Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 15:10:46 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t13521463399s7we5mp0ycjqf0.htm/, Retrieved Sun, 05 Feb 2023 22:54:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186274, Retrieved Sun, 05 Feb 2023 22:54:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact58
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multipleregression] [2012-11-05 20:10:46] [239167cccea8953a8e1721fd6db07280] [Current]
- RMPD    [Decomposition by Loess] [WS8 loes] [2012-11-12 23:36:30] [6e5c9f686e58f6d348ebade5a40c0120]
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Dataseries X:
46	26	95556	47.38555556
48	20	54565	24.06138889
37	24	63016	31.4825
75	25	79774	42.36388889
31	15	31258	23.94611111
18	16	52491	10.34916667
79	20	91256	85.01527778
16	18	22807	9.097222222
38	19	77411	32.36166667
24	20	48821	36.26083333
65	30	52295	44.96555556
74	37	63262	35.63166667
43	23	50466	28.43055556
42	36	62932	53.61777778
55	29	38439	39.32611111
121	35	70817	70.43305556
42	24	105965	50.30833333
102	22	73795	55.12
36	19	82043	31.62583333
50	30	74349	44.42777778
48	27	82204	46.33944444
56	26	55709	79.63194444
19	15	37137	25.46027778
32	30	70780	30.07722222
77	28	55027	40.65055556
90	24	56699	40.31722222
81	21	65911	44.92777778
55	27	56316	44.69583333
34	21	26982	29.69111111
38	30	54628	52.26388889
53	30	96750	52.61138889
48	33	53009	35.96777778
63	30	64664	56.675
25	20	36990	17.42527778
56	27	85224	67.67361111
37	25	37048	46.45972222
83	30	59635	73.48
50	20	42051	33.89555556
26	8	26998	22.49
108	24	63717	58.27638889
55	25	55071	62.27916667
41	25	40001	32.21416667
49	21	54506	38.38638889
31	21	35838	22.52944444
49	21	50838	25.86805556
96	26	86997	84.93222222
42	26	33032	21.88888889
55	30	61704	44.12083333
70	34	117986	61.59583333
39	30	56733	36.41888889
53	18	55064	35.75944444
24	4	5950	6.718888889
209	31	84607	71.57277778
17	18	32551	18.06361111
58	14	31701	27.24055556
27	20	71170	48.21861111
58	36	101773	50.01166667
114	24	101653	54.79611111
75	26	81493	58.90555556
51	22	55901	39.32833333
86	31	109104	68.08527778
77	21	114425	57.46638889
62	31	36311	40.47111111
60	26	70027	47.39861111
39	24	73713	39.46222222
35	15	40671	31.89444444
86	19	89041	31.51694444
102	28	57231	40.35694444
49	24	68608	41.94416667
35	18	59155	25.50333333
33	25	55827	33.00194444
28	20	22618	19.2975
44	25	58425	35.175
37	24	65724	40.53
33	23	56979	27.33138889
45	25	72369	53.035
57	20	79194	55.22138889
58	23	202316	29.49805556
36	22	44970	24.81055556
42	25	49319	33.43388889
30	18	36252	27.44194444
67	30	75741	76.37583333
53	22	38417	36.88833333
59	25	64102	37.56972222
25	8	56622	22.48694444
39	21	15430	30.34361111
36	22	72571	26.84277778
114	24	67271	62.83083333
54	30	43460	47.57944444
70	27	99501	32.72638889
51	24	28340	37.10027778
49	25	76013	42.27583333
42	21	37361	31.11222222
51	24	48204	47.11472222
51	24	76168	52.07861111
27	20	85168	36.25916667
29	20	125410	39.53861111
54	24	123328	52.71222222
92	40	83038	56.00083333
72	22	120087	68.565
63	31	91939	43.31861111
41	26	103646	50.71694444
111	20	29467	29.54194444
14	19	43750	12.02416667
45	15	34497	35.41472222
91	21	66477	35.53611111
29	22	71181	41.39055556
64	24	74482	52.12583333
32	19	174949	20.58666667
65	24	46765	26.11277778
42	23	90257	49.0625
55	27	51370	39.42583333
10	1	1168	6.371666667
53	24	51360	34.97972222
25	11	25162	17.1825
33	27	21067	25.35833333
66	22	58233	70.86111111
16	0	855	5.848333333
35	17	85903	46.97027778
19	8	14116	8.726111111
76	24	57637	52.41694444
35	31	94137	38.20666667
46	24	62147	21.435
29	20	62832	20.71305556
34	8	8773	10.615
25	22	63785	25.26694444
48	33	65196	53.95111111
38	33	73087	37.5725
50	31	72631	67.85333333
65	33	86281	56.04111111
72	35	162365	71.22277778
23	21	56530	38.65111111
29	20	35606	21.24166667
194	24	70111	52.63944444
114	29	92046	77.87055556
15	20	63989	14.16638889
86	27	104911	70.35388889
50	24	43448	28.6775
33	26	60029	46.68305556
50	26	38650	35.76888889
72	12	47261	21.04055556
81	21	73586	69.23111111
54	24	83042	42.32388889
63	21	37238	48.12777778
69	30	63958	54.77694444
39	32	78956	18.75194444
49	24	99518	38.72472222
67	29	111436	51.49055556
0	0	0	0
10	0	6023	4.08
1	0	0	0.027222222
2	0	0	0.126388889
0	0	0	0
0	0	0	0
58	20	42564	38.30138889
72	27	38885	51.46888889
0	0	0	0
4	0	0	0.056388889
5	0	1644	1.999722222
20	5	6179	12.96111111
5	1	3926	4.874166667
27	23	23238	20.43527778
2	0	0	0.269166667
33	16	49288	29.29916667


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 15 seconds R Server 'Herman Ole Andreas Wold' @ wold.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 & 15 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186274&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186274&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186274&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 15 seconds R Server 'Herman Ole Andreas Wold' @ wold.wessa.net

 Multiple Linear Regression - Estimated Regression Equation AantalurenRFC[t] = -0.0992742116382974 + 0.246966684905508#logins[t] + 0.80493782844676otaal#peer_reviews[t] + 0.000133114330761128totaal#karakterscompendium[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
AantalurenRFC[t] =  -0.0992742116382974 +  0.246966684905508#logins[t] +  0.80493782844676otaal#peer_reviews[t] +  0.000133114330761128totaal#karakterscompendium[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186274&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]AantalurenRFC[t] =  -0.0992742116382974 +  0.246966684905508#logins[t] +  0.80493782844676otaal#peer_reviews[t] +  0.000133114330761128totaal#karakterscompendium[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186274&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186274&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 AantalurenRFC[t] = -0.0992742116382974 + 0.246966684905508#logins[t] + 0.80493782844676otaal#peer_reviews[t] + 0.000133114330761128totaal#karakterscompendium[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) -0.0992742116382974 2.355258 -0.0422 0.966432 0.483216 #logins 0.246966684905508 0.034272 7.2061 0 0 otaal#peer_reviews 0.80493782844676 0.13594 5.9213 0 0 totaal#karakterscompendium 0.000133114330761128 3.3e-05 4.0445 8.1e-05 4.1e-05

\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) & -0.0992742116382974 & 2.355258 & -0.0422 & 0.966432 & 0.483216 \tabularnewline
#logins & 0.246966684905508 & 0.034272 & 7.2061 & 0 & 0 \tabularnewline
otaal#peer_reviews & 0.80493782844676 & 0.13594 & 5.9213 & 0 & 0 \tabularnewline
totaal#karakterscompendium & 0.000133114330761128 & 3.3e-05 & 4.0445 & 8.1e-05 & 4.1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186274&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]-0.0992742116382974[/C][C]2.355258[/C][C]-0.0422[/C][C]0.966432[/C][C]0.483216[/C][/ROW]
[ROW][C]#logins[/C][C]0.246966684905508[/C][C]0.034272[/C][C]7.2061[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]otaal#peer_reviews[/C][C]0.80493782844676[/C][C]0.13594[/C][C]5.9213[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]totaal#karakterscompendium[/C][C]0.000133114330761128[/C][C]3.3e-05[/C][C]4.0445[/C][C]8.1e-05[/C][C]4.1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186274&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186274&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) -0.0992742116382974 2.355258 -0.0422 0.966432 0.483216 #logins 0.246966684905508 0.034272 7.2061 0 0 otaal#peer_reviews 0.80493782844676 0.13594 5.9213 0 0 totaal#karakterscompendium 0.000133114330761128 3.3e-05 4.0445 8.1e-05 4.1e-05

 Multiple Linear Regression - Regression Statistics Multiple R 0.823788024047727 R-squared 0.678626708564458 Adjusted R-squared 0.672600959350041 F-TEST (value) 112.62113380704 F-TEST (DF numerator) 3 F-TEST (DF denominator) 160 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.0474264069462 Sum Squared Residuals 19527.3008347026

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.823788024047727 \tabularnewline
R-squared & 0.678626708564458 \tabularnewline
Adjusted R-squared & 0.672600959350041 \tabularnewline
F-TEST (value) & 112.62113380704 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.0474264069462 \tabularnewline
Sum Squared Residuals & 19527.3008347026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186274&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.823788024047727[/C][/ROW]
[ROW][C]R-squared[/C][C]0.678626708564458[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.672600959350041[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]112.62113380704[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/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]11.0474264069462[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19527.3008347026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186274&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186274&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 0.823788024047727 R-squared 0.678626708564458 Adjusted R-squared 0.672600959350041 F-TEST (value) 112.62113380704 F-TEST (DF numerator) 3 F-TEST (DF denominator) 160 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.0474264069462 Sum Squared Residuals 19527.3008347026

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 47.38555556 44.9094498238412 2.47610573615882 2 24.06138889 35.1172666907422 -11.0558778007422 3 31.4825 36.745333679831 -5.26283367983099 4 42.36388889 49.165735489582 -6.80184659958204 5 23.94611111 23.7916481980652 0.154462911934808 6 10.34916667 24.2124357077914 -13.8632690377914 7 85.01527778 47.6573318327695 37.3579459472305 8 9.097222222 21.3770122005606 -12.2797899785606 9 32.36166667 34.8837920138091 -2.52212534380914 10 36.26083333 28.4254575371181 7.83537579288187 11 44.96555556 47.0629090877757 -2.09735352777571 12 35.63166667 56.3800389165099 -20.7483722465099 13 28.43055556 35.7516111097651 -7.32105554976512 14 53.61777778 47.6282394419357 5.98953833806429 15 39.32611111 41.9438722432477 -2.61776113324769 16 70.43305556 67.3832762190756 3.04977934092442 17 50.30833333 43.6972944962182 6.61103883378178 18 55.12 52.6231319130697 2.49686808693031 19 31.62583333 35.0064442240837 -3.38061089408367 20 44.42777778 46.294112264799 -1.86633448479901 21 46.33944444 44.4309784777764 1.90846596222362 22 79.63194444 42.0749099350576 37.5570345049424 23 25.46027778 21.6106271297438 3.84965065025623 24 30.07722222 41.3736268900134 -11.2964046700134 25 40.65055556 48.7803020013877 -8.12974644138769 26 40.31722222 48.9936847524049 -8.67646253240486 27 44.92777778 45.5824203178865 -0.654642537886524 28 44.69583333 42.7136814773708 1.98215185262915 29 29.69111111 28.7929783451277 0.898132764872311 30 52.26388889 40.7053643289927 11.5585245610073 31 52.61138889 50.0169064428956 2.59448244710443 32 35.96777778 45.3743325618858 -9.4065547818858 33 56.675 48.2154668751491 8.45953312485091 34 17.42527778 27.0975485747887 -9.67227079478873 35 67.67361111 46.808717235919 20.8648938740809 36 46.45972222 34.0935585670728 12.3661636529272 37 73.48 52.4853686038615 20.9946313961385 38 33.89555556 33.9454073254085 -0.0498517654085003 39 22.49 16.3551829253679 6.13481707463207 40 58.27638889 54.3732814539856 3.9031074360144 41 62.27916667 40.9380784786797 21.3410881913203 42 32.21416667 35.4745119254324 -3.26034525543242 43 38.38638889 36.1613174585796 2.2250714314204 44 22.52944444 29.2309388036317 -6.70149436363172 45 25.86805556 35.6730540933478 -9.80499853334778 46 84.93222222 56.1184585121321 28.8137637078679 47 21.88888889 35.5987426677104 -13.7098537777104 48 44.12083333 45.8457149768521 -1.72488164685208 49 61.59583333 60.2619073281196 1.33392600188044 50 36.41888889 41.2325366801504 -4.8136477901504 51 35.75944444 34.8086485094261 0.950795930573938 52 6.718888889 9.83970780790965 -3.12081891890965 53 71.57277778 87.7322397981692 -16.1594620181692 54 18.06361111 22.9210449244025 -4.8574338144025 55 27.24055556 29.7137805105943 -2.47322495059433 56 48.21861111 32.1413297700151 16.0772813399849 57 50.01166667 56.7500001215168 -6.73833345151682 58 54.79611111 60.9049068151728 -6.10879570517281 59 58.90555556 50.1994968526072 8.70605870739283 60 39.32833333 37.6458831482492 1.68245018175085 61 68.08527778 60.6162393154471 7.46903846455293 62 57.46638889 51.0524622208099 6.41392666919013 63 40.47111111 44.9992473986201 -4.52813628862007 64 47.39861111 44.9687076625175 2.42990344748254 65 39.46222222 38.6631910457938 0.799031174206211 66 31.89444444 26.0325201331417 5.86192430685828 67 31.51694444 48.2863125560254 -16.7693681160254 68 40.35694444 55.2478531090229 -14.8909086690229 69 41.94416667 40.4533092363133 1.49085743368669 70 25.50333333 30.9078189082707 -5.4044855782707 71 33.00194444 35.605445844814 -2.60350140481396 72 19.2975 25.9253294678063 -6.62782946780632 73 35.175 38.667910410092 -3.49291041009197 74 40.53 37.1058072875321 3.42419271246788 75 27.33138889 34.1489178969573 -6.81752900695726 76 53.035 40.7710233231306 12.2639766768694 77 55.22138889 40.6184397072076 14.6029491827924 78 29.49805556 59.669522509425 -30.171466949425 79 24.81055556 32.4863101251166 -7.67575456511664 80 33.43388889 36.9618379443701 -3.52794905437011 81 27.44194444 26.624267966321 0.817676473678963 82 76.37583333 50.6778410566121 25.6979922733879 83 36.88833333 35.8124455590326 1.0758877709674 84 37.56972222 43.1281007394055 -5.5583785194055 85 22.48694444 20.0515951749301 2.43534926506992 86 30.34361111 28.4900750207027 1.85353608929732 87 26.84277778 36.1603987684545 -9.31762098845454 88 62.83083333 56.3281698949437 6.50266343505629 89 47.57944444 43.1702104415406 4.40923399845944 90 32.72638889 52.1667241248728 -19.4403352348728 91 37.10027778 35.5869947350352 1.51328304496478 92 42.27583333 42.2439586840462 0.0318746459537728 93 31.11222222 32.1503054633415 -1.0380832433415 94 47.11472222 38.2311778012743 8.88354441872573 95 52.07861111 41.9535869466785 10.1250241633215 96 36.25916667 34.0046641720094 2.25450249799062 97 39.53861111 39.8553844403097 -0.316773330309714 98 52.71222222 48.9721588400898 3.74006337991022 99 56.00083333 65.8727217352814 -9.87188840528139 100 68.565 51.3762599654986 17.1887400345014 101 43.31861111 52.6510980751056 -9.33248696510562 102 50.71694444 44.7515113351712 5.96543310482883 103 29.54194444 47.3352643663465 -17.7933199263464 104 12.02416667 24.4758300883266 -12.4516634183266 105 35.41472222 27.6803391040776 7.7343831159224 106 35.53611111 48.1274298781524 -12.5913187681524 107 41.39055556 34.246603054358 7.14395250564199 108 52.12583333 44.9397230887868 7.1861102412132 109 20.58666667 46.385697498155 -25.799030828155 110 26.11277778 41.4971598679861 -15.3843820879861 111 49.0625 40.8013967601757 8.26110323982434 112 39.42583333 42.0552979974263 -2.62946466742631 113 6.371666667 3.33080800419254 3.04085866280746 114 34.97972222 39.1452199989674 -4.16549777896741 115 17.1825 18.2786318145253 -1.09613181452526 116 25.35833333 32.5882673644507 -7.22993403445067 117 70.86111111 41.6608060411667 29.2003050688333 118 5.848333333 3.9660054996506 1.8823278333494 119 46.97027778 33.6634231990226 13.3068545809774 120 8.726111111 12.9116373221645 -4.18552621116452 121 52.41694444 45.6610124059817 6.75593203401831 122 38.20666667 46.0286161967644 -7.82194952676436 123 21.435 38.8523574905491 -17.4173574905491 124 20.71305556 31.5253558499398 -10.8123002899398 125 10.615 15.9049077264904 -5.28990772649043 126 25.26694444 32.2742227244267 -7.00727828442668 127 53.95111111 46.9965969108717 6.95451419912833 128 37.5725 45.5773352458527 -8.00483524585265 129 67.85333333 46.8703596729982 20.9829736570018 130 56.04111111 54.0017462183637 2.03936489163631 131 71.22277778 67.4682594112254 3.75451836877456 132 38.65111111 30.0096070564969 8.64150405350309 133 21.24166667 27.9011850806374 -6.65951841063736 134 52.63944444 76.463549386746 -23.824104946746 135 77.87055556 63.6507665817845 14.2197889782155 136 14.16638889 28.2218355419533 -14.0554466519533 137 70.35388889 56.8383396127786 13.5155492772214 138 28.6775 37.3511193592688 -8.67361935926884 139 46.68305556 36.969730091119 9.71332546888102 140 35.76888889 38.3223124571705 -2.55342356717046 141 21.04055556 33.6326974290211 -12.5921418690211 142 69.23111111 46.6040728064782 22.6270383035218 143 42.32388889 43.609514911047 -1.28562602104698 144 48.12777778 37.3202327836736 10.8075449963264 145 54.77694444 49.6032882670648 5.17365617293522 146 18.75194444 45.8006121095485 -27.0486676695485 147 38.72472222 44.5678732001398 -5.84315098013978 148 51.49055556 54.6244192646839 -3.13386370468385 149 0 -0.0992742116382943 0.0992742116382943 150 4.08 3.17214025159106 0.907859748408937 151 0.027222222 0.147692473267216 -0.120470251267216 152 0.126388889 0.394659158172725 -0.268270269172725 153 0 -0.0992742116382943 0.0992742116382943 154 0 -0.0992742116382943 0.0992742116382943 155 38.30138889 35.989428456333 2.31196043366698 156 51.46888889 44.5917992212673 6.87708966873274 157 0 -0.0992742116382943 0.0992742116382943 158 0.056388889 0.888592527983742 -0.832203638983742 159 1.999722222 1.35439917266054 0.645323049339463 160 12.96111111 9.68726207847868 3.27384903152132 161 4.874166667 2.4631039039042 2.4110627630958 162 20.43527778 28.175707153313 -7.74042937331299 163 0.269166667 0.394659158172722 -0.125492491172722 164 29.29916667 27.4905707799461 1.80859589005389

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 47.38555556 & 44.9094498238412 & 2.47610573615882 \tabularnewline
2 & 24.06138889 & 35.1172666907422 & -11.0558778007422 \tabularnewline
3 & 31.4825 & 36.745333679831 & -5.26283367983099 \tabularnewline
4 & 42.36388889 & 49.165735489582 & -6.80184659958204 \tabularnewline
5 & 23.94611111 & 23.7916481980652 & 0.154462911934808 \tabularnewline
6 & 10.34916667 & 24.2124357077914 & -13.8632690377914 \tabularnewline
7 & 85.01527778 & 47.6573318327695 & 37.3579459472305 \tabularnewline
8 & 9.097222222 & 21.3770122005606 & -12.2797899785606 \tabularnewline
9 & 32.36166667 & 34.8837920138091 & -2.52212534380914 \tabularnewline
10 & 36.26083333 & 28.4254575371181 & 7.83537579288187 \tabularnewline
11 & 44.96555556 & 47.0629090877757 & -2.09735352777571 \tabularnewline
12 & 35.63166667 & 56.3800389165099 & -20.7483722465099 \tabularnewline
13 & 28.43055556 & 35.7516111097651 & -7.32105554976512 \tabularnewline
14 & 53.61777778 & 47.6282394419357 & 5.98953833806429 \tabularnewline
15 & 39.32611111 & 41.9438722432477 & -2.61776113324769 \tabularnewline
16 & 70.43305556 & 67.3832762190756 & 3.04977934092442 \tabularnewline
17 & 50.30833333 & 43.6972944962182 & 6.61103883378178 \tabularnewline
18 & 55.12 & 52.6231319130697 & 2.49686808693031 \tabularnewline
19 & 31.62583333 & 35.0064442240837 & -3.38061089408367 \tabularnewline
20 & 44.42777778 & 46.294112264799 & -1.86633448479901 \tabularnewline
21 & 46.33944444 & 44.4309784777764 & 1.90846596222362 \tabularnewline
22 & 79.63194444 & 42.0749099350576 & 37.5570345049424 \tabularnewline
23 & 25.46027778 & 21.6106271297438 & 3.84965065025623 \tabularnewline
24 & 30.07722222 & 41.3736268900134 & -11.2964046700134 \tabularnewline
25 & 40.65055556 & 48.7803020013877 & -8.12974644138769 \tabularnewline
26 & 40.31722222 & 48.9936847524049 & -8.67646253240486 \tabularnewline
27 & 44.92777778 & 45.5824203178865 & -0.654642537886524 \tabularnewline
28 & 44.69583333 & 42.7136814773708 & 1.98215185262915 \tabularnewline
29 & 29.69111111 & 28.7929783451277 & 0.898132764872311 \tabularnewline
30 & 52.26388889 & 40.7053643289927 & 11.5585245610073 \tabularnewline
31 & 52.61138889 & 50.0169064428956 & 2.59448244710443 \tabularnewline
32 & 35.96777778 & 45.3743325618858 & -9.4065547818858 \tabularnewline
33 & 56.675 & 48.2154668751491 & 8.45953312485091 \tabularnewline
34 & 17.42527778 & 27.0975485747887 & -9.67227079478873 \tabularnewline
35 & 67.67361111 & 46.808717235919 & 20.8648938740809 \tabularnewline
36 & 46.45972222 & 34.0935585670728 & 12.3661636529272 \tabularnewline
37 & 73.48 & 52.4853686038615 & 20.9946313961385 \tabularnewline
38 & 33.89555556 & 33.9454073254085 & -0.0498517654085003 \tabularnewline
39 & 22.49 & 16.3551829253679 & 6.13481707463207 \tabularnewline
40 & 58.27638889 & 54.3732814539856 & 3.9031074360144 \tabularnewline
41 & 62.27916667 & 40.9380784786797 & 21.3410881913203 \tabularnewline
42 & 32.21416667 & 35.4745119254324 & -3.26034525543242 \tabularnewline
43 & 38.38638889 & 36.1613174585796 & 2.2250714314204 \tabularnewline
44 & 22.52944444 & 29.2309388036317 & -6.70149436363172 \tabularnewline
45 & 25.86805556 & 35.6730540933478 & -9.80499853334778 \tabularnewline
46 & 84.93222222 & 56.1184585121321 & 28.8137637078679 \tabularnewline
47 & 21.88888889 & 35.5987426677104 & -13.7098537777104 \tabularnewline
48 & 44.12083333 & 45.8457149768521 & -1.72488164685208 \tabularnewline
49 & 61.59583333 & 60.2619073281196 & 1.33392600188044 \tabularnewline
50 & 36.41888889 & 41.2325366801504 & -4.8136477901504 \tabularnewline
51 & 35.75944444 & 34.8086485094261 & 0.950795930573938 \tabularnewline
52 & 6.718888889 & 9.83970780790965 & -3.12081891890965 \tabularnewline
53 & 71.57277778 & 87.7322397981692 & -16.1594620181692 \tabularnewline
54 & 18.06361111 & 22.9210449244025 & -4.8574338144025 \tabularnewline
55 & 27.24055556 & 29.7137805105943 & -2.47322495059433 \tabularnewline
56 & 48.21861111 & 32.1413297700151 & 16.0772813399849 \tabularnewline
57 & 50.01166667 & 56.7500001215168 & -6.73833345151682 \tabularnewline
58 & 54.79611111 & 60.9049068151728 & -6.10879570517281 \tabularnewline
59 & 58.90555556 & 50.1994968526072 & 8.70605870739283 \tabularnewline
60 & 39.32833333 & 37.6458831482492 & 1.68245018175085 \tabularnewline
61 & 68.08527778 & 60.6162393154471 & 7.46903846455293 \tabularnewline
62 & 57.46638889 & 51.0524622208099 & 6.41392666919013 \tabularnewline
63 & 40.47111111 & 44.9992473986201 & -4.52813628862007 \tabularnewline
64 & 47.39861111 & 44.9687076625175 & 2.42990344748254 \tabularnewline
65 & 39.46222222 & 38.6631910457938 & 0.799031174206211 \tabularnewline
66 & 31.89444444 & 26.0325201331417 & 5.86192430685828 \tabularnewline
67 & 31.51694444 & 48.2863125560254 & -16.7693681160254 \tabularnewline
68 & 40.35694444 & 55.2478531090229 & -14.8909086690229 \tabularnewline
69 & 41.94416667 & 40.4533092363133 & 1.49085743368669 \tabularnewline
70 & 25.50333333 & 30.9078189082707 & -5.4044855782707 \tabularnewline
71 & 33.00194444 & 35.605445844814 & -2.60350140481396 \tabularnewline
72 & 19.2975 & 25.9253294678063 & -6.62782946780632 \tabularnewline
73 & 35.175 & 38.667910410092 & -3.49291041009197 \tabularnewline
74 & 40.53 & 37.1058072875321 & 3.42419271246788 \tabularnewline
75 & 27.33138889 & 34.1489178969573 & -6.81752900695726 \tabularnewline
76 & 53.035 & 40.7710233231306 & 12.2639766768694 \tabularnewline
77 & 55.22138889 & 40.6184397072076 & 14.6029491827924 \tabularnewline
78 & 29.49805556 & 59.669522509425 & -30.171466949425 \tabularnewline
79 & 24.81055556 & 32.4863101251166 & -7.67575456511664 \tabularnewline
80 & 33.43388889 & 36.9618379443701 & -3.52794905437011 \tabularnewline
81 & 27.44194444 & 26.624267966321 & 0.817676473678963 \tabularnewline
82 & 76.37583333 & 50.6778410566121 & 25.6979922733879 \tabularnewline
83 & 36.88833333 & 35.8124455590326 & 1.0758877709674 \tabularnewline
84 & 37.56972222 & 43.1281007394055 & -5.5583785194055 \tabularnewline
85 & 22.48694444 & 20.0515951749301 & 2.43534926506992 \tabularnewline
86 & 30.34361111 & 28.4900750207027 & 1.85353608929732 \tabularnewline
87 & 26.84277778 & 36.1603987684545 & -9.31762098845454 \tabularnewline
88 & 62.83083333 & 56.3281698949437 & 6.50266343505629 \tabularnewline
89 & 47.57944444 & 43.1702104415406 & 4.40923399845944 \tabularnewline
90 & 32.72638889 & 52.1667241248728 & -19.4403352348728 \tabularnewline
91 & 37.10027778 & 35.5869947350352 & 1.51328304496478 \tabularnewline
92 & 42.27583333 & 42.2439586840462 & 0.0318746459537728 \tabularnewline
93 & 31.11222222 & 32.1503054633415 & -1.0380832433415 \tabularnewline
94 & 47.11472222 & 38.2311778012743 & 8.88354441872573 \tabularnewline
95 & 52.07861111 & 41.9535869466785 & 10.1250241633215 \tabularnewline
96 & 36.25916667 & 34.0046641720094 & 2.25450249799062 \tabularnewline
97 & 39.53861111 & 39.8553844403097 & -0.316773330309714 \tabularnewline
98 & 52.71222222 & 48.9721588400898 & 3.74006337991022 \tabularnewline
99 & 56.00083333 & 65.8727217352814 & -9.87188840528139 \tabularnewline
100 & 68.565 & 51.3762599654986 & 17.1887400345014 \tabularnewline
101 & 43.31861111 & 52.6510980751056 & -9.33248696510562 \tabularnewline
102 & 50.71694444 & 44.7515113351712 & 5.96543310482883 \tabularnewline
103 & 29.54194444 & 47.3352643663465 & -17.7933199263464 \tabularnewline
104 & 12.02416667 & 24.4758300883266 & -12.4516634183266 \tabularnewline
105 & 35.41472222 & 27.6803391040776 & 7.7343831159224 \tabularnewline
106 & 35.53611111 & 48.1274298781524 & -12.5913187681524 \tabularnewline
107 & 41.39055556 & 34.246603054358 & 7.14395250564199 \tabularnewline
108 & 52.12583333 & 44.9397230887868 & 7.1861102412132 \tabularnewline
109 & 20.58666667 & 46.385697498155 & -25.799030828155 \tabularnewline
110 & 26.11277778 & 41.4971598679861 & -15.3843820879861 \tabularnewline
111 & 49.0625 & 40.8013967601757 & 8.26110323982434 \tabularnewline
112 & 39.42583333 & 42.0552979974263 & -2.62946466742631 \tabularnewline
113 & 6.371666667 & 3.33080800419254 & 3.04085866280746 \tabularnewline
114 & 34.97972222 & 39.1452199989674 & -4.16549777896741 \tabularnewline
115 & 17.1825 & 18.2786318145253 & -1.09613181452526 \tabularnewline
116 & 25.35833333 & 32.5882673644507 & -7.22993403445067 \tabularnewline
117 & 70.86111111 & 41.6608060411667 & 29.2003050688333 \tabularnewline
118 & 5.848333333 & 3.9660054996506 & 1.8823278333494 \tabularnewline
119 & 46.97027778 & 33.6634231990226 & 13.3068545809774 \tabularnewline
120 & 8.726111111 & 12.9116373221645 & -4.18552621116452 \tabularnewline
121 & 52.41694444 & 45.6610124059817 & 6.75593203401831 \tabularnewline
122 & 38.20666667 & 46.0286161967644 & -7.82194952676436 \tabularnewline
123 & 21.435 & 38.8523574905491 & -17.4173574905491 \tabularnewline
124 & 20.71305556 & 31.5253558499398 & -10.8123002899398 \tabularnewline
125 & 10.615 & 15.9049077264904 & -5.28990772649043 \tabularnewline
126 & 25.26694444 & 32.2742227244267 & -7.00727828442668 \tabularnewline
127 & 53.95111111 & 46.9965969108717 & 6.95451419912833 \tabularnewline
128 & 37.5725 & 45.5773352458527 & -8.00483524585265 \tabularnewline
129 & 67.85333333 & 46.8703596729982 & 20.9829736570018 \tabularnewline
130 & 56.04111111 & 54.0017462183637 & 2.03936489163631 \tabularnewline
131 & 71.22277778 & 67.4682594112254 & 3.75451836877456 \tabularnewline
132 & 38.65111111 & 30.0096070564969 & 8.64150405350309 \tabularnewline
133 & 21.24166667 & 27.9011850806374 & -6.65951841063736 \tabularnewline
134 & 52.63944444 & 76.463549386746 & -23.824104946746 \tabularnewline
135 & 77.87055556 & 63.6507665817845 & 14.2197889782155 \tabularnewline
136 & 14.16638889 & 28.2218355419533 & -14.0554466519533 \tabularnewline
137 & 70.35388889 & 56.8383396127786 & 13.5155492772214 \tabularnewline
138 & 28.6775 & 37.3511193592688 & -8.67361935926884 \tabularnewline
139 & 46.68305556 & 36.969730091119 & 9.71332546888102 \tabularnewline
140 & 35.76888889 & 38.3223124571705 & -2.55342356717046 \tabularnewline
141 & 21.04055556 & 33.6326974290211 & -12.5921418690211 \tabularnewline
142 & 69.23111111 & 46.6040728064782 & 22.6270383035218 \tabularnewline
143 & 42.32388889 & 43.609514911047 & -1.28562602104698 \tabularnewline
144 & 48.12777778 & 37.3202327836736 & 10.8075449963264 \tabularnewline
145 & 54.77694444 & 49.6032882670648 & 5.17365617293522 \tabularnewline
146 & 18.75194444 & 45.8006121095485 & -27.0486676695485 \tabularnewline
147 & 38.72472222 & 44.5678732001398 & -5.84315098013978 \tabularnewline
148 & 51.49055556 & 54.6244192646839 & -3.13386370468385 \tabularnewline
149 & 0 & -0.0992742116382943 & 0.0992742116382943 \tabularnewline
150 & 4.08 & 3.17214025159106 & 0.907859748408937 \tabularnewline
151 & 0.027222222 & 0.147692473267216 & -0.120470251267216 \tabularnewline
152 & 0.126388889 & 0.394659158172725 & -0.268270269172725 \tabularnewline
153 & 0 & -0.0992742116382943 & 0.0992742116382943 \tabularnewline
154 & 0 & -0.0992742116382943 & 0.0992742116382943 \tabularnewline
155 & 38.30138889 & 35.989428456333 & 2.31196043366698 \tabularnewline
156 & 51.46888889 & 44.5917992212673 & 6.87708966873274 \tabularnewline
157 & 0 & -0.0992742116382943 & 0.0992742116382943 \tabularnewline
158 & 0.056388889 & 0.888592527983742 & -0.832203638983742 \tabularnewline
159 & 1.999722222 & 1.35439917266054 & 0.645323049339463 \tabularnewline
160 & 12.96111111 & 9.68726207847868 & 3.27384903152132 \tabularnewline
161 & 4.874166667 & 2.4631039039042 & 2.4110627630958 \tabularnewline
162 & 20.43527778 & 28.175707153313 & -7.74042937331299 \tabularnewline
163 & 0.269166667 & 0.394659158172722 & -0.125492491172722 \tabularnewline
164 & 29.29916667 & 27.4905707799461 & 1.80859589005389 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186274&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]47.38555556[/C][C]44.9094498238412[/C][C]2.47610573615882[/C][/ROW]
[ROW][C]2[/C][C]24.06138889[/C][C]35.1172666907422[/C][C]-11.0558778007422[/C][/ROW]
[ROW][C]3[/C][C]31.4825[/C][C]36.745333679831[/C][C]-5.26283367983099[/C][/ROW]
[ROW][C]4[/C][C]42.36388889[/C][C]49.165735489582[/C][C]-6.80184659958204[/C][/ROW]
[ROW][C]5[/C][C]23.94611111[/C][C]23.7916481980652[/C][C]0.154462911934808[/C][/ROW]
[ROW][C]6[/C][C]10.34916667[/C][C]24.2124357077914[/C][C]-13.8632690377914[/C][/ROW]
[ROW][C]7[/C][C]85.01527778[/C][C]47.6573318327695[/C][C]37.3579459472305[/C][/ROW]
[ROW][C]8[/C][C]9.097222222[/C][C]21.3770122005606[/C][C]-12.2797899785606[/C][/ROW]
[ROW][C]9[/C][C]32.36166667[/C][C]34.8837920138091[/C][C]-2.52212534380914[/C][/ROW]
[ROW][C]10[/C][C]36.26083333[/C][C]28.4254575371181[/C][C]7.83537579288187[/C][/ROW]
[ROW][C]11[/C][C]44.96555556[/C][C]47.0629090877757[/C][C]-2.09735352777571[/C][/ROW]
[ROW][C]12[/C][C]35.63166667[/C][C]56.3800389165099[/C][C]-20.7483722465099[/C][/ROW]
[ROW][C]13[/C][C]28.43055556[/C][C]35.7516111097651[/C][C]-7.32105554976512[/C][/ROW]
[ROW][C]14[/C][C]53.61777778[/C][C]47.6282394419357[/C][C]5.98953833806429[/C][/ROW]
[ROW][C]15[/C][C]39.32611111[/C][C]41.9438722432477[/C][C]-2.61776113324769[/C][/ROW]
[ROW][C]16[/C][C]70.43305556[/C][C]67.3832762190756[/C][C]3.04977934092442[/C][/ROW]
[ROW][C]17[/C][C]50.30833333[/C][C]43.6972944962182[/C][C]6.61103883378178[/C][/ROW]
[ROW][C]18[/C][C]55.12[/C][C]52.6231319130697[/C][C]2.49686808693031[/C][/ROW]
[ROW][C]19[/C][C]31.62583333[/C][C]35.0064442240837[/C][C]-3.38061089408367[/C][/ROW]
[ROW][C]20[/C][C]44.42777778[/C][C]46.294112264799[/C][C]-1.86633448479901[/C][/ROW]
[ROW][C]21[/C][C]46.33944444[/C][C]44.4309784777764[/C][C]1.90846596222362[/C][/ROW]
[ROW][C]22[/C][C]79.63194444[/C][C]42.0749099350576[/C][C]37.5570345049424[/C][/ROW]
[ROW][C]23[/C][C]25.46027778[/C][C]21.6106271297438[/C][C]3.84965065025623[/C][/ROW]
[ROW][C]24[/C][C]30.07722222[/C][C]41.3736268900134[/C][C]-11.2964046700134[/C][/ROW]
[ROW][C]25[/C][C]40.65055556[/C][C]48.7803020013877[/C][C]-8.12974644138769[/C][/ROW]
[ROW][C]26[/C][C]40.31722222[/C][C]48.9936847524049[/C][C]-8.67646253240486[/C][/ROW]
[ROW][C]27[/C][C]44.92777778[/C][C]45.5824203178865[/C][C]-0.654642537886524[/C][/ROW]
[ROW][C]28[/C][C]44.69583333[/C][C]42.7136814773708[/C][C]1.98215185262915[/C][/ROW]
[ROW][C]29[/C][C]29.69111111[/C][C]28.7929783451277[/C][C]0.898132764872311[/C][/ROW]
[ROW][C]30[/C][C]52.26388889[/C][C]40.7053643289927[/C][C]11.5585245610073[/C][/ROW]
[ROW][C]31[/C][C]52.61138889[/C][C]50.0169064428956[/C][C]2.59448244710443[/C][/ROW]
[ROW][C]32[/C][C]35.96777778[/C][C]45.3743325618858[/C][C]-9.4065547818858[/C][/ROW]
[ROW][C]33[/C][C]56.675[/C][C]48.2154668751491[/C][C]8.45953312485091[/C][/ROW]
[ROW][C]34[/C][C]17.42527778[/C][C]27.0975485747887[/C][C]-9.67227079478873[/C][/ROW]
[ROW][C]35[/C][C]67.67361111[/C][C]46.808717235919[/C][C]20.8648938740809[/C][/ROW]
[ROW][C]36[/C][C]46.45972222[/C][C]34.0935585670728[/C][C]12.3661636529272[/C][/ROW]
[ROW][C]37[/C][C]73.48[/C][C]52.4853686038615[/C][C]20.9946313961385[/C][/ROW]
[ROW][C]38[/C][C]33.89555556[/C][C]33.9454073254085[/C][C]-0.0498517654085003[/C][/ROW]
[ROW][C]39[/C][C]22.49[/C][C]16.3551829253679[/C][C]6.13481707463207[/C][/ROW]
[ROW][C]40[/C][C]58.27638889[/C][C]54.3732814539856[/C][C]3.9031074360144[/C][/ROW]
[ROW][C]41[/C][C]62.27916667[/C][C]40.9380784786797[/C][C]21.3410881913203[/C][/ROW]
[ROW][C]42[/C][C]32.21416667[/C][C]35.4745119254324[/C][C]-3.26034525543242[/C][/ROW]
[ROW][C]43[/C][C]38.38638889[/C][C]36.1613174585796[/C][C]2.2250714314204[/C][/ROW]
[ROW][C]44[/C][C]22.52944444[/C][C]29.2309388036317[/C][C]-6.70149436363172[/C][/ROW]
[ROW][C]45[/C][C]25.86805556[/C][C]35.6730540933478[/C][C]-9.80499853334778[/C][/ROW]
[ROW][C]46[/C][C]84.93222222[/C][C]56.1184585121321[/C][C]28.8137637078679[/C][/ROW]
[ROW][C]47[/C][C]21.88888889[/C][C]35.5987426677104[/C][C]-13.7098537777104[/C][/ROW]
[ROW][C]48[/C][C]44.12083333[/C][C]45.8457149768521[/C][C]-1.72488164685208[/C][/ROW]
[ROW][C]49[/C][C]61.59583333[/C][C]60.2619073281196[/C][C]1.33392600188044[/C][/ROW]
[ROW][C]50[/C][C]36.41888889[/C][C]41.2325366801504[/C][C]-4.8136477901504[/C][/ROW]
[ROW][C]51[/C][C]35.75944444[/C][C]34.8086485094261[/C][C]0.950795930573938[/C][/ROW]
[ROW][C]52[/C][C]6.718888889[/C][C]9.83970780790965[/C][C]-3.12081891890965[/C][/ROW]
[ROW][C]53[/C][C]71.57277778[/C][C]87.7322397981692[/C][C]-16.1594620181692[/C][/ROW]
[ROW][C]54[/C][C]18.06361111[/C][C]22.9210449244025[/C][C]-4.8574338144025[/C][/ROW]
[ROW][C]55[/C][C]27.24055556[/C][C]29.7137805105943[/C][C]-2.47322495059433[/C][/ROW]
[ROW][C]56[/C][C]48.21861111[/C][C]32.1413297700151[/C][C]16.0772813399849[/C][/ROW]
[ROW][C]57[/C][C]50.01166667[/C][C]56.7500001215168[/C][C]-6.73833345151682[/C][/ROW]
[ROW][C]58[/C][C]54.79611111[/C][C]60.9049068151728[/C][C]-6.10879570517281[/C][/ROW]
[ROW][C]59[/C][C]58.90555556[/C][C]50.1994968526072[/C][C]8.70605870739283[/C][/ROW]
[ROW][C]60[/C][C]39.32833333[/C][C]37.6458831482492[/C][C]1.68245018175085[/C][/ROW]
[ROW][C]61[/C][C]68.08527778[/C][C]60.6162393154471[/C][C]7.46903846455293[/C][/ROW]
[ROW][C]62[/C][C]57.46638889[/C][C]51.0524622208099[/C][C]6.41392666919013[/C][/ROW]
[ROW][C]63[/C][C]40.47111111[/C][C]44.9992473986201[/C][C]-4.52813628862007[/C][/ROW]
[ROW][C]64[/C][C]47.39861111[/C][C]44.9687076625175[/C][C]2.42990344748254[/C][/ROW]
[ROW][C]65[/C][C]39.46222222[/C][C]38.6631910457938[/C][C]0.799031174206211[/C][/ROW]
[ROW][C]66[/C][C]31.89444444[/C][C]26.0325201331417[/C][C]5.86192430685828[/C][/ROW]
[ROW][C]67[/C][C]31.51694444[/C][C]48.2863125560254[/C][C]-16.7693681160254[/C][/ROW]
[ROW][C]68[/C][C]40.35694444[/C][C]55.2478531090229[/C][C]-14.8909086690229[/C][/ROW]
[ROW][C]69[/C][C]41.94416667[/C][C]40.4533092363133[/C][C]1.49085743368669[/C][/ROW]
[ROW][C]70[/C][C]25.50333333[/C][C]30.9078189082707[/C][C]-5.4044855782707[/C][/ROW]
[ROW][C]71[/C][C]33.00194444[/C][C]35.605445844814[/C][C]-2.60350140481396[/C][/ROW]
[ROW][C]72[/C][C]19.2975[/C][C]25.9253294678063[/C][C]-6.62782946780632[/C][/ROW]
[ROW][C]73[/C][C]35.175[/C][C]38.667910410092[/C][C]-3.49291041009197[/C][/ROW]
[ROW][C]74[/C][C]40.53[/C][C]37.1058072875321[/C][C]3.42419271246788[/C][/ROW]
[ROW][C]75[/C][C]27.33138889[/C][C]34.1489178969573[/C][C]-6.81752900695726[/C][/ROW]
[ROW][C]76[/C][C]53.035[/C][C]40.7710233231306[/C][C]12.2639766768694[/C][/ROW]
[ROW][C]77[/C][C]55.22138889[/C][C]40.6184397072076[/C][C]14.6029491827924[/C][/ROW]
[ROW][C]78[/C][C]29.49805556[/C][C]59.669522509425[/C][C]-30.171466949425[/C][/ROW]
[ROW][C]79[/C][C]24.81055556[/C][C]32.4863101251166[/C][C]-7.67575456511664[/C][/ROW]
[ROW][C]80[/C][C]33.43388889[/C][C]36.9618379443701[/C][C]-3.52794905437011[/C][/ROW]
[ROW][C]81[/C][C]27.44194444[/C][C]26.624267966321[/C][C]0.817676473678963[/C][/ROW]
[ROW][C]82[/C][C]76.37583333[/C][C]50.6778410566121[/C][C]25.6979922733879[/C][/ROW]
[ROW][C]83[/C][C]36.88833333[/C][C]35.8124455590326[/C][C]1.0758877709674[/C][/ROW]
[ROW][C]84[/C][C]37.56972222[/C][C]43.1281007394055[/C][C]-5.5583785194055[/C][/ROW]
[ROW][C]85[/C][C]22.48694444[/C][C]20.0515951749301[/C][C]2.43534926506992[/C][/ROW]
[ROW][C]86[/C][C]30.34361111[/C][C]28.4900750207027[/C][C]1.85353608929732[/C][/ROW]
[ROW][C]87[/C][C]26.84277778[/C][C]36.1603987684545[/C][C]-9.31762098845454[/C][/ROW]
[ROW][C]88[/C][C]62.83083333[/C][C]56.3281698949437[/C][C]6.50266343505629[/C][/ROW]
[ROW][C]89[/C][C]47.57944444[/C][C]43.1702104415406[/C][C]4.40923399845944[/C][/ROW]
[ROW][C]90[/C][C]32.72638889[/C][C]52.1667241248728[/C][C]-19.4403352348728[/C][/ROW]
[ROW][C]91[/C][C]37.10027778[/C][C]35.5869947350352[/C][C]1.51328304496478[/C][/ROW]
[ROW][C]92[/C][C]42.27583333[/C][C]42.2439586840462[/C][C]0.0318746459537728[/C][/ROW]
[ROW][C]93[/C][C]31.11222222[/C][C]32.1503054633415[/C][C]-1.0380832433415[/C][/ROW]
[ROW][C]94[/C][C]47.11472222[/C][C]38.2311778012743[/C][C]8.88354441872573[/C][/ROW]
[ROW][C]95[/C][C]52.07861111[/C][C]41.9535869466785[/C][C]10.1250241633215[/C][/ROW]
[ROW][C]96[/C][C]36.25916667[/C][C]34.0046641720094[/C][C]2.25450249799062[/C][/ROW]
[ROW][C]97[/C][C]39.53861111[/C][C]39.8553844403097[/C][C]-0.316773330309714[/C][/ROW]
[ROW][C]98[/C][C]52.71222222[/C][C]48.9721588400898[/C][C]3.74006337991022[/C][/ROW]
[ROW][C]99[/C][C]56.00083333[/C][C]65.8727217352814[/C][C]-9.87188840528139[/C][/ROW]
[ROW][C]100[/C][C]68.565[/C][C]51.3762599654986[/C][C]17.1887400345014[/C][/ROW]
[ROW][C]101[/C][C]43.31861111[/C][C]52.6510980751056[/C][C]-9.33248696510562[/C][/ROW]
[ROW][C]102[/C][C]50.71694444[/C][C]44.7515113351712[/C][C]5.96543310482883[/C][/ROW]
[ROW][C]103[/C][C]29.54194444[/C][C]47.3352643663465[/C][C]-17.7933199263464[/C][/ROW]
[ROW][C]104[/C][C]12.02416667[/C][C]24.4758300883266[/C][C]-12.4516634183266[/C][/ROW]
[ROW][C]105[/C][C]35.41472222[/C][C]27.6803391040776[/C][C]7.7343831159224[/C][/ROW]
[ROW][C]106[/C][C]35.53611111[/C][C]48.1274298781524[/C][C]-12.5913187681524[/C][/ROW]
[ROW][C]107[/C][C]41.39055556[/C][C]34.246603054358[/C][C]7.14395250564199[/C][/ROW]
[ROW][C]108[/C][C]52.12583333[/C][C]44.9397230887868[/C][C]7.1861102412132[/C][/ROW]
[ROW][C]109[/C][C]20.58666667[/C][C]46.385697498155[/C][C]-25.799030828155[/C][/ROW]
[ROW][C]110[/C][C]26.11277778[/C][C]41.4971598679861[/C][C]-15.3843820879861[/C][/ROW]
[ROW][C]111[/C][C]49.0625[/C][C]40.8013967601757[/C][C]8.26110323982434[/C][/ROW]
[ROW][C]112[/C][C]39.42583333[/C][C]42.0552979974263[/C][C]-2.62946466742631[/C][/ROW]
[ROW][C]113[/C][C]6.371666667[/C][C]3.33080800419254[/C][C]3.04085866280746[/C][/ROW]
[ROW][C]114[/C][C]34.97972222[/C][C]39.1452199989674[/C][C]-4.16549777896741[/C][/ROW]
[ROW][C]115[/C][C]17.1825[/C][C]18.2786318145253[/C][C]-1.09613181452526[/C][/ROW]
[ROW][C]116[/C][C]25.35833333[/C][C]32.5882673644507[/C][C]-7.22993403445067[/C][/ROW]
[ROW][C]117[/C][C]70.86111111[/C][C]41.6608060411667[/C][C]29.2003050688333[/C][/ROW]
[ROW][C]118[/C][C]5.848333333[/C][C]3.9660054996506[/C][C]1.8823278333494[/C][/ROW]
[ROW][C]119[/C][C]46.97027778[/C][C]33.6634231990226[/C][C]13.3068545809774[/C][/ROW]
[ROW][C]120[/C][C]8.726111111[/C][C]12.9116373221645[/C][C]-4.18552621116452[/C][/ROW]
[ROW][C]121[/C][C]52.41694444[/C][C]45.6610124059817[/C][C]6.75593203401831[/C][/ROW]
[ROW][C]122[/C][C]38.20666667[/C][C]46.0286161967644[/C][C]-7.82194952676436[/C][/ROW]
[ROW][C]123[/C][C]21.435[/C][C]38.8523574905491[/C][C]-17.4173574905491[/C][/ROW]
[ROW][C]124[/C][C]20.71305556[/C][C]31.5253558499398[/C][C]-10.8123002899398[/C][/ROW]
[ROW][C]125[/C][C]10.615[/C][C]15.9049077264904[/C][C]-5.28990772649043[/C][/ROW]
[ROW][C]126[/C][C]25.26694444[/C][C]32.2742227244267[/C][C]-7.00727828442668[/C][/ROW]
[ROW][C]127[/C][C]53.95111111[/C][C]46.9965969108717[/C][C]6.95451419912833[/C][/ROW]
[ROW][C]128[/C][C]37.5725[/C][C]45.5773352458527[/C][C]-8.00483524585265[/C][/ROW]
[ROW][C]129[/C][C]67.85333333[/C][C]46.8703596729982[/C][C]20.9829736570018[/C][/ROW]
[ROW][C]130[/C][C]56.04111111[/C][C]54.0017462183637[/C][C]2.03936489163631[/C][/ROW]
[ROW][C]131[/C][C]71.22277778[/C][C]67.4682594112254[/C][C]3.75451836877456[/C][/ROW]
[ROW][C]132[/C][C]38.65111111[/C][C]30.0096070564969[/C][C]8.64150405350309[/C][/ROW]
[ROW][C]133[/C][C]21.24166667[/C][C]27.9011850806374[/C][C]-6.65951841063736[/C][/ROW]
[ROW][C]134[/C][C]52.63944444[/C][C]76.463549386746[/C][C]-23.824104946746[/C][/ROW]
[ROW][C]135[/C][C]77.87055556[/C][C]63.6507665817845[/C][C]14.2197889782155[/C][/ROW]
[ROW][C]136[/C][C]14.16638889[/C][C]28.2218355419533[/C][C]-14.0554466519533[/C][/ROW]
[ROW][C]137[/C][C]70.35388889[/C][C]56.8383396127786[/C][C]13.5155492772214[/C][/ROW]
[ROW][C]138[/C][C]28.6775[/C][C]37.3511193592688[/C][C]-8.67361935926884[/C][/ROW]
[ROW][C]139[/C][C]46.68305556[/C][C]36.969730091119[/C][C]9.71332546888102[/C][/ROW]
[ROW][C]140[/C][C]35.76888889[/C][C]38.3223124571705[/C][C]-2.55342356717046[/C][/ROW]
[ROW][C]141[/C][C]21.04055556[/C][C]33.6326974290211[/C][C]-12.5921418690211[/C][/ROW]
[ROW][C]142[/C][C]69.23111111[/C][C]46.6040728064782[/C][C]22.6270383035218[/C][/ROW]
[ROW][C]143[/C][C]42.32388889[/C][C]43.609514911047[/C][C]-1.28562602104698[/C][/ROW]
[ROW][C]144[/C][C]48.12777778[/C][C]37.3202327836736[/C][C]10.8075449963264[/C][/ROW]
[ROW][C]145[/C][C]54.77694444[/C][C]49.6032882670648[/C][C]5.17365617293522[/C][/ROW]
[ROW][C]146[/C][C]18.75194444[/C][C]45.8006121095485[/C][C]-27.0486676695485[/C][/ROW]
[ROW][C]147[/C][C]38.72472222[/C][C]44.5678732001398[/C][C]-5.84315098013978[/C][/ROW]
[ROW][C]148[/C][C]51.49055556[/C][C]54.6244192646839[/C][C]-3.13386370468385[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-0.0992742116382943[/C][C]0.0992742116382943[/C][/ROW]
[ROW][C]150[/C][C]4.08[/C][C]3.17214025159106[/C][C]0.907859748408937[/C][/ROW]
[ROW][C]151[/C][C]0.027222222[/C][C]0.147692473267216[/C][C]-0.120470251267216[/C][/ROW]
[ROW][C]152[/C][C]0.126388889[/C][C]0.394659158172725[/C][C]-0.268270269172725[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-0.0992742116382943[/C][C]0.0992742116382943[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-0.0992742116382943[/C][C]0.0992742116382943[/C][/ROW]
[ROW][C]155[/C][C]38.30138889[/C][C]35.989428456333[/C][C]2.31196043366698[/C][/ROW]
[ROW][C]156[/C][C]51.46888889[/C][C]44.5917992212673[/C][C]6.87708966873274[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-0.0992742116382943[/C][C]0.0992742116382943[/C][/ROW]
[ROW][C]158[/C][C]0.056388889[/C][C]0.888592527983742[/C][C]-0.832203638983742[/C][/ROW]
[ROW][C]159[/C][C]1.999722222[/C][C]1.35439917266054[/C][C]0.645323049339463[/C][/ROW]
[ROW][C]160[/C][C]12.96111111[/C][C]9.68726207847868[/C][C]3.27384903152132[/C][/ROW]
[ROW][C]161[/C][C]4.874166667[/C][C]2.4631039039042[/C][C]2.4110627630958[/C][/ROW]
[ROW][C]162[/C][C]20.43527778[/C][C]28.175707153313[/C][C]-7.74042937331299[/C][/ROW]
[ROW][C]163[/C][C]0.269166667[/C][C]0.394659158172722[/C][C]-0.125492491172722[/C][/ROW]
[ROW][C]164[/C][C]29.29916667[/C][C]27.4905707799461[/C][C]1.80859589005389[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186274&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186274&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 47.38555556 44.9094498238412 2.47610573615882 2 24.06138889 35.1172666907422 -11.0558778007422 3 31.4825 36.745333679831 -5.26283367983099 4 42.36388889 49.165735489582 -6.80184659958204 5 23.94611111 23.7916481980652 0.154462911934808 6 10.34916667 24.2124357077914 -13.8632690377914 7 85.01527778 47.6573318327695 37.3579459472305 8 9.097222222 21.3770122005606 -12.2797899785606 9 32.36166667 34.8837920138091 -2.52212534380914 10 36.26083333 28.4254575371181 7.83537579288187 11 44.96555556 47.0629090877757 -2.09735352777571 12 35.63166667 56.3800389165099 -20.7483722465099 13 28.43055556 35.7516111097651 -7.32105554976512 14 53.61777778 47.6282394419357 5.98953833806429 15 39.32611111 41.9438722432477 -2.61776113324769 16 70.43305556 67.3832762190756 3.04977934092442 17 50.30833333 43.6972944962182 6.61103883378178 18 55.12 52.6231319130697 2.49686808693031 19 31.62583333 35.0064442240837 -3.38061089408367 20 44.42777778 46.294112264799 -1.86633448479901 21 46.33944444 44.4309784777764 1.90846596222362 22 79.63194444 42.0749099350576 37.5570345049424 23 25.46027778 21.6106271297438 3.84965065025623 24 30.07722222 41.3736268900134 -11.2964046700134 25 40.65055556 48.7803020013877 -8.12974644138769 26 40.31722222 48.9936847524049 -8.67646253240486 27 44.92777778 45.5824203178865 -0.654642537886524 28 44.69583333 42.7136814773708 1.98215185262915 29 29.69111111 28.7929783451277 0.898132764872311 30 52.26388889 40.7053643289927 11.5585245610073 31 52.61138889 50.0169064428956 2.59448244710443 32 35.96777778 45.3743325618858 -9.4065547818858 33 56.675 48.2154668751491 8.45953312485091 34 17.42527778 27.0975485747887 -9.67227079478873 35 67.67361111 46.808717235919 20.8648938740809 36 46.45972222 34.0935585670728 12.3661636529272 37 73.48 52.4853686038615 20.9946313961385 38 33.89555556 33.9454073254085 -0.0498517654085003 39 22.49 16.3551829253679 6.13481707463207 40 58.27638889 54.3732814539856 3.9031074360144 41 62.27916667 40.9380784786797 21.3410881913203 42 32.21416667 35.4745119254324 -3.26034525543242 43 38.38638889 36.1613174585796 2.2250714314204 44 22.52944444 29.2309388036317 -6.70149436363172 45 25.86805556 35.6730540933478 -9.80499853334778 46 84.93222222 56.1184585121321 28.8137637078679 47 21.88888889 35.5987426677104 -13.7098537777104 48 44.12083333 45.8457149768521 -1.72488164685208 49 61.59583333 60.2619073281196 1.33392600188044 50 36.41888889 41.2325366801504 -4.8136477901504 51 35.75944444 34.8086485094261 0.950795930573938 52 6.718888889 9.83970780790965 -3.12081891890965 53 71.57277778 87.7322397981692 -16.1594620181692 54 18.06361111 22.9210449244025 -4.8574338144025 55 27.24055556 29.7137805105943 -2.47322495059433 56 48.21861111 32.1413297700151 16.0772813399849 57 50.01166667 56.7500001215168 -6.73833345151682 58 54.79611111 60.9049068151728 -6.10879570517281 59 58.90555556 50.1994968526072 8.70605870739283 60 39.32833333 37.6458831482492 1.68245018175085 61 68.08527778 60.6162393154471 7.46903846455293 62 57.46638889 51.0524622208099 6.41392666919013 63 40.47111111 44.9992473986201 -4.52813628862007 64 47.39861111 44.9687076625175 2.42990344748254 65 39.46222222 38.6631910457938 0.799031174206211 66 31.89444444 26.0325201331417 5.86192430685828 67 31.51694444 48.2863125560254 -16.7693681160254 68 40.35694444 55.2478531090229 -14.8909086690229 69 41.94416667 40.4533092363133 1.49085743368669 70 25.50333333 30.9078189082707 -5.4044855782707 71 33.00194444 35.605445844814 -2.60350140481396 72 19.2975 25.9253294678063 -6.62782946780632 73 35.175 38.667910410092 -3.49291041009197 74 40.53 37.1058072875321 3.42419271246788 75 27.33138889 34.1489178969573 -6.81752900695726 76 53.035 40.7710233231306 12.2639766768694 77 55.22138889 40.6184397072076 14.6029491827924 78 29.49805556 59.669522509425 -30.171466949425 79 24.81055556 32.4863101251166 -7.67575456511664 80 33.43388889 36.9618379443701 -3.52794905437011 81 27.44194444 26.624267966321 0.817676473678963 82 76.37583333 50.6778410566121 25.6979922733879 83 36.88833333 35.8124455590326 1.0758877709674 84 37.56972222 43.1281007394055 -5.5583785194055 85 22.48694444 20.0515951749301 2.43534926506992 86 30.34361111 28.4900750207027 1.85353608929732 87 26.84277778 36.1603987684545 -9.31762098845454 88 62.83083333 56.3281698949437 6.50266343505629 89 47.57944444 43.1702104415406 4.40923399845944 90 32.72638889 52.1667241248728 -19.4403352348728 91 37.10027778 35.5869947350352 1.51328304496478 92 42.27583333 42.2439586840462 0.0318746459537728 93 31.11222222 32.1503054633415 -1.0380832433415 94 47.11472222 38.2311778012743 8.88354441872573 95 52.07861111 41.9535869466785 10.1250241633215 96 36.25916667 34.0046641720094 2.25450249799062 97 39.53861111 39.8553844403097 -0.316773330309714 98 52.71222222 48.9721588400898 3.74006337991022 99 56.00083333 65.8727217352814 -9.87188840528139 100 68.565 51.3762599654986 17.1887400345014 101 43.31861111 52.6510980751056 -9.33248696510562 102 50.71694444 44.7515113351712 5.96543310482883 103 29.54194444 47.3352643663465 -17.7933199263464 104 12.02416667 24.4758300883266 -12.4516634183266 105 35.41472222 27.6803391040776 7.7343831159224 106 35.53611111 48.1274298781524 -12.5913187681524 107 41.39055556 34.246603054358 7.14395250564199 108 52.12583333 44.9397230887868 7.1861102412132 109 20.58666667 46.385697498155 -25.799030828155 110 26.11277778 41.4971598679861 -15.3843820879861 111 49.0625 40.8013967601757 8.26110323982434 112 39.42583333 42.0552979974263 -2.62946466742631 113 6.371666667 3.33080800419254 3.04085866280746 114 34.97972222 39.1452199989674 -4.16549777896741 115 17.1825 18.2786318145253 -1.09613181452526 116 25.35833333 32.5882673644507 -7.22993403445067 117 70.86111111 41.6608060411667 29.2003050688333 118 5.848333333 3.9660054996506 1.8823278333494 119 46.97027778 33.6634231990226 13.3068545809774 120 8.726111111 12.9116373221645 -4.18552621116452 121 52.41694444 45.6610124059817 6.75593203401831 122 38.20666667 46.0286161967644 -7.82194952676436 123 21.435 38.8523574905491 -17.4173574905491 124 20.71305556 31.5253558499398 -10.8123002899398 125 10.615 15.9049077264904 -5.28990772649043 126 25.26694444 32.2742227244267 -7.00727828442668 127 53.95111111 46.9965969108717 6.95451419912833 128 37.5725 45.5773352458527 -8.00483524585265 129 67.85333333 46.8703596729982 20.9829736570018 130 56.04111111 54.0017462183637 2.03936489163631 131 71.22277778 67.4682594112254 3.75451836877456 132 38.65111111 30.0096070564969 8.64150405350309 133 21.24166667 27.9011850806374 -6.65951841063736 134 52.63944444 76.463549386746 -23.824104946746 135 77.87055556 63.6507665817845 14.2197889782155 136 14.16638889 28.2218355419533 -14.0554466519533 137 70.35388889 56.8383396127786 13.5155492772214 138 28.6775 37.3511193592688 -8.67361935926884 139 46.68305556 36.969730091119 9.71332546888102 140 35.76888889 38.3223124571705 -2.55342356717046 141 21.04055556 33.6326974290211 -12.5921418690211 142 69.23111111 46.6040728064782 22.6270383035218 143 42.32388889 43.609514911047 -1.28562602104698 144 48.12777778 37.3202327836736 10.8075449963264 145 54.77694444 49.6032882670648 5.17365617293522 146 18.75194444 45.8006121095485 -27.0486676695485 147 38.72472222 44.5678732001398 -5.84315098013978 148 51.49055556 54.6244192646839 -3.13386370468385 149 0 -0.0992742116382943 0.0992742116382943 150 4.08 3.17214025159106 0.907859748408937 151 0.027222222 0.147692473267216 -0.120470251267216 152 0.126388889 0.394659158172725 -0.268270269172725 153 0 -0.0992742116382943 0.0992742116382943 154 0 -0.0992742116382943 0.0992742116382943 155 38.30138889 35.989428456333 2.31196043366698 156 51.46888889 44.5917992212673 6.87708966873274 157 0 -0.0992742116382943 0.0992742116382943 158 0.056388889 0.888592527983742 -0.832203638983742 159 1.999722222 1.35439917266054 0.645323049339463 160 12.96111111 9.68726207847868 3.27384903152132 161 4.874166667 2.4631039039042 2.4110627630958 162 20.43527778 28.175707153313 -7.74042937331299 163 0.269166667 0.394659158172722 -0.125492491172722 164 29.29916667 27.4905707799461 1.80859589005389

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.800761753447256 0.398476493105487 0.199238246552744 8 0.787223839498735 0.42555232100253 0.212776160501265 9 0.728222183541206 0.543555632917587 0.271777816458794 10 0.841532173309467 0.316935653381065 0.158467826690533 11 0.788945935933636 0.422108128132729 0.211054064066364 12 0.750692216842163 0.498615566315674 0.249307783157837 13 0.6682995716161 0.663400856767801 0.3317004283839 14 0.85978659445891 0.280426811082179 0.14021340554109 15 0.824334332088893 0.351331335822214 0.175665667911107 16 0.765964472082097 0.468071055835806 0.234035527917903 17 0.701570081665396 0.596859836669207 0.298429918334604 18 0.653859972387731 0.692280055224538 0.346140027612269 19 0.608404014096148 0.783191971807704 0.391595985903852 20 0.533619618532312 0.932760762935375 0.466380381467688 21 0.459488026407476 0.918976052814952 0.540511973592524 22 0.959447842654085 0.0811043146918299 0.0405521573459149 23 0.948079155165011 0.103841689669978 0.0519208448349888 24 0.938335859178154 0.123328281643692 0.0616641408218458 25 0.928420544950292 0.143158910099416 0.071579455049708 26 0.926328914305019 0.147342171389963 0.0736710856949813 27 0.905521630875898 0.188956738248203 0.0944783691241017 28 0.880767849746706 0.238464300506587 0.119232150253294 29 0.85705279323353 0.28589441353294 0.14294720676647 30 0.876147927228508 0.247704145542984 0.123852072771492 31 0.843876041686028 0.312247916627945 0.156123958313972 32 0.81929733402673 0.361405331946539 0.18070266597327 33 0.803749743258396 0.392500513483208 0.196250256741604 34 0.778985228471544 0.442029543056912 0.221014771528456 35 0.837169468831078 0.325661062337844 0.162830531168922 36 0.863716735956364 0.272566528087273 0.136283264043636 37 0.913881832806553 0.172236334386895 0.0861181671934473 38 0.890689615376556 0.218620769246889 0.109310384623444 39 0.872867168122474 0.254265663755052 0.127132831877526 40 0.84616429029043 0.307671419419141 0.15383570970957 41 0.906090254111872 0.187819491776257 0.0939097458881283 42 0.883391271117238 0.233217457765523 0.116608728882761 43 0.856603048567652 0.286793902864697 0.143396951432348 44 0.8332065032706 0.3335869934588 0.1667934967294 45 0.828870626000747 0.342258747998507 0.171129373999253 46 0.91725592733945 0.165488145321101 0.0827440726605503 47 0.917881370614913 0.164237258770174 0.0821186293850868 48 0.898017744214409 0.203964511571182 0.101982255785591 49 0.882680986682345 0.23463802663531 0.117319013317655 50 0.859614342185659 0.280771315628682 0.140385657814341 51 0.831806447047868 0.336387105904264 0.168193552952132 52 0.800450405739428 0.399099188521144 0.199549594260572 53 0.886226854876339 0.227546290247321 0.113773145123661 54 0.864913564655399 0.270172870689203 0.135086435344601 55 0.837764623791988 0.324470752416025 0.162235376208012 56 0.851888931663496 0.296222136673009 0.148111068336504 57 0.84608402556802 0.30783194886396 0.15391597443198 58 0.843318115159082 0.313363769681836 0.156681884840918 59 0.826879445111875 0.346241109776249 0.173120554888124 60 0.795581777898327 0.408836444203347 0.204418222101673 61 0.771203605196693 0.457592789606615 0.228796394803308 62 0.748085605031371 0.503828789937257 0.251914394968629 63 0.713494889275492 0.573010221449015 0.286505110724508 64 0.67407338768561 0.65185322462878 0.32592661231439 65 0.632879635155726 0.734240729688547 0.367120364844274 66 0.59988461841328 0.80023076317344 0.40011538158672 67 0.6870011846326 0.625997630734799 0.3129988153674 68 0.709014528567364 0.581970942865273 0.290985471432636 69 0.669045106235875 0.661909787528251 0.330954893764125 70 0.641207079425543 0.717585841148915 0.358792920574457 71 0.600462809726933 0.799074380546133 0.399537190273067 72 0.56782316328283 0.86435367343434 0.43217683671717 73 0.527571957401795 0.944856085196411 0.472428042598205 74 0.484887023208049 0.969774046416098 0.515112976791951 75 0.45906318759695 0.918126375193899 0.54093681240305 76 0.462346727434926 0.924693454869852 0.537653272565074 77 0.485566060164618 0.971132120329237 0.514433939835382 78 0.816981237075394 0.366037525849211 0.183018762924606 79 0.800483816418502 0.399032367162995 0.199516183581498 80 0.770550439237102 0.458899121525796 0.229449560762898 81 0.734798532427829 0.530402935144343 0.265201467572171 82 0.868423211168818 0.263153577662363 0.131576788831182 83 0.842966821850612 0.314066356298777 0.157033178149388 84 0.821090315640787 0.357819368718426 0.178909684359213 85 0.791361267596632 0.417277464806736 0.208638732403368 86 0.758369402279535 0.483261195440931 0.241630597720465 87 0.746830759092561 0.506338481814878 0.253169240907439 88 0.723855607588568 0.552288784822864 0.276144392411432 89 0.691753125828382 0.616493748343236 0.308246874171618 90 0.766364688513956 0.467270622972088 0.233635311486044 91 0.731026713150454 0.537946573699092 0.268973286849546 92 0.6917965096668 0.616406980666401 0.3082034903332 93 0.650476250823536 0.699047498352929 0.349523749176464 94 0.636988546767977 0.726022906464046 0.363011453232023 95 0.631180329386239 0.737639341227522 0.368819670613761 96 0.58867469242639 0.822650615147219 0.41132530757361 97 0.543312438078865 0.913375123842271 0.456687561921135 98 0.502148574354804 0.995702851290392 0.497851425645196 99 0.486261448452226 0.972522896904453 0.513738551547774 100 0.555698592448254 0.888602815103493 0.444301407551746 101 0.537754974075097 0.924490051849807 0.462245025924903 102 0.507323357404792 0.985353285190417 0.492676642595208 103 0.579625250661842 0.840749498676316 0.420374749338158 104 0.589377223570303 0.821245552859395 0.410622776429697 105 0.565192217396463 0.869615565207073 0.434807782603537 106 0.576878054417035 0.84624389116593 0.423121945582965 107 0.552278790223398 0.895442419553204 0.447721209776602 108 0.526035814868835 0.947928370262331 0.473964185131166 109 0.74197895463052 0.516042090738961 0.25802104536948 110 0.771242383001956 0.457515233996088 0.228757616998044 111 0.748476444697027 0.503047110605947 0.251523555302973 112 0.708574681897248 0.582850636205504 0.291425318102752 113 0.667657644305916 0.664684711388168 0.332342355694084 114 0.626491425179047 0.747017149641905 0.373508574820953 115 0.578340722614282 0.843318554771435 0.421659277385718 116 0.541594160440342 0.916811679119316 0.458405839559658 117 0.809658576536929 0.380682846926142 0.190341423463071 118 0.773708333795413 0.452583332409173 0.226291666204587 119 0.786368855571903 0.427262288856194 0.213631144428097 120 0.751004327802036 0.497991344395928 0.248995672197964 121 0.727915213628223 0.544169572743553 0.272084786371777 122 0.705356299152588 0.589287401694824 0.294643700847412 123 0.770930712886286 0.458138574227427 0.229069287113714 124 0.774941243790638 0.450117512418725 0.225058756209362 125 0.739695115812445 0.52060976837511 0.260304884187555 126 0.718496331039938 0.563007337920124 0.281503668960062 127 0.68787186533133 0.624256269337341 0.31212813466867 128 0.67094114406518 0.658117711869641 0.32905885593482 129 0.794111882159573 0.411776235680853 0.205888117840427 130 0.752041770803752 0.495916458392497 0.247958229196248 131 0.703865572630235 0.592268854739529 0.296134427369765 132 0.68506292787319 0.629874144253619 0.314937072126809 133 0.644318099357088 0.711363801285824 0.355681900642912 134 0.983519796168776 0.0329604076624476 0.0164802038312238 135 0.977426922615454 0.0451461547690928 0.0225730773845464 136 0.971210442417419 0.0575791151651626 0.0287895575825813 137 0.971173569992637 0.0576528600147259 0.028826430007363 138 0.968861901563875 0.0622761968722495 0.0311380984361248 139 0.988447582117837 0.0231048357643259 0.0115524178821629 140 0.981514585907358 0.0369708281852837 0.0184854140926418 141 0.999999951862804 9.62743925794442e-08 4.81371962897221e-08 142 0.99999994230381 1.15392379214597e-07 5.76961896072986e-08 143 0.999999806705432 3.86589135712995e-07 1.93294567856498e-07 144 0.999999374845176 1.25030964758685e-06 6.25154823793426e-07 145 0.999999264699861 1.47060027886255e-06 7.35300139431276e-07 146 0.999999999810212 3.79575812322054e-10 1.89787906161027e-10 147 0.999999998908669 2.1826611102213e-09 1.09133055511065e-09 148 0.999999998226016 3.54796820221849e-09 1.77398410110924e-09 149 0.999999986996974 2.60060515242326e-08 1.30030257621163e-08 150 0.999999949915677 1.0016864700133e-07 5.00843235006651e-08 151 0.999999614904606 7.70190787247422e-07 3.85095393623711e-07 152 0.999997331047972 5.33790405516174e-06 2.66895202758087e-06 153 0.999981984464935 3.60310701309463e-05 1.80155350654732e-05 154 0.999886205662745 0.00022758867451098 0.00011379433725549 155 0.999963709925333 7.25801493348042e-05 3.62900746674021e-05 156 0.999642552551745 0.000714894896510025 0.000357447448255013 157 0.997141703905035 0.00571659218993006 0.00285829609496503

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.800761753447256 & 0.398476493105487 & 0.199238246552744 \tabularnewline
8 & 0.787223839498735 & 0.42555232100253 & 0.212776160501265 \tabularnewline
9 & 0.728222183541206 & 0.543555632917587 & 0.271777816458794 \tabularnewline
10 & 0.841532173309467 & 0.316935653381065 & 0.158467826690533 \tabularnewline
11 & 0.788945935933636 & 0.422108128132729 & 0.211054064066364 \tabularnewline
12 & 0.750692216842163 & 0.498615566315674 & 0.249307783157837 \tabularnewline
13 & 0.6682995716161 & 0.663400856767801 & 0.3317004283839 \tabularnewline
14 & 0.85978659445891 & 0.280426811082179 & 0.14021340554109 \tabularnewline
15 & 0.824334332088893 & 0.351331335822214 & 0.175665667911107 \tabularnewline
16 & 0.765964472082097 & 0.468071055835806 & 0.234035527917903 \tabularnewline
17 & 0.701570081665396 & 0.596859836669207 & 0.298429918334604 \tabularnewline
18 & 0.653859972387731 & 0.692280055224538 & 0.346140027612269 \tabularnewline
19 & 0.608404014096148 & 0.783191971807704 & 0.391595985903852 \tabularnewline
20 & 0.533619618532312 & 0.932760762935375 & 0.466380381467688 \tabularnewline
21 & 0.459488026407476 & 0.918976052814952 & 0.540511973592524 \tabularnewline
22 & 0.959447842654085 & 0.0811043146918299 & 0.0405521573459149 \tabularnewline
23 & 0.948079155165011 & 0.103841689669978 & 0.0519208448349888 \tabularnewline
24 & 0.938335859178154 & 0.123328281643692 & 0.0616641408218458 \tabularnewline
25 & 0.928420544950292 & 0.143158910099416 & 0.071579455049708 \tabularnewline
26 & 0.926328914305019 & 0.147342171389963 & 0.0736710856949813 \tabularnewline
27 & 0.905521630875898 & 0.188956738248203 & 0.0944783691241017 \tabularnewline
28 & 0.880767849746706 & 0.238464300506587 & 0.119232150253294 \tabularnewline
29 & 0.85705279323353 & 0.28589441353294 & 0.14294720676647 \tabularnewline
30 & 0.876147927228508 & 0.247704145542984 & 0.123852072771492 \tabularnewline
31 & 0.843876041686028 & 0.312247916627945 & 0.156123958313972 \tabularnewline
32 & 0.81929733402673 & 0.361405331946539 & 0.18070266597327 \tabularnewline
33 & 0.803749743258396 & 0.392500513483208 & 0.196250256741604 \tabularnewline
34 & 0.778985228471544 & 0.442029543056912 & 0.221014771528456 \tabularnewline
35 & 0.837169468831078 & 0.325661062337844 & 0.162830531168922 \tabularnewline
36 & 0.863716735956364 & 0.272566528087273 & 0.136283264043636 \tabularnewline
37 & 0.913881832806553 & 0.172236334386895 & 0.0861181671934473 \tabularnewline
38 & 0.890689615376556 & 0.218620769246889 & 0.109310384623444 \tabularnewline
39 & 0.872867168122474 & 0.254265663755052 & 0.127132831877526 \tabularnewline
40 & 0.84616429029043 & 0.307671419419141 & 0.15383570970957 \tabularnewline
41 & 0.906090254111872 & 0.187819491776257 & 0.0939097458881283 \tabularnewline
42 & 0.883391271117238 & 0.233217457765523 & 0.116608728882761 \tabularnewline
43 & 0.856603048567652 & 0.286793902864697 & 0.143396951432348 \tabularnewline
44 & 0.8332065032706 & 0.3335869934588 & 0.1667934967294 \tabularnewline
45 & 0.828870626000747 & 0.342258747998507 & 0.171129373999253 \tabularnewline
46 & 0.91725592733945 & 0.165488145321101 & 0.0827440726605503 \tabularnewline
47 & 0.917881370614913 & 0.164237258770174 & 0.0821186293850868 \tabularnewline
48 & 0.898017744214409 & 0.203964511571182 & 0.101982255785591 \tabularnewline
49 & 0.882680986682345 & 0.23463802663531 & 0.117319013317655 \tabularnewline
50 & 0.859614342185659 & 0.280771315628682 & 0.140385657814341 \tabularnewline
51 & 0.831806447047868 & 0.336387105904264 & 0.168193552952132 \tabularnewline
52 & 0.800450405739428 & 0.399099188521144 & 0.199549594260572 \tabularnewline
53 & 0.886226854876339 & 0.227546290247321 & 0.113773145123661 \tabularnewline
54 & 0.864913564655399 & 0.270172870689203 & 0.135086435344601 \tabularnewline
55 & 0.837764623791988 & 0.324470752416025 & 0.162235376208012 \tabularnewline
56 & 0.851888931663496 & 0.296222136673009 & 0.148111068336504 \tabularnewline
57 & 0.84608402556802 & 0.30783194886396 & 0.15391597443198 \tabularnewline
58 & 0.843318115159082 & 0.313363769681836 & 0.156681884840918 \tabularnewline
59 & 0.826879445111875 & 0.346241109776249 & 0.173120554888124 \tabularnewline
60 & 0.795581777898327 & 0.408836444203347 & 0.204418222101673 \tabularnewline
61 & 0.771203605196693 & 0.457592789606615 & 0.228796394803308 \tabularnewline
62 & 0.748085605031371 & 0.503828789937257 & 0.251914394968629 \tabularnewline
63 & 0.713494889275492 & 0.573010221449015 & 0.286505110724508 \tabularnewline
64 & 0.67407338768561 & 0.65185322462878 & 0.32592661231439 \tabularnewline
65 & 0.632879635155726 & 0.734240729688547 & 0.367120364844274 \tabularnewline
66 & 0.59988461841328 & 0.80023076317344 & 0.40011538158672 \tabularnewline
67 & 0.6870011846326 & 0.625997630734799 & 0.3129988153674 \tabularnewline
68 & 0.709014528567364 & 0.581970942865273 & 0.290985471432636 \tabularnewline
69 & 0.669045106235875 & 0.661909787528251 & 0.330954893764125 \tabularnewline
70 & 0.641207079425543 & 0.717585841148915 & 0.358792920574457 \tabularnewline
71 & 0.600462809726933 & 0.799074380546133 & 0.399537190273067 \tabularnewline
72 & 0.56782316328283 & 0.86435367343434 & 0.43217683671717 \tabularnewline
73 & 0.527571957401795 & 0.944856085196411 & 0.472428042598205 \tabularnewline
74 & 0.484887023208049 & 0.969774046416098 & 0.515112976791951 \tabularnewline
75 & 0.45906318759695 & 0.918126375193899 & 0.54093681240305 \tabularnewline
76 & 0.462346727434926 & 0.924693454869852 & 0.537653272565074 \tabularnewline
77 & 0.485566060164618 & 0.971132120329237 & 0.514433939835382 \tabularnewline
78 & 0.816981237075394 & 0.366037525849211 & 0.183018762924606 \tabularnewline
79 & 0.800483816418502 & 0.399032367162995 & 0.199516183581498 \tabularnewline
80 & 0.770550439237102 & 0.458899121525796 & 0.229449560762898 \tabularnewline
81 & 0.734798532427829 & 0.530402935144343 & 0.265201467572171 \tabularnewline
82 & 0.868423211168818 & 0.263153577662363 & 0.131576788831182 \tabularnewline
83 & 0.842966821850612 & 0.314066356298777 & 0.157033178149388 \tabularnewline
84 & 0.821090315640787 & 0.357819368718426 & 0.178909684359213 \tabularnewline
85 & 0.791361267596632 & 0.417277464806736 & 0.208638732403368 \tabularnewline
86 & 0.758369402279535 & 0.483261195440931 & 0.241630597720465 \tabularnewline
87 & 0.746830759092561 & 0.506338481814878 & 0.253169240907439 \tabularnewline
88 & 0.723855607588568 & 0.552288784822864 & 0.276144392411432 \tabularnewline
89 & 0.691753125828382 & 0.616493748343236 & 0.308246874171618 \tabularnewline
90 & 0.766364688513956 & 0.467270622972088 & 0.233635311486044 \tabularnewline
91 & 0.731026713150454 & 0.537946573699092 & 0.268973286849546 \tabularnewline
92 & 0.6917965096668 & 0.616406980666401 & 0.3082034903332 \tabularnewline
93 & 0.650476250823536 & 0.699047498352929 & 0.349523749176464 \tabularnewline
94 & 0.636988546767977 & 0.726022906464046 & 0.363011453232023 \tabularnewline
95 & 0.631180329386239 & 0.737639341227522 & 0.368819670613761 \tabularnewline
96 & 0.58867469242639 & 0.822650615147219 & 0.41132530757361 \tabularnewline
97 & 0.543312438078865 & 0.913375123842271 & 0.456687561921135 \tabularnewline
98 & 0.502148574354804 & 0.995702851290392 & 0.497851425645196 \tabularnewline
99 & 0.486261448452226 & 0.972522896904453 & 0.513738551547774 \tabularnewline
100 & 0.555698592448254 & 0.888602815103493 & 0.444301407551746 \tabularnewline
101 & 0.537754974075097 & 0.924490051849807 & 0.462245025924903 \tabularnewline
102 & 0.507323357404792 & 0.985353285190417 & 0.492676642595208 \tabularnewline
103 & 0.579625250661842 & 0.840749498676316 & 0.420374749338158 \tabularnewline
104 & 0.589377223570303 & 0.821245552859395 & 0.410622776429697 \tabularnewline
105 & 0.565192217396463 & 0.869615565207073 & 0.434807782603537 \tabularnewline
106 & 0.576878054417035 & 0.84624389116593 & 0.423121945582965 \tabularnewline
107 & 0.552278790223398 & 0.895442419553204 & 0.447721209776602 \tabularnewline
108 & 0.526035814868835 & 0.947928370262331 & 0.473964185131166 \tabularnewline
109 & 0.74197895463052 & 0.516042090738961 & 0.25802104536948 \tabularnewline
110 & 0.771242383001956 & 0.457515233996088 & 0.228757616998044 \tabularnewline
111 & 0.748476444697027 & 0.503047110605947 & 0.251523555302973 \tabularnewline
112 & 0.708574681897248 & 0.582850636205504 & 0.291425318102752 \tabularnewline
113 & 0.667657644305916 & 0.664684711388168 & 0.332342355694084 \tabularnewline
114 & 0.626491425179047 & 0.747017149641905 & 0.373508574820953 \tabularnewline
115 & 0.578340722614282 & 0.843318554771435 & 0.421659277385718 \tabularnewline
116 & 0.541594160440342 & 0.916811679119316 & 0.458405839559658 \tabularnewline
117 & 0.809658576536929 & 0.380682846926142 & 0.190341423463071 \tabularnewline
118 & 0.773708333795413 & 0.452583332409173 & 0.226291666204587 \tabularnewline
119 & 0.786368855571903 & 0.427262288856194 & 0.213631144428097 \tabularnewline
120 & 0.751004327802036 & 0.497991344395928 & 0.248995672197964 \tabularnewline
121 & 0.727915213628223 & 0.544169572743553 & 0.272084786371777 \tabularnewline
122 & 0.705356299152588 & 0.589287401694824 & 0.294643700847412 \tabularnewline
123 & 0.770930712886286 & 0.458138574227427 & 0.229069287113714 \tabularnewline
124 & 0.774941243790638 & 0.450117512418725 & 0.225058756209362 \tabularnewline
125 & 0.739695115812445 & 0.52060976837511 & 0.260304884187555 \tabularnewline
126 & 0.718496331039938 & 0.563007337920124 & 0.281503668960062 \tabularnewline
127 & 0.68787186533133 & 0.624256269337341 & 0.31212813466867 \tabularnewline
128 & 0.67094114406518 & 0.658117711869641 & 0.32905885593482 \tabularnewline
129 & 0.794111882159573 & 0.411776235680853 & 0.205888117840427 \tabularnewline
130 & 0.752041770803752 & 0.495916458392497 & 0.247958229196248 \tabularnewline
131 & 0.703865572630235 & 0.592268854739529 & 0.296134427369765 \tabularnewline
132 & 0.68506292787319 & 0.629874144253619 & 0.314937072126809 \tabularnewline
133 & 0.644318099357088 & 0.711363801285824 & 0.355681900642912 \tabularnewline
134 & 0.983519796168776 & 0.0329604076624476 & 0.0164802038312238 \tabularnewline
135 & 0.977426922615454 & 0.0451461547690928 & 0.0225730773845464 \tabularnewline
136 & 0.971210442417419 & 0.0575791151651626 & 0.0287895575825813 \tabularnewline
137 & 0.971173569992637 & 0.0576528600147259 & 0.028826430007363 \tabularnewline
138 & 0.968861901563875 & 0.0622761968722495 & 0.0311380984361248 \tabularnewline
139 & 0.988447582117837 & 0.0231048357643259 & 0.0115524178821629 \tabularnewline
140 & 0.981514585907358 & 0.0369708281852837 & 0.0184854140926418 \tabularnewline
141 & 0.999999951862804 & 9.62743925794442e-08 & 4.81371962897221e-08 \tabularnewline
142 & 0.99999994230381 & 1.15392379214597e-07 & 5.76961896072986e-08 \tabularnewline
143 & 0.999999806705432 & 3.86589135712995e-07 & 1.93294567856498e-07 \tabularnewline
144 & 0.999999374845176 & 1.25030964758685e-06 & 6.25154823793426e-07 \tabularnewline
145 & 0.999999264699861 & 1.47060027886255e-06 & 7.35300139431276e-07 \tabularnewline
146 & 0.999999999810212 & 3.79575812322054e-10 & 1.89787906161027e-10 \tabularnewline
147 & 0.999999998908669 & 2.1826611102213e-09 & 1.09133055511065e-09 \tabularnewline
148 & 0.999999998226016 & 3.54796820221849e-09 & 1.77398410110924e-09 \tabularnewline
149 & 0.999999986996974 & 2.60060515242326e-08 & 1.30030257621163e-08 \tabularnewline
150 & 0.999999949915677 & 1.0016864700133e-07 & 5.00843235006651e-08 \tabularnewline
151 & 0.999999614904606 & 7.70190787247422e-07 & 3.85095393623711e-07 \tabularnewline
152 & 0.999997331047972 & 5.33790405516174e-06 & 2.66895202758087e-06 \tabularnewline
153 & 0.999981984464935 & 3.60310701309463e-05 & 1.80155350654732e-05 \tabularnewline
154 & 0.999886205662745 & 0.00022758867451098 & 0.00011379433725549 \tabularnewline
155 & 0.999963709925333 & 7.25801493348042e-05 & 3.62900746674021e-05 \tabularnewline
156 & 0.999642552551745 & 0.000714894896510025 & 0.000357447448255013 \tabularnewline
157 & 0.997141703905035 & 0.00571659218993006 & 0.00285829609496503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186274&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]7[/C][C]0.800761753447256[/C][C]0.398476493105487[/C][C]0.199238246552744[/C][/ROW]
[ROW][C]8[/C][C]0.787223839498735[/C][C]0.42555232100253[/C][C]0.212776160501265[/C][/ROW]
[ROW][C]9[/C][C]0.728222183541206[/C][C]0.543555632917587[/C][C]0.271777816458794[/C][/ROW]
[ROW][C]10[/C][C]0.841532173309467[/C][C]0.316935653381065[/C][C]0.158467826690533[/C][/ROW]
[ROW][C]11[/C][C]0.788945935933636[/C][C]0.422108128132729[/C][C]0.211054064066364[/C][/ROW]
[ROW][C]12[/C][C]0.750692216842163[/C][C]0.498615566315674[/C][C]0.249307783157837[/C][/ROW]
[ROW][C]13[/C][C]0.6682995716161[/C][C]0.663400856767801[/C][C]0.3317004283839[/C][/ROW]
[ROW][C]14[/C][C]0.85978659445891[/C][C]0.280426811082179[/C][C]0.14021340554109[/C][/ROW]
[ROW][C]15[/C][C]0.824334332088893[/C][C]0.351331335822214[/C][C]0.175665667911107[/C][/ROW]
[ROW][C]16[/C][C]0.765964472082097[/C][C]0.468071055835806[/C][C]0.234035527917903[/C][/ROW]
[ROW][C]17[/C][C]0.701570081665396[/C][C]0.596859836669207[/C][C]0.298429918334604[/C][/ROW]
[ROW][C]18[/C][C]0.653859972387731[/C][C]0.692280055224538[/C][C]0.346140027612269[/C][/ROW]
[ROW][C]19[/C][C]0.608404014096148[/C][C]0.783191971807704[/C][C]0.391595985903852[/C][/ROW]
[ROW][C]20[/C][C]0.533619618532312[/C][C]0.932760762935375[/C][C]0.466380381467688[/C][/ROW]
[ROW][C]21[/C][C]0.459488026407476[/C][C]0.918976052814952[/C][C]0.540511973592524[/C][/ROW]
[ROW][C]22[/C][C]0.959447842654085[/C][C]0.0811043146918299[/C][C]0.0405521573459149[/C][/ROW]
[ROW][C]23[/C][C]0.948079155165011[/C][C]0.103841689669978[/C][C]0.0519208448349888[/C][/ROW]
[ROW][C]24[/C][C]0.938335859178154[/C][C]0.123328281643692[/C][C]0.0616641408218458[/C][/ROW]
[ROW][C]25[/C][C]0.928420544950292[/C][C]0.143158910099416[/C][C]0.071579455049708[/C][/ROW]
[ROW][C]26[/C][C]0.926328914305019[/C][C]0.147342171389963[/C][C]0.0736710856949813[/C][/ROW]
[ROW][C]27[/C][C]0.905521630875898[/C][C]0.188956738248203[/C][C]0.0944783691241017[/C][/ROW]
[ROW][C]28[/C][C]0.880767849746706[/C][C]0.238464300506587[/C][C]0.119232150253294[/C][/ROW]
[ROW][C]29[/C][C]0.85705279323353[/C][C]0.28589441353294[/C][C]0.14294720676647[/C][/ROW]
[ROW][C]30[/C][C]0.876147927228508[/C][C]0.247704145542984[/C][C]0.123852072771492[/C][/ROW]
[ROW][C]31[/C][C]0.843876041686028[/C][C]0.312247916627945[/C][C]0.156123958313972[/C][/ROW]
[ROW][C]32[/C][C]0.81929733402673[/C][C]0.361405331946539[/C][C]0.18070266597327[/C][/ROW]
[ROW][C]33[/C][C]0.803749743258396[/C][C]0.392500513483208[/C][C]0.196250256741604[/C][/ROW]
[ROW][C]34[/C][C]0.778985228471544[/C][C]0.442029543056912[/C][C]0.221014771528456[/C][/ROW]
[ROW][C]35[/C][C]0.837169468831078[/C][C]0.325661062337844[/C][C]0.162830531168922[/C][/ROW]
[ROW][C]36[/C][C]0.863716735956364[/C][C]0.272566528087273[/C][C]0.136283264043636[/C][/ROW]
[ROW][C]37[/C][C]0.913881832806553[/C][C]0.172236334386895[/C][C]0.0861181671934473[/C][/ROW]
[ROW][C]38[/C][C]0.890689615376556[/C][C]0.218620769246889[/C][C]0.109310384623444[/C][/ROW]
[ROW][C]39[/C][C]0.872867168122474[/C][C]0.254265663755052[/C][C]0.127132831877526[/C][/ROW]
[ROW][C]40[/C][C]0.84616429029043[/C][C]0.307671419419141[/C][C]0.15383570970957[/C][/ROW]
[ROW][C]41[/C][C]0.906090254111872[/C][C]0.187819491776257[/C][C]0.0939097458881283[/C][/ROW]
[ROW][C]42[/C][C]0.883391271117238[/C][C]0.233217457765523[/C][C]0.116608728882761[/C][/ROW]
[ROW][C]43[/C][C]0.856603048567652[/C][C]0.286793902864697[/C][C]0.143396951432348[/C][/ROW]
[ROW][C]44[/C][C]0.8332065032706[/C][C]0.3335869934588[/C][C]0.1667934967294[/C][/ROW]
[ROW][C]45[/C][C]0.828870626000747[/C][C]0.342258747998507[/C][C]0.171129373999253[/C][/ROW]
[ROW][C]46[/C][C]0.91725592733945[/C][C]0.165488145321101[/C][C]0.0827440726605503[/C][/ROW]
[ROW][C]47[/C][C]0.917881370614913[/C][C]0.164237258770174[/C][C]0.0821186293850868[/C][/ROW]
[ROW][C]48[/C][C]0.898017744214409[/C][C]0.203964511571182[/C][C]0.101982255785591[/C][/ROW]
[ROW][C]49[/C][C]0.882680986682345[/C][C]0.23463802663531[/C][C]0.117319013317655[/C][/ROW]
[ROW][C]50[/C][C]0.859614342185659[/C][C]0.280771315628682[/C][C]0.140385657814341[/C][/ROW]
[ROW][C]51[/C][C]0.831806447047868[/C][C]0.336387105904264[/C][C]0.168193552952132[/C][/ROW]
[ROW][C]52[/C][C]0.800450405739428[/C][C]0.399099188521144[/C][C]0.199549594260572[/C][/ROW]
[ROW][C]53[/C][C]0.886226854876339[/C][C]0.227546290247321[/C][C]0.113773145123661[/C][/ROW]
[ROW][C]54[/C][C]0.864913564655399[/C][C]0.270172870689203[/C][C]0.135086435344601[/C][/ROW]
[ROW][C]55[/C][C]0.837764623791988[/C][C]0.324470752416025[/C][C]0.162235376208012[/C][/ROW]
[ROW][C]56[/C][C]0.851888931663496[/C][C]0.296222136673009[/C][C]0.148111068336504[/C][/ROW]
[ROW][C]57[/C][C]0.84608402556802[/C][C]0.30783194886396[/C][C]0.15391597443198[/C][/ROW]
[ROW][C]58[/C][C]0.843318115159082[/C][C]0.313363769681836[/C][C]0.156681884840918[/C][/ROW]
[ROW][C]59[/C][C]0.826879445111875[/C][C]0.346241109776249[/C][C]0.173120554888124[/C][/ROW]
[ROW][C]60[/C][C]0.795581777898327[/C][C]0.408836444203347[/C][C]0.204418222101673[/C][/ROW]
[ROW][C]61[/C][C]0.771203605196693[/C][C]0.457592789606615[/C][C]0.228796394803308[/C][/ROW]
[ROW][C]62[/C][C]0.748085605031371[/C][C]0.503828789937257[/C][C]0.251914394968629[/C][/ROW]
[ROW][C]63[/C][C]0.713494889275492[/C][C]0.573010221449015[/C][C]0.286505110724508[/C][/ROW]
[ROW][C]64[/C][C]0.67407338768561[/C][C]0.65185322462878[/C][C]0.32592661231439[/C][/ROW]
[ROW][C]65[/C][C]0.632879635155726[/C][C]0.734240729688547[/C][C]0.367120364844274[/C][/ROW]
[ROW][C]66[/C][C]0.59988461841328[/C][C]0.80023076317344[/C][C]0.40011538158672[/C][/ROW]
[ROW][C]67[/C][C]0.6870011846326[/C][C]0.625997630734799[/C][C]0.3129988153674[/C][/ROW]
[ROW][C]68[/C][C]0.709014528567364[/C][C]0.581970942865273[/C][C]0.290985471432636[/C][/ROW]
[ROW][C]69[/C][C]0.669045106235875[/C][C]0.661909787528251[/C][C]0.330954893764125[/C][/ROW]
[ROW][C]70[/C][C]0.641207079425543[/C][C]0.717585841148915[/C][C]0.358792920574457[/C][/ROW]
[ROW][C]71[/C][C]0.600462809726933[/C][C]0.799074380546133[/C][C]0.399537190273067[/C][/ROW]
[ROW][C]72[/C][C]0.56782316328283[/C][C]0.86435367343434[/C][C]0.43217683671717[/C][/ROW]
[ROW][C]73[/C][C]0.527571957401795[/C][C]0.944856085196411[/C][C]0.472428042598205[/C][/ROW]
[ROW][C]74[/C][C]0.484887023208049[/C][C]0.969774046416098[/C][C]0.515112976791951[/C][/ROW]
[ROW][C]75[/C][C]0.45906318759695[/C][C]0.918126375193899[/C][C]0.54093681240305[/C][/ROW]
[ROW][C]76[/C][C]0.462346727434926[/C][C]0.924693454869852[/C][C]0.537653272565074[/C][/ROW]
[ROW][C]77[/C][C]0.485566060164618[/C][C]0.971132120329237[/C][C]0.514433939835382[/C][/ROW]
[ROW][C]78[/C][C]0.816981237075394[/C][C]0.366037525849211[/C][C]0.183018762924606[/C][/ROW]
[ROW][C]79[/C][C]0.800483816418502[/C][C]0.399032367162995[/C][C]0.199516183581498[/C][/ROW]
[ROW][C]80[/C][C]0.770550439237102[/C][C]0.458899121525796[/C][C]0.229449560762898[/C][/ROW]
[ROW][C]81[/C][C]0.734798532427829[/C][C]0.530402935144343[/C][C]0.265201467572171[/C][/ROW]
[ROW][C]82[/C][C]0.868423211168818[/C][C]0.263153577662363[/C][C]0.131576788831182[/C][/ROW]
[ROW][C]83[/C][C]0.842966821850612[/C][C]0.314066356298777[/C][C]0.157033178149388[/C][/ROW]
[ROW][C]84[/C][C]0.821090315640787[/C][C]0.357819368718426[/C][C]0.178909684359213[/C][/ROW]
[ROW][C]85[/C][C]0.791361267596632[/C][C]0.417277464806736[/C][C]0.208638732403368[/C][/ROW]
[ROW][C]86[/C][C]0.758369402279535[/C][C]0.483261195440931[/C][C]0.241630597720465[/C][/ROW]
[ROW][C]87[/C][C]0.746830759092561[/C][C]0.506338481814878[/C][C]0.253169240907439[/C][/ROW]
[ROW][C]88[/C][C]0.723855607588568[/C][C]0.552288784822864[/C][C]0.276144392411432[/C][/ROW]
[ROW][C]89[/C][C]0.691753125828382[/C][C]0.616493748343236[/C][C]0.308246874171618[/C][/ROW]
[ROW][C]90[/C][C]0.766364688513956[/C][C]0.467270622972088[/C][C]0.233635311486044[/C][/ROW]
[ROW][C]91[/C][C]0.731026713150454[/C][C]0.537946573699092[/C][C]0.268973286849546[/C][/ROW]
[ROW][C]92[/C][C]0.6917965096668[/C][C]0.616406980666401[/C][C]0.3082034903332[/C][/ROW]
[ROW][C]93[/C][C]0.650476250823536[/C][C]0.699047498352929[/C][C]0.349523749176464[/C][/ROW]
[ROW][C]94[/C][C]0.636988546767977[/C][C]0.726022906464046[/C][C]0.363011453232023[/C][/ROW]
[ROW][C]95[/C][C]0.631180329386239[/C][C]0.737639341227522[/C][C]0.368819670613761[/C][/ROW]
[ROW][C]96[/C][C]0.58867469242639[/C][C]0.822650615147219[/C][C]0.41132530757361[/C][/ROW]
[ROW][C]97[/C][C]0.543312438078865[/C][C]0.913375123842271[/C][C]0.456687561921135[/C][/ROW]
[ROW][C]98[/C][C]0.502148574354804[/C][C]0.995702851290392[/C][C]0.497851425645196[/C][/ROW]
[ROW][C]99[/C][C]0.486261448452226[/C][C]0.972522896904453[/C][C]0.513738551547774[/C][/ROW]
[ROW][C]100[/C][C]0.555698592448254[/C][C]0.888602815103493[/C][C]0.444301407551746[/C][/ROW]
[ROW][C]101[/C][C]0.537754974075097[/C][C]0.924490051849807[/C][C]0.462245025924903[/C][/ROW]
[ROW][C]102[/C][C]0.507323357404792[/C][C]0.985353285190417[/C][C]0.492676642595208[/C][/ROW]
[ROW][C]103[/C][C]0.579625250661842[/C][C]0.840749498676316[/C][C]0.420374749338158[/C][/ROW]
[ROW][C]104[/C][C]0.589377223570303[/C][C]0.821245552859395[/C][C]0.410622776429697[/C][/ROW]
[ROW][C]105[/C][C]0.565192217396463[/C][C]0.869615565207073[/C][C]0.434807782603537[/C][/ROW]
[ROW][C]106[/C][C]0.576878054417035[/C][C]0.84624389116593[/C][C]0.423121945582965[/C][/ROW]
[ROW][C]107[/C][C]0.552278790223398[/C][C]0.895442419553204[/C][C]0.447721209776602[/C][/ROW]
[ROW][C]108[/C][C]0.526035814868835[/C][C]0.947928370262331[/C][C]0.473964185131166[/C][/ROW]
[ROW][C]109[/C][C]0.74197895463052[/C][C]0.516042090738961[/C][C]0.25802104536948[/C][/ROW]
[ROW][C]110[/C][C]0.771242383001956[/C][C]0.457515233996088[/C][C]0.228757616998044[/C][/ROW]
[ROW][C]111[/C][C]0.748476444697027[/C][C]0.503047110605947[/C][C]0.251523555302973[/C][/ROW]
[ROW][C]112[/C][C]0.708574681897248[/C][C]0.582850636205504[/C][C]0.291425318102752[/C][/ROW]
[ROW][C]113[/C][C]0.667657644305916[/C][C]0.664684711388168[/C][C]0.332342355694084[/C][/ROW]
[ROW][C]114[/C][C]0.626491425179047[/C][C]0.747017149641905[/C][C]0.373508574820953[/C][/ROW]
[ROW][C]115[/C][C]0.578340722614282[/C][C]0.843318554771435[/C][C]0.421659277385718[/C][/ROW]
[ROW][C]116[/C][C]0.541594160440342[/C][C]0.916811679119316[/C][C]0.458405839559658[/C][/ROW]
[ROW][C]117[/C][C]0.809658576536929[/C][C]0.380682846926142[/C][C]0.190341423463071[/C][/ROW]
[ROW][C]118[/C][C]0.773708333795413[/C][C]0.452583332409173[/C][C]0.226291666204587[/C][/ROW]
[ROW][C]119[/C][C]0.786368855571903[/C][C]0.427262288856194[/C][C]0.213631144428097[/C][/ROW]
[ROW][C]120[/C][C]0.751004327802036[/C][C]0.497991344395928[/C][C]0.248995672197964[/C][/ROW]
[ROW][C]121[/C][C]0.727915213628223[/C][C]0.544169572743553[/C][C]0.272084786371777[/C][/ROW]
[ROW][C]122[/C][C]0.705356299152588[/C][C]0.589287401694824[/C][C]0.294643700847412[/C][/ROW]
[ROW][C]123[/C][C]0.770930712886286[/C][C]0.458138574227427[/C][C]0.229069287113714[/C][/ROW]
[ROW][C]124[/C][C]0.774941243790638[/C][C]0.450117512418725[/C][C]0.225058756209362[/C][/ROW]
[ROW][C]125[/C][C]0.739695115812445[/C][C]0.52060976837511[/C][C]0.260304884187555[/C][/ROW]
[ROW][C]126[/C][C]0.718496331039938[/C][C]0.563007337920124[/C][C]0.281503668960062[/C][/ROW]
[ROW][C]127[/C][C]0.68787186533133[/C][C]0.624256269337341[/C][C]0.31212813466867[/C][/ROW]
[ROW][C]128[/C][C]0.67094114406518[/C][C]0.658117711869641[/C][C]0.32905885593482[/C][/ROW]
[ROW][C]129[/C][C]0.794111882159573[/C][C]0.411776235680853[/C][C]0.205888117840427[/C][/ROW]
[ROW][C]130[/C][C]0.752041770803752[/C][C]0.495916458392497[/C][C]0.247958229196248[/C][/ROW]
[ROW][C]131[/C][C]0.703865572630235[/C][C]0.592268854739529[/C][C]0.296134427369765[/C][/ROW]
[ROW][C]132[/C][C]0.68506292787319[/C][C]0.629874144253619[/C][C]0.314937072126809[/C][/ROW]
[ROW][C]133[/C][C]0.644318099357088[/C][C]0.711363801285824[/C][C]0.355681900642912[/C][/ROW]
[ROW][C]134[/C][C]0.983519796168776[/C][C]0.0329604076624476[/C][C]0.0164802038312238[/C][/ROW]
[ROW][C]135[/C][C]0.977426922615454[/C][C]0.0451461547690928[/C][C]0.0225730773845464[/C][/ROW]
[ROW][C]136[/C][C]0.971210442417419[/C][C]0.0575791151651626[/C][C]0.0287895575825813[/C][/ROW]
[ROW][C]137[/C][C]0.971173569992637[/C][C]0.0576528600147259[/C][C]0.028826430007363[/C][/ROW]
[ROW][C]138[/C][C]0.968861901563875[/C][C]0.0622761968722495[/C][C]0.0311380984361248[/C][/ROW]
[ROW][C]139[/C][C]0.988447582117837[/C][C]0.0231048357643259[/C][C]0.0115524178821629[/C][/ROW]
[ROW][C]140[/C][C]0.981514585907358[/C][C]0.0369708281852837[/C][C]0.0184854140926418[/C][/ROW]
[ROW][C]141[/C][C]0.999999951862804[/C][C]9.62743925794442e-08[/C][C]4.81371962897221e-08[/C][/ROW]
[ROW][C]142[/C][C]0.99999994230381[/C][C]1.15392379214597e-07[/C][C]5.76961896072986e-08[/C][/ROW]
[ROW][C]143[/C][C]0.999999806705432[/C][C]3.86589135712995e-07[/C][C]1.93294567856498e-07[/C][/ROW]
[ROW][C]144[/C][C]0.999999374845176[/C][C]1.25030964758685e-06[/C][C]6.25154823793426e-07[/C][/ROW]
[ROW][C]145[/C][C]0.999999264699861[/C][C]1.47060027886255e-06[/C][C]7.35300139431276e-07[/C][/ROW]
[ROW][C]146[/C][C]0.999999999810212[/C][C]3.79575812322054e-10[/C][C]1.89787906161027e-10[/C][/ROW]
[ROW][C]147[/C][C]0.999999998908669[/C][C]2.1826611102213e-09[/C][C]1.09133055511065e-09[/C][/ROW]
[ROW][C]148[/C][C]0.999999998226016[/C][C]3.54796820221849e-09[/C][C]1.77398410110924e-09[/C][/ROW]
[ROW][C]149[/C][C]0.999999986996974[/C][C]2.60060515242326e-08[/C][C]1.30030257621163e-08[/C][/ROW]
[ROW][C]150[/C][C]0.999999949915677[/C][C]1.0016864700133e-07[/C][C]5.00843235006651e-08[/C][/ROW]
[ROW][C]151[/C][C]0.999999614904606[/C][C]7.70190787247422e-07[/C][C]3.85095393623711e-07[/C][/ROW]
[ROW][C]152[/C][C]0.999997331047972[/C][C]5.33790405516174e-06[/C][C]2.66895202758087e-06[/C][/ROW]
[ROW][C]153[/C][C]0.999981984464935[/C][C]3.60310701309463e-05[/C][C]1.80155350654732e-05[/C][/ROW]
[ROW][C]154[/C][C]0.999886205662745[/C][C]0.00022758867451098[/C][C]0.00011379433725549[/C][/ROW]
[ROW][C]155[/C][C]0.999963709925333[/C][C]7.25801493348042e-05[/C][C]3.62900746674021e-05[/C][/ROW]
[ROW][C]156[/C][C]0.999642552551745[/C][C]0.000714894896510025[/C][C]0.000357447448255013[/C][/ROW]
[ROW][C]157[/C][C]0.997141703905035[/C][C]0.00571659218993006[/C][C]0.00285829609496503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186274&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186274&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 7 0.800761753447256 0.398476493105487 0.199238246552744 8 0.787223839498735 0.42555232100253 0.212776160501265 9 0.728222183541206 0.543555632917587 0.271777816458794 10 0.841532173309467 0.316935653381065 0.158467826690533 11 0.788945935933636 0.422108128132729 0.211054064066364 12 0.750692216842163 0.498615566315674 0.249307783157837 13 0.6682995716161 0.663400856767801 0.3317004283839 14 0.85978659445891 0.280426811082179 0.14021340554109 15 0.824334332088893 0.351331335822214 0.175665667911107 16 0.765964472082097 0.468071055835806 0.234035527917903 17 0.701570081665396 0.596859836669207 0.298429918334604 18 0.653859972387731 0.692280055224538 0.346140027612269 19 0.608404014096148 0.783191971807704 0.391595985903852 20 0.533619618532312 0.932760762935375 0.466380381467688 21 0.459488026407476 0.918976052814952 0.540511973592524 22 0.959447842654085 0.0811043146918299 0.0405521573459149 23 0.948079155165011 0.103841689669978 0.0519208448349888 24 0.938335859178154 0.123328281643692 0.0616641408218458 25 0.928420544950292 0.143158910099416 0.071579455049708 26 0.926328914305019 0.147342171389963 0.0736710856949813 27 0.905521630875898 0.188956738248203 0.0944783691241017 28 0.880767849746706 0.238464300506587 0.119232150253294 29 0.85705279323353 0.28589441353294 0.14294720676647 30 0.876147927228508 0.247704145542984 0.123852072771492 31 0.843876041686028 0.312247916627945 0.156123958313972 32 0.81929733402673 0.361405331946539 0.18070266597327 33 0.803749743258396 0.392500513483208 0.196250256741604 34 0.778985228471544 0.442029543056912 0.221014771528456 35 0.837169468831078 0.325661062337844 0.162830531168922 36 0.863716735956364 0.272566528087273 0.136283264043636 37 0.913881832806553 0.172236334386895 0.0861181671934473 38 0.890689615376556 0.218620769246889 0.109310384623444 39 0.872867168122474 0.254265663755052 0.127132831877526 40 0.84616429029043 0.307671419419141 0.15383570970957 41 0.906090254111872 0.187819491776257 0.0939097458881283 42 0.883391271117238 0.233217457765523 0.116608728882761 43 0.856603048567652 0.286793902864697 0.143396951432348 44 0.8332065032706 0.3335869934588 0.1667934967294 45 0.828870626000747 0.342258747998507 0.171129373999253 46 0.91725592733945 0.165488145321101 0.0827440726605503 47 0.917881370614913 0.164237258770174 0.0821186293850868 48 0.898017744214409 0.203964511571182 0.101982255785591 49 0.882680986682345 0.23463802663531 0.117319013317655 50 0.859614342185659 0.280771315628682 0.140385657814341 51 0.831806447047868 0.336387105904264 0.168193552952132 52 0.800450405739428 0.399099188521144 0.199549594260572 53 0.886226854876339 0.227546290247321 0.113773145123661 54 0.864913564655399 0.270172870689203 0.135086435344601 55 0.837764623791988 0.324470752416025 0.162235376208012 56 0.851888931663496 0.296222136673009 0.148111068336504 57 0.84608402556802 0.30783194886396 0.15391597443198 58 0.843318115159082 0.313363769681836 0.156681884840918 59 0.826879445111875 0.346241109776249 0.173120554888124 60 0.795581777898327 0.408836444203347 0.204418222101673 61 0.771203605196693 0.457592789606615 0.228796394803308 62 0.748085605031371 0.503828789937257 0.251914394968629 63 0.713494889275492 0.573010221449015 0.286505110724508 64 0.67407338768561 0.65185322462878 0.32592661231439 65 0.632879635155726 0.734240729688547 0.367120364844274 66 0.59988461841328 0.80023076317344 0.40011538158672 67 0.6870011846326 0.625997630734799 0.3129988153674 68 0.709014528567364 0.581970942865273 0.290985471432636 69 0.669045106235875 0.661909787528251 0.330954893764125 70 0.641207079425543 0.717585841148915 0.358792920574457 71 0.600462809726933 0.799074380546133 0.399537190273067 72 0.56782316328283 0.86435367343434 0.43217683671717 73 0.527571957401795 0.944856085196411 0.472428042598205 74 0.484887023208049 0.969774046416098 0.515112976791951 75 0.45906318759695 0.918126375193899 0.54093681240305 76 0.462346727434926 0.924693454869852 0.537653272565074 77 0.485566060164618 0.971132120329237 0.514433939835382 78 0.816981237075394 0.366037525849211 0.183018762924606 79 0.800483816418502 0.399032367162995 0.199516183581498 80 0.770550439237102 0.458899121525796 0.229449560762898 81 0.734798532427829 0.530402935144343 0.265201467572171 82 0.868423211168818 0.263153577662363 0.131576788831182 83 0.842966821850612 0.314066356298777 0.157033178149388 84 0.821090315640787 0.357819368718426 0.178909684359213 85 0.791361267596632 0.417277464806736 0.208638732403368 86 0.758369402279535 0.483261195440931 0.241630597720465 87 0.746830759092561 0.506338481814878 0.253169240907439 88 0.723855607588568 0.552288784822864 0.276144392411432 89 0.691753125828382 0.616493748343236 0.308246874171618 90 0.766364688513956 0.467270622972088 0.233635311486044 91 0.731026713150454 0.537946573699092 0.268973286849546 92 0.6917965096668 0.616406980666401 0.3082034903332 93 0.650476250823536 0.699047498352929 0.349523749176464 94 0.636988546767977 0.726022906464046 0.363011453232023 95 0.631180329386239 0.737639341227522 0.368819670613761 96 0.58867469242639 0.822650615147219 0.41132530757361 97 0.543312438078865 0.913375123842271 0.456687561921135 98 0.502148574354804 0.995702851290392 0.497851425645196 99 0.486261448452226 0.972522896904453 0.513738551547774 100 0.555698592448254 0.888602815103493 0.444301407551746 101 0.537754974075097 0.924490051849807 0.462245025924903 102 0.507323357404792 0.985353285190417 0.492676642595208 103 0.579625250661842 0.840749498676316 0.420374749338158 104 0.589377223570303 0.821245552859395 0.410622776429697 105 0.565192217396463 0.869615565207073 0.434807782603537 106 0.576878054417035 0.84624389116593 0.423121945582965 107 0.552278790223398 0.895442419553204 0.447721209776602 108 0.526035814868835 0.947928370262331 0.473964185131166 109 0.74197895463052 0.516042090738961 0.25802104536948 110 0.771242383001956 0.457515233996088 0.228757616998044 111 0.748476444697027 0.503047110605947 0.251523555302973 112 0.708574681897248 0.582850636205504 0.291425318102752 113 0.667657644305916 0.664684711388168 0.332342355694084 114 0.626491425179047 0.747017149641905 0.373508574820953 115 0.578340722614282 0.843318554771435 0.421659277385718 116 0.541594160440342 0.916811679119316 0.458405839559658 117 0.809658576536929 0.380682846926142 0.190341423463071 118 0.773708333795413 0.452583332409173 0.226291666204587 119 0.786368855571903 0.427262288856194 0.213631144428097 120 0.751004327802036 0.497991344395928 0.248995672197964 121 0.727915213628223 0.544169572743553 0.272084786371777 122 0.705356299152588 0.589287401694824 0.294643700847412 123 0.770930712886286 0.458138574227427 0.229069287113714 124 0.774941243790638 0.450117512418725 0.225058756209362 125 0.739695115812445 0.52060976837511 0.260304884187555 126 0.718496331039938 0.563007337920124 0.281503668960062 127 0.68787186533133 0.624256269337341 0.31212813466867 128 0.67094114406518 0.658117711869641 0.32905885593482 129 0.794111882159573 0.411776235680853 0.205888117840427 130 0.752041770803752 0.495916458392497 0.247958229196248 131 0.703865572630235 0.592268854739529 0.296134427369765 132 0.68506292787319 0.629874144253619 0.314937072126809 133 0.644318099357088 0.711363801285824 0.355681900642912 134 0.983519796168776 0.0329604076624476 0.0164802038312238 135 0.977426922615454 0.0451461547690928 0.0225730773845464 136 0.971210442417419 0.0575791151651626 0.0287895575825813 137 0.971173569992637 0.0576528600147259 0.028826430007363 138 0.968861901563875 0.0622761968722495 0.0311380984361248 139 0.988447582117837 0.0231048357643259 0.0115524178821629 140 0.981514585907358 0.0369708281852837 0.0184854140926418 141 0.999999951862804 9.62743925794442e-08 4.81371962897221e-08 142 0.99999994230381 1.15392379214597e-07 5.76961896072986e-08 143 0.999999806705432 3.86589135712995e-07 1.93294567856498e-07 144 0.999999374845176 1.25030964758685e-06 6.25154823793426e-07 145 0.999999264699861 1.47060027886255e-06 7.35300139431276e-07 146 0.999999999810212 3.79575812322054e-10 1.89787906161027e-10 147 0.999999998908669 2.1826611102213e-09 1.09133055511065e-09 148 0.999999998226016 3.54796820221849e-09 1.77398410110924e-09 149 0.999999986996974 2.60060515242326e-08 1.30030257621163e-08 150 0.999999949915677 1.0016864700133e-07 5.00843235006651e-08 151 0.999999614904606 7.70190787247422e-07 3.85095393623711e-07 152 0.999997331047972 5.33790405516174e-06 2.66895202758087e-06 153 0.999981984464935 3.60310701309463e-05 1.80155350654732e-05 154 0.999886205662745 0.00022758867451098 0.00011379433725549 155 0.999963709925333 7.25801493348042e-05 3.62900746674021e-05 156 0.999642552551745 0.000714894896510025 0.000357447448255013 157 0.997141703905035 0.00571659218993006 0.00285829609496503

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.112582781456954 NOK 5% type I error level 21 0.139072847682119 NOK 10% type I error level 25 0.165562913907285 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 & 17 & 0.112582781456954 & NOK \tabularnewline
5% type I error level & 21 & 0.139072847682119 & NOK \tabularnewline
10% type I error level & 25 & 0.165562913907285 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186274&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]17[/C][C]0.112582781456954[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.139072847682119[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.165562913907285[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186274&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186274&T=6

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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 17 0.112582781456954 NOK 5% type I error level 21 0.139072847682119 NOK 10% type I error level 25 0.165562913907285 NOK

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Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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 testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}