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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 02 Dec 2010 12:15:29 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/02/t129129212393u5f81ywogofjd.htm/, Retrieved Sun, 05 May 2024 13:35:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104229, Retrieved Sun, 05 May 2024 13:35:04 +0000

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User-defined keywords

Estimated Impact

152

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]
-   PD  [Multiple Regression] [Multiple Lineair ...] [2010-11-29 11:22:54] [65eb19f81eab2b6e672eafaed2a27190]
-   PD    [Multiple Regression] [Paper interactiem...] [2010-12-01 14:29:23] [65eb19f81eab2b6e672eafaed2a27190]
-   PD        [Multiple Regression] [Paper vrienden vi...] [2010-12-02 12:15:29] [e192c8164fa91adb027f71579ac0a49a] [Current]

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Dataseries X:

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Histogram

Boxplots

15	11	12	13	6
12	12	7	11	4
15	12	13	14	6
12	11	11	12	5
14	11	16	12	5
8	10	10	6	4
11	11	15	10	5
15	9	5	11	3
4	10	4	10	2
13	12	7	12	5
19	12	15	15	6
10	12	5	13	6
15	13	16	18	8
6	9	15	11	6
7	12	13	12	3
14	12	13	13	6
16	12	15	14	6
14	13	10	16	8
15	11	17	16	6
12	12	9	13	4
9	15	6	8	4
12	11	11	14	2
14	12	13	15	6
12	10	12	13	6
14	11	10	16	6
10	13	4	13	6
14	6	13	12	6
16	12	15	15	7
10	12	8	11	4
8	10	10	14	3
12	12	8	13	5
11	12	7	13	6
8	11	9	12	4
13	9	14	14	6
11	10	5	13	3
12	12	7	12	3
16	12	16	14	6
13	12	16	16	6
5	14	4	5	2
14	10	12	15	6
13	10	8	8	4
16	11	17	16	7
15	10	12	16	6
11	10	12	14	5
15	12	13	13	6
16	11	14	14	6
13	8	14	14	5
11	12	15	12	6
12	10	14	13	7
12	7	11	15	5
10	11	13	15	6
8	7	4	13	6
9	11	8	10	4
12	8	13	13	5
14	11	15	14	6
12	12	15	13	6
11	8	8	13	4
14	14	17	18	6
7	14	12	12	4
16	11	13	14	7
16	12	14	16	8
11	14	7	13	6
16	9	16	16	6
13	13	11	15	6
11	8	10	14	5
13	11	14	13	6
14	9	19	12	6
15	12	14	16	4
10	7	8	9	5
15	11	15	15	8
11	12	8	16	6
11	11	8	12	6
6	12	6	11	2
11	9	7	13	2
12	11	16	13	4
13	13	15	14	6
12	12	10	15	6
8	12	8	14	5
9	11	9	12	4
10	12	8	16	4
16	12	14	14	6
15	11	14	13	5
14	11	14	12	6
12	8	15	13	7
12	9	7	12	6
12	12	12	13	4
8	13	7	10	3
16	12	12	15	8
11	6	6	9	4
12	12	10	13	4
9	11	12	13	5
14	13	13	13	5
15	11	14	15	7
8	12	8	13	4
12	10	14	14	5
10	10	10	11	5
16	11	14	15	8
8	11	10	15	2
9	9	6	12	5
8	7	9	15	4
11	11	11	14	5
16	12	16	16	7
13	12	14	14	6
5	15	8	12	3
15	11	16	11	5
15	10	16	13	6
12	13	14	12	5
12	13	12	12	6
16	11	16	16	7
12	12	15	13	6
10	12	11	12	6
12	12	6	14	5
4	8	6	4	4
11	5	16	14	6
16	11	16	15	6
7	12	8	12	3
9	12	11	11	4
14	11	12	12	4
11	12	13	11	4
10	10	11	12	5
6	7	9	11	4
14	12	15	13	6
11	12	11	12	6
11	9	12	12	4
16	12	8	14	4
7	12	7	12	4
8	11	10	12	4
10	11	9	12	4
14	12	13	13	5
9	12	11	11	4
13	11	12	13	7
13	12	5	12	3
12	12	12	14	5
11	8	14	15	5
12	11	14	13	5
14	11	13	16	6
11	6	14	17	6
13	13	14	13	3
14	12	15	14	6
13	12	13	13	5
16	12	14	16	8
13	12	11	13	6
12	12	14	14	4
9	10	11	13	3
14	12	8	14	4
15	12	12	16	7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104229&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104229&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104229&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
FindingFriends[t] = + 10.1709445126328 + 0.100818898434514Popularity[t] -0.0457479600331225KnowingPeople[t] + 0.0459202753566864Liked[t] -0.0882532612200294Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FindingFriends[t] =  +  10.1709445126328 +  0.100818898434514Popularity[t] -0.0457479600331225KnowingPeople[t] +  0.0459202753566864Liked[t] -0.0882532612200294Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104229&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FindingFriends[t] =  +  10.1709445126328 +  0.100818898434514Popularity[t] -0.0457479600331225KnowingPeople[t] +  0.0459202753566864Liked[t] -0.0882532612200294Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104229&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104229&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
FindingFriends[t] = + 10.1709445126328 + 0.100818898434514Popularity[t] -0.0457479600331225KnowingPeople[t] + 0.0459202753566864Liked[t] -0.0882532612200294Celebrity[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	10.1709445126328	0.92659	10.9768	0	0
Popularity	0.100818898434514	0.073729	1.3674	0.173667	0.086834
KnowingPeople	-0.0457479600331225	0.056098	-0.8155	0.41616	0.20808
Liked	0.0459202753566864	0.087762	0.5232	0.60163	0.300815
Celebrity	-0.0882532612200294	0.145183	-0.6079	0.544248	0.272124

\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) & 10.1709445126328 & 0.92659 & 10.9768 & 0 & 0 \tabularnewline
Popularity & 0.100818898434514 & 0.073729 & 1.3674 & 0.173667 & 0.086834 \tabularnewline
KnowingPeople & -0.0457479600331225 & 0.056098 & -0.8155 & 0.41616 & 0.20808 \tabularnewline
Liked & 0.0459202753566864 & 0.087762 & 0.5232 & 0.60163 & 0.300815 \tabularnewline
Celebrity & -0.0882532612200294 & 0.145183 & -0.6079 & 0.544248 & 0.272124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104229&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]10.1709445126328[/C][C]0.92659[/C][C]10.9768[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Popularity[/C][C]0.100818898434514[/C][C]0.073729[/C][C]1.3674[/C][C]0.173667[/C][C]0.086834[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]-0.0457479600331225[/C][C]0.056098[/C][C]-0.8155[/C][C]0.41616[/C][C]0.20808[/C][/ROW]
[ROW][C]Liked[/C][C]0.0459202753566864[/C][C]0.087762[/C][C]0.5232[/C][C]0.60163[/C][C]0.300815[/C][/ROW]
[ROW][C]Celebrity[/C][C]-0.0882532612200294[/C][C]0.145183[/C][C]-0.6079[/C][C]0.544248[/C][C]0.272124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104229&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104229&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	10.1709445126328	0.92659	10.9768	0	0
Popularity	0.100818898434514	0.073729	1.3674	0.173667	0.086834
KnowingPeople	-0.0457479600331225	0.056098	-0.8155	0.41616	0.20808
Liked	0.0459202753566864	0.087762	0.5232	0.60163	0.300815
Celebrity	-0.0882532612200294	0.145183	-0.6079	0.544248	0.272124

Multiple Linear Regression - Regression Statistics
Multiple R	0.142043863917955
R-squared	0.0201764592767424
Adjusted R-squared	-0.00761995322604503
F-TEST (value)	0.725865586960875
F-TEST (DF numerator)	4
F-TEST (DF denominator)	141
p-value	0.57567568711713
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.80338023245963
Sum Squared Residuals	458.557417058485

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.142043863917955 \tabularnewline
R-squared & 0.0201764592767424 \tabularnewline
Adjusted R-squared & -0.00761995322604503 \tabularnewline
F-TEST (value) & 0.725865586960875 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 141 \tabularnewline
p-value & 0.57567568711713 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.80338023245963 \tabularnewline
Sum Squared Residuals & 458.557417058485 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104229&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.142043863917955[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0201764592767424[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00761995322604503[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.725865586960875[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]141[/C][/ROW]
[ROW][C]p-value[/C][C]0.57567568711713[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.80338023245963[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]458.557417058485[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104229&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104229&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.142043863917955
R-squared	0.0201764592767424
Adjusted R-squared	-0.00761995322604503
F-TEST (value)	0.725865586960875
F-TEST (DF numerator)	4
F-TEST (DF denominator)	141
p-value	0.57567568711713
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.80338023245963
Sum Squared Residuals	458.557417058485

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	11.2016964810699	-0.201696481069856
2	12	11.2126455576586	0.787354442341446
3	12	11.2018687963934	0.79813120360664
4	11	10.9873207316627	0.0126792683372818
5	11	10.9602187283661	0.0397812716338672
6	10	10.4425247070377	-0.442524707037697
7	11	10.6116694423823	0.388330557617659
8	9	11.6948514342484	-2.69485143424837
9	10	10.6739244973652	-0.673924497365182
10	12	11.2711314702297	0.728868529770278
11	12	11.5595687454219	0.440431254578146
12	12	11.0178377091291	0.982162290870917
13	13	11.0717994952807	1.92820050471932
14	9	10.0652419643464	-1.06524196434643
15	12	10.5682368418640	1.43176315813604
16	12	11.0551296226022	0.944870377397843
17	12	11.2111917747616	0.788808225238374
18	13	11.1536278063315	1.84637219366847
19	11	11.1107175069742	-0.110717506974240
20	12	11.2129901883057	0.78700981169432
21	15	10.8181759963181	4.18182400368193
22	11	11.3439210660362	-0.343921066036179
23	12	11.1469701733155	0.85302982668447
24	10	10.8992397857663	-0.899239785766253
25	11	11.3301343287716	-0.330134328771585
26	13	11.0635856691622	1.93641433083779
27	6	11.0092093472455	-5.00920934724547
28	12	11.1688587888983	0.831141211101717
29	12	10.9652598007564	1.03474019924360
30	10	10.8981401711112	-0.898140171111218
31	12	11.1704848871188	0.829515112881227
32	12	11.0271606874974	0.972839312502648
33	11	10.7637943192109	0.236205680789062
34	9	10.9544830394912	-1.95448303949121
35	10	11.3834163912237	-1.38341639122369
36	12	11.3468190942353	0.653180905764733
37	12	11.1654438147285	0.834556185271496
38	12	10.9548276701383	1.04517232986166
39	14	10.5451420190163	3.45485798098374
40	10	11.1927181333487	-1.19271813334865
41	10	11.1299556699899	-1.12995566998988
42	11	11.1232831441887	-0.123283144188725
43	10	11.3394573071399	-1.33945730713985
44	10	10.9325944239085	-0.932594423908455
45	12	11.1559485210367	0.844051478963329
46	11	11.2569397347947	-0.256939734794749
47	8	11.0427363007112	-3.04273630071124
48	12	10.6152567318757	1.38474326812432
49	10	10.7194906044800	-0.719490604479978
50	7	11.1250815577328	-4.12508155773278
51	11	10.7436945795775	0.256305420422524
52	7	10.8619478722932	-3.86194787229318
53	11	10.8185206269652	0.181479373034799
54	8	10.9417450869532	-2.94174508695316
55	11	11.0095539778926	-0.0095539778925989
56	12	10.7619959056669	1.23800409433311
57	8	11.1579192499043	-3.15791924990429
58	14	11.1017391592531	2.8982608407469
59	14	10.5257315406771	3.47426845932294
60	11	11.2144344336078	-0.214434433607842
61	12	11.1722737630681	0.827726236931937
62	14	11.0271606874974	2.97283931250265
63	9	11.2572843654419	-2.25728436544188
64	13	11.1376471949473	1.86235280505274
65	8	11.0240903439747	-3.0240903439747
66	11	10.9085627641345	0.0914372358654787
67	9	10.7347215870467	-1.73472158704674
68	12	11.4244679095137	0.575532090486333
69	7	10.785165988823	-3.785165988823
70	11	10.9797866292437	0.0202133707562597
71	12	11.1191735535343	0.880826446465711
72	11	10.9354924521075	0.0645075478924571
73	12	10.8299866495247	1.17001335047535
74	9	11.3801737323775	-2.38017373237747
75	11	10.8927544680738	0.107245531926179
76	13	10.9087350794581	2.09126492054191
77	12	11.0825762565459	0.917423743454129
78	12	10.8131295687374	1.18687043126260
79	11	10.8646132176455	0.135386782354548
80	12	11.1948611775398	0.805138822460166
81	12	11.2569397347947	0.743060265205251
82	11	11.1984538222236	-0.198453822223578
83	11	10.9634613872123	0.0365386127876514
84	8	10.6737426444469	-2.67374264444686
85	9	11.0820593105752	-2.08205931057518
86	12	11.0757463082063	0.924253691793688
87	13	10.8517029497838	2.14829705021616
88	12	11.2178494077776	0.782150592222378
89	6	11.0657340685438	-5.06573406854379
90	12	11.1672422282726	0.832757771727443
91	11	10.6850363516827	0.314963648317259
92	13	11.1433828838222	1.85661711617781
93	11	11.1137878504969	-0.113787850496892
94	12	10.8554625546007	1.14453744539925
95	10	10.9419174022767	-0.941917402276723
96	10	10.7855106194701	-0.785510619470127
97	11	11.1263534877114	-0.126353487711377
98	11	11.0323137076879	-0.0323137076879334
99	9	10.9136038365248	-1.91360383652479
100	7	10.901555145281	-3.901555145281
101	11	10.9783423839416	0.0216576160584226
102	12	11.1690311042218	0.830968895778153
103	12	10.9544830394912	1.04551696050879
104	15	10.5953388451605	4.40466115483945
105	11	11.0151173514440	-0.0151173514439601
106	10	11.0187046409373	-1.01870464093730
107	13	10.8500768515634	2.14992314843665
108	13	10.8533195104096	2.14668048959043
109	11	11.1690311042218	-0.169031104221847
110	12	10.7619959056669	1.23800409433311
111	12	10.6974296735737	1.30257032642634
112	12	11.3079010825417	0.692098917458296
113	8	10.1304004027188	-2.13040040271876
114	5	10.6613493225559	-5.66134932255594
115	11	11.2113640900852	-0.21136409008519
116	12	10.7969766420296	1.20302335797042
117	12	10.7271970222225	1.27280297777748
118	11	11.2314638297187	-0.231463829718652
119	12	10.8373388990253	1.16266110097470
120	10	10.7856829347937	-0.78568293479369
121	7	10.5162362469852	-3.51623624698522
122	12	10.9636337025359	1.03636629746409
123	12	10.7982485720082	1.20175142799182
124	9	10.9290071344151	-1.92900713441511
125	12	11.7079340174335	0.292065982566457
126	12	10.7544713408427	1.24552865915733
127	11	10.7180463591778	0.281953640822185
128	11	10.9654321160800	0.0345678839200346
129	12	11.1433828838222	0.856617116177813
130	12	10.7271970222225	1.27280297777748
131	11	10.9118054229807	0.0881945770192629
132	12	11.5391339127360	0.460866087263974
133	12	11.0334133223430	0.966586677657031
134	8	10.8870187791989	-2.88701877919890
135	11	10.8959971269200	0.104002873079963
136	11	11.1928904486722	-0.192890448672217
137	6	10.8906060686922	-4.89060606869224
138	13	11.1733225477946	1.82667745220539
139	12	11.0095539778926	0.9904460221074
140	12	11.0425639853877	0.957436014612327
141	12	11.1722737630681	0.827726236931937
142	12	11.0458066442339	0.95419335576611
143	12	11.0301706634968	0.969829336503247
144	10	10.9072908341559	-0.907290834155922
145	12	11.5062962205645	0.493703779435484
146	12	11.2512040459198	0.748795954080176

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 11.2016964810699 & -0.201696481069856 \tabularnewline
2 & 12 & 11.2126455576586 & 0.787354442341446 \tabularnewline
3 & 12 & 11.2018687963934 & 0.79813120360664 \tabularnewline
4 & 11 & 10.9873207316627 & 0.0126792683372818 \tabularnewline
5 & 11 & 10.9602187283661 & 0.0397812716338672 \tabularnewline
6 & 10 & 10.4425247070377 & -0.442524707037697 \tabularnewline
7 & 11 & 10.6116694423823 & 0.388330557617659 \tabularnewline
8 & 9 & 11.6948514342484 & -2.69485143424837 \tabularnewline
9 & 10 & 10.6739244973652 & -0.673924497365182 \tabularnewline
10 & 12 & 11.2711314702297 & 0.728868529770278 \tabularnewline
11 & 12 & 11.5595687454219 & 0.440431254578146 \tabularnewline
12 & 12 & 11.0178377091291 & 0.982162290870917 \tabularnewline
13 & 13 & 11.0717994952807 & 1.92820050471932 \tabularnewline
14 & 9 & 10.0652419643464 & -1.06524196434643 \tabularnewline
15 & 12 & 10.5682368418640 & 1.43176315813604 \tabularnewline
16 & 12 & 11.0551296226022 & 0.944870377397843 \tabularnewline
17 & 12 & 11.2111917747616 & 0.788808225238374 \tabularnewline
18 & 13 & 11.1536278063315 & 1.84637219366847 \tabularnewline
19 & 11 & 11.1107175069742 & -0.110717506974240 \tabularnewline
20 & 12 & 11.2129901883057 & 0.78700981169432 \tabularnewline
21 & 15 & 10.8181759963181 & 4.18182400368193 \tabularnewline
22 & 11 & 11.3439210660362 & -0.343921066036179 \tabularnewline
23 & 12 & 11.1469701733155 & 0.85302982668447 \tabularnewline
24 & 10 & 10.8992397857663 & -0.899239785766253 \tabularnewline
25 & 11 & 11.3301343287716 & -0.330134328771585 \tabularnewline
26 & 13 & 11.0635856691622 & 1.93641433083779 \tabularnewline
27 & 6 & 11.0092093472455 & -5.00920934724547 \tabularnewline
28 & 12 & 11.1688587888983 & 0.831141211101717 \tabularnewline
29 & 12 & 10.9652598007564 & 1.03474019924360 \tabularnewline
30 & 10 & 10.8981401711112 & -0.898140171111218 \tabularnewline
31 & 12 & 11.1704848871188 & 0.829515112881227 \tabularnewline
32 & 12 & 11.0271606874974 & 0.972839312502648 \tabularnewline
33 & 11 & 10.7637943192109 & 0.236205680789062 \tabularnewline
34 & 9 & 10.9544830394912 & -1.95448303949121 \tabularnewline
35 & 10 & 11.3834163912237 & -1.38341639122369 \tabularnewline
36 & 12 & 11.3468190942353 & 0.653180905764733 \tabularnewline
37 & 12 & 11.1654438147285 & 0.834556185271496 \tabularnewline
38 & 12 & 10.9548276701383 & 1.04517232986166 \tabularnewline
39 & 14 & 10.5451420190163 & 3.45485798098374 \tabularnewline
40 & 10 & 11.1927181333487 & -1.19271813334865 \tabularnewline
41 & 10 & 11.1299556699899 & -1.12995566998988 \tabularnewline
42 & 11 & 11.1232831441887 & -0.123283144188725 \tabularnewline
43 & 10 & 11.3394573071399 & -1.33945730713985 \tabularnewline
44 & 10 & 10.9325944239085 & -0.932594423908455 \tabularnewline
45 & 12 & 11.1559485210367 & 0.844051478963329 \tabularnewline
46 & 11 & 11.2569397347947 & -0.256939734794749 \tabularnewline
47 & 8 & 11.0427363007112 & -3.04273630071124 \tabularnewline
48 & 12 & 10.6152567318757 & 1.38474326812432 \tabularnewline
49 & 10 & 10.7194906044800 & -0.719490604479978 \tabularnewline
50 & 7 & 11.1250815577328 & -4.12508155773278 \tabularnewline
51 & 11 & 10.7436945795775 & 0.256305420422524 \tabularnewline
52 & 7 & 10.8619478722932 & -3.86194787229318 \tabularnewline
53 & 11 & 10.8185206269652 & 0.181479373034799 \tabularnewline
54 & 8 & 10.9417450869532 & -2.94174508695316 \tabularnewline
55 & 11 & 11.0095539778926 & -0.0095539778925989 \tabularnewline
56 & 12 & 10.7619959056669 & 1.23800409433311 \tabularnewline
57 & 8 & 11.1579192499043 & -3.15791924990429 \tabularnewline
58 & 14 & 11.1017391592531 & 2.8982608407469 \tabularnewline
59 & 14 & 10.5257315406771 & 3.47426845932294 \tabularnewline
60 & 11 & 11.2144344336078 & -0.214434433607842 \tabularnewline
61 & 12 & 11.1722737630681 & 0.827726236931937 \tabularnewline
62 & 14 & 11.0271606874974 & 2.97283931250265 \tabularnewline
63 & 9 & 11.2572843654419 & -2.25728436544188 \tabularnewline
64 & 13 & 11.1376471949473 & 1.86235280505274 \tabularnewline
65 & 8 & 11.0240903439747 & -3.0240903439747 \tabularnewline
66 & 11 & 10.9085627641345 & 0.0914372358654787 \tabularnewline
67 & 9 & 10.7347215870467 & -1.73472158704674 \tabularnewline
68 & 12 & 11.4244679095137 & 0.575532090486333 \tabularnewline
69 & 7 & 10.785165988823 & -3.785165988823 \tabularnewline
70 & 11 & 10.9797866292437 & 0.0202133707562597 \tabularnewline
71 & 12 & 11.1191735535343 & 0.880826446465711 \tabularnewline
72 & 11 & 10.9354924521075 & 0.0645075478924571 \tabularnewline
73 & 12 & 10.8299866495247 & 1.17001335047535 \tabularnewline
74 & 9 & 11.3801737323775 & -2.38017373237747 \tabularnewline
75 & 11 & 10.8927544680738 & 0.107245531926179 \tabularnewline
76 & 13 & 10.9087350794581 & 2.09126492054191 \tabularnewline
77 & 12 & 11.0825762565459 & 0.917423743454129 \tabularnewline
78 & 12 & 10.8131295687374 & 1.18687043126260 \tabularnewline
79 & 11 & 10.8646132176455 & 0.135386782354548 \tabularnewline
80 & 12 & 11.1948611775398 & 0.805138822460166 \tabularnewline
81 & 12 & 11.2569397347947 & 0.743060265205251 \tabularnewline
82 & 11 & 11.1984538222236 & -0.198453822223578 \tabularnewline
83 & 11 & 10.9634613872123 & 0.0365386127876514 \tabularnewline
84 & 8 & 10.6737426444469 & -2.67374264444686 \tabularnewline
85 & 9 & 11.0820593105752 & -2.08205931057518 \tabularnewline
86 & 12 & 11.0757463082063 & 0.924253691793688 \tabularnewline
87 & 13 & 10.8517029497838 & 2.14829705021616 \tabularnewline
88 & 12 & 11.2178494077776 & 0.782150592222378 \tabularnewline
89 & 6 & 11.0657340685438 & -5.06573406854379 \tabularnewline
90 & 12 & 11.1672422282726 & 0.832757771727443 \tabularnewline
91 & 11 & 10.6850363516827 & 0.314963648317259 \tabularnewline
92 & 13 & 11.1433828838222 & 1.85661711617781 \tabularnewline
93 & 11 & 11.1137878504969 & -0.113787850496892 \tabularnewline
94 & 12 & 10.8554625546007 & 1.14453744539925 \tabularnewline
95 & 10 & 10.9419174022767 & -0.941917402276723 \tabularnewline
96 & 10 & 10.7855106194701 & -0.785510619470127 \tabularnewline
97 & 11 & 11.1263534877114 & -0.126353487711377 \tabularnewline
98 & 11 & 11.0323137076879 & -0.0323137076879334 \tabularnewline
99 & 9 & 10.9136038365248 & -1.91360383652479 \tabularnewline
100 & 7 & 10.901555145281 & -3.901555145281 \tabularnewline
101 & 11 & 10.9783423839416 & 0.0216576160584226 \tabularnewline
102 & 12 & 11.1690311042218 & 0.830968895778153 \tabularnewline
103 & 12 & 10.9544830394912 & 1.04551696050879 \tabularnewline
104 & 15 & 10.5953388451605 & 4.40466115483945 \tabularnewline
105 & 11 & 11.0151173514440 & -0.0151173514439601 \tabularnewline
106 & 10 & 11.0187046409373 & -1.01870464093730 \tabularnewline
107 & 13 & 10.8500768515634 & 2.14992314843665 \tabularnewline
108 & 13 & 10.8533195104096 & 2.14668048959043 \tabularnewline
109 & 11 & 11.1690311042218 & -0.169031104221847 \tabularnewline
110 & 12 & 10.7619959056669 & 1.23800409433311 \tabularnewline
111 & 12 & 10.6974296735737 & 1.30257032642634 \tabularnewline
112 & 12 & 11.3079010825417 & 0.692098917458296 \tabularnewline
113 & 8 & 10.1304004027188 & -2.13040040271876 \tabularnewline
114 & 5 & 10.6613493225559 & -5.66134932255594 \tabularnewline
115 & 11 & 11.2113640900852 & -0.21136409008519 \tabularnewline
116 & 12 & 10.7969766420296 & 1.20302335797042 \tabularnewline
117 & 12 & 10.7271970222225 & 1.27280297777748 \tabularnewline
118 & 11 & 11.2314638297187 & -0.231463829718652 \tabularnewline
119 & 12 & 10.8373388990253 & 1.16266110097470 \tabularnewline
120 & 10 & 10.7856829347937 & -0.78568293479369 \tabularnewline
121 & 7 & 10.5162362469852 & -3.51623624698522 \tabularnewline
122 & 12 & 10.9636337025359 & 1.03636629746409 \tabularnewline
123 & 12 & 10.7982485720082 & 1.20175142799182 \tabularnewline
124 & 9 & 10.9290071344151 & -1.92900713441511 \tabularnewline
125 & 12 & 11.7079340174335 & 0.292065982566457 \tabularnewline
126 & 12 & 10.7544713408427 & 1.24552865915733 \tabularnewline
127 & 11 & 10.7180463591778 & 0.281953640822185 \tabularnewline
128 & 11 & 10.9654321160800 & 0.0345678839200346 \tabularnewline
129 & 12 & 11.1433828838222 & 0.856617116177813 \tabularnewline
130 & 12 & 10.7271970222225 & 1.27280297777748 \tabularnewline
131 & 11 & 10.9118054229807 & 0.0881945770192629 \tabularnewline
132 & 12 & 11.5391339127360 & 0.460866087263974 \tabularnewline
133 & 12 & 11.0334133223430 & 0.966586677657031 \tabularnewline
134 & 8 & 10.8870187791989 & -2.88701877919890 \tabularnewline
135 & 11 & 10.8959971269200 & 0.104002873079963 \tabularnewline
136 & 11 & 11.1928904486722 & -0.192890448672217 \tabularnewline
137 & 6 & 10.8906060686922 & -4.89060606869224 \tabularnewline
138 & 13 & 11.1733225477946 & 1.82667745220539 \tabularnewline
139 & 12 & 11.0095539778926 & 0.9904460221074 \tabularnewline
140 & 12 & 11.0425639853877 & 0.957436014612327 \tabularnewline
141 & 12 & 11.1722737630681 & 0.827726236931937 \tabularnewline
142 & 12 & 11.0458066442339 & 0.95419335576611 \tabularnewline
143 & 12 & 11.0301706634968 & 0.969829336503247 \tabularnewline
144 & 10 & 10.9072908341559 & -0.907290834155922 \tabularnewline
145 & 12 & 11.5062962205645 & 0.493703779435484 \tabularnewline
146 & 12 & 11.2512040459198 & 0.748795954080176 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104229&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]11[/C][C]11.2016964810699[/C][C]-0.201696481069856[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.2126455576586[/C][C]0.787354442341446[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]11.2018687963934[/C][C]0.79813120360664[/C][/ROW]
[ROW][C]4[/C][C]11[/C][C]10.9873207316627[/C][C]0.0126792683372818[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]10.9602187283661[/C][C]0.0397812716338672[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]10.4425247070377[/C][C]-0.442524707037697[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]10.6116694423823[/C][C]0.388330557617659[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]11.6948514342484[/C][C]-2.69485143424837[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.6739244973652[/C][C]-0.673924497365182[/C][/ROW]
[ROW][C]10[/C][C]12[/C][C]11.2711314702297[/C][C]0.728868529770278[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]11.5595687454219[/C][C]0.440431254578146[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.0178377091291[/C][C]0.982162290870917[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]11.0717994952807[/C][C]1.92820050471932[/C][/ROW]
[ROW][C]14[/C][C]9[/C][C]10.0652419643464[/C][C]-1.06524196434643[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]10.5682368418640[/C][C]1.43176315813604[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.0551296226022[/C][C]0.944870377397843[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]11.2111917747616[/C][C]0.788808225238374[/C][/ROW]
[ROW][C]18[/C][C]13[/C][C]11.1536278063315[/C][C]1.84637219366847[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]11.1107175069742[/C][C]-0.110717506974240[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.2129901883057[/C][C]0.78700981169432[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]10.8181759963181[/C][C]4.18182400368193[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.3439210660362[/C][C]-0.343921066036179[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]11.1469701733155[/C][C]0.85302982668447[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]10.8992397857663[/C][C]-0.899239785766253[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.3301343287716[/C][C]-0.330134328771585[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]11.0635856691622[/C][C]1.93641433083779[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]11.0092093472455[/C][C]-5.00920934724547[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]11.1688587888983[/C][C]0.831141211101717[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]10.9652598007564[/C][C]1.03474019924360[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.8981401711112[/C][C]-0.898140171111218[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.1704848871188[/C][C]0.829515112881227[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]11.0271606874974[/C][C]0.972839312502648[/C][/ROW]
[ROW][C]33[/C][C]11[/C][C]10.7637943192109[/C][C]0.236205680789062[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]10.9544830394912[/C][C]-1.95448303949121[/C][/ROW]
[ROW][C]35[/C][C]10[/C][C]11.3834163912237[/C][C]-1.38341639122369[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]11.3468190942353[/C][C]0.653180905764733[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]11.1654438147285[/C][C]0.834556185271496[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.9548276701383[/C][C]1.04517232986166[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]10.5451420190163[/C][C]3.45485798098374[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.1927181333487[/C][C]-1.19271813334865[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.1299556699899[/C][C]-1.12995566998988[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]11.1232831441887[/C][C]-0.123283144188725[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]11.3394573071399[/C][C]-1.33945730713985[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.9325944239085[/C][C]-0.932594423908455[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]11.1559485210367[/C][C]0.844051478963329[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]11.2569397347947[/C][C]-0.256939734794749[/C][/ROW]
[ROW][C]47[/C][C]8[/C][C]11.0427363007112[/C][C]-3.04273630071124[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]10.6152567318757[/C][C]1.38474326812432[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]10.7194906044800[/C][C]-0.719490604479978[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]11.1250815577328[/C][C]-4.12508155773278[/C][/ROW]
[ROW][C]51[/C][C]11[/C][C]10.7436945795775[/C][C]0.256305420422524[/C][/ROW]
[ROW][C]52[/C][C]7[/C][C]10.8619478722932[/C][C]-3.86194787229318[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]10.8185206269652[/C][C]0.181479373034799[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.9417450869532[/C][C]-2.94174508695316[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]11.0095539778926[/C][C]-0.0095539778925989[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]10.7619959056669[/C][C]1.23800409433311[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]11.1579192499043[/C][C]-3.15791924990429[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]11.1017391592531[/C][C]2.8982608407469[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]10.5257315406771[/C][C]3.47426845932294[/C][/ROW]
[ROW][C]60[/C][C]11[/C][C]11.2144344336078[/C][C]-0.214434433607842[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.1722737630681[/C][C]0.827726236931937[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]11.0271606874974[/C][C]2.97283931250265[/C][/ROW]
[ROW][C]63[/C][C]9[/C][C]11.2572843654419[/C][C]-2.25728436544188[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]11.1376471949473[/C][C]1.86235280505274[/C][/ROW]
[ROW][C]65[/C][C]8[/C][C]11.0240903439747[/C][C]-3.0240903439747[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]10.9085627641345[/C][C]0.0914372358654787[/C][/ROW]
[ROW][C]67[/C][C]9[/C][C]10.7347215870467[/C][C]-1.73472158704674[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]11.4244679095137[/C][C]0.575532090486333[/C][/ROW]
[ROW][C]69[/C][C]7[/C][C]10.785165988823[/C][C]-3.785165988823[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.9797866292437[/C][C]0.0202133707562597[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]11.1191735535343[/C][C]0.880826446465711[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.9354924521075[/C][C]0.0645075478924571[/C][/ROW]
[ROW][C]73[/C][C]12[/C][C]10.8299866495247[/C][C]1.17001335047535[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]11.3801737323775[/C][C]-2.38017373237747[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]10.8927544680738[/C][C]0.107245531926179[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]10.9087350794581[/C][C]2.09126492054191[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]11.0825762565459[/C][C]0.917423743454129[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]10.8131295687374[/C][C]1.18687043126260[/C][/ROW]
[ROW][C]79[/C][C]11[/C][C]10.8646132176455[/C][C]0.135386782354548[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.1948611775398[/C][C]0.805138822460166[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.2569397347947[/C][C]0.743060265205251[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]11.1984538222236[/C][C]-0.198453822223578[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]10.9634613872123[/C][C]0.0365386127876514[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.6737426444469[/C][C]-2.67374264444686[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]11.0820593105752[/C][C]-2.08205931057518[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.0757463082063[/C][C]0.924253691793688[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]10.8517029497838[/C][C]2.14829705021616[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.2178494077776[/C][C]0.782150592222378[/C][/ROW]
[ROW][C]89[/C][C]6[/C][C]11.0657340685438[/C][C]-5.06573406854379[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.1672422282726[/C][C]0.832757771727443[/C][/ROW]
[ROW][C]91[/C][C]11[/C][C]10.6850363516827[/C][C]0.314963648317259[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]11.1433828838222[/C][C]1.85661711617781[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]11.1137878504969[/C][C]-0.113787850496892[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.8554625546007[/C][C]1.14453744539925[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]10.9419174022767[/C][C]-0.941917402276723[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]10.7855106194701[/C][C]-0.785510619470127[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]11.1263534877114[/C][C]-0.126353487711377[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]11.0323137076879[/C][C]-0.0323137076879334[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]10.9136038365248[/C][C]-1.91360383652479[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]10.901555145281[/C][C]-3.901555145281[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]10.9783423839416[/C][C]0.0216576160584226[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.1690311042218[/C][C]0.830968895778153[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.9544830394912[/C][C]1.04551696050879[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]10.5953388451605[/C][C]4.40466115483945[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.0151173514440[/C][C]-0.0151173514439601[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]11.0187046409373[/C][C]-1.01870464093730[/C][/ROW]
[ROW][C]107[/C][C]13[/C][C]10.8500768515634[/C][C]2.14992314843665[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]10.8533195104096[/C][C]2.14668048959043[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.1690311042218[/C][C]-0.169031104221847[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]10.7619959056669[/C][C]1.23800409433311[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]10.6974296735737[/C][C]1.30257032642634[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]11.3079010825417[/C][C]0.692098917458296[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]10.1304004027188[/C][C]-2.13040040271876[/C][/ROW]
[ROW][C]114[/C][C]5[/C][C]10.6613493225559[/C][C]-5.66134932255594[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]11.2113640900852[/C][C]-0.21136409008519[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]10.7969766420296[/C][C]1.20302335797042[/C][/ROW]
[ROW][C]117[/C][C]12[/C][C]10.7271970222225[/C][C]1.27280297777748[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]11.2314638297187[/C][C]-0.231463829718652[/C][/ROW]
[ROW][C]119[/C][C]12[/C][C]10.8373388990253[/C][C]1.16266110097470[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]10.7856829347937[/C][C]-0.78568293479369[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]10.5162362469852[/C][C]-3.51623624698522[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]10.9636337025359[/C][C]1.03636629746409[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.7982485720082[/C][C]1.20175142799182[/C][/ROW]
[ROW][C]124[/C][C]9[/C][C]10.9290071344151[/C][C]-1.92900713441511[/C][/ROW]
[ROW][C]125[/C][C]12[/C][C]11.7079340174335[/C][C]0.292065982566457[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.7544713408427[/C][C]1.24552865915733[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]10.7180463591778[/C][C]0.281953640822185[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]10.9654321160800[/C][C]0.0345678839200346[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]11.1433828838222[/C][C]0.856617116177813[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]10.7271970222225[/C][C]1.27280297777748[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.9118054229807[/C][C]0.0881945770192629[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.5391339127360[/C][C]0.460866087263974[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]11.0334133223430[/C][C]0.966586677657031[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]10.8870187791989[/C][C]-2.88701877919890[/C][/ROW]
[ROW][C]135[/C][C]11[/C][C]10.8959971269200[/C][C]0.104002873079963[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]11.1928904486722[/C][C]-0.192890448672217[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]10.8906060686922[/C][C]-4.89060606869224[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]11.1733225477946[/C][C]1.82667745220539[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]11.0095539778926[/C][C]0.9904460221074[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.0425639853877[/C][C]0.957436014612327[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.1722737630681[/C][C]0.827726236931937[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.0458066442339[/C][C]0.95419335576611[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]11.0301706634968[/C][C]0.969829336503247[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]10.9072908341559[/C][C]-0.907290834155922[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.5062962205645[/C][C]0.493703779435484[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]11.2512040459198[/C][C]0.748795954080176[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104229&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104229&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	11.2016964810699	-0.201696481069856
2	12	11.2126455576586	0.787354442341446
3	12	11.2018687963934	0.79813120360664
4	11	10.9873207316627	0.0126792683372818
5	11	10.9602187283661	0.0397812716338672
6	10	10.4425247070377	-0.442524707037697
7	11	10.6116694423823	0.388330557617659
8	9	11.6948514342484	-2.69485143424837
9	10	10.6739244973652	-0.673924497365182
10	12	11.2711314702297	0.728868529770278
11	12	11.5595687454219	0.440431254578146
12	12	11.0178377091291	0.982162290870917
13	13	11.0717994952807	1.92820050471932
14	9	10.0652419643464	-1.06524196434643
15	12	10.5682368418640	1.43176315813604
16	12	11.0551296226022	0.944870377397843
17	12	11.2111917747616	0.788808225238374
18	13	11.1536278063315	1.84637219366847
19	11	11.1107175069742	-0.110717506974240
20	12	11.2129901883057	0.78700981169432
21	15	10.8181759963181	4.18182400368193
22	11	11.3439210660362	-0.343921066036179
23	12	11.1469701733155	0.85302982668447
24	10	10.8992397857663	-0.899239785766253
25	11	11.3301343287716	-0.330134328771585
26	13	11.0635856691622	1.93641433083779
27	6	11.0092093472455	-5.00920934724547
28	12	11.1688587888983	0.831141211101717
29	12	10.9652598007564	1.03474019924360
30	10	10.8981401711112	-0.898140171111218
31	12	11.1704848871188	0.829515112881227
32	12	11.0271606874974	0.972839312502648
33	11	10.7637943192109	0.236205680789062
34	9	10.9544830394912	-1.95448303949121
35	10	11.3834163912237	-1.38341639122369
36	12	11.3468190942353	0.653180905764733
37	12	11.1654438147285	0.834556185271496
38	12	10.9548276701383	1.04517232986166
39	14	10.5451420190163	3.45485798098374
40	10	11.1927181333487	-1.19271813334865
41	10	11.1299556699899	-1.12995566998988
42	11	11.1232831441887	-0.123283144188725
43	10	11.3394573071399	-1.33945730713985
44	10	10.9325944239085	-0.932594423908455
45	12	11.1559485210367	0.844051478963329
46	11	11.2569397347947	-0.256939734794749
47	8	11.0427363007112	-3.04273630071124
48	12	10.6152567318757	1.38474326812432
49	10	10.7194906044800	-0.719490604479978
50	7	11.1250815577328	-4.12508155773278
51	11	10.7436945795775	0.256305420422524
52	7	10.8619478722932	-3.86194787229318
53	11	10.8185206269652	0.181479373034799
54	8	10.9417450869532	-2.94174508695316
55	11	11.0095539778926	-0.0095539778925989
56	12	10.7619959056669	1.23800409433311
57	8	11.1579192499043	-3.15791924990429
58	14	11.1017391592531	2.8982608407469
59	14	10.5257315406771	3.47426845932294
60	11	11.2144344336078	-0.214434433607842
61	12	11.1722737630681	0.827726236931937
62	14	11.0271606874974	2.97283931250265
63	9	11.2572843654419	-2.25728436544188
64	13	11.1376471949473	1.86235280505274
65	8	11.0240903439747	-3.0240903439747
66	11	10.9085627641345	0.0914372358654787
67	9	10.7347215870467	-1.73472158704674
68	12	11.4244679095137	0.575532090486333
69	7	10.785165988823	-3.785165988823
70	11	10.9797866292437	0.0202133707562597
71	12	11.1191735535343	0.880826446465711
72	11	10.9354924521075	0.0645075478924571
73	12	10.8299866495247	1.17001335047535
74	9	11.3801737323775	-2.38017373237747
75	11	10.8927544680738	0.107245531926179
76	13	10.9087350794581	2.09126492054191
77	12	11.0825762565459	0.917423743454129
78	12	10.8131295687374	1.18687043126260
79	11	10.8646132176455	0.135386782354548
80	12	11.1948611775398	0.805138822460166
81	12	11.2569397347947	0.743060265205251
82	11	11.1984538222236	-0.198453822223578
83	11	10.9634613872123	0.0365386127876514
84	8	10.6737426444469	-2.67374264444686
85	9	11.0820593105752	-2.08205931057518
86	12	11.0757463082063	0.924253691793688
87	13	10.8517029497838	2.14829705021616
88	12	11.2178494077776	0.782150592222378
89	6	11.0657340685438	-5.06573406854379
90	12	11.1672422282726	0.832757771727443
91	11	10.6850363516827	0.314963648317259
92	13	11.1433828838222	1.85661711617781
93	11	11.1137878504969	-0.113787850496892
94	12	10.8554625546007	1.14453744539925
95	10	10.9419174022767	-0.941917402276723
96	10	10.7855106194701	-0.785510619470127
97	11	11.1263534877114	-0.126353487711377
98	11	11.0323137076879	-0.0323137076879334
99	9	10.9136038365248	-1.91360383652479
100	7	10.901555145281	-3.901555145281
101	11	10.9783423839416	0.0216576160584226
102	12	11.1690311042218	0.830968895778153
103	12	10.9544830394912	1.04551696050879
104	15	10.5953388451605	4.40466115483945
105	11	11.0151173514440	-0.0151173514439601
106	10	11.0187046409373	-1.01870464093730
107	13	10.8500768515634	2.14992314843665
108	13	10.8533195104096	2.14668048959043
109	11	11.1690311042218	-0.169031104221847
110	12	10.7619959056669	1.23800409433311
111	12	10.6974296735737	1.30257032642634
112	12	11.3079010825417	0.692098917458296
113	8	10.1304004027188	-2.13040040271876
114	5	10.6613493225559	-5.66134932255594
115	11	11.2113640900852	-0.21136409008519
116	12	10.7969766420296	1.20302335797042
117	12	10.7271970222225	1.27280297777748
118	11	11.2314638297187	-0.231463829718652
119	12	10.8373388990253	1.16266110097470
120	10	10.7856829347937	-0.78568293479369
121	7	10.5162362469852	-3.51623624698522
122	12	10.9636337025359	1.03636629746409
123	12	10.7982485720082	1.20175142799182
124	9	10.9290071344151	-1.92900713441511
125	12	11.7079340174335	0.292065982566457
126	12	10.7544713408427	1.24552865915733
127	11	10.7180463591778	0.281953640822185
128	11	10.9654321160800	0.0345678839200346
129	12	11.1433828838222	0.856617116177813
130	12	10.7271970222225	1.27280297777748
131	11	10.9118054229807	0.0881945770192629
132	12	11.5391339127360	0.460866087263974
133	12	11.0334133223430	0.966586677657031
134	8	10.8870187791989	-2.88701877919890
135	11	10.8959971269200	0.104002873079963
136	11	11.1928904486722	-0.192890448672217
137	6	10.8906060686922	-4.89060606869224
138	13	11.1733225477946	1.82667745220539
139	12	11.0095539778926	0.9904460221074
140	12	11.0425639853877	0.957436014612327
141	12	11.1722737630681	0.827726236931937
142	12	11.0458066442339	0.95419335576611
143	12	11.0301706634968	0.969829336503247
144	10	10.9072908341559	-0.907290834155922
145	12	11.5062962205645	0.493703779435484
146	12	11.2512040459198	0.748795954080176

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0919674854131228	0.183934970826246	0.908032514586877
9	0.0751755854506615	0.150351170901323	0.924824414549339
10	0.0364859219372154	0.0729718438744307	0.963514078062785
11	0.0167287242135186	0.0334574484270372	0.983271275786481
12	0.00820259365482063	0.0164051873096413	0.99179740634518
13	0.00366075445231982	0.00732150890463965	0.99633924554768
14	0.0201748429089669	0.0403496858179338	0.979825157091033
15	0.0247359049522437	0.0494718099044875	0.975264095047756
16	0.0140070051540866	0.0280140103081732	0.985992994845913
17	0.00709147700938566	0.0141829540187713	0.992908522990614
18	0.00380368808735597	0.00760737617471195	0.996196311912644
19	0.00316202798387758	0.00632405596775517	0.996837972016122
20	0.00182991562593755	0.00365983125187511	0.998170084374062
21	0.0862433111644597	0.172486622328919	0.91375668883554
22	0.0595048745608822	0.119009749121764	0.940495125439118
23	0.0398471877098824	0.0796943754197648	0.960152812290118
24	0.0412655056307178	0.0825310112614356	0.958734494369282
25	0.0322597921298826	0.0645195842597653	0.967740207870117
26	0.0244795095324369	0.0489590190648738	0.975520490467563
27	0.342067634133476	0.684135268266952	0.657932365866524
28	0.289413157480203	0.578826314960405	0.710586842519797
29	0.246313713366817	0.492627426733635	0.753686286633183
30	0.218391997347098	0.436783994694197	0.781608002652902
31	0.176940145659200	0.353880291318400	0.8230598543408
32	0.142050250490071	0.284100500980141	0.85794974950993
33	0.109800936563802	0.219601873127605	0.890199063436198
34	0.128011056169752	0.256022112339504	0.871988943830248
35	0.120239625694544	0.240479251389087	0.879760374305456
36	0.098539370078695	0.19707874015739	0.901460629921305
37	0.0817738093345542	0.163547618669108	0.918226190665446
38	0.0664524726834492	0.132904945366898	0.93354752731655
39	0.127525760878865	0.255051521757730	0.872474239121135
40	0.116701016343749	0.233402032687498	0.883298983656251
41	0.104473010043853	0.208946020087706	0.895526989956147
42	0.08102397959612	0.16204795919224	0.91897602040388
43	0.073724772073222	0.147449544146444	0.926275227926778
44	0.0624560398690059	0.124912079738012	0.937543960130994
45	0.0502836183328673	0.100567236665735	0.949716381667133
46	0.03760792199064	0.07521584398128	0.96239207800936
47	0.0634974834710746	0.126994966942149	0.936502516528925
48	0.054971388364802	0.109942776729604	0.945028611635198
49	0.0475311131322985	0.095062226264597	0.952468886867702
50	0.140682874019765	0.281365748039530	0.859317125980235
51	0.113580697371040	0.227161394742080	0.88641930262896
52	0.281892768217913	0.563785536435825	0.718107231782087
53	0.240826728374009	0.481653456748018	0.759173271625991
54	0.305921202501871	0.611842405003741	0.69407879749813
55	0.262917461913029	0.525834923826059	0.73708253808697
56	0.241081708049891	0.482163416099782	0.758918291950109
57	0.315299814926633	0.630599629853265	0.684700185073367
58	0.416200775388789	0.832401550777578	0.583799224611211
59	0.546785721455997	0.906428557088006	0.453214278544003
60	0.49832168570349	0.99664337140698	0.50167831429651
61	0.457908812162321	0.915817624324641	0.542091187837679
62	0.533272834780053	0.933454330439893	0.466727165219947
63	0.55697188861851	0.88605622276298	0.44302811138149
64	0.559091203399351	0.881817593201298	0.440908796600649
65	0.641731734484134	0.716536531031731	0.358268265515866
66	0.59540731646458	0.80918536707084	0.40459268353542
67	0.592934380947382	0.814131238105236	0.407065619052618
68	0.56123485154538	0.87753029690924	0.43876514845462
69	0.71433598013998	0.57132803972004	0.28566401986002
70	0.67176213064903	0.65647573870194	0.32823786935097
71	0.637929641527045	0.72414071694591	0.362070358472955
72	0.591984738399252	0.816030523201495	0.408015261600748
73	0.564248161566784	0.871503676866432	0.435751838433216
74	0.609863089088958	0.780273821822084	0.390136910911042
75	0.563873233351463	0.872253533297073	0.436126766648537
76	0.581531952882251	0.836936094235498	0.418468047117749
77	0.547187015146868	0.905625969706263	0.452812984853132
78	0.52806039695727	0.94387920608546	0.47193960304273
79	0.479698976677853	0.959397953355706	0.520301023322147
80	0.441953761666891	0.883907523333782	0.558046238333109
81	0.402195415392598	0.804390830785195	0.597804584607402
82	0.361019260807661	0.722038521615322	0.638980739192339
83	0.316272789512975	0.632545579025951	0.683727210487024
84	0.362160114654175	0.72432022930835	0.637839885345825
85	0.374414746733095	0.74882949346619	0.625585253266905
86	0.338072587251130	0.676145174502259	0.66192741274887
87	0.352014109757006	0.704028219514012	0.647985890242994
88	0.31608268142868	0.63216536285736	0.68391731857132
89	0.714651113877129	0.570697772245742	0.285348886122871
90	0.676295254778605	0.64740949044279	0.323704745221395
91	0.638636626405152	0.722726747189696	0.361363373594848
92	0.629287822435034	0.741424355129933	0.370712177564966
93	0.580148827434237	0.839702345131526	0.419851172565763
94	0.559020555378522	0.881958889242956	0.440979444621478
95	0.519340034663897	0.961319930672207	0.480659965336103
96	0.480793178984582	0.961586357969163	0.519206821015418
97	0.429583250014853	0.859166500029706	0.570416749985147
98	0.384415059160387	0.768830118320775	0.615584940839613
99	0.39836325547761	0.79672651095522	0.60163674452239
100	0.548230679547204	0.903538640905592	0.451769320452796
101	0.494957118796237	0.989914237592475	0.505042881203763
102	0.455957102869968	0.911914205739935	0.544042897130032
103	0.424342302235027	0.848684604470053	0.575657697764973
104	0.774256402943643	0.451487194112714	0.225743597056357
105	0.749665415689497	0.500669168621006	0.250334584310503
106	0.746705255703438	0.506589488593123	0.253294744296562
107	0.757682279540764	0.484635440918471	0.242317720459236
108	0.767530596129791	0.464938807740419	0.232469403870209
109	0.721224965356999	0.557550069286002	0.278775034643001
110	0.713971097313712	0.572057805372577	0.286028902686288
111	0.718399515086487	0.563200969827025	0.281600484913513
112	0.669455829180564	0.661088341638871	0.330544170819436
113	0.794296668578292	0.411406662843415	0.205703331421708
114	0.977623932438222	0.0447521351235555	0.0223760675617778
115	0.968603783975398	0.0627924320492042	0.0313962160246021
116	0.97778803663627	0.0444239267274582	0.0222119633637291
117	0.972706276336176	0.0545874473276489	0.0272937236638244
118	0.97710977353966	0.0457804529206819	0.0228902264603409
119	0.96625234490422	0.0674953101915601	0.0337476550957800
120	0.955922428452405	0.0881551430951895	0.0440775715475948
121	0.986790256520304	0.0264194869593910	0.0132097434796955
122	0.979268776408945	0.0414624471821097	0.0207312235910549
123	0.967841319982552	0.0643173600348971	0.0321586800174485
124	0.991655186137832	0.0166896277243359	0.00834481386216794
125	0.987871172961734	0.0242576540765327	0.0121288270382663
126	0.995955877443234	0.0080882451135311	0.00404412255676555
127	0.996254233284033	0.00749153343193462	0.00374576671596731
128	0.992674334070865	0.0146513318582708	0.0073256659291354
129	0.991844420347716	0.016311159304568	0.008155579652284
130	0.993321285260185	0.0133574294796295	0.00667871473981474
131	0.987423489524193	0.0251530209516149	0.0125765104758074
132	0.987986354653389	0.0240272906932227	0.0120136453466113
133	0.990955383323317	0.0180892333533657	0.00904461667668284
134	0.986975938818012	0.026048122363976	0.013024061181988
135	0.973935077077702	0.0521298458445953	0.0260649229222977
136	0.945293246826528	0.109413506346944	0.0547067531734722
137	0.996164776465406	0.00767044706918774	0.00383522353459387
138	0.983743295812945	0.0325134083741105	0.0162567041870552

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0919674854131228 & 0.183934970826246 & 0.908032514586877 \tabularnewline
9 & 0.0751755854506615 & 0.150351170901323 & 0.924824414549339 \tabularnewline
10 & 0.0364859219372154 & 0.0729718438744307 & 0.963514078062785 \tabularnewline
11 & 0.0167287242135186 & 0.0334574484270372 & 0.983271275786481 \tabularnewline
12 & 0.00820259365482063 & 0.0164051873096413 & 0.99179740634518 \tabularnewline
13 & 0.00366075445231982 & 0.00732150890463965 & 0.99633924554768 \tabularnewline
14 & 0.0201748429089669 & 0.0403496858179338 & 0.979825157091033 \tabularnewline
15 & 0.0247359049522437 & 0.0494718099044875 & 0.975264095047756 \tabularnewline
16 & 0.0140070051540866 & 0.0280140103081732 & 0.985992994845913 \tabularnewline
17 & 0.00709147700938566 & 0.0141829540187713 & 0.992908522990614 \tabularnewline
18 & 0.00380368808735597 & 0.00760737617471195 & 0.996196311912644 \tabularnewline
19 & 0.00316202798387758 & 0.00632405596775517 & 0.996837972016122 \tabularnewline
20 & 0.00182991562593755 & 0.00365983125187511 & 0.998170084374062 \tabularnewline
21 & 0.0862433111644597 & 0.172486622328919 & 0.91375668883554 \tabularnewline
22 & 0.0595048745608822 & 0.119009749121764 & 0.940495125439118 \tabularnewline
23 & 0.0398471877098824 & 0.0796943754197648 & 0.960152812290118 \tabularnewline
24 & 0.0412655056307178 & 0.0825310112614356 & 0.958734494369282 \tabularnewline
25 & 0.0322597921298826 & 0.0645195842597653 & 0.967740207870117 \tabularnewline
26 & 0.0244795095324369 & 0.0489590190648738 & 0.975520490467563 \tabularnewline
27 & 0.342067634133476 & 0.684135268266952 & 0.657932365866524 \tabularnewline
28 & 0.289413157480203 & 0.578826314960405 & 0.710586842519797 \tabularnewline
29 & 0.246313713366817 & 0.492627426733635 & 0.753686286633183 \tabularnewline
30 & 0.218391997347098 & 0.436783994694197 & 0.781608002652902 \tabularnewline
31 & 0.176940145659200 & 0.353880291318400 & 0.8230598543408 \tabularnewline
32 & 0.142050250490071 & 0.284100500980141 & 0.85794974950993 \tabularnewline
33 & 0.109800936563802 & 0.219601873127605 & 0.890199063436198 \tabularnewline
34 & 0.128011056169752 & 0.256022112339504 & 0.871988943830248 \tabularnewline
35 & 0.120239625694544 & 0.240479251389087 & 0.879760374305456 \tabularnewline
36 & 0.098539370078695 & 0.19707874015739 & 0.901460629921305 \tabularnewline
37 & 0.0817738093345542 & 0.163547618669108 & 0.918226190665446 \tabularnewline
38 & 0.0664524726834492 & 0.132904945366898 & 0.93354752731655 \tabularnewline
39 & 0.127525760878865 & 0.255051521757730 & 0.872474239121135 \tabularnewline
40 & 0.116701016343749 & 0.233402032687498 & 0.883298983656251 \tabularnewline
41 & 0.104473010043853 & 0.208946020087706 & 0.895526989956147 \tabularnewline
42 & 0.08102397959612 & 0.16204795919224 & 0.91897602040388 \tabularnewline
43 & 0.073724772073222 & 0.147449544146444 & 0.926275227926778 \tabularnewline
44 & 0.0624560398690059 & 0.124912079738012 & 0.937543960130994 \tabularnewline
45 & 0.0502836183328673 & 0.100567236665735 & 0.949716381667133 \tabularnewline
46 & 0.03760792199064 & 0.07521584398128 & 0.96239207800936 \tabularnewline
47 & 0.0634974834710746 & 0.126994966942149 & 0.936502516528925 \tabularnewline
48 & 0.054971388364802 & 0.109942776729604 & 0.945028611635198 \tabularnewline
49 & 0.0475311131322985 & 0.095062226264597 & 0.952468886867702 \tabularnewline
50 & 0.140682874019765 & 0.281365748039530 & 0.859317125980235 \tabularnewline
51 & 0.113580697371040 & 0.227161394742080 & 0.88641930262896 \tabularnewline
52 & 0.281892768217913 & 0.563785536435825 & 0.718107231782087 \tabularnewline
53 & 0.240826728374009 & 0.481653456748018 & 0.759173271625991 \tabularnewline
54 & 0.305921202501871 & 0.611842405003741 & 0.69407879749813 \tabularnewline
55 & 0.262917461913029 & 0.525834923826059 & 0.73708253808697 \tabularnewline
56 & 0.241081708049891 & 0.482163416099782 & 0.758918291950109 \tabularnewline
57 & 0.315299814926633 & 0.630599629853265 & 0.684700185073367 \tabularnewline
58 & 0.416200775388789 & 0.832401550777578 & 0.583799224611211 \tabularnewline
59 & 0.546785721455997 & 0.906428557088006 & 0.453214278544003 \tabularnewline
60 & 0.49832168570349 & 0.99664337140698 & 0.50167831429651 \tabularnewline
61 & 0.457908812162321 & 0.915817624324641 & 0.542091187837679 \tabularnewline
62 & 0.533272834780053 & 0.933454330439893 & 0.466727165219947 \tabularnewline
63 & 0.55697188861851 & 0.88605622276298 & 0.44302811138149 \tabularnewline
64 & 0.559091203399351 & 0.881817593201298 & 0.440908796600649 \tabularnewline
65 & 0.641731734484134 & 0.716536531031731 & 0.358268265515866 \tabularnewline
66 & 0.59540731646458 & 0.80918536707084 & 0.40459268353542 \tabularnewline
67 & 0.592934380947382 & 0.814131238105236 & 0.407065619052618 \tabularnewline
68 & 0.56123485154538 & 0.87753029690924 & 0.43876514845462 \tabularnewline
69 & 0.71433598013998 & 0.57132803972004 & 0.28566401986002 \tabularnewline
70 & 0.67176213064903 & 0.65647573870194 & 0.32823786935097 \tabularnewline
71 & 0.637929641527045 & 0.72414071694591 & 0.362070358472955 \tabularnewline
72 & 0.591984738399252 & 0.816030523201495 & 0.408015261600748 \tabularnewline
73 & 0.564248161566784 & 0.871503676866432 & 0.435751838433216 \tabularnewline
74 & 0.609863089088958 & 0.780273821822084 & 0.390136910911042 \tabularnewline
75 & 0.563873233351463 & 0.872253533297073 & 0.436126766648537 \tabularnewline
76 & 0.581531952882251 & 0.836936094235498 & 0.418468047117749 \tabularnewline
77 & 0.547187015146868 & 0.905625969706263 & 0.452812984853132 \tabularnewline
78 & 0.52806039695727 & 0.94387920608546 & 0.47193960304273 \tabularnewline
79 & 0.479698976677853 & 0.959397953355706 & 0.520301023322147 \tabularnewline
80 & 0.441953761666891 & 0.883907523333782 & 0.558046238333109 \tabularnewline
81 & 0.402195415392598 & 0.804390830785195 & 0.597804584607402 \tabularnewline
82 & 0.361019260807661 & 0.722038521615322 & 0.638980739192339 \tabularnewline
83 & 0.316272789512975 & 0.632545579025951 & 0.683727210487024 \tabularnewline
84 & 0.362160114654175 & 0.72432022930835 & 0.637839885345825 \tabularnewline
85 & 0.374414746733095 & 0.74882949346619 & 0.625585253266905 \tabularnewline
86 & 0.338072587251130 & 0.676145174502259 & 0.66192741274887 \tabularnewline
87 & 0.352014109757006 & 0.704028219514012 & 0.647985890242994 \tabularnewline
88 & 0.31608268142868 & 0.63216536285736 & 0.68391731857132 \tabularnewline
89 & 0.714651113877129 & 0.570697772245742 & 0.285348886122871 \tabularnewline
90 & 0.676295254778605 & 0.64740949044279 & 0.323704745221395 \tabularnewline
91 & 0.638636626405152 & 0.722726747189696 & 0.361363373594848 \tabularnewline
92 & 0.629287822435034 & 0.741424355129933 & 0.370712177564966 \tabularnewline
93 & 0.580148827434237 & 0.839702345131526 & 0.419851172565763 \tabularnewline
94 & 0.559020555378522 & 0.881958889242956 & 0.440979444621478 \tabularnewline
95 & 0.519340034663897 & 0.961319930672207 & 0.480659965336103 \tabularnewline
96 & 0.480793178984582 & 0.961586357969163 & 0.519206821015418 \tabularnewline
97 & 0.429583250014853 & 0.859166500029706 & 0.570416749985147 \tabularnewline
98 & 0.384415059160387 & 0.768830118320775 & 0.615584940839613 \tabularnewline
99 & 0.39836325547761 & 0.79672651095522 & 0.60163674452239 \tabularnewline
100 & 0.548230679547204 & 0.903538640905592 & 0.451769320452796 \tabularnewline
101 & 0.494957118796237 & 0.989914237592475 & 0.505042881203763 \tabularnewline
102 & 0.455957102869968 & 0.911914205739935 & 0.544042897130032 \tabularnewline
103 & 0.424342302235027 & 0.848684604470053 & 0.575657697764973 \tabularnewline
104 & 0.774256402943643 & 0.451487194112714 & 0.225743597056357 \tabularnewline
105 & 0.749665415689497 & 0.500669168621006 & 0.250334584310503 \tabularnewline
106 & 0.746705255703438 & 0.506589488593123 & 0.253294744296562 \tabularnewline
107 & 0.757682279540764 & 0.484635440918471 & 0.242317720459236 \tabularnewline
108 & 0.767530596129791 & 0.464938807740419 & 0.232469403870209 \tabularnewline
109 & 0.721224965356999 & 0.557550069286002 & 0.278775034643001 \tabularnewline
110 & 0.713971097313712 & 0.572057805372577 & 0.286028902686288 \tabularnewline
111 & 0.718399515086487 & 0.563200969827025 & 0.281600484913513 \tabularnewline
112 & 0.669455829180564 & 0.661088341638871 & 0.330544170819436 \tabularnewline
113 & 0.794296668578292 & 0.411406662843415 & 0.205703331421708 \tabularnewline
114 & 0.977623932438222 & 0.0447521351235555 & 0.0223760675617778 \tabularnewline
115 & 0.968603783975398 & 0.0627924320492042 & 0.0313962160246021 \tabularnewline
116 & 0.97778803663627 & 0.0444239267274582 & 0.0222119633637291 \tabularnewline
117 & 0.972706276336176 & 0.0545874473276489 & 0.0272937236638244 \tabularnewline
118 & 0.97710977353966 & 0.0457804529206819 & 0.0228902264603409 \tabularnewline
119 & 0.96625234490422 & 0.0674953101915601 & 0.0337476550957800 \tabularnewline
120 & 0.955922428452405 & 0.0881551430951895 & 0.0440775715475948 \tabularnewline
121 & 0.986790256520304 & 0.0264194869593910 & 0.0132097434796955 \tabularnewline
122 & 0.979268776408945 & 0.0414624471821097 & 0.0207312235910549 \tabularnewline
123 & 0.967841319982552 & 0.0643173600348971 & 0.0321586800174485 \tabularnewline
124 & 0.991655186137832 & 0.0166896277243359 & 0.00834481386216794 \tabularnewline
125 & 0.987871172961734 & 0.0242576540765327 & 0.0121288270382663 \tabularnewline
126 & 0.995955877443234 & 0.0080882451135311 & 0.00404412255676555 \tabularnewline
127 & 0.996254233284033 & 0.00749153343193462 & 0.00374576671596731 \tabularnewline
128 & 0.992674334070865 & 0.0146513318582708 & 0.0073256659291354 \tabularnewline
129 & 0.991844420347716 & 0.016311159304568 & 0.008155579652284 \tabularnewline
130 & 0.993321285260185 & 0.0133574294796295 & 0.00667871473981474 \tabularnewline
131 & 0.987423489524193 & 0.0251530209516149 & 0.0125765104758074 \tabularnewline
132 & 0.987986354653389 & 0.0240272906932227 & 0.0120136453466113 \tabularnewline
133 & 0.990955383323317 & 0.0180892333533657 & 0.00904461667668284 \tabularnewline
134 & 0.986975938818012 & 0.026048122363976 & 0.013024061181988 \tabularnewline
135 & 0.973935077077702 & 0.0521298458445953 & 0.0260649229222977 \tabularnewline
136 & 0.945293246826528 & 0.109413506346944 & 0.0547067531734722 \tabularnewline
137 & 0.996164776465406 & 0.00767044706918774 & 0.00383522353459387 \tabularnewline
138 & 0.983743295812945 & 0.0325134083741105 & 0.0162567041870552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104229&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]0.0919674854131228[/C][C]0.183934970826246[/C][C]0.908032514586877[/C][/ROW]
[ROW][C]9[/C][C]0.0751755854506615[/C][C]0.150351170901323[/C][C]0.924824414549339[/C][/ROW]
[ROW][C]10[/C][C]0.0364859219372154[/C][C]0.0729718438744307[/C][C]0.963514078062785[/C][/ROW]
[ROW][C]11[/C][C]0.0167287242135186[/C][C]0.0334574484270372[/C][C]0.983271275786481[/C][/ROW]
[ROW][C]12[/C][C]0.00820259365482063[/C][C]0.0164051873096413[/C][C]0.99179740634518[/C][/ROW]
[ROW][C]13[/C][C]0.00366075445231982[/C][C]0.00732150890463965[/C][C]0.99633924554768[/C][/ROW]
[ROW][C]14[/C][C]0.0201748429089669[/C][C]0.0403496858179338[/C][C]0.979825157091033[/C][/ROW]
[ROW][C]15[/C][C]0.0247359049522437[/C][C]0.0494718099044875[/C][C]0.975264095047756[/C][/ROW]
[ROW][C]16[/C][C]0.0140070051540866[/C][C]0.0280140103081732[/C][C]0.985992994845913[/C][/ROW]
[ROW][C]17[/C][C]0.00709147700938566[/C][C]0.0141829540187713[/C][C]0.992908522990614[/C][/ROW]
[ROW][C]18[/C][C]0.00380368808735597[/C][C]0.00760737617471195[/C][C]0.996196311912644[/C][/ROW]
[ROW][C]19[/C][C]0.00316202798387758[/C][C]0.00632405596775517[/C][C]0.996837972016122[/C][/ROW]
[ROW][C]20[/C][C]0.00182991562593755[/C][C]0.00365983125187511[/C][C]0.998170084374062[/C][/ROW]
[ROW][C]21[/C][C]0.0862433111644597[/C][C]0.172486622328919[/C][C]0.91375668883554[/C][/ROW]
[ROW][C]22[/C][C]0.0595048745608822[/C][C]0.119009749121764[/C][C]0.940495125439118[/C][/ROW]
[ROW][C]23[/C][C]0.0398471877098824[/C][C]0.0796943754197648[/C][C]0.960152812290118[/C][/ROW]
[ROW][C]24[/C][C]0.0412655056307178[/C][C]0.0825310112614356[/C][C]0.958734494369282[/C][/ROW]
[ROW][C]25[/C][C]0.0322597921298826[/C][C]0.0645195842597653[/C][C]0.967740207870117[/C][/ROW]
[ROW][C]26[/C][C]0.0244795095324369[/C][C]0.0489590190648738[/C][C]0.975520490467563[/C][/ROW]
[ROW][C]27[/C][C]0.342067634133476[/C][C]0.684135268266952[/C][C]0.657932365866524[/C][/ROW]
[ROW][C]28[/C][C]0.289413157480203[/C][C]0.578826314960405[/C][C]0.710586842519797[/C][/ROW]
[ROW][C]29[/C][C]0.246313713366817[/C][C]0.492627426733635[/C][C]0.753686286633183[/C][/ROW]
[ROW][C]30[/C][C]0.218391997347098[/C][C]0.436783994694197[/C][C]0.781608002652902[/C][/ROW]
[ROW][C]31[/C][C]0.176940145659200[/C][C]0.353880291318400[/C][C]0.8230598543408[/C][/ROW]
[ROW][C]32[/C][C]0.142050250490071[/C][C]0.284100500980141[/C][C]0.85794974950993[/C][/ROW]
[ROW][C]33[/C][C]0.109800936563802[/C][C]0.219601873127605[/C][C]0.890199063436198[/C][/ROW]
[ROW][C]34[/C][C]0.128011056169752[/C][C]0.256022112339504[/C][C]0.871988943830248[/C][/ROW]
[ROW][C]35[/C][C]0.120239625694544[/C][C]0.240479251389087[/C][C]0.879760374305456[/C][/ROW]
[ROW][C]36[/C][C]0.098539370078695[/C][C]0.19707874015739[/C][C]0.901460629921305[/C][/ROW]
[ROW][C]37[/C][C]0.0817738093345542[/C][C]0.163547618669108[/C][C]0.918226190665446[/C][/ROW]
[ROW][C]38[/C][C]0.0664524726834492[/C][C]0.132904945366898[/C][C]0.93354752731655[/C][/ROW]
[ROW][C]39[/C][C]0.127525760878865[/C][C]0.255051521757730[/C][C]0.872474239121135[/C][/ROW]
[ROW][C]40[/C][C]0.116701016343749[/C][C]0.233402032687498[/C][C]0.883298983656251[/C][/ROW]
[ROW][C]41[/C][C]0.104473010043853[/C][C]0.208946020087706[/C][C]0.895526989956147[/C][/ROW]
[ROW][C]42[/C][C]0.08102397959612[/C][C]0.16204795919224[/C][C]0.91897602040388[/C][/ROW]
[ROW][C]43[/C][C]0.073724772073222[/C][C]0.147449544146444[/C][C]0.926275227926778[/C][/ROW]
[ROW][C]44[/C][C]0.0624560398690059[/C][C]0.124912079738012[/C][C]0.937543960130994[/C][/ROW]
[ROW][C]45[/C][C]0.0502836183328673[/C][C]0.100567236665735[/C][C]0.949716381667133[/C][/ROW]
[ROW][C]46[/C][C]0.03760792199064[/C][C]0.07521584398128[/C][C]0.96239207800936[/C][/ROW]
[ROW][C]47[/C][C]0.0634974834710746[/C][C]0.126994966942149[/C][C]0.936502516528925[/C][/ROW]
[ROW][C]48[/C][C]0.054971388364802[/C][C]0.109942776729604[/C][C]0.945028611635198[/C][/ROW]
[ROW][C]49[/C][C]0.0475311131322985[/C][C]0.095062226264597[/C][C]0.952468886867702[/C][/ROW]
[ROW][C]50[/C][C]0.140682874019765[/C][C]0.281365748039530[/C][C]0.859317125980235[/C][/ROW]
[ROW][C]51[/C][C]0.113580697371040[/C][C]0.227161394742080[/C][C]0.88641930262896[/C][/ROW]
[ROW][C]52[/C][C]0.281892768217913[/C][C]0.563785536435825[/C][C]0.718107231782087[/C][/ROW]
[ROW][C]53[/C][C]0.240826728374009[/C][C]0.481653456748018[/C][C]0.759173271625991[/C][/ROW]
[ROW][C]54[/C][C]0.305921202501871[/C][C]0.611842405003741[/C][C]0.69407879749813[/C][/ROW]
[ROW][C]55[/C][C]0.262917461913029[/C][C]0.525834923826059[/C][C]0.73708253808697[/C][/ROW]
[ROW][C]56[/C][C]0.241081708049891[/C][C]0.482163416099782[/C][C]0.758918291950109[/C][/ROW]
[ROW][C]57[/C][C]0.315299814926633[/C][C]0.630599629853265[/C][C]0.684700185073367[/C][/ROW]
[ROW][C]58[/C][C]0.416200775388789[/C][C]0.832401550777578[/C][C]0.583799224611211[/C][/ROW]
[ROW][C]59[/C][C]0.546785721455997[/C][C]0.906428557088006[/C][C]0.453214278544003[/C][/ROW]
[ROW][C]60[/C][C]0.49832168570349[/C][C]0.99664337140698[/C][C]0.50167831429651[/C][/ROW]
[ROW][C]61[/C][C]0.457908812162321[/C][C]0.915817624324641[/C][C]0.542091187837679[/C][/ROW]
[ROW][C]62[/C][C]0.533272834780053[/C][C]0.933454330439893[/C][C]0.466727165219947[/C][/ROW]
[ROW][C]63[/C][C]0.55697188861851[/C][C]0.88605622276298[/C][C]0.44302811138149[/C][/ROW]
[ROW][C]64[/C][C]0.559091203399351[/C][C]0.881817593201298[/C][C]0.440908796600649[/C][/ROW]
[ROW][C]65[/C][C]0.641731734484134[/C][C]0.716536531031731[/C][C]0.358268265515866[/C][/ROW]
[ROW][C]66[/C][C]0.59540731646458[/C][C]0.80918536707084[/C][C]0.40459268353542[/C][/ROW]
[ROW][C]67[/C][C]0.592934380947382[/C][C]0.814131238105236[/C][C]0.407065619052618[/C][/ROW]
[ROW][C]68[/C][C]0.56123485154538[/C][C]0.87753029690924[/C][C]0.43876514845462[/C][/ROW]
[ROW][C]69[/C][C]0.71433598013998[/C][C]0.57132803972004[/C][C]0.28566401986002[/C][/ROW]
[ROW][C]70[/C][C]0.67176213064903[/C][C]0.65647573870194[/C][C]0.32823786935097[/C][/ROW]
[ROW][C]71[/C][C]0.637929641527045[/C][C]0.72414071694591[/C][C]0.362070358472955[/C][/ROW]
[ROW][C]72[/C][C]0.591984738399252[/C][C]0.816030523201495[/C][C]0.408015261600748[/C][/ROW]
[ROW][C]73[/C][C]0.564248161566784[/C][C]0.871503676866432[/C][C]0.435751838433216[/C][/ROW]
[ROW][C]74[/C][C]0.609863089088958[/C][C]0.780273821822084[/C][C]0.390136910911042[/C][/ROW]
[ROW][C]75[/C][C]0.563873233351463[/C][C]0.872253533297073[/C][C]0.436126766648537[/C][/ROW]
[ROW][C]76[/C][C]0.581531952882251[/C][C]0.836936094235498[/C][C]0.418468047117749[/C][/ROW]
[ROW][C]77[/C][C]0.547187015146868[/C][C]0.905625969706263[/C][C]0.452812984853132[/C][/ROW]
[ROW][C]78[/C][C]0.52806039695727[/C][C]0.94387920608546[/C][C]0.47193960304273[/C][/ROW]
[ROW][C]79[/C][C]0.479698976677853[/C][C]0.959397953355706[/C][C]0.520301023322147[/C][/ROW]
[ROW][C]80[/C][C]0.441953761666891[/C][C]0.883907523333782[/C][C]0.558046238333109[/C][/ROW]
[ROW][C]81[/C][C]0.402195415392598[/C][C]0.804390830785195[/C][C]0.597804584607402[/C][/ROW]
[ROW][C]82[/C][C]0.361019260807661[/C][C]0.722038521615322[/C][C]0.638980739192339[/C][/ROW]
[ROW][C]83[/C][C]0.316272789512975[/C][C]0.632545579025951[/C][C]0.683727210487024[/C][/ROW]
[ROW][C]84[/C][C]0.362160114654175[/C][C]0.72432022930835[/C][C]0.637839885345825[/C][/ROW]
[ROW][C]85[/C][C]0.374414746733095[/C][C]0.74882949346619[/C][C]0.625585253266905[/C][/ROW]
[ROW][C]86[/C][C]0.338072587251130[/C][C]0.676145174502259[/C][C]0.66192741274887[/C][/ROW]
[ROW][C]87[/C][C]0.352014109757006[/C][C]0.704028219514012[/C][C]0.647985890242994[/C][/ROW]
[ROW][C]88[/C][C]0.31608268142868[/C][C]0.63216536285736[/C][C]0.68391731857132[/C][/ROW]
[ROW][C]89[/C][C]0.714651113877129[/C][C]0.570697772245742[/C][C]0.285348886122871[/C][/ROW]
[ROW][C]90[/C][C]0.676295254778605[/C][C]0.64740949044279[/C][C]0.323704745221395[/C][/ROW]
[ROW][C]91[/C][C]0.638636626405152[/C][C]0.722726747189696[/C][C]0.361363373594848[/C][/ROW]
[ROW][C]92[/C][C]0.629287822435034[/C][C]0.741424355129933[/C][C]0.370712177564966[/C][/ROW]
[ROW][C]93[/C][C]0.580148827434237[/C][C]0.839702345131526[/C][C]0.419851172565763[/C][/ROW]
[ROW][C]94[/C][C]0.559020555378522[/C][C]0.881958889242956[/C][C]0.440979444621478[/C][/ROW]
[ROW][C]95[/C][C]0.519340034663897[/C][C]0.961319930672207[/C][C]0.480659965336103[/C][/ROW]
[ROW][C]96[/C][C]0.480793178984582[/C][C]0.961586357969163[/C][C]0.519206821015418[/C][/ROW]
[ROW][C]97[/C][C]0.429583250014853[/C][C]0.859166500029706[/C][C]0.570416749985147[/C][/ROW]
[ROW][C]98[/C][C]0.384415059160387[/C][C]0.768830118320775[/C][C]0.615584940839613[/C][/ROW]
[ROW][C]99[/C][C]0.39836325547761[/C][C]0.79672651095522[/C][C]0.60163674452239[/C][/ROW]
[ROW][C]100[/C][C]0.548230679547204[/C][C]0.903538640905592[/C][C]0.451769320452796[/C][/ROW]
[ROW][C]101[/C][C]0.494957118796237[/C][C]0.989914237592475[/C][C]0.505042881203763[/C][/ROW]
[ROW][C]102[/C][C]0.455957102869968[/C][C]0.911914205739935[/C][C]0.544042897130032[/C][/ROW]
[ROW][C]103[/C][C]0.424342302235027[/C][C]0.848684604470053[/C][C]0.575657697764973[/C][/ROW]
[ROW][C]104[/C][C]0.774256402943643[/C][C]0.451487194112714[/C][C]0.225743597056357[/C][/ROW]
[ROW][C]105[/C][C]0.749665415689497[/C][C]0.500669168621006[/C][C]0.250334584310503[/C][/ROW]
[ROW][C]106[/C][C]0.746705255703438[/C][C]0.506589488593123[/C][C]0.253294744296562[/C][/ROW]
[ROW][C]107[/C][C]0.757682279540764[/C][C]0.484635440918471[/C][C]0.242317720459236[/C][/ROW]
[ROW][C]108[/C][C]0.767530596129791[/C][C]0.464938807740419[/C][C]0.232469403870209[/C][/ROW]
[ROW][C]109[/C][C]0.721224965356999[/C][C]0.557550069286002[/C][C]0.278775034643001[/C][/ROW]
[ROW][C]110[/C][C]0.713971097313712[/C][C]0.572057805372577[/C][C]0.286028902686288[/C][/ROW]
[ROW][C]111[/C][C]0.718399515086487[/C][C]0.563200969827025[/C][C]0.281600484913513[/C][/ROW]
[ROW][C]112[/C][C]0.669455829180564[/C][C]0.661088341638871[/C][C]0.330544170819436[/C][/ROW]
[ROW][C]113[/C][C]0.794296668578292[/C][C]0.411406662843415[/C][C]0.205703331421708[/C][/ROW]
[ROW][C]114[/C][C]0.977623932438222[/C][C]0.0447521351235555[/C][C]0.0223760675617778[/C][/ROW]
[ROW][C]115[/C][C]0.968603783975398[/C][C]0.0627924320492042[/C][C]0.0313962160246021[/C][/ROW]
[ROW][C]116[/C][C]0.97778803663627[/C][C]0.0444239267274582[/C][C]0.0222119633637291[/C][/ROW]
[ROW][C]117[/C][C]0.972706276336176[/C][C]0.0545874473276489[/C][C]0.0272937236638244[/C][/ROW]
[ROW][C]118[/C][C]0.97710977353966[/C][C]0.0457804529206819[/C][C]0.0228902264603409[/C][/ROW]
[ROW][C]119[/C][C]0.96625234490422[/C][C]0.0674953101915601[/C][C]0.0337476550957800[/C][/ROW]
[ROW][C]120[/C][C]0.955922428452405[/C][C]0.0881551430951895[/C][C]0.0440775715475948[/C][/ROW]
[ROW][C]121[/C][C]0.986790256520304[/C][C]0.0264194869593910[/C][C]0.0132097434796955[/C][/ROW]
[ROW][C]122[/C][C]0.979268776408945[/C][C]0.0414624471821097[/C][C]0.0207312235910549[/C][/ROW]
[ROW][C]123[/C][C]0.967841319982552[/C][C]0.0643173600348971[/C][C]0.0321586800174485[/C][/ROW]
[ROW][C]124[/C][C]0.991655186137832[/C][C]0.0166896277243359[/C][C]0.00834481386216794[/C][/ROW]
[ROW][C]125[/C][C]0.987871172961734[/C][C]0.0242576540765327[/C][C]0.0121288270382663[/C][/ROW]
[ROW][C]126[/C][C]0.995955877443234[/C][C]0.0080882451135311[/C][C]0.00404412255676555[/C][/ROW]
[ROW][C]127[/C][C]0.996254233284033[/C][C]0.00749153343193462[/C][C]0.00374576671596731[/C][/ROW]
[ROW][C]128[/C][C]0.992674334070865[/C][C]0.0146513318582708[/C][C]0.0073256659291354[/C][/ROW]
[ROW][C]129[/C][C]0.991844420347716[/C][C]0.016311159304568[/C][C]0.008155579652284[/C][/ROW]
[ROW][C]130[/C][C]0.993321285260185[/C][C]0.0133574294796295[/C][C]0.00667871473981474[/C][/ROW]
[ROW][C]131[/C][C]0.987423489524193[/C][C]0.0251530209516149[/C][C]0.0125765104758074[/C][/ROW]
[ROW][C]132[/C][C]0.987986354653389[/C][C]0.0240272906932227[/C][C]0.0120136453466113[/C][/ROW]
[ROW][C]133[/C][C]0.990955383323317[/C][C]0.0180892333533657[/C][C]0.00904461667668284[/C][/ROW]
[ROW][C]134[/C][C]0.986975938818012[/C][C]0.026048122363976[/C][C]0.013024061181988[/C][/ROW]
[ROW][C]135[/C][C]0.973935077077702[/C][C]0.0521298458445953[/C][C]0.0260649229222977[/C][/ROW]
[ROW][C]136[/C][C]0.945293246826528[/C][C]0.109413506346944[/C][C]0.0547067531734722[/C][/ROW]
[ROW][C]137[/C][C]0.996164776465406[/C][C]0.00767044706918774[/C][C]0.00383522353459387[/C][/ROW]
[ROW][C]138[/C][C]0.983743295812945[/C][C]0.0325134083741105[/C][C]0.0162567041870552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104229&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104229&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0919674854131228	0.183934970826246	0.908032514586877
9	0.0751755854506615	0.150351170901323	0.924824414549339
10	0.0364859219372154	0.0729718438744307	0.963514078062785
11	0.0167287242135186	0.0334574484270372	0.983271275786481
12	0.00820259365482063	0.0164051873096413	0.99179740634518
13	0.00366075445231982	0.00732150890463965	0.99633924554768
14	0.0201748429089669	0.0403496858179338	0.979825157091033
15	0.0247359049522437	0.0494718099044875	0.975264095047756
16	0.0140070051540866	0.0280140103081732	0.985992994845913
17	0.00709147700938566	0.0141829540187713	0.992908522990614
18	0.00380368808735597	0.00760737617471195	0.996196311912644
19	0.00316202798387758	0.00632405596775517	0.996837972016122
20	0.00182991562593755	0.00365983125187511	0.998170084374062
21	0.0862433111644597	0.172486622328919	0.91375668883554
22	0.0595048745608822	0.119009749121764	0.940495125439118
23	0.0398471877098824	0.0796943754197648	0.960152812290118
24	0.0412655056307178	0.0825310112614356	0.958734494369282
25	0.0322597921298826	0.0645195842597653	0.967740207870117
26	0.0244795095324369	0.0489590190648738	0.975520490467563
27	0.342067634133476	0.684135268266952	0.657932365866524
28	0.289413157480203	0.578826314960405	0.710586842519797
29	0.246313713366817	0.492627426733635	0.753686286633183
30	0.218391997347098	0.436783994694197	0.781608002652902
31	0.176940145659200	0.353880291318400	0.8230598543408
32	0.142050250490071	0.284100500980141	0.85794974950993
33	0.109800936563802	0.219601873127605	0.890199063436198
34	0.128011056169752	0.256022112339504	0.871988943830248
35	0.120239625694544	0.240479251389087	0.879760374305456
36	0.098539370078695	0.19707874015739	0.901460629921305
37	0.0817738093345542	0.163547618669108	0.918226190665446
38	0.0664524726834492	0.132904945366898	0.93354752731655
39	0.127525760878865	0.255051521757730	0.872474239121135
40	0.116701016343749	0.233402032687498	0.883298983656251
41	0.104473010043853	0.208946020087706	0.895526989956147
42	0.08102397959612	0.16204795919224	0.91897602040388
43	0.073724772073222	0.147449544146444	0.926275227926778
44	0.0624560398690059	0.124912079738012	0.937543960130994
45	0.0502836183328673	0.100567236665735	0.949716381667133
46	0.03760792199064	0.07521584398128	0.96239207800936
47	0.0634974834710746	0.126994966942149	0.936502516528925
48	0.054971388364802	0.109942776729604	0.945028611635198
49	0.0475311131322985	0.095062226264597	0.952468886867702
50	0.140682874019765	0.281365748039530	0.859317125980235
51	0.113580697371040	0.227161394742080	0.88641930262896
52	0.281892768217913	0.563785536435825	0.718107231782087
53	0.240826728374009	0.481653456748018	0.759173271625991
54	0.305921202501871	0.611842405003741	0.69407879749813
55	0.262917461913029	0.525834923826059	0.73708253808697
56	0.241081708049891	0.482163416099782	0.758918291950109
57	0.315299814926633	0.630599629853265	0.684700185073367
58	0.416200775388789	0.832401550777578	0.583799224611211
59	0.546785721455997	0.906428557088006	0.453214278544003
60	0.49832168570349	0.99664337140698	0.50167831429651
61	0.457908812162321	0.915817624324641	0.542091187837679
62	0.533272834780053	0.933454330439893	0.466727165219947
63	0.55697188861851	0.88605622276298	0.44302811138149
64	0.559091203399351	0.881817593201298	0.440908796600649
65	0.641731734484134	0.716536531031731	0.358268265515866
66	0.59540731646458	0.80918536707084	0.40459268353542
67	0.592934380947382	0.814131238105236	0.407065619052618
68	0.56123485154538	0.87753029690924	0.43876514845462
69	0.71433598013998	0.57132803972004	0.28566401986002
70	0.67176213064903	0.65647573870194	0.32823786935097
71	0.637929641527045	0.72414071694591	0.362070358472955
72	0.591984738399252	0.816030523201495	0.408015261600748
73	0.564248161566784	0.871503676866432	0.435751838433216
74	0.609863089088958	0.780273821822084	0.390136910911042
75	0.563873233351463	0.872253533297073	0.436126766648537
76	0.581531952882251	0.836936094235498	0.418468047117749
77	0.547187015146868	0.905625969706263	0.452812984853132
78	0.52806039695727	0.94387920608546	0.47193960304273
79	0.479698976677853	0.959397953355706	0.520301023322147
80	0.441953761666891	0.883907523333782	0.558046238333109
81	0.402195415392598	0.804390830785195	0.597804584607402
82	0.361019260807661	0.722038521615322	0.638980739192339
83	0.316272789512975	0.632545579025951	0.683727210487024
84	0.362160114654175	0.72432022930835	0.637839885345825
85	0.374414746733095	0.74882949346619	0.625585253266905
86	0.338072587251130	0.676145174502259	0.66192741274887
87	0.352014109757006	0.704028219514012	0.647985890242994
88	0.31608268142868	0.63216536285736	0.68391731857132
89	0.714651113877129	0.570697772245742	0.285348886122871
90	0.676295254778605	0.64740949044279	0.323704745221395
91	0.638636626405152	0.722726747189696	0.361363373594848
92	0.629287822435034	0.741424355129933	0.370712177564966
93	0.580148827434237	0.839702345131526	0.419851172565763
94	0.559020555378522	0.881958889242956	0.440979444621478
95	0.519340034663897	0.961319930672207	0.480659965336103
96	0.480793178984582	0.961586357969163	0.519206821015418
97	0.429583250014853	0.859166500029706	0.570416749985147
98	0.384415059160387	0.768830118320775	0.615584940839613
99	0.39836325547761	0.79672651095522	0.60163674452239
100	0.548230679547204	0.903538640905592	0.451769320452796
101	0.494957118796237	0.989914237592475	0.505042881203763
102	0.455957102869968	0.911914205739935	0.544042897130032
103	0.424342302235027	0.848684604470053	0.575657697764973
104	0.774256402943643	0.451487194112714	0.225743597056357
105	0.749665415689497	0.500669168621006	0.250334584310503
106	0.746705255703438	0.506589488593123	0.253294744296562
107	0.757682279540764	0.484635440918471	0.242317720459236
108	0.767530596129791	0.464938807740419	0.232469403870209
109	0.721224965356999	0.557550069286002	0.278775034643001
110	0.713971097313712	0.572057805372577	0.286028902686288
111	0.718399515086487	0.563200969827025	0.281600484913513
112	0.669455829180564	0.661088341638871	0.330544170819436
113	0.794296668578292	0.411406662843415	0.205703331421708
114	0.977623932438222	0.0447521351235555	0.0223760675617778
115	0.968603783975398	0.0627924320492042	0.0313962160246021
116	0.97778803663627	0.0444239267274582	0.0222119633637291
117	0.972706276336176	0.0545874473276489	0.0272937236638244
118	0.97710977353966	0.0457804529206819	0.0228902264603409
119	0.96625234490422	0.0674953101915601	0.0337476550957800
120	0.955922428452405	0.0881551430951895	0.0440775715475948
121	0.986790256520304	0.0264194869593910	0.0132097434796955
122	0.979268776408945	0.0414624471821097	0.0207312235910549
123	0.967841319982552	0.0643173600348971	0.0321586800174485
124	0.991655186137832	0.0166896277243359	0.00834481386216794
125	0.987871172961734	0.0242576540765327	0.0121288270382663
126	0.995955877443234	0.0080882451135311	0.00404412255676555
127	0.996254233284033	0.00749153343193462	0.00374576671596731
128	0.992674334070865	0.0146513318582708	0.0073256659291354
129	0.991844420347716	0.016311159304568	0.008155579652284
130	0.993321285260185	0.0133574294796295	0.00667871473981474
131	0.987423489524193	0.0251530209516149	0.0125765104758074
132	0.987986354653389	0.0240272906932227	0.0120136453466113
133	0.990955383323317	0.0180892333533657	0.00904461667668284
134	0.986975938818012	0.026048122363976	0.013024061181988
135	0.973935077077702	0.0521298458445953	0.0260649229222977
136	0.945293246826528	0.109413506346944	0.0547067531734722
137	0.996164776465406	0.00767044706918774	0.00383522353459387
138	0.983743295812945	0.0325134083741105	0.0162567041870552

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.0534351145038168	NOK
5% type I error level	29	0.221374045801527	NOK
10% type I error level	41	0.312977099236641	NOK

\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 & 7 & 0.0534351145038168 & NOK \tabularnewline
5% type I error level & 29 & 0.221374045801527 & NOK \tabularnewline
10% type I error level & 41 & 0.312977099236641 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104229&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]7[/C][C]0.0534351145038168[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.221374045801527[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.312977099236641[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104229&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104229&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.0534351145038168	NOK
5% type I error level	29	0.221374045801527	NOK
10% type I error level	41	0.312977099236641	NOK

Figure 1

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code