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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 19 Dec 2012 09:14:52 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/19/t1355926551dg76grcx9t57u0t.htm/, Retrieved Fri, 03 May 2024 18:30:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201979, Retrieved Fri, 03 May 2024 18:30:22 +0000

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

-       [Multiple Regression] [] [2012-12-19 14:14:52] [9556601f32d45cd6b13539aa40ba329c] [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	11 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201979&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201979&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201979&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	11 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
weeks[t] = + 3.60756189327435 -0.179786952486727uselimit[t] + 0.399208217517742T40[t] -1.59378146047458T20[t] -0.358443823282814Used[t] + 0.339910161109647CorrectAnalysis[t] + 0.213213949183679useful[t] + 0.144838684250187outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
weeks[t] =  +  3.60756189327435 -0.179786952486727uselimit[t] +  0.399208217517742T40[t] -1.59378146047458T20[t] -0.358443823282814Used[t] +  0.339910161109647CorrectAnalysis[t] +  0.213213949183679useful[t] +  0.144838684250187outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201979&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]weeks[t] =  +  3.60756189327435 -0.179786952486727uselimit[t] +  0.399208217517742T40[t] -1.59378146047458T20[t] -0.358443823282814Used[t] +  0.339910161109647CorrectAnalysis[t] +  0.213213949183679useful[t] +  0.144838684250187outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201979&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201979&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
weeks[t] = + 3.60756189327435 -0.179786952486727uselimit[t] + 0.399208217517742T40[t] -1.59378146047458T20[t] -0.358443823282814Used[t] + 0.339910161109647CorrectAnalysis[t] + 0.213213949183679useful[t] + 0.144838684250187outcome[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)	3.60756189327435	0.086894	41.5169	0	0
uselimit	-0.179786952486727	0.100355	-1.7915	0.075282	0.037641
T40	0.399208217517742	0.142636	2.7988	0.005823	0.002911
T20	-1.59378146047458	0.105271	-15.1398	0	0
Used	-0.358443823282814	0.118178	-3.0331	0.002866	0.001433
CorrectAnalysis	0.339910161109647	0.200342	1.6966	0.091895	0.045947
useful	0.213213949183679	0.110272	1.9335	0.055106	0.027553
outcome	0.144838684250187	0.096177	1.506	0.134237	0.067118

\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) & 3.60756189327435 & 0.086894 & 41.5169 & 0 & 0 \tabularnewline
uselimit & -0.179786952486727 & 0.100355 & -1.7915 & 0.075282 & 0.037641 \tabularnewline
T40 & 0.399208217517742 & 0.142636 & 2.7988 & 0.005823 & 0.002911 \tabularnewline
T20 & -1.59378146047458 & 0.105271 & -15.1398 & 0 & 0 \tabularnewline
Used & -0.358443823282814 & 0.118178 & -3.0331 & 0.002866 & 0.001433 \tabularnewline
CorrectAnalysis & 0.339910161109647 & 0.200342 & 1.6966 & 0.091895 & 0.045947 \tabularnewline
useful & 0.213213949183679 & 0.110272 & 1.9335 & 0.055106 & 0.027553 \tabularnewline
outcome & 0.144838684250187 & 0.096177 & 1.506 & 0.134237 & 0.067118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201979&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]3.60756189327435[/C][C]0.086894[/C][C]41.5169[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]uselimit[/C][C]-0.179786952486727[/C][C]0.100355[/C][C]-1.7915[/C][C]0.075282[/C][C]0.037641[/C][/ROW]
[ROW][C]T40[/C][C]0.399208217517742[/C][C]0.142636[/C][C]2.7988[/C][C]0.005823[/C][C]0.002911[/C][/ROW]
[ROW][C]T20[/C][C]-1.59378146047458[/C][C]0.105271[/C][C]-15.1398[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Used[/C][C]-0.358443823282814[/C][C]0.118178[/C][C]-3.0331[/C][C]0.002866[/C][C]0.001433[/C][/ROW]
[ROW][C]CorrectAnalysis[/C][C]0.339910161109647[/C][C]0.200342[/C][C]1.6966[/C][C]0.091895[/C][C]0.045947[/C][/ROW]
[ROW][C]useful[/C][C]0.213213949183679[/C][C]0.110272[/C][C]1.9335[/C][C]0.055106[/C][C]0.027553[/C][/ROW]
[ROW][C]outcome[/C][C]0.144838684250187[/C][C]0.096177[/C][C]1.506[/C][C]0.134237[/C][C]0.067118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201979&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201979&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)	3.60756189327435	0.086894	41.5169	0	0
uselimit	-0.179786952486727	0.100355	-1.7915	0.075282	0.037641
T40	0.399208217517742	0.142636	2.7988	0.005823	0.002911
T20	-1.59378146047458	0.105271	-15.1398	0	0
Used	-0.358443823282814	0.118178	-3.0331	0.002866	0.001433
CorrectAnalysis	0.339910161109647	0.200342	1.6966	0.091895	0.045947
useful	0.213213949183679	0.110272	1.9335	0.055106	0.027553
outcome	0.144838684250187	0.096177	1.506	0.134237	0.067118

Multiple Linear Regression - Regression Statistics
Multiple R	0.829661987508761
R-squared	0.688339013516987
Adjusted R-squared	0.67339636348013
F-TEST (value)	46.0653907987649
F-TEST (DF numerator)	7
F-TEST (DF denominator)	146
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.569427070770201
Sum Squared Residuals	47.340089583186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.829661987508761 \tabularnewline
R-squared & 0.688339013516987 \tabularnewline
Adjusted R-squared & 0.67339636348013 \tabularnewline
F-TEST (value) & 46.0653907987649 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.569427070770201 \tabularnewline
Sum Squared Residuals & 47.340089583186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201979&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.829661987508761[/C][/ROW]
[ROW][C]R-squared[/C][C]0.688339013516987[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.67339636348013[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]46.0653907987649[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.569427070770201[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]47.340089583186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201979&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201979&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	0.829661987508761
R-squared	0.688339013516987
Adjusted R-squared	0.67339636348013
F-TEST (value)	46.0653907987649
F-TEST (DF numerator)	7
F-TEST (DF denominator)	146
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.569427070770201
Sum Squared Residuals	47.340089583186

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4	3.97182184255556	0.028178157444444
2	4	3.60756189327436	0.392438106725644
3	4	3.60756189327435	0.392438106725645
4	4	3.60756189327436	0.392438106725645
5	4	3.60756189327435	0.392438106725645
6	4	3.78582757422149	0.214172425778507
7	4	3.60756189327436	0.392438106725645
8	4	4.0067701107921	-0.00677011079209737
9	4	3.75240057752454	0.247599422475458
10	4	3.42777494078763	0.572225059212372
11	4	3.82698315830537	0.17301684169463
12	4	3.60756189327436	0.392438106725645
13	4	3.46233201917522	0.537667980824781
14	4	3.82698315830537	0.17301684169463
15	4	3.60717070342541	0.392829296574594
16	4	4.00637892094315	-0.00637892094314828
17	4	4.02166344531588	-0.021663445315881
18	4	3.82698315830537	0.17301684169463
19	4	3.75240057752454	0.247599422475458
20	4	4.3462890820528	-0.346289082052795
21	4	3.64098888997131	0.359011110028693
22	4	3.42738375093868	0.572616249061321
23	4	3.96561452670822	0.0343854732917796
24	4	3.78582757422149	0.214172425778507
25	4	3.79316497175947	0.20683502824053
26	4	3.46233201917522	0.537667980824781
27	4	3.57261362503781	0.427386374962185
28	4	3.24911806999154	0.750881930008459
29	4	3.75240057752454	0.247599422475458
30	4	3.82077584245803	0.179224157541966
31	4	3.60756189327436	0.392438106725645
32	4	3.42777494078763	0.572225059212372
33	4	3.64098888997131	0.359011110028693
34	4	4.15160879504228	-0.151608795042284
35	4	3.60756189327436	0.392438106725645
36	4	3.60756189327436	0.392438106725645
37	4	3.68175328420623	0.318246715793766
38	4	3.39395675424173	0.606043245758273
39	4	3.96561452670822	0.0343854732917796
40	4	4.21998405997578	-0.219984059975776
41	4	3.94708086453505	0.0529191354649474
42	4	3.39395675424173	0.606043245758273
43	4	3.78582757422149	0.214172425778507
44	4	3.82698315830537	0.17301684169463
45	4	3.82077584245803	0.179224157541966
46	4	3.96561452670822	0.0343854732917796
47	4	3.60756189327436	0.392438106725645
48	4	3.75240057752454	0.247599422475458
49	4	3.96561452670822	0.0343854732917796
50	4	3.60756189327436	0.392438106725645
51	4	3.64832628750928	0.351673712490717
52	4	4.02166344531588	-0.021663445315881
53	4	3.75240057752454	0.247599422475458
54	4	3.58902823110119	0.410971768898813
55	4	3.60756189327436	0.392438106725645
56	4	3.79316497175947	0.20683502824053
57	4	3.60717070342541	0.392829296574594
58	4	3.75240057752454	0.247599422475458
59	4	3.75240057752454	0.247599422475458
60	4	4.16650212956607	-0.166502129566068
61	4	3.97182184255556	0.0281781574444432
62	4	3.46233201917522	0.537667980824781
63	4	3.60756189327436	0.392438106725645
64	4	3.97182184255556	0.0281781574444432
65	4	3.60756189327436	0.392438106725645
66	4	3.60756189327436	0.392438106725645
67	4	4.20145039780261	-0.201450397802608
68	4	3.42777494078763	0.572225059212372
69	4	3.75240057752454	0.247599422475458
70	4	3.24911806999154	0.750881930008459
71	4	3.60756189327436	0.392438106725645
72	4	3.75240057752454	0.247599422475458
73	4	3.39395675424173	0.606043245758273
74	4	3.06933111750481	0.930668882495186
75	4	3.75240057752454	0.247599422475458
76	4	4.36482274422596	-0.364822744225963
77	4	3.75240057752454	0.247599422475458
78	4	3.60717070342541	0.392829296574594
79	4	4.13307513286912	-0.133075132869116
80	4	4.21998405997578	-0.219984059975776
81	4	3.60756189327436	0.392438106725645
82	4	3.214169801755	0.785830198245
83	4	3.60756189327436	0.392438106725645
84	4	3.58902823110119	0.410971768898813
85	4	3.96561452670822	0.0343854732917796
86	4	3.42777494078763	0.572225059212372
87	2	1.97883216456323	0.0211678354367684
88	2	3.214169801755	-1.214169801755
89	2	2.01378043279977	-0.0137804327997723
90	2	2.15861911704996	-0.158619117049959
91	2	2.22699438198345	-0.226994381983451
92	2	3.42777494078763	-1.42777494078763
93	2	2.04720742949672	-0.0472074294967236
94	2	2.01378043279977	-0.0137804327997723
95	2	3.60756189327435	-1.60756189327435
96	2	2.15861911704996	-0.158619117049959
97	2	3.42777494078763	-1.42777494078763
98	2	2.01378043279977	-0.0137804327997723
99	2	1.83399348031304	0.166006519686955
100	2	2.15861911704996	-0.158619117049959
101	2	1.97883216456323	0.0211678354367684
102	2	2.01378043279977	-0.0137804327997723
103	2	2.01378043279977	-0.0137804327997723
104	2	2.01378043279977	-0.0137804327997723
105	2	3.24911806999154	-1.24911806999154
106	2	2.01378043279977	-0.0137804327997723
107	2	2.01378043279977	-0.0137804327997723
108	2	3.06933111750481	-1.06933111750481
109	2	2.01378043279977	-0.0137804327997723
110	2	1.83399348031304	0.166006519686955
111	2	3.28254506668849	-1.28254506668849
112	2	3.60756189327435	-1.60756189327435
113	2	1.65533660951696	0.344663390483042
114	2	3.06933111750481	-1.06933111750481
115	2	1.83399348031304	0.166006519686955
116	2	2.01378043279977	-0.0137804327997723
117	2	1.97883216456323	0.0211678354367684
118	2	1.83399348031304	0.166006519686955
119	2	2.01378043279977	-0.0137804327997723
120	2	2.15861911704996	-0.158619117049959
121	2	1.83399348031304	0.166006519686955
122	2	2.01378043279977	-0.0137804327997723
123	2	3.06933111750481	-1.06933111750481
124	2	2.01338924295082	-0.0133892429508232
125	2	2.15861911704996	-0.158619117049959
126	2	3.60756189327435	-1.60756189327435
127	2	2.22699438198345	-0.226994381983451
128	2	2.15861911704996	-0.158619117049959
129	2	2.01378043279977	-0.0137804327997723
130	2	2.15861911704996	-0.158619117049959
131	2	1.83399348031304	0.166006519686955
132	2	1.97883216456323	0.0211678354367684
133	2	1.47554965703023	0.524450342969769
134	2	2.01378043279977	-0.0137804327997723
135	2	2.01378043279977	-0.0137804327997723
136	2	2.01378043279977	-0.0137804327997723
137	2	1.8336022904641	0.166397709535904
138	2	3.42738375093868	-1.42738375093868
139	2	3.60756189327435	-1.60756189327435
140	2	2.01378043279977	-0.0137804327997723
141	2	2.14008545487679	-0.140085454876791
142	2	3.39395675424173	-1.39395675424173
143	2	1.83399348031304	0.166006519686955
144	2	2.37183306623364	-0.371833066233638
145	2	2.22699438198345	-0.226994381983451
146	2	3.75240057752454	-1.75240057752454
147	2	3.24911806999154	-1.24911806999154
148	2	3.60756189327435	-1.60756189327435
149	2	1.83399348031304	0.166006519686955
150	2	2.37183306623364	-0.371833066233638
151	2	2.15861911704996	-0.158619117049959
152	2	1.81545981813988	0.184540181860123
153	2	2.02867376732356	-0.0286737673235558
154	2	1.47554965703023	0.524450342969769

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 3.97182184255556 & 0.028178157444444 \tabularnewline
2 & 4 & 3.60756189327436 & 0.392438106725644 \tabularnewline
3 & 4 & 3.60756189327435 & 0.392438106725645 \tabularnewline
4 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
5 & 4 & 3.60756189327435 & 0.392438106725645 \tabularnewline
6 & 4 & 3.78582757422149 & 0.214172425778507 \tabularnewline
7 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
8 & 4 & 4.0067701107921 & -0.00677011079209737 \tabularnewline
9 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
10 & 4 & 3.42777494078763 & 0.572225059212372 \tabularnewline
11 & 4 & 3.82698315830537 & 0.17301684169463 \tabularnewline
12 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
13 & 4 & 3.46233201917522 & 0.537667980824781 \tabularnewline
14 & 4 & 3.82698315830537 & 0.17301684169463 \tabularnewline
15 & 4 & 3.60717070342541 & 0.392829296574594 \tabularnewline
16 & 4 & 4.00637892094315 & -0.00637892094314828 \tabularnewline
17 & 4 & 4.02166344531588 & -0.021663445315881 \tabularnewline
18 & 4 & 3.82698315830537 & 0.17301684169463 \tabularnewline
19 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
20 & 4 & 4.3462890820528 & -0.346289082052795 \tabularnewline
21 & 4 & 3.64098888997131 & 0.359011110028693 \tabularnewline
22 & 4 & 3.42738375093868 & 0.572616249061321 \tabularnewline
23 & 4 & 3.96561452670822 & 0.0343854732917796 \tabularnewline
24 & 4 & 3.78582757422149 & 0.214172425778507 \tabularnewline
25 & 4 & 3.79316497175947 & 0.20683502824053 \tabularnewline
26 & 4 & 3.46233201917522 & 0.537667980824781 \tabularnewline
27 & 4 & 3.57261362503781 & 0.427386374962185 \tabularnewline
28 & 4 & 3.24911806999154 & 0.750881930008459 \tabularnewline
29 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
30 & 4 & 3.82077584245803 & 0.179224157541966 \tabularnewline
31 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
32 & 4 & 3.42777494078763 & 0.572225059212372 \tabularnewline
33 & 4 & 3.64098888997131 & 0.359011110028693 \tabularnewline
34 & 4 & 4.15160879504228 & -0.151608795042284 \tabularnewline
35 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
36 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
37 & 4 & 3.68175328420623 & 0.318246715793766 \tabularnewline
38 & 4 & 3.39395675424173 & 0.606043245758273 \tabularnewline
39 & 4 & 3.96561452670822 & 0.0343854732917796 \tabularnewline
40 & 4 & 4.21998405997578 & -0.219984059975776 \tabularnewline
41 & 4 & 3.94708086453505 & 0.0529191354649474 \tabularnewline
42 & 4 & 3.39395675424173 & 0.606043245758273 \tabularnewline
43 & 4 & 3.78582757422149 & 0.214172425778507 \tabularnewline
44 & 4 & 3.82698315830537 & 0.17301684169463 \tabularnewline
45 & 4 & 3.82077584245803 & 0.179224157541966 \tabularnewline
46 & 4 & 3.96561452670822 & 0.0343854732917796 \tabularnewline
47 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
48 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
49 & 4 & 3.96561452670822 & 0.0343854732917796 \tabularnewline
50 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
51 & 4 & 3.64832628750928 & 0.351673712490717 \tabularnewline
52 & 4 & 4.02166344531588 & -0.021663445315881 \tabularnewline
53 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
54 & 4 & 3.58902823110119 & 0.410971768898813 \tabularnewline
55 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
56 & 4 & 3.79316497175947 & 0.20683502824053 \tabularnewline
57 & 4 & 3.60717070342541 & 0.392829296574594 \tabularnewline
58 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
59 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
60 & 4 & 4.16650212956607 & -0.166502129566068 \tabularnewline
61 & 4 & 3.97182184255556 & 0.0281781574444432 \tabularnewline
62 & 4 & 3.46233201917522 & 0.537667980824781 \tabularnewline
63 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
64 & 4 & 3.97182184255556 & 0.0281781574444432 \tabularnewline
65 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
66 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
67 & 4 & 4.20145039780261 & -0.201450397802608 \tabularnewline
68 & 4 & 3.42777494078763 & 0.572225059212372 \tabularnewline
69 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
70 & 4 & 3.24911806999154 & 0.750881930008459 \tabularnewline
71 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
72 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
73 & 4 & 3.39395675424173 & 0.606043245758273 \tabularnewline
74 & 4 & 3.06933111750481 & 0.930668882495186 \tabularnewline
75 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
76 & 4 & 4.36482274422596 & -0.364822744225963 \tabularnewline
77 & 4 & 3.75240057752454 & 0.247599422475458 \tabularnewline
78 & 4 & 3.60717070342541 & 0.392829296574594 \tabularnewline
79 & 4 & 4.13307513286912 & -0.133075132869116 \tabularnewline
80 & 4 & 4.21998405997578 & -0.219984059975776 \tabularnewline
81 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
82 & 4 & 3.214169801755 & 0.785830198245 \tabularnewline
83 & 4 & 3.60756189327436 & 0.392438106725645 \tabularnewline
84 & 4 & 3.58902823110119 & 0.410971768898813 \tabularnewline
85 & 4 & 3.96561452670822 & 0.0343854732917796 \tabularnewline
86 & 4 & 3.42777494078763 & 0.572225059212372 \tabularnewline
87 & 2 & 1.97883216456323 & 0.0211678354367684 \tabularnewline
88 & 2 & 3.214169801755 & -1.214169801755 \tabularnewline
89 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
90 & 2 & 2.15861911704996 & -0.158619117049959 \tabularnewline
91 & 2 & 2.22699438198345 & -0.226994381983451 \tabularnewline
92 & 2 & 3.42777494078763 & -1.42777494078763 \tabularnewline
93 & 2 & 2.04720742949672 & -0.0472074294967236 \tabularnewline
94 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
95 & 2 & 3.60756189327435 & -1.60756189327435 \tabularnewline
96 & 2 & 2.15861911704996 & -0.158619117049959 \tabularnewline
97 & 2 & 3.42777494078763 & -1.42777494078763 \tabularnewline
98 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
99 & 2 & 1.83399348031304 & 0.166006519686955 \tabularnewline
100 & 2 & 2.15861911704996 & -0.158619117049959 \tabularnewline
101 & 2 & 1.97883216456323 & 0.0211678354367684 \tabularnewline
102 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
103 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
104 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
105 & 2 & 3.24911806999154 & -1.24911806999154 \tabularnewline
106 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
107 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
108 & 2 & 3.06933111750481 & -1.06933111750481 \tabularnewline
109 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
110 & 2 & 1.83399348031304 & 0.166006519686955 \tabularnewline
111 & 2 & 3.28254506668849 & -1.28254506668849 \tabularnewline
112 & 2 & 3.60756189327435 & -1.60756189327435 \tabularnewline
113 & 2 & 1.65533660951696 & 0.344663390483042 \tabularnewline
114 & 2 & 3.06933111750481 & -1.06933111750481 \tabularnewline
115 & 2 & 1.83399348031304 & 0.166006519686955 \tabularnewline
116 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
117 & 2 & 1.97883216456323 & 0.0211678354367684 \tabularnewline
118 & 2 & 1.83399348031304 & 0.166006519686955 \tabularnewline
119 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
120 & 2 & 2.15861911704996 & -0.158619117049959 \tabularnewline
121 & 2 & 1.83399348031304 & 0.166006519686955 \tabularnewline
122 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
123 & 2 & 3.06933111750481 & -1.06933111750481 \tabularnewline
124 & 2 & 2.01338924295082 & -0.0133892429508232 \tabularnewline
125 & 2 & 2.15861911704996 & -0.158619117049959 \tabularnewline
126 & 2 & 3.60756189327435 & -1.60756189327435 \tabularnewline
127 & 2 & 2.22699438198345 & -0.226994381983451 \tabularnewline
128 & 2 & 2.15861911704996 & -0.158619117049959 \tabularnewline
129 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
130 & 2 & 2.15861911704996 & -0.158619117049959 \tabularnewline
131 & 2 & 1.83399348031304 & 0.166006519686955 \tabularnewline
132 & 2 & 1.97883216456323 & 0.0211678354367684 \tabularnewline
133 & 2 & 1.47554965703023 & 0.524450342969769 \tabularnewline
134 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
135 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
136 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
137 & 2 & 1.8336022904641 & 0.166397709535904 \tabularnewline
138 & 2 & 3.42738375093868 & -1.42738375093868 \tabularnewline
139 & 2 & 3.60756189327435 & -1.60756189327435 \tabularnewline
140 & 2 & 2.01378043279977 & -0.0137804327997723 \tabularnewline
141 & 2 & 2.14008545487679 & -0.140085454876791 \tabularnewline
142 & 2 & 3.39395675424173 & -1.39395675424173 \tabularnewline
143 & 2 & 1.83399348031304 & 0.166006519686955 \tabularnewline
144 & 2 & 2.37183306623364 & -0.371833066233638 \tabularnewline
145 & 2 & 2.22699438198345 & -0.226994381983451 \tabularnewline
146 & 2 & 3.75240057752454 & -1.75240057752454 \tabularnewline
147 & 2 & 3.24911806999154 & -1.24911806999154 \tabularnewline
148 & 2 & 3.60756189327435 & -1.60756189327435 \tabularnewline
149 & 2 & 1.83399348031304 & 0.166006519686955 \tabularnewline
150 & 2 & 2.37183306623364 & -0.371833066233638 \tabularnewline
151 & 2 & 2.15861911704996 & -0.158619117049959 \tabularnewline
152 & 2 & 1.81545981813988 & 0.184540181860123 \tabularnewline
153 & 2 & 2.02867376732356 & -0.0286737673235558 \tabularnewline
154 & 2 & 1.47554965703023 & 0.524450342969769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201979&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]4[/C][C]3.97182184255556[/C][C]0.028178157444444[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725644[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.60756189327435[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]3.60756189327435[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]3.78582757422149[/C][C]0.214172425778507[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.0067701107921[/C][C]-0.00677011079209737[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.42777494078763[/C][C]0.572225059212372[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]3.82698315830537[/C][C]0.17301684169463[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]3.46233201917522[/C][C]0.537667980824781[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]3.82698315830537[/C][C]0.17301684169463[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]3.60717070342541[/C][C]0.392829296574594[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]4.00637892094315[/C][C]-0.00637892094314828[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]4.02166344531588[/C][C]-0.021663445315881[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.82698315830537[/C][C]0.17301684169463[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]4.3462890820528[/C][C]-0.346289082052795[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.64098888997131[/C][C]0.359011110028693[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]3.42738375093868[/C][C]0.572616249061321[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]3.96561452670822[/C][C]0.0343854732917796[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]3.78582757422149[/C][C]0.214172425778507[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]3.79316497175947[/C][C]0.20683502824053[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.46233201917522[/C][C]0.537667980824781[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]3.57261362503781[/C][C]0.427386374962185[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.24911806999154[/C][C]0.750881930008459[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]3.82077584245803[/C][C]0.179224157541966[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]3.42777494078763[/C][C]0.572225059212372[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.64098888997131[/C][C]0.359011110028693[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]4.15160879504228[/C][C]-0.151608795042284[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]3.68175328420623[/C][C]0.318246715793766[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]3.39395675424173[/C][C]0.606043245758273[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.96561452670822[/C][C]0.0343854732917796[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.21998405997578[/C][C]-0.219984059975776[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]3.94708086453505[/C][C]0.0529191354649474[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]3.39395675424173[/C][C]0.606043245758273[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]3.78582757422149[/C][C]0.214172425778507[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]3.82698315830537[/C][C]0.17301684169463[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]3.82077584245803[/C][C]0.179224157541966[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]3.96561452670822[/C][C]0.0343854732917796[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]3.96561452670822[/C][C]0.0343854732917796[/C][/ROW]
[ROW][C]50[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]3.64832628750928[/C][C]0.351673712490717[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]4.02166344531588[/C][C]-0.021663445315881[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]3.58902823110119[/C][C]0.410971768898813[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]3.79316497175947[/C][C]0.20683502824053[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]3.60717070342541[/C][C]0.392829296574594[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]4.16650212956607[/C][C]-0.166502129566068[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]3.97182184255556[/C][C]0.0281781574444432[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.46233201917522[/C][C]0.537667980824781[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]3.97182184255556[/C][C]0.0281781574444432[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]4.20145039780261[/C][C]-0.201450397802608[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]3.42777494078763[/C][C]0.572225059212372[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]3.24911806999154[/C][C]0.750881930008459[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.39395675424173[/C][C]0.606043245758273[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]3.06933111750481[/C][C]0.930668882495186[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]4.36482274422596[/C][C]-0.364822744225963[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]3.75240057752454[/C][C]0.247599422475458[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]3.60717070342541[/C][C]0.392829296574594[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]4.13307513286912[/C][C]-0.133075132869116[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]4.21998405997578[/C][C]-0.219984059975776[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]3.214169801755[/C][C]0.785830198245[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]3.60756189327436[/C][C]0.392438106725645[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]3.58902823110119[/C][C]0.410971768898813[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]3.96561452670822[/C][C]0.0343854732917796[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]3.42777494078763[/C][C]0.572225059212372[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]1.97883216456323[/C][C]0.0211678354367684[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]3.214169801755[/C][C]-1.214169801755[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]2.15861911704996[/C][C]-0.158619117049959[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]2.22699438198345[/C][C]-0.226994381983451[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]3.42777494078763[/C][C]-1.42777494078763[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]2.04720742949672[/C][C]-0.0472074294967236[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]3.60756189327435[/C][C]-1.60756189327435[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]2.15861911704996[/C][C]-0.158619117049959[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]3.42777494078763[/C][C]-1.42777494078763[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]1.83399348031304[/C][C]0.166006519686955[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]2.15861911704996[/C][C]-0.158619117049959[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]1.97883216456323[/C][C]0.0211678354367684[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]3.24911806999154[/C][C]-1.24911806999154[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]108[/C][C]2[/C][C]3.06933111750481[/C][C]-1.06933111750481[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.83399348031304[/C][C]0.166006519686955[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]3.28254506668849[/C][C]-1.28254506668849[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]3.60756189327435[/C][C]-1.60756189327435[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]1.65533660951696[/C][C]0.344663390483042[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]3.06933111750481[/C][C]-1.06933111750481[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]1.83399348031304[/C][C]0.166006519686955[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]117[/C][C]2[/C][C]1.97883216456323[/C][C]0.0211678354367684[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.83399348031304[/C][C]0.166006519686955[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]2.15861911704996[/C][C]-0.158619117049959[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]1.83399348031304[/C][C]0.166006519686955[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]3.06933111750481[/C][C]-1.06933111750481[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]2.01338924295082[/C][C]-0.0133892429508232[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]2.15861911704996[/C][C]-0.158619117049959[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]3.60756189327435[/C][C]-1.60756189327435[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]2.22699438198345[/C][C]-0.226994381983451[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]2.15861911704996[/C][C]-0.158619117049959[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]2.15861911704996[/C][C]-0.158619117049959[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.83399348031304[/C][C]0.166006519686955[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.97883216456323[/C][C]0.0211678354367684[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.47554965703023[/C][C]0.524450342969769[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]137[/C][C]2[/C][C]1.8336022904641[/C][C]0.166397709535904[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]3.42738375093868[/C][C]-1.42738375093868[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]3.60756189327435[/C][C]-1.60756189327435[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]2.01378043279977[/C][C]-0.0137804327997723[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]2.14008545487679[/C][C]-0.140085454876791[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]3.39395675424173[/C][C]-1.39395675424173[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]1.83399348031304[/C][C]0.166006519686955[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]2.37183306623364[/C][C]-0.371833066233638[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]2.22699438198345[/C][C]-0.226994381983451[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]3.75240057752454[/C][C]-1.75240057752454[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]3.24911806999154[/C][C]-1.24911806999154[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]3.60756189327435[/C][C]-1.60756189327435[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]1.83399348031304[/C][C]0.166006519686955[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]2.37183306623364[/C][C]-0.371833066233638[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]2.15861911704996[/C][C]-0.158619117049959[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.81545981813988[/C][C]0.184540181860123[/C][/ROW]
[ROW][C]153[/C][C]2[/C][C]2.02867376732356[/C][C]-0.0286737673235558[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.47554965703023[/C][C]0.524450342969769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201979&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201979&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	4	3.97182184255556	0.028178157444444
2	4	3.60756189327436	0.392438106725644
3	4	3.60756189327435	0.392438106725645
4	4	3.60756189327436	0.392438106725645
5	4	3.60756189327435	0.392438106725645
6	4	3.78582757422149	0.214172425778507
7	4	3.60756189327436	0.392438106725645
8	4	4.0067701107921	-0.00677011079209737
9	4	3.75240057752454	0.247599422475458
10	4	3.42777494078763	0.572225059212372
11	4	3.82698315830537	0.17301684169463
12	4	3.60756189327436	0.392438106725645
13	4	3.46233201917522	0.537667980824781
14	4	3.82698315830537	0.17301684169463
15	4	3.60717070342541	0.392829296574594
16	4	4.00637892094315	-0.00637892094314828
17	4	4.02166344531588	-0.021663445315881
18	4	3.82698315830537	0.17301684169463
19	4	3.75240057752454	0.247599422475458
20	4	4.3462890820528	-0.346289082052795
21	4	3.64098888997131	0.359011110028693
22	4	3.42738375093868	0.572616249061321
23	4	3.96561452670822	0.0343854732917796
24	4	3.78582757422149	0.214172425778507
25	4	3.79316497175947	0.20683502824053
26	4	3.46233201917522	0.537667980824781
27	4	3.57261362503781	0.427386374962185
28	4	3.24911806999154	0.750881930008459
29	4	3.75240057752454	0.247599422475458
30	4	3.82077584245803	0.179224157541966
31	4	3.60756189327436	0.392438106725645
32	4	3.42777494078763	0.572225059212372
33	4	3.64098888997131	0.359011110028693
34	4	4.15160879504228	-0.151608795042284
35	4	3.60756189327436	0.392438106725645
36	4	3.60756189327436	0.392438106725645
37	4	3.68175328420623	0.318246715793766
38	4	3.39395675424173	0.606043245758273
39	4	3.96561452670822	0.0343854732917796
40	4	4.21998405997578	-0.219984059975776
41	4	3.94708086453505	0.0529191354649474
42	4	3.39395675424173	0.606043245758273
43	4	3.78582757422149	0.214172425778507
44	4	3.82698315830537	0.17301684169463
45	4	3.82077584245803	0.179224157541966
46	4	3.96561452670822	0.0343854732917796
47	4	3.60756189327436	0.392438106725645
48	4	3.75240057752454	0.247599422475458
49	4	3.96561452670822	0.0343854732917796
50	4	3.60756189327436	0.392438106725645
51	4	3.64832628750928	0.351673712490717
52	4	4.02166344531588	-0.021663445315881
53	4	3.75240057752454	0.247599422475458
54	4	3.58902823110119	0.410971768898813
55	4	3.60756189327436	0.392438106725645
56	4	3.79316497175947	0.20683502824053
57	4	3.60717070342541	0.392829296574594
58	4	3.75240057752454	0.247599422475458
59	4	3.75240057752454	0.247599422475458
60	4	4.16650212956607	-0.166502129566068
61	4	3.97182184255556	0.0281781574444432
62	4	3.46233201917522	0.537667980824781
63	4	3.60756189327436	0.392438106725645
64	4	3.97182184255556	0.0281781574444432
65	4	3.60756189327436	0.392438106725645
66	4	3.60756189327436	0.392438106725645
67	4	4.20145039780261	-0.201450397802608
68	4	3.42777494078763	0.572225059212372
69	4	3.75240057752454	0.247599422475458
70	4	3.24911806999154	0.750881930008459
71	4	3.60756189327436	0.392438106725645
72	4	3.75240057752454	0.247599422475458
73	4	3.39395675424173	0.606043245758273
74	4	3.06933111750481	0.930668882495186
75	4	3.75240057752454	0.247599422475458
76	4	4.36482274422596	-0.364822744225963
77	4	3.75240057752454	0.247599422475458
78	4	3.60717070342541	0.392829296574594
79	4	4.13307513286912	-0.133075132869116
80	4	4.21998405997578	-0.219984059975776
81	4	3.60756189327436	0.392438106725645
82	4	3.214169801755	0.785830198245
83	4	3.60756189327436	0.392438106725645
84	4	3.58902823110119	0.410971768898813
85	4	3.96561452670822	0.0343854732917796
86	4	3.42777494078763	0.572225059212372
87	2	1.97883216456323	0.0211678354367684
88	2	3.214169801755	-1.214169801755
89	2	2.01378043279977	-0.0137804327997723
90	2	2.15861911704996	-0.158619117049959
91	2	2.22699438198345	-0.226994381983451
92	2	3.42777494078763	-1.42777494078763
93	2	2.04720742949672	-0.0472074294967236
94	2	2.01378043279977	-0.0137804327997723
95	2	3.60756189327435	-1.60756189327435
96	2	2.15861911704996	-0.158619117049959
97	2	3.42777494078763	-1.42777494078763
98	2	2.01378043279977	-0.0137804327997723
99	2	1.83399348031304	0.166006519686955
100	2	2.15861911704996	-0.158619117049959
101	2	1.97883216456323	0.0211678354367684
102	2	2.01378043279977	-0.0137804327997723
103	2	2.01378043279977	-0.0137804327997723
104	2	2.01378043279977	-0.0137804327997723
105	2	3.24911806999154	-1.24911806999154
106	2	2.01378043279977	-0.0137804327997723
107	2	2.01378043279977	-0.0137804327997723
108	2	3.06933111750481	-1.06933111750481
109	2	2.01378043279977	-0.0137804327997723
110	2	1.83399348031304	0.166006519686955
111	2	3.28254506668849	-1.28254506668849
112	2	3.60756189327435	-1.60756189327435
113	2	1.65533660951696	0.344663390483042
114	2	3.06933111750481	-1.06933111750481
115	2	1.83399348031304	0.166006519686955
116	2	2.01378043279977	-0.0137804327997723
117	2	1.97883216456323	0.0211678354367684
118	2	1.83399348031304	0.166006519686955
119	2	2.01378043279977	-0.0137804327997723
120	2	2.15861911704996	-0.158619117049959
121	2	1.83399348031304	0.166006519686955
122	2	2.01378043279977	-0.0137804327997723
123	2	3.06933111750481	-1.06933111750481
124	2	2.01338924295082	-0.0133892429508232
125	2	2.15861911704996	-0.158619117049959
126	2	3.60756189327435	-1.60756189327435
127	2	2.22699438198345	-0.226994381983451
128	2	2.15861911704996	-0.158619117049959
129	2	2.01378043279977	-0.0137804327997723
130	2	2.15861911704996	-0.158619117049959
131	2	1.83399348031304	0.166006519686955
132	2	1.97883216456323	0.0211678354367684
133	2	1.47554965703023	0.524450342969769
134	2	2.01378043279977	-0.0137804327997723
135	2	2.01378043279977	-0.0137804327997723
136	2	2.01378043279977	-0.0137804327997723
137	2	1.8336022904641	0.166397709535904
138	2	3.42738375093868	-1.42738375093868
139	2	3.60756189327435	-1.60756189327435
140	2	2.01378043279977	-0.0137804327997723
141	2	2.14008545487679	-0.140085454876791
142	2	3.39395675424173	-1.39395675424173
143	2	1.83399348031304	0.166006519686955
144	2	2.37183306623364	-0.371833066233638
145	2	2.22699438198345	-0.226994381983451
146	2	3.75240057752454	-1.75240057752454
147	2	3.24911806999154	-1.24911806999154
148	2	3.60756189327435	-1.60756189327435
149	2	1.83399348031304	0.166006519686955
150	2	2.37183306623364	-0.371833066233638
151	2	2.15861911704996	-0.158619117049959
152	2	1.81545981813988	0.184540181860123
153	2	2.02867376732356	-0.0286737673235558
154	2	1.47554965703023	0.524450342969769

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	5.82205790957935e-49	1.16441158191587e-48	1
12	8.36008829268061e-61	1.67201765853612e-60	1
13	2.98125545625662e-86	5.96251091251324e-86	1
14	3.53464395244205e-90	7.0692879048841e-90	1
15	5.53477348095206e-105	1.10695469619041e-104	1
16	0	0	1
17	2.80648197816894e-145	5.61296395633788e-145	1
18	3.9140570204587e-151	7.82811404091741e-151	1
19	7.11421089491722e-165	1.42284217898344e-164	1
20	1.06346902493561e-188	2.12693804987122e-188	1
21	6.76285148983158e-221	1.35257029796632e-220	1
22	1.5792121643312e-212	3.15842432866239e-212	1
23	6.07137006008037e-224	1.21427401201607e-223	1
24	7.18394621088993e-242	1.43678924217799e-241	1
25	5.17427314915009e-260	1.03485462983002e-259	1
26	1.20339773966256e-300	2.40679547932513e-300	1
27	8.0756516919114e-289	1.61513033838228e-288	1
28	5.41197745203462e-299	1.08239549040692e-298	1
29	6.76474682285835e-319	1.35294936457167e-318	1
30	0	0	1
31	0	0	1
32	0	0	1
33	0	0	1
34	0	0	1
35	0	0	1
36	0	0	1
37	0	0	1
38	0	0	1
39	0	0	1
40	0	0	1
41	0	0	1
42	0	0	1
43	0	0	1
44	0	0	1
45	0	0	1
46	0	0	1
47	0	0	1
48	0	0	1
49	0	0	1
50	0	0	1
51	0	0	1
52	0	0	1
53	0	0	1
54	0	0	1
55	0	0	1
56	0	0	1
57	0	0	1
58	0	0	1
59	0	0	1
60	0	0	1
61	0	0	1
62	0	0	1
63	0	0	1
64	0	0	1
65	0	0	1
66	0	0	1
67	0	0	1
68	0	0	1
69	0	0	1
70	0	0	1
71	0	0	1
72	0	0	1
73	0	0	1
74	0	0	1
75	0	0	1
76	0	0	1
77	0	0	1
78	0	0	1
79	0	0	1
80	0	0	1
81	0	0	1
82	0	0	1
83	0	0	1
84	0	0	1
85	0	0	1
86	1	1.72556705773827e-18	8.62783528869134e-19
87	2.7196633862642e-08	5.43932677252839e-08	0.999999972803366
88	1	0	0
89	1	0	0
90	1	0	0
91	1	0	0
92	1	0	0
93	1	0	0
94	1	0	0
95	1	0	0
96	1	0	0
97	1	0	0
98	1	0	0
99	1	0	0
100	1	0	0
101	1	0	0
102	1	0	0
103	1	0	0
104	1	0	0
105	1	0	0
106	1	0	0
107	1	0	0
108	1	0	0
109	1	0	0
110	1	0	0
111	1	0	0
112	1	0	0
113	1	0	0
114	1	0	0
115	1	0	0
116	1	0	0
117	1	0	0
118	1	0	0
119	1	0	0
120	1	0	0
121	1	0	0
122	1	0	0
123	1	0	0
124	1	0	0
125	1	8.89318162514244e-323	4.44659081257122e-323
126	1	2.60949473370241e-303	1.3047473668512e-303
127	1	2.40614422669591e-292	1.20307211334795e-292
128	1	1.25126173464601e-303	6.25630867323005e-304
129	1	6.29350469393274e-263	3.14675234696637e-263
130	1	2.25099975117603e-244	1.12549987558802e-244
131	1	3.31604677393526e-227	1.65802338696763e-227
132	1	4.70558793362626e-215	2.35279396681313e-215
133	1	1.42775286175333e-222	7.13876430876665e-223
134	1	3.61038382563511e-190	1.80519191281755e-190
135	1	2.80213070567385e-166	1.40106535283692e-166
136	1	9.74805642865301e-153	4.8740282143265e-153
137	1	7.89176785898121e-148	3.94588392949061e-148
138	1	0	0
139	1	3.00597299693505e-106	1.50298649846752e-106
140	1	9.65029309880746e-92	4.82514654940373e-92
141	1	6.55115127186497e-87	3.27557563593248e-87
142	1	4.17980621595277e-61	2.08990310797638e-61
143	1	4.8285363108379e-49	2.41426815541895e-49

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 5.82205790957935e-49 & 1.16441158191587e-48 & 1 \tabularnewline
12 & 8.36008829268061e-61 & 1.67201765853612e-60 & 1 \tabularnewline
13 & 2.98125545625662e-86 & 5.96251091251324e-86 & 1 \tabularnewline
14 & 3.53464395244205e-90 & 7.0692879048841e-90 & 1 \tabularnewline
15 & 5.53477348095206e-105 & 1.10695469619041e-104 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 2.80648197816894e-145 & 5.61296395633788e-145 & 1 \tabularnewline
18 & 3.9140570204587e-151 & 7.82811404091741e-151 & 1 \tabularnewline
19 & 7.11421089491722e-165 & 1.42284217898344e-164 & 1 \tabularnewline
20 & 1.06346902493561e-188 & 2.12693804987122e-188 & 1 \tabularnewline
21 & 6.76285148983158e-221 & 1.35257029796632e-220 & 1 \tabularnewline
22 & 1.5792121643312e-212 & 3.15842432866239e-212 & 1 \tabularnewline
23 & 6.07137006008037e-224 & 1.21427401201607e-223 & 1 \tabularnewline
24 & 7.18394621088993e-242 & 1.43678924217799e-241 & 1 \tabularnewline
25 & 5.17427314915009e-260 & 1.03485462983002e-259 & 1 \tabularnewline
26 & 1.20339773966256e-300 & 2.40679547932513e-300 & 1 \tabularnewline
27 & 8.0756516919114e-289 & 1.61513033838228e-288 & 1 \tabularnewline
28 & 5.41197745203462e-299 & 1.08239549040692e-298 & 1 \tabularnewline
29 & 6.76474682285835e-319 & 1.35294936457167e-318 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 0 & 0 & 1 \tabularnewline
78 & 0 & 0 & 1 \tabularnewline
79 & 0 & 0 & 1 \tabularnewline
80 & 0 & 0 & 1 \tabularnewline
81 & 0 & 0 & 1 \tabularnewline
82 & 0 & 0 & 1 \tabularnewline
83 & 0 & 0 & 1 \tabularnewline
84 & 0 & 0 & 1 \tabularnewline
85 & 0 & 0 & 1 \tabularnewline
86 & 1 & 1.72556705773827e-18 & 8.62783528869134e-19 \tabularnewline
87 & 2.7196633862642e-08 & 5.43932677252839e-08 & 0.999999972803366 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 8.89318162514244e-323 & 4.44659081257122e-323 \tabularnewline
126 & 1 & 2.60949473370241e-303 & 1.3047473668512e-303 \tabularnewline
127 & 1 & 2.40614422669591e-292 & 1.20307211334795e-292 \tabularnewline
128 & 1 & 1.25126173464601e-303 & 6.25630867323005e-304 \tabularnewline
129 & 1 & 6.29350469393274e-263 & 3.14675234696637e-263 \tabularnewline
130 & 1 & 2.25099975117603e-244 & 1.12549987558802e-244 \tabularnewline
131 & 1 & 3.31604677393526e-227 & 1.65802338696763e-227 \tabularnewline
132 & 1 & 4.70558793362626e-215 & 2.35279396681313e-215 \tabularnewline
133 & 1 & 1.42775286175333e-222 & 7.13876430876665e-223 \tabularnewline
134 & 1 & 3.61038382563511e-190 & 1.80519191281755e-190 \tabularnewline
135 & 1 & 2.80213070567385e-166 & 1.40106535283692e-166 \tabularnewline
136 & 1 & 9.74805642865301e-153 & 4.8740282143265e-153 \tabularnewline
137 & 1 & 7.89176785898121e-148 & 3.94588392949061e-148 \tabularnewline
138 & 1 & 0 & 0 \tabularnewline
139 & 1 & 3.00597299693505e-106 & 1.50298649846752e-106 \tabularnewline
140 & 1 & 9.65029309880746e-92 & 4.82514654940373e-92 \tabularnewline
141 & 1 & 6.55115127186497e-87 & 3.27557563593248e-87 \tabularnewline
142 & 1 & 4.17980621595277e-61 & 2.08990310797638e-61 \tabularnewline
143 & 1 & 4.8285363108379e-49 & 2.41426815541895e-49 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201979&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]11[/C][C]5.82205790957935e-49[/C][C]1.16441158191587e-48[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]8.36008829268061e-61[/C][C]1.67201765853612e-60[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]2.98125545625662e-86[/C][C]5.96251091251324e-86[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]3.53464395244205e-90[/C][C]7.0692879048841e-90[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]5.53477348095206e-105[/C][C]1.10695469619041e-104[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]2.80648197816894e-145[/C][C]5.61296395633788e-145[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]3.9140570204587e-151[/C][C]7.82811404091741e-151[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]7.11421089491722e-165[/C][C]1.42284217898344e-164[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]1.06346902493561e-188[/C][C]2.12693804987122e-188[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]6.76285148983158e-221[/C][C]1.35257029796632e-220[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]1.5792121643312e-212[/C][C]3.15842432866239e-212[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]6.07137006008037e-224[/C][C]1.21427401201607e-223[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]7.18394621088993e-242[/C][C]1.43678924217799e-241[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]5.17427314915009e-260[/C][C]1.03485462983002e-259[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]1.20339773966256e-300[/C][C]2.40679547932513e-300[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]8.0756516919114e-289[/C][C]1.61513033838228e-288[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]5.41197745203462e-299[/C][C]1.08239549040692e-298[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]6.76474682285835e-319[/C][C]1.35294936457167e-318[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.72556705773827e-18[/C][C]8.62783528869134e-19[/C][/ROW]
[ROW][C]87[/C][C]2.7196633862642e-08[/C][C]5.43932677252839e-08[/C][C]0.999999972803366[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]8.89318162514244e-323[/C][C]4.44659081257122e-323[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]2.60949473370241e-303[/C][C]1.3047473668512e-303[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]2.40614422669591e-292[/C][C]1.20307211334795e-292[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.25126173464601e-303[/C][C]6.25630867323005e-304[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]6.29350469393274e-263[/C][C]3.14675234696637e-263[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]2.25099975117603e-244[/C][C]1.12549987558802e-244[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]3.31604677393526e-227[/C][C]1.65802338696763e-227[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]4.70558793362626e-215[/C][C]2.35279396681313e-215[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.42775286175333e-222[/C][C]7.13876430876665e-223[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]3.61038382563511e-190[/C][C]1.80519191281755e-190[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]2.80213070567385e-166[/C][C]1.40106535283692e-166[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]9.74805642865301e-153[/C][C]4.8740282143265e-153[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]7.89176785898121e-148[/C][C]3.94588392949061e-148[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]3.00597299693505e-106[/C][C]1.50298649846752e-106[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]9.65029309880746e-92[/C][C]4.82514654940373e-92[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]6.55115127186497e-87[/C][C]3.27557563593248e-87[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]4.17980621595277e-61[/C][C]2.08990310797638e-61[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]4.8285363108379e-49[/C][C]2.41426815541895e-49[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201979&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201979&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
11	5.82205790957935e-49	1.16441158191587e-48	1
12	8.36008829268061e-61	1.67201765853612e-60	1
13	2.98125545625662e-86	5.96251091251324e-86	1
14	3.53464395244205e-90	7.0692879048841e-90	1
15	5.53477348095206e-105	1.10695469619041e-104	1
16	0	0	1
17	2.80648197816894e-145	5.61296395633788e-145	1
18	3.9140570204587e-151	7.82811404091741e-151	1
19	7.11421089491722e-165	1.42284217898344e-164	1
20	1.06346902493561e-188	2.12693804987122e-188	1
21	6.76285148983158e-221	1.35257029796632e-220	1
22	1.5792121643312e-212	3.15842432866239e-212	1
23	6.07137006008037e-224	1.21427401201607e-223	1
24	7.18394621088993e-242	1.43678924217799e-241	1
25	5.17427314915009e-260	1.03485462983002e-259	1
26	1.20339773966256e-300	2.40679547932513e-300	1
27	8.0756516919114e-289	1.61513033838228e-288	1
28	5.41197745203462e-299	1.08239549040692e-298	1
29	6.76474682285835e-319	1.35294936457167e-318	1
30	0	0	1
31	0	0	1
32	0	0	1
33	0	0	1
34	0	0	1
35	0	0	1
36	0	0	1
37	0	0	1
38	0	0	1
39	0	0	1
40	0	0	1
41	0	0	1
42	0	0	1
43	0	0	1
44	0	0	1
45	0	0	1
46	0	0	1
47	0	0	1
48	0	0	1
49	0	0	1
50	0	0	1
51	0	0	1
52	0	0	1
53	0	0	1
54	0	0	1
55	0	0	1
56	0	0	1
57	0	0	1
58	0	0	1
59	0	0	1
60	0	0	1
61	0	0	1
62	0	0	1
63	0	0	1
64	0	0	1
65	0	0	1
66	0	0	1
67	0	0	1
68	0	0	1
69	0	0	1
70	0	0	1
71	0	0	1
72	0	0	1
73	0	0	1
74	0	0	1
75	0	0	1
76	0	0	1
77	0	0	1
78	0	0	1
79	0	0	1
80	0	0	1
81	0	0	1
82	0	0	1
83	0	0	1
84	0	0	1
85	0	0	1
86	1	1.72556705773827e-18	8.62783528869134e-19
87	2.7196633862642e-08	5.43932677252839e-08	0.999999972803366
88	1	0	0
89	1	0	0
90	1	0	0
91	1	0	0
92	1	0	0
93	1	0	0
94	1	0	0
95	1	0	0
96	1	0	0
97	1	0	0
98	1	0	0
99	1	0	0
100	1	0	0
101	1	0	0
102	1	0	0
103	1	0	0
104	1	0	0
105	1	0	0
106	1	0	0
107	1	0	0
108	1	0	0
109	1	0	0
110	1	0	0
111	1	0	0
112	1	0	0
113	1	0	0
114	1	0	0
115	1	0	0
116	1	0	0
117	1	0	0
118	1	0	0
119	1	0	0
120	1	0	0
121	1	0	0
122	1	0	0
123	1	0	0
124	1	0	0
125	1	8.89318162514244e-323	4.44659081257122e-323
126	1	2.60949473370241e-303	1.3047473668512e-303
127	1	2.40614422669591e-292	1.20307211334795e-292
128	1	1.25126173464601e-303	6.25630867323005e-304
129	1	6.29350469393274e-263	3.14675234696637e-263
130	1	2.25099975117603e-244	1.12549987558802e-244
131	1	3.31604677393526e-227	1.65802338696763e-227
132	1	4.70558793362626e-215	2.35279396681313e-215
133	1	1.42775286175333e-222	7.13876430876665e-223
134	1	3.61038382563511e-190	1.80519191281755e-190
135	1	2.80213070567385e-166	1.40106535283692e-166
136	1	9.74805642865301e-153	4.8740282143265e-153
137	1	7.89176785898121e-148	3.94588392949061e-148
138	1	0	0
139	1	3.00597299693505e-106	1.50298649846752e-106
140	1	9.65029309880746e-92	4.82514654940373e-92
141	1	6.55115127186497e-87	3.27557563593248e-87
142	1	4.17980621595277e-61	2.08990310797638e-61
143	1	4.8285363108379e-49	2.41426815541895e-49

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	133	1	NOK
5% type I error level	133	1	NOK
10% type I error level	133	1	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 & 133 & 1 & NOK \tabularnewline
5% type I error level & 133 & 1 & NOK \tabularnewline
10% type I error level & 133 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201979&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]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201979&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201979&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	133	1	NOK
5% type I error level	133	1	NOK
10% type I error level	133	1	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code