471179519729631[/C][C]0.00942359039459262[/C][C]0.995288204802704[/C][/ROW]
[ROW][C]9[/C][C]0.00138970547707386[/C][C]0.00277941095414771[/C][C]0.998610294522926[/C][/ROW]
[ROW][C]10[/C][C]0.000813670050581358[/C][C]0.00162734010116272[/C][C]0.999186329949419[/C][/ROW]
[ROW][C]11[/C][C]0.000314823017378798[/C][C]0.000629646034757596[/C][C]0.99968517698262[/C][/ROW]
[ROW][C]12[/C][C]9.18930629443465e-05[/C][C]0.000183786125888693[/C][C]0.999908106937056[/C][/ROW]
[ROW][C]13[/C][C]2.47106487751642e-05[/C][C]4.94212975503284e-05[/C][C]0.999975289351225[/C][/ROW]
[ROW][C]14[/C][C]7.07656147277191e-06[/C][C]1.41531229455438e-05[/C][C]0.999992923438527[/C][/ROW]
[ROW][C]15[/C][C]1.85432410759590e-06[/C][C]3.70864821519179e-06[/C][C]0.999998145675892[/C][/ROW]
[ROW][C]16[/C][C]4.34760639019229e-07[/C][C]8.69521278038459e-07[/C][C]0.99999956523936[/C][/ROW]
[ROW][C]17[/C][C]1.10406242674004e-07[/C][C]2.20812485348008e-07[/C][C]0.999999889593757[/C][/ROW]
[ROW][C]18[/C][C]2.40585492371650e-07[/C][C]4.81170984743301e-07[/C][C]0.999999759414508[/C][/ROW]
[ROW][C]19[/C][C]6.83934173886577e-08[/C][C]1.36786834777315e-07[/C][C]0.999999931606583[/C][/ROW]
[ROW][C]20[/C][C]1.62846223681723e-08[/C][C]3.25692447363447e-08[/C][C]0.999999983715378[/C][/ROW]
[ROW][C]21[/C][C]5.03093332144809e-09[/C][C]1.00618666428962e-08[/C][C]0.999999994969067[/C][/ROW]
[ROW][C]22[/C][C]6.96324192289415e-09[/C][C]1.39264838457883e-08[/C][C]0.999999993036758[/C][/ROW]
[ROW][C]23[/C][C]5.89022359943381e-09[/C][C]1.17804471988676e-08[/C][C]0.999999994109776[/C][/ROW]
[ROW][C]24[/C][C]6.29746946809062e-09[/C][C]1.25949389361812e-08[/C][C]0.99999999370253[/C][/ROW]
[ROW][C]25[/C][C]2.84534478016425e-09[/C][C]5.69068956032851e-09[/C][C]0.999999997154655[/C][/ROW]
[ROW][C]26[/C][C]3.49567558664325e-09[/C][C]6.9913511732865e-09[/C][C]0.999999996504324[/C][/ROW]
[ROW][C]27[/C][C]4.90647524734652e-09[/C][C]9.81295049469304e-09[/C][C]0.999999995093525[/C][/ROW]
[ROW][C]28[/C][C]7.97585378841928e-09[/C][C]1.59517075768386e-08[/C][C]0.999999992024146[/C][/ROW]
[ROW][C]29[/C][C]7.809777220857e-09[/C][C]1.5619554441714e-08[/C][C]0.999999992190223[/C][/ROW]
[ROW][C]30[/C][C]2.3231610064275e-09[/C][C]4.646322012855e-09[/C][C]0.99999999767684[/C][/ROW]
[ROW][C]31[/C][C]7.38897032803957e-10[/C][C]1.47779406560791e-09[/C][C]0.999999999261103[/C][/ROW]
[ROW][C]32[/C][C]2.06882451630177e-10[/C][C]4.13764903260354e-10[/C][C]0.999999999793117[/C][/ROW]
[ROW][C]33[/C][C]1.75128942818334e-10[/C][C]3.50257885636668e-10[/C][C]0.99999999982487[/C][/ROW]
[ROW][C]34[/C][C]6.86504671135594e-11[/C][C]1.37300934227119e-10[/C][C]0.99999999993135[/C][/ROW]
[ROW][C]35[/C][C]2.35075430045574e-11[/C][C]4.70150860091147e-11[/C][C]0.999999999976492[/C][/ROW]
[ROW][C]36[/C][C]6.51215297246021e-12[/C][C]1.30243059449204e-11[/C][C]0.999999999993488[/C][/ROW]
[ROW][C]37[/C][C]3.3320703752254e-12[/C][C]6.6641407504508e-12[/C][C]0.999999999996668[/C][/ROW]
[ROW][C]38[/C][C]1.26817395071544e-12[/C][C]2.53634790143088e-12[/C][C]0.999999999998732[/C][/ROW]
[ROW][C]39[/C][C]3.21086264875726e-13[/C][C]6.42172529751452e-13[/C][C]0.999999999999679[/C][/ROW]
[ROW][C]40[/C][C]7.67833682532071e-14[/C][C]1.53566736506414e-13[/C][C]0.999999999999923[/C][/ROW]
[ROW][C]41[/C][C]2.55917850321551e-14[/C][C]5.11835700643103e-14[/C][C]0.999999999999974[/C][/ROW]
[ROW][C]42[/C][C]9.74484560012574e-13[/C][C]1.94896912002515e-12[/C][C]0.999999999999025[/C][/ROW]
[ROW][C]43[/C][C]2.94769181198869e-12[/C][C]5.89538362397737e-12[/C][C]0.999999999997052[/C][/ROW]
[ROW][C]44[/C][C]2.22322743825744e-11[/C][C]4.44645487651487e-11[/C][C]0.999999999977768[/C][/ROW]
[ROW][C]45[/C][C]2.49186815864726e-11[/C][C]4.98373631729452e-11[/C][C]0.999999999975081[/C][/ROW]
[ROW][C]46[/C][C]1.34962952069873e-11[/C][C]2.69925904139746e-11[/C][C]0.999999999986504[/C][/ROW]
[ROW][C]47[/C][C]7.87604645213027e-12[/C][C]1.57520929042605e-11[/C][C]0.999999999992124[/C][/ROW]
[ROW][C]48[/C][C]3.04170971944714e-12[/C][C]6.08341943889428e-12[/C][C]0.999999999996958[/C][/ROW]
[ROW][C]49[/C][C]1.18215998599462e-12[/C][C]2.36431997198923e-12[/C][C]0.999999999998818[/C][/ROW]
[ROW][C]50[/C][C]4.31934692960309e-13[/C][C]8.63869385920619e-13[/C][C]0.999999999999568[/C][/ROW]
[ROW][C]51[/C][C]2.22110175816047e-13[/C][C]4.44220351632094e-13[/C][C]0.999999999999778[/C][/ROW]
[ROW][C]52[/C][C]9.66742863667377e-14[/C][C]1.93348572733475e-13[/C][C]0.999999999999903[/C][/ROW]
[ROW][C]53[/C][C]3.06730803002057e-13[/C][C]6.13461606004114e-13[/C][C]0.999999999999693[/C][/ROW]
[ROW][C]54[/C][C]3.42639894093472e-11[/C][C]6.85279788186943e-11[/C][C]0.999999999965736[/C][/ROW]
[ROW][C]55[/C][C]3.64310520151234e-10[/C][C]7.28621040302467e-10[/C][C]0.99999999963569[/C][/ROW]
[ROW][C]56[/C][C]4.22703230456607e-07[/C][C]8.45406460913214e-07[/C][C]0.99999957729677[/C][/ROW]
[ROW][C]57[/C][C]0.000100030612360649[/C][C]0.000200061224721298[/C][C]0.99989996938764[/C][/ROW]
[ROW][C]58[/C][C]0.00573643137261794[/C][C]0.0114728627452359[/C][C]0.994263568627382[/C][/ROW]
[ROW][C]59[/C][C]0.117214327166318[/C][C]0.234428654332636[/C][C]0.882785672833682[/C][/ROW]
[ROW][C]60[/C][C]0.640875888403288[/C][C]0.718248223193423[/C][C]0.359124111596712[/C][/ROW]
[ROW][C]61[/C][C]0.925295512748117[/C][C]0.149408974503766[/C][C]0.0747044872518831[/C][/ROW]
[ROW][C]62[/C][C]0.98654613189888[/C][C]0.0269077362022381[/C][C]0.0134538681011190[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58957&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58957&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
50.001040599665622890.002081199331245790.998959400334377
60.01962889923989750.03925779847979510.980371100760102
70.01524936223076520.03049872446153050.984750637769235
80.004711795197296310.009423590394592620.995288204802704
90.001389705477073860.002779410954147710.998610294522926
100.0008136700505813580.001627340101162720.999186329949419
110.0003148230173787980.0006296460347575960.99968517698262
129.18930629443465e-050.0001837861258886930.999908106937056
132.47106487751642e-054.94212975503284e-050.999975289351225
147.07656147277191e-061.41531229455438e-050.999992923438527
151.85432410759590e-063.70864821519179e-060.999998145675892
164.34760639019229e-078.69521278038459e-070.99999956523936
171.10406242674004e-072.20812485348008e-070.999999889593757
182.40585492371650e-074.81170984743301e-070.999999759414508
196.83934173886577e-081.36786834777315e-070.999999931606583
201.62846223681723e-083.25692447363447e-080.999999983715378
215.03093332144809e-091.00618666428962e-080.999999994969067
226.96324192289415e-091.39264838457883e-080.999999993036758
235.89022359943381e-091.17804471988676e-080.999999994109776
246.29746946809062e-091.25949389361812e-080.99999999370253
252.84534478016425e-095.69068956032851e-090.999999997154655
263.49567558664325e-096.9913511732865e-090.999999996504324
274.90647524734652e-099.81295049469304e-090.999999995093525
287.97585378841928e-091.59517075768386e-080.999999992024146
297.809777220857e-091.5619554441714e-080.999999992190223
302.3231610064275e-094.646322012855e-090.99999999767684
317.38897032803957e-101.47779406560791e-090.999999999261103
322.06882451630177e-104.13764903260354e-100.999999999793117
331.75128942818334e-103.50257885636668e-100.99999999982487
346.86504671135594e-111.37300934227119e-100.99999999993135
352.35075430045574e-114.70150860091147e-110.999999999976492
366.51215297246021e-121.30243059449204e-110.999999999993488
373.3320703752254e-126.6641407504508e-120.999999999996668
381.26817395071544e-122.53634790143088e-120.999999999998732
393.21086264875726e-136.42172529751452e-130.999999999999679
407.67833682532071e-141.53566736506414e-130.999999999999923
412.55917850321551e-145.11835700643103e-140.999999999999974
429.74484560012574e-131.94896912002515e-120.999999999999025
432.94769181198869e-125.89538362397737e-120.999999999997052
442.22322743825744e-114.44645487651487e-110.999999999977768
452.49186815864726e-114.98373631729452e-110.999999999975081
461.34962952069873e-112.69925904139746e-110.999999999986504
477.87604645213027e-121.57520929042605e-110.999999999992124
483.04170971944714e-126.08341943889428e-120.999999999996958
491.18215998599462e-122.36431997198923e-120.999999999998818
504.31934692960309e-138.63869385920619e-130.999999999999568
512.22110175816047e-134.44220351632094e-130.999999999999778
529.66742863667377e-141.93348572733475e-130.999999999999903
533.06730803002057e-136.13461606004114e-130.999999999999693
543.42639894093472e-116.85279788186943e-110.999999999965736
553.64310520151234e-107.28621040302467e-100.99999999963569
564.22703230456607e-078.45406460913214e-070.99999957729677
570.0001000306123606490.0002000612247212980.99989996938764
580.005736431372617940.01147286274523590.994263568627382
590.1172143271663180.2344286543326360.882785672833682
600.6408758884032880.7182482231934230.359124111596712
610.9252955127481170.1494089745037660.0747044872518831
620.986546131898880.02690773620223810.0134538681011190







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level510.879310344827586NOK
5% type I error level550.948275862068966NOK
10% type I error level550.948275862068966NOK

\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 & 51 & 0.879310344827586 & NOK \tabularnewline
5% type I error level & 55 & 0.948275862068966 & NOK \tabularnewline
10% type I error level & 55 & 0.948275862068966 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58957&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]51[/C][C]0.879310344827586[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]55[/C][C]0.948275862068966[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.948275862068966[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58957&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58957&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 level510.879310344827586NOK
5% type I error level550.948275862068966NOK
10% type I error level550.948275862068966NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}