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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 20 Nov 2011 18:34:53 -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/20/t1321832138mm1qdd9vqc0w5gb.htm/, Retrieved Thu, 28 Mar 2024 10:32:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145678, Retrieved Thu, 28 Mar 2024 10:32:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Workshop 7: Multi...] [2011-11-20 23:34:53] [0a33c029fc36ba20d75a47e4ff91377b] [Current]
- R         [Multiple Regression] [Workshop 7: Multi...] [2011-11-20 23:57:48] [a9a952c1cbc7081c25fad93a34aab827]
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Dataseries X:
170588	46	65	26	99
86621	48	54	20	77
113337	37	58	24	90
144530	72	77	25	96
81530	31	41	15	41
35523	17	0	16	64
305115	78	111	20	76
32750	16	1	18	67
115885	37	37	19	72
130539	24	60	20	75
156990	63	64	26	97
128274	74	71	37	139
102350	43	38	23	76
192887	42	76	36	123
129796	55	61	28	106
245478	120	125	35	133
169569	42	85	20	76
185279	100	69	22	83
109598	36	77	19	72
155012	49	100	28	107
154730	46	78	27	99
280379	56	76	25	88
90938	17	40	15	56
101324	31	81	26	104
139502	77	102	27	103
145120	90	70	24	90
161729	80	75	21	78
160905	54	93	27	103
106888	34	42	21	81
187289	38	95	30	114
181853	53	87	25	95
129340	47	44	33	118
196862	63	87	30	113
62731	25	28	20	75
234863	55	87	27	103
167255	37	71	25	93
264528	83	70	30	114
121976	49	50	20	76
80964	26	30	8	27
209631	107	87	24	92
213310	55	78	22	84
115911	40	48	25	92
131337	46	52	20	72
81106	31	31	21	79
93125	49	30	21	57
305708	95	70	26	99
78800	42	20	26	82
157566	54	84	30	113
213487	68	81	26	97
131108	39	79	30	110
128734	53	72	18	78
24188	24	8	4	12
257662	200	67	31	114
65029	17	21	18	67
98066	58	30	14	52
173587	27	70	20	76
179571	58	87	35	134
197067	113	87	24	92
208823	74	116	26	93
134088	50	54	20	75
245107	86	96	31	118
201409	76	93	21	77
141760	61	49	31	122
170635	59	49	26	99
129100	38	38	19	72
108811	34	64	15	58
113450	85	64	19	73
142286	100	66	28	103
143937	49	98	20	76
89882	35	99	17	65
118807	33	56	25	95
69471	28	22	20	76
126630	44	51	25	95
145908	37	61	24	92
96981	33	94	22	84
189066	44	98	25	95
191467	55	76	20	76
106193	58	57	23	87
89318	36	75	22	84
120362	42	48	25	95
98791	30	48	18	69
274949	66	109	30	115
132798	53	27	22	83
128075	57	83	25	47
80953	25	49	8	28
109237	39	24	21	79
96634	35	46	22	83
226183	114	44	24	92
167226	53	49	30	98
117805	70	108	27	103
121630	48	42	21	77
152193	49	110	25	95
112004	42	28	21	78
169613	51	79	24	92
176577	51	49	20	76
130533	27	64	20	76
142339	29	75	20	67
189764	54	118	24	92
201603	92	95	40	151
243180	72	106	22	83
155931	63	73	31	118
182557	40	108	26	98
106351	108	30	20	76
43287	14	13	19	71
127394	44	69	15	57
127930	91	75	21	79
135306	29	80	22	83
175663	63	106	24	92
74112	32	28	19	75
89059	65	70	20	79
166142	41	51	23	88
141933	55	90	27	99
22938	10	12	1	0
125927	53	87	24	91
61857	25	23	11	32
91185	31	57	27	101
236316	64	85	22	84
21054	16	4	0	0
169093	35	56	17	60
31414	19	18	8	25
183059	74	86	23	86
137544	35	40	31	115
75032	45	16	23	88
71908	28	18	17	59
38214	34	16	8	27
90961	25	42	22	83
193662	48	78	33	126
127185	38	30	33	125
242153	49	104	31	119
201748	65	121	33	127
254599	71	111	35	133
139144	23	57	21	79
76470	29	28	20	76
183260	190	56	24	92
280039	113	82	29	109
50999	15	2	20	76
253056	85	91	27	100
98466	48	41	24	87
168059	33	84	26	97
128768	50	55	26	95
75746	72	3	12	48
244909	79	54	21	80
152366	54	93	24	91
173260	63	41	21	79
197033	68	94	30	114
67507	39	101	32	120
139409	49	70	24	89
185366	67	114	29	111
0	0	0	0	0
14688	10	4	0	0
98	1	0	0	0
455	2	0	0	0
0	0	0	0	0
0	0	0	0	0
128873	57	42	20	74
185288	71	97	27	107
0	0	0	0	0
203	4	0	0	0
7199	5	7	0	0
46660	20	12	5	15
17547	5	0	1	4
73567	27	37	23	82
969	2	0	0	0
105477	33	39	16	54




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145678&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]5 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=145678&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145678&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Total_Time_spent_in_RFC[t] = + 3514.23367219296 + 795.618481365439Number_of_Logins[t] + 924.142165626854Total_Number_of_Blogged_Computations[t] + 970.67494192235Total_Number_of_Reviewed_Compendiums[t] + 192.29959937223Total_Number_of_submitted_Feedback_Messages_in_PeerReviews[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Time_spent_in_RFC[t] =  +  3514.23367219296 +  795.618481365439Number_of_Logins[t] +  924.142165626854Total_Number_of_Blogged_Computations[t] +  970.67494192235Total_Number_of_Reviewed_Compendiums[t] +  192.29959937223Total_Number_of_submitted_Feedback_Messages_in_PeerReviews[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145678&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Time_spent_in_RFC[t] =  +  3514.23367219296 +  795.618481365439Number_of_Logins[t] +  924.142165626854Total_Number_of_Blogged_Computations[t] +  970.67494192235Total_Number_of_Reviewed_Compendiums[t] +  192.29959937223Total_Number_of_submitted_Feedback_Messages_in_PeerReviews[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145678&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145678&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Total_Time_spent_in_RFC[t] = + 3514.23367219296 + 795.618481365439Number_of_Logins[t] + 924.142165626854Total_Number_of_Blogged_Computations[t] + 970.67494192235Total_Number_of_Reviewed_Compendiums[t] + 192.29959937223Total_Number_of_submitted_Feedback_Messages_in_PeerReviews[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3514.233672192967484.4470610.46950.6393280.319664
Number_of_Logins795.618481365439113.5953857.00400
Total_Number_of_Blogged_Computations924.142165626854122.4830697.545100
Total_Number_of_Reviewed_Compendiums970.674941922352014.6590660.48180.6306060.315303
Total_Number_of_submitted_Feedback_Messages_in_PeerReviews192.29959937223532.8850880.36090.7186790.35934

\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) & 3514.23367219296 & 7484.447061 & 0.4695 & 0.639328 & 0.319664 \tabularnewline
Number_of_Logins & 795.618481365439 & 113.595385 & 7.004 & 0 & 0 \tabularnewline
Total_Number_of_Blogged_Computations & 924.142165626854 & 122.483069 & 7.5451 & 0 & 0 \tabularnewline
Total_Number_of_Reviewed_Compendiums & 970.67494192235 & 2014.659066 & 0.4818 & 0.630606 & 0.315303 \tabularnewline
Total_Number_of_submitted_Feedback_Messages_in_PeerReviews & 192.29959937223 & 532.885088 & 0.3609 & 0.718679 & 0.35934 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145678&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]3514.23367219296[/C][C]7484.447061[/C][C]0.4695[/C][C]0.639328[/C][C]0.319664[/C][/ROW]
[ROW][C]Number_of_Logins[/C][C]795.618481365439[/C][C]113.595385[/C][C]7.004[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Blogged_Computations[/C][C]924.142165626854[/C][C]122.483069[/C][C]7.5451[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Reviewed_Compendiums[/C][C]970.67494192235[/C][C]2014.659066[/C][C]0.4818[/C][C]0.630606[/C][C]0.315303[/C][/ROW]
[ROW][C]Total_Number_of_submitted_Feedback_Messages_in_PeerReviews[/C][C]192.29959937223[/C][C]532.885088[/C][C]0.3609[/C][C]0.718679[/C][C]0.35934[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145678&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145678&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3514.233672192967484.4470610.46950.6393280.319664
Number_of_Logins795.618481365439113.5953857.00400
Total_Number_of_Blogged_Computations924.142165626854122.4830697.545100
Total_Number_of_Reviewed_Compendiums970.674941922352014.6590660.48180.6306060.315303
Total_Number_of_submitted_Feedback_Messages_in_PeerReviews192.29959937223532.8850880.36090.7186790.35934







Multiple Linear Regression - Regression Statistics
Multiple R0.860892998403522
R-squared0.741136754700207
Adjusted R-squared0.734624471799583
F-TEST (value)113.805982634634
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation35212.9045305305
Sum Squared Residuals197151834630.725

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.860892998403522 \tabularnewline
R-squared & 0.741136754700207 \tabularnewline
Adjusted R-squared & 0.734624471799583 \tabularnewline
F-TEST (value) & 113.805982634634 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 35212.9045305305 \tabularnewline
Sum Squared Residuals & 197151834630.725 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145678&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.860892998403522[/C][/ROW]
[ROW][C]R-squared[/C][C]0.741136754700207[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.734624471799583[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]113.805982634634[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/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]35212.9045305305[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]197151834630.725[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145678&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145678&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.860892998403522
R-squared0.741136754700207
Adjusted R-squared0.734624471799583
F-TEST (value)113.805982634634
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation35212.9045305305
Sum Squared Residuals197151834630.725







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1170588144457.13340858126130.8665914195
286621125828.165711693-39207.1657116929
3113337127155.525638709-13818.5256387088
4144530174685.346171565-30155.3461715651
58153088512.6430883193-6982.64308831926
63552344877.7212859857-9354.72128598573
7305115202180.523994014102934.476005986
83275047524.4936522085-14774.4936522085
911588599433.77266223316451.227337767
10130539111893.57595393918645.424046061
11156990156673.906227422316.093772578347
12128274190648.712216609-62374.712216609
13102350109783.523881231-7433.5238812308
14192887165762.16310917127124.8368908289
15129796151208.578157812-21412.5781578123
16245478274055.691823191-28577.6918231912
17169569149510.5623585620058.4376414395
18185279184157.6067071771121.39329282345
19109598135603.840805942-26005.8408059417
20155012182668.711328439-27656.7113284393
21154730157441.656503652-2711.65650365196
22280379159492.911509113120886.088490887
239093879334.336174159711603.6638258403
24101324148270.62883499-46946.6288349897
25139502205054.439798514-65552.439798514
26145120180413.011138599-35293.0111385993
27161729171857.917134845-10128.9171348454
28160905178437.935236467-17532.9352364672
29106888105339.6743244661548.32567553429
30187289172583.64428473514705.3557152645
31181853168617.71708251813235.2829174819
32129340136294.183393311-6954.18339331082
33196862184888.66939448411973.3306055156
346273183116.6451352451-20385.6451352451
35234863173688.70072407261174.2992759285
36167255140716.94753189726538.052468103
37264528185282.92180550979245.0781944911
38121976122734.915931179-758.915931178631
398096464882.06787492916081.932125071
40209631210033.541336213-402.541336212767
41213310156864.35413574656445.6458642543
42115911121656.233567203-5745.2335672034
43131337121427.1464208479909.8535791529
448110692402.6558597295-11296.6558597295
4593125101569.055172492-8444.05517249154
46305708188063.149823621117644.850176379
477880096419.1688405824-17619.1688405824
48157566174955.676565315-17389.6765653149
49213487176362.41544990537124.5845500946
50131108157823.789718582-26715.7897185823
51128734144691.766815331-15957.7668153309
522418836192.5095101345-12004.5095101345
53257662276568.532570307-18906.532570307
546502966802.9554461111-1773.95544611106
5598066100973.398914463-2907.39891446289
56173587123714.15265367649872.8473463239
57179571189802.243284086-10231.2432840858
58197067214807.252224405-17740.2522244054
59208823212711.903737549-3888.90373754898
60134088127034.8034756797053.19652432074
61245107213437.34689531531669.6531046853
62201409185117.70259129516291.2974087052
63141760150881.401474205-9121.40147420545
64170635140013.89901630230621.1009836984
65129100101153.53330922527946.4666907747
66108811115423.861531161-6612.86153116113
67113450162767.597839071-49317.5978390714
68142286191055.221849275-48769.2218492747
69143937167093.739881268-23156.7398812676
7089882151851.902888917-61969.9028889168
71118807124056.940320777-5249.94032077685
726947180150.9471849525-10679.9471849525
73126630128188.032787662-1558.0327876624
74145908130312.55133433415595.4486656662
7596981154147.022195736-57166.0221957357
76189066171622.71457212517443.2854278755
77191467151536.32312566939930.6768743305
78106193141391.797841717-35198.7978417171
7989318138975.176492922-49657.1764929218
80120362123824.369328051-3462.36932805096
8198791102482.433374531-3691.43337453128
82274949207991.25168111666957.7483188841
83132798107949.56712667324848.432873327
84128075158873.241575605-30798.2415756054
858095381837.450139846-884.450139846035
8610923792298.608551265116938.3914487349
8796634111187.135609005-14553.1356090054
88226183175864.75758381650318.2424161839
89167226138930.58829642628295.4117035739
90117805205029.963422717-87224.963422717
91121630115709.1346660935920.86533390706
92152193186690.512966474-34497.512966474
9311200498189.733058496613814.2669415034
94169613158085.76905473311527.2309452666
95176577123402.01072828353174.9892717173
96130533118169.29965991512363.7003400851
97142339128195.40405019114143.5959498089
98189764196514.168958277-6750.16895827698
99201603232368.876874465-30765.8768744652
100243180196073.54935713847106.4506428621
101155931173882.852014492-17951.8520144919
102182557179229.236042973327.76395702963
103106351151193.563019202-44842.5630192024
1044328758762.8360164112-15475.8360164112
105127394127808.457573578-414.457573577555
106127930180802.020029237-52872.0200292375
107135306137834.258352126-2528.25835212578
108175663192585.029303044-16922.0293030437
1097411287715.2995628808-13603.2995628808
11089059154524.553743679-65465.5537436794
111166142122513.73026411643628.2697358842
112141933175691.928823463-33758.9288234632
1132293823530.799415292-592.799415291966
114125927166877.843743107-40950.8437431068
1156185761490.9770568038366.022943196216
11691185126484.993003751-35299.9930037509
117236316170493.91562742365822.0843725774
1182105419940.69803654741113.30196345258
119169093111152.29177010157940.7082298991
1203141447838.4333191042-16424.4333191042
121183059180729.5167473712329.4832526293
122137544120531.94427245717012.0557275433
1237503293351.2283926376-18319.2283926376
1247190870273.26050735011634.73949264987
1253821458309.0254070766-20095.0254070766
1269096199534.3821328436-8573.38213284358
127193662170049.03230096723612.9676990329
128127185117541.7239378529643.27606214848
129242153191584.9000091850568.0999908195
130201748223504.959205507-21756.9592055066
131254599222132.39591750932466.6040824913
132139144110065.40431510429078.5956848958
1337647086491.4186600791-10021.4186600791
134183260247421.468155112-64161.4681551117
135280039218309.00929521161729.9907047892
1365099951325.0636146647-326.063614664708
137253056200676.92502942552379.0749705746
13898466119620.013319955-21154.0133199554
139168059151288.19509899616770.8049010044
140128768137628.987280285-8860.9872802848
1417574684449.6709003204-8703.67090032036
142244909152039.9123740692869.0876259395
143152366173218.315218233-20852.3152182334
144173260127103.86891969246156.1310803079
145197033195528.0565600721504.94343992823
14667507182019.26323994-114512.26323994
147139409147600.353803244-8191.3538032441
148185366211667.707651204-26301.7076512044
14903514.23367219298-3514.23367219298
1501468815166.9871483548-478.987148354788
151984309.85215355842-4211.85215355842
1524555105.47063492385-4650.47063492385
15303514.23367219298-3514.23367219298
15403514.23367219298-3514.23367219298
155128873121322.1272583437550.87274165715
156185288196429.216479676-11141.216479676
15703514.23367219298-3514.23367219298
1582036696.70759765473-6493.70759765473
159719913961.3212384082-6762.32123840815
1604666038254.17798721928405.82201278081
161175479232.199418431448314.80058156856
1627356797283.2836099903-23716.2836099903
1639695105.47063492385-4136.47063492385
16410547791726.165453557813750.8345464422

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 170588 & 144457.133408581 & 26130.8665914195 \tabularnewline
2 & 86621 & 125828.165711693 & -39207.1657116929 \tabularnewline
3 & 113337 & 127155.525638709 & -13818.5256387088 \tabularnewline
4 & 144530 & 174685.346171565 & -30155.3461715651 \tabularnewline
5 & 81530 & 88512.6430883193 & -6982.64308831926 \tabularnewline
6 & 35523 & 44877.7212859857 & -9354.72128598573 \tabularnewline
7 & 305115 & 202180.523994014 & 102934.476005986 \tabularnewline
8 & 32750 & 47524.4936522085 & -14774.4936522085 \tabularnewline
9 & 115885 & 99433.772662233 & 16451.227337767 \tabularnewline
10 & 130539 & 111893.575953939 & 18645.424046061 \tabularnewline
11 & 156990 & 156673.906227422 & 316.093772578347 \tabularnewline
12 & 128274 & 190648.712216609 & -62374.712216609 \tabularnewline
13 & 102350 & 109783.523881231 & -7433.5238812308 \tabularnewline
14 & 192887 & 165762.163109171 & 27124.8368908289 \tabularnewline
15 & 129796 & 151208.578157812 & -21412.5781578123 \tabularnewline
16 & 245478 & 274055.691823191 & -28577.6918231912 \tabularnewline
17 & 169569 & 149510.56235856 & 20058.4376414395 \tabularnewline
18 & 185279 & 184157.606707177 & 1121.39329282345 \tabularnewline
19 & 109598 & 135603.840805942 & -26005.8408059417 \tabularnewline
20 & 155012 & 182668.711328439 & -27656.7113284393 \tabularnewline
21 & 154730 & 157441.656503652 & -2711.65650365196 \tabularnewline
22 & 280379 & 159492.911509113 & 120886.088490887 \tabularnewline
23 & 90938 & 79334.3361741597 & 11603.6638258403 \tabularnewline
24 & 101324 & 148270.62883499 & -46946.6288349897 \tabularnewline
25 & 139502 & 205054.439798514 & -65552.439798514 \tabularnewline
26 & 145120 & 180413.011138599 & -35293.0111385993 \tabularnewline
27 & 161729 & 171857.917134845 & -10128.9171348454 \tabularnewline
28 & 160905 & 178437.935236467 & -17532.9352364672 \tabularnewline
29 & 106888 & 105339.674324466 & 1548.32567553429 \tabularnewline
30 & 187289 & 172583.644284735 & 14705.3557152645 \tabularnewline
31 & 181853 & 168617.717082518 & 13235.2829174819 \tabularnewline
32 & 129340 & 136294.183393311 & -6954.18339331082 \tabularnewline
33 & 196862 & 184888.669394484 & 11973.3306055156 \tabularnewline
34 & 62731 & 83116.6451352451 & -20385.6451352451 \tabularnewline
35 & 234863 & 173688.700724072 & 61174.2992759285 \tabularnewline
36 & 167255 & 140716.947531897 & 26538.052468103 \tabularnewline
37 & 264528 & 185282.921805509 & 79245.0781944911 \tabularnewline
38 & 121976 & 122734.915931179 & -758.915931178631 \tabularnewline
39 & 80964 & 64882.067874929 & 16081.932125071 \tabularnewline
40 & 209631 & 210033.541336213 & -402.541336212767 \tabularnewline
41 & 213310 & 156864.354135746 & 56445.6458642543 \tabularnewline
42 & 115911 & 121656.233567203 & -5745.2335672034 \tabularnewline
43 & 131337 & 121427.146420847 & 9909.8535791529 \tabularnewline
44 & 81106 & 92402.6558597295 & -11296.6558597295 \tabularnewline
45 & 93125 & 101569.055172492 & -8444.05517249154 \tabularnewline
46 & 305708 & 188063.149823621 & 117644.850176379 \tabularnewline
47 & 78800 & 96419.1688405824 & -17619.1688405824 \tabularnewline
48 & 157566 & 174955.676565315 & -17389.6765653149 \tabularnewline
49 & 213487 & 176362.415449905 & 37124.5845500946 \tabularnewline
50 & 131108 & 157823.789718582 & -26715.7897185823 \tabularnewline
51 & 128734 & 144691.766815331 & -15957.7668153309 \tabularnewline
52 & 24188 & 36192.5095101345 & -12004.5095101345 \tabularnewline
53 & 257662 & 276568.532570307 & -18906.532570307 \tabularnewline
54 & 65029 & 66802.9554461111 & -1773.95544611106 \tabularnewline
55 & 98066 & 100973.398914463 & -2907.39891446289 \tabularnewline
56 & 173587 & 123714.152653676 & 49872.8473463239 \tabularnewline
57 & 179571 & 189802.243284086 & -10231.2432840858 \tabularnewline
58 & 197067 & 214807.252224405 & -17740.2522244054 \tabularnewline
59 & 208823 & 212711.903737549 & -3888.90373754898 \tabularnewline
60 & 134088 & 127034.803475679 & 7053.19652432074 \tabularnewline
61 & 245107 & 213437.346895315 & 31669.6531046853 \tabularnewline
62 & 201409 & 185117.702591295 & 16291.2974087052 \tabularnewline
63 & 141760 & 150881.401474205 & -9121.40147420545 \tabularnewline
64 & 170635 & 140013.899016302 & 30621.1009836984 \tabularnewline
65 & 129100 & 101153.533309225 & 27946.4666907747 \tabularnewline
66 & 108811 & 115423.861531161 & -6612.86153116113 \tabularnewline
67 & 113450 & 162767.597839071 & -49317.5978390714 \tabularnewline
68 & 142286 & 191055.221849275 & -48769.2218492747 \tabularnewline
69 & 143937 & 167093.739881268 & -23156.7398812676 \tabularnewline
70 & 89882 & 151851.902888917 & -61969.9028889168 \tabularnewline
71 & 118807 & 124056.940320777 & -5249.94032077685 \tabularnewline
72 & 69471 & 80150.9471849525 & -10679.9471849525 \tabularnewline
73 & 126630 & 128188.032787662 & -1558.0327876624 \tabularnewline
74 & 145908 & 130312.551334334 & 15595.4486656662 \tabularnewline
75 & 96981 & 154147.022195736 & -57166.0221957357 \tabularnewline
76 & 189066 & 171622.714572125 & 17443.2854278755 \tabularnewline
77 & 191467 & 151536.323125669 & 39930.6768743305 \tabularnewline
78 & 106193 & 141391.797841717 & -35198.7978417171 \tabularnewline
79 & 89318 & 138975.176492922 & -49657.1764929218 \tabularnewline
80 & 120362 & 123824.369328051 & -3462.36932805096 \tabularnewline
81 & 98791 & 102482.433374531 & -3691.43337453128 \tabularnewline
82 & 274949 & 207991.251681116 & 66957.7483188841 \tabularnewline
83 & 132798 & 107949.567126673 & 24848.432873327 \tabularnewline
84 & 128075 & 158873.241575605 & -30798.2415756054 \tabularnewline
85 & 80953 & 81837.450139846 & -884.450139846035 \tabularnewline
86 & 109237 & 92298.6085512651 & 16938.3914487349 \tabularnewline
87 & 96634 & 111187.135609005 & -14553.1356090054 \tabularnewline
88 & 226183 & 175864.757583816 & 50318.2424161839 \tabularnewline
89 & 167226 & 138930.588296426 & 28295.4117035739 \tabularnewline
90 & 117805 & 205029.963422717 & -87224.963422717 \tabularnewline
91 & 121630 & 115709.134666093 & 5920.86533390706 \tabularnewline
92 & 152193 & 186690.512966474 & -34497.512966474 \tabularnewline
93 & 112004 & 98189.7330584966 & 13814.2669415034 \tabularnewline
94 & 169613 & 158085.769054733 & 11527.2309452666 \tabularnewline
95 & 176577 & 123402.010728283 & 53174.9892717173 \tabularnewline
96 & 130533 & 118169.299659915 & 12363.7003400851 \tabularnewline
97 & 142339 & 128195.404050191 & 14143.5959498089 \tabularnewline
98 & 189764 & 196514.168958277 & -6750.16895827698 \tabularnewline
99 & 201603 & 232368.876874465 & -30765.8768744652 \tabularnewline
100 & 243180 & 196073.549357138 & 47106.4506428621 \tabularnewline
101 & 155931 & 173882.852014492 & -17951.8520144919 \tabularnewline
102 & 182557 & 179229.23604297 & 3327.76395702963 \tabularnewline
103 & 106351 & 151193.563019202 & -44842.5630192024 \tabularnewline
104 & 43287 & 58762.8360164112 & -15475.8360164112 \tabularnewline
105 & 127394 & 127808.457573578 & -414.457573577555 \tabularnewline
106 & 127930 & 180802.020029237 & -52872.0200292375 \tabularnewline
107 & 135306 & 137834.258352126 & -2528.25835212578 \tabularnewline
108 & 175663 & 192585.029303044 & -16922.0293030437 \tabularnewline
109 & 74112 & 87715.2995628808 & -13603.2995628808 \tabularnewline
110 & 89059 & 154524.553743679 & -65465.5537436794 \tabularnewline
111 & 166142 & 122513.730264116 & 43628.2697358842 \tabularnewline
112 & 141933 & 175691.928823463 & -33758.9288234632 \tabularnewline
113 & 22938 & 23530.799415292 & -592.799415291966 \tabularnewline
114 & 125927 & 166877.843743107 & -40950.8437431068 \tabularnewline
115 & 61857 & 61490.9770568038 & 366.022943196216 \tabularnewline
116 & 91185 & 126484.993003751 & -35299.9930037509 \tabularnewline
117 & 236316 & 170493.915627423 & 65822.0843725774 \tabularnewline
118 & 21054 & 19940.6980365474 & 1113.30196345258 \tabularnewline
119 & 169093 & 111152.291770101 & 57940.7082298991 \tabularnewline
120 & 31414 & 47838.4333191042 & -16424.4333191042 \tabularnewline
121 & 183059 & 180729.516747371 & 2329.4832526293 \tabularnewline
122 & 137544 & 120531.944272457 & 17012.0557275433 \tabularnewline
123 & 75032 & 93351.2283926376 & -18319.2283926376 \tabularnewline
124 & 71908 & 70273.2605073501 & 1634.73949264987 \tabularnewline
125 & 38214 & 58309.0254070766 & -20095.0254070766 \tabularnewline
126 & 90961 & 99534.3821328436 & -8573.38213284358 \tabularnewline
127 & 193662 & 170049.032300967 & 23612.9676990329 \tabularnewline
128 & 127185 & 117541.723937852 & 9643.27606214848 \tabularnewline
129 & 242153 & 191584.90000918 & 50568.0999908195 \tabularnewline
130 & 201748 & 223504.959205507 & -21756.9592055066 \tabularnewline
131 & 254599 & 222132.395917509 & 32466.6040824913 \tabularnewline
132 & 139144 & 110065.404315104 & 29078.5956848958 \tabularnewline
133 & 76470 & 86491.4186600791 & -10021.4186600791 \tabularnewline
134 & 183260 & 247421.468155112 & -64161.4681551117 \tabularnewline
135 & 280039 & 218309.009295211 & 61729.9907047892 \tabularnewline
136 & 50999 & 51325.0636146647 & -326.063614664708 \tabularnewline
137 & 253056 & 200676.925029425 & 52379.0749705746 \tabularnewline
138 & 98466 & 119620.013319955 & -21154.0133199554 \tabularnewline
139 & 168059 & 151288.195098996 & 16770.8049010044 \tabularnewline
140 & 128768 & 137628.987280285 & -8860.9872802848 \tabularnewline
141 & 75746 & 84449.6709003204 & -8703.67090032036 \tabularnewline
142 & 244909 & 152039.91237406 & 92869.0876259395 \tabularnewline
143 & 152366 & 173218.315218233 & -20852.3152182334 \tabularnewline
144 & 173260 & 127103.868919692 & 46156.1310803079 \tabularnewline
145 & 197033 & 195528.056560072 & 1504.94343992823 \tabularnewline
146 & 67507 & 182019.26323994 & -114512.26323994 \tabularnewline
147 & 139409 & 147600.353803244 & -8191.3538032441 \tabularnewline
148 & 185366 & 211667.707651204 & -26301.7076512044 \tabularnewline
149 & 0 & 3514.23367219298 & -3514.23367219298 \tabularnewline
150 & 14688 & 15166.9871483548 & -478.987148354788 \tabularnewline
151 & 98 & 4309.85215355842 & -4211.85215355842 \tabularnewline
152 & 455 & 5105.47063492385 & -4650.47063492385 \tabularnewline
153 & 0 & 3514.23367219298 & -3514.23367219298 \tabularnewline
154 & 0 & 3514.23367219298 & -3514.23367219298 \tabularnewline
155 & 128873 & 121322.127258343 & 7550.87274165715 \tabularnewline
156 & 185288 & 196429.216479676 & -11141.216479676 \tabularnewline
157 & 0 & 3514.23367219298 & -3514.23367219298 \tabularnewline
158 & 203 & 6696.70759765473 & -6493.70759765473 \tabularnewline
159 & 7199 & 13961.3212384082 & -6762.32123840815 \tabularnewline
160 & 46660 & 38254.1779872192 & 8405.82201278081 \tabularnewline
161 & 17547 & 9232.19941843144 & 8314.80058156856 \tabularnewline
162 & 73567 & 97283.2836099903 & -23716.2836099903 \tabularnewline
163 & 969 & 5105.47063492385 & -4136.47063492385 \tabularnewline
164 & 105477 & 91726.1654535578 & 13750.8345464422 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145678&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]170588[/C][C]144457.133408581[/C][C]26130.8665914195[/C][/ROW]
[ROW][C]2[/C][C]86621[/C][C]125828.165711693[/C][C]-39207.1657116929[/C][/ROW]
[ROW][C]3[/C][C]113337[/C][C]127155.525638709[/C][C]-13818.5256387088[/C][/ROW]
[ROW][C]4[/C][C]144530[/C][C]174685.346171565[/C][C]-30155.3461715651[/C][/ROW]
[ROW][C]5[/C][C]81530[/C][C]88512.6430883193[/C][C]-6982.64308831926[/C][/ROW]
[ROW][C]6[/C][C]35523[/C][C]44877.7212859857[/C][C]-9354.72128598573[/C][/ROW]
[ROW][C]7[/C][C]305115[/C][C]202180.523994014[/C][C]102934.476005986[/C][/ROW]
[ROW][C]8[/C][C]32750[/C][C]47524.4936522085[/C][C]-14774.4936522085[/C][/ROW]
[ROW][C]9[/C][C]115885[/C][C]99433.772662233[/C][C]16451.227337767[/C][/ROW]
[ROW][C]10[/C][C]130539[/C][C]111893.575953939[/C][C]18645.424046061[/C][/ROW]
[ROW][C]11[/C][C]156990[/C][C]156673.906227422[/C][C]316.093772578347[/C][/ROW]
[ROW][C]12[/C][C]128274[/C][C]190648.712216609[/C][C]-62374.712216609[/C][/ROW]
[ROW][C]13[/C][C]102350[/C][C]109783.523881231[/C][C]-7433.5238812308[/C][/ROW]
[ROW][C]14[/C][C]192887[/C][C]165762.163109171[/C][C]27124.8368908289[/C][/ROW]
[ROW][C]15[/C][C]129796[/C][C]151208.578157812[/C][C]-21412.5781578123[/C][/ROW]
[ROW][C]16[/C][C]245478[/C][C]274055.691823191[/C][C]-28577.6918231912[/C][/ROW]
[ROW][C]17[/C][C]169569[/C][C]149510.56235856[/C][C]20058.4376414395[/C][/ROW]
[ROW][C]18[/C][C]185279[/C][C]184157.606707177[/C][C]1121.39329282345[/C][/ROW]
[ROW][C]19[/C][C]109598[/C][C]135603.840805942[/C][C]-26005.8408059417[/C][/ROW]
[ROW][C]20[/C][C]155012[/C][C]182668.711328439[/C][C]-27656.7113284393[/C][/ROW]
[ROW][C]21[/C][C]154730[/C][C]157441.656503652[/C][C]-2711.65650365196[/C][/ROW]
[ROW][C]22[/C][C]280379[/C][C]159492.911509113[/C][C]120886.088490887[/C][/ROW]
[ROW][C]23[/C][C]90938[/C][C]79334.3361741597[/C][C]11603.6638258403[/C][/ROW]
[ROW][C]24[/C][C]101324[/C][C]148270.62883499[/C][C]-46946.6288349897[/C][/ROW]
[ROW][C]25[/C][C]139502[/C][C]205054.439798514[/C][C]-65552.439798514[/C][/ROW]
[ROW][C]26[/C][C]145120[/C][C]180413.011138599[/C][C]-35293.0111385993[/C][/ROW]
[ROW][C]27[/C][C]161729[/C][C]171857.917134845[/C][C]-10128.9171348454[/C][/ROW]
[ROW][C]28[/C][C]160905[/C][C]178437.935236467[/C][C]-17532.9352364672[/C][/ROW]
[ROW][C]29[/C][C]106888[/C][C]105339.674324466[/C][C]1548.32567553429[/C][/ROW]
[ROW][C]30[/C][C]187289[/C][C]172583.644284735[/C][C]14705.3557152645[/C][/ROW]
[ROW][C]31[/C][C]181853[/C][C]168617.717082518[/C][C]13235.2829174819[/C][/ROW]
[ROW][C]32[/C][C]129340[/C][C]136294.183393311[/C][C]-6954.18339331082[/C][/ROW]
[ROW][C]33[/C][C]196862[/C][C]184888.669394484[/C][C]11973.3306055156[/C][/ROW]
[ROW][C]34[/C][C]62731[/C][C]83116.6451352451[/C][C]-20385.6451352451[/C][/ROW]
[ROW][C]35[/C][C]234863[/C][C]173688.700724072[/C][C]61174.2992759285[/C][/ROW]
[ROW][C]36[/C][C]167255[/C][C]140716.947531897[/C][C]26538.052468103[/C][/ROW]
[ROW][C]37[/C][C]264528[/C][C]185282.921805509[/C][C]79245.0781944911[/C][/ROW]
[ROW][C]38[/C][C]121976[/C][C]122734.915931179[/C][C]-758.915931178631[/C][/ROW]
[ROW][C]39[/C][C]80964[/C][C]64882.067874929[/C][C]16081.932125071[/C][/ROW]
[ROW][C]40[/C][C]209631[/C][C]210033.541336213[/C][C]-402.541336212767[/C][/ROW]
[ROW][C]41[/C][C]213310[/C][C]156864.354135746[/C][C]56445.6458642543[/C][/ROW]
[ROW][C]42[/C][C]115911[/C][C]121656.233567203[/C][C]-5745.2335672034[/C][/ROW]
[ROW][C]43[/C][C]131337[/C][C]121427.146420847[/C][C]9909.8535791529[/C][/ROW]
[ROW][C]44[/C][C]81106[/C][C]92402.6558597295[/C][C]-11296.6558597295[/C][/ROW]
[ROW][C]45[/C][C]93125[/C][C]101569.055172492[/C][C]-8444.05517249154[/C][/ROW]
[ROW][C]46[/C][C]305708[/C][C]188063.149823621[/C][C]117644.850176379[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]96419.1688405824[/C][C]-17619.1688405824[/C][/ROW]
[ROW][C]48[/C][C]157566[/C][C]174955.676565315[/C][C]-17389.6765653149[/C][/ROW]
[ROW][C]49[/C][C]213487[/C][C]176362.415449905[/C][C]37124.5845500946[/C][/ROW]
[ROW][C]50[/C][C]131108[/C][C]157823.789718582[/C][C]-26715.7897185823[/C][/ROW]
[ROW][C]51[/C][C]128734[/C][C]144691.766815331[/C][C]-15957.7668153309[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]36192.5095101345[/C][C]-12004.5095101345[/C][/ROW]
[ROW][C]53[/C][C]257662[/C][C]276568.532570307[/C][C]-18906.532570307[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]66802.9554461111[/C][C]-1773.95544611106[/C][/ROW]
[ROW][C]55[/C][C]98066[/C][C]100973.398914463[/C][C]-2907.39891446289[/C][/ROW]
[ROW][C]56[/C][C]173587[/C][C]123714.152653676[/C][C]49872.8473463239[/C][/ROW]
[ROW][C]57[/C][C]179571[/C][C]189802.243284086[/C][C]-10231.2432840858[/C][/ROW]
[ROW][C]58[/C][C]197067[/C][C]214807.252224405[/C][C]-17740.2522244054[/C][/ROW]
[ROW][C]59[/C][C]208823[/C][C]212711.903737549[/C][C]-3888.90373754898[/C][/ROW]
[ROW][C]60[/C][C]134088[/C][C]127034.803475679[/C][C]7053.19652432074[/C][/ROW]
[ROW][C]61[/C][C]245107[/C][C]213437.346895315[/C][C]31669.6531046853[/C][/ROW]
[ROW][C]62[/C][C]201409[/C][C]185117.702591295[/C][C]16291.2974087052[/C][/ROW]
[ROW][C]63[/C][C]141760[/C][C]150881.401474205[/C][C]-9121.40147420545[/C][/ROW]
[ROW][C]64[/C][C]170635[/C][C]140013.899016302[/C][C]30621.1009836984[/C][/ROW]
[ROW][C]65[/C][C]129100[/C][C]101153.533309225[/C][C]27946.4666907747[/C][/ROW]
[ROW][C]66[/C][C]108811[/C][C]115423.861531161[/C][C]-6612.86153116113[/C][/ROW]
[ROW][C]67[/C][C]113450[/C][C]162767.597839071[/C][C]-49317.5978390714[/C][/ROW]
[ROW][C]68[/C][C]142286[/C][C]191055.221849275[/C][C]-48769.2218492747[/C][/ROW]
[ROW][C]69[/C][C]143937[/C][C]167093.739881268[/C][C]-23156.7398812676[/C][/ROW]
[ROW][C]70[/C][C]89882[/C][C]151851.902888917[/C][C]-61969.9028889168[/C][/ROW]
[ROW][C]71[/C][C]118807[/C][C]124056.940320777[/C][C]-5249.94032077685[/C][/ROW]
[ROW][C]72[/C][C]69471[/C][C]80150.9471849525[/C][C]-10679.9471849525[/C][/ROW]
[ROW][C]73[/C][C]126630[/C][C]128188.032787662[/C][C]-1558.0327876624[/C][/ROW]
[ROW][C]74[/C][C]145908[/C][C]130312.551334334[/C][C]15595.4486656662[/C][/ROW]
[ROW][C]75[/C][C]96981[/C][C]154147.022195736[/C][C]-57166.0221957357[/C][/ROW]
[ROW][C]76[/C][C]189066[/C][C]171622.714572125[/C][C]17443.2854278755[/C][/ROW]
[ROW][C]77[/C][C]191467[/C][C]151536.323125669[/C][C]39930.6768743305[/C][/ROW]
[ROW][C]78[/C][C]106193[/C][C]141391.797841717[/C][C]-35198.7978417171[/C][/ROW]
[ROW][C]79[/C][C]89318[/C][C]138975.176492922[/C][C]-49657.1764929218[/C][/ROW]
[ROW][C]80[/C][C]120362[/C][C]123824.369328051[/C][C]-3462.36932805096[/C][/ROW]
[ROW][C]81[/C][C]98791[/C][C]102482.433374531[/C][C]-3691.43337453128[/C][/ROW]
[ROW][C]82[/C][C]274949[/C][C]207991.251681116[/C][C]66957.7483188841[/C][/ROW]
[ROW][C]83[/C][C]132798[/C][C]107949.567126673[/C][C]24848.432873327[/C][/ROW]
[ROW][C]84[/C][C]128075[/C][C]158873.241575605[/C][C]-30798.2415756054[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]81837.450139846[/C][C]-884.450139846035[/C][/ROW]
[ROW][C]86[/C][C]109237[/C][C]92298.6085512651[/C][C]16938.3914487349[/C][/ROW]
[ROW][C]87[/C][C]96634[/C][C]111187.135609005[/C][C]-14553.1356090054[/C][/ROW]
[ROW][C]88[/C][C]226183[/C][C]175864.757583816[/C][C]50318.2424161839[/C][/ROW]
[ROW][C]89[/C][C]167226[/C][C]138930.588296426[/C][C]28295.4117035739[/C][/ROW]
[ROW][C]90[/C][C]117805[/C][C]205029.963422717[/C][C]-87224.963422717[/C][/ROW]
[ROW][C]91[/C][C]121630[/C][C]115709.134666093[/C][C]5920.86533390706[/C][/ROW]
[ROW][C]92[/C][C]152193[/C][C]186690.512966474[/C][C]-34497.512966474[/C][/ROW]
[ROW][C]93[/C][C]112004[/C][C]98189.7330584966[/C][C]13814.2669415034[/C][/ROW]
[ROW][C]94[/C][C]169613[/C][C]158085.769054733[/C][C]11527.2309452666[/C][/ROW]
[ROW][C]95[/C][C]176577[/C][C]123402.010728283[/C][C]53174.9892717173[/C][/ROW]
[ROW][C]96[/C][C]130533[/C][C]118169.299659915[/C][C]12363.7003400851[/C][/ROW]
[ROW][C]97[/C][C]142339[/C][C]128195.404050191[/C][C]14143.5959498089[/C][/ROW]
[ROW][C]98[/C][C]189764[/C][C]196514.168958277[/C][C]-6750.16895827698[/C][/ROW]
[ROW][C]99[/C][C]201603[/C][C]232368.876874465[/C][C]-30765.8768744652[/C][/ROW]
[ROW][C]100[/C][C]243180[/C][C]196073.549357138[/C][C]47106.4506428621[/C][/ROW]
[ROW][C]101[/C][C]155931[/C][C]173882.852014492[/C][C]-17951.8520144919[/C][/ROW]
[ROW][C]102[/C][C]182557[/C][C]179229.23604297[/C][C]3327.76395702963[/C][/ROW]
[ROW][C]103[/C][C]106351[/C][C]151193.563019202[/C][C]-44842.5630192024[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]58762.8360164112[/C][C]-15475.8360164112[/C][/ROW]
[ROW][C]105[/C][C]127394[/C][C]127808.457573578[/C][C]-414.457573577555[/C][/ROW]
[ROW][C]106[/C][C]127930[/C][C]180802.020029237[/C][C]-52872.0200292375[/C][/ROW]
[ROW][C]107[/C][C]135306[/C][C]137834.258352126[/C][C]-2528.25835212578[/C][/ROW]
[ROW][C]108[/C][C]175663[/C][C]192585.029303044[/C][C]-16922.0293030437[/C][/ROW]
[ROW][C]109[/C][C]74112[/C][C]87715.2995628808[/C][C]-13603.2995628808[/C][/ROW]
[ROW][C]110[/C][C]89059[/C][C]154524.553743679[/C][C]-65465.5537436794[/C][/ROW]
[ROW][C]111[/C][C]166142[/C][C]122513.730264116[/C][C]43628.2697358842[/C][/ROW]
[ROW][C]112[/C][C]141933[/C][C]175691.928823463[/C][C]-33758.9288234632[/C][/ROW]
[ROW][C]113[/C][C]22938[/C][C]23530.799415292[/C][C]-592.799415291966[/C][/ROW]
[ROW][C]114[/C][C]125927[/C][C]166877.843743107[/C][C]-40950.8437431068[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]61490.9770568038[/C][C]366.022943196216[/C][/ROW]
[ROW][C]116[/C][C]91185[/C][C]126484.993003751[/C][C]-35299.9930037509[/C][/ROW]
[ROW][C]117[/C][C]236316[/C][C]170493.915627423[/C][C]65822.0843725774[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]19940.6980365474[/C][C]1113.30196345258[/C][/ROW]
[ROW][C]119[/C][C]169093[/C][C]111152.291770101[/C][C]57940.7082298991[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]47838.4333191042[/C][C]-16424.4333191042[/C][/ROW]
[ROW][C]121[/C][C]183059[/C][C]180729.516747371[/C][C]2329.4832526293[/C][/ROW]
[ROW][C]122[/C][C]137544[/C][C]120531.944272457[/C][C]17012.0557275433[/C][/ROW]
[ROW][C]123[/C][C]75032[/C][C]93351.2283926376[/C][C]-18319.2283926376[/C][/ROW]
[ROW][C]124[/C][C]71908[/C][C]70273.2605073501[/C][C]1634.73949264987[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]58309.0254070766[/C][C]-20095.0254070766[/C][/ROW]
[ROW][C]126[/C][C]90961[/C][C]99534.3821328436[/C][C]-8573.38213284358[/C][/ROW]
[ROW][C]127[/C][C]193662[/C][C]170049.032300967[/C][C]23612.9676990329[/C][/ROW]
[ROW][C]128[/C][C]127185[/C][C]117541.723937852[/C][C]9643.27606214848[/C][/ROW]
[ROW][C]129[/C][C]242153[/C][C]191584.90000918[/C][C]50568.0999908195[/C][/ROW]
[ROW][C]130[/C][C]201748[/C][C]223504.959205507[/C][C]-21756.9592055066[/C][/ROW]
[ROW][C]131[/C][C]254599[/C][C]222132.395917509[/C][C]32466.6040824913[/C][/ROW]
[ROW][C]132[/C][C]139144[/C][C]110065.404315104[/C][C]29078.5956848958[/C][/ROW]
[ROW][C]133[/C][C]76470[/C][C]86491.4186600791[/C][C]-10021.4186600791[/C][/ROW]
[ROW][C]134[/C][C]183260[/C][C]247421.468155112[/C][C]-64161.4681551117[/C][/ROW]
[ROW][C]135[/C][C]280039[/C][C]218309.009295211[/C][C]61729.9907047892[/C][/ROW]
[ROW][C]136[/C][C]50999[/C][C]51325.0636146647[/C][C]-326.063614664708[/C][/ROW]
[ROW][C]137[/C][C]253056[/C][C]200676.925029425[/C][C]52379.0749705746[/C][/ROW]
[ROW][C]138[/C][C]98466[/C][C]119620.013319955[/C][C]-21154.0133199554[/C][/ROW]
[ROW][C]139[/C][C]168059[/C][C]151288.195098996[/C][C]16770.8049010044[/C][/ROW]
[ROW][C]140[/C][C]128768[/C][C]137628.987280285[/C][C]-8860.9872802848[/C][/ROW]
[ROW][C]141[/C][C]75746[/C][C]84449.6709003204[/C][C]-8703.67090032036[/C][/ROW]
[ROW][C]142[/C][C]244909[/C][C]152039.91237406[/C][C]92869.0876259395[/C][/ROW]
[ROW][C]143[/C][C]152366[/C][C]173218.315218233[/C][C]-20852.3152182334[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]127103.868919692[/C][C]46156.1310803079[/C][/ROW]
[ROW][C]145[/C][C]197033[/C][C]195528.056560072[/C][C]1504.94343992823[/C][/ROW]
[ROW][C]146[/C][C]67507[/C][C]182019.26323994[/C][C]-114512.26323994[/C][/ROW]
[ROW][C]147[/C][C]139409[/C][C]147600.353803244[/C][C]-8191.3538032441[/C][/ROW]
[ROW][C]148[/C][C]185366[/C][C]211667.707651204[/C][C]-26301.7076512044[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]3514.23367219298[/C][C]-3514.23367219298[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]15166.9871483548[/C][C]-478.987148354788[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]4309.85215355842[/C][C]-4211.85215355842[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]5105.47063492385[/C][C]-4650.47063492385[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]3514.23367219298[/C][C]-3514.23367219298[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]3514.23367219298[/C][C]-3514.23367219298[/C][/ROW]
[ROW][C]155[/C][C]128873[/C][C]121322.127258343[/C][C]7550.87274165715[/C][/ROW]
[ROW][C]156[/C][C]185288[/C][C]196429.216479676[/C][C]-11141.216479676[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]3514.23367219298[/C][C]-3514.23367219298[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]6696.70759765473[/C][C]-6493.70759765473[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]13961.3212384082[/C][C]-6762.32123840815[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]38254.1779872192[/C][C]8405.82201278081[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]9232.19941843144[/C][C]8314.80058156856[/C][/ROW]
[ROW][C]162[/C][C]73567[/C][C]97283.2836099903[/C][C]-23716.2836099903[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]5105.47063492385[/C][C]-4136.47063492385[/C][/ROW]
[ROW][C]164[/C][C]105477[/C][C]91726.1654535578[/C][C]13750.8345464422[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145678&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145678&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1170588144457.13340858126130.8665914195
286621125828.165711693-39207.1657116929
3113337127155.525638709-13818.5256387088
4144530174685.346171565-30155.3461715651
58153088512.6430883193-6982.64308831926
63552344877.7212859857-9354.72128598573
7305115202180.523994014102934.476005986
83275047524.4936522085-14774.4936522085
911588599433.77266223316451.227337767
10130539111893.57595393918645.424046061
11156990156673.906227422316.093772578347
12128274190648.712216609-62374.712216609
13102350109783.523881231-7433.5238812308
14192887165762.16310917127124.8368908289
15129796151208.578157812-21412.5781578123
16245478274055.691823191-28577.6918231912
17169569149510.5623585620058.4376414395
18185279184157.6067071771121.39329282345
19109598135603.840805942-26005.8408059417
20155012182668.711328439-27656.7113284393
21154730157441.656503652-2711.65650365196
22280379159492.911509113120886.088490887
239093879334.336174159711603.6638258403
24101324148270.62883499-46946.6288349897
25139502205054.439798514-65552.439798514
26145120180413.011138599-35293.0111385993
27161729171857.917134845-10128.9171348454
28160905178437.935236467-17532.9352364672
29106888105339.6743244661548.32567553429
30187289172583.64428473514705.3557152645
31181853168617.71708251813235.2829174819
32129340136294.183393311-6954.18339331082
33196862184888.66939448411973.3306055156
346273183116.6451352451-20385.6451352451
35234863173688.70072407261174.2992759285
36167255140716.94753189726538.052468103
37264528185282.92180550979245.0781944911
38121976122734.915931179-758.915931178631
398096464882.06787492916081.932125071
40209631210033.541336213-402.541336212767
41213310156864.35413574656445.6458642543
42115911121656.233567203-5745.2335672034
43131337121427.1464208479909.8535791529
448110692402.6558597295-11296.6558597295
4593125101569.055172492-8444.05517249154
46305708188063.149823621117644.850176379
477880096419.1688405824-17619.1688405824
48157566174955.676565315-17389.6765653149
49213487176362.41544990537124.5845500946
50131108157823.789718582-26715.7897185823
51128734144691.766815331-15957.7668153309
522418836192.5095101345-12004.5095101345
53257662276568.532570307-18906.532570307
546502966802.9554461111-1773.95544611106
5598066100973.398914463-2907.39891446289
56173587123714.15265367649872.8473463239
57179571189802.243284086-10231.2432840858
58197067214807.252224405-17740.2522244054
59208823212711.903737549-3888.90373754898
60134088127034.8034756797053.19652432074
61245107213437.34689531531669.6531046853
62201409185117.70259129516291.2974087052
63141760150881.401474205-9121.40147420545
64170635140013.89901630230621.1009836984
65129100101153.53330922527946.4666907747
66108811115423.861531161-6612.86153116113
67113450162767.597839071-49317.5978390714
68142286191055.221849275-48769.2218492747
69143937167093.739881268-23156.7398812676
7089882151851.902888917-61969.9028889168
71118807124056.940320777-5249.94032077685
726947180150.9471849525-10679.9471849525
73126630128188.032787662-1558.0327876624
74145908130312.55133433415595.4486656662
7596981154147.022195736-57166.0221957357
76189066171622.71457212517443.2854278755
77191467151536.32312566939930.6768743305
78106193141391.797841717-35198.7978417171
7989318138975.176492922-49657.1764929218
80120362123824.369328051-3462.36932805096
8198791102482.433374531-3691.43337453128
82274949207991.25168111666957.7483188841
83132798107949.56712667324848.432873327
84128075158873.241575605-30798.2415756054
858095381837.450139846-884.450139846035
8610923792298.608551265116938.3914487349
8796634111187.135609005-14553.1356090054
88226183175864.75758381650318.2424161839
89167226138930.58829642628295.4117035739
90117805205029.963422717-87224.963422717
91121630115709.1346660935920.86533390706
92152193186690.512966474-34497.512966474
9311200498189.733058496613814.2669415034
94169613158085.76905473311527.2309452666
95176577123402.01072828353174.9892717173
96130533118169.29965991512363.7003400851
97142339128195.40405019114143.5959498089
98189764196514.168958277-6750.16895827698
99201603232368.876874465-30765.8768744652
100243180196073.54935713847106.4506428621
101155931173882.852014492-17951.8520144919
102182557179229.236042973327.76395702963
103106351151193.563019202-44842.5630192024
1044328758762.8360164112-15475.8360164112
105127394127808.457573578-414.457573577555
106127930180802.020029237-52872.0200292375
107135306137834.258352126-2528.25835212578
108175663192585.029303044-16922.0293030437
1097411287715.2995628808-13603.2995628808
11089059154524.553743679-65465.5537436794
111166142122513.73026411643628.2697358842
112141933175691.928823463-33758.9288234632
1132293823530.799415292-592.799415291966
114125927166877.843743107-40950.8437431068
1156185761490.9770568038366.022943196216
11691185126484.993003751-35299.9930037509
117236316170493.91562742365822.0843725774
1182105419940.69803654741113.30196345258
119169093111152.29177010157940.7082298991
1203141447838.4333191042-16424.4333191042
121183059180729.5167473712329.4832526293
122137544120531.94427245717012.0557275433
1237503293351.2283926376-18319.2283926376
1247190870273.26050735011634.73949264987
1253821458309.0254070766-20095.0254070766
1269096199534.3821328436-8573.38213284358
127193662170049.03230096723612.9676990329
128127185117541.7239378529643.27606214848
129242153191584.9000091850568.0999908195
130201748223504.959205507-21756.9592055066
131254599222132.39591750932466.6040824913
132139144110065.40431510429078.5956848958
1337647086491.4186600791-10021.4186600791
134183260247421.468155112-64161.4681551117
135280039218309.00929521161729.9907047892
1365099951325.0636146647-326.063614664708
137253056200676.92502942552379.0749705746
13898466119620.013319955-21154.0133199554
139168059151288.19509899616770.8049010044
140128768137628.987280285-8860.9872802848
1417574684449.6709003204-8703.67090032036
142244909152039.9123740692869.0876259395
143152366173218.315218233-20852.3152182334
144173260127103.86891969246156.1310803079
145197033195528.0565600721504.94343992823
14667507182019.26323994-114512.26323994
147139409147600.353803244-8191.3538032441
148185366211667.707651204-26301.7076512044
14903514.23367219298-3514.23367219298
1501468815166.9871483548-478.987148354788
151984309.85215355842-4211.85215355842
1524555105.47063492385-4650.47063492385
15303514.23367219298-3514.23367219298
15403514.23367219298-3514.23367219298
155128873121322.1272583437550.87274165715
156185288196429.216479676-11141.216479676
15703514.23367219298-3514.23367219298
1582036696.70759765473-6493.70759765473
159719913961.3212384082-6762.32123840815
1604666038254.17798721928405.82201278081
161175479232.199418431448314.80058156856
1627356797283.2836099903-23716.2836099903
1639695105.47063492385-4136.47063492385
16410547791726.165453557813750.8345464422







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7649218939035790.4701562121928430.235078106096421
90.6563775604940220.6872448790119570.343622439505978
100.6382013704462750.7235972591074510.361798629553725
110.5655075018403890.8689849963192210.434492498159611
120.4639765918823530.9279531837647070.536023408117646
130.4185184822542630.8370369645085260.581481517745737
140.458260280724020.9165205614480410.54173971927598
150.3684881739160770.7369763478321550.631511826083923
160.2953651740247680.5907303480495360.704634825975232
170.2895417104343010.5790834208686010.710458289565699
180.2625634639035450.5251269278070890.737436536096455
190.402904125460960.805808250921920.59709587453904
200.4136466288771710.8272932577543420.586353371122829
210.3392768622048050.6785537244096110.660723137795195
220.8742302041591720.2515395916816550.125769795840828
230.8351722244179940.3296555511640120.164827775582006
240.8343806413771910.3312387172456170.165619358622809
250.9126132731698490.1747734536603010.0873867268301506
260.9025762792000150.1948474415999690.0974237207999845
270.876634812387850.2467303752242990.12336518761215
280.846826667785590.3063466644288190.15317333221441
290.814568069635260.3708638607294810.18543193036474
300.7844334490700340.4311331018599310.215566550929966
310.7434014028771260.5131971942457490.256598597122874
320.7016141283688280.5967717432623430.298385871631172
330.6666645208003850.6666709583992310.333335479199615
340.6201416328856040.7597167342287910.379858367114396
350.7315688033842190.5368623932315620.268431196615781
360.7019566083298540.5960867833402920.298043391670146
370.9085406950841390.1829186098317220.0914593049158609
380.88404563672850.2319087265430.1159543632715
390.8575943870577320.2848112258845350.142405612942267
400.8250756143325930.3498487713348130.174924385667407
410.8590071435544710.2819857128910590.140992856445529
420.8277423288690380.3445153422619250.172257671130962
430.7932401335194220.4135197329611560.206759866480578
440.7557862138243850.4884275723512290.244213786175615
450.7417313313534930.5165373372930140.258268668646507
460.9699481506008870.06010369879822650.0300518493991133
470.9625550314368340.07488993712633210.0374449685631661
480.9539403092447080.09211938151058360.0460596907552918
490.9519409760855270.09611804782894680.0480590239144734
500.9454373484118040.1091253031763920.0545626515881962
510.935027113477620.129945773044760.06497288652238
520.9236206250212290.1527587499575420.076379374978771
530.9118272747944270.1763454504111460.0881727252055729
540.8915613450581870.2168773098836270.108438654941813
550.8676754719205670.2646490561588650.132324528079433
560.8821175447408570.2357649105182870.117882455259143
570.8578901086634980.2842197826730040.142109891336502
580.8412161615316220.3175676769367560.158783838468378
590.8242304769138050.351539046172390.175769523086195
600.7928446804659860.4143106390680280.207155319534014
610.7853303047226270.4293393905547460.214669695277373
620.7573273533466290.4853452933067420.242672646653371
630.723184402295370.5536311954092590.27681559770463
640.7202490035325540.5595019929348910.279750996467446
650.7051856634142320.5896286731715360.294814336585768
660.6711663649493590.6576672701012820.328833635050641
670.7130228642706520.5739542714586960.286977135729348
680.7412998865372660.5174002269254670.258700113462734
690.7325701910650430.5348596178699130.267429808934957
700.8188131855050410.3623736289899170.181186814494959
710.787446401044390.4251071979112190.21255359895561
720.755364466549670.489271066900660.24463553345033
730.7179530877804480.5640938244391030.282046912219552
740.6858093203426610.6283813593146780.314190679657339
750.7504670054746910.4990659890506180.249532994525309
760.721378068428430.5572438631431390.27862193157157
770.7304440716094550.539111856781090.269555928390545
780.7282879282147710.5434241435704570.271712071785229
790.762776239465240.474447521069520.23722376053476
800.7265806958748960.5468386082502090.273419304125104
810.6876314012128970.6247371975742070.312368598787103
820.7816648486654640.4366703026690710.218335151334536
830.7656099109991350.468780178001730.234390089000865
840.7719892785697420.4560214428605160.228010721430258
850.7359128839928180.5281742320143650.264087116007182
860.7066936394038870.5866127211922250.293306360596113
870.6723496652772010.6553006694455990.327650334722799
880.7187375877011440.5625248245977110.281262412298856
890.6944402128352650.611119574329470.305559787164735
900.8576291051376680.2847417897246640.142370894862332
910.8306295047181710.3387409905636580.169370495281829
920.829417659270670.341164681458660.17058234072933
930.8036688899648250.392662220070350.196331110035175
940.7739664046252070.4520671907495870.226033595374793
950.8188585850789360.3622828298421280.181141414921064
960.7907021760388270.4185956479223460.209297823961173
970.7587160623228370.4825678753543250.241283937677163
980.7222644337622160.5554711324755680.277735566237784
990.7079319283209910.5841361433580180.292068071679009
1000.739047453777740.5219050924445210.26095254622226
1010.7076026052368770.5847947895262460.292397394763123
1020.6659260340433130.6681479319133740.334073965956687
1030.6868246122858290.6263507754283420.313175387714171
1040.6528771954935020.6942456090129950.347122804506498
1050.6078950037823310.7842099924353370.392104996217669
1060.6605359626837740.6789280746324520.339464037316226
1070.6151485722657710.7697028554684580.384851427734229
1080.5774978016756190.8450043966487610.422502198324381
1090.5350765303792780.9298469392414430.464923469620722
1100.6508635455844840.6982729088310310.349136454415516
1110.6714222187005820.6571555625988360.328577781299418
1120.6733250836782090.6533498326435820.326674916321791
1130.6269288410741120.7461423178517760.373071158925888
1140.6535536762832980.6928926474334030.346446323716702
1150.6060008751292250.787998249741550.393999124870775
1160.6104093739934750.7791812520130510.389590626006525
1170.7107009680483290.5785980639033420.289299031951671
1180.6647661989870510.6704676020258990.335233801012949
1190.7353547460118970.5292905079762070.264645253988103
1200.6996398277563390.6007203444873220.300360172243661
1210.6515211371171750.6969577257656510.348478862882825
1220.6098537953129110.7802924093741790.390146204687089
1230.5746075104898180.8507849790203650.425392489510182
1240.5204179043006210.9591641913987580.479582095699379
1250.482953328251090.965906656502180.51704667174891
1260.4327594118310380.8655188236620770.567240588168962
1270.3969824186435220.7939648372870450.603017581356478
1280.3460895884070030.6921791768140050.653910411592997
1290.4079591529888440.8159183059776870.592040847011156
1300.3653387357184630.7306774714369250.634661264281537
1310.3652543981965790.7305087963931570.634745601803421
1320.3594835246129130.7189670492258260.640516475387087
1330.3060171923481830.6120343846963670.693982807651817
1340.9427315301914140.1145369396171720.0572684698085858
1350.9315841857291850.1368316285416310.0684158142708155
1360.9593747082748210.08125058345035760.0406252917251788
1370.9471112599275810.1057774801448390.0528887400724193
1380.931993173171720.136013653656560.0680068268282802
1390.9819628485139680.0360743029720640.018037151486032
1400.9721649103520760.05567017929584790.0278350896479239
1410.9999944948848941.10102302116248e-055.5051151058124e-06
1420.999996335827427.3283451591825e-063.66417257959125e-06
1430.9999903222987431.93554025130961e-059.67770125654806e-06
1440.9999750502155844.98995688315701e-052.49497844157851e-05
1450.9999763547519824.72904960369593e-052.36452480184797e-05
1460.9999999969005556.1988892111615e-093.09944460558075e-09
1470.999999984143623.17127594219789e-081.58563797109895e-08
1480.9999999690917216.1816557259119e-083.09082786295595e-08
1490.9999997965272294.06945542075298e-072.03472771037649e-07
1500.9999990262897971.94742040594328e-069.73710202971641e-07
1510.9999936028881541.27942236912738e-056.39711184563689e-06
1520.9999613516818267.72966363471193e-053.86483181735596e-05
1530.9997804159949080.0004391680101848410.000219584005092421
1540.9988422512133840.002315497573231830.00115774878661591
1550.9942907610004940.01141847799901260.0057092389995063
1560.9747849380345350.05043012393092910.0252150619654646

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.764921893903579 & 0.470156212192843 & 0.235078106096421 \tabularnewline
9 & 0.656377560494022 & 0.687244879011957 & 0.343622439505978 \tabularnewline
10 & 0.638201370446275 & 0.723597259107451 & 0.361798629553725 \tabularnewline
11 & 0.565507501840389 & 0.868984996319221 & 0.434492498159611 \tabularnewline
12 & 0.463976591882353 & 0.927953183764707 & 0.536023408117646 \tabularnewline
13 & 0.418518482254263 & 0.837036964508526 & 0.581481517745737 \tabularnewline
14 & 0.45826028072402 & 0.916520561448041 & 0.54173971927598 \tabularnewline
15 & 0.368488173916077 & 0.736976347832155 & 0.631511826083923 \tabularnewline
16 & 0.295365174024768 & 0.590730348049536 & 0.704634825975232 \tabularnewline
17 & 0.289541710434301 & 0.579083420868601 & 0.710458289565699 \tabularnewline
18 & 0.262563463903545 & 0.525126927807089 & 0.737436536096455 \tabularnewline
19 & 0.40290412546096 & 0.80580825092192 & 0.59709587453904 \tabularnewline
20 & 0.413646628877171 & 0.827293257754342 & 0.586353371122829 \tabularnewline
21 & 0.339276862204805 & 0.678553724409611 & 0.660723137795195 \tabularnewline
22 & 0.874230204159172 & 0.251539591681655 & 0.125769795840828 \tabularnewline
23 & 0.835172224417994 & 0.329655551164012 & 0.164827775582006 \tabularnewline
24 & 0.834380641377191 & 0.331238717245617 & 0.165619358622809 \tabularnewline
25 & 0.912613273169849 & 0.174773453660301 & 0.0873867268301506 \tabularnewline
26 & 0.902576279200015 & 0.194847441599969 & 0.0974237207999845 \tabularnewline
27 & 0.87663481238785 & 0.246730375224299 & 0.12336518761215 \tabularnewline
28 & 0.84682666778559 & 0.306346664428819 & 0.15317333221441 \tabularnewline
29 & 0.81456806963526 & 0.370863860729481 & 0.18543193036474 \tabularnewline
30 & 0.784433449070034 & 0.431133101859931 & 0.215566550929966 \tabularnewline
31 & 0.743401402877126 & 0.513197194245749 & 0.256598597122874 \tabularnewline
32 & 0.701614128368828 & 0.596771743262343 & 0.298385871631172 \tabularnewline
33 & 0.666664520800385 & 0.666670958399231 & 0.333335479199615 \tabularnewline
34 & 0.620141632885604 & 0.759716734228791 & 0.379858367114396 \tabularnewline
35 & 0.731568803384219 & 0.536862393231562 & 0.268431196615781 \tabularnewline
36 & 0.701956608329854 & 0.596086783340292 & 0.298043391670146 \tabularnewline
37 & 0.908540695084139 & 0.182918609831722 & 0.0914593049158609 \tabularnewline
38 & 0.8840456367285 & 0.231908726543 & 0.1159543632715 \tabularnewline
39 & 0.857594387057732 & 0.284811225884535 & 0.142405612942267 \tabularnewline
40 & 0.825075614332593 & 0.349848771334813 & 0.174924385667407 \tabularnewline
41 & 0.859007143554471 & 0.281985712891059 & 0.140992856445529 \tabularnewline
42 & 0.827742328869038 & 0.344515342261925 & 0.172257671130962 \tabularnewline
43 & 0.793240133519422 & 0.413519732961156 & 0.206759866480578 \tabularnewline
44 & 0.755786213824385 & 0.488427572351229 & 0.244213786175615 \tabularnewline
45 & 0.741731331353493 & 0.516537337293014 & 0.258268668646507 \tabularnewline
46 & 0.969948150600887 & 0.0601036987982265 & 0.0300518493991133 \tabularnewline
47 & 0.962555031436834 & 0.0748899371263321 & 0.0374449685631661 \tabularnewline
48 & 0.953940309244708 & 0.0921193815105836 & 0.0460596907552918 \tabularnewline
49 & 0.951940976085527 & 0.0961180478289468 & 0.0480590239144734 \tabularnewline
50 & 0.945437348411804 & 0.109125303176392 & 0.0545626515881962 \tabularnewline
51 & 0.93502711347762 & 0.12994577304476 & 0.06497288652238 \tabularnewline
52 & 0.923620625021229 & 0.152758749957542 & 0.076379374978771 \tabularnewline
53 & 0.911827274794427 & 0.176345450411146 & 0.0881727252055729 \tabularnewline
54 & 0.891561345058187 & 0.216877309883627 & 0.108438654941813 \tabularnewline
55 & 0.867675471920567 & 0.264649056158865 & 0.132324528079433 \tabularnewline
56 & 0.882117544740857 & 0.235764910518287 & 0.117882455259143 \tabularnewline
57 & 0.857890108663498 & 0.284219782673004 & 0.142109891336502 \tabularnewline
58 & 0.841216161531622 & 0.317567676936756 & 0.158783838468378 \tabularnewline
59 & 0.824230476913805 & 0.35153904617239 & 0.175769523086195 \tabularnewline
60 & 0.792844680465986 & 0.414310639068028 & 0.207155319534014 \tabularnewline
61 & 0.785330304722627 & 0.429339390554746 & 0.214669695277373 \tabularnewline
62 & 0.757327353346629 & 0.485345293306742 & 0.242672646653371 \tabularnewline
63 & 0.72318440229537 & 0.553631195409259 & 0.27681559770463 \tabularnewline
64 & 0.720249003532554 & 0.559501992934891 & 0.279750996467446 \tabularnewline
65 & 0.705185663414232 & 0.589628673171536 & 0.294814336585768 \tabularnewline
66 & 0.671166364949359 & 0.657667270101282 & 0.328833635050641 \tabularnewline
67 & 0.713022864270652 & 0.573954271458696 & 0.286977135729348 \tabularnewline
68 & 0.741299886537266 & 0.517400226925467 & 0.258700113462734 \tabularnewline
69 & 0.732570191065043 & 0.534859617869913 & 0.267429808934957 \tabularnewline
70 & 0.818813185505041 & 0.362373628989917 & 0.181186814494959 \tabularnewline
71 & 0.78744640104439 & 0.425107197911219 & 0.21255359895561 \tabularnewline
72 & 0.75536446654967 & 0.48927106690066 & 0.24463553345033 \tabularnewline
73 & 0.717953087780448 & 0.564093824439103 & 0.282046912219552 \tabularnewline
74 & 0.685809320342661 & 0.628381359314678 & 0.314190679657339 \tabularnewline
75 & 0.750467005474691 & 0.499065989050618 & 0.249532994525309 \tabularnewline
76 & 0.72137806842843 & 0.557243863143139 & 0.27862193157157 \tabularnewline
77 & 0.730444071609455 & 0.53911185678109 & 0.269555928390545 \tabularnewline
78 & 0.728287928214771 & 0.543424143570457 & 0.271712071785229 \tabularnewline
79 & 0.76277623946524 & 0.47444752106952 & 0.23722376053476 \tabularnewline
80 & 0.726580695874896 & 0.546838608250209 & 0.273419304125104 \tabularnewline
81 & 0.687631401212897 & 0.624737197574207 & 0.312368598787103 \tabularnewline
82 & 0.781664848665464 & 0.436670302669071 & 0.218335151334536 \tabularnewline
83 & 0.765609910999135 & 0.46878017800173 & 0.234390089000865 \tabularnewline
84 & 0.771989278569742 & 0.456021442860516 & 0.228010721430258 \tabularnewline
85 & 0.735912883992818 & 0.528174232014365 & 0.264087116007182 \tabularnewline
86 & 0.706693639403887 & 0.586612721192225 & 0.293306360596113 \tabularnewline
87 & 0.672349665277201 & 0.655300669445599 & 0.327650334722799 \tabularnewline
88 & 0.718737587701144 & 0.562524824597711 & 0.281262412298856 \tabularnewline
89 & 0.694440212835265 & 0.61111957432947 & 0.305559787164735 \tabularnewline
90 & 0.857629105137668 & 0.284741789724664 & 0.142370894862332 \tabularnewline
91 & 0.830629504718171 & 0.338740990563658 & 0.169370495281829 \tabularnewline
92 & 0.82941765927067 & 0.34116468145866 & 0.17058234072933 \tabularnewline
93 & 0.803668889964825 & 0.39266222007035 & 0.196331110035175 \tabularnewline
94 & 0.773966404625207 & 0.452067190749587 & 0.226033595374793 \tabularnewline
95 & 0.818858585078936 & 0.362282829842128 & 0.181141414921064 \tabularnewline
96 & 0.790702176038827 & 0.418595647922346 & 0.209297823961173 \tabularnewline
97 & 0.758716062322837 & 0.482567875354325 & 0.241283937677163 \tabularnewline
98 & 0.722264433762216 & 0.555471132475568 & 0.277735566237784 \tabularnewline
99 & 0.707931928320991 & 0.584136143358018 & 0.292068071679009 \tabularnewline
100 & 0.73904745377774 & 0.521905092444521 & 0.26095254622226 \tabularnewline
101 & 0.707602605236877 & 0.584794789526246 & 0.292397394763123 \tabularnewline
102 & 0.665926034043313 & 0.668147931913374 & 0.334073965956687 \tabularnewline
103 & 0.686824612285829 & 0.626350775428342 & 0.313175387714171 \tabularnewline
104 & 0.652877195493502 & 0.694245609012995 & 0.347122804506498 \tabularnewline
105 & 0.607895003782331 & 0.784209992435337 & 0.392104996217669 \tabularnewline
106 & 0.660535962683774 & 0.678928074632452 & 0.339464037316226 \tabularnewline
107 & 0.615148572265771 & 0.769702855468458 & 0.384851427734229 \tabularnewline
108 & 0.577497801675619 & 0.845004396648761 & 0.422502198324381 \tabularnewline
109 & 0.535076530379278 & 0.929846939241443 & 0.464923469620722 \tabularnewline
110 & 0.650863545584484 & 0.698272908831031 & 0.349136454415516 \tabularnewline
111 & 0.671422218700582 & 0.657155562598836 & 0.328577781299418 \tabularnewline
112 & 0.673325083678209 & 0.653349832643582 & 0.326674916321791 \tabularnewline
113 & 0.626928841074112 & 0.746142317851776 & 0.373071158925888 \tabularnewline
114 & 0.653553676283298 & 0.692892647433403 & 0.346446323716702 \tabularnewline
115 & 0.606000875129225 & 0.78799824974155 & 0.393999124870775 \tabularnewline
116 & 0.610409373993475 & 0.779181252013051 & 0.389590626006525 \tabularnewline
117 & 0.710700968048329 & 0.578598063903342 & 0.289299031951671 \tabularnewline
118 & 0.664766198987051 & 0.670467602025899 & 0.335233801012949 \tabularnewline
119 & 0.735354746011897 & 0.529290507976207 & 0.264645253988103 \tabularnewline
120 & 0.699639827756339 & 0.600720344487322 & 0.300360172243661 \tabularnewline
121 & 0.651521137117175 & 0.696957725765651 & 0.348478862882825 \tabularnewline
122 & 0.609853795312911 & 0.780292409374179 & 0.390146204687089 \tabularnewline
123 & 0.574607510489818 & 0.850784979020365 & 0.425392489510182 \tabularnewline
124 & 0.520417904300621 & 0.959164191398758 & 0.479582095699379 \tabularnewline
125 & 0.48295332825109 & 0.96590665650218 & 0.51704667174891 \tabularnewline
126 & 0.432759411831038 & 0.865518823662077 & 0.567240588168962 \tabularnewline
127 & 0.396982418643522 & 0.793964837287045 & 0.603017581356478 \tabularnewline
128 & 0.346089588407003 & 0.692179176814005 & 0.653910411592997 \tabularnewline
129 & 0.407959152988844 & 0.815918305977687 & 0.592040847011156 \tabularnewline
130 & 0.365338735718463 & 0.730677471436925 & 0.634661264281537 \tabularnewline
131 & 0.365254398196579 & 0.730508796393157 & 0.634745601803421 \tabularnewline
132 & 0.359483524612913 & 0.718967049225826 & 0.640516475387087 \tabularnewline
133 & 0.306017192348183 & 0.612034384696367 & 0.693982807651817 \tabularnewline
134 & 0.942731530191414 & 0.114536939617172 & 0.0572684698085858 \tabularnewline
135 & 0.931584185729185 & 0.136831628541631 & 0.0684158142708155 \tabularnewline
136 & 0.959374708274821 & 0.0812505834503576 & 0.0406252917251788 \tabularnewline
137 & 0.947111259927581 & 0.105777480144839 & 0.0528887400724193 \tabularnewline
138 & 0.93199317317172 & 0.13601365365656 & 0.0680068268282802 \tabularnewline
139 & 0.981962848513968 & 0.036074302972064 & 0.018037151486032 \tabularnewline
140 & 0.972164910352076 & 0.0556701792958479 & 0.0278350896479239 \tabularnewline
141 & 0.999994494884894 & 1.10102302116248e-05 & 5.5051151058124e-06 \tabularnewline
142 & 0.99999633582742 & 7.3283451591825e-06 & 3.66417257959125e-06 \tabularnewline
143 & 0.999990322298743 & 1.93554025130961e-05 & 9.67770125654806e-06 \tabularnewline
144 & 0.999975050215584 & 4.98995688315701e-05 & 2.49497844157851e-05 \tabularnewline
145 & 0.999976354751982 & 4.72904960369593e-05 & 2.36452480184797e-05 \tabularnewline
146 & 0.999999996900555 & 6.1988892111615e-09 & 3.09944460558075e-09 \tabularnewline
147 & 0.99999998414362 & 3.17127594219789e-08 & 1.58563797109895e-08 \tabularnewline
148 & 0.999999969091721 & 6.1816557259119e-08 & 3.09082786295595e-08 \tabularnewline
149 & 0.999999796527229 & 4.06945542075298e-07 & 2.03472771037649e-07 \tabularnewline
150 & 0.999999026289797 & 1.94742040594328e-06 & 9.73710202971641e-07 \tabularnewline
151 & 0.999993602888154 & 1.27942236912738e-05 & 6.39711184563689e-06 \tabularnewline
152 & 0.999961351681826 & 7.72966363471193e-05 & 3.86483181735596e-05 \tabularnewline
153 & 0.999780415994908 & 0.000439168010184841 & 0.000219584005092421 \tabularnewline
154 & 0.998842251213384 & 0.00231549757323183 & 0.00115774878661591 \tabularnewline
155 & 0.994290761000494 & 0.0114184779990126 & 0.0057092389995063 \tabularnewline
156 & 0.974784938034535 & 0.0504301239309291 & 0.0252150619654646 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145678&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.764921893903579[/C][C]0.470156212192843[/C][C]0.235078106096421[/C][/ROW]
[ROW][C]9[/C][C]0.656377560494022[/C][C]0.687244879011957[/C][C]0.343622439505978[/C][/ROW]
[ROW][C]10[/C][C]0.638201370446275[/C][C]0.723597259107451[/C][C]0.361798629553725[/C][/ROW]
[ROW][C]11[/C][C]0.565507501840389[/C][C]0.868984996319221[/C][C]0.434492498159611[/C][/ROW]
[ROW][C]12[/C][C]0.463976591882353[/C][C]0.927953183764707[/C][C]0.536023408117646[/C][/ROW]
[ROW][C]13[/C][C]0.418518482254263[/C][C]0.837036964508526[/C][C]0.581481517745737[/C][/ROW]
[ROW][C]14[/C][C]0.45826028072402[/C][C]0.916520561448041[/C][C]0.54173971927598[/C][/ROW]
[ROW][C]15[/C][C]0.368488173916077[/C][C]0.736976347832155[/C][C]0.631511826083923[/C][/ROW]
[ROW][C]16[/C][C]0.295365174024768[/C][C]0.590730348049536[/C][C]0.704634825975232[/C][/ROW]
[ROW][C]17[/C][C]0.289541710434301[/C][C]0.579083420868601[/C][C]0.710458289565699[/C][/ROW]
[ROW][C]18[/C][C]0.262563463903545[/C][C]0.525126927807089[/C][C]0.737436536096455[/C][/ROW]
[ROW][C]19[/C][C]0.40290412546096[/C][C]0.80580825092192[/C][C]0.59709587453904[/C][/ROW]
[ROW][C]20[/C][C]0.413646628877171[/C][C]0.827293257754342[/C][C]0.586353371122829[/C][/ROW]
[ROW][C]21[/C][C]0.339276862204805[/C][C]0.678553724409611[/C][C]0.660723137795195[/C][/ROW]
[ROW][C]22[/C][C]0.874230204159172[/C][C]0.251539591681655[/C][C]0.125769795840828[/C][/ROW]
[ROW][C]23[/C][C]0.835172224417994[/C][C]0.329655551164012[/C][C]0.164827775582006[/C][/ROW]
[ROW][C]24[/C][C]0.834380641377191[/C][C]0.331238717245617[/C][C]0.165619358622809[/C][/ROW]
[ROW][C]25[/C][C]0.912613273169849[/C][C]0.174773453660301[/C][C]0.0873867268301506[/C][/ROW]
[ROW][C]26[/C][C]0.902576279200015[/C][C]0.194847441599969[/C][C]0.0974237207999845[/C][/ROW]
[ROW][C]27[/C][C]0.87663481238785[/C][C]0.246730375224299[/C][C]0.12336518761215[/C][/ROW]
[ROW][C]28[/C][C]0.84682666778559[/C][C]0.306346664428819[/C][C]0.15317333221441[/C][/ROW]
[ROW][C]29[/C][C]0.81456806963526[/C][C]0.370863860729481[/C][C]0.18543193036474[/C][/ROW]
[ROW][C]30[/C][C]0.784433449070034[/C][C]0.431133101859931[/C][C]0.215566550929966[/C][/ROW]
[ROW][C]31[/C][C]0.743401402877126[/C][C]0.513197194245749[/C][C]0.256598597122874[/C][/ROW]
[ROW][C]32[/C][C]0.701614128368828[/C][C]0.596771743262343[/C][C]0.298385871631172[/C][/ROW]
[ROW][C]33[/C][C]0.666664520800385[/C][C]0.666670958399231[/C][C]0.333335479199615[/C][/ROW]
[ROW][C]34[/C][C]0.620141632885604[/C][C]0.759716734228791[/C][C]0.379858367114396[/C][/ROW]
[ROW][C]35[/C][C]0.731568803384219[/C][C]0.536862393231562[/C][C]0.268431196615781[/C][/ROW]
[ROW][C]36[/C][C]0.701956608329854[/C][C]0.596086783340292[/C][C]0.298043391670146[/C][/ROW]
[ROW][C]37[/C][C]0.908540695084139[/C][C]0.182918609831722[/C][C]0.0914593049158609[/C][/ROW]
[ROW][C]38[/C][C]0.8840456367285[/C][C]0.231908726543[/C][C]0.1159543632715[/C][/ROW]
[ROW][C]39[/C][C]0.857594387057732[/C][C]0.284811225884535[/C][C]0.142405612942267[/C][/ROW]
[ROW][C]40[/C][C]0.825075614332593[/C][C]0.349848771334813[/C][C]0.174924385667407[/C][/ROW]
[ROW][C]41[/C][C]0.859007143554471[/C][C]0.281985712891059[/C][C]0.140992856445529[/C][/ROW]
[ROW][C]42[/C][C]0.827742328869038[/C][C]0.344515342261925[/C][C]0.172257671130962[/C][/ROW]
[ROW][C]43[/C][C]0.793240133519422[/C][C]0.413519732961156[/C][C]0.206759866480578[/C][/ROW]
[ROW][C]44[/C][C]0.755786213824385[/C][C]0.488427572351229[/C][C]0.244213786175615[/C][/ROW]
[ROW][C]45[/C][C]0.741731331353493[/C][C]0.516537337293014[/C][C]0.258268668646507[/C][/ROW]
[ROW][C]46[/C][C]0.969948150600887[/C][C]0.0601036987982265[/C][C]0.0300518493991133[/C][/ROW]
[ROW][C]47[/C][C]0.962555031436834[/C][C]0.0748899371263321[/C][C]0.0374449685631661[/C][/ROW]
[ROW][C]48[/C][C]0.953940309244708[/C][C]0.0921193815105836[/C][C]0.0460596907552918[/C][/ROW]
[ROW][C]49[/C][C]0.951940976085527[/C][C]0.0961180478289468[/C][C]0.0480590239144734[/C][/ROW]
[ROW][C]50[/C][C]0.945437348411804[/C][C]0.109125303176392[/C][C]0.0545626515881962[/C][/ROW]
[ROW][C]51[/C][C]0.93502711347762[/C][C]0.12994577304476[/C][C]0.06497288652238[/C][/ROW]
[ROW][C]52[/C][C]0.923620625021229[/C][C]0.152758749957542[/C][C]0.076379374978771[/C][/ROW]
[ROW][C]53[/C][C]0.911827274794427[/C][C]0.176345450411146[/C][C]0.0881727252055729[/C][/ROW]
[ROW][C]54[/C][C]0.891561345058187[/C][C]0.216877309883627[/C][C]0.108438654941813[/C][/ROW]
[ROW][C]55[/C][C]0.867675471920567[/C][C]0.264649056158865[/C][C]0.132324528079433[/C][/ROW]
[ROW][C]56[/C][C]0.882117544740857[/C][C]0.235764910518287[/C][C]0.117882455259143[/C][/ROW]
[ROW][C]57[/C][C]0.857890108663498[/C][C]0.284219782673004[/C][C]0.142109891336502[/C][/ROW]
[ROW][C]58[/C][C]0.841216161531622[/C][C]0.317567676936756[/C][C]0.158783838468378[/C][/ROW]
[ROW][C]59[/C][C]0.824230476913805[/C][C]0.35153904617239[/C][C]0.175769523086195[/C][/ROW]
[ROW][C]60[/C][C]0.792844680465986[/C][C]0.414310639068028[/C][C]0.207155319534014[/C][/ROW]
[ROW][C]61[/C][C]0.785330304722627[/C][C]0.429339390554746[/C][C]0.214669695277373[/C][/ROW]
[ROW][C]62[/C][C]0.757327353346629[/C][C]0.485345293306742[/C][C]0.242672646653371[/C][/ROW]
[ROW][C]63[/C][C]0.72318440229537[/C][C]0.553631195409259[/C][C]0.27681559770463[/C][/ROW]
[ROW][C]64[/C][C]0.720249003532554[/C][C]0.559501992934891[/C][C]0.279750996467446[/C][/ROW]
[ROW][C]65[/C][C]0.705185663414232[/C][C]0.589628673171536[/C][C]0.294814336585768[/C][/ROW]
[ROW][C]66[/C][C]0.671166364949359[/C][C]0.657667270101282[/C][C]0.328833635050641[/C][/ROW]
[ROW][C]67[/C][C]0.713022864270652[/C][C]0.573954271458696[/C][C]0.286977135729348[/C][/ROW]
[ROW][C]68[/C][C]0.741299886537266[/C][C]0.517400226925467[/C][C]0.258700113462734[/C][/ROW]
[ROW][C]69[/C][C]0.732570191065043[/C][C]0.534859617869913[/C][C]0.267429808934957[/C][/ROW]
[ROW][C]70[/C][C]0.818813185505041[/C][C]0.362373628989917[/C][C]0.181186814494959[/C][/ROW]
[ROW][C]71[/C][C]0.78744640104439[/C][C]0.425107197911219[/C][C]0.21255359895561[/C][/ROW]
[ROW][C]72[/C][C]0.75536446654967[/C][C]0.48927106690066[/C][C]0.24463553345033[/C][/ROW]
[ROW][C]73[/C][C]0.717953087780448[/C][C]0.564093824439103[/C][C]0.282046912219552[/C][/ROW]
[ROW][C]74[/C][C]0.685809320342661[/C][C]0.628381359314678[/C][C]0.314190679657339[/C][/ROW]
[ROW][C]75[/C][C]0.750467005474691[/C][C]0.499065989050618[/C][C]0.249532994525309[/C][/ROW]
[ROW][C]76[/C][C]0.72137806842843[/C][C]0.557243863143139[/C][C]0.27862193157157[/C][/ROW]
[ROW][C]77[/C][C]0.730444071609455[/C][C]0.53911185678109[/C][C]0.269555928390545[/C][/ROW]
[ROW][C]78[/C][C]0.728287928214771[/C][C]0.543424143570457[/C][C]0.271712071785229[/C][/ROW]
[ROW][C]79[/C][C]0.76277623946524[/C][C]0.47444752106952[/C][C]0.23722376053476[/C][/ROW]
[ROW][C]80[/C][C]0.726580695874896[/C][C]0.546838608250209[/C][C]0.273419304125104[/C][/ROW]
[ROW][C]81[/C][C]0.687631401212897[/C][C]0.624737197574207[/C][C]0.312368598787103[/C][/ROW]
[ROW][C]82[/C][C]0.781664848665464[/C][C]0.436670302669071[/C][C]0.218335151334536[/C][/ROW]
[ROW][C]83[/C][C]0.765609910999135[/C][C]0.46878017800173[/C][C]0.234390089000865[/C][/ROW]
[ROW][C]84[/C][C]0.771989278569742[/C][C]0.456021442860516[/C][C]0.228010721430258[/C][/ROW]
[ROW][C]85[/C][C]0.735912883992818[/C][C]0.528174232014365[/C][C]0.264087116007182[/C][/ROW]
[ROW][C]86[/C][C]0.706693639403887[/C][C]0.586612721192225[/C][C]0.293306360596113[/C][/ROW]
[ROW][C]87[/C][C]0.672349665277201[/C][C]0.655300669445599[/C][C]0.327650334722799[/C][/ROW]
[ROW][C]88[/C][C]0.718737587701144[/C][C]0.562524824597711[/C][C]0.281262412298856[/C][/ROW]
[ROW][C]89[/C][C]0.694440212835265[/C][C]0.61111957432947[/C][C]0.305559787164735[/C][/ROW]
[ROW][C]90[/C][C]0.857629105137668[/C][C]0.284741789724664[/C][C]0.142370894862332[/C][/ROW]
[ROW][C]91[/C][C]0.830629504718171[/C][C]0.338740990563658[/C][C]0.169370495281829[/C][/ROW]
[ROW][C]92[/C][C]0.82941765927067[/C][C]0.34116468145866[/C][C]0.17058234072933[/C][/ROW]
[ROW][C]93[/C][C]0.803668889964825[/C][C]0.39266222007035[/C][C]0.196331110035175[/C][/ROW]
[ROW][C]94[/C][C]0.773966404625207[/C][C]0.452067190749587[/C][C]0.226033595374793[/C][/ROW]
[ROW][C]95[/C][C]0.818858585078936[/C][C]0.362282829842128[/C][C]0.181141414921064[/C][/ROW]
[ROW][C]96[/C][C]0.790702176038827[/C][C]0.418595647922346[/C][C]0.209297823961173[/C][/ROW]
[ROW][C]97[/C][C]0.758716062322837[/C][C]0.482567875354325[/C][C]0.241283937677163[/C][/ROW]
[ROW][C]98[/C][C]0.722264433762216[/C][C]0.555471132475568[/C][C]0.277735566237784[/C][/ROW]
[ROW][C]99[/C][C]0.707931928320991[/C][C]0.584136143358018[/C][C]0.292068071679009[/C][/ROW]
[ROW][C]100[/C][C]0.73904745377774[/C][C]0.521905092444521[/C][C]0.26095254622226[/C][/ROW]
[ROW][C]101[/C][C]0.707602605236877[/C][C]0.584794789526246[/C][C]0.292397394763123[/C][/ROW]
[ROW][C]102[/C][C]0.665926034043313[/C][C]0.668147931913374[/C][C]0.334073965956687[/C][/ROW]
[ROW][C]103[/C][C]0.686824612285829[/C][C]0.626350775428342[/C][C]0.313175387714171[/C][/ROW]
[ROW][C]104[/C][C]0.652877195493502[/C][C]0.694245609012995[/C][C]0.347122804506498[/C][/ROW]
[ROW][C]105[/C][C]0.607895003782331[/C][C]0.784209992435337[/C][C]0.392104996217669[/C][/ROW]
[ROW][C]106[/C][C]0.660535962683774[/C][C]0.678928074632452[/C][C]0.339464037316226[/C][/ROW]
[ROW][C]107[/C][C]0.615148572265771[/C][C]0.769702855468458[/C][C]0.384851427734229[/C][/ROW]
[ROW][C]108[/C][C]0.577497801675619[/C][C]0.845004396648761[/C][C]0.422502198324381[/C][/ROW]
[ROW][C]109[/C][C]0.535076530379278[/C][C]0.929846939241443[/C][C]0.464923469620722[/C][/ROW]
[ROW][C]110[/C][C]0.650863545584484[/C][C]0.698272908831031[/C][C]0.349136454415516[/C][/ROW]
[ROW][C]111[/C][C]0.671422218700582[/C][C]0.657155562598836[/C][C]0.328577781299418[/C][/ROW]
[ROW][C]112[/C][C]0.673325083678209[/C][C]0.653349832643582[/C][C]0.326674916321791[/C][/ROW]
[ROW][C]113[/C][C]0.626928841074112[/C][C]0.746142317851776[/C][C]0.373071158925888[/C][/ROW]
[ROW][C]114[/C][C]0.653553676283298[/C][C]0.692892647433403[/C][C]0.346446323716702[/C][/ROW]
[ROW][C]115[/C][C]0.606000875129225[/C][C]0.78799824974155[/C][C]0.393999124870775[/C][/ROW]
[ROW][C]116[/C][C]0.610409373993475[/C][C]0.779181252013051[/C][C]0.389590626006525[/C][/ROW]
[ROW][C]117[/C][C]0.710700968048329[/C][C]0.578598063903342[/C][C]0.289299031951671[/C][/ROW]
[ROW][C]118[/C][C]0.664766198987051[/C][C]0.670467602025899[/C][C]0.335233801012949[/C][/ROW]
[ROW][C]119[/C][C]0.735354746011897[/C][C]0.529290507976207[/C][C]0.264645253988103[/C][/ROW]
[ROW][C]120[/C][C]0.699639827756339[/C][C]0.600720344487322[/C][C]0.300360172243661[/C][/ROW]
[ROW][C]121[/C][C]0.651521137117175[/C][C]0.696957725765651[/C][C]0.348478862882825[/C][/ROW]
[ROW][C]122[/C][C]0.609853795312911[/C][C]0.780292409374179[/C][C]0.390146204687089[/C][/ROW]
[ROW][C]123[/C][C]0.574607510489818[/C][C]0.850784979020365[/C][C]0.425392489510182[/C][/ROW]
[ROW][C]124[/C][C]0.520417904300621[/C][C]0.959164191398758[/C][C]0.479582095699379[/C][/ROW]
[ROW][C]125[/C][C]0.48295332825109[/C][C]0.96590665650218[/C][C]0.51704667174891[/C][/ROW]
[ROW][C]126[/C][C]0.432759411831038[/C][C]0.865518823662077[/C][C]0.567240588168962[/C][/ROW]
[ROW][C]127[/C][C]0.396982418643522[/C][C]0.793964837287045[/C][C]0.603017581356478[/C][/ROW]
[ROW][C]128[/C][C]0.346089588407003[/C][C]0.692179176814005[/C][C]0.653910411592997[/C][/ROW]
[ROW][C]129[/C][C]0.407959152988844[/C][C]0.815918305977687[/C][C]0.592040847011156[/C][/ROW]
[ROW][C]130[/C][C]0.365338735718463[/C][C]0.730677471436925[/C][C]0.634661264281537[/C][/ROW]
[ROW][C]131[/C][C]0.365254398196579[/C][C]0.730508796393157[/C][C]0.634745601803421[/C][/ROW]
[ROW][C]132[/C][C]0.359483524612913[/C][C]0.718967049225826[/C][C]0.640516475387087[/C][/ROW]
[ROW][C]133[/C][C]0.306017192348183[/C][C]0.612034384696367[/C][C]0.693982807651817[/C][/ROW]
[ROW][C]134[/C][C]0.942731530191414[/C][C]0.114536939617172[/C][C]0.0572684698085858[/C][/ROW]
[ROW][C]135[/C][C]0.931584185729185[/C][C]0.136831628541631[/C][C]0.0684158142708155[/C][/ROW]
[ROW][C]136[/C][C]0.959374708274821[/C][C]0.0812505834503576[/C][C]0.0406252917251788[/C][/ROW]
[ROW][C]137[/C][C]0.947111259927581[/C][C]0.105777480144839[/C][C]0.0528887400724193[/C][/ROW]
[ROW][C]138[/C][C]0.93199317317172[/C][C]0.13601365365656[/C][C]0.0680068268282802[/C][/ROW]
[ROW][C]139[/C][C]0.981962848513968[/C][C]0.036074302972064[/C][C]0.018037151486032[/C][/ROW]
[ROW][C]140[/C][C]0.972164910352076[/C][C]0.0556701792958479[/C][C]0.0278350896479239[/C][/ROW]
[ROW][C]141[/C][C]0.999994494884894[/C][C]1.10102302116248e-05[/C][C]5.5051151058124e-06[/C][/ROW]
[ROW][C]142[/C][C]0.99999633582742[/C][C]7.3283451591825e-06[/C][C]3.66417257959125e-06[/C][/ROW]
[ROW][C]143[/C][C]0.999990322298743[/C][C]1.93554025130961e-05[/C][C]9.67770125654806e-06[/C][/ROW]
[ROW][C]144[/C][C]0.999975050215584[/C][C]4.98995688315701e-05[/C][C]2.49497844157851e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999976354751982[/C][C]4.72904960369593e-05[/C][C]2.36452480184797e-05[/C][/ROW]
[ROW][C]146[/C][C]0.999999996900555[/C][C]6.1988892111615e-09[/C][C]3.09944460558075e-09[/C][/ROW]
[ROW][C]147[/C][C]0.99999998414362[/C][C]3.17127594219789e-08[/C][C]1.58563797109895e-08[/C][/ROW]
[ROW][C]148[/C][C]0.999999969091721[/C][C]6.1816557259119e-08[/C][C]3.09082786295595e-08[/C][/ROW]
[ROW][C]149[/C][C]0.999999796527229[/C][C]4.06945542075298e-07[/C][C]2.03472771037649e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999999026289797[/C][C]1.94742040594328e-06[/C][C]9.73710202971641e-07[/C][/ROW]
[ROW][C]151[/C][C]0.999993602888154[/C][C]1.27942236912738e-05[/C][C]6.39711184563689e-06[/C][/ROW]
[ROW][C]152[/C][C]0.999961351681826[/C][C]7.72966363471193e-05[/C][C]3.86483181735596e-05[/C][/ROW]
[ROW][C]153[/C][C]0.999780415994908[/C][C]0.000439168010184841[/C][C]0.000219584005092421[/C][/ROW]
[ROW][C]154[/C][C]0.998842251213384[/C][C]0.00231549757323183[/C][C]0.00115774878661591[/C][/ROW]
[ROW][C]155[/C][C]0.994290761000494[/C][C]0.0114184779990126[/C][C]0.0057092389995063[/C][/ROW]
[ROW][C]156[/C][C]0.974784938034535[/C][C]0.0504301239309291[/C][C]0.0252150619654646[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145678&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145678&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7649218939035790.4701562121928430.235078106096421
90.6563775604940220.6872448790119570.343622439505978
100.6382013704462750.7235972591074510.361798629553725
110.5655075018403890.8689849963192210.434492498159611
120.4639765918823530.9279531837647070.536023408117646
130.4185184822542630.8370369645085260.581481517745737
140.458260280724020.9165205614480410.54173971927598
150.3684881739160770.7369763478321550.631511826083923
160.2953651740247680.5907303480495360.704634825975232
170.2895417104343010.5790834208686010.710458289565699
180.2625634639035450.5251269278070890.737436536096455
190.402904125460960.805808250921920.59709587453904
200.4136466288771710.8272932577543420.586353371122829
210.3392768622048050.6785537244096110.660723137795195
220.8742302041591720.2515395916816550.125769795840828
230.8351722244179940.3296555511640120.164827775582006
240.8343806413771910.3312387172456170.165619358622809
250.9126132731698490.1747734536603010.0873867268301506
260.9025762792000150.1948474415999690.0974237207999845
270.876634812387850.2467303752242990.12336518761215
280.846826667785590.3063466644288190.15317333221441
290.814568069635260.3708638607294810.18543193036474
300.7844334490700340.4311331018599310.215566550929966
310.7434014028771260.5131971942457490.256598597122874
320.7016141283688280.5967717432623430.298385871631172
330.6666645208003850.6666709583992310.333335479199615
340.6201416328856040.7597167342287910.379858367114396
350.7315688033842190.5368623932315620.268431196615781
360.7019566083298540.5960867833402920.298043391670146
370.9085406950841390.1829186098317220.0914593049158609
380.88404563672850.2319087265430.1159543632715
390.8575943870577320.2848112258845350.142405612942267
400.8250756143325930.3498487713348130.174924385667407
410.8590071435544710.2819857128910590.140992856445529
420.8277423288690380.3445153422619250.172257671130962
430.7932401335194220.4135197329611560.206759866480578
440.7557862138243850.4884275723512290.244213786175615
450.7417313313534930.5165373372930140.258268668646507
460.9699481506008870.06010369879822650.0300518493991133
470.9625550314368340.07488993712633210.0374449685631661
480.9539403092447080.09211938151058360.0460596907552918
490.9519409760855270.09611804782894680.0480590239144734
500.9454373484118040.1091253031763920.0545626515881962
510.935027113477620.129945773044760.06497288652238
520.9236206250212290.1527587499575420.076379374978771
530.9118272747944270.1763454504111460.0881727252055729
540.8915613450581870.2168773098836270.108438654941813
550.8676754719205670.2646490561588650.132324528079433
560.8821175447408570.2357649105182870.117882455259143
570.8578901086634980.2842197826730040.142109891336502
580.8412161615316220.3175676769367560.158783838468378
590.8242304769138050.351539046172390.175769523086195
600.7928446804659860.4143106390680280.207155319534014
610.7853303047226270.4293393905547460.214669695277373
620.7573273533466290.4853452933067420.242672646653371
630.723184402295370.5536311954092590.27681559770463
640.7202490035325540.5595019929348910.279750996467446
650.7051856634142320.5896286731715360.294814336585768
660.6711663649493590.6576672701012820.328833635050641
670.7130228642706520.5739542714586960.286977135729348
680.7412998865372660.5174002269254670.258700113462734
690.7325701910650430.5348596178699130.267429808934957
700.8188131855050410.3623736289899170.181186814494959
710.787446401044390.4251071979112190.21255359895561
720.755364466549670.489271066900660.24463553345033
730.7179530877804480.5640938244391030.282046912219552
740.6858093203426610.6283813593146780.314190679657339
750.7504670054746910.4990659890506180.249532994525309
760.721378068428430.5572438631431390.27862193157157
770.7304440716094550.539111856781090.269555928390545
780.7282879282147710.5434241435704570.271712071785229
790.762776239465240.474447521069520.23722376053476
800.7265806958748960.5468386082502090.273419304125104
810.6876314012128970.6247371975742070.312368598787103
820.7816648486654640.4366703026690710.218335151334536
830.7656099109991350.468780178001730.234390089000865
840.7719892785697420.4560214428605160.228010721430258
850.7359128839928180.5281742320143650.264087116007182
860.7066936394038870.5866127211922250.293306360596113
870.6723496652772010.6553006694455990.327650334722799
880.7187375877011440.5625248245977110.281262412298856
890.6944402128352650.611119574329470.305559787164735
900.8576291051376680.2847417897246640.142370894862332
910.8306295047181710.3387409905636580.169370495281829
920.829417659270670.341164681458660.17058234072933
930.8036688899648250.392662220070350.196331110035175
940.7739664046252070.4520671907495870.226033595374793
950.8188585850789360.3622828298421280.181141414921064
960.7907021760388270.4185956479223460.209297823961173
970.7587160623228370.4825678753543250.241283937677163
980.7222644337622160.5554711324755680.277735566237784
990.7079319283209910.5841361433580180.292068071679009
1000.739047453777740.5219050924445210.26095254622226
1010.7076026052368770.5847947895262460.292397394763123
1020.6659260340433130.6681479319133740.334073965956687
1030.6868246122858290.6263507754283420.313175387714171
1040.6528771954935020.6942456090129950.347122804506498
1050.6078950037823310.7842099924353370.392104996217669
1060.6605359626837740.6789280746324520.339464037316226
1070.6151485722657710.7697028554684580.384851427734229
1080.5774978016756190.8450043966487610.422502198324381
1090.5350765303792780.9298469392414430.464923469620722
1100.6508635455844840.6982729088310310.349136454415516
1110.6714222187005820.6571555625988360.328577781299418
1120.6733250836782090.6533498326435820.326674916321791
1130.6269288410741120.7461423178517760.373071158925888
1140.6535536762832980.6928926474334030.346446323716702
1150.6060008751292250.787998249741550.393999124870775
1160.6104093739934750.7791812520130510.389590626006525
1170.7107009680483290.5785980639033420.289299031951671
1180.6647661989870510.6704676020258990.335233801012949
1190.7353547460118970.5292905079762070.264645253988103
1200.6996398277563390.6007203444873220.300360172243661
1210.6515211371171750.6969577257656510.348478862882825
1220.6098537953129110.7802924093741790.390146204687089
1230.5746075104898180.8507849790203650.425392489510182
1240.5204179043006210.9591641913987580.479582095699379
1250.482953328251090.965906656502180.51704667174891
1260.4327594118310380.8655188236620770.567240588168962
1270.3969824186435220.7939648372870450.603017581356478
1280.3460895884070030.6921791768140050.653910411592997
1290.4079591529888440.8159183059776870.592040847011156
1300.3653387357184630.7306774714369250.634661264281537
1310.3652543981965790.7305087963931570.634745601803421
1320.3594835246129130.7189670492258260.640516475387087
1330.3060171923481830.6120343846963670.693982807651817
1340.9427315301914140.1145369396171720.0572684698085858
1350.9315841857291850.1368316285416310.0684158142708155
1360.9593747082748210.08125058345035760.0406252917251788
1370.9471112599275810.1057774801448390.0528887400724193
1380.931993173171720.136013653656560.0680068268282802
1390.9819628485139680.0360743029720640.018037151486032
1400.9721649103520760.05567017929584790.0278350896479239
1410.9999944948848941.10102302116248e-055.5051151058124e-06
1420.999996335827427.3283451591825e-063.66417257959125e-06
1430.9999903222987431.93554025130961e-059.67770125654806e-06
1440.9999750502155844.98995688315701e-052.49497844157851e-05
1450.9999763547519824.72904960369593e-052.36452480184797e-05
1460.9999999969005556.1988892111615e-093.09944460558075e-09
1470.999999984143623.17127594219789e-081.58563797109895e-08
1480.9999999690917216.1816557259119e-083.09082786295595e-08
1490.9999997965272294.06945542075298e-072.03472771037649e-07
1500.9999990262897971.94742040594328e-069.73710202971641e-07
1510.9999936028881541.27942236912738e-056.39711184563689e-06
1520.9999613516818267.72966363471193e-053.86483181735596e-05
1530.9997804159949080.0004391680101848410.000219584005092421
1540.9988422512133840.002315497573231830.00115774878661591
1550.9942907610004940.01141847799901260.0057092389995063
1560.9747849380345350.05043012393092910.0252150619654646







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.0939597315436242NOK
5% type I error level160.10738255033557NOK
10% type I error level230.154362416107383NOK

\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 & 14 & 0.0939597315436242 & NOK \tabularnewline
5% type I error level & 16 & 0.10738255033557 & NOK \tabularnewline
10% type I error level & 23 & 0.154362416107383 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145678&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]14[/C][C]0.0939597315436242[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.10738255033557[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.154362416107383[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145678&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145678&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.0939597315436242NOK
5% type I error level160.10738255033557NOK
10% type I error level230.154362416107383NOK



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')
}