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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 17 Dec 2017 18:07:38 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/17/t1513530582pntxqdf48gjflpp.htm/, Retrieved Wed, 15 May 2024 05:34:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310027, Retrieved Wed, 15 May 2024 05:34:02 +0000

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No (this computation is public)

User-defined keywords

zondag avond

Estimated Impact

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

-       [Multiple Regression] [Multiple regression ] [2017-12-17 17:07:38] [54bb40d47798e07418502d5b4e7efc34] [Current]

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Dataseries X:

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Histogram

Boxplots

57,7	63,2
60,1	68,6
66,5	77,7
63,4	68,1
71,4	75,1
68,5	73,3
61,6	60,5
68,3	65,9
69,3	77,7
76,1	77,1
73,3	77,7
69,7	71,3
67,4	76
63,7	75,3
73	81,7
67,5	72,5
74,4	77,4
72,9	81,1
71,7	65,1
75,6	68,7
72,5	75,6
80	79,7
75,4	75,3
71	67,7
70,6	73,2
67,5	72,2
74,1	79,3
73,2	77,5
74	75,6
73	77,4
74	69,2
73	67,1
76	77,9
81,7	82,7
73,5	75,7
77	70,1
73,6	76,4
70,4	74,3
74,7	80,5
76,8	78
72,7	73,5
76	78,8
77,5	71,2
73,6	66,2
78,5	82,7
84,3	83,8
74,4	75
78,5	80,4
72,7	74,6
71,3	77,7
84,4	89,8
79,1	82,4
76,2	77
84,9	89,6
77,1	75,7
78,7	75,1
84,7	89,9
83,7	88,8
82,5	86,5
85,2	90
76	84
72,2	82,7
83,2	91,7
80,2	87,5
81,1	82
86	92,2
76	73,1
83,9	75,6
87,9	91,6
85	87,5
88,1	90,1
87,4	91,3
79,5	87,6
75,2	88,4
87,3	100,7
79,5	85,3
87,6	92
89,1	96,8
83	77,9
88,3	80,9
88,9	95,3
93,9	99,3
91,7	96,1
87,2	92,5
87,8	93,7
81	92,1
93,7	103,6
87,5	92,5
91,4	95,7
93,8	103,4
89,5	89
93,3	89,1
92,8	98,7
104,1	109,4
99,9	101,1
93,4	95,4
99	101,4
93,2	102,1
95,7	103,6
102,6	106
98,8	98,4
98	106,6
101,5	95,8
94,9	87,2
104,7	108,5
108,4	107
97	92
102,3	94,9
90,8	84,4
89,6	85
99,9	94
99,2	84,5
94	88,2
103	92,1
99,8	81,1
94,9	81,2
102	96,1
103,2	95,3
98	92,1
101,1	91,7
88,2	90,3
90,3	96,1
105,5	108,7
99,4	95,9
94,3	95,1
105,9	109,4
98	91,2
99	91,4
103,9	107,4
104,3	105,6
105,7	105,3
105,5	103,7
97,4	99,5
95,4	103,2
110,5	123,1
102,8	102,2
110	110
104,3	106,2
96,5	91,3
105,6	99,3
111,3	111,8
108,5	104,4
109,1	102,4
107,7	101
102,3	100,6
102,4	104,5
110,8	117,4
101,7	97,4
108,9	99,5
111,5	106,4
104	95,2
109,9	94
106,8	104,1
118,4	105,8
111,8	101,1
105	93,5
104,9	97,9
96,5	96,8
106,3	108,4
105,6	103,5
109,3	101,3
105,1	107,4
111,5	100,7
103,1	91,1
106,5	105
114,4	112,8
104,7	105,6
105,5	101
100,5	101,9
96,4	103,5
105,1	109,5
108,4	105
105,7	102,9
109	108,5
107,2	96,9
101,6	88,4
112,7	112,4
115,9	111,3
105	101,6
110,4	101,2
100,9	101,8
98,5	98,8
111,3	114,4
109,6	104,5
103,4	97,6
115,7	109,1
110,4	94,5
105,2	90,4
113,2	111,8
117,4	110,5
112,3	106,8
113,9	101,8
102,2	103,7
106,9	107,4
118	117,5
113,8	109,6
114,9	102,8
118,8	115,5
106,3	97,8
114,2	100,2
117,3	112,9
114,7	108,7
117	109
116,6	113,9
106,5	106,9
105,7	109,6
121	124,5
107,8	104,2
119,7	110,8
121	118,7
108,8	102,1
115	105,1

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	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310027&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&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	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
FBT[t] = -2.12338 + 0.348474EXFBT[t] + 0.144804`FBT(t-1)`[t] + 0.548703`FBT(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FBT[t] =  -2.12338 +  0.348474EXFBT[t] +  0.144804`FBT(t-1)`[t] +  0.548703`FBT(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FBT[t] =  -2.12338 +  0.348474EXFBT[t] +  0.144804`FBT(t-1)`[t] +  0.548703`FBT(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&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
FBT[t] = -2.12338 + 0.348474EXFBT[t] + 0.144804`FBT(t-1)`[t] + 0.548703`FBT(t-1s)`[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)	-2.123	1.736	-1.2230e+00	0.2227	0.1113
EXFBT	+0.3485	0.03192	+1.0920e+01	5.827e-22	2.914e-22
`FBT(t-1)`	+0.1448	0.03828	+3.7820e+00	0.0002064	0.0001032
`FBT(t-1s)`	+0.5487	0.04396	+1.2480e+01	1.174e-26	5.869e-27

\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) & -2.123 &  1.736 & -1.2230e+00 &  0.2227 &  0.1113 \tabularnewline
EXFBT & +0.3485 &  0.03192 & +1.0920e+01 &  5.827e-22 &  2.914e-22 \tabularnewline
`FBT(t-1)` & +0.1448 &  0.03828 & +3.7820e+00 &  0.0002064 &  0.0001032 \tabularnewline
`FBT(t-1s)` & +0.5487 &  0.04396 & +1.2480e+01 &  1.174e-26 &  5.869e-27 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&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]-2.123[/C][C] 1.736[/C][C]-1.2230e+00[/C][C] 0.2227[/C][C] 0.1113[/C][/ROW]
[ROW][C]EXFBT[/C][C]+0.3485[/C][C] 0.03192[/C][C]+1.0920e+01[/C][C] 5.827e-22[/C][C] 2.914e-22[/C][/ROW]
[ROW][C]`FBT(t-1)`[/C][C]+0.1448[/C][C] 0.03828[/C][C]+3.7820e+00[/C][C] 0.0002064[/C][C] 0.0001032[/C][/ROW]
[ROW][C]`FBT(t-1s)`[/C][C]+0.5487[/C][C] 0.04396[/C][C]+1.2480e+01[/C][C] 1.174e-26[/C][C] 5.869e-27[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&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)	-2.123	1.736	-1.2230e+00	0.2227	0.1113
EXFBT	+0.3485	0.03192	+1.0920e+01	5.827e-22	2.914e-22
`FBT(t-1)`	+0.1448	0.03828	+3.7820e+00	0.0002064	0.0001032
`FBT(t-1s)`	+0.5487	0.04396	+1.2480e+01	1.174e-26	5.869e-27

Multiple Linear Regression - Regression Statistics
Multiple R	0.9762
R-squared	0.9529
Adjusted R-squared	0.9522
F-TEST (value)	1315
F-TEST (DF numerator)	3
F-TEST (DF denominator)	195
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.177
Sum Squared Residuals	1969

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9762 \tabularnewline
R-squared &  0.9529 \tabularnewline
Adjusted R-squared &  0.9522 \tabularnewline
F-TEST (value) &  1315 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 195 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.177 \tabularnewline
Sum Squared Residuals &  1969 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9762[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9529[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9522[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1315[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]195[/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] 3.177[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&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	0.9762
R-squared	0.9529
Adjusted R-squared	0.9522
F-TEST (value)	1315
F-TEST (DF numerator)	3
F-TEST (DF denominator)	195
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.177
Sum Squared Residuals	1969

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	63.7	66.85	-3.154
2	73	72.06	0.9403
3	67.5	68.5	-0.9995
4	74.4	73.8	0.5998
5	72.9	74.5	-1.597
6	71.7	64.92	6.781
7	75.6	69.68	5.924
8	72.5	73.19	-0.6936
9	80	77.9	2.095
10	75.4	75.92	-0.521
11	71	70.63	0.3688
12	70.6	70.65	-0.04861
13	67.5	68.21	-0.712
14	74.1	75.34	-1.24
15	73.2	72.65	0.5492
16	74	75.64	-1.644
17	73	75.56	-2.564
18	74	71.9	2.096
19	73	73.46	-0.4567
20	76	75.37	0.6256
21	81.7	81.6	0.1032
22	73.5	77.46	-3.959
23	77	71.91	5.094
24	73.6	74.39	-0.7884
25	70.4	71.46	-1.063
26	74.7	76.78	-2.082
27	76.8	76.04	0.7605
28	72.7	75.21	-2.514
29	76	75.92	0.08103
30	77.5	74.3	3.203
31	73.6	72.22	1.377
32	78.5	79.05	-0.5545
33	84.3	83.27	1.025
34	74.4	76.55	-2.149
35	78.5	78.92	-0.4175
36	72.7	75.62	-2.924
37	71.3	74.11	-2.809
38	84.4	80.48	3.918
39	79.1	80.95	-1.853
40	76.2	76.05	0.1461
41	84.9	81.84	3.065
42	77.1	79.07	-1.974
43	78.7	75.6	3.104
44	84.7	83.67	1.026
45	83.7	87.34	-3.642
46	82.5	80.96	1.537
47	85.2	84.26	0.9412
48	76	79.38	-3.376
49	72.2	76.82	-4.623
50	83.2	86.6	-3.397
51	80.2	83.82	-3.618
52	81.1	79.88	1.224
53	86	88.33	-2.334
54	76	78.11	-2.108
55	83.9	78.41	5.491
56	87.9	88.42	-0.5211
57	85	87.02	-2.023
58	88.1	86.85	1.25
59	87.4	89.2	-1.799
60	79.5	82.76	-3.26
61	75.2	79.81	-4.61
62	87.3	89.51	-2.209
63	79.5	84.25	-4.749
64	87.6	85.95	1.652
65	89.1	91.48	-2.382
66	83	79.63	3.374
67	88.3	84.12	4.177
68	88.9	92.1	-3.203
69	93.9	91.99	1.907
70	91.7	93.3	-1.603
71	87.2	91.35	-4.146
72	87.8	86.78	1.023
73	81	83.95	-2.947
74	93.7	93.61	0.09054
75	87.5	87.3	0.1995
76	91.4	91.96	-0.5624
77	93.8	96.03	-2.233
78	89.5	88.02	1.484
79	93.3	90.34	2.964
80	92.8	94.56	-1.761
81	104.1	101	3.139
82	99.9	98.5	1.402
83	93.4	93.43	-0.0339
84	99	94.91	4.087
85	93.2	92.24	0.9636
86	95.7	98.89	-3.188
87	102.6	96.68	5.916
88	98.8	97.17	1.625
89	98	100.8	-2.799
90	101.5	94.56	6.94
91	94.9	94.16	0.7448
92	104.7	100.3	4.352
93	108.4	107.4	0.9557
94	97	100.4	-3.448
95	102.3	96.24	6.058
96	90.8	96.42	-5.623
97	89.6	91.78	-2.184
98	99.9	96.12	3.781
99	99.2	98.09	1.114
100	94	97.19	-3.188
101	103	97.36	5.644
102	99.8	96.75	3.054
103	94.9	92.7	2.204
104	102	102.6	-0.5561
105	103.2	105.3	-2.136
106	98	98.14	-0.1391
107	101.1	100.2	0.9452
108	88.2	93.81	-5.606
109	90.3	93.3	-3.001
110	105.5	103.6	1.853
111	99.4	101	-1.603
112	94.3	96.99	-2.688
113	105.9	106.2	-0.2711
114	98	99.75	-1.753
115	99	95.99	3.01
116	103.9	105.6	-1.706
117	104.3	106.3	-2.047
118	105.7	103.4	2.253
119	105.5	104.8	0.7069
120	97.4	96.22	1.178
121	95.4	97.49	-2.091
122	110.5	112.5	-1.976
123	102.8	104	-1.233
124	110	102.8	7.163
125	104.3	108.9	-4.621
126	96.5	98.57	-2.068
127	105.6	100.8	4.825
128	111.3	109.1	2.162
129	108.5	107.6	0.8962
130	109.1	107.3	1.83
131	107.7	106.8	0.9412
132	102.3	102	0.3278
133	102.4	101.5	0.9481
134	110.8	114.2	-3.447
135	101.7	104.3	-2.569
136	108.9	107.6	1.266
137	111.5	108	3.547
138	104	100.1	3.853
139	109.9	103.6	6.264
140	106.8	111.1	-4.337
141	118.4	109.7	8.655
142	111.8	110.1	1.684
143	105	105.7	-0.7434
144	104.9	103.3	1.571
145	96.5	103	-6.486
146	106.3	110.4	-4.121
147	105.6	105.1	0.4605
148	109.3	108.2	1.078
149	105.1	112.3	-7.21
150	111.5	105.3	6.248
151	103.1	106.1	-2.971
152	106.5	108	-1.497
153	114.4	117.6	-3.173
154	104.7	112.6	-7.886
155	105.5	105.8	-0.3473
156	100.5	106.2	-5.722
157	96.4	101.4	-5.046
158	105.1	108.3	-3.221
159	108.4	107.6	0.7716
160	105.7	109.4	-3.705
161	109	108.7	0.3394
162	107.2	108.6	-1.408
163	101.6	100.8	0.824
164	112.7	110.2	2.506
165	115.9	115.8	0.1471
166	105	107.5	-2.514
167	110.4	106.2	4.165
168	100.9	104.5	-3.582
169	98.5	99.81	-1.312
170	111.3	109.7	1.626
171	109.6	109.9	-0.2883
172	103.4	105.8	-2.356
173	115.7	110.7	5.023
174	110.4	106.4	4.018
175	105.2	101.1	4.087
176	113.2	113.9	-0.7083
177	117.4	116.4	1.03
178	112.3	109.7	2.593
179	113.9	110.2	3.71
180	102.2	105.9	-3.671
181	106.9	104.1	2.751
182	118	115.4	2.627
183	113.8	113.3	0.5059
184	114.9	106.9	7.986
185	118.8	118.2	0.5517
186	106.3	109.7	-3.437
187	114.2	105.9	8.29
188	117.3	115.9	1.431
189	114.7	117.2	-2.459
190	117	114.1	2.911
191	116.6	117	-0.4072
192	106.5	108.1	-1.59
193	105.7	110.1	-4.447
194	121	121.3	-0.3144
195	107.8	114.2	-6.351
196	119.7	115.1	4.557
197	121	121.8	-0.7595
198	108.8	109.3	-0.5043
199	115	112.9	2.082

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  63.7 &  66.85 & -3.154 \tabularnewline
2 &  73 &  72.06 &  0.9403 \tabularnewline
3 &  67.5 &  68.5 & -0.9995 \tabularnewline
4 &  74.4 &  73.8 &  0.5998 \tabularnewline
5 &  72.9 &  74.5 & -1.597 \tabularnewline
6 &  71.7 &  64.92 &  6.781 \tabularnewline
7 &  75.6 &  69.68 &  5.924 \tabularnewline
8 &  72.5 &  73.19 & -0.6936 \tabularnewline
9 &  80 &  77.9 &  2.095 \tabularnewline
10 &  75.4 &  75.92 & -0.521 \tabularnewline
11 &  71 &  70.63 &  0.3688 \tabularnewline
12 &  70.6 &  70.65 & -0.04861 \tabularnewline
13 &  67.5 &  68.21 & -0.712 \tabularnewline
14 &  74.1 &  75.34 & -1.24 \tabularnewline
15 &  73.2 &  72.65 &  0.5492 \tabularnewline
16 &  74 &  75.64 & -1.644 \tabularnewline
17 &  73 &  75.56 & -2.564 \tabularnewline
18 &  74 &  71.9 &  2.096 \tabularnewline
19 &  73 &  73.46 & -0.4567 \tabularnewline
20 &  76 &  75.37 &  0.6256 \tabularnewline
21 &  81.7 &  81.6 &  0.1032 \tabularnewline
22 &  73.5 &  77.46 & -3.959 \tabularnewline
23 &  77 &  71.91 &  5.094 \tabularnewline
24 &  73.6 &  74.39 & -0.7884 \tabularnewline
25 &  70.4 &  71.46 & -1.063 \tabularnewline
26 &  74.7 &  76.78 & -2.082 \tabularnewline
27 &  76.8 &  76.04 &  0.7605 \tabularnewline
28 &  72.7 &  75.21 & -2.514 \tabularnewline
29 &  76 &  75.92 &  0.08103 \tabularnewline
30 &  77.5 &  74.3 &  3.203 \tabularnewline
31 &  73.6 &  72.22 &  1.377 \tabularnewline
32 &  78.5 &  79.05 & -0.5545 \tabularnewline
33 &  84.3 &  83.27 &  1.025 \tabularnewline
34 &  74.4 &  76.55 & -2.149 \tabularnewline
35 &  78.5 &  78.92 & -0.4175 \tabularnewline
36 &  72.7 &  75.62 & -2.924 \tabularnewline
37 &  71.3 &  74.11 & -2.809 \tabularnewline
38 &  84.4 &  80.48 &  3.918 \tabularnewline
39 &  79.1 &  80.95 & -1.853 \tabularnewline
40 &  76.2 &  76.05 &  0.1461 \tabularnewline
41 &  84.9 &  81.84 &  3.065 \tabularnewline
42 &  77.1 &  79.07 & -1.974 \tabularnewline
43 &  78.7 &  75.6 &  3.104 \tabularnewline
44 &  84.7 &  83.67 &  1.026 \tabularnewline
45 &  83.7 &  87.34 & -3.642 \tabularnewline
46 &  82.5 &  80.96 &  1.537 \tabularnewline
47 &  85.2 &  84.26 &  0.9412 \tabularnewline
48 &  76 &  79.38 & -3.376 \tabularnewline
49 &  72.2 &  76.82 & -4.623 \tabularnewline
50 &  83.2 &  86.6 & -3.397 \tabularnewline
51 &  80.2 &  83.82 & -3.618 \tabularnewline
52 &  81.1 &  79.88 &  1.224 \tabularnewline
53 &  86 &  88.33 & -2.334 \tabularnewline
54 &  76 &  78.11 & -2.108 \tabularnewline
55 &  83.9 &  78.41 &  5.491 \tabularnewline
56 &  87.9 &  88.42 & -0.5211 \tabularnewline
57 &  85 &  87.02 & -2.023 \tabularnewline
58 &  88.1 &  86.85 &  1.25 \tabularnewline
59 &  87.4 &  89.2 & -1.799 \tabularnewline
60 &  79.5 &  82.76 & -3.26 \tabularnewline
61 &  75.2 &  79.81 & -4.61 \tabularnewline
62 &  87.3 &  89.51 & -2.209 \tabularnewline
63 &  79.5 &  84.25 & -4.749 \tabularnewline
64 &  87.6 &  85.95 &  1.652 \tabularnewline
65 &  89.1 &  91.48 & -2.382 \tabularnewline
66 &  83 &  79.63 &  3.374 \tabularnewline
67 &  88.3 &  84.12 &  4.177 \tabularnewline
68 &  88.9 &  92.1 & -3.203 \tabularnewline
69 &  93.9 &  91.99 &  1.907 \tabularnewline
70 &  91.7 &  93.3 & -1.603 \tabularnewline
71 &  87.2 &  91.35 & -4.146 \tabularnewline
72 &  87.8 &  86.78 &  1.023 \tabularnewline
73 &  81 &  83.95 & -2.947 \tabularnewline
74 &  93.7 &  93.61 &  0.09054 \tabularnewline
75 &  87.5 &  87.3 &  0.1995 \tabularnewline
76 &  91.4 &  91.96 & -0.5624 \tabularnewline
77 &  93.8 &  96.03 & -2.233 \tabularnewline
78 &  89.5 &  88.02 &  1.484 \tabularnewline
79 &  93.3 &  90.34 &  2.964 \tabularnewline
80 &  92.8 &  94.56 & -1.761 \tabularnewline
81 &  104.1 &  101 &  3.139 \tabularnewline
82 &  99.9 &  98.5 &  1.402 \tabularnewline
83 &  93.4 &  93.43 & -0.0339 \tabularnewline
84 &  99 &  94.91 &  4.087 \tabularnewline
85 &  93.2 &  92.24 &  0.9636 \tabularnewline
86 &  95.7 &  98.89 & -3.188 \tabularnewline
87 &  102.6 &  96.68 &  5.916 \tabularnewline
88 &  98.8 &  97.17 &  1.625 \tabularnewline
89 &  98 &  100.8 & -2.799 \tabularnewline
90 &  101.5 &  94.56 &  6.94 \tabularnewline
91 &  94.9 &  94.16 &  0.7448 \tabularnewline
92 &  104.7 &  100.3 &  4.352 \tabularnewline
93 &  108.4 &  107.4 &  0.9557 \tabularnewline
94 &  97 &  100.4 & -3.448 \tabularnewline
95 &  102.3 &  96.24 &  6.058 \tabularnewline
96 &  90.8 &  96.42 & -5.623 \tabularnewline
97 &  89.6 &  91.78 & -2.184 \tabularnewline
98 &  99.9 &  96.12 &  3.781 \tabularnewline
99 &  99.2 &  98.09 &  1.114 \tabularnewline
100 &  94 &  97.19 & -3.188 \tabularnewline
101 &  103 &  97.36 &  5.644 \tabularnewline
102 &  99.8 &  96.75 &  3.054 \tabularnewline
103 &  94.9 &  92.7 &  2.204 \tabularnewline
104 &  102 &  102.6 & -0.5561 \tabularnewline
105 &  103.2 &  105.3 & -2.136 \tabularnewline
106 &  98 &  98.14 & -0.1391 \tabularnewline
107 &  101.1 &  100.2 &  0.9452 \tabularnewline
108 &  88.2 &  93.81 & -5.606 \tabularnewline
109 &  90.3 &  93.3 & -3.001 \tabularnewline
110 &  105.5 &  103.6 &  1.853 \tabularnewline
111 &  99.4 &  101 & -1.603 \tabularnewline
112 &  94.3 &  96.99 & -2.688 \tabularnewline
113 &  105.9 &  106.2 & -0.2711 \tabularnewline
114 &  98 &  99.75 & -1.753 \tabularnewline
115 &  99 &  95.99 &  3.01 \tabularnewline
116 &  103.9 &  105.6 & -1.706 \tabularnewline
117 &  104.3 &  106.3 & -2.047 \tabularnewline
118 &  105.7 &  103.4 &  2.253 \tabularnewline
119 &  105.5 &  104.8 &  0.7069 \tabularnewline
120 &  97.4 &  96.22 &  1.178 \tabularnewline
121 &  95.4 &  97.49 & -2.091 \tabularnewline
122 &  110.5 &  112.5 & -1.976 \tabularnewline
123 &  102.8 &  104 & -1.233 \tabularnewline
124 &  110 &  102.8 &  7.163 \tabularnewline
125 &  104.3 &  108.9 & -4.621 \tabularnewline
126 &  96.5 &  98.57 & -2.068 \tabularnewline
127 &  105.6 &  100.8 &  4.825 \tabularnewline
128 &  111.3 &  109.1 &  2.162 \tabularnewline
129 &  108.5 &  107.6 &  0.8962 \tabularnewline
130 &  109.1 &  107.3 &  1.83 \tabularnewline
131 &  107.7 &  106.8 &  0.9412 \tabularnewline
132 &  102.3 &  102 &  0.3278 \tabularnewline
133 &  102.4 &  101.5 &  0.9481 \tabularnewline
134 &  110.8 &  114.2 & -3.447 \tabularnewline
135 &  101.7 &  104.3 & -2.569 \tabularnewline
136 &  108.9 &  107.6 &  1.266 \tabularnewline
137 &  111.5 &  108 &  3.547 \tabularnewline
138 &  104 &  100.1 &  3.853 \tabularnewline
139 &  109.9 &  103.6 &  6.264 \tabularnewline
140 &  106.8 &  111.1 & -4.337 \tabularnewline
141 &  118.4 &  109.7 &  8.655 \tabularnewline
142 &  111.8 &  110.1 &  1.684 \tabularnewline
143 &  105 &  105.7 & -0.7434 \tabularnewline
144 &  104.9 &  103.3 &  1.571 \tabularnewline
145 &  96.5 &  103 & -6.486 \tabularnewline
146 &  106.3 &  110.4 & -4.121 \tabularnewline
147 &  105.6 &  105.1 &  0.4605 \tabularnewline
148 &  109.3 &  108.2 &  1.078 \tabularnewline
149 &  105.1 &  112.3 & -7.21 \tabularnewline
150 &  111.5 &  105.3 &  6.248 \tabularnewline
151 &  103.1 &  106.1 & -2.971 \tabularnewline
152 &  106.5 &  108 & -1.497 \tabularnewline
153 &  114.4 &  117.6 & -3.173 \tabularnewline
154 &  104.7 &  112.6 & -7.886 \tabularnewline
155 &  105.5 &  105.8 & -0.3473 \tabularnewline
156 &  100.5 &  106.2 & -5.722 \tabularnewline
157 &  96.4 &  101.4 & -5.046 \tabularnewline
158 &  105.1 &  108.3 & -3.221 \tabularnewline
159 &  108.4 &  107.6 &  0.7716 \tabularnewline
160 &  105.7 &  109.4 & -3.705 \tabularnewline
161 &  109 &  108.7 &  0.3394 \tabularnewline
162 &  107.2 &  108.6 & -1.408 \tabularnewline
163 &  101.6 &  100.8 &  0.824 \tabularnewline
164 &  112.7 &  110.2 &  2.506 \tabularnewline
165 &  115.9 &  115.8 &  0.1471 \tabularnewline
166 &  105 &  107.5 & -2.514 \tabularnewline
167 &  110.4 &  106.2 &  4.165 \tabularnewline
168 &  100.9 &  104.5 & -3.582 \tabularnewline
169 &  98.5 &  99.81 & -1.312 \tabularnewline
170 &  111.3 &  109.7 &  1.626 \tabularnewline
171 &  109.6 &  109.9 & -0.2883 \tabularnewline
172 &  103.4 &  105.8 & -2.356 \tabularnewline
173 &  115.7 &  110.7 &  5.023 \tabularnewline
174 &  110.4 &  106.4 &  4.018 \tabularnewline
175 &  105.2 &  101.1 &  4.087 \tabularnewline
176 &  113.2 &  113.9 & -0.7083 \tabularnewline
177 &  117.4 &  116.4 &  1.03 \tabularnewline
178 &  112.3 &  109.7 &  2.593 \tabularnewline
179 &  113.9 &  110.2 &  3.71 \tabularnewline
180 &  102.2 &  105.9 & -3.671 \tabularnewline
181 &  106.9 &  104.1 &  2.751 \tabularnewline
182 &  118 &  115.4 &  2.627 \tabularnewline
183 &  113.8 &  113.3 &  0.5059 \tabularnewline
184 &  114.9 &  106.9 &  7.986 \tabularnewline
185 &  118.8 &  118.2 &  0.5517 \tabularnewline
186 &  106.3 &  109.7 & -3.437 \tabularnewline
187 &  114.2 &  105.9 &  8.29 \tabularnewline
188 &  117.3 &  115.9 &  1.431 \tabularnewline
189 &  114.7 &  117.2 & -2.459 \tabularnewline
190 &  117 &  114.1 &  2.911 \tabularnewline
191 &  116.6 &  117 & -0.4072 \tabularnewline
192 &  106.5 &  108.1 & -1.59 \tabularnewline
193 &  105.7 &  110.1 & -4.447 \tabularnewline
194 &  121 &  121.3 & -0.3144 \tabularnewline
195 &  107.8 &  114.2 & -6.351 \tabularnewline
196 &  119.7 &  115.1 &  4.557 \tabularnewline
197 &  121 &  121.8 & -0.7595 \tabularnewline
198 &  108.8 &  109.3 & -0.5043 \tabularnewline
199 &  115 &  112.9 &  2.082 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=5

[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] 63.7[/C][C] 66.85[/C][C]-3.154[/C][/ROW]
[ROW][C]2[/C][C] 73[/C][C] 72.06[/C][C] 0.9403[/C][/ROW]
[ROW][C]3[/C][C] 67.5[/C][C] 68.5[/C][C]-0.9995[/C][/ROW]
[ROW][C]4[/C][C] 74.4[/C][C] 73.8[/C][C] 0.5998[/C][/ROW]
[ROW][C]5[/C][C] 72.9[/C][C] 74.5[/C][C]-1.597[/C][/ROW]
[ROW][C]6[/C][C] 71.7[/C][C] 64.92[/C][C] 6.781[/C][/ROW]
[ROW][C]7[/C][C] 75.6[/C][C] 69.68[/C][C] 5.924[/C][/ROW]
[ROW][C]8[/C][C] 72.5[/C][C] 73.19[/C][C]-0.6936[/C][/ROW]
[ROW][C]9[/C][C] 80[/C][C] 77.9[/C][C] 2.095[/C][/ROW]
[ROW][C]10[/C][C] 75.4[/C][C] 75.92[/C][C]-0.521[/C][/ROW]
[ROW][C]11[/C][C] 71[/C][C] 70.63[/C][C] 0.3688[/C][/ROW]
[ROW][C]12[/C][C] 70.6[/C][C] 70.65[/C][C]-0.04861[/C][/ROW]
[ROW][C]13[/C][C] 67.5[/C][C] 68.21[/C][C]-0.712[/C][/ROW]
[ROW][C]14[/C][C] 74.1[/C][C] 75.34[/C][C]-1.24[/C][/ROW]
[ROW][C]15[/C][C] 73.2[/C][C] 72.65[/C][C] 0.5492[/C][/ROW]
[ROW][C]16[/C][C] 74[/C][C] 75.64[/C][C]-1.644[/C][/ROW]
[ROW][C]17[/C][C] 73[/C][C] 75.56[/C][C]-2.564[/C][/ROW]
[ROW][C]18[/C][C] 74[/C][C] 71.9[/C][C] 2.096[/C][/ROW]
[ROW][C]19[/C][C] 73[/C][C] 73.46[/C][C]-0.4567[/C][/ROW]
[ROW][C]20[/C][C] 76[/C][C] 75.37[/C][C] 0.6256[/C][/ROW]
[ROW][C]21[/C][C] 81.7[/C][C] 81.6[/C][C] 0.1032[/C][/ROW]
[ROW][C]22[/C][C] 73.5[/C][C] 77.46[/C][C]-3.959[/C][/ROW]
[ROW][C]23[/C][C] 77[/C][C] 71.91[/C][C] 5.094[/C][/ROW]
[ROW][C]24[/C][C] 73.6[/C][C] 74.39[/C][C]-0.7884[/C][/ROW]
[ROW][C]25[/C][C] 70.4[/C][C] 71.46[/C][C]-1.063[/C][/ROW]
[ROW][C]26[/C][C] 74.7[/C][C] 76.78[/C][C]-2.082[/C][/ROW]
[ROW][C]27[/C][C] 76.8[/C][C] 76.04[/C][C] 0.7605[/C][/ROW]
[ROW][C]28[/C][C] 72.7[/C][C] 75.21[/C][C]-2.514[/C][/ROW]
[ROW][C]29[/C][C] 76[/C][C] 75.92[/C][C] 0.08103[/C][/ROW]
[ROW][C]30[/C][C] 77.5[/C][C] 74.3[/C][C] 3.203[/C][/ROW]
[ROW][C]31[/C][C] 73.6[/C][C] 72.22[/C][C] 1.377[/C][/ROW]
[ROW][C]32[/C][C] 78.5[/C][C] 79.05[/C][C]-0.5545[/C][/ROW]
[ROW][C]33[/C][C] 84.3[/C][C] 83.27[/C][C] 1.025[/C][/ROW]
[ROW][C]34[/C][C] 74.4[/C][C] 76.55[/C][C]-2.149[/C][/ROW]
[ROW][C]35[/C][C] 78.5[/C][C] 78.92[/C][C]-0.4175[/C][/ROW]
[ROW][C]36[/C][C] 72.7[/C][C] 75.62[/C][C]-2.924[/C][/ROW]
[ROW][C]37[/C][C] 71.3[/C][C] 74.11[/C][C]-2.809[/C][/ROW]
[ROW][C]38[/C][C] 84.4[/C][C] 80.48[/C][C] 3.918[/C][/ROW]
[ROW][C]39[/C][C] 79.1[/C][C] 80.95[/C][C]-1.853[/C][/ROW]
[ROW][C]40[/C][C] 76.2[/C][C] 76.05[/C][C] 0.1461[/C][/ROW]
[ROW][C]41[/C][C] 84.9[/C][C] 81.84[/C][C] 3.065[/C][/ROW]
[ROW][C]42[/C][C] 77.1[/C][C] 79.07[/C][C]-1.974[/C][/ROW]
[ROW][C]43[/C][C] 78.7[/C][C] 75.6[/C][C] 3.104[/C][/ROW]
[ROW][C]44[/C][C] 84.7[/C][C] 83.67[/C][C] 1.026[/C][/ROW]
[ROW][C]45[/C][C] 83.7[/C][C] 87.34[/C][C]-3.642[/C][/ROW]
[ROW][C]46[/C][C] 82.5[/C][C] 80.96[/C][C] 1.537[/C][/ROW]
[ROW][C]47[/C][C] 85.2[/C][C] 84.26[/C][C] 0.9412[/C][/ROW]
[ROW][C]48[/C][C] 76[/C][C] 79.38[/C][C]-3.376[/C][/ROW]
[ROW][C]49[/C][C] 72.2[/C][C] 76.82[/C][C]-4.623[/C][/ROW]
[ROW][C]50[/C][C] 83.2[/C][C] 86.6[/C][C]-3.397[/C][/ROW]
[ROW][C]51[/C][C] 80.2[/C][C] 83.82[/C][C]-3.618[/C][/ROW]
[ROW][C]52[/C][C] 81.1[/C][C] 79.88[/C][C] 1.224[/C][/ROW]
[ROW][C]53[/C][C] 86[/C][C] 88.33[/C][C]-2.334[/C][/ROW]
[ROW][C]54[/C][C] 76[/C][C] 78.11[/C][C]-2.108[/C][/ROW]
[ROW][C]55[/C][C] 83.9[/C][C] 78.41[/C][C] 5.491[/C][/ROW]
[ROW][C]56[/C][C] 87.9[/C][C] 88.42[/C][C]-0.5211[/C][/ROW]
[ROW][C]57[/C][C] 85[/C][C] 87.02[/C][C]-2.023[/C][/ROW]
[ROW][C]58[/C][C] 88.1[/C][C] 86.85[/C][C] 1.25[/C][/ROW]
[ROW][C]59[/C][C] 87.4[/C][C] 89.2[/C][C]-1.799[/C][/ROW]
[ROW][C]60[/C][C] 79.5[/C][C] 82.76[/C][C]-3.26[/C][/ROW]
[ROW][C]61[/C][C] 75.2[/C][C] 79.81[/C][C]-4.61[/C][/ROW]
[ROW][C]62[/C][C] 87.3[/C][C] 89.51[/C][C]-2.209[/C][/ROW]
[ROW][C]63[/C][C] 79.5[/C][C] 84.25[/C][C]-4.749[/C][/ROW]
[ROW][C]64[/C][C] 87.6[/C][C] 85.95[/C][C] 1.652[/C][/ROW]
[ROW][C]65[/C][C] 89.1[/C][C] 91.48[/C][C]-2.382[/C][/ROW]
[ROW][C]66[/C][C] 83[/C][C] 79.63[/C][C] 3.374[/C][/ROW]
[ROW][C]67[/C][C] 88.3[/C][C] 84.12[/C][C] 4.177[/C][/ROW]
[ROW][C]68[/C][C] 88.9[/C][C] 92.1[/C][C]-3.203[/C][/ROW]
[ROW][C]69[/C][C] 93.9[/C][C] 91.99[/C][C] 1.907[/C][/ROW]
[ROW][C]70[/C][C] 91.7[/C][C] 93.3[/C][C]-1.603[/C][/ROW]
[ROW][C]71[/C][C] 87.2[/C][C] 91.35[/C][C]-4.146[/C][/ROW]
[ROW][C]72[/C][C] 87.8[/C][C] 86.78[/C][C] 1.023[/C][/ROW]
[ROW][C]73[/C][C] 81[/C][C] 83.95[/C][C]-2.947[/C][/ROW]
[ROW][C]74[/C][C] 93.7[/C][C] 93.61[/C][C] 0.09054[/C][/ROW]
[ROW][C]75[/C][C] 87.5[/C][C] 87.3[/C][C] 0.1995[/C][/ROW]
[ROW][C]76[/C][C] 91.4[/C][C] 91.96[/C][C]-0.5624[/C][/ROW]
[ROW][C]77[/C][C] 93.8[/C][C] 96.03[/C][C]-2.233[/C][/ROW]
[ROW][C]78[/C][C] 89.5[/C][C] 88.02[/C][C] 1.484[/C][/ROW]
[ROW][C]79[/C][C] 93.3[/C][C] 90.34[/C][C] 2.964[/C][/ROW]
[ROW][C]80[/C][C] 92.8[/C][C] 94.56[/C][C]-1.761[/C][/ROW]
[ROW][C]81[/C][C] 104.1[/C][C] 101[/C][C] 3.139[/C][/ROW]
[ROW][C]82[/C][C] 99.9[/C][C] 98.5[/C][C] 1.402[/C][/ROW]
[ROW][C]83[/C][C] 93.4[/C][C] 93.43[/C][C]-0.0339[/C][/ROW]
[ROW][C]84[/C][C] 99[/C][C] 94.91[/C][C] 4.087[/C][/ROW]
[ROW][C]85[/C][C] 93.2[/C][C] 92.24[/C][C] 0.9636[/C][/ROW]
[ROW][C]86[/C][C] 95.7[/C][C] 98.89[/C][C]-3.188[/C][/ROW]
[ROW][C]87[/C][C] 102.6[/C][C] 96.68[/C][C] 5.916[/C][/ROW]
[ROW][C]88[/C][C] 98.8[/C][C] 97.17[/C][C] 1.625[/C][/ROW]
[ROW][C]89[/C][C] 98[/C][C] 100.8[/C][C]-2.799[/C][/ROW]
[ROW][C]90[/C][C] 101.5[/C][C] 94.56[/C][C] 6.94[/C][/ROW]
[ROW][C]91[/C][C] 94.9[/C][C] 94.16[/C][C] 0.7448[/C][/ROW]
[ROW][C]92[/C][C] 104.7[/C][C] 100.3[/C][C] 4.352[/C][/ROW]
[ROW][C]93[/C][C] 108.4[/C][C] 107.4[/C][C] 0.9557[/C][/ROW]
[ROW][C]94[/C][C] 97[/C][C] 100.4[/C][C]-3.448[/C][/ROW]
[ROW][C]95[/C][C] 102.3[/C][C] 96.24[/C][C] 6.058[/C][/ROW]
[ROW][C]96[/C][C] 90.8[/C][C] 96.42[/C][C]-5.623[/C][/ROW]
[ROW][C]97[/C][C] 89.6[/C][C] 91.78[/C][C]-2.184[/C][/ROW]
[ROW][C]98[/C][C] 99.9[/C][C] 96.12[/C][C] 3.781[/C][/ROW]
[ROW][C]99[/C][C] 99.2[/C][C] 98.09[/C][C] 1.114[/C][/ROW]
[ROW][C]100[/C][C] 94[/C][C] 97.19[/C][C]-3.188[/C][/ROW]
[ROW][C]101[/C][C] 103[/C][C] 97.36[/C][C] 5.644[/C][/ROW]
[ROW][C]102[/C][C] 99.8[/C][C] 96.75[/C][C] 3.054[/C][/ROW]
[ROW][C]103[/C][C] 94.9[/C][C] 92.7[/C][C] 2.204[/C][/ROW]
[ROW][C]104[/C][C] 102[/C][C] 102.6[/C][C]-0.5561[/C][/ROW]
[ROW][C]105[/C][C] 103.2[/C][C] 105.3[/C][C]-2.136[/C][/ROW]
[ROW][C]106[/C][C] 98[/C][C] 98.14[/C][C]-0.1391[/C][/ROW]
[ROW][C]107[/C][C] 101.1[/C][C] 100.2[/C][C] 0.9452[/C][/ROW]
[ROW][C]108[/C][C] 88.2[/C][C] 93.81[/C][C]-5.606[/C][/ROW]
[ROW][C]109[/C][C] 90.3[/C][C] 93.3[/C][C]-3.001[/C][/ROW]
[ROW][C]110[/C][C] 105.5[/C][C] 103.6[/C][C] 1.853[/C][/ROW]
[ROW][C]111[/C][C] 99.4[/C][C] 101[/C][C]-1.603[/C][/ROW]
[ROW][C]112[/C][C] 94.3[/C][C] 96.99[/C][C]-2.688[/C][/ROW]
[ROW][C]113[/C][C] 105.9[/C][C] 106.2[/C][C]-0.2711[/C][/ROW]
[ROW][C]114[/C][C] 98[/C][C] 99.75[/C][C]-1.753[/C][/ROW]
[ROW][C]115[/C][C] 99[/C][C] 95.99[/C][C] 3.01[/C][/ROW]
[ROW][C]116[/C][C] 103.9[/C][C] 105.6[/C][C]-1.706[/C][/ROW]
[ROW][C]117[/C][C] 104.3[/C][C] 106.3[/C][C]-2.047[/C][/ROW]
[ROW][C]118[/C][C] 105.7[/C][C] 103.4[/C][C] 2.253[/C][/ROW]
[ROW][C]119[/C][C] 105.5[/C][C] 104.8[/C][C] 0.7069[/C][/ROW]
[ROW][C]120[/C][C] 97.4[/C][C] 96.22[/C][C] 1.178[/C][/ROW]
[ROW][C]121[/C][C] 95.4[/C][C] 97.49[/C][C]-2.091[/C][/ROW]
[ROW][C]122[/C][C] 110.5[/C][C] 112.5[/C][C]-1.976[/C][/ROW]
[ROW][C]123[/C][C] 102.8[/C][C] 104[/C][C]-1.233[/C][/ROW]
[ROW][C]124[/C][C] 110[/C][C] 102.8[/C][C] 7.163[/C][/ROW]
[ROW][C]125[/C][C] 104.3[/C][C] 108.9[/C][C]-4.621[/C][/ROW]
[ROW][C]126[/C][C] 96.5[/C][C] 98.57[/C][C]-2.068[/C][/ROW]
[ROW][C]127[/C][C] 105.6[/C][C] 100.8[/C][C] 4.825[/C][/ROW]
[ROW][C]128[/C][C] 111.3[/C][C] 109.1[/C][C] 2.162[/C][/ROW]
[ROW][C]129[/C][C] 108.5[/C][C] 107.6[/C][C] 0.8962[/C][/ROW]
[ROW][C]130[/C][C] 109.1[/C][C] 107.3[/C][C] 1.83[/C][/ROW]
[ROW][C]131[/C][C] 107.7[/C][C] 106.8[/C][C] 0.9412[/C][/ROW]
[ROW][C]132[/C][C] 102.3[/C][C] 102[/C][C] 0.3278[/C][/ROW]
[ROW][C]133[/C][C] 102.4[/C][C] 101.5[/C][C] 0.9481[/C][/ROW]
[ROW][C]134[/C][C] 110.8[/C][C] 114.2[/C][C]-3.447[/C][/ROW]
[ROW][C]135[/C][C] 101.7[/C][C] 104.3[/C][C]-2.569[/C][/ROW]
[ROW][C]136[/C][C] 108.9[/C][C] 107.6[/C][C] 1.266[/C][/ROW]
[ROW][C]137[/C][C] 111.5[/C][C] 108[/C][C] 3.547[/C][/ROW]
[ROW][C]138[/C][C] 104[/C][C] 100.1[/C][C] 3.853[/C][/ROW]
[ROW][C]139[/C][C] 109.9[/C][C] 103.6[/C][C] 6.264[/C][/ROW]
[ROW][C]140[/C][C] 106.8[/C][C] 111.1[/C][C]-4.337[/C][/ROW]
[ROW][C]141[/C][C] 118.4[/C][C] 109.7[/C][C] 8.655[/C][/ROW]
[ROW][C]142[/C][C] 111.8[/C][C] 110.1[/C][C] 1.684[/C][/ROW]
[ROW][C]143[/C][C] 105[/C][C] 105.7[/C][C]-0.7434[/C][/ROW]
[ROW][C]144[/C][C] 104.9[/C][C] 103.3[/C][C] 1.571[/C][/ROW]
[ROW][C]145[/C][C] 96.5[/C][C] 103[/C][C]-6.486[/C][/ROW]
[ROW][C]146[/C][C] 106.3[/C][C] 110.4[/C][C]-4.121[/C][/ROW]
[ROW][C]147[/C][C] 105.6[/C][C] 105.1[/C][C] 0.4605[/C][/ROW]
[ROW][C]148[/C][C] 109.3[/C][C] 108.2[/C][C] 1.078[/C][/ROW]
[ROW][C]149[/C][C] 105.1[/C][C] 112.3[/C][C]-7.21[/C][/ROW]
[ROW][C]150[/C][C] 111.5[/C][C] 105.3[/C][C] 6.248[/C][/ROW]
[ROW][C]151[/C][C] 103.1[/C][C] 106.1[/C][C]-2.971[/C][/ROW]
[ROW][C]152[/C][C] 106.5[/C][C] 108[/C][C]-1.497[/C][/ROW]
[ROW][C]153[/C][C] 114.4[/C][C] 117.6[/C][C]-3.173[/C][/ROW]
[ROW][C]154[/C][C] 104.7[/C][C] 112.6[/C][C]-7.886[/C][/ROW]
[ROW][C]155[/C][C] 105.5[/C][C] 105.8[/C][C]-0.3473[/C][/ROW]
[ROW][C]156[/C][C] 100.5[/C][C] 106.2[/C][C]-5.722[/C][/ROW]
[ROW][C]157[/C][C] 96.4[/C][C] 101.4[/C][C]-5.046[/C][/ROW]
[ROW][C]158[/C][C] 105.1[/C][C] 108.3[/C][C]-3.221[/C][/ROW]
[ROW][C]159[/C][C] 108.4[/C][C] 107.6[/C][C] 0.7716[/C][/ROW]
[ROW][C]160[/C][C] 105.7[/C][C] 109.4[/C][C]-3.705[/C][/ROW]
[ROW][C]161[/C][C] 109[/C][C] 108.7[/C][C] 0.3394[/C][/ROW]
[ROW][C]162[/C][C] 107.2[/C][C] 108.6[/C][C]-1.408[/C][/ROW]
[ROW][C]163[/C][C] 101.6[/C][C] 100.8[/C][C] 0.824[/C][/ROW]
[ROW][C]164[/C][C] 112.7[/C][C] 110.2[/C][C] 2.506[/C][/ROW]
[ROW][C]165[/C][C] 115.9[/C][C] 115.8[/C][C] 0.1471[/C][/ROW]
[ROW][C]166[/C][C] 105[/C][C] 107.5[/C][C]-2.514[/C][/ROW]
[ROW][C]167[/C][C] 110.4[/C][C] 106.2[/C][C] 4.165[/C][/ROW]
[ROW][C]168[/C][C] 100.9[/C][C] 104.5[/C][C]-3.582[/C][/ROW]
[ROW][C]169[/C][C] 98.5[/C][C] 99.81[/C][C]-1.312[/C][/ROW]
[ROW][C]170[/C][C] 111.3[/C][C] 109.7[/C][C] 1.626[/C][/ROW]
[ROW][C]171[/C][C] 109.6[/C][C] 109.9[/C][C]-0.2883[/C][/ROW]
[ROW][C]172[/C][C] 103.4[/C][C] 105.8[/C][C]-2.356[/C][/ROW]
[ROW][C]173[/C][C] 115.7[/C][C] 110.7[/C][C] 5.023[/C][/ROW]
[ROW][C]174[/C][C] 110.4[/C][C] 106.4[/C][C] 4.018[/C][/ROW]
[ROW][C]175[/C][C] 105.2[/C][C] 101.1[/C][C] 4.087[/C][/ROW]
[ROW][C]176[/C][C] 113.2[/C][C] 113.9[/C][C]-0.7083[/C][/ROW]
[ROW][C]177[/C][C] 117.4[/C][C] 116.4[/C][C] 1.03[/C][/ROW]
[ROW][C]178[/C][C] 112.3[/C][C] 109.7[/C][C] 2.593[/C][/ROW]
[ROW][C]179[/C][C] 113.9[/C][C] 110.2[/C][C] 3.71[/C][/ROW]
[ROW][C]180[/C][C] 102.2[/C][C] 105.9[/C][C]-3.671[/C][/ROW]
[ROW][C]181[/C][C] 106.9[/C][C] 104.1[/C][C] 2.751[/C][/ROW]
[ROW][C]182[/C][C] 118[/C][C] 115.4[/C][C] 2.627[/C][/ROW]
[ROW][C]183[/C][C] 113.8[/C][C] 113.3[/C][C] 0.5059[/C][/ROW]
[ROW][C]184[/C][C] 114.9[/C][C] 106.9[/C][C] 7.986[/C][/ROW]
[ROW][C]185[/C][C] 118.8[/C][C] 118.2[/C][C] 0.5517[/C][/ROW]
[ROW][C]186[/C][C] 106.3[/C][C] 109.7[/C][C]-3.437[/C][/ROW]
[ROW][C]187[/C][C] 114.2[/C][C] 105.9[/C][C] 8.29[/C][/ROW]
[ROW][C]188[/C][C] 117.3[/C][C] 115.9[/C][C] 1.431[/C][/ROW]
[ROW][C]189[/C][C] 114.7[/C][C] 117.2[/C][C]-2.459[/C][/ROW]
[ROW][C]190[/C][C] 117[/C][C] 114.1[/C][C] 2.911[/C][/ROW]
[ROW][C]191[/C][C] 116.6[/C][C] 117[/C][C]-0.4072[/C][/ROW]
[ROW][C]192[/C][C] 106.5[/C][C] 108.1[/C][C]-1.59[/C][/ROW]
[ROW][C]193[/C][C] 105.7[/C][C] 110.1[/C][C]-4.447[/C][/ROW]
[ROW][C]194[/C][C] 121[/C][C] 121.3[/C][C]-0.3144[/C][/ROW]
[ROW][C]195[/C][C] 107.8[/C][C] 114.2[/C][C]-6.351[/C][/ROW]
[ROW][C]196[/C][C] 119.7[/C][C] 115.1[/C][C] 4.557[/C][/ROW]
[ROW][C]197[/C][C] 121[/C][C] 121.8[/C][C]-0.7595[/C][/ROW]
[ROW][C]198[/C][C] 108.8[/C][C] 109.3[/C][C]-0.5043[/C][/ROW]
[ROW][C]199[/C][C] 115[/C][C] 112.9[/C][C] 2.082[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&T=5

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	63.7	66.85	-3.154
2	73	72.06	0.9403
3	67.5	68.5	-0.9995
4	74.4	73.8	0.5998
5	72.9	74.5	-1.597
6	71.7	64.92	6.781
7	75.6	69.68	5.924
8	72.5	73.19	-0.6936
9	80	77.9	2.095
10	75.4	75.92	-0.521
11	71	70.63	0.3688
12	70.6	70.65	-0.04861
13	67.5	68.21	-0.712
14	74.1	75.34	-1.24
15	73.2	72.65	0.5492
16	74	75.64	-1.644
17	73	75.56	-2.564
18	74	71.9	2.096
19	73	73.46	-0.4567
20	76	75.37	0.6256
21	81.7	81.6	0.1032
22	73.5	77.46	-3.959
23	77	71.91	5.094
24	73.6	74.39	-0.7884
25	70.4	71.46	-1.063
26	74.7	76.78	-2.082
27	76.8	76.04	0.7605
28	72.7	75.21	-2.514
29	76	75.92	0.08103
30	77.5	74.3	3.203
31	73.6	72.22	1.377
32	78.5	79.05	-0.5545
33	84.3	83.27	1.025
34	74.4	76.55	-2.149
35	78.5	78.92	-0.4175
36	72.7	75.62	-2.924
37	71.3	74.11	-2.809
38	84.4	80.48	3.918
39	79.1	80.95	-1.853
40	76.2	76.05	0.1461
41	84.9	81.84	3.065
42	77.1	79.07	-1.974
43	78.7	75.6	3.104
44	84.7	83.67	1.026
45	83.7	87.34	-3.642
46	82.5	80.96	1.537
47	85.2	84.26	0.9412
48	76	79.38	-3.376
49	72.2	76.82	-4.623
50	83.2	86.6	-3.397
51	80.2	83.82	-3.618
52	81.1	79.88	1.224
53	86	88.33	-2.334
54	76	78.11	-2.108
55	83.9	78.41	5.491
56	87.9	88.42	-0.5211
57	85	87.02	-2.023
58	88.1	86.85	1.25
59	87.4	89.2	-1.799
60	79.5	82.76	-3.26
61	75.2	79.81	-4.61
62	87.3	89.51	-2.209
63	79.5	84.25	-4.749
64	87.6	85.95	1.652
65	89.1	91.48	-2.382
66	83	79.63	3.374
67	88.3	84.12	4.177
68	88.9	92.1	-3.203
69	93.9	91.99	1.907
70	91.7	93.3	-1.603
71	87.2	91.35	-4.146
72	87.8	86.78	1.023
73	81	83.95	-2.947
74	93.7	93.61	0.09054
75	87.5	87.3	0.1995
76	91.4	91.96	-0.5624
77	93.8	96.03	-2.233
78	89.5	88.02	1.484
79	93.3	90.34	2.964
80	92.8	94.56	-1.761
81	104.1	101	3.139
82	99.9	98.5	1.402
83	93.4	93.43	-0.0339
84	99	94.91	4.087
85	93.2	92.24	0.9636
86	95.7	98.89	-3.188
87	102.6	96.68	5.916
88	98.8	97.17	1.625
89	98	100.8	-2.799
90	101.5	94.56	6.94
91	94.9	94.16	0.7448
92	104.7	100.3	4.352
93	108.4	107.4	0.9557
94	97	100.4	-3.448
95	102.3	96.24	6.058
96	90.8	96.42	-5.623
97	89.6	91.78	-2.184
98	99.9	96.12	3.781
99	99.2	98.09	1.114
100	94	97.19	-3.188
101	103	97.36	5.644
102	99.8	96.75	3.054
103	94.9	92.7	2.204
104	102	102.6	-0.5561
105	103.2	105.3	-2.136
106	98	98.14	-0.1391
107	101.1	100.2	0.9452
108	88.2	93.81	-5.606
109	90.3	93.3	-3.001
110	105.5	103.6	1.853
111	99.4	101	-1.603
112	94.3	96.99	-2.688
113	105.9	106.2	-0.2711
114	98	99.75	-1.753
115	99	95.99	3.01
116	103.9	105.6	-1.706
117	104.3	106.3	-2.047
118	105.7	103.4	2.253
119	105.5	104.8	0.7069
120	97.4	96.22	1.178
121	95.4	97.49	-2.091
122	110.5	112.5	-1.976
123	102.8	104	-1.233
124	110	102.8	7.163
125	104.3	108.9	-4.621
126	96.5	98.57	-2.068
127	105.6	100.8	4.825
128	111.3	109.1	2.162
129	108.5	107.6	0.8962
130	109.1	107.3	1.83
131	107.7	106.8	0.9412
132	102.3	102	0.3278
133	102.4	101.5	0.9481
134	110.8	114.2	-3.447
135	101.7	104.3	-2.569
136	108.9	107.6	1.266
137	111.5	108	3.547
138	104	100.1	3.853
139	109.9	103.6	6.264
140	106.8	111.1	-4.337
141	118.4	109.7	8.655
142	111.8	110.1	1.684
143	105	105.7	-0.7434
144	104.9	103.3	1.571
145	96.5	103	-6.486
146	106.3	110.4	-4.121
147	105.6	105.1	0.4605
148	109.3	108.2	1.078
149	105.1	112.3	-7.21
150	111.5	105.3	6.248
151	103.1	106.1	-2.971
152	106.5	108	-1.497
153	114.4	117.6	-3.173
154	104.7	112.6	-7.886
155	105.5	105.8	-0.3473
156	100.5	106.2	-5.722
157	96.4	101.4	-5.046
158	105.1	108.3	-3.221
159	108.4	107.6	0.7716
160	105.7	109.4	-3.705
161	109	108.7	0.3394
162	107.2	108.6	-1.408
163	101.6	100.8	0.824
164	112.7	110.2	2.506
165	115.9	115.8	0.1471
166	105	107.5	-2.514
167	110.4	106.2	4.165
168	100.9	104.5	-3.582
169	98.5	99.81	-1.312
170	111.3	109.7	1.626
171	109.6	109.9	-0.2883
172	103.4	105.8	-2.356
173	115.7	110.7	5.023
174	110.4	106.4	4.018
175	105.2	101.1	4.087
176	113.2	113.9	-0.7083
177	117.4	116.4	1.03
178	112.3	109.7	2.593
179	113.9	110.2	3.71
180	102.2	105.9	-3.671
181	106.9	104.1	2.751
182	118	115.4	2.627
183	113.8	113.3	0.5059
184	114.9	106.9	7.986
185	118.8	118.2	0.5517
186	106.3	109.7	-3.437
187	114.2	105.9	8.29
188	117.3	115.9	1.431
189	114.7	117.2	-2.459
190	117	114.1	2.911
191	116.6	117	-0.4072
192	106.5	108.1	-1.59
193	105.7	110.1	-4.447
194	121	121.3	-0.3144
195	107.8	114.2	-6.351
196	119.7	115.1	4.557
197	121	121.8	-0.7595
198	108.8	109.3	-0.5043
199	115	112.9	2.082

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.478	0.956	0.522
8	0.3388	0.6776	0.6612
9	0.2125	0.4249	0.7875
10	0.1389	0.2779	0.8611
11	0.255	0.5099	0.745
12	0.1981	0.3961	0.8019
13	0.149	0.2981	0.851
14	0.1317	0.2634	0.8683
15	0.1051	0.2103	0.8949
16	0.1068	0.2136	0.8932
17	0.09199	0.184	0.908
18	0.06288	0.1258	0.9371
19	0.07887	0.1577	0.9211
20	0.05776	0.1155	0.9422
21	0.04575	0.0915	0.9543
22	0.04862	0.09724	0.9514
23	0.06498	0.13	0.935
24	0.0447	0.08941	0.9553
25	0.03193	0.06387	0.9681
26	0.02357	0.04714	0.9764
27	0.01791	0.03582	0.9821
28	0.0175	0.035	0.9825
29	0.01183	0.02367	0.9882
30	0.01048	0.02096	0.9895
31	0.007118	0.01424	0.9929
32	0.004889	0.009777	0.9951
33	0.005558	0.01112	0.9944
34	0.003642	0.007283	0.9964
35	0.002265	0.00453	0.9977
36	0.002177	0.004354	0.9978
37	0.001961	0.003921	0.998
38	0.01024	0.02047	0.9898
39	0.007253	0.01451	0.9927
40	0.005154	0.01031	0.9948
41	0.01016	0.02033	0.9898
42	0.007259	0.01452	0.9927
43	0.008135	0.01627	0.9919
44	0.00702	0.01404	0.993
45	0.006037	0.01207	0.994
46	0.006757	0.01351	0.9932
47	0.005712	0.01142	0.9943
48	0.004833	0.009666	0.9952
49	0.007295	0.01459	0.9927
50	0.007843	0.01569	0.9922
51	0.006875	0.01375	0.9931
52	0.005788	0.01158	0.9942
53	0.004255	0.008509	0.9957
54	0.003241	0.006482	0.9968
55	0.00731	0.01462	0.9927
56	0.005517	0.01103	0.9945
57	0.003995	0.007989	0.996
58	0.003933	0.007866	0.9961
59	0.002816	0.005631	0.9972
60	0.002228	0.004457	0.9978
61	0.002546	0.005091	0.9975
62	0.001903	0.003805	0.9981
63	0.002274	0.004548	0.9977
64	0.002267	0.004535	0.9977
65	0.001722	0.003444	0.9983
66	0.00298	0.005959	0.997
67	0.004147	0.008294	0.9959
68	0.003511	0.007022	0.9965
69	0.004968	0.009937	0.995
70	0.003781	0.007561	0.9962
71	0.003966	0.007932	0.996
72	0.00393	0.007859	0.9961
73	0.003429	0.006858	0.9966
74	0.00276	0.005519	0.9972
75	0.002499	0.004997	0.9975
76	0.001868	0.003736	0.9981
77	0.001511	0.003022	0.9985
78	0.001422	0.002844	0.9986
79	0.001519	0.003037	0.9985
80	0.001183	0.002366	0.9988
81	0.001849	0.003698	0.9982
82	0.00173	0.00346	0.9983
83	0.001295	0.00259	0.9987
84	0.002132	0.004265	0.9979
85	0.00187	0.00374	0.9981
86	0.001965	0.003929	0.998
87	0.005585	0.01117	0.9944
88	0.004471	0.008943	0.9955
89	0.00434	0.00868	0.9957
90	0.01247	0.02493	0.9875
91	0.009482	0.01896	0.9905
92	0.01208	0.02416	0.9879
93	0.009239	0.01848	0.9908
94	0.01105	0.0221	0.9889
95	0.02007	0.04015	0.9799
96	0.03745	0.07489	0.9626
97	0.03418	0.06835	0.9658
98	0.03621	0.07243	0.9638
99	0.02929	0.05858	0.9707
100	0.03025	0.0605	0.9698
101	0.04595	0.0919	0.9541
102	0.04368	0.08735	0.9563
103	0.03807	0.07615	0.9619
104	0.03104	0.06207	0.969
105	0.02804	0.05608	0.972
106	0.0221	0.04419	0.9779
107	0.01752	0.03505	0.9825
108	0.03063	0.06126	0.9694
109	0.03203	0.06406	0.968
110	0.02665	0.05329	0.9734
111	0.02259	0.04519	0.9774
112	0.0223	0.04459	0.9777
113	0.01753	0.03506	0.9825
114	0.01499	0.02998	0.985
115	0.01371	0.02741	0.9863
116	0.01162	0.02323	0.9884
117	0.01004	0.02009	0.99
118	0.008572	0.01714	0.9914
119	0.006513	0.01303	0.9935
120	0.005042	0.01008	0.995
121	0.004818	0.009636	0.9952
122	0.00423	0.008461	0.9958
123	0.003351	0.006701	0.9967
124	0.008629	0.01726	0.9914
125	0.01158	0.02315	0.9884
126	0.01043	0.02086	0.9896
127	0.01286	0.02571	0.9871
128	0.01083	0.02166	0.9892
129	0.008306	0.01661	0.9917
130	0.006718	0.01344	0.9933
131	0.005062	0.01012	0.9949
132	0.003719	0.007437	0.9963
133	0.002724	0.005448	0.9973
134	0.002856	0.005711	0.9971
135	0.00258	0.00516	0.9974
136	0.001926	0.003852	0.9981
137	0.001948	0.003897	0.9981
138	0.002037	0.004074	0.998
139	0.00455	0.0091	0.9954
140	0.005622	0.01124	0.9944
141	0.02839	0.05677	0.9716
142	0.02369	0.04738	0.9763
143	0.0184	0.03679	0.9816
144	0.01476	0.02952	0.9852
145	0.03218	0.06436	0.9678
146	0.03845	0.0769	0.9616
147	0.02984	0.05968	0.9702
148	0.02337	0.04674	0.9766
149	0.05827	0.1165	0.9417
150	0.09721	0.1944	0.9028
151	0.0924	0.1848	0.9076
152	0.07892	0.1578	0.9211
153	0.08087	0.1617	0.9191
154	0.2034	0.4068	0.7966
155	0.1721	0.3442	0.8279
156	0.2632	0.5264	0.7368
157	0.3566	0.7133	0.6434
158	0.4338	0.8677	0.5662
159	0.3868	0.7735	0.6132
160	0.4478	0.8956	0.5522
161	0.4011	0.8022	0.5989
162	0.3997	0.7995	0.6003
163	0.3667	0.7333	0.6333
164	0.3247	0.6494	0.6753
165	0.2777	0.5553	0.7223
166	0.257	0.514	0.743
167	0.2375	0.475	0.7625
168	0.2717	0.5434	0.7283
169	0.3223	0.6447	0.6777
170	0.29	0.5801	0.71
171	0.2534	0.5067	0.7467
172	0.3306	0.6612	0.6694
173	0.3061	0.6122	0.6939
174	0.2851	0.5702	0.7149
175	0.2444	0.4887	0.7556
176	0.2423	0.4846	0.7577
177	0.1937	0.3875	0.8063
178	0.1887	0.3774	0.8113
179	0.1638	0.3277	0.8362
180	0.2043	0.4085	0.7957
181	0.1909	0.3818	0.8091
182	0.1454	0.2908	0.8546
183	0.1082	0.2164	0.8918
184	0.2732	0.5464	0.7268
185	0.2147	0.4293	0.7853
186	0.2045	0.409	0.7955
187	0.384	0.768	0.616
188	0.3207	0.6414	0.6793
189	0.2639	0.5279	0.7361
190	0.2671	0.5343	0.7329
191	0.1761	0.3522	0.8239
192	0.122	0.2441	0.878

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.478 &  0.956 &  0.522 \tabularnewline
8 &  0.3388 &  0.6776 &  0.6612 \tabularnewline
9 &  0.2125 &  0.4249 &  0.7875 \tabularnewline
10 &  0.1389 &  0.2779 &  0.8611 \tabularnewline
11 &  0.255 &  0.5099 &  0.745 \tabularnewline
12 &  0.1981 &  0.3961 &  0.8019 \tabularnewline
13 &  0.149 &  0.2981 &  0.851 \tabularnewline
14 &  0.1317 &  0.2634 &  0.8683 \tabularnewline
15 &  0.1051 &  0.2103 &  0.8949 \tabularnewline
16 &  0.1068 &  0.2136 &  0.8932 \tabularnewline
17 &  0.09199 &  0.184 &  0.908 \tabularnewline
18 &  0.06288 &  0.1258 &  0.9371 \tabularnewline
19 &  0.07887 &  0.1577 &  0.9211 \tabularnewline
20 &  0.05776 &  0.1155 &  0.9422 \tabularnewline
21 &  0.04575 &  0.0915 &  0.9543 \tabularnewline
22 &  0.04862 &  0.09724 &  0.9514 \tabularnewline
23 &  0.06498 &  0.13 &  0.935 \tabularnewline
24 &  0.0447 &  0.08941 &  0.9553 \tabularnewline
25 &  0.03193 &  0.06387 &  0.9681 \tabularnewline
26 &  0.02357 &  0.04714 &  0.9764 \tabularnewline
27 &  0.01791 &  0.03582 &  0.9821 \tabularnewline
28 &  0.0175 &  0.035 &  0.9825 \tabularnewline
29 &  0.01183 &  0.02367 &  0.9882 \tabularnewline
30 &  0.01048 &  0.02096 &  0.9895 \tabularnewline
31 &  0.007118 &  0.01424 &  0.9929 \tabularnewline
32 &  0.004889 &  0.009777 &  0.9951 \tabularnewline
33 &  0.005558 &  0.01112 &  0.9944 \tabularnewline
34 &  0.003642 &  0.007283 &  0.9964 \tabularnewline
35 &  0.002265 &  0.00453 &  0.9977 \tabularnewline
36 &  0.002177 &  0.004354 &  0.9978 \tabularnewline
37 &  0.001961 &  0.003921 &  0.998 \tabularnewline
38 &  0.01024 &  0.02047 &  0.9898 \tabularnewline
39 &  0.007253 &  0.01451 &  0.9927 \tabularnewline
40 &  0.005154 &  0.01031 &  0.9948 \tabularnewline
41 &  0.01016 &  0.02033 &  0.9898 \tabularnewline
42 &  0.007259 &  0.01452 &  0.9927 \tabularnewline
43 &  0.008135 &  0.01627 &  0.9919 \tabularnewline
44 &  0.00702 &  0.01404 &  0.993 \tabularnewline
45 &  0.006037 &  0.01207 &  0.994 \tabularnewline
46 &  0.006757 &  0.01351 &  0.9932 \tabularnewline
47 &  0.005712 &  0.01142 &  0.9943 \tabularnewline
48 &  0.004833 &  0.009666 &  0.9952 \tabularnewline
49 &  0.007295 &  0.01459 &  0.9927 \tabularnewline
50 &  0.007843 &  0.01569 &  0.9922 \tabularnewline
51 &  0.006875 &  0.01375 &  0.9931 \tabularnewline
52 &  0.005788 &  0.01158 &  0.9942 \tabularnewline
53 &  0.004255 &  0.008509 &  0.9957 \tabularnewline
54 &  0.003241 &  0.006482 &  0.9968 \tabularnewline
55 &  0.00731 &  0.01462 &  0.9927 \tabularnewline
56 &  0.005517 &  0.01103 &  0.9945 \tabularnewline
57 &  0.003995 &  0.007989 &  0.996 \tabularnewline
58 &  0.003933 &  0.007866 &  0.9961 \tabularnewline
59 &  0.002816 &  0.005631 &  0.9972 \tabularnewline
60 &  0.002228 &  0.004457 &  0.9978 \tabularnewline
61 &  0.002546 &  0.005091 &  0.9975 \tabularnewline
62 &  0.001903 &  0.003805 &  0.9981 \tabularnewline
63 &  0.002274 &  0.004548 &  0.9977 \tabularnewline
64 &  0.002267 &  0.004535 &  0.9977 \tabularnewline
65 &  0.001722 &  0.003444 &  0.9983 \tabularnewline
66 &  0.00298 &  0.005959 &  0.997 \tabularnewline
67 &  0.004147 &  0.008294 &  0.9959 \tabularnewline
68 &  0.003511 &  0.007022 &  0.9965 \tabularnewline
69 &  0.004968 &  0.009937 &  0.995 \tabularnewline
70 &  0.003781 &  0.007561 &  0.9962 \tabularnewline
71 &  0.003966 &  0.007932 &  0.996 \tabularnewline
72 &  0.00393 &  0.007859 &  0.9961 \tabularnewline
73 &  0.003429 &  0.006858 &  0.9966 \tabularnewline
74 &  0.00276 &  0.005519 &  0.9972 \tabularnewline
75 &  0.002499 &  0.004997 &  0.9975 \tabularnewline
76 &  0.001868 &  0.003736 &  0.9981 \tabularnewline
77 &  0.001511 &  0.003022 &  0.9985 \tabularnewline
78 &  0.001422 &  0.002844 &  0.9986 \tabularnewline
79 &  0.001519 &  0.003037 &  0.9985 \tabularnewline
80 &  0.001183 &  0.002366 &  0.9988 \tabularnewline
81 &  0.001849 &  0.003698 &  0.9982 \tabularnewline
82 &  0.00173 &  0.00346 &  0.9983 \tabularnewline
83 &  0.001295 &  0.00259 &  0.9987 \tabularnewline
84 &  0.002132 &  0.004265 &  0.9979 \tabularnewline
85 &  0.00187 &  0.00374 &  0.9981 \tabularnewline
86 &  0.001965 &  0.003929 &  0.998 \tabularnewline
87 &  0.005585 &  0.01117 &  0.9944 \tabularnewline
88 &  0.004471 &  0.008943 &  0.9955 \tabularnewline
89 &  0.00434 &  0.00868 &  0.9957 \tabularnewline
90 &  0.01247 &  0.02493 &  0.9875 \tabularnewline
91 &  0.009482 &  0.01896 &  0.9905 \tabularnewline
92 &  0.01208 &  0.02416 &  0.9879 \tabularnewline
93 &  0.009239 &  0.01848 &  0.9908 \tabularnewline
94 &  0.01105 &  0.0221 &  0.9889 \tabularnewline
95 &  0.02007 &  0.04015 &  0.9799 \tabularnewline
96 &  0.03745 &  0.07489 &  0.9626 \tabularnewline
97 &  0.03418 &  0.06835 &  0.9658 \tabularnewline
98 &  0.03621 &  0.07243 &  0.9638 \tabularnewline
99 &  0.02929 &  0.05858 &  0.9707 \tabularnewline
100 &  0.03025 &  0.0605 &  0.9698 \tabularnewline
101 &  0.04595 &  0.0919 &  0.9541 \tabularnewline
102 &  0.04368 &  0.08735 &  0.9563 \tabularnewline
103 &  0.03807 &  0.07615 &  0.9619 \tabularnewline
104 &  0.03104 &  0.06207 &  0.969 \tabularnewline
105 &  0.02804 &  0.05608 &  0.972 \tabularnewline
106 &  0.0221 &  0.04419 &  0.9779 \tabularnewline
107 &  0.01752 &  0.03505 &  0.9825 \tabularnewline
108 &  0.03063 &  0.06126 &  0.9694 \tabularnewline
109 &  0.03203 &  0.06406 &  0.968 \tabularnewline
110 &  0.02665 &  0.05329 &  0.9734 \tabularnewline
111 &  0.02259 &  0.04519 &  0.9774 \tabularnewline
112 &  0.0223 &  0.04459 &  0.9777 \tabularnewline
113 &  0.01753 &  0.03506 &  0.9825 \tabularnewline
114 &  0.01499 &  0.02998 &  0.985 \tabularnewline
115 &  0.01371 &  0.02741 &  0.9863 \tabularnewline
116 &  0.01162 &  0.02323 &  0.9884 \tabularnewline
117 &  0.01004 &  0.02009 &  0.99 \tabularnewline
118 &  0.008572 &  0.01714 &  0.9914 \tabularnewline
119 &  0.006513 &  0.01303 &  0.9935 \tabularnewline
120 &  0.005042 &  0.01008 &  0.995 \tabularnewline
121 &  0.004818 &  0.009636 &  0.9952 \tabularnewline
122 &  0.00423 &  0.008461 &  0.9958 \tabularnewline
123 &  0.003351 &  0.006701 &  0.9967 \tabularnewline
124 &  0.008629 &  0.01726 &  0.9914 \tabularnewline
125 &  0.01158 &  0.02315 &  0.9884 \tabularnewline
126 &  0.01043 &  0.02086 &  0.9896 \tabularnewline
127 &  0.01286 &  0.02571 &  0.9871 \tabularnewline
128 &  0.01083 &  0.02166 &  0.9892 \tabularnewline
129 &  0.008306 &  0.01661 &  0.9917 \tabularnewline
130 &  0.006718 &  0.01344 &  0.9933 \tabularnewline
131 &  0.005062 &  0.01012 &  0.9949 \tabularnewline
132 &  0.003719 &  0.007437 &  0.9963 \tabularnewline
133 &  0.002724 &  0.005448 &  0.9973 \tabularnewline
134 &  0.002856 &  0.005711 &  0.9971 \tabularnewline
135 &  0.00258 &  0.00516 &  0.9974 \tabularnewline
136 &  0.001926 &  0.003852 &  0.9981 \tabularnewline
137 &  0.001948 &  0.003897 &  0.9981 \tabularnewline
138 &  0.002037 &  0.004074 &  0.998 \tabularnewline
139 &  0.00455 &  0.0091 &  0.9954 \tabularnewline
140 &  0.005622 &  0.01124 &  0.9944 \tabularnewline
141 &  0.02839 &  0.05677 &  0.9716 \tabularnewline
142 &  0.02369 &  0.04738 &  0.9763 \tabularnewline
143 &  0.0184 &  0.03679 &  0.9816 \tabularnewline
144 &  0.01476 &  0.02952 &  0.9852 \tabularnewline
145 &  0.03218 &  0.06436 &  0.9678 \tabularnewline
146 &  0.03845 &  0.0769 &  0.9616 \tabularnewline
147 &  0.02984 &  0.05968 &  0.9702 \tabularnewline
148 &  0.02337 &  0.04674 &  0.9766 \tabularnewline
149 &  0.05827 &  0.1165 &  0.9417 \tabularnewline
150 &  0.09721 &  0.1944 &  0.9028 \tabularnewline
151 &  0.0924 &  0.1848 &  0.9076 \tabularnewline
152 &  0.07892 &  0.1578 &  0.9211 \tabularnewline
153 &  0.08087 &  0.1617 &  0.9191 \tabularnewline
154 &  0.2034 &  0.4068 &  0.7966 \tabularnewline
155 &  0.1721 &  0.3442 &  0.8279 \tabularnewline
156 &  0.2632 &  0.5264 &  0.7368 \tabularnewline
157 &  0.3566 &  0.7133 &  0.6434 \tabularnewline
158 &  0.4338 &  0.8677 &  0.5662 \tabularnewline
159 &  0.3868 &  0.7735 &  0.6132 \tabularnewline
160 &  0.4478 &  0.8956 &  0.5522 \tabularnewline
161 &  0.4011 &  0.8022 &  0.5989 \tabularnewline
162 &  0.3997 &  0.7995 &  0.6003 \tabularnewline
163 &  0.3667 &  0.7333 &  0.6333 \tabularnewline
164 &  0.3247 &  0.6494 &  0.6753 \tabularnewline
165 &  0.2777 &  0.5553 &  0.7223 \tabularnewline
166 &  0.257 &  0.514 &  0.743 \tabularnewline
167 &  0.2375 &  0.475 &  0.7625 \tabularnewline
168 &  0.2717 &  0.5434 &  0.7283 \tabularnewline
169 &  0.3223 &  0.6447 &  0.6777 \tabularnewline
170 &  0.29 &  0.5801 &  0.71 \tabularnewline
171 &  0.2534 &  0.5067 &  0.7467 \tabularnewline
172 &  0.3306 &  0.6612 &  0.6694 \tabularnewline
173 &  0.3061 &  0.6122 &  0.6939 \tabularnewline
174 &  0.2851 &  0.5702 &  0.7149 \tabularnewline
175 &  0.2444 &  0.4887 &  0.7556 \tabularnewline
176 &  0.2423 &  0.4846 &  0.7577 \tabularnewline
177 &  0.1937 &  0.3875 &  0.8063 \tabularnewline
178 &  0.1887 &  0.3774 &  0.8113 \tabularnewline
179 &  0.1638 &  0.3277 &  0.8362 \tabularnewline
180 &  0.2043 &  0.4085 &  0.7957 \tabularnewline
181 &  0.1909 &  0.3818 &  0.8091 \tabularnewline
182 &  0.1454 &  0.2908 &  0.8546 \tabularnewline
183 &  0.1082 &  0.2164 &  0.8918 \tabularnewline
184 &  0.2732 &  0.5464 &  0.7268 \tabularnewline
185 &  0.2147 &  0.4293 &  0.7853 \tabularnewline
186 &  0.2045 &  0.409 &  0.7955 \tabularnewline
187 &  0.384 &  0.768 &  0.616 \tabularnewline
188 &  0.3207 &  0.6414 &  0.6793 \tabularnewline
189 &  0.2639 &  0.5279 &  0.7361 \tabularnewline
190 &  0.2671 &  0.5343 &  0.7329 \tabularnewline
191 &  0.1761 &  0.3522 &  0.8239 \tabularnewline
192 &  0.122 &  0.2441 &  0.878 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C] 0.478[/C][C] 0.956[/C][C] 0.522[/C][/ROW]
[ROW][C]8[/C][C] 0.3388[/C][C] 0.6776[/C][C] 0.6612[/C][/ROW]
[ROW][C]9[/C][C] 0.2125[/C][C] 0.4249[/C][C] 0.7875[/C][/ROW]
[ROW][C]10[/C][C] 0.1389[/C][C] 0.2779[/C][C] 0.8611[/C][/ROW]
[ROW][C]11[/C][C] 0.255[/C][C] 0.5099[/C][C] 0.745[/C][/ROW]
[ROW][C]12[/C][C] 0.1981[/C][C] 0.3961[/C][C] 0.8019[/C][/ROW]
[ROW][C]13[/C][C] 0.149[/C][C] 0.2981[/C][C] 0.851[/C][/ROW]
[ROW][C]14[/C][C] 0.1317[/C][C] 0.2634[/C][C] 0.8683[/C][/ROW]
[ROW][C]15[/C][C] 0.1051[/C][C] 0.2103[/C][C] 0.8949[/C][/ROW]
[ROW][C]16[/C][C] 0.1068[/C][C] 0.2136[/C][C] 0.8932[/C][/ROW]
[ROW][C]17[/C][C] 0.09199[/C][C] 0.184[/C][C] 0.908[/C][/ROW]
[ROW][C]18[/C][C] 0.06288[/C][C] 0.1258[/C][C] 0.9371[/C][/ROW]
[ROW][C]19[/C][C] 0.07887[/C][C] 0.1577[/C][C] 0.9211[/C][/ROW]
[ROW][C]20[/C][C] 0.05776[/C][C] 0.1155[/C][C] 0.9422[/C][/ROW]
[ROW][C]21[/C][C] 0.04575[/C][C] 0.0915[/C][C] 0.9543[/C][/ROW]
[ROW][C]22[/C][C] 0.04862[/C][C] 0.09724[/C][C] 0.9514[/C][/ROW]
[ROW][C]23[/C][C] 0.06498[/C][C] 0.13[/C][C] 0.935[/C][/ROW]
[ROW][C]24[/C][C] 0.0447[/C][C] 0.08941[/C][C] 0.9553[/C][/ROW]
[ROW][C]25[/C][C] 0.03193[/C][C] 0.06387[/C][C] 0.9681[/C][/ROW]
[ROW][C]26[/C][C] 0.02357[/C][C] 0.04714[/C][C] 0.9764[/C][/ROW]
[ROW][C]27[/C][C] 0.01791[/C][C] 0.03582[/C][C] 0.9821[/C][/ROW]
[ROW][C]28[/C][C] 0.0175[/C][C] 0.035[/C][C] 0.9825[/C][/ROW]
[ROW][C]29[/C][C] 0.01183[/C][C] 0.02367[/C][C] 0.9882[/C][/ROW]
[ROW][C]30[/C][C] 0.01048[/C][C] 0.02096[/C][C] 0.9895[/C][/ROW]
[ROW][C]31[/C][C] 0.007118[/C][C] 0.01424[/C][C] 0.9929[/C][/ROW]
[ROW][C]32[/C][C] 0.004889[/C][C] 0.009777[/C][C] 0.9951[/C][/ROW]
[ROW][C]33[/C][C] 0.005558[/C][C] 0.01112[/C][C] 0.9944[/C][/ROW]
[ROW][C]34[/C][C] 0.003642[/C][C] 0.007283[/C][C] 0.9964[/C][/ROW]
[ROW][C]35[/C][C] 0.002265[/C][C] 0.00453[/C][C] 0.9977[/C][/ROW]
[ROW][C]36[/C][C] 0.002177[/C][C] 0.004354[/C][C] 0.9978[/C][/ROW]
[ROW][C]37[/C][C] 0.001961[/C][C] 0.003921[/C][C] 0.998[/C][/ROW]
[ROW][C]38[/C][C] 0.01024[/C][C] 0.02047[/C][C] 0.9898[/C][/ROW]
[ROW][C]39[/C][C] 0.007253[/C][C] 0.01451[/C][C] 0.9927[/C][/ROW]
[ROW][C]40[/C][C] 0.005154[/C][C] 0.01031[/C][C] 0.9948[/C][/ROW]
[ROW][C]41[/C][C] 0.01016[/C][C] 0.02033[/C][C] 0.9898[/C][/ROW]
[ROW][C]42[/C][C] 0.007259[/C][C] 0.01452[/C][C] 0.9927[/C][/ROW]
[ROW][C]43[/C][C] 0.008135[/C][C] 0.01627[/C][C] 0.9919[/C][/ROW]
[ROW][C]44[/C][C] 0.00702[/C][C] 0.01404[/C][C] 0.993[/C][/ROW]
[ROW][C]45[/C][C] 0.006037[/C][C] 0.01207[/C][C] 0.994[/C][/ROW]
[ROW][C]46[/C][C] 0.006757[/C][C] 0.01351[/C][C] 0.9932[/C][/ROW]
[ROW][C]47[/C][C] 0.005712[/C][C] 0.01142[/C][C] 0.9943[/C][/ROW]
[ROW][C]48[/C][C] 0.004833[/C][C] 0.009666[/C][C] 0.9952[/C][/ROW]
[ROW][C]49[/C][C] 0.007295[/C][C] 0.01459[/C][C] 0.9927[/C][/ROW]
[ROW][C]50[/C][C] 0.007843[/C][C] 0.01569[/C][C] 0.9922[/C][/ROW]
[ROW][C]51[/C][C] 0.006875[/C][C] 0.01375[/C][C] 0.9931[/C][/ROW]
[ROW][C]52[/C][C] 0.005788[/C][C] 0.01158[/C][C] 0.9942[/C][/ROW]
[ROW][C]53[/C][C] 0.004255[/C][C] 0.008509[/C][C] 0.9957[/C][/ROW]
[ROW][C]54[/C][C] 0.003241[/C][C] 0.006482[/C][C] 0.9968[/C][/ROW]
[ROW][C]55[/C][C] 0.00731[/C][C] 0.01462[/C][C] 0.9927[/C][/ROW]
[ROW][C]56[/C][C] 0.005517[/C][C] 0.01103[/C][C] 0.9945[/C][/ROW]
[ROW][C]57[/C][C] 0.003995[/C][C] 0.007989[/C][C] 0.996[/C][/ROW]
[ROW][C]58[/C][C] 0.003933[/C][C] 0.007866[/C][C] 0.9961[/C][/ROW]
[ROW][C]59[/C][C] 0.002816[/C][C] 0.005631[/C][C] 0.9972[/C][/ROW]
[ROW][C]60[/C][C] 0.002228[/C][C] 0.004457[/C][C] 0.9978[/C][/ROW]
[ROW][C]61[/C][C] 0.002546[/C][C] 0.005091[/C][C] 0.9975[/C][/ROW]
[ROW][C]62[/C][C] 0.001903[/C][C] 0.003805[/C][C] 0.9981[/C][/ROW]
[ROW][C]63[/C][C] 0.002274[/C][C] 0.004548[/C][C] 0.9977[/C][/ROW]
[ROW][C]64[/C][C] 0.002267[/C][C] 0.004535[/C][C] 0.9977[/C][/ROW]
[ROW][C]65[/C][C] 0.001722[/C][C] 0.003444[/C][C] 0.9983[/C][/ROW]
[ROW][C]66[/C][C] 0.00298[/C][C] 0.005959[/C][C] 0.997[/C][/ROW]
[ROW][C]67[/C][C] 0.004147[/C][C] 0.008294[/C][C] 0.9959[/C][/ROW]
[ROW][C]68[/C][C] 0.003511[/C][C] 0.007022[/C][C] 0.9965[/C][/ROW]
[ROW][C]69[/C][C] 0.004968[/C][C] 0.009937[/C][C] 0.995[/C][/ROW]
[ROW][C]70[/C][C] 0.003781[/C][C] 0.007561[/C][C] 0.9962[/C][/ROW]
[ROW][C]71[/C][C] 0.003966[/C][C] 0.007932[/C][C] 0.996[/C][/ROW]
[ROW][C]72[/C][C] 0.00393[/C][C] 0.007859[/C][C] 0.9961[/C][/ROW]
[ROW][C]73[/C][C] 0.003429[/C][C] 0.006858[/C][C] 0.9966[/C][/ROW]
[ROW][C]74[/C][C] 0.00276[/C][C] 0.005519[/C][C] 0.9972[/C][/ROW]
[ROW][C]75[/C][C] 0.002499[/C][C] 0.004997[/C][C] 0.9975[/C][/ROW]
[ROW][C]76[/C][C] 0.001868[/C][C] 0.003736[/C][C] 0.9981[/C][/ROW]
[ROW][C]77[/C][C] 0.001511[/C][C] 0.003022[/C][C] 0.9985[/C][/ROW]
[ROW][C]78[/C][C] 0.001422[/C][C] 0.002844[/C][C] 0.9986[/C][/ROW]
[ROW][C]79[/C][C] 0.001519[/C][C] 0.003037[/C][C] 0.9985[/C][/ROW]
[ROW][C]80[/C][C] 0.001183[/C][C] 0.002366[/C][C] 0.9988[/C][/ROW]
[ROW][C]81[/C][C] 0.001849[/C][C] 0.003698[/C][C] 0.9982[/C][/ROW]
[ROW][C]82[/C][C] 0.00173[/C][C] 0.00346[/C][C] 0.9983[/C][/ROW]
[ROW][C]83[/C][C] 0.001295[/C][C] 0.00259[/C][C] 0.9987[/C][/ROW]
[ROW][C]84[/C][C] 0.002132[/C][C] 0.004265[/C][C] 0.9979[/C][/ROW]
[ROW][C]85[/C][C] 0.00187[/C][C] 0.00374[/C][C] 0.9981[/C][/ROW]
[ROW][C]86[/C][C] 0.001965[/C][C] 0.003929[/C][C] 0.998[/C][/ROW]
[ROW][C]87[/C][C] 0.005585[/C][C] 0.01117[/C][C] 0.9944[/C][/ROW]
[ROW][C]88[/C][C] 0.004471[/C][C] 0.008943[/C][C] 0.9955[/C][/ROW]
[ROW][C]89[/C][C] 0.00434[/C][C] 0.00868[/C][C] 0.9957[/C][/ROW]
[ROW][C]90[/C][C] 0.01247[/C][C] 0.02493[/C][C] 0.9875[/C][/ROW]
[ROW][C]91[/C][C] 0.009482[/C][C] 0.01896[/C][C] 0.9905[/C][/ROW]
[ROW][C]92[/C][C] 0.01208[/C][C] 0.02416[/C][C] 0.9879[/C][/ROW]
[ROW][C]93[/C][C] 0.009239[/C][C] 0.01848[/C][C] 0.9908[/C][/ROW]
[ROW][C]94[/C][C] 0.01105[/C][C] 0.0221[/C][C] 0.9889[/C][/ROW]
[ROW][C]95[/C][C] 0.02007[/C][C] 0.04015[/C][C] 0.9799[/C][/ROW]
[ROW][C]96[/C][C] 0.03745[/C][C] 0.07489[/C][C] 0.9626[/C][/ROW]
[ROW][C]97[/C][C] 0.03418[/C][C] 0.06835[/C][C] 0.9658[/C][/ROW]
[ROW][C]98[/C][C] 0.03621[/C][C] 0.07243[/C][C] 0.9638[/C][/ROW]
[ROW][C]99[/C][C] 0.02929[/C][C] 0.05858[/C][C] 0.9707[/C][/ROW]
[ROW][C]100[/C][C] 0.03025[/C][C] 0.0605[/C][C] 0.9698[/C][/ROW]
[ROW][C]101[/C][C] 0.04595[/C][C] 0.0919[/C][C] 0.9541[/C][/ROW]
[ROW][C]102[/C][C] 0.04368[/C][C] 0.08735[/C][C] 0.9563[/C][/ROW]
[ROW][C]103[/C][C] 0.03807[/C][C] 0.07615[/C][C] 0.9619[/C][/ROW]
[ROW][C]104[/C][C] 0.03104[/C][C] 0.06207[/C][C] 0.969[/C][/ROW]
[ROW][C]105[/C][C] 0.02804[/C][C] 0.05608[/C][C] 0.972[/C][/ROW]
[ROW][C]106[/C][C] 0.0221[/C][C] 0.04419[/C][C] 0.9779[/C][/ROW]
[ROW][C]107[/C][C] 0.01752[/C][C] 0.03505[/C][C] 0.9825[/C][/ROW]
[ROW][C]108[/C][C] 0.03063[/C][C] 0.06126[/C][C] 0.9694[/C][/ROW]
[ROW][C]109[/C][C] 0.03203[/C][C] 0.06406[/C][C] 0.968[/C][/ROW]
[ROW][C]110[/C][C] 0.02665[/C][C] 0.05329[/C][C] 0.9734[/C][/ROW]
[ROW][C]111[/C][C] 0.02259[/C][C] 0.04519[/C][C] 0.9774[/C][/ROW]
[ROW][C]112[/C][C] 0.0223[/C][C] 0.04459[/C][C] 0.9777[/C][/ROW]
[ROW][C]113[/C][C] 0.01753[/C][C] 0.03506[/C][C] 0.9825[/C][/ROW]
[ROW][C]114[/C][C] 0.01499[/C][C] 0.02998[/C][C] 0.985[/C][/ROW]
[ROW][C]115[/C][C] 0.01371[/C][C] 0.02741[/C][C] 0.9863[/C][/ROW]
[ROW][C]116[/C][C] 0.01162[/C][C] 0.02323[/C][C] 0.9884[/C][/ROW]
[ROW][C]117[/C][C] 0.01004[/C][C] 0.02009[/C][C] 0.99[/C][/ROW]
[ROW][C]118[/C][C] 0.008572[/C][C] 0.01714[/C][C] 0.9914[/C][/ROW]
[ROW][C]119[/C][C] 0.006513[/C][C] 0.01303[/C][C] 0.9935[/C][/ROW]
[ROW][C]120[/C][C] 0.005042[/C][C] 0.01008[/C][C] 0.995[/C][/ROW]
[ROW][C]121[/C][C] 0.004818[/C][C] 0.009636[/C][C] 0.9952[/C][/ROW]
[ROW][C]122[/C][C] 0.00423[/C][C] 0.008461[/C][C] 0.9958[/C][/ROW]
[ROW][C]123[/C][C] 0.003351[/C][C] 0.006701[/C][C] 0.9967[/C][/ROW]
[ROW][C]124[/C][C] 0.008629[/C][C] 0.01726[/C][C] 0.9914[/C][/ROW]
[ROW][C]125[/C][C] 0.01158[/C][C] 0.02315[/C][C] 0.9884[/C][/ROW]
[ROW][C]126[/C][C] 0.01043[/C][C] 0.02086[/C][C] 0.9896[/C][/ROW]
[ROW][C]127[/C][C] 0.01286[/C][C] 0.02571[/C][C] 0.9871[/C][/ROW]
[ROW][C]128[/C][C] 0.01083[/C][C] 0.02166[/C][C] 0.9892[/C][/ROW]
[ROW][C]129[/C][C] 0.008306[/C][C] 0.01661[/C][C] 0.9917[/C][/ROW]
[ROW][C]130[/C][C] 0.006718[/C][C] 0.01344[/C][C] 0.9933[/C][/ROW]
[ROW][C]131[/C][C] 0.005062[/C][C] 0.01012[/C][C] 0.9949[/C][/ROW]
[ROW][C]132[/C][C] 0.003719[/C][C] 0.007437[/C][C] 0.9963[/C][/ROW]
[ROW][C]133[/C][C] 0.002724[/C][C] 0.005448[/C][C] 0.9973[/C][/ROW]
[ROW][C]134[/C][C] 0.002856[/C][C] 0.005711[/C][C] 0.9971[/C][/ROW]
[ROW][C]135[/C][C] 0.00258[/C][C] 0.00516[/C][C] 0.9974[/C][/ROW]
[ROW][C]136[/C][C] 0.001926[/C][C] 0.003852[/C][C] 0.9981[/C][/ROW]
[ROW][C]137[/C][C] 0.001948[/C][C] 0.003897[/C][C] 0.9981[/C][/ROW]
[ROW][C]138[/C][C] 0.002037[/C][C] 0.004074[/C][C] 0.998[/C][/ROW]
[ROW][C]139[/C][C] 0.00455[/C][C] 0.0091[/C][C] 0.9954[/C][/ROW]
[ROW][C]140[/C][C] 0.005622[/C][C] 0.01124[/C][C] 0.9944[/C][/ROW]
[ROW][C]141[/C][C] 0.02839[/C][C] 0.05677[/C][C] 0.9716[/C][/ROW]
[ROW][C]142[/C][C] 0.02369[/C][C] 0.04738[/C][C] 0.9763[/C][/ROW]
[ROW][C]143[/C][C] 0.0184[/C][C] 0.03679[/C][C] 0.9816[/C][/ROW]
[ROW][C]144[/C][C] 0.01476[/C][C] 0.02952[/C][C] 0.9852[/C][/ROW]
[ROW][C]145[/C][C] 0.03218[/C][C] 0.06436[/C][C] 0.9678[/C][/ROW]
[ROW][C]146[/C][C] 0.03845[/C][C] 0.0769[/C][C] 0.9616[/C][/ROW]
[ROW][C]147[/C][C] 0.02984[/C][C] 0.05968[/C][C] 0.9702[/C][/ROW]
[ROW][C]148[/C][C] 0.02337[/C][C] 0.04674[/C][C] 0.9766[/C][/ROW]
[ROW][C]149[/C][C] 0.05827[/C][C] 0.1165[/C][C] 0.9417[/C][/ROW]
[ROW][C]150[/C][C] 0.09721[/C][C] 0.1944[/C][C] 0.9028[/C][/ROW]
[ROW][C]151[/C][C] 0.0924[/C][C] 0.1848[/C][C] 0.9076[/C][/ROW]
[ROW][C]152[/C][C] 0.07892[/C][C] 0.1578[/C][C] 0.9211[/C][/ROW]
[ROW][C]153[/C][C] 0.08087[/C][C] 0.1617[/C][C] 0.9191[/C][/ROW]
[ROW][C]154[/C][C] 0.2034[/C][C] 0.4068[/C][C] 0.7966[/C][/ROW]
[ROW][C]155[/C][C] 0.1721[/C][C] 0.3442[/C][C] 0.8279[/C][/ROW]
[ROW][C]156[/C][C] 0.2632[/C][C] 0.5264[/C][C] 0.7368[/C][/ROW]
[ROW][C]157[/C][C] 0.3566[/C][C] 0.7133[/C][C] 0.6434[/C][/ROW]
[ROW][C]158[/C][C] 0.4338[/C][C] 0.8677[/C][C] 0.5662[/C][/ROW]
[ROW][C]159[/C][C] 0.3868[/C][C] 0.7735[/C][C] 0.6132[/C][/ROW]
[ROW][C]160[/C][C] 0.4478[/C][C] 0.8956[/C][C] 0.5522[/C][/ROW]
[ROW][C]161[/C][C] 0.4011[/C][C] 0.8022[/C][C] 0.5989[/C][/ROW]
[ROW][C]162[/C][C] 0.3997[/C][C] 0.7995[/C][C] 0.6003[/C][/ROW]
[ROW][C]163[/C][C] 0.3667[/C][C] 0.7333[/C][C] 0.6333[/C][/ROW]
[ROW][C]164[/C][C] 0.3247[/C][C] 0.6494[/C][C] 0.6753[/C][/ROW]
[ROW][C]165[/C][C] 0.2777[/C][C] 0.5553[/C][C] 0.7223[/C][/ROW]
[ROW][C]166[/C][C] 0.257[/C][C] 0.514[/C][C] 0.743[/C][/ROW]
[ROW][C]167[/C][C] 0.2375[/C][C] 0.475[/C][C] 0.7625[/C][/ROW]
[ROW][C]168[/C][C] 0.2717[/C][C] 0.5434[/C][C] 0.7283[/C][/ROW]
[ROW][C]169[/C][C] 0.3223[/C][C] 0.6447[/C][C] 0.6777[/C][/ROW]
[ROW][C]170[/C][C] 0.29[/C][C] 0.5801[/C][C] 0.71[/C][/ROW]
[ROW][C]171[/C][C] 0.2534[/C][C] 0.5067[/C][C] 0.7467[/C][/ROW]
[ROW][C]172[/C][C] 0.3306[/C][C] 0.6612[/C][C] 0.6694[/C][/ROW]
[ROW][C]173[/C][C] 0.3061[/C][C] 0.6122[/C][C] 0.6939[/C][/ROW]
[ROW][C]174[/C][C] 0.2851[/C][C] 0.5702[/C][C] 0.7149[/C][/ROW]
[ROW][C]175[/C][C] 0.2444[/C][C] 0.4887[/C][C] 0.7556[/C][/ROW]
[ROW][C]176[/C][C] 0.2423[/C][C] 0.4846[/C][C] 0.7577[/C][/ROW]
[ROW][C]177[/C][C] 0.1937[/C][C] 0.3875[/C][C] 0.8063[/C][/ROW]
[ROW][C]178[/C][C] 0.1887[/C][C] 0.3774[/C][C] 0.8113[/C][/ROW]
[ROW][C]179[/C][C] 0.1638[/C][C] 0.3277[/C][C] 0.8362[/C][/ROW]
[ROW][C]180[/C][C] 0.2043[/C][C] 0.4085[/C][C] 0.7957[/C][/ROW]
[ROW][C]181[/C][C] 0.1909[/C][C] 0.3818[/C][C] 0.8091[/C][/ROW]
[ROW][C]182[/C][C] 0.1454[/C][C] 0.2908[/C][C] 0.8546[/C][/ROW]
[ROW][C]183[/C][C] 0.1082[/C][C] 0.2164[/C][C] 0.8918[/C][/ROW]
[ROW][C]184[/C][C] 0.2732[/C][C] 0.5464[/C][C] 0.7268[/C][/ROW]
[ROW][C]185[/C][C] 0.2147[/C][C] 0.4293[/C][C] 0.7853[/C][/ROW]
[ROW][C]186[/C][C] 0.2045[/C][C] 0.409[/C][C] 0.7955[/C][/ROW]
[ROW][C]187[/C][C] 0.384[/C][C] 0.768[/C][C] 0.616[/C][/ROW]
[ROW][C]188[/C][C] 0.3207[/C][C] 0.6414[/C][C] 0.6793[/C][/ROW]
[ROW][C]189[/C][C] 0.2639[/C][C] 0.5279[/C][C] 0.7361[/C][/ROW]
[ROW][C]190[/C][C] 0.2671[/C][C] 0.5343[/C][C] 0.7329[/C][/ROW]
[ROW][C]191[/C][C] 0.1761[/C][C] 0.3522[/C][C] 0.8239[/C][/ROW]
[ROW][C]192[/C][C] 0.122[/C][C] 0.2441[/C][C] 0.878[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.478	0.956	0.522
8	0.3388	0.6776	0.6612
9	0.2125	0.4249	0.7875
10	0.1389	0.2779	0.8611
11	0.255	0.5099	0.745
12	0.1981	0.3961	0.8019
13	0.149	0.2981	0.851
14	0.1317	0.2634	0.8683
15	0.1051	0.2103	0.8949
16	0.1068	0.2136	0.8932
17	0.09199	0.184	0.908
18	0.06288	0.1258	0.9371
19	0.07887	0.1577	0.9211
20	0.05776	0.1155	0.9422
21	0.04575	0.0915	0.9543
22	0.04862	0.09724	0.9514
23	0.06498	0.13	0.935
24	0.0447	0.08941	0.9553
25	0.03193	0.06387	0.9681
26	0.02357	0.04714	0.9764
27	0.01791	0.03582	0.9821
28	0.0175	0.035	0.9825
29	0.01183	0.02367	0.9882
30	0.01048	0.02096	0.9895
31	0.007118	0.01424	0.9929
32	0.004889	0.009777	0.9951
33	0.005558	0.01112	0.9944
34	0.003642	0.007283	0.9964
35	0.002265	0.00453	0.9977
36	0.002177	0.004354	0.9978
37	0.001961	0.003921	0.998
38	0.01024	0.02047	0.9898
39	0.007253	0.01451	0.9927
40	0.005154	0.01031	0.9948
41	0.01016	0.02033	0.9898
42	0.007259	0.01452	0.9927
43	0.008135	0.01627	0.9919
44	0.00702	0.01404	0.993
45	0.006037	0.01207	0.994
46	0.006757	0.01351	0.9932
47	0.005712	0.01142	0.9943
48	0.004833	0.009666	0.9952
49	0.007295	0.01459	0.9927
50	0.007843	0.01569	0.9922
51	0.006875	0.01375	0.9931
52	0.005788	0.01158	0.9942
53	0.004255	0.008509	0.9957
54	0.003241	0.006482	0.9968
55	0.00731	0.01462	0.9927
56	0.005517	0.01103	0.9945
57	0.003995	0.007989	0.996
58	0.003933	0.007866	0.9961
59	0.002816	0.005631	0.9972
60	0.002228	0.004457	0.9978
61	0.002546	0.005091	0.9975
62	0.001903	0.003805	0.9981
63	0.002274	0.004548	0.9977
64	0.002267	0.004535	0.9977
65	0.001722	0.003444	0.9983
66	0.00298	0.005959	0.997
67	0.004147	0.008294	0.9959
68	0.003511	0.007022	0.9965
69	0.004968	0.009937	0.995
70	0.003781	0.007561	0.9962
71	0.003966	0.007932	0.996
72	0.00393	0.007859	0.9961
73	0.003429	0.006858	0.9966
74	0.00276	0.005519	0.9972
75	0.002499	0.004997	0.9975
76	0.001868	0.003736	0.9981
77	0.001511	0.003022	0.9985
78	0.001422	0.002844	0.9986
79	0.001519	0.003037	0.9985
80	0.001183	0.002366	0.9988
81	0.001849	0.003698	0.9982
82	0.00173	0.00346	0.9983
83	0.001295	0.00259	0.9987
84	0.002132	0.004265	0.9979
85	0.00187	0.00374	0.9981
86	0.001965	0.003929	0.998
87	0.005585	0.01117	0.9944
88	0.004471	0.008943	0.9955
89	0.00434	0.00868	0.9957
90	0.01247	0.02493	0.9875
91	0.009482	0.01896	0.9905
92	0.01208	0.02416	0.9879
93	0.009239	0.01848	0.9908
94	0.01105	0.0221	0.9889
95	0.02007	0.04015	0.9799
96	0.03745	0.07489	0.9626
97	0.03418	0.06835	0.9658
98	0.03621	0.07243	0.9638
99	0.02929	0.05858	0.9707
100	0.03025	0.0605	0.9698
101	0.04595	0.0919	0.9541
102	0.04368	0.08735	0.9563
103	0.03807	0.07615	0.9619
104	0.03104	0.06207	0.969
105	0.02804	0.05608	0.972
106	0.0221	0.04419	0.9779
107	0.01752	0.03505	0.9825
108	0.03063	0.06126	0.9694
109	0.03203	0.06406	0.968
110	0.02665	0.05329	0.9734
111	0.02259	0.04519	0.9774
112	0.0223	0.04459	0.9777
113	0.01753	0.03506	0.9825
114	0.01499	0.02998	0.985
115	0.01371	0.02741	0.9863
116	0.01162	0.02323	0.9884
117	0.01004	0.02009	0.99
118	0.008572	0.01714	0.9914
119	0.006513	0.01303	0.9935
120	0.005042	0.01008	0.995
121	0.004818	0.009636	0.9952
122	0.00423	0.008461	0.9958
123	0.003351	0.006701	0.9967
124	0.008629	0.01726	0.9914
125	0.01158	0.02315	0.9884
126	0.01043	0.02086	0.9896
127	0.01286	0.02571	0.9871
128	0.01083	0.02166	0.9892
129	0.008306	0.01661	0.9917
130	0.006718	0.01344	0.9933
131	0.005062	0.01012	0.9949
132	0.003719	0.007437	0.9963
133	0.002724	0.005448	0.9973
134	0.002856	0.005711	0.9971
135	0.00258	0.00516	0.9974
136	0.001926	0.003852	0.9981
137	0.001948	0.003897	0.9981
138	0.002037	0.004074	0.998
139	0.00455	0.0091	0.9954
140	0.005622	0.01124	0.9944
141	0.02839	0.05677	0.9716
142	0.02369	0.04738	0.9763
143	0.0184	0.03679	0.9816
144	0.01476	0.02952	0.9852
145	0.03218	0.06436	0.9678
146	0.03845	0.0769	0.9616
147	0.02984	0.05968	0.9702
148	0.02337	0.04674	0.9766
149	0.05827	0.1165	0.9417
150	0.09721	0.1944	0.9028
151	0.0924	0.1848	0.9076
152	0.07892	0.1578	0.9211
153	0.08087	0.1617	0.9191
154	0.2034	0.4068	0.7966
155	0.1721	0.3442	0.8279
156	0.2632	0.5264	0.7368
157	0.3566	0.7133	0.6434
158	0.4338	0.8677	0.5662
159	0.3868	0.7735	0.6132
160	0.4478	0.8956	0.5522
161	0.4011	0.8022	0.5989
162	0.3997	0.7995	0.6003
163	0.3667	0.7333	0.6333
164	0.3247	0.6494	0.6753
165	0.2777	0.5553	0.7223
166	0.257	0.514	0.743
167	0.2375	0.475	0.7625
168	0.2717	0.5434	0.7283
169	0.3223	0.6447	0.6777
170	0.29	0.5801	0.71
171	0.2534	0.5067	0.7467
172	0.3306	0.6612	0.6694
173	0.3061	0.6122	0.6939
174	0.2851	0.5702	0.7149
175	0.2444	0.4887	0.7556
176	0.2423	0.4846	0.7577
177	0.1937	0.3875	0.8063
178	0.1887	0.3774	0.8113
179	0.1638	0.3277	0.8362
180	0.2043	0.4085	0.7957
181	0.1909	0.3818	0.8091
182	0.1454	0.2908	0.8546
183	0.1082	0.2164	0.8918
184	0.2732	0.5464	0.7268
185	0.2147	0.4293	0.7853
186	0.2045	0.409	0.7955
187	0.384	0.768	0.616
188	0.3207	0.6414	0.6793
189	0.2639	0.5279	0.7361
190	0.2671	0.5343	0.7329
191	0.1761	0.3522	0.8239
192	0.122	0.2441	0.878

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	51	0.2742	NOK
5% type I error level	106	0.569892	NOK
10% type I error level	127	0.682796	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 & 51 &  0.2742 & NOK \tabularnewline
5% type I error level & 106 & 0.569892 & NOK \tabularnewline
10% type I error level & 127 & 0.682796 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=7

[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]51[/C][C] 0.2742[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]106[/C][C]0.569892[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]127[/C][C]0.682796[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&T=7

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	51	0.2742	NOK
5% type I error level	106	0.569892	NOK
10% type I error level	127	0.682796	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.3217, df1 = 2, df2 = 193, p-value = 0.1008
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.3316, df1 = 6, df2 = 189, p-value = 0.03396
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.494, df1 = 2, df2 = 193, p-value = 0.08523

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3217, df1 = 2, df2 = 193, p-value = 0.1008
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.3316, df1 = 6, df2 = 189, p-value = 0.03396
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.494, df1 = 2, df2 = 193, p-value = 0.08523
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3217, df1 = 2, df2 = 193, p-value = 0.1008
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.3316, df1 = 6, df2 = 189, p-value = 0.03396
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.494, df1 = 2, df2 = 193, p-value = 0.08523
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.3217, df1 = 2, df2 = 193, p-value = 0.1008
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.3316, df1 = 6, df2 = 189, p-value = 0.03396
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.494, df1 = 2, df2 = 193, p-value = 0.08523

Variance Inflation Factors (Multicollinearity)
> vif EXFBT `FBT(t-1)` `FBT(t-1s)` 3.341142 6.120612 8.545021

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      EXFBT  `FBT(t-1)` `FBT(t-1s)` 
   3.341142    6.120612    8.545021 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310027&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      EXFBT  `FBT(t-1)` `FBT(t-1s)` 
   3.341142    6.120612    8.545021 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310027&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310027&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif EXFBT `FBT(t-1)` `FBT(t-1s)` 3.341142 6.120612 8.545021

Figure 1

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 1 ; par5 = 1 ; par6 = 12 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code