FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 17 Nov 2011 07:23:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/17/t1321532675527blk74kph08zm.htm/, Retrieved Fri, 19 Apr 2024 09:45:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=144378, Retrieved Fri, 19 Apr 2024 09:45:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [mini tutorial] [2011-11-17 12:23:50] [05d3841c0e91f0207133db830e88168b] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
65	44	21387	68	127	1
54	48	12341	72	90	4
58	37	11397	37	68	9
75	68	25533	70	111	2
41	29	6630	30	51	1
0	17	7745	53	33	2
111	77	25304	74	123	0
1	16	1271	22	5	0
36	35	18035	68	63	5
60	24	13284	47	66	0
63	60	15628	87	99	0
71	72	13990	123	72	7
38	41	8532	69	55	6
76	39	13953	89	116	3
61	51	7210	45	71	4
125	100	22436	122	125	0
84	39	20238	75	123	4
69	97	10244	45	74	3
77	34	17390	53	116	0
95	47	9917	86	117	5
78	45	29625	82	98	0
76	54	13193	76	101	1
40	17	6815	51	43	3
81	31	11807	104	103	5
102	73	21472	83	107	0
70	85	19589	78	77	0
75	74	12266	59	87	4
93	52	18391	83	99	0
42	32	6711	71	46	0
95	32	9004	81	96	0
87	52	34301	93	92	3
44	45	8061	72	96	4
84	60	19463	107	96	1
28	23	2053	75	15	4
87	51	29618	84	147	1
71	37	3963	69	56	0
68	79	17609	90	81	0
50	45	11738	51	69	2
30	26	11082	18	34	1
86	101	22648	75	98	2
75	53	16538	59	82	8
46	38	10149	63	64	5
52	43	19787	68	61	3
31	27	7740	47	45	4
30	49	5873	29	37	1
70	88	11694	69	64	2
20	42	7935	66	21	2
84	51	15093	106	104	0
81	63	14533	73	126	6
79	38	15834	87	104	3
70	51	15699	65	87	0
8	24	2694	7	7	0
67	186	13834	111	130	6
21	17	3597	61	21	5
30	57	5296	41	35	3
70	27	21637	70	97	1
87	54	18081	112	103	5
87	101	29016	71	210	5
112	69	27279	90	151	0
54	49	12889	69	57	9
96	82	21550	85	117	6
93	70	34042	47	152	6
49	55	8190	50	52	5
49	57	16163	76	83	6
38	37	23471	60	87	2
64	32	14220	35	80	0
62	80	12759	72	88	3
66	94	18142	88	83	8
98	48	13883	66	140	2
97	31	14069	58	76	5
56	33	11131	81	70	11
22	28	3007	63	26	6
51	43	12530	91	66	5
56	35	13205	50	89	1
94	30	13025	75	100	0
98	44	18778	85	98	3
76	55	19793	75	109	3
57	58	8238	70	51	6
75	36	11285	78	82	1
48	37	10490	61	65	0
48	29	10457	55	46	1
109	65	17313	60	104	0
27	52	9592	83	36	5
83	48	14282	38	123	2
49	25	7905	27	59	0
24	37	4525	62	27	0
43	34	21179	82	84	5
44	95	13724	79	61	1
49	52	18446	59	46	0
106	66	25961	80	125	1
42	46	6602	36	58	1
108	47	16795	88	152	2
27	41	5463	63	52	4
79	48	11299	73	85	1
49	48	20390	71	95	4
64	27	18558	76	78	0
75	29	26262	67	144	2
115	51	25267	66	149	0
92	88	21091	123	101	7
106	69	32425	65	205	7
73	60	24380	87	61	6
105	37	20460	77	145	0
30	101	6515	37	28	0
13	14	7409	64	49	4
69	43	12300	22	68	4
72	90	27127	35	142	0
80	27	27687	61	82	0
106	60	19255	80	105	0
28	32	15070	54	52	0
70	61	6291	60	56	0
51	39	16577	87	81	4
90	55	13027	75	100	0
12	10	238	0	11	0
84	47	17103	54	87	0
23	25	3913	30	31	4
57	31	5654	66	67	0
84	53	14354	56	150	1
4	16	338	0	4	0
56	33	8852	32	75	5
18	19	3988	9	39	0
86	71	15964	78	88	1
39	34	14784	90	67	7
16	42	2667	56	24	5
18	27	7164	35	58	2
16	34	1888	21	16	0
42	25	12367	78	49	1
75	45	20505	114	109	0
30	36	18330	83	124	0
104	45	24993	89	115	2
121	61	11869	83	128	0
106	69	31156	116	159	2
57	23	15234	76	75	0
28	27	6645	57	30	0
56	178	15007	91	83	4
81	100	16597	89	135	4
2	15	317	66	8	8
88	77	27627	82	115	0
41	41	8658	63	60	4
83	29	20493	75	99	0
55	44	8877	59	98	1
3	72	867	19	36	0
54	77	13259	57	93	9
89	49	20613	62	158	0
41	63	2805	78	16	3
94	63	20588	73	100	7
101	39	9812	112	49	5
70	46	20001	79	89	2
111	63	23042	84	153	1
0	0	0	0	0	9
4	10	2065	0	5	0
0	1	0	0	0	0
0	2	0	0	0	0
0	0	0	0	0	1
0	0	0	0	0	0
42	55	10902	48	80	2
97	66	11309	55	122	1
0	0	0	0	0	0
0	4	0	0	0	0
7	5	556	0	6	0
12	20	2089	13	13	0
0	5	2658	4	3	0
37	27	1419	31	18	0
0	2	0	0	0	0
39	30	10699	29	49	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144378&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144378&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
geblogde_berekeningen[t] = + 2.39111196049004 + 0.0329154194555798logins[t] + 0.000283194200281519revisions[t] + 0.298785577171909LFB[t] + 0.45787361453206hyperlinks[t] -1.21032764362543gedeelde_documenten[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
geblogde_berekeningen[t] =  +  2.39111196049004 +  0.0329154194555798logins[t] +  0.000283194200281519revisions[t] +  0.298785577171909LFB[t] +  0.45787361453206hyperlinks[t] -1.21032764362543gedeelde_documenten[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144378&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]geblogde_berekeningen[t] =  +  2.39111196049004 +  0.0329154194555798logins[t] +  0.000283194200281519revisions[t] +  0.298785577171909LFB[t] +  0.45787361453206hyperlinks[t] -1.21032764362543gedeelde_documenten[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144378&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
geblogde_berekeningen[t] = + 2.39111196049004 + 0.0329154194555798logins[t] + 0.000283194200281519revisions[t] + 0.298785577171909LFB[t] + 0.45787361453206hyperlinks[t] -1.21032764362543gedeelde_documenten[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.391111960490042.9815320.8020.4237720.211886
logins0.03291541945557980.0534790.61550.5391190.269559
revisions0.0002831942002815190.0002651.06890.2867310.143365
LFB0.2987855771719090.0582515.12931e-060
hyperlinks0.457873614532060.0505989.049200
gedeelde_documenten-1.210327643625430.478198-2.5310.0123510.006175

\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) & 2.39111196049004 & 2.981532 & 0.802 & 0.423772 & 0.211886 \tabularnewline
logins & 0.0329154194555798 & 0.053479 & 0.6155 & 0.539119 & 0.269559 \tabularnewline
revisions & 0.000283194200281519 & 0.000265 & 1.0689 & 0.286731 & 0.143365 \tabularnewline
LFB & 0.298785577171909 & 0.058251 & 5.1293 & 1e-06 & 0 \tabularnewline
hyperlinks & 0.45787361453206 & 0.050598 & 9.0492 & 0 & 0 \tabularnewline
gedeelde_documenten & -1.21032764362543 & 0.478198 & -2.531 & 0.012351 & 0.006175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144378&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]2.39111196049004[/C][C]2.981532[/C][C]0.802[/C][C]0.423772[/C][C]0.211886[/C][/ROW]
[ROW][C]logins[/C][C]0.0329154194555798[/C][C]0.053479[/C][C]0.6155[/C][C]0.539119[/C][C]0.269559[/C][/ROW]
[ROW][C]revisions[/C][C]0.000283194200281519[/C][C]0.000265[/C][C]1.0689[/C][C]0.286731[/C][C]0.143365[/C][/ROW]
[ROW][C]LFB[/C][C]0.298785577171909[/C][C]0.058251[/C][C]5.1293[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]hyperlinks[/C][C]0.45787361453206[/C][C]0.050598[/C][C]9.0492[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]gedeelde_documenten[/C][C]-1.21032764362543[/C][C]0.478198[/C][C]-2.531[/C][C]0.012351[/C][C]0.006175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144378&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144378&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.391111960490042.9815320.8020.4237720.211886
logins0.03291541945557980.0534790.61550.5391190.269559
revisions0.0002831942002815190.0002651.06890.2867310.143365
LFB0.2987855771719090.0582515.12931e-060
hyperlinks0.457873614532060.0505989.049200
gedeelde_documenten-1.210327643625430.478198-2.5310.0123510.006175






Multiple Linear Regression - Regression Statistics
Multiple R0.886430013382404
R-squared0.785758168625129
Adjusted R-squared0.778978363834785
F-TEST (value)115.89687209641
F-TEST (DF numerator)5
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.1580327067202
Sum Squared Residuals36303.0209750037

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.886430013382404 \tabularnewline
R-squared & 0.785758168625129 \tabularnewline
Adjusted R-squared & 0.778978363834785 \tabularnewline
F-TEST (value) & 115.89687209641 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 15.1580327067202 \tabularnewline
Sum Squared Residuals & 36303.0209750037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144378&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.886430013382404[/C][/ROW]
[ROW][C]R-squared[/C][C]0.785758168625129[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.778978363834785[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]115.89687209641[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]15.1580327067202[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]36303.0209750037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144378&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144378&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.886430013382404
R-squared0.785758168625129
Adjusted R-squared0.778978363834785
F-TEST (value)115.89687209641
F-TEST (DF numerator)5
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.1580327067202
Sum Squared Residuals36303.0209750037






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16587.1531054275924-22.1531054275923
25465.3458280097932-11.3458280097932
35838.134070131866719.8659298681333
47581.1784643270989-6.1784643270989
54136.32803068523524.67196931476481
6033.6688227548335-33.6688227548335
711190.520132600657920.4798673993421
8112.1403492707794-11.1403492707794
93651.7623777885949-15.7623777885949
106051.20561447015938.79438552984068
116380.1156291424544-17.1156291424544
127169.96814175630531.03185824369468
133844.6941448373431-6.69414483734315
147683.7004947189282-7.70049471892822
156147.224695566764813.7753044332352
16125105.72244121504519.2775587849555
178483.29215984538980.707840154610169
186952.181964552597216.8180354474028
197777.3839582407055-0.383958240705476
209579.961707878002115.0382921219979
217881.6339655715695-3.63396557156953
227675.64733698458310.352663015416888
234036.17628949592313.82371050407692
248178.93820799089042.06179200910957
2510284.66636310939117.333636890609
267069.29795714190650.702042858093498
277560.922556003786214.0774439962138
289379.439629053513.5603709465
294247.6208839088381-5.62088390883815
309574.151784708405720.8482152915943
318780.09700631909736.90299368090267
324466.7822522614139-22.7822522614139
338484.5934419567506-0.593441956750596
342828.1652762325187-0.165276232518665
3587103.65252635169-16.6525263516903
367150.988408334719220.0115916652808
376873.9566614928068-5.95666149280676
385051.6071279101242-1.60712791012422
393026.12078663341393.87921336658612
408676.98922879826369.01077120173638
417554.310459171659620.6895408283404
424648.5918003122141-2.59180031221414
435254.0267654413196-2.02676544131955
443135.2776756024903-4.27767560249034
453030.062944884113-0.0629448841129551
467056.098802718335813.9011972816642
472032.9352442681266-12.9352442681266
488487.6341755091301-3.63417550913013
498180.82190140171920.178098598280827
507978.10821306147360.891786938526362
517067.77173108340752.22826891659254
5289.24062154491015-1.24062154491015
536797.8578916394195-30.8578916394195
542125.7589515241801-4.75895152418006
553030.4118895959431-0.411889595943083
567073.5257045652999-3.52570456529992
578783.86230766831033.13769233168971
5887125.248329053683-38.2483290536831
59112108.4171482322173.58285176778257
605443.476109621802210.5238903781978
619682.89903247002513.100967529975
629390.71343396256432.28656603743568
634939.2178891269889.78211087301204
644962.2938067380817-13.2938067380817
653865.5973173625069-27.5973173625069
666454.55881127465349.44118872534661
676266.8120770226507-4.81207702265073
686665.23689021910740.763109780892643
699889.30413601744978.69586398255031
709753.472069129653843.5279308703462
715649.56873613414686.43126386585318
722227.630548143404-5.63054814340403
735158.7124061902399-7.7124061902399
745661.7624339644761-5.76243396447613
759475.263458743923418.7365412560766
769875.794616462299722.2053835377003
777678.4928821777304-2.49288217773043
785743.637738992248913.3622610077511
797566.41249737848028.58750262151978
804854.56739479337-6.56739479336996
814842.592086246355.40791375364999
8210974.979545956225434.0204540437746
832742.050127351576-15.050127351576
848373.26728289550369.73271710449644
854940.5344014411388.46559855886199
862435.7777296136443-11.7777296136443
874366.4181689204044-23.4181689204044
884459.7286574528447-15.7286574528447
894948.01704931219050.982950687809531
9010691.842254624701614.1577453752984
914241.87749214312810.122507856871944
92108102.1636481813815.8363518186194
932743.0793428173024-16.0793428173024
947966.691140088487812.3088599115122
954969.6158406233475-20.6158406233475
966466.9571920531809-2.95719205318087
977594.3146840883788-19.3146840883788
9811599.168282869860715.8317171301393
999285.78408530522346.21591469477661
100106118.657707836922-12.6577078369216
1017357.932981569347415.0670184306526
10210598.8012993674926.19870063250798
1033031.436107102596-1.43610710259601
1041341.6668871393253-28.6668871393253
1056940.15714157200328.842858427997
1067288.5112572470983-16.5112572470983
1078066.892182708100513.1078172918995
10810681.798517153864524.2014828461355
1092847.6559911042613-19.6559911042613
1107049.748584305361320.2514156946387
1115166.6101209938756-15.6101209938756
1129076.086910618713413.9130893812866
113127.824276134565494.17572386543451
1148464.751032713889419.2489672861106
1152322.63847514373050.361524856269484
1165755.41005023899871.58994976100129
1178492.4033056002871-8.40330560028712
11844.84497276960271-0.844972769602709
1195643.834177204694612.1658227953054
1201824.6920245621663-6.69202456216627
1218671.636844409735114.3631555902649
1223956.7929198926835-17.7929198926835
1231626.1981593620444-10.1981593620444
1241839.9021410932329-21.9021410932329
1251617.6453818252343-1.6453818252343
1264251.2470146096155-9.24701460961548
1277593.6489826943558-18.6489826943558
1283090.342547859295-60.342547859295
12910487.77690523586316.223094764137
13012191.167210075793829.8327899242062
131106118.525850782184-12.5258507821841
1325764.5105720100266-7.5105720100266
1332835.928640081422-7.92864008142199
1345672.8516389415109-16.8516389415109
1358193.9463718037466-12.9463718037466
136216.674831674412-14.674831674412
1378889.9052884290306-1.90528842903063
1384147.6471372034583-6.64713720345833
1398376.88756399763816.11243600236189
1405567.6429409660936-12.6429409660936
141327.1669276223563-24.1669276223563
1425457.4005464177538-3.40054641775383
14389100.71018644494-11.7101864449402
1444132.25941303902688.74058696097322
1459469.421600662964824.5783993370352
14610156.301668349617544.6983316503825
1477071.5035454579606-1.50354545796064
148111104.9324680112986.06753198870162
1490-8.501836832138858.50183683213885
15045.59443025128747-1.59443025128747
15102.42402737994562-2.42402737994562
15202.4569427994012-2.4569427994012
15301.1807843168646-1.1807843168646
15402.39111196049004-2.39111196049004
1554255.8397847815816-13.8397847815816
1569778.849632929282818.1503670707172
15702.39111196049004-2.39111196049004
15802.52277363831236-2.52277363831236
15975.460386720316821.53961327968317
1601213.4775825261413-1.47758252614132
16105.87718239440003-5.87718239440003
1623721.185758809896415.8142411901036
16302.4569427994012-2.4569427994012
1643935.08840285577483.91159714422516

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 65 & 87.1531054275924 & -22.1531054275923 \tabularnewline
2 & 54 & 65.3458280097932 & -11.3458280097932 \tabularnewline
3 & 58 & 38.1340701318667 & 19.8659298681333 \tabularnewline
4 & 75 & 81.1784643270989 & -6.1784643270989 \tabularnewline
5 & 41 & 36.3280306852352 & 4.67196931476481 \tabularnewline
6 & 0 & 33.6688227548335 & -33.6688227548335 \tabularnewline
7 & 111 & 90.5201326006579 & 20.4798673993421 \tabularnewline
8 & 1 & 12.1403492707794 & -11.1403492707794 \tabularnewline
9 & 36 & 51.7623777885949 & -15.7623777885949 \tabularnewline
10 & 60 & 51.2056144701593 & 8.79438552984068 \tabularnewline
11 & 63 & 80.1156291424544 & -17.1156291424544 \tabularnewline
12 & 71 & 69.9681417563053 & 1.03185824369468 \tabularnewline
13 & 38 & 44.6941448373431 & -6.69414483734315 \tabularnewline
14 & 76 & 83.7004947189282 & -7.70049471892822 \tabularnewline
15 & 61 & 47.2246955667648 & 13.7753044332352 \tabularnewline
16 & 125 & 105.722441215045 & 19.2775587849555 \tabularnewline
17 & 84 & 83.2921598453898 & 0.707840154610169 \tabularnewline
18 & 69 & 52.1819645525972 & 16.8180354474028 \tabularnewline
19 & 77 & 77.3839582407055 & -0.383958240705476 \tabularnewline
20 & 95 & 79.9617078780021 & 15.0382921219979 \tabularnewline
21 & 78 & 81.6339655715695 & -3.63396557156953 \tabularnewline
22 & 76 & 75.6473369845831 & 0.352663015416888 \tabularnewline
23 & 40 & 36.1762894959231 & 3.82371050407692 \tabularnewline
24 & 81 & 78.9382079908904 & 2.06179200910957 \tabularnewline
25 & 102 & 84.666363109391 & 17.333636890609 \tabularnewline
26 & 70 & 69.2979571419065 & 0.702042858093498 \tabularnewline
27 & 75 & 60.9225560037862 & 14.0774439962138 \tabularnewline
28 & 93 & 79.4396290535 & 13.5603709465 \tabularnewline
29 & 42 & 47.6208839088381 & -5.62088390883815 \tabularnewline
30 & 95 & 74.1517847084057 & 20.8482152915943 \tabularnewline
31 & 87 & 80.0970063190973 & 6.90299368090267 \tabularnewline
32 & 44 & 66.7822522614139 & -22.7822522614139 \tabularnewline
33 & 84 & 84.5934419567506 & -0.593441956750596 \tabularnewline
34 & 28 & 28.1652762325187 & -0.165276232518665 \tabularnewline
35 & 87 & 103.65252635169 & -16.6525263516903 \tabularnewline
36 & 71 & 50.9884083347192 & 20.0115916652808 \tabularnewline
37 & 68 & 73.9566614928068 & -5.95666149280676 \tabularnewline
38 & 50 & 51.6071279101242 & -1.60712791012422 \tabularnewline
39 & 30 & 26.1207866334139 & 3.87921336658612 \tabularnewline
40 & 86 & 76.9892287982636 & 9.01077120173638 \tabularnewline
41 & 75 & 54.3104591716596 & 20.6895408283404 \tabularnewline
42 & 46 & 48.5918003122141 & -2.59180031221414 \tabularnewline
43 & 52 & 54.0267654413196 & -2.02676544131955 \tabularnewline
44 & 31 & 35.2776756024903 & -4.27767560249034 \tabularnewline
45 & 30 & 30.062944884113 & -0.0629448841129551 \tabularnewline
46 & 70 & 56.0988027183358 & 13.9011972816642 \tabularnewline
47 & 20 & 32.9352442681266 & -12.9352442681266 \tabularnewline
48 & 84 & 87.6341755091301 & -3.63417550913013 \tabularnewline
49 & 81 & 80.8219014017192 & 0.178098598280827 \tabularnewline
50 & 79 & 78.1082130614736 & 0.891786938526362 \tabularnewline
51 & 70 & 67.7717310834075 & 2.22826891659254 \tabularnewline
52 & 8 & 9.24062154491015 & -1.24062154491015 \tabularnewline
53 & 67 & 97.8578916394195 & -30.8578916394195 \tabularnewline
54 & 21 & 25.7589515241801 & -4.75895152418006 \tabularnewline
55 & 30 & 30.4118895959431 & -0.411889595943083 \tabularnewline
56 & 70 & 73.5257045652999 & -3.52570456529992 \tabularnewline
57 & 87 & 83.8623076683103 & 3.13769233168971 \tabularnewline
58 & 87 & 125.248329053683 & -38.2483290536831 \tabularnewline
59 & 112 & 108.417148232217 & 3.58285176778257 \tabularnewline
60 & 54 & 43.4761096218022 & 10.5238903781978 \tabularnewline
61 & 96 & 82.899032470025 & 13.100967529975 \tabularnewline
62 & 93 & 90.7134339625643 & 2.28656603743568 \tabularnewline
63 & 49 & 39.217889126988 & 9.78211087301204 \tabularnewline
64 & 49 & 62.2938067380817 & -13.2938067380817 \tabularnewline
65 & 38 & 65.5973173625069 & -27.5973173625069 \tabularnewline
66 & 64 & 54.5588112746534 & 9.44118872534661 \tabularnewline
67 & 62 & 66.8120770226507 & -4.81207702265073 \tabularnewline
68 & 66 & 65.2368902191074 & 0.763109780892643 \tabularnewline
69 & 98 & 89.3041360174497 & 8.69586398255031 \tabularnewline
70 & 97 & 53.4720691296538 & 43.5279308703462 \tabularnewline
71 & 56 & 49.5687361341468 & 6.43126386585318 \tabularnewline
72 & 22 & 27.630548143404 & -5.63054814340403 \tabularnewline
73 & 51 & 58.7124061902399 & -7.7124061902399 \tabularnewline
74 & 56 & 61.7624339644761 & -5.76243396447613 \tabularnewline
75 & 94 & 75.2634587439234 & 18.7365412560766 \tabularnewline
76 & 98 & 75.7946164622997 & 22.2053835377003 \tabularnewline
77 & 76 & 78.4928821777304 & -2.49288217773043 \tabularnewline
78 & 57 & 43.6377389922489 & 13.3622610077511 \tabularnewline
79 & 75 & 66.4124973784802 & 8.58750262151978 \tabularnewline
80 & 48 & 54.56739479337 & -6.56739479336996 \tabularnewline
81 & 48 & 42.59208624635 & 5.40791375364999 \tabularnewline
82 & 109 & 74.9795459562254 & 34.0204540437746 \tabularnewline
83 & 27 & 42.050127351576 & -15.050127351576 \tabularnewline
84 & 83 & 73.2672828955036 & 9.73271710449644 \tabularnewline
85 & 49 & 40.534401441138 & 8.46559855886199 \tabularnewline
86 & 24 & 35.7777296136443 & -11.7777296136443 \tabularnewline
87 & 43 & 66.4181689204044 & -23.4181689204044 \tabularnewline
88 & 44 & 59.7286574528447 & -15.7286574528447 \tabularnewline
89 & 49 & 48.0170493121905 & 0.982950687809531 \tabularnewline
90 & 106 & 91.8422546247016 & 14.1577453752984 \tabularnewline
91 & 42 & 41.8774921431281 & 0.122507856871944 \tabularnewline
92 & 108 & 102.163648181381 & 5.8363518186194 \tabularnewline
93 & 27 & 43.0793428173024 & -16.0793428173024 \tabularnewline
94 & 79 & 66.6911400884878 & 12.3088599115122 \tabularnewline
95 & 49 & 69.6158406233475 & -20.6158406233475 \tabularnewline
96 & 64 & 66.9571920531809 & -2.95719205318087 \tabularnewline
97 & 75 & 94.3146840883788 & -19.3146840883788 \tabularnewline
98 & 115 & 99.1682828698607 & 15.8317171301393 \tabularnewline
99 & 92 & 85.7840853052234 & 6.21591469477661 \tabularnewline
100 & 106 & 118.657707836922 & -12.6577078369216 \tabularnewline
101 & 73 & 57.9329815693474 & 15.0670184306526 \tabularnewline
102 & 105 & 98.801299367492 & 6.19870063250798 \tabularnewline
103 & 30 & 31.436107102596 & -1.43610710259601 \tabularnewline
104 & 13 & 41.6668871393253 & -28.6668871393253 \tabularnewline
105 & 69 & 40.157141572003 & 28.842858427997 \tabularnewline
106 & 72 & 88.5112572470983 & -16.5112572470983 \tabularnewline
107 & 80 & 66.8921827081005 & 13.1078172918995 \tabularnewline
108 & 106 & 81.7985171538645 & 24.2014828461355 \tabularnewline
109 & 28 & 47.6559911042613 & -19.6559911042613 \tabularnewline
110 & 70 & 49.7485843053613 & 20.2514156946387 \tabularnewline
111 & 51 & 66.6101209938756 & -15.6101209938756 \tabularnewline
112 & 90 & 76.0869106187134 & 13.9130893812866 \tabularnewline
113 & 12 & 7.82427613456549 & 4.17572386543451 \tabularnewline
114 & 84 & 64.7510327138894 & 19.2489672861106 \tabularnewline
115 & 23 & 22.6384751437305 & 0.361524856269484 \tabularnewline
116 & 57 & 55.4100502389987 & 1.58994976100129 \tabularnewline
117 & 84 & 92.4033056002871 & -8.40330560028712 \tabularnewline
118 & 4 & 4.84497276960271 & -0.844972769602709 \tabularnewline
119 & 56 & 43.8341772046946 & 12.1658227953054 \tabularnewline
120 & 18 & 24.6920245621663 & -6.69202456216627 \tabularnewline
121 & 86 & 71.6368444097351 & 14.3631555902649 \tabularnewline
122 & 39 & 56.7929198926835 & -17.7929198926835 \tabularnewline
123 & 16 & 26.1981593620444 & -10.1981593620444 \tabularnewline
124 & 18 & 39.9021410932329 & -21.9021410932329 \tabularnewline
125 & 16 & 17.6453818252343 & -1.6453818252343 \tabularnewline
126 & 42 & 51.2470146096155 & -9.24701460961548 \tabularnewline
127 & 75 & 93.6489826943558 & -18.6489826943558 \tabularnewline
128 & 30 & 90.342547859295 & -60.342547859295 \tabularnewline
129 & 104 & 87.776905235863 & 16.223094764137 \tabularnewline
130 & 121 & 91.1672100757938 & 29.8327899242062 \tabularnewline
131 & 106 & 118.525850782184 & -12.5258507821841 \tabularnewline
132 & 57 & 64.5105720100266 & -7.5105720100266 \tabularnewline
133 & 28 & 35.928640081422 & -7.92864008142199 \tabularnewline
134 & 56 & 72.8516389415109 & -16.8516389415109 \tabularnewline
135 & 81 & 93.9463718037466 & -12.9463718037466 \tabularnewline
136 & 2 & 16.674831674412 & -14.674831674412 \tabularnewline
137 & 88 & 89.9052884290306 & -1.90528842903063 \tabularnewline
138 & 41 & 47.6471372034583 & -6.64713720345833 \tabularnewline
139 & 83 & 76.8875639976381 & 6.11243600236189 \tabularnewline
140 & 55 & 67.6429409660936 & -12.6429409660936 \tabularnewline
141 & 3 & 27.1669276223563 & -24.1669276223563 \tabularnewline
142 & 54 & 57.4005464177538 & -3.40054641775383 \tabularnewline
143 & 89 & 100.71018644494 & -11.7101864449402 \tabularnewline
144 & 41 & 32.2594130390268 & 8.74058696097322 \tabularnewline
145 & 94 & 69.4216006629648 & 24.5783993370352 \tabularnewline
146 & 101 & 56.3016683496175 & 44.6983316503825 \tabularnewline
147 & 70 & 71.5035454579606 & -1.50354545796064 \tabularnewline
148 & 111 & 104.932468011298 & 6.06753198870162 \tabularnewline
149 & 0 & -8.50183683213885 & 8.50183683213885 \tabularnewline
150 & 4 & 5.59443025128747 & -1.59443025128747 \tabularnewline
151 & 0 & 2.42402737994562 & -2.42402737994562 \tabularnewline
152 & 0 & 2.4569427994012 & -2.4569427994012 \tabularnewline
153 & 0 & 1.1807843168646 & -1.1807843168646 \tabularnewline
154 & 0 & 2.39111196049004 & -2.39111196049004 \tabularnewline
155 & 42 & 55.8397847815816 & -13.8397847815816 \tabularnewline
156 & 97 & 78.8496329292828 & 18.1503670707172 \tabularnewline
157 & 0 & 2.39111196049004 & -2.39111196049004 \tabularnewline
158 & 0 & 2.52277363831236 & -2.52277363831236 \tabularnewline
159 & 7 & 5.46038672031682 & 1.53961327968317 \tabularnewline
160 & 12 & 13.4775825261413 & -1.47758252614132 \tabularnewline
161 & 0 & 5.87718239440003 & -5.87718239440003 \tabularnewline
162 & 37 & 21.1857588098964 & 15.8142411901036 \tabularnewline
163 & 0 & 2.4569427994012 & -2.4569427994012 \tabularnewline
164 & 39 & 35.0884028557748 & 3.91159714422516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144378&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]65[/C][C]87.1531054275924[/C][C]-22.1531054275923[/C][/ROW]
[ROW][C]2[/C][C]54[/C][C]65.3458280097932[/C][C]-11.3458280097932[/C][/ROW]
[ROW][C]3[/C][C]58[/C][C]38.1340701318667[/C][C]19.8659298681333[/C][/ROW]
[ROW][C]4[/C][C]75[/C][C]81.1784643270989[/C][C]-6.1784643270989[/C][/ROW]
[ROW][C]5[/C][C]41[/C][C]36.3280306852352[/C][C]4.67196931476481[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]33.6688227548335[/C][C]-33.6688227548335[/C][/ROW]
[ROW][C]7[/C][C]111[/C][C]90.5201326006579[/C][C]20.4798673993421[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]12.1403492707794[/C][C]-11.1403492707794[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]51.7623777885949[/C][C]-15.7623777885949[/C][/ROW]
[ROW][C]10[/C][C]60[/C][C]51.2056144701593[/C][C]8.79438552984068[/C][/ROW]
[ROW][C]11[/C][C]63[/C][C]80.1156291424544[/C][C]-17.1156291424544[/C][/ROW]
[ROW][C]12[/C][C]71[/C][C]69.9681417563053[/C][C]1.03185824369468[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]44.6941448373431[/C][C]-6.69414483734315[/C][/ROW]
[ROW][C]14[/C][C]76[/C][C]83.7004947189282[/C][C]-7.70049471892822[/C][/ROW]
[ROW][C]15[/C][C]61[/C][C]47.2246955667648[/C][C]13.7753044332352[/C][/ROW]
[ROW][C]16[/C][C]125[/C][C]105.722441215045[/C][C]19.2775587849555[/C][/ROW]
[ROW][C]17[/C][C]84[/C][C]83.2921598453898[/C][C]0.707840154610169[/C][/ROW]
[ROW][C]18[/C][C]69[/C][C]52.1819645525972[/C][C]16.8180354474028[/C][/ROW]
[ROW][C]19[/C][C]77[/C][C]77.3839582407055[/C][C]-0.383958240705476[/C][/ROW]
[ROW][C]20[/C][C]95[/C][C]79.9617078780021[/C][C]15.0382921219979[/C][/ROW]
[ROW][C]21[/C][C]78[/C][C]81.6339655715695[/C][C]-3.63396557156953[/C][/ROW]
[ROW][C]22[/C][C]76[/C][C]75.6473369845831[/C][C]0.352663015416888[/C][/ROW]
[ROW][C]23[/C][C]40[/C][C]36.1762894959231[/C][C]3.82371050407692[/C][/ROW]
[ROW][C]24[/C][C]81[/C][C]78.9382079908904[/C][C]2.06179200910957[/C][/ROW]
[ROW][C]25[/C][C]102[/C][C]84.666363109391[/C][C]17.333636890609[/C][/ROW]
[ROW][C]26[/C][C]70[/C][C]69.2979571419065[/C][C]0.702042858093498[/C][/ROW]
[ROW][C]27[/C][C]75[/C][C]60.9225560037862[/C][C]14.0774439962138[/C][/ROW]
[ROW][C]28[/C][C]93[/C][C]79.4396290535[/C][C]13.5603709465[/C][/ROW]
[ROW][C]29[/C][C]42[/C][C]47.6208839088381[/C][C]-5.62088390883815[/C][/ROW]
[ROW][C]30[/C][C]95[/C][C]74.1517847084057[/C][C]20.8482152915943[/C][/ROW]
[ROW][C]31[/C][C]87[/C][C]80.0970063190973[/C][C]6.90299368090267[/C][/ROW]
[ROW][C]32[/C][C]44[/C][C]66.7822522614139[/C][C]-22.7822522614139[/C][/ROW]
[ROW][C]33[/C][C]84[/C][C]84.5934419567506[/C][C]-0.593441956750596[/C][/ROW]
[ROW][C]34[/C][C]28[/C][C]28.1652762325187[/C][C]-0.165276232518665[/C][/ROW]
[ROW][C]35[/C][C]87[/C][C]103.65252635169[/C][C]-16.6525263516903[/C][/ROW]
[ROW][C]36[/C][C]71[/C][C]50.9884083347192[/C][C]20.0115916652808[/C][/ROW]
[ROW][C]37[/C][C]68[/C][C]73.9566614928068[/C][C]-5.95666149280676[/C][/ROW]
[ROW][C]38[/C][C]50[/C][C]51.6071279101242[/C][C]-1.60712791012422[/C][/ROW]
[ROW][C]39[/C][C]30[/C][C]26.1207866334139[/C][C]3.87921336658612[/C][/ROW]
[ROW][C]40[/C][C]86[/C][C]76.9892287982636[/C][C]9.01077120173638[/C][/ROW]
[ROW][C]41[/C][C]75[/C][C]54.3104591716596[/C][C]20.6895408283404[/C][/ROW]
[ROW][C]42[/C][C]46[/C][C]48.5918003122141[/C][C]-2.59180031221414[/C][/ROW]
[ROW][C]43[/C][C]52[/C][C]54.0267654413196[/C][C]-2.02676544131955[/C][/ROW]
[ROW][C]44[/C][C]31[/C][C]35.2776756024903[/C][C]-4.27767560249034[/C][/ROW]
[ROW][C]45[/C][C]30[/C][C]30.062944884113[/C][C]-0.0629448841129551[/C][/ROW]
[ROW][C]46[/C][C]70[/C][C]56.0988027183358[/C][C]13.9011972816642[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]32.9352442681266[/C][C]-12.9352442681266[/C][/ROW]
[ROW][C]48[/C][C]84[/C][C]87.6341755091301[/C][C]-3.63417550913013[/C][/ROW]
[ROW][C]49[/C][C]81[/C][C]80.8219014017192[/C][C]0.178098598280827[/C][/ROW]
[ROW][C]50[/C][C]79[/C][C]78.1082130614736[/C][C]0.891786938526362[/C][/ROW]
[ROW][C]51[/C][C]70[/C][C]67.7717310834075[/C][C]2.22826891659254[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]9.24062154491015[/C][C]-1.24062154491015[/C][/ROW]
[ROW][C]53[/C][C]67[/C][C]97.8578916394195[/C][C]-30.8578916394195[/C][/ROW]
[ROW][C]54[/C][C]21[/C][C]25.7589515241801[/C][C]-4.75895152418006[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]30.4118895959431[/C][C]-0.411889595943083[/C][/ROW]
[ROW][C]56[/C][C]70[/C][C]73.5257045652999[/C][C]-3.52570456529992[/C][/ROW]
[ROW][C]57[/C][C]87[/C][C]83.8623076683103[/C][C]3.13769233168971[/C][/ROW]
[ROW][C]58[/C][C]87[/C][C]125.248329053683[/C][C]-38.2483290536831[/C][/ROW]
[ROW][C]59[/C][C]112[/C][C]108.417148232217[/C][C]3.58285176778257[/C][/ROW]
[ROW][C]60[/C][C]54[/C][C]43.4761096218022[/C][C]10.5238903781978[/C][/ROW]
[ROW][C]61[/C][C]96[/C][C]82.899032470025[/C][C]13.100967529975[/C][/ROW]
[ROW][C]62[/C][C]93[/C][C]90.7134339625643[/C][C]2.28656603743568[/C][/ROW]
[ROW][C]63[/C][C]49[/C][C]39.217889126988[/C][C]9.78211087301204[/C][/ROW]
[ROW][C]64[/C][C]49[/C][C]62.2938067380817[/C][C]-13.2938067380817[/C][/ROW]
[ROW][C]65[/C][C]38[/C][C]65.5973173625069[/C][C]-27.5973173625069[/C][/ROW]
[ROW][C]66[/C][C]64[/C][C]54.5588112746534[/C][C]9.44118872534661[/C][/ROW]
[ROW][C]67[/C][C]62[/C][C]66.8120770226507[/C][C]-4.81207702265073[/C][/ROW]
[ROW][C]68[/C][C]66[/C][C]65.2368902191074[/C][C]0.763109780892643[/C][/ROW]
[ROW][C]69[/C][C]98[/C][C]89.3041360174497[/C][C]8.69586398255031[/C][/ROW]
[ROW][C]70[/C][C]97[/C][C]53.4720691296538[/C][C]43.5279308703462[/C][/ROW]
[ROW][C]71[/C][C]56[/C][C]49.5687361341468[/C][C]6.43126386585318[/C][/ROW]
[ROW][C]72[/C][C]22[/C][C]27.630548143404[/C][C]-5.63054814340403[/C][/ROW]
[ROW][C]73[/C][C]51[/C][C]58.7124061902399[/C][C]-7.7124061902399[/C][/ROW]
[ROW][C]74[/C][C]56[/C][C]61.7624339644761[/C][C]-5.76243396447613[/C][/ROW]
[ROW][C]75[/C][C]94[/C][C]75.2634587439234[/C][C]18.7365412560766[/C][/ROW]
[ROW][C]76[/C][C]98[/C][C]75.7946164622997[/C][C]22.2053835377003[/C][/ROW]
[ROW][C]77[/C][C]76[/C][C]78.4928821777304[/C][C]-2.49288217773043[/C][/ROW]
[ROW][C]78[/C][C]57[/C][C]43.6377389922489[/C][C]13.3622610077511[/C][/ROW]
[ROW][C]79[/C][C]75[/C][C]66.4124973784802[/C][C]8.58750262151978[/C][/ROW]
[ROW][C]80[/C][C]48[/C][C]54.56739479337[/C][C]-6.56739479336996[/C][/ROW]
[ROW][C]81[/C][C]48[/C][C]42.59208624635[/C][C]5.40791375364999[/C][/ROW]
[ROW][C]82[/C][C]109[/C][C]74.9795459562254[/C][C]34.0204540437746[/C][/ROW]
[ROW][C]83[/C][C]27[/C][C]42.050127351576[/C][C]-15.050127351576[/C][/ROW]
[ROW][C]84[/C][C]83[/C][C]73.2672828955036[/C][C]9.73271710449644[/C][/ROW]
[ROW][C]85[/C][C]49[/C][C]40.534401441138[/C][C]8.46559855886199[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]35.7777296136443[/C][C]-11.7777296136443[/C][/ROW]
[ROW][C]87[/C][C]43[/C][C]66.4181689204044[/C][C]-23.4181689204044[/C][/ROW]
[ROW][C]88[/C][C]44[/C][C]59.7286574528447[/C][C]-15.7286574528447[/C][/ROW]
[ROW][C]89[/C][C]49[/C][C]48.0170493121905[/C][C]0.982950687809531[/C][/ROW]
[ROW][C]90[/C][C]106[/C][C]91.8422546247016[/C][C]14.1577453752984[/C][/ROW]
[ROW][C]91[/C][C]42[/C][C]41.8774921431281[/C][C]0.122507856871944[/C][/ROW]
[ROW][C]92[/C][C]108[/C][C]102.163648181381[/C][C]5.8363518186194[/C][/ROW]
[ROW][C]93[/C][C]27[/C][C]43.0793428173024[/C][C]-16.0793428173024[/C][/ROW]
[ROW][C]94[/C][C]79[/C][C]66.6911400884878[/C][C]12.3088599115122[/C][/ROW]
[ROW][C]95[/C][C]49[/C][C]69.6158406233475[/C][C]-20.6158406233475[/C][/ROW]
[ROW][C]96[/C][C]64[/C][C]66.9571920531809[/C][C]-2.95719205318087[/C][/ROW]
[ROW][C]97[/C][C]75[/C][C]94.3146840883788[/C][C]-19.3146840883788[/C][/ROW]
[ROW][C]98[/C][C]115[/C][C]99.1682828698607[/C][C]15.8317171301393[/C][/ROW]
[ROW][C]99[/C][C]92[/C][C]85.7840853052234[/C][C]6.21591469477661[/C][/ROW]
[ROW][C]100[/C][C]106[/C][C]118.657707836922[/C][C]-12.6577078369216[/C][/ROW]
[ROW][C]101[/C][C]73[/C][C]57.9329815693474[/C][C]15.0670184306526[/C][/ROW]
[ROW][C]102[/C][C]105[/C][C]98.801299367492[/C][C]6.19870063250798[/C][/ROW]
[ROW][C]103[/C][C]30[/C][C]31.436107102596[/C][C]-1.43610710259601[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]41.6668871393253[/C][C]-28.6668871393253[/C][/ROW]
[ROW][C]105[/C][C]69[/C][C]40.157141572003[/C][C]28.842858427997[/C][/ROW]
[ROW][C]106[/C][C]72[/C][C]88.5112572470983[/C][C]-16.5112572470983[/C][/ROW]
[ROW][C]107[/C][C]80[/C][C]66.8921827081005[/C][C]13.1078172918995[/C][/ROW]
[ROW][C]108[/C][C]106[/C][C]81.7985171538645[/C][C]24.2014828461355[/C][/ROW]
[ROW][C]109[/C][C]28[/C][C]47.6559911042613[/C][C]-19.6559911042613[/C][/ROW]
[ROW][C]110[/C][C]70[/C][C]49.7485843053613[/C][C]20.2514156946387[/C][/ROW]
[ROW][C]111[/C][C]51[/C][C]66.6101209938756[/C][C]-15.6101209938756[/C][/ROW]
[ROW][C]112[/C][C]90[/C][C]76.0869106187134[/C][C]13.9130893812866[/C][/ROW]
[ROW][C]113[/C][C]12[/C][C]7.82427613456549[/C][C]4.17572386543451[/C][/ROW]
[ROW][C]114[/C][C]84[/C][C]64.7510327138894[/C][C]19.2489672861106[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]22.6384751437305[/C][C]0.361524856269484[/C][/ROW]
[ROW][C]116[/C][C]57[/C][C]55.4100502389987[/C][C]1.58994976100129[/C][/ROW]
[ROW][C]117[/C][C]84[/C][C]92.4033056002871[/C][C]-8.40330560028712[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]4.84497276960271[/C][C]-0.844972769602709[/C][/ROW]
[ROW][C]119[/C][C]56[/C][C]43.8341772046946[/C][C]12.1658227953054[/C][/ROW]
[ROW][C]120[/C][C]18[/C][C]24.6920245621663[/C][C]-6.69202456216627[/C][/ROW]
[ROW][C]121[/C][C]86[/C][C]71.6368444097351[/C][C]14.3631555902649[/C][/ROW]
[ROW][C]122[/C][C]39[/C][C]56.7929198926835[/C][C]-17.7929198926835[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]26.1981593620444[/C][C]-10.1981593620444[/C][/ROW]
[ROW][C]124[/C][C]18[/C][C]39.9021410932329[/C][C]-21.9021410932329[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]17.6453818252343[/C][C]-1.6453818252343[/C][/ROW]
[ROW][C]126[/C][C]42[/C][C]51.2470146096155[/C][C]-9.24701460961548[/C][/ROW]
[ROW][C]127[/C][C]75[/C][C]93.6489826943558[/C][C]-18.6489826943558[/C][/ROW]
[ROW][C]128[/C][C]30[/C][C]90.342547859295[/C][C]-60.342547859295[/C][/ROW]
[ROW][C]129[/C][C]104[/C][C]87.776905235863[/C][C]16.223094764137[/C][/ROW]
[ROW][C]130[/C][C]121[/C][C]91.1672100757938[/C][C]29.8327899242062[/C][/ROW]
[ROW][C]131[/C][C]106[/C][C]118.525850782184[/C][C]-12.5258507821841[/C][/ROW]
[ROW][C]132[/C][C]57[/C][C]64.5105720100266[/C][C]-7.5105720100266[/C][/ROW]
[ROW][C]133[/C][C]28[/C][C]35.928640081422[/C][C]-7.92864008142199[/C][/ROW]
[ROW][C]134[/C][C]56[/C][C]72.8516389415109[/C][C]-16.8516389415109[/C][/ROW]
[ROW][C]135[/C][C]81[/C][C]93.9463718037466[/C][C]-12.9463718037466[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]16.674831674412[/C][C]-14.674831674412[/C][/ROW]
[ROW][C]137[/C][C]88[/C][C]89.9052884290306[/C][C]-1.90528842903063[/C][/ROW]
[ROW][C]138[/C][C]41[/C][C]47.6471372034583[/C][C]-6.64713720345833[/C][/ROW]
[ROW][C]139[/C][C]83[/C][C]76.8875639976381[/C][C]6.11243600236189[/C][/ROW]
[ROW][C]140[/C][C]55[/C][C]67.6429409660936[/C][C]-12.6429409660936[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]27.1669276223563[/C][C]-24.1669276223563[/C][/ROW]
[ROW][C]142[/C][C]54[/C][C]57.4005464177538[/C][C]-3.40054641775383[/C][/ROW]
[ROW][C]143[/C][C]89[/C][C]100.71018644494[/C][C]-11.7101864449402[/C][/ROW]
[ROW][C]144[/C][C]41[/C][C]32.2594130390268[/C][C]8.74058696097322[/C][/ROW]
[ROW][C]145[/C][C]94[/C][C]69.4216006629648[/C][C]24.5783993370352[/C][/ROW]
[ROW][C]146[/C][C]101[/C][C]56.3016683496175[/C][C]44.6983316503825[/C][/ROW]
[ROW][C]147[/C][C]70[/C][C]71.5035454579606[/C][C]-1.50354545796064[/C][/ROW]
[ROW][C]148[/C][C]111[/C][C]104.932468011298[/C][C]6.06753198870162[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-8.50183683213885[/C][C]8.50183683213885[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]5.59443025128747[/C][C]-1.59443025128747[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]2.42402737994562[/C][C]-2.42402737994562[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]2.4569427994012[/C][C]-2.4569427994012[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]1.1807843168646[/C][C]-1.1807843168646[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]2.39111196049004[/C][C]-2.39111196049004[/C][/ROW]
[ROW][C]155[/C][C]42[/C][C]55.8397847815816[/C][C]-13.8397847815816[/C][/ROW]
[ROW][C]156[/C][C]97[/C][C]78.8496329292828[/C][C]18.1503670707172[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]2.39111196049004[/C][C]-2.39111196049004[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]2.52277363831236[/C][C]-2.52277363831236[/C][/ROW]
[ROW][C]159[/C][C]7[/C][C]5.46038672031682[/C][C]1.53961327968317[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]13.4775825261413[/C][C]-1.47758252614132[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]5.87718239440003[/C][C]-5.87718239440003[/C][/ROW]
[ROW][C]162[/C][C]37[/C][C]21.1857588098964[/C][C]15.8142411901036[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]2.4569427994012[/C][C]-2.4569427994012[/C][/ROW]
[ROW][C]164[/C][C]39[/C][C]35.0884028557748[/C][C]3.91159714422516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144378&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144378&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16587.1531054275924-22.1531054275923
25465.3458280097932-11.3458280097932
35838.134070131866719.8659298681333
47581.1784643270989-6.1784643270989
54136.32803068523524.67196931476481
6033.6688227548335-33.6688227548335
711190.520132600657920.4798673993421
8112.1403492707794-11.1403492707794
93651.7623777885949-15.7623777885949
106051.20561447015938.79438552984068
116380.1156291424544-17.1156291424544
127169.96814175630531.03185824369468
133844.6941448373431-6.69414483734315
147683.7004947189282-7.70049471892822
156147.224695566764813.7753044332352
16125105.72244121504519.2775587849555
178483.29215984538980.707840154610169
186952.181964552597216.8180354474028
197777.3839582407055-0.383958240705476
209579.961707878002115.0382921219979
217881.6339655715695-3.63396557156953
227675.64733698458310.352663015416888
234036.17628949592313.82371050407692
248178.93820799089042.06179200910957
2510284.66636310939117.333636890609
267069.29795714190650.702042858093498
277560.922556003786214.0774439962138
289379.439629053513.5603709465
294247.6208839088381-5.62088390883815
309574.151784708405720.8482152915943
318780.09700631909736.90299368090267
324466.7822522614139-22.7822522614139
338484.5934419567506-0.593441956750596
342828.1652762325187-0.165276232518665
3587103.65252635169-16.6525263516903
367150.988408334719220.0115916652808
376873.9566614928068-5.95666149280676
385051.6071279101242-1.60712791012422
393026.12078663341393.87921336658612
408676.98922879826369.01077120173638
417554.310459171659620.6895408283404
424648.5918003122141-2.59180031221414
435254.0267654413196-2.02676544131955
443135.2776756024903-4.27767560249034
453030.062944884113-0.0629448841129551
467056.098802718335813.9011972816642
472032.9352442681266-12.9352442681266
488487.6341755091301-3.63417550913013
498180.82190140171920.178098598280827
507978.10821306147360.891786938526362
517067.77173108340752.22826891659254
5289.24062154491015-1.24062154491015
536797.8578916394195-30.8578916394195
542125.7589515241801-4.75895152418006
553030.4118895959431-0.411889595943083
567073.5257045652999-3.52570456529992
578783.86230766831033.13769233168971
5887125.248329053683-38.2483290536831
59112108.4171482322173.58285176778257
605443.476109621802210.5238903781978
619682.89903247002513.100967529975
629390.71343396256432.28656603743568
634939.2178891269889.78211087301204
644962.2938067380817-13.2938067380817
653865.5973173625069-27.5973173625069
666454.55881127465349.44118872534661
676266.8120770226507-4.81207702265073
686665.23689021910740.763109780892643
699889.30413601744978.69586398255031
709753.472069129653843.5279308703462
715649.56873613414686.43126386585318
722227.630548143404-5.63054814340403
735158.7124061902399-7.7124061902399
745661.7624339644761-5.76243396447613
759475.263458743923418.7365412560766
769875.794616462299722.2053835377003
777678.4928821777304-2.49288217773043
785743.637738992248913.3622610077511
797566.41249737848028.58750262151978
804854.56739479337-6.56739479336996
814842.592086246355.40791375364999
8210974.979545956225434.0204540437746
832742.050127351576-15.050127351576
848373.26728289550369.73271710449644
854940.5344014411388.46559855886199
862435.7777296136443-11.7777296136443
874366.4181689204044-23.4181689204044
884459.7286574528447-15.7286574528447
894948.01704931219050.982950687809531
9010691.842254624701614.1577453752984
914241.87749214312810.122507856871944
92108102.1636481813815.8363518186194
932743.0793428173024-16.0793428173024
947966.691140088487812.3088599115122
954969.6158406233475-20.6158406233475
966466.9571920531809-2.95719205318087
977594.3146840883788-19.3146840883788
9811599.168282869860715.8317171301393
999285.78408530522346.21591469477661
100106118.657707836922-12.6577078369216
1017357.932981569347415.0670184306526
10210598.8012993674926.19870063250798
1033031.436107102596-1.43610710259601
1041341.6668871393253-28.6668871393253
1056940.15714157200328.842858427997
1067288.5112572470983-16.5112572470983
1078066.892182708100513.1078172918995
10810681.798517153864524.2014828461355
1092847.6559911042613-19.6559911042613
1107049.748584305361320.2514156946387
1115166.6101209938756-15.6101209938756
1129076.086910618713413.9130893812866
113127.824276134565494.17572386543451
1148464.751032713889419.2489672861106
1152322.63847514373050.361524856269484
1165755.41005023899871.58994976100129
1178492.4033056002871-8.40330560028712
11844.84497276960271-0.844972769602709
1195643.834177204694612.1658227953054
1201824.6920245621663-6.69202456216627
1218671.636844409735114.3631555902649
1223956.7929198926835-17.7929198926835
1231626.1981593620444-10.1981593620444
1241839.9021410932329-21.9021410932329
1251617.6453818252343-1.6453818252343
1264251.2470146096155-9.24701460961548
1277593.6489826943558-18.6489826943558
1283090.342547859295-60.342547859295
12910487.77690523586316.223094764137
13012191.167210075793829.8327899242062
131106118.525850782184-12.5258507821841
1325764.5105720100266-7.5105720100266
1332835.928640081422-7.92864008142199
1345672.8516389415109-16.8516389415109
1358193.9463718037466-12.9463718037466
136216.674831674412-14.674831674412
1378889.9052884290306-1.90528842903063
1384147.6471372034583-6.64713720345833
1398376.88756399763816.11243600236189
1405567.6429409660936-12.6429409660936
141327.1669276223563-24.1669276223563
1425457.4005464177538-3.40054641775383
14389100.71018644494-11.7101864449402
1444132.25941303902688.74058696097322
1459469.421600662964824.5783993370352
14610156.301668349617544.6983316503825
1477071.5035454579606-1.50354545796064
148111104.9324680112986.06753198870162
1490-8.501836832138858.50183683213885
15045.59443025128747-1.59443025128747
15102.42402737994562-2.42402737994562
15202.4569427994012-2.4569427994012
15301.1807843168646-1.1807843168646
15402.39111196049004-2.39111196049004
1554255.8397847815816-13.8397847815816
1569778.849632929282818.1503670707172
15702.39111196049004-2.39111196049004
15802.52277363831236-2.52277363831236
15975.460386720316821.53961327968317
1601213.4775825261413-1.47758252614132
16105.87718239440003-5.87718239440003
1623721.185758809896415.8142411901036
16302.4569427994012-2.4569427994012
1643935.08840285577483.91159714422516






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3305750005900820.6611500011801650.669424999409918
100.6876698238740960.6246603522518070.312330176125904
110.5688379760916050.8623240478167890.431162023908395
120.6198003745381210.7603992509237570.380199625461879
130.508390696181190.983218607637620.49160930381881
140.4858062765682130.9716125531364270.514193723431787
150.3889729391275890.7779458782551770.611027060872411
160.3627904513834520.7255809027669030.637209548616548
170.3289007506131810.6578015012263630.671099249386819
180.3966134736776590.7932269473553180.603386526322341
190.3217480623590090.6434961247180180.678251937640991
200.2950614972563340.5901229945126690.704938502743666
210.2734181713190790.5468363426381580.726581828680921
220.2107374864349050.4214749728698110.789262513565095
230.2206123541992550.441224708398510.779387645800745
240.1910809534636070.3821619069272150.808919046536393
250.1917632537329750.383526507465950.808236746267025
260.1505092523187440.3010185046374870.849490747681256
270.1165924817772430.2331849635544850.883407518222757
280.129524597575920.259049195151840.87047540242408
290.09935539325158830.1987107865031770.900644606748412
300.1492689295020460.2985378590040910.850731070497954
310.1619531118408180.3239062236816370.838046888159182
320.2683172587616330.5366345175232660.731682741238367
330.2192468167113430.4384936334226850.780753183288657
340.1917219960559530.3834439921119050.808278003944047
350.2047793739112590.4095587478225180.795220626088741
360.2437537941375480.4875075882750950.756246205862452
370.2287255601019430.4574511202038850.771274439898057
380.1884356460472120.3768712920944250.811564353952788
390.1581411584062250.3162823168124490.841858841593775
400.129963584192630.2599271683852610.87003641580737
410.1437386360119910.2874772720239820.856261363988009
420.1157476542922350.2314953085844690.884252345707765
430.0908559917609420.1817119835218840.909144008239058
440.07122381466855410.1424476293371080.928776185331446
450.05583879001077060.1116775800215410.944161209989229
460.04549534705465480.09099069410930970.954504652945345
470.04063524078109190.08127048156218380.959364759218908
480.03040097549994460.06080195099988920.969599024500055
490.02483094055234490.04966188110468970.975169059447655
500.01830837855653210.03661675711306420.981691621443468
510.01318641369105920.02637282738211850.986813586308941
520.009321977174098520.0186439543481970.990678022825901
530.09703472055716890.1940694411143380.902965279442831
540.07780494145652810.1556098829130560.922195058543472
550.06097142528902470.1219428505780490.939028574710975
560.04817059121880690.09634118243761380.951829408781193
570.03784107559496210.07568215118992430.962158924405038
580.139035810685560.2780716213711190.86096418931444
590.1162159216362930.2324318432725870.883784078363707
600.1045666832025530.2091333664051050.895433316797447
610.1007037112192260.2014074224384520.899296288780774
620.08156740227965120.1631348045593020.918432597720349
630.07046457588501830.1409291517700370.929535424114982
640.06785621530931550.1357124306186310.932143784690684
650.1136634772460340.2273269544920680.886336522753966
660.1006295023346090.2012590046692180.899370497665391
670.083098198617950.16619639723590.91690180138205
680.06635279467112220.1327055893422440.933647205328878
690.05802921422891710.1160584284578340.941970785771083
700.2304415688453340.4608831376906680.769558431154666
710.2020089242901060.4040178485802120.797991075709894
720.1769749292764410.3539498585528810.823025070723559
730.1553922086468590.3107844172937180.844607791353141
740.1330560101499530.2661120202999060.866943989850047
750.1459225645543670.2918451291087350.854077435445633
760.1773983807037860.3547967614075720.822601619296214
770.1495737688374570.2991475376749130.850426231162543
780.1431723885779110.2863447771558230.856827611422089
790.1258527500281790.2517055000563580.874147249971821
800.1080180176537290.2160360353074570.891981982346271
810.09008611384906980.180172227698140.90991388615093
820.1848016985524040.3696033971048080.815198301447596
830.1823384630342030.3646769260684070.817661536965797
840.1633924457778990.3267848915557980.836607554222101
850.1429287341204380.2858574682408760.857071265879562
860.1336913837694960.2673827675389920.866308616230504
870.1666240081850820.3332480163701640.833375991814918
880.1678642972882830.3357285945765650.832135702711717
890.1407493238610080.2814986477220170.859250676138992
900.1365270372198120.2730540744396230.863472962780188
910.1133204877571440.2266409755142880.886679512242856
920.0966418033510310.1932836067020620.903358196648969
930.09825867081220340.1965173416244070.901741329187797
940.09129602195940140.1825920439188030.908703978040599
950.1071109589698670.2142219179397340.892889041030133
960.08792999104608260.1758599820921650.912070008953917
970.09902468977857110.1980493795571420.900975310221429
980.100333814013550.2006676280270990.89966618598645
990.0850942811933840.1701885623867680.914905718806616
1000.07855695614630140.1571139122926030.921443043853699
1010.07649143159046170.1529828631809230.923508568409538
1020.0640806285482990.1281612570965980.935919371451701
1030.05082340963130620.1016468192626120.949176590368694
1040.08749347126577020.174986942531540.91250652873423
1050.1428198812680450.2856397625360890.857180118731955
1060.1419127144184670.2838254288369340.858087285581533
1070.1358887762398480.2717775524796970.864111223760152
1080.1838080885639350.3676161771278710.816191911436065
1090.1979279211912360.3958558423824720.802072078808764
1100.2251762768092180.4503525536184370.774823723190782
1110.2250670596165530.4501341192331050.774932940383447
1120.2254703352438110.4509406704876220.774529664756189
1130.1941553637332770.3883107274665540.805844636266723
1140.2247934581391720.4495869162783430.775206541860828
1150.1889403942354310.3778807884708620.811059605764569
1160.1586668166332770.3173336332665530.841333183366723
1170.135081825016190.2701636500323790.86491817498381
1180.1103316530874420.2206633061748840.889668346912558
1190.1021793214509520.2043586429019040.897820678549048
1200.08375770146844350.1675154029368870.916242298531557
1210.08666716034671470.1733343206934290.913332839653285
1220.1009129986234390.2018259972468780.899087001376561
1230.09163408979925470.1832681795985090.908365910200745
1240.1092076238453570.2184152476907130.890792376154643
1250.08672363108494520.173447262169890.913276368915055
1260.07431911037290740.1486382207458150.925680889627093
1270.0840718209922810.1681436419845620.915928179007719
1280.8027095527954450.394580894409110.197290447204555
1290.7943995025622250.411200994875550.205600497437775
1300.903578741565540.1928425168689210.0964212584344603
1310.9171772036794010.1656455926411980.0828227963205991
1320.9220731441054120.1558537117891760.0779268558945882
1330.9202003323661790.1595993352676430.0797996676338214
1340.8999005082803890.2001989834392230.100099491719611
1350.8852226897551730.2295546204896540.114777310244827
1360.9857483597550560.02850328048988840.0142516402449442
1370.9777582290588180.04448354188236410.022241770941182
1380.9872662583887960.0254674832224080.012733741611204
1390.9808119089347940.03837618213041290.0191880910652064
1400.9925522800792410.01489543984151740.00744771992075869
1410.9905471488057690.01890570238846270.00945285119423135
1420.9907421278655310.01851574426893890.00925787213446945
1430.99474260863930.01051478272140070.00525739136070037
1440.9962281166254960.007543766749007350.00377188337450367
1450.9993330203390760.001333959321847040.000666979660923521
1460.998953344727040.002093310545920060.00104665527296003
1470.9975215615176930.004956876964613310.00247843848230666
1480.9945414371890090.01091712562198160.00545856281099078
1490.9946237664029130.01075246719417420.00537623359708711
1500.9925401019064990.0149197961870020.00745989809350101
1510.9829965409255740.03400691814885220.0170034590744261
1520.9612891796217090.07742164075658240.0387108203782912
1530.93441041128140.1311791774372010.0655895887186003
1540.8697587263358730.2604825473282530.130241273664126
1550.9908521117643450.01829577647131040.00914788823565521

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.330575000590082 & 0.661150001180165 & 0.669424999409918 \tabularnewline
10 & 0.687669823874096 & 0.624660352251807 & 0.312330176125904 \tabularnewline
11 & 0.568837976091605 & 0.862324047816789 & 0.431162023908395 \tabularnewline
12 & 0.619800374538121 & 0.760399250923757 & 0.380199625461879 \tabularnewline
13 & 0.50839069618119 & 0.98321860763762 & 0.49160930381881 \tabularnewline
14 & 0.485806276568213 & 0.971612553136427 & 0.514193723431787 \tabularnewline
15 & 0.388972939127589 & 0.777945878255177 & 0.611027060872411 \tabularnewline
16 & 0.362790451383452 & 0.725580902766903 & 0.637209548616548 \tabularnewline
17 & 0.328900750613181 & 0.657801501226363 & 0.671099249386819 \tabularnewline
18 & 0.396613473677659 & 0.793226947355318 & 0.603386526322341 \tabularnewline
19 & 0.321748062359009 & 0.643496124718018 & 0.678251937640991 \tabularnewline
20 & 0.295061497256334 & 0.590122994512669 & 0.704938502743666 \tabularnewline
21 & 0.273418171319079 & 0.546836342638158 & 0.726581828680921 \tabularnewline
22 & 0.210737486434905 & 0.421474972869811 & 0.789262513565095 \tabularnewline
23 & 0.220612354199255 & 0.44122470839851 & 0.779387645800745 \tabularnewline
24 & 0.191080953463607 & 0.382161906927215 & 0.808919046536393 \tabularnewline
25 & 0.191763253732975 & 0.38352650746595 & 0.808236746267025 \tabularnewline
26 & 0.150509252318744 & 0.301018504637487 & 0.849490747681256 \tabularnewline
27 & 0.116592481777243 & 0.233184963554485 & 0.883407518222757 \tabularnewline
28 & 0.12952459757592 & 0.25904919515184 & 0.87047540242408 \tabularnewline
29 & 0.0993553932515883 & 0.198710786503177 & 0.900644606748412 \tabularnewline
30 & 0.149268929502046 & 0.298537859004091 & 0.850731070497954 \tabularnewline
31 & 0.161953111840818 & 0.323906223681637 & 0.838046888159182 \tabularnewline
32 & 0.268317258761633 & 0.536634517523266 & 0.731682741238367 \tabularnewline
33 & 0.219246816711343 & 0.438493633422685 & 0.780753183288657 \tabularnewline
34 & 0.191721996055953 & 0.383443992111905 & 0.808278003944047 \tabularnewline
35 & 0.204779373911259 & 0.409558747822518 & 0.795220626088741 \tabularnewline
36 & 0.243753794137548 & 0.487507588275095 & 0.756246205862452 \tabularnewline
37 & 0.228725560101943 & 0.457451120203885 & 0.771274439898057 \tabularnewline
38 & 0.188435646047212 & 0.376871292094425 & 0.811564353952788 \tabularnewline
39 & 0.158141158406225 & 0.316282316812449 & 0.841858841593775 \tabularnewline
40 & 0.12996358419263 & 0.259927168385261 & 0.87003641580737 \tabularnewline
41 & 0.143738636011991 & 0.287477272023982 & 0.856261363988009 \tabularnewline
42 & 0.115747654292235 & 0.231495308584469 & 0.884252345707765 \tabularnewline
43 & 0.090855991760942 & 0.181711983521884 & 0.909144008239058 \tabularnewline
44 & 0.0712238146685541 & 0.142447629337108 & 0.928776185331446 \tabularnewline
45 & 0.0558387900107706 & 0.111677580021541 & 0.944161209989229 \tabularnewline
46 & 0.0454953470546548 & 0.0909906941093097 & 0.954504652945345 \tabularnewline
47 & 0.0406352407810919 & 0.0812704815621838 & 0.959364759218908 \tabularnewline
48 & 0.0304009754999446 & 0.0608019509998892 & 0.969599024500055 \tabularnewline
49 & 0.0248309405523449 & 0.0496618811046897 & 0.975169059447655 \tabularnewline
50 & 0.0183083785565321 & 0.0366167571130642 & 0.981691621443468 \tabularnewline
51 & 0.0131864136910592 & 0.0263728273821185 & 0.986813586308941 \tabularnewline
52 & 0.00932197717409852 & 0.018643954348197 & 0.990678022825901 \tabularnewline
53 & 0.0970347205571689 & 0.194069441114338 & 0.902965279442831 \tabularnewline
54 & 0.0778049414565281 & 0.155609882913056 & 0.922195058543472 \tabularnewline
55 & 0.0609714252890247 & 0.121942850578049 & 0.939028574710975 \tabularnewline
56 & 0.0481705912188069 & 0.0963411824376138 & 0.951829408781193 \tabularnewline
57 & 0.0378410755949621 & 0.0756821511899243 & 0.962158924405038 \tabularnewline
58 & 0.13903581068556 & 0.278071621371119 & 0.86096418931444 \tabularnewline
59 & 0.116215921636293 & 0.232431843272587 & 0.883784078363707 \tabularnewline
60 & 0.104566683202553 & 0.209133366405105 & 0.895433316797447 \tabularnewline
61 & 0.100703711219226 & 0.201407422438452 & 0.899296288780774 \tabularnewline
62 & 0.0815674022796512 & 0.163134804559302 & 0.918432597720349 \tabularnewline
63 & 0.0704645758850183 & 0.140929151770037 & 0.929535424114982 \tabularnewline
64 & 0.0678562153093155 & 0.135712430618631 & 0.932143784690684 \tabularnewline
65 & 0.113663477246034 & 0.227326954492068 & 0.886336522753966 \tabularnewline
66 & 0.100629502334609 & 0.201259004669218 & 0.899370497665391 \tabularnewline
67 & 0.08309819861795 & 0.1661963972359 & 0.91690180138205 \tabularnewline
68 & 0.0663527946711222 & 0.132705589342244 & 0.933647205328878 \tabularnewline
69 & 0.0580292142289171 & 0.116058428457834 & 0.941970785771083 \tabularnewline
70 & 0.230441568845334 & 0.460883137690668 & 0.769558431154666 \tabularnewline
71 & 0.202008924290106 & 0.404017848580212 & 0.797991075709894 \tabularnewline
72 & 0.176974929276441 & 0.353949858552881 & 0.823025070723559 \tabularnewline
73 & 0.155392208646859 & 0.310784417293718 & 0.844607791353141 \tabularnewline
74 & 0.133056010149953 & 0.266112020299906 & 0.866943989850047 \tabularnewline
75 & 0.145922564554367 & 0.291845129108735 & 0.854077435445633 \tabularnewline
76 & 0.177398380703786 & 0.354796761407572 & 0.822601619296214 \tabularnewline
77 & 0.149573768837457 & 0.299147537674913 & 0.850426231162543 \tabularnewline
78 & 0.143172388577911 & 0.286344777155823 & 0.856827611422089 \tabularnewline
79 & 0.125852750028179 & 0.251705500056358 & 0.874147249971821 \tabularnewline
80 & 0.108018017653729 & 0.216036035307457 & 0.891981982346271 \tabularnewline
81 & 0.0900861138490698 & 0.18017222769814 & 0.90991388615093 \tabularnewline
82 & 0.184801698552404 & 0.369603397104808 & 0.815198301447596 \tabularnewline
83 & 0.182338463034203 & 0.364676926068407 & 0.817661536965797 \tabularnewline
84 & 0.163392445777899 & 0.326784891555798 & 0.836607554222101 \tabularnewline
85 & 0.142928734120438 & 0.285857468240876 & 0.857071265879562 \tabularnewline
86 & 0.133691383769496 & 0.267382767538992 & 0.866308616230504 \tabularnewline
87 & 0.166624008185082 & 0.333248016370164 & 0.833375991814918 \tabularnewline
88 & 0.167864297288283 & 0.335728594576565 & 0.832135702711717 \tabularnewline
89 & 0.140749323861008 & 0.281498647722017 & 0.859250676138992 \tabularnewline
90 & 0.136527037219812 & 0.273054074439623 & 0.863472962780188 \tabularnewline
91 & 0.113320487757144 & 0.226640975514288 & 0.886679512242856 \tabularnewline
92 & 0.096641803351031 & 0.193283606702062 & 0.903358196648969 \tabularnewline
93 & 0.0982586708122034 & 0.196517341624407 & 0.901741329187797 \tabularnewline
94 & 0.0912960219594014 & 0.182592043918803 & 0.908703978040599 \tabularnewline
95 & 0.107110958969867 & 0.214221917939734 & 0.892889041030133 \tabularnewline
96 & 0.0879299910460826 & 0.175859982092165 & 0.912070008953917 \tabularnewline
97 & 0.0990246897785711 & 0.198049379557142 & 0.900975310221429 \tabularnewline
98 & 0.10033381401355 & 0.200667628027099 & 0.89966618598645 \tabularnewline
99 & 0.085094281193384 & 0.170188562386768 & 0.914905718806616 \tabularnewline
100 & 0.0785569561463014 & 0.157113912292603 & 0.921443043853699 \tabularnewline
101 & 0.0764914315904617 & 0.152982863180923 & 0.923508568409538 \tabularnewline
102 & 0.064080628548299 & 0.128161257096598 & 0.935919371451701 \tabularnewline
103 & 0.0508234096313062 & 0.101646819262612 & 0.949176590368694 \tabularnewline
104 & 0.0874934712657702 & 0.17498694253154 & 0.91250652873423 \tabularnewline
105 & 0.142819881268045 & 0.285639762536089 & 0.857180118731955 \tabularnewline
106 & 0.141912714418467 & 0.283825428836934 & 0.858087285581533 \tabularnewline
107 & 0.135888776239848 & 0.271777552479697 & 0.864111223760152 \tabularnewline
108 & 0.183808088563935 & 0.367616177127871 & 0.816191911436065 \tabularnewline
109 & 0.197927921191236 & 0.395855842382472 & 0.802072078808764 \tabularnewline
110 & 0.225176276809218 & 0.450352553618437 & 0.774823723190782 \tabularnewline
111 & 0.225067059616553 & 0.450134119233105 & 0.774932940383447 \tabularnewline
112 & 0.225470335243811 & 0.450940670487622 & 0.774529664756189 \tabularnewline
113 & 0.194155363733277 & 0.388310727466554 & 0.805844636266723 \tabularnewline
114 & 0.224793458139172 & 0.449586916278343 & 0.775206541860828 \tabularnewline
115 & 0.188940394235431 & 0.377880788470862 & 0.811059605764569 \tabularnewline
116 & 0.158666816633277 & 0.317333633266553 & 0.841333183366723 \tabularnewline
117 & 0.13508182501619 & 0.270163650032379 & 0.86491817498381 \tabularnewline
118 & 0.110331653087442 & 0.220663306174884 & 0.889668346912558 \tabularnewline
119 & 0.102179321450952 & 0.204358642901904 & 0.897820678549048 \tabularnewline
120 & 0.0837577014684435 & 0.167515402936887 & 0.916242298531557 \tabularnewline
121 & 0.0866671603467147 & 0.173334320693429 & 0.913332839653285 \tabularnewline
122 & 0.100912998623439 & 0.201825997246878 & 0.899087001376561 \tabularnewline
123 & 0.0916340897992547 & 0.183268179598509 & 0.908365910200745 \tabularnewline
124 & 0.109207623845357 & 0.218415247690713 & 0.890792376154643 \tabularnewline
125 & 0.0867236310849452 & 0.17344726216989 & 0.913276368915055 \tabularnewline
126 & 0.0743191103729074 & 0.148638220745815 & 0.925680889627093 \tabularnewline
127 & 0.084071820992281 & 0.168143641984562 & 0.915928179007719 \tabularnewline
128 & 0.802709552795445 & 0.39458089440911 & 0.197290447204555 \tabularnewline
129 & 0.794399502562225 & 0.41120099487555 & 0.205600497437775 \tabularnewline
130 & 0.90357874156554 & 0.192842516868921 & 0.0964212584344603 \tabularnewline
131 & 0.917177203679401 & 0.165645592641198 & 0.0828227963205991 \tabularnewline
132 & 0.922073144105412 & 0.155853711789176 & 0.0779268558945882 \tabularnewline
133 & 0.920200332366179 & 0.159599335267643 & 0.0797996676338214 \tabularnewline
134 & 0.899900508280389 & 0.200198983439223 & 0.100099491719611 \tabularnewline
135 & 0.885222689755173 & 0.229554620489654 & 0.114777310244827 \tabularnewline
136 & 0.985748359755056 & 0.0285032804898884 & 0.0142516402449442 \tabularnewline
137 & 0.977758229058818 & 0.0444835418823641 & 0.022241770941182 \tabularnewline
138 & 0.987266258388796 & 0.025467483222408 & 0.012733741611204 \tabularnewline
139 & 0.980811908934794 & 0.0383761821304129 & 0.0191880910652064 \tabularnewline
140 & 0.992552280079241 & 0.0148954398415174 & 0.00744771992075869 \tabularnewline
141 & 0.990547148805769 & 0.0189057023884627 & 0.00945285119423135 \tabularnewline
142 & 0.990742127865531 & 0.0185157442689389 & 0.00925787213446945 \tabularnewline
143 & 0.9947426086393 & 0.0105147827214007 & 0.00525739136070037 \tabularnewline
144 & 0.996228116625496 & 0.00754376674900735 & 0.00377188337450367 \tabularnewline
145 & 0.999333020339076 & 0.00133395932184704 & 0.000666979660923521 \tabularnewline
146 & 0.99895334472704 & 0.00209331054592006 & 0.00104665527296003 \tabularnewline
147 & 0.997521561517693 & 0.00495687696461331 & 0.00247843848230666 \tabularnewline
148 & 0.994541437189009 & 0.0109171256219816 & 0.00545856281099078 \tabularnewline
149 & 0.994623766402913 & 0.0107524671941742 & 0.00537623359708711 \tabularnewline
150 & 0.992540101906499 & 0.014919796187002 & 0.00745989809350101 \tabularnewline
151 & 0.982996540925574 & 0.0340069181488522 & 0.0170034590744261 \tabularnewline
152 & 0.961289179621709 & 0.0774216407565824 & 0.0387108203782912 \tabularnewline
153 & 0.9344104112814 & 0.131179177437201 & 0.0655895887186003 \tabularnewline
154 & 0.869758726335873 & 0.260482547328253 & 0.130241273664126 \tabularnewline
155 & 0.990852111764345 & 0.0182957764713104 & 0.00914788823565521 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144378&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]9[/C][C]0.330575000590082[/C][C]0.661150001180165[/C][C]0.669424999409918[/C][/ROW]
[ROW][C]10[/C][C]0.687669823874096[/C][C]0.624660352251807[/C][C]0.312330176125904[/C][/ROW]
[ROW][C]11[/C][C]0.568837976091605[/C][C]0.862324047816789[/C][C]0.431162023908395[/C][/ROW]
[ROW][C]12[/C][C]0.619800374538121[/C][C]0.760399250923757[/C][C]0.380199625461879[/C][/ROW]
[ROW][C]13[/C][C]0.50839069618119[/C][C]0.98321860763762[/C][C]0.49160930381881[/C][/ROW]
[ROW][C]14[/C][C]0.485806276568213[/C][C]0.971612553136427[/C][C]0.514193723431787[/C][/ROW]
[ROW][C]15[/C][C]0.388972939127589[/C][C]0.777945878255177[/C][C]0.611027060872411[/C][/ROW]
[ROW][C]16[/C][C]0.362790451383452[/C][C]0.725580902766903[/C][C]0.637209548616548[/C][/ROW]
[ROW][C]17[/C][C]0.328900750613181[/C][C]0.657801501226363[/C][C]0.671099249386819[/C][/ROW]
[ROW][C]18[/C][C]0.396613473677659[/C][C]0.793226947355318[/C][C]0.603386526322341[/C][/ROW]
[ROW][C]19[/C][C]0.321748062359009[/C][C]0.643496124718018[/C][C]0.678251937640991[/C][/ROW]
[ROW][C]20[/C][C]0.295061497256334[/C][C]0.590122994512669[/C][C]0.704938502743666[/C][/ROW]
[ROW][C]21[/C][C]0.273418171319079[/C][C]0.546836342638158[/C][C]0.726581828680921[/C][/ROW]
[ROW][C]22[/C][C]0.210737486434905[/C][C]0.421474972869811[/C][C]0.789262513565095[/C][/ROW]
[ROW][C]23[/C][C]0.220612354199255[/C][C]0.44122470839851[/C][C]0.779387645800745[/C][/ROW]
[ROW][C]24[/C][C]0.191080953463607[/C][C]0.382161906927215[/C][C]0.808919046536393[/C][/ROW]
[ROW][C]25[/C][C]0.191763253732975[/C][C]0.38352650746595[/C][C]0.808236746267025[/C][/ROW]
[ROW][C]26[/C][C]0.150509252318744[/C][C]0.301018504637487[/C][C]0.849490747681256[/C][/ROW]
[ROW][C]27[/C][C]0.116592481777243[/C][C]0.233184963554485[/C][C]0.883407518222757[/C][/ROW]
[ROW][C]28[/C][C]0.12952459757592[/C][C]0.25904919515184[/C][C]0.87047540242408[/C][/ROW]
[ROW][C]29[/C][C]0.0993553932515883[/C][C]0.198710786503177[/C][C]0.900644606748412[/C][/ROW]
[ROW][C]30[/C][C]0.149268929502046[/C][C]0.298537859004091[/C][C]0.850731070497954[/C][/ROW]
[ROW][C]31[/C][C]0.161953111840818[/C][C]0.323906223681637[/C][C]0.838046888159182[/C][/ROW]
[ROW][C]32[/C][C]0.268317258761633[/C][C]0.536634517523266[/C][C]0.731682741238367[/C][/ROW]
[ROW][C]33[/C][C]0.219246816711343[/C][C]0.438493633422685[/C][C]0.780753183288657[/C][/ROW]
[ROW][C]34[/C][C]0.191721996055953[/C][C]0.383443992111905[/C][C]0.808278003944047[/C][/ROW]
[ROW][C]35[/C][C]0.204779373911259[/C][C]0.409558747822518[/C][C]0.795220626088741[/C][/ROW]
[ROW][C]36[/C][C]0.243753794137548[/C][C]0.487507588275095[/C][C]0.756246205862452[/C][/ROW]
[ROW][C]37[/C][C]0.228725560101943[/C][C]0.457451120203885[/C][C]0.771274439898057[/C][/ROW]
[ROW][C]38[/C][C]0.188435646047212[/C][C]0.376871292094425[/C][C]0.811564353952788[/C][/ROW]
[ROW][C]39[/C][C]0.158141158406225[/C][C]0.316282316812449[/C][C]0.841858841593775[/C][/ROW]
[ROW][C]40[/C][C]0.12996358419263[/C][C]0.259927168385261[/C][C]0.87003641580737[/C][/ROW]
[ROW][C]41[/C][C]0.143738636011991[/C][C]0.287477272023982[/C][C]0.856261363988009[/C][/ROW]
[ROW][C]42[/C][C]0.115747654292235[/C][C]0.231495308584469[/C][C]0.884252345707765[/C][/ROW]
[ROW][C]43[/C][C]0.090855991760942[/C][C]0.181711983521884[/C][C]0.909144008239058[/C][/ROW]
[ROW][C]44[/C][C]0.0712238146685541[/C][C]0.142447629337108[/C][C]0.928776185331446[/C][/ROW]
[ROW][C]45[/C][C]0.0558387900107706[/C][C]0.111677580021541[/C][C]0.944161209989229[/C][/ROW]
[ROW][C]46[/C][C]0.0454953470546548[/C][C]0.0909906941093097[/C][C]0.954504652945345[/C][/ROW]
[ROW][C]47[/C][C]0.0406352407810919[/C][C]0.0812704815621838[/C][C]0.959364759218908[/C][/ROW]
[ROW][C]48[/C][C]0.0304009754999446[/C][C]0.0608019509998892[/C][C]0.969599024500055[/C][/ROW]
[ROW][C]49[/C][C]0.0248309405523449[/C][C]0.0496618811046897[/C][C]0.975169059447655[/C][/ROW]
[ROW][C]50[/C][C]0.0183083785565321[/C][C]0.0366167571130642[/C][C]0.981691621443468[/C][/ROW]
[ROW][C]51[/C][C]0.0131864136910592[/C][C]0.0263728273821185[/C][C]0.986813586308941[/C][/ROW]
[ROW][C]52[/C][C]0.00932197717409852[/C][C]0.018643954348197[/C][C]0.990678022825901[/C][/ROW]
[ROW][C]53[/C][C]0.0970347205571689[/C][C]0.194069441114338[/C][C]0.902965279442831[/C][/ROW]
[ROW][C]54[/C][C]0.0778049414565281[/C][C]0.155609882913056[/C][C]0.922195058543472[/C][/ROW]
[ROW][C]55[/C][C]0.0609714252890247[/C][C]0.121942850578049[/C][C]0.939028574710975[/C][/ROW]
[ROW][C]56[/C][C]0.0481705912188069[/C][C]0.0963411824376138[/C][C]0.951829408781193[/C][/ROW]
[ROW][C]57[/C][C]0.0378410755949621[/C][C]0.0756821511899243[/C][C]0.962158924405038[/C][/ROW]
[ROW][C]58[/C][C]0.13903581068556[/C][C]0.278071621371119[/C][C]0.86096418931444[/C][/ROW]
[ROW][C]59[/C][C]0.116215921636293[/C][C]0.232431843272587[/C][C]0.883784078363707[/C][/ROW]
[ROW][C]60[/C][C]0.104566683202553[/C][C]0.209133366405105[/C][C]0.895433316797447[/C][/ROW]
[ROW][C]61[/C][C]0.100703711219226[/C][C]0.201407422438452[/C][C]0.899296288780774[/C][/ROW]
[ROW][C]62[/C][C]0.0815674022796512[/C][C]0.163134804559302[/C][C]0.918432597720349[/C][/ROW]
[ROW][C]63[/C][C]0.0704645758850183[/C][C]0.140929151770037[/C][C]0.929535424114982[/C][/ROW]
[ROW][C]64[/C][C]0.0678562153093155[/C][C]0.135712430618631[/C][C]0.932143784690684[/C][/ROW]
[ROW][C]65[/C][C]0.113663477246034[/C][C]0.227326954492068[/C][C]0.886336522753966[/C][/ROW]
[ROW][C]66[/C][C]0.100629502334609[/C][C]0.201259004669218[/C][C]0.899370497665391[/C][/ROW]
[ROW][C]67[/C][C]0.08309819861795[/C][C]0.1661963972359[/C][C]0.91690180138205[/C][/ROW]
[ROW][C]68[/C][C]0.0663527946711222[/C][C]0.132705589342244[/C][C]0.933647205328878[/C][/ROW]
[ROW][C]69[/C][C]0.0580292142289171[/C][C]0.116058428457834[/C][C]0.941970785771083[/C][/ROW]
[ROW][C]70[/C][C]0.230441568845334[/C][C]0.460883137690668[/C][C]0.769558431154666[/C][/ROW]
[ROW][C]71[/C][C]0.202008924290106[/C][C]0.404017848580212[/C][C]0.797991075709894[/C][/ROW]
[ROW][C]72[/C][C]0.176974929276441[/C][C]0.353949858552881[/C][C]0.823025070723559[/C][/ROW]
[ROW][C]73[/C][C]0.155392208646859[/C][C]0.310784417293718[/C][C]0.844607791353141[/C][/ROW]
[ROW][C]74[/C][C]0.133056010149953[/C][C]0.266112020299906[/C][C]0.866943989850047[/C][/ROW]
[ROW][C]75[/C][C]0.145922564554367[/C][C]0.291845129108735[/C][C]0.854077435445633[/C][/ROW]
[ROW][C]76[/C][C]0.177398380703786[/C][C]0.354796761407572[/C][C]0.822601619296214[/C][/ROW]
[ROW][C]77[/C][C]0.149573768837457[/C][C]0.299147537674913[/C][C]0.850426231162543[/C][/ROW]
[ROW][C]78[/C][C]0.143172388577911[/C][C]0.286344777155823[/C][C]0.856827611422089[/C][/ROW]
[ROW][C]79[/C][C]0.125852750028179[/C][C]0.251705500056358[/C][C]0.874147249971821[/C][/ROW]
[ROW][C]80[/C][C]0.108018017653729[/C][C]0.216036035307457[/C][C]0.891981982346271[/C][/ROW]
[ROW][C]81[/C][C]0.0900861138490698[/C][C]0.18017222769814[/C][C]0.90991388615093[/C][/ROW]
[ROW][C]82[/C][C]0.184801698552404[/C][C]0.369603397104808[/C][C]0.815198301447596[/C][/ROW]
[ROW][C]83[/C][C]0.182338463034203[/C][C]0.364676926068407[/C][C]0.817661536965797[/C][/ROW]
[ROW][C]84[/C][C]0.163392445777899[/C][C]0.326784891555798[/C][C]0.836607554222101[/C][/ROW]
[ROW][C]85[/C][C]0.142928734120438[/C][C]0.285857468240876[/C][C]0.857071265879562[/C][/ROW]
[ROW][C]86[/C][C]0.133691383769496[/C][C]0.267382767538992[/C][C]0.866308616230504[/C][/ROW]
[ROW][C]87[/C][C]0.166624008185082[/C][C]0.333248016370164[/C][C]0.833375991814918[/C][/ROW]
[ROW][C]88[/C][C]0.167864297288283[/C][C]0.335728594576565[/C][C]0.832135702711717[/C][/ROW]
[ROW][C]89[/C][C]0.140749323861008[/C][C]0.281498647722017[/C][C]0.859250676138992[/C][/ROW]
[ROW][C]90[/C][C]0.136527037219812[/C][C]0.273054074439623[/C][C]0.863472962780188[/C][/ROW]
[ROW][C]91[/C][C]0.113320487757144[/C][C]0.226640975514288[/C][C]0.886679512242856[/C][/ROW]
[ROW][C]92[/C][C]0.096641803351031[/C][C]0.193283606702062[/C][C]0.903358196648969[/C][/ROW]
[ROW][C]93[/C][C]0.0982586708122034[/C][C]0.196517341624407[/C][C]0.901741329187797[/C][/ROW]
[ROW][C]94[/C][C]0.0912960219594014[/C][C]0.182592043918803[/C][C]0.908703978040599[/C][/ROW]
[ROW][C]95[/C][C]0.107110958969867[/C][C]0.214221917939734[/C][C]0.892889041030133[/C][/ROW]
[ROW][C]96[/C][C]0.0879299910460826[/C][C]0.175859982092165[/C][C]0.912070008953917[/C][/ROW]
[ROW][C]97[/C][C]0.0990246897785711[/C][C]0.198049379557142[/C][C]0.900975310221429[/C][/ROW]
[ROW][C]98[/C][C]0.10033381401355[/C][C]0.200667628027099[/C][C]0.89966618598645[/C][/ROW]
[ROW][C]99[/C][C]0.085094281193384[/C][C]0.170188562386768[/C][C]0.914905718806616[/C][/ROW]
[ROW][C]100[/C][C]0.0785569561463014[/C][C]0.157113912292603[/C][C]0.921443043853699[/C][/ROW]
[ROW][C]101[/C][C]0.0764914315904617[/C][C]0.152982863180923[/C][C]0.923508568409538[/C][/ROW]
[ROW][C]102[/C][C]0.064080628548299[/C][C]0.128161257096598[/C][C]0.935919371451701[/C][/ROW]
[ROW][C]103[/C][C]0.0508234096313062[/C][C]0.101646819262612[/C][C]0.949176590368694[/C][/ROW]
[ROW][C]104[/C][C]0.0874934712657702[/C][C]0.17498694253154[/C][C]0.91250652873423[/C][/ROW]
[ROW][C]105[/C][C]0.142819881268045[/C][C]0.285639762536089[/C][C]0.857180118731955[/C][/ROW]
[ROW][C]106[/C][C]0.141912714418467[/C][C]0.283825428836934[/C][C]0.858087285581533[/C][/ROW]
[ROW][C]107[/C][C]0.135888776239848[/C][C]0.271777552479697[/C][C]0.864111223760152[/C][/ROW]
[ROW][C]108[/C][C]0.183808088563935[/C][C]0.367616177127871[/C][C]0.816191911436065[/C][/ROW]
[ROW][C]109[/C][C]0.197927921191236[/C][C]0.395855842382472[/C][C]0.802072078808764[/C][/ROW]
[ROW][C]110[/C][C]0.225176276809218[/C][C]0.450352553618437[/C][C]0.774823723190782[/C][/ROW]
[ROW][C]111[/C][C]0.225067059616553[/C][C]0.450134119233105[/C][C]0.774932940383447[/C][/ROW]
[ROW][C]112[/C][C]0.225470335243811[/C][C]0.450940670487622[/C][C]0.774529664756189[/C][/ROW]
[ROW][C]113[/C][C]0.194155363733277[/C][C]0.388310727466554[/C][C]0.805844636266723[/C][/ROW]
[ROW][C]114[/C][C]0.224793458139172[/C][C]0.449586916278343[/C][C]0.775206541860828[/C][/ROW]
[ROW][C]115[/C][C]0.188940394235431[/C][C]0.377880788470862[/C][C]0.811059605764569[/C][/ROW]
[ROW][C]116[/C][C]0.158666816633277[/C][C]0.317333633266553[/C][C]0.841333183366723[/C][/ROW]
[ROW][C]117[/C][C]0.13508182501619[/C][C]0.270163650032379[/C][C]0.86491817498381[/C][/ROW]
[ROW][C]118[/C][C]0.110331653087442[/C][C]0.220663306174884[/C][C]0.889668346912558[/C][/ROW]
[ROW][C]119[/C][C]0.102179321450952[/C][C]0.204358642901904[/C][C]0.897820678549048[/C][/ROW]
[ROW][C]120[/C][C]0.0837577014684435[/C][C]0.167515402936887[/C][C]0.916242298531557[/C][/ROW]
[ROW][C]121[/C][C]0.0866671603467147[/C][C]0.173334320693429[/C][C]0.913332839653285[/C][/ROW]
[ROW][C]122[/C][C]0.100912998623439[/C][C]0.201825997246878[/C][C]0.899087001376561[/C][/ROW]
[ROW][C]123[/C][C]0.0916340897992547[/C][C]0.183268179598509[/C][C]0.908365910200745[/C][/ROW]
[ROW][C]124[/C][C]0.109207623845357[/C][C]0.218415247690713[/C][C]0.890792376154643[/C][/ROW]
[ROW][C]125[/C][C]0.0867236310849452[/C][C]0.17344726216989[/C][C]0.913276368915055[/C][/ROW]
[ROW][C]126[/C][C]0.0743191103729074[/C][C]0.148638220745815[/C][C]0.925680889627093[/C][/ROW]
[ROW][C]127[/C][C]0.084071820992281[/C][C]0.168143641984562[/C][C]0.915928179007719[/C][/ROW]
[ROW][C]128[/C][C]0.802709552795445[/C][C]0.39458089440911[/C][C]0.197290447204555[/C][/ROW]
[ROW][C]129[/C][C]0.794399502562225[/C][C]0.41120099487555[/C][C]0.205600497437775[/C][/ROW]
[ROW][C]130[/C][C]0.90357874156554[/C][C]0.192842516868921[/C][C]0.0964212584344603[/C][/ROW]
[ROW][C]131[/C][C]0.917177203679401[/C][C]0.165645592641198[/C][C]0.0828227963205991[/C][/ROW]
[ROW][C]132[/C][C]0.922073144105412[/C][C]0.155853711789176[/C][C]0.0779268558945882[/C][/ROW]
[ROW][C]133[/C][C]0.920200332366179[/C][C]0.159599335267643[/C][C]0.0797996676338214[/C][/ROW]
[ROW][C]134[/C][C]0.899900508280389[/C][C]0.200198983439223[/C][C]0.100099491719611[/C][/ROW]
[ROW][C]135[/C][C]0.885222689755173[/C][C]0.229554620489654[/C][C]0.114777310244827[/C][/ROW]
[ROW][C]136[/C][C]0.985748359755056[/C][C]0.0285032804898884[/C][C]0.0142516402449442[/C][/ROW]
[ROW][C]137[/C][C]0.977758229058818[/C][C]0.0444835418823641[/C][C]0.022241770941182[/C][/ROW]
[ROW][C]138[/C][C]0.987266258388796[/C][C]0.025467483222408[/C][C]0.012733741611204[/C][/ROW]
[ROW][C]139[/C][C]0.980811908934794[/C][C]0.0383761821304129[/C][C]0.0191880910652064[/C][/ROW]
[ROW][C]140[/C][C]0.992552280079241[/C][C]0.0148954398415174[/C][C]0.00744771992075869[/C][/ROW]
[ROW][C]141[/C][C]0.990547148805769[/C][C]0.0189057023884627[/C][C]0.00945285119423135[/C][/ROW]
[ROW][C]142[/C][C]0.990742127865531[/C][C]0.0185157442689389[/C][C]0.00925787213446945[/C][/ROW]
[ROW][C]143[/C][C]0.9947426086393[/C][C]0.0105147827214007[/C][C]0.00525739136070037[/C][/ROW]
[ROW][C]144[/C][C]0.996228116625496[/C][C]0.00754376674900735[/C][C]0.00377188337450367[/C][/ROW]
[ROW][C]145[/C][C]0.999333020339076[/C][C]0.00133395932184704[/C][C]0.000666979660923521[/C][/ROW]
[ROW][C]146[/C][C]0.99895334472704[/C][C]0.00209331054592006[/C][C]0.00104665527296003[/C][/ROW]
[ROW][C]147[/C][C]0.997521561517693[/C][C]0.00495687696461331[/C][C]0.00247843848230666[/C][/ROW]
[ROW][C]148[/C][C]0.994541437189009[/C][C]0.0109171256219816[/C][C]0.00545856281099078[/C][/ROW]
[ROW][C]149[/C][C]0.994623766402913[/C][C]0.0107524671941742[/C][C]0.00537623359708711[/C][/ROW]
[ROW][C]150[/C][C]0.992540101906499[/C][C]0.014919796187002[/C][C]0.00745989809350101[/C][/ROW]
[ROW][C]151[/C][C]0.982996540925574[/C][C]0.0340069181488522[/C][C]0.0170034590744261[/C][/ROW]
[ROW][C]152[/C][C]0.961289179621709[/C][C]0.0774216407565824[/C][C]0.0387108203782912[/C][/ROW]
[ROW][C]153[/C][C]0.9344104112814[/C][C]0.131179177437201[/C][C]0.0655895887186003[/C][/ROW]
[ROW][C]154[/C][C]0.869758726335873[/C][C]0.260482547328253[/C][C]0.130241273664126[/C][/ROW]
[ROW][C]155[/C][C]0.990852111764345[/C][C]0.0182957764713104[/C][C]0.00914788823565521[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144378&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144378&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3305750005900820.6611500011801650.669424999409918
100.6876698238740960.6246603522518070.312330176125904
110.5688379760916050.8623240478167890.431162023908395
120.6198003745381210.7603992509237570.380199625461879
130.508390696181190.983218607637620.49160930381881
140.4858062765682130.9716125531364270.514193723431787
150.3889729391275890.7779458782551770.611027060872411
160.3627904513834520.7255809027669030.637209548616548
170.3289007506131810.6578015012263630.671099249386819
180.3966134736776590.7932269473553180.603386526322341
190.3217480623590090.6434961247180180.678251937640991
200.2950614972563340.5901229945126690.704938502743666
210.2734181713190790.5468363426381580.726581828680921
220.2107374864349050.4214749728698110.789262513565095
230.2206123541992550.441224708398510.779387645800745
240.1910809534636070.3821619069272150.808919046536393
250.1917632537329750.383526507465950.808236746267025
260.1505092523187440.3010185046374870.849490747681256
270.1165924817772430.2331849635544850.883407518222757
280.129524597575920.259049195151840.87047540242408
290.09935539325158830.1987107865031770.900644606748412
300.1492689295020460.2985378590040910.850731070497954
310.1619531118408180.3239062236816370.838046888159182
320.2683172587616330.5366345175232660.731682741238367
330.2192468167113430.4384936334226850.780753183288657
340.1917219960559530.3834439921119050.808278003944047
350.2047793739112590.4095587478225180.795220626088741
360.2437537941375480.4875075882750950.756246205862452
370.2287255601019430.4574511202038850.771274439898057
380.1884356460472120.3768712920944250.811564353952788
390.1581411584062250.3162823168124490.841858841593775
400.129963584192630.2599271683852610.87003641580737
410.1437386360119910.2874772720239820.856261363988009
420.1157476542922350.2314953085844690.884252345707765
430.0908559917609420.1817119835218840.909144008239058
440.07122381466855410.1424476293371080.928776185331446
450.05583879001077060.1116775800215410.944161209989229
460.04549534705465480.09099069410930970.954504652945345
470.04063524078109190.08127048156218380.959364759218908
480.03040097549994460.06080195099988920.969599024500055
490.02483094055234490.04966188110468970.975169059447655
500.01830837855653210.03661675711306420.981691621443468
510.01318641369105920.02637282738211850.986813586308941
520.009321977174098520.0186439543481970.990678022825901
530.09703472055716890.1940694411143380.902965279442831
540.07780494145652810.1556098829130560.922195058543472
550.06097142528902470.1219428505780490.939028574710975
560.04817059121880690.09634118243761380.951829408781193
570.03784107559496210.07568215118992430.962158924405038
580.139035810685560.2780716213711190.86096418931444
590.1162159216362930.2324318432725870.883784078363707
600.1045666832025530.2091333664051050.895433316797447
610.1007037112192260.2014074224384520.899296288780774
620.08156740227965120.1631348045593020.918432597720349
630.07046457588501830.1409291517700370.929535424114982
640.06785621530931550.1357124306186310.932143784690684
650.1136634772460340.2273269544920680.886336522753966
660.1006295023346090.2012590046692180.899370497665391
670.083098198617950.16619639723590.91690180138205
680.06635279467112220.1327055893422440.933647205328878
690.05802921422891710.1160584284578340.941970785771083
700.2304415688453340.4608831376906680.769558431154666
710.2020089242901060.4040178485802120.797991075709894
720.1769749292764410.3539498585528810.823025070723559
730.1553922086468590.3107844172937180.844607791353141
740.1330560101499530.2661120202999060.866943989850047
750.1459225645543670.2918451291087350.854077435445633
760.1773983807037860.3547967614075720.822601619296214
770.1495737688374570.2991475376749130.850426231162543
780.1431723885779110.2863447771558230.856827611422089
790.1258527500281790.2517055000563580.874147249971821
800.1080180176537290.2160360353074570.891981982346271
810.09008611384906980.180172227698140.90991388615093
820.1848016985524040.3696033971048080.815198301447596
830.1823384630342030.3646769260684070.817661536965797
840.1633924457778990.3267848915557980.836607554222101
850.1429287341204380.2858574682408760.857071265879562
860.1336913837694960.2673827675389920.866308616230504
870.1666240081850820.3332480163701640.833375991814918
880.1678642972882830.3357285945765650.832135702711717
890.1407493238610080.2814986477220170.859250676138992
900.1365270372198120.2730540744396230.863472962780188
910.1133204877571440.2266409755142880.886679512242856
920.0966418033510310.1932836067020620.903358196648969
930.09825867081220340.1965173416244070.901741329187797
940.09129602195940140.1825920439188030.908703978040599
950.1071109589698670.2142219179397340.892889041030133
960.08792999104608260.1758599820921650.912070008953917
970.09902468977857110.1980493795571420.900975310221429
980.100333814013550.2006676280270990.89966618598645
990.0850942811933840.1701885623867680.914905718806616
1000.07855695614630140.1571139122926030.921443043853699
1010.07649143159046170.1529828631809230.923508568409538
1020.0640806285482990.1281612570965980.935919371451701
1030.05082340963130620.1016468192626120.949176590368694
1040.08749347126577020.174986942531540.91250652873423
1050.1428198812680450.2856397625360890.857180118731955
1060.1419127144184670.2838254288369340.858087285581533
1070.1358887762398480.2717775524796970.864111223760152
1080.1838080885639350.3676161771278710.816191911436065
1090.1979279211912360.3958558423824720.802072078808764
1100.2251762768092180.4503525536184370.774823723190782
1110.2250670596165530.4501341192331050.774932940383447
1120.2254703352438110.4509406704876220.774529664756189
1130.1941553637332770.3883107274665540.805844636266723
1140.2247934581391720.4495869162783430.775206541860828
1150.1889403942354310.3778807884708620.811059605764569
1160.1586668166332770.3173336332665530.841333183366723
1170.135081825016190.2701636500323790.86491817498381
1180.1103316530874420.2206633061748840.889668346912558
1190.1021793214509520.2043586429019040.897820678549048
1200.08375770146844350.1675154029368870.916242298531557
1210.08666716034671470.1733343206934290.913332839653285
1220.1009129986234390.2018259972468780.899087001376561
1230.09163408979925470.1832681795985090.908365910200745
1240.1092076238453570.2184152476907130.890792376154643
1250.08672363108494520.173447262169890.913276368915055
1260.07431911037290740.1486382207458150.925680889627093
1270.0840718209922810.1681436419845620.915928179007719
1280.8027095527954450.394580894409110.197290447204555
1290.7943995025622250.411200994875550.205600497437775
1300.903578741565540.1928425168689210.0964212584344603
1310.9171772036794010.1656455926411980.0828227963205991
1320.9220731441054120.1558537117891760.0779268558945882
1330.9202003323661790.1595993352676430.0797996676338214
1340.8999005082803890.2001989834392230.100099491719611
1350.8852226897551730.2295546204896540.114777310244827
1360.9857483597550560.02850328048988840.0142516402449442
1370.9777582290588180.04448354188236410.022241770941182
1380.9872662583887960.0254674832224080.012733741611204
1390.9808119089347940.03837618213041290.0191880910652064
1400.9925522800792410.01489543984151740.00744771992075869
1410.9905471488057690.01890570238846270.00945285119423135
1420.9907421278655310.01851574426893890.00925787213446945
1430.99474260863930.01051478272140070.00525739136070037
1440.9962281166254960.007543766749007350.00377188337450367
1450.9993330203390760.001333959321847040.000666979660923521
1460.998953344727040.002093310545920060.00104665527296003
1470.9975215615176930.004956876964613310.00247843848230666
1480.9945414371890090.01091712562198160.00545856281099078
1490.9946237664029130.01075246719417420.00537623359708711
1500.9925401019064990.0149197961870020.00745989809350101
1510.9829965409255740.03400691814885220.0170034590744261
1520.9612891796217090.07742164075658240.0387108203782912
1530.93441041128140.1311791774372010.0655895887186003
1540.8697587263358730.2604825473282530.130241273664126
1550.9908521117643450.01829577647131040.00914788823565521






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0272108843537415NOK
5% type I error level210.142857142857143NOK
10% type I error level270.183673469387755NOK

\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 & 4 & 0.0272108843537415 & NOK \tabularnewline
5% type I error level & 21 & 0.142857142857143 & NOK \tabularnewline
10% type I error level & 27 & 0.183673469387755 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144378&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]4[/C][C]0.0272108843537415[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.183673469387755[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144378&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144378&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0272108843537415NOK
5% type I error level210.142857142857143NOK
10% type I error level270.183673469387755NOK


Figures (Output of Computation)

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


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}