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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 computationWed, 07 Nov 2012 05:51:34 -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/2012/Nov/07/t13522855450rz4mfb1koi0ssi.htm/, Retrieved Fri, 29 Mar 2024 06:52:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186534, Retrieved Fri, 29 Mar 2024 06:52:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [eerste poging met...] [2012-11-05 21:00:14] [aa97672bca82024903c7d587f1b28e35]
- R  D    [Multiple Regression] [Multiple linear r...] [2012-11-07 10:51:34] [447cab31e466d1c88f957d20e303ed40] [Current]
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Dataseries X:
93.7	76.6	76.4	85.7
114.7	83.8	83.8	116
121.2	95.1	95	130.6
98.6	82.2	82	105.1
111.5	89.2	89	130.7
107.5	86.9	86.7	113.9
69.1	72	71.8	40.1
88.3	79.4	79.2	112.2
114.7	89.1	89.1	120.6
115.5	89.8	89.7	123.1
109.5	88.9	88.8	112.2
97.7	83.2	83.1	90.5
102	90.8	90.7	89.2
107.5	89.3	89.4	107.9
120.5	99.2	99.2	111.1
101.9	86.7	86.6	92
107.6	93.5	93.3	115
113.9	96.7	96.7	116.4
70.9	80.5	80.2	53.4
93.4	84.1	83.8	109
114.8	92.9	92.9	105.3
117.8	97.2	97.3	120.4
105.2	92.4	92.4	102.3
95.1	83.6	83.5	68.6
97.5	89.9	89.8	91.9
103.2	88	88	95.1
111.6	97.4	97.4	113
105.4	92.8	92.8	106.3
97.8	90.6	90.5	106.5
104.4	93.9	94.1	109.6
75	83.5	83.2	49
82.2	80.5	80.1	95.3
116.2	97.7	97.6	114.9
115	102	102	118
91.5	94.2	94.1	102.9
89.5	87.1	86.9	67
90.7	92.5	92.4	84.5
100.1	92.8	92.8	95.9
100.1	99.8	99.8	114
93.5	97	96.9	106.6
84.4	91.6	91.5	100
101.2	98	97.9	111.6
75.3	88.7	88.5	56.5
76.5	82.3	81.9	90.2
105.6	102.4	102.4	122.3
110.4	104.5	104.5	118.8
91.5	93.9	93.9	94.5
88.1	97.1	97.1	77.4
88.2	91.3	91.2	89.3
99.3	93.2	93.2	99.5
117.1	108	108.1	122.2
100.5	98.2	98.2	104.6
83.9	92	91.9	97.4
110.7	106.5	106.5	121
66.9	89.5	89.3	48.3
85.9	87.8	87.5	103.4
112.1	105.2	105.3	119.8
105.5	104.3	104.3	113.9
104	99.6	99.7	100.4
97.8	101	101	85.9
91.4	94	94	79.4
104.4	96.1	96.2	95.8
111.2	108.3	108.3	103.3
102.3	102.9	103	117.8
94.6	96	95.9	102.8
109.4	109	109	123.8
69.1	87.6	87.4	41.8
86.9	89.9	89.7	107.8
118.3	108.8	108.9	124.4
102.3	102.3	102.4	109.1
108.8	103.9	104	107.1
101.2	101.3	101.3	86.9
99.1	97.4	97.3	86.3
105.5	98.1	98.1	98.6
119.8	111.4	111.5	121.6
94.5	94.2	94.1	102.9
101.4	104.5	104.5	116.5
116.5	110	110	124.3
66.7	89.7	89.3	44.2
91.6	92.6	92.5	110.5
119.8	108.6	108.7	124.2
116.4	110.6	110.7	116.3
111.7	107	107.1	113.3
102.4	101.4	101.4	83.9
99.3	107.2	107.3	95.6
109.3	105.1	105.2	106.8
119	114.1	114.2	122.7
102.5	103.1	103	102.7
104.9	107.6	107.6	108.4
122.4	113.8	113.9	120
76.4	100.2	100.1	49.4
103.2	100.2	100.2	111.2
120.8	109	109.1	113.3
124.9	119.6	119.8	125.8
110.2	112.2	112.4	109.9
99.7	100	100	74.3
97.1	111.4	111.6	106.7
109.3	113.1	113.3	114.8
109.4	113.7	113.9	93.6
117	117.1	117.4	126.4
107.1	108.5	108.5	109.4
118.5	117	117.3	117.3
85.1	103.7	103.7	57.1
85.3	95.2	95.1	97.4
129.7	116.4	116.6	122.7
128	116.6	116.9	115.7
103.3	98.8	98.8	95.5
103.9	97.7	97.7	77.6
96.2	89.8	89.6	86.3
106.3	93	93.1	101.3
114.8	100.4	100.4	116.6
101.9	93.5	93.5	100.2
90.9	90.9	90.8	98.8
108.5	103.4	103.4	113.8
75.6	91.1	90.9	53.2
90.6	89.5	89.3	93.6
121.1	108.1	108.2	117.7
116.6	107.8	107.9	117.9
105.7	99.9	100	87.2
101.1	94.6	94.6	68.9
97	94.3	94.2	74
105.4	99.9	99.9	83.9
117.9	113.8	113.9	121.1
104.5	105.4	105.5	98.1
97.4	101.2	101.2	89.1
115.8	115	115.1	116.1
73.1	94.4	94.2	48.9
90.6	95.5	95.4	98.6
124.1	113.3	113.5	114.8
110.1	108.7	108.8	109.2
103.4	106.9	107	91.4
109.4	102.8	102.8	58.6
92.1	104.7	104.7	81.9
107.8	108.2	108.5	105.2
116.2	128	128.4	122.4
97.5	108.4	108.5	92.2
104.8	115.5	115.6	113.9
106.2	111.5	111.7	104.1
73.6	93.8	93.7	43.1
100.7	106	106.1	100.1
123.4	118.4	118.7	118
109.1	110.8	111	103.8
100.1	110.5	110.8	103.4
105.9	104.1	104.2	79.5
104.8	105	105.1	87.2
110.8	102.8	102.9	98.3
118.4	113.8	114	145.7
93.1	102	102.1	107.9
105.4	106.1	106.2	107.6
113.6	109.6	109.7	111.6
75	97.3	97.3	48.9
94	98.2	98.2	104.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186534&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186534&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186534&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 time8 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
vervaardigingmeubels[t] = + 54.6896103726068 -31.4214447834521winningrondstoffen[t] + 31.5139663075632industrie[t] + 0.384318238038688bouw[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
vervaardigingmeubels[t] =  +  54.6896103726068 -31.4214447834521winningrondstoffen[t] +  31.5139663075632industrie[t] +  0.384318238038688bouw[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186534&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]vervaardigingmeubels[t] =  +  54.6896103726068 -31.4214447834521winningrondstoffen[t] +  31.5139663075632industrie[t] +  0.384318238038688bouw[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186534&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186534&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
vervaardigingmeubels[t] = + 54.6896103726068 -31.4214447834521winningrondstoffen[t] + 31.5139663075632industrie[t] + 0.384318238038688bouw[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)54.68961037260689.8100495.574900
winningrondstoffen-31.42144478345216.81932-4.60779e-064e-06
industrie31.51396630756326.7417984.67447e-063e-06
bouw0.3843182380386880.0294413.054500

\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) & 54.6896103726068 & 9.810049 & 5.5749 & 0 & 0 \tabularnewline
winningrondstoffen & -31.4214447834521 & 6.81932 & -4.6077 & 9e-06 & 4e-06 \tabularnewline
industrie & 31.5139663075632 & 6.741798 & 4.6744 & 7e-06 & 3e-06 \tabularnewline
bouw & 0.384318238038688 & 0.02944 & 13.0545 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186534&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]54.6896103726068[/C][C]9.810049[/C][C]5.5749[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]winningrondstoffen[/C][C]-31.4214447834521[/C][C]6.81932[/C][C]-4.6077[/C][C]9e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]industrie[/C][C]31.5139663075632[/C][C]6.741798[/C][C]4.6744[/C][C]7e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]bouw[/C][C]0.384318238038688[/C][C]0.02944[/C][C]13.0545[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186534&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186534&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)54.68961037260689.8100495.574900
winningrondstoffen-31.42144478345216.81932-4.60779e-064e-06
industrie31.51396630756326.7417984.67447e-063e-06
bouw0.3843182380386880.0294413.054500







Multiple Linear Regression - Regression Statistics
Multiple R0.871229340532814
R-squared0.759040563805242
Adjusted R-squared0.754156250909402
F-TEST (value)155.403754835561
F-TEST (DF numerator)3
F-TEST (DF denominator)148
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.70804072902335
Sum Squared Residuals6659.67594249095

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.871229340532814 \tabularnewline
R-squared & 0.759040563805242 \tabularnewline
Adjusted R-squared & 0.754156250909402 \tabularnewline
F-TEST (value) & 155.403754835561 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.70804072902335 \tabularnewline
Sum Squared Residuals & 6659.67594249095 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186534&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.871229340532814[/C][/ROW]
[ROW][C]R-squared[/C][C]0.759040563805242[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.754156250909402[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]155.403754835561[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/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]6.70804072902335[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6659.67594249095[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186534&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186534&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 R0.871229340532814
R-squared0.759040563805242
Adjusted R-squared0.754156250909402
F-TEST (value)155.403754835561
F-TEST (DF numerator)3
F-TEST (DF denominator)148
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.70804072902335
Sum Squared Residuals6659.67594249095







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
193.788.41003885791055.28996114208951
2114.7107.0238297056017.67617029439873
3121.2110.52897257266510.6710274273351
498.696.3839332108892.21606678911099
5111.5106.8701307734574.62986922654309
6107.5100.2007848689517.29921513104851
769.170.4595281924414-1.35952819244142
888.398.8535324334527-10.5535324334527
9114.7109.2820576783685.41794232163222
10115.5107.1562217095868.34377829041392
11109.5102.8838835432646.61611645673599
1297.794.01680509039143.68319490960864
1310294.22035496418567.77964503581437
14107.5107.571116990855-0.071116990855209
15120.5106.56550181052213.934498189478
16101.994.91710778183826.98289221816184
17107.6101.2341769899276.36582301007292
18113.9108.3710846618495.52891533815063
1970.973.2059970825439-2.30599708254393
2093.494.9071686042948-1.5071686042948
21114.8103.75357042799811.0464295720021
22117.8113.1060150068164.6939849931842
23105.2102.5543549518262.64564504817371
2495.185.6372442869899.46275571301102
2597.595.17474483518972.32525516481034
26103.299.3801689318593.81983106814098
27111.6107.1291677193964.47083228060441
28105.4104.1286365136251.27136348637454
2997.8100.850556177433-3.05055617743273
30104.4111.801453637188-7.40145363718759
317571.79256140750693.20743859249312
3282.286.1575346256084-3.95753462560839
33116.2104.73573219814611.4642678018542
34115109.47635792055.52364207950009
3591.599.8000880072928-8.30008800729285
3689.582.19476380975967.30523619024034
3790.792.5713458363926-1.8713458363926
38100.1100.131726838023-0.0317268380231115
39100.1107.735537615301-7.63553761530083
4093.5101.481125755547-7.98112575554741
4184.498.4450091542923-14.0450091542923
42101.2103.495238469852-2.29523846985191
4375.378.3074567489307-3.00745674893069
4476.584.3640503550114-7.8640503550114
45105.6111.165934953711-5.56593495371071
46110.4110.0151163212090.38488367879148
4791.599.6954549812911-8.19545498129113
4888.193.4196819879849-5.31968198798496
4988.294.305047550045-6.10504755004503
5099.3101.552281104607-2.25228110460682
51117.1114.7970202956852.3029797043151
52100.5103.974911739159-3.47491173915944
5383.997.4827903450362-13.5827903450361
54110.7111.045659493116-0.345659493115766
5566.975.2300644163023-8.33006441630229
5685.993.0973161104891-7.19731611048909
57112.1113.615596256881-1.5155962568811
58105.5108.113452649997-2.61345264999673
59104105.641701903909-1.64170190390903
6097.897.0472209553470.752779044653035
6191.493.901501739318-2.50150173931804
62104.4103.5500126745420.849987325457665
63111.2104.4097654232316.79023457676909
64102.3112.634160275348-10.3341602753483
6594.699.9281949268893-5.32819492688933
66109.4112.353054369902-2.95305436990173
6769.172.5562049732402-3.45620497324025
6886.998.1340081892487-11.2340081892487
69118.3115.7165376386592.58346236134064
70102.3109.235078689945-6.93507868994548
71108.8108.6144766524450.185523347554633
72101.297.4592956506193.74070434938103
7399.193.7164741330065.38352586699398
74105.5101.6597501585163.84024984148379
75119.8114.8810025348394.91899746516068
7694.599.8000880072928-5.30008800729285
77101.4109.13118437372-7.73118437371954
78116.5112.6377350130323.86226498696784
7966.767.3700706836532-0.670070683653173
8091.6102.57287217781-10.9728721778096
81119.8115.6211696862294.1788303137706
82116.4112.7700986539463.62990134605415
83111.7111.284066453030.415933546970458
84102.496.3155930889146.084406911086
8599.3104.500137944567-5.20013794456694
86109.3108.6102070099670.689792990032528
87119115.5535607117823.44643928821777
88102.5100.5466659242741.953334075726
89104.9106.30502337035-1.40502337035045
90122.4114.4881450118447.91185498815554
9176.479.7941914168894-3.39419141688939
92103.2106.696455158437-3.49645515843693
93120.8111.4691095012529.33089049874835
94124.9120.4052122630694.49478773693083
95110.2113.609892999832-3.4098929998321
9699.792.49660786998717.20339213001289
9797.1112.306057418819-15.206057418819
98109.3115.576321737922-6.27632173792166
99109.4107.4842880059681.91571199403191
100117123.555896026371-6.55589602637123
101107.1106.7726109800890.327389019910907
102118.5120.049347907808-1.54934790780769
10385.186.2286638149326-1.12866381493261
10485.397.7788592221911-12.4788592221911
105129.7118.91775684799310.7822431520067
106128119.3974301173028.60256988269822
107103.3100.5331286874742.76687131252598
108103.993.552058550059310.3479414499407
10996.289.86191391900586.33808608099424
110106.3105.376946259010.923053740989636
111114.8108.7902779486686.00972205133195
112101.9101.8490603284670.0509396715327991
11390.997.9190622017677-7.01906220176768
114108.5107.9917514544930.508248545507101
11575.677.26125822127-1.66125822127002
11690.692.6396805994549-2.03968059945489
117121.1113.0768403769228.02315962307761
118116.6113.1259475672973.47405243270318
119105.7100.5964576190315.10354238096878
120101.189.921673154378411.1783268456216
1219788.70254308038638.29745691961369
122105.496.17681080274749.22318919725259
123117.9114.9108950736872.98910492631299
124104.5105.294394796264-0.794394796263757
12597.498.295543621893-0.895543621892971
126115.8113.1003297124262.69967028757361
12773.175.9140108272697-2.81401082726974
12890.698.2677975650713-7.66779756507131
129124.1115.5948260427448.50517395725617
130110.1109.865648268060.234351731940278
131103.4102.8582448875710.541755112428868
132109.486.721871800290722.6781281997093
13392.195.8522776424031-3.75227764240314
134107.8114.584907815362-6.78490781536215
135116.2126.178504317783-9.97850431778322
13697.5103.304481764169-5.8044817641687
137104.8112.301090350797-7.50109035079676
138106.2111.31608215233-5.11608215232999
13973.676.7808487629353-3.1808487629353
140100.7106.118544186808-5.41854418680778
141123.4120.447900808192.9520991918098
142109.1111.136021614041-2.03602161404062
143100.1114.105934492348-14.005934492348
144105.998.02579758740027.87420241259978
145104.8101.0683173919983.73168260800237
146110.8105.1307024811835.66929751881682
147118.4127.516520360195-9.11652036019488
14893.1108.746140347065-15.6461403470653
149105.4109.010183124509-3.61018312450946
150113.6110.8712814110532.728718588947
1517582.4851165087045-7.48511650870454
15294103.859616267748-9.85961626774784

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 93.7 & 88.4100388579105 & 5.28996114208951 \tabularnewline
2 & 114.7 & 107.023829705601 & 7.67617029439873 \tabularnewline
3 & 121.2 & 110.528972572665 & 10.6710274273351 \tabularnewline
4 & 98.6 & 96.383933210889 & 2.21606678911099 \tabularnewline
5 & 111.5 & 106.870130773457 & 4.62986922654309 \tabularnewline
6 & 107.5 & 100.200784868951 & 7.29921513104851 \tabularnewline
7 & 69.1 & 70.4595281924414 & -1.35952819244142 \tabularnewline
8 & 88.3 & 98.8535324334527 & -10.5535324334527 \tabularnewline
9 & 114.7 & 109.282057678368 & 5.41794232163222 \tabularnewline
10 & 115.5 & 107.156221709586 & 8.34377829041392 \tabularnewline
11 & 109.5 & 102.883883543264 & 6.61611645673599 \tabularnewline
12 & 97.7 & 94.0168050903914 & 3.68319490960864 \tabularnewline
13 & 102 & 94.2203549641856 & 7.77964503581437 \tabularnewline
14 & 107.5 & 107.571116990855 & -0.071116990855209 \tabularnewline
15 & 120.5 & 106.565501810522 & 13.934498189478 \tabularnewline
16 & 101.9 & 94.9171077818382 & 6.98289221816184 \tabularnewline
17 & 107.6 & 101.234176989927 & 6.36582301007292 \tabularnewline
18 & 113.9 & 108.371084661849 & 5.52891533815063 \tabularnewline
19 & 70.9 & 73.2059970825439 & -2.30599708254393 \tabularnewline
20 & 93.4 & 94.9071686042948 & -1.5071686042948 \tabularnewline
21 & 114.8 & 103.753570427998 & 11.0464295720021 \tabularnewline
22 & 117.8 & 113.106015006816 & 4.6939849931842 \tabularnewline
23 & 105.2 & 102.554354951826 & 2.64564504817371 \tabularnewline
24 & 95.1 & 85.637244286989 & 9.46275571301102 \tabularnewline
25 & 97.5 & 95.1747448351897 & 2.32525516481034 \tabularnewline
26 & 103.2 & 99.380168931859 & 3.81983106814098 \tabularnewline
27 & 111.6 & 107.129167719396 & 4.47083228060441 \tabularnewline
28 & 105.4 & 104.128636513625 & 1.27136348637454 \tabularnewline
29 & 97.8 & 100.850556177433 & -3.05055617743273 \tabularnewline
30 & 104.4 & 111.801453637188 & -7.40145363718759 \tabularnewline
31 & 75 & 71.7925614075069 & 3.20743859249312 \tabularnewline
32 & 82.2 & 86.1575346256084 & -3.95753462560839 \tabularnewline
33 & 116.2 & 104.735732198146 & 11.4642678018542 \tabularnewline
34 & 115 & 109.4763579205 & 5.52364207950009 \tabularnewline
35 & 91.5 & 99.8000880072928 & -8.30008800729285 \tabularnewline
36 & 89.5 & 82.1947638097596 & 7.30523619024034 \tabularnewline
37 & 90.7 & 92.5713458363926 & -1.8713458363926 \tabularnewline
38 & 100.1 & 100.131726838023 & -0.0317268380231115 \tabularnewline
39 & 100.1 & 107.735537615301 & -7.63553761530083 \tabularnewline
40 & 93.5 & 101.481125755547 & -7.98112575554741 \tabularnewline
41 & 84.4 & 98.4450091542923 & -14.0450091542923 \tabularnewline
42 & 101.2 & 103.495238469852 & -2.29523846985191 \tabularnewline
43 & 75.3 & 78.3074567489307 & -3.00745674893069 \tabularnewline
44 & 76.5 & 84.3640503550114 & -7.8640503550114 \tabularnewline
45 & 105.6 & 111.165934953711 & -5.56593495371071 \tabularnewline
46 & 110.4 & 110.015116321209 & 0.38488367879148 \tabularnewline
47 & 91.5 & 99.6954549812911 & -8.19545498129113 \tabularnewline
48 & 88.1 & 93.4196819879849 & -5.31968198798496 \tabularnewline
49 & 88.2 & 94.305047550045 & -6.10504755004503 \tabularnewline
50 & 99.3 & 101.552281104607 & -2.25228110460682 \tabularnewline
51 & 117.1 & 114.797020295685 & 2.3029797043151 \tabularnewline
52 & 100.5 & 103.974911739159 & -3.47491173915944 \tabularnewline
53 & 83.9 & 97.4827903450362 & -13.5827903450361 \tabularnewline
54 & 110.7 & 111.045659493116 & -0.345659493115766 \tabularnewline
55 & 66.9 & 75.2300644163023 & -8.33006441630229 \tabularnewline
56 & 85.9 & 93.0973161104891 & -7.19731611048909 \tabularnewline
57 & 112.1 & 113.615596256881 & -1.5155962568811 \tabularnewline
58 & 105.5 & 108.113452649997 & -2.61345264999673 \tabularnewline
59 & 104 & 105.641701903909 & -1.64170190390903 \tabularnewline
60 & 97.8 & 97.047220955347 & 0.752779044653035 \tabularnewline
61 & 91.4 & 93.901501739318 & -2.50150173931804 \tabularnewline
62 & 104.4 & 103.550012674542 & 0.849987325457665 \tabularnewline
63 & 111.2 & 104.409765423231 & 6.79023457676909 \tabularnewline
64 & 102.3 & 112.634160275348 & -10.3341602753483 \tabularnewline
65 & 94.6 & 99.9281949268893 & -5.32819492688933 \tabularnewline
66 & 109.4 & 112.353054369902 & -2.95305436990173 \tabularnewline
67 & 69.1 & 72.5562049732402 & -3.45620497324025 \tabularnewline
68 & 86.9 & 98.1340081892487 & -11.2340081892487 \tabularnewline
69 & 118.3 & 115.716537638659 & 2.58346236134064 \tabularnewline
70 & 102.3 & 109.235078689945 & -6.93507868994548 \tabularnewline
71 & 108.8 & 108.614476652445 & 0.185523347554633 \tabularnewline
72 & 101.2 & 97.459295650619 & 3.74070434938103 \tabularnewline
73 & 99.1 & 93.716474133006 & 5.38352586699398 \tabularnewline
74 & 105.5 & 101.659750158516 & 3.84024984148379 \tabularnewline
75 & 119.8 & 114.881002534839 & 4.91899746516068 \tabularnewline
76 & 94.5 & 99.8000880072928 & -5.30008800729285 \tabularnewline
77 & 101.4 & 109.13118437372 & -7.73118437371954 \tabularnewline
78 & 116.5 & 112.637735013032 & 3.86226498696784 \tabularnewline
79 & 66.7 & 67.3700706836532 & -0.670070683653173 \tabularnewline
80 & 91.6 & 102.57287217781 & -10.9728721778096 \tabularnewline
81 & 119.8 & 115.621169686229 & 4.1788303137706 \tabularnewline
82 & 116.4 & 112.770098653946 & 3.62990134605415 \tabularnewline
83 & 111.7 & 111.28406645303 & 0.415933546970458 \tabularnewline
84 & 102.4 & 96.315593088914 & 6.084406911086 \tabularnewline
85 & 99.3 & 104.500137944567 & -5.20013794456694 \tabularnewline
86 & 109.3 & 108.610207009967 & 0.689792990032528 \tabularnewline
87 & 119 & 115.553560711782 & 3.44643928821777 \tabularnewline
88 & 102.5 & 100.546665924274 & 1.953334075726 \tabularnewline
89 & 104.9 & 106.30502337035 & -1.40502337035045 \tabularnewline
90 & 122.4 & 114.488145011844 & 7.91185498815554 \tabularnewline
91 & 76.4 & 79.7941914168894 & -3.39419141688939 \tabularnewline
92 & 103.2 & 106.696455158437 & -3.49645515843693 \tabularnewline
93 & 120.8 & 111.469109501252 & 9.33089049874835 \tabularnewline
94 & 124.9 & 120.405212263069 & 4.49478773693083 \tabularnewline
95 & 110.2 & 113.609892999832 & -3.4098929998321 \tabularnewline
96 & 99.7 & 92.4966078699871 & 7.20339213001289 \tabularnewline
97 & 97.1 & 112.306057418819 & -15.206057418819 \tabularnewline
98 & 109.3 & 115.576321737922 & -6.27632173792166 \tabularnewline
99 & 109.4 & 107.484288005968 & 1.91571199403191 \tabularnewline
100 & 117 & 123.555896026371 & -6.55589602637123 \tabularnewline
101 & 107.1 & 106.772610980089 & 0.327389019910907 \tabularnewline
102 & 118.5 & 120.049347907808 & -1.54934790780769 \tabularnewline
103 & 85.1 & 86.2286638149326 & -1.12866381493261 \tabularnewline
104 & 85.3 & 97.7788592221911 & -12.4788592221911 \tabularnewline
105 & 129.7 & 118.917756847993 & 10.7822431520067 \tabularnewline
106 & 128 & 119.397430117302 & 8.60256988269822 \tabularnewline
107 & 103.3 & 100.533128687474 & 2.76687131252598 \tabularnewline
108 & 103.9 & 93.5520585500593 & 10.3479414499407 \tabularnewline
109 & 96.2 & 89.8619139190058 & 6.33808608099424 \tabularnewline
110 & 106.3 & 105.37694625901 & 0.923053740989636 \tabularnewline
111 & 114.8 & 108.790277948668 & 6.00972205133195 \tabularnewline
112 & 101.9 & 101.849060328467 & 0.0509396715327991 \tabularnewline
113 & 90.9 & 97.9190622017677 & -7.01906220176768 \tabularnewline
114 & 108.5 & 107.991751454493 & 0.508248545507101 \tabularnewline
115 & 75.6 & 77.26125822127 & -1.66125822127002 \tabularnewline
116 & 90.6 & 92.6396805994549 & -2.03968059945489 \tabularnewline
117 & 121.1 & 113.076840376922 & 8.02315962307761 \tabularnewline
118 & 116.6 & 113.125947567297 & 3.47405243270318 \tabularnewline
119 & 105.7 & 100.596457619031 & 5.10354238096878 \tabularnewline
120 & 101.1 & 89.9216731543784 & 11.1783268456216 \tabularnewline
121 & 97 & 88.7025430803863 & 8.29745691961369 \tabularnewline
122 & 105.4 & 96.1768108027474 & 9.22318919725259 \tabularnewline
123 & 117.9 & 114.910895073687 & 2.98910492631299 \tabularnewline
124 & 104.5 & 105.294394796264 & -0.794394796263757 \tabularnewline
125 & 97.4 & 98.295543621893 & -0.895543621892971 \tabularnewline
126 & 115.8 & 113.100329712426 & 2.69967028757361 \tabularnewline
127 & 73.1 & 75.9140108272697 & -2.81401082726974 \tabularnewline
128 & 90.6 & 98.2677975650713 & -7.66779756507131 \tabularnewline
129 & 124.1 & 115.594826042744 & 8.50517395725617 \tabularnewline
130 & 110.1 & 109.86564826806 & 0.234351731940278 \tabularnewline
131 & 103.4 & 102.858244887571 & 0.541755112428868 \tabularnewline
132 & 109.4 & 86.7218718002907 & 22.6781281997093 \tabularnewline
133 & 92.1 & 95.8522776424031 & -3.75227764240314 \tabularnewline
134 & 107.8 & 114.584907815362 & -6.78490781536215 \tabularnewline
135 & 116.2 & 126.178504317783 & -9.97850431778322 \tabularnewline
136 & 97.5 & 103.304481764169 & -5.8044817641687 \tabularnewline
137 & 104.8 & 112.301090350797 & -7.50109035079676 \tabularnewline
138 & 106.2 & 111.31608215233 & -5.11608215232999 \tabularnewline
139 & 73.6 & 76.7808487629353 & -3.1808487629353 \tabularnewline
140 & 100.7 & 106.118544186808 & -5.41854418680778 \tabularnewline
141 & 123.4 & 120.44790080819 & 2.9520991918098 \tabularnewline
142 & 109.1 & 111.136021614041 & -2.03602161404062 \tabularnewline
143 & 100.1 & 114.105934492348 & -14.005934492348 \tabularnewline
144 & 105.9 & 98.0257975874002 & 7.87420241259978 \tabularnewline
145 & 104.8 & 101.068317391998 & 3.73168260800237 \tabularnewline
146 & 110.8 & 105.130702481183 & 5.66929751881682 \tabularnewline
147 & 118.4 & 127.516520360195 & -9.11652036019488 \tabularnewline
148 & 93.1 & 108.746140347065 & -15.6461403470653 \tabularnewline
149 & 105.4 & 109.010183124509 & -3.61018312450946 \tabularnewline
150 & 113.6 & 110.871281411053 & 2.728718588947 \tabularnewline
151 & 75 & 82.4851165087045 & -7.48511650870454 \tabularnewline
152 & 94 & 103.859616267748 & -9.85961626774784 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186534&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]93.7[/C][C]88.4100388579105[/C][C]5.28996114208951[/C][/ROW]
[ROW][C]2[/C][C]114.7[/C][C]107.023829705601[/C][C]7.67617029439873[/C][/ROW]
[ROW][C]3[/C][C]121.2[/C][C]110.528972572665[/C][C]10.6710274273351[/C][/ROW]
[ROW][C]4[/C][C]98.6[/C][C]96.383933210889[/C][C]2.21606678911099[/C][/ROW]
[ROW][C]5[/C][C]111.5[/C][C]106.870130773457[/C][C]4.62986922654309[/C][/ROW]
[ROW][C]6[/C][C]107.5[/C][C]100.200784868951[/C][C]7.29921513104851[/C][/ROW]
[ROW][C]7[/C][C]69.1[/C][C]70.4595281924414[/C][C]-1.35952819244142[/C][/ROW]
[ROW][C]8[/C][C]88.3[/C][C]98.8535324334527[/C][C]-10.5535324334527[/C][/ROW]
[ROW][C]9[/C][C]114.7[/C][C]109.282057678368[/C][C]5.41794232163222[/C][/ROW]
[ROW][C]10[/C][C]115.5[/C][C]107.156221709586[/C][C]8.34377829041392[/C][/ROW]
[ROW][C]11[/C][C]109.5[/C][C]102.883883543264[/C][C]6.61611645673599[/C][/ROW]
[ROW][C]12[/C][C]97.7[/C][C]94.0168050903914[/C][C]3.68319490960864[/C][/ROW]
[ROW][C]13[/C][C]102[/C][C]94.2203549641856[/C][C]7.77964503581437[/C][/ROW]
[ROW][C]14[/C][C]107.5[/C][C]107.571116990855[/C][C]-0.071116990855209[/C][/ROW]
[ROW][C]15[/C][C]120.5[/C][C]106.565501810522[/C][C]13.934498189478[/C][/ROW]
[ROW][C]16[/C][C]101.9[/C][C]94.9171077818382[/C][C]6.98289221816184[/C][/ROW]
[ROW][C]17[/C][C]107.6[/C][C]101.234176989927[/C][C]6.36582301007292[/C][/ROW]
[ROW][C]18[/C][C]113.9[/C][C]108.371084661849[/C][C]5.52891533815063[/C][/ROW]
[ROW][C]19[/C][C]70.9[/C][C]73.2059970825439[/C][C]-2.30599708254393[/C][/ROW]
[ROW][C]20[/C][C]93.4[/C][C]94.9071686042948[/C][C]-1.5071686042948[/C][/ROW]
[ROW][C]21[/C][C]114.8[/C][C]103.753570427998[/C][C]11.0464295720021[/C][/ROW]
[ROW][C]22[/C][C]117.8[/C][C]113.106015006816[/C][C]4.6939849931842[/C][/ROW]
[ROW][C]23[/C][C]105.2[/C][C]102.554354951826[/C][C]2.64564504817371[/C][/ROW]
[ROW][C]24[/C][C]95.1[/C][C]85.637244286989[/C][C]9.46275571301102[/C][/ROW]
[ROW][C]25[/C][C]97.5[/C][C]95.1747448351897[/C][C]2.32525516481034[/C][/ROW]
[ROW][C]26[/C][C]103.2[/C][C]99.380168931859[/C][C]3.81983106814098[/C][/ROW]
[ROW][C]27[/C][C]111.6[/C][C]107.129167719396[/C][C]4.47083228060441[/C][/ROW]
[ROW][C]28[/C][C]105.4[/C][C]104.128636513625[/C][C]1.27136348637454[/C][/ROW]
[ROW][C]29[/C][C]97.8[/C][C]100.850556177433[/C][C]-3.05055617743273[/C][/ROW]
[ROW][C]30[/C][C]104.4[/C][C]111.801453637188[/C][C]-7.40145363718759[/C][/ROW]
[ROW][C]31[/C][C]75[/C][C]71.7925614075069[/C][C]3.20743859249312[/C][/ROW]
[ROW][C]32[/C][C]82.2[/C][C]86.1575346256084[/C][C]-3.95753462560839[/C][/ROW]
[ROW][C]33[/C][C]116.2[/C][C]104.735732198146[/C][C]11.4642678018542[/C][/ROW]
[ROW][C]34[/C][C]115[/C][C]109.4763579205[/C][C]5.52364207950009[/C][/ROW]
[ROW][C]35[/C][C]91.5[/C][C]99.8000880072928[/C][C]-8.30008800729285[/C][/ROW]
[ROW][C]36[/C][C]89.5[/C][C]82.1947638097596[/C][C]7.30523619024034[/C][/ROW]
[ROW][C]37[/C][C]90.7[/C][C]92.5713458363926[/C][C]-1.8713458363926[/C][/ROW]
[ROW][C]38[/C][C]100.1[/C][C]100.131726838023[/C][C]-0.0317268380231115[/C][/ROW]
[ROW][C]39[/C][C]100.1[/C][C]107.735537615301[/C][C]-7.63553761530083[/C][/ROW]
[ROW][C]40[/C][C]93.5[/C][C]101.481125755547[/C][C]-7.98112575554741[/C][/ROW]
[ROW][C]41[/C][C]84.4[/C][C]98.4450091542923[/C][C]-14.0450091542923[/C][/ROW]
[ROW][C]42[/C][C]101.2[/C][C]103.495238469852[/C][C]-2.29523846985191[/C][/ROW]
[ROW][C]43[/C][C]75.3[/C][C]78.3074567489307[/C][C]-3.00745674893069[/C][/ROW]
[ROW][C]44[/C][C]76.5[/C][C]84.3640503550114[/C][C]-7.8640503550114[/C][/ROW]
[ROW][C]45[/C][C]105.6[/C][C]111.165934953711[/C][C]-5.56593495371071[/C][/ROW]
[ROW][C]46[/C][C]110.4[/C][C]110.015116321209[/C][C]0.38488367879148[/C][/ROW]
[ROW][C]47[/C][C]91.5[/C][C]99.6954549812911[/C][C]-8.19545498129113[/C][/ROW]
[ROW][C]48[/C][C]88.1[/C][C]93.4196819879849[/C][C]-5.31968198798496[/C][/ROW]
[ROW][C]49[/C][C]88.2[/C][C]94.305047550045[/C][C]-6.10504755004503[/C][/ROW]
[ROW][C]50[/C][C]99.3[/C][C]101.552281104607[/C][C]-2.25228110460682[/C][/ROW]
[ROW][C]51[/C][C]117.1[/C][C]114.797020295685[/C][C]2.3029797043151[/C][/ROW]
[ROW][C]52[/C][C]100.5[/C][C]103.974911739159[/C][C]-3.47491173915944[/C][/ROW]
[ROW][C]53[/C][C]83.9[/C][C]97.4827903450362[/C][C]-13.5827903450361[/C][/ROW]
[ROW][C]54[/C][C]110.7[/C][C]111.045659493116[/C][C]-0.345659493115766[/C][/ROW]
[ROW][C]55[/C][C]66.9[/C][C]75.2300644163023[/C][C]-8.33006441630229[/C][/ROW]
[ROW][C]56[/C][C]85.9[/C][C]93.0973161104891[/C][C]-7.19731611048909[/C][/ROW]
[ROW][C]57[/C][C]112.1[/C][C]113.615596256881[/C][C]-1.5155962568811[/C][/ROW]
[ROW][C]58[/C][C]105.5[/C][C]108.113452649997[/C][C]-2.61345264999673[/C][/ROW]
[ROW][C]59[/C][C]104[/C][C]105.641701903909[/C][C]-1.64170190390903[/C][/ROW]
[ROW][C]60[/C][C]97.8[/C][C]97.047220955347[/C][C]0.752779044653035[/C][/ROW]
[ROW][C]61[/C][C]91.4[/C][C]93.901501739318[/C][C]-2.50150173931804[/C][/ROW]
[ROW][C]62[/C][C]104.4[/C][C]103.550012674542[/C][C]0.849987325457665[/C][/ROW]
[ROW][C]63[/C][C]111.2[/C][C]104.409765423231[/C][C]6.79023457676909[/C][/ROW]
[ROW][C]64[/C][C]102.3[/C][C]112.634160275348[/C][C]-10.3341602753483[/C][/ROW]
[ROW][C]65[/C][C]94.6[/C][C]99.9281949268893[/C][C]-5.32819492688933[/C][/ROW]
[ROW][C]66[/C][C]109.4[/C][C]112.353054369902[/C][C]-2.95305436990173[/C][/ROW]
[ROW][C]67[/C][C]69.1[/C][C]72.5562049732402[/C][C]-3.45620497324025[/C][/ROW]
[ROW][C]68[/C][C]86.9[/C][C]98.1340081892487[/C][C]-11.2340081892487[/C][/ROW]
[ROW][C]69[/C][C]118.3[/C][C]115.716537638659[/C][C]2.58346236134064[/C][/ROW]
[ROW][C]70[/C][C]102.3[/C][C]109.235078689945[/C][C]-6.93507868994548[/C][/ROW]
[ROW][C]71[/C][C]108.8[/C][C]108.614476652445[/C][C]0.185523347554633[/C][/ROW]
[ROW][C]72[/C][C]101.2[/C][C]97.459295650619[/C][C]3.74070434938103[/C][/ROW]
[ROW][C]73[/C][C]99.1[/C][C]93.716474133006[/C][C]5.38352586699398[/C][/ROW]
[ROW][C]74[/C][C]105.5[/C][C]101.659750158516[/C][C]3.84024984148379[/C][/ROW]
[ROW][C]75[/C][C]119.8[/C][C]114.881002534839[/C][C]4.91899746516068[/C][/ROW]
[ROW][C]76[/C][C]94.5[/C][C]99.8000880072928[/C][C]-5.30008800729285[/C][/ROW]
[ROW][C]77[/C][C]101.4[/C][C]109.13118437372[/C][C]-7.73118437371954[/C][/ROW]
[ROW][C]78[/C][C]116.5[/C][C]112.637735013032[/C][C]3.86226498696784[/C][/ROW]
[ROW][C]79[/C][C]66.7[/C][C]67.3700706836532[/C][C]-0.670070683653173[/C][/ROW]
[ROW][C]80[/C][C]91.6[/C][C]102.57287217781[/C][C]-10.9728721778096[/C][/ROW]
[ROW][C]81[/C][C]119.8[/C][C]115.621169686229[/C][C]4.1788303137706[/C][/ROW]
[ROW][C]82[/C][C]116.4[/C][C]112.770098653946[/C][C]3.62990134605415[/C][/ROW]
[ROW][C]83[/C][C]111.7[/C][C]111.28406645303[/C][C]0.415933546970458[/C][/ROW]
[ROW][C]84[/C][C]102.4[/C][C]96.315593088914[/C][C]6.084406911086[/C][/ROW]
[ROW][C]85[/C][C]99.3[/C][C]104.500137944567[/C][C]-5.20013794456694[/C][/ROW]
[ROW][C]86[/C][C]109.3[/C][C]108.610207009967[/C][C]0.689792990032528[/C][/ROW]
[ROW][C]87[/C][C]119[/C][C]115.553560711782[/C][C]3.44643928821777[/C][/ROW]
[ROW][C]88[/C][C]102.5[/C][C]100.546665924274[/C][C]1.953334075726[/C][/ROW]
[ROW][C]89[/C][C]104.9[/C][C]106.30502337035[/C][C]-1.40502337035045[/C][/ROW]
[ROW][C]90[/C][C]122.4[/C][C]114.488145011844[/C][C]7.91185498815554[/C][/ROW]
[ROW][C]91[/C][C]76.4[/C][C]79.7941914168894[/C][C]-3.39419141688939[/C][/ROW]
[ROW][C]92[/C][C]103.2[/C][C]106.696455158437[/C][C]-3.49645515843693[/C][/ROW]
[ROW][C]93[/C][C]120.8[/C][C]111.469109501252[/C][C]9.33089049874835[/C][/ROW]
[ROW][C]94[/C][C]124.9[/C][C]120.405212263069[/C][C]4.49478773693083[/C][/ROW]
[ROW][C]95[/C][C]110.2[/C][C]113.609892999832[/C][C]-3.4098929998321[/C][/ROW]
[ROW][C]96[/C][C]99.7[/C][C]92.4966078699871[/C][C]7.20339213001289[/C][/ROW]
[ROW][C]97[/C][C]97.1[/C][C]112.306057418819[/C][C]-15.206057418819[/C][/ROW]
[ROW][C]98[/C][C]109.3[/C][C]115.576321737922[/C][C]-6.27632173792166[/C][/ROW]
[ROW][C]99[/C][C]109.4[/C][C]107.484288005968[/C][C]1.91571199403191[/C][/ROW]
[ROW][C]100[/C][C]117[/C][C]123.555896026371[/C][C]-6.55589602637123[/C][/ROW]
[ROW][C]101[/C][C]107.1[/C][C]106.772610980089[/C][C]0.327389019910907[/C][/ROW]
[ROW][C]102[/C][C]118.5[/C][C]120.049347907808[/C][C]-1.54934790780769[/C][/ROW]
[ROW][C]103[/C][C]85.1[/C][C]86.2286638149326[/C][C]-1.12866381493261[/C][/ROW]
[ROW][C]104[/C][C]85.3[/C][C]97.7788592221911[/C][C]-12.4788592221911[/C][/ROW]
[ROW][C]105[/C][C]129.7[/C][C]118.917756847993[/C][C]10.7822431520067[/C][/ROW]
[ROW][C]106[/C][C]128[/C][C]119.397430117302[/C][C]8.60256988269822[/C][/ROW]
[ROW][C]107[/C][C]103.3[/C][C]100.533128687474[/C][C]2.76687131252598[/C][/ROW]
[ROW][C]108[/C][C]103.9[/C][C]93.5520585500593[/C][C]10.3479414499407[/C][/ROW]
[ROW][C]109[/C][C]96.2[/C][C]89.8619139190058[/C][C]6.33808608099424[/C][/ROW]
[ROW][C]110[/C][C]106.3[/C][C]105.37694625901[/C][C]0.923053740989636[/C][/ROW]
[ROW][C]111[/C][C]114.8[/C][C]108.790277948668[/C][C]6.00972205133195[/C][/ROW]
[ROW][C]112[/C][C]101.9[/C][C]101.849060328467[/C][C]0.0509396715327991[/C][/ROW]
[ROW][C]113[/C][C]90.9[/C][C]97.9190622017677[/C][C]-7.01906220176768[/C][/ROW]
[ROW][C]114[/C][C]108.5[/C][C]107.991751454493[/C][C]0.508248545507101[/C][/ROW]
[ROW][C]115[/C][C]75.6[/C][C]77.26125822127[/C][C]-1.66125822127002[/C][/ROW]
[ROW][C]116[/C][C]90.6[/C][C]92.6396805994549[/C][C]-2.03968059945489[/C][/ROW]
[ROW][C]117[/C][C]121.1[/C][C]113.076840376922[/C][C]8.02315962307761[/C][/ROW]
[ROW][C]118[/C][C]116.6[/C][C]113.125947567297[/C][C]3.47405243270318[/C][/ROW]
[ROW][C]119[/C][C]105.7[/C][C]100.596457619031[/C][C]5.10354238096878[/C][/ROW]
[ROW][C]120[/C][C]101.1[/C][C]89.9216731543784[/C][C]11.1783268456216[/C][/ROW]
[ROW][C]121[/C][C]97[/C][C]88.7025430803863[/C][C]8.29745691961369[/C][/ROW]
[ROW][C]122[/C][C]105.4[/C][C]96.1768108027474[/C][C]9.22318919725259[/C][/ROW]
[ROW][C]123[/C][C]117.9[/C][C]114.910895073687[/C][C]2.98910492631299[/C][/ROW]
[ROW][C]124[/C][C]104.5[/C][C]105.294394796264[/C][C]-0.794394796263757[/C][/ROW]
[ROW][C]125[/C][C]97.4[/C][C]98.295543621893[/C][C]-0.895543621892971[/C][/ROW]
[ROW][C]126[/C][C]115.8[/C][C]113.100329712426[/C][C]2.69967028757361[/C][/ROW]
[ROW][C]127[/C][C]73.1[/C][C]75.9140108272697[/C][C]-2.81401082726974[/C][/ROW]
[ROW][C]128[/C][C]90.6[/C][C]98.2677975650713[/C][C]-7.66779756507131[/C][/ROW]
[ROW][C]129[/C][C]124.1[/C][C]115.594826042744[/C][C]8.50517395725617[/C][/ROW]
[ROW][C]130[/C][C]110.1[/C][C]109.86564826806[/C][C]0.234351731940278[/C][/ROW]
[ROW][C]131[/C][C]103.4[/C][C]102.858244887571[/C][C]0.541755112428868[/C][/ROW]
[ROW][C]132[/C][C]109.4[/C][C]86.7218718002907[/C][C]22.6781281997093[/C][/ROW]
[ROW][C]133[/C][C]92.1[/C][C]95.8522776424031[/C][C]-3.75227764240314[/C][/ROW]
[ROW][C]134[/C][C]107.8[/C][C]114.584907815362[/C][C]-6.78490781536215[/C][/ROW]
[ROW][C]135[/C][C]116.2[/C][C]126.178504317783[/C][C]-9.97850431778322[/C][/ROW]
[ROW][C]136[/C][C]97.5[/C][C]103.304481764169[/C][C]-5.8044817641687[/C][/ROW]
[ROW][C]137[/C][C]104.8[/C][C]112.301090350797[/C][C]-7.50109035079676[/C][/ROW]
[ROW][C]138[/C][C]106.2[/C][C]111.31608215233[/C][C]-5.11608215232999[/C][/ROW]
[ROW][C]139[/C][C]73.6[/C][C]76.7808487629353[/C][C]-3.1808487629353[/C][/ROW]
[ROW][C]140[/C][C]100.7[/C][C]106.118544186808[/C][C]-5.41854418680778[/C][/ROW]
[ROW][C]141[/C][C]123.4[/C][C]120.44790080819[/C][C]2.9520991918098[/C][/ROW]
[ROW][C]142[/C][C]109.1[/C][C]111.136021614041[/C][C]-2.03602161404062[/C][/ROW]
[ROW][C]143[/C][C]100.1[/C][C]114.105934492348[/C][C]-14.005934492348[/C][/ROW]
[ROW][C]144[/C][C]105.9[/C][C]98.0257975874002[/C][C]7.87420241259978[/C][/ROW]
[ROW][C]145[/C][C]104.8[/C][C]101.068317391998[/C][C]3.73168260800237[/C][/ROW]
[ROW][C]146[/C][C]110.8[/C][C]105.130702481183[/C][C]5.66929751881682[/C][/ROW]
[ROW][C]147[/C][C]118.4[/C][C]127.516520360195[/C][C]-9.11652036019488[/C][/ROW]
[ROW][C]148[/C][C]93.1[/C][C]108.746140347065[/C][C]-15.6461403470653[/C][/ROW]
[ROW][C]149[/C][C]105.4[/C][C]109.010183124509[/C][C]-3.61018312450946[/C][/ROW]
[ROW][C]150[/C][C]113.6[/C][C]110.871281411053[/C][C]2.728718588947[/C][/ROW]
[ROW][C]151[/C][C]75[/C][C]82.4851165087045[/C][C]-7.48511650870454[/C][/ROW]
[ROW][C]152[/C][C]94[/C][C]103.859616267748[/C][C]-9.85961626774784[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186534&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186534&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
193.788.41003885791055.28996114208951
2114.7107.0238297056017.67617029439873
3121.2110.52897257266510.6710274273351
498.696.3839332108892.21606678911099
5111.5106.8701307734574.62986922654309
6107.5100.2007848689517.29921513104851
769.170.4595281924414-1.35952819244142
888.398.8535324334527-10.5535324334527
9114.7109.2820576783685.41794232163222
10115.5107.1562217095868.34377829041392
11109.5102.8838835432646.61611645673599
1297.794.01680509039143.68319490960864
1310294.22035496418567.77964503581437
14107.5107.571116990855-0.071116990855209
15120.5106.56550181052213.934498189478
16101.994.91710778183826.98289221816184
17107.6101.2341769899276.36582301007292
18113.9108.3710846618495.52891533815063
1970.973.2059970825439-2.30599708254393
2093.494.9071686042948-1.5071686042948
21114.8103.75357042799811.0464295720021
22117.8113.1060150068164.6939849931842
23105.2102.5543549518262.64564504817371
2495.185.6372442869899.46275571301102
2597.595.17474483518972.32525516481034
26103.299.3801689318593.81983106814098
27111.6107.1291677193964.47083228060441
28105.4104.1286365136251.27136348637454
2997.8100.850556177433-3.05055617743273
30104.4111.801453637188-7.40145363718759
317571.79256140750693.20743859249312
3282.286.1575346256084-3.95753462560839
33116.2104.73573219814611.4642678018542
34115109.47635792055.52364207950009
3591.599.8000880072928-8.30008800729285
3689.582.19476380975967.30523619024034
3790.792.5713458363926-1.8713458363926
38100.1100.131726838023-0.0317268380231115
39100.1107.735537615301-7.63553761530083
4093.5101.481125755547-7.98112575554741
4184.498.4450091542923-14.0450091542923
42101.2103.495238469852-2.29523846985191
4375.378.3074567489307-3.00745674893069
4476.584.3640503550114-7.8640503550114
45105.6111.165934953711-5.56593495371071
46110.4110.0151163212090.38488367879148
4791.599.6954549812911-8.19545498129113
4888.193.4196819879849-5.31968198798496
4988.294.305047550045-6.10504755004503
5099.3101.552281104607-2.25228110460682
51117.1114.7970202956852.3029797043151
52100.5103.974911739159-3.47491173915944
5383.997.4827903450362-13.5827903450361
54110.7111.045659493116-0.345659493115766
5566.975.2300644163023-8.33006441630229
5685.993.0973161104891-7.19731611048909
57112.1113.615596256881-1.5155962568811
58105.5108.113452649997-2.61345264999673
59104105.641701903909-1.64170190390903
6097.897.0472209553470.752779044653035
6191.493.901501739318-2.50150173931804
62104.4103.5500126745420.849987325457665
63111.2104.4097654232316.79023457676909
64102.3112.634160275348-10.3341602753483
6594.699.9281949268893-5.32819492688933
66109.4112.353054369902-2.95305436990173
6769.172.5562049732402-3.45620497324025
6886.998.1340081892487-11.2340081892487
69118.3115.7165376386592.58346236134064
70102.3109.235078689945-6.93507868994548
71108.8108.6144766524450.185523347554633
72101.297.4592956506193.74070434938103
7399.193.7164741330065.38352586699398
74105.5101.6597501585163.84024984148379
75119.8114.8810025348394.91899746516068
7694.599.8000880072928-5.30008800729285
77101.4109.13118437372-7.73118437371954
78116.5112.6377350130323.86226498696784
7966.767.3700706836532-0.670070683653173
8091.6102.57287217781-10.9728721778096
81119.8115.6211696862294.1788303137706
82116.4112.7700986539463.62990134605415
83111.7111.284066453030.415933546970458
84102.496.3155930889146.084406911086
8599.3104.500137944567-5.20013794456694
86109.3108.6102070099670.689792990032528
87119115.5535607117823.44643928821777
88102.5100.5466659242741.953334075726
89104.9106.30502337035-1.40502337035045
90122.4114.4881450118447.91185498815554
9176.479.7941914168894-3.39419141688939
92103.2106.696455158437-3.49645515843693
93120.8111.4691095012529.33089049874835
94124.9120.4052122630694.49478773693083
95110.2113.609892999832-3.4098929998321
9699.792.49660786998717.20339213001289
9797.1112.306057418819-15.206057418819
98109.3115.576321737922-6.27632173792166
99109.4107.4842880059681.91571199403191
100117123.555896026371-6.55589602637123
101107.1106.7726109800890.327389019910907
102118.5120.049347907808-1.54934790780769
10385.186.2286638149326-1.12866381493261
10485.397.7788592221911-12.4788592221911
105129.7118.91775684799310.7822431520067
106128119.3974301173028.60256988269822
107103.3100.5331286874742.76687131252598
108103.993.552058550059310.3479414499407
10996.289.86191391900586.33808608099424
110106.3105.376946259010.923053740989636
111114.8108.7902779486686.00972205133195
112101.9101.8490603284670.0509396715327991
11390.997.9190622017677-7.01906220176768
114108.5107.9917514544930.508248545507101
11575.677.26125822127-1.66125822127002
11690.692.6396805994549-2.03968059945489
117121.1113.0768403769228.02315962307761
118116.6113.1259475672973.47405243270318
119105.7100.5964576190315.10354238096878
120101.189.921673154378411.1783268456216
1219788.70254308038638.29745691961369
122105.496.17681080274749.22318919725259
123117.9114.9108950736872.98910492631299
124104.5105.294394796264-0.794394796263757
12597.498.295543621893-0.895543621892971
126115.8113.1003297124262.69967028757361
12773.175.9140108272697-2.81401082726974
12890.698.2677975650713-7.66779756507131
129124.1115.5948260427448.50517395725617
130110.1109.865648268060.234351731940278
131103.4102.8582448875710.541755112428868
132109.486.721871800290722.6781281997093
13392.195.8522776424031-3.75227764240314
134107.8114.584907815362-6.78490781536215
135116.2126.178504317783-9.97850431778322
13697.5103.304481764169-5.8044817641687
137104.8112.301090350797-7.50109035079676
138106.2111.31608215233-5.11608215232999
13973.676.7808487629353-3.1808487629353
140100.7106.118544186808-5.41854418680778
141123.4120.447900808192.9520991918098
142109.1111.136021614041-2.03602161404062
143100.1114.105934492348-14.005934492348
144105.998.02579758740027.87420241259978
145104.8101.0683173919983.73168260800237
146110.8105.1307024811835.66929751881682
147118.4127.516520360195-9.11652036019488
14893.1108.746140347065-15.6461403470653
149105.4109.010183124509-3.61018312450946
150113.6110.8712814110532.728718588947
1517582.4851165087045-7.48511650870454
15294103.859616267748-9.85961626774784







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.08204426189295510.164088523785910.917955738107045
80.4126669857437870.8253339714875740.587333014256213
90.3394597080611870.6789194161223740.660540291938813
100.2309983693730020.4619967387460040.769001630626998
110.1543981711601950.308796342320390.845601828839805
120.1003414390920930.2006828781841850.899658560907907
130.07791369833644170.1558273966728830.922086301663558
140.1166167696570230.2332335393140460.883383230342977
150.08418899297269070.1683779859453810.915811007027309
160.05568647522142360.1113729504428470.944313524778576
170.04366484864641390.08732969729282780.956335151353586
180.04187180142432140.08374360284864280.958128198575679
190.04124350205418310.08248700410836610.958756497945817
200.03154808924918570.06309617849837140.968451910750814
210.02581960125824430.05163920251648860.974180398741756
220.03002934117875110.06005868235750210.969970658821249
230.02763382958683860.05526765917367720.972366170413161
240.03402477168372480.06804954336744960.965975228316275
250.02865428766342520.05730857532685050.971345712336575
260.02122323580661060.04244647161322120.978776764193389
270.02000530228634990.04001060457269980.97999469771365
280.02068502612033440.04137005224066870.979314973879666
290.03310156708818290.06620313417636590.966898432911817
300.1014552318031490.2029104636062980.898544768196851
310.07843420069605040.1568684013921010.92156579930395
320.08107789431073350.1621557886214670.918922105689266
330.0862643817989840.1725287635979680.913735618201016
340.0785437212882460.1570874425764920.921456278711754
350.2110912199186380.4221824398372760.788908780081362
360.2028461522688310.4056923045376620.797153847731169
370.2059570411856110.4119140823712220.794042958814389
380.1878115909354440.3756231818708890.812188409064556
390.3203340005121960.6406680010243910.679665999487804
400.4305909267815570.8611818535631140.569409073218443
410.6792217492222230.6415565015555530.320778250777777
420.6525548772586640.6948902454826720.347445122741336
430.6141417961943390.7717164076113210.385858203805661
440.6242905980859640.7514188038280730.375709401914036
450.6341285051360050.731742989727990.365871494863995
460.5870432588204510.8259134823590990.412956741179549
470.6245294610017630.7509410779964730.375470538998237
480.6041227495400310.7917545009199370.395877250459969
490.5919401176534280.8161197646931430.408059882346571
500.553221328825890.893557342348220.44677867117411
510.5090818611449110.9818362777101770.490918138855088
520.4733481691985120.9466963383970240.526651830801488
530.6139370576894910.7721258846210180.386062942310509
540.5656665120006260.8686669759987480.434333487999374
550.5660774955144280.8678450089711440.433922504485572
560.5612848694062560.8774302611874880.438715130593744
570.5161182282017220.9677635435965550.483881771798278
580.4712694323523090.9425388647046190.528730567647691
590.4256789944593650.8513579889187290.574321005540635
600.3843939015598480.7687878031196950.615606098440152
610.3406062585479550.681212517095910.659393741452045
620.3039572694194910.6079145388389820.696042730580509
630.3190177343029120.6380354686058240.680982265697088
640.3787619805086650.7575239610173290.621238019491336
650.3545684339152630.7091368678305260.645431566084737
660.3173425560597590.6346851121195190.682657443940241
670.2844253791704350.5688507583408710.715574620829565
680.3501033521706360.7002067043412730.649896647829364
690.3156188317638180.6312376635276360.684381168236182
700.3120542682285860.6241085364571730.687945731771414
710.2720427084561640.5440854169123290.727957291543836
720.2530920062763940.5061840125527880.746907993723606
730.2482800648391240.4965601296782480.751719935160876
740.2284837916337550.456967583267510.771516208366245
750.2135606530559270.4271213061118550.786439346944073
760.1962268071605270.3924536143210530.803773192839473
770.2038080060366180.4076160120732360.796191993963382
780.1830209487562220.3660418975124440.816979051243778
790.1677247556266480.3354495112532950.832275244373352
800.2152451354002990.4304902708005980.784754864599701
810.1955386918318290.3910773836636580.804461308168171
820.1728927222264080.3457854444528150.827107277773592
830.1445729238851750.289145847770350.855427076114825
840.1398730768379490.2797461536758980.860126923162051
850.129621143524080.259242287048160.87037885647592
860.1067448912910660.2134897825821330.893255108708934
870.09054216371773480.181084327435470.909457836282265
880.07399464959953430.1479892991990690.926005350400466
890.05976054638732780.1195210927746560.940239453612672
900.0631970825429080.1263941650858160.936802917457092
910.05810469833405130.1162093966681030.941895301665949
920.04814613152263910.09629226304527810.951853868477361
930.05997919137077880.1199583827415580.940020808629221
940.05204616941795330.1040923388359070.947953830582047
950.04346722902620840.08693445805241670.956532770973792
960.043468462844360.086936925688720.95653153715564
970.1088928512930160.2177857025860330.891107148706984
980.1039108087398060.2078216174796120.896089191260194
990.08452621885095360.1690524377019070.915473781149046
1000.08033918410785560.1606783682157110.919660815892144
1010.06346917804059430.1269383560811890.936530821959406
1020.04982489326518730.09964978653037460.950175106734813
1030.04127053001079040.08254106002158080.95872946998921
1040.07185336732271250.1437067346454250.928146632677288
1050.1039975326587430.2079950653174860.896002467341257
1060.1285848440350520.2571696880701040.871415155964948
1070.1073732298321110.2147464596642220.892626770167889
1080.1353283060920560.2706566121841120.864671693907944
1090.1274984171561640.2549968343123280.872501582843836
1100.1093515987193270.2187031974386530.890648401280673
1110.1148906882413510.2297813764827010.885109311758649
1120.09544239918496170.1908847983699230.904557600815038
1130.08647431992059110.1729486398411820.913525680079409
1140.06823970076037280.1364794015207460.931760299239627
1150.05924412985258560.1184882597051710.940755870147414
1160.04570730531974230.09141461063948470.954292694680258
1170.05857336880250830.1171467376050170.941426631197492
1180.0536563705932170.1073127411864340.946343629406783
1190.05201648537669910.1040329707533980.947983514623301
1200.08833935405368130.1766787081073630.911660645946319
1210.1037968170344560.2075936340689120.896203182965544
1220.1458257304818690.2916514609637370.854174269518131
1230.1233149540955330.2466299081910660.876685045904467
1240.09793415188921230.1958683037784250.902065848110788
1250.07495021350431690.1499004270086340.925049786495683
1260.05833731754950890.1166746350990180.941662682450491
1270.0559305769525560.1118611539051120.944069423047444
1280.04617338765487880.09234677530975750.953826612345121
1290.07904937226940520.158098744538810.920950627730595
1300.06179906096580120.1235981219316020.938200939034199
1310.04466634672028560.08933269344057120.955333653279714
1320.4057470956274290.8114941912548580.594252904372571
1330.3427389139344620.6854778278689240.657261086065538
1340.288444284525960.576888569051920.71155571547404
1350.3359780488218940.6719560976437890.664021951178106
1360.294171156900690.5883423138013810.70582884309931
1370.4724040570382390.9448081140764770.527595942961761
1380.426099284307350.8521985686146990.57390071569265
1390.3960648052933070.7921296105866140.603935194706693
1400.337713249284590.675426498569180.66228675071541
1410.2485727726333330.4971455452666650.751427227366667
1420.1698551605930730.3397103211861460.830144839406927
1430.1913505381316450.3827010762632890.808649461868355
1440.17050755534060.3410151106811990.8294924446594
1450.1196977533609190.2393955067218390.880302246639081

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0820442618929551 & 0.16408852378591 & 0.917955738107045 \tabularnewline
8 & 0.412666985743787 & 0.825333971487574 & 0.587333014256213 \tabularnewline
9 & 0.339459708061187 & 0.678919416122374 & 0.660540291938813 \tabularnewline
10 & 0.230998369373002 & 0.461996738746004 & 0.769001630626998 \tabularnewline
11 & 0.154398171160195 & 0.30879634232039 & 0.845601828839805 \tabularnewline
12 & 0.100341439092093 & 0.200682878184185 & 0.899658560907907 \tabularnewline
13 & 0.0779136983364417 & 0.155827396672883 & 0.922086301663558 \tabularnewline
14 & 0.116616769657023 & 0.233233539314046 & 0.883383230342977 \tabularnewline
15 & 0.0841889929726907 & 0.168377985945381 & 0.915811007027309 \tabularnewline
16 & 0.0556864752214236 & 0.111372950442847 & 0.944313524778576 \tabularnewline
17 & 0.0436648486464139 & 0.0873296972928278 & 0.956335151353586 \tabularnewline
18 & 0.0418718014243214 & 0.0837436028486428 & 0.958128198575679 \tabularnewline
19 & 0.0412435020541831 & 0.0824870041083661 & 0.958756497945817 \tabularnewline
20 & 0.0315480892491857 & 0.0630961784983714 & 0.968451910750814 \tabularnewline
21 & 0.0258196012582443 & 0.0516392025164886 & 0.974180398741756 \tabularnewline
22 & 0.0300293411787511 & 0.0600586823575021 & 0.969970658821249 \tabularnewline
23 & 0.0276338295868386 & 0.0552676591736772 & 0.972366170413161 \tabularnewline
24 & 0.0340247716837248 & 0.0680495433674496 & 0.965975228316275 \tabularnewline
25 & 0.0286542876634252 & 0.0573085753268505 & 0.971345712336575 \tabularnewline
26 & 0.0212232358066106 & 0.0424464716132212 & 0.978776764193389 \tabularnewline
27 & 0.0200053022863499 & 0.0400106045726998 & 0.97999469771365 \tabularnewline
28 & 0.0206850261203344 & 0.0413700522406687 & 0.979314973879666 \tabularnewline
29 & 0.0331015670881829 & 0.0662031341763659 & 0.966898432911817 \tabularnewline
30 & 0.101455231803149 & 0.202910463606298 & 0.898544768196851 \tabularnewline
31 & 0.0784342006960504 & 0.156868401392101 & 0.92156579930395 \tabularnewline
32 & 0.0810778943107335 & 0.162155788621467 & 0.918922105689266 \tabularnewline
33 & 0.086264381798984 & 0.172528763597968 & 0.913735618201016 \tabularnewline
34 & 0.078543721288246 & 0.157087442576492 & 0.921456278711754 \tabularnewline
35 & 0.211091219918638 & 0.422182439837276 & 0.788908780081362 \tabularnewline
36 & 0.202846152268831 & 0.405692304537662 & 0.797153847731169 \tabularnewline
37 & 0.205957041185611 & 0.411914082371222 & 0.794042958814389 \tabularnewline
38 & 0.187811590935444 & 0.375623181870889 & 0.812188409064556 \tabularnewline
39 & 0.320334000512196 & 0.640668001024391 & 0.679665999487804 \tabularnewline
40 & 0.430590926781557 & 0.861181853563114 & 0.569409073218443 \tabularnewline
41 & 0.679221749222223 & 0.641556501555553 & 0.320778250777777 \tabularnewline
42 & 0.652554877258664 & 0.694890245482672 & 0.347445122741336 \tabularnewline
43 & 0.614141796194339 & 0.771716407611321 & 0.385858203805661 \tabularnewline
44 & 0.624290598085964 & 0.751418803828073 & 0.375709401914036 \tabularnewline
45 & 0.634128505136005 & 0.73174298972799 & 0.365871494863995 \tabularnewline
46 & 0.587043258820451 & 0.825913482359099 & 0.412956741179549 \tabularnewline
47 & 0.624529461001763 & 0.750941077996473 & 0.375470538998237 \tabularnewline
48 & 0.604122749540031 & 0.791754500919937 & 0.395877250459969 \tabularnewline
49 & 0.591940117653428 & 0.816119764693143 & 0.408059882346571 \tabularnewline
50 & 0.55322132882589 & 0.89355734234822 & 0.44677867117411 \tabularnewline
51 & 0.509081861144911 & 0.981836277710177 & 0.490918138855088 \tabularnewline
52 & 0.473348169198512 & 0.946696338397024 & 0.526651830801488 \tabularnewline
53 & 0.613937057689491 & 0.772125884621018 & 0.386062942310509 \tabularnewline
54 & 0.565666512000626 & 0.868666975998748 & 0.434333487999374 \tabularnewline
55 & 0.566077495514428 & 0.867845008971144 & 0.433922504485572 \tabularnewline
56 & 0.561284869406256 & 0.877430261187488 & 0.438715130593744 \tabularnewline
57 & 0.516118228201722 & 0.967763543596555 & 0.483881771798278 \tabularnewline
58 & 0.471269432352309 & 0.942538864704619 & 0.528730567647691 \tabularnewline
59 & 0.425678994459365 & 0.851357988918729 & 0.574321005540635 \tabularnewline
60 & 0.384393901559848 & 0.768787803119695 & 0.615606098440152 \tabularnewline
61 & 0.340606258547955 & 0.68121251709591 & 0.659393741452045 \tabularnewline
62 & 0.303957269419491 & 0.607914538838982 & 0.696042730580509 \tabularnewline
63 & 0.319017734302912 & 0.638035468605824 & 0.680982265697088 \tabularnewline
64 & 0.378761980508665 & 0.757523961017329 & 0.621238019491336 \tabularnewline
65 & 0.354568433915263 & 0.709136867830526 & 0.645431566084737 \tabularnewline
66 & 0.317342556059759 & 0.634685112119519 & 0.682657443940241 \tabularnewline
67 & 0.284425379170435 & 0.568850758340871 & 0.715574620829565 \tabularnewline
68 & 0.350103352170636 & 0.700206704341273 & 0.649896647829364 \tabularnewline
69 & 0.315618831763818 & 0.631237663527636 & 0.684381168236182 \tabularnewline
70 & 0.312054268228586 & 0.624108536457173 & 0.687945731771414 \tabularnewline
71 & 0.272042708456164 & 0.544085416912329 & 0.727957291543836 \tabularnewline
72 & 0.253092006276394 & 0.506184012552788 & 0.746907993723606 \tabularnewline
73 & 0.248280064839124 & 0.496560129678248 & 0.751719935160876 \tabularnewline
74 & 0.228483791633755 & 0.45696758326751 & 0.771516208366245 \tabularnewline
75 & 0.213560653055927 & 0.427121306111855 & 0.786439346944073 \tabularnewline
76 & 0.196226807160527 & 0.392453614321053 & 0.803773192839473 \tabularnewline
77 & 0.203808006036618 & 0.407616012073236 & 0.796191993963382 \tabularnewline
78 & 0.183020948756222 & 0.366041897512444 & 0.816979051243778 \tabularnewline
79 & 0.167724755626648 & 0.335449511253295 & 0.832275244373352 \tabularnewline
80 & 0.215245135400299 & 0.430490270800598 & 0.784754864599701 \tabularnewline
81 & 0.195538691831829 & 0.391077383663658 & 0.804461308168171 \tabularnewline
82 & 0.172892722226408 & 0.345785444452815 & 0.827107277773592 \tabularnewline
83 & 0.144572923885175 & 0.28914584777035 & 0.855427076114825 \tabularnewline
84 & 0.139873076837949 & 0.279746153675898 & 0.860126923162051 \tabularnewline
85 & 0.12962114352408 & 0.25924228704816 & 0.87037885647592 \tabularnewline
86 & 0.106744891291066 & 0.213489782582133 & 0.893255108708934 \tabularnewline
87 & 0.0905421637177348 & 0.18108432743547 & 0.909457836282265 \tabularnewline
88 & 0.0739946495995343 & 0.147989299199069 & 0.926005350400466 \tabularnewline
89 & 0.0597605463873278 & 0.119521092774656 & 0.940239453612672 \tabularnewline
90 & 0.063197082542908 & 0.126394165085816 & 0.936802917457092 \tabularnewline
91 & 0.0581046983340513 & 0.116209396668103 & 0.941895301665949 \tabularnewline
92 & 0.0481461315226391 & 0.0962922630452781 & 0.951853868477361 \tabularnewline
93 & 0.0599791913707788 & 0.119958382741558 & 0.940020808629221 \tabularnewline
94 & 0.0520461694179533 & 0.104092338835907 & 0.947953830582047 \tabularnewline
95 & 0.0434672290262084 & 0.0869344580524167 & 0.956532770973792 \tabularnewline
96 & 0.04346846284436 & 0.08693692568872 & 0.95653153715564 \tabularnewline
97 & 0.108892851293016 & 0.217785702586033 & 0.891107148706984 \tabularnewline
98 & 0.103910808739806 & 0.207821617479612 & 0.896089191260194 \tabularnewline
99 & 0.0845262188509536 & 0.169052437701907 & 0.915473781149046 \tabularnewline
100 & 0.0803391841078556 & 0.160678368215711 & 0.919660815892144 \tabularnewline
101 & 0.0634691780405943 & 0.126938356081189 & 0.936530821959406 \tabularnewline
102 & 0.0498248932651873 & 0.0996497865303746 & 0.950175106734813 \tabularnewline
103 & 0.0412705300107904 & 0.0825410600215808 & 0.95872946998921 \tabularnewline
104 & 0.0718533673227125 & 0.143706734645425 & 0.928146632677288 \tabularnewline
105 & 0.103997532658743 & 0.207995065317486 & 0.896002467341257 \tabularnewline
106 & 0.128584844035052 & 0.257169688070104 & 0.871415155964948 \tabularnewline
107 & 0.107373229832111 & 0.214746459664222 & 0.892626770167889 \tabularnewline
108 & 0.135328306092056 & 0.270656612184112 & 0.864671693907944 \tabularnewline
109 & 0.127498417156164 & 0.254996834312328 & 0.872501582843836 \tabularnewline
110 & 0.109351598719327 & 0.218703197438653 & 0.890648401280673 \tabularnewline
111 & 0.114890688241351 & 0.229781376482701 & 0.885109311758649 \tabularnewline
112 & 0.0954423991849617 & 0.190884798369923 & 0.904557600815038 \tabularnewline
113 & 0.0864743199205911 & 0.172948639841182 & 0.913525680079409 \tabularnewline
114 & 0.0682397007603728 & 0.136479401520746 & 0.931760299239627 \tabularnewline
115 & 0.0592441298525856 & 0.118488259705171 & 0.940755870147414 \tabularnewline
116 & 0.0457073053197423 & 0.0914146106394847 & 0.954292694680258 \tabularnewline
117 & 0.0585733688025083 & 0.117146737605017 & 0.941426631197492 \tabularnewline
118 & 0.053656370593217 & 0.107312741186434 & 0.946343629406783 \tabularnewline
119 & 0.0520164853766991 & 0.104032970753398 & 0.947983514623301 \tabularnewline
120 & 0.0883393540536813 & 0.176678708107363 & 0.911660645946319 \tabularnewline
121 & 0.103796817034456 & 0.207593634068912 & 0.896203182965544 \tabularnewline
122 & 0.145825730481869 & 0.291651460963737 & 0.854174269518131 \tabularnewline
123 & 0.123314954095533 & 0.246629908191066 & 0.876685045904467 \tabularnewline
124 & 0.0979341518892123 & 0.195868303778425 & 0.902065848110788 \tabularnewline
125 & 0.0749502135043169 & 0.149900427008634 & 0.925049786495683 \tabularnewline
126 & 0.0583373175495089 & 0.116674635099018 & 0.941662682450491 \tabularnewline
127 & 0.055930576952556 & 0.111861153905112 & 0.944069423047444 \tabularnewline
128 & 0.0461733876548788 & 0.0923467753097575 & 0.953826612345121 \tabularnewline
129 & 0.0790493722694052 & 0.15809874453881 & 0.920950627730595 \tabularnewline
130 & 0.0617990609658012 & 0.123598121931602 & 0.938200939034199 \tabularnewline
131 & 0.0446663467202856 & 0.0893326934405712 & 0.955333653279714 \tabularnewline
132 & 0.405747095627429 & 0.811494191254858 & 0.594252904372571 \tabularnewline
133 & 0.342738913934462 & 0.685477827868924 & 0.657261086065538 \tabularnewline
134 & 0.28844428452596 & 0.57688856905192 & 0.71155571547404 \tabularnewline
135 & 0.335978048821894 & 0.671956097643789 & 0.664021951178106 \tabularnewline
136 & 0.29417115690069 & 0.588342313801381 & 0.70582884309931 \tabularnewline
137 & 0.472404057038239 & 0.944808114076477 & 0.527595942961761 \tabularnewline
138 & 0.42609928430735 & 0.852198568614699 & 0.57390071569265 \tabularnewline
139 & 0.396064805293307 & 0.792129610586614 & 0.603935194706693 \tabularnewline
140 & 0.33771324928459 & 0.67542649856918 & 0.66228675071541 \tabularnewline
141 & 0.248572772633333 & 0.497145545266665 & 0.751427227366667 \tabularnewline
142 & 0.169855160593073 & 0.339710321186146 & 0.830144839406927 \tabularnewline
143 & 0.191350538131645 & 0.382701076263289 & 0.808649461868355 \tabularnewline
144 & 0.1705075553406 & 0.341015110681199 & 0.8294924446594 \tabularnewline
145 & 0.119697753360919 & 0.239395506721839 & 0.880302246639081 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186534&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]7[/C][C]0.0820442618929551[/C][C]0.16408852378591[/C][C]0.917955738107045[/C][/ROW]
[ROW][C]8[/C][C]0.412666985743787[/C][C]0.825333971487574[/C][C]0.587333014256213[/C][/ROW]
[ROW][C]9[/C][C]0.339459708061187[/C][C]0.678919416122374[/C][C]0.660540291938813[/C][/ROW]
[ROW][C]10[/C][C]0.230998369373002[/C][C]0.461996738746004[/C][C]0.769001630626998[/C][/ROW]
[ROW][C]11[/C][C]0.154398171160195[/C][C]0.30879634232039[/C][C]0.845601828839805[/C][/ROW]
[ROW][C]12[/C][C]0.100341439092093[/C][C]0.200682878184185[/C][C]0.899658560907907[/C][/ROW]
[ROW][C]13[/C][C]0.0779136983364417[/C][C]0.155827396672883[/C][C]0.922086301663558[/C][/ROW]
[ROW][C]14[/C][C]0.116616769657023[/C][C]0.233233539314046[/C][C]0.883383230342977[/C][/ROW]
[ROW][C]15[/C][C]0.0841889929726907[/C][C]0.168377985945381[/C][C]0.915811007027309[/C][/ROW]
[ROW][C]16[/C][C]0.0556864752214236[/C][C]0.111372950442847[/C][C]0.944313524778576[/C][/ROW]
[ROW][C]17[/C][C]0.0436648486464139[/C][C]0.0873296972928278[/C][C]0.956335151353586[/C][/ROW]
[ROW][C]18[/C][C]0.0418718014243214[/C][C]0.0837436028486428[/C][C]0.958128198575679[/C][/ROW]
[ROW][C]19[/C][C]0.0412435020541831[/C][C]0.0824870041083661[/C][C]0.958756497945817[/C][/ROW]
[ROW][C]20[/C][C]0.0315480892491857[/C][C]0.0630961784983714[/C][C]0.968451910750814[/C][/ROW]
[ROW][C]21[/C][C]0.0258196012582443[/C][C]0.0516392025164886[/C][C]0.974180398741756[/C][/ROW]
[ROW][C]22[/C][C]0.0300293411787511[/C][C]0.0600586823575021[/C][C]0.969970658821249[/C][/ROW]
[ROW][C]23[/C][C]0.0276338295868386[/C][C]0.0552676591736772[/C][C]0.972366170413161[/C][/ROW]
[ROW][C]24[/C][C]0.0340247716837248[/C][C]0.0680495433674496[/C][C]0.965975228316275[/C][/ROW]
[ROW][C]25[/C][C]0.0286542876634252[/C][C]0.0573085753268505[/C][C]0.971345712336575[/C][/ROW]
[ROW][C]26[/C][C]0.0212232358066106[/C][C]0.0424464716132212[/C][C]0.978776764193389[/C][/ROW]
[ROW][C]27[/C][C]0.0200053022863499[/C][C]0.0400106045726998[/C][C]0.97999469771365[/C][/ROW]
[ROW][C]28[/C][C]0.0206850261203344[/C][C]0.0413700522406687[/C][C]0.979314973879666[/C][/ROW]
[ROW][C]29[/C][C]0.0331015670881829[/C][C]0.0662031341763659[/C][C]0.966898432911817[/C][/ROW]
[ROW][C]30[/C][C]0.101455231803149[/C][C]0.202910463606298[/C][C]0.898544768196851[/C][/ROW]
[ROW][C]31[/C][C]0.0784342006960504[/C][C]0.156868401392101[/C][C]0.92156579930395[/C][/ROW]
[ROW][C]32[/C][C]0.0810778943107335[/C][C]0.162155788621467[/C][C]0.918922105689266[/C][/ROW]
[ROW][C]33[/C][C]0.086264381798984[/C][C]0.172528763597968[/C][C]0.913735618201016[/C][/ROW]
[ROW][C]34[/C][C]0.078543721288246[/C][C]0.157087442576492[/C][C]0.921456278711754[/C][/ROW]
[ROW][C]35[/C][C]0.211091219918638[/C][C]0.422182439837276[/C][C]0.788908780081362[/C][/ROW]
[ROW][C]36[/C][C]0.202846152268831[/C][C]0.405692304537662[/C][C]0.797153847731169[/C][/ROW]
[ROW][C]37[/C][C]0.205957041185611[/C][C]0.411914082371222[/C][C]0.794042958814389[/C][/ROW]
[ROW][C]38[/C][C]0.187811590935444[/C][C]0.375623181870889[/C][C]0.812188409064556[/C][/ROW]
[ROW][C]39[/C][C]0.320334000512196[/C][C]0.640668001024391[/C][C]0.679665999487804[/C][/ROW]
[ROW][C]40[/C][C]0.430590926781557[/C][C]0.861181853563114[/C][C]0.569409073218443[/C][/ROW]
[ROW][C]41[/C][C]0.679221749222223[/C][C]0.641556501555553[/C][C]0.320778250777777[/C][/ROW]
[ROW][C]42[/C][C]0.652554877258664[/C][C]0.694890245482672[/C][C]0.347445122741336[/C][/ROW]
[ROW][C]43[/C][C]0.614141796194339[/C][C]0.771716407611321[/C][C]0.385858203805661[/C][/ROW]
[ROW][C]44[/C][C]0.624290598085964[/C][C]0.751418803828073[/C][C]0.375709401914036[/C][/ROW]
[ROW][C]45[/C][C]0.634128505136005[/C][C]0.73174298972799[/C][C]0.365871494863995[/C][/ROW]
[ROW][C]46[/C][C]0.587043258820451[/C][C]0.825913482359099[/C][C]0.412956741179549[/C][/ROW]
[ROW][C]47[/C][C]0.624529461001763[/C][C]0.750941077996473[/C][C]0.375470538998237[/C][/ROW]
[ROW][C]48[/C][C]0.604122749540031[/C][C]0.791754500919937[/C][C]0.395877250459969[/C][/ROW]
[ROW][C]49[/C][C]0.591940117653428[/C][C]0.816119764693143[/C][C]0.408059882346571[/C][/ROW]
[ROW][C]50[/C][C]0.55322132882589[/C][C]0.89355734234822[/C][C]0.44677867117411[/C][/ROW]
[ROW][C]51[/C][C]0.509081861144911[/C][C]0.981836277710177[/C][C]0.490918138855088[/C][/ROW]
[ROW][C]52[/C][C]0.473348169198512[/C][C]0.946696338397024[/C][C]0.526651830801488[/C][/ROW]
[ROW][C]53[/C][C]0.613937057689491[/C][C]0.772125884621018[/C][C]0.386062942310509[/C][/ROW]
[ROW][C]54[/C][C]0.565666512000626[/C][C]0.868666975998748[/C][C]0.434333487999374[/C][/ROW]
[ROW][C]55[/C][C]0.566077495514428[/C][C]0.867845008971144[/C][C]0.433922504485572[/C][/ROW]
[ROW][C]56[/C][C]0.561284869406256[/C][C]0.877430261187488[/C][C]0.438715130593744[/C][/ROW]
[ROW][C]57[/C][C]0.516118228201722[/C][C]0.967763543596555[/C][C]0.483881771798278[/C][/ROW]
[ROW][C]58[/C][C]0.471269432352309[/C][C]0.942538864704619[/C][C]0.528730567647691[/C][/ROW]
[ROW][C]59[/C][C]0.425678994459365[/C][C]0.851357988918729[/C][C]0.574321005540635[/C][/ROW]
[ROW][C]60[/C][C]0.384393901559848[/C][C]0.768787803119695[/C][C]0.615606098440152[/C][/ROW]
[ROW][C]61[/C][C]0.340606258547955[/C][C]0.68121251709591[/C][C]0.659393741452045[/C][/ROW]
[ROW][C]62[/C][C]0.303957269419491[/C][C]0.607914538838982[/C][C]0.696042730580509[/C][/ROW]
[ROW][C]63[/C][C]0.319017734302912[/C][C]0.638035468605824[/C][C]0.680982265697088[/C][/ROW]
[ROW][C]64[/C][C]0.378761980508665[/C][C]0.757523961017329[/C][C]0.621238019491336[/C][/ROW]
[ROW][C]65[/C][C]0.354568433915263[/C][C]0.709136867830526[/C][C]0.645431566084737[/C][/ROW]
[ROW][C]66[/C][C]0.317342556059759[/C][C]0.634685112119519[/C][C]0.682657443940241[/C][/ROW]
[ROW][C]67[/C][C]0.284425379170435[/C][C]0.568850758340871[/C][C]0.715574620829565[/C][/ROW]
[ROW][C]68[/C][C]0.350103352170636[/C][C]0.700206704341273[/C][C]0.649896647829364[/C][/ROW]
[ROW][C]69[/C][C]0.315618831763818[/C][C]0.631237663527636[/C][C]0.684381168236182[/C][/ROW]
[ROW][C]70[/C][C]0.312054268228586[/C][C]0.624108536457173[/C][C]0.687945731771414[/C][/ROW]
[ROW][C]71[/C][C]0.272042708456164[/C][C]0.544085416912329[/C][C]0.727957291543836[/C][/ROW]
[ROW][C]72[/C][C]0.253092006276394[/C][C]0.506184012552788[/C][C]0.746907993723606[/C][/ROW]
[ROW][C]73[/C][C]0.248280064839124[/C][C]0.496560129678248[/C][C]0.751719935160876[/C][/ROW]
[ROW][C]74[/C][C]0.228483791633755[/C][C]0.45696758326751[/C][C]0.771516208366245[/C][/ROW]
[ROW][C]75[/C][C]0.213560653055927[/C][C]0.427121306111855[/C][C]0.786439346944073[/C][/ROW]
[ROW][C]76[/C][C]0.196226807160527[/C][C]0.392453614321053[/C][C]0.803773192839473[/C][/ROW]
[ROW][C]77[/C][C]0.203808006036618[/C][C]0.407616012073236[/C][C]0.796191993963382[/C][/ROW]
[ROW][C]78[/C][C]0.183020948756222[/C][C]0.366041897512444[/C][C]0.816979051243778[/C][/ROW]
[ROW][C]79[/C][C]0.167724755626648[/C][C]0.335449511253295[/C][C]0.832275244373352[/C][/ROW]
[ROW][C]80[/C][C]0.215245135400299[/C][C]0.430490270800598[/C][C]0.784754864599701[/C][/ROW]
[ROW][C]81[/C][C]0.195538691831829[/C][C]0.391077383663658[/C][C]0.804461308168171[/C][/ROW]
[ROW][C]82[/C][C]0.172892722226408[/C][C]0.345785444452815[/C][C]0.827107277773592[/C][/ROW]
[ROW][C]83[/C][C]0.144572923885175[/C][C]0.28914584777035[/C][C]0.855427076114825[/C][/ROW]
[ROW][C]84[/C][C]0.139873076837949[/C][C]0.279746153675898[/C][C]0.860126923162051[/C][/ROW]
[ROW][C]85[/C][C]0.12962114352408[/C][C]0.25924228704816[/C][C]0.87037885647592[/C][/ROW]
[ROW][C]86[/C][C]0.106744891291066[/C][C]0.213489782582133[/C][C]0.893255108708934[/C][/ROW]
[ROW][C]87[/C][C]0.0905421637177348[/C][C]0.18108432743547[/C][C]0.909457836282265[/C][/ROW]
[ROW][C]88[/C][C]0.0739946495995343[/C][C]0.147989299199069[/C][C]0.926005350400466[/C][/ROW]
[ROW][C]89[/C][C]0.0597605463873278[/C][C]0.119521092774656[/C][C]0.940239453612672[/C][/ROW]
[ROW][C]90[/C][C]0.063197082542908[/C][C]0.126394165085816[/C][C]0.936802917457092[/C][/ROW]
[ROW][C]91[/C][C]0.0581046983340513[/C][C]0.116209396668103[/C][C]0.941895301665949[/C][/ROW]
[ROW][C]92[/C][C]0.0481461315226391[/C][C]0.0962922630452781[/C][C]0.951853868477361[/C][/ROW]
[ROW][C]93[/C][C]0.0599791913707788[/C][C]0.119958382741558[/C][C]0.940020808629221[/C][/ROW]
[ROW][C]94[/C][C]0.0520461694179533[/C][C]0.104092338835907[/C][C]0.947953830582047[/C][/ROW]
[ROW][C]95[/C][C]0.0434672290262084[/C][C]0.0869344580524167[/C][C]0.956532770973792[/C][/ROW]
[ROW][C]96[/C][C]0.04346846284436[/C][C]0.08693692568872[/C][C]0.95653153715564[/C][/ROW]
[ROW][C]97[/C][C]0.108892851293016[/C][C]0.217785702586033[/C][C]0.891107148706984[/C][/ROW]
[ROW][C]98[/C][C]0.103910808739806[/C][C]0.207821617479612[/C][C]0.896089191260194[/C][/ROW]
[ROW][C]99[/C][C]0.0845262188509536[/C][C]0.169052437701907[/C][C]0.915473781149046[/C][/ROW]
[ROW][C]100[/C][C]0.0803391841078556[/C][C]0.160678368215711[/C][C]0.919660815892144[/C][/ROW]
[ROW][C]101[/C][C]0.0634691780405943[/C][C]0.126938356081189[/C][C]0.936530821959406[/C][/ROW]
[ROW][C]102[/C][C]0.0498248932651873[/C][C]0.0996497865303746[/C][C]0.950175106734813[/C][/ROW]
[ROW][C]103[/C][C]0.0412705300107904[/C][C]0.0825410600215808[/C][C]0.95872946998921[/C][/ROW]
[ROW][C]104[/C][C]0.0718533673227125[/C][C]0.143706734645425[/C][C]0.928146632677288[/C][/ROW]
[ROW][C]105[/C][C]0.103997532658743[/C][C]0.207995065317486[/C][C]0.896002467341257[/C][/ROW]
[ROW][C]106[/C][C]0.128584844035052[/C][C]0.257169688070104[/C][C]0.871415155964948[/C][/ROW]
[ROW][C]107[/C][C]0.107373229832111[/C][C]0.214746459664222[/C][C]0.892626770167889[/C][/ROW]
[ROW][C]108[/C][C]0.135328306092056[/C][C]0.270656612184112[/C][C]0.864671693907944[/C][/ROW]
[ROW][C]109[/C][C]0.127498417156164[/C][C]0.254996834312328[/C][C]0.872501582843836[/C][/ROW]
[ROW][C]110[/C][C]0.109351598719327[/C][C]0.218703197438653[/C][C]0.890648401280673[/C][/ROW]
[ROW][C]111[/C][C]0.114890688241351[/C][C]0.229781376482701[/C][C]0.885109311758649[/C][/ROW]
[ROW][C]112[/C][C]0.0954423991849617[/C][C]0.190884798369923[/C][C]0.904557600815038[/C][/ROW]
[ROW][C]113[/C][C]0.0864743199205911[/C][C]0.172948639841182[/C][C]0.913525680079409[/C][/ROW]
[ROW][C]114[/C][C]0.0682397007603728[/C][C]0.136479401520746[/C][C]0.931760299239627[/C][/ROW]
[ROW][C]115[/C][C]0.0592441298525856[/C][C]0.118488259705171[/C][C]0.940755870147414[/C][/ROW]
[ROW][C]116[/C][C]0.0457073053197423[/C][C]0.0914146106394847[/C][C]0.954292694680258[/C][/ROW]
[ROW][C]117[/C][C]0.0585733688025083[/C][C]0.117146737605017[/C][C]0.941426631197492[/C][/ROW]
[ROW][C]118[/C][C]0.053656370593217[/C][C]0.107312741186434[/C][C]0.946343629406783[/C][/ROW]
[ROW][C]119[/C][C]0.0520164853766991[/C][C]0.104032970753398[/C][C]0.947983514623301[/C][/ROW]
[ROW][C]120[/C][C]0.0883393540536813[/C][C]0.176678708107363[/C][C]0.911660645946319[/C][/ROW]
[ROW][C]121[/C][C]0.103796817034456[/C][C]0.207593634068912[/C][C]0.896203182965544[/C][/ROW]
[ROW][C]122[/C][C]0.145825730481869[/C][C]0.291651460963737[/C][C]0.854174269518131[/C][/ROW]
[ROW][C]123[/C][C]0.123314954095533[/C][C]0.246629908191066[/C][C]0.876685045904467[/C][/ROW]
[ROW][C]124[/C][C]0.0979341518892123[/C][C]0.195868303778425[/C][C]0.902065848110788[/C][/ROW]
[ROW][C]125[/C][C]0.0749502135043169[/C][C]0.149900427008634[/C][C]0.925049786495683[/C][/ROW]
[ROW][C]126[/C][C]0.0583373175495089[/C][C]0.116674635099018[/C][C]0.941662682450491[/C][/ROW]
[ROW][C]127[/C][C]0.055930576952556[/C][C]0.111861153905112[/C][C]0.944069423047444[/C][/ROW]
[ROW][C]128[/C][C]0.0461733876548788[/C][C]0.0923467753097575[/C][C]0.953826612345121[/C][/ROW]
[ROW][C]129[/C][C]0.0790493722694052[/C][C]0.15809874453881[/C][C]0.920950627730595[/C][/ROW]
[ROW][C]130[/C][C]0.0617990609658012[/C][C]0.123598121931602[/C][C]0.938200939034199[/C][/ROW]
[ROW][C]131[/C][C]0.0446663467202856[/C][C]0.0893326934405712[/C][C]0.955333653279714[/C][/ROW]
[ROW][C]132[/C][C]0.405747095627429[/C][C]0.811494191254858[/C][C]0.594252904372571[/C][/ROW]
[ROW][C]133[/C][C]0.342738913934462[/C][C]0.685477827868924[/C][C]0.657261086065538[/C][/ROW]
[ROW][C]134[/C][C]0.28844428452596[/C][C]0.57688856905192[/C][C]0.71155571547404[/C][/ROW]
[ROW][C]135[/C][C]0.335978048821894[/C][C]0.671956097643789[/C][C]0.664021951178106[/C][/ROW]
[ROW][C]136[/C][C]0.29417115690069[/C][C]0.588342313801381[/C][C]0.70582884309931[/C][/ROW]
[ROW][C]137[/C][C]0.472404057038239[/C][C]0.944808114076477[/C][C]0.527595942961761[/C][/ROW]
[ROW][C]138[/C][C]0.42609928430735[/C][C]0.852198568614699[/C][C]0.57390071569265[/C][/ROW]
[ROW][C]139[/C][C]0.396064805293307[/C][C]0.792129610586614[/C][C]0.603935194706693[/C][/ROW]
[ROW][C]140[/C][C]0.33771324928459[/C][C]0.67542649856918[/C][C]0.66228675071541[/C][/ROW]
[ROW][C]141[/C][C]0.248572772633333[/C][C]0.497145545266665[/C][C]0.751427227366667[/C][/ROW]
[ROW][C]142[/C][C]0.169855160593073[/C][C]0.339710321186146[/C][C]0.830144839406927[/C][/ROW]
[ROW][C]143[/C][C]0.191350538131645[/C][C]0.382701076263289[/C][C]0.808649461868355[/C][/ROW]
[ROW][C]144[/C][C]0.1705075553406[/C][C]0.341015110681199[/C][C]0.8294924446594[/C][/ROW]
[ROW][C]145[/C][C]0.119697753360919[/C][C]0.239395506721839[/C][C]0.880302246639081[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186534&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186534&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
70.08204426189295510.164088523785910.917955738107045
80.4126669857437870.8253339714875740.587333014256213
90.3394597080611870.6789194161223740.660540291938813
100.2309983693730020.4619967387460040.769001630626998
110.1543981711601950.308796342320390.845601828839805
120.1003414390920930.2006828781841850.899658560907907
130.07791369833644170.1558273966728830.922086301663558
140.1166167696570230.2332335393140460.883383230342977
150.08418899297269070.1683779859453810.915811007027309
160.05568647522142360.1113729504428470.944313524778576
170.04366484864641390.08732969729282780.956335151353586
180.04187180142432140.08374360284864280.958128198575679
190.04124350205418310.08248700410836610.958756497945817
200.03154808924918570.06309617849837140.968451910750814
210.02581960125824430.05163920251648860.974180398741756
220.03002934117875110.06005868235750210.969970658821249
230.02763382958683860.05526765917367720.972366170413161
240.03402477168372480.06804954336744960.965975228316275
250.02865428766342520.05730857532685050.971345712336575
260.02122323580661060.04244647161322120.978776764193389
270.02000530228634990.04001060457269980.97999469771365
280.02068502612033440.04137005224066870.979314973879666
290.03310156708818290.06620313417636590.966898432911817
300.1014552318031490.2029104636062980.898544768196851
310.07843420069605040.1568684013921010.92156579930395
320.08107789431073350.1621557886214670.918922105689266
330.0862643817989840.1725287635979680.913735618201016
340.0785437212882460.1570874425764920.921456278711754
350.2110912199186380.4221824398372760.788908780081362
360.2028461522688310.4056923045376620.797153847731169
370.2059570411856110.4119140823712220.794042958814389
380.1878115909354440.3756231818708890.812188409064556
390.3203340005121960.6406680010243910.679665999487804
400.4305909267815570.8611818535631140.569409073218443
410.6792217492222230.6415565015555530.320778250777777
420.6525548772586640.6948902454826720.347445122741336
430.6141417961943390.7717164076113210.385858203805661
440.6242905980859640.7514188038280730.375709401914036
450.6341285051360050.731742989727990.365871494863995
460.5870432588204510.8259134823590990.412956741179549
470.6245294610017630.7509410779964730.375470538998237
480.6041227495400310.7917545009199370.395877250459969
490.5919401176534280.8161197646931430.408059882346571
500.553221328825890.893557342348220.44677867117411
510.5090818611449110.9818362777101770.490918138855088
520.4733481691985120.9466963383970240.526651830801488
530.6139370576894910.7721258846210180.386062942310509
540.5656665120006260.8686669759987480.434333487999374
550.5660774955144280.8678450089711440.433922504485572
560.5612848694062560.8774302611874880.438715130593744
570.5161182282017220.9677635435965550.483881771798278
580.4712694323523090.9425388647046190.528730567647691
590.4256789944593650.8513579889187290.574321005540635
600.3843939015598480.7687878031196950.615606098440152
610.3406062585479550.681212517095910.659393741452045
620.3039572694194910.6079145388389820.696042730580509
630.3190177343029120.6380354686058240.680982265697088
640.3787619805086650.7575239610173290.621238019491336
650.3545684339152630.7091368678305260.645431566084737
660.3173425560597590.6346851121195190.682657443940241
670.2844253791704350.5688507583408710.715574620829565
680.3501033521706360.7002067043412730.649896647829364
690.3156188317638180.6312376635276360.684381168236182
700.3120542682285860.6241085364571730.687945731771414
710.2720427084561640.5440854169123290.727957291543836
720.2530920062763940.5061840125527880.746907993723606
730.2482800648391240.4965601296782480.751719935160876
740.2284837916337550.456967583267510.771516208366245
750.2135606530559270.4271213061118550.786439346944073
760.1962268071605270.3924536143210530.803773192839473
770.2038080060366180.4076160120732360.796191993963382
780.1830209487562220.3660418975124440.816979051243778
790.1677247556266480.3354495112532950.832275244373352
800.2152451354002990.4304902708005980.784754864599701
810.1955386918318290.3910773836636580.804461308168171
820.1728927222264080.3457854444528150.827107277773592
830.1445729238851750.289145847770350.855427076114825
840.1398730768379490.2797461536758980.860126923162051
850.129621143524080.259242287048160.87037885647592
860.1067448912910660.2134897825821330.893255108708934
870.09054216371773480.181084327435470.909457836282265
880.07399464959953430.1479892991990690.926005350400466
890.05976054638732780.1195210927746560.940239453612672
900.0631970825429080.1263941650858160.936802917457092
910.05810469833405130.1162093966681030.941895301665949
920.04814613152263910.09629226304527810.951853868477361
930.05997919137077880.1199583827415580.940020808629221
940.05204616941795330.1040923388359070.947953830582047
950.04346722902620840.08693445805241670.956532770973792
960.043468462844360.086936925688720.95653153715564
970.1088928512930160.2177857025860330.891107148706984
980.1039108087398060.2078216174796120.896089191260194
990.08452621885095360.1690524377019070.915473781149046
1000.08033918410785560.1606783682157110.919660815892144
1010.06346917804059430.1269383560811890.936530821959406
1020.04982489326518730.09964978653037460.950175106734813
1030.04127053001079040.08254106002158080.95872946998921
1040.07185336732271250.1437067346454250.928146632677288
1050.1039975326587430.2079950653174860.896002467341257
1060.1285848440350520.2571696880701040.871415155964948
1070.1073732298321110.2147464596642220.892626770167889
1080.1353283060920560.2706566121841120.864671693907944
1090.1274984171561640.2549968343123280.872501582843836
1100.1093515987193270.2187031974386530.890648401280673
1110.1148906882413510.2297813764827010.885109311758649
1120.09544239918496170.1908847983699230.904557600815038
1130.08647431992059110.1729486398411820.913525680079409
1140.06823970076037280.1364794015207460.931760299239627
1150.05924412985258560.1184882597051710.940755870147414
1160.04570730531974230.09141461063948470.954292694680258
1170.05857336880250830.1171467376050170.941426631197492
1180.0536563705932170.1073127411864340.946343629406783
1190.05201648537669910.1040329707533980.947983514623301
1200.08833935405368130.1766787081073630.911660645946319
1210.1037968170344560.2075936340689120.896203182965544
1220.1458257304818690.2916514609637370.854174269518131
1230.1233149540955330.2466299081910660.876685045904467
1240.09793415188921230.1958683037784250.902065848110788
1250.07495021350431690.1499004270086340.925049786495683
1260.05833731754950890.1166746350990180.941662682450491
1270.0559305769525560.1118611539051120.944069423047444
1280.04617338765487880.09234677530975750.953826612345121
1290.07904937226940520.158098744538810.920950627730595
1300.06179906096580120.1235981219316020.938200939034199
1310.04466634672028560.08933269344057120.955333653279714
1320.4057470956274290.8114941912548580.594252904372571
1330.3427389139344620.6854778278689240.657261086065538
1340.288444284525960.576888569051920.71155571547404
1350.3359780488218940.6719560976437890.664021951178106
1360.294171156900690.5883423138013810.70582884309931
1370.4724040570382390.9448081140764770.527595942961761
1380.426099284307350.8521985686146990.57390071569265
1390.3960648052933070.7921296105866140.603935194706693
1400.337713249284590.675426498569180.66228675071541
1410.2485727726333330.4971455452666650.751427227366667
1420.1698551605930730.3397103211861460.830144839406927
1430.1913505381316450.3827010762632890.808649461868355
1440.17050755534060.3410151106811990.8294924446594
1450.1196977533609190.2393955067218390.880302246639081







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0215827338129496OK
10% type I error level210.151079136690647NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0215827338129496 & OK \tabularnewline
10% type I error level & 21 & 0.151079136690647 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186534&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0215827338129496[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.151079136690647[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186534&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186534&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 level00OK
5% type I error level30.0215827338129496OK
10% type I error level210.151079136690647NOK



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')
}