FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 20:29:46 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t12905452502qviomya2rkvhvk.htm/, Retrieved Thu, 25 Apr 2024 20:13:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99645, Retrieved Thu, 25 Apr 2024 20:13:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact206

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2010-11-23 20:29:46] [23ca1b0f6f6de1e008a90be3f55e3db8] [Current]
-    D      [Multiple Regression] [] [2010-11-23 21:25:10] [1908ef7bb1a3d37a854f5aaad1a1c348]
-    D        [Multiple Regression] [] [2010-11-23 21:38:46] [1908ef7bb1a3d37a854f5aaad1a1c348]
- R PD      [Multiple Regression] [MLRM 1] [2010-12-10 14:33:44] [6501d0caa85bd8c4ed4905f18a69a94d]
- R PD      [Multiple Regression] [MLRM 1] [2010-12-10 14:54:22] [6501d0caa85bd8c4ed4905f18a69a94d]
- RMPD        [] [MLRM 2] [-0001-11-30 00:00:00] [6501d0caa85bd8c4ed4905f18a69a94d]
- RMPD        [] [MLRM 2] [-0001-11-30 00:00:00] [6501d0caa85bd8c4ed4905f18a69a94d]
-    D        [Multiple Regression] [MLRM 2] [2010-12-17 18:56:54] [6501d0caa85bd8c4ed4905f18a69a94d]
-   PD          [Multiple Regression] [MRLM 3] [2010-12-17 19:06:06] [6501d0caa85bd8c4ed4905f18a69a94d]
-   P           [Multiple Regression] [MRLM 4] [2010-12-17 19:28:52] [6501d0caa85bd8c4ed4905f18a69a94d]
-    D          [Multiple Regression] [MRLM 2] [2010-12-21 15:18:59] [6501d0caa85bd8c4ed4905f18a69a94d]
- R PD          [Multiple Regression] [MRLM 3] [2010-12-21 16:18:26] [6501d0caa85bd8c4ed4905f18a69a94d]
-   PD          [Multiple Regression] [MRLM 3] [2010-12-21 16:43:33] [6501d0caa85bd8c4ed4905f18a69a94d]
-   PD            [Multiple Regression] [MRLM 4] [2010-12-22 14:43:02] [6501d0caa85bd8c4ed4905f18a69a94d]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
24	11	12	26	14
25	7	8	23	11
17	17	8	25	6
18	10	8	23	12
18	12	9	19	8
16	12	7	29	10
20	11	4	25	10
16	11	11	21	11
18	12	7	22	16
17	13	7	25	11
23	14	12	24	13
30	16	10	18	12
23	11	10	22	8
18	10	8	15	12
15	11	8	22	11
12	15	4	28	4
21	9	9	20	9
15	11	8	12	8
20	17	7	24	8
31	17	11	20	14
27	11	9	21	15
34	18	11	20	16
21	14	13	21	9
31	10	8	23	14
19	11	8	28	11
16	15	9	24	8
20	15	6	24	9
21	13	9	24	9
22	16	9	23	9
17	13	6	23	9
24	9	6	29	10
25	18	16	24	16
26	18	5	18	11
25	12	7	25	8
17	17	9	21	9
32	9	6	26	16
33	9	6	22	11
13	12	5	22	16
32	18	12	22	12
25	12	7	23	12
29	18	10	30	14
22	14	9	23	9
18	15	8	17	10
17	16	5	23	9
20	10	8	23	10
15	11	8	25	12
20	14	10	24	14
33	9	6	24	14
29	12	8	23	10
23	17	7	21	14
26	5	4	24	16
18	12	8	24	9
20	12	8	28	10
11	6	4	16	6
28	24	20	20	8
26	12	8	29	13
22	12	8	27	10
17	14	6	22	8
12	7	4	28	7
14	13	8	16	15
17	12	9	25	9
21	13	6	24	10
19	14	7	28	12
18	8	9	24	13
10	11	5	23	10
29	9	5	30	11
31	11	8	24	8
19	13	8	21	9
9	10	6	25	13
20	11	8	25	11
28	12	7	22	8
19	9	7	23	9
30	15	9	26	9
29	18	11	23	15
26	15	6	25	9
23	12	8	21	10
13	13	6	25	14
21	14	9	24	12
19	10	8	29	12
28	13	6	22	11
23	13	10	27	14
18	11	8	26	6
21	13	8	22	12
20	16	10	24	8
23	8	5	27	14
21	16	7	24	11
21	11	5	24	10
15	9	8	29	14
28	16	14	22	12
19	12	7	21	10
26	14	8	24	14
10	8	6	24	5
16	9	5	23	11
22	15	6	20	10
19	11	10	27	9
31	21	12	26	10
31	14	9	25	16
29	18	12	21	13
19	12	7	21	9
22	13	8	19	10
23	15	10	21	10
15	12	6	21	7
20	19	10	16	9
18	15	10	22	8
23	11	10	29	14
25	11	5	15	14
21	10	7	17	8
24	13	10	15	9
25	15	11	21	14
17	12	6	21	14
13	12	7	19	8
28	16	12	24	8
21	9	11	20	8
25	18	11	17	7
9	8	11	23	6
16	13	5	24	8
19	17	8	14	6
17	9	6	19	11
25	15	9	24	14
20	8	4	13	11
29	7	4	22	11
14	12	7	16	11
22	14	11	19	14
15	6	6	25	8
19	8	7	25	20
20	17	8	23	11
15	10	4	24	8
20	11	8	26	11
18	14	9	26	10
33	11	8	25	14
22	13	11	18	11
16	12	8	21	9
17	11	5	26	9
16	9	4	23	8
21	12	8	23	10
26	20	10	22	13
18	12	6	20	13
18	13	9	13	12
17	12	9	24	8
22	12	13	15	13
30	9	9	14	14
30	15	10	22	12
24	24	20	10	14
21	7	5	24	15
21	17	11	22	13
29	11	6	24	16
31	17	9	19	9
20	11	7	20	9
16	12	9	13	9
22	14	10	20	8
20	11	9	22	7
28	16	8	24	16
38	21	7	29	11
22	14	6	12	9
20	20	13	20	11
17	13	6	21	9
28	11	8	24	14
22	15	10	22	13
31	19	16	20	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99645&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99645&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99645&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
D[t] = + 6.49442505844273 + 0.199709095209500CM[t] -0.152437526053681PE[t] + 0.153754981742715PC[t] + 0.0350480394488463O[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
D[t] =  +  6.49442505844273 +  0.199709095209500CM[t] -0.152437526053681PE[t] +  0.153754981742715PC[t] +  0.0350480394488463O[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99645&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]D[t] =  +  6.49442505844273 +  0.199709095209500CM[t] -0.152437526053681PE[t] +  0.153754981742715PC[t] +  0.0350480394488463O[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99645&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99645&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
D[t] = + 6.49442505844273 + 0.199709095209500CM[t] -0.152437526053681PE[t] + 0.153754981742715PC[t] + 0.0350480394488463O[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.494425058442731.6081824.03848.5e-054.2e-05
CM0.1997090952095000.0385615.17911e-060
PE-0.1524375260536810.075086-2.03020.0440610.022031
PC0.1537549817427150.0959611.60230.1111470.055573
O0.03504803944884630.0539190.650.5166520.258326

\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) & 6.49442505844273 & 1.608182 & 4.0384 & 8.5e-05 & 4.2e-05 \tabularnewline
CM & 0.199709095209500 & 0.038561 & 5.1791 & 1e-06 & 0 \tabularnewline
PE & -0.152437526053681 & 0.075086 & -2.0302 & 0.044061 & 0.022031 \tabularnewline
PC & 0.153754981742715 & 0.095961 & 1.6023 & 0.111147 & 0.055573 \tabularnewline
O & 0.0350480394488463 & 0.053919 & 0.65 & 0.516652 & 0.258326 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99645&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]6.49442505844273[/C][C]1.608182[/C][C]4.0384[/C][C]8.5e-05[/C][C]4.2e-05[/C][/ROW]
[ROW][C]CM[/C][C]0.199709095209500[/C][C]0.038561[/C][C]5.1791[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]PE[/C][C]-0.152437526053681[/C][C]0.075086[/C][C]-2.0302[/C][C]0.044061[/C][C]0.022031[/C][/ROW]
[ROW][C]PC[/C][C]0.153754981742715[/C][C]0.095961[/C][C]1.6023[/C][C]0.111147[/C][C]0.055573[/C][/ROW]
[ROW][C]O[/C][C]0.0350480394488463[/C][C]0.053919[/C][C]0.65[/C][C]0.516652[/C][C]0.258326[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99645&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99645&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.494425058442731.6081824.03848.5e-054.2e-05
CM0.1997090952095000.0385615.17911e-060
PE-0.1524375260536810.075086-2.03020.0440610.022031
PC0.1537549817427150.0959611.60230.1111470.055573
O0.03504803944884630.0539190.650.5166520.258326






Multiple Linear Regression - Regression Statistics
Multiple R0.427261498314918
R-squared0.182552387942309
Adjusted R-squared0.161319982434317
F-TEST (value)8.59781939797614
F-TEST (DF numerator)4
F-TEST (DF denominator)154
p-value2.73614794954469e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.56470383287756
Sum Squared Residuals1012.96668555803

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.427261498314918 \tabularnewline
R-squared & 0.182552387942309 \tabularnewline
Adjusted R-squared & 0.161319982434317 \tabularnewline
F-TEST (value) & 8.59781939797614 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 2.73614794954469e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.56470383287756 \tabularnewline
Sum Squared Residuals & 1012.96668555803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99645&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.427261498314918[/C][/ROW]
[ROW][C]R-squared[/C][C]0.182552387942309[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.161319982434317[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.59781939797614[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]2.73614794954469e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.56470383287756[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1012.96668555803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99645&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99645&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.427261498314918
R-squared0.182552387942309
Adjusted R-squared0.161319982434317
F-TEST (value)8.59781939797614
F-TEST (DF numerator)4
F-TEST (DF denominator)154
p-value2.73614794954469e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.56470383287756
Sum Squared Residuals1012.96668555803






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11412.36693936346281.63306063653715
21112.4562345175697-1.45623451756966
369.40428257425454-3.40428257425454
41210.60095827294211.39904172705789
5810.3096460447821-2.30964604478208
6109.953198285366110.046801714633887
71010.3030150892343-0.303015089234263
81110.44027142279990.559728577200115
91610.10728019964325.89271980035681
10119.860277696726551.13972230327345
111311.63982161119461.36017838880540
121212.2151120253752-0.215112025375229
13811.7195281469725-3.71952814697252
141210.32057395735131.67942604264866
15119.814345421811091.18565457818891
1648.20073634169008-4.20073634169008
17911.4011339480205-2.40113394802047
1889.46386502732262-1.46386502732262
1989.81460683869148-1.81460683869148
201412.48623465517151.51376534482854
211512.32956150661902.67043849338104
221612.93292441474633.06707558525372
23911.2890142841718-2.28901428417177
241413.19717651066560.802823489334384
251110.82347003934220.176529960657836
2689.62815547344627-1.62815547344627
2799.96572690905612-0.965726909056124
28910.9315760016011-1.93157600160113
29910.6389244792007-1.63892447920074
3099.63642663608614-0.636426636086139
311011.8544286434604-1.85442864346044
321612.04450962436973.95549037563026
331110.34262568371630.657374316283711
34811.6103879844562-3.61038798445623
3599.41784539820187-0.417845398201869
361613.34695728678992.65304271321009
371113.4064742242040-2.40647422420402
38168.801224760110267.19877523988974
391212.7573572849677-0.757357284967684
401211.54029190555850.459708094441463
411412.13110435144451.86889564855548
42910.9437995313081-1.94379953130811
43109.628482405980630.371517594019369
4499.02535907618238-0.0253590761823804
451011.0003764633611-1.00037646336111
46129.919489540157622.08051045984238
471410.73318436208073.26681563791933
481413.47657030310170.523429696898287
491012.4928832681393-2.49288326813925
501410.30859000597343.69140999402656
511612.38084677736453.61915322263549
52910.3311312602836-1.33113126028360
531010.8707416084980-0.870741608497983
5468.95238850757755-2.95238850757755
55812.2038395228516-4.20383952285163
561312.10404421920380.89595578079617
571011.2351117594681-1.23511175946814
5889.44894107058361-1.44894107058361
5979.42023655011952-2.42023655011952
60159.099473037801155.90052696219885
61910.3202251862657-1.32022518626566
621010.4703110563730-0.470311056372986
631210.21240247943841.78759752056159
641311.09463634624101.90536365375896
65108.389583039984281.61041696001572
661112.7342671772141-1.73426717721407
67813.0797870240608-5.07978702406078
68910.2732587110929-1.27325871109288
69138.566162531468874.43383746853113
701110.91803501620510.081964983794875
71812.1043711517382-4.10437115173819
72910.7993499124626-1.79934991246258
73912.4941788852770-3.49417888527697
741512.03952305704532.96047694295469
75911.1990295197620-2.19902951976197
761011.2245326179846-1.22453261798456
77148.907686334145835.09231366585417
781210.77913847554751.22086152445255
791211.01095560484470.98904439515531
801111.7981786439418-0.798178643941795
811411.58989329210942.41010670789061
82610.5536648652350-4.55366486523497
831210.70772494096071.29227505903928
84810.4283093099733-2.42830930997330
851411.58330601366422.41669398633578
861110.16675345995470.833246540045342
871010.6214311267376-0.621431126737633
881410.36455675006043.63544324993963
891212.5709059197225-0.570905919722476
901010.2719412554038-0.271941255403843
911411.62392896985222.37607103014776
9259.03569863933689-4.03569863933689
93119.892712663348651.10728733665135
941010.2249529416797-0.224952941679739
95911.0959319633787-2.09593196337875
961012.2405277693925-2.24052776939253
971612.81127746709133.1887225329087
981312.12318195989030.876818040109663
99910.2719412554038-1.27194125540384
1001010.8022899178237-0.802289917823686
1011011.0747300033089-1.07473000330895
10279.31934989282313-2.31934989282313
10399.6906124162215-0.690612416221491
104810.1112325667103-2.11123256671029
1051411.96486442311442.03513557688556
1061411.10483515253602.89516484746398
107810.8360423401348-2.83604234013482
108911.3690259139327-2.36902591393273
1091411.62790317547072.37209682452934
110149.718768083242134.28123191675787
11189.00359060524915-1.00359060524915
112812.3334920351347-4.33349203513474
113811.7086439115059-3.7086439115059
114711.0303984395142-4.03039843951424
11569.56971641339212-3.56971641339212
11689.31801059858277-1.31801059858277
11769.41817233073623-3.41817233073623
1181110.10598458250550.894015417494522
1191411.42553733033182.57446266966823
1201110.33975119400920.660248805990849
1211112.6050029319880-1.60500293198795
122119.098155582112111.90184441788789
1231411.11111733699822.88888266300185
124810.3741672069406-2.3741672069406
1252011.02188351741408.97811648258605
126119.933313780985351.06668621901465
12789.4218590997916-1.42185909979160
1281110.95308305565400.0469169443460286
1291010.2501072688166-0.250107268816644
1301413.51425325392860.485746746071372
1311111.2285068236030-0.228506823602986
13299.82656895151806-0.826568951518058
13399.89269082479732-0.892690824797324
13489.73895768160593-1.73895768160593
1351010.8952105064633-0.895210506463252
1361310.94671769811792.05328230188211
137139.883429139002783.11657086099722
138129.946920282035322.05307971796468
139810.2851771468168-2.28517714681681
1401311.58331019479561.41668980520444
1411412.98822756821291.01177243178710
1421212.5077417092243-0.507741709224296
1431411.05452274752522.94547725247484
1441511.23118123095243.76881876904764
1451310.55923978197412.44076021802585
1461612.37285887015633.62714112984365
147912.1436766522372-3.14367665223718
148910.5890398372182-1.58903983721818
14999.69993961767-0.699939617670003
150810.9924103947043-2.99241039470428
151710.9666458796013-3.9666458796013
1521611.71847210816394.28152789183612
1531112.974860645492-1.97486064549199
154910.0970061521426-1.09700615214265
1551110.13963199319130.860368006808658
15699.56633055718845-0.566330557188446
1571412.48065973843231.51934026156772
1581310.91006894754832.08993105245171
1591612.95013451177773.04986548822233

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 12.3669393634628 & 1.63306063653715 \tabularnewline
2 & 11 & 12.4562345175697 & -1.45623451756966 \tabularnewline
3 & 6 & 9.40428257425454 & -3.40428257425454 \tabularnewline
4 & 12 & 10.6009582729421 & 1.39904172705789 \tabularnewline
5 & 8 & 10.3096460447821 & -2.30964604478208 \tabularnewline
6 & 10 & 9.95319828536611 & 0.046801714633887 \tabularnewline
7 & 10 & 10.3030150892343 & -0.303015089234263 \tabularnewline
8 & 11 & 10.4402714227999 & 0.559728577200115 \tabularnewline
9 & 16 & 10.1072801996432 & 5.89271980035681 \tabularnewline
10 & 11 & 9.86027769672655 & 1.13972230327345 \tabularnewline
11 & 13 & 11.6398216111946 & 1.36017838880540 \tabularnewline
12 & 12 & 12.2151120253752 & -0.215112025375229 \tabularnewline
13 & 8 & 11.7195281469725 & -3.71952814697252 \tabularnewline
14 & 12 & 10.3205739573513 & 1.67942604264866 \tabularnewline
15 & 11 & 9.81434542181109 & 1.18565457818891 \tabularnewline
16 & 4 & 8.20073634169008 & -4.20073634169008 \tabularnewline
17 & 9 & 11.4011339480205 & -2.40113394802047 \tabularnewline
18 & 8 & 9.46386502732262 & -1.46386502732262 \tabularnewline
19 & 8 & 9.81460683869148 & -1.81460683869148 \tabularnewline
20 & 14 & 12.4862346551715 & 1.51376534482854 \tabularnewline
21 & 15 & 12.3295615066190 & 2.67043849338104 \tabularnewline
22 & 16 & 12.9329244147463 & 3.06707558525372 \tabularnewline
23 & 9 & 11.2890142841718 & -2.28901428417177 \tabularnewline
24 & 14 & 13.1971765106656 & 0.802823489334384 \tabularnewline
25 & 11 & 10.8234700393422 & 0.176529960657836 \tabularnewline
26 & 8 & 9.62815547344627 & -1.62815547344627 \tabularnewline
27 & 9 & 9.96572690905612 & -0.965726909056124 \tabularnewline
28 & 9 & 10.9315760016011 & -1.93157600160113 \tabularnewline
29 & 9 & 10.6389244792007 & -1.63892447920074 \tabularnewline
30 & 9 & 9.63642663608614 & -0.636426636086139 \tabularnewline
31 & 10 & 11.8544286434604 & -1.85442864346044 \tabularnewline
32 & 16 & 12.0445096243697 & 3.95549037563026 \tabularnewline
33 & 11 & 10.3426256837163 & 0.657374316283711 \tabularnewline
34 & 8 & 11.6103879844562 & -3.61038798445623 \tabularnewline
35 & 9 & 9.41784539820187 & -0.417845398201869 \tabularnewline
36 & 16 & 13.3469572867899 & 2.65304271321009 \tabularnewline
37 & 11 & 13.4064742242040 & -2.40647422420402 \tabularnewline
38 & 16 & 8.80122476011026 & 7.19877523988974 \tabularnewline
39 & 12 & 12.7573572849677 & -0.757357284967684 \tabularnewline
40 & 12 & 11.5402919055585 & 0.459708094441463 \tabularnewline
41 & 14 & 12.1311043514445 & 1.86889564855548 \tabularnewline
42 & 9 & 10.9437995313081 & -1.94379953130811 \tabularnewline
43 & 10 & 9.62848240598063 & 0.371517594019369 \tabularnewline
44 & 9 & 9.02535907618238 & -0.0253590761823804 \tabularnewline
45 & 10 & 11.0003764633611 & -1.00037646336111 \tabularnewline
46 & 12 & 9.91948954015762 & 2.08051045984238 \tabularnewline
47 & 14 & 10.7331843620807 & 3.26681563791933 \tabularnewline
48 & 14 & 13.4765703031017 & 0.523429696898287 \tabularnewline
49 & 10 & 12.4928832681393 & -2.49288326813925 \tabularnewline
50 & 14 & 10.3085900059734 & 3.69140999402656 \tabularnewline
51 & 16 & 12.3808467773645 & 3.61915322263549 \tabularnewline
52 & 9 & 10.3311312602836 & -1.33113126028360 \tabularnewline
53 & 10 & 10.8707416084980 & -0.870741608497983 \tabularnewline
54 & 6 & 8.95238850757755 & -2.95238850757755 \tabularnewline
55 & 8 & 12.2038395228516 & -4.20383952285163 \tabularnewline
56 & 13 & 12.1040442192038 & 0.89595578079617 \tabularnewline
57 & 10 & 11.2351117594681 & -1.23511175946814 \tabularnewline
58 & 8 & 9.44894107058361 & -1.44894107058361 \tabularnewline
59 & 7 & 9.42023655011952 & -2.42023655011952 \tabularnewline
60 & 15 & 9.09947303780115 & 5.90052696219885 \tabularnewline
61 & 9 & 10.3202251862657 & -1.32022518626566 \tabularnewline
62 & 10 & 10.4703110563730 & -0.470311056372986 \tabularnewline
63 & 12 & 10.2124024794384 & 1.78759752056159 \tabularnewline
64 & 13 & 11.0946363462410 & 1.90536365375896 \tabularnewline
65 & 10 & 8.38958303998428 & 1.61041696001572 \tabularnewline
66 & 11 & 12.7342671772141 & -1.73426717721407 \tabularnewline
67 & 8 & 13.0797870240608 & -5.07978702406078 \tabularnewline
68 & 9 & 10.2732587110929 & -1.27325871109288 \tabularnewline
69 & 13 & 8.56616253146887 & 4.43383746853113 \tabularnewline
70 & 11 & 10.9180350162051 & 0.081964983794875 \tabularnewline
71 & 8 & 12.1043711517382 & -4.10437115173819 \tabularnewline
72 & 9 & 10.7993499124626 & -1.79934991246258 \tabularnewline
73 & 9 & 12.4941788852770 & -3.49417888527697 \tabularnewline
74 & 15 & 12.0395230570453 & 2.96047694295469 \tabularnewline
75 & 9 & 11.1990295197620 & -2.19902951976197 \tabularnewline
76 & 10 & 11.2245326179846 & -1.22453261798456 \tabularnewline
77 & 14 & 8.90768633414583 & 5.09231366585417 \tabularnewline
78 & 12 & 10.7791384755475 & 1.22086152445255 \tabularnewline
79 & 12 & 11.0109556048447 & 0.98904439515531 \tabularnewline
80 & 11 & 11.7981786439418 & -0.798178643941795 \tabularnewline
81 & 14 & 11.5898932921094 & 2.41010670789061 \tabularnewline
82 & 6 & 10.5536648652350 & -4.55366486523497 \tabularnewline
83 & 12 & 10.7077249409607 & 1.29227505903928 \tabularnewline
84 & 8 & 10.4283093099733 & -2.42830930997330 \tabularnewline
85 & 14 & 11.5833060136642 & 2.41669398633578 \tabularnewline
86 & 11 & 10.1667534599547 & 0.833246540045342 \tabularnewline
87 & 10 & 10.6214311267376 & -0.621431126737633 \tabularnewline
88 & 14 & 10.3645567500604 & 3.63544324993963 \tabularnewline
89 & 12 & 12.5709059197225 & -0.570905919722476 \tabularnewline
90 & 10 & 10.2719412554038 & -0.271941255403843 \tabularnewline
91 & 14 & 11.6239289698522 & 2.37607103014776 \tabularnewline
92 & 5 & 9.03569863933689 & -4.03569863933689 \tabularnewline
93 & 11 & 9.89271266334865 & 1.10728733665135 \tabularnewline
94 & 10 & 10.2249529416797 & -0.224952941679739 \tabularnewline
95 & 9 & 11.0959319633787 & -2.09593196337875 \tabularnewline
96 & 10 & 12.2405277693925 & -2.24052776939253 \tabularnewline
97 & 16 & 12.8112774670913 & 3.1887225329087 \tabularnewline
98 & 13 & 12.1231819598903 & 0.876818040109663 \tabularnewline
99 & 9 & 10.2719412554038 & -1.27194125540384 \tabularnewline
100 & 10 & 10.8022899178237 & -0.802289917823686 \tabularnewline
101 & 10 & 11.0747300033089 & -1.07473000330895 \tabularnewline
102 & 7 & 9.31934989282313 & -2.31934989282313 \tabularnewline
103 & 9 & 9.6906124162215 & -0.690612416221491 \tabularnewline
104 & 8 & 10.1112325667103 & -2.11123256671029 \tabularnewline
105 & 14 & 11.9648644231144 & 2.03513557688556 \tabularnewline
106 & 14 & 11.1048351525360 & 2.89516484746398 \tabularnewline
107 & 8 & 10.8360423401348 & -2.83604234013482 \tabularnewline
108 & 9 & 11.3690259139327 & -2.36902591393273 \tabularnewline
109 & 14 & 11.6279031754707 & 2.37209682452934 \tabularnewline
110 & 14 & 9.71876808324213 & 4.28123191675787 \tabularnewline
111 & 8 & 9.00359060524915 & -1.00359060524915 \tabularnewline
112 & 8 & 12.3334920351347 & -4.33349203513474 \tabularnewline
113 & 8 & 11.7086439115059 & -3.7086439115059 \tabularnewline
114 & 7 & 11.0303984395142 & -4.03039843951424 \tabularnewline
115 & 6 & 9.56971641339212 & -3.56971641339212 \tabularnewline
116 & 8 & 9.31801059858277 & -1.31801059858277 \tabularnewline
117 & 6 & 9.41817233073623 & -3.41817233073623 \tabularnewline
118 & 11 & 10.1059845825055 & 0.894015417494522 \tabularnewline
119 & 14 & 11.4255373303318 & 2.57446266966823 \tabularnewline
120 & 11 & 10.3397511940092 & 0.660248805990849 \tabularnewline
121 & 11 & 12.6050029319880 & -1.60500293198795 \tabularnewline
122 & 11 & 9.09815558211211 & 1.90184441788789 \tabularnewline
123 & 14 & 11.1111173369982 & 2.88888266300185 \tabularnewline
124 & 8 & 10.3741672069406 & -2.3741672069406 \tabularnewline
125 & 20 & 11.0218835174140 & 8.97811648258605 \tabularnewline
126 & 11 & 9.93331378098535 & 1.06668621901465 \tabularnewline
127 & 8 & 9.4218590997916 & -1.42185909979160 \tabularnewline
128 & 11 & 10.9530830556540 & 0.0469169443460286 \tabularnewline
129 & 10 & 10.2501072688166 & -0.250107268816644 \tabularnewline
130 & 14 & 13.5142532539286 & 0.485746746071372 \tabularnewline
131 & 11 & 11.2285068236030 & -0.228506823602986 \tabularnewline
132 & 9 & 9.82656895151806 & -0.826568951518058 \tabularnewline
133 & 9 & 9.89269082479732 & -0.892690824797324 \tabularnewline
134 & 8 & 9.73895768160593 & -1.73895768160593 \tabularnewline
135 & 10 & 10.8952105064633 & -0.895210506463252 \tabularnewline
136 & 13 & 10.9467176981179 & 2.05328230188211 \tabularnewline
137 & 13 & 9.88342913900278 & 3.11657086099722 \tabularnewline
138 & 12 & 9.94692028203532 & 2.05307971796468 \tabularnewline
139 & 8 & 10.2851771468168 & -2.28517714681681 \tabularnewline
140 & 13 & 11.5833101947956 & 1.41668980520444 \tabularnewline
141 & 14 & 12.9882275682129 & 1.01177243178710 \tabularnewline
142 & 12 & 12.5077417092243 & -0.507741709224296 \tabularnewline
143 & 14 & 11.0545227475252 & 2.94547725247484 \tabularnewline
144 & 15 & 11.2311812309524 & 3.76881876904764 \tabularnewline
145 & 13 & 10.5592397819741 & 2.44076021802585 \tabularnewline
146 & 16 & 12.3728588701563 & 3.62714112984365 \tabularnewline
147 & 9 & 12.1436766522372 & -3.14367665223718 \tabularnewline
148 & 9 & 10.5890398372182 & -1.58903983721818 \tabularnewline
149 & 9 & 9.69993961767 & -0.699939617670003 \tabularnewline
150 & 8 & 10.9924103947043 & -2.99241039470428 \tabularnewline
151 & 7 & 10.9666458796013 & -3.9666458796013 \tabularnewline
152 & 16 & 11.7184721081639 & 4.28152789183612 \tabularnewline
153 & 11 & 12.974860645492 & -1.97486064549199 \tabularnewline
154 & 9 & 10.0970061521426 & -1.09700615214265 \tabularnewline
155 & 11 & 10.1396319931913 & 0.860368006808658 \tabularnewline
156 & 9 & 9.56633055718845 & -0.566330557188446 \tabularnewline
157 & 14 & 12.4806597384323 & 1.51934026156772 \tabularnewline
158 & 13 & 10.9100689475483 & 2.08993105245171 \tabularnewline
159 & 16 & 12.9501345117777 & 3.04986548822233 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99645&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]14[/C][C]12.3669393634628[/C][C]1.63306063653715[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]12.4562345175697[/C][C]-1.45623451756966[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]9.40428257425454[/C][C]-3.40428257425454[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.6009582729421[/C][C]1.39904172705789[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]10.3096460447821[/C][C]-2.30964604478208[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.95319828536611[/C][C]0.046801714633887[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]10.3030150892343[/C][C]-0.303015089234263[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]10.4402714227999[/C][C]0.559728577200115[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]10.1072801996432[/C][C]5.89271980035681[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]9.86027769672655[/C][C]1.13972230327345[/C][/ROW]
[ROW][C]11[/C][C]13[/C][C]11.6398216111946[/C][C]1.36017838880540[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]12.2151120253752[/C][C]-0.215112025375229[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]11.7195281469725[/C][C]-3.71952814697252[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.3205739573513[/C][C]1.67942604264866[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]9.81434542181109[/C][C]1.18565457818891[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]8.20073634169008[/C][C]-4.20073634169008[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]11.4011339480205[/C][C]-2.40113394802047[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]9.46386502732262[/C][C]-1.46386502732262[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]9.81460683869148[/C][C]-1.81460683869148[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]12.4862346551715[/C][C]1.51376534482854[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]12.3295615066190[/C][C]2.67043849338104[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]12.9329244147463[/C][C]3.06707558525372[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]11.2890142841718[/C][C]-2.28901428417177[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]13.1971765106656[/C][C]0.802823489334384[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.8234700393422[/C][C]0.176529960657836[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]9.62815547344627[/C][C]-1.62815547344627[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.96572690905612[/C][C]-0.965726909056124[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]10.9315760016011[/C][C]-1.93157600160113[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.6389244792007[/C][C]-1.63892447920074[/C][/ROW]
[ROW][C]30[/C][C]9[/C][C]9.63642663608614[/C][C]-0.636426636086139[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]11.8544286434604[/C][C]-1.85442864346044[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]12.0445096243697[/C][C]3.95549037563026[/C][/ROW]
[ROW][C]33[/C][C]11[/C][C]10.3426256837163[/C][C]0.657374316283711[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]11.6103879844562[/C][C]-3.61038798445623[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.41784539820187[/C][C]-0.417845398201869[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.3469572867899[/C][C]2.65304271321009[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]13.4064742242040[/C][C]-2.40647422420402[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]8.80122476011026[/C][C]7.19877523988974[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]12.7573572849677[/C][C]-0.757357284967684[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]11.5402919055585[/C][C]0.459708094441463[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.1311043514445[/C][C]1.86889564855548[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]10.9437995313081[/C][C]-1.94379953130811[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]9.62848240598063[/C][C]0.371517594019369[/C][/ROW]
[ROW][C]44[/C][C]9[/C][C]9.02535907618238[/C][C]-0.0253590761823804[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]11.0003764633611[/C][C]-1.00037646336111[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.91948954015762[/C][C]2.08051045984238[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]10.7331843620807[/C][C]3.26681563791933[/C][/ROW]
[ROW][C]48[/C][C]14[/C][C]13.4765703031017[/C][C]0.523429696898287[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]12.4928832681393[/C][C]-2.49288326813925[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]10.3085900059734[/C][C]3.69140999402656[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]12.3808467773645[/C][C]3.61915322263549[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]10.3311312602836[/C][C]-1.33113126028360[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]10.8707416084980[/C][C]-0.870741608497983[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]8.95238850757755[/C][C]-2.95238850757755[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]12.2038395228516[/C][C]-4.20383952285163[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]12.1040442192038[/C][C]0.89595578079617[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]11.2351117594681[/C][C]-1.23511175946814[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]9.44894107058361[/C][C]-1.44894107058361[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]9.42023655011952[/C][C]-2.42023655011952[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]9.09947303780115[/C][C]5.90052696219885[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]10.3202251862657[/C][C]-1.32022518626566[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]10.4703110563730[/C][C]-0.470311056372986[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]10.2124024794384[/C][C]1.78759752056159[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]11.0946363462410[/C][C]1.90536365375896[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]8.38958303998428[/C][C]1.61041696001572[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]12.7342671772141[/C][C]-1.73426717721407[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]13.0797870240608[/C][C]-5.07978702406078[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]10.2732587110929[/C][C]-1.27325871109288[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]8.56616253146887[/C][C]4.43383746853113[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.9180350162051[/C][C]0.081964983794875[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]12.1043711517382[/C][C]-4.10437115173819[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]10.7993499124626[/C][C]-1.79934991246258[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]12.4941788852770[/C][C]-3.49417888527697[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]12.0395230570453[/C][C]2.96047694295469[/C][/ROW]
[ROW][C]75[/C][C]9[/C][C]11.1990295197620[/C][C]-2.19902951976197[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]11.2245326179846[/C][C]-1.22453261798456[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]8.90768633414583[/C][C]5.09231366585417[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]10.7791384755475[/C][C]1.22086152445255[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.0109556048447[/C][C]0.98904439515531[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.7981786439418[/C][C]-0.798178643941795[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]11.5898932921094[/C][C]2.41010670789061[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]10.5536648652350[/C][C]-4.55366486523497[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]10.7077249409607[/C][C]1.29227505903928[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.4283093099733[/C][C]-2.42830930997330[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]11.5833060136642[/C][C]2.41669398633578[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.1667534599547[/C][C]0.833246540045342[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]10.6214311267376[/C][C]-0.621431126737633[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]10.3645567500604[/C][C]3.63544324993963[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]12.5709059197225[/C][C]-0.570905919722476[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.2719412554038[/C][C]-0.271941255403843[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]11.6239289698522[/C][C]2.37607103014776[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]9.03569863933689[/C][C]-4.03569863933689[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]9.89271266334865[/C][C]1.10728733665135[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]10.2249529416797[/C][C]-0.224952941679739[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]11.0959319633787[/C][C]-2.09593196337875[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]12.2405277693925[/C][C]-2.24052776939253[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.8112774670913[/C][C]3.1887225329087[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]12.1231819598903[/C][C]0.876818040109663[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]10.2719412554038[/C][C]-1.27194125540384[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.8022899178237[/C][C]-0.802289917823686[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]11.0747300033089[/C][C]-1.07473000330895[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]9.31934989282313[/C][C]-2.31934989282313[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]9.6906124162215[/C][C]-0.690612416221491[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]10.1112325667103[/C][C]-2.11123256671029[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]11.9648644231144[/C][C]2.03513557688556[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]11.1048351525360[/C][C]2.89516484746398[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]10.8360423401348[/C][C]-2.83604234013482[/C][/ROW]
[ROW][C]108[/C][C]9[/C][C]11.3690259139327[/C][C]-2.36902591393273[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]11.6279031754707[/C][C]2.37209682452934[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]9.71876808324213[/C][C]4.28123191675787[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]9.00359060524915[/C][C]-1.00359060524915[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]12.3334920351347[/C][C]-4.33349203513474[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]11.7086439115059[/C][C]-3.7086439115059[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]11.0303984395142[/C][C]-4.03039843951424[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]9.56971641339212[/C][C]-3.56971641339212[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]9.31801059858277[/C][C]-1.31801059858277[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]9.41817233073623[/C][C]-3.41817233073623[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]10.1059845825055[/C][C]0.894015417494522[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]11.4255373303318[/C][C]2.57446266966823[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]10.3397511940092[/C][C]0.660248805990849[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]12.6050029319880[/C][C]-1.60500293198795[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]9.09815558211211[/C][C]1.90184441788789[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]11.1111173369982[/C][C]2.88888266300185[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]10.3741672069406[/C][C]-2.3741672069406[/C][/ROW]
[ROW][C]125[/C][C]20[/C][C]11.0218835174140[/C][C]8.97811648258605[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]9.93331378098535[/C][C]1.06668621901465[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]9.4218590997916[/C][C]-1.42185909979160[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]10.9530830556540[/C][C]0.0469169443460286[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]10.2501072688166[/C][C]-0.250107268816644[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]13.5142532539286[/C][C]0.485746746071372[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]11.2285068236030[/C][C]-0.228506823602986[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]9.82656895151806[/C][C]-0.826568951518058[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]9.89269082479732[/C][C]-0.892690824797324[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]9.73895768160593[/C][C]-1.73895768160593[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]10.8952105064633[/C][C]-0.895210506463252[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]10.9467176981179[/C][C]2.05328230188211[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]9.88342913900278[/C][C]3.11657086099722[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]9.94692028203532[/C][C]2.05307971796468[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]10.2851771468168[/C][C]-2.28517714681681[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]11.5833101947956[/C][C]1.41668980520444[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]12.9882275682129[/C][C]1.01177243178710[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]12.5077417092243[/C][C]-0.507741709224296[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.0545227475252[/C][C]2.94547725247484[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]11.2311812309524[/C][C]3.76881876904764[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]10.5592397819741[/C][C]2.44076021802585[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]12.3728588701563[/C][C]3.62714112984365[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]12.1436766522372[/C][C]-3.14367665223718[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]10.5890398372182[/C][C]-1.58903983721818[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]9.69993961767[/C][C]-0.699939617670003[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]10.9924103947043[/C][C]-2.99241039470428[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]10.9666458796013[/C][C]-3.9666458796013[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]11.7184721081639[/C][C]4.28152789183612[/C][/ROW]
[ROW][C]153[/C][C]11[/C][C]12.974860645492[/C][C]-1.97486064549199[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]10.0970061521426[/C][C]-1.09700615214265[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]10.1396319931913[/C][C]0.860368006808658[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]9.56633055718845[/C][C]-0.566330557188446[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]12.4806597384323[/C][C]1.51934026156772[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]10.9100689475483[/C][C]2.08993105245171[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]12.9501345117777[/C][C]3.04986548822233[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99645&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99645&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11412.36693936346281.63306063653715
21112.4562345175697-1.45623451756966
369.40428257425454-3.40428257425454
41210.60095827294211.39904172705789
5810.3096460447821-2.30964604478208
6109.953198285366110.046801714633887
71010.3030150892343-0.303015089234263
81110.44027142279990.559728577200115
91610.10728019964325.89271980035681
10119.860277696726551.13972230327345
111311.63982161119461.36017838880540
121212.2151120253752-0.215112025375229
13811.7195281469725-3.71952814697252
141210.32057395735131.67942604264866
15119.814345421811091.18565457818891
1648.20073634169008-4.20073634169008
17911.4011339480205-2.40113394802047
1889.46386502732262-1.46386502732262
1989.81460683869148-1.81460683869148
201412.48623465517151.51376534482854
211512.32956150661902.67043849338104
221612.93292441474633.06707558525372
23911.2890142841718-2.28901428417177
241413.19717651066560.802823489334384
251110.82347003934220.176529960657836
2689.62815547344627-1.62815547344627
2799.96572690905612-0.965726909056124
28910.9315760016011-1.93157600160113
29910.6389244792007-1.63892447920074
3099.63642663608614-0.636426636086139
311011.8544286434604-1.85442864346044
321612.04450962436973.95549037563026
331110.34262568371630.657374316283711
34811.6103879844562-3.61038798445623
3599.41784539820187-0.417845398201869
361613.34695728678992.65304271321009
371113.4064742242040-2.40647422420402
38168.801224760110267.19877523988974
391212.7573572849677-0.757357284967684
401211.54029190555850.459708094441463
411412.13110435144451.86889564855548
42910.9437995313081-1.94379953130811
43109.628482405980630.371517594019369
4499.02535907618238-0.0253590761823804
451011.0003764633611-1.00037646336111
46129.919489540157622.08051045984238
471410.73318436208073.26681563791933
481413.47657030310170.523429696898287
491012.4928832681393-2.49288326813925
501410.30859000597343.69140999402656
511612.38084677736453.61915322263549
52910.3311312602836-1.33113126028360
531010.8707416084980-0.870741608497983
5468.95238850757755-2.95238850757755
55812.2038395228516-4.20383952285163
561312.10404421920380.89595578079617
571011.2351117594681-1.23511175946814
5889.44894107058361-1.44894107058361
5979.42023655011952-2.42023655011952
60159.099473037801155.90052696219885
61910.3202251862657-1.32022518626566
621010.4703110563730-0.470311056372986
631210.21240247943841.78759752056159
641311.09463634624101.90536365375896
65108.389583039984281.61041696001572
661112.7342671772141-1.73426717721407
67813.0797870240608-5.07978702406078
68910.2732587110929-1.27325871109288
69138.566162531468874.43383746853113
701110.91803501620510.081964983794875
71812.1043711517382-4.10437115173819
72910.7993499124626-1.79934991246258
73912.4941788852770-3.49417888527697
741512.03952305704532.96047694295469
75911.1990295197620-2.19902951976197
761011.2245326179846-1.22453261798456
77148.907686334145835.09231366585417
781210.77913847554751.22086152445255
791211.01095560484470.98904439515531
801111.7981786439418-0.798178643941795
811411.58989329210942.41010670789061
82610.5536648652350-4.55366486523497
831210.70772494096071.29227505903928
84810.4283093099733-2.42830930997330
851411.58330601366422.41669398633578
861110.16675345995470.833246540045342
871010.6214311267376-0.621431126737633
881410.36455675006043.63544324993963
891212.5709059197225-0.570905919722476
901010.2719412554038-0.271941255403843
911411.62392896985222.37607103014776
9259.03569863933689-4.03569863933689
93119.892712663348651.10728733665135
941010.2249529416797-0.224952941679739
95911.0959319633787-2.09593196337875
961012.2405277693925-2.24052776939253
971612.81127746709133.1887225329087
981312.12318195989030.876818040109663
99910.2719412554038-1.27194125540384
1001010.8022899178237-0.802289917823686
1011011.0747300033089-1.07473000330895
10279.31934989282313-2.31934989282313
10399.6906124162215-0.690612416221491
104810.1112325667103-2.11123256671029
1051411.96486442311442.03513557688556
1061411.10483515253602.89516484746398
107810.8360423401348-2.83604234013482
108911.3690259139327-2.36902591393273
1091411.62790317547072.37209682452934
110149.718768083242134.28123191675787
11189.00359060524915-1.00359060524915
112812.3334920351347-4.33349203513474
113811.7086439115059-3.7086439115059
114711.0303984395142-4.03039843951424
11569.56971641339212-3.56971641339212
11689.31801059858277-1.31801059858277
11769.41817233073623-3.41817233073623
1181110.10598458250550.894015417494522
1191411.42553733033182.57446266966823
1201110.33975119400920.660248805990849
1211112.6050029319880-1.60500293198795
122119.098155582112111.90184441788789
1231411.11111733699822.88888266300185
124810.3741672069406-2.3741672069406
1252011.02188351741408.97811648258605
126119.933313780985351.06668621901465
12789.4218590997916-1.42185909979160
1281110.95308305565400.0469169443460286
1291010.2501072688166-0.250107268816644
1301413.51425325392860.485746746071372
1311111.2285068236030-0.228506823602986
13299.82656895151806-0.826568951518058
13399.89269082479732-0.892690824797324
13489.73895768160593-1.73895768160593
1351010.8952105064633-0.895210506463252
1361310.94671769811792.05328230188211
137139.883429139002783.11657086099722
138129.946920282035322.05307971796468
139810.2851771468168-2.28517714681681
1401311.58331019479561.41668980520444
1411412.98822756821291.01177243178710
1421212.5077417092243-0.507741709224296
1431411.05452274752522.94547725247484
1441511.23118123095243.76881876904764
1451310.55923978197412.44076021802585
1461612.37285887015633.62714112984365
147912.1436766522372-3.14367665223718
148910.5890398372182-1.58903983721818
14999.69993961767-0.699939617670003
150810.9924103947043-2.99241039470428
151710.9666458796013-3.9666458796013
1521611.71847210816394.28152789183612
1531112.974860645492-1.97486064549199
154910.0970061521426-1.09700615214265
1551110.13963199319130.860368006808658
15699.56633055718845-0.566330557188446
1571412.48065973843231.51934026156772
1581310.91006894754832.08993105245171
1591612.95013451177773.04986548822233






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.2669434620135040.5338869240270090.733056537986496
90.9019544080636520.1960911838726970.0980455919363483
100.841208729400620.3175825411987610.158791270599381
110.7954734167480.4090531665040010.204526583252000
120.7131607261755160.5736785476489690.286839273824484
130.7930321262806130.4139357474387740.206967873719387
140.730236194729650.53952761054070.26976380527035
150.6491306027866050.7017387944267910.350869397213395
160.7033720684209920.5932558631580150.296627931579008
170.7218337120588240.5563325758823530.278166287941176
180.6751081110102950.649783777979410.324891888989705
190.6050399788550010.7899200422899970.394960021144999
200.5685994992127460.8628010015745080.431400500787254
210.5494090709533110.9011818580933770.450590929046689
220.5298330976599810.9403338046800390.470166902340019
230.5161460912712410.9677078174575190.483853908728759
240.4528915946628620.9057831893257250.547108405337138
250.3858371915935680.7716743831871350.614162808406432
260.3285941225581070.6571882451162150.671405877441893
270.2723303710210310.5446607420420610.727669628978969
280.2451950836752430.4903901673504850.754804916324757
290.2080080614153690.4160161228307370.791991938584631
300.1651979867669820.3303959735339650.834802013233018
310.1542896032668880.3085792065337750.845710396733112
320.1906217805033490.3812435610066980.809378219496651
330.1569679566684020.3139359133368030.843032043331598
340.1955457986526700.3910915973053410.80445420134733
350.1570148379188360.3140296758376720.842985162081164
360.1554459933178610.3108919866357220.844554006682139
370.1680072199597080.3360144399194150.831992780040292
380.617209331802850.7655813363943010.382790668197150
390.573540186823990.852919626352020.42645981317601
400.5211332443082420.9577335113835150.478866755691758
410.4966105871898410.9932211743796810.503389412810159
420.473274636731760.946549273463520.52672536326824
430.4208455071438450.841691014287690.579154492856155
440.3706631727029100.7413263454058190.62933682729709
450.3271924897021580.6543849794043160.672807510297842
460.3173419064417620.6346838128835250.682658093558238
470.3417083727373780.6834167454747570.658291627262622
480.2975835406255260.5951670812510520.702416459374474
490.2964127516206910.5928255032413810.70358724837931
500.3415938042743040.6831876085486070.658406195725696
510.3961996070407960.7923992140815920.603800392959204
520.3609591475731840.7219182951463680.639040852426816
530.3191609301986970.6383218603973940.680839069801303
540.3256007557863370.6512015115726740.674399244213663
550.4096887561455790.8193775122911580.590311243854421
560.3668573946184430.7337147892368850.633142605381557
570.331973804849140.663947609698280.66802619515086
580.3011673775845290.6023347551690590.69883262241547
590.2924356718981580.5848713437963160.707564328101842
600.4855912158979260.9711824317958510.514408784102074
610.448231864555890.896463729111780.55176813544411
620.4027765853855870.8055531707711730.597223414614413
630.3822801562462960.7645603124925910.617719843753704
640.3630370207234860.7260740414469720.636962979276514
650.3357710291414420.6715420582828840.664228970858558
660.3111283553714830.6222567107429670.688871644628517
670.4381829519855380.8763659039710760.561817048014462
680.4040184794695650.808036958939130.595981520530435
690.4910176227348730.9820352454697460.508982377265127
700.4443144808845170.8886289617690350.555685519115483
710.5147664922656630.9704670154686750.485233507734337
720.4919461578256730.9838923156513460.508053842174327
730.5293544357506580.9412911284986840.470645564249342
740.5459893559728110.9080212880543780.454010644027189
750.5328910135641840.9342179728716320.467108986435816
760.4979593621614470.9959187243228940.502040637838553
770.6367510441414210.7264979117171570.363248955858579
780.6025792499394750.794841500121050.397420750060525
790.5636926111287430.8726147777425140.436307388871257
800.5247085893500520.9505828212998950.475291410649948
810.5177227329477980.9645545341044040.482277267052202
820.6139895213347230.7720209573305540.386010478665277
830.5798066820082730.8403866359834550.420193317991727
840.5725900391151870.8548199217696270.427409960884813
850.5628985790873730.8742028418252540.437101420912627
860.522489091285690.955021817428620.47751090871431
870.4787677514735610.9575355029471220.521232248526439
880.5278789044295240.9442421911409510.472121095570476
890.485624558151220.971249116302440.51437544184878
900.4395421973993780.8790843947987550.560457802600622
910.4310677650834820.8621355301669640.568932234916518
920.4884974775383270.9769949550766540.511502522461673
930.4518038372761690.9036076745523380.548196162723831
940.4054943503605680.8109887007211350.594505649639432
950.3895746277703560.7791492555407120.610425372229644
960.3845566247344880.7691132494689750.615443375265512
970.3972333270318460.7944666540636910.602766672968154
980.3556968181434410.7113936362868820.644303181856559
990.3215116730608320.6430233461216640.678488326939168
1000.2837170610805940.5674341221611870.716282938919406
1010.2516148594572860.5032297189145710.748385140542714
1020.2398159878856990.4796319757713990.7601840121143
1030.2055098523706870.4110197047413730.794490147629313
1040.1931254765749190.3862509531498390.80687452342508
1050.1763853501202330.3527707002404660.823614649879767
1060.1825323783977330.3650647567954660.817467621602267
1070.1866757129805150.3733514259610300.813324287019485
1080.1823248036841970.3646496073683950.817675196315803
1090.1727280766762270.3454561533524550.827271923323773
1100.2344217412479150.4688434824958290.765578258752085
1110.2005429760386190.4010859520772390.79945702396138
1120.2890126074978290.5780252149956580.710987392502171
1130.3645710518279080.7291421036558160.635428948172092
1140.4520551831606430.9041103663212850.547944816839357
1150.5434119512200940.9131760975598130.456588048779906
1160.4986325447560840.9972650895121680.501367455243916
1170.5200104962628560.9599790074742890.479989503737144
1180.4713005317717970.9426010635435950.528699468228203
1190.4555951688087770.9111903376175540.544404831191223
1200.4096494165949590.8192988331899180.590350583405041
1210.3817009137364340.7634018274728670.618299086263566
1220.3651317242883280.7302634485766560.634868275711672
1230.3550213483885560.7100426967771120.644978651611444
1240.3714591514183860.7429183028367720.628540848581614
1250.8879528356689090.2240943286621830.112047164331091
1260.8642019267447870.2715961465104260.135798073255213
1270.8315738306449980.3368523387100040.168426169355002
1280.7888014454115460.4223971091769090.211198554588454
1290.740357774068280.5192844518634410.259642225931721
1300.6871873280878750.6256253438242510.312812671912125
1310.6358321339687440.7283357320625130.364167866031256
1320.5780303858964830.8439392282070340.421969614103517
1330.5148787700863790.9702424598272410.485121229913621
1340.4670790209218570.9341580418437140.532920979078143
1350.4163713319240130.8327426638480270.583628668075987
1360.3812882134547730.7625764269095460.618711786545227
1370.430010625865820.860021251731640.56998937413418
1380.4166673195936650.833334639187330.583332680406335
1390.4336839695246890.8673679390493780.566316030475311
1400.3619303852806910.7238607705613820.638069614719309
1410.2913488195244590.5826976390489180.708651180475541
1420.2448634162807560.4897268325615130.755136583719244
1430.2303894797359330.4607789594718670.769610520264067
1440.2435468686517700.4870937373035410.75645313134823
1450.2142421327334540.4284842654669090.785757867266546
1460.2884900704698980.5769801409397960.711509929530102
1470.3205215644794340.6410431289588690.679478435520566
1480.2307917467747150.461583493549430.769208253225285
1490.1495430675617540.2990861351235080.850456932438246
1500.1649302798042870.3298605596085730.835069720195713
1510.4968480048416170.9936960096832350.503151995158383

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.266943462013504 & 0.533886924027009 & 0.733056537986496 \tabularnewline
9 & 0.901954408063652 & 0.196091183872697 & 0.0980455919363483 \tabularnewline
10 & 0.84120872940062 & 0.317582541198761 & 0.158791270599381 \tabularnewline
11 & 0.795473416748 & 0.409053166504001 & 0.204526583252000 \tabularnewline
12 & 0.713160726175516 & 0.573678547648969 & 0.286839273824484 \tabularnewline
13 & 0.793032126280613 & 0.413935747438774 & 0.206967873719387 \tabularnewline
14 & 0.73023619472965 & 0.5395276105407 & 0.26976380527035 \tabularnewline
15 & 0.649130602786605 & 0.701738794426791 & 0.350869397213395 \tabularnewline
16 & 0.703372068420992 & 0.593255863158015 & 0.296627931579008 \tabularnewline
17 & 0.721833712058824 & 0.556332575882353 & 0.278166287941176 \tabularnewline
18 & 0.675108111010295 & 0.64978377797941 & 0.324891888989705 \tabularnewline
19 & 0.605039978855001 & 0.789920042289997 & 0.394960021144999 \tabularnewline
20 & 0.568599499212746 & 0.862801001574508 & 0.431400500787254 \tabularnewline
21 & 0.549409070953311 & 0.901181858093377 & 0.450590929046689 \tabularnewline
22 & 0.529833097659981 & 0.940333804680039 & 0.470166902340019 \tabularnewline
23 & 0.516146091271241 & 0.967707817457519 & 0.483853908728759 \tabularnewline
24 & 0.452891594662862 & 0.905783189325725 & 0.547108405337138 \tabularnewline
25 & 0.385837191593568 & 0.771674383187135 & 0.614162808406432 \tabularnewline
26 & 0.328594122558107 & 0.657188245116215 & 0.671405877441893 \tabularnewline
27 & 0.272330371021031 & 0.544660742042061 & 0.727669628978969 \tabularnewline
28 & 0.245195083675243 & 0.490390167350485 & 0.754804916324757 \tabularnewline
29 & 0.208008061415369 & 0.416016122830737 & 0.791991938584631 \tabularnewline
30 & 0.165197986766982 & 0.330395973533965 & 0.834802013233018 \tabularnewline
31 & 0.154289603266888 & 0.308579206533775 & 0.845710396733112 \tabularnewline
32 & 0.190621780503349 & 0.381243561006698 & 0.809378219496651 \tabularnewline
33 & 0.156967956668402 & 0.313935913336803 & 0.843032043331598 \tabularnewline
34 & 0.195545798652670 & 0.391091597305341 & 0.80445420134733 \tabularnewline
35 & 0.157014837918836 & 0.314029675837672 & 0.842985162081164 \tabularnewline
36 & 0.155445993317861 & 0.310891986635722 & 0.844554006682139 \tabularnewline
37 & 0.168007219959708 & 0.336014439919415 & 0.831992780040292 \tabularnewline
38 & 0.61720933180285 & 0.765581336394301 & 0.382790668197150 \tabularnewline
39 & 0.57354018682399 & 0.85291962635202 & 0.42645981317601 \tabularnewline
40 & 0.521133244308242 & 0.957733511383515 & 0.478866755691758 \tabularnewline
41 & 0.496610587189841 & 0.993221174379681 & 0.503389412810159 \tabularnewline
42 & 0.47327463673176 & 0.94654927346352 & 0.52672536326824 \tabularnewline
43 & 0.420845507143845 & 0.84169101428769 & 0.579154492856155 \tabularnewline
44 & 0.370663172702910 & 0.741326345405819 & 0.62933682729709 \tabularnewline
45 & 0.327192489702158 & 0.654384979404316 & 0.672807510297842 \tabularnewline
46 & 0.317341906441762 & 0.634683812883525 & 0.682658093558238 \tabularnewline
47 & 0.341708372737378 & 0.683416745474757 & 0.658291627262622 \tabularnewline
48 & 0.297583540625526 & 0.595167081251052 & 0.702416459374474 \tabularnewline
49 & 0.296412751620691 & 0.592825503241381 & 0.70358724837931 \tabularnewline
50 & 0.341593804274304 & 0.683187608548607 & 0.658406195725696 \tabularnewline
51 & 0.396199607040796 & 0.792399214081592 & 0.603800392959204 \tabularnewline
52 & 0.360959147573184 & 0.721918295146368 & 0.639040852426816 \tabularnewline
53 & 0.319160930198697 & 0.638321860397394 & 0.680839069801303 \tabularnewline
54 & 0.325600755786337 & 0.651201511572674 & 0.674399244213663 \tabularnewline
55 & 0.409688756145579 & 0.819377512291158 & 0.590311243854421 \tabularnewline
56 & 0.366857394618443 & 0.733714789236885 & 0.633142605381557 \tabularnewline
57 & 0.33197380484914 & 0.66394760969828 & 0.66802619515086 \tabularnewline
58 & 0.301167377584529 & 0.602334755169059 & 0.69883262241547 \tabularnewline
59 & 0.292435671898158 & 0.584871343796316 & 0.707564328101842 \tabularnewline
60 & 0.485591215897926 & 0.971182431795851 & 0.514408784102074 \tabularnewline
61 & 0.44823186455589 & 0.89646372911178 & 0.55176813544411 \tabularnewline
62 & 0.402776585385587 & 0.805553170771173 & 0.597223414614413 \tabularnewline
63 & 0.382280156246296 & 0.764560312492591 & 0.617719843753704 \tabularnewline
64 & 0.363037020723486 & 0.726074041446972 & 0.636962979276514 \tabularnewline
65 & 0.335771029141442 & 0.671542058282884 & 0.664228970858558 \tabularnewline
66 & 0.311128355371483 & 0.622256710742967 & 0.688871644628517 \tabularnewline
67 & 0.438182951985538 & 0.876365903971076 & 0.561817048014462 \tabularnewline
68 & 0.404018479469565 & 0.80803695893913 & 0.595981520530435 \tabularnewline
69 & 0.491017622734873 & 0.982035245469746 & 0.508982377265127 \tabularnewline
70 & 0.444314480884517 & 0.888628961769035 & 0.555685519115483 \tabularnewline
71 & 0.514766492265663 & 0.970467015468675 & 0.485233507734337 \tabularnewline
72 & 0.491946157825673 & 0.983892315651346 & 0.508053842174327 \tabularnewline
73 & 0.529354435750658 & 0.941291128498684 & 0.470645564249342 \tabularnewline
74 & 0.545989355972811 & 0.908021288054378 & 0.454010644027189 \tabularnewline
75 & 0.532891013564184 & 0.934217972871632 & 0.467108986435816 \tabularnewline
76 & 0.497959362161447 & 0.995918724322894 & 0.502040637838553 \tabularnewline
77 & 0.636751044141421 & 0.726497911717157 & 0.363248955858579 \tabularnewline
78 & 0.602579249939475 & 0.79484150012105 & 0.397420750060525 \tabularnewline
79 & 0.563692611128743 & 0.872614777742514 & 0.436307388871257 \tabularnewline
80 & 0.524708589350052 & 0.950582821299895 & 0.475291410649948 \tabularnewline
81 & 0.517722732947798 & 0.964554534104404 & 0.482277267052202 \tabularnewline
82 & 0.613989521334723 & 0.772020957330554 & 0.386010478665277 \tabularnewline
83 & 0.579806682008273 & 0.840386635983455 & 0.420193317991727 \tabularnewline
84 & 0.572590039115187 & 0.854819921769627 & 0.427409960884813 \tabularnewline
85 & 0.562898579087373 & 0.874202841825254 & 0.437101420912627 \tabularnewline
86 & 0.52248909128569 & 0.95502181742862 & 0.47751090871431 \tabularnewline
87 & 0.478767751473561 & 0.957535502947122 & 0.521232248526439 \tabularnewline
88 & 0.527878904429524 & 0.944242191140951 & 0.472121095570476 \tabularnewline
89 & 0.48562455815122 & 0.97124911630244 & 0.51437544184878 \tabularnewline
90 & 0.439542197399378 & 0.879084394798755 & 0.560457802600622 \tabularnewline
91 & 0.431067765083482 & 0.862135530166964 & 0.568932234916518 \tabularnewline
92 & 0.488497477538327 & 0.976994955076654 & 0.511502522461673 \tabularnewline
93 & 0.451803837276169 & 0.903607674552338 & 0.548196162723831 \tabularnewline
94 & 0.405494350360568 & 0.810988700721135 & 0.594505649639432 \tabularnewline
95 & 0.389574627770356 & 0.779149255540712 & 0.610425372229644 \tabularnewline
96 & 0.384556624734488 & 0.769113249468975 & 0.615443375265512 \tabularnewline
97 & 0.397233327031846 & 0.794466654063691 & 0.602766672968154 \tabularnewline
98 & 0.355696818143441 & 0.711393636286882 & 0.644303181856559 \tabularnewline
99 & 0.321511673060832 & 0.643023346121664 & 0.678488326939168 \tabularnewline
100 & 0.283717061080594 & 0.567434122161187 & 0.716282938919406 \tabularnewline
101 & 0.251614859457286 & 0.503229718914571 & 0.748385140542714 \tabularnewline
102 & 0.239815987885699 & 0.479631975771399 & 0.7601840121143 \tabularnewline
103 & 0.205509852370687 & 0.411019704741373 & 0.794490147629313 \tabularnewline
104 & 0.193125476574919 & 0.386250953149839 & 0.80687452342508 \tabularnewline
105 & 0.176385350120233 & 0.352770700240466 & 0.823614649879767 \tabularnewline
106 & 0.182532378397733 & 0.365064756795466 & 0.817467621602267 \tabularnewline
107 & 0.186675712980515 & 0.373351425961030 & 0.813324287019485 \tabularnewline
108 & 0.182324803684197 & 0.364649607368395 & 0.817675196315803 \tabularnewline
109 & 0.172728076676227 & 0.345456153352455 & 0.827271923323773 \tabularnewline
110 & 0.234421741247915 & 0.468843482495829 & 0.765578258752085 \tabularnewline
111 & 0.200542976038619 & 0.401085952077239 & 0.79945702396138 \tabularnewline
112 & 0.289012607497829 & 0.578025214995658 & 0.710987392502171 \tabularnewline
113 & 0.364571051827908 & 0.729142103655816 & 0.635428948172092 \tabularnewline
114 & 0.452055183160643 & 0.904110366321285 & 0.547944816839357 \tabularnewline
115 & 0.543411951220094 & 0.913176097559813 & 0.456588048779906 \tabularnewline
116 & 0.498632544756084 & 0.997265089512168 & 0.501367455243916 \tabularnewline
117 & 0.520010496262856 & 0.959979007474289 & 0.479989503737144 \tabularnewline
118 & 0.471300531771797 & 0.942601063543595 & 0.528699468228203 \tabularnewline
119 & 0.455595168808777 & 0.911190337617554 & 0.544404831191223 \tabularnewline
120 & 0.409649416594959 & 0.819298833189918 & 0.590350583405041 \tabularnewline
121 & 0.381700913736434 & 0.763401827472867 & 0.618299086263566 \tabularnewline
122 & 0.365131724288328 & 0.730263448576656 & 0.634868275711672 \tabularnewline
123 & 0.355021348388556 & 0.710042696777112 & 0.644978651611444 \tabularnewline
124 & 0.371459151418386 & 0.742918302836772 & 0.628540848581614 \tabularnewline
125 & 0.887952835668909 & 0.224094328662183 & 0.112047164331091 \tabularnewline
126 & 0.864201926744787 & 0.271596146510426 & 0.135798073255213 \tabularnewline
127 & 0.831573830644998 & 0.336852338710004 & 0.168426169355002 \tabularnewline
128 & 0.788801445411546 & 0.422397109176909 & 0.211198554588454 \tabularnewline
129 & 0.74035777406828 & 0.519284451863441 & 0.259642225931721 \tabularnewline
130 & 0.687187328087875 & 0.625625343824251 & 0.312812671912125 \tabularnewline
131 & 0.635832133968744 & 0.728335732062513 & 0.364167866031256 \tabularnewline
132 & 0.578030385896483 & 0.843939228207034 & 0.421969614103517 \tabularnewline
133 & 0.514878770086379 & 0.970242459827241 & 0.485121229913621 \tabularnewline
134 & 0.467079020921857 & 0.934158041843714 & 0.532920979078143 \tabularnewline
135 & 0.416371331924013 & 0.832742663848027 & 0.583628668075987 \tabularnewline
136 & 0.381288213454773 & 0.762576426909546 & 0.618711786545227 \tabularnewline
137 & 0.43001062586582 & 0.86002125173164 & 0.56998937413418 \tabularnewline
138 & 0.416667319593665 & 0.83333463918733 & 0.583332680406335 \tabularnewline
139 & 0.433683969524689 & 0.867367939049378 & 0.566316030475311 \tabularnewline
140 & 0.361930385280691 & 0.723860770561382 & 0.638069614719309 \tabularnewline
141 & 0.291348819524459 & 0.582697639048918 & 0.708651180475541 \tabularnewline
142 & 0.244863416280756 & 0.489726832561513 & 0.755136583719244 \tabularnewline
143 & 0.230389479735933 & 0.460778959471867 & 0.769610520264067 \tabularnewline
144 & 0.243546868651770 & 0.487093737303541 & 0.75645313134823 \tabularnewline
145 & 0.214242132733454 & 0.428484265466909 & 0.785757867266546 \tabularnewline
146 & 0.288490070469898 & 0.576980140939796 & 0.711509929530102 \tabularnewline
147 & 0.320521564479434 & 0.641043128958869 & 0.679478435520566 \tabularnewline
148 & 0.230791746774715 & 0.46158349354943 & 0.769208253225285 \tabularnewline
149 & 0.149543067561754 & 0.299086135123508 & 0.850456932438246 \tabularnewline
150 & 0.164930279804287 & 0.329860559608573 & 0.835069720195713 \tabularnewline
151 & 0.496848004841617 & 0.993696009683235 & 0.503151995158383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99645&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]8[/C][C]0.266943462013504[/C][C]0.533886924027009[/C][C]0.733056537986496[/C][/ROW]
[ROW][C]9[/C][C]0.901954408063652[/C][C]0.196091183872697[/C][C]0.0980455919363483[/C][/ROW]
[ROW][C]10[/C][C]0.84120872940062[/C][C]0.317582541198761[/C][C]0.158791270599381[/C][/ROW]
[ROW][C]11[/C][C]0.795473416748[/C][C]0.409053166504001[/C][C]0.204526583252000[/C][/ROW]
[ROW][C]12[/C][C]0.713160726175516[/C][C]0.573678547648969[/C][C]0.286839273824484[/C][/ROW]
[ROW][C]13[/C][C]0.793032126280613[/C][C]0.413935747438774[/C][C]0.206967873719387[/C][/ROW]
[ROW][C]14[/C][C]0.73023619472965[/C][C]0.5395276105407[/C][C]0.26976380527035[/C][/ROW]
[ROW][C]15[/C][C]0.649130602786605[/C][C]0.701738794426791[/C][C]0.350869397213395[/C][/ROW]
[ROW][C]16[/C][C]0.703372068420992[/C][C]0.593255863158015[/C][C]0.296627931579008[/C][/ROW]
[ROW][C]17[/C][C]0.721833712058824[/C][C]0.556332575882353[/C][C]0.278166287941176[/C][/ROW]
[ROW][C]18[/C][C]0.675108111010295[/C][C]0.64978377797941[/C][C]0.324891888989705[/C][/ROW]
[ROW][C]19[/C][C]0.605039978855001[/C][C]0.789920042289997[/C][C]0.394960021144999[/C][/ROW]
[ROW][C]20[/C][C]0.568599499212746[/C][C]0.862801001574508[/C][C]0.431400500787254[/C][/ROW]
[ROW][C]21[/C][C]0.549409070953311[/C][C]0.901181858093377[/C][C]0.450590929046689[/C][/ROW]
[ROW][C]22[/C][C]0.529833097659981[/C][C]0.940333804680039[/C][C]0.470166902340019[/C][/ROW]
[ROW][C]23[/C][C]0.516146091271241[/C][C]0.967707817457519[/C][C]0.483853908728759[/C][/ROW]
[ROW][C]24[/C][C]0.452891594662862[/C][C]0.905783189325725[/C][C]0.547108405337138[/C][/ROW]
[ROW][C]25[/C][C]0.385837191593568[/C][C]0.771674383187135[/C][C]0.614162808406432[/C][/ROW]
[ROW][C]26[/C][C]0.328594122558107[/C][C]0.657188245116215[/C][C]0.671405877441893[/C][/ROW]
[ROW][C]27[/C][C]0.272330371021031[/C][C]0.544660742042061[/C][C]0.727669628978969[/C][/ROW]
[ROW][C]28[/C][C]0.245195083675243[/C][C]0.490390167350485[/C][C]0.754804916324757[/C][/ROW]
[ROW][C]29[/C][C]0.208008061415369[/C][C]0.416016122830737[/C][C]0.791991938584631[/C][/ROW]
[ROW][C]30[/C][C]0.165197986766982[/C][C]0.330395973533965[/C][C]0.834802013233018[/C][/ROW]
[ROW][C]31[/C][C]0.154289603266888[/C][C]0.308579206533775[/C][C]0.845710396733112[/C][/ROW]
[ROW][C]32[/C][C]0.190621780503349[/C][C]0.381243561006698[/C][C]0.809378219496651[/C][/ROW]
[ROW][C]33[/C][C]0.156967956668402[/C][C]0.313935913336803[/C][C]0.843032043331598[/C][/ROW]
[ROW][C]34[/C][C]0.195545798652670[/C][C]0.391091597305341[/C][C]0.80445420134733[/C][/ROW]
[ROW][C]35[/C][C]0.157014837918836[/C][C]0.314029675837672[/C][C]0.842985162081164[/C][/ROW]
[ROW][C]36[/C][C]0.155445993317861[/C][C]0.310891986635722[/C][C]0.844554006682139[/C][/ROW]
[ROW][C]37[/C][C]0.168007219959708[/C][C]0.336014439919415[/C][C]0.831992780040292[/C][/ROW]
[ROW][C]38[/C][C]0.61720933180285[/C][C]0.765581336394301[/C][C]0.382790668197150[/C][/ROW]
[ROW][C]39[/C][C]0.57354018682399[/C][C]0.85291962635202[/C][C]0.42645981317601[/C][/ROW]
[ROW][C]40[/C][C]0.521133244308242[/C][C]0.957733511383515[/C][C]0.478866755691758[/C][/ROW]
[ROW][C]41[/C][C]0.496610587189841[/C][C]0.993221174379681[/C][C]0.503389412810159[/C][/ROW]
[ROW][C]42[/C][C]0.47327463673176[/C][C]0.94654927346352[/C][C]0.52672536326824[/C][/ROW]
[ROW][C]43[/C][C]0.420845507143845[/C][C]0.84169101428769[/C][C]0.579154492856155[/C][/ROW]
[ROW][C]44[/C][C]0.370663172702910[/C][C]0.741326345405819[/C][C]0.62933682729709[/C][/ROW]
[ROW][C]45[/C][C]0.327192489702158[/C][C]0.654384979404316[/C][C]0.672807510297842[/C][/ROW]
[ROW][C]46[/C][C]0.317341906441762[/C][C]0.634683812883525[/C][C]0.682658093558238[/C][/ROW]
[ROW][C]47[/C][C]0.341708372737378[/C][C]0.683416745474757[/C][C]0.658291627262622[/C][/ROW]
[ROW][C]48[/C][C]0.297583540625526[/C][C]0.595167081251052[/C][C]0.702416459374474[/C][/ROW]
[ROW][C]49[/C][C]0.296412751620691[/C][C]0.592825503241381[/C][C]0.70358724837931[/C][/ROW]
[ROW][C]50[/C][C]0.341593804274304[/C][C]0.683187608548607[/C][C]0.658406195725696[/C][/ROW]
[ROW][C]51[/C][C]0.396199607040796[/C][C]0.792399214081592[/C][C]0.603800392959204[/C][/ROW]
[ROW][C]52[/C][C]0.360959147573184[/C][C]0.721918295146368[/C][C]0.639040852426816[/C][/ROW]
[ROW][C]53[/C][C]0.319160930198697[/C][C]0.638321860397394[/C][C]0.680839069801303[/C][/ROW]
[ROW][C]54[/C][C]0.325600755786337[/C][C]0.651201511572674[/C][C]0.674399244213663[/C][/ROW]
[ROW][C]55[/C][C]0.409688756145579[/C][C]0.819377512291158[/C][C]0.590311243854421[/C][/ROW]
[ROW][C]56[/C][C]0.366857394618443[/C][C]0.733714789236885[/C][C]0.633142605381557[/C][/ROW]
[ROW][C]57[/C][C]0.33197380484914[/C][C]0.66394760969828[/C][C]0.66802619515086[/C][/ROW]
[ROW][C]58[/C][C]0.301167377584529[/C][C]0.602334755169059[/C][C]0.69883262241547[/C][/ROW]
[ROW][C]59[/C][C]0.292435671898158[/C][C]0.584871343796316[/C][C]0.707564328101842[/C][/ROW]
[ROW][C]60[/C][C]0.485591215897926[/C][C]0.971182431795851[/C][C]0.514408784102074[/C][/ROW]
[ROW][C]61[/C][C]0.44823186455589[/C][C]0.89646372911178[/C][C]0.55176813544411[/C][/ROW]
[ROW][C]62[/C][C]0.402776585385587[/C][C]0.805553170771173[/C][C]0.597223414614413[/C][/ROW]
[ROW][C]63[/C][C]0.382280156246296[/C][C]0.764560312492591[/C][C]0.617719843753704[/C][/ROW]
[ROW][C]64[/C][C]0.363037020723486[/C][C]0.726074041446972[/C][C]0.636962979276514[/C][/ROW]
[ROW][C]65[/C][C]0.335771029141442[/C][C]0.671542058282884[/C][C]0.664228970858558[/C][/ROW]
[ROW][C]66[/C][C]0.311128355371483[/C][C]0.622256710742967[/C][C]0.688871644628517[/C][/ROW]
[ROW][C]67[/C][C]0.438182951985538[/C][C]0.876365903971076[/C][C]0.561817048014462[/C][/ROW]
[ROW][C]68[/C][C]0.404018479469565[/C][C]0.80803695893913[/C][C]0.595981520530435[/C][/ROW]
[ROW][C]69[/C][C]0.491017622734873[/C][C]0.982035245469746[/C][C]0.508982377265127[/C][/ROW]
[ROW][C]70[/C][C]0.444314480884517[/C][C]0.888628961769035[/C][C]0.555685519115483[/C][/ROW]
[ROW][C]71[/C][C]0.514766492265663[/C][C]0.970467015468675[/C][C]0.485233507734337[/C][/ROW]
[ROW][C]72[/C][C]0.491946157825673[/C][C]0.983892315651346[/C][C]0.508053842174327[/C][/ROW]
[ROW][C]73[/C][C]0.529354435750658[/C][C]0.941291128498684[/C][C]0.470645564249342[/C][/ROW]
[ROW][C]74[/C][C]0.545989355972811[/C][C]0.908021288054378[/C][C]0.454010644027189[/C][/ROW]
[ROW][C]75[/C][C]0.532891013564184[/C][C]0.934217972871632[/C][C]0.467108986435816[/C][/ROW]
[ROW][C]76[/C][C]0.497959362161447[/C][C]0.995918724322894[/C][C]0.502040637838553[/C][/ROW]
[ROW][C]77[/C][C]0.636751044141421[/C][C]0.726497911717157[/C][C]0.363248955858579[/C][/ROW]
[ROW][C]78[/C][C]0.602579249939475[/C][C]0.79484150012105[/C][C]0.397420750060525[/C][/ROW]
[ROW][C]79[/C][C]0.563692611128743[/C][C]0.872614777742514[/C][C]0.436307388871257[/C][/ROW]
[ROW][C]80[/C][C]0.524708589350052[/C][C]0.950582821299895[/C][C]0.475291410649948[/C][/ROW]
[ROW][C]81[/C][C]0.517722732947798[/C][C]0.964554534104404[/C][C]0.482277267052202[/C][/ROW]
[ROW][C]82[/C][C]0.613989521334723[/C][C]0.772020957330554[/C][C]0.386010478665277[/C][/ROW]
[ROW][C]83[/C][C]0.579806682008273[/C][C]0.840386635983455[/C][C]0.420193317991727[/C][/ROW]
[ROW][C]84[/C][C]0.572590039115187[/C][C]0.854819921769627[/C][C]0.427409960884813[/C][/ROW]
[ROW][C]85[/C][C]0.562898579087373[/C][C]0.874202841825254[/C][C]0.437101420912627[/C][/ROW]
[ROW][C]86[/C][C]0.52248909128569[/C][C]0.95502181742862[/C][C]0.47751090871431[/C][/ROW]
[ROW][C]87[/C][C]0.478767751473561[/C][C]0.957535502947122[/C][C]0.521232248526439[/C][/ROW]
[ROW][C]88[/C][C]0.527878904429524[/C][C]0.944242191140951[/C][C]0.472121095570476[/C][/ROW]
[ROW][C]89[/C][C]0.48562455815122[/C][C]0.97124911630244[/C][C]0.51437544184878[/C][/ROW]
[ROW][C]90[/C][C]0.439542197399378[/C][C]0.879084394798755[/C][C]0.560457802600622[/C][/ROW]
[ROW][C]91[/C][C]0.431067765083482[/C][C]0.862135530166964[/C][C]0.568932234916518[/C][/ROW]
[ROW][C]92[/C][C]0.488497477538327[/C][C]0.976994955076654[/C][C]0.511502522461673[/C][/ROW]
[ROW][C]93[/C][C]0.451803837276169[/C][C]0.903607674552338[/C][C]0.548196162723831[/C][/ROW]
[ROW][C]94[/C][C]0.405494350360568[/C][C]0.810988700721135[/C][C]0.594505649639432[/C][/ROW]
[ROW][C]95[/C][C]0.389574627770356[/C][C]0.779149255540712[/C][C]0.610425372229644[/C][/ROW]
[ROW][C]96[/C][C]0.384556624734488[/C][C]0.769113249468975[/C][C]0.615443375265512[/C][/ROW]
[ROW][C]97[/C][C]0.397233327031846[/C][C]0.794466654063691[/C][C]0.602766672968154[/C][/ROW]
[ROW][C]98[/C][C]0.355696818143441[/C][C]0.711393636286882[/C][C]0.644303181856559[/C][/ROW]
[ROW][C]99[/C][C]0.321511673060832[/C][C]0.643023346121664[/C][C]0.678488326939168[/C][/ROW]
[ROW][C]100[/C][C]0.283717061080594[/C][C]0.567434122161187[/C][C]0.716282938919406[/C][/ROW]
[ROW][C]101[/C][C]0.251614859457286[/C][C]0.503229718914571[/C][C]0.748385140542714[/C][/ROW]
[ROW][C]102[/C][C]0.239815987885699[/C][C]0.479631975771399[/C][C]0.7601840121143[/C][/ROW]
[ROW][C]103[/C][C]0.205509852370687[/C][C]0.411019704741373[/C][C]0.794490147629313[/C][/ROW]
[ROW][C]104[/C][C]0.193125476574919[/C][C]0.386250953149839[/C][C]0.80687452342508[/C][/ROW]
[ROW][C]105[/C][C]0.176385350120233[/C][C]0.352770700240466[/C][C]0.823614649879767[/C][/ROW]
[ROW][C]106[/C][C]0.182532378397733[/C][C]0.365064756795466[/C][C]0.817467621602267[/C][/ROW]
[ROW][C]107[/C][C]0.186675712980515[/C][C]0.373351425961030[/C][C]0.813324287019485[/C][/ROW]
[ROW][C]108[/C][C]0.182324803684197[/C][C]0.364649607368395[/C][C]0.817675196315803[/C][/ROW]
[ROW][C]109[/C][C]0.172728076676227[/C][C]0.345456153352455[/C][C]0.827271923323773[/C][/ROW]
[ROW][C]110[/C][C]0.234421741247915[/C][C]0.468843482495829[/C][C]0.765578258752085[/C][/ROW]
[ROW][C]111[/C][C]0.200542976038619[/C][C]0.401085952077239[/C][C]0.79945702396138[/C][/ROW]
[ROW][C]112[/C][C]0.289012607497829[/C][C]0.578025214995658[/C][C]0.710987392502171[/C][/ROW]
[ROW][C]113[/C][C]0.364571051827908[/C][C]0.729142103655816[/C][C]0.635428948172092[/C][/ROW]
[ROW][C]114[/C][C]0.452055183160643[/C][C]0.904110366321285[/C][C]0.547944816839357[/C][/ROW]
[ROW][C]115[/C][C]0.543411951220094[/C][C]0.913176097559813[/C][C]0.456588048779906[/C][/ROW]
[ROW][C]116[/C][C]0.498632544756084[/C][C]0.997265089512168[/C][C]0.501367455243916[/C][/ROW]
[ROW][C]117[/C][C]0.520010496262856[/C][C]0.959979007474289[/C][C]0.479989503737144[/C][/ROW]
[ROW][C]118[/C][C]0.471300531771797[/C][C]0.942601063543595[/C][C]0.528699468228203[/C][/ROW]
[ROW][C]119[/C][C]0.455595168808777[/C][C]0.911190337617554[/C][C]0.544404831191223[/C][/ROW]
[ROW][C]120[/C][C]0.409649416594959[/C][C]0.819298833189918[/C][C]0.590350583405041[/C][/ROW]
[ROW][C]121[/C][C]0.381700913736434[/C][C]0.763401827472867[/C][C]0.618299086263566[/C][/ROW]
[ROW][C]122[/C][C]0.365131724288328[/C][C]0.730263448576656[/C][C]0.634868275711672[/C][/ROW]
[ROW][C]123[/C][C]0.355021348388556[/C][C]0.710042696777112[/C][C]0.644978651611444[/C][/ROW]
[ROW][C]124[/C][C]0.371459151418386[/C][C]0.742918302836772[/C][C]0.628540848581614[/C][/ROW]
[ROW][C]125[/C][C]0.887952835668909[/C][C]0.224094328662183[/C][C]0.112047164331091[/C][/ROW]
[ROW][C]126[/C][C]0.864201926744787[/C][C]0.271596146510426[/C][C]0.135798073255213[/C][/ROW]
[ROW][C]127[/C][C]0.831573830644998[/C][C]0.336852338710004[/C][C]0.168426169355002[/C][/ROW]
[ROW][C]128[/C][C]0.788801445411546[/C][C]0.422397109176909[/C][C]0.211198554588454[/C][/ROW]
[ROW][C]129[/C][C]0.74035777406828[/C][C]0.519284451863441[/C][C]0.259642225931721[/C][/ROW]
[ROW][C]130[/C][C]0.687187328087875[/C][C]0.625625343824251[/C][C]0.312812671912125[/C][/ROW]
[ROW][C]131[/C][C]0.635832133968744[/C][C]0.728335732062513[/C][C]0.364167866031256[/C][/ROW]
[ROW][C]132[/C][C]0.578030385896483[/C][C]0.843939228207034[/C][C]0.421969614103517[/C][/ROW]
[ROW][C]133[/C][C]0.514878770086379[/C][C]0.970242459827241[/C][C]0.485121229913621[/C][/ROW]
[ROW][C]134[/C][C]0.467079020921857[/C][C]0.934158041843714[/C][C]0.532920979078143[/C][/ROW]
[ROW][C]135[/C][C]0.416371331924013[/C][C]0.832742663848027[/C][C]0.583628668075987[/C][/ROW]
[ROW][C]136[/C][C]0.381288213454773[/C][C]0.762576426909546[/C][C]0.618711786545227[/C][/ROW]
[ROW][C]137[/C][C]0.43001062586582[/C][C]0.86002125173164[/C][C]0.56998937413418[/C][/ROW]
[ROW][C]138[/C][C]0.416667319593665[/C][C]0.83333463918733[/C][C]0.583332680406335[/C][/ROW]
[ROW][C]139[/C][C]0.433683969524689[/C][C]0.867367939049378[/C][C]0.566316030475311[/C][/ROW]
[ROW][C]140[/C][C]0.361930385280691[/C][C]0.723860770561382[/C][C]0.638069614719309[/C][/ROW]
[ROW][C]141[/C][C]0.291348819524459[/C][C]0.582697639048918[/C][C]0.708651180475541[/C][/ROW]
[ROW][C]142[/C][C]0.244863416280756[/C][C]0.489726832561513[/C][C]0.755136583719244[/C][/ROW]
[ROW][C]143[/C][C]0.230389479735933[/C][C]0.460778959471867[/C][C]0.769610520264067[/C][/ROW]
[ROW][C]144[/C][C]0.243546868651770[/C][C]0.487093737303541[/C][C]0.75645313134823[/C][/ROW]
[ROW][C]145[/C][C]0.214242132733454[/C][C]0.428484265466909[/C][C]0.785757867266546[/C][/ROW]
[ROW][C]146[/C][C]0.288490070469898[/C][C]0.576980140939796[/C][C]0.711509929530102[/C][/ROW]
[ROW][C]147[/C][C]0.320521564479434[/C][C]0.641043128958869[/C][C]0.679478435520566[/C][/ROW]
[ROW][C]148[/C][C]0.230791746774715[/C][C]0.46158349354943[/C][C]0.769208253225285[/C][/ROW]
[ROW][C]149[/C][C]0.149543067561754[/C][C]0.299086135123508[/C][C]0.850456932438246[/C][/ROW]
[ROW][C]150[/C][C]0.164930279804287[/C][C]0.329860559608573[/C][C]0.835069720195713[/C][/ROW]
[ROW][C]151[/C][C]0.496848004841617[/C][C]0.993696009683235[/C][C]0.503151995158383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99645&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99645&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.2669434620135040.5338869240270090.733056537986496
90.9019544080636520.1960911838726970.0980455919363483
100.841208729400620.3175825411987610.158791270599381
110.7954734167480.4090531665040010.204526583252000
120.7131607261755160.5736785476489690.286839273824484
130.7930321262806130.4139357474387740.206967873719387
140.730236194729650.53952761054070.26976380527035
150.6491306027866050.7017387944267910.350869397213395
160.7033720684209920.5932558631580150.296627931579008
170.7218337120588240.5563325758823530.278166287941176
180.6751081110102950.649783777979410.324891888989705
190.6050399788550010.7899200422899970.394960021144999
200.5685994992127460.8628010015745080.431400500787254
210.5494090709533110.9011818580933770.450590929046689
220.5298330976599810.9403338046800390.470166902340019
230.5161460912712410.9677078174575190.483853908728759
240.4528915946628620.9057831893257250.547108405337138
250.3858371915935680.7716743831871350.614162808406432
260.3285941225581070.6571882451162150.671405877441893
270.2723303710210310.5446607420420610.727669628978969
280.2451950836752430.4903901673504850.754804916324757
290.2080080614153690.4160161228307370.791991938584631
300.1651979867669820.3303959735339650.834802013233018
310.1542896032668880.3085792065337750.845710396733112
320.1906217805033490.3812435610066980.809378219496651
330.1569679566684020.3139359133368030.843032043331598
340.1955457986526700.3910915973053410.80445420134733
350.1570148379188360.3140296758376720.842985162081164
360.1554459933178610.3108919866357220.844554006682139
370.1680072199597080.3360144399194150.831992780040292
380.617209331802850.7655813363943010.382790668197150
390.573540186823990.852919626352020.42645981317601
400.5211332443082420.9577335113835150.478866755691758
410.4966105871898410.9932211743796810.503389412810159
420.473274636731760.946549273463520.52672536326824
430.4208455071438450.841691014287690.579154492856155
440.3706631727029100.7413263454058190.62933682729709
450.3271924897021580.6543849794043160.672807510297842
460.3173419064417620.6346838128835250.682658093558238
470.3417083727373780.6834167454747570.658291627262622
480.2975835406255260.5951670812510520.702416459374474
490.2964127516206910.5928255032413810.70358724837931
500.3415938042743040.6831876085486070.658406195725696
510.3961996070407960.7923992140815920.603800392959204
520.3609591475731840.7219182951463680.639040852426816
530.3191609301986970.6383218603973940.680839069801303
540.3256007557863370.6512015115726740.674399244213663
550.4096887561455790.8193775122911580.590311243854421
560.3668573946184430.7337147892368850.633142605381557
570.331973804849140.663947609698280.66802619515086
580.3011673775845290.6023347551690590.69883262241547
590.2924356718981580.5848713437963160.707564328101842
600.4855912158979260.9711824317958510.514408784102074
610.448231864555890.896463729111780.55176813544411
620.4027765853855870.8055531707711730.597223414614413
630.3822801562462960.7645603124925910.617719843753704
640.3630370207234860.7260740414469720.636962979276514
650.3357710291414420.6715420582828840.664228970858558
660.3111283553714830.6222567107429670.688871644628517
670.4381829519855380.8763659039710760.561817048014462
680.4040184794695650.808036958939130.595981520530435
690.4910176227348730.9820352454697460.508982377265127
700.4443144808845170.8886289617690350.555685519115483
710.5147664922656630.9704670154686750.485233507734337
720.4919461578256730.9838923156513460.508053842174327
730.5293544357506580.9412911284986840.470645564249342
740.5459893559728110.9080212880543780.454010644027189
750.5328910135641840.9342179728716320.467108986435816
760.4979593621614470.9959187243228940.502040637838553
770.6367510441414210.7264979117171570.363248955858579
780.6025792499394750.794841500121050.397420750060525
790.5636926111287430.8726147777425140.436307388871257
800.5247085893500520.9505828212998950.475291410649948
810.5177227329477980.9645545341044040.482277267052202
820.6139895213347230.7720209573305540.386010478665277
830.5798066820082730.8403866359834550.420193317991727
840.5725900391151870.8548199217696270.427409960884813
850.5628985790873730.8742028418252540.437101420912627
860.522489091285690.955021817428620.47751090871431
870.4787677514735610.9575355029471220.521232248526439
880.5278789044295240.9442421911409510.472121095570476
890.485624558151220.971249116302440.51437544184878
900.4395421973993780.8790843947987550.560457802600622
910.4310677650834820.8621355301669640.568932234916518
920.4884974775383270.9769949550766540.511502522461673
930.4518038372761690.9036076745523380.548196162723831
940.4054943503605680.8109887007211350.594505649639432
950.3895746277703560.7791492555407120.610425372229644
960.3845566247344880.7691132494689750.615443375265512
970.3972333270318460.7944666540636910.602766672968154
980.3556968181434410.7113936362868820.644303181856559
990.3215116730608320.6430233461216640.678488326939168
1000.2837170610805940.5674341221611870.716282938919406
1010.2516148594572860.5032297189145710.748385140542714
1020.2398159878856990.4796319757713990.7601840121143
1030.2055098523706870.4110197047413730.794490147629313
1040.1931254765749190.3862509531498390.80687452342508
1050.1763853501202330.3527707002404660.823614649879767
1060.1825323783977330.3650647567954660.817467621602267
1070.1866757129805150.3733514259610300.813324287019485
1080.1823248036841970.3646496073683950.817675196315803
1090.1727280766762270.3454561533524550.827271923323773
1100.2344217412479150.4688434824958290.765578258752085
1110.2005429760386190.4010859520772390.79945702396138
1120.2890126074978290.5780252149956580.710987392502171
1130.3645710518279080.7291421036558160.635428948172092
1140.4520551831606430.9041103663212850.547944816839357
1150.5434119512200940.9131760975598130.456588048779906
1160.4986325447560840.9972650895121680.501367455243916
1170.5200104962628560.9599790074742890.479989503737144
1180.4713005317717970.9426010635435950.528699468228203
1190.4555951688087770.9111903376175540.544404831191223
1200.4096494165949590.8192988331899180.590350583405041
1210.3817009137364340.7634018274728670.618299086263566
1220.3651317242883280.7302634485766560.634868275711672
1230.3550213483885560.7100426967771120.644978651611444
1240.3714591514183860.7429183028367720.628540848581614
1250.8879528356689090.2240943286621830.112047164331091
1260.8642019267447870.2715961465104260.135798073255213
1270.8315738306449980.3368523387100040.168426169355002
1280.7888014454115460.4223971091769090.211198554588454
1290.740357774068280.5192844518634410.259642225931721
1300.6871873280878750.6256253438242510.312812671912125
1310.6358321339687440.7283357320625130.364167866031256
1320.5780303858964830.8439392282070340.421969614103517
1330.5148787700863790.9702424598272410.485121229913621
1340.4670790209218570.9341580418437140.532920979078143
1350.4163713319240130.8327426638480270.583628668075987
1360.3812882134547730.7625764269095460.618711786545227
1370.430010625865820.860021251731640.56998937413418
1380.4166673195936650.833334639187330.583332680406335
1390.4336839695246890.8673679390493780.566316030475311
1400.3619303852806910.7238607705613820.638069614719309
1410.2913488195244590.5826976390489180.708651180475541
1420.2448634162807560.4897268325615130.755136583719244
1430.2303894797359330.4607789594718670.769610520264067
1440.2435468686517700.4870937373035410.75645313134823
1450.2142421327334540.4284842654669090.785757867266546
1460.2884900704698980.5769801409397960.711509929530102
1470.3205215644794340.6410431289588690.679478435520566
1480.2307917467747150.461583493549430.769208253225285
1490.1495430675617540.2990861351235080.850456932438246
1500.1649302798042870.3298605596085730.835069720195713
1510.4968480048416170.9936960096832350.503151995158383






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99645&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99645&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99645&T=6

As an alternative you can also use a QR Code:  
QR Code


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

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


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}