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Author

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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 14 Dec 2010 14:47:16 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t12923382669r2vm6qqzr4zi3m.htm/, Retrieved Thu, 02 May 2024 22:47:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109707, Retrieved Thu, 02 May 2024 22:47:14 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

130

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

-     [Multiple Regression] [multi lineair reg...] [2009-12-11 16:54:58] [517ac0676608e46c618c738721d88e41]
- R  D    [Multiple Regression] [] [2010-12-14 14:47:16] [5f45e5b827d1a020c3ecc9d930121b4e] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109707&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109707&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109707&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
CO2-uitstoot[t] = + 61.25 + 47.5Dummy[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CO2-uitstoot[t] =  +  61.25 +  47.5Dummy[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109707&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CO2-uitstoot[t] =  +  61.25 +  47.5Dummy[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109707&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109707&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
CO2-uitstoot[t] = + 61.25 + 47.5Dummy[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)	61.25	2.975289	20.5862	0	0
Dummy	47.5	19.735836	2.4068	0.01714	0.00857

\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) & 61.25 & 2.975289 & 20.5862 & 0 & 0 \tabularnewline
Dummy & 47.5 & 19.735836 & 2.4068 & 0.01714 & 0.00857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109707&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]61.25[/C][C]2.975289[/C][C]20.5862[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]47.5[/C][C]19.735836[/C][C]2.4068[/C][C]0.01714[/C][C]0.00857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109707&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109707&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)	61.25	2.975289	20.5862	0	0
Dummy	47.5	19.735836	2.4068	0.01714	0.00857

Multiple Linear Regression - Regression Statistics
Multiple R	0.179494911537253
R-squared	0.0322184232677663
Adjusted R-squared	0.0266564601830982
F-TEST (value)	5.79263522200982
F-TEST (DF numerator)	1
F-TEST (DF denominator)	174
p-value	0.0171398615537394
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	39.0205516141925
Sum Squared Residuals	264933

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.179494911537253 \tabularnewline
R-squared & 0.0322184232677663 \tabularnewline
Adjusted R-squared & 0.0266564601830982 \tabularnewline
F-TEST (value) & 5.79263522200982 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 174 \tabularnewline
p-value & 0.0171398615537394 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 39.0205516141925 \tabularnewline
Sum Squared Residuals & 264933 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109707&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.179494911537253[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0322184232677663[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0266564601830982[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.79263522200982[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]174[/C][/ROW]
[ROW][C]p-value[/C][C]0.0171398615537394[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]39.0205516141925[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]264933[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109707&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109707&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	0.179494911537253
R-squared	0.0322184232677663
Adjusted R-squared	0.0266564601830982
F-TEST (value)	5.79263522200982
F-TEST (DF numerator)	1
F-TEST (DF denominator)	174
p-value	0.0171398615537394
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	39.0205516141925
Sum Squared Residuals	264933

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5	61.2499999999999	-56.2499999999999
2	4	61.25	-57.25
3	5	61.25	-56.25
4	6	61.25	-55.25
5	6	61.25	-55.25
6	6	61.25	-55.25
7	7	61.25	-54.25
8	8	61.25	-53.25
9	7	61.25	-54.25
10	8	61.25	-53.25
11	7	61.25	-54.25
12	8	61.25	-53.25
13	8	61.25	-53.25
14	9	61.25	-52.25
15	9	61.25	-52.25
16	8	61.25	-53.25
17	9	61.25	-52.25
18	9	61.25	-52.25
19	10	61.25	-51.25
20	11	61.25	-50.25
21	12	61.25	-49.25
22	13	61.25	-48.25
23	13	61.25	-48.25
24	13	61.25	-48.25
25	14	61.25	-47.25
26	14	61.25	-47.25
27	15	61.25	-46.25
28	15	61.25	-46.25
29	16	61.25	-45.25
30	16	61.25	-45.25
31	17	61.25	-44.25
32	18	61.25	-43.25
33	19	61.25	-42.25
34	20	61.25	-41.25
35	22	61.25	-39.25
36	20	61.25	-41.25
37	22	61.25	-39.25
38	25	61.25	-36.25
39	24	61.25	-37.25
40	25	61.25	-36.25
41	28	61.25	-33.25
42	26	61.25	-35.25
43	27	61.25	-34.25
44	26	61.25	-35.25
45	25	61.25	-36.25
46	27	61.25	-34.25
47	28	61.25	-33.25
48	30	61.25	-31.25
49	31	61.25	-30.25
50	32	61.25	-29.25
51	34	61.25	-27.25
52	34	61.25	-27.25
53	33	61.25	-28.25
54	32	61.25	-29.25
55	34	61.25	-27.25
56	36	61.25	-25.25
57	37	61.25	-24.25
58	40	61.25	-21.25
59	38	61.25	-23.25
60	38	61.25	-23.25
61	36	61.25	-25.25
62	40	61.25	-21.25
63	40	61.25	-21.25
64	42	61.25	-19.25
65	44	61.25	-17.25
66	45	61.25	-16.25
67	47	61.25	-14.25
68	49	61.25	-12.25
69	47	61.25	-14.25
70	49	61.25	-12.25
71	52	61.25	-9.25
72	50	61.25	-11.25
73	50	61.25	-11.25
74	57	61.25	-4.25
75	58	61.25	-3.25
76	58	61.25	-3.25
77	58	61.25	-3.25
78	61	61.25	-0.250000000000002
79	61	61.25	-0.250000000000002
80	64	61.25	2.75
81	68	61.25	6.75
82	40	61.25	-21.25
83	34	61.25	-27.25
84	46	61.25	-15.25
85	36	61.25	-25.25
86	34	61.25	-27.25
87	45	61.25	-16.25
88	55	61.25	-6.25
89	50	61.25	-11.25
90	56	61.25	-5.25
91	72	61.25	10.75
92	76	61.25	14.75
93	78	61.25	16.75
94	77	61.25	15.75
95	90	61.25	28.75
96	88	61.25	26.75
97	97	61.25	35.75
98	93	61.25	31.75
99	84	61.25	22.75
100	67	61.25	5.75
101	72	61.25	10.75
102	75	61.25	13.75
103	71	61.25	9.75
104	75	61.25	13.75
105	90	61.25	28.75
106	78	61.25	16.75
107	73	61.25	11.75
108	62	61.25	0.749999999999999
109	65	61.25	3.75
110	61	61.25	-0.250000000000002
111	58	61.25	-3.25
112	33	61.25	-28.25
113	39	61.25	-22.25
114	56	61.25	-5.25
115	79	61.25	17.75
116	82	61.25	20.75
117	79	61.25	17.75
118	73	61.25	11.75
119	87	61.25	25.75
120	85	61.25	23.75
121	83	61.25	21.75
122	82	61.25	20.75
123	83	61.25	21.75
124	92	61.25	30.75
125	95	61.25	33.75
126	97	61.25	35.75
127	87	61.25	25.75
128	84	61.25	22.75
129	84	61.25	22.75
130	89	61.25	27.75
131	103	61.25	41.75
132	106	61.25	44.75
133	109	61.25	47.75
134	106	61.25	44.75
135	105	61.25	43.75
136	115	61.25	53.75
137	120	61.25	58.75
138	124	61.25	62.75
139	121	61.25	59.75
140	131	61.25	69.75
141	139	61.25	77.75
142	133	61.25	71.75
143	119	61.25	57.75
144	123	61.25	61.75
145	120	61.25	58.75
146	128	61.25	66.75
147	134	61.25	72.75
148	126	61.25	64.75
149	115	61.25	53.75
150	106	61.25	44.75
151	99	61.25	37.75
152	100	61.25	38.75
153	99	61.25	37.75
154	99	61.25	37.75
155	100	61.25	38.75
156	100	61.25	38.75
157	108	61.25	46.75
158	109	61.25	47.75
159	115	61.25	53.75
160	114	61.25	52.75
161	108	61.25	46.75
162	113	61.25	51.75
163	118	61.25	56.75
164	122	61.25	60.75
165	118	61.25	56.75
166	121	61.25	59.75
167	118	61.25	56.75
168	121	61.25	59.75
169	121	61.25	59.75
170	112	61.25	50.75
171	119	61.25	57.75
172	116	61.25	54.75
173	110	108.75	1.25
174	111	108.75	2.25
175	106	108.75	-2.75
176	108	108.75	-0.749999999999998

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 61.2499999999999 & -56.2499999999999 \tabularnewline
2 & 4 & 61.25 & -57.25 \tabularnewline
3 & 5 & 61.25 & -56.25 \tabularnewline
4 & 6 & 61.25 & -55.25 \tabularnewline
5 & 6 & 61.25 & -55.25 \tabularnewline
6 & 6 & 61.25 & -55.25 \tabularnewline
7 & 7 & 61.25 & -54.25 \tabularnewline
8 & 8 & 61.25 & -53.25 \tabularnewline
9 & 7 & 61.25 & -54.25 \tabularnewline
10 & 8 & 61.25 & -53.25 \tabularnewline
11 & 7 & 61.25 & -54.25 \tabularnewline
12 & 8 & 61.25 & -53.25 \tabularnewline
13 & 8 & 61.25 & -53.25 \tabularnewline
14 & 9 & 61.25 & -52.25 \tabularnewline
15 & 9 & 61.25 & -52.25 \tabularnewline
16 & 8 & 61.25 & -53.25 \tabularnewline
17 & 9 & 61.25 & -52.25 \tabularnewline
18 & 9 & 61.25 & -52.25 \tabularnewline
19 & 10 & 61.25 & -51.25 \tabularnewline
20 & 11 & 61.25 & -50.25 \tabularnewline
21 & 12 & 61.25 & -49.25 \tabularnewline
22 & 13 & 61.25 & -48.25 \tabularnewline
23 & 13 & 61.25 & -48.25 \tabularnewline
24 & 13 & 61.25 & -48.25 \tabularnewline
25 & 14 & 61.25 & -47.25 \tabularnewline
26 & 14 & 61.25 & -47.25 \tabularnewline
27 & 15 & 61.25 & -46.25 \tabularnewline
28 & 15 & 61.25 & -46.25 \tabularnewline
29 & 16 & 61.25 & -45.25 \tabularnewline
30 & 16 & 61.25 & -45.25 \tabularnewline
31 & 17 & 61.25 & -44.25 \tabularnewline
32 & 18 & 61.25 & -43.25 \tabularnewline
33 & 19 & 61.25 & -42.25 \tabularnewline
34 & 20 & 61.25 & -41.25 \tabularnewline
35 & 22 & 61.25 & -39.25 \tabularnewline
36 & 20 & 61.25 & -41.25 \tabularnewline
37 & 22 & 61.25 & -39.25 \tabularnewline
38 & 25 & 61.25 & -36.25 \tabularnewline
39 & 24 & 61.25 & -37.25 \tabularnewline
40 & 25 & 61.25 & -36.25 \tabularnewline
41 & 28 & 61.25 & -33.25 \tabularnewline
42 & 26 & 61.25 & -35.25 \tabularnewline
43 & 27 & 61.25 & -34.25 \tabularnewline
44 & 26 & 61.25 & -35.25 \tabularnewline
45 & 25 & 61.25 & -36.25 \tabularnewline
46 & 27 & 61.25 & -34.25 \tabularnewline
47 & 28 & 61.25 & -33.25 \tabularnewline
48 & 30 & 61.25 & -31.25 \tabularnewline
49 & 31 & 61.25 & -30.25 \tabularnewline
50 & 32 & 61.25 & -29.25 \tabularnewline
51 & 34 & 61.25 & -27.25 \tabularnewline
52 & 34 & 61.25 & -27.25 \tabularnewline
53 & 33 & 61.25 & -28.25 \tabularnewline
54 & 32 & 61.25 & -29.25 \tabularnewline
55 & 34 & 61.25 & -27.25 \tabularnewline
56 & 36 & 61.25 & -25.25 \tabularnewline
57 & 37 & 61.25 & -24.25 \tabularnewline
58 & 40 & 61.25 & -21.25 \tabularnewline
59 & 38 & 61.25 & -23.25 \tabularnewline
60 & 38 & 61.25 & -23.25 \tabularnewline
61 & 36 & 61.25 & -25.25 \tabularnewline
62 & 40 & 61.25 & -21.25 \tabularnewline
63 & 40 & 61.25 & -21.25 \tabularnewline
64 & 42 & 61.25 & -19.25 \tabularnewline
65 & 44 & 61.25 & -17.25 \tabularnewline
66 & 45 & 61.25 & -16.25 \tabularnewline
67 & 47 & 61.25 & -14.25 \tabularnewline
68 & 49 & 61.25 & -12.25 \tabularnewline
69 & 47 & 61.25 & -14.25 \tabularnewline
70 & 49 & 61.25 & -12.25 \tabularnewline
71 & 52 & 61.25 & -9.25 \tabularnewline
72 & 50 & 61.25 & -11.25 \tabularnewline
73 & 50 & 61.25 & -11.25 \tabularnewline
74 & 57 & 61.25 & -4.25 \tabularnewline
75 & 58 & 61.25 & -3.25 \tabularnewline
76 & 58 & 61.25 & -3.25 \tabularnewline
77 & 58 & 61.25 & -3.25 \tabularnewline
78 & 61 & 61.25 & -0.250000000000002 \tabularnewline
79 & 61 & 61.25 & -0.250000000000002 \tabularnewline
80 & 64 & 61.25 & 2.75 \tabularnewline
81 & 68 & 61.25 & 6.75 \tabularnewline
82 & 40 & 61.25 & -21.25 \tabularnewline
83 & 34 & 61.25 & -27.25 \tabularnewline
84 & 46 & 61.25 & -15.25 \tabularnewline
85 & 36 & 61.25 & -25.25 \tabularnewline
86 & 34 & 61.25 & -27.25 \tabularnewline
87 & 45 & 61.25 & -16.25 \tabularnewline
88 & 55 & 61.25 & -6.25 \tabularnewline
89 & 50 & 61.25 & -11.25 \tabularnewline
90 & 56 & 61.25 & -5.25 \tabularnewline
91 & 72 & 61.25 & 10.75 \tabularnewline
92 & 76 & 61.25 & 14.75 \tabularnewline
93 & 78 & 61.25 & 16.75 \tabularnewline
94 & 77 & 61.25 & 15.75 \tabularnewline
95 & 90 & 61.25 & 28.75 \tabularnewline
96 & 88 & 61.25 & 26.75 \tabularnewline
97 & 97 & 61.25 & 35.75 \tabularnewline
98 & 93 & 61.25 & 31.75 \tabularnewline
99 & 84 & 61.25 & 22.75 \tabularnewline
100 & 67 & 61.25 & 5.75 \tabularnewline
101 & 72 & 61.25 & 10.75 \tabularnewline
102 & 75 & 61.25 & 13.75 \tabularnewline
103 & 71 & 61.25 & 9.75 \tabularnewline
104 & 75 & 61.25 & 13.75 \tabularnewline
105 & 90 & 61.25 & 28.75 \tabularnewline
106 & 78 & 61.25 & 16.75 \tabularnewline
107 & 73 & 61.25 & 11.75 \tabularnewline
108 & 62 & 61.25 & 0.749999999999999 \tabularnewline
109 & 65 & 61.25 & 3.75 \tabularnewline
110 & 61 & 61.25 & -0.250000000000002 \tabularnewline
111 & 58 & 61.25 & -3.25 \tabularnewline
112 & 33 & 61.25 & -28.25 \tabularnewline
113 & 39 & 61.25 & -22.25 \tabularnewline
114 & 56 & 61.25 & -5.25 \tabularnewline
115 & 79 & 61.25 & 17.75 \tabularnewline
116 & 82 & 61.25 & 20.75 \tabularnewline
117 & 79 & 61.25 & 17.75 \tabularnewline
118 & 73 & 61.25 & 11.75 \tabularnewline
119 & 87 & 61.25 & 25.75 \tabularnewline
120 & 85 & 61.25 & 23.75 \tabularnewline
121 & 83 & 61.25 & 21.75 \tabularnewline
122 & 82 & 61.25 & 20.75 \tabularnewline
123 & 83 & 61.25 & 21.75 \tabularnewline
124 & 92 & 61.25 & 30.75 \tabularnewline
125 & 95 & 61.25 & 33.75 \tabularnewline
126 & 97 & 61.25 & 35.75 \tabularnewline
127 & 87 & 61.25 & 25.75 \tabularnewline
128 & 84 & 61.25 & 22.75 \tabularnewline
129 & 84 & 61.25 & 22.75 \tabularnewline
130 & 89 & 61.25 & 27.75 \tabularnewline
131 & 103 & 61.25 & 41.75 \tabularnewline
132 & 106 & 61.25 & 44.75 \tabularnewline
133 & 109 & 61.25 & 47.75 \tabularnewline
134 & 106 & 61.25 & 44.75 \tabularnewline
135 & 105 & 61.25 & 43.75 \tabularnewline
136 & 115 & 61.25 & 53.75 \tabularnewline
137 & 120 & 61.25 & 58.75 \tabularnewline
138 & 124 & 61.25 & 62.75 \tabularnewline
139 & 121 & 61.25 & 59.75 \tabularnewline
140 & 131 & 61.25 & 69.75 \tabularnewline
141 & 139 & 61.25 & 77.75 \tabularnewline
142 & 133 & 61.25 & 71.75 \tabularnewline
143 & 119 & 61.25 & 57.75 \tabularnewline
144 & 123 & 61.25 & 61.75 \tabularnewline
145 & 120 & 61.25 & 58.75 \tabularnewline
146 & 128 & 61.25 & 66.75 \tabularnewline
147 & 134 & 61.25 & 72.75 \tabularnewline
148 & 126 & 61.25 & 64.75 \tabularnewline
149 & 115 & 61.25 & 53.75 \tabularnewline
150 & 106 & 61.25 & 44.75 \tabularnewline
151 & 99 & 61.25 & 37.75 \tabularnewline
152 & 100 & 61.25 & 38.75 \tabularnewline
153 & 99 & 61.25 & 37.75 \tabularnewline
154 & 99 & 61.25 & 37.75 \tabularnewline
155 & 100 & 61.25 & 38.75 \tabularnewline
156 & 100 & 61.25 & 38.75 \tabularnewline
157 & 108 & 61.25 & 46.75 \tabularnewline
158 & 109 & 61.25 & 47.75 \tabularnewline
159 & 115 & 61.25 & 53.75 \tabularnewline
160 & 114 & 61.25 & 52.75 \tabularnewline
161 & 108 & 61.25 & 46.75 \tabularnewline
162 & 113 & 61.25 & 51.75 \tabularnewline
163 & 118 & 61.25 & 56.75 \tabularnewline
164 & 122 & 61.25 & 60.75 \tabularnewline
165 & 118 & 61.25 & 56.75 \tabularnewline
166 & 121 & 61.25 & 59.75 \tabularnewline
167 & 118 & 61.25 & 56.75 \tabularnewline
168 & 121 & 61.25 & 59.75 \tabularnewline
169 & 121 & 61.25 & 59.75 \tabularnewline
170 & 112 & 61.25 & 50.75 \tabularnewline
171 & 119 & 61.25 & 57.75 \tabularnewline
172 & 116 & 61.25 & 54.75 \tabularnewline
173 & 110 & 108.75 & 1.25 \tabularnewline
174 & 111 & 108.75 & 2.25 \tabularnewline
175 & 106 & 108.75 & -2.75 \tabularnewline
176 & 108 & 108.75 & -0.749999999999998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109707&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]5[/C][C]61.2499999999999[/C][C]-56.2499999999999[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]61.25[/C][C]-57.25[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]61.25[/C][C]-56.25[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]61.25[/C][C]-55.25[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]61.25[/C][C]-55.25[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]61.25[/C][C]-55.25[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]61.25[/C][C]-54.25[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]61.25[/C][C]-53.25[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]61.25[/C][C]-54.25[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]61.25[/C][C]-53.25[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]61.25[/C][C]-54.25[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]61.25[/C][C]-53.25[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]61.25[/C][C]-53.25[/C][/ROW]
[ROW][C]14[/C][C]9[/C][C]61.25[/C][C]-52.25[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]61.25[/C][C]-52.25[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]61.25[/C][C]-53.25[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]61.25[/C][C]-52.25[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]61.25[/C][C]-52.25[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]61.25[/C][C]-51.25[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]61.25[/C][C]-50.25[/C][/ROW]
[ROW][C]21[/C][C]12[/C][C]61.25[/C][C]-49.25[/C][/ROW]
[ROW][C]22[/C][C]13[/C][C]61.25[/C][C]-48.25[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]61.25[/C][C]-48.25[/C][/ROW]
[ROW][C]24[/C][C]13[/C][C]61.25[/C][C]-48.25[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]61.25[/C][C]-47.25[/C][/ROW]
[ROW][C]26[/C][C]14[/C][C]61.25[/C][C]-47.25[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]61.25[/C][C]-46.25[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]61.25[/C][C]-46.25[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]61.25[/C][C]-45.25[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]61.25[/C][C]-45.25[/C][/ROW]
[ROW][C]31[/C][C]17[/C][C]61.25[/C][C]-44.25[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]61.25[/C][C]-43.25[/C][/ROW]
[ROW][C]33[/C][C]19[/C][C]61.25[/C][C]-42.25[/C][/ROW]
[ROW][C]34[/C][C]20[/C][C]61.25[/C][C]-41.25[/C][/ROW]
[ROW][C]35[/C][C]22[/C][C]61.25[/C][C]-39.25[/C][/ROW]
[ROW][C]36[/C][C]20[/C][C]61.25[/C][C]-41.25[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]61.25[/C][C]-39.25[/C][/ROW]
[ROW][C]38[/C][C]25[/C][C]61.25[/C][C]-36.25[/C][/ROW]
[ROW][C]39[/C][C]24[/C][C]61.25[/C][C]-37.25[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]61.25[/C][C]-36.25[/C][/ROW]
[ROW][C]41[/C][C]28[/C][C]61.25[/C][C]-33.25[/C][/ROW]
[ROW][C]42[/C][C]26[/C][C]61.25[/C][C]-35.25[/C][/ROW]
[ROW][C]43[/C][C]27[/C][C]61.25[/C][C]-34.25[/C][/ROW]
[ROW][C]44[/C][C]26[/C][C]61.25[/C][C]-35.25[/C][/ROW]
[ROW][C]45[/C][C]25[/C][C]61.25[/C][C]-36.25[/C][/ROW]
[ROW][C]46[/C][C]27[/C][C]61.25[/C][C]-34.25[/C][/ROW]
[ROW][C]47[/C][C]28[/C][C]61.25[/C][C]-33.25[/C][/ROW]
[ROW][C]48[/C][C]30[/C][C]61.25[/C][C]-31.25[/C][/ROW]
[ROW][C]49[/C][C]31[/C][C]61.25[/C][C]-30.25[/C][/ROW]
[ROW][C]50[/C][C]32[/C][C]61.25[/C][C]-29.25[/C][/ROW]
[ROW][C]51[/C][C]34[/C][C]61.25[/C][C]-27.25[/C][/ROW]
[ROW][C]52[/C][C]34[/C][C]61.25[/C][C]-27.25[/C][/ROW]
[ROW][C]53[/C][C]33[/C][C]61.25[/C][C]-28.25[/C][/ROW]
[ROW][C]54[/C][C]32[/C][C]61.25[/C][C]-29.25[/C][/ROW]
[ROW][C]55[/C][C]34[/C][C]61.25[/C][C]-27.25[/C][/ROW]
[ROW][C]56[/C][C]36[/C][C]61.25[/C][C]-25.25[/C][/ROW]
[ROW][C]57[/C][C]37[/C][C]61.25[/C][C]-24.25[/C][/ROW]
[ROW][C]58[/C][C]40[/C][C]61.25[/C][C]-21.25[/C][/ROW]
[ROW][C]59[/C][C]38[/C][C]61.25[/C][C]-23.25[/C][/ROW]
[ROW][C]60[/C][C]38[/C][C]61.25[/C][C]-23.25[/C][/ROW]
[ROW][C]61[/C][C]36[/C][C]61.25[/C][C]-25.25[/C][/ROW]
[ROW][C]62[/C][C]40[/C][C]61.25[/C][C]-21.25[/C][/ROW]
[ROW][C]63[/C][C]40[/C][C]61.25[/C][C]-21.25[/C][/ROW]
[ROW][C]64[/C][C]42[/C][C]61.25[/C][C]-19.25[/C][/ROW]
[ROW][C]65[/C][C]44[/C][C]61.25[/C][C]-17.25[/C][/ROW]
[ROW][C]66[/C][C]45[/C][C]61.25[/C][C]-16.25[/C][/ROW]
[ROW][C]67[/C][C]47[/C][C]61.25[/C][C]-14.25[/C][/ROW]
[ROW][C]68[/C][C]49[/C][C]61.25[/C][C]-12.25[/C][/ROW]
[ROW][C]69[/C][C]47[/C][C]61.25[/C][C]-14.25[/C][/ROW]
[ROW][C]70[/C][C]49[/C][C]61.25[/C][C]-12.25[/C][/ROW]
[ROW][C]71[/C][C]52[/C][C]61.25[/C][C]-9.25[/C][/ROW]
[ROW][C]72[/C][C]50[/C][C]61.25[/C][C]-11.25[/C][/ROW]
[ROW][C]73[/C][C]50[/C][C]61.25[/C][C]-11.25[/C][/ROW]
[ROW][C]74[/C][C]57[/C][C]61.25[/C][C]-4.25[/C][/ROW]
[ROW][C]75[/C][C]58[/C][C]61.25[/C][C]-3.25[/C][/ROW]
[ROW][C]76[/C][C]58[/C][C]61.25[/C][C]-3.25[/C][/ROW]
[ROW][C]77[/C][C]58[/C][C]61.25[/C][C]-3.25[/C][/ROW]
[ROW][C]78[/C][C]61[/C][C]61.25[/C][C]-0.250000000000002[/C][/ROW]
[ROW][C]79[/C][C]61[/C][C]61.25[/C][C]-0.250000000000002[/C][/ROW]
[ROW][C]80[/C][C]64[/C][C]61.25[/C][C]2.75[/C][/ROW]
[ROW][C]81[/C][C]68[/C][C]61.25[/C][C]6.75[/C][/ROW]
[ROW][C]82[/C][C]40[/C][C]61.25[/C][C]-21.25[/C][/ROW]
[ROW][C]83[/C][C]34[/C][C]61.25[/C][C]-27.25[/C][/ROW]
[ROW][C]84[/C][C]46[/C][C]61.25[/C][C]-15.25[/C][/ROW]
[ROW][C]85[/C][C]36[/C][C]61.25[/C][C]-25.25[/C][/ROW]
[ROW][C]86[/C][C]34[/C][C]61.25[/C][C]-27.25[/C][/ROW]
[ROW][C]87[/C][C]45[/C][C]61.25[/C][C]-16.25[/C][/ROW]
[ROW][C]88[/C][C]55[/C][C]61.25[/C][C]-6.25[/C][/ROW]
[ROW][C]89[/C][C]50[/C][C]61.25[/C][C]-11.25[/C][/ROW]
[ROW][C]90[/C][C]56[/C][C]61.25[/C][C]-5.25[/C][/ROW]
[ROW][C]91[/C][C]72[/C][C]61.25[/C][C]10.75[/C][/ROW]
[ROW][C]92[/C][C]76[/C][C]61.25[/C][C]14.75[/C][/ROW]
[ROW][C]93[/C][C]78[/C][C]61.25[/C][C]16.75[/C][/ROW]
[ROW][C]94[/C][C]77[/C][C]61.25[/C][C]15.75[/C][/ROW]
[ROW][C]95[/C][C]90[/C][C]61.25[/C][C]28.75[/C][/ROW]
[ROW][C]96[/C][C]88[/C][C]61.25[/C][C]26.75[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]61.25[/C][C]35.75[/C][/ROW]
[ROW][C]98[/C][C]93[/C][C]61.25[/C][C]31.75[/C][/ROW]
[ROW][C]99[/C][C]84[/C][C]61.25[/C][C]22.75[/C][/ROW]
[ROW][C]100[/C][C]67[/C][C]61.25[/C][C]5.75[/C][/ROW]
[ROW][C]101[/C][C]72[/C][C]61.25[/C][C]10.75[/C][/ROW]
[ROW][C]102[/C][C]75[/C][C]61.25[/C][C]13.75[/C][/ROW]
[ROW][C]103[/C][C]71[/C][C]61.25[/C][C]9.75[/C][/ROW]
[ROW][C]104[/C][C]75[/C][C]61.25[/C][C]13.75[/C][/ROW]
[ROW][C]105[/C][C]90[/C][C]61.25[/C][C]28.75[/C][/ROW]
[ROW][C]106[/C][C]78[/C][C]61.25[/C][C]16.75[/C][/ROW]
[ROW][C]107[/C][C]73[/C][C]61.25[/C][C]11.75[/C][/ROW]
[ROW][C]108[/C][C]62[/C][C]61.25[/C][C]0.749999999999999[/C][/ROW]
[ROW][C]109[/C][C]65[/C][C]61.25[/C][C]3.75[/C][/ROW]
[ROW][C]110[/C][C]61[/C][C]61.25[/C][C]-0.250000000000002[/C][/ROW]
[ROW][C]111[/C][C]58[/C][C]61.25[/C][C]-3.25[/C][/ROW]
[ROW][C]112[/C][C]33[/C][C]61.25[/C][C]-28.25[/C][/ROW]
[ROW][C]113[/C][C]39[/C][C]61.25[/C][C]-22.25[/C][/ROW]
[ROW][C]114[/C][C]56[/C][C]61.25[/C][C]-5.25[/C][/ROW]
[ROW][C]115[/C][C]79[/C][C]61.25[/C][C]17.75[/C][/ROW]
[ROW][C]116[/C][C]82[/C][C]61.25[/C][C]20.75[/C][/ROW]
[ROW][C]117[/C][C]79[/C][C]61.25[/C][C]17.75[/C][/ROW]
[ROW][C]118[/C][C]73[/C][C]61.25[/C][C]11.75[/C][/ROW]
[ROW][C]119[/C][C]87[/C][C]61.25[/C][C]25.75[/C][/ROW]
[ROW][C]120[/C][C]85[/C][C]61.25[/C][C]23.75[/C][/ROW]
[ROW][C]121[/C][C]83[/C][C]61.25[/C][C]21.75[/C][/ROW]
[ROW][C]122[/C][C]82[/C][C]61.25[/C][C]20.75[/C][/ROW]
[ROW][C]123[/C][C]83[/C][C]61.25[/C][C]21.75[/C][/ROW]
[ROW][C]124[/C][C]92[/C][C]61.25[/C][C]30.75[/C][/ROW]
[ROW][C]125[/C][C]95[/C][C]61.25[/C][C]33.75[/C][/ROW]
[ROW][C]126[/C][C]97[/C][C]61.25[/C][C]35.75[/C][/ROW]
[ROW][C]127[/C][C]87[/C][C]61.25[/C][C]25.75[/C][/ROW]
[ROW][C]128[/C][C]84[/C][C]61.25[/C][C]22.75[/C][/ROW]
[ROW][C]129[/C][C]84[/C][C]61.25[/C][C]22.75[/C][/ROW]
[ROW][C]130[/C][C]89[/C][C]61.25[/C][C]27.75[/C][/ROW]
[ROW][C]131[/C][C]103[/C][C]61.25[/C][C]41.75[/C][/ROW]
[ROW][C]132[/C][C]106[/C][C]61.25[/C][C]44.75[/C][/ROW]
[ROW][C]133[/C][C]109[/C][C]61.25[/C][C]47.75[/C][/ROW]
[ROW][C]134[/C][C]106[/C][C]61.25[/C][C]44.75[/C][/ROW]
[ROW][C]135[/C][C]105[/C][C]61.25[/C][C]43.75[/C][/ROW]
[ROW][C]136[/C][C]115[/C][C]61.25[/C][C]53.75[/C][/ROW]
[ROW][C]137[/C][C]120[/C][C]61.25[/C][C]58.75[/C][/ROW]
[ROW][C]138[/C][C]124[/C][C]61.25[/C][C]62.75[/C][/ROW]
[ROW][C]139[/C][C]121[/C][C]61.25[/C][C]59.75[/C][/ROW]
[ROW][C]140[/C][C]131[/C][C]61.25[/C][C]69.75[/C][/ROW]
[ROW][C]141[/C][C]139[/C][C]61.25[/C][C]77.75[/C][/ROW]
[ROW][C]142[/C][C]133[/C][C]61.25[/C][C]71.75[/C][/ROW]
[ROW][C]143[/C][C]119[/C][C]61.25[/C][C]57.75[/C][/ROW]
[ROW][C]144[/C][C]123[/C][C]61.25[/C][C]61.75[/C][/ROW]
[ROW][C]145[/C][C]120[/C][C]61.25[/C][C]58.75[/C][/ROW]
[ROW][C]146[/C][C]128[/C][C]61.25[/C][C]66.75[/C][/ROW]
[ROW][C]147[/C][C]134[/C][C]61.25[/C][C]72.75[/C][/ROW]
[ROW][C]148[/C][C]126[/C][C]61.25[/C][C]64.75[/C][/ROW]
[ROW][C]149[/C][C]115[/C][C]61.25[/C][C]53.75[/C][/ROW]
[ROW][C]150[/C][C]106[/C][C]61.25[/C][C]44.75[/C][/ROW]
[ROW][C]151[/C][C]99[/C][C]61.25[/C][C]37.75[/C][/ROW]
[ROW][C]152[/C][C]100[/C][C]61.25[/C][C]38.75[/C][/ROW]
[ROW][C]153[/C][C]99[/C][C]61.25[/C][C]37.75[/C][/ROW]
[ROW][C]154[/C][C]99[/C][C]61.25[/C][C]37.75[/C][/ROW]
[ROW][C]155[/C][C]100[/C][C]61.25[/C][C]38.75[/C][/ROW]
[ROW][C]156[/C][C]100[/C][C]61.25[/C][C]38.75[/C][/ROW]
[ROW][C]157[/C][C]108[/C][C]61.25[/C][C]46.75[/C][/ROW]
[ROW][C]158[/C][C]109[/C][C]61.25[/C][C]47.75[/C][/ROW]
[ROW][C]159[/C][C]115[/C][C]61.25[/C][C]53.75[/C][/ROW]
[ROW][C]160[/C][C]114[/C][C]61.25[/C][C]52.75[/C][/ROW]
[ROW][C]161[/C][C]108[/C][C]61.25[/C][C]46.75[/C][/ROW]
[ROW][C]162[/C][C]113[/C][C]61.25[/C][C]51.75[/C][/ROW]
[ROW][C]163[/C][C]118[/C][C]61.25[/C][C]56.75[/C][/ROW]
[ROW][C]164[/C][C]122[/C][C]61.25[/C][C]60.75[/C][/ROW]
[ROW][C]165[/C][C]118[/C][C]61.25[/C][C]56.75[/C][/ROW]
[ROW][C]166[/C][C]121[/C][C]61.25[/C][C]59.75[/C][/ROW]
[ROW][C]167[/C][C]118[/C][C]61.25[/C][C]56.75[/C][/ROW]
[ROW][C]168[/C][C]121[/C][C]61.25[/C][C]59.75[/C][/ROW]
[ROW][C]169[/C][C]121[/C][C]61.25[/C][C]59.75[/C][/ROW]
[ROW][C]170[/C][C]112[/C][C]61.25[/C][C]50.75[/C][/ROW]
[ROW][C]171[/C][C]119[/C][C]61.25[/C][C]57.75[/C][/ROW]
[ROW][C]172[/C][C]116[/C][C]61.25[/C][C]54.75[/C][/ROW]
[ROW][C]173[/C][C]110[/C][C]108.75[/C][C]1.25[/C][/ROW]
[ROW][C]174[/C][C]111[/C][C]108.75[/C][C]2.25[/C][/ROW]
[ROW][C]175[/C][C]106[/C][C]108.75[/C][C]-2.75[/C][/ROW]
[ROW][C]176[/C][C]108[/C][C]108.75[/C][C]-0.749999999999998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109707&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109707&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	5	61.2499999999999	-56.2499999999999
2	4	61.25	-57.25
3	5	61.25	-56.25
4	6	61.25	-55.25
5	6	61.25	-55.25
6	6	61.25	-55.25
7	7	61.25	-54.25
8	8	61.25	-53.25
9	7	61.25	-54.25
10	8	61.25	-53.25
11	7	61.25	-54.25
12	8	61.25	-53.25
13	8	61.25	-53.25
14	9	61.25	-52.25
15	9	61.25	-52.25
16	8	61.25	-53.25
17	9	61.25	-52.25
18	9	61.25	-52.25
19	10	61.25	-51.25
20	11	61.25	-50.25
21	12	61.25	-49.25
22	13	61.25	-48.25
23	13	61.25	-48.25
24	13	61.25	-48.25
25	14	61.25	-47.25
26	14	61.25	-47.25
27	15	61.25	-46.25
28	15	61.25	-46.25
29	16	61.25	-45.25
30	16	61.25	-45.25
31	17	61.25	-44.25
32	18	61.25	-43.25
33	19	61.25	-42.25
34	20	61.25	-41.25
35	22	61.25	-39.25
36	20	61.25	-41.25
37	22	61.25	-39.25
38	25	61.25	-36.25
39	24	61.25	-37.25
40	25	61.25	-36.25
41	28	61.25	-33.25
42	26	61.25	-35.25
43	27	61.25	-34.25
44	26	61.25	-35.25
45	25	61.25	-36.25
46	27	61.25	-34.25
47	28	61.25	-33.25
48	30	61.25	-31.25
49	31	61.25	-30.25
50	32	61.25	-29.25
51	34	61.25	-27.25
52	34	61.25	-27.25
53	33	61.25	-28.25
54	32	61.25	-29.25
55	34	61.25	-27.25
56	36	61.25	-25.25
57	37	61.25	-24.25
58	40	61.25	-21.25
59	38	61.25	-23.25
60	38	61.25	-23.25
61	36	61.25	-25.25
62	40	61.25	-21.25
63	40	61.25	-21.25
64	42	61.25	-19.25
65	44	61.25	-17.25
66	45	61.25	-16.25
67	47	61.25	-14.25
68	49	61.25	-12.25
69	47	61.25	-14.25
70	49	61.25	-12.25
71	52	61.25	-9.25
72	50	61.25	-11.25
73	50	61.25	-11.25
74	57	61.25	-4.25
75	58	61.25	-3.25
76	58	61.25	-3.25
77	58	61.25	-3.25
78	61	61.25	-0.250000000000002
79	61	61.25	-0.250000000000002
80	64	61.25	2.75
81	68	61.25	6.75
82	40	61.25	-21.25
83	34	61.25	-27.25
84	46	61.25	-15.25
85	36	61.25	-25.25
86	34	61.25	-27.25
87	45	61.25	-16.25
88	55	61.25	-6.25
89	50	61.25	-11.25
90	56	61.25	-5.25
91	72	61.25	10.75
92	76	61.25	14.75
93	78	61.25	16.75
94	77	61.25	15.75
95	90	61.25	28.75
96	88	61.25	26.75
97	97	61.25	35.75
98	93	61.25	31.75
99	84	61.25	22.75
100	67	61.25	5.75
101	72	61.25	10.75
102	75	61.25	13.75
103	71	61.25	9.75
104	75	61.25	13.75
105	90	61.25	28.75
106	78	61.25	16.75
107	73	61.25	11.75
108	62	61.25	0.749999999999999
109	65	61.25	3.75
110	61	61.25	-0.250000000000002
111	58	61.25	-3.25
112	33	61.25	-28.25
113	39	61.25	-22.25
114	56	61.25	-5.25
115	79	61.25	17.75
116	82	61.25	20.75
117	79	61.25	17.75
118	73	61.25	11.75
119	87	61.25	25.75
120	85	61.25	23.75
121	83	61.25	21.75
122	82	61.25	20.75
123	83	61.25	21.75
124	92	61.25	30.75
125	95	61.25	33.75
126	97	61.25	35.75
127	87	61.25	25.75
128	84	61.25	22.75
129	84	61.25	22.75
130	89	61.25	27.75
131	103	61.25	41.75
132	106	61.25	44.75
133	109	61.25	47.75
134	106	61.25	44.75
135	105	61.25	43.75
136	115	61.25	53.75
137	120	61.25	58.75
138	124	61.25	62.75
139	121	61.25	59.75
140	131	61.25	69.75
141	139	61.25	77.75
142	133	61.25	71.75
143	119	61.25	57.75
144	123	61.25	61.75
145	120	61.25	58.75
146	128	61.25	66.75
147	134	61.25	72.75
148	126	61.25	64.75
149	115	61.25	53.75
150	106	61.25	44.75
151	99	61.25	37.75
152	100	61.25	38.75
153	99	61.25	37.75
154	99	61.25	37.75
155	100	61.25	38.75
156	100	61.25	38.75
157	108	61.25	46.75
158	109	61.25	47.75
159	115	61.25	53.75
160	114	61.25	52.75
161	108	61.25	46.75
162	113	61.25	51.75
163	118	61.25	56.75
164	122	61.25	60.75
165	118	61.25	56.75
166	121	61.25	59.75
167	118	61.25	56.75
168	121	61.25	59.75
169	121	61.25	59.75
170	112	61.25	50.75
171	119	61.25	57.75
172	116	61.25	54.75
173	110	108.75	1.25
174	111	108.75	2.25
175	106	108.75	-2.75
176	108	108.75	-0.749999999999998

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	2.21515374435362e-05	4.43030748870724e-05	0.999977848462556
6	6.57679846073018e-07	1.31535969214604e-06	0.999999342320154
7	5.28665283840724e-08	1.05733056768145e-07	0.999999947133472
8	9.2552174220246e-09	1.85104348440492e-08	0.999999990744783
9	4.32125950972926e-10	8.64251901945852e-10	0.999999999567874
10	4.06797294499577e-11	8.13594588999154e-11	0.99999999995932
11	1.73165959924573e-12	3.46331919849146e-12	0.999999999998268
12	1.33580949459035e-13	2.6716189891807e-13	0.999999999999866
13	9.3254265079507e-15	1.86508530159014e-14	0.99999999999999
14	1.45122719679988e-15	2.90245439359976e-15	0.999999999999999
15	1.78968072354008e-16	3.57936144708015e-16	1
16	1.03623208263145e-17	2.07246416526291e-17	1
17	1.08900305901702e-18	2.17800611803405e-18	1
18	1.05451795687361e-19	2.10903591374721e-19	1
19	2.17132290648129e-20	4.34264581296257e-20	1
20	9.39846507534841e-21	1.87969301506968e-20	1
21	7.22398483383608e-21	1.44479696676722e-20	1
22	8.11307871163928e-21	1.62261574232786e-20	1
23	5.11921906194586e-21	1.02384381238917e-20	1
24	2.32749031594418e-21	4.65498063188836e-21	1
25	1.60008941994935e-21	3.2001788398987e-21	1
26	8.28786280260541e-22	1.65757256052108e-21	1
27	6.17907463758426e-22	1.23581492751685e-21	1
28	3.59970128628871e-22	7.19940257257741e-22	1
29	2.9052046186236e-22	5.8104092372472e-22	1
30	1.88762325740178e-22	3.77524651480356e-22	1
31	1.64790922930058e-22	3.29581845860117e-22	1
32	1.85259380913127e-22	3.70518761826253e-22	1
33	2.56696060002162e-22	5.13392120004324e-22	1
34	4.21000610075691e-22	8.42001220151382e-22	1
35	1.21628420903583e-21	2.43256841807167e-21	1
36	1.16692001928721e-21	2.33384003857442e-21	1
37	1.91949672135537e-21	3.83899344271075e-21	1
38	7.43825728250929e-21	1.48765145650186e-20	1
39	1.4042910046023e-20	2.80858200920459e-20	1
40	2.9087049747163e-20	5.81740994943261e-20	1
41	1.19776980268725e-19	2.3955396053745e-19	1
42	2.05419851298605e-19	4.1083970259721e-19	1
43	3.87921632389118e-19	7.75843264778237e-19	1
44	5.12940485859346e-19	1.02588097171869e-18	1
45	5.24576013100668e-19	1.04915202620134e-18	1
46	7.42006165874435e-19	1.48401233174887e-18	1
47	1.18038529429858e-18	2.36077058859716e-18	1
48	2.54537298537119e-18	5.09074597074238e-18	1
49	5.9025860734117e-18	1.18051721468234e-17	1
50	1.45678024048873e-17	2.91356048097746e-17	1
51	4.5783749977071e-17	9.1567499954142e-17	1
52	1.2216029925462e-16	2.4432059850924e-16	1
53	2.46369430264138e-16	4.92738860528276e-16	1
54	4.046766765073e-16	8.093533530146e-16	1
55	8.300185235424e-16	1.6600370470848e-15	1
56	2.09603731219794e-15	4.19207462439588e-15	0.999999999999998
57	5.53340347328185e-15	1.10668069465637e-14	0.999999999999994
58	2.0059049625047e-14	4.01180992500941e-14	0.99999999999998
59	4.87868414019479e-14	9.75736828038958e-14	0.999999999999951
60	1.10526351011025e-13	2.21052702022049e-13	0.99999999999989
61	1.96566028145476e-13	3.93132056290952e-13	0.999999999999803
62	5.00843799090323e-13	1.00168759818065e-12	0.9999999999995
63	1.19512967999512e-12	2.39025935999025e-12	0.999999999998805
64	3.26621351701692e-12	6.53242703403383e-12	0.999999999996734
65	1.00384910971043e-11	2.00769821942087e-11	0.999999999989961
66	3.08350311643701e-11	6.16700623287402e-11	0.999999999969165
67	1.03281140911375e-10	2.0656228182275e-10	0.999999999896719
68	3.6916665607294e-10	7.3833331214588e-10	0.999999999630833
69	9.9301744070032e-10	1.98603488140064e-09	0.999999999006983
70	2.86345554774264e-09	5.72691109548529e-09	0.999999997136544
71	9.37493082252903e-09	1.87498616450581e-08	0.999999990625069
72	2.39569929837122e-08	4.79139859674244e-08	0.999999976043007
73	5.71408412039268e-08	1.14281682407854e-07	0.999999942859159
74	1.94600739194467e-07	3.89201478388934e-07	0.99999980539926
75	6.10386959143062e-07	1.22077391828612e-06	0.99999938961304
76	1.6756595245087e-06	3.3513190490174e-06	0.999998324340476
77	4.12247700287882e-06	8.24495400575763e-06	0.999995877522997
78	1.06133542880592e-05	2.12267085761185e-05	0.999989386645712
79	2.44746861810533e-05	4.89493723621066e-05	0.99997552531382
80	5.80529451371888e-05	0.000116105890274378	0.999941947054863
81	0.000145400174204448	0.000290800348408896	0.999854599825795
82	0.00021429155096419	0.000428583101928381	0.999785708449036
83	0.000366725346144119	0.000733450692288239	0.999633274653856
84	0.000561375888565764	0.00112275177713153	0.999438624111434
85	0.00101280766585171	0.00202561533170342	0.998987192334148
86	0.00203893974340071	0.00407787948680141	0.9979610602566
87	0.0034936204161045	0.006987240832209	0.996506379583896
88	0.00585235420922774	0.0117047084184555	0.994147645790772
89	0.00980835938368476	0.0196167187673695	0.990191640616315
90	0.0161400588469416	0.0322801176938832	0.983859941153058
91	0.0298031331990612	0.0596062663981224	0.970196866800939
92	0.0531043069577587	0.106208613915517	0.946895693042241
93	0.0875088870510504	0.175017774102101	0.91249111294895
94	0.12924059167724	0.258481183354479	0.87075940832276
95	0.21025934587545	0.4205186917509	0.78974065412455
96	0.292695101140985	0.58539020228197	0.707304898859015
97	0.412202198773491	0.824404397546982	0.587797801226509
98	0.505356138330019	0.989287723339961	0.494643861669981
99	0.56093254604404	0.878134907911921	0.439067453955961
100	0.600009131553663	0.799981736892674	0.399990868446337
101	0.636162495858384	0.727675008283231	0.363837504141616
102	0.670217136354961	0.659565727290078	0.329782863645039
103	0.702725261747505	0.59454947650499	0.297274738252495
104	0.732067658921743	0.535864682156514	0.267932341078257
105	0.768970778512558	0.462058442974885	0.231029221487442
106	0.790906319426599	0.418187361146802	0.209093680573401
107	0.81247115862623	0.375057682747541	0.187528841373771
108	0.846492910464911	0.307014179070177	0.153507089535089
109	0.874293579145106	0.251412841709788	0.125706420854894
110	0.906095166375591	0.187809667248817	0.0939048336244086
111	0.938193224405907	0.123613551188186	0.061806775594093
112	0.989028191692501	0.0219436166149978	0.0109718083074989
113	0.998942444450507	0.00211511109898571	0.00105755554949285
114	0.999826947237963	0.000346105524074253	0.000173052762037126
115	0.999913040961887	0.000173918076224944	8.6959038112472e-05
116	0.999953478560975	9.30428780490198e-05	4.65214390245099e-05
117	0.999979474740173	4.105051965454e-05	2.052525982727e-05
118	0.999994759337411	1.0481325177621e-05	5.24066258881049e-06
119	0.999997185555284	5.62888943094957e-06	2.81444471547478e-06
120	0.999998684906054	2.63018789101223e-06	1.31509394550611e-06
121	0.99999950213906	9.95721879213336e-07	4.97860939606668e-07
122	0.99999984834229	3.03315419803329e-07	1.51657709901665e-07
123	0.999999957614864	8.47702711498887e-08	4.23851355749444e-08
124	0.999999977620547	4.47589067030494e-08	2.23794533515247e-08
125	0.999999985996353	2.80072946716826e-08	1.40036473358413e-08
126	0.999999990177621	1.96447575885381e-08	9.82237879426905e-09
127	0.99999999711756	5.76487897224107e-09	2.88243948612053e-09
128	0.999999999570982	8.58036511488659e-10	4.29018255744329e-10
129	0.999999999961212	7.75749938838015e-11	3.87874969419007e-11
130	0.999999999994663	1.06737009186411e-11	5.33685045932057e-12
131	0.999999999995684	8.63120749831957e-12	4.31560374915979e-12
132	0.999999999995544	8.9115492457928e-12	4.4557746228964e-12
133	0.999999999994433	1.11345943286655e-11	5.56729716433276e-12
134	0.999999999993804	1.23912278095895e-11	6.19561390479476e-12
135	0.999999999993462	1.30752742613852e-11	6.53763713069262e-12
136	0.999999999989688	2.06247294420309e-11	1.03123647210154e-11
137	0.999999999985081	2.98369641058642e-11	1.49184820529321e-11
138	0.999999999982464	3.50716688248067e-11	1.75358344124033e-11
139	0.999999999973065	5.38690588169702e-11	2.69345294084851e-11
140	0.999999999984386	3.12287842639898e-11	1.56143921319949e-11
141	0.999999999998344	3.31265670952793e-12	1.65632835476396e-12
142	0.9999999999995	1.00151060402465e-12	5.00755302012327e-13
143	0.999999999998903	2.1938024822031e-12	1.09690124110155e-12
144	0.999999999998299	3.40269301240706e-12	1.70134650620353e-12
145	0.999999999996397	7.20502932898138e-12	3.60251466449069e-12
146	0.999999999997363	5.27389055984395e-12	2.63694527992198e-12
147	0.999999999999661	6.77757149127059e-13	3.38878574563529e-13
148	0.999999999999768	4.64010339395509e-13	2.32005169697754e-13
149	0.999999999999214	1.57273683061881e-12	7.86368415309403e-13
150	0.99999999999741	5.17814238580308e-12	2.58907119290154e-12
151	0.999999999996792	6.41614922562462e-12	3.20807461281231e-12
152	0.999999999995911	8.17696536718644e-12	4.08848268359322e-12
153	0.999999999996773	6.45411703992088e-12	3.22705851996044e-12
154	0.999999999998409	3.1827766378281e-12	1.59138831891405e-12
155	0.999999999999479	1.04170206653071e-12	5.20851033265355e-13
156	0.999999999999964	7.13894103647715e-14	3.56947051823857e-14
157	0.99999999999995	9.89403131971015e-14	4.94701565985507e-14
158	0.99999999999993	1.40668737481329e-13	7.03343687406646e-14
159	0.999999999999444	1.11246427117081e-12	5.56232135585407e-13
160	0.999999999996355	7.29012358115776e-12	3.64506179057888e-12
161	0.999999999999119	1.76279830234036e-12	8.81399151170178e-13
162	0.999999999997869	4.2622871731719e-12	2.13114358658595e-12
163	0.999999999973663	5.26738543735649e-11	2.63369271867825e-11
164	0.999999999840905	3.18189308518937e-10	1.59094654259468e-10
165	0.999999997964574	4.07085303369629e-09	2.03542651684815e-09
166	0.9999999838497	3.23006002549004e-08	1.61503001274502e-08
167	0.999999797620107	4.04759785781143e-07	2.02379892890571e-07
168	0.999998639892827	2.72021434644751e-06	1.36010717322375e-06
169	0.999994279510577	1.14409788451143e-05	5.72048942255713e-06
170	0.999980288601388	3.94227972238934e-05	1.97113986119467e-05
171	0.99975266597555	0.000494668048901318	0.000247334024450659

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 2.21515374435362e-05 & 4.43030748870724e-05 & 0.999977848462556 \tabularnewline
6 & 6.57679846073018e-07 & 1.31535969214604e-06 & 0.999999342320154 \tabularnewline
7 & 5.28665283840724e-08 & 1.05733056768145e-07 & 0.999999947133472 \tabularnewline
8 & 9.2552174220246e-09 & 1.85104348440492e-08 & 0.999999990744783 \tabularnewline
9 & 4.32125950972926e-10 & 8.64251901945852e-10 & 0.999999999567874 \tabularnewline
10 & 4.06797294499577e-11 & 8.13594588999154e-11 & 0.99999999995932 \tabularnewline
11 & 1.73165959924573e-12 & 3.46331919849146e-12 & 0.999999999998268 \tabularnewline
12 & 1.33580949459035e-13 & 2.6716189891807e-13 & 0.999999999999866 \tabularnewline
13 & 9.3254265079507e-15 & 1.86508530159014e-14 & 0.99999999999999 \tabularnewline
14 & 1.45122719679988e-15 & 2.90245439359976e-15 & 0.999999999999999 \tabularnewline
15 & 1.78968072354008e-16 & 3.57936144708015e-16 & 1 \tabularnewline
16 & 1.03623208263145e-17 & 2.07246416526291e-17 & 1 \tabularnewline
17 & 1.08900305901702e-18 & 2.17800611803405e-18 & 1 \tabularnewline
18 & 1.05451795687361e-19 & 2.10903591374721e-19 & 1 \tabularnewline
19 & 2.17132290648129e-20 & 4.34264581296257e-20 & 1 \tabularnewline
20 & 9.39846507534841e-21 & 1.87969301506968e-20 & 1 \tabularnewline
21 & 7.22398483383608e-21 & 1.44479696676722e-20 & 1 \tabularnewline
22 & 8.11307871163928e-21 & 1.62261574232786e-20 & 1 \tabularnewline
23 & 5.11921906194586e-21 & 1.02384381238917e-20 & 1 \tabularnewline
24 & 2.32749031594418e-21 & 4.65498063188836e-21 & 1 \tabularnewline
25 & 1.60008941994935e-21 & 3.2001788398987e-21 & 1 \tabularnewline
26 & 8.28786280260541e-22 & 1.65757256052108e-21 & 1 \tabularnewline
27 & 6.17907463758426e-22 & 1.23581492751685e-21 & 1 \tabularnewline
28 & 3.59970128628871e-22 & 7.19940257257741e-22 & 1 \tabularnewline
29 & 2.9052046186236e-22 & 5.8104092372472e-22 & 1 \tabularnewline
30 & 1.88762325740178e-22 & 3.77524651480356e-22 & 1 \tabularnewline
31 & 1.64790922930058e-22 & 3.29581845860117e-22 & 1 \tabularnewline
32 & 1.85259380913127e-22 & 3.70518761826253e-22 & 1 \tabularnewline
33 & 2.56696060002162e-22 & 5.13392120004324e-22 & 1 \tabularnewline
34 & 4.21000610075691e-22 & 8.42001220151382e-22 & 1 \tabularnewline
35 & 1.21628420903583e-21 & 2.43256841807167e-21 & 1 \tabularnewline
36 & 1.16692001928721e-21 & 2.33384003857442e-21 & 1 \tabularnewline
37 & 1.91949672135537e-21 & 3.83899344271075e-21 & 1 \tabularnewline
38 & 7.43825728250929e-21 & 1.48765145650186e-20 & 1 \tabularnewline
39 & 1.4042910046023e-20 & 2.80858200920459e-20 & 1 \tabularnewline
40 & 2.9087049747163e-20 & 5.81740994943261e-20 & 1 \tabularnewline
41 & 1.19776980268725e-19 & 2.3955396053745e-19 & 1 \tabularnewline
42 & 2.05419851298605e-19 & 4.1083970259721e-19 & 1 \tabularnewline
43 & 3.87921632389118e-19 & 7.75843264778237e-19 & 1 \tabularnewline
44 & 5.12940485859346e-19 & 1.02588097171869e-18 & 1 \tabularnewline
45 & 5.24576013100668e-19 & 1.04915202620134e-18 & 1 \tabularnewline
46 & 7.42006165874435e-19 & 1.48401233174887e-18 & 1 \tabularnewline
47 & 1.18038529429858e-18 & 2.36077058859716e-18 & 1 \tabularnewline
48 & 2.54537298537119e-18 & 5.09074597074238e-18 & 1 \tabularnewline
49 & 5.9025860734117e-18 & 1.18051721468234e-17 & 1 \tabularnewline
50 & 1.45678024048873e-17 & 2.91356048097746e-17 & 1 \tabularnewline
51 & 4.5783749977071e-17 & 9.1567499954142e-17 & 1 \tabularnewline
52 & 1.2216029925462e-16 & 2.4432059850924e-16 & 1 \tabularnewline
53 & 2.46369430264138e-16 & 4.92738860528276e-16 & 1 \tabularnewline
54 & 4.046766765073e-16 & 8.093533530146e-16 & 1 \tabularnewline
55 & 8.300185235424e-16 & 1.6600370470848e-15 & 1 \tabularnewline
56 & 2.09603731219794e-15 & 4.19207462439588e-15 & 0.999999999999998 \tabularnewline
57 & 5.53340347328185e-15 & 1.10668069465637e-14 & 0.999999999999994 \tabularnewline
58 & 2.0059049625047e-14 & 4.01180992500941e-14 & 0.99999999999998 \tabularnewline
59 & 4.87868414019479e-14 & 9.75736828038958e-14 & 0.999999999999951 \tabularnewline
60 & 1.10526351011025e-13 & 2.21052702022049e-13 & 0.99999999999989 \tabularnewline
61 & 1.96566028145476e-13 & 3.93132056290952e-13 & 0.999999999999803 \tabularnewline
62 & 5.00843799090323e-13 & 1.00168759818065e-12 & 0.9999999999995 \tabularnewline
63 & 1.19512967999512e-12 & 2.39025935999025e-12 & 0.999999999998805 \tabularnewline
64 & 3.26621351701692e-12 & 6.53242703403383e-12 & 0.999999999996734 \tabularnewline
65 & 1.00384910971043e-11 & 2.00769821942087e-11 & 0.999999999989961 \tabularnewline
66 & 3.08350311643701e-11 & 6.16700623287402e-11 & 0.999999999969165 \tabularnewline
67 & 1.03281140911375e-10 & 2.0656228182275e-10 & 0.999999999896719 \tabularnewline
68 & 3.6916665607294e-10 & 7.3833331214588e-10 & 0.999999999630833 \tabularnewline
69 & 9.9301744070032e-10 & 1.98603488140064e-09 & 0.999999999006983 \tabularnewline
70 & 2.86345554774264e-09 & 5.72691109548529e-09 & 0.999999997136544 \tabularnewline
71 & 9.37493082252903e-09 & 1.87498616450581e-08 & 0.999999990625069 \tabularnewline
72 & 2.39569929837122e-08 & 4.79139859674244e-08 & 0.999999976043007 \tabularnewline
73 & 5.71408412039268e-08 & 1.14281682407854e-07 & 0.999999942859159 \tabularnewline
74 & 1.94600739194467e-07 & 3.89201478388934e-07 & 0.99999980539926 \tabularnewline
75 & 6.10386959143062e-07 & 1.22077391828612e-06 & 0.99999938961304 \tabularnewline
76 & 1.6756595245087e-06 & 3.3513190490174e-06 & 0.999998324340476 \tabularnewline
77 & 4.12247700287882e-06 & 8.24495400575763e-06 & 0.999995877522997 \tabularnewline
78 & 1.06133542880592e-05 & 2.12267085761185e-05 & 0.999989386645712 \tabularnewline
79 & 2.44746861810533e-05 & 4.89493723621066e-05 & 0.99997552531382 \tabularnewline
80 & 5.80529451371888e-05 & 0.000116105890274378 & 0.999941947054863 \tabularnewline
81 & 0.000145400174204448 & 0.000290800348408896 & 0.999854599825795 \tabularnewline
82 & 0.00021429155096419 & 0.000428583101928381 & 0.999785708449036 \tabularnewline
83 & 0.000366725346144119 & 0.000733450692288239 & 0.999633274653856 \tabularnewline
84 & 0.000561375888565764 & 0.00112275177713153 & 0.999438624111434 \tabularnewline
85 & 0.00101280766585171 & 0.00202561533170342 & 0.998987192334148 \tabularnewline
86 & 0.00203893974340071 & 0.00407787948680141 & 0.9979610602566 \tabularnewline
87 & 0.0034936204161045 & 0.006987240832209 & 0.996506379583896 \tabularnewline
88 & 0.00585235420922774 & 0.0117047084184555 & 0.994147645790772 \tabularnewline
89 & 0.00980835938368476 & 0.0196167187673695 & 0.990191640616315 \tabularnewline
90 & 0.0161400588469416 & 0.0322801176938832 & 0.983859941153058 \tabularnewline
91 & 0.0298031331990612 & 0.0596062663981224 & 0.970196866800939 \tabularnewline
92 & 0.0531043069577587 & 0.106208613915517 & 0.946895693042241 \tabularnewline
93 & 0.0875088870510504 & 0.175017774102101 & 0.91249111294895 \tabularnewline
94 & 0.12924059167724 & 0.258481183354479 & 0.87075940832276 \tabularnewline
95 & 0.21025934587545 & 0.4205186917509 & 0.78974065412455 \tabularnewline
96 & 0.292695101140985 & 0.58539020228197 & 0.707304898859015 \tabularnewline
97 & 0.412202198773491 & 0.824404397546982 & 0.587797801226509 \tabularnewline
98 & 0.505356138330019 & 0.989287723339961 & 0.494643861669981 \tabularnewline
99 & 0.56093254604404 & 0.878134907911921 & 0.439067453955961 \tabularnewline
100 & 0.600009131553663 & 0.799981736892674 & 0.399990868446337 \tabularnewline
101 & 0.636162495858384 & 0.727675008283231 & 0.363837504141616 \tabularnewline
102 & 0.670217136354961 & 0.659565727290078 & 0.329782863645039 \tabularnewline
103 & 0.702725261747505 & 0.59454947650499 & 0.297274738252495 \tabularnewline
104 & 0.732067658921743 & 0.535864682156514 & 0.267932341078257 \tabularnewline
105 & 0.768970778512558 & 0.462058442974885 & 0.231029221487442 \tabularnewline
106 & 0.790906319426599 & 0.418187361146802 & 0.209093680573401 \tabularnewline
107 & 0.81247115862623 & 0.375057682747541 & 0.187528841373771 \tabularnewline
108 & 0.846492910464911 & 0.307014179070177 & 0.153507089535089 \tabularnewline
109 & 0.874293579145106 & 0.251412841709788 & 0.125706420854894 \tabularnewline
110 & 0.906095166375591 & 0.187809667248817 & 0.0939048336244086 \tabularnewline
111 & 0.938193224405907 & 0.123613551188186 & 0.061806775594093 \tabularnewline
112 & 0.989028191692501 & 0.0219436166149978 & 0.0109718083074989 \tabularnewline
113 & 0.998942444450507 & 0.00211511109898571 & 0.00105755554949285 \tabularnewline
114 & 0.999826947237963 & 0.000346105524074253 & 0.000173052762037126 \tabularnewline
115 & 0.999913040961887 & 0.000173918076224944 & 8.6959038112472e-05 \tabularnewline
116 & 0.999953478560975 & 9.30428780490198e-05 & 4.65214390245099e-05 \tabularnewline
117 & 0.999979474740173 & 4.105051965454e-05 & 2.052525982727e-05 \tabularnewline
118 & 0.999994759337411 & 1.0481325177621e-05 & 5.24066258881049e-06 \tabularnewline
119 & 0.999997185555284 & 5.62888943094957e-06 & 2.81444471547478e-06 \tabularnewline
120 & 0.999998684906054 & 2.63018789101223e-06 & 1.31509394550611e-06 \tabularnewline
121 & 0.99999950213906 & 9.95721879213336e-07 & 4.97860939606668e-07 \tabularnewline
122 & 0.99999984834229 & 3.03315419803329e-07 & 1.51657709901665e-07 \tabularnewline
123 & 0.999999957614864 & 8.47702711498887e-08 & 4.23851355749444e-08 \tabularnewline
124 & 0.999999977620547 & 4.47589067030494e-08 & 2.23794533515247e-08 \tabularnewline
125 & 0.999999985996353 & 2.80072946716826e-08 & 1.40036473358413e-08 \tabularnewline
126 & 0.999999990177621 & 1.96447575885381e-08 & 9.82237879426905e-09 \tabularnewline
127 & 0.99999999711756 & 5.76487897224107e-09 & 2.88243948612053e-09 \tabularnewline
128 & 0.999999999570982 & 8.58036511488659e-10 & 4.29018255744329e-10 \tabularnewline
129 & 0.999999999961212 & 7.75749938838015e-11 & 3.87874969419007e-11 \tabularnewline
130 & 0.999999999994663 & 1.06737009186411e-11 & 5.33685045932057e-12 \tabularnewline
131 & 0.999999999995684 & 8.63120749831957e-12 & 4.31560374915979e-12 \tabularnewline
132 & 0.999999999995544 & 8.9115492457928e-12 & 4.4557746228964e-12 \tabularnewline
133 & 0.999999999994433 & 1.11345943286655e-11 & 5.56729716433276e-12 \tabularnewline
134 & 0.999999999993804 & 1.23912278095895e-11 & 6.19561390479476e-12 \tabularnewline
135 & 0.999999999993462 & 1.30752742613852e-11 & 6.53763713069262e-12 \tabularnewline
136 & 0.999999999989688 & 2.06247294420309e-11 & 1.03123647210154e-11 \tabularnewline
137 & 0.999999999985081 & 2.98369641058642e-11 & 1.49184820529321e-11 \tabularnewline
138 & 0.999999999982464 & 3.50716688248067e-11 & 1.75358344124033e-11 \tabularnewline
139 & 0.999999999973065 & 5.38690588169702e-11 & 2.69345294084851e-11 \tabularnewline
140 & 0.999999999984386 & 3.12287842639898e-11 & 1.56143921319949e-11 \tabularnewline
141 & 0.999999999998344 & 3.31265670952793e-12 & 1.65632835476396e-12 \tabularnewline
142 & 0.9999999999995 & 1.00151060402465e-12 & 5.00755302012327e-13 \tabularnewline
143 & 0.999999999998903 & 2.1938024822031e-12 & 1.09690124110155e-12 \tabularnewline
144 & 0.999999999998299 & 3.40269301240706e-12 & 1.70134650620353e-12 \tabularnewline
145 & 0.999999999996397 & 7.20502932898138e-12 & 3.60251466449069e-12 \tabularnewline
146 & 0.999999999997363 & 5.27389055984395e-12 & 2.63694527992198e-12 \tabularnewline
147 & 0.999999999999661 & 6.77757149127059e-13 & 3.38878574563529e-13 \tabularnewline
148 & 0.999999999999768 & 4.64010339395509e-13 & 2.32005169697754e-13 \tabularnewline
149 & 0.999999999999214 & 1.57273683061881e-12 & 7.86368415309403e-13 \tabularnewline
150 & 0.99999999999741 & 5.17814238580308e-12 & 2.58907119290154e-12 \tabularnewline
151 & 0.999999999996792 & 6.41614922562462e-12 & 3.20807461281231e-12 \tabularnewline
152 & 0.999999999995911 & 8.17696536718644e-12 & 4.08848268359322e-12 \tabularnewline
153 & 0.999999999996773 & 6.45411703992088e-12 & 3.22705851996044e-12 \tabularnewline
154 & 0.999999999998409 & 3.1827766378281e-12 & 1.59138831891405e-12 \tabularnewline
155 & 0.999999999999479 & 1.04170206653071e-12 & 5.20851033265355e-13 \tabularnewline
156 & 0.999999999999964 & 7.13894103647715e-14 & 3.56947051823857e-14 \tabularnewline
157 & 0.99999999999995 & 9.89403131971015e-14 & 4.94701565985507e-14 \tabularnewline
158 & 0.99999999999993 & 1.40668737481329e-13 & 7.03343687406646e-14 \tabularnewline
159 & 0.999999999999444 & 1.11246427117081e-12 & 5.56232135585407e-13 \tabularnewline
160 & 0.999999999996355 & 7.29012358115776e-12 & 3.64506179057888e-12 \tabularnewline
161 & 0.999999999999119 & 1.76279830234036e-12 & 8.81399151170178e-13 \tabularnewline
162 & 0.999999999997869 & 4.2622871731719e-12 & 2.13114358658595e-12 \tabularnewline
163 & 0.999999999973663 & 5.26738543735649e-11 & 2.63369271867825e-11 \tabularnewline
164 & 0.999999999840905 & 3.18189308518937e-10 & 1.59094654259468e-10 \tabularnewline
165 & 0.999999997964574 & 4.07085303369629e-09 & 2.03542651684815e-09 \tabularnewline
166 & 0.9999999838497 & 3.23006002549004e-08 & 1.61503001274502e-08 \tabularnewline
167 & 0.999999797620107 & 4.04759785781143e-07 & 2.02379892890571e-07 \tabularnewline
168 & 0.999998639892827 & 2.72021434644751e-06 & 1.36010717322375e-06 \tabularnewline
169 & 0.999994279510577 & 1.14409788451143e-05 & 5.72048942255713e-06 \tabularnewline
170 & 0.999980288601388 & 3.94227972238934e-05 & 1.97113986119467e-05 \tabularnewline
171 & 0.99975266597555 & 0.000494668048901318 & 0.000247334024450659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109707&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]5[/C][C]2.21515374435362e-05[/C][C]4.43030748870724e-05[/C][C]0.999977848462556[/C][/ROW]
[ROW][C]6[/C][C]6.57679846073018e-07[/C][C]1.31535969214604e-06[/C][C]0.999999342320154[/C][/ROW]
[ROW][C]7[/C][C]5.28665283840724e-08[/C][C]1.05733056768145e-07[/C][C]0.999999947133472[/C][/ROW]
[ROW][C]8[/C][C]9.2552174220246e-09[/C][C]1.85104348440492e-08[/C][C]0.999999990744783[/C][/ROW]
[ROW][C]9[/C][C]4.32125950972926e-10[/C][C]8.64251901945852e-10[/C][C]0.999999999567874[/C][/ROW]
[ROW][C]10[/C][C]4.06797294499577e-11[/C][C]8.13594588999154e-11[/C][C]0.99999999995932[/C][/ROW]
[ROW][C]11[/C][C]1.73165959924573e-12[/C][C]3.46331919849146e-12[/C][C]0.999999999998268[/C][/ROW]
[ROW][C]12[/C][C]1.33580949459035e-13[/C][C]2.6716189891807e-13[/C][C]0.999999999999866[/C][/ROW]
[ROW][C]13[/C][C]9.3254265079507e-15[/C][C]1.86508530159014e-14[/C][C]0.99999999999999[/C][/ROW]
[ROW][C]14[/C][C]1.45122719679988e-15[/C][C]2.90245439359976e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]15[/C][C]1.78968072354008e-16[/C][C]3.57936144708015e-16[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]1.03623208263145e-17[/C][C]2.07246416526291e-17[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]1.08900305901702e-18[/C][C]2.17800611803405e-18[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]1.05451795687361e-19[/C][C]2.10903591374721e-19[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]2.17132290648129e-20[/C][C]4.34264581296257e-20[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]9.39846507534841e-21[/C][C]1.87969301506968e-20[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]7.22398483383608e-21[/C][C]1.44479696676722e-20[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]8.11307871163928e-21[/C][C]1.62261574232786e-20[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]5.11921906194586e-21[/C][C]1.02384381238917e-20[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]2.32749031594418e-21[/C][C]4.65498063188836e-21[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.60008941994935e-21[/C][C]3.2001788398987e-21[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]8.28786280260541e-22[/C][C]1.65757256052108e-21[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]6.17907463758426e-22[/C][C]1.23581492751685e-21[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]3.59970128628871e-22[/C][C]7.19940257257741e-22[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]2.9052046186236e-22[/C][C]5.8104092372472e-22[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.88762325740178e-22[/C][C]3.77524651480356e-22[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.64790922930058e-22[/C][C]3.29581845860117e-22[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]1.85259380913127e-22[/C][C]3.70518761826253e-22[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]2.56696060002162e-22[/C][C]5.13392120004324e-22[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]4.21000610075691e-22[/C][C]8.42001220151382e-22[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]1.21628420903583e-21[/C][C]2.43256841807167e-21[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]1.16692001928721e-21[/C][C]2.33384003857442e-21[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]1.91949672135537e-21[/C][C]3.83899344271075e-21[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]7.43825728250929e-21[/C][C]1.48765145650186e-20[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]1.4042910046023e-20[/C][C]2.80858200920459e-20[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]2.9087049747163e-20[/C][C]5.81740994943261e-20[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]1.19776980268725e-19[/C][C]2.3955396053745e-19[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]2.05419851298605e-19[/C][C]4.1083970259721e-19[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]3.87921632389118e-19[/C][C]7.75843264778237e-19[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]5.12940485859346e-19[/C][C]1.02588097171869e-18[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]5.24576013100668e-19[/C][C]1.04915202620134e-18[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]7.42006165874435e-19[/C][C]1.48401233174887e-18[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]1.18038529429858e-18[/C][C]2.36077058859716e-18[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]2.54537298537119e-18[/C][C]5.09074597074238e-18[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]5.9025860734117e-18[/C][C]1.18051721468234e-17[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]1.45678024048873e-17[/C][C]2.91356048097746e-17[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]4.5783749977071e-17[/C][C]9.1567499954142e-17[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]1.2216029925462e-16[/C][C]2.4432059850924e-16[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]2.46369430264138e-16[/C][C]4.92738860528276e-16[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]4.046766765073e-16[/C][C]8.093533530146e-16[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]8.300185235424e-16[/C][C]1.6600370470848e-15[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]2.09603731219794e-15[/C][C]4.19207462439588e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]57[/C][C]5.53340347328185e-15[/C][C]1.10668069465637e-14[/C][C]0.999999999999994[/C][/ROW]
[ROW][C]58[/C][C]2.0059049625047e-14[/C][C]4.01180992500941e-14[/C][C]0.99999999999998[/C][/ROW]
[ROW][C]59[/C][C]4.87868414019479e-14[/C][C]9.75736828038958e-14[/C][C]0.999999999999951[/C][/ROW]
[ROW][C]60[/C][C]1.10526351011025e-13[/C][C]2.21052702022049e-13[/C][C]0.99999999999989[/C][/ROW]
[ROW][C]61[/C][C]1.96566028145476e-13[/C][C]3.93132056290952e-13[/C][C]0.999999999999803[/C][/ROW]
[ROW][C]62[/C][C]5.00843799090323e-13[/C][C]1.00168759818065e-12[/C][C]0.9999999999995[/C][/ROW]
[ROW][C]63[/C][C]1.19512967999512e-12[/C][C]2.39025935999025e-12[/C][C]0.999999999998805[/C][/ROW]
[ROW][C]64[/C][C]3.26621351701692e-12[/C][C]6.53242703403383e-12[/C][C]0.999999999996734[/C][/ROW]
[ROW][C]65[/C][C]1.00384910971043e-11[/C][C]2.00769821942087e-11[/C][C]0.999999999989961[/C][/ROW]
[ROW][C]66[/C][C]3.08350311643701e-11[/C][C]6.16700623287402e-11[/C][C]0.999999999969165[/C][/ROW]
[ROW][C]67[/C][C]1.03281140911375e-10[/C][C]2.0656228182275e-10[/C][C]0.999999999896719[/C][/ROW]
[ROW][C]68[/C][C]3.6916665607294e-10[/C][C]7.3833331214588e-10[/C][C]0.999999999630833[/C][/ROW]
[ROW][C]69[/C][C]9.9301744070032e-10[/C][C]1.98603488140064e-09[/C][C]0.999999999006983[/C][/ROW]
[ROW][C]70[/C][C]2.86345554774264e-09[/C][C]5.72691109548529e-09[/C][C]0.999999997136544[/C][/ROW]
[ROW][C]71[/C][C]9.37493082252903e-09[/C][C]1.87498616450581e-08[/C][C]0.999999990625069[/C][/ROW]
[ROW][C]72[/C][C]2.39569929837122e-08[/C][C]4.79139859674244e-08[/C][C]0.999999976043007[/C][/ROW]
[ROW][C]73[/C][C]5.71408412039268e-08[/C][C]1.14281682407854e-07[/C][C]0.999999942859159[/C][/ROW]
[ROW][C]74[/C][C]1.94600739194467e-07[/C][C]3.89201478388934e-07[/C][C]0.99999980539926[/C][/ROW]
[ROW][C]75[/C][C]6.10386959143062e-07[/C][C]1.22077391828612e-06[/C][C]0.99999938961304[/C][/ROW]
[ROW][C]76[/C][C]1.6756595245087e-06[/C][C]3.3513190490174e-06[/C][C]0.999998324340476[/C][/ROW]
[ROW][C]77[/C][C]4.12247700287882e-06[/C][C]8.24495400575763e-06[/C][C]0.999995877522997[/C][/ROW]
[ROW][C]78[/C][C]1.06133542880592e-05[/C][C]2.12267085761185e-05[/C][C]0.999989386645712[/C][/ROW]
[ROW][C]79[/C][C]2.44746861810533e-05[/C][C]4.89493723621066e-05[/C][C]0.99997552531382[/C][/ROW]
[ROW][C]80[/C][C]5.80529451371888e-05[/C][C]0.000116105890274378[/C][C]0.999941947054863[/C][/ROW]
[ROW][C]81[/C][C]0.000145400174204448[/C][C]0.000290800348408896[/C][C]0.999854599825795[/C][/ROW]
[ROW][C]82[/C][C]0.00021429155096419[/C][C]0.000428583101928381[/C][C]0.999785708449036[/C][/ROW]
[ROW][C]83[/C][C]0.000366725346144119[/C][C]0.000733450692288239[/C][C]0.999633274653856[/C][/ROW]
[ROW][C]84[/C][C]0.000561375888565764[/C][C]0.00112275177713153[/C][C]0.999438624111434[/C][/ROW]
[ROW][C]85[/C][C]0.00101280766585171[/C][C]0.00202561533170342[/C][C]0.998987192334148[/C][/ROW]
[ROW][C]86[/C][C]0.00203893974340071[/C][C]0.00407787948680141[/C][C]0.9979610602566[/C][/ROW]
[ROW][C]87[/C][C]0.0034936204161045[/C][C]0.006987240832209[/C][C]0.996506379583896[/C][/ROW]
[ROW][C]88[/C][C]0.00585235420922774[/C][C]0.0117047084184555[/C][C]0.994147645790772[/C][/ROW]
[ROW][C]89[/C][C]0.00980835938368476[/C][C]0.0196167187673695[/C][C]0.990191640616315[/C][/ROW]
[ROW][C]90[/C][C]0.0161400588469416[/C][C]0.0322801176938832[/C][C]0.983859941153058[/C][/ROW]
[ROW][C]91[/C][C]0.0298031331990612[/C][C]0.0596062663981224[/C][C]0.970196866800939[/C][/ROW]
[ROW][C]92[/C][C]0.0531043069577587[/C][C]0.106208613915517[/C][C]0.946895693042241[/C][/ROW]
[ROW][C]93[/C][C]0.0875088870510504[/C][C]0.175017774102101[/C][C]0.91249111294895[/C][/ROW]
[ROW][C]94[/C][C]0.12924059167724[/C][C]0.258481183354479[/C][C]0.87075940832276[/C][/ROW]
[ROW][C]95[/C][C]0.21025934587545[/C][C]0.4205186917509[/C][C]0.78974065412455[/C][/ROW]
[ROW][C]96[/C][C]0.292695101140985[/C][C]0.58539020228197[/C][C]0.707304898859015[/C][/ROW]
[ROW][C]97[/C][C]0.412202198773491[/C][C]0.824404397546982[/C][C]0.587797801226509[/C][/ROW]
[ROW][C]98[/C][C]0.505356138330019[/C][C]0.989287723339961[/C][C]0.494643861669981[/C][/ROW]
[ROW][C]99[/C][C]0.56093254604404[/C][C]0.878134907911921[/C][C]0.439067453955961[/C][/ROW]
[ROW][C]100[/C][C]0.600009131553663[/C][C]0.799981736892674[/C][C]0.399990868446337[/C][/ROW]
[ROW][C]101[/C][C]0.636162495858384[/C][C]0.727675008283231[/C][C]0.363837504141616[/C][/ROW]
[ROW][C]102[/C][C]0.670217136354961[/C][C]0.659565727290078[/C][C]0.329782863645039[/C][/ROW]
[ROW][C]103[/C][C]0.702725261747505[/C][C]0.59454947650499[/C][C]0.297274738252495[/C][/ROW]
[ROW][C]104[/C][C]0.732067658921743[/C][C]0.535864682156514[/C][C]0.267932341078257[/C][/ROW]
[ROW][C]105[/C][C]0.768970778512558[/C][C]0.462058442974885[/C][C]0.231029221487442[/C][/ROW]
[ROW][C]106[/C][C]0.790906319426599[/C][C]0.418187361146802[/C][C]0.209093680573401[/C][/ROW]
[ROW][C]107[/C][C]0.81247115862623[/C][C]0.375057682747541[/C][C]0.187528841373771[/C][/ROW]
[ROW][C]108[/C][C]0.846492910464911[/C][C]0.307014179070177[/C][C]0.153507089535089[/C][/ROW]
[ROW][C]109[/C][C]0.874293579145106[/C][C]0.251412841709788[/C][C]0.125706420854894[/C][/ROW]
[ROW][C]110[/C][C]0.906095166375591[/C][C]0.187809667248817[/C][C]0.0939048336244086[/C][/ROW]
[ROW][C]111[/C][C]0.938193224405907[/C][C]0.123613551188186[/C][C]0.061806775594093[/C][/ROW]
[ROW][C]112[/C][C]0.989028191692501[/C][C]0.0219436166149978[/C][C]0.0109718083074989[/C][/ROW]
[ROW][C]113[/C][C]0.998942444450507[/C][C]0.00211511109898571[/C][C]0.00105755554949285[/C][/ROW]
[ROW][C]114[/C][C]0.999826947237963[/C][C]0.000346105524074253[/C][C]0.000173052762037126[/C][/ROW]
[ROW][C]115[/C][C]0.999913040961887[/C][C]0.000173918076224944[/C][C]8.6959038112472e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999953478560975[/C][C]9.30428780490198e-05[/C][C]4.65214390245099e-05[/C][/ROW]
[ROW][C]117[/C][C]0.999979474740173[/C][C]4.105051965454e-05[/C][C]2.052525982727e-05[/C][/ROW]
[ROW][C]118[/C][C]0.999994759337411[/C][C]1.0481325177621e-05[/C][C]5.24066258881049e-06[/C][/ROW]
[ROW][C]119[/C][C]0.999997185555284[/C][C]5.62888943094957e-06[/C][C]2.81444471547478e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999998684906054[/C][C]2.63018789101223e-06[/C][C]1.31509394550611e-06[/C][/ROW]
[ROW][C]121[/C][C]0.99999950213906[/C][C]9.95721879213336e-07[/C][C]4.97860939606668e-07[/C][/ROW]
[ROW][C]122[/C][C]0.99999984834229[/C][C]3.03315419803329e-07[/C][C]1.51657709901665e-07[/C][/ROW]
[ROW][C]123[/C][C]0.999999957614864[/C][C]8.47702711498887e-08[/C][C]4.23851355749444e-08[/C][/ROW]
[ROW][C]124[/C][C]0.999999977620547[/C][C]4.47589067030494e-08[/C][C]2.23794533515247e-08[/C][/ROW]
[ROW][C]125[/C][C]0.999999985996353[/C][C]2.80072946716826e-08[/C][C]1.40036473358413e-08[/C][/ROW]
[ROW][C]126[/C][C]0.999999990177621[/C][C]1.96447575885381e-08[/C][C]9.82237879426905e-09[/C][/ROW]
[ROW][C]127[/C][C]0.99999999711756[/C][C]5.76487897224107e-09[/C][C]2.88243948612053e-09[/C][/ROW]
[ROW][C]128[/C][C]0.999999999570982[/C][C]8.58036511488659e-10[/C][C]4.29018255744329e-10[/C][/ROW]
[ROW][C]129[/C][C]0.999999999961212[/C][C]7.75749938838015e-11[/C][C]3.87874969419007e-11[/C][/ROW]
[ROW][C]130[/C][C]0.999999999994663[/C][C]1.06737009186411e-11[/C][C]5.33685045932057e-12[/C][/ROW]
[ROW][C]131[/C][C]0.999999999995684[/C][C]8.63120749831957e-12[/C][C]4.31560374915979e-12[/C][/ROW]
[ROW][C]132[/C][C]0.999999999995544[/C][C]8.9115492457928e-12[/C][C]4.4557746228964e-12[/C][/ROW]
[ROW][C]133[/C][C]0.999999999994433[/C][C]1.11345943286655e-11[/C][C]5.56729716433276e-12[/C][/ROW]
[ROW][C]134[/C][C]0.999999999993804[/C][C]1.23912278095895e-11[/C][C]6.19561390479476e-12[/C][/ROW]
[ROW][C]135[/C][C]0.999999999993462[/C][C]1.30752742613852e-11[/C][C]6.53763713069262e-12[/C][/ROW]
[ROW][C]136[/C][C]0.999999999989688[/C][C]2.06247294420309e-11[/C][C]1.03123647210154e-11[/C][/ROW]
[ROW][C]137[/C][C]0.999999999985081[/C][C]2.98369641058642e-11[/C][C]1.49184820529321e-11[/C][/ROW]
[ROW][C]138[/C][C]0.999999999982464[/C][C]3.50716688248067e-11[/C][C]1.75358344124033e-11[/C][/ROW]
[ROW][C]139[/C][C]0.999999999973065[/C][C]5.38690588169702e-11[/C][C]2.69345294084851e-11[/C][/ROW]
[ROW][C]140[/C][C]0.999999999984386[/C][C]3.12287842639898e-11[/C][C]1.56143921319949e-11[/C][/ROW]
[ROW][C]141[/C][C]0.999999999998344[/C][C]3.31265670952793e-12[/C][C]1.65632835476396e-12[/C][/ROW]
[ROW][C]142[/C][C]0.9999999999995[/C][C]1.00151060402465e-12[/C][C]5.00755302012327e-13[/C][/ROW]
[ROW][C]143[/C][C]0.999999999998903[/C][C]2.1938024822031e-12[/C][C]1.09690124110155e-12[/C][/ROW]
[ROW][C]144[/C][C]0.999999999998299[/C][C]3.40269301240706e-12[/C][C]1.70134650620353e-12[/C][/ROW]
[ROW][C]145[/C][C]0.999999999996397[/C][C]7.20502932898138e-12[/C][C]3.60251466449069e-12[/C][/ROW]
[ROW][C]146[/C][C]0.999999999997363[/C][C]5.27389055984395e-12[/C][C]2.63694527992198e-12[/C][/ROW]
[ROW][C]147[/C][C]0.999999999999661[/C][C]6.77757149127059e-13[/C][C]3.38878574563529e-13[/C][/ROW]
[ROW][C]148[/C][C]0.999999999999768[/C][C]4.64010339395509e-13[/C][C]2.32005169697754e-13[/C][/ROW]
[ROW][C]149[/C][C]0.999999999999214[/C][C]1.57273683061881e-12[/C][C]7.86368415309403e-13[/C][/ROW]
[ROW][C]150[/C][C]0.99999999999741[/C][C]5.17814238580308e-12[/C][C]2.58907119290154e-12[/C][/ROW]
[ROW][C]151[/C][C]0.999999999996792[/C][C]6.41614922562462e-12[/C][C]3.20807461281231e-12[/C][/ROW]
[ROW][C]152[/C][C]0.999999999995911[/C][C]8.17696536718644e-12[/C][C]4.08848268359322e-12[/C][/ROW]
[ROW][C]153[/C][C]0.999999999996773[/C][C]6.45411703992088e-12[/C][C]3.22705851996044e-12[/C][/ROW]
[ROW][C]154[/C][C]0.999999999998409[/C][C]3.1827766378281e-12[/C][C]1.59138831891405e-12[/C][/ROW]
[ROW][C]155[/C][C]0.999999999999479[/C][C]1.04170206653071e-12[/C][C]5.20851033265355e-13[/C][/ROW]
[ROW][C]156[/C][C]0.999999999999964[/C][C]7.13894103647715e-14[/C][C]3.56947051823857e-14[/C][/ROW]
[ROW][C]157[/C][C]0.99999999999995[/C][C]9.89403131971015e-14[/C][C]4.94701565985507e-14[/C][/ROW]
[ROW][C]158[/C][C]0.99999999999993[/C][C]1.40668737481329e-13[/C][C]7.03343687406646e-14[/C][/ROW]
[ROW][C]159[/C][C]0.999999999999444[/C][C]1.11246427117081e-12[/C][C]5.56232135585407e-13[/C][/ROW]
[ROW][C]160[/C][C]0.999999999996355[/C][C]7.29012358115776e-12[/C][C]3.64506179057888e-12[/C][/ROW]
[ROW][C]161[/C][C]0.999999999999119[/C][C]1.76279830234036e-12[/C][C]8.81399151170178e-13[/C][/ROW]
[ROW][C]162[/C][C]0.999999999997869[/C][C]4.2622871731719e-12[/C][C]2.13114358658595e-12[/C][/ROW]
[ROW][C]163[/C][C]0.999999999973663[/C][C]5.26738543735649e-11[/C][C]2.63369271867825e-11[/C][/ROW]
[ROW][C]164[/C][C]0.999999999840905[/C][C]3.18189308518937e-10[/C][C]1.59094654259468e-10[/C][/ROW]
[ROW][C]165[/C][C]0.999999997964574[/C][C]4.07085303369629e-09[/C][C]2.03542651684815e-09[/C][/ROW]
[ROW][C]166[/C][C]0.9999999838497[/C][C]3.23006002549004e-08[/C][C]1.61503001274502e-08[/C][/ROW]
[ROW][C]167[/C][C]0.999999797620107[/C][C]4.04759785781143e-07[/C][C]2.02379892890571e-07[/C][/ROW]
[ROW][C]168[/C][C]0.999998639892827[/C][C]2.72021434644751e-06[/C][C]1.36010717322375e-06[/C][/ROW]
[ROW][C]169[/C][C]0.999994279510577[/C][C]1.14409788451143e-05[/C][C]5.72048942255713e-06[/C][/ROW]
[ROW][C]170[/C][C]0.999980288601388[/C][C]3.94227972238934e-05[/C][C]1.97113986119467e-05[/C][/ROW]
[ROW][C]171[/C][C]0.99975266597555[/C][C]0.000494668048901318[/C][C]0.000247334024450659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109707&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109707&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
5	2.21515374435362e-05	4.43030748870724e-05	0.999977848462556
6	6.57679846073018e-07	1.31535969214604e-06	0.999999342320154
7	5.28665283840724e-08	1.05733056768145e-07	0.999999947133472
8	9.2552174220246e-09	1.85104348440492e-08	0.999999990744783
9	4.32125950972926e-10	8.64251901945852e-10	0.999999999567874
10	4.06797294499577e-11	8.13594588999154e-11	0.99999999995932
11	1.73165959924573e-12	3.46331919849146e-12	0.999999999998268
12	1.33580949459035e-13	2.6716189891807e-13	0.999999999999866
13	9.3254265079507e-15	1.86508530159014e-14	0.99999999999999
14	1.45122719679988e-15	2.90245439359976e-15	0.999999999999999
15	1.78968072354008e-16	3.57936144708015e-16	1
16	1.03623208263145e-17	2.07246416526291e-17	1
17	1.08900305901702e-18	2.17800611803405e-18	1
18	1.05451795687361e-19	2.10903591374721e-19	1
19	2.17132290648129e-20	4.34264581296257e-20	1
20	9.39846507534841e-21	1.87969301506968e-20	1
21	7.22398483383608e-21	1.44479696676722e-20	1
22	8.11307871163928e-21	1.62261574232786e-20	1
23	5.11921906194586e-21	1.02384381238917e-20	1
24	2.32749031594418e-21	4.65498063188836e-21	1
25	1.60008941994935e-21	3.2001788398987e-21	1
26	8.28786280260541e-22	1.65757256052108e-21	1
27	6.17907463758426e-22	1.23581492751685e-21	1
28	3.59970128628871e-22	7.19940257257741e-22	1
29	2.9052046186236e-22	5.8104092372472e-22	1
30	1.88762325740178e-22	3.77524651480356e-22	1
31	1.64790922930058e-22	3.29581845860117e-22	1
32	1.85259380913127e-22	3.70518761826253e-22	1
33	2.56696060002162e-22	5.13392120004324e-22	1
34	4.21000610075691e-22	8.42001220151382e-22	1
35	1.21628420903583e-21	2.43256841807167e-21	1
36	1.16692001928721e-21	2.33384003857442e-21	1
37	1.91949672135537e-21	3.83899344271075e-21	1
38	7.43825728250929e-21	1.48765145650186e-20	1
39	1.4042910046023e-20	2.80858200920459e-20	1
40	2.9087049747163e-20	5.81740994943261e-20	1
41	1.19776980268725e-19	2.3955396053745e-19	1
42	2.05419851298605e-19	4.1083970259721e-19	1
43	3.87921632389118e-19	7.75843264778237e-19	1
44	5.12940485859346e-19	1.02588097171869e-18	1
45	5.24576013100668e-19	1.04915202620134e-18	1
46	7.42006165874435e-19	1.48401233174887e-18	1
47	1.18038529429858e-18	2.36077058859716e-18	1
48	2.54537298537119e-18	5.09074597074238e-18	1
49	5.9025860734117e-18	1.18051721468234e-17	1
50	1.45678024048873e-17	2.91356048097746e-17	1
51	4.5783749977071e-17	9.1567499954142e-17	1
52	1.2216029925462e-16	2.4432059850924e-16	1
53	2.46369430264138e-16	4.92738860528276e-16	1
54	4.046766765073e-16	8.093533530146e-16	1
55	8.300185235424e-16	1.6600370470848e-15	1
56	2.09603731219794e-15	4.19207462439588e-15	0.999999999999998
57	5.53340347328185e-15	1.10668069465637e-14	0.999999999999994
58	2.0059049625047e-14	4.01180992500941e-14	0.99999999999998
59	4.87868414019479e-14	9.75736828038958e-14	0.999999999999951
60	1.10526351011025e-13	2.21052702022049e-13	0.99999999999989
61	1.96566028145476e-13	3.93132056290952e-13	0.999999999999803
62	5.00843799090323e-13	1.00168759818065e-12	0.9999999999995
63	1.19512967999512e-12	2.39025935999025e-12	0.999999999998805
64	3.26621351701692e-12	6.53242703403383e-12	0.999999999996734
65	1.00384910971043e-11	2.00769821942087e-11	0.999999999989961
66	3.08350311643701e-11	6.16700623287402e-11	0.999999999969165
67	1.03281140911375e-10	2.0656228182275e-10	0.999999999896719
68	3.6916665607294e-10	7.3833331214588e-10	0.999999999630833
69	9.9301744070032e-10	1.98603488140064e-09	0.999999999006983
70	2.86345554774264e-09	5.72691109548529e-09	0.999999997136544
71	9.37493082252903e-09	1.87498616450581e-08	0.999999990625069
72	2.39569929837122e-08	4.79139859674244e-08	0.999999976043007
73	5.71408412039268e-08	1.14281682407854e-07	0.999999942859159
74	1.94600739194467e-07	3.89201478388934e-07	0.99999980539926
75	6.10386959143062e-07	1.22077391828612e-06	0.99999938961304
76	1.6756595245087e-06	3.3513190490174e-06	0.999998324340476
77	4.12247700287882e-06	8.24495400575763e-06	0.999995877522997
78	1.06133542880592e-05	2.12267085761185e-05	0.999989386645712
79	2.44746861810533e-05	4.89493723621066e-05	0.99997552531382
80	5.80529451371888e-05	0.000116105890274378	0.999941947054863
81	0.000145400174204448	0.000290800348408896	0.999854599825795
82	0.00021429155096419	0.000428583101928381	0.999785708449036
83	0.000366725346144119	0.000733450692288239	0.999633274653856
84	0.000561375888565764	0.00112275177713153	0.999438624111434
85	0.00101280766585171	0.00202561533170342	0.998987192334148
86	0.00203893974340071	0.00407787948680141	0.9979610602566
87	0.0034936204161045	0.006987240832209	0.996506379583896
88	0.00585235420922774	0.0117047084184555	0.994147645790772
89	0.00980835938368476	0.0196167187673695	0.990191640616315
90	0.0161400588469416	0.0322801176938832	0.983859941153058
91	0.0298031331990612	0.0596062663981224	0.970196866800939
92	0.0531043069577587	0.106208613915517	0.946895693042241
93	0.0875088870510504	0.175017774102101	0.91249111294895
94	0.12924059167724	0.258481183354479	0.87075940832276
95	0.21025934587545	0.4205186917509	0.78974065412455
96	0.292695101140985	0.58539020228197	0.707304898859015
97	0.412202198773491	0.824404397546982	0.587797801226509
98	0.505356138330019	0.989287723339961	0.494643861669981
99	0.56093254604404	0.878134907911921	0.439067453955961
100	0.600009131553663	0.799981736892674	0.399990868446337
101	0.636162495858384	0.727675008283231	0.363837504141616
102	0.670217136354961	0.659565727290078	0.329782863645039
103	0.702725261747505	0.59454947650499	0.297274738252495
104	0.732067658921743	0.535864682156514	0.267932341078257
105	0.768970778512558	0.462058442974885	0.231029221487442
106	0.790906319426599	0.418187361146802	0.209093680573401
107	0.81247115862623	0.375057682747541	0.187528841373771
108	0.846492910464911	0.307014179070177	0.153507089535089
109	0.874293579145106	0.251412841709788	0.125706420854894
110	0.906095166375591	0.187809667248817	0.0939048336244086
111	0.938193224405907	0.123613551188186	0.061806775594093
112	0.989028191692501	0.0219436166149978	0.0109718083074989
113	0.998942444450507	0.00211511109898571	0.00105755554949285
114	0.999826947237963	0.000346105524074253	0.000173052762037126
115	0.999913040961887	0.000173918076224944	8.6959038112472e-05
116	0.999953478560975	9.30428780490198e-05	4.65214390245099e-05
117	0.999979474740173	4.105051965454e-05	2.052525982727e-05
118	0.999994759337411	1.0481325177621e-05	5.24066258881049e-06
119	0.999997185555284	5.62888943094957e-06	2.81444471547478e-06
120	0.999998684906054	2.63018789101223e-06	1.31509394550611e-06
121	0.99999950213906	9.95721879213336e-07	4.97860939606668e-07
122	0.99999984834229	3.03315419803329e-07	1.51657709901665e-07
123	0.999999957614864	8.47702711498887e-08	4.23851355749444e-08
124	0.999999977620547	4.47589067030494e-08	2.23794533515247e-08
125	0.999999985996353	2.80072946716826e-08	1.40036473358413e-08
126	0.999999990177621	1.96447575885381e-08	9.82237879426905e-09
127	0.99999999711756	5.76487897224107e-09	2.88243948612053e-09
128	0.999999999570982	8.58036511488659e-10	4.29018255744329e-10
129	0.999999999961212	7.75749938838015e-11	3.87874969419007e-11
130	0.999999999994663	1.06737009186411e-11	5.33685045932057e-12
131	0.999999999995684	8.63120749831957e-12	4.31560374915979e-12
132	0.999999999995544	8.9115492457928e-12	4.4557746228964e-12
133	0.999999999994433	1.11345943286655e-11	5.56729716433276e-12
134	0.999999999993804	1.23912278095895e-11	6.19561390479476e-12
135	0.999999999993462	1.30752742613852e-11	6.53763713069262e-12
136	0.999999999989688	2.06247294420309e-11	1.03123647210154e-11
137	0.999999999985081	2.98369641058642e-11	1.49184820529321e-11
138	0.999999999982464	3.50716688248067e-11	1.75358344124033e-11
139	0.999999999973065	5.38690588169702e-11	2.69345294084851e-11
140	0.999999999984386	3.12287842639898e-11	1.56143921319949e-11
141	0.999999999998344	3.31265670952793e-12	1.65632835476396e-12
142	0.9999999999995	1.00151060402465e-12	5.00755302012327e-13
143	0.999999999998903	2.1938024822031e-12	1.09690124110155e-12
144	0.999999999998299	3.40269301240706e-12	1.70134650620353e-12
145	0.999999999996397	7.20502932898138e-12	3.60251466449069e-12
146	0.999999999997363	5.27389055984395e-12	2.63694527992198e-12
147	0.999999999999661	6.77757149127059e-13	3.38878574563529e-13
148	0.999999999999768	4.64010339395509e-13	2.32005169697754e-13
149	0.999999999999214	1.57273683061881e-12	7.86368415309403e-13
150	0.99999999999741	5.17814238580308e-12	2.58907119290154e-12
151	0.999999999996792	6.41614922562462e-12	3.20807461281231e-12
152	0.999999999995911	8.17696536718644e-12	4.08848268359322e-12
153	0.999999999996773	6.45411703992088e-12	3.22705851996044e-12
154	0.999999999998409	3.1827766378281e-12	1.59138831891405e-12
155	0.999999999999479	1.04170206653071e-12	5.20851033265355e-13
156	0.999999999999964	7.13894103647715e-14	3.56947051823857e-14
157	0.99999999999995	9.89403131971015e-14	4.94701565985507e-14
158	0.99999999999993	1.40668737481329e-13	7.03343687406646e-14
159	0.999999999999444	1.11246427117081e-12	5.56232135585407e-13
160	0.999999999996355	7.29012358115776e-12	3.64506179057888e-12
161	0.999999999999119	1.76279830234036e-12	8.81399151170178e-13
162	0.999999999997869	4.2622871731719e-12	2.13114358658595e-12
163	0.999999999973663	5.26738543735649e-11	2.63369271867825e-11
164	0.999999999840905	3.18189308518937e-10	1.59094654259468e-10
165	0.999999997964574	4.07085303369629e-09	2.03542651684815e-09
166	0.9999999838497	3.23006002549004e-08	1.61503001274502e-08
167	0.999999797620107	4.04759785781143e-07	2.02379892890571e-07
168	0.999998639892827	2.72021434644751e-06	1.36010717322375e-06
169	0.999994279510577	1.14409788451143e-05	5.72048942255713e-06
170	0.999980288601388	3.94227972238934e-05	1.97113986119467e-05
171	0.99975266597555	0.000494668048901318	0.000247334024450659

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	142	0.850299401197605	NOK
5% type I error level	146	0.874251497005988	NOK
10% type I error level	147	0.880239520958084	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 & 142 & 0.850299401197605 & NOK \tabularnewline
5% type I error level & 146 & 0.874251497005988 & NOK \tabularnewline
10% type I error level & 147 & 0.880239520958084 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109707&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]142[/C][C]0.850299401197605[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]146[/C][C]0.874251497005988[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]147[/C][C]0.880239520958084[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109707&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109707&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	142	0.850299401197605	NOK
5% type I error level	146	0.874251497005988	NOK
10% type I error level	147	0.880239520958084	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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code