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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 12 Dec 2011 11:34:43 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/12/t13237076953imo24ww3tols8f.htm/, Retrieved Fri, 03 May 2024 08:18:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154110, Retrieved Fri, 03 May 2024 08:18:53 +0000

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

-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2011-12-12 16:34:43] [2e63149daec6ba44c7d6fab36a0b0c34] [Current]
-   P       [Multiple Regression] [] [2011-12-12 16:38:32] [0fa8c500575976cf9d2f7efbe256ddfb] 

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Dataseries X:

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13	15	2	9	42	12
12	18	1	9	51	15
15	11	1	9	42	14
12	16	1	8	46	10
10	12	2	14	41	10
12	17	2	14	49	9
15	15	1	15	47	18
9	19	1	11	33	11
11	18	1	8	47	12
11	10	2	14	42	11
11	14	1	9	32	15
15	18	1	6	53	17
7	18	2	14	41	14
11	14	2	8	41	24
11	14	1	11	33	7
10	12	1	16	37	18
14	16	2	11	43	11
6	13	2	13	33	14
11	16	1	7	49	18
15	14	2	9	42	12
11	9	1	15	43	11
12	9	2	16	37	5
14	17	1	10	43	12
15	13	2	14	42	11
9	15	2	12	43	10
13	17	1	6	46	11
13	16	2	4	33	15
16	12	1	12	42	16
13	11	1	14	40	14
12	16	2	13	44	8
14	17	1	9	42	13
11	17	2	14	52	18
9	16	1	14	44	17
16	13	2	10	45	10
12	12	1	14	46	13
10	12	2	8	36	11
13	16	1	8	45	12
16	14	1	10	49	12
14	12	2	9	43	12
15	12	1	9	43	9
5	14	1	11	37	18
8	8	2	15	32	7
11	15	1	9	45	14
16	14	2	9	45	16
17	11	1	10	45	12
9	13	2	8	45	17
9	14	1	8	31	12
13	15	1	14	33	9
10	16	1	10	44	12
6	10	2	11	49	9
12	11	2	9	44	13
8	12	2	12	41	10
14	14	2	13	44	10
12	15	1	14	38	11
11	16	1	15	33	13
16	9	1	11	47	13
8	11	2	9	37	13
15	15	1	8	48	6
7	15	2	7	40	7
16	13	2	10	50	13
14	17	1	10	54	21
16	17	1	10	43	11
9	15	1	9	54	9
14	13	1	13	44	18
11	15	2	11	47	9
13	13	2	8	33	9
15	15	1	10	45	15
5	10	2	14	33	9
15	15	1	11	44	11
13	14	1	10	47	14
11	15	2	16	45	14
11	16	2	11	43	8
12	7	1	16	43	12
12	13	1	6	33	8
12	15	1	11	46	11
14	13	1	14	47	17
6	16	1	9	47	16
7	16	2	9	0	11
14	12	1	11	43	13
13	15	2	12	46	11
12	14	2	20	36	8
9	11	2	11	42	11
12	14	1	12	44	13
16	15	1	9	47	13
10	9	2	10	41	15
14	15	1	14	47	15
10	17	1	8	46	12
16	16	1	10	47	12
15	14	1	8	46	15
12	15	2	7	46	12
10	16	1	11	36	21
8	10	1	14	30	24
8	17	2	8	48	11
11	15	2	14	45	12
13	15	1	10	49	15
16	13	1	9	55	17
14	14	2	16	11	12
11	16	1	8	52	16
4	11	2	12	33	13
14	18	1	8	47	15
9	14	1	16	33	11
14	14	1	13	44	15
8	14	1	13	42	12
8	14	1	8	55	14
11	15	1	9	42	12
12	14	1	11	46	20
14	15	1	9	46	17
15	15	2	8	47	12
16	12	1	14	33	11
16	19	1	7	53	11
14	13	2	11	42	9
12	15	1	11	44	12
14	17	2	10	55	11
8	9	2	14	40	8
16	15	2	10	46	12
12	16	1	9	53	15
12	17	1	8	44	10
11	11	1	14	35	14
4	15	1	12	40	16
16	11	1	12	44	18
15	15	1	6	46	6
10	17	1	16	45	16
13	14	1	8	53	11
15	12	2	13	45	20
12	14	1	12	48	10
14	15	2	11	46	16
7	16	1	12	55	15
19	16	1	9	47	14
12	14	1	11	43	7
12	11	2	16	38	9
8	14	2	10	40	12
12	13	1	13	47	12
10	13	1	11	47	13
8	14	2	11	42	17
10	16	2	9	53	11
14	16	2	11	43	11
16	12	1	12	44	14
13	11	1	10	42	13
16	13	1	13	51	12
9	15	1	9	54	11
14	13	2	14	41	15
14	16	2	14	51	11
12	13	1	8	51	13

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154110&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154110&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154110&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	6 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Doubtsaboutexpectations[t] = + 33.7628490052607 + 0.576297541832057Popularity[t] + 0.478113214787678Gender[t] -1.43366386194623Belonging[t] -0.413115473681593Happiness[t] + 0.180267632695104Parentalexpectations[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Doubtsaboutexpectations[t] =  +  33.7628490052607 +  0.576297541832057Popularity[t] +  0.478113214787678Gender[t] -1.43366386194623Belonging[t] -0.413115473681593Happiness[t] +  0.180267632695104Parentalexpectations[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154110&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Doubtsaboutexpectations[t] =  +  33.7628490052607 +  0.576297541832057Popularity[t] +  0.478113214787678Gender[t] -1.43366386194623Belonging[t] -0.413115473681593Happiness[t] +  0.180267632695104Parentalexpectations[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154110&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154110&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
Doubtsaboutexpectations[t] = + 33.7628490052607 + 0.576297541832057Popularity[t] + 0.478113214787678Gender[t] -1.43366386194623Belonging[t] -0.413115473681593Happiness[t] + 0.180267632695104Parentalexpectations[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)	33.7628490052607	6.588503	5.1245	1e-06	0
Popularity	0.576297541832057	0.195775	2.9437	0.003811	0.001906
Gender	0.478113214787678	0.261714	1.8269	0.069898	0.034949
Belonging	-1.43366386194623	1.23424	-1.1616	0.247428	0.123714
Happiness	-0.413115473681593	0.223849	-1.8455	0.067122	0.033561
Parentalexpectations	0.180267632695104	0.172462	1.0453	0.297743	0.148872

\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) & 33.7628490052607 & 6.588503 & 5.1245 & 1e-06 & 0 \tabularnewline
Popularity & 0.576297541832057 & 0.195775 & 2.9437 & 0.003811 & 0.001906 \tabularnewline
Gender & 0.478113214787678 & 0.261714 & 1.8269 & 0.069898 & 0.034949 \tabularnewline
Belonging & -1.43366386194623 & 1.23424 & -1.1616 & 0.247428 & 0.123714 \tabularnewline
Happiness & -0.413115473681593 & 0.223849 & -1.8455 & 0.067122 & 0.033561 \tabularnewline
Parentalexpectations & 0.180267632695104 & 0.172462 & 1.0453 & 0.297743 & 0.148872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154110&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]33.7628490052607[/C][C]6.588503[/C][C]5.1245[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Popularity[/C][C]0.576297541832057[/C][C]0.195775[/C][C]2.9437[/C][C]0.003811[/C][C]0.001906[/C][/ROW]
[ROW][C]Gender[/C][C]0.478113214787678[/C][C]0.261714[/C][C]1.8269[/C][C]0.069898[/C][C]0.034949[/C][/ROW]
[ROW][C]Belonging[/C][C]-1.43366386194623[/C][C]1.23424[/C][C]-1.1616[/C][C]0.247428[/C][C]0.123714[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.413115473681593[/C][C]0.223849[/C][C]-1.8455[/C][C]0.067122[/C][C]0.033561[/C][/ROW]
[ROW][C]Parentalexpectations[/C][C]0.180267632695104[/C][C]0.172462[/C][C]1.0453[/C][C]0.297743[/C][C]0.148872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154110&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154110&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)	33.7628490052607	6.588503	5.1245	1e-06	0
Popularity	0.576297541832057	0.195775	2.9437	0.003811	0.001906
Gender	0.478113214787678	0.261714	1.8269	0.069898	0.034949
Belonging	-1.43366386194623	1.23424	-1.1616	0.247428	0.123714
Happiness	-0.413115473681593	0.223849	-1.8455	0.067122	0.033561
Parentalexpectations	0.180267632695104	0.172462	1.0453	0.297743	0.148872

Multiple Linear Regression - Regression Statistics
Multiple R	0.417432508416781
R-squared	0.174249899083126
Adjusted R-squared	0.14411303408616
F-TEST (value)	5.78195174251427
F-TEST (DF numerator)	5
F-TEST (DF denominator)	137
p-value	7.09848962429094e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.80662163087911
Sum Squared Residuals	6347.22342955535

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.417432508416781 \tabularnewline
R-squared & 0.174249899083126 \tabularnewline
Adjusted R-squared & 0.14411303408616 \tabularnewline
F-TEST (value) & 5.78195174251427 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 7.09848962429094e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.80662163087911 \tabularnewline
Sum Squared Residuals & 6347.22342955535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154110&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.417432508416781[/C][/ROW]
[ROW][C]R-squared[/C][C]0.174249899083126[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.14411303408616[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.78195174251427[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/C][/ROW]
[ROW][C]p-value[/C][C]7.09848962429094e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.80662163087911[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6347.22342955535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154110&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154110&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	0.417432508416781
R-squared	0.174249899083126
Adjusted R-squared	0.14411303408616
F-TEST (value)	5.78195174251427
F-TEST (DF numerator)	5
F-TEST (DF denominator)	137
p-value	7.09848962429094e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.80662163087911
Sum Squared Residuals	6347.22342955535

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	42	44.004259876207	-2.00425987620701
2	51	46.8367687387696	4.16323126123041
3	42	45.0386012280569	-3.0386012280569
4	46	45.3923196194003	0.607680380599704
5	41	38.4149149725497	2.58508502745031
6	49	41.7778084974571	7.22219150254292
7	47	45.1934317758985	1.80656822410153
8	33	44.0386878499175	-11.0386878499175
9	47	46.1327837725338	0.867216227466199
10	42	38.2152537175015	3.78474628249851
11	32	44.3480183377868	-12.3480183377868
12	53	50.1655430507007	2.83445694929927
13	41	40.27577216656	0.724227833440004
14	41	44.9498786437781	-3.94987864377811
15	33	42.0796463288628	-9.0796463288628
16	37	40.4644889486936	-3.46448894869356
17	43	44.0521720527685	-1.0521720527685
18	33	37.7220240244711	-4.72202402447114
19	49	46.6712786128107	2.32872138718934
20	42	44.6787417450835	-2.67874174508349
21	43	38.7576888909785	4.24231110902155
22	37	36.4056013010121	0.594398698987939
23	43	46.5573322358791	-3.55733223587911
24	42	41.9547835291928	0.0452164708072503
25	43	40.0991880224438	2.90081197755615
26	46	47.4532289560783	-1.45322895607832
27	33	47.088753357488	-14.088753357488
28	42	45.2142008290221	-3.21420082902206
29	40	41.8204287759848	-1.82042877598482
30	44	41.5325431236559	2.46745687634411
31	42	47.1507153422558	-5.1507153422558
32	52	42.823919649881	9.17608035011904
33	44	42.4466075806803	1.55339241931971
34	45	44.0032753330561	0.996724666943926
35	46	41.5419768162453	4.45802318375466
36	36	41.0738754473343	-5.07387544733435
37	45	46.3291524266226	-1.32915242662256
38	49	46.2755876751802	2.72441232481981
39	43	43.1462177736761	-0.146217773676084
40	43	44.6153762793691	-1.61537627936906
41	37	40.6048050375166	-3.6048050375166
42	32	34.395948657968	-2.39594865796796
43	45	44.6458639198794	0.354136080120617
44	45	45.976109817696	-0.976109817695967
45	45	45.4175455726492	-0.417545572649212
46	45	42.0572969164606	2.94270308353941
47	31	43.067735829719	-12.067735829719
48	33	42.83154347166	-9.83154347166001
49	44	43.7740288537632	0.225971146236797
50	49	36.2125771639958	12.7874228360042
51	44	41.6957771079194	2.3042228920806
52	41	38.0885508362488	2.91144916375124
53	44	42.0894470431349	1.91055295686514
54	38	42.6157811952182	-4.61578119521816
55	33	42.4650166598824	-9.4650166598824
56	47	43.6521737602553	3.34782623974469
57	37	39.3905869405912	-2.39058694059117
58	48	45.9220284993284	2.07797150067163
59	40	40.4713674091024	-0.471367409102387
60	50	44.5440782311414	5.45592176885862
61	54	48.179740930135	5.82025906986496
62	43	47.5296596868481	-4.52965968684811
63	54	42.5919306727397	11.4080693272603
64	44	44.4871387518542	-0.487138751854241
65	47	41.4846309470945	5.51536905290555
66	33	42.920346022228	-9.920346022228
67	45	46.7182062462211	-1.71820624622112
68	33	34.3969332011189	-1.39693320111894
69	44	45.5840202417591	-1.58402024175911
70	47	44.9072303150742	2.09276968492577
71	45	40.320391742162	4.679608257838
72	43	41.782476529187	1.21752347081298
73	43	38.1449121622487	4.85508783775134
74	33	44.4236756570102	-11.4236756570102
75	46	43.8551276162629	2.14487238373706
76	47	43.8937556454775	3.10624435452246
77	47	42.603024690897	4.39697530910302
78	0	40.8443202073073	-40.8443202073073
79	43	43.9339183209542	-0.933918320954231
80	46	42.5846458224672	3.41535417753282
81	36	37.6845083783094	-1.68450837830939
82	42	38.7801182696698	3.21988173033017
83	44	43.3244341931839	0.67556580681612
84	47	47.3470839963446	-0.347083996344562
85	41	39.5343753863885	1.46562461361146
86	47	44.4894468096627	2.51055319033731
87	46	45.0783730159141	0.921626984085934
88	47	47.2318141047555	-0.231814104755543
89	46	47.0663239787966	-1.06632397879663
90	46	44.2541932817382	1.74580671826181
91	36	44.9833220743375	-8.98332207433755
92	30	40.2635041789879	-10.2635041789879
93	48	42.3118464376086	5.68815356239138
94	45	40.786087424135	4.21391257586502
95	49	45.565611162557	3.43438883744299
96	55	47.1119280975496	7.88807190245038
97	11	41.2106358874803	-30.2106358874803
98	52	45.8976278737389	6.10237212626114
99	33	35.8460503522182	-2.84605035221817
100	47	48.4024792961153	-1.40247929611528
101	33	39.5825444075711	-6.58254440757113
102	44	44.4244490685566	-0.424449068556608
103	42	40.425860919479	1.57413908052104
104	55	42.8519735532771	12.1480264467229
105	42	44.2853286544892	-2.28532865448918
106	46	44.9994230957312	1.0005769042688
107	46	46.9155594434609	-0.915559443460864
108	47	45.5699704335528	1.43002956644723
109	33	43.4866317181834	-10.4866317181834
110	53	49.7252325374683	3.27476746253175
111	42	42.2572971430153	-0.257297143015264
112	44	44.035395248958	-0.0353952489580461
113	55	44.9434007412378	10.0565992587622
114	40	35.4674449791323	4.53255502086767
115	46	45.3200370280216	0.679962971978363
116	53	45.8805423091942	7.11945769080578
117	44	45.870432834188	-1.87043283418797
118	35	40.6678336923207	-5.66783369232071
119	40	39.7329699714004	0.267030028599584
120	44	45.0966228796246	-1.09662287962459
121	46	46.7482594466916	-0.748259446691557
122	45	42.4945197572417	2.50548024275826
123	53	45.1926583643521	7.8073416356479
124	45	43.5121944823426	1.4878055176574
125	48	42.7836312950986	5.21636870490143
126	46	44.4753970014563	1.52460299854365
127	55	41.7597081789892	13.2402918210108
128	47	49.7343574693235	-2.73435746932351
129	43	42.6559438706948	0.344056129305151
130	38	38.0828982613678	-0.082898261367832
131	40	40.2315434785775	-0.231543478577507
132	47	42.2529378720195	4.7470621279805
133	47	42.1068413684137	4.89315863158632
134	42	40.7197661683714	1.28023383162857
135	53	42.5732128328035	10.4267871671965
136	43	44.0521720527685	-1.0521720527685
137	44	44.8536655636319	-0.853665563631855
138	42	43.2926230380161	-1.29262303801609
139	51	44.5581280393477	6.44187196065227
140	54	42.95246593813	11.04753406187
141	41	42.0995565181411	-1.09955651814111
142	51	42.8128256317237	8.18717436827627
143	51	44.4987828731226	6.50121712687743

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 42 & 44.004259876207 & -2.00425987620701 \tabularnewline
2 & 51 & 46.8367687387696 & 4.16323126123041 \tabularnewline
3 & 42 & 45.0386012280569 & -3.0386012280569 \tabularnewline
4 & 46 & 45.3923196194003 & 0.607680380599704 \tabularnewline
5 & 41 & 38.4149149725497 & 2.58508502745031 \tabularnewline
6 & 49 & 41.7778084974571 & 7.22219150254292 \tabularnewline
7 & 47 & 45.1934317758985 & 1.80656822410153 \tabularnewline
8 & 33 & 44.0386878499175 & -11.0386878499175 \tabularnewline
9 & 47 & 46.1327837725338 & 0.867216227466199 \tabularnewline
10 & 42 & 38.2152537175015 & 3.78474628249851 \tabularnewline
11 & 32 & 44.3480183377868 & -12.3480183377868 \tabularnewline
12 & 53 & 50.1655430507007 & 2.83445694929927 \tabularnewline
13 & 41 & 40.27577216656 & 0.724227833440004 \tabularnewline
14 & 41 & 44.9498786437781 & -3.94987864377811 \tabularnewline
15 & 33 & 42.0796463288628 & -9.0796463288628 \tabularnewline
16 & 37 & 40.4644889486936 & -3.46448894869356 \tabularnewline
17 & 43 & 44.0521720527685 & -1.0521720527685 \tabularnewline
18 & 33 & 37.7220240244711 & -4.72202402447114 \tabularnewline
19 & 49 & 46.6712786128107 & 2.32872138718934 \tabularnewline
20 & 42 & 44.6787417450835 & -2.67874174508349 \tabularnewline
21 & 43 & 38.7576888909785 & 4.24231110902155 \tabularnewline
22 & 37 & 36.4056013010121 & 0.594398698987939 \tabularnewline
23 & 43 & 46.5573322358791 & -3.55733223587911 \tabularnewline
24 & 42 & 41.9547835291928 & 0.0452164708072503 \tabularnewline
25 & 43 & 40.0991880224438 & 2.90081197755615 \tabularnewline
26 & 46 & 47.4532289560783 & -1.45322895607832 \tabularnewline
27 & 33 & 47.088753357488 & -14.088753357488 \tabularnewline
28 & 42 & 45.2142008290221 & -3.21420082902206 \tabularnewline
29 & 40 & 41.8204287759848 & -1.82042877598482 \tabularnewline
30 & 44 & 41.5325431236559 & 2.46745687634411 \tabularnewline
31 & 42 & 47.1507153422558 & -5.1507153422558 \tabularnewline
32 & 52 & 42.823919649881 & 9.17608035011904 \tabularnewline
33 & 44 & 42.4466075806803 & 1.55339241931971 \tabularnewline
34 & 45 & 44.0032753330561 & 0.996724666943926 \tabularnewline
35 & 46 & 41.5419768162453 & 4.45802318375466 \tabularnewline
36 & 36 & 41.0738754473343 & -5.07387544733435 \tabularnewline
37 & 45 & 46.3291524266226 & -1.32915242662256 \tabularnewline
38 & 49 & 46.2755876751802 & 2.72441232481981 \tabularnewline
39 & 43 & 43.1462177736761 & -0.146217773676084 \tabularnewline
40 & 43 & 44.6153762793691 & -1.61537627936906 \tabularnewline
41 & 37 & 40.6048050375166 & -3.6048050375166 \tabularnewline
42 & 32 & 34.395948657968 & -2.39594865796796 \tabularnewline
43 & 45 & 44.6458639198794 & 0.354136080120617 \tabularnewline
44 & 45 & 45.976109817696 & -0.976109817695967 \tabularnewline
45 & 45 & 45.4175455726492 & -0.417545572649212 \tabularnewline
46 & 45 & 42.0572969164606 & 2.94270308353941 \tabularnewline
47 & 31 & 43.067735829719 & -12.067735829719 \tabularnewline
48 & 33 & 42.83154347166 & -9.83154347166001 \tabularnewline
49 & 44 & 43.7740288537632 & 0.225971146236797 \tabularnewline
50 & 49 & 36.2125771639958 & 12.7874228360042 \tabularnewline
51 & 44 & 41.6957771079194 & 2.3042228920806 \tabularnewline
52 & 41 & 38.0885508362488 & 2.91144916375124 \tabularnewline
53 & 44 & 42.0894470431349 & 1.91055295686514 \tabularnewline
54 & 38 & 42.6157811952182 & -4.61578119521816 \tabularnewline
55 & 33 & 42.4650166598824 & -9.4650166598824 \tabularnewline
56 & 47 & 43.6521737602553 & 3.34782623974469 \tabularnewline
57 & 37 & 39.3905869405912 & -2.39058694059117 \tabularnewline
58 & 48 & 45.9220284993284 & 2.07797150067163 \tabularnewline
59 & 40 & 40.4713674091024 & -0.471367409102387 \tabularnewline
60 & 50 & 44.5440782311414 & 5.45592176885862 \tabularnewline
61 & 54 & 48.179740930135 & 5.82025906986496 \tabularnewline
62 & 43 & 47.5296596868481 & -4.52965968684811 \tabularnewline
63 & 54 & 42.5919306727397 & 11.4080693272603 \tabularnewline
64 & 44 & 44.4871387518542 & -0.487138751854241 \tabularnewline
65 & 47 & 41.4846309470945 & 5.51536905290555 \tabularnewline
66 & 33 & 42.920346022228 & -9.920346022228 \tabularnewline
67 & 45 & 46.7182062462211 & -1.71820624622112 \tabularnewline
68 & 33 & 34.3969332011189 & -1.39693320111894 \tabularnewline
69 & 44 & 45.5840202417591 & -1.58402024175911 \tabularnewline
70 & 47 & 44.9072303150742 & 2.09276968492577 \tabularnewline
71 & 45 & 40.320391742162 & 4.679608257838 \tabularnewline
72 & 43 & 41.782476529187 & 1.21752347081298 \tabularnewline
73 & 43 & 38.1449121622487 & 4.85508783775134 \tabularnewline
74 & 33 & 44.4236756570102 & -11.4236756570102 \tabularnewline
75 & 46 & 43.8551276162629 & 2.14487238373706 \tabularnewline
76 & 47 & 43.8937556454775 & 3.10624435452246 \tabularnewline
77 & 47 & 42.603024690897 & 4.39697530910302 \tabularnewline
78 & 0 & 40.8443202073073 & -40.8443202073073 \tabularnewline
79 & 43 & 43.9339183209542 & -0.933918320954231 \tabularnewline
80 & 46 & 42.5846458224672 & 3.41535417753282 \tabularnewline
81 & 36 & 37.6845083783094 & -1.68450837830939 \tabularnewline
82 & 42 & 38.7801182696698 & 3.21988173033017 \tabularnewline
83 & 44 & 43.3244341931839 & 0.67556580681612 \tabularnewline
84 & 47 & 47.3470839963446 & -0.347083996344562 \tabularnewline
85 & 41 & 39.5343753863885 & 1.46562461361146 \tabularnewline
86 & 47 & 44.4894468096627 & 2.51055319033731 \tabularnewline
87 & 46 & 45.0783730159141 & 0.921626984085934 \tabularnewline
88 & 47 & 47.2318141047555 & -0.231814104755543 \tabularnewline
89 & 46 & 47.0663239787966 & -1.06632397879663 \tabularnewline
90 & 46 & 44.2541932817382 & 1.74580671826181 \tabularnewline
91 & 36 & 44.9833220743375 & -8.98332207433755 \tabularnewline
92 & 30 & 40.2635041789879 & -10.2635041789879 \tabularnewline
93 & 48 & 42.3118464376086 & 5.68815356239138 \tabularnewline
94 & 45 & 40.786087424135 & 4.21391257586502 \tabularnewline
95 & 49 & 45.565611162557 & 3.43438883744299 \tabularnewline
96 & 55 & 47.1119280975496 & 7.88807190245038 \tabularnewline
97 & 11 & 41.2106358874803 & -30.2106358874803 \tabularnewline
98 & 52 & 45.8976278737389 & 6.10237212626114 \tabularnewline
99 & 33 & 35.8460503522182 & -2.84605035221817 \tabularnewline
100 & 47 & 48.4024792961153 & -1.40247929611528 \tabularnewline
101 & 33 & 39.5825444075711 & -6.58254440757113 \tabularnewline
102 & 44 & 44.4244490685566 & -0.424449068556608 \tabularnewline
103 & 42 & 40.425860919479 & 1.57413908052104 \tabularnewline
104 & 55 & 42.8519735532771 & 12.1480264467229 \tabularnewline
105 & 42 & 44.2853286544892 & -2.28532865448918 \tabularnewline
106 & 46 & 44.9994230957312 & 1.0005769042688 \tabularnewline
107 & 46 & 46.9155594434609 & -0.915559443460864 \tabularnewline
108 & 47 & 45.5699704335528 & 1.43002956644723 \tabularnewline
109 & 33 & 43.4866317181834 & -10.4866317181834 \tabularnewline
110 & 53 & 49.7252325374683 & 3.27476746253175 \tabularnewline
111 & 42 & 42.2572971430153 & -0.257297143015264 \tabularnewline
112 & 44 & 44.035395248958 & -0.0353952489580461 \tabularnewline
113 & 55 & 44.9434007412378 & 10.0565992587622 \tabularnewline
114 & 40 & 35.4674449791323 & 4.53255502086767 \tabularnewline
115 & 46 & 45.3200370280216 & 0.679962971978363 \tabularnewline
116 & 53 & 45.8805423091942 & 7.11945769080578 \tabularnewline
117 & 44 & 45.870432834188 & -1.87043283418797 \tabularnewline
118 & 35 & 40.6678336923207 & -5.66783369232071 \tabularnewline
119 & 40 & 39.7329699714004 & 0.267030028599584 \tabularnewline
120 & 44 & 45.0966228796246 & -1.09662287962459 \tabularnewline
121 & 46 & 46.7482594466916 & -0.748259446691557 \tabularnewline
122 & 45 & 42.4945197572417 & 2.50548024275826 \tabularnewline
123 & 53 & 45.1926583643521 & 7.8073416356479 \tabularnewline
124 & 45 & 43.5121944823426 & 1.4878055176574 \tabularnewline
125 & 48 & 42.7836312950986 & 5.21636870490143 \tabularnewline
126 & 46 & 44.4753970014563 & 1.52460299854365 \tabularnewline
127 & 55 & 41.7597081789892 & 13.2402918210108 \tabularnewline
128 & 47 & 49.7343574693235 & -2.73435746932351 \tabularnewline
129 & 43 & 42.6559438706948 & 0.344056129305151 \tabularnewline
130 & 38 & 38.0828982613678 & -0.082898261367832 \tabularnewline
131 & 40 & 40.2315434785775 & -0.231543478577507 \tabularnewline
132 & 47 & 42.2529378720195 & 4.7470621279805 \tabularnewline
133 & 47 & 42.1068413684137 & 4.89315863158632 \tabularnewline
134 & 42 & 40.7197661683714 & 1.28023383162857 \tabularnewline
135 & 53 & 42.5732128328035 & 10.4267871671965 \tabularnewline
136 & 43 & 44.0521720527685 & -1.0521720527685 \tabularnewline
137 & 44 & 44.8536655636319 & -0.853665563631855 \tabularnewline
138 & 42 & 43.2926230380161 & -1.29262303801609 \tabularnewline
139 & 51 & 44.5581280393477 & 6.44187196065227 \tabularnewline
140 & 54 & 42.95246593813 & 11.04753406187 \tabularnewline
141 & 41 & 42.0995565181411 & -1.09955651814111 \tabularnewline
142 & 51 & 42.8128256317237 & 8.18717436827627 \tabularnewline
143 & 51 & 44.4987828731226 & 6.50121712687743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154110&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]42[/C][C]44.004259876207[/C][C]-2.00425987620701[/C][/ROW]
[ROW][C]2[/C][C]51[/C][C]46.8367687387696[/C][C]4.16323126123041[/C][/ROW]
[ROW][C]3[/C][C]42[/C][C]45.0386012280569[/C][C]-3.0386012280569[/C][/ROW]
[ROW][C]4[/C][C]46[/C][C]45.3923196194003[/C][C]0.607680380599704[/C][/ROW]
[ROW][C]5[/C][C]41[/C][C]38.4149149725497[/C][C]2.58508502745031[/C][/ROW]
[ROW][C]6[/C][C]49[/C][C]41.7778084974571[/C][C]7.22219150254292[/C][/ROW]
[ROW][C]7[/C][C]47[/C][C]45.1934317758985[/C][C]1.80656822410153[/C][/ROW]
[ROW][C]8[/C][C]33[/C][C]44.0386878499175[/C][C]-11.0386878499175[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]46.1327837725338[/C][C]0.867216227466199[/C][/ROW]
[ROW][C]10[/C][C]42[/C][C]38.2152537175015[/C][C]3.78474628249851[/C][/ROW]
[ROW][C]11[/C][C]32[/C][C]44.3480183377868[/C][C]-12.3480183377868[/C][/ROW]
[ROW][C]12[/C][C]53[/C][C]50.1655430507007[/C][C]2.83445694929927[/C][/ROW]
[ROW][C]13[/C][C]41[/C][C]40.27577216656[/C][C]0.724227833440004[/C][/ROW]
[ROW][C]14[/C][C]41[/C][C]44.9498786437781[/C][C]-3.94987864377811[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]42.0796463288628[/C][C]-9.0796463288628[/C][/ROW]
[ROW][C]16[/C][C]37[/C][C]40.4644889486936[/C][C]-3.46448894869356[/C][/ROW]
[ROW][C]17[/C][C]43[/C][C]44.0521720527685[/C][C]-1.0521720527685[/C][/ROW]
[ROW][C]18[/C][C]33[/C][C]37.7220240244711[/C][C]-4.72202402447114[/C][/ROW]
[ROW][C]19[/C][C]49[/C][C]46.6712786128107[/C][C]2.32872138718934[/C][/ROW]
[ROW][C]20[/C][C]42[/C][C]44.6787417450835[/C][C]-2.67874174508349[/C][/ROW]
[ROW][C]21[/C][C]43[/C][C]38.7576888909785[/C][C]4.24231110902155[/C][/ROW]
[ROW][C]22[/C][C]37[/C][C]36.4056013010121[/C][C]0.594398698987939[/C][/ROW]
[ROW][C]23[/C][C]43[/C][C]46.5573322358791[/C][C]-3.55733223587911[/C][/ROW]
[ROW][C]24[/C][C]42[/C][C]41.9547835291928[/C][C]0.0452164708072503[/C][/ROW]
[ROW][C]25[/C][C]43[/C][C]40.0991880224438[/C][C]2.90081197755615[/C][/ROW]
[ROW][C]26[/C][C]46[/C][C]47.4532289560783[/C][C]-1.45322895607832[/C][/ROW]
[ROW][C]27[/C][C]33[/C][C]47.088753357488[/C][C]-14.088753357488[/C][/ROW]
[ROW][C]28[/C][C]42[/C][C]45.2142008290221[/C][C]-3.21420082902206[/C][/ROW]
[ROW][C]29[/C][C]40[/C][C]41.8204287759848[/C][C]-1.82042877598482[/C][/ROW]
[ROW][C]30[/C][C]44[/C][C]41.5325431236559[/C][C]2.46745687634411[/C][/ROW]
[ROW][C]31[/C][C]42[/C][C]47.1507153422558[/C][C]-5.1507153422558[/C][/ROW]
[ROW][C]32[/C][C]52[/C][C]42.823919649881[/C][C]9.17608035011904[/C][/ROW]
[ROW][C]33[/C][C]44[/C][C]42.4466075806803[/C][C]1.55339241931971[/C][/ROW]
[ROW][C]34[/C][C]45[/C][C]44.0032753330561[/C][C]0.996724666943926[/C][/ROW]
[ROW][C]35[/C][C]46[/C][C]41.5419768162453[/C][C]4.45802318375466[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]41.0738754473343[/C][C]-5.07387544733435[/C][/ROW]
[ROW][C]37[/C][C]45[/C][C]46.3291524266226[/C][C]-1.32915242662256[/C][/ROW]
[ROW][C]38[/C][C]49[/C][C]46.2755876751802[/C][C]2.72441232481981[/C][/ROW]
[ROW][C]39[/C][C]43[/C][C]43.1462177736761[/C][C]-0.146217773676084[/C][/ROW]
[ROW][C]40[/C][C]43[/C][C]44.6153762793691[/C][C]-1.61537627936906[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]40.6048050375166[/C][C]-3.6048050375166[/C][/ROW]
[ROW][C]42[/C][C]32[/C][C]34.395948657968[/C][C]-2.39594865796796[/C][/ROW]
[ROW][C]43[/C][C]45[/C][C]44.6458639198794[/C][C]0.354136080120617[/C][/ROW]
[ROW][C]44[/C][C]45[/C][C]45.976109817696[/C][C]-0.976109817695967[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]45.4175455726492[/C][C]-0.417545572649212[/C][/ROW]
[ROW][C]46[/C][C]45[/C][C]42.0572969164606[/C][C]2.94270308353941[/C][/ROW]
[ROW][C]47[/C][C]31[/C][C]43.067735829719[/C][C]-12.067735829719[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]42.83154347166[/C][C]-9.83154347166001[/C][/ROW]
[ROW][C]49[/C][C]44[/C][C]43.7740288537632[/C][C]0.225971146236797[/C][/ROW]
[ROW][C]50[/C][C]49[/C][C]36.2125771639958[/C][C]12.7874228360042[/C][/ROW]
[ROW][C]51[/C][C]44[/C][C]41.6957771079194[/C][C]2.3042228920806[/C][/ROW]
[ROW][C]52[/C][C]41[/C][C]38.0885508362488[/C][C]2.91144916375124[/C][/ROW]
[ROW][C]53[/C][C]44[/C][C]42.0894470431349[/C][C]1.91055295686514[/C][/ROW]
[ROW][C]54[/C][C]38[/C][C]42.6157811952182[/C][C]-4.61578119521816[/C][/ROW]
[ROW][C]55[/C][C]33[/C][C]42.4650166598824[/C][C]-9.4650166598824[/C][/ROW]
[ROW][C]56[/C][C]47[/C][C]43.6521737602553[/C][C]3.34782623974469[/C][/ROW]
[ROW][C]57[/C][C]37[/C][C]39.3905869405912[/C][C]-2.39058694059117[/C][/ROW]
[ROW][C]58[/C][C]48[/C][C]45.9220284993284[/C][C]2.07797150067163[/C][/ROW]
[ROW][C]59[/C][C]40[/C][C]40.4713674091024[/C][C]-0.471367409102387[/C][/ROW]
[ROW][C]60[/C][C]50[/C][C]44.5440782311414[/C][C]5.45592176885862[/C][/ROW]
[ROW][C]61[/C][C]54[/C][C]48.179740930135[/C][C]5.82025906986496[/C][/ROW]
[ROW][C]62[/C][C]43[/C][C]47.5296596868481[/C][C]-4.52965968684811[/C][/ROW]
[ROW][C]63[/C][C]54[/C][C]42.5919306727397[/C][C]11.4080693272603[/C][/ROW]
[ROW][C]64[/C][C]44[/C][C]44.4871387518542[/C][C]-0.487138751854241[/C][/ROW]
[ROW][C]65[/C][C]47[/C][C]41.4846309470945[/C][C]5.51536905290555[/C][/ROW]
[ROW][C]66[/C][C]33[/C][C]42.920346022228[/C][C]-9.920346022228[/C][/ROW]
[ROW][C]67[/C][C]45[/C][C]46.7182062462211[/C][C]-1.71820624622112[/C][/ROW]
[ROW][C]68[/C][C]33[/C][C]34.3969332011189[/C][C]-1.39693320111894[/C][/ROW]
[ROW][C]69[/C][C]44[/C][C]45.5840202417591[/C][C]-1.58402024175911[/C][/ROW]
[ROW][C]70[/C][C]47[/C][C]44.9072303150742[/C][C]2.09276968492577[/C][/ROW]
[ROW][C]71[/C][C]45[/C][C]40.320391742162[/C][C]4.679608257838[/C][/ROW]
[ROW][C]72[/C][C]43[/C][C]41.782476529187[/C][C]1.21752347081298[/C][/ROW]
[ROW][C]73[/C][C]43[/C][C]38.1449121622487[/C][C]4.85508783775134[/C][/ROW]
[ROW][C]74[/C][C]33[/C][C]44.4236756570102[/C][C]-11.4236756570102[/C][/ROW]
[ROW][C]75[/C][C]46[/C][C]43.8551276162629[/C][C]2.14487238373706[/C][/ROW]
[ROW][C]76[/C][C]47[/C][C]43.8937556454775[/C][C]3.10624435452246[/C][/ROW]
[ROW][C]77[/C][C]47[/C][C]42.603024690897[/C][C]4.39697530910302[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]40.8443202073073[/C][C]-40.8443202073073[/C][/ROW]
[ROW][C]79[/C][C]43[/C][C]43.9339183209542[/C][C]-0.933918320954231[/C][/ROW]
[ROW][C]80[/C][C]46[/C][C]42.5846458224672[/C][C]3.41535417753282[/C][/ROW]
[ROW][C]81[/C][C]36[/C][C]37.6845083783094[/C][C]-1.68450837830939[/C][/ROW]
[ROW][C]82[/C][C]42[/C][C]38.7801182696698[/C][C]3.21988173033017[/C][/ROW]
[ROW][C]83[/C][C]44[/C][C]43.3244341931839[/C][C]0.67556580681612[/C][/ROW]
[ROW][C]84[/C][C]47[/C][C]47.3470839963446[/C][C]-0.347083996344562[/C][/ROW]
[ROW][C]85[/C][C]41[/C][C]39.5343753863885[/C][C]1.46562461361146[/C][/ROW]
[ROW][C]86[/C][C]47[/C][C]44.4894468096627[/C][C]2.51055319033731[/C][/ROW]
[ROW][C]87[/C][C]46[/C][C]45.0783730159141[/C][C]0.921626984085934[/C][/ROW]
[ROW][C]88[/C][C]47[/C][C]47.2318141047555[/C][C]-0.231814104755543[/C][/ROW]
[ROW][C]89[/C][C]46[/C][C]47.0663239787966[/C][C]-1.06632397879663[/C][/ROW]
[ROW][C]90[/C][C]46[/C][C]44.2541932817382[/C][C]1.74580671826181[/C][/ROW]
[ROW][C]91[/C][C]36[/C][C]44.9833220743375[/C][C]-8.98332207433755[/C][/ROW]
[ROW][C]92[/C][C]30[/C][C]40.2635041789879[/C][C]-10.2635041789879[/C][/ROW]
[ROW][C]93[/C][C]48[/C][C]42.3118464376086[/C][C]5.68815356239138[/C][/ROW]
[ROW][C]94[/C][C]45[/C][C]40.786087424135[/C][C]4.21391257586502[/C][/ROW]
[ROW][C]95[/C][C]49[/C][C]45.565611162557[/C][C]3.43438883744299[/C][/ROW]
[ROW][C]96[/C][C]55[/C][C]47.1119280975496[/C][C]7.88807190245038[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]41.2106358874803[/C][C]-30.2106358874803[/C][/ROW]
[ROW][C]98[/C][C]52[/C][C]45.8976278737389[/C][C]6.10237212626114[/C][/ROW]
[ROW][C]99[/C][C]33[/C][C]35.8460503522182[/C][C]-2.84605035221817[/C][/ROW]
[ROW][C]100[/C][C]47[/C][C]48.4024792961153[/C][C]-1.40247929611528[/C][/ROW]
[ROW][C]101[/C][C]33[/C][C]39.5825444075711[/C][C]-6.58254440757113[/C][/ROW]
[ROW][C]102[/C][C]44[/C][C]44.4244490685566[/C][C]-0.424449068556608[/C][/ROW]
[ROW][C]103[/C][C]42[/C][C]40.425860919479[/C][C]1.57413908052104[/C][/ROW]
[ROW][C]104[/C][C]55[/C][C]42.8519735532771[/C][C]12.1480264467229[/C][/ROW]
[ROW][C]105[/C][C]42[/C][C]44.2853286544892[/C][C]-2.28532865448918[/C][/ROW]
[ROW][C]106[/C][C]46[/C][C]44.9994230957312[/C][C]1.0005769042688[/C][/ROW]
[ROW][C]107[/C][C]46[/C][C]46.9155594434609[/C][C]-0.915559443460864[/C][/ROW]
[ROW][C]108[/C][C]47[/C][C]45.5699704335528[/C][C]1.43002956644723[/C][/ROW]
[ROW][C]109[/C][C]33[/C][C]43.4866317181834[/C][C]-10.4866317181834[/C][/ROW]
[ROW][C]110[/C][C]53[/C][C]49.7252325374683[/C][C]3.27476746253175[/C][/ROW]
[ROW][C]111[/C][C]42[/C][C]42.2572971430153[/C][C]-0.257297143015264[/C][/ROW]
[ROW][C]112[/C][C]44[/C][C]44.035395248958[/C][C]-0.0353952489580461[/C][/ROW]
[ROW][C]113[/C][C]55[/C][C]44.9434007412378[/C][C]10.0565992587622[/C][/ROW]
[ROW][C]114[/C][C]40[/C][C]35.4674449791323[/C][C]4.53255502086767[/C][/ROW]
[ROW][C]115[/C][C]46[/C][C]45.3200370280216[/C][C]0.679962971978363[/C][/ROW]
[ROW][C]116[/C][C]53[/C][C]45.8805423091942[/C][C]7.11945769080578[/C][/ROW]
[ROW][C]117[/C][C]44[/C][C]45.870432834188[/C][C]-1.87043283418797[/C][/ROW]
[ROW][C]118[/C][C]35[/C][C]40.6678336923207[/C][C]-5.66783369232071[/C][/ROW]
[ROW][C]119[/C][C]40[/C][C]39.7329699714004[/C][C]0.267030028599584[/C][/ROW]
[ROW][C]120[/C][C]44[/C][C]45.0966228796246[/C][C]-1.09662287962459[/C][/ROW]
[ROW][C]121[/C][C]46[/C][C]46.7482594466916[/C][C]-0.748259446691557[/C][/ROW]
[ROW][C]122[/C][C]45[/C][C]42.4945197572417[/C][C]2.50548024275826[/C][/ROW]
[ROW][C]123[/C][C]53[/C][C]45.1926583643521[/C][C]7.8073416356479[/C][/ROW]
[ROW][C]124[/C][C]45[/C][C]43.5121944823426[/C][C]1.4878055176574[/C][/ROW]
[ROW][C]125[/C][C]48[/C][C]42.7836312950986[/C][C]5.21636870490143[/C][/ROW]
[ROW][C]126[/C][C]46[/C][C]44.4753970014563[/C][C]1.52460299854365[/C][/ROW]
[ROW][C]127[/C][C]55[/C][C]41.7597081789892[/C][C]13.2402918210108[/C][/ROW]
[ROW][C]128[/C][C]47[/C][C]49.7343574693235[/C][C]-2.73435746932351[/C][/ROW]
[ROW][C]129[/C][C]43[/C][C]42.6559438706948[/C][C]0.344056129305151[/C][/ROW]
[ROW][C]130[/C][C]38[/C][C]38.0828982613678[/C][C]-0.082898261367832[/C][/ROW]
[ROW][C]131[/C][C]40[/C][C]40.2315434785775[/C][C]-0.231543478577507[/C][/ROW]
[ROW][C]132[/C][C]47[/C][C]42.2529378720195[/C][C]4.7470621279805[/C][/ROW]
[ROW][C]133[/C][C]47[/C][C]42.1068413684137[/C][C]4.89315863158632[/C][/ROW]
[ROW][C]134[/C][C]42[/C][C]40.7197661683714[/C][C]1.28023383162857[/C][/ROW]
[ROW][C]135[/C][C]53[/C][C]42.5732128328035[/C][C]10.4267871671965[/C][/ROW]
[ROW][C]136[/C][C]43[/C][C]44.0521720527685[/C][C]-1.0521720527685[/C][/ROW]
[ROW][C]137[/C][C]44[/C][C]44.8536655636319[/C][C]-0.853665563631855[/C][/ROW]
[ROW][C]138[/C][C]42[/C][C]43.2926230380161[/C][C]-1.29262303801609[/C][/ROW]
[ROW][C]139[/C][C]51[/C][C]44.5581280393477[/C][C]6.44187196065227[/C][/ROW]
[ROW][C]140[/C][C]54[/C][C]42.95246593813[/C][C]11.04753406187[/C][/ROW]
[ROW][C]141[/C][C]41[/C][C]42.0995565181411[/C][C]-1.09955651814111[/C][/ROW]
[ROW][C]142[/C][C]51[/C][C]42.8128256317237[/C][C]8.18717436827627[/C][/ROW]
[ROW][C]143[/C][C]51[/C][C]44.4987828731226[/C][C]6.50121712687743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154110&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154110&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	42	44.004259876207	-2.00425987620701
2	51	46.8367687387696	4.16323126123041
3	42	45.0386012280569	-3.0386012280569
4	46	45.3923196194003	0.607680380599704
5	41	38.4149149725497	2.58508502745031
6	49	41.7778084974571	7.22219150254292
7	47	45.1934317758985	1.80656822410153
8	33	44.0386878499175	-11.0386878499175
9	47	46.1327837725338	0.867216227466199
10	42	38.2152537175015	3.78474628249851
11	32	44.3480183377868	-12.3480183377868
12	53	50.1655430507007	2.83445694929927
13	41	40.27577216656	0.724227833440004
14	41	44.9498786437781	-3.94987864377811
15	33	42.0796463288628	-9.0796463288628
16	37	40.4644889486936	-3.46448894869356
17	43	44.0521720527685	-1.0521720527685
18	33	37.7220240244711	-4.72202402447114
19	49	46.6712786128107	2.32872138718934
20	42	44.6787417450835	-2.67874174508349
21	43	38.7576888909785	4.24231110902155
22	37	36.4056013010121	0.594398698987939
23	43	46.5573322358791	-3.55733223587911
24	42	41.9547835291928	0.0452164708072503
25	43	40.0991880224438	2.90081197755615
26	46	47.4532289560783	-1.45322895607832
27	33	47.088753357488	-14.088753357488
28	42	45.2142008290221	-3.21420082902206
29	40	41.8204287759848	-1.82042877598482
30	44	41.5325431236559	2.46745687634411
31	42	47.1507153422558	-5.1507153422558
32	52	42.823919649881	9.17608035011904
33	44	42.4466075806803	1.55339241931971
34	45	44.0032753330561	0.996724666943926
35	46	41.5419768162453	4.45802318375466
36	36	41.0738754473343	-5.07387544733435
37	45	46.3291524266226	-1.32915242662256
38	49	46.2755876751802	2.72441232481981
39	43	43.1462177736761	-0.146217773676084
40	43	44.6153762793691	-1.61537627936906
41	37	40.6048050375166	-3.6048050375166
42	32	34.395948657968	-2.39594865796796
43	45	44.6458639198794	0.354136080120617
44	45	45.976109817696	-0.976109817695967
45	45	45.4175455726492	-0.417545572649212
46	45	42.0572969164606	2.94270308353941
47	31	43.067735829719	-12.067735829719
48	33	42.83154347166	-9.83154347166001
49	44	43.7740288537632	0.225971146236797
50	49	36.2125771639958	12.7874228360042
51	44	41.6957771079194	2.3042228920806
52	41	38.0885508362488	2.91144916375124
53	44	42.0894470431349	1.91055295686514
54	38	42.6157811952182	-4.61578119521816
55	33	42.4650166598824	-9.4650166598824
56	47	43.6521737602553	3.34782623974469
57	37	39.3905869405912	-2.39058694059117
58	48	45.9220284993284	2.07797150067163
59	40	40.4713674091024	-0.471367409102387
60	50	44.5440782311414	5.45592176885862
61	54	48.179740930135	5.82025906986496
62	43	47.5296596868481	-4.52965968684811
63	54	42.5919306727397	11.4080693272603
64	44	44.4871387518542	-0.487138751854241
65	47	41.4846309470945	5.51536905290555
66	33	42.920346022228	-9.920346022228
67	45	46.7182062462211	-1.71820624622112
68	33	34.3969332011189	-1.39693320111894
69	44	45.5840202417591	-1.58402024175911
70	47	44.9072303150742	2.09276968492577
71	45	40.320391742162	4.679608257838
72	43	41.782476529187	1.21752347081298
73	43	38.1449121622487	4.85508783775134
74	33	44.4236756570102	-11.4236756570102
75	46	43.8551276162629	2.14487238373706
76	47	43.8937556454775	3.10624435452246
77	47	42.603024690897	4.39697530910302
78	0	40.8443202073073	-40.8443202073073
79	43	43.9339183209542	-0.933918320954231
80	46	42.5846458224672	3.41535417753282
81	36	37.6845083783094	-1.68450837830939
82	42	38.7801182696698	3.21988173033017
83	44	43.3244341931839	0.67556580681612
84	47	47.3470839963446	-0.347083996344562
85	41	39.5343753863885	1.46562461361146
86	47	44.4894468096627	2.51055319033731
87	46	45.0783730159141	0.921626984085934
88	47	47.2318141047555	-0.231814104755543
89	46	47.0663239787966	-1.06632397879663
90	46	44.2541932817382	1.74580671826181
91	36	44.9833220743375	-8.98332207433755
92	30	40.2635041789879	-10.2635041789879
93	48	42.3118464376086	5.68815356239138
94	45	40.786087424135	4.21391257586502
95	49	45.565611162557	3.43438883744299
96	55	47.1119280975496	7.88807190245038
97	11	41.2106358874803	-30.2106358874803
98	52	45.8976278737389	6.10237212626114
99	33	35.8460503522182	-2.84605035221817
100	47	48.4024792961153	-1.40247929611528
101	33	39.5825444075711	-6.58254440757113
102	44	44.4244490685566	-0.424449068556608
103	42	40.425860919479	1.57413908052104
104	55	42.8519735532771	12.1480264467229
105	42	44.2853286544892	-2.28532865448918
106	46	44.9994230957312	1.0005769042688
107	46	46.9155594434609	-0.915559443460864
108	47	45.5699704335528	1.43002956644723
109	33	43.4866317181834	-10.4866317181834
110	53	49.7252325374683	3.27476746253175
111	42	42.2572971430153	-0.257297143015264
112	44	44.035395248958	-0.0353952489580461
113	55	44.9434007412378	10.0565992587622
114	40	35.4674449791323	4.53255502086767
115	46	45.3200370280216	0.679962971978363
116	53	45.8805423091942	7.11945769080578
117	44	45.870432834188	-1.87043283418797
118	35	40.6678336923207	-5.66783369232071
119	40	39.7329699714004	0.267030028599584
120	44	45.0966228796246	-1.09662287962459
121	46	46.7482594466916	-0.748259446691557
122	45	42.4945197572417	2.50548024275826
123	53	45.1926583643521	7.8073416356479
124	45	43.5121944823426	1.4878055176574
125	48	42.7836312950986	5.21636870490143
126	46	44.4753970014563	1.52460299854365
127	55	41.7597081789892	13.2402918210108
128	47	49.7343574693235	-2.73435746932351
129	43	42.6559438706948	0.344056129305151
130	38	38.0828982613678	-0.082898261367832
131	40	40.2315434785775	-0.231543478577507
132	47	42.2529378720195	4.7470621279805
133	47	42.1068413684137	4.89315863158632
134	42	40.7197661683714	1.28023383162857
135	53	42.5732128328035	10.4267871671965
136	43	44.0521720527685	-1.0521720527685
137	44	44.8536655636319	-0.853665563631855
138	42	43.2926230380161	-1.29262303801609
139	51	44.5581280393477	6.44187196065227
140	54	42.95246593813	11.04753406187
141	41	42.0995565181411	-1.09955651814111
142	51	42.8128256317237	8.18717436827627
143	51	44.4987828731226	6.50121712687743

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.625479598830677	0.749040802338646	0.374520401169323
10	0.48978863462332	0.97957726924664	0.51021136537668
11	0.474338660514287	0.948677321028573	0.525661339485713
12	0.369242547572502	0.738485095145004	0.630757452427498
13	0.28139104572854	0.56278209145708	0.71860895427146
14	0.192075279610892	0.384150559221783	0.807924720389108
15	0.17166142711748	0.34332285423496	0.82833857288252
16	0.125844022450332	0.251688044900663	0.874155977549668
17	0.144721649968746	0.289443299937492	0.855278350031254
18	0.0983061725848847	0.196612345169769	0.901693827415115
19	0.114338625660729	0.228677251321459	0.88566137433927
20	0.100355345061708	0.200710690123416	0.899644654938292
21	0.117473136842152	0.234946273684303	0.882526863157848
22	0.0814774426745284	0.162954885349057	0.918522557325472
23	0.0626062325093801	0.12521246501876	0.93739376749062
24	0.0461221575759451	0.0922443151518901	0.953877842424055
25	0.0364422938838962	0.0728845877677924	0.963557706116104
26	0.0242733352926843	0.0485466705853686	0.975726664707316
27	0.0600328475122004	0.120065695024401	0.9399671524878
28	0.0465530033764702	0.0931060067529404	0.95344699662353
29	0.0322644290169215	0.064528858033843	0.967735570983078
30	0.021885400922732	0.043770801845464	0.978114599077268
31	0.0171853466829017	0.0343706933658035	0.982814653317098
32	0.0192589611838022	0.0385179223676043	0.980741038816198
33	0.0128339538011563	0.0256679076023125	0.987166046198844
34	0.00848461382060324	0.0169692276412065	0.991515386179397
35	0.00730537334364162	0.0146107466872832	0.992694626656358
36	0.00491177241641231	0.00982354483282463	0.995088227583588
37	0.00325279116521318	0.00650558233042637	0.996747208834787
38	0.00236295507723267	0.00472591015446535	0.997637044922767
39	0.00152424228584383	0.00304848457168765	0.998475757714156
40	0.000946264288637119	0.00189252857727424	0.999053735711363
41	0.000595651408963466	0.00119130281792693	0.999404348591037
42	0.000347862533781218	0.000695725067562437	0.999652137466219
43	0.000243585395748413	0.000487170791496826	0.999756414604252
44	0.000139895356023048	0.000279790712046095	0.999860104643977
45	7.86207430430638e-05	0.000157241486086128	0.999921379256957
46	9.93624592464413e-05	0.000198724918492883	0.999900637540754
47	0.000177055510202217	0.000354111020404434	0.999822944489798
48	0.000566704699634689	0.00113340939926938	0.999433295300365
49	0.000397170271791238	0.000794340543582476	0.999602829728209
50	0.0044244395442087	0.0088488790884174	0.995575560455791
51	0.00321309209460317	0.00642618418920635	0.996786907905397
52	0.00230252549001733	0.00460505098003467	0.997697474509983
53	0.00152203883988141	0.00304407767976281	0.998477961160119
54	0.00120395913717536	0.00240791827435072	0.998796040862825
55	0.0020435686528183	0.00408713730563661	0.997956431347182
56	0.00163860073497577	0.00327720146995155	0.998361399265024
57	0.00110806384769146	0.00221612769538292	0.998891936152309
58	0.000917739650250278	0.00183547930050056	0.99908226034975
59	0.00059928133823191	0.00119856267646382	0.999400718661768
60	0.000497624690026418	0.000995249380052836	0.999502375309974
61	0.000533329253905137	0.00106665850781027	0.999466670746095
62	0.000400558965733986	0.000801117931467973	0.999599441034266
63	0.0018834186135391	0.0037668372270782	0.99811658138646
64	0.00124204751161695	0.00248409502323391	0.998757952488383
65	0.00107889834091087	0.00215779668182175	0.99892110165909
66	0.00186592272656232	0.00373184545312464	0.998134077273438
67	0.00126569957793323	0.00253139915586646	0.998734300422067
68	0.00085559004004255	0.0017111800800851	0.999144409959957
69	0.000564573422286361	0.00112914684457272	0.999435426577714
70	0.00039891182699547	0.00079782365399094	0.999601088173005
71	0.000301337008530648	0.000602674017061295	0.99969866299147
72	0.000190551569050377	0.000381103138100754	0.99980944843095
73	0.000155192316221532	0.000310384632443064	0.999844807683778
74	0.000339927224344045	0.00067985444868809	0.999660072775656
75	0.000237952979395826	0.000475905958791651	0.999762047020604
76	0.000164171763506554	0.000328343527013107	0.999835828236493
77	0.000147613137363423	0.000295226274726845	0.999852386862637
78	0.895765879239367	0.208468241521266	0.104234120760633
79	0.871536685851211	0.256926628297578	0.128463314148789
80	0.849033315550945	0.30193336889811	0.150966684449055
81	0.832311290034144	0.335377419931711	0.167688709965856
82	0.804666708916258	0.390666582167485	0.195333291083742
83	0.768370056234955	0.46325988753009	0.231629943765045
84	0.730195616494614	0.539608767010773	0.269804383505387
85	0.687106366796468	0.625787266407064	0.312893633203532
86	0.660107309729116	0.679785380541768	0.339892690270884
87	0.638865525486242	0.722268949027516	0.361134474513758
88	0.591136008230444	0.817727983539111	0.408863991769556
89	0.549962971564927	0.900074056870145	0.450037028435073
90	0.518301177254986	0.963397645490029	0.481698822745014
91	0.587563984971085	0.82487203005783	0.412436015028915
92	0.662472876937074	0.675054246125852	0.337527123062926
93	0.64361933508679	0.712761329826419	0.35638066491321
94	0.621559295856643	0.756881408286713	0.378440704143357
95	0.579326705380457	0.841346589239085	0.420673294619543
96	0.584887752033365	0.83022449593327	0.415112247966635
97	0.998529304112251	0.00294139177549746	0.00147069588774873
98	0.998021377107474	0.0039572457850524	0.0019786228925262
99	0.998517000651073	0.00296599869785322	0.00148299934892661
100	0.998283791478668	0.00343241704266313	0.00171620852133156
101	0.99885410165978	0.00229179668043973	0.00114589834021987
102	0.998129210647506	0.00374157870498808	0.00187078935249404
103	0.99726721218381	0.00546557563237975	0.00273278781618988
104	0.998413010877918	0.00317397824416376	0.00158698912208188
105	0.998393982653714	0.003212034692572	0.001606017346286
106	0.997364296080438	0.00527140783912367	0.00263570391956184
107	0.99616526112611	0.00766947774778049	0.00383473887389024
108	0.994058855097942	0.0118822898041153	0.00594114490205767
109	0.997546025614996	0.00490794877000809	0.00245397438500404
110	0.996153821946647	0.00769235610670506	0.00384617805335253
111	0.994132555303736	0.0117348893925271	0.00586744469626353
112	0.992105286486273	0.0157894270274544	0.0078947135137272
113	0.993234058281636	0.0135318834367273	0.00676594171836367
114	0.99076594005831	0.0184681198833785	0.00923405994168925
115	0.985298988740902	0.0294020225181953	0.0147010112590977
116	0.980537996974147	0.038924006051707	0.0194620030258535
117	0.985886636101466	0.0282267277970686	0.0141133638985343
118	0.988752618193024	0.0224947636139522	0.0112473818069761
119	0.994915974257321	0.0101680514853573	0.00508402574267863
120	0.990966249035018	0.0180675019299636	0.0090337509649818
121	0.989187878645253	0.0216242427094935	0.0108121213547468
122	0.993758473418765	0.0124830531624708	0.00624152658123539
123	0.99378960246765	0.0124207950646996	0.00621039753234978
124	0.991870673756406	0.0162586524871871	0.00812932624359354
125	0.985117712902062	0.029764574195876	0.014882287097938
126	0.972965053553615	0.054069892892771	0.0270349464463855
127	0.957188567054805	0.0856228658903896	0.0428114329451948
128	0.961471489415025	0.0770570211699509	0.0385285105849754
129	0.983176279830487	0.0336474403390263	0.0168237201695132
130	0.971796168726138	0.0564076625477231	0.0282038312738615
131	0.95039050098662	0.099218998026762	0.049609499013381
132	0.906213123271642	0.187573753456715	0.0937868767283575
133	0.846983848070659	0.306032303858682	0.153016151929341
134	0.728984590377957	0.542030819244085	0.271015409622043

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.625479598830677 & 0.749040802338646 & 0.374520401169323 \tabularnewline
10 & 0.48978863462332 & 0.97957726924664 & 0.51021136537668 \tabularnewline
11 & 0.474338660514287 & 0.948677321028573 & 0.525661339485713 \tabularnewline
12 & 0.369242547572502 & 0.738485095145004 & 0.630757452427498 \tabularnewline
13 & 0.28139104572854 & 0.56278209145708 & 0.71860895427146 \tabularnewline
14 & 0.192075279610892 & 0.384150559221783 & 0.807924720389108 \tabularnewline
15 & 0.17166142711748 & 0.34332285423496 & 0.82833857288252 \tabularnewline
16 & 0.125844022450332 & 0.251688044900663 & 0.874155977549668 \tabularnewline
17 & 0.144721649968746 & 0.289443299937492 & 0.855278350031254 \tabularnewline
18 & 0.0983061725848847 & 0.196612345169769 & 0.901693827415115 \tabularnewline
19 & 0.114338625660729 & 0.228677251321459 & 0.88566137433927 \tabularnewline
20 & 0.100355345061708 & 0.200710690123416 & 0.899644654938292 \tabularnewline
21 & 0.117473136842152 & 0.234946273684303 & 0.882526863157848 \tabularnewline
22 & 0.0814774426745284 & 0.162954885349057 & 0.918522557325472 \tabularnewline
23 & 0.0626062325093801 & 0.12521246501876 & 0.93739376749062 \tabularnewline
24 & 0.0461221575759451 & 0.0922443151518901 & 0.953877842424055 \tabularnewline
25 & 0.0364422938838962 & 0.0728845877677924 & 0.963557706116104 \tabularnewline
26 & 0.0242733352926843 & 0.0485466705853686 & 0.975726664707316 \tabularnewline
27 & 0.0600328475122004 & 0.120065695024401 & 0.9399671524878 \tabularnewline
28 & 0.0465530033764702 & 0.0931060067529404 & 0.95344699662353 \tabularnewline
29 & 0.0322644290169215 & 0.064528858033843 & 0.967735570983078 \tabularnewline
30 & 0.021885400922732 & 0.043770801845464 & 0.978114599077268 \tabularnewline
31 & 0.0171853466829017 & 0.0343706933658035 & 0.982814653317098 \tabularnewline
32 & 0.0192589611838022 & 0.0385179223676043 & 0.980741038816198 \tabularnewline
33 & 0.0128339538011563 & 0.0256679076023125 & 0.987166046198844 \tabularnewline
34 & 0.00848461382060324 & 0.0169692276412065 & 0.991515386179397 \tabularnewline
35 & 0.00730537334364162 & 0.0146107466872832 & 0.992694626656358 \tabularnewline
36 & 0.00491177241641231 & 0.00982354483282463 & 0.995088227583588 \tabularnewline
37 & 0.00325279116521318 & 0.00650558233042637 & 0.996747208834787 \tabularnewline
38 & 0.00236295507723267 & 0.00472591015446535 & 0.997637044922767 \tabularnewline
39 & 0.00152424228584383 & 0.00304848457168765 & 0.998475757714156 \tabularnewline
40 & 0.000946264288637119 & 0.00189252857727424 & 0.999053735711363 \tabularnewline
41 & 0.000595651408963466 & 0.00119130281792693 & 0.999404348591037 \tabularnewline
42 & 0.000347862533781218 & 0.000695725067562437 & 0.999652137466219 \tabularnewline
43 & 0.000243585395748413 & 0.000487170791496826 & 0.999756414604252 \tabularnewline
44 & 0.000139895356023048 & 0.000279790712046095 & 0.999860104643977 \tabularnewline
45 & 7.86207430430638e-05 & 0.000157241486086128 & 0.999921379256957 \tabularnewline
46 & 9.93624592464413e-05 & 0.000198724918492883 & 0.999900637540754 \tabularnewline
47 & 0.000177055510202217 & 0.000354111020404434 & 0.999822944489798 \tabularnewline
48 & 0.000566704699634689 & 0.00113340939926938 & 0.999433295300365 \tabularnewline
49 & 0.000397170271791238 & 0.000794340543582476 & 0.999602829728209 \tabularnewline
50 & 0.0044244395442087 & 0.0088488790884174 & 0.995575560455791 \tabularnewline
51 & 0.00321309209460317 & 0.00642618418920635 & 0.996786907905397 \tabularnewline
52 & 0.00230252549001733 & 0.00460505098003467 & 0.997697474509983 \tabularnewline
53 & 0.00152203883988141 & 0.00304407767976281 & 0.998477961160119 \tabularnewline
54 & 0.00120395913717536 & 0.00240791827435072 & 0.998796040862825 \tabularnewline
55 & 0.0020435686528183 & 0.00408713730563661 & 0.997956431347182 \tabularnewline
56 & 0.00163860073497577 & 0.00327720146995155 & 0.998361399265024 \tabularnewline
57 & 0.00110806384769146 & 0.00221612769538292 & 0.998891936152309 \tabularnewline
58 & 0.000917739650250278 & 0.00183547930050056 & 0.99908226034975 \tabularnewline
59 & 0.00059928133823191 & 0.00119856267646382 & 0.999400718661768 \tabularnewline
60 & 0.000497624690026418 & 0.000995249380052836 & 0.999502375309974 \tabularnewline
61 & 0.000533329253905137 & 0.00106665850781027 & 0.999466670746095 \tabularnewline
62 & 0.000400558965733986 & 0.000801117931467973 & 0.999599441034266 \tabularnewline
63 & 0.0018834186135391 & 0.0037668372270782 & 0.99811658138646 \tabularnewline
64 & 0.00124204751161695 & 0.00248409502323391 & 0.998757952488383 \tabularnewline
65 & 0.00107889834091087 & 0.00215779668182175 & 0.99892110165909 \tabularnewline
66 & 0.00186592272656232 & 0.00373184545312464 & 0.998134077273438 \tabularnewline
67 & 0.00126569957793323 & 0.00253139915586646 & 0.998734300422067 \tabularnewline
68 & 0.00085559004004255 & 0.0017111800800851 & 0.999144409959957 \tabularnewline
69 & 0.000564573422286361 & 0.00112914684457272 & 0.999435426577714 \tabularnewline
70 & 0.00039891182699547 & 0.00079782365399094 & 0.999601088173005 \tabularnewline
71 & 0.000301337008530648 & 0.000602674017061295 & 0.99969866299147 \tabularnewline
72 & 0.000190551569050377 & 0.000381103138100754 & 0.99980944843095 \tabularnewline
73 & 0.000155192316221532 & 0.000310384632443064 & 0.999844807683778 \tabularnewline
74 & 0.000339927224344045 & 0.00067985444868809 & 0.999660072775656 \tabularnewline
75 & 0.000237952979395826 & 0.000475905958791651 & 0.999762047020604 \tabularnewline
76 & 0.000164171763506554 & 0.000328343527013107 & 0.999835828236493 \tabularnewline
77 & 0.000147613137363423 & 0.000295226274726845 & 0.999852386862637 \tabularnewline
78 & 0.895765879239367 & 0.208468241521266 & 0.104234120760633 \tabularnewline
79 & 0.871536685851211 & 0.256926628297578 & 0.128463314148789 \tabularnewline
80 & 0.849033315550945 & 0.30193336889811 & 0.150966684449055 \tabularnewline
81 & 0.832311290034144 & 0.335377419931711 & 0.167688709965856 \tabularnewline
82 & 0.804666708916258 & 0.390666582167485 & 0.195333291083742 \tabularnewline
83 & 0.768370056234955 & 0.46325988753009 & 0.231629943765045 \tabularnewline
84 & 0.730195616494614 & 0.539608767010773 & 0.269804383505387 \tabularnewline
85 & 0.687106366796468 & 0.625787266407064 & 0.312893633203532 \tabularnewline
86 & 0.660107309729116 & 0.679785380541768 & 0.339892690270884 \tabularnewline
87 & 0.638865525486242 & 0.722268949027516 & 0.361134474513758 \tabularnewline
88 & 0.591136008230444 & 0.817727983539111 & 0.408863991769556 \tabularnewline
89 & 0.549962971564927 & 0.900074056870145 & 0.450037028435073 \tabularnewline
90 & 0.518301177254986 & 0.963397645490029 & 0.481698822745014 \tabularnewline
91 & 0.587563984971085 & 0.82487203005783 & 0.412436015028915 \tabularnewline
92 & 0.662472876937074 & 0.675054246125852 & 0.337527123062926 \tabularnewline
93 & 0.64361933508679 & 0.712761329826419 & 0.35638066491321 \tabularnewline
94 & 0.621559295856643 & 0.756881408286713 & 0.378440704143357 \tabularnewline
95 & 0.579326705380457 & 0.841346589239085 & 0.420673294619543 \tabularnewline
96 & 0.584887752033365 & 0.83022449593327 & 0.415112247966635 \tabularnewline
97 & 0.998529304112251 & 0.00294139177549746 & 0.00147069588774873 \tabularnewline
98 & 0.998021377107474 & 0.0039572457850524 & 0.0019786228925262 \tabularnewline
99 & 0.998517000651073 & 0.00296599869785322 & 0.00148299934892661 \tabularnewline
100 & 0.998283791478668 & 0.00343241704266313 & 0.00171620852133156 \tabularnewline
101 & 0.99885410165978 & 0.00229179668043973 & 0.00114589834021987 \tabularnewline
102 & 0.998129210647506 & 0.00374157870498808 & 0.00187078935249404 \tabularnewline
103 & 0.99726721218381 & 0.00546557563237975 & 0.00273278781618988 \tabularnewline
104 & 0.998413010877918 & 0.00317397824416376 & 0.00158698912208188 \tabularnewline
105 & 0.998393982653714 & 0.003212034692572 & 0.001606017346286 \tabularnewline
106 & 0.997364296080438 & 0.00527140783912367 & 0.00263570391956184 \tabularnewline
107 & 0.99616526112611 & 0.00766947774778049 & 0.00383473887389024 \tabularnewline
108 & 0.994058855097942 & 0.0118822898041153 & 0.00594114490205767 \tabularnewline
109 & 0.997546025614996 & 0.00490794877000809 & 0.00245397438500404 \tabularnewline
110 & 0.996153821946647 & 0.00769235610670506 & 0.00384617805335253 \tabularnewline
111 & 0.994132555303736 & 0.0117348893925271 & 0.00586744469626353 \tabularnewline
112 & 0.992105286486273 & 0.0157894270274544 & 0.0078947135137272 \tabularnewline
113 & 0.993234058281636 & 0.0135318834367273 & 0.00676594171836367 \tabularnewline
114 & 0.99076594005831 & 0.0184681198833785 & 0.00923405994168925 \tabularnewline
115 & 0.985298988740902 & 0.0294020225181953 & 0.0147010112590977 \tabularnewline
116 & 0.980537996974147 & 0.038924006051707 & 0.0194620030258535 \tabularnewline
117 & 0.985886636101466 & 0.0282267277970686 & 0.0141133638985343 \tabularnewline
118 & 0.988752618193024 & 0.0224947636139522 & 0.0112473818069761 \tabularnewline
119 & 0.994915974257321 & 0.0101680514853573 & 0.00508402574267863 \tabularnewline
120 & 0.990966249035018 & 0.0180675019299636 & 0.0090337509649818 \tabularnewline
121 & 0.989187878645253 & 0.0216242427094935 & 0.0108121213547468 \tabularnewline
122 & 0.993758473418765 & 0.0124830531624708 & 0.00624152658123539 \tabularnewline
123 & 0.99378960246765 & 0.0124207950646996 & 0.00621039753234978 \tabularnewline
124 & 0.991870673756406 & 0.0162586524871871 & 0.00812932624359354 \tabularnewline
125 & 0.985117712902062 & 0.029764574195876 & 0.014882287097938 \tabularnewline
126 & 0.972965053553615 & 0.054069892892771 & 0.0270349464463855 \tabularnewline
127 & 0.957188567054805 & 0.0856228658903896 & 0.0428114329451948 \tabularnewline
128 & 0.961471489415025 & 0.0770570211699509 & 0.0385285105849754 \tabularnewline
129 & 0.983176279830487 & 0.0336474403390263 & 0.0168237201695132 \tabularnewline
130 & 0.971796168726138 & 0.0564076625477231 & 0.0282038312738615 \tabularnewline
131 & 0.95039050098662 & 0.099218998026762 & 0.049609499013381 \tabularnewline
132 & 0.906213123271642 & 0.187573753456715 & 0.0937868767283575 \tabularnewline
133 & 0.846983848070659 & 0.306032303858682 & 0.153016151929341 \tabularnewline
134 & 0.728984590377957 & 0.542030819244085 & 0.271015409622043 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154110&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.625479598830677[/C][C]0.749040802338646[/C][C]0.374520401169323[/C][/ROW]
[ROW][C]10[/C][C]0.48978863462332[/C][C]0.97957726924664[/C][C]0.51021136537668[/C][/ROW]
[ROW][C]11[/C][C]0.474338660514287[/C][C]0.948677321028573[/C][C]0.525661339485713[/C][/ROW]
[ROW][C]12[/C][C]0.369242547572502[/C][C]0.738485095145004[/C][C]0.630757452427498[/C][/ROW]
[ROW][C]13[/C][C]0.28139104572854[/C][C]0.56278209145708[/C][C]0.71860895427146[/C][/ROW]
[ROW][C]14[/C][C]0.192075279610892[/C][C]0.384150559221783[/C][C]0.807924720389108[/C][/ROW]
[ROW][C]15[/C][C]0.17166142711748[/C][C]0.34332285423496[/C][C]0.82833857288252[/C][/ROW]
[ROW][C]16[/C][C]0.125844022450332[/C][C]0.251688044900663[/C][C]0.874155977549668[/C][/ROW]
[ROW][C]17[/C][C]0.144721649968746[/C][C]0.289443299937492[/C][C]0.855278350031254[/C][/ROW]
[ROW][C]18[/C][C]0.0983061725848847[/C][C]0.196612345169769[/C][C]0.901693827415115[/C][/ROW]
[ROW][C]19[/C][C]0.114338625660729[/C][C]0.228677251321459[/C][C]0.88566137433927[/C][/ROW]
[ROW][C]20[/C][C]0.100355345061708[/C][C]0.200710690123416[/C][C]0.899644654938292[/C][/ROW]
[ROW][C]21[/C][C]0.117473136842152[/C][C]0.234946273684303[/C][C]0.882526863157848[/C][/ROW]
[ROW][C]22[/C][C]0.0814774426745284[/C][C]0.162954885349057[/C][C]0.918522557325472[/C][/ROW]
[ROW][C]23[/C][C]0.0626062325093801[/C][C]0.12521246501876[/C][C]0.93739376749062[/C][/ROW]
[ROW][C]24[/C][C]0.0461221575759451[/C][C]0.0922443151518901[/C][C]0.953877842424055[/C][/ROW]
[ROW][C]25[/C][C]0.0364422938838962[/C][C]0.0728845877677924[/C][C]0.963557706116104[/C][/ROW]
[ROW][C]26[/C][C]0.0242733352926843[/C][C]0.0485466705853686[/C][C]0.975726664707316[/C][/ROW]
[ROW][C]27[/C][C]0.0600328475122004[/C][C]0.120065695024401[/C][C]0.9399671524878[/C][/ROW]
[ROW][C]28[/C][C]0.0465530033764702[/C][C]0.0931060067529404[/C][C]0.95344699662353[/C][/ROW]
[ROW][C]29[/C][C]0.0322644290169215[/C][C]0.064528858033843[/C][C]0.967735570983078[/C][/ROW]
[ROW][C]30[/C][C]0.021885400922732[/C][C]0.043770801845464[/C][C]0.978114599077268[/C][/ROW]
[ROW][C]31[/C][C]0.0171853466829017[/C][C]0.0343706933658035[/C][C]0.982814653317098[/C][/ROW]
[ROW][C]32[/C][C]0.0192589611838022[/C][C]0.0385179223676043[/C][C]0.980741038816198[/C][/ROW]
[ROW][C]33[/C][C]0.0128339538011563[/C][C]0.0256679076023125[/C][C]0.987166046198844[/C][/ROW]
[ROW][C]34[/C][C]0.00848461382060324[/C][C]0.0169692276412065[/C][C]0.991515386179397[/C][/ROW]
[ROW][C]35[/C][C]0.00730537334364162[/C][C]0.0146107466872832[/C][C]0.992694626656358[/C][/ROW]
[ROW][C]36[/C][C]0.00491177241641231[/C][C]0.00982354483282463[/C][C]0.995088227583588[/C][/ROW]
[ROW][C]37[/C][C]0.00325279116521318[/C][C]0.00650558233042637[/C][C]0.996747208834787[/C][/ROW]
[ROW][C]38[/C][C]0.00236295507723267[/C][C]0.00472591015446535[/C][C]0.997637044922767[/C][/ROW]
[ROW][C]39[/C][C]0.00152424228584383[/C][C]0.00304848457168765[/C][C]0.998475757714156[/C][/ROW]
[ROW][C]40[/C][C]0.000946264288637119[/C][C]0.00189252857727424[/C][C]0.999053735711363[/C][/ROW]
[ROW][C]41[/C][C]0.000595651408963466[/C][C]0.00119130281792693[/C][C]0.999404348591037[/C][/ROW]
[ROW][C]42[/C][C]0.000347862533781218[/C][C]0.000695725067562437[/C][C]0.999652137466219[/C][/ROW]
[ROW][C]43[/C][C]0.000243585395748413[/C][C]0.000487170791496826[/C][C]0.999756414604252[/C][/ROW]
[ROW][C]44[/C][C]0.000139895356023048[/C][C]0.000279790712046095[/C][C]0.999860104643977[/C][/ROW]
[ROW][C]45[/C][C]7.86207430430638e-05[/C][C]0.000157241486086128[/C][C]0.999921379256957[/C][/ROW]
[ROW][C]46[/C][C]9.93624592464413e-05[/C][C]0.000198724918492883[/C][C]0.999900637540754[/C][/ROW]
[ROW][C]47[/C][C]0.000177055510202217[/C][C]0.000354111020404434[/C][C]0.999822944489798[/C][/ROW]
[ROW][C]48[/C][C]0.000566704699634689[/C][C]0.00113340939926938[/C][C]0.999433295300365[/C][/ROW]
[ROW][C]49[/C][C]0.000397170271791238[/C][C]0.000794340543582476[/C][C]0.999602829728209[/C][/ROW]
[ROW][C]50[/C][C]0.0044244395442087[/C][C]0.0088488790884174[/C][C]0.995575560455791[/C][/ROW]
[ROW][C]51[/C][C]0.00321309209460317[/C][C]0.00642618418920635[/C][C]0.996786907905397[/C][/ROW]
[ROW][C]52[/C][C]0.00230252549001733[/C][C]0.00460505098003467[/C][C]0.997697474509983[/C][/ROW]
[ROW][C]53[/C][C]0.00152203883988141[/C][C]0.00304407767976281[/C][C]0.998477961160119[/C][/ROW]
[ROW][C]54[/C][C]0.00120395913717536[/C][C]0.00240791827435072[/C][C]0.998796040862825[/C][/ROW]
[ROW][C]55[/C][C]0.0020435686528183[/C][C]0.00408713730563661[/C][C]0.997956431347182[/C][/ROW]
[ROW][C]56[/C][C]0.00163860073497577[/C][C]0.00327720146995155[/C][C]0.998361399265024[/C][/ROW]
[ROW][C]57[/C][C]0.00110806384769146[/C][C]0.00221612769538292[/C][C]0.998891936152309[/C][/ROW]
[ROW][C]58[/C][C]0.000917739650250278[/C][C]0.00183547930050056[/C][C]0.99908226034975[/C][/ROW]
[ROW][C]59[/C][C]0.00059928133823191[/C][C]0.00119856267646382[/C][C]0.999400718661768[/C][/ROW]
[ROW][C]60[/C][C]0.000497624690026418[/C][C]0.000995249380052836[/C][C]0.999502375309974[/C][/ROW]
[ROW][C]61[/C][C]0.000533329253905137[/C][C]0.00106665850781027[/C][C]0.999466670746095[/C][/ROW]
[ROW][C]62[/C][C]0.000400558965733986[/C][C]0.000801117931467973[/C][C]0.999599441034266[/C][/ROW]
[ROW][C]63[/C][C]0.0018834186135391[/C][C]0.0037668372270782[/C][C]0.99811658138646[/C][/ROW]
[ROW][C]64[/C][C]0.00124204751161695[/C][C]0.00248409502323391[/C][C]0.998757952488383[/C][/ROW]
[ROW][C]65[/C][C]0.00107889834091087[/C][C]0.00215779668182175[/C][C]0.99892110165909[/C][/ROW]
[ROW][C]66[/C][C]0.00186592272656232[/C][C]0.00373184545312464[/C][C]0.998134077273438[/C][/ROW]
[ROW][C]67[/C][C]0.00126569957793323[/C][C]0.00253139915586646[/C][C]0.998734300422067[/C][/ROW]
[ROW][C]68[/C][C]0.00085559004004255[/C][C]0.0017111800800851[/C][C]0.999144409959957[/C][/ROW]
[ROW][C]69[/C][C]0.000564573422286361[/C][C]0.00112914684457272[/C][C]0.999435426577714[/C][/ROW]
[ROW][C]70[/C][C]0.00039891182699547[/C][C]0.00079782365399094[/C][C]0.999601088173005[/C][/ROW]
[ROW][C]71[/C][C]0.000301337008530648[/C][C]0.000602674017061295[/C][C]0.99969866299147[/C][/ROW]
[ROW][C]72[/C][C]0.000190551569050377[/C][C]0.000381103138100754[/C][C]0.99980944843095[/C][/ROW]
[ROW][C]73[/C][C]0.000155192316221532[/C][C]0.000310384632443064[/C][C]0.999844807683778[/C][/ROW]
[ROW][C]74[/C][C]0.000339927224344045[/C][C]0.00067985444868809[/C][C]0.999660072775656[/C][/ROW]
[ROW][C]75[/C][C]0.000237952979395826[/C][C]0.000475905958791651[/C][C]0.999762047020604[/C][/ROW]
[ROW][C]76[/C][C]0.000164171763506554[/C][C]0.000328343527013107[/C][C]0.999835828236493[/C][/ROW]
[ROW][C]77[/C][C]0.000147613137363423[/C][C]0.000295226274726845[/C][C]0.999852386862637[/C][/ROW]
[ROW][C]78[/C][C]0.895765879239367[/C][C]0.208468241521266[/C][C]0.104234120760633[/C][/ROW]
[ROW][C]79[/C][C]0.871536685851211[/C][C]0.256926628297578[/C][C]0.128463314148789[/C][/ROW]
[ROW][C]80[/C][C]0.849033315550945[/C][C]0.30193336889811[/C][C]0.150966684449055[/C][/ROW]
[ROW][C]81[/C][C]0.832311290034144[/C][C]0.335377419931711[/C][C]0.167688709965856[/C][/ROW]
[ROW][C]82[/C][C]0.804666708916258[/C][C]0.390666582167485[/C][C]0.195333291083742[/C][/ROW]
[ROW][C]83[/C][C]0.768370056234955[/C][C]0.46325988753009[/C][C]0.231629943765045[/C][/ROW]
[ROW][C]84[/C][C]0.730195616494614[/C][C]0.539608767010773[/C][C]0.269804383505387[/C][/ROW]
[ROW][C]85[/C][C]0.687106366796468[/C][C]0.625787266407064[/C][C]0.312893633203532[/C][/ROW]
[ROW][C]86[/C][C]0.660107309729116[/C][C]0.679785380541768[/C][C]0.339892690270884[/C][/ROW]
[ROW][C]87[/C][C]0.638865525486242[/C][C]0.722268949027516[/C][C]0.361134474513758[/C][/ROW]
[ROW][C]88[/C][C]0.591136008230444[/C][C]0.817727983539111[/C][C]0.408863991769556[/C][/ROW]
[ROW][C]89[/C][C]0.549962971564927[/C][C]0.900074056870145[/C][C]0.450037028435073[/C][/ROW]
[ROW][C]90[/C][C]0.518301177254986[/C][C]0.963397645490029[/C][C]0.481698822745014[/C][/ROW]
[ROW][C]91[/C][C]0.587563984971085[/C][C]0.82487203005783[/C][C]0.412436015028915[/C][/ROW]
[ROW][C]92[/C][C]0.662472876937074[/C][C]0.675054246125852[/C][C]0.337527123062926[/C][/ROW]
[ROW][C]93[/C][C]0.64361933508679[/C][C]0.712761329826419[/C][C]0.35638066491321[/C][/ROW]
[ROW][C]94[/C][C]0.621559295856643[/C][C]0.756881408286713[/C][C]0.378440704143357[/C][/ROW]
[ROW][C]95[/C][C]0.579326705380457[/C][C]0.841346589239085[/C][C]0.420673294619543[/C][/ROW]
[ROW][C]96[/C][C]0.584887752033365[/C][C]0.83022449593327[/C][C]0.415112247966635[/C][/ROW]
[ROW][C]97[/C][C]0.998529304112251[/C][C]0.00294139177549746[/C][C]0.00147069588774873[/C][/ROW]
[ROW][C]98[/C][C]0.998021377107474[/C][C]0.0039572457850524[/C][C]0.0019786228925262[/C][/ROW]
[ROW][C]99[/C][C]0.998517000651073[/C][C]0.00296599869785322[/C][C]0.00148299934892661[/C][/ROW]
[ROW][C]100[/C][C]0.998283791478668[/C][C]0.00343241704266313[/C][C]0.00171620852133156[/C][/ROW]
[ROW][C]101[/C][C]0.99885410165978[/C][C]0.00229179668043973[/C][C]0.00114589834021987[/C][/ROW]
[ROW][C]102[/C][C]0.998129210647506[/C][C]0.00374157870498808[/C][C]0.00187078935249404[/C][/ROW]
[ROW][C]103[/C][C]0.99726721218381[/C][C]0.00546557563237975[/C][C]0.00273278781618988[/C][/ROW]
[ROW][C]104[/C][C]0.998413010877918[/C][C]0.00317397824416376[/C][C]0.00158698912208188[/C][/ROW]
[ROW][C]105[/C][C]0.998393982653714[/C][C]0.003212034692572[/C][C]0.001606017346286[/C][/ROW]
[ROW][C]106[/C][C]0.997364296080438[/C][C]0.00527140783912367[/C][C]0.00263570391956184[/C][/ROW]
[ROW][C]107[/C][C]0.99616526112611[/C][C]0.00766947774778049[/C][C]0.00383473887389024[/C][/ROW]
[ROW][C]108[/C][C]0.994058855097942[/C][C]0.0118822898041153[/C][C]0.00594114490205767[/C][/ROW]
[ROW][C]109[/C][C]0.997546025614996[/C][C]0.00490794877000809[/C][C]0.00245397438500404[/C][/ROW]
[ROW][C]110[/C][C]0.996153821946647[/C][C]0.00769235610670506[/C][C]0.00384617805335253[/C][/ROW]
[ROW][C]111[/C][C]0.994132555303736[/C][C]0.0117348893925271[/C][C]0.00586744469626353[/C][/ROW]
[ROW][C]112[/C][C]0.992105286486273[/C][C]0.0157894270274544[/C][C]0.0078947135137272[/C][/ROW]
[ROW][C]113[/C][C]0.993234058281636[/C][C]0.0135318834367273[/C][C]0.00676594171836367[/C][/ROW]
[ROW][C]114[/C][C]0.99076594005831[/C][C]0.0184681198833785[/C][C]0.00923405994168925[/C][/ROW]
[ROW][C]115[/C][C]0.985298988740902[/C][C]0.0294020225181953[/C][C]0.0147010112590977[/C][/ROW]
[ROW][C]116[/C][C]0.980537996974147[/C][C]0.038924006051707[/C][C]0.0194620030258535[/C][/ROW]
[ROW][C]117[/C][C]0.985886636101466[/C][C]0.0282267277970686[/C][C]0.0141133638985343[/C][/ROW]
[ROW][C]118[/C][C]0.988752618193024[/C][C]0.0224947636139522[/C][C]0.0112473818069761[/C][/ROW]
[ROW][C]119[/C][C]0.994915974257321[/C][C]0.0101680514853573[/C][C]0.00508402574267863[/C][/ROW]
[ROW][C]120[/C][C]0.990966249035018[/C][C]0.0180675019299636[/C][C]0.0090337509649818[/C][/ROW]
[ROW][C]121[/C][C]0.989187878645253[/C][C]0.0216242427094935[/C][C]0.0108121213547468[/C][/ROW]
[ROW][C]122[/C][C]0.993758473418765[/C][C]0.0124830531624708[/C][C]0.00624152658123539[/C][/ROW]
[ROW][C]123[/C][C]0.99378960246765[/C][C]0.0124207950646996[/C][C]0.00621039753234978[/C][/ROW]
[ROW][C]124[/C][C]0.991870673756406[/C][C]0.0162586524871871[/C][C]0.00812932624359354[/C][/ROW]
[ROW][C]125[/C][C]0.985117712902062[/C][C]0.029764574195876[/C][C]0.014882287097938[/C][/ROW]
[ROW][C]126[/C][C]0.972965053553615[/C][C]0.054069892892771[/C][C]0.0270349464463855[/C][/ROW]
[ROW][C]127[/C][C]0.957188567054805[/C][C]0.0856228658903896[/C][C]0.0428114329451948[/C][/ROW]
[ROW][C]128[/C][C]0.961471489415025[/C][C]0.0770570211699509[/C][C]0.0385285105849754[/C][/ROW]
[ROW][C]129[/C][C]0.983176279830487[/C][C]0.0336474403390263[/C][C]0.0168237201695132[/C][/ROW]
[ROW][C]130[/C][C]0.971796168726138[/C][C]0.0564076625477231[/C][C]0.0282038312738615[/C][/ROW]
[ROW][C]131[/C][C]0.95039050098662[/C][C]0.099218998026762[/C][C]0.049609499013381[/C][/ROW]
[ROW][C]132[/C][C]0.906213123271642[/C][C]0.187573753456715[/C][C]0.0937868767283575[/C][/ROW]
[ROW][C]133[/C][C]0.846983848070659[/C][C]0.306032303858682[/C][C]0.153016151929341[/C][/ROW]
[ROW][C]134[/C][C]0.728984590377957[/C][C]0.542030819244085[/C][C]0.271015409622043[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154110&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154110&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.625479598830677	0.749040802338646	0.374520401169323
10	0.48978863462332	0.97957726924664	0.51021136537668
11	0.474338660514287	0.948677321028573	0.525661339485713
12	0.369242547572502	0.738485095145004	0.630757452427498
13	0.28139104572854	0.56278209145708	0.71860895427146
14	0.192075279610892	0.384150559221783	0.807924720389108
15	0.17166142711748	0.34332285423496	0.82833857288252
16	0.125844022450332	0.251688044900663	0.874155977549668
17	0.144721649968746	0.289443299937492	0.855278350031254
18	0.0983061725848847	0.196612345169769	0.901693827415115
19	0.114338625660729	0.228677251321459	0.88566137433927
20	0.100355345061708	0.200710690123416	0.899644654938292
21	0.117473136842152	0.234946273684303	0.882526863157848
22	0.0814774426745284	0.162954885349057	0.918522557325472
23	0.0626062325093801	0.12521246501876	0.93739376749062
24	0.0461221575759451	0.0922443151518901	0.953877842424055
25	0.0364422938838962	0.0728845877677924	0.963557706116104
26	0.0242733352926843	0.0485466705853686	0.975726664707316
27	0.0600328475122004	0.120065695024401	0.9399671524878
28	0.0465530033764702	0.0931060067529404	0.95344699662353
29	0.0322644290169215	0.064528858033843	0.967735570983078
30	0.021885400922732	0.043770801845464	0.978114599077268
31	0.0171853466829017	0.0343706933658035	0.982814653317098
32	0.0192589611838022	0.0385179223676043	0.980741038816198
33	0.0128339538011563	0.0256679076023125	0.987166046198844
34	0.00848461382060324	0.0169692276412065	0.991515386179397
35	0.00730537334364162	0.0146107466872832	0.992694626656358
36	0.00491177241641231	0.00982354483282463	0.995088227583588
37	0.00325279116521318	0.00650558233042637	0.996747208834787
38	0.00236295507723267	0.00472591015446535	0.997637044922767
39	0.00152424228584383	0.00304848457168765	0.998475757714156
40	0.000946264288637119	0.00189252857727424	0.999053735711363
41	0.000595651408963466	0.00119130281792693	0.999404348591037
42	0.000347862533781218	0.000695725067562437	0.999652137466219
43	0.000243585395748413	0.000487170791496826	0.999756414604252
44	0.000139895356023048	0.000279790712046095	0.999860104643977
45	7.86207430430638e-05	0.000157241486086128	0.999921379256957
46	9.93624592464413e-05	0.000198724918492883	0.999900637540754
47	0.000177055510202217	0.000354111020404434	0.999822944489798
48	0.000566704699634689	0.00113340939926938	0.999433295300365
49	0.000397170271791238	0.000794340543582476	0.999602829728209
50	0.0044244395442087	0.0088488790884174	0.995575560455791
51	0.00321309209460317	0.00642618418920635	0.996786907905397
52	0.00230252549001733	0.00460505098003467	0.997697474509983
53	0.00152203883988141	0.00304407767976281	0.998477961160119
54	0.00120395913717536	0.00240791827435072	0.998796040862825
55	0.0020435686528183	0.00408713730563661	0.997956431347182
56	0.00163860073497577	0.00327720146995155	0.998361399265024
57	0.00110806384769146	0.00221612769538292	0.998891936152309
58	0.000917739650250278	0.00183547930050056	0.99908226034975
59	0.00059928133823191	0.00119856267646382	0.999400718661768
60	0.000497624690026418	0.000995249380052836	0.999502375309974
61	0.000533329253905137	0.00106665850781027	0.999466670746095
62	0.000400558965733986	0.000801117931467973	0.999599441034266
63	0.0018834186135391	0.0037668372270782	0.99811658138646
64	0.00124204751161695	0.00248409502323391	0.998757952488383
65	0.00107889834091087	0.00215779668182175	0.99892110165909
66	0.00186592272656232	0.00373184545312464	0.998134077273438
67	0.00126569957793323	0.00253139915586646	0.998734300422067
68	0.00085559004004255	0.0017111800800851	0.999144409959957
69	0.000564573422286361	0.00112914684457272	0.999435426577714
70	0.00039891182699547	0.00079782365399094	0.999601088173005
71	0.000301337008530648	0.000602674017061295	0.99969866299147
72	0.000190551569050377	0.000381103138100754	0.99980944843095
73	0.000155192316221532	0.000310384632443064	0.999844807683778
74	0.000339927224344045	0.00067985444868809	0.999660072775656
75	0.000237952979395826	0.000475905958791651	0.999762047020604
76	0.000164171763506554	0.000328343527013107	0.999835828236493
77	0.000147613137363423	0.000295226274726845	0.999852386862637
78	0.895765879239367	0.208468241521266	0.104234120760633
79	0.871536685851211	0.256926628297578	0.128463314148789
80	0.849033315550945	0.30193336889811	0.150966684449055
81	0.832311290034144	0.335377419931711	0.167688709965856
82	0.804666708916258	0.390666582167485	0.195333291083742
83	0.768370056234955	0.46325988753009	0.231629943765045
84	0.730195616494614	0.539608767010773	0.269804383505387
85	0.687106366796468	0.625787266407064	0.312893633203532
86	0.660107309729116	0.679785380541768	0.339892690270884
87	0.638865525486242	0.722268949027516	0.361134474513758
88	0.591136008230444	0.817727983539111	0.408863991769556
89	0.549962971564927	0.900074056870145	0.450037028435073
90	0.518301177254986	0.963397645490029	0.481698822745014
91	0.587563984971085	0.82487203005783	0.412436015028915
92	0.662472876937074	0.675054246125852	0.337527123062926
93	0.64361933508679	0.712761329826419	0.35638066491321
94	0.621559295856643	0.756881408286713	0.378440704143357
95	0.579326705380457	0.841346589239085	0.420673294619543
96	0.584887752033365	0.83022449593327	0.415112247966635
97	0.998529304112251	0.00294139177549746	0.00147069588774873
98	0.998021377107474	0.0039572457850524	0.0019786228925262
99	0.998517000651073	0.00296599869785322	0.00148299934892661
100	0.998283791478668	0.00343241704266313	0.00171620852133156
101	0.99885410165978	0.00229179668043973	0.00114589834021987
102	0.998129210647506	0.00374157870498808	0.00187078935249404
103	0.99726721218381	0.00546557563237975	0.00273278781618988
104	0.998413010877918	0.00317397824416376	0.00158698912208188
105	0.998393982653714	0.003212034692572	0.001606017346286
106	0.997364296080438	0.00527140783912367	0.00263570391956184
107	0.99616526112611	0.00766947774778049	0.00383473887389024
108	0.994058855097942	0.0118822898041153	0.00594114490205767
109	0.997546025614996	0.00490794877000809	0.00245397438500404
110	0.996153821946647	0.00769235610670506	0.00384617805335253
111	0.994132555303736	0.0117348893925271	0.00586744469626353
112	0.992105286486273	0.0157894270274544	0.0078947135137272
113	0.993234058281636	0.0135318834367273	0.00676594171836367
114	0.99076594005831	0.0184681198833785	0.00923405994168925
115	0.985298988740902	0.0294020225181953	0.0147010112590977
116	0.980537996974147	0.038924006051707	0.0194620030258535
117	0.985886636101466	0.0282267277970686	0.0141133638985343
118	0.988752618193024	0.0224947636139522	0.0112473818069761
119	0.994915974257321	0.0101680514853573	0.00508402574267863
120	0.990966249035018	0.0180675019299636	0.0090337509649818
121	0.989187878645253	0.0216242427094935	0.0108121213547468
122	0.993758473418765	0.0124830531624708	0.00624152658123539
123	0.99378960246765	0.0124207950646996	0.00621039753234978
124	0.991870673756406	0.0162586524871871	0.00812932624359354
125	0.985117712902062	0.029764574195876	0.014882287097938
126	0.972965053553615	0.054069892892771	0.0270349464463855
127	0.957188567054805	0.0856228658903896	0.0428114329451948
128	0.961471489415025	0.0770570211699509	0.0385285105849754
129	0.983176279830487	0.0336474403390263	0.0168237201695132
130	0.971796168726138	0.0564076625477231	0.0282038312738615
131	0.95039050098662	0.099218998026762	0.049609499013381
132	0.906213123271642	0.187573753456715	0.0937868767283575
133	0.846983848070659	0.306032303858682	0.153016151929341
134	0.728984590377957	0.542030819244085	0.271015409622043

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	55	0.436507936507937	NOK
5% type I error level	79	0.626984126984127	NOK
10% type I error level	88	0.698412698412698	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 & 55 & 0.436507936507937 & NOK \tabularnewline
5% type I error level & 79 & 0.626984126984127 & NOK \tabularnewline
10% type I error level & 88 & 0.698412698412698 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154110&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]55[/C][C]0.436507936507937[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]79[/C][C]0.626984126984127[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]88[/C][C]0.698412698412698[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154110&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154110&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	55	0.436507936507937	NOK
5% type I error level	79	0.626984126984127	NOK
10% type I error level	88	0.698412698412698	NOK

Figure 1

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code