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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 31 Oct 2012 05:55:58 -0400

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Oct/31/t135167740011a8pmeqi4zw2so.htm/, Retrieved Mon, 29 Apr 2024 00:50:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185430, Retrieved Mon, 29 Apr 2024 00:50:59 +0000

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Estimated Impact

129

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [WS 7 - 2 ] [2012-10-31 09:55:58] [18a55f974a2e8651a7d8da0218fcbdb6] [Current]

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14	501	11	20	91,81	1303,2
14	485	11	19	91,98	-58,7
15	464	11	18	91,72	-378,9
13	460	11	13	90,27	175,6
8	467	11	17	91,89	233,7
7	460	9	17	92,07	706,8
3	448	8	13	92,92	-23,6
3	443	6	14	93,34	420,9
4	436	7	13	93,6	722,1
4	431	8	17	92,41	1401,3
0	484	6	17	93,6	-94,9
-4	510	5	15	93,77	1043,6
-14	513	2	9	93,6	1300,1
-18	503	3	10	93,6	721,1
-8	471	3	9	93,51	-45,6
-1	471	7	14	92,66	787,5
1	476	8	18	94,2	694,3
2	475	7	18	94,37	1054,7
0	470	7	12	94,45	821,9
1	461	6	16	94,62	1100,7
0	455	6	12	94,37	862,4
-1	456	7	19	93,43	1656,1
-3	517	5	13	94,79	-174
-3	525	5	12	94,88	1337,6
-3	523	5	13	94,79	1394,9
-4	519	4	11	94,62	915,7
-8	509	4	10	94,71	-481,1
-9	512	4	16	93,77	167,9
-13	519	1	12	95,73	208,2
-18	517	-1	6	95,99	382,2
-11	510	3	8	95,82	1004
-9	509	4	6	95,47	864,7
-10	501	3	8	95,82	1052,9
-13	507	2	8	94,71	1417,6
-11	569	1	9	96,33	-197,7
-5	580	4	13	96,5	1262,1
-15	578	3	8	96,16	1147,2
-6	565	5	11	96,33	700,2
-6	547	6	8	96,33	45,3
-3	555	6	10	95,05	458,5
-1	562	6	15	96,84	610,2
-3	561	6	12	96,92	786,4
-4	555	6	13	97,44	787,2
-6	544	5	12	97,78	1040
0	537	6	15	97,69	324,1
-4	543	5	13	96,67	1343
-2	594	6	13	98,29	-501,2
-2	611	5	16	98,2	800,4
-6	613	7	14	98,71	916,7
-7	611	4	12	98,54	695,8
-6	594	5	15	98,2	28
-6	595	6	14	96,92	495,6
-3	591	6	19	99,06	366,2
-2	589	5	16	99,65	633
-5	584	3	16	99,82	848,3
-11	573	2	11	99,99	472,2
-11	567	3	13	100,33	357,8
-11	569	3	12	99,31	824,3
-10	621	2	11	101,1	-880,1
-14	629	0	6	101,1	1066,8
-8	628	4	9	100,93	1052,8
-9	612	4	6	100,85	-32,1
-5	595	5	15	100,93	-1331,4
-1	597	6	17	99,6	-767,1
-2	593	6	13	101,88	-236,7
-5	590	5	12	101,81	-184,9
-4	580	5	13	102,38	-143,4
-6	574	3	10	102,74	493,9
-2	573	5	14	102,82	549,7
-2	573	5	13	101,72	982,7
-2	620	5	10	103,47	-856,3
-2	626	3	11	102,98	967
2	620	6	12	102,68	659,4
1	588	6	7	102,9	577,2
-8	566	4	11	103,03	-213,1
-1	557	6	9	101,29	17,7
1	561	5	13	103,69	390,1
-1	549	4	12	103,68	509,3
2	532	5	5	104,2	410
2	526	5	13	104,08	212,5
1	511	4	11	104,16	818
-1	499	3	8	103,05	422,7
-2	555	2	8	104,66	-158
-2	565	3	8	104,46	427,2
-1	542	2	8	104,95	243,4
-8	527	-1	0	105,85	-419,3
-4	510	0	3	106,23	-1459,8
-6	514	-2	0	104,86	-1389,8
-3	517	1	-1	107,44	-2,1
-3	508	-2	-1	108,23	-938,6
-7	493	-2	-4	108,45	-839,9
-9	490	-2	1	109,39	-297,6
-11	469	-6	-1	110,15	-376,3
-13	478	-4	0	109,13	-79,4
-11	528	-2	-1	110,28	-2091,3
-9	534	0	6	110,17	-1023
-17	518	-5	0	109,99	-765,6
-22	506	-4	-3	109,26	-1592,3
-25	502	-5	-3	109,11	-1588,8
-20	516	-1	4	107,06	-1318
-24	528	-2	1	109,53	-402,4
-24	533	-4	0	108,92	-814,5
-22	536	-1	-4	109,24	-98,4
-19	537	1	-2	109,12	-305,9
-18	524	1	3	109	-18,4
-17	536	-2	2	107,23	610,3
-11	587	1	5	109,49	-917,3
-11	597	1	6	109,04	88,4
-12	581	3	6	109,02	-740,2
-10	564	3	3	109,23	29,3
-15	558	1	4	109,46	-893,2
-15	575	1	7	107,9	-1030,2
-15	580	0	5	110,42	-403,4
-13	575	2	6	110,98	-46,9
-8	563	2	1	111,48	-321,2
-13	552	-1	3	111,88	-239,9
-9	537	1	6	111,89	640,9
-7	545	0	0	109,85	511,6
-4	601	1	3	112,1	-665,1
-4	604	1	4	112,24	657,7
-2	586	3	7	112,39	-207,7
0	564	2	6	112,52	-885,2
-2	549	0	6	113,16	-1595,8
-3	551	0	6	111,84	-1374,9
1	556	3	6	114,33	-316,6
-2	548	-2	2	114,82	-283,4
-1	540	0	2	115,2	-175,8
1	531	1	2	115,4	-694,2
-3	521	-1	3	115,74	-249,9
-4	519	-2	-1	114,19	268,2
-9	572	-1	-4	115,94	-2105,1
-9	581	-1	4	116,03	-762,8
-7	563	1	5	116,24	-117,1
-14	548	-2	3	116,66	-1094,4
-12	539	-5	-1	116,79	-2095,2
-16	541	-5	-4	115,48	-1587,6
-20	562	-6	0	118,16	-528
-12	559	-4	-1	118,38	-324,2
-12	546	-3	-1	118,51	-276,1
-10	536	-3	3	118,42	-139,1
-10	528	-1	2	118,24	268
-13	530	-2	-4	116,47	570,5
-16	582	-3	-3	118,96	-316,5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185430&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185430&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185430&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
i[t] = -50.7450835493624 -0.0477251527818315w[t] + 2.06608926081329f[t] + 0.290398884525233s[t] + 0.6060795964509c[t] -0.000397523236439083h[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
i[t] =  -50.7450835493624 -0.0477251527818315w[t] +  2.06608926081329f[t] +  0.290398884525233s[t] +  0.6060795964509c[t] -0.000397523236439083h[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185430&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]i[t] =  -50.7450835493624 -0.0477251527818315w[t] +  2.06608926081329f[t] +  0.290398884525233s[t] +  0.6060795964509c[t] -0.000397523236439083h[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185430&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185430&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
i[t] = -50.7450835493624 -0.0477251527818315w[t] + 2.06608926081329f[t] + 0.290398884525233s[t] + 0.6060795964509c[t] -0.000397523236439083h[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-50.7450835493624	8.803085	-5.7645	0	0
w	-0.0477251527818315	0.008559	-5.5758	0	0
f	2.06608926081329	0.202272	10.2144	0	0
s	0.290398884525233	0.131239	2.2128	0.02857	0.014285
c	0.6060795964509	0.088077	6.8812	0	0
h	-0.000397523236439083	0.000536	-0.7419	0.459443	0.229721

\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) & -50.7450835493624 & 8.803085 & -5.7645 & 0 & 0 \tabularnewline
w & -0.0477251527818315 & 0.008559 & -5.5758 & 0 & 0 \tabularnewline
f & 2.06608926081329 & 0.202272 & 10.2144 & 0 & 0 \tabularnewline
s & 0.290398884525233 & 0.131239 & 2.2128 & 0.02857 & 0.014285 \tabularnewline
c & 0.6060795964509 & 0.088077 & 6.8812 & 0 & 0 \tabularnewline
h & -0.000397523236439083 & 0.000536 & -0.7419 & 0.459443 & 0.229721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185430&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]-50.7450835493624[/C][C]8.803085[/C][C]-5.7645[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]w[/C][C]-0.0477251527818315[/C][C]0.008559[/C][C]-5.5758[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]f[/C][C]2.06608926081329[/C][C]0.202272[/C][C]10.2144[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]s[/C][C]0.290398884525233[/C][C]0.131239[/C][C]2.2128[/C][C]0.02857[/C][C]0.014285[/C][/ROW]
[ROW][C]c[/C][C]0.6060795964509[/C][C]0.088077[/C][C]6.8812[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]h[/C][C]-0.000397523236439083[/C][C]0.000536[/C][C]-0.7419[/C][C]0.459443[/C][C]0.229721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185430&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185430&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-50.7450835493624	8.803085	-5.7645	0	0
w	-0.0477251527818315	0.008559	-5.5758	0	0
f	2.06608926081329	0.202272	10.2144	0	0
s	0.290398884525233	0.131239	2.2128	0.02857	0.014285
c	0.6060795964509	0.088077	6.8812	0	0
h	-0.000397523236439083	0.000536	-0.7419	0.459443	0.229721

Multiple Linear Regression - Regression Statistics
Multiple R	0.841174135041901
R-squared	0.70757392546349
Adjusted R-squared	0.696901440991354
F-TEST (value)	66.2988948178731
F-TEST (DF numerator)	5
F-TEST (DF denominator)	137
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.12683988179144
Sum Squared Residuals	2333.22061516238

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.841174135041901 \tabularnewline
R-squared & 0.70757392546349 \tabularnewline
Adjusted R-squared & 0.696901440991354 \tabularnewline
F-TEST (value) & 66.2988948178731 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.12683988179144 \tabularnewline
Sum Squared Residuals & 2333.22061516238 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185430&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.841174135041901[/C][/ROW]
[ROW][C]R-squared[/C][C]0.70757392546349[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.696901440991354[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]66.2988948178731[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/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]4.12683988179144[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2333.22061516238[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185430&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185430&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.841174135041901
R-squared	0.70757392546349
Adjusted R-squared	0.696901440991354
F-TEST (value)	66.2988948178731
F-TEST (DF numerator)	5
F-TEST (DF denominator)	137
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.12683988179144
Sum Squared Residuals	2333.22061516238

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	9.00568993482063	4.99431006517937
2	14	10.1233139219078	3.87668607809224
3	15	10.8048494910315	4.19515050896847
4	13	8.44451363007342	4.55548636992658
5	8	10.2307859449149	-2.23078594491488
6	7	6.35370957696295	0.646290423037048
7	3	4.50424524030909	-1.50424524030909
8	3	0.978945719029109	2.02105428097089
9	4	3.12655886105176	0.87344113894824
10	4	5.60163692190914	-1.60163692190914
11	0	0.256034288982226	-0.256034288982226
12	-4	-3.98125338649839	-0.0187466135016118
13	-14	-12.2700881759784	-1.72991182402158
14	-18	-9.20618254892336	-8.79381745107664
15	-8	-7.71914264273271	-0.280857357267286
16	-1	1.15086455788593	-2.15086455788593
17	1	5.11033533706151	-4.11033533706151
18	2	3.05173738601406	-1.05173738601406
19	0	1.68899961993091	-1.68899961993091
20	1	1.20623632533247	-0.206236325332474
21	0	0.274201592053238	-0.274201592053238
22	-1	3.44012887833578	-4.44012887833578
23	-3	-3.79390174395352	0.793901743953522
24	-3	-5.01245081125415	2.01245081125415
25	-3	-4.70392686629379	1.70392686629379
26	-4	-7.07245368152526	3.07245368152526
27	-8	-6.27579341789349	-1.72420658210651
28	-9	-5.5042829702004	-3.4957170297996
29	-13	-12.0263265375987	-0.973673462401261
30	-18	-17.7170364088762	-0.282963591123778
31	-11	-8.88801900691426	-2.11198099308574
32	-9	-7.51175523429146	-1.48824476570854
33	-10	-8.47793151813965	-1.52206848186035
34	-13	-11.6480967720338	-1.35190322796624
35	-11	-14.7587783907249	3.75877839072485
36	-5	-8.40116263994132	3.40116263994132
37	-15	-11.9841876607436	-3.01581233925643
38	-6	-6.07965908129257	0.0796590812925652
39	-6	-3.76537575643805	-2.23462424356195
40	-3	-4.50641769439602	1.50641769439602
41	-1	-2.36392113856337	1.36392113856337
42	-3	-3.20894986590173	0.208949865901734
43	-4	-2.3173566931202	-1.6826433068798
44	-6	-4.04329496923706	-1.95670503076294
45	0	-0.541893264089099	0.541893264089099
46	-4	-4.49836882463154	0.49836882463154
47	-2	-3.15130105680024	1.15130105680024
48	-2	-5.72948466955866	3.72948466955866
49	-6	-2.01068558075412	-3.98931441924588
50	-7	-8.70952147514804	1.70952147514804
51	-6	-4.90150900896721	-1.09849099103279
52	-6	-4.13520753427705	-1.86479246572295
53	-3	-1.14386265732341	-1.85613734267659
54	-2	-3.73417050372465	1.73417050372465
55	-5	-7.61027648285076	2.61027648285076
56	-11	-10.3508414650687	-0.649158534931326
57	-11	-7.16605979747199	-3.83394020252801
58	-11	-8.35555476573963	-2.64444523426036
59	-10	-11.4313297738995	1.43132977389951
60	-14	-18.1712419294302	4.17124192943016
61	-8	-9.08543128590597	1.08543128590597
62	-9	-8.81023890347569	-0.18976109652431
63	-5	-2.75424377582279	-2.24575622417721
64	-1	-1.23321527712497	0.233215277124975
65	-2	-1.03289504879782	-0.967104951202182
66	-5	-3.30922501118995	-1.69077498881005
67	-4	-2.21260644318161	-1.78739355681839
68	-6	-6.9647836055532	0.964783605553202
69	-2	-1.59697982192109	-0.403020178078907
70	-2	-2.72619382392044	0.726193823920437
71	-2	-4.04878813264167	2.04878813264167
72	-2	-9.19870180569431	7.19870180569431
73	2	-2.48322995344484	4.48322995344484
74	1	-2.24200556579791	3.24200556579791
75	-8	-3.76968222682683	-4.23031777317317
76	-1	-0.935101960008946	-0.0648980399910543
77	1	-0.723942915616385	1.72394291561639
78	-1	-2.56117479332097	1.56117479332097
79	2	-1.36191467936032	3.36191467936032
80	2	1.25340860115515	0.746591398844849
81	1	-0.869815088928926	1.86981508892893
82	-1	-3.75000658663206	2.75000658663206
83	-2	-7.28207450954179	5.28207450954179
84	-2	-6.04708329380115	4.04708329380115
85	-1	-6.64545026751386	5.64545026751386
86	-8	-13.6421215488341	5.64212154883413
87	-4	-9.2495748629878	5.2495748629878
88	-6	-15.3020063230059	9.30200632300587
89	-3	-8.52527051979993	5.52527051979993
90	-3	-13.4429285350819	10.4429285350819
91	-7	-13.5041459291475	6.50414592914746
92	-9	-11.5548380786329	2.55483807863287
93	-11	-18.9058591105076	7.90585911050758
94	-13	-15.6490339166709	2.64903391667094
95	-11	-12.6967433833508	1.69674338335084
96	-9	-7.3094664158361	-1.6905335841639
97	-17	-18.8301203909652	1.83012039096521
98	-22	-17.1763315961906	-4.82366840380939
99	-25	-19.1438235166717	-5.85617648332825
100	-20	-10.8649388858397	-9.13506111416034
101	-24	-13.2418823056605	-10.7581176943395
102	-24	-18.10897470382	-5.89102529618001
103	-22	-13.3061988365763	-8.6938011634237
104	-19	-8.63119117869408	-10.3688088213059
105	-18	-6.74578725195445	-11.2542127480455
106	-17	-15.1298394967689	-1.87016050323113
107	-11	-8.51736146866334	-2.48263853133666
108	-11	-9.3767390492461	-1.62326095075389
109	-12	-4.16369192132583	-7.83630807867417
110	-10	-4.40217839279557	-5.59782160720443
111	-15	-7.45149362040717	-7.54850637959283
112	-15	-8.28264805119385	-6.71735194880615
113	-15	-9.89000782651051	-5.10999217348949
114	-13	-5.03111711622757	-7.96888288377243
115	-8	-5.49832928349107	-2.50167071650893
116	-13	-10.3807094168225	-2.61929058317754
117	-9	-5.00553462058375	-3.99446537941625
118	-7	-10.3808210330914	3.38082103309135
119	-4	-8.28469899015253	4.28469899015253
120	-4	-8.57846815763129	4.57846815763129
121	-2	-2.28111168407402	0.281111684074025
122	0	-3.23953412798616	3.23953412798616
123	-2	-5.98546440434307	3.98546440434307
124	-3	-6.96875266015131	3.96875266015131
125	1	0.079328712418648	0.920671287581352
126	-2	-10.7471306766829	8.74713067668291
127	-1	-6.04561418639118	5.04561418639118
128	1	-3.22270658548121	4.22270658548121
129	-3	-6.55778720592082	3.55778720592082
130	-4	-10.8354018625694	6.83540186256936
131	-9	-10.1658611619389	1.16586116193889
132	-9	-8.7512447373651	-0.248755262634896
133	-7	-3.59901861965436	-3.40098138034564
134	-14	-9.01915398993592	-4.98084601006408
135	-12	-15.4728593328734	3.47285933287337
136	-16	-17.4352533581799	1.4352533581799
137	-20	-18.1388975921532	-1.86110240784684
138	-12	-14.1016202210734	2.10162022107341
139	-12	-11.3554344942304	-0.644565505769591
140	-10	-9.8255952753839	-0.174404724616103
141	-10	-5.87294045294342	-4.12705954705658
142	-13	-10.9698849912127	-2.03011500878731
143	-16	-13.3655420062718	-2.63445799372823

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 9.00568993482063 & 4.99431006517937 \tabularnewline
2 & 14 & 10.1233139219078 & 3.87668607809224 \tabularnewline
3 & 15 & 10.8048494910315 & 4.19515050896847 \tabularnewline
4 & 13 & 8.44451363007342 & 4.55548636992658 \tabularnewline
5 & 8 & 10.2307859449149 & -2.23078594491488 \tabularnewline
6 & 7 & 6.35370957696295 & 0.646290423037048 \tabularnewline
7 & 3 & 4.50424524030909 & -1.50424524030909 \tabularnewline
8 & 3 & 0.978945719029109 & 2.02105428097089 \tabularnewline
9 & 4 & 3.12655886105176 & 0.87344113894824 \tabularnewline
10 & 4 & 5.60163692190914 & -1.60163692190914 \tabularnewline
11 & 0 & 0.256034288982226 & -0.256034288982226 \tabularnewline
12 & -4 & -3.98125338649839 & -0.0187466135016118 \tabularnewline
13 & -14 & -12.2700881759784 & -1.72991182402158 \tabularnewline
14 & -18 & -9.20618254892336 & -8.79381745107664 \tabularnewline
15 & -8 & -7.71914264273271 & -0.280857357267286 \tabularnewline
16 & -1 & 1.15086455788593 & -2.15086455788593 \tabularnewline
17 & 1 & 5.11033533706151 & -4.11033533706151 \tabularnewline
18 & 2 & 3.05173738601406 & -1.05173738601406 \tabularnewline
19 & 0 & 1.68899961993091 & -1.68899961993091 \tabularnewline
20 & 1 & 1.20623632533247 & -0.206236325332474 \tabularnewline
21 & 0 & 0.274201592053238 & -0.274201592053238 \tabularnewline
22 & -1 & 3.44012887833578 & -4.44012887833578 \tabularnewline
23 & -3 & -3.79390174395352 & 0.793901743953522 \tabularnewline
24 & -3 & -5.01245081125415 & 2.01245081125415 \tabularnewline
25 & -3 & -4.70392686629379 & 1.70392686629379 \tabularnewline
26 & -4 & -7.07245368152526 & 3.07245368152526 \tabularnewline
27 & -8 & -6.27579341789349 & -1.72420658210651 \tabularnewline
28 & -9 & -5.5042829702004 & -3.4957170297996 \tabularnewline
29 & -13 & -12.0263265375987 & -0.973673462401261 \tabularnewline
30 & -18 & -17.7170364088762 & -0.282963591123778 \tabularnewline
31 & -11 & -8.88801900691426 & -2.11198099308574 \tabularnewline
32 & -9 & -7.51175523429146 & -1.48824476570854 \tabularnewline
33 & -10 & -8.47793151813965 & -1.52206848186035 \tabularnewline
34 & -13 & -11.6480967720338 & -1.35190322796624 \tabularnewline
35 & -11 & -14.7587783907249 & 3.75877839072485 \tabularnewline
36 & -5 & -8.40116263994132 & 3.40116263994132 \tabularnewline
37 & -15 & -11.9841876607436 & -3.01581233925643 \tabularnewline
38 & -6 & -6.07965908129257 & 0.0796590812925652 \tabularnewline
39 & -6 & -3.76537575643805 & -2.23462424356195 \tabularnewline
40 & -3 & -4.50641769439602 & 1.50641769439602 \tabularnewline
41 & -1 & -2.36392113856337 & 1.36392113856337 \tabularnewline
42 & -3 & -3.20894986590173 & 0.208949865901734 \tabularnewline
43 & -4 & -2.3173566931202 & -1.6826433068798 \tabularnewline
44 & -6 & -4.04329496923706 & -1.95670503076294 \tabularnewline
45 & 0 & -0.541893264089099 & 0.541893264089099 \tabularnewline
46 & -4 & -4.49836882463154 & 0.49836882463154 \tabularnewline
47 & -2 & -3.15130105680024 & 1.15130105680024 \tabularnewline
48 & -2 & -5.72948466955866 & 3.72948466955866 \tabularnewline
49 & -6 & -2.01068558075412 & -3.98931441924588 \tabularnewline
50 & -7 & -8.70952147514804 & 1.70952147514804 \tabularnewline
51 & -6 & -4.90150900896721 & -1.09849099103279 \tabularnewline
52 & -6 & -4.13520753427705 & -1.86479246572295 \tabularnewline
53 & -3 & -1.14386265732341 & -1.85613734267659 \tabularnewline
54 & -2 & -3.73417050372465 & 1.73417050372465 \tabularnewline
55 & -5 & -7.61027648285076 & 2.61027648285076 \tabularnewline
56 & -11 & -10.3508414650687 & -0.649158534931326 \tabularnewline
57 & -11 & -7.16605979747199 & -3.83394020252801 \tabularnewline
58 & -11 & -8.35555476573963 & -2.64444523426036 \tabularnewline
59 & -10 & -11.4313297738995 & 1.43132977389951 \tabularnewline
60 & -14 & -18.1712419294302 & 4.17124192943016 \tabularnewline
61 & -8 & -9.08543128590597 & 1.08543128590597 \tabularnewline
62 & -9 & -8.81023890347569 & -0.18976109652431 \tabularnewline
63 & -5 & -2.75424377582279 & -2.24575622417721 \tabularnewline
64 & -1 & -1.23321527712497 & 0.233215277124975 \tabularnewline
65 & -2 & -1.03289504879782 & -0.967104951202182 \tabularnewline
66 & -5 & -3.30922501118995 & -1.69077498881005 \tabularnewline
67 & -4 & -2.21260644318161 & -1.78739355681839 \tabularnewline
68 & -6 & -6.9647836055532 & 0.964783605553202 \tabularnewline
69 & -2 & -1.59697982192109 & -0.403020178078907 \tabularnewline
70 & -2 & -2.72619382392044 & 0.726193823920437 \tabularnewline
71 & -2 & -4.04878813264167 & 2.04878813264167 \tabularnewline
72 & -2 & -9.19870180569431 & 7.19870180569431 \tabularnewline
73 & 2 & -2.48322995344484 & 4.48322995344484 \tabularnewline
74 & 1 & -2.24200556579791 & 3.24200556579791 \tabularnewline
75 & -8 & -3.76968222682683 & -4.23031777317317 \tabularnewline
76 & -1 & -0.935101960008946 & -0.0648980399910543 \tabularnewline
77 & 1 & -0.723942915616385 & 1.72394291561639 \tabularnewline
78 & -1 & -2.56117479332097 & 1.56117479332097 \tabularnewline
79 & 2 & -1.36191467936032 & 3.36191467936032 \tabularnewline
80 & 2 & 1.25340860115515 & 0.746591398844849 \tabularnewline
81 & 1 & -0.869815088928926 & 1.86981508892893 \tabularnewline
82 & -1 & -3.75000658663206 & 2.75000658663206 \tabularnewline
83 & -2 & -7.28207450954179 & 5.28207450954179 \tabularnewline
84 & -2 & -6.04708329380115 & 4.04708329380115 \tabularnewline
85 & -1 & -6.64545026751386 & 5.64545026751386 \tabularnewline
86 & -8 & -13.6421215488341 & 5.64212154883413 \tabularnewline
87 & -4 & -9.2495748629878 & 5.2495748629878 \tabularnewline
88 & -6 & -15.3020063230059 & 9.30200632300587 \tabularnewline
89 & -3 & -8.52527051979993 & 5.52527051979993 \tabularnewline
90 & -3 & -13.4429285350819 & 10.4429285350819 \tabularnewline
91 & -7 & -13.5041459291475 & 6.50414592914746 \tabularnewline
92 & -9 & -11.5548380786329 & 2.55483807863287 \tabularnewline
93 & -11 & -18.9058591105076 & 7.90585911050758 \tabularnewline
94 & -13 & -15.6490339166709 & 2.64903391667094 \tabularnewline
95 & -11 & -12.6967433833508 & 1.69674338335084 \tabularnewline
96 & -9 & -7.3094664158361 & -1.6905335841639 \tabularnewline
97 & -17 & -18.8301203909652 & 1.83012039096521 \tabularnewline
98 & -22 & -17.1763315961906 & -4.82366840380939 \tabularnewline
99 & -25 & -19.1438235166717 & -5.85617648332825 \tabularnewline
100 & -20 & -10.8649388858397 & -9.13506111416034 \tabularnewline
101 & -24 & -13.2418823056605 & -10.7581176943395 \tabularnewline
102 & -24 & -18.10897470382 & -5.89102529618001 \tabularnewline
103 & -22 & -13.3061988365763 & -8.6938011634237 \tabularnewline
104 & -19 & -8.63119117869408 & -10.3688088213059 \tabularnewline
105 & -18 & -6.74578725195445 & -11.2542127480455 \tabularnewline
106 & -17 & -15.1298394967689 & -1.87016050323113 \tabularnewline
107 & -11 & -8.51736146866334 & -2.48263853133666 \tabularnewline
108 & -11 & -9.3767390492461 & -1.62326095075389 \tabularnewline
109 & -12 & -4.16369192132583 & -7.83630807867417 \tabularnewline
110 & -10 & -4.40217839279557 & -5.59782160720443 \tabularnewline
111 & -15 & -7.45149362040717 & -7.54850637959283 \tabularnewline
112 & -15 & -8.28264805119385 & -6.71735194880615 \tabularnewline
113 & -15 & -9.89000782651051 & -5.10999217348949 \tabularnewline
114 & -13 & -5.03111711622757 & -7.96888288377243 \tabularnewline
115 & -8 & -5.49832928349107 & -2.50167071650893 \tabularnewline
116 & -13 & -10.3807094168225 & -2.61929058317754 \tabularnewline
117 & -9 & -5.00553462058375 & -3.99446537941625 \tabularnewline
118 & -7 & -10.3808210330914 & 3.38082103309135 \tabularnewline
119 & -4 & -8.28469899015253 & 4.28469899015253 \tabularnewline
120 & -4 & -8.57846815763129 & 4.57846815763129 \tabularnewline
121 & -2 & -2.28111168407402 & 0.281111684074025 \tabularnewline
122 & 0 & -3.23953412798616 & 3.23953412798616 \tabularnewline
123 & -2 & -5.98546440434307 & 3.98546440434307 \tabularnewline
124 & -3 & -6.96875266015131 & 3.96875266015131 \tabularnewline
125 & 1 & 0.079328712418648 & 0.920671287581352 \tabularnewline
126 & -2 & -10.7471306766829 & 8.74713067668291 \tabularnewline
127 & -1 & -6.04561418639118 & 5.04561418639118 \tabularnewline
128 & 1 & -3.22270658548121 & 4.22270658548121 \tabularnewline
129 & -3 & -6.55778720592082 & 3.55778720592082 \tabularnewline
130 & -4 & -10.8354018625694 & 6.83540186256936 \tabularnewline
131 & -9 & -10.1658611619389 & 1.16586116193889 \tabularnewline
132 & -9 & -8.7512447373651 & -0.248755262634896 \tabularnewline
133 & -7 & -3.59901861965436 & -3.40098138034564 \tabularnewline
134 & -14 & -9.01915398993592 & -4.98084601006408 \tabularnewline
135 & -12 & -15.4728593328734 & 3.47285933287337 \tabularnewline
136 & -16 & -17.4352533581799 & 1.4352533581799 \tabularnewline
137 & -20 & -18.1388975921532 & -1.86110240784684 \tabularnewline
138 & -12 & -14.1016202210734 & 2.10162022107341 \tabularnewline
139 & -12 & -11.3554344942304 & -0.644565505769591 \tabularnewline
140 & -10 & -9.8255952753839 & -0.174404724616103 \tabularnewline
141 & -10 & -5.87294045294342 & -4.12705954705658 \tabularnewline
142 & -13 & -10.9698849912127 & -2.03011500878731 \tabularnewline
143 & -16 & -13.3655420062718 & -2.63445799372823 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185430&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]9.00568993482063[/C][C]4.99431006517937[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]10.1233139219078[/C][C]3.87668607809224[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]10.8048494910315[/C][C]4.19515050896847[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]8.44451363007342[/C][C]4.55548636992658[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]10.2307859449149[/C][C]-2.23078594491488[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]6.35370957696295[/C][C]0.646290423037048[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]4.50424524030909[/C][C]-1.50424524030909[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]0.978945719029109[/C][C]2.02105428097089[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.12655886105176[/C][C]0.87344113894824[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.60163692190914[/C][C]-1.60163692190914[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.256034288982226[/C][C]-0.256034288982226[/C][/ROW]
[ROW][C]12[/C][C]-4[/C][C]-3.98125338649839[/C][C]-0.0187466135016118[/C][/ROW]
[ROW][C]13[/C][C]-14[/C][C]-12.2700881759784[/C][C]-1.72991182402158[/C][/ROW]
[ROW][C]14[/C][C]-18[/C][C]-9.20618254892336[/C][C]-8.79381745107664[/C][/ROW]
[ROW][C]15[/C][C]-8[/C][C]-7.71914264273271[/C][C]-0.280857357267286[/C][/ROW]
[ROW][C]16[/C][C]-1[/C][C]1.15086455788593[/C][C]-2.15086455788593[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]5.11033533706151[/C][C]-4.11033533706151[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]3.05173738601406[/C][C]-1.05173738601406[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]1.68899961993091[/C][C]-1.68899961993091[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.20623632533247[/C][C]-0.206236325332474[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.274201592053238[/C][C]-0.274201592053238[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]3.44012887833578[/C][C]-4.44012887833578[/C][/ROW]
[ROW][C]23[/C][C]-3[/C][C]-3.79390174395352[/C][C]0.793901743953522[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]-5.01245081125415[/C][C]2.01245081125415[/C][/ROW]
[ROW][C]25[/C][C]-3[/C][C]-4.70392686629379[/C][C]1.70392686629379[/C][/ROW]
[ROW][C]26[/C][C]-4[/C][C]-7.07245368152526[/C][C]3.07245368152526[/C][/ROW]
[ROW][C]27[/C][C]-8[/C][C]-6.27579341789349[/C][C]-1.72420658210651[/C][/ROW]
[ROW][C]28[/C][C]-9[/C][C]-5.5042829702004[/C][C]-3.4957170297996[/C][/ROW]
[ROW][C]29[/C][C]-13[/C][C]-12.0263265375987[/C][C]-0.973673462401261[/C][/ROW]
[ROW][C]30[/C][C]-18[/C][C]-17.7170364088762[/C][C]-0.282963591123778[/C][/ROW]
[ROW][C]31[/C][C]-11[/C][C]-8.88801900691426[/C][C]-2.11198099308574[/C][/ROW]
[ROW][C]32[/C][C]-9[/C][C]-7.51175523429146[/C][C]-1.48824476570854[/C][/ROW]
[ROW][C]33[/C][C]-10[/C][C]-8.47793151813965[/C][C]-1.52206848186035[/C][/ROW]
[ROW][C]34[/C][C]-13[/C][C]-11.6480967720338[/C][C]-1.35190322796624[/C][/ROW]
[ROW][C]35[/C][C]-11[/C][C]-14.7587783907249[/C][C]3.75877839072485[/C][/ROW]
[ROW][C]36[/C][C]-5[/C][C]-8.40116263994132[/C][C]3.40116263994132[/C][/ROW]
[ROW][C]37[/C][C]-15[/C][C]-11.9841876607436[/C][C]-3.01581233925643[/C][/ROW]
[ROW][C]38[/C][C]-6[/C][C]-6.07965908129257[/C][C]0.0796590812925652[/C][/ROW]
[ROW][C]39[/C][C]-6[/C][C]-3.76537575643805[/C][C]-2.23462424356195[/C][/ROW]
[ROW][C]40[/C][C]-3[/C][C]-4.50641769439602[/C][C]1.50641769439602[/C][/ROW]
[ROW][C]41[/C][C]-1[/C][C]-2.36392113856337[/C][C]1.36392113856337[/C][/ROW]
[ROW][C]42[/C][C]-3[/C][C]-3.20894986590173[/C][C]0.208949865901734[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-2.3173566931202[/C][C]-1.6826433068798[/C][/ROW]
[ROW][C]44[/C][C]-6[/C][C]-4.04329496923706[/C][C]-1.95670503076294[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]-0.541893264089099[/C][C]0.541893264089099[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-4.49836882463154[/C][C]0.49836882463154[/C][/ROW]
[ROW][C]47[/C][C]-2[/C][C]-3.15130105680024[/C][C]1.15130105680024[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C]-5.72948466955866[/C][C]3.72948466955866[/C][/ROW]
[ROW][C]49[/C][C]-6[/C][C]-2.01068558075412[/C][C]-3.98931441924588[/C][/ROW]
[ROW][C]50[/C][C]-7[/C][C]-8.70952147514804[/C][C]1.70952147514804[/C][/ROW]
[ROW][C]51[/C][C]-6[/C][C]-4.90150900896721[/C][C]-1.09849099103279[/C][/ROW]
[ROW][C]52[/C][C]-6[/C][C]-4.13520753427705[/C][C]-1.86479246572295[/C][/ROW]
[ROW][C]53[/C][C]-3[/C][C]-1.14386265732341[/C][C]-1.85613734267659[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-3.73417050372465[/C][C]1.73417050372465[/C][/ROW]
[ROW][C]55[/C][C]-5[/C][C]-7.61027648285076[/C][C]2.61027648285076[/C][/ROW]
[ROW][C]56[/C][C]-11[/C][C]-10.3508414650687[/C][C]-0.649158534931326[/C][/ROW]
[ROW][C]57[/C][C]-11[/C][C]-7.16605979747199[/C][C]-3.83394020252801[/C][/ROW]
[ROW][C]58[/C][C]-11[/C][C]-8.35555476573963[/C][C]-2.64444523426036[/C][/ROW]
[ROW][C]59[/C][C]-10[/C][C]-11.4313297738995[/C][C]1.43132977389951[/C][/ROW]
[ROW][C]60[/C][C]-14[/C][C]-18.1712419294302[/C][C]4.17124192943016[/C][/ROW]
[ROW][C]61[/C][C]-8[/C][C]-9.08543128590597[/C][C]1.08543128590597[/C][/ROW]
[ROW][C]62[/C][C]-9[/C][C]-8.81023890347569[/C][C]-0.18976109652431[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-2.75424377582279[/C][C]-2.24575622417721[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]-1.23321527712497[/C][C]0.233215277124975[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]-1.03289504879782[/C][C]-0.967104951202182[/C][/ROW]
[ROW][C]66[/C][C]-5[/C][C]-3.30922501118995[/C][C]-1.69077498881005[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-2.21260644318161[/C][C]-1.78739355681839[/C][/ROW]
[ROW][C]68[/C][C]-6[/C][C]-6.9647836055532[/C][C]0.964783605553202[/C][/ROW]
[ROW][C]69[/C][C]-2[/C][C]-1.59697982192109[/C][C]-0.403020178078907[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C]-2.72619382392044[/C][C]0.726193823920437[/C][/ROW]
[ROW][C]71[/C][C]-2[/C][C]-4.04878813264167[/C][C]2.04878813264167[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-9.19870180569431[/C][C]7.19870180569431[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]-2.48322995344484[/C][C]4.48322995344484[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]-2.24200556579791[/C][C]3.24200556579791[/C][/ROW]
[ROW][C]75[/C][C]-8[/C][C]-3.76968222682683[/C][C]-4.23031777317317[/C][/ROW]
[ROW][C]76[/C][C]-1[/C][C]-0.935101960008946[/C][C]-0.0648980399910543[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]-0.723942915616385[/C][C]1.72394291561639[/C][/ROW]
[ROW][C]78[/C][C]-1[/C][C]-2.56117479332097[/C][C]1.56117479332097[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]-1.36191467936032[/C][C]3.36191467936032[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.25340860115515[/C][C]0.746591398844849[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]-0.869815088928926[/C][C]1.86981508892893[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]-3.75000658663206[/C][C]2.75000658663206[/C][/ROW]
[ROW][C]83[/C][C]-2[/C][C]-7.28207450954179[/C][C]5.28207450954179[/C][/ROW]
[ROW][C]84[/C][C]-2[/C][C]-6.04708329380115[/C][C]4.04708329380115[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-6.64545026751386[/C][C]5.64545026751386[/C][/ROW]
[ROW][C]86[/C][C]-8[/C][C]-13.6421215488341[/C][C]5.64212154883413[/C][/ROW]
[ROW][C]87[/C][C]-4[/C][C]-9.2495748629878[/C][C]5.2495748629878[/C][/ROW]
[ROW][C]88[/C][C]-6[/C][C]-15.3020063230059[/C][C]9.30200632300587[/C][/ROW]
[ROW][C]89[/C][C]-3[/C][C]-8.52527051979993[/C][C]5.52527051979993[/C][/ROW]
[ROW][C]90[/C][C]-3[/C][C]-13.4429285350819[/C][C]10.4429285350819[/C][/ROW]
[ROW][C]91[/C][C]-7[/C][C]-13.5041459291475[/C][C]6.50414592914746[/C][/ROW]
[ROW][C]92[/C][C]-9[/C][C]-11.5548380786329[/C][C]2.55483807863287[/C][/ROW]
[ROW][C]93[/C][C]-11[/C][C]-18.9058591105076[/C][C]7.90585911050758[/C][/ROW]
[ROW][C]94[/C][C]-13[/C][C]-15.6490339166709[/C][C]2.64903391667094[/C][/ROW]
[ROW][C]95[/C][C]-11[/C][C]-12.6967433833508[/C][C]1.69674338335084[/C][/ROW]
[ROW][C]96[/C][C]-9[/C][C]-7.3094664158361[/C][C]-1.6905335841639[/C][/ROW]
[ROW][C]97[/C][C]-17[/C][C]-18.8301203909652[/C][C]1.83012039096521[/C][/ROW]
[ROW][C]98[/C][C]-22[/C][C]-17.1763315961906[/C][C]-4.82366840380939[/C][/ROW]
[ROW][C]99[/C][C]-25[/C][C]-19.1438235166717[/C][C]-5.85617648332825[/C][/ROW]
[ROW][C]100[/C][C]-20[/C][C]-10.8649388858397[/C][C]-9.13506111416034[/C][/ROW]
[ROW][C]101[/C][C]-24[/C][C]-13.2418823056605[/C][C]-10.7581176943395[/C][/ROW]
[ROW][C]102[/C][C]-24[/C][C]-18.10897470382[/C][C]-5.89102529618001[/C][/ROW]
[ROW][C]103[/C][C]-22[/C][C]-13.3061988365763[/C][C]-8.6938011634237[/C][/ROW]
[ROW][C]104[/C][C]-19[/C][C]-8.63119117869408[/C][C]-10.3688088213059[/C][/ROW]
[ROW][C]105[/C][C]-18[/C][C]-6.74578725195445[/C][C]-11.2542127480455[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-15.1298394967689[/C][C]-1.87016050323113[/C][/ROW]
[ROW][C]107[/C][C]-11[/C][C]-8.51736146866334[/C][C]-2.48263853133666[/C][/ROW]
[ROW][C]108[/C][C]-11[/C][C]-9.3767390492461[/C][C]-1.62326095075389[/C][/ROW]
[ROW][C]109[/C][C]-12[/C][C]-4.16369192132583[/C][C]-7.83630807867417[/C][/ROW]
[ROW][C]110[/C][C]-10[/C][C]-4.40217839279557[/C][C]-5.59782160720443[/C][/ROW]
[ROW][C]111[/C][C]-15[/C][C]-7.45149362040717[/C][C]-7.54850637959283[/C][/ROW]
[ROW][C]112[/C][C]-15[/C][C]-8.28264805119385[/C][C]-6.71735194880615[/C][/ROW]
[ROW][C]113[/C][C]-15[/C][C]-9.89000782651051[/C][C]-5.10999217348949[/C][/ROW]
[ROW][C]114[/C][C]-13[/C][C]-5.03111711622757[/C][C]-7.96888288377243[/C][/ROW]
[ROW][C]115[/C][C]-8[/C][C]-5.49832928349107[/C][C]-2.50167071650893[/C][/ROW]
[ROW][C]116[/C][C]-13[/C][C]-10.3807094168225[/C][C]-2.61929058317754[/C][/ROW]
[ROW][C]117[/C][C]-9[/C][C]-5.00553462058375[/C][C]-3.99446537941625[/C][/ROW]
[ROW][C]118[/C][C]-7[/C][C]-10.3808210330914[/C][C]3.38082103309135[/C][/ROW]
[ROW][C]119[/C][C]-4[/C][C]-8.28469899015253[/C][C]4.28469899015253[/C][/ROW]
[ROW][C]120[/C][C]-4[/C][C]-8.57846815763129[/C][C]4.57846815763129[/C][/ROW]
[ROW][C]121[/C][C]-2[/C][C]-2.28111168407402[/C][C]0.281111684074025[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-3.23953412798616[/C][C]3.23953412798616[/C][/ROW]
[ROW][C]123[/C][C]-2[/C][C]-5.98546440434307[/C][C]3.98546440434307[/C][/ROW]
[ROW][C]124[/C][C]-3[/C][C]-6.96875266015131[/C][C]3.96875266015131[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0.079328712418648[/C][C]0.920671287581352[/C][/ROW]
[ROW][C]126[/C][C]-2[/C][C]-10.7471306766829[/C][C]8.74713067668291[/C][/ROW]
[ROW][C]127[/C][C]-1[/C][C]-6.04561418639118[/C][C]5.04561418639118[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]-3.22270658548121[/C][C]4.22270658548121[/C][/ROW]
[ROW][C]129[/C][C]-3[/C][C]-6.55778720592082[/C][C]3.55778720592082[/C][/ROW]
[ROW][C]130[/C][C]-4[/C][C]-10.8354018625694[/C][C]6.83540186256936[/C][/ROW]
[ROW][C]131[/C][C]-9[/C][C]-10.1658611619389[/C][C]1.16586116193889[/C][/ROW]
[ROW][C]132[/C][C]-9[/C][C]-8.7512447373651[/C][C]-0.248755262634896[/C][/ROW]
[ROW][C]133[/C][C]-7[/C][C]-3.59901861965436[/C][C]-3.40098138034564[/C][/ROW]
[ROW][C]134[/C][C]-14[/C][C]-9.01915398993592[/C][C]-4.98084601006408[/C][/ROW]
[ROW][C]135[/C][C]-12[/C][C]-15.4728593328734[/C][C]3.47285933287337[/C][/ROW]
[ROW][C]136[/C][C]-16[/C][C]-17.4352533581799[/C][C]1.4352533581799[/C][/ROW]
[ROW][C]137[/C][C]-20[/C][C]-18.1388975921532[/C][C]-1.86110240784684[/C][/ROW]
[ROW][C]138[/C][C]-12[/C][C]-14.1016202210734[/C][C]2.10162022107341[/C][/ROW]
[ROW][C]139[/C][C]-12[/C][C]-11.3554344942304[/C][C]-0.644565505769591[/C][/ROW]
[ROW][C]140[/C][C]-10[/C][C]-9.8255952753839[/C][C]-0.174404724616103[/C][/ROW]
[ROW][C]141[/C][C]-10[/C][C]-5.87294045294342[/C][C]-4.12705954705658[/C][/ROW]
[ROW][C]142[/C][C]-13[/C][C]-10.9698849912127[/C][C]-2.03011500878731[/C][/ROW]
[ROW][C]143[/C][C]-16[/C][C]-13.3655420062718[/C][C]-2.63445799372823[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185430&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185430&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	9.00568993482063	4.99431006517937
2	14	10.1233139219078	3.87668607809224
3	15	10.8048494910315	4.19515050896847
4	13	8.44451363007342	4.55548636992658
5	8	10.2307859449149	-2.23078594491488
6	7	6.35370957696295	0.646290423037048
7	3	4.50424524030909	-1.50424524030909
8	3	0.978945719029109	2.02105428097089
9	4	3.12655886105176	0.87344113894824
10	4	5.60163692190914	-1.60163692190914
11	0	0.256034288982226	-0.256034288982226
12	-4	-3.98125338649839	-0.0187466135016118
13	-14	-12.2700881759784	-1.72991182402158
14	-18	-9.20618254892336	-8.79381745107664
15	-8	-7.71914264273271	-0.280857357267286
16	-1	1.15086455788593	-2.15086455788593
17	1	5.11033533706151	-4.11033533706151
18	2	3.05173738601406	-1.05173738601406
19	0	1.68899961993091	-1.68899961993091
20	1	1.20623632533247	-0.206236325332474
21	0	0.274201592053238	-0.274201592053238
22	-1	3.44012887833578	-4.44012887833578
23	-3	-3.79390174395352	0.793901743953522
24	-3	-5.01245081125415	2.01245081125415
25	-3	-4.70392686629379	1.70392686629379
26	-4	-7.07245368152526	3.07245368152526
27	-8	-6.27579341789349	-1.72420658210651
28	-9	-5.5042829702004	-3.4957170297996
29	-13	-12.0263265375987	-0.973673462401261
30	-18	-17.7170364088762	-0.282963591123778
31	-11	-8.88801900691426	-2.11198099308574
32	-9	-7.51175523429146	-1.48824476570854
33	-10	-8.47793151813965	-1.52206848186035
34	-13	-11.6480967720338	-1.35190322796624
35	-11	-14.7587783907249	3.75877839072485
36	-5	-8.40116263994132	3.40116263994132
37	-15	-11.9841876607436	-3.01581233925643
38	-6	-6.07965908129257	0.0796590812925652
39	-6	-3.76537575643805	-2.23462424356195
40	-3	-4.50641769439602	1.50641769439602
41	-1	-2.36392113856337	1.36392113856337
42	-3	-3.20894986590173	0.208949865901734
43	-4	-2.3173566931202	-1.6826433068798
44	-6	-4.04329496923706	-1.95670503076294
45	0	-0.541893264089099	0.541893264089099
46	-4	-4.49836882463154	0.49836882463154
47	-2	-3.15130105680024	1.15130105680024
48	-2	-5.72948466955866	3.72948466955866
49	-6	-2.01068558075412	-3.98931441924588
50	-7	-8.70952147514804	1.70952147514804
51	-6	-4.90150900896721	-1.09849099103279
52	-6	-4.13520753427705	-1.86479246572295
53	-3	-1.14386265732341	-1.85613734267659
54	-2	-3.73417050372465	1.73417050372465
55	-5	-7.61027648285076	2.61027648285076
56	-11	-10.3508414650687	-0.649158534931326
57	-11	-7.16605979747199	-3.83394020252801
58	-11	-8.35555476573963	-2.64444523426036
59	-10	-11.4313297738995	1.43132977389951
60	-14	-18.1712419294302	4.17124192943016
61	-8	-9.08543128590597	1.08543128590597
62	-9	-8.81023890347569	-0.18976109652431
63	-5	-2.75424377582279	-2.24575622417721
64	-1	-1.23321527712497	0.233215277124975
65	-2	-1.03289504879782	-0.967104951202182
66	-5	-3.30922501118995	-1.69077498881005
67	-4	-2.21260644318161	-1.78739355681839
68	-6	-6.9647836055532	0.964783605553202
69	-2	-1.59697982192109	-0.403020178078907
70	-2	-2.72619382392044	0.726193823920437
71	-2	-4.04878813264167	2.04878813264167
72	-2	-9.19870180569431	7.19870180569431
73	2	-2.48322995344484	4.48322995344484
74	1	-2.24200556579791	3.24200556579791
75	-8	-3.76968222682683	-4.23031777317317
76	-1	-0.935101960008946	-0.0648980399910543
77	1	-0.723942915616385	1.72394291561639
78	-1	-2.56117479332097	1.56117479332097
79	2	-1.36191467936032	3.36191467936032
80	2	1.25340860115515	0.746591398844849
81	1	-0.869815088928926	1.86981508892893
82	-1	-3.75000658663206	2.75000658663206
83	-2	-7.28207450954179	5.28207450954179
84	-2	-6.04708329380115	4.04708329380115
85	-1	-6.64545026751386	5.64545026751386
86	-8	-13.6421215488341	5.64212154883413
87	-4	-9.2495748629878	5.2495748629878
88	-6	-15.3020063230059	9.30200632300587
89	-3	-8.52527051979993	5.52527051979993
90	-3	-13.4429285350819	10.4429285350819
91	-7	-13.5041459291475	6.50414592914746
92	-9	-11.5548380786329	2.55483807863287
93	-11	-18.9058591105076	7.90585911050758
94	-13	-15.6490339166709	2.64903391667094
95	-11	-12.6967433833508	1.69674338335084
96	-9	-7.3094664158361	-1.6905335841639
97	-17	-18.8301203909652	1.83012039096521
98	-22	-17.1763315961906	-4.82366840380939
99	-25	-19.1438235166717	-5.85617648332825
100	-20	-10.8649388858397	-9.13506111416034
101	-24	-13.2418823056605	-10.7581176943395
102	-24	-18.10897470382	-5.89102529618001
103	-22	-13.3061988365763	-8.6938011634237
104	-19	-8.63119117869408	-10.3688088213059
105	-18	-6.74578725195445	-11.2542127480455
106	-17	-15.1298394967689	-1.87016050323113
107	-11	-8.51736146866334	-2.48263853133666
108	-11	-9.3767390492461	-1.62326095075389
109	-12	-4.16369192132583	-7.83630807867417
110	-10	-4.40217839279557	-5.59782160720443
111	-15	-7.45149362040717	-7.54850637959283
112	-15	-8.28264805119385	-6.71735194880615
113	-15	-9.89000782651051	-5.10999217348949
114	-13	-5.03111711622757	-7.96888288377243
115	-8	-5.49832928349107	-2.50167071650893
116	-13	-10.3807094168225	-2.61929058317754
117	-9	-5.00553462058375	-3.99446537941625
118	-7	-10.3808210330914	3.38082103309135
119	-4	-8.28469899015253	4.28469899015253
120	-4	-8.57846815763129	4.57846815763129
121	-2	-2.28111168407402	0.281111684074025
122	0	-3.23953412798616	3.23953412798616
123	-2	-5.98546440434307	3.98546440434307
124	-3	-6.96875266015131	3.96875266015131
125	1	0.079328712418648	0.920671287581352
126	-2	-10.7471306766829	8.74713067668291
127	-1	-6.04561418639118	5.04561418639118
128	1	-3.22270658548121	4.22270658548121
129	-3	-6.55778720592082	3.55778720592082
130	-4	-10.8354018625694	6.83540186256936
131	-9	-10.1658611619389	1.16586116193889
132	-9	-8.7512447373651	-0.248755262634896
133	-7	-3.59901861965436	-3.40098138034564
134	-14	-9.01915398993592	-4.98084601006408
135	-12	-15.4728593328734	3.47285933287337
136	-16	-17.4352533581799	1.4352533581799
137	-20	-18.1388975921532	-1.86110240784684
138	-12	-14.1016202210734	2.10162022107341
139	-12	-11.3554344942304	-0.644565505769591
140	-10	-9.8255952753839	-0.174404724616103
141	-10	-5.87294045294342	-4.12705954705658
142	-13	-10.9698849912127	-2.03011500878731
143	-16	-13.3655420062718	-2.63445799372823

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.380203718014839	0.760407436029678	0.619796281985161
10	0.226550814236603	0.453101628473206	0.773449185763397
11	0.225356945191359	0.450713890382719	0.774643054808641
12	0.148880281064088	0.297760562128177	0.851119718935912
13	0.0927993060601099	0.18559861212022	0.90720069393989
14	0.274180449420143	0.548360898840286	0.725819550579857
15	0.229882630052417	0.459765260104835	0.770117369947583
16	0.175203295716327	0.350406591432654	0.824796704283673
17	0.145653874771241	0.291307749542482	0.854346125228759
18	0.102161356560239	0.204322713120478	0.897838643439761
19	0.0744928606909608	0.148985721381922	0.925507139309039
20	0.0554367618084156	0.110873523616831	0.944563238191584
21	0.040179182276557	0.080358364553114	0.959820817723443
22	0.0393559099938772	0.0787118199877543	0.960644090006123
23	0.0278057892886827	0.0556115785773654	0.972194210711317
24	0.0261908753378593	0.0523817506757186	0.973809124662141
25	0.0196369044513221	0.0392738089026442	0.980363095548678
26	0.0188199303457894	0.0376398606915788	0.981180069654211
27	0.0132951885834173	0.0265903771668347	0.986704811416583
28	0.00925411648281004	0.0185082329656201	0.99074588351719
29	0.00682429232689369	0.0136485846537874	0.993175707673106
30	0.00524229322943982	0.0104845864588796	0.99475770677056
31	0.00376053708263028	0.00752107416526056	0.99623946291737
32	0.00264884456093867	0.00529768912187734	0.997351155439061
33	0.00158441816249061	0.00316883632498123	0.998415581837509
34	0.000949812198403093	0.00189962439680619	0.999050187801597
35	0.000904483403243494	0.00180896680648699	0.999095516596757
36	0.000554389651512199	0.0011087793030244	0.999445610348488
37	0.000953302437418091	0.00190660487483618	0.999046697562582
38	0.000623563627330854	0.00124712725466171	0.999376436372669
39	0.000679545218193717	0.00135909043638743	0.999320454781806
40	0.000391468983994301	0.000782937967988602	0.999608531016006
41	0.00022239110071842	0.00044478220143684	0.999777608899282
42	0.000125209204463756	0.000250418408927513	0.999874790795536
43	8.33169694265974e-05	0.000166633938853195	0.999916683030573
44	4.96246428269877e-05	9.92492856539754e-05	0.999950375357173
45	2.78024802882699e-05	5.56049605765398e-05	0.999972197519712
46	1.55892528133105e-05	3.1178505626621e-05	0.999984410747187
47	8.24671349256182e-06	1.64934269851236e-05	0.999991753286507
48	6.04615357661854e-06	1.20923071532371e-05	0.999993953846423
49	1.53017721988539e-05	3.06035443977078e-05	0.999984698227801
50	8.84288310965543e-06	1.76857662193109e-05	0.99999115711689
51	5.5463109965572e-06	1.10926219931144e-05	0.999994453689003
52	4.73319384298891e-06	9.46638768597783e-06	0.999995266806157
53	2.92119304197508e-06	5.84238608395016e-06	0.999997078806958
54	2.13393822064544e-06	4.26787644129088e-06	0.999997866061779
55	2.17871888513884e-06	4.35743777027769e-06	0.999997821281115
56	1.16689885369866e-06	2.33379770739733e-06	0.999998833101146
57	9.74733630320527e-07	1.94946726064105e-06	0.99999902526637
58	6.32652037017185e-07	1.26530407403437e-06	0.999999367347963
59	3.68444349092926e-07	7.36888698185853e-07	0.999999631555651
60	8.14988804120111e-07	1.62997760824022e-06	0.999999185011196
61	4.370814176251e-07	8.74162835250201e-07	0.999999562918582
62	2.1970638554784e-07	4.39412771095681e-07	0.999999780293614
63	1.45337239123758e-07	2.90674478247517e-07	0.999999854662761
64	7.11390290635137e-08	1.42278058127027e-07	0.999999928860971
65	3.46204011183065e-08	6.9240802236613e-08	0.999999965379599
66	1.74366281871731e-08	3.48732563743462e-08	0.999999982563372
67	8.71125656256738e-09	1.74225131251348e-08	0.999999991288743
68	6.96460508261457e-09	1.39292101652291e-08	0.999999993035395
69	3.5766419933135e-09	7.153283986627e-09	0.999999996423358
70	1.952292162945e-09	3.90458432589e-09	0.999999998047708
71	1.25097442019289e-09	2.50194884038579e-09	0.999999998749026
72	1.66675306909105e-08	3.3335061381821e-08	0.999999983332469
73	2.22501539586714e-08	4.45003079173428e-08	0.999999977749846
74	1.86712522326342e-08	3.73425044652683e-08	0.999999981328748
75	1.88569740495659e-08	3.77139480991317e-08	0.999999981143026
76	9.08828827445203e-09	1.81765765489041e-08	0.999999990911712
77	5.75161544249509e-09	1.15032308849902e-08	0.999999994248385
78	3.55904408793514e-09	7.11808817587028e-09	0.999999996440956
79	3.0185266447348e-09	6.03705328946961e-09	0.999999996981473
80	1.49017575891915e-09	2.9803515178383e-09	0.999999998509824
81	8.40326722840788e-10	1.68065344568158e-09	0.999999999159673
82	5.93164724498254e-10	1.18632944899651e-09	0.999999999406835
83	1.43253430229696e-09	2.86506860459392e-09	0.999999998567466
84	1.68840218600847e-09	3.37680437201693e-09	0.999999998311598
85	4.51836068861085e-09	9.0367213772217e-09	0.999999995481639
86	8.42938420222746e-09	1.68587684044549e-08	0.999999991570616
87	9.0744758766794e-09	1.81489517533588e-08	0.999999990925524
88	1.3483966754649e-07	2.6967933509298e-07	0.999999865160332
89	1.87962172133763e-07	3.75924344267525e-07	0.999999812037828
90	2.890890997546e-06	5.78178199509201e-06	0.999997109109002
91	7.5220338864421e-06	1.50440677728842e-05	0.999992477966114
92	7.36893557152935e-06	1.47378711430587e-05	0.999992631064428
93	2.47121955306865e-05	4.94243910613729e-05	0.999975287804469
94	4.11734770093537e-05	8.23469540187073e-05	0.999958826522991
95	6.21628308656893e-05	0.000124325661731379	0.999937837169134
96	8.13222777652265e-05	0.000162644555530453	0.999918677722235
97	0.000108510511874915	0.000217021023749829	0.999891489488125
98	0.000342659104251809	0.000685318208503619	0.999657340895748
99	0.000782407847718238	0.00156481569543648	0.999217592152282
100	0.00277081207197825	0.00554162414395651	0.997229187928022
101	0.0187846067330948	0.0375692134661896	0.981215393266905
102	0.0206339341134728	0.0412678682269457	0.979366065886527
103	0.045042385166465	0.0900847703329299	0.954957614833535
104	0.121333962088823	0.242667924177646	0.878666037911177
105	0.320910137502184	0.641820275004369	0.679089862497816
106	0.273557156822902	0.547114313645803	0.726442843177098
107	0.232896465549358	0.465792931098717	0.767103534450641
108	0.19870146106996	0.39740292213992	0.80129853893004
109	0.251577337674793	0.503154675349587	0.748422662325207
110	0.266120148562807	0.532240297125614	0.733879851437193
111	0.384398546605598	0.768797093211196	0.615601453394402
112	0.493424786791488	0.986849573582977	0.506575213208512
113	0.554325717737469	0.891348564525061	0.445674282262531
114	0.787560239573279	0.424879520853442	0.212439760426721
115	0.823255490619436	0.353489018761128	0.176744509380564
116	0.878006113234678	0.243987773530644	0.121993886765322
117	0.958489524756021	0.0830209504879574	0.0415104752439787
118	0.97775276951179	0.0444944609764205	0.0222472304882102
119	0.968135883596209	0.0637282328075828	0.0318641164037914
120	0.956875461387336	0.0862490772253274	0.0431245386126637
121	0.942670190118318	0.114659619763364	0.0573298098816819
122	0.918464592597931	0.163070814804139	0.0815354074020694
123	0.886257998717252	0.227484002565495	0.113742001282748
124	0.877461994526938	0.245076010946124	0.122538005473062
125	0.831126498495759	0.337747003008481	0.168873501504241
126	0.888855640843487	0.222288718313025	0.111144359156513
127	0.882878471779914	0.234243056440172	0.117121528220086
128	0.877848628197274	0.244302743605451	0.122151371802726
129	0.858412774131913	0.283174451736174	0.141587225868087
130	0.941685273438707	0.116629453122586	0.0583147265612932
131	0.891173743323433	0.217652513353133	0.108826256676567
132	0.858294254825502	0.283411490348996	0.141705745174498
133	0.922797042217372	0.154405915565255	0.0772029577826277
134	0.824287319613511	0.351425360772978	0.175712680386489

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.380203718014839 & 0.760407436029678 & 0.619796281985161 \tabularnewline
10 & 0.226550814236603 & 0.453101628473206 & 0.773449185763397 \tabularnewline
11 & 0.225356945191359 & 0.450713890382719 & 0.774643054808641 \tabularnewline
12 & 0.148880281064088 & 0.297760562128177 & 0.851119718935912 \tabularnewline
13 & 0.0927993060601099 & 0.18559861212022 & 0.90720069393989 \tabularnewline
14 & 0.274180449420143 & 0.548360898840286 & 0.725819550579857 \tabularnewline
15 & 0.229882630052417 & 0.459765260104835 & 0.770117369947583 \tabularnewline
16 & 0.175203295716327 & 0.350406591432654 & 0.824796704283673 \tabularnewline
17 & 0.145653874771241 & 0.291307749542482 & 0.854346125228759 \tabularnewline
18 & 0.102161356560239 & 0.204322713120478 & 0.897838643439761 \tabularnewline
19 & 0.0744928606909608 & 0.148985721381922 & 0.925507139309039 \tabularnewline
20 & 0.0554367618084156 & 0.110873523616831 & 0.944563238191584 \tabularnewline
21 & 0.040179182276557 & 0.080358364553114 & 0.959820817723443 \tabularnewline
22 & 0.0393559099938772 & 0.0787118199877543 & 0.960644090006123 \tabularnewline
23 & 0.0278057892886827 & 0.0556115785773654 & 0.972194210711317 \tabularnewline
24 & 0.0261908753378593 & 0.0523817506757186 & 0.973809124662141 \tabularnewline
25 & 0.0196369044513221 & 0.0392738089026442 & 0.980363095548678 \tabularnewline
26 & 0.0188199303457894 & 0.0376398606915788 & 0.981180069654211 \tabularnewline
27 & 0.0132951885834173 & 0.0265903771668347 & 0.986704811416583 \tabularnewline
28 & 0.00925411648281004 & 0.0185082329656201 & 0.99074588351719 \tabularnewline
29 & 0.00682429232689369 & 0.0136485846537874 & 0.993175707673106 \tabularnewline
30 & 0.00524229322943982 & 0.0104845864588796 & 0.99475770677056 \tabularnewline
31 & 0.00376053708263028 & 0.00752107416526056 & 0.99623946291737 \tabularnewline
32 & 0.00264884456093867 & 0.00529768912187734 & 0.997351155439061 \tabularnewline
33 & 0.00158441816249061 & 0.00316883632498123 & 0.998415581837509 \tabularnewline
34 & 0.000949812198403093 & 0.00189962439680619 & 0.999050187801597 \tabularnewline
35 & 0.000904483403243494 & 0.00180896680648699 & 0.999095516596757 \tabularnewline
36 & 0.000554389651512199 & 0.0011087793030244 & 0.999445610348488 \tabularnewline
37 & 0.000953302437418091 & 0.00190660487483618 & 0.999046697562582 \tabularnewline
38 & 0.000623563627330854 & 0.00124712725466171 & 0.999376436372669 \tabularnewline
39 & 0.000679545218193717 & 0.00135909043638743 & 0.999320454781806 \tabularnewline
40 & 0.000391468983994301 & 0.000782937967988602 & 0.999608531016006 \tabularnewline
41 & 0.00022239110071842 & 0.00044478220143684 & 0.999777608899282 \tabularnewline
42 & 0.000125209204463756 & 0.000250418408927513 & 0.999874790795536 \tabularnewline
43 & 8.33169694265974e-05 & 0.000166633938853195 & 0.999916683030573 \tabularnewline
44 & 4.96246428269877e-05 & 9.92492856539754e-05 & 0.999950375357173 \tabularnewline
45 & 2.78024802882699e-05 & 5.56049605765398e-05 & 0.999972197519712 \tabularnewline
46 & 1.55892528133105e-05 & 3.1178505626621e-05 & 0.999984410747187 \tabularnewline
47 & 8.24671349256182e-06 & 1.64934269851236e-05 & 0.999991753286507 \tabularnewline
48 & 6.04615357661854e-06 & 1.20923071532371e-05 & 0.999993953846423 \tabularnewline
49 & 1.53017721988539e-05 & 3.06035443977078e-05 & 0.999984698227801 \tabularnewline
50 & 8.84288310965543e-06 & 1.76857662193109e-05 & 0.99999115711689 \tabularnewline
51 & 5.5463109965572e-06 & 1.10926219931144e-05 & 0.999994453689003 \tabularnewline
52 & 4.73319384298891e-06 & 9.46638768597783e-06 & 0.999995266806157 \tabularnewline
53 & 2.92119304197508e-06 & 5.84238608395016e-06 & 0.999997078806958 \tabularnewline
54 & 2.13393822064544e-06 & 4.26787644129088e-06 & 0.999997866061779 \tabularnewline
55 & 2.17871888513884e-06 & 4.35743777027769e-06 & 0.999997821281115 \tabularnewline
56 & 1.16689885369866e-06 & 2.33379770739733e-06 & 0.999998833101146 \tabularnewline
57 & 9.74733630320527e-07 & 1.94946726064105e-06 & 0.99999902526637 \tabularnewline
58 & 6.32652037017185e-07 & 1.26530407403437e-06 & 0.999999367347963 \tabularnewline
59 & 3.68444349092926e-07 & 7.36888698185853e-07 & 0.999999631555651 \tabularnewline
60 & 8.14988804120111e-07 & 1.62997760824022e-06 & 0.999999185011196 \tabularnewline
61 & 4.370814176251e-07 & 8.74162835250201e-07 & 0.999999562918582 \tabularnewline
62 & 2.1970638554784e-07 & 4.39412771095681e-07 & 0.999999780293614 \tabularnewline
63 & 1.45337239123758e-07 & 2.90674478247517e-07 & 0.999999854662761 \tabularnewline
64 & 7.11390290635137e-08 & 1.42278058127027e-07 & 0.999999928860971 \tabularnewline
65 & 3.46204011183065e-08 & 6.9240802236613e-08 & 0.999999965379599 \tabularnewline
66 & 1.74366281871731e-08 & 3.48732563743462e-08 & 0.999999982563372 \tabularnewline
67 & 8.71125656256738e-09 & 1.74225131251348e-08 & 0.999999991288743 \tabularnewline
68 & 6.96460508261457e-09 & 1.39292101652291e-08 & 0.999999993035395 \tabularnewline
69 & 3.5766419933135e-09 & 7.153283986627e-09 & 0.999999996423358 \tabularnewline
70 & 1.952292162945e-09 & 3.90458432589e-09 & 0.999999998047708 \tabularnewline
71 & 1.25097442019289e-09 & 2.50194884038579e-09 & 0.999999998749026 \tabularnewline
72 & 1.66675306909105e-08 & 3.3335061381821e-08 & 0.999999983332469 \tabularnewline
73 & 2.22501539586714e-08 & 4.45003079173428e-08 & 0.999999977749846 \tabularnewline
74 & 1.86712522326342e-08 & 3.73425044652683e-08 & 0.999999981328748 \tabularnewline
75 & 1.88569740495659e-08 & 3.77139480991317e-08 & 0.999999981143026 \tabularnewline
76 & 9.08828827445203e-09 & 1.81765765489041e-08 & 0.999999990911712 \tabularnewline
77 & 5.75161544249509e-09 & 1.15032308849902e-08 & 0.999999994248385 \tabularnewline
78 & 3.55904408793514e-09 & 7.11808817587028e-09 & 0.999999996440956 \tabularnewline
79 & 3.0185266447348e-09 & 6.03705328946961e-09 & 0.999999996981473 \tabularnewline
80 & 1.49017575891915e-09 & 2.9803515178383e-09 & 0.999999998509824 \tabularnewline
81 & 8.40326722840788e-10 & 1.68065344568158e-09 & 0.999999999159673 \tabularnewline
82 & 5.93164724498254e-10 & 1.18632944899651e-09 & 0.999999999406835 \tabularnewline
83 & 1.43253430229696e-09 & 2.86506860459392e-09 & 0.999999998567466 \tabularnewline
84 & 1.68840218600847e-09 & 3.37680437201693e-09 & 0.999999998311598 \tabularnewline
85 & 4.51836068861085e-09 & 9.0367213772217e-09 & 0.999999995481639 \tabularnewline
86 & 8.42938420222746e-09 & 1.68587684044549e-08 & 0.999999991570616 \tabularnewline
87 & 9.0744758766794e-09 & 1.81489517533588e-08 & 0.999999990925524 \tabularnewline
88 & 1.3483966754649e-07 & 2.6967933509298e-07 & 0.999999865160332 \tabularnewline
89 & 1.87962172133763e-07 & 3.75924344267525e-07 & 0.999999812037828 \tabularnewline
90 & 2.890890997546e-06 & 5.78178199509201e-06 & 0.999997109109002 \tabularnewline
91 & 7.5220338864421e-06 & 1.50440677728842e-05 & 0.999992477966114 \tabularnewline
92 & 7.36893557152935e-06 & 1.47378711430587e-05 & 0.999992631064428 \tabularnewline
93 & 2.47121955306865e-05 & 4.94243910613729e-05 & 0.999975287804469 \tabularnewline
94 & 4.11734770093537e-05 & 8.23469540187073e-05 & 0.999958826522991 \tabularnewline
95 & 6.21628308656893e-05 & 0.000124325661731379 & 0.999937837169134 \tabularnewline
96 & 8.13222777652265e-05 & 0.000162644555530453 & 0.999918677722235 \tabularnewline
97 & 0.000108510511874915 & 0.000217021023749829 & 0.999891489488125 \tabularnewline
98 & 0.000342659104251809 & 0.000685318208503619 & 0.999657340895748 \tabularnewline
99 & 0.000782407847718238 & 0.00156481569543648 & 0.999217592152282 \tabularnewline
100 & 0.00277081207197825 & 0.00554162414395651 & 0.997229187928022 \tabularnewline
101 & 0.0187846067330948 & 0.0375692134661896 & 0.981215393266905 \tabularnewline
102 & 0.0206339341134728 & 0.0412678682269457 & 0.979366065886527 \tabularnewline
103 & 0.045042385166465 & 0.0900847703329299 & 0.954957614833535 \tabularnewline
104 & 0.121333962088823 & 0.242667924177646 & 0.878666037911177 \tabularnewline
105 & 0.320910137502184 & 0.641820275004369 & 0.679089862497816 \tabularnewline
106 & 0.273557156822902 & 0.547114313645803 & 0.726442843177098 \tabularnewline
107 & 0.232896465549358 & 0.465792931098717 & 0.767103534450641 \tabularnewline
108 & 0.19870146106996 & 0.39740292213992 & 0.80129853893004 \tabularnewline
109 & 0.251577337674793 & 0.503154675349587 & 0.748422662325207 \tabularnewline
110 & 0.266120148562807 & 0.532240297125614 & 0.733879851437193 \tabularnewline
111 & 0.384398546605598 & 0.768797093211196 & 0.615601453394402 \tabularnewline
112 & 0.493424786791488 & 0.986849573582977 & 0.506575213208512 \tabularnewline
113 & 0.554325717737469 & 0.891348564525061 & 0.445674282262531 \tabularnewline
114 & 0.787560239573279 & 0.424879520853442 & 0.212439760426721 \tabularnewline
115 & 0.823255490619436 & 0.353489018761128 & 0.176744509380564 \tabularnewline
116 & 0.878006113234678 & 0.243987773530644 & 0.121993886765322 \tabularnewline
117 & 0.958489524756021 & 0.0830209504879574 & 0.0415104752439787 \tabularnewline
118 & 0.97775276951179 & 0.0444944609764205 & 0.0222472304882102 \tabularnewline
119 & 0.968135883596209 & 0.0637282328075828 & 0.0318641164037914 \tabularnewline
120 & 0.956875461387336 & 0.0862490772253274 & 0.0431245386126637 \tabularnewline
121 & 0.942670190118318 & 0.114659619763364 & 0.0573298098816819 \tabularnewline
122 & 0.918464592597931 & 0.163070814804139 & 0.0815354074020694 \tabularnewline
123 & 0.886257998717252 & 0.227484002565495 & 0.113742001282748 \tabularnewline
124 & 0.877461994526938 & 0.245076010946124 & 0.122538005473062 \tabularnewline
125 & 0.831126498495759 & 0.337747003008481 & 0.168873501504241 \tabularnewline
126 & 0.888855640843487 & 0.222288718313025 & 0.111144359156513 \tabularnewline
127 & 0.882878471779914 & 0.234243056440172 & 0.117121528220086 \tabularnewline
128 & 0.877848628197274 & 0.244302743605451 & 0.122151371802726 \tabularnewline
129 & 0.858412774131913 & 0.283174451736174 & 0.141587225868087 \tabularnewline
130 & 0.941685273438707 & 0.116629453122586 & 0.0583147265612932 \tabularnewline
131 & 0.891173743323433 & 0.217652513353133 & 0.108826256676567 \tabularnewline
132 & 0.858294254825502 & 0.283411490348996 & 0.141705745174498 \tabularnewline
133 & 0.922797042217372 & 0.154405915565255 & 0.0772029577826277 \tabularnewline
134 & 0.824287319613511 & 0.351425360772978 & 0.175712680386489 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185430&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.380203718014839[/C][C]0.760407436029678[/C][C]0.619796281985161[/C][/ROW]
[ROW][C]10[/C][C]0.226550814236603[/C][C]0.453101628473206[/C][C]0.773449185763397[/C][/ROW]
[ROW][C]11[/C][C]0.225356945191359[/C][C]0.450713890382719[/C][C]0.774643054808641[/C][/ROW]
[ROW][C]12[/C][C]0.148880281064088[/C][C]0.297760562128177[/C][C]0.851119718935912[/C][/ROW]
[ROW][C]13[/C][C]0.0927993060601099[/C][C]0.18559861212022[/C][C]0.90720069393989[/C][/ROW]
[ROW][C]14[/C][C]0.274180449420143[/C][C]0.548360898840286[/C][C]0.725819550579857[/C][/ROW]
[ROW][C]15[/C][C]0.229882630052417[/C][C]0.459765260104835[/C][C]0.770117369947583[/C][/ROW]
[ROW][C]16[/C][C]0.175203295716327[/C][C]0.350406591432654[/C][C]0.824796704283673[/C][/ROW]
[ROW][C]17[/C][C]0.145653874771241[/C][C]0.291307749542482[/C][C]0.854346125228759[/C][/ROW]
[ROW][C]18[/C][C]0.102161356560239[/C][C]0.204322713120478[/C][C]0.897838643439761[/C][/ROW]
[ROW][C]19[/C][C]0.0744928606909608[/C][C]0.148985721381922[/C][C]0.925507139309039[/C][/ROW]
[ROW][C]20[/C][C]0.0554367618084156[/C][C]0.110873523616831[/C][C]0.944563238191584[/C][/ROW]
[ROW][C]21[/C][C]0.040179182276557[/C][C]0.080358364553114[/C][C]0.959820817723443[/C][/ROW]
[ROW][C]22[/C][C]0.0393559099938772[/C][C]0.0787118199877543[/C][C]0.960644090006123[/C][/ROW]
[ROW][C]23[/C][C]0.0278057892886827[/C][C]0.0556115785773654[/C][C]0.972194210711317[/C][/ROW]
[ROW][C]24[/C][C]0.0261908753378593[/C][C]0.0523817506757186[/C][C]0.973809124662141[/C][/ROW]
[ROW][C]25[/C][C]0.0196369044513221[/C][C]0.0392738089026442[/C][C]0.980363095548678[/C][/ROW]
[ROW][C]26[/C][C]0.0188199303457894[/C][C]0.0376398606915788[/C][C]0.981180069654211[/C][/ROW]
[ROW][C]27[/C][C]0.0132951885834173[/C][C]0.0265903771668347[/C][C]0.986704811416583[/C][/ROW]
[ROW][C]28[/C][C]0.00925411648281004[/C][C]0.0185082329656201[/C][C]0.99074588351719[/C][/ROW]
[ROW][C]29[/C][C]0.00682429232689369[/C][C]0.0136485846537874[/C][C]0.993175707673106[/C][/ROW]
[ROW][C]30[/C][C]0.00524229322943982[/C][C]0.0104845864588796[/C][C]0.99475770677056[/C][/ROW]
[ROW][C]31[/C][C]0.00376053708263028[/C][C]0.00752107416526056[/C][C]0.99623946291737[/C][/ROW]
[ROW][C]32[/C][C]0.00264884456093867[/C][C]0.00529768912187734[/C][C]0.997351155439061[/C][/ROW]
[ROW][C]33[/C][C]0.00158441816249061[/C][C]0.00316883632498123[/C][C]0.998415581837509[/C][/ROW]
[ROW][C]34[/C][C]0.000949812198403093[/C][C]0.00189962439680619[/C][C]0.999050187801597[/C][/ROW]
[ROW][C]35[/C][C]0.000904483403243494[/C][C]0.00180896680648699[/C][C]0.999095516596757[/C][/ROW]
[ROW][C]36[/C][C]0.000554389651512199[/C][C]0.0011087793030244[/C][C]0.999445610348488[/C][/ROW]
[ROW][C]37[/C][C]0.000953302437418091[/C][C]0.00190660487483618[/C][C]0.999046697562582[/C][/ROW]
[ROW][C]38[/C][C]0.000623563627330854[/C][C]0.00124712725466171[/C][C]0.999376436372669[/C][/ROW]
[ROW][C]39[/C][C]0.000679545218193717[/C][C]0.00135909043638743[/C][C]0.999320454781806[/C][/ROW]
[ROW][C]40[/C][C]0.000391468983994301[/C][C]0.000782937967988602[/C][C]0.999608531016006[/C][/ROW]
[ROW][C]41[/C][C]0.00022239110071842[/C][C]0.00044478220143684[/C][C]0.999777608899282[/C][/ROW]
[ROW][C]42[/C][C]0.000125209204463756[/C][C]0.000250418408927513[/C][C]0.999874790795536[/C][/ROW]
[ROW][C]43[/C][C]8.33169694265974e-05[/C][C]0.000166633938853195[/C][C]0.999916683030573[/C][/ROW]
[ROW][C]44[/C][C]4.96246428269877e-05[/C][C]9.92492856539754e-05[/C][C]0.999950375357173[/C][/ROW]
[ROW][C]45[/C][C]2.78024802882699e-05[/C][C]5.56049605765398e-05[/C][C]0.999972197519712[/C][/ROW]
[ROW][C]46[/C][C]1.55892528133105e-05[/C][C]3.1178505626621e-05[/C][C]0.999984410747187[/C][/ROW]
[ROW][C]47[/C][C]8.24671349256182e-06[/C][C]1.64934269851236e-05[/C][C]0.999991753286507[/C][/ROW]
[ROW][C]48[/C][C]6.04615357661854e-06[/C][C]1.20923071532371e-05[/C][C]0.999993953846423[/C][/ROW]
[ROW][C]49[/C][C]1.53017721988539e-05[/C][C]3.06035443977078e-05[/C][C]0.999984698227801[/C][/ROW]
[ROW][C]50[/C][C]8.84288310965543e-06[/C][C]1.76857662193109e-05[/C][C]0.99999115711689[/C][/ROW]
[ROW][C]51[/C][C]5.5463109965572e-06[/C][C]1.10926219931144e-05[/C][C]0.999994453689003[/C][/ROW]
[ROW][C]52[/C][C]4.73319384298891e-06[/C][C]9.46638768597783e-06[/C][C]0.999995266806157[/C][/ROW]
[ROW][C]53[/C][C]2.92119304197508e-06[/C][C]5.84238608395016e-06[/C][C]0.999997078806958[/C][/ROW]
[ROW][C]54[/C][C]2.13393822064544e-06[/C][C]4.26787644129088e-06[/C][C]0.999997866061779[/C][/ROW]
[ROW][C]55[/C][C]2.17871888513884e-06[/C][C]4.35743777027769e-06[/C][C]0.999997821281115[/C][/ROW]
[ROW][C]56[/C][C]1.16689885369866e-06[/C][C]2.33379770739733e-06[/C][C]0.999998833101146[/C][/ROW]
[ROW][C]57[/C][C]9.74733630320527e-07[/C][C]1.94946726064105e-06[/C][C]0.99999902526637[/C][/ROW]
[ROW][C]58[/C][C]6.32652037017185e-07[/C][C]1.26530407403437e-06[/C][C]0.999999367347963[/C][/ROW]
[ROW][C]59[/C][C]3.68444349092926e-07[/C][C]7.36888698185853e-07[/C][C]0.999999631555651[/C][/ROW]
[ROW][C]60[/C][C]8.14988804120111e-07[/C][C]1.62997760824022e-06[/C][C]0.999999185011196[/C][/ROW]
[ROW][C]61[/C][C]4.370814176251e-07[/C][C]8.74162835250201e-07[/C][C]0.999999562918582[/C][/ROW]
[ROW][C]62[/C][C]2.1970638554784e-07[/C][C]4.39412771095681e-07[/C][C]0.999999780293614[/C][/ROW]
[ROW][C]63[/C][C]1.45337239123758e-07[/C][C]2.90674478247517e-07[/C][C]0.999999854662761[/C][/ROW]
[ROW][C]64[/C][C]7.11390290635137e-08[/C][C]1.42278058127027e-07[/C][C]0.999999928860971[/C][/ROW]
[ROW][C]65[/C][C]3.46204011183065e-08[/C][C]6.9240802236613e-08[/C][C]0.999999965379599[/C][/ROW]
[ROW][C]66[/C][C]1.74366281871731e-08[/C][C]3.48732563743462e-08[/C][C]0.999999982563372[/C][/ROW]
[ROW][C]67[/C][C]8.71125656256738e-09[/C][C]1.74225131251348e-08[/C][C]0.999999991288743[/C][/ROW]
[ROW][C]68[/C][C]6.96460508261457e-09[/C][C]1.39292101652291e-08[/C][C]0.999999993035395[/C][/ROW]
[ROW][C]69[/C][C]3.5766419933135e-09[/C][C]7.153283986627e-09[/C][C]0.999999996423358[/C][/ROW]
[ROW][C]70[/C][C]1.952292162945e-09[/C][C]3.90458432589e-09[/C][C]0.999999998047708[/C][/ROW]
[ROW][C]71[/C][C]1.25097442019289e-09[/C][C]2.50194884038579e-09[/C][C]0.999999998749026[/C][/ROW]
[ROW][C]72[/C][C]1.66675306909105e-08[/C][C]3.3335061381821e-08[/C][C]0.999999983332469[/C][/ROW]
[ROW][C]73[/C][C]2.22501539586714e-08[/C][C]4.45003079173428e-08[/C][C]0.999999977749846[/C][/ROW]
[ROW][C]74[/C][C]1.86712522326342e-08[/C][C]3.73425044652683e-08[/C][C]0.999999981328748[/C][/ROW]
[ROW][C]75[/C][C]1.88569740495659e-08[/C][C]3.77139480991317e-08[/C][C]0.999999981143026[/C][/ROW]
[ROW][C]76[/C][C]9.08828827445203e-09[/C][C]1.81765765489041e-08[/C][C]0.999999990911712[/C][/ROW]
[ROW][C]77[/C][C]5.75161544249509e-09[/C][C]1.15032308849902e-08[/C][C]0.999999994248385[/C][/ROW]
[ROW][C]78[/C][C]3.55904408793514e-09[/C][C]7.11808817587028e-09[/C][C]0.999999996440956[/C][/ROW]
[ROW][C]79[/C][C]3.0185266447348e-09[/C][C]6.03705328946961e-09[/C][C]0.999999996981473[/C][/ROW]
[ROW][C]80[/C][C]1.49017575891915e-09[/C][C]2.9803515178383e-09[/C][C]0.999999998509824[/C][/ROW]
[ROW][C]81[/C][C]8.40326722840788e-10[/C][C]1.68065344568158e-09[/C][C]0.999999999159673[/C][/ROW]
[ROW][C]82[/C][C]5.93164724498254e-10[/C][C]1.18632944899651e-09[/C][C]0.999999999406835[/C][/ROW]
[ROW][C]83[/C][C]1.43253430229696e-09[/C][C]2.86506860459392e-09[/C][C]0.999999998567466[/C][/ROW]
[ROW][C]84[/C][C]1.68840218600847e-09[/C][C]3.37680437201693e-09[/C][C]0.999999998311598[/C][/ROW]
[ROW][C]85[/C][C]4.51836068861085e-09[/C][C]9.0367213772217e-09[/C][C]0.999999995481639[/C][/ROW]
[ROW][C]86[/C][C]8.42938420222746e-09[/C][C]1.68587684044549e-08[/C][C]0.999999991570616[/C][/ROW]
[ROW][C]87[/C][C]9.0744758766794e-09[/C][C]1.81489517533588e-08[/C][C]0.999999990925524[/C][/ROW]
[ROW][C]88[/C][C]1.3483966754649e-07[/C][C]2.6967933509298e-07[/C][C]0.999999865160332[/C][/ROW]
[ROW][C]89[/C][C]1.87962172133763e-07[/C][C]3.75924344267525e-07[/C][C]0.999999812037828[/C][/ROW]
[ROW][C]90[/C][C]2.890890997546e-06[/C][C]5.78178199509201e-06[/C][C]0.999997109109002[/C][/ROW]
[ROW][C]91[/C][C]7.5220338864421e-06[/C][C]1.50440677728842e-05[/C][C]0.999992477966114[/C][/ROW]
[ROW][C]92[/C][C]7.36893557152935e-06[/C][C]1.47378711430587e-05[/C][C]0.999992631064428[/C][/ROW]
[ROW][C]93[/C][C]2.47121955306865e-05[/C][C]4.94243910613729e-05[/C][C]0.999975287804469[/C][/ROW]
[ROW][C]94[/C][C]4.11734770093537e-05[/C][C]8.23469540187073e-05[/C][C]0.999958826522991[/C][/ROW]
[ROW][C]95[/C][C]6.21628308656893e-05[/C][C]0.000124325661731379[/C][C]0.999937837169134[/C][/ROW]
[ROW][C]96[/C][C]8.13222777652265e-05[/C][C]0.000162644555530453[/C][C]0.999918677722235[/C][/ROW]
[ROW][C]97[/C][C]0.000108510511874915[/C][C]0.000217021023749829[/C][C]0.999891489488125[/C][/ROW]
[ROW][C]98[/C][C]0.000342659104251809[/C][C]0.000685318208503619[/C][C]0.999657340895748[/C][/ROW]
[ROW][C]99[/C][C]0.000782407847718238[/C][C]0.00156481569543648[/C][C]0.999217592152282[/C][/ROW]
[ROW][C]100[/C][C]0.00277081207197825[/C][C]0.00554162414395651[/C][C]0.997229187928022[/C][/ROW]
[ROW][C]101[/C][C]0.0187846067330948[/C][C]0.0375692134661896[/C][C]0.981215393266905[/C][/ROW]
[ROW][C]102[/C][C]0.0206339341134728[/C][C]0.0412678682269457[/C][C]0.979366065886527[/C][/ROW]
[ROW][C]103[/C][C]0.045042385166465[/C][C]0.0900847703329299[/C][C]0.954957614833535[/C][/ROW]
[ROW][C]104[/C][C]0.121333962088823[/C][C]0.242667924177646[/C][C]0.878666037911177[/C][/ROW]
[ROW][C]105[/C][C]0.320910137502184[/C][C]0.641820275004369[/C][C]0.679089862497816[/C][/ROW]
[ROW][C]106[/C][C]0.273557156822902[/C][C]0.547114313645803[/C][C]0.726442843177098[/C][/ROW]
[ROW][C]107[/C][C]0.232896465549358[/C][C]0.465792931098717[/C][C]0.767103534450641[/C][/ROW]
[ROW][C]108[/C][C]0.19870146106996[/C][C]0.39740292213992[/C][C]0.80129853893004[/C][/ROW]
[ROW][C]109[/C][C]0.251577337674793[/C][C]0.503154675349587[/C][C]0.748422662325207[/C][/ROW]
[ROW][C]110[/C][C]0.266120148562807[/C][C]0.532240297125614[/C][C]0.733879851437193[/C][/ROW]
[ROW][C]111[/C][C]0.384398546605598[/C][C]0.768797093211196[/C][C]0.615601453394402[/C][/ROW]
[ROW][C]112[/C][C]0.493424786791488[/C][C]0.986849573582977[/C][C]0.506575213208512[/C][/ROW]
[ROW][C]113[/C][C]0.554325717737469[/C][C]0.891348564525061[/C][C]0.445674282262531[/C][/ROW]
[ROW][C]114[/C][C]0.787560239573279[/C][C]0.424879520853442[/C][C]0.212439760426721[/C][/ROW]
[ROW][C]115[/C][C]0.823255490619436[/C][C]0.353489018761128[/C][C]0.176744509380564[/C][/ROW]
[ROW][C]116[/C][C]0.878006113234678[/C][C]0.243987773530644[/C][C]0.121993886765322[/C][/ROW]
[ROW][C]117[/C][C]0.958489524756021[/C][C]0.0830209504879574[/C][C]0.0415104752439787[/C][/ROW]
[ROW][C]118[/C][C]0.97775276951179[/C][C]0.0444944609764205[/C][C]0.0222472304882102[/C][/ROW]
[ROW][C]119[/C][C]0.968135883596209[/C][C]0.0637282328075828[/C][C]0.0318641164037914[/C][/ROW]
[ROW][C]120[/C][C]0.956875461387336[/C][C]0.0862490772253274[/C][C]0.0431245386126637[/C][/ROW]
[ROW][C]121[/C][C]0.942670190118318[/C][C]0.114659619763364[/C][C]0.0573298098816819[/C][/ROW]
[ROW][C]122[/C][C]0.918464592597931[/C][C]0.163070814804139[/C][C]0.0815354074020694[/C][/ROW]
[ROW][C]123[/C][C]0.886257998717252[/C][C]0.227484002565495[/C][C]0.113742001282748[/C][/ROW]
[ROW][C]124[/C][C]0.877461994526938[/C][C]0.245076010946124[/C][C]0.122538005473062[/C][/ROW]
[ROW][C]125[/C][C]0.831126498495759[/C][C]0.337747003008481[/C][C]0.168873501504241[/C][/ROW]
[ROW][C]126[/C][C]0.888855640843487[/C][C]0.222288718313025[/C][C]0.111144359156513[/C][/ROW]
[ROW][C]127[/C][C]0.882878471779914[/C][C]0.234243056440172[/C][C]0.117121528220086[/C][/ROW]
[ROW][C]128[/C][C]0.877848628197274[/C][C]0.244302743605451[/C][C]0.122151371802726[/C][/ROW]
[ROW][C]129[/C][C]0.858412774131913[/C][C]0.283174451736174[/C][C]0.141587225868087[/C][/ROW]
[ROW][C]130[/C][C]0.941685273438707[/C][C]0.116629453122586[/C][C]0.0583147265612932[/C][/ROW]
[ROW][C]131[/C][C]0.891173743323433[/C][C]0.217652513353133[/C][C]0.108826256676567[/C][/ROW]
[ROW][C]132[/C][C]0.858294254825502[/C][C]0.283411490348996[/C][C]0.141705745174498[/C][/ROW]
[ROW][C]133[/C][C]0.922797042217372[/C][C]0.154405915565255[/C][C]0.0772029577826277[/C][/ROW]
[ROW][C]134[/C][C]0.824287319613511[/C][C]0.351425360772978[/C][C]0.175712680386489[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185430&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185430&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.380203718014839	0.760407436029678	0.619796281985161
10	0.226550814236603	0.453101628473206	0.773449185763397
11	0.225356945191359	0.450713890382719	0.774643054808641
12	0.148880281064088	0.297760562128177	0.851119718935912
13	0.0927993060601099	0.18559861212022	0.90720069393989
14	0.274180449420143	0.548360898840286	0.725819550579857
15	0.229882630052417	0.459765260104835	0.770117369947583
16	0.175203295716327	0.350406591432654	0.824796704283673
17	0.145653874771241	0.291307749542482	0.854346125228759
18	0.102161356560239	0.204322713120478	0.897838643439761
19	0.0744928606909608	0.148985721381922	0.925507139309039
20	0.0554367618084156	0.110873523616831	0.944563238191584
21	0.040179182276557	0.080358364553114	0.959820817723443
22	0.0393559099938772	0.0787118199877543	0.960644090006123
23	0.0278057892886827	0.0556115785773654	0.972194210711317
24	0.0261908753378593	0.0523817506757186	0.973809124662141
25	0.0196369044513221	0.0392738089026442	0.980363095548678
26	0.0188199303457894	0.0376398606915788	0.981180069654211
27	0.0132951885834173	0.0265903771668347	0.986704811416583
28	0.00925411648281004	0.0185082329656201	0.99074588351719
29	0.00682429232689369	0.0136485846537874	0.993175707673106
30	0.00524229322943982	0.0104845864588796	0.99475770677056
31	0.00376053708263028	0.00752107416526056	0.99623946291737
32	0.00264884456093867	0.00529768912187734	0.997351155439061
33	0.00158441816249061	0.00316883632498123	0.998415581837509
34	0.000949812198403093	0.00189962439680619	0.999050187801597
35	0.000904483403243494	0.00180896680648699	0.999095516596757
36	0.000554389651512199	0.0011087793030244	0.999445610348488
37	0.000953302437418091	0.00190660487483618	0.999046697562582
38	0.000623563627330854	0.00124712725466171	0.999376436372669
39	0.000679545218193717	0.00135909043638743	0.999320454781806
40	0.000391468983994301	0.000782937967988602	0.999608531016006
41	0.00022239110071842	0.00044478220143684	0.999777608899282
42	0.000125209204463756	0.000250418408927513	0.999874790795536
43	8.33169694265974e-05	0.000166633938853195	0.999916683030573
44	4.96246428269877e-05	9.92492856539754e-05	0.999950375357173
45	2.78024802882699e-05	5.56049605765398e-05	0.999972197519712
46	1.55892528133105e-05	3.1178505626621e-05	0.999984410747187
47	8.24671349256182e-06	1.64934269851236e-05	0.999991753286507
48	6.04615357661854e-06	1.20923071532371e-05	0.999993953846423
49	1.53017721988539e-05	3.06035443977078e-05	0.999984698227801
50	8.84288310965543e-06	1.76857662193109e-05	0.99999115711689
51	5.5463109965572e-06	1.10926219931144e-05	0.999994453689003
52	4.73319384298891e-06	9.46638768597783e-06	0.999995266806157
53	2.92119304197508e-06	5.84238608395016e-06	0.999997078806958
54	2.13393822064544e-06	4.26787644129088e-06	0.999997866061779
55	2.17871888513884e-06	4.35743777027769e-06	0.999997821281115
56	1.16689885369866e-06	2.33379770739733e-06	0.999998833101146
57	9.74733630320527e-07	1.94946726064105e-06	0.99999902526637
58	6.32652037017185e-07	1.26530407403437e-06	0.999999367347963
59	3.68444349092926e-07	7.36888698185853e-07	0.999999631555651
60	8.14988804120111e-07	1.62997760824022e-06	0.999999185011196
61	4.370814176251e-07	8.74162835250201e-07	0.999999562918582
62	2.1970638554784e-07	4.39412771095681e-07	0.999999780293614
63	1.45337239123758e-07	2.90674478247517e-07	0.999999854662761
64	7.11390290635137e-08	1.42278058127027e-07	0.999999928860971
65	3.46204011183065e-08	6.9240802236613e-08	0.999999965379599
66	1.74366281871731e-08	3.48732563743462e-08	0.999999982563372
67	8.71125656256738e-09	1.74225131251348e-08	0.999999991288743
68	6.96460508261457e-09	1.39292101652291e-08	0.999999993035395
69	3.5766419933135e-09	7.153283986627e-09	0.999999996423358
70	1.952292162945e-09	3.90458432589e-09	0.999999998047708
71	1.25097442019289e-09	2.50194884038579e-09	0.999999998749026
72	1.66675306909105e-08	3.3335061381821e-08	0.999999983332469
73	2.22501539586714e-08	4.45003079173428e-08	0.999999977749846
74	1.86712522326342e-08	3.73425044652683e-08	0.999999981328748
75	1.88569740495659e-08	3.77139480991317e-08	0.999999981143026
76	9.08828827445203e-09	1.81765765489041e-08	0.999999990911712
77	5.75161544249509e-09	1.15032308849902e-08	0.999999994248385
78	3.55904408793514e-09	7.11808817587028e-09	0.999999996440956
79	3.0185266447348e-09	6.03705328946961e-09	0.999999996981473
80	1.49017575891915e-09	2.9803515178383e-09	0.999999998509824
81	8.40326722840788e-10	1.68065344568158e-09	0.999999999159673
82	5.93164724498254e-10	1.18632944899651e-09	0.999999999406835
83	1.43253430229696e-09	2.86506860459392e-09	0.999999998567466
84	1.68840218600847e-09	3.37680437201693e-09	0.999999998311598
85	4.51836068861085e-09	9.0367213772217e-09	0.999999995481639
86	8.42938420222746e-09	1.68587684044549e-08	0.999999991570616
87	9.0744758766794e-09	1.81489517533588e-08	0.999999990925524
88	1.3483966754649e-07	2.6967933509298e-07	0.999999865160332
89	1.87962172133763e-07	3.75924344267525e-07	0.999999812037828
90	2.890890997546e-06	5.78178199509201e-06	0.999997109109002
91	7.5220338864421e-06	1.50440677728842e-05	0.999992477966114
92	7.36893557152935e-06	1.47378711430587e-05	0.999992631064428
93	2.47121955306865e-05	4.94243910613729e-05	0.999975287804469
94	4.11734770093537e-05	8.23469540187073e-05	0.999958826522991
95	6.21628308656893e-05	0.000124325661731379	0.999937837169134
96	8.13222777652265e-05	0.000162644555530453	0.999918677722235
97	0.000108510511874915	0.000217021023749829	0.999891489488125
98	0.000342659104251809	0.000685318208503619	0.999657340895748
99	0.000782407847718238	0.00156481569543648	0.999217592152282
100	0.00277081207197825	0.00554162414395651	0.997229187928022
101	0.0187846067330948	0.0375692134661896	0.981215393266905
102	0.0206339341134728	0.0412678682269457	0.979366065886527
103	0.045042385166465	0.0900847703329299	0.954957614833535
104	0.121333962088823	0.242667924177646	0.878666037911177
105	0.320910137502184	0.641820275004369	0.679089862497816
106	0.273557156822902	0.547114313645803	0.726442843177098
107	0.232896465549358	0.465792931098717	0.767103534450641
108	0.19870146106996	0.39740292213992	0.80129853893004
109	0.251577337674793	0.503154675349587	0.748422662325207
110	0.266120148562807	0.532240297125614	0.733879851437193
111	0.384398546605598	0.768797093211196	0.615601453394402
112	0.493424786791488	0.986849573582977	0.506575213208512
113	0.554325717737469	0.891348564525061	0.445674282262531
114	0.787560239573279	0.424879520853442	0.212439760426721
115	0.823255490619436	0.353489018761128	0.176744509380564
116	0.878006113234678	0.243987773530644	0.121993886765322
117	0.958489524756021	0.0830209504879574	0.0415104752439787
118	0.97775276951179	0.0444944609764205	0.0222472304882102
119	0.968135883596209	0.0637282328075828	0.0318641164037914
120	0.956875461387336	0.0862490772253274	0.0431245386126637
121	0.942670190118318	0.114659619763364	0.0573298098816819
122	0.918464592597931	0.163070814804139	0.0815354074020694
123	0.886257998717252	0.227484002565495	0.113742001282748
124	0.877461994526938	0.245076010946124	0.122538005473062
125	0.831126498495759	0.337747003008481	0.168873501504241
126	0.888855640843487	0.222288718313025	0.111144359156513
127	0.882878471779914	0.234243056440172	0.117121528220086
128	0.877848628197274	0.244302743605451	0.122151371802726
129	0.858412774131913	0.283174451736174	0.141587225868087
130	0.941685273438707	0.116629453122586	0.0583147265612932
131	0.891173743323433	0.217652513353133	0.108826256676567
132	0.858294254825502	0.283411490348996	0.141705745174498
133	0.922797042217372	0.154405915565255	0.0772029577826277
134	0.824287319613511	0.351425360772978	0.175712680386489

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	70	0.555555555555556	NOK
5% type I error level	79	0.626984126984127	NOK
10% type I error level	87	0.69047619047619	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 70 & 0.555555555555556 & NOK \tabularnewline
5% type I error level & 79 & 0.626984126984127 & NOK \tabularnewline
10% type I error level & 87 & 0.69047619047619 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185430&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]70[/C][C]0.555555555555556[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]79[/C][C]0.626984126984127[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]87[/C][C]0.69047619047619[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185430&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185430&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	70	0.555555555555556	NOK
5% type I error level	79	0.626984126984127	NOK
10% type I error level	87	0.69047619047619	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code