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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 26 Nov 2010 11:00:47 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/26/t1290769162i0q02xiz9ggbc3n.htm/, Retrieved Sat, 14 Jun 2025 16:38:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=101756, Retrieved Sat, 14 Jun 2025 16:38:38 +0000

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Estimated Impact

210

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

-     [Multiple Regression] [HPC Retail Sales] [2008-03-08 13:40:54] [1c0f2c85e8a48e42648374b3bcceca26]
-  M D    [Multiple Regression] [W8 Regressiemodel] [2010-11-26 11:00:47] [59f7d3e7fcb6374015f4e6b9053b0f01] [Current]
-   PD      [Multiple Regression] [W8 Regressiemodel] [2010-11-26 12:03:41] [56d90b683fcd93137645f9226b43c62b] 
-    D        [Multiple Regression] [Paper Multiple Re...] [2010-12-11 10:24:16] [56d90b683fcd93137645f9226b43c62b] 
-    D        [Multiple Regression] [Paper MR Time Series] [2010-12-11 10:40:35] [56d90b683fcd93137645f9226b43c62b] 

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101756&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101756&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101756&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	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
Pas[t] = + 11516.7062045455 -32.8866979166674M1[t] + 745.405183712119M2[t] + 3028.25015625000M3[t] + 2861.47703787879M4[t] + 4766.35437405303M5[t] + 3852.05461931818M6[t] + 1868.78450094697M7[t] + 620.103837121209M8[t] + 3368.07762784091M9[t] + 2685.14005492424M10[t] + 1822.64866382575M11[t] + 6.44775473484847t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Pas[t] =  +  11516.7062045455 -32.8866979166674M1[t] +  745.405183712119M2[t] +  3028.25015625000M3[t] +  2861.47703787879M4[t] +  4766.35437405303M5[t] +  3852.05461931818M6[t] +  1868.78450094697M7[t] +  620.103837121209M8[t] +  3368.07762784091M9[t] +  2685.14005492424M10[t] +  1822.64866382575M11[t] +  6.44775473484847t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101756&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Pas[t] =  +  11516.7062045455 -32.8866979166674M1[t] +  745.405183712119M2[t] +  3028.25015625000M3[t] +  2861.47703787879M4[t] +  4766.35437405303M5[t] +  3852.05461931818M6[t] +  1868.78450094697M7[t] +  620.103837121209M8[t] +  3368.07762784091M9[t] +  2685.14005492424M10[t] +  1822.64866382575M11[t] +  6.44775473484847t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101756&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101756&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
Pas[t] = + 11516.7062045455 -32.8866979166674M1[t] + 745.405183712119M2[t] + 3028.25015625000M3[t] + 2861.47703787879M4[t] + 4766.35437405303M5[t] + 3852.05461931818M6[t] + 1868.78450094697M7[t] + 620.103837121209M8[t] + 3368.07762784091M9[t] + 2685.14005492424M10[t] + 1822.64866382575M11[t] + 6.44775473484847t + 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)	11516.7062045455	2087.765023	5.5163	0	0
M1	-32.8866979166674	2594.083062	-0.0127	0.989906	0.494953
M2	745.405183712119	2593.29756	0.2874	0.774279	0.387139
M3	3028.25015625000	2592.586662	1.168	0.245125	0.122562
M4	2861.47703787879	2591.950431	1.104	0.271827	0.135913
M5	4766.35437405303	2591.388921	1.8393	0.068363	0.034182
M6	3852.05461931818	2590.90218	1.4868	0.139722	0.069861
M7	1868.78450094697	2590.490251	0.7214	0.472077	0.236039
M8	620.103837121209	2590.153169	0.2394	0.811201	0.4056
M9	3368.07762784091	2589.890964	1.3005	0.195953	0.097976
M10	2685.14005492424	2589.703659	1.0369	0.301907	0.150954
M11	1822.64866382575	2589.591269	0.7038	0.48291	0.241455
t	6.44775473484847	13.929582	0.4629	0.644294	0.322147

\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) & 11516.7062045455 & 2087.765023 & 5.5163 & 0 & 0 \tabularnewline
M1 & -32.8866979166674 & 2594.083062 & -0.0127 & 0.989906 & 0.494953 \tabularnewline
M2 & 745.405183712119 & 2593.29756 & 0.2874 & 0.774279 & 0.387139 \tabularnewline
M3 & 3028.25015625000 & 2592.586662 & 1.168 & 0.245125 & 0.122562 \tabularnewline
M4 & 2861.47703787879 & 2591.950431 & 1.104 & 0.271827 & 0.135913 \tabularnewline
M5 & 4766.35437405303 & 2591.388921 & 1.8393 & 0.068363 & 0.034182 \tabularnewline
M6 & 3852.05461931818 & 2590.90218 & 1.4868 & 0.139722 & 0.069861 \tabularnewline
M7 & 1868.78450094697 & 2590.490251 & 0.7214 & 0.472077 & 0.236039 \tabularnewline
M8 & 620.103837121209 & 2590.153169 & 0.2394 & 0.811201 & 0.4056 \tabularnewline
M9 & 3368.07762784091 & 2589.890964 & 1.3005 & 0.195953 & 0.097976 \tabularnewline
M10 & 2685.14005492424 & 2589.703659 & 1.0369 & 0.301907 & 0.150954 \tabularnewline
M11 & 1822.64866382575 & 2589.591269 & 0.7038 & 0.48291 & 0.241455 \tabularnewline
t & 6.44775473484847 & 13.929582 & 0.4629 & 0.644294 & 0.322147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101756&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]11516.7062045455[/C][C]2087.765023[/C][C]5.5163[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-32.8866979166674[/C][C]2594.083062[/C][C]-0.0127[/C][C]0.989906[/C][C]0.494953[/C][/ROW]
[ROW][C]M2[/C][C]745.405183712119[/C][C]2593.29756[/C][C]0.2874[/C][C]0.774279[/C][C]0.387139[/C][/ROW]
[ROW][C]M3[/C][C]3028.25015625000[/C][C]2592.586662[/C][C]1.168[/C][C]0.245125[/C][C]0.122562[/C][/ROW]
[ROW][C]M4[/C][C]2861.47703787879[/C][C]2591.950431[/C][C]1.104[/C][C]0.271827[/C][C]0.135913[/C][/ROW]
[ROW][C]M5[/C][C]4766.35437405303[/C][C]2591.388921[/C][C]1.8393[/C][C]0.068363[/C][C]0.034182[/C][/ROW]
[ROW][C]M6[/C][C]3852.05461931818[/C][C]2590.90218[/C][C]1.4868[/C][C]0.139722[/C][C]0.069861[/C][/ROW]
[ROW][C]M7[/C][C]1868.78450094697[/C][C]2590.490251[/C][C]0.7214[/C][C]0.472077[/C][C]0.236039[/C][/ROW]
[ROW][C]M8[/C][C]620.103837121209[/C][C]2590.153169[/C][C]0.2394[/C][C]0.811201[/C][C]0.4056[/C][/ROW]
[ROW][C]M9[/C][C]3368.07762784091[/C][C]2589.890964[/C][C]1.3005[/C][C]0.195953[/C][C]0.097976[/C][/ROW]
[ROW][C]M10[/C][C]2685.14005492424[/C][C]2589.703659[/C][C]1.0369[/C][C]0.301907[/C][C]0.150954[/C][/ROW]
[ROW][C]M11[/C][C]1822.64866382575[/C][C]2589.591269[/C][C]0.7038[/C][C]0.48291[/C][C]0.241455[/C][/ROW]
[ROW][C]t[/C][C]6.44775473484847[/C][C]13.929582[/C][C]0.4629[/C][C]0.644294[/C][C]0.322147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101756&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101756&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)	11516.7062045455	2087.765023	5.5163	0	0
M1	-32.8866979166674	2594.083062	-0.0127	0.989906	0.494953
M2	745.405183712119	2593.29756	0.2874	0.774279	0.387139
M3	3028.25015625000	2592.586662	1.168	0.245125	0.122562
M4	2861.47703787879	2591.950431	1.104	0.271827	0.135913
M5	4766.35437405303	2591.388921	1.8393	0.068363	0.034182
M6	3852.05461931818	2590.90218	1.4868	0.139722	0.069861
M7	1868.78450094697	2590.490251	0.7214	0.472077	0.236039
M8	620.103837121209	2590.153169	0.2394	0.811201	0.4056
M9	3368.07762784091	2589.890964	1.3005	0.195953	0.097976
M10	2685.14005492424	2589.703659	1.0369	0.301907	0.150954
M11	1822.64866382575	2589.591269	0.7038	0.48291	0.241455
t	6.44775473484847	13.929582	0.4629	0.644294	0.322147

Multiple Linear Regression - Regression Statistics
Multiple R	0.253366532834073
R-squared	0.0641945999603596
Adjusted R-squared	-0.0301723311360749
F-TEST (value)	0.680265843283159
F-TEST (DF numerator)	12
F-TEST (DF denominator)	119
p-value	0.767537094382899
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6073.04198876287
Sum Squared Residuals	4388938840.67594

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.253366532834073 \tabularnewline
R-squared & 0.0641945999603596 \tabularnewline
Adjusted R-squared & -0.0301723311360749 \tabularnewline
F-TEST (value) & 0.680265843283159 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 119 \tabularnewline
p-value & 0.767537094382899 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6073.04198876287 \tabularnewline
Sum Squared Residuals & 4388938840.67594 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101756&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.253366532834073[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0641945999603596[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0301723311360749[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.680265843283159[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]119[/C][/ROW]
[ROW][C]p-value[/C][C]0.767537094382899[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6073.04198876287[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4388938840.67594[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101756&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101756&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	0.253366532834073
R-squared	0.0641945999603596
Adjusted R-squared	-0.0301723311360749
F-TEST (value)	0.680265843283159
F-TEST (DF numerator)	12
F-TEST (DF denominator)	119
p-value	0.767537094382899
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6073.04198876287
Sum Squared Residuals	4388938840.67594

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14.458	11490.2672613636	-11475.8092613636
2	13.594	12275.0068977273	-12261.4128977273
3	17.814	14564.299625	-14546.485625
4	20.235	14403.9742613636	-14383.7392613636
5	21.811	16315.2993522727	-16293.4883522727
6	21.439	15407.4473522727	-15386.0083522727
7	21.393	13430.6249886364	-13409.2319886364
8	19.831	12188.3920795455	-12168.5610795455
9	20.468	14942.813625	-14922.345625
10	21.08	14266.3238068182	-14245.2438068182
11	21.6	13410.2801704545	-13388.6801704545
12	17.39	11594.0792613636	-11576.6892613636
13	17848	11567.6403181818	6280.35968181818
14	19592	12352.3799545455	7239.62004545455
15	21092	14641.6726818182	6450.32731818182
16	20889	14481.3473181818	6407.65268181818
17	25890	16392.6724090909	9497.32759090909
18	24965	15484.8204090909	9480.1795909091
19	22225	13507.9980454545	8717.00195454545
20	20977	12265.7651363636	8711.23486363637
21	22897	15020.1866818182	7876.81331818181
22	22785	14343.6968636364	8441.30313636363
23	22769	13487.6532272727	9281.34677272727
24	19637	11671.4523181818	7965.54768181818
25	20203	11645.013375	8557.986625
26	20450	12429.7530113636	8020.24698863637
27	23083	14719.0457386364	8363.95426136363
28	21738	14558.720375	7179.279625
29	26766	16470.0454659091	10295.9545340909
30	25280	15562.1934659091	9717.8065340909
31	22574	13585.3711022727	8988.62889772727
32	22729	12343.1381931818	10385.8618068182
33	21378	15097.5597386364	6280.44026136364
34	22902	14421.0699204545	8480.93007954545
35	24989	13565.0262840909	11423.9737159091
36	21116	11748.825375	9367.174625
37	15169	11722.3864318182	3446.61356818182
38	15846	12507.1260681818	3338.87393181818
39	20927	14796.4187954545	6130.58120454545
40	18273	14636.0934318182	3636.90656818182
41	22538	16547.4185227273	5990.58147727273
42	15596	15639.5665227273	-43.5665227272722
43	14034	13662.7441590909	371.255840909091
44	11366	12420.51125	-1054.51125000000
45	14861	15174.9327954545	-313.932795454544
46	15149	14498.4429772727	650.557022727276
47	13577	13642.3993409091	-65.3993409090896
48	13026	11826.1984318182	1199.80156818181
49	13190	11799.7594886364	1390.24051136363
50	13196	12584.499125	611.500875
51	15826	14873.7918522727	952.208147727272
52	14733	14713.4664886364	19.5335113636345
53	16307	16624.7915795455	-317.791579545453
54	15703	15716.9395795455	-13.9395795454540
55	14589	13740.1172159091	848.88278409091
56	12043	12497.8843068182	-454.884306818183
57	15057	15252.3058522727	-195.305852272726
58	14053	14575.8160340909	-522.816034090907
59	12698	13719.7723977273	-1021.77239772727
60	10888	11903.5714886364	-1015.57148863637
61	10045	11877.1325454545	-1832.13254545455
62	11549	12661.8721818182	-1112.87218181818
63	13767	14951.1649090909	-1184.16490909091
64	12424	14790.8395454545	-2366.83954545455
65	13116	16702.1646363636	-3586.16463636363
66	14211	15794.3126363636	-1583.31263636364
67	12266	13817.4902727273	-1551.49027272727
68	12602	12575.2573636364	26.7426363636362
69	15714	15329.6789090909	384.321090909093
70	13742	14653.1890909091	-911.18909090909
71	12745	13797.1454545455	-1052.14545454545
72	10491	11980.9445454545	-1489.94454545455
73	10057	11954.5056022727	-1897.50560227273
74	10900	12739.2452386364	-1839.24523863636
75	11771	15028.5379659091	-3257.53796590909
76	11992	14868.2126022727	-2876.21260227273
77	11993	16779.5376931818	-4786.53769318182
78	14504	15871.6856931818	-1367.68569318182
79	11727	13894.8633295455	-2167.86332954546
80	11477	12652.6304204545	-1175.63042045455
81	13578	15407.0519659091	-1829.05196590909
82	11555	14730.5621477273	-3175.56214772727
83	11846	13874.5185113636	-2028.51851136364
84	11397	12058.3176022727	-661.31760227273
85	10066	12031.8786590909	-1965.87865909091
86	10269	12816.6182954545	-2547.61829545454
87	14279	15105.9110227273	-826.911022727272
88	13870	14945.5856590909	-1075.58565909091
89	13695	16856.91075	-3161.91075
90	14420	15949.05875	-1529.05875
91	11424	13972.2363863636	-2548.23638636364
92	9704	12730.0034772727	-3026.00347727273
93	12464	15484.4250227273	-3020.42502272727
94	14301	14807.9352045455	-506.935204545452
95	13464	13951.8915681818	-487.891568181817
96	9893	12135.6906590909	-2242.69065909091
97	11572	12109.2517159091	-537.251715909092
98	12380	12893.9913522727	-513.991352272726
99	16692	15183.2840795455	1508.71592045455
100	16052	15022.9587159091	1029.04128409091
101	16459	16934.2838068182	-475.28380681818
102	14761	16026.4318068182	-1265.43180681818
103	13654	14049.6094431818	-395.609443181818
104	13480	12807.3765340909	672.623465909092
105	18068	15561.7980795455	2506.20192045455
106	16560	14885.3082613636	1674.69173863637
107	14530	14029.264625	500.735375000003
108	10650	12213.0637159091	-1563.06371590909
109	11651	12186.6247727273	-535.624772727274
110	13735	12971.3644090909	763.635590909092
111	13360	15260.6571363636	-1900.65713636364
112	17818	15100.3317727273	2717.66822727273
113	20613	17011.6568636364	3601.34313636364
114	16231	16103.8048636364	127.195136363638
115	13862	14126.9825	-264.982499999999
116	12004	12884.7495909091	-880.749590909091
117	17734	15639.1711363636	2094.82886363637
118	15034	14962.6813181818	71.3186818181851
119	12609	14106.6376818182	-1497.63768181818
120	12320	12290.4367727273	29.5632272727255
121	10833	12263.9978295455	-1430.99782954546
122	11350	13048.7374659091	-1698.73746590909
123	13648	15338.0301931818	-1690.03019318182
124	14890	15177.7048295455	-287.704829545454
125	16325	17089.0299204545	-764.029920454543
126	18045	16181.1779204545	1863.82207954546
127	15616	14204.3555568182	1411.64444318182
128	11926	12962.1226477273	-1036.12264772727
129	16855	15716.5441931818	1138.45580681818
130	15083	15040.054375	42.9456250000042
131	12520	14184.0107386364	-1664.01073863636
132	12355	12367.8098295455	-12.8098295454563

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14.458 & 11490.2672613636 & -11475.8092613636 \tabularnewline
2 & 13.594 & 12275.0068977273 & -12261.4128977273 \tabularnewline
3 & 17.814 & 14564.299625 & -14546.485625 \tabularnewline
4 & 20.235 & 14403.9742613636 & -14383.7392613636 \tabularnewline
5 & 21.811 & 16315.2993522727 & -16293.4883522727 \tabularnewline
6 & 21.439 & 15407.4473522727 & -15386.0083522727 \tabularnewline
7 & 21.393 & 13430.6249886364 & -13409.2319886364 \tabularnewline
8 & 19.831 & 12188.3920795455 & -12168.5610795455 \tabularnewline
9 & 20.468 & 14942.813625 & -14922.345625 \tabularnewline
10 & 21.08 & 14266.3238068182 & -14245.2438068182 \tabularnewline
11 & 21.6 & 13410.2801704545 & -13388.6801704545 \tabularnewline
12 & 17.39 & 11594.0792613636 & -11576.6892613636 \tabularnewline
13 & 17848 & 11567.6403181818 & 6280.35968181818 \tabularnewline
14 & 19592 & 12352.3799545455 & 7239.62004545455 \tabularnewline
15 & 21092 & 14641.6726818182 & 6450.32731818182 \tabularnewline
16 & 20889 & 14481.3473181818 & 6407.65268181818 \tabularnewline
17 & 25890 & 16392.6724090909 & 9497.32759090909 \tabularnewline
18 & 24965 & 15484.8204090909 & 9480.1795909091 \tabularnewline
19 & 22225 & 13507.9980454545 & 8717.00195454545 \tabularnewline
20 & 20977 & 12265.7651363636 & 8711.23486363637 \tabularnewline
21 & 22897 & 15020.1866818182 & 7876.81331818181 \tabularnewline
22 & 22785 & 14343.6968636364 & 8441.30313636363 \tabularnewline
23 & 22769 & 13487.6532272727 & 9281.34677272727 \tabularnewline
24 & 19637 & 11671.4523181818 & 7965.54768181818 \tabularnewline
25 & 20203 & 11645.013375 & 8557.986625 \tabularnewline
26 & 20450 & 12429.7530113636 & 8020.24698863637 \tabularnewline
27 & 23083 & 14719.0457386364 & 8363.95426136363 \tabularnewline
28 & 21738 & 14558.720375 & 7179.279625 \tabularnewline
29 & 26766 & 16470.0454659091 & 10295.9545340909 \tabularnewline
30 & 25280 & 15562.1934659091 & 9717.8065340909 \tabularnewline
31 & 22574 & 13585.3711022727 & 8988.62889772727 \tabularnewline
32 & 22729 & 12343.1381931818 & 10385.8618068182 \tabularnewline
33 & 21378 & 15097.5597386364 & 6280.44026136364 \tabularnewline
34 & 22902 & 14421.0699204545 & 8480.93007954545 \tabularnewline
35 & 24989 & 13565.0262840909 & 11423.9737159091 \tabularnewline
36 & 21116 & 11748.825375 & 9367.174625 \tabularnewline
37 & 15169 & 11722.3864318182 & 3446.61356818182 \tabularnewline
38 & 15846 & 12507.1260681818 & 3338.87393181818 \tabularnewline
39 & 20927 & 14796.4187954545 & 6130.58120454545 \tabularnewline
40 & 18273 & 14636.0934318182 & 3636.90656818182 \tabularnewline
41 & 22538 & 16547.4185227273 & 5990.58147727273 \tabularnewline
42 & 15596 & 15639.5665227273 & -43.5665227272722 \tabularnewline
43 & 14034 & 13662.7441590909 & 371.255840909091 \tabularnewline
44 & 11366 & 12420.51125 & -1054.51125000000 \tabularnewline
45 & 14861 & 15174.9327954545 & -313.932795454544 \tabularnewline
46 & 15149 & 14498.4429772727 & 650.557022727276 \tabularnewline
47 & 13577 & 13642.3993409091 & -65.3993409090896 \tabularnewline
48 & 13026 & 11826.1984318182 & 1199.80156818181 \tabularnewline
49 & 13190 & 11799.7594886364 & 1390.24051136363 \tabularnewline
50 & 13196 & 12584.499125 & 611.500875 \tabularnewline
51 & 15826 & 14873.7918522727 & 952.208147727272 \tabularnewline
52 & 14733 & 14713.4664886364 & 19.5335113636345 \tabularnewline
53 & 16307 & 16624.7915795455 & -317.791579545453 \tabularnewline
54 & 15703 & 15716.9395795455 & -13.9395795454540 \tabularnewline
55 & 14589 & 13740.1172159091 & 848.88278409091 \tabularnewline
56 & 12043 & 12497.8843068182 & -454.884306818183 \tabularnewline
57 & 15057 & 15252.3058522727 & -195.305852272726 \tabularnewline
58 & 14053 & 14575.8160340909 & -522.816034090907 \tabularnewline
59 & 12698 & 13719.7723977273 & -1021.77239772727 \tabularnewline
60 & 10888 & 11903.5714886364 & -1015.57148863637 \tabularnewline
61 & 10045 & 11877.1325454545 & -1832.13254545455 \tabularnewline
62 & 11549 & 12661.8721818182 & -1112.87218181818 \tabularnewline
63 & 13767 & 14951.1649090909 & -1184.16490909091 \tabularnewline
64 & 12424 & 14790.8395454545 & -2366.83954545455 \tabularnewline
65 & 13116 & 16702.1646363636 & -3586.16463636363 \tabularnewline
66 & 14211 & 15794.3126363636 & -1583.31263636364 \tabularnewline
67 & 12266 & 13817.4902727273 & -1551.49027272727 \tabularnewline
68 & 12602 & 12575.2573636364 & 26.7426363636362 \tabularnewline
69 & 15714 & 15329.6789090909 & 384.321090909093 \tabularnewline
70 & 13742 & 14653.1890909091 & -911.18909090909 \tabularnewline
71 & 12745 & 13797.1454545455 & -1052.14545454545 \tabularnewline
72 & 10491 & 11980.9445454545 & -1489.94454545455 \tabularnewline
73 & 10057 & 11954.5056022727 & -1897.50560227273 \tabularnewline
74 & 10900 & 12739.2452386364 & -1839.24523863636 \tabularnewline
75 & 11771 & 15028.5379659091 & -3257.53796590909 \tabularnewline
76 & 11992 & 14868.2126022727 & -2876.21260227273 \tabularnewline
77 & 11993 & 16779.5376931818 & -4786.53769318182 \tabularnewline
78 & 14504 & 15871.6856931818 & -1367.68569318182 \tabularnewline
79 & 11727 & 13894.8633295455 & -2167.86332954546 \tabularnewline
80 & 11477 & 12652.6304204545 & -1175.63042045455 \tabularnewline
81 & 13578 & 15407.0519659091 & -1829.05196590909 \tabularnewline
82 & 11555 & 14730.5621477273 & -3175.56214772727 \tabularnewline
83 & 11846 & 13874.5185113636 & -2028.51851136364 \tabularnewline
84 & 11397 & 12058.3176022727 & -661.31760227273 \tabularnewline
85 & 10066 & 12031.8786590909 & -1965.87865909091 \tabularnewline
86 & 10269 & 12816.6182954545 & -2547.61829545454 \tabularnewline
87 & 14279 & 15105.9110227273 & -826.911022727272 \tabularnewline
88 & 13870 & 14945.5856590909 & -1075.58565909091 \tabularnewline
89 & 13695 & 16856.91075 & -3161.91075 \tabularnewline
90 & 14420 & 15949.05875 & -1529.05875 \tabularnewline
91 & 11424 & 13972.2363863636 & -2548.23638636364 \tabularnewline
92 & 9704 & 12730.0034772727 & -3026.00347727273 \tabularnewline
93 & 12464 & 15484.4250227273 & -3020.42502272727 \tabularnewline
94 & 14301 & 14807.9352045455 & -506.935204545452 \tabularnewline
95 & 13464 & 13951.8915681818 & -487.891568181817 \tabularnewline
96 & 9893 & 12135.6906590909 & -2242.69065909091 \tabularnewline
97 & 11572 & 12109.2517159091 & -537.251715909092 \tabularnewline
98 & 12380 & 12893.9913522727 & -513.991352272726 \tabularnewline
99 & 16692 & 15183.2840795455 & 1508.71592045455 \tabularnewline
100 & 16052 & 15022.9587159091 & 1029.04128409091 \tabularnewline
101 & 16459 & 16934.2838068182 & -475.28380681818 \tabularnewline
102 & 14761 & 16026.4318068182 & -1265.43180681818 \tabularnewline
103 & 13654 & 14049.6094431818 & -395.609443181818 \tabularnewline
104 & 13480 & 12807.3765340909 & 672.623465909092 \tabularnewline
105 & 18068 & 15561.7980795455 & 2506.20192045455 \tabularnewline
106 & 16560 & 14885.3082613636 & 1674.69173863637 \tabularnewline
107 & 14530 & 14029.264625 & 500.735375000003 \tabularnewline
108 & 10650 & 12213.0637159091 & -1563.06371590909 \tabularnewline
109 & 11651 & 12186.6247727273 & -535.624772727274 \tabularnewline
110 & 13735 & 12971.3644090909 & 763.635590909092 \tabularnewline
111 & 13360 & 15260.6571363636 & -1900.65713636364 \tabularnewline
112 & 17818 & 15100.3317727273 & 2717.66822727273 \tabularnewline
113 & 20613 & 17011.6568636364 & 3601.34313636364 \tabularnewline
114 & 16231 & 16103.8048636364 & 127.195136363638 \tabularnewline
115 & 13862 & 14126.9825 & -264.982499999999 \tabularnewline
116 & 12004 & 12884.7495909091 & -880.749590909091 \tabularnewline
117 & 17734 & 15639.1711363636 & 2094.82886363637 \tabularnewline
118 & 15034 & 14962.6813181818 & 71.3186818181851 \tabularnewline
119 & 12609 & 14106.6376818182 & -1497.63768181818 \tabularnewline
120 & 12320 & 12290.4367727273 & 29.5632272727255 \tabularnewline
121 & 10833 & 12263.9978295455 & -1430.99782954546 \tabularnewline
122 & 11350 & 13048.7374659091 & -1698.73746590909 \tabularnewline
123 & 13648 & 15338.0301931818 & -1690.03019318182 \tabularnewline
124 & 14890 & 15177.7048295455 & -287.704829545454 \tabularnewline
125 & 16325 & 17089.0299204545 & -764.029920454543 \tabularnewline
126 & 18045 & 16181.1779204545 & 1863.82207954546 \tabularnewline
127 & 15616 & 14204.3555568182 & 1411.64444318182 \tabularnewline
128 & 11926 & 12962.1226477273 & -1036.12264772727 \tabularnewline
129 & 16855 & 15716.5441931818 & 1138.45580681818 \tabularnewline
130 & 15083 & 15040.054375 & 42.9456250000042 \tabularnewline
131 & 12520 & 14184.0107386364 & -1664.01073863636 \tabularnewline
132 & 12355 & 12367.8098295455 & -12.8098295454563 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101756&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14.458[/C][C]11490.2672613636[/C][C]-11475.8092613636[/C][/ROW]
[ROW][C]2[/C][C]13.594[/C][C]12275.0068977273[/C][C]-12261.4128977273[/C][/ROW]
[ROW][C]3[/C][C]17.814[/C][C]14564.299625[/C][C]-14546.485625[/C][/ROW]
[ROW][C]4[/C][C]20.235[/C][C]14403.9742613636[/C][C]-14383.7392613636[/C][/ROW]
[ROW][C]5[/C][C]21.811[/C][C]16315.2993522727[/C][C]-16293.4883522727[/C][/ROW]
[ROW][C]6[/C][C]21.439[/C][C]15407.4473522727[/C][C]-15386.0083522727[/C][/ROW]
[ROW][C]7[/C][C]21.393[/C][C]13430.6249886364[/C][C]-13409.2319886364[/C][/ROW]
[ROW][C]8[/C][C]19.831[/C][C]12188.3920795455[/C][C]-12168.5610795455[/C][/ROW]
[ROW][C]9[/C][C]20.468[/C][C]14942.813625[/C][C]-14922.345625[/C][/ROW]
[ROW][C]10[/C][C]21.08[/C][C]14266.3238068182[/C][C]-14245.2438068182[/C][/ROW]
[ROW][C]11[/C][C]21.6[/C][C]13410.2801704545[/C][C]-13388.6801704545[/C][/ROW]
[ROW][C]12[/C][C]17.39[/C][C]11594.0792613636[/C][C]-11576.6892613636[/C][/ROW]
[ROW][C]13[/C][C]17848[/C][C]11567.6403181818[/C][C]6280.35968181818[/C][/ROW]
[ROW][C]14[/C][C]19592[/C][C]12352.3799545455[/C][C]7239.62004545455[/C][/ROW]
[ROW][C]15[/C][C]21092[/C][C]14641.6726818182[/C][C]6450.32731818182[/C][/ROW]
[ROW][C]16[/C][C]20889[/C][C]14481.3473181818[/C][C]6407.65268181818[/C][/ROW]
[ROW][C]17[/C][C]25890[/C][C]16392.6724090909[/C][C]9497.32759090909[/C][/ROW]
[ROW][C]18[/C][C]24965[/C][C]15484.8204090909[/C][C]9480.1795909091[/C][/ROW]
[ROW][C]19[/C][C]22225[/C][C]13507.9980454545[/C][C]8717.00195454545[/C][/ROW]
[ROW][C]20[/C][C]20977[/C][C]12265.7651363636[/C][C]8711.23486363637[/C][/ROW]
[ROW][C]21[/C][C]22897[/C][C]15020.1866818182[/C][C]7876.81331818181[/C][/ROW]
[ROW][C]22[/C][C]22785[/C][C]14343.6968636364[/C][C]8441.30313636363[/C][/ROW]
[ROW][C]23[/C][C]22769[/C][C]13487.6532272727[/C][C]9281.34677272727[/C][/ROW]
[ROW][C]24[/C][C]19637[/C][C]11671.4523181818[/C][C]7965.54768181818[/C][/ROW]
[ROW][C]25[/C][C]20203[/C][C]11645.013375[/C][C]8557.986625[/C][/ROW]
[ROW][C]26[/C][C]20450[/C][C]12429.7530113636[/C][C]8020.24698863637[/C][/ROW]
[ROW][C]27[/C][C]23083[/C][C]14719.0457386364[/C][C]8363.95426136363[/C][/ROW]
[ROW][C]28[/C][C]21738[/C][C]14558.720375[/C][C]7179.279625[/C][/ROW]
[ROW][C]29[/C][C]26766[/C][C]16470.0454659091[/C][C]10295.9545340909[/C][/ROW]
[ROW][C]30[/C][C]25280[/C][C]15562.1934659091[/C][C]9717.8065340909[/C][/ROW]
[ROW][C]31[/C][C]22574[/C][C]13585.3711022727[/C][C]8988.62889772727[/C][/ROW]
[ROW][C]32[/C][C]22729[/C][C]12343.1381931818[/C][C]10385.8618068182[/C][/ROW]
[ROW][C]33[/C][C]21378[/C][C]15097.5597386364[/C][C]6280.44026136364[/C][/ROW]
[ROW][C]34[/C][C]22902[/C][C]14421.0699204545[/C][C]8480.93007954545[/C][/ROW]
[ROW][C]35[/C][C]24989[/C][C]13565.0262840909[/C][C]11423.9737159091[/C][/ROW]
[ROW][C]36[/C][C]21116[/C][C]11748.825375[/C][C]9367.174625[/C][/ROW]
[ROW][C]37[/C][C]15169[/C][C]11722.3864318182[/C][C]3446.61356818182[/C][/ROW]
[ROW][C]38[/C][C]15846[/C][C]12507.1260681818[/C][C]3338.87393181818[/C][/ROW]
[ROW][C]39[/C][C]20927[/C][C]14796.4187954545[/C][C]6130.58120454545[/C][/ROW]
[ROW][C]40[/C][C]18273[/C][C]14636.0934318182[/C][C]3636.90656818182[/C][/ROW]
[ROW][C]41[/C][C]22538[/C][C]16547.4185227273[/C][C]5990.58147727273[/C][/ROW]
[ROW][C]42[/C][C]15596[/C][C]15639.5665227273[/C][C]-43.5665227272722[/C][/ROW]
[ROW][C]43[/C][C]14034[/C][C]13662.7441590909[/C][C]371.255840909091[/C][/ROW]
[ROW][C]44[/C][C]11366[/C][C]12420.51125[/C][C]-1054.51125000000[/C][/ROW]
[ROW][C]45[/C][C]14861[/C][C]15174.9327954545[/C][C]-313.932795454544[/C][/ROW]
[ROW][C]46[/C][C]15149[/C][C]14498.4429772727[/C][C]650.557022727276[/C][/ROW]
[ROW][C]47[/C][C]13577[/C][C]13642.3993409091[/C][C]-65.3993409090896[/C][/ROW]
[ROW][C]48[/C][C]13026[/C][C]11826.1984318182[/C][C]1199.80156818181[/C][/ROW]
[ROW][C]49[/C][C]13190[/C][C]11799.7594886364[/C][C]1390.24051136363[/C][/ROW]
[ROW][C]50[/C][C]13196[/C][C]12584.499125[/C][C]611.500875[/C][/ROW]
[ROW][C]51[/C][C]15826[/C][C]14873.7918522727[/C][C]952.208147727272[/C][/ROW]
[ROW][C]52[/C][C]14733[/C][C]14713.4664886364[/C][C]19.5335113636345[/C][/ROW]
[ROW][C]53[/C][C]16307[/C][C]16624.7915795455[/C][C]-317.791579545453[/C][/ROW]
[ROW][C]54[/C][C]15703[/C][C]15716.9395795455[/C][C]-13.9395795454540[/C][/ROW]
[ROW][C]55[/C][C]14589[/C][C]13740.1172159091[/C][C]848.88278409091[/C][/ROW]
[ROW][C]56[/C][C]12043[/C][C]12497.8843068182[/C][C]-454.884306818183[/C][/ROW]
[ROW][C]57[/C][C]15057[/C][C]15252.3058522727[/C][C]-195.305852272726[/C][/ROW]
[ROW][C]58[/C][C]14053[/C][C]14575.8160340909[/C][C]-522.816034090907[/C][/ROW]
[ROW][C]59[/C][C]12698[/C][C]13719.7723977273[/C][C]-1021.77239772727[/C][/ROW]
[ROW][C]60[/C][C]10888[/C][C]11903.5714886364[/C][C]-1015.57148863637[/C][/ROW]
[ROW][C]61[/C][C]10045[/C][C]11877.1325454545[/C][C]-1832.13254545455[/C][/ROW]
[ROW][C]62[/C][C]11549[/C][C]12661.8721818182[/C][C]-1112.87218181818[/C][/ROW]
[ROW][C]63[/C][C]13767[/C][C]14951.1649090909[/C][C]-1184.16490909091[/C][/ROW]
[ROW][C]64[/C][C]12424[/C][C]14790.8395454545[/C][C]-2366.83954545455[/C][/ROW]
[ROW][C]65[/C][C]13116[/C][C]16702.1646363636[/C][C]-3586.16463636363[/C][/ROW]
[ROW][C]66[/C][C]14211[/C][C]15794.3126363636[/C][C]-1583.31263636364[/C][/ROW]
[ROW][C]67[/C][C]12266[/C][C]13817.4902727273[/C][C]-1551.49027272727[/C][/ROW]
[ROW][C]68[/C][C]12602[/C][C]12575.2573636364[/C][C]26.7426363636362[/C][/ROW]
[ROW][C]69[/C][C]15714[/C][C]15329.6789090909[/C][C]384.321090909093[/C][/ROW]
[ROW][C]70[/C][C]13742[/C][C]14653.1890909091[/C][C]-911.18909090909[/C][/ROW]
[ROW][C]71[/C][C]12745[/C][C]13797.1454545455[/C][C]-1052.14545454545[/C][/ROW]
[ROW][C]72[/C][C]10491[/C][C]11980.9445454545[/C][C]-1489.94454545455[/C][/ROW]
[ROW][C]73[/C][C]10057[/C][C]11954.5056022727[/C][C]-1897.50560227273[/C][/ROW]
[ROW][C]74[/C][C]10900[/C][C]12739.2452386364[/C][C]-1839.24523863636[/C][/ROW]
[ROW][C]75[/C][C]11771[/C][C]15028.5379659091[/C][C]-3257.53796590909[/C][/ROW]
[ROW][C]76[/C][C]11992[/C][C]14868.2126022727[/C][C]-2876.21260227273[/C][/ROW]
[ROW][C]77[/C][C]11993[/C][C]16779.5376931818[/C][C]-4786.53769318182[/C][/ROW]
[ROW][C]78[/C][C]14504[/C][C]15871.6856931818[/C][C]-1367.68569318182[/C][/ROW]
[ROW][C]79[/C][C]11727[/C][C]13894.8633295455[/C][C]-2167.86332954546[/C][/ROW]
[ROW][C]80[/C][C]11477[/C][C]12652.6304204545[/C][C]-1175.63042045455[/C][/ROW]
[ROW][C]81[/C][C]13578[/C][C]15407.0519659091[/C][C]-1829.05196590909[/C][/ROW]
[ROW][C]82[/C][C]11555[/C][C]14730.5621477273[/C][C]-3175.56214772727[/C][/ROW]
[ROW][C]83[/C][C]11846[/C][C]13874.5185113636[/C][C]-2028.51851136364[/C][/ROW]
[ROW][C]84[/C][C]11397[/C][C]12058.3176022727[/C][C]-661.31760227273[/C][/ROW]
[ROW][C]85[/C][C]10066[/C][C]12031.8786590909[/C][C]-1965.87865909091[/C][/ROW]
[ROW][C]86[/C][C]10269[/C][C]12816.6182954545[/C][C]-2547.61829545454[/C][/ROW]
[ROW][C]87[/C][C]14279[/C][C]15105.9110227273[/C][C]-826.911022727272[/C][/ROW]
[ROW][C]88[/C][C]13870[/C][C]14945.5856590909[/C][C]-1075.58565909091[/C][/ROW]
[ROW][C]89[/C][C]13695[/C][C]16856.91075[/C][C]-3161.91075[/C][/ROW]
[ROW][C]90[/C][C]14420[/C][C]15949.05875[/C][C]-1529.05875[/C][/ROW]
[ROW][C]91[/C][C]11424[/C][C]13972.2363863636[/C][C]-2548.23638636364[/C][/ROW]
[ROW][C]92[/C][C]9704[/C][C]12730.0034772727[/C][C]-3026.00347727273[/C][/ROW]
[ROW][C]93[/C][C]12464[/C][C]15484.4250227273[/C][C]-3020.42502272727[/C][/ROW]
[ROW][C]94[/C][C]14301[/C][C]14807.9352045455[/C][C]-506.935204545452[/C][/ROW]
[ROW][C]95[/C][C]13464[/C][C]13951.8915681818[/C][C]-487.891568181817[/C][/ROW]
[ROW][C]96[/C][C]9893[/C][C]12135.6906590909[/C][C]-2242.69065909091[/C][/ROW]
[ROW][C]97[/C][C]11572[/C][C]12109.2517159091[/C][C]-537.251715909092[/C][/ROW]
[ROW][C]98[/C][C]12380[/C][C]12893.9913522727[/C][C]-513.991352272726[/C][/ROW]
[ROW][C]99[/C][C]16692[/C][C]15183.2840795455[/C][C]1508.71592045455[/C][/ROW]
[ROW][C]100[/C][C]16052[/C][C]15022.9587159091[/C][C]1029.04128409091[/C][/ROW]
[ROW][C]101[/C][C]16459[/C][C]16934.2838068182[/C][C]-475.28380681818[/C][/ROW]
[ROW][C]102[/C][C]14761[/C][C]16026.4318068182[/C][C]-1265.43180681818[/C][/ROW]
[ROW][C]103[/C][C]13654[/C][C]14049.6094431818[/C][C]-395.609443181818[/C][/ROW]
[ROW][C]104[/C][C]13480[/C][C]12807.3765340909[/C][C]672.623465909092[/C][/ROW]
[ROW][C]105[/C][C]18068[/C][C]15561.7980795455[/C][C]2506.20192045455[/C][/ROW]
[ROW][C]106[/C][C]16560[/C][C]14885.3082613636[/C][C]1674.69173863637[/C][/ROW]
[ROW][C]107[/C][C]14530[/C][C]14029.264625[/C][C]500.735375000003[/C][/ROW]
[ROW][C]108[/C][C]10650[/C][C]12213.0637159091[/C][C]-1563.06371590909[/C][/ROW]
[ROW][C]109[/C][C]11651[/C][C]12186.6247727273[/C][C]-535.624772727274[/C][/ROW]
[ROW][C]110[/C][C]13735[/C][C]12971.3644090909[/C][C]763.635590909092[/C][/ROW]
[ROW][C]111[/C][C]13360[/C][C]15260.6571363636[/C][C]-1900.65713636364[/C][/ROW]
[ROW][C]112[/C][C]17818[/C][C]15100.3317727273[/C][C]2717.66822727273[/C][/ROW]
[ROW][C]113[/C][C]20613[/C][C]17011.6568636364[/C][C]3601.34313636364[/C][/ROW]
[ROW][C]114[/C][C]16231[/C][C]16103.8048636364[/C][C]127.195136363638[/C][/ROW]
[ROW][C]115[/C][C]13862[/C][C]14126.9825[/C][C]-264.982499999999[/C][/ROW]
[ROW][C]116[/C][C]12004[/C][C]12884.7495909091[/C][C]-880.749590909091[/C][/ROW]
[ROW][C]117[/C][C]17734[/C][C]15639.1711363636[/C][C]2094.82886363637[/C][/ROW]
[ROW][C]118[/C][C]15034[/C][C]14962.6813181818[/C][C]71.3186818181851[/C][/ROW]
[ROW][C]119[/C][C]12609[/C][C]14106.6376818182[/C][C]-1497.63768181818[/C][/ROW]
[ROW][C]120[/C][C]12320[/C][C]12290.4367727273[/C][C]29.5632272727255[/C][/ROW]
[ROW][C]121[/C][C]10833[/C][C]12263.9978295455[/C][C]-1430.99782954546[/C][/ROW]
[ROW][C]122[/C][C]11350[/C][C]13048.7374659091[/C][C]-1698.73746590909[/C][/ROW]
[ROW][C]123[/C][C]13648[/C][C]15338.0301931818[/C][C]-1690.03019318182[/C][/ROW]
[ROW][C]124[/C][C]14890[/C][C]15177.7048295455[/C][C]-287.704829545454[/C][/ROW]
[ROW][C]125[/C][C]16325[/C][C]17089.0299204545[/C][C]-764.029920454543[/C][/ROW]
[ROW][C]126[/C][C]18045[/C][C]16181.1779204545[/C][C]1863.82207954546[/C][/ROW]
[ROW][C]127[/C][C]15616[/C][C]14204.3555568182[/C][C]1411.64444318182[/C][/ROW]
[ROW][C]128[/C][C]11926[/C][C]12962.1226477273[/C][C]-1036.12264772727[/C][/ROW]
[ROW][C]129[/C][C]16855[/C][C]15716.5441931818[/C][C]1138.45580681818[/C][/ROW]
[ROW][C]130[/C][C]15083[/C][C]15040.054375[/C][C]42.9456250000042[/C][/ROW]
[ROW][C]131[/C][C]12520[/C][C]14184.0107386364[/C][C]-1664.01073863636[/C][/ROW]
[ROW][C]132[/C][C]12355[/C][C]12367.8098295455[/C][C]-12.8098295454563[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101756&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101756&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14.458	11490.2672613636	-11475.8092613636
2	13.594	12275.0068977273	-12261.4128977273
3	17.814	14564.299625	-14546.485625
4	20.235	14403.9742613636	-14383.7392613636
5	21.811	16315.2993522727	-16293.4883522727
6	21.439	15407.4473522727	-15386.0083522727
7	21.393	13430.6249886364	-13409.2319886364
8	19.831	12188.3920795455	-12168.5610795455
9	20.468	14942.813625	-14922.345625
10	21.08	14266.3238068182	-14245.2438068182
11	21.6	13410.2801704545	-13388.6801704545
12	17.39	11594.0792613636	-11576.6892613636
13	17848	11567.6403181818	6280.35968181818
14	19592	12352.3799545455	7239.62004545455
15	21092	14641.6726818182	6450.32731818182
16	20889	14481.3473181818	6407.65268181818
17	25890	16392.6724090909	9497.32759090909
18	24965	15484.8204090909	9480.1795909091
19	22225	13507.9980454545	8717.00195454545
20	20977	12265.7651363636	8711.23486363637
21	22897	15020.1866818182	7876.81331818181
22	22785	14343.6968636364	8441.30313636363
23	22769	13487.6532272727	9281.34677272727
24	19637	11671.4523181818	7965.54768181818
25	20203	11645.013375	8557.986625
26	20450	12429.7530113636	8020.24698863637
27	23083	14719.0457386364	8363.95426136363
28	21738	14558.720375	7179.279625
29	26766	16470.0454659091	10295.9545340909
30	25280	15562.1934659091	9717.8065340909
31	22574	13585.3711022727	8988.62889772727
32	22729	12343.1381931818	10385.8618068182
33	21378	15097.5597386364	6280.44026136364
34	22902	14421.0699204545	8480.93007954545
35	24989	13565.0262840909	11423.9737159091
36	21116	11748.825375	9367.174625
37	15169	11722.3864318182	3446.61356818182
38	15846	12507.1260681818	3338.87393181818
39	20927	14796.4187954545	6130.58120454545
40	18273	14636.0934318182	3636.90656818182
41	22538	16547.4185227273	5990.58147727273
42	15596	15639.5665227273	-43.5665227272722
43	14034	13662.7441590909	371.255840909091
44	11366	12420.51125	-1054.51125000000
45	14861	15174.9327954545	-313.932795454544
46	15149	14498.4429772727	650.557022727276
47	13577	13642.3993409091	-65.3993409090896
48	13026	11826.1984318182	1199.80156818181
49	13190	11799.7594886364	1390.24051136363
50	13196	12584.499125	611.500875
51	15826	14873.7918522727	952.208147727272
52	14733	14713.4664886364	19.5335113636345
53	16307	16624.7915795455	-317.791579545453
54	15703	15716.9395795455	-13.9395795454540
55	14589	13740.1172159091	848.88278409091
56	12043	12497.8843068182	-454.884306818183
57	15057	15252.3058522727	-195.305852272726
58	14053	14575.8160340909	-522.816034090907
59	12698	13719.7723977273	-1021.77239772727
60	10888	11903.5714886364	-1015.57148863637
61	10045	11877.1325454545	-1832.13254545455
62	11549	12661.8721818182	-1112.87218181818
63	13767	14951.1649090909	-1184.16490909091
64	12424	14790.8395454545	-2366.83954545455
65	13116	16702.1646363636	-3586.16463636363
66	14211	15794.3126363636	-1583.31263636364
67	12266	13817.4902727273	-1551.49027272727
68	12602	12575.2573636364	26.7426363636362
69	15714	15329.6789090909	384.321090909093
70	13742	14653.1890909091	-911.18909090909
71	12745	13797.1454545455	-1052.14545454545
72	10491	11980.9445454545	-1489.94454545455
73	10057	11954.5056022727	-1897.50560227273
74	10900	12739.2452386364	-1839.24523863636
75	11771	15028.5379659091	-3257.53796590909
76	11992	14868.2126022727	-2876.21260227273
77	11993	16779.5376931818	-4786.53769318182
78	14504	15871.6856931818	-1367.68569318182
79	11727	13894.8633295455	-2167.86332954546
80	11477	12652.6304204545	-1175.63042045455
81	13578	15407.0519659091	-1829.05196590909
82	11555	14730.5621477273	-3175.56214772727
83	11846	13874.5185113636	-2028.51851136364
84	11397	12058.3176022727	-661.31760227273
85	10066	12031.8786590909	-1965.87865909091
86	10269	12816.6182954545	-2547.61829545454
87	14279	15105.9110227273	-826.911022727272
88	13870	14945.5856590909	-1075.58565909091
89	13695	16856.91075	-3161.91075
90	14420	15949.05875	-1529.05875
91	11424	13972.2363863636	-2548.23638636364
92	9704	12730.0034772727	-3026.00347727273
93	12464	15484.4250227273	-3020.42502272727
94	14301	14807.9352045455	-506.935204545452
95	13464	13951.8915681818	-487.891568181817
96	9893	12135.6906590909	-2242.69065909091
97	11572	12109.2517159091	-537.251715909092
98	12380	12893.9913522727	-513.991352272726
99	16692	15183.2840795455	1508.71592045455
100	16052	15022.9587159091	1029.04128409091
101	16459	16934.2838068182	-475.28380681818
102	14761	16026.4318068182	-1265.43180681818
103	13654	14049.6094431818	-395.609443181818
104	13480	12807.3765340909	672.623465909092
105	18068	15561.7980795455	2506.20192045455
106	16560	14885.3082613636	1674.69173863637
107	14530	14029.264625	500.735375000003
108	10650	12213.0637159091	-1563.06371590909
109	11651	12186.6247727273	-535.624772727274
110	13735	12971.3644090909	763.635590909092
111	13360	15260.6571363636	-1900.65713636364
112	17818	15100.3317727273	2717.66822727273
113	20613	17011.6568636364	3601.34313636364
114	16231	16103.8048636364	127.195136363638
115	13862	14126.9825	-264.982499999999
116	12004	12884.7495909091	-880.749590909091
117	17734	15639.1711363636	2094.82886363637
118	15034	14962.6813181818	71.3186818181851
119	12609	14106.6376818182	-1497.63768181818
120	12320	12290.4367727273	29.5632272727255
121	10833	12263.9978295455	-1430.99782954546
122	11350	13048.7374659091	-1698.73746590909
123	13648	15338.0301931818	-1690.03019318182
124	14890	15177.7048295455	-287.704829545454
125	16325	17089.0299204545	-764.029920454543
126	18045	16181.1779204545	1863.82207954546
127	15616	14204.3555568182	1411.64444318182
128	11926	12962.1226477273	-1036.12264772727
129	16855	15716.5441931818	1138.45580681818
130	15083	15040.054375	42.9456250000042
131	12520	14184.0107386364	-1664.01073863636
132	12355	12367.8098295455	-12.8098295454563

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0446694702171001	0.0893389404342002	0.9553305297829
17	0.217795139174473	0.435590278348946	0.782204860825527
18	0.206948424615960	0.413896849231920	0.79305157538404
19	0.123002155567594	0.246004311135189	0.876997844432406
20	0.0713426550017686	0.142685310003537	0.928657344998231
21	0.0408406913809466	0.0816813827618932	0.959159308619053
22	0.0225191683580043	0.0450383367160085	0.977480831641996
23	0.0124562644601262	0.0249125289202524	0.987543735539874
24	0.00920881500578142	0.0184176300115628	0.990791184994219
25	0.989571375209978	0.0208572495800439	0.0104286247900220
26	0.99988700383339	0.000225992333219593	0.000112996166609796
27	0.999988354674786	2.32906504274421e-05	1.16453252137211e-05
28	0.999998132574066	3.73485186844505e-06	1.86742593422252e-06
29	0.999999574468331	8.51063337608523e-07	4.25531668804261e-07
30	0.999999914148836	1.71702327833412e-07	8.58511639167061e-08
31	0.999999985300168	2.93996636958791e-08	1.46998318479396e-08
32	0.999999998926972	2.14605675328575e-09	1.07302837664287e-09
33	0.99999999964541	7.09180906061965e-10	3.54590453030983e-10
34	0.999999999945367	1.09265111156789e-10	5.46325555783946e-11
35	0.999999999999762	4.7653891972433e-13	2.38269459862165e-13
36	1	1.91329174150928e-15	9.56645870754638e-16
37	1	7.8066204954338e-19	3.9033102477169e-19
38	1	3.69556328537189e-21	1.84778164268595e-21
39	1	9.86093781566029e-24	4.93046890783014e-24
40	1	4.36821141735872e-25	2.18410570867936e-25
41	1	1.05992621743059e-28	5.29963108715296e-29
42	1	1.30134624421135e-29	6.50673122105673e-30
43	1	3.13412898732319e-30	1.56706449366159e-30
44	1	8.66840979567252e-31	4.33420489783626e-31
45	1	1.06822080726673e-30	5.34110403633363e-31
46	1	1.05786641852041e-30	5.28933209260205e-31
47	1	8.13912135642193e-31	4.06956067821096e-31
48	1	4.52487437541523e-31	2.26243718770762e-31
49	1	1.01984837220099e-31	5.09924186100497e-32
50	1	6.0865066063739e-32	3.04325330318695e-32
51	1	2.80563036045276e-32	1.40281518022638e-32
52	1	5.37436586866497e-32	2.68718293433248e-32
53	1	5.04670452763168e-32	2.52335226381584e-32
54	1	1.02214817307039e-31	5.11074086535195e-32
55	1	8.05388788693352e-32	4.02694394346676e-32
56	1	1.63607705432227e-31	8.18038527161135e-32
57	1	6.50564532722289e-31	3.25282266361145e-31
58	1	1.96841100336608e-30	9.84205501683039e-31
59	1	4.96832028371087e-30	2.48416014185543e-30
60	1	1.31415574018369e-29	6.57077870091843e-30
61	1	3.99781116601239e-29	1.99890558300620e-29
62	1	1.12334424053747e-28	5.61672120268735e-29
63	1	3.18940627190774e-28	1.59470313595387e-28
64	1	1.18240212709421e-27	5.91201063547105e-28
65	1	3.31436246355277e-27	1.65718123177638e-27
66	1	1.56223291974110e-26	7.81116459870548e-27
67	1	6.87657093711081e-26	3.43828546855541e-26
68	1	1.13190559507520e-25	5.65952797537598e-26
69	1	3.63200832673205e-25	1.81600416336602e-25
70	1	1.51292993941597e-24	7.56464969707984e-25
71	1	4.54038756860062e-24	2.27019378430031e-24
72	1	1.73569558207494e-23	8.67847791037469e-24
73	1	7.46037915896143e-23	3.73018957948071e-23
74	1	3.27516659137081e-22	1.63758329568541e-22
75	1	1.25688207486982e-21	6.2844103743491e-22
76	1	3.24029247958044e-21	1.62014623979022e-21
77	1	2.47537819770185e-21	1.23768909885092e-21
78	1	1.29641431436841e-20	6.48207157184203e-21
79	1	6.29727439672095e-20	3.14863719836048e-20
80	1	2.54259644977851e-19	1.27129822488926e-19
81	1	1.02208179426063e-18	5.11040897130313e-19
82	1	2.08215305619356e-18	1.04107652809678e-18
83	1	1.02484472507311e-17	5.12422362536555e-18
84	1	3.73944531377065e-17	1.86972265688533e-17
85	1	1.80495537517788e-16	9.02477687588941e-17
86	1	7.0941935887671e-16	3.54709679438355e-16
87	0.999999999999998	3.15141190595537e-15	1.57570595297769e-15
88	0.999999999999994	1.18330028544004e-14	5.91650142720019e-15
89	0.999999999999993	1.32085544240374e-14	6.6042772120187e-15
90	0.999999999999974	5.24192599970922e-14	2.62096299985461e-14
91	0.999999999999943	1.14847958459624e-13	5.74239792298119e-14
92	0.999999999999876	2.47122402176736e-13	1.23561201088368e-13
93	0.99999999999999	1.90360410643387e-14	9.51802053216936e-15
94	0.999999999999964	7.25831813073968e-14	3.62915906536984e-14
95	0.9999999999998	4.00130313613739e-13	2.00065156806869e-13
96	0.999999999999583	8.3478179288123e-13	4.17390896440615e-13
97	0.999999999997718	4.56301685163247e-12	2.28150842581624e-12
98	0.999999999988383	2.32341676778831e-11	1.16170838389415e-11
99	0.99999999998565	2.87006607660736e-11	1.43503303830368e-11
100	0.999999999925504	1.48991945792713e-10	7.44959728963564e-11
101	0.999999999843074	3.13852264540182e-10	1.56926132270091e-10
102	0.999999999831731	3.36537443494355e-10	1.68268721747177e-10
103	0.999999999527287	9.45425183683165e-10	4.72712591841582e-10
104	0.999999997505618	4.98876375657457e-09	2.49438187828728e-09
105	0.999999986133296	2.77334073040907e-08	1.38667036520454e-08
106	0.999999932517554	1.34964892834633e-07	6.74824464173167e-08
107	0.999999748705998	5.02588004602093e-07	2.51294002301047e-07
108	0.999999517051373	9.6589725301962e-07	4.8294862650981e-07
109	0.999997387718965	5.22456206978607e-06	2.61228103489303e-06
110	0.999991301296299	1.73974074028618e-05	8.69870370143092e-06
111	0.999958301496567	8.33970068654296e-05	4.16985034327148e-05
112	0.999906803214395	0.000186393571209182	9.3196785604591e-05
113	0.999992312187483	1.53756250334749e-05	7.68781251673744e-06
114	0.99997845279414	4.30944117199411e-05	2.15472058599705e-05
115	0.999993622138568	1.27557228635836e-05	6.37786143179179e-06
116	0.999844893339378	0.000310213321244287	0.000155106660622144

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0446694702171001 & 0.0893389404342002 & 0.9553305297829 \tabularnewline
17 & 0.217795139174473 & 0.435590278348946 & 0.782204860825527 \tabularnewline
18 & 0.206948424615960 & 0.413896849231920 & 0.79305157538404 \tabularnewline
19 & 0.123002155567594 & 0.246004311135189 & 0.876997844432406 \tabularnewline
20 & 0.0713426550017686 & 0.142685310003537 & 0.928657344998231 \tabularnewline
21 & 0.0408406913809466 & 0.0816813827618932 & 0.959159308619053 \tabularnewline
22 & 0.0225191683580043 & 0.0450383367160085 & 0.977480831641996 \tabularnewline
23 & 0.0124562644601262 & 0.0249125289202524 & 0.987543735539874 \tabularnewline
24 & 0.00920881500578142 & 0.0184176300115628 & 0.990791184994219 \tabularnewline
25 & 0.989571375209978 & 0.0208572495800439 & 0.0104286247900220 \tabularnewline
26 & 0.99988700383339 & 0.000225992333219593 & 0.000112996166609796 \tabularnewline
27 & 0.999988354674786 & 2.32906504274421e-05 & 1.16453252137211e-05 \tabularnewline
28 & 0.999998132574066 & 3.73485186844505e-06 & 1.86742593422252e-06 \tabularnewline
29 & 0.999999574468331 & 8.51063337608523e-07 & 4.25531668804261e-07 \tabularnewline
30 & 0.999999914148836 & 1.71702327833412e-07 & 8.58511639167061e-08 \tabularnewline
31 & 0.999999985300168 & 2.93996636958791e-08 & 1.46998318479396e-08 \tabularnewline
32 & 0.999999998926972 & 2.14605675328575e-09 & 1.07302837664287e-09 \tabularnewline
33 & 0.99999999964541 & 7.09180906061965e-10 & 3.54590453030983e-10 \tabularnewline
34 & 0.999999999945367 & 1.09265111156789e-10 & 5.46325555783946e-11 \tabularnewline
35 & 0.999999999999762 & 4.7653891972433e-13 & 2.38269459862165e-13 \tabularnewline
36 & 1 & 1.91329174150928e-15 & 9.56645870754638e-16 \tabularnewline
37 & 1 & 7.8066204954338e-19 & 3.9033102477169e-19 \tabularnewline
38 & 1 & 3.69556328537189e-21 & 1.84778164268595e-21 \tabularnewline
39 & 1 & 9.86093781566029e-24 & 4.93046890783014e-24 \tabularnewline
40 & 1 & 4.36821141735872e-25 & 2.18410570867936e-25 \tabularnewline
41 & 1 & 1.05992621743059e-28 & 5.29963108715296e-29 \tabularnewline
42 & 1 & 1.30134624421135e-29 & 6.50673122105673e-30 \tabularnewline
43 & 1 & 3.13412898732319e-30 & 1.56706449366159e-30 \tabularnewline
44 & 1 & 8.66840979567252e-31 & 4.33420489783626e-31 \tabularnewline
45 & 1 & 1.06822080726673e-30 & 5.34110403633363e-31 \tabularnewline
46 & 1 & 1.05786641852041e-30 & 5.28933209260205e-31 \tabularnewline
47 & 1 & 8.13912135642193e-31 & 4.06956067821096e-31 \tabularnewline
48 & 1 & 4.52487437541523e-31 & 2.26243718770762e-31 \tabularnewline
49 & 1 & 1.01984837220099e-31 & 5.09924186100497e-32 \tabularnewline
50 & 1 & 6.0865066063739e-32 & 3.04325330318695e-32 \tabularnewline
51 & 1 & 2.80563036045276e-32 & 1.40281518022638e-32 \tabularnewline
52 & 1 & 5.37436586866497e-32 & 2.68718293433248e-32 \tabularnewline
53 & 1 & 5.04670452763168e-32 & 2.52335226381584e-32 \tabularnewline
54 & 1 & 1.02214817307039e-31 & 5.11074086535195e-32 \tabularnewline
55 & 1 & 8.05388788693352e-32 & 4.02694394346676e-32 \tabularnewline
56 & 1 & 1.63607705432227e-31 & 8.18038527161135e-32 \tabularnewline
57 & 1 & 6.50564532722289e-31 & 3.25282266361145e-31 \tabularnewline
58 & 1 & 1.96841100336608e-30 & 9.84205501683039e-31 \tabularnewline
59 & 1 & 4.96832028371087e-30 & 2.48416014185543e-30 \tabularnewline
60 & 1 & 1.31415574018369e-29 & 6.57077870091843e-30 \tabularnewline
61 & 1 & 3.99781116601239e-29 & 1.99890558300620e-29 \tabularnewline
62 & 1 & 1.12334424053747e-28 & 5.61672120268735e-29 \tabularnewline
63 & 1 & 3.18940627190774e-28 & 1.59470313595387e-28 \tabularnewline
64 & 1 & 1.18240212709421e-27 & 5.91201063547105e-28 \tabularnewline
65 & 1 & 3.31436246355277e-27 & 1.65718123177638e-27 \tabularnewline
66 & 1 & 1.56223291974110e-26 & 7.81116459870548e-27 \tabularnewline
67 & 1 & 6.87657093711081e-26 & 3.43828546855541e-26 \tabularnewline
68 & 1 & 1.13190559507520e-25 & 5.65952797537598e-26 \tabularnewline
69 & 1 & 3.63200832673205e-25 & 1.81600416336602e-25 \tabularnewline
70 & 1 & 1.51292993941597e-24 & 7.56464969707984e-25 \tabularnewline
71 & 1 & 4.54038756860062e-24 & 2.27019378430031e-24 \tabularnewline
72 & 1 & 1.73569558207494e-23 & 8.67847791037469e-24 \tabularnewline
73 & 1 & 7.46037915896143e-23 & 3.73018957948071e-23 \tabularnewline
74 & 1 & 3.27516659137081e-22 & 1.63758329568541e-22 \tabularnewline
75 & 1 & 1.25688207486982e-21 & 6.2844103743491e-22 \tabularnewline
76 & 1 & 3.24029247958044e-21 & 1.62014623979022e-21 \tabularnewline
77 & 1 & 2.47537819770185e-21 & 1.23768909885092e-21 \tabularnewline
78 & 1 & 1.29641431436841e-20 & 6.48207157184203e-21 \tabularnewline
79 & 1 & 6.29727439672095e-20 & 3.14863719836048e-20 \tabularnewline
80 & 1 & 2.54259644977851e-19 & 1.27129822488926e-19 \tabularnewline
81 & 1 & 1.02208179426063e-18 & 5.11040897130313e-19 \tabularnewline
82 & 1 & 2.08215305619356e-18 & 1.04107652809678e-18 \tabularnewline
83 & 1 & 1.02484472507311e-17 & 5.12422362536555e-18 \tabularnewline
84 & 1 & 3.73944531377065e-17 & 1.86972265688533e-17 \tabularnewline
85 & 1 & 1.80495537517788e-16 & 9.02477687588941e-17 \tabularnewline
86 & 1 & 7.0941935887671e-16 & 3.54709679438355e-16 \tabularnewline
87 & 0.999999999999998 & 3.15141190595537e-15 & 1.57570595297769e-15 \tabularnewline
88 & 0.999999999999994 & 1.18330028544004e-14 & 5.91650142720019e-15 \tabularnewline
89 & 0.999999999999993 & 1.32085544240374e-14 & 6.6042772120187e-15 \tabularnewline
90 & 0.999999999999974 & 5.24192599970922e-14 & 2.62096299985461e-14 \tabularnewline
91 & 0.999999999999943 & 1.14847958459624e-13 & 5.74239792298119e-14 \tabularnewline
92 & 0.999999999999876 & 2.47122402176736e-13 & 1.23561201088368e-13 \tabularnewline
93 & 0.99999999999999 & 1.90360410643387e-14 & 9.51802053216936e-15 \tabularnewline
94 & 0.999999999999964 & 7.25831813073968e-14 & 3.62915906536984e-14 \tabularnewline
95 & 0.9999999999998 & 4.00130313613739e-13 & 2.00065156806869e-13 \tabularnewline
96 & 0.999999999999583 & 8.3478179288123e-13 & 4.17390896440615e-13 \tabularnewline
97 & 0.999999999997718 & 4.56301685163247e-12 & 2.28150842581624e-12 \tabularnewline
98 & 0.999999999988383 & 2.32341676778831e-11 & 1.16170838389415e-11 \tabularnewline
99 & 0.99999999998565 & 2.87006607660736e-11 & 1.43503303830368e-11 \tabularnewline
100 & 0.999999999925504 & 1.48991945792713e-10 & 7.44959728963564e-11 \tabularnewline
101 & 0.999999999843074 & 3.13852264540182e-10 & 1.56926132270091e-10 \tabularnewline
102 & 0.999999999831731 & 3.36537443494355e-10 & 1.68268721747177e-10 \tabularnewline
103 & 0.999999999527287 & 9.45425183683165e-10 & 4.72712591841582e-10 \tabularnewline
104 & 0.999999997505618 & 4.98876375657457e-09 & 2.49438187828728e-09 \tabularnewline
105 & 0.999999986133296 & 2.77334073040907e-08 & 1.38667036520454e-08 \tabularnewline
106 & 0.999999932517554 & 1.34964892834633e-07 & 6.74824464173167e-08 \tabularnewline
107 & 0.999999748705998 & 5.02588004602093e-07 & 2.51294002301047e-07 \tabularnewline
108 & 0.999999517051373 & 9.6589725301962e-07 & 4.8294862650981e-07 \tabularnewline
109 & 0.999997387718965 & 5.22456206978607e-06 & 2.61228103489303e-06 \tabularnewline
110 & 0.999991301296299 & 1.73974074028618e-05 & 8.69870370143092e-06 \tabularnewline
111 & 0.999958301496567 & 8.33970068654296e-05 & 4.16985034327148e-05 \tabularnewline
112 & 0.999906803214395 & 0.000186393571209182 & 9.3196785604591e-05 \tabularnewline
113 & 0.999992312187483 & 1.53756250334749e-05 & 7.68781251673744e-06 \tabularnewline
114 & 0.99997845279414 & 4.30944117199411e-05 & 2.15472058599705e-05 \tabularnewline
115 & 0.999993622138568 & 1.27557228635836e-05 & 6.37786143179179e-06 \tabularnewline
116 & 0.999844893339378 & 0.000310213321244287 & 0.000155106660622144 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101756&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]16[/C][C]0.0446694702171001[/C][C]0.0893389404342002[/C][C]0.9553305297829[/C][/ROW]
[ROW][C]17[/C][C]0.217795139174473[/C][C]0.435590278348946[/C][C]0.782204860825527[/C][/ROW]
[ROW][C]18[/C][C]0.206948424615960[/C][C]0.413896849231920[/C][C]0.79305157538404[/C][/ROW]
[ROW][C]19[/C][C]0.123002155567594[/C][C]0.246004311135189[/C][C]0.876997844432406[/C][/ROW]
[ROW][C]20[/C][C]0.0713426550017686[/C][C]0.142685310003537[/C][C]0.928657344998231[/C][/ROW]
[ROW][C]21[/C][C]0.0408406913809466[/C][C]0.0816813827618932[/C][C]0.959159308619053[/C][/ROW]
[ROW][C]22[/C][C]0.0225191683580043[/C][C]0.0450383367160085[/C][C]0.977480831641996[/C][/ROW]
[ROW][C]23[/C][C]0.0124562644601262[/C][C]0.0249125289202524[/C][C]0.987543735539874[/C][/ROW]
[ROW][C]24[/C][C]0.00920881500578142[/C][C]0.0184176300115628[/C][C]0.990791184994219[/C][/ROW]
[ROW][C]25[/C][C]0.989571375209978[/C][C]0.0208572495800439[/C][C]0.0104286247900220[/C][/ROW]
[ROW][C]26[/C][C]0.99988700383339[/C][C]0.000225992333219593[/C][C]0.000112996166609796[/C][/ROW]
[ROW][C]27[/C][C]0.999988354674786[/C][C]2.32906504274421e-05[/C][C]1.16453252137211e-05[/C][/ROW]
[ROW][C]28[/C][C]0.999998132574066[/C][C]3.73485186844505e-06[/C][C]1.86742593422252e-06[/C][/ROW]
[ROW][C]29[/C][C]0.999999574468331[/C][C]8.51063337608523e-07[/C][C]4.25531668804261e-07[/C][/ROW]
[ROW][C]30[/C][C]0.999999914148836[/C][C]1.71702327833412e-07[/C][C]8.58511639167061e-08[/C][/ROW]
[ROW][C]31[/C][C]0.999999985300168[/C][C]2.93996636958791e-08[/C][C]1.46998318479396e-08[/C][/ROW]
[ROW][C]32[/C][C]0.999999998926972[/C][C]2.14605675328575e-09[/C][C]1.07302837664287e-09[/C][/ROW]
[ROW][C]33[/C][C]0.99999999964541[/C][C]7.09180906061965e-10[/C][C]3.54590453030983e-10[/C][/ROW]
[ROW][C]34[/C][C]0.999999999945367[/C][C]1.09265111156789e-10[/C][C]5.46325555783946e-11[/C][/ROW]
[ROW][C]35[/C][C]0.999999999999762[/C][C]4.7653891972433e-13[/C][C]2.38269459862165e-13[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.91329174150928e-15[/C][C]9.56645870754638e-16[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]7.8066204954338e-19[/C][C]3.9033102477169e-19[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]3.69556328537189e-21[/C][C]1.84778164268595e-21[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]9.86093781566029e-24[/C][C]4.93046890783014e-24[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]4.36821141735872e-25[/C][C]2.18410570867936e-25[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.05992621743059e-28[/C][C]5.29963108715296e-29[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.30134624421135e-29[/C][C]6.50673122105673e-30[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]3.13412898732319e-30[/C][C]1.56706449366159e-30[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]8.66840979567252e-31[/C][C]4.33420489783626e-31[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.06822080726673e-30[/C][C]5.34110403633363e-31[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]1.05786641852041e-30[/C][C]5.28933209260205e-31[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]8.13912135642193e-31[/C][C]4.06956067821096e-31[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]4.52487437541523e-31[/C][C]2.26243718770762e-31[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.01984837220099e-31[/C][C]5.09924186100497e-32[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]6.0865066063739e-32[/C][C]3.04325330318695e-32[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]2.80563036045276e-32[/C][C]1.40281518022638e-32[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]5.37436586866497e-32[/C][C]2.68718293433248e-32[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]5.04670452763168e-32[/C][C]2.52335226381584e-32[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.02214817307039e-31[/C][C]5.11074086535195e-32[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]8.05388788693352e-32[/C][C]4.02694394346676e-32[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.63607705432227e-31[/C][C]8.18038527161135e-32[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]6.50564532722289e-31[/C][C]3.25282266361145e-31[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.96841100336608e-30[/C][C]9.84205501683039e-31[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]4.96832028371087e-30[/C][C]2.48416014185543e-30[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]1.31415574018369e-29[/C][C]6.57077870091843e-30[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]3.99781116601239e-29[/C][C]1.99890558300620e-29[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.12334424053747e-28[/C][C]5.61672120268735e-29[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]3.18940627190774e-28[/C][C]1.59470313595387e-28[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]1.18240212709421e-27[/C][C]5.91201063547105e-28[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]3.31436246355277e-27[/C][C]1.65718123177638e-27[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.56223291974110e-26[/C][C]7.81116459870548e-27[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]6.87657093711081e-26[/C][C]3.43828546855541e-26[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.13190559507520e-25[/C][C]5.65952797537598e-26[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]3.63200832673205e-25[/C][C]1.81600416336602e-25[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.51292993941597e-24[/C][C]7.56464969707984e-25[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]4.54038756860062e-24[/C][C]2.27019378430031e-24[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]1.73569558207494e-23[/C][C]8.67847791037469e-24[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]7.46037915896143e-23[/C][C]3.73018957948071e-23[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]3.27516659137081e-22[/C][C]1.63758329568541e-22[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.25688207486982e-21[/C][C]6.2844103743491e-22[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]3.24029247958044e-21[/C][C]1.62014623979022e-21[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]2.47537819770185e-21[/C][C]1.23768909885092e-21[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.29641431436841e-20[/C][C]6.48207157184203e-21[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]6.29727439672095e-20[/C][C]3.14863719836048e-20[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]2.54259644977851e-19[/C][C]1.27129822488926e-19[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.02208179426063e-18[/C][C]5.11040897130313e-19[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]2.08215305619356e-18[/C][C]1.04107652809678e-18[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.02484472507311e-17[/C][C]5.12422362536555e-18[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]3.73944531377065e-17[/C][C]1.86972265688533e-17[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]1.80495537517788e-16[/C][C]9.02477687588941e-17[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]7.0941935887671e-16[/C][C]3.54709679438355e-16[/C][/ROW]
[ROW][C]87[/C][C]0.999999999999998[/C][C]3.15141190595537e-15[/C][C]1.57570595297769e-15[/C][/ROW]
[ROW][C]88[/C][C]0.999999999999994[/C][C]1.18330028544004e-14[/C][C]5.91650142720019e-15[/C][/ROW]
[ROW][C]89[/C][C]0.999999999999993[/C][C]1.32085544240374e-14[/C][C]6.6042772120187e-15[/C][/ROW]
[ROW][C]90[/C][C]0.999999999999974[/C][C]5.24192599970922e-14[/C][C]2.62096299985461e-14[/C][/ROW]
[ROW][C]91[/C][C]0.999999999999943[/C][C]1.14847958459624e-13[/C][C]5.74239792298119e-14[/C][/ROW]
[ROW][C]92[/C][C]0.999999999999876[/C][C]2.47122402176736e-13[/C][C]1.23561201088368e-13[/C][/ROW]
[ROW][C]93[/C][C]0.99999999999999[/C][C]1.90360410643387e-14[/C][C]9.51802053216936e-15[/C][/ROW]
[ROW][C]94[/C][C]0.999999999999964[/C][C]7.25831813073968e-14[/C][C]3.62915906536984e-14[/C][/ROW]
[ROW][C]95[/C][C]0.9999999999998[/C][C]4.00130313613739e-13[/C][C]2.00065156806869e-13[/C][/ROW]
[ROW][C]96[/C][C]0.999999999999583[/C][C]8.3478179288123e-13[/C][C]4.17390896440615e-13[/C][/ROW]
[ROW][C]97[/C][C]0.999999999997718[/C][C]4.56301685163247e-12[/C][C]2.28150842581624e-12[/C][/ROW]
[ROW][C]98[/C][C]0.999999999988383[/C][C]2.32341676778831e-11[/C][C]1.16170838389415e-11[/C][/ROW]
[ROW][C]99[/C][C]0.99999999998565[/C][C]2.87006607660736e-11[/C][C]1.43503303830368e-11[/C][/ROW]
[ROW][C]100[/C][C]0.999999999925504[/C][C]1.48991945792713e-10[/C][C]7.44959728963564e-11[/C][/ROW]
[ROW][C]101[/C][C]0.999999999843074[/C][C]3.13852264540182e-10[/C][C]1.56926132270091e-10[/C][/ROW]
[ROW][C]102[/C][C]0.999999999831731[/C][C]3.36537443494355e-10[/C][C]1.68268721747177e-10[/C][/ROW]
[ROW][C]103[/C][C]0.999999999527287[/C][C]9.45425183683165e-10[/C][C]4.72712591841582e-10[/C][/ROW]
[ROW][C]104[/C][C]0.999999997505618[/C][C]4.98876375657457e-09[/C][C]2.49438187828728e-09[/C][/ROW]
[ROW][C]105[/C][C]0.999999986133296[/C][C]2.77334073040907e-08[/C][C]1.38667036520454e-08[/C][/ROW]
[ROW][C]106[/C][C]0.999999932517554[/C][C]1.34964892834633e-07[/C][C]6.74824464173167e-08[/C][/ROW]
[ROW][C]107[/C][C]0.999999748705998[/C][C]5.02588004602093e-07[/C][C]2.51294002301047e-07[/C][/ROW]
[ROW][C]108[/C][C]0.999999517051373[/C][C]9.6589725301962e-07[/C][C]4.8294862650981e-07[/C][/ROW]
[ROW][C]109[/C][C]0.999997387718965[/C][C]5.22456206978607e-06[/C][C]2.61228103489303e-06[/C][/ROW]
[ROW][C]110[/C][C]0.999991301296299[/C][C]1.73974074028618e-05[/C][C]8.69870370143092e-06[/C][/ROW]
[ROW][C]111[/C][C]0.999958301496567[/C][C]8.33970068654296e-05[/C][C]4.16985034327148e-05[/C][/ROW]
[ROW][C]112[/C][C]0.999906803214395[/C][C]0.000186393571209182[/C][C]9.3196785604591e-05[/C][/ROW]
[ROW][C]113[/C][C]0.999992312187483[/C][C]1.53756250334749e-05[/C][C]7.68781251673744e-06[/C][/ROW]
[ROW][C]114[/C][C]0.99997845279414[/C][C]4.30944117199411e-05[/C][C]2.15472058599705e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999993622138568[/C][C]1.27557228635836e-05[/C][C]6.37786143179179e-06[/C][/ROW]
[ROW][C]116[/C][C]0.999844893339378[/C][C]0.000310213321244287[/C][C]0.000155106660622144[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101756&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101756&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
16	0.0446694702171001	0.0893389404342002	0.9553305297829
17	0.217795139174473	0.435590278348946	0.782204860825527
18	0.206948424615960	0.413896849231920	0.79305157538404
19	0.123002155567594	0.246004311135189	0.876997844432406
20	0.0713426550017686	0.142685310003537	0.928657344998231
21	0.0408406913809466	0.0816813827618932	0.959159308619053
22	0.0225191683580043	0.0450383367160085	0.977480831641996
23	0.0124562644601262	0.0249125289202524	0.987543735539874
24	0.00920881500578142	0.0184176300115628	0.990791184994219
25	0.989571375209978	0.0208572495800439	0.0104286247900220
26	0.99988700383339	0.000225992333219593	0.000112996166609796
27	0.999988354674786	2.32906504274421e-05	1.16453252137211e-05
28	0.999998132574066	3.73485186844505e-06	1.86742593422252e-06
29	0.999999574468331	8.51063337608523e-07	4.25531668804261e-07
30	0.999999914148836	1.71702327833412e-07	8.58511639167061e-08
31	0.999999985300168	2.93996636958791e-08	1.46998318479396e-08
32	0.999999998926972	2.14605675328575e-09	1.07302837664287e-09
33	0.99999999964541	7.09180906061965e-10	3.54590453030983e-10
34	0.999999999945367	1.09265111156789e-10	5.46325555783946e-11
35	0.999999999999762	4.7653891972433e-13	2.38269459862165e-13
36	1	1.91329174150928e-15	9.56645870754638e-16
37	1	7.8066204954338e-19	3.9033102477169e-19
38	1	3.69556328537189e-21	1.84778164268595e-21
39	1	9.86093781566029e-24	4.93046890783014e-24
40	1	4.36821141735872e-25	2.18410570867936e-25
41	1	1.05992621743059e-28	5.29963108715296e-29
42	1	1.30134624421135e-29	6.50673122105673e-30
43	1	3.13412898732319e-30	1.56706449366159e-30
44	1	8.66840979567252e-31	4.33420489783626e-31
45	1	1.06822080726673e-30	5.34110403633363e-31
46	1	1.05786641852041e-30	5.28933209260205e-31
47	1	8.13912135642193e-31	4.06956067821096e-31
48	1	4.52487437541523e-31	2.26243718770762e-31
49	1	1.01984837220099e-31	5.09924186100497e-32
50	1	6.0865066063739e-32	3.04325330318695e-32
51	1	2.80563036045276e-32	1.40281518022638e-32
52	1	5.37436586866497e-32	2.68718293433248e-32
53	1	5.04670452763168e-32	2.52335226381584e-32
54	1	1.02214817307039e-31	5.11074086535195e-32
55	1	8.05388788693352e-32	4.02694394346676e-32
56	1	1.63607705432227e-31	8.18038527161135e-32
57	1	6.50564532722289e-31	3.25282266361145e-31
58	1	1.96841100336608e-30	9.84205501683039e-31
59	1	4.96832028371087e-30	2.48416014185543e-30
60	1	1.31415574018369e-29	6.57077870091843e-30
61	1	3.99781116601239e-29	1.99890558300620e-29
62	1	1.12334424053747e-28	5.61672120268735e-29
63	1	3.18940627190774e-28	1.59470313595387e-28
64	1	1.18240212709421e-27	5.91201063547105e-28
65	1	3.31436246355277e-27	1.65718123177638e-27
66	1	1.56223291974110e-26	7.81116459870548e-27
67	1	6.87657093711081e-26	3.43828546855541e-26
68	1	1.13190559507520e-25	5.65952797537598e-26
69	1	3.63200832673205e-25	1.81600416336602e-25
70	1	1.51292993941597e-24	7.56464969707984e-25
71	1	4.54038756860062e-24	2.27019378430031e-24
72	1	1.73569558207494e-23	8.67847791037469e-24
73	1	7.46037915896143e-23	3.73018957948071e-23
74	1	3.27516659137081e-22	1.63758329568541e-22
75	1	1.25688207486982e-21	6.2844103743491e-22
76	1	3.24029247958044e-21	1.62014623979022e-21
77	1	2.47537819770185e-21	1.23768909885092e-21
78	1	1.29641431436841e-20	6.48207157184203e-21
79	1	6.29727439672095e-20	3.14863719836048e-20
80	1	2.54259644977851e-19	1.27129822488926e-19
81	1	1.02208179426063e-18	5.11040897130313e-19
82	1	2.08215305619356e-18	1.04107652809678e-18
83	1	1.02484472507311e-17	5.12422362536555e-18
84	1	3.73944531377065e-17	1.86972265688533e-17
85	1	1.80495537517788e-16	9.02477687588941e-17
86	1	7.0941935887671e-16	3.54709679438355e-16
87	0.999999999999998	3.15141190595537e-15	1.57570595297769e-15
88	0.999999999999994	1.18330028544004e-14	5.91650142720019e-15
89	0.999999999999993	1.32085544240374e-14	6.6042772120187e-15
90	0.999999999999974	5.24192599970922e-14	2.62096299985461e-14
91	0.999999999999943	1.14847958459624e-13	5.74239792298119e-14
92	0.999999999999876	2.47122402176736e-13	1.23561201088368e-13
93	0.99999999999999	1.90360410643387e-14	9.51802053216936e-15
94	0.999999999999964	7.25831813073968e-14	3.62915906536984e-14
95	0.9999999999998	4.00130313613739e-13	2.00065156806869e-13
96	0.999999999999583	8.3478179288123e-13	4.17390896440615e-13
97	0.999999999997718	4.56301685163247e-12	2.28150842581624e-12
98	0.999999999988383	2.32341676778831e-11	1.16170838389415e-11
99	0.99999999998565	2.87006607660736e-11	1.43503303830368e-11
100	0.999999999925504	1.48991945792713e-10	7.44959728963564e-11
101	0.999999999843074	3.13852264540182e-10	1.56926132270091e-10
102	0.999999999831731	3.36537443494355e-10	1.68268721747177e-10
103	0.999999999527287	9.45425183683165e-10	4.72712591841582e-10
104	0.999999997505618	4.98876375657457e-09	2.49438187828728e-09
105	0.999999986133296	2.77334073040907e-08	1.38667036520454e-08
106	0.999999932517554	1.34964892834633e-07	6.74824464173167e-08
107	0.999999748705998	5.02588004602093e-07	2.51294002301047e-07
108	0.999999517051373	9.6589725301962e-07	4.8294862650981e-07
109	0.999997387718965	5.22456206978607e-06	2.61228103489303e-06
110	0.999991301296299	1.73974074028618e-05	8.69870370143092e-06
111	0.999958301496567	8.33970068654296e-05	4.16985034327148e-05
112	0.999906803214395	0.000186393571209182	9.3196785604591e-05
113	0.999992312187483	1.53756250334749e-05	7.68781251673744e-06
114	0.99997845279414	4.30944117199411e-05	2.15472058599705e-05
115	0.999993622138568	1.27557228635836e-05	6.37786143179179e-06
116	0.999844893339378	0.000310213321244287	0.000155106660622144

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	91	0.900990099009901	NOK
5% type I error level	95	0.94059405940594	NOK
10% type I error level	97	0.96039603960396	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 & 91 & 0.900990099009901 & NOK \tabularnewline
5% type I error level & 95 & 0.94059405940594 & NOK \tabularnewline
10% type I error level & 97 & 0.96039603960396 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101756&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]91[/C][C]0.900990099009901[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]95[/C][C]0.94059405940594[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]97[/C][C]0.96039603960396[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101756&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101756&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	91	0.900990099009901	NOK
5% type I error level	95	0.94059405940594	NOK
10% type I error level	97	0.96039603960396	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code