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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 18 Dec 2017 12:08:29 +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/18/t1513595446ly3lx05nub0luqv.htm/, Retrieved Tue, 14 May 2024 10:55:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310133, Retrieved Tue, 14 May 2024 10:55:14 +0000

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

-       [Multiple Regression] [] [2017-12-18 11:08:29] [d45155ea4037f62d47a0a82219388c6c] [Current]

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Dataseries X:

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99.5	62.4
89.9	67.4
96	76.1
86.9	67.4
85.6	74.5
82.5	72.6
80.5	60.5
82.7	66.1
87.7	76.5
92.2	76.8
93.9	77
94.5	71
94.8	74.8
85	73.7
87.4	80.5
79.5	71.8
80.5	76.9
79.8	79.9
78.8	65.9
81.5	69.5
82.6	75.1
89.5	79.6
90.7	75.2
90.7	68
95.7	72.8
86.6	71.5
92.4	78.5
86.3	76.8
84.7	75.3
83.1	76.7
82.2	69.7
84.5	67.8
81.2	77.5
88.2	82.5
89.1	75.3
89.1	70.9
98	76
91.7	73.7
90.9	79.7
87.1	77.8
84.5	73.3
83.5	78.3
85.9	71.9
89	67
87.6	82
92.9	83.7
89.1	74.8
96.9	80
104.1	74.3
93	76.8
98	89
85.9	81.9
84.8	76.8
81.5	88.9
85.3	75.8
79.3	75.5
82.3	89.1
87.8	88
95	85.9
104.4	89.3
103.5	82.9
99.5	81.2
96.6	90.5
88.1	86.4
86.4	81.8
83.6	91.3
85.7	73.4
79.8	76.6
81.9	91
87.1	87
92	89.7
106.1	90.7
108.5	86.5
101.4	86.6
100.1	98.8
84.4	84.4
81.6	91.4
81.5	95.7
80.9	78.5
79.9	81.7
81.2	94.3
90.5	98.5
91.7	95.4
102.7	91.7
104.8	92.8
98.7	90.5
100.8	102.2
93.6	91.8
88.1	95
86.8	102
80.8	88.9
84.6	89.6
82	97.9
93.6	108.6
99.7	100.8
102.1	95.1
106.6	101
95.9	100.9
92.1	102.5
85.9	105.4
79.3	98.4
83.7	105.3
84.1	96.5
83.2	88.1
85	107.9
93.1	107
95.4	92.5
107.3	95.7
112.5	85.2
97.8	85.5
99.1	94.7
85.6	86.2
87.2	88.8
86	93.4
92.7	83.4
98.8	82.9
99.2	96.7
101.4	96.2
98.8	92.8
113.2	92.8
119.2	90
107.4	95.4
111.6	108.3
94.8	96.3
97.7	95
87.3	109
91.4	92
93.4	92.3
90.8	107
96.1	105.5
102.6	105.4
107.7	103.9
111.4	99.2
98.9	102.2
100.7	121.5
91	102.3
94.8	110
87.3	105.9
88.8	91.9
92.3	100
90.9	111.7
95.2	104.9
98.2	103.3
103.5	101.8
109.7	100.8
116.4	104.2
87.5	116.5
87.2	97.9
85.5	100.7
79	107
81.8	96.3
78.2	96
78.9	104.5
76.9	107.4
84.4	102.4
93.1	94.9
101.6	98.8
97.1	96.8
99.3	108.2
77.8	103.8
74.3	102.3
80.4	107.2
85.3	102
80.1	92.6
78.8	105.2
91.8	113
100	105.6
108.4	101.6
101.7	101.7
94.4	102.7
89.5	109
69.8	105.5
72.5	103.3
69.1	108.6
71.9	98.2
67	90
63.8	112.4
73.2	111.9
74.2	102.1
84.7	102.4
97.8	101.7
87.4	98.7
81.8	114
68.6	105.1
64.9	98.3
64.1	110
63.6	96.5
59.8	92.2
66.3	112
78.1	111.4
86.8	107.5
89	103.4
111.3	103.5
99.7	107.4
103.7	117.6
90.4	110.2
77.6	104.3
73.9	115.9
81.5	98.9
88.2	101.9
78	113.5
84.7	109.5
94.8	110
101.5	114.2
112.4	106.9
96.6	109.2
96.9	124.2
76.1	104.7
76.9	111.9
83.8	119
89.4	102.9
89.1	106.3

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=310133&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=310133&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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
(1-Bs)(1-B)EnergySupply[t] = + 0.0680189 + 0.141952`(1-Bs)(1-B)Totind`[t] -0.268862`(1-Bs)(1-B)EnergySupply(t-1)`[t] -0.372563`(1-Bs)(1-B)EnergySupply(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)EnergySupply[t] =  +  0.0680189 +  0.141952`(1-Bs)(1-B)Totind`[t] -0.268862`(1-Bs)(1-B)EnergySupply(t-1)`[t] -0.372563`(1-Bs)(1-B)EnergySupply(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310133&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)EnergySupply[t] =  +  0.0680189 +  0.141952`(1-Bs)(1-B)Totind`[t] -0.268862`(1-Bs)(1-B)EnergySupply(t-1)`[t] -0.372563`(1-Bs)(1-B)EnergySupply(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310133&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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
(1-Bs)(1-B)EnergySupply[t] = + 0.0680189 + 0.141952`(1-Bs)(1-B)Totind`[t] -0.268862`(1-Bs)(1-B)EnergySupply(t-1)`[t] -0.372563`(1-Bs)(1-B)EnergySupply(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)	+0.06802	0.4288	+1.5860e-01	0.8742	0.4371
`(1-Bs)(1-B)Totind`	+0.1419	0.07569	+1.8750e+00	0.06233	0.03116
`(1-Bs)(1-B)EnergySupply(t-1)`	-0.2689	0.0648	-4.1490e+00	5.117e-05	2.558e-05
`(1-Bs)(1-B)EnergySupply(t-1s)`	-0.3726	0.06876	-5.4180e+00	1.891e-07	9.457e-08

\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) & +0.06802 &  0.4288 & +1.5860e-01 &  0.8742 &  0.4371 \tabularnewline
`(1-Bs)(1-B)Totind` & +0.1419 &  0.07569 & +1.8750e+00 &  0.06233 &  0.03116 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-1)` & -0.2689 &  0.0648 & -4.1490e+00 &  5.117e-05 &  2.558e-05 \tabularnewline
`(1-Bs)(1-B)EnergySupply(t-1s)` & -0.3726 &  0.06876 & -5.4180e+00 &  1.891e-07 &  9.457e-08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310133&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]+0.06802[/C][C] 0.4288[/C][C]+1.5860e-01[/C][C] 0.8742[/C][C] 0.4371[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Totind`[/C][C]+0.1419[/C][C] 0.07569[/C][C]+1.8750e+00[/C][C] 0.06233[/C][C] 0.03116[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-1)`[/C][C]-0.2689[/C][C] 0.0648[/C][C]-4.1490e+00[/C][C] 5.117e-05[/C][C] 2.558e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)EnergySupply(t-1s)`[/C][C]-0.3726[/C][C] 0.06876[/C][C]-5.4180e+00[/C][C] 1.891e-07[/C][C] 9.457e-08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310133&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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)	+0.06802	0.4288	+1.5860e-01	0.8742	0.4371
`(1-Bs)(1-B)Totind`	+0.1419	0.07569	+1.8750e+00	0.06233	0.03116
`(1-Bs)(1-B)EnergySupply(t-1)`	-0.2689	0.0648	-4.1490e+00	5.117e-05	2.558e-05
`(1-Bs)(1-B)EnergySupply(t-1s)`	-0.3726	0.06876	-5.4180e+00	1.891e-07	9.457e-08

Multiple Linear Regression - Regression Statistics
Multiple R	0.507
R-squared	0.2571
Adjusted R-squared	0.2448
F-TEST (value)	20.99
F-TEST (DF numerator)	3
F-TEST (DF denominator)	182
p-value	1.006e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.846
Sum Squared Residuals	6220

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.507 \tabularnewline
R-squared &  0.2571 \tabularnewline
Adjusted R-squared &  0.2448 \tabularnewline
F-TEST (value) &  20.99 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 182 \tabularnewline
p-value &  1.006e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  5.846 \tabularnewline
Sum Squared Residuals &  6220 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310133&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.507[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2571[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2448[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 20.99[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]182[/C][/ROW]
[ROW][C]p-value[/C][C] 1.006e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 5.846[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 6220[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310133&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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	0.507
R-squared	0.2571
Adjusted R-squared	0.2448
F-TEST (value)	20.99
F-TEST (DF numerator)	3
F-TEST (DF denominator)	182
p-value	1.006e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.846
Sum Squared Residuals	6220

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=310133&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=310133&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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	3.4	1.287	2.113
2	1.8	-0.2995	2.1
3	-2.6	-2.21	-0.3903
4	-0.9	-0.3542	-0.5458
5	0.1	0.9311	-0.8311
6	-0.4	-0.9259	0.5259
7	-4.4	2.211	-6.611
8	0.1	0.4278	-0.3278
9	-0.3	-0.1701	-0.1299
10	0	0.7697	-0.7697
11	3.9	-1.64	5.54
12	2.8	-1.383	4.183
13	-6.6	-2.093	-4.507
14	2.3	1.144	1.156
15	-1	-0.007556	-0.9924
16	0.6	1.183	-0.5832
17	3.3	-0.04538	3.345
18	0.8	-1.096	1.896
19	1.9	2.245	-0.3446
20	-1.7	-0.9485	-0.7515
21	-4.7	0.3955	-5.096
22	7.8	2.694	5.106
23	-1.7	-5.015	3.315
24	-4.8	0.1633	-4.963
25	5.8	4.698	1.102
26	-8.3	-3.086	-5.214
27	1.5	2.587	-1.087
28	-2.3	0.449	-2.749
29	1.4	-1.494	2.894
30	-9.1	0.04654	-9.147
31	4.4	1.608	2.792
32	0.2	-0.8791	1.079
33	11	2.731	8.269
34	1.6	-6.051	7.651
35	-8.1	0.1718	-8.272
36	7.1	3.438	3.662
37	-7.9	-4.413	-3.487
38	3.6	5.71	-2.11
39	-0.6	-1.388	0.7878
40	0.5	0.7172	-0.2172
41	-1.7	-1.269	-0.4306
42	0.1	4.412	-4.312
43	-0.9	-1.485	0.5846
44	-0.3	-0.1762	-0.1238
45	-2.3	-3.268	0.9681
46	4.7	-0.2504	4.95
47	3.3	2.134	1.166
48	-3.1	-3.209	0.1089
49	1.6	4.256	-2.656
50	-7.2	-3.165	-4.035
51	-1.1	3.874	-4.974
52	2.7	-0.5607	3.261
53	-2.7	0.07481	-2.775
54	4.9	0.7567	4.143
55	-0.8	-1.17	0.3696
56	4.1	1.559	2.541
57	-3.7	-1.001	-2.699
58	-3.1	-1.355	-1.745
59	-0.3	0.4244	-0.7244
60	1	0.9629	0.03706
61	3.4	-0.8679	4.268
62	8.5	2.404	6.096
63	-2.7	-2.347	-0.3531
64	-1.2	0.1713	-1.371
65	-5.4	1.979	-7.379
66	4.8	-0.6606	5.461
67	-3.9	-1.535	-2.365
68	2.3	0.5118	1.788
69	4.9	0.1609	4.739
70	-8.6	-0.3784	-8.222
71	2.4	3.173	-0.7734
72	-4.6	-0.6375	-3.962
73	-5.9	-1.396	-4.504
74	1	0.3755	0.6245
75	-1.1	-0.6428	-0.4572
76	5.7	0.7966	4.903
77	6.4	1.158	5.242
78	-4.7	-4.733	0.03276
79	4.4	4.417	-0.01711
80	-3.5	-3.619	0.1185
81	-3.8	-1.768	-2.032
82	9.5	5.557	3.943
83	0.7	-5.708	6.408
84	-4	1.65	-5.65
85	5.1	4.42	0.6796
86	-7.3	-3.294	-4.006
87	8.2	3.803	4.397
88	-5.6	-4.587	-1.013
89	6.3	-0.9811	7.281
90	7	1.247	5.753
91	-1.4	-4.305	2.905
92	-5.9	1.805	-7.705
93	-4.9	4.646	-9.546
94	2.5	-2.608	5.108
95	0.8	0.2281	0.5719
96	2.9	2.067	0.8329
97	2.9	-2.087	4.987
98	-3.3	1.511	-4.811
99	1.3	-2.653	3.953
100	-9.2	3.139	-12.34
101	-2.6	-0.7993	-1.801
102	-4.1	-1.727	-2.373
103	-3	1.82	-4.82
104	3.1	2.931	0.1692
105	9.1	1.529	7.571
106	-9.3	-3.523	-5.777
107	-2.3	2.001	-4.301
108	-0.7	-0.7347	0.03471
109	-2.4	0.08428	-2.484
110	7.1	0.9207	6.179
111	0.9	-1.048	1.948
112	2.9	0.6843	2.216
113	-2.6	0.6828	-3.283
114	1.5	3.402	-1.902
115	1.2	0.3566	0.8434
116	-1	-2.162	1.162
117	-3.5	-3.266	-0.2336
118	0.2	4.474	-4.274
119	2.5	1.396	1.104
120	19.2	-0.2866	19.49
121	-30.7	-5.194	-25.51
122	9.4	5.762	3.638
123	-5.5	-3.49	-2.01
124	1	1.943	-0.9426
125	1.3	1.236	0.06374
126	-7.1	-2.033	-5.067
127	2.1	1.076	1.024
128	-6.3	1.253	-7.553
129	4.5	2.583	1.917
130	3.4	-2.068	5.468
131	2.3	-1.082	3.382
132	-11.2	-8.47	-2.73
133	31.1	14.39	16.71
134	-21.2	-9.78	-11.42
135	-1.8	7.207	-9.007
136	12.6	-0.01932	12.62
137	2.1	-3.023	5.123
138	-1.6	0.8568	-2.457
139	-2	0.2978	-2.298
140	15	3.648	11.35
141	0.7	-5.982	6.682
142	-0.3	-0.8901	0.5901
143	-15.2	-1.248	-13.95
144	-2.8	8.753	-11.55
145	-7.1	-11.49	4.39
146	1.8	10	-8.203
147	6.2	0.1553	6.045
148	-9.5	-6.236	-3.264
149	-2.1	1.102	-3.202
150	0.3	1.399	-1.099
151	-1.9	2.124	-4.024
152	-3.6	-6.188	2.588
153	-7.2	0.4344	-7.634
154	2.1	2.726	-0.626
155	19.8	5.053	14.75
156	-3.1	-4.78	1.68
157	-0.7	4.824	-5.524
158	6.5	-1.181	7.681
159	-6.4	-4.642	-1.758
160	2.6	6.237	-3.637
161	-3.3	-0.2887	-3.011
162	1.1	1.397	-0.2971
163	9.7	0.1111	9.589
164	2.4	-1.213	3.613
165	7.7	2.943	4.757
166	-8.3	-3.409	-4.891
167	9.2	-4.964	14.16
168	-1.2	-0.2711	-0.9289
169	9.6	-0.07251	9.673
170	-0.1	-4.722	4.622
171	-9.1	2.607	-11.71
172	-2.9	1.532	-4.432
173	8.1	1.58	6.52
174	10.5	-1.483	11.98
175	-16.7	-7.533	-9.167
176	-5.1	3.181	-8.281
177	1.4	-0.8049	2.205
178	4.5	3.962	0.5379
179	-11.4	-5.62	-5.78
180	-4.2	3.353	-7.553
181	-3.7	-1.698	-2.002
182	-7.5	-0.6176	-6.882
183	13.6	7.334	6.266
184	10.6	-3.147	13.75
185	-2	-5.672	3.672
186	-7	-3.249	-3.751

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  3.4 &  1.287 &  2.113 \tabularnewline
2 &  1.8 & -0.2995 &  2.1 \tabularnewline
3 & -2.6 & -2.21 & -0.3903 \tabularnewline
4 & -0.9 & -0.3542 & -0.5458 \tabularnewline
5 &  0.1 &  0.9311 & -0.8311 \tabularnewline
6 & -0.4 & -0.9259 &  0.5259 \tabularnewline
7 & -4.4 &  2.211 & -6.611 \tabularnewline
8 &  0.1 &  0.4278 & -0.3278 \tabularnewline
9 & -0.3 & -0.1701 & -0.1299 \tabularnewline
10 &  0 &  0.7697 & -0.7697 \tabularnewline
11 &  3.9 & -1.64 &  5.54 \tabularnewline
12 &  2.8 & -1.383 &  4.183 \tabularnewline
13 & -6.6 & -2.093 & -4.507 \tabularnewline
14 &  2.3 &  1.144 &  1.156 \tabularnewline
15 & -1 & -0.007556 & -0.9924 \tabularnewline
16 &  0.6 &  1.183 & -0.5832 \tabularnewline
17 &  3.3 & -0.04538 &  3.345 \tabularnewline
18 &  0.8 & -1.096 &  1.896 \tabularnewline
19 &  1.9 &  2.245 & -0.3446 \tabularnewline
20 & -1.7 & -0.9485 & -0.7515 \tabularnewline
21 & -4.7 &  0.3955 & -5.096 \tabularnewline
22 &  7.8 &  2.694 &  5.106 \tabularnewline
23 & -1.7 & -5.015 &  3.315 \tabularnewline
24 & -4.8 &  0.1633 & -4.963 \tabularnewline
25 &  5.8 &  4.698 &  1.102 \tabularnewline
26 & -8.3 & -3.086 & -5.214 \tabularnewline
27 &  1.5 &  2.587 & -1.087 \tabularnewline
28 & -2.3 &  0.449 & -2.749 \tabularnewline
29 &  1.4 & -1.494 &  2.894 \tabularnewline
30 & -9.1 &  0.04654 & -9.147 \tabularnewline
31 &  4.4 &  1.608 &  2.792 \tabularnewline
32 &  0.2 & -0.8791 &  1.079 \tabularnewline
33 &  11 &  2.731 &  8.269 \tabularnewline
34 &  1.6 & -6.051 &  7.651 \tabularnewline
35 & -8.1 &  0.1718 & -8.272 \tabularnewline
36 &  7.1 &  3.438 &  3.662 \tabularnewline
37 & -7.9 & -4.413 & -3.487 \tabularnewline
38 &  3.6 &  5.71 & -2.11 \tabularnewline
39 & -0.6 & -1.388 &  0.7878 \tabularnewline
40 &  0.5 &  0.7172 & -0.2172 \tabularnewline
41 & -1.7 & -1.269 & -0.4306 \tabularnewline
42 &  0.1 &  4.412 & -4.312 \tabularnewline
43 & -0.9 & -1.485 &  0.5846 \tabularnewline
44 & -0.3 & -0.1762 & -0.1238 \tabularnewline
45 & -2.3 & -3.268 &  0.9681 \tabularnewline
46 &  4.7 & -0.2504 &  4.95 \tabularnewline
47 &  3.3 &  2.134 &  1.166 \tabularnewline
48 & -3.1 & -3.209 &  0.1089 \tabularnewline
49 &  1.6 &  4.256 & -2.656 \tabularnewline
50 & -7.2 & -3.165 & -4.035 \tabularnewline
51 & -1.1 &  3.874 & -4.974 \tabularnewline
52 &  2.7 & -0.5607 &  3.261 \tabularnewline
53 & -2.7 &  0.07481 & -2.775 \tabularnewline
54 &  4.9 &  0.7567 &  4.143 \tabularnewline
55 & -0.8 & -1.17 &  0.3696 \tabularnewline
56 &  4.1 &  1.559 &  2.541 \tabularnewline
57 & -3.7 & -1.001 & -2.699 \tabularnewline
58 & -3.1 & -1.355 & -1.745 \tabularnewline
59 & -0.3 &  0.4244 & -0.7244 \tabularnewline
60 &  1 &  0.9629 &  0.03706 \tabularnewline
61 &  3.4 & -0.8679 &  4.268 \tabularnewline
62 &  8.5 &  2.404 &  6.096 \tabularnewline
63 & -2.7 & -2.347 & -0.3531 \tabularnewline
64 & -1.2 &  0.1713 & -1.371 \tabularnewline
65 & -5.4 &  1.979 & -7.379 \tabularnewline
66 &  4.8 & -0.6606 &  5.461 \tabularnewline
67 & -3.9 & -1.535 & -2.365 \tabularnewline
68 &  2.3 &  0.5118 &  1.788 \tabularnewline
69 &  4.9 &  0.1609 &  4.739 \tabularnewline
70 & -8.6 & -0.3784 & -8.222 \tabularnewline
71 &  2.4 &  3.173 & -0.7734 \tabularnewline
72 & -4.6 & -0.6375 & -3.962 \tabularnewline
73 & -5.9 & -1.396 & -4.504 \tabularnewline
74 &  1 &  0.3755 &  0.6245 \tabularnewline
75 & -1.1 & -0.6428 & -0.4572 \tabularnewline
76 &  5.7 &  0.7966 &  4.903 \tabularnewline
77 &  6.4 &  1.158 &  5.242 \tabularnewline
78 & -4.7 & -4.733 &  0.03276 \tabularnewline
79 &  4.4 &  4.417 & -0.01711 \tabularnewline
80 & -3.5 & -3.619 &  0.1185 \tabularnewline
81 & -3.8 & -1.768 & -2.032 \tabularnewline
82 &  9.5 &  5.557 &  3.943 \tabularnewline
83 &  0.7 & -5.708 &  6.408 \tabularnewline
84 & -4 &  1.65 & -5.65 \tabularnewline
85 &  5.1 &  4.42 &  0.6796 \tabularnewline
86 & -7.3 & -3.294 & -4.006 \tabularnewline
87 &  8.2 &  3.803 &  4.397 \tabularnewline
88 & -5.6 & -4.587 & -1.013 \tabularnewline
89 &  6.3 & -0.9811 &  7.281 \tabularnewline
90 &  7 &  1.247 &  5.753 \tabularnewline
91 & -1.4 & -4.305 &  2.905 \tabularnewline
92 & -5.9 &  1.805 & -7.705 \tabularnewline
93 & -4.9 &  4.646 & -9.546 \tabularnewline
94 &  2.5 & -2.608 &  5.108 \tabularnewline
95 &  0.8 &  0.2281 &  0.5719 \tabularnewline
96 &  2.9 &  2.067 &  0.8329 \tabularnewline
97 &  2.9 & -2.087 &  4.987 \tabularnewline
98 & -3.3 &  1.511 & -4.811 \tabularnewline
99 &  1.3 & -2.653 &  3.953 \tabularnewline
100 & -9.2 &  3.139 & -12.34 \tabularnewline
101 & -2.6 & -0.7993 & -1.801 \tabularnewline
102 & -4.1 & -1.727 & -2.373 \tabularnewline
103 & -3 &  1.82 & -4.82 \tabularnewline
104 &  3.1 &  2.931 &  0.1692 \tabularnewline
105 &  9.1 &  1.529 &  7.571 \tabularnewline
106 & -9.3 & -3.523 & -5.777 \tabularnewline
107 & -2.3 &  2.001 & -4.301 \tabularnewline
108 & -0.7 & -0.7347 &  0.03471 \tabularnewline
109 & -2.4 &  0.08428 & -2.484 \tabularnewline
110 &  7.1 &  0.9207 &  6.179 \tabularnewline
111 &  0.9 & -1.048 &  1.948 \tabularnewline
112 &  2.9 &  0.6843 &  2.216 \tabularnewline
113 & -2.6 &  0.6828 & -3.283 \tabularnewline
114 &  1.5 &  3.402 & -1.902 \tabularnewline
115 &  1.2 &  0.3566 &  0.8434 \tabularnewline
116 & -1 & -2.162 &  1.162 \tabularnewline
117 & -3.5 & -3.266 & -0.2336 \tabularnewline
118 &  0.2 &  4.474 & -4.274 \tabularnewline
119 &  2.5 &  1.396 &  1.104 \tabularnewline
120 &  19.2 & -0.2866 &  19.49 \tabularnewline
121 & -30.7 & -5.194 & -25.51 \tabularnewline
122 &  9.4 &  5.762 &  3.638 \tabularnewline
123 & -5.5 & -3.49 & -2.01 \tabularnewline
124 &  1 &  1.943 & -0.9426 \tabularnewline
125 &  1.3 &  1.236 &  0.06374 \tabularnewline
126 & -7.1 & -2.033 & -5.067 \tabularnewline
127 &  2.1 &  1.076 &  1.024 \tabularnewline
128 & -6.3 &  1.253 & -7.553 \tabularnewline
129 &  4.5 &  2.583 &  1.917 \tabularnewline
130 &  3.4 & -2.068 &  5.468 \tabularnewline
131 &  2.3 & -1.082 &  3.382 \tabularnewline
132 & -11.2 & -8.47 & -2.73 \tabularnewline
133 &  31.1 &  14.39 &  16.71 \tabularnewline
134 & -21.2 & -9.78 & -11.42 \tabularnewline
135 & -1.8 &  7.207 & -9.007 \tabularnewline
136 &  12.6 & -0.01932 &  12.62 \tabularnewline
137 &  2.1 & -3.023 &  5.123 \tabularnewline
138 & -1.6 &  0.8568 & -2.457 \tabularnewline
139 & -2 &  0.2978 & -2.298 \tabularnewline
140 &  15 &  3.648 &  11.35 \tabularnewline
141 &  0.7 & -5.982 &  6.682 \tabularnewline
142 & -0.3 & -0.8901 &  0.5901 \tabularnewline
143 & -15.2 & -1.248 & -13.95 \tabularnewline
144 & -2.8 &  8.753 & -11.55 \tabularnewline
145 & -7.1 & -11.49 &  4.39 \tabularnewline
146 &  1.8 &  10 & -8.203 \tabularnewline
147 &  6.2 &  0.1553 &  6.045 \tabularnewline
148 & -9.5 & -6.236 & -3.264 \tabularnewline
149 & -2.1 &  1.102 & -3.202 \tabularnewline
150 &  0.3 &  1.399 & -1.099 \tabularnewline
151 & -1.9 &  2.124 & -4.024 \tabularnewline
152 & -3.6 & -6.188 &  2.588 \tabularnewline
153 & -7.2 &  0.4344 & -7.634 \tabularnewline
154 &  2.1 &  2.726 & -0.626 \tabularnewline
155 &  19.8 &  5.053 &  14.75 \tabularnewline
156 & -3.1 & -4.78 &  1.68 \tabularnewline
157 & -0.7 &  4.824 & -5.524 \tabularnewline
158 &  6.5 & -1.181 &  7.681 \tabularnewline
159 & -6.4 & -4.642 & -1.758 \tabularnewline
160 &  2.6 &  6.237 & -3.637 \tabularnewline
161 & -3.3 & -0.2887 & -3.011 \tabularnewline
162 &  1.1 &  1.397 & -0.2971 \tabularnewline
163 &  9.7 &  0.1111 &  9.589 \tabularnewline
164 &  2.4 & -1.213 &  3.613 \tabularnewline
165 &  7.7 &  2.943 &  4.757 \tabularnewline
166 & -8.3 & -3.409 & -4.891 \tabularnewline
167 &  9.2 & -4.964 &  14.16 \tabularnewline
168 & -1.2 & -0.2711 & -0.9289 \tabularnewline
169 &  9.6 & -0.07251 &  9.673 \tabularnewline
170 & -0.1 & -4.722 &  4.622 \tabularnewline
171 & -9.1 &  2.607 & -11.71 \tabularnewline
172 & -2.9 &  1.532 & -4.432 \tabularnewline
173 &  8.1 &  1.58 &  6.52 \tabularnewline
174 &  10.5 & -1.483 &  11.98 \tabularnewline
175 & -16.7 & -7.533 & -9.167 \tabularnewline
176 & -5.1 &  3.181 & -8.281 \tabularnewline
177 &  1.4 & -0.8049 &  2.205 \tabularnewline
178 &  4.5 &  3.962 &  0.5379 \tabularnewline
179 & -11.4 & -5.62 & -5.78 \tabularnewline
180 & -4.2 &  3.353 & -7.553 \tabularnewline
181 & -3.7 & -1.698 & -2.002 \tabularnewline
182 & -7.5 & -0.6176 & -6.882 \tabularnewline
183 &  13.6 &  7.334 &  6.266 \tabularnewline
184 &  10.6 & -3.147 &  13.75 \tabularnewline
185 & -2 & -5.672 &  3.672 \tabularnewline
186 & -7 & -3.249 & -3.751 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310133&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] 3.4[/C][C] 1.287[/C][C] 2.113[/C][/ROW]
[ROW][C]2[/C][C] 1.8[/C][C]-0.2995[/C][C] 2.1[/C][/ROW]
[ROW][C]3[/C][C]-2.6[/C][C]-2.21[/C][C]-0.3903[/C][/ROW]
[ROW][C]4[/C][C]-0.9[/C][C]-0.3542[/C][C]-0.5458[/C][/ROW]
[ROW][C]5[/C][C] 0.1[/C][C] 0.9311[/C][C]-0.8311[/C][/ROW]
[ROW][C]6[/C][C]-0.4[/C][C]-0.9259[/C][C] 0.5259[/C][/ROW]
[ROW][C]7[/C][C]-4.4[/C][C] 2.211[/C][C]-6.611[/C][/ROW]
[ROW][C]8[/C][C] 0.1[/C][C] 0.4278[/C][C]-0.3278[/C][/ROW]
[ROW][C]9[/C][C]-0.3[/C][C]-0.1701[/C][C]-0.1299[/C][/ROW]
[ROW][C]10[/C][C] 0[/C][C] 0.7697[/C][C]-0.7697[/C][/ROW]
[ROW][C]11[/C][C] 3.9[/C][C]-1.64[/C][C] 5.54[/C][/ROW]
[ROW][C]12[/C][C] 2.8[/C][C]-1.383[/C][C] 4.183[/C][/ROW]
[ROW][C]13[/C][C]-6.6[/C][C]-2.093[/C][C]-4.507[/C][/ROW]
[ROW][C]14[/C][C] 2.3[/C][C] 1.144[/C][C] 1.156[/C][/ROW]
[ROW][C]15[/C][C]-1[/C][C]-0.007556[/C][C]-0.9924[/C][/ROW]
[ROW][C]16[/C][C] 0.6[/C][C] 1.183[/C][C]-0.5832[/C][/ROW]
[ROW][C]17[/C][C] 3.3[/C][C]-0.04538[/C][C] 3.345[/C][/ROW]
[ROW][C]18[/C][C] 0.8[/C][C]-1.096[/C][C] 1.896[/C][/ROW]
[ROW][C]19[/C][C] 1.9[/C][C] 2.245[/C][C]-0.3446[/C][/ROW]
[ROW][C]20[/C][C]-1.7[/C][C]-0.9485[/C][C]-0.7515[/C][/ROW]
[ROW][C]21[/C][C]-4.7[/C][C] 0.3955[/C][C]-5.096[/C][/ROW]
[ROW][C]22[/C][C] 7.8[/C][C] 2.694[/C][C] 5.106[/C][/ROW]
[ROW][C]23[/C][C]-1.7[/C][C]-5.015[/C][C] 3.315[/C][/ROW]
[ROW][C]24[/C][C]-4.8[/C][C] 0.1633[/C][C]-4.963[/C][/ROW]
[ROW][C]25[/C][C] 5.8[/C][C] 4.698[/C][C] 1.102[/C][/ROW]
[ROW][C]26[/C][C]-8.3[/C][C]-3.086[/C][C]-5.214[/C][/ROW]
[ROW][C]27[/C][C] 1.5[/C][C] 2.587[/C][C]-1.087[/C][/ROW]
[ROW][C]28[/C][C]-2.3[/C][C] 0.449[/C][C]-2.749[/C][/ROW]
[ROW][C]29[/C][C] 1.4[/C][C]-1.494[/C][C] 2.894[/C][/ROW]
[ROW][C]30[/C][C]-9.1[/C][C] 0.04654[/C][C]-9.147[/C][/ROW]
[ROW][C]31[/C][C] 4.4[/C][C] 1.608[/C][C] 2.792[/C][/ROW]
[ROW][C]32[/C][C] 0.2[/C][C]-0.8791[/C][C] 1.079[/C][/ROW]
[ROW][C]33[/C][C] 11[/C][C] 2.731[/C][C] 8.269[/C][/ROW]
[ROW][C]34[/C][C] 1.6[/C][C]-6.051[/C][C] 7.651[/C][/ROW]
[ROW][C]35[/C][C]-8.1[/C][C] 0.1718[/C][C]-8.272[/C][/ROW]
[ROW][C]36[/C][C] 7.1[/C][C] 3.438[/C][C] 3.662[/C][/ROW]
[ROW][C]37[/C][C]-7.9[/C][C]-4.413[/C][C]-3.487[/C][/ROW]
[ROW][C]38[/C][C] 3.6[/C][C] 5.71[/C][C]-2.11[/C][/ROW]
[ROW][C]39[/C][C]-0.6[/C][C]-1.388[/C][C] 0.7878[/C][/ROW]
[ROW][C]40[/C][C] 0.5[/C][C] 0.7172[/C][C]-0.2172[/C][/ROW]
[ROW][C]41[/C][C]-1.7[/C][C]-1.269[/C][C]-0.4306[/C][/ROW]
[ROW][C]42[/C][C] 0.1[/C][C] 4.412[/C][C]-4.312[/C][/ROW]
[ROW][C]43[/C][C]-0.9[/C][C]-1.485[/C][C] 0.5846[/C][/ROW]
[ROW][C]44[/C][C]-0.3[/C][C]-0.1762[/C][C]-0.1238[/C][/ROW]
[ROW][C]45[/C][C]-2.3[/C][C]-3.268[/C][C] 0.9681[/C][/ROW]
[ROW][C]46[/C][C] 4.7[/C][C]-0.2504[/C][C] 4.95[/C][/ROW]
[ROW][C]47[/C][C] 3.3[/C][C] 2.134[/C][C] 1.166[/C][/ROW]
[ROW][C]48[/C][C]-3.1[/C][C]-3.209[/C][C] 0.1089[/C][/ROW]
[ROW][C]49[/C][C] 1.6[/C][C] 4.256[/C][C]-2.656[/C][/ROW]
[ROW][C]50[/C][C]-7.2[/C][C]-3.165[/C][C]-4.035[/C][/ROW]
[ROW][C]51[/C][C]-1.1[/C][C] 3.874[/C][C]-4.974[/C][/ROW]
[ROW][C]52[/C][C] 2.7[/C][C]-0.5607[/C][C] 3.261[/C][/ROW]
[ROW][C]53[/C][C]-2.7[/C][C] 0.07481[/C][C]-2.775[/C][/ROW]
[ROW][C]54[/C][C] 4.9[/C][C] 0.7567[/C][C] 4.143[/C][/ROW]
[ROW][C]55[/C][C]-0.8[/C][C]-1.17[/C][C] 0.3696[/C][/ROW]
[ROW][C]56[/C][C] 4.1[/C][C] 1.559[/C][C] 2.541[/C][/ROW]
[ROW][C]57[/C][C]-3.7[/C][C]-1.001[/C][C]-2.699[/C][/ROW]
[ROW][C]58[/C][C]-3.1[/C][C]-1.355[/C][C]-1.745[/C][/ROW]
[ROW][C]59[/C][C]-0.3[/C][C] 0.4244[/C][C]-0.7244[/C][/ROW]
[ROW][C]60[/C][C] 1[/C][C] 0.9629[/C][C] 0.03706[/C][/ROW]
[ROW][C]61[/C][C] 3.4[/C][C]-0.8679[/C][C] 4.268[/C][/ROW]
[ROW][C]62[/C][C] 8.5[/C][C] 2.404[/C][C] 6.096[/C][/ROW]
[ROW][C]63[/C][C]-2.7[/C][C]-2.347[/C][C]-0.3531[/C][/ROW]
[ROW][C]64[/C][C]-1.2[/C][C] 0.1713[/C][C]-1.371[/C][/ROW]
[ROW][C]65[/C][C]-5.4[/C][C] 1.979[/C][C]-7.379[/C][/ROW]
[ROW][C]66[/C][C] 4.8[/C][C]-0.6606[/C][C] 5.461[/C][/ROW]
[ROW][C]67[/C][C]-3.9[/C][C]-1.535[/C][C]-2.365[/C][/ROW]
[ROW][C]68[/C][C] 2.3[/C][C] 0.5118[/C][C] 1.788[/C][/ROW]
[ROW][C]69[/C][C] 4.9[/C][C] 0.1609[/C][C] 4.739[/C][/ROW]
[ROW][C]70[/C][C]-8.6[/C][C]-0.3784[/C][C]-8.222[/C][/ROW]
[ROW][C]71[/C][C] 2.4[/C][C] 3.173[/C][C]-0.7734[/C][/ROW]
[ROW][C]72[/C][C]-4.6[/C][C]-0.6375[/C][C]-3.962[/C][/ROW]
[ROW][C]73[/C][C]-5.9[/C][C]-1.396[/C][C]-4.504[/C][/ROW]
[ROW][C]74[/C][C] 1[/C][C] 0.3755[/C][C] 0.6245[/C][/ROW]
[ROW][C]75[/C][C]-1.1[/C][C]-0.6428[/C][C]-0.4572[/C][/ROW]
[ROW][C]76[/C][C] 5.7[/C][C] 0.7966[/C][C] 4.903[/C][/ROW]
[ROW][C]77[/C][C] 6.4[/C][C] 1.158[/C][C] 5.242[/C][/ROW]
[ROW][C]78[/C][C]-4.7[/C][C]-4.733[/C][C] 0.03276[/C][/ROW]
[ROW][C]79[/C][C] 4.4[/C][C] 4.417[/C][C]-0.01711[/C][/ROW]
[ROW][C]80[/C][C]-3.5[/C][C]-3.619[/C][C] 0.1185[/C][/ROW]
[ROW][C]81[/C][C]-3.8[/C][C]-1.768[/C][C]-2.032[/C][/ROW]
[ROW][C]82[/C][C] 9.5[/C][C] 5.557[/C][C] 3.943[/C][/ROW]
[ROW][C]83[/C][C] 0.7[/C][C]-5.708[/C][C] 6.408[/C][/ROW]
[ROW][C]84[/C][C]-4[/C][C] 1.65[/C][C]-5.65[/C][/ROW]
[ROW][C]85[/C][C] 5.1[/C][C] 4.42[/C][C] 0.6796[/C][/ROW]
[ROW][C]86[/C][C]-7.3[/C][C]-3.294[/C][C]-4.006[/C][/ROW]
[ROW][C]87[/C][C] 8.2[/C][C] 3.803[/C][C] 4.397[/C][/ROW]
[ROW][C]88[/C][C]-5.6[/C][C]-4.587[/C][C]-1.013[/C][/ROW]
[ROW][C]89[/C][C] 6.3[/C][C]-0.9811[/C][C] 7.281[/C][/ROW]
[ROW][C]90[/C][C] 7[/C][C] 1.247[/C][C] 5.753[/C][/ROW]
[ROW][C]91[/C][C]-1.4[/C][C]-4.305[/C][C] 2.905[/C][/ROW]
[ROW][C]92[/C][C]-5.9[/C][C] 1.805[/C][C]-7.705[/C][/ROW]
[ROW][C]93[/C][C]-4.9[/C][C] 4.646[/C][C]-9.546[/C][/ROW]
[ROW][C]94[/C][C] 2.5[/C][C]-2.608[/C][C] 5.108[/C][/ROW]
[ROW][C]95[/C][C] 0.8[/C][C] 0.2281[/C][C] 0.5719[/C][/ROW]
[ROW][C]96[/C][C] 2.9[/C][C] 2.067[/C][C] 0.8329[/C][/ROW]
[ROW][C]97[/C][C] 2.9[/C][C]-2.087[/C][C] 4.987[/C][/ROW]
[ROW][C]98[/C][C]-3.3[/C][C] 1.511[/C][C]-4.811[/C][/ROW]
[ROW][C]99[/C][C] 1.3[/C][C]-2.653[/C][C] 3.953[/C][/ROW]
[ROW][C]100[/C][C]-9.2[/C][C] 3.139[/C][C]-12.34[/C][/ROW]
[ROW][C]101[/C][C]-2.6[/C][C]-0.7993[/C][C]-1.801[/C][/ROW]
[ROW][C]102[/C][C]-4.1[/C][C]-1.727[/C][C]-2.373[/C][/ROW]
[ROW][C]103[/C][C]-3[/C][C] 1.82[/C][C]-4.82[/C][/ROW]
[ROW][C]104[/C][C] 3.1[/C][C] 2.931[/C][C] 0.1692[/C][/ROW]
[ROW][C]105[/C][C] 9.1[/C][C] 1.529[/C][C] 7.571[/C][/ROW]
[ROW][C]106[/C][C]-9.3[/C][C]-3.523[/C][C]-5.777[/C][/ROW]
[ROW][C]107[/C][C]-2.3[/C][C] 2.001[/C][C]-4.301[/C][/ROW]
[ROW][C]108[/C][C]-0.7[/C][C]-0.7347[/C][C] 0.03471[/C][/ROW]
[ROW][C]109[/C][C]-2.4[/C][C] 0.08428[/C][C]-2.484[/C][/ROW]
[ROW][C]110[/C][C] 7.1[/C][C] 0.9207[/C][C] 6.179[/C][/ROW]
[ROW][C]111[/C][C] 0.9[/C][C]-1.048[/C][C] 1.948[/C][/ROW]
[ROW][C]112[/C][C] 2.9[/C][C] 0.6843[/C][C] 2.216[/C][/ROW]
[ROW][C]113[/C][C]-2.6[/C][C] 0.6828[/C][C]-3.283[/C][/ROW]
[ROW][C]114[/C][C] 1.5[/C][C] 3.402[/C][C]-1.902[/C][/ROW]
[ROW][C]115[/C][C] 1.2[/C][C] 0.3566[/C][C] 0.8434[/C][/ROW]
[ROW][C]116[/C][C]-1[/C][C]-2.162[/C][C] 1.162[/C][/ROW]
[ROW][C]117[/C][C]-3.5[/C][C]-3.266[/C][C]-0.2336[/C][/ROW]
[ROW][C]118[/C][C] 0.2[/C][C] 4.474[/C][C]-4.274[/C][/ROW]
[ROW][C]119[/C][C] 2.5[/C][C] 1.396[/C][C] 1.104[/C][/ROW]
[ROW][C]120[/C][C] 19.2[/C][C]-0.2866[/C][C] 19.49[/C][/ROW]
[ROW][C]121[/C][C]-30.7[/C][C]-5.194[/C][C]-25.51[/C][/ROW]
[ROW][C]122[/C][C] 9.4[/C][C] 5.762[/C][C] 3.638[/C][/ROW]
[ROW][C]123[/C][C]-5.5[/C][C]-3.49[/C][C]-2.01[/C][/ROW]
[ROW][C]124[/C][C] 1[/C][C] 1.943[/C][C]-0.9426[/C][/ROW]
[ROW][C]125[/C][C] 1.3[/C][C] 1.236[/C][C] 0.06374[/C][/ROW]
[ROW][C]126[/C][C]-7.1[/C][C]-2.033[/C][C]-5.067[/C][/ROW]
[ROW][C]127[/C][C] 2.1[/C][C] 1.076[/C][C] 1.024[/C][/ROW]
[ROW][C]128[/C][C]-6.3[/C][C] 1.253[/C][C]-7.553[/C][/ROW]
[ROW][C]129[/C][C] 4.5[/C][C] 2.583[/C][C] 1.917[/C][/ROW]
[ROW][C]130[/C][C] 3.4[/C][C]-2.068[/C][C] 5.468[/C][/ROW]
[ROW][C]131[/C][C] 2.3[/C][C]-1.082[/C][C] 3.382[/C][/ROW]
[ROW][C]132[/C][C]-11.2[/C][C]-8.47[/C][C]-2.73[/C][/ROW]
[ROW][C]133[/C][C] 31.1[/C][C] 14.39[/C][C] 16.71[/C][/ROW]
[ROW][C]134[/C][C]-21.2[/C][C]-9.78[/C][C]-11.42[/C][/ROW]
[ROW][C]135[/C][C]-1.8[/C][C] 7.207[/C][C]-9.007[/C][/ROW]
[ROW][C]136[/C][C] 12.6[/C][C]-0.01932[/C][C] 12.62[/C][/ROW]
[ROW][C]137[/C][C] 2.1[/C][C]-3.023[/C][C] 5.123[/C][/ROW]
[ROW][C]138[/C][C]-1.6[/C][C] 0.8568[/C][C]-2.457[/C][/ROW]
[ROW][C]139[/C][C]-2[/C][C] 0.2978[/C][C]-2.298[/C][/ROW]
[ROW][C]140[/C][C] 15[/C][C] 3.648[/C][C] 11.35[/C][/ROW]
[ROW][C]141[/C][C] 0.7[/C][C]-5.982[/C][C] 6.682[/C][/ROW]
[ROW][C]142[/C][C]-0.3[/C][C]-0.8901[/C][C] 0.5901[/C][/ROW]
[ROW][C]143[/C][C]-15.2[/C][C]-1.248[/C][C]-13.95[/C][/ROW]
[ROW][C]144[/C][C]-2.8[/C][C] 8.753[/C][C]-11.55[/C][/ROW]
[ROW][C]145[/C][C]-7.1[/C][C]-11.49[/C][C] 4.39[/C][/ROW]
[ROW][C]146[/C][C] 1.8[/C][C] 10[/C][C]-8.203[/C][/ROW]
[ROW][C]147[/C][C] 6.2[/C][C] 0.1553[/C][C] 6.045[/C][/ROW]
[ROW][C]148[/C][C]-9.5[/C][C]-6.236[/C][C]-3.264[/C][/ROW]
[ROW][C]149[/C][C]-2.1[/C][C] 1.102[/C][C]-3.202[/C][/ROW]
[ROW][C]150[/C][C] 0.3[/C][C] 1.399[/C][C]-1.099[/C][/ROW]
[ROW][C]151[/C][C]-1.9[/C][C] 2.124[/C][C]-4.024[/C][/ROW]
[ROW][C]152[/C][C]-3.6[/C][C]-6.188[/C][C] 2.588[/C][/ROW]
[ROW][C]153[/C][C]-7.2[/C][C] 0.4344[/C][C]-7.634[/C][/ROW]
[ROW][C]154[/C][C] 2.1[/C][C] 2.726[/C][C]-0.626[/C][/ROW]
[ROW][C]155[/C][C] 19.8[/C][C] 5.053[/C][C] 14.75[/C][/ROW]
[ROW][C]156[/C][C]-3.1[/C][C]-4.78[/C][C] 1.68[/C][/ROW]
[ROW][C]157[/C][C]-0.7[/C][C] 4.824[/C][C]-5.524[/C][/ROW]
[ROW][C]158[/C][C] 6.5[/C][C]-1.181[/C][C] 7.681[/C][/ROW]
[ROW][C]159[/C][C]-6.4[/C][C]-4.642[/C][C]-1.758[/C][/ROW]
[ROW][C]160[/C][C] 2.6[/C][C] 6.237[/C][C]-3.637[/C][/ROW]
[ROW][C]161[/C][C]-3.3[/C][C]-0.2887[/C][C]-3.011[/C][/ROW]
[ROW][C]162[/C][C] 1.1[/C][C] 1.397[/C][C]-0.2971[/C][/ROW]
[ROW][C]163[/C][C] 9.7[/C][C] 0.1111[/C][C] 9.589[/C][/ROW]
[ROW][C]164[/C][C] 2.4[/C][C]-1.213[/C][C] 3.613[/C][/ROW]
[ROW][C]165[/C][C] 7.7[/C][C] 2.943[/C][C] 4.757[/C][/ROW]
[ROW][C]166[/C][C]-8.3[/C][C]-3.409[/C][C]-4.891[/C][/ROW]
[ROW][C]167[/C][C] 9.2[/C][C]-4.964[/C][C] 14.16[/C][/ROW]
[ROW][C]168[/C][C]-1.2[/C][C]-0.2711[/C][C]-0.9289[/C][/ROW]
[ROW][C]169[/C][C] 9.6[/C][C]-0.07251[/C][C] 9.673[/C][/ROW]
[ROW][C]170[/C][C]-0.1[/C][C]-4.722[/C][C] 4.622[/C][/ROW]
[ROW][C]171[/C][C]-9.1[/C][C] 2.607[/C][C]-11.71[/C][/ROW]
[ROW][C]172[/C][C]-2.9[/C][C] 1.532[/C][C]-4.432[/C][/ROW]
[ROW][C]173[/C][C] 8.1[/C][C] 1.58[/C][C] 6.52[/C][/ROW]
[ROW][C]174[/C][C] 10.5[/C][C]-1.483[/C][C] 11.98[/C][/ROW]
[ROW][C]175[/C][C]-16.7[/C][C]-7.533[/C][C]-9.167[/C][/ROW]
[ROW][C]176[/C][C]-5.1[/C][C] 3.181[/C][C]-8.281[/C][/ROW]
[ROW][C]177[/C][C] 1.4[/C][C]-0.8049[/C][C] 2.205[/C][/ROW]
[ROW][C]178[/C][C] 4.5[/C][C] 3.962[/C][C] 0.5379[/C][/ROW]
[ROW][C]179[/C][C]-11.4[/C][C]-5.62[/C][C]-5.78[/C][/ROW]
[ROW][C]180[/C][C]-4.2[/C][C] 3.353[/C][C]-7.553[/C][/ROW]
[ROW][C]181[/C][C]-3.7[/C][C]-1.698[/C][C]-2.002[/C][/ROW]
[ROW][C]182[/C][C]-7.5[/C][C]-0.6176[/C][C]-6.882[/C][/ROW]
[ROW][C]183[/C][C] 13.6[/C][C] 7.334[/C][C] 6.266[/C][/ROW]
[ROW][C]184[/C][C] 10.6[/C][C]-3.147[/C][C] 13.75[/C][/ROW]
[ROW][C]185[/C][C]-2[/C][C]-5.672[/C][C] 3.672[/C][/ROW]
[ROW][C]186[/C][C]-7[/C][C]-3.249[/C][C]-3.751[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310133&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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	3.4	1.287	2.113
2	1.8	-0.2995	2.1
3	-2.6	-2.21	-0.3903
4	-0.9	-0.3542	-0.5458
5	0.1	0.9311	-0.8311
6	-0.4	-0.9259	0.5259
7	-4.4	2.211	-6.611
8	0.1	0.4278	-0.3278
9	-0.3	-0.1701	-0.1299
10	0	0.7697	-0.7697
11	3.9	-1.64	5.54
12	2.8	-1.383	4.183
13	-6.6	-2.093	-4.507
14	2.3	1.144	1.156
15	-1	-0.007556	-0.9924
16	0.6	1.183	-0.5832
17	3.3	-0.04538	3.345
18	0.8	-1.096	1.896
19	1.9	2.245	-0.3446
20	-1.7	-0.9485	-0.7515
21	-4.7	0.3955	-5.096
22	7.8	2.694	5.106
23	-1.7	-5.015	3.315
24	-4.8	0.1633	-4.963
25	5.8	4.698	1.102
26	-8.3	-3.086	-5.214
27	1.5	2.587	-1.087
28	-2.3	0.449	-2.749
29	1.4	-1.494	2.894
30	-9.1	0.04654	-9.147
31	4.4	1.608	2.792
32	0.2	-0.8791	1.079
33	11	2.731	8.269
34	1.6	-6.051	7.651
35	-8.1	0.1718	-8.272
36	7.1	3.438	3.662
37	-7.9	-4.413	-3.487
38	3.6	5.71	-2.11
39	-0.6	-1.388	0.7878
40	0.5	0.7172	-0.2172
41	-1.7	-1.269	-0.4306
42	0.1	4.412	-4.312
43	-0.9	-1.485	0.5846
44	-0.3	-0.1762	-0.1238
45	-2.3	-3.268	0.9681
46	4.7	-0.2504	4.95
47	3.3	2.134	1.166
48	-3.1	-3.209	0.1089
49	1.6	4.256	-2.656
50	-7.2	-3.165	-4.035
51	-1.1	3.874	-4.974
52	2.7	-0.5607	3.261
53	-2.7	0.07481	-2.775
54	4.9	0.7567	4.143
55	-0.8	-1.17	0.3696
56	4.1	1.559	2.541
57	-3.7	-1.001	-2.699
58	-3.1	-1.355	-1.745
59	-0.3	0.4244	-0.7244
60	1	0.9629	0.03706
61	3.4	-0.8679	4.268
62	8.5	2.404	6.096
63	-2.7	-2.347	-0.3531
64	-1.2	0.1713	-1.371
65	-5.4	1.979	-7.379
66	4.8	-0.6606	5.461
67	-3.9	-1.535	-2.365
68	2.3	0.5118	1.788
69	4.9	0.1609	4.739
70	-8.6	-0.3784	-8.222
71	2.4	3.173	-0.7734
72	-4.6	-0.6375	-3.962
73	-5.9	-1.396	-4.504
74	1	0.3755	0.6245
75	-1.1	-0.6428	-0.4572
76	5.7	0.7966	4.903
77	6.4	1.158	5.242
78	-4.7	-4.733	0.03276
79	4.4	4.417	-0.01711
80	-3.5	-3.619	0.1185
81	-3.8	-1.768	-2.032
82	9.5	5.557	3.943
83	0.7	-5.708	6.408
84	-4	1.65	-5.65
85	5.1	4.42	0.6796
86	-7.3	-3.294	-4.006
87	8.2	3.803	4.397
88	-5.6	-4.587	-1.013
89	6.3	-0.9811	7.281
90	7	1.247	5.753
91	-1.4	-4.305	2.905
92	-5.9	1.805	-7.705
93	-4.9	4.646	-9.546
94	2.5	-2.608	5.108
95	0.8	0.2281	0.5719
96	2.9	2.067	0.8329
97	2.9	-2.087	4.987
98	-3.3	1.511	-4.811
99	1.3	-2.653	3.953
100	-9.2	3.139	-12.34
101	-2.6	-0.7993	-1.801
102	-4.1	-1.727	-2.373
103	-3	1.82	-4.82
104	3.1	2.931	0.1692
105	9.1	1.529	7.571
106	-9.3	-3.523	-5.777
107	-2.3	2.001	-4.301
108	-0.7	-0.7347	0.03471
109	-2.4	0.08428	-2.484
110	7.1	0.9207	6.179
111	0.9	-1.048	1.948
112	2.9	0.6843	2.216
113	-2.6	0.6828	-3.283
114	1.5	3.402	-1.902
115	1.2	0.3566	0.8434
116	-1	-2.162	1.162
117	-3.5	-3.266	-0.2336
118	0.2	4.474	-4.274
119	2.5	1.396	1.104
120	19.2	-0.2866	19.49
121	-30.7	-5.194	-25.51
122	9.4	5.762	3.638
123	-5.5	-3.49	-2.01
124	1	1.943	-0.9426
125	1.3	1.236	0.06374
126	-7.1	-2.033	-5.067
127	2.1	1.076	1.024
128	-6.3	1.253	-7.553
129	4.5	2.583	1.917
130	3.4	-2.068	5.468
131	2.3	-1.082	3.382
132	-11.2	-8.47	-2.73
133	31.1	14.39	16.71
134	-21.2	-9.78	-11.42
135	-1.8	7.207	-9.007
136	12.6	-0.01932	12.62
137	2.1	-3.023	5.123
138	-1.6	0.8568	-2.457
139	-2	0.2978	-2.298
140	15	3.648	11.35
141	0.7	-5.982	6.682
142	-0.3	-0.8901	0.5901
143	-15.2	-1.248	-13.95
144	-2.8	8.753	-11.55
145	-7.1	-11.49	4.39
146	1.8	10	-8.203
147	6.2	0.1553	6.045
148	-9.5	-6.236	-3.264
149	-2.1	1.102	-3.202
150	0.3	1.399	-1.099
151	-1.9	2.124	-4.024
152	-3.6	-6.188	2.588
153	-7.2	0.4344	-7.634
154	2.1	2.726	-0.626
155	19.8	5.053	14.75
156	-3.1	-4.78	1.68
157	-0.7	4.824	-5.524
158	6.5	-1.181	7.681
159	-6.4	-4.642	-1.758
160	2.6	6.237	-3.637
161	-3.3	-0.2887	-3.011
162	1.1	1.397	-0.2971
163	9.7	0.1111	9.589
164	2.4	-1.213	3.613
165	7.7	2.943	4.757
166	-8.3	-3.409	-4.891
167	9.2	-4.964	14.16
168	-1.2	-0.2711	-0.9289
169	9.6	-0.07251	9.673
170	-0.1	-4.722	4.622
171	-9.1	2.607	-11.71
172	-2.9	1.532	-4.432
173	8.1	1.58	6.52
174	10.5	-1.483	11.98
175	-16.7	-7.533	-9.167
176	-5.1	3.181	-8.281
177	1.4	-0.8049	2.205
178	4.5	3.962	0.5379
179	-11.4	-5.62	-5.78
180	-4.2	3.353	-7.553
181	-3.7	-1.698	-2.002
182	-7.5	-0.6176	-6.882
183	13.6	7.334	6.266
184	10.6	-3.147	13.75
185	-2	-5.672	3.672
186	-7	-3.249	-3.751

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.2028	0.4056	0.7972
8	0.1017	0.2034	0.8983
9	0.04373	0.08745	0.9563
10	0.0173	0.03461	0.9827
11	0.01286	0.02572	0.9871
12	0.005951	0.0119	0.994
13	0.03374	0.06747	0.9663
14	0.0198	0.03959	0.9802
15	0.009846	0.01969	0.9902
16	0.004708	0.009417	0.9953
17	0.003583	0.007166	0.9964
18	0.001822	0.003644	0.9982
19	0.0008795	0.001759	0.9991
20	0.0004214	0.0008428	0.9996
21	0.0005292	0.001058	0.9995
22	0.0007202	0.00144	0.9993
23	0.000401	0.000802	0.9996
24	0.0008723	0.001745	0.9991
25	0.0006068	0.001214	0.9994
26	0.0009276	0.001855	0.9991
27	0.0004884	0.0009768	0.9995
28	0.0003373	0.0006746	0.9997
29	0.0002171	0.0004342	0.9998
30	0.001099	0.002197	0.9989
31	0.0006623	0.001325	0.9993
32	0.0003977	0.0007954	0.9996
33	0.001677	0.003353	0.9983
34	0.003227	0.006454	0.9968
35	0.006712	0.01342	0.9933
36	0.005976	0.01195	0.994
37	0.004968	0.009936	0.995
38	0.003356	0.006712	0.9966
39	0.002156	0.004313	0.9978
40	0.001346	0.002693	0.9987
41	0.0008339	0.001668	0.9992
42	0.0006155	0.001231	0.9994
43	0.0003701	0.0007403	0.9996
44	0.0002192	0.0004384	0.9998
45	0.0001291	0.0002581	0.9999
46	0.0001213	0.0002426	0.9999
47	8.255e-05	0.0001651	0.9999
48	4.758e-05	9.515e-05	1
49	2.903e-05	5.807e-05	1
50	2.707e-05	5.414e-05	1
51	2.617e-05	5.234e-05	1
52	1.87e-05	3.74e-05	1
53	1.205e-05	2.41e-05	1
54	1.038e-05	2.075e-05	1
55	5.812e-06	1.162e-05	1
56	4.027e-06	8.054e-06	1
57	2.561e-06	5.122e-06	1
58	1.616e-06	3.231e-06	1
59	8.845e-07	1.769e-06	1
60	4.704e-07	9.409e-07	1
61	4.063e-07	8.127e-07	1
62	7.346e-07	1.469e-06	1
63	3.949e-07	7.898e-07	1
64	2.208e-07	4.415e-07	1
65	4.55e-07	9.1e-07	1
66	4.806e-07	9.611e-07	1
67	2.972e-07	5.944e-07	1
68	1.684e-07	3.369e-07	1
69	1.705e-07	3.409e-07	1
70	4.377e-07	8.754e-07	1
71	2.424e-07	4.847e-07	1
72	1.873e-07	3.747e-07	1
73	1.904e-07	3.808e-07	1
74	1.025e-07	2.05e-07	1
75	5.456e-08	1.091e-07	1
76	5.626e-08	1.125e-07	1
77	6.783e-08	1.357e-07	1
78	3.606e-08	7.211e-08	1
79	1.896e-08	3.791e-08	1
80	9.848e-09	1.97e-08	1
81	5.706e-09	1.141e-08	1
82	4.668e-09	9.335e-09	1
83	6.847e-09	1.369e-08	1
84	7.762e-09	1.552e-08	1
85	4.124e-09	8.248e-09	1
86	3.25e-09	6.501e-09	1
87	2.766e-09	5.532e-09	1
88	1.468e-09	2.937e-09	1
89	2.59e-09	5.179e-09	1
90	3.14e-09	6.28e-09	1
91	1.93e-09	3.861e-09	1
92	4.062e-09	8.125e-09	1
93	1.672e-08	3.344e-08	1
94	1.414e-08	2.827e-08	1
95	7.541e-09	1.508e-08	1
96	4.086e-09	8.173e-09	1
97	3.333e-09	6.667e-09	1
98	2.563e-09	5.126e-09	1
99	1.697e-09	3.393e-09	1
100	2.029e-08	4.058e-08	1
101	1.258e-08	2.517e-08	1
102	8.416e-09	1.683e-08	1
103	7.006e-09	1.401e-08	1
104	3.845e-09	7.689e-09	1
105	8.182e-09	1.636e-08	1
106	9.684e-09	1.937e-08	1
107	7.406e-09	1.481e-08	1
108	3.932e-09	7.864e-09	1
109	2.436e-09	4.873e-09	1
110	3.237e-09	6.475e-09	1
111	1.806e-09	3.612e-09	1
112	1.131e-09	2.262e-09	1
113	7.218e-10	1.444e-09	1
114	3.955e-10	7.91e-10	1
115	2.045e-10	4.09e-10	1
116	1.045e-10	2.09e-10	1
117	5.262e-11	1.052e-10	1
118	3.531e-11	7.062e-11	1
119	1.785e-11	3.569e-11	1
120	2.521e-08	5.042e-08	1
121	0.0001782	0.0003563	0.9998
122	0.0001551	0.0003103	0.9998
123	0.0001096	0.0002192	0.9999
124	7.211e-05	0.0001442	0.9999
125	4.63e-05	9.259e-05	1
126	4.326e-05	8.653e-05	1
127	2.756e-05	5.513e-05	1
128	3.519e-05	7.039e-05	1
129	2.285e-05	4.57e-05	1
130	2.054e-05	4.108e-05	1
131	1.47e-05	2.94e-05	1
132	1.059e-05	2.119e-05	1
133	0.0001912	0.0003824	0.9998
134	0.002026	0.004052	0.998
135	0.00302	0.00604	0.997
136	0.01032	0.02064	0.9897
137	0.009072	0.01814	0.9909
138	0.006747	0.01349	0.9933
139	0.005193	0.01039	0.9948
140	0.01173	0.02346	0.9883
141	0.0111	0.02219	0.9889
142	0.00805	0.0161	0.9919
143	0.02742	0.05483	0.9726
144	0.04095	0.0819	0.959
145	0.03504	0.07008	0.965
146	0.03962	0.07924	0.9604
147	0.03762	0.07524	0.9624
148	0.03309	0.06619	0.9669
149	0.0255	0.05101	0.9745
150	0.01887	0.03775	0.9811
151	0.01839	0.03678	0.9816
152	0.01505	0.03009	0.985
153	0.01657	0.03314	0.9834
154	0.01175	0.02351	0.9882
155	0.05266	0.1053	0.9473
156	0.0403	0.0806	0.9597
157	0.04316	0.08632	0.9568
158	0.06004	0.1201	0.94
159	0.04626	0.09251	0.9537
160	0.0379	0.07581	0.9621
161	0.02862	0.05724	0.9714
162	0.02033	0.04066	0.9797
163	0.03551	0.07101	0.9645
164	0.02572	0.05144	0.9743
165	0.01892	0.03784	0.9811
166	0.01602	0.03203	0.984
167	0.1109	0.2219	0.8891
168	0.1345	0.2691	0.8655
169	0.2835	0.5669	0.7165
170	0.2231	0.4463	0.7769
171	0.6488	0.7023	0.3512
172	0.5617	0.8767	0.4383
173	0.6439	0.7122	0.3561
174	0.6267	0.7467	0.3733
175	0.782	0.436	0.218
176	0.7147	0.5707	0.2853
177	0.7275	0.545	0.2725
178	0.9465	0.1071	0.05353
179	0.9185	0.1629	0.08145

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.2028 &  0.4056 &  0.7972 \tabularnewline
8 &  0.1017 &  0.2034 &  0.8983 \tabularnewline
9 &  0.04373 &  0.08745 &  0.9563 \tabularnewline
10 &  0.0173 &  0.03461 &  0.9827 \tabularnewline
11 &  0.01286 &  0.02572 &  0.9871 \tabularnewline
12 &  0.005951 &  0.0119 &  0.994 \tabularnewline
13 &  0.03374 &  0.06747 &  0.9663 \tabularnewline
14 &  0.0198 &  0.03959 &  0.9802 \tabularnewline
15 &  0.009846 &  0.01969 &  0.9902 \tabularnewline
16 &  0.004708 &  0.009417 &  0.9953 \tabularnewline
17 &  0.003583 &  0.007166 &  0.9964 \tabularnewline
18 &  0.001822 &  0.003644 &  0.9982 \tabularnewline
19 &  0.0008795 &  0.001759 &  0.9991 \tabularnewline
20 &  0.0004214 &  0.0008428 &  0.9996 \tabularnewline
21 &  0.0005292 &  0.001058 &  0.9995 \tabularnewline
22 &  0.0007202 &  0.00144 &  0.9993 \tabularnewline
23 &  0.000401 &  0.000802 &  0.9996 \tabularnewline
24 &  0.0008723 &  0.001745 &  0.9991 \tabularnewline
25 &  0.0006068 &  0.001214 &  0.9994 \tabularnewline
26 &  0.0009276 &  0.001855 &  0.9991 \tabularnewline
27 &  0.0004884 &  0.0009768 &  0.9995 \tabularnewline
28 &  0.0003373 &  0.0006746 &  0.9997 \tabularnewline
29 &  0.0002171 &  0.0004342 &  0.9998 \tabularnewline
30 &  0.001099 &  0.002197 &  0.9989 \tabularnewline
31 &  0.0006623 &  0.001325 &  0.9993 \tabularnewline
32 &  0.0003977 &  0.0007954 &  0.9996 \tabularnewline
33 &  0.001677 &  0.003353 &  0.9983 \tabularnewline
34 &  0.003227 &  0.006454 &  0.9968 \tabularnewline
35 &  0.006712 &  0.01342 &  0.9933 \tabularnewline
36 &  0.005976 &  0.01195 &  0.994 \tabularnewline
37 &  0.004968 &  0.009936 &  0.995 \tabularnewline
38 &  0.003356 &  0.006712 &  0.9966 \tabularnewline
39 &  0.002156 &  0.004313 &  0.9978 \tabularnewline
40 &  0.001346 &  0.002693 &  0.9987 \tabularnewline
41 &  0.0008339 &  0.001668 &  0.9992 \tabularnewline
42 &  0.0006155 &  0.001231 &  0.9994 \tabularnewline
43 &  0.0003701 &  0.0007403 &  0.9996 \tabularnewline
44 &  0.0002192 &  0.0004384 &  0.9998 \tabularnewline
45 &  0.0001291 &  0.0002581 &  0.9999 \tabularnewline
46 &  0.0001213 &  0.0002426 &  0.9999 \tabularnewline
47 &  8.255e-05 &  0.0001651 &  0.9999 \tabularnewline
48 &  4.758e-05 &  9.515e-05 &  1 \tabularnewline
49 &  2.903e-05 &  5.807e-05 &  1 \tabularnewline
50 &  2.707e-05 &  5.414e-05 &  1 \tabularnewline
51 &  2.617e-05 &  5.234e-05 &  1 \tabularnewline
52 &  1.87e-05 &  3.74e-05 &  1 \tabularnewline
53 &  1.205e-05 &  2.41e-05 &  1 \tabularnewline
54 &  1.038e-05 &  2.075e-05 &  1 \tabularnewline
55 &  5.812e-06 &  1.162e-05 &  1 \tabularnewline
56 &  4.027e-06 &  8.054e-06 &  1 \tabularnewline
57 &  2.561e-06 &  5.122e-06 &  1 \tabularnewline
58 &  1.616e-06 &  3.231e-06 &  1 \tabularnewline
59 &  8.845e-07 &  1.769e-06 &  1 \tabularnewline
60 &  4.704e-07 &  9.409e-07 &  1 \tabularnewline
61 &  4.063e-07 &  8.127e-07 &  1 \tabularnewline
62 &  7.346e-07 &  1.469e-06 &  1 \tabularnewline
63 &  3.949e-07 &  7.898e-07 &  1 \tabularnewline
64 &  2.208e-07 &  4.415e-07 &  1 \tabularnewline
65 &  4.55e-07 &  9.1e-07 &  1 \tabularnewline
66 &  4.806e-07 &  9.611e-07 &  1 \tabularnewline
67 &  2.972e-07 &  5.944e-07 &  1 \tabularnewline
68 &  1.684e-07 &  3.369e-07 &  1 \tabularnewline
69 &  1.705e-07 &  3.409e-07 &  1 \tabularnewline
70 &  4.377e-07 &  8.754e-07 &  1 \tabularnewline
71 &  2.424e-07 &  4.847e-07 &  1 \tabularnewline
72 &  1.873e-07 &  3.747e-07 &  1 \tabularnewline
73 &  1.904e-07 &  3.808e-07 &  1 \tabularnewline
74 &  1.025e-07 &  2.05e-07 &  1 \tabularnewline
75 &  5.456e-08 &  1.091e-07 &  1 \tabularnewline
76 &  5.626e-08 &  1.125e-07 &  1 \tabularnewline
77 &  6.783e-08 &  1.357e-07 &  1 \tabularnewline
78 &  3.606e-08 &  7.211e-08 &  1 \tabularnewline
79 &  1.896e-08 &  3.791e-08 &  1 \tabularnewline
80 &  9.848e-09 &  1.97e-08 &  1 \tabularnewline
81 &  5.706e-09 &  1.141e-08 &  1 \tabularnewline
82 &  4.668e-09 &  9.335e-09 &  1 \tabularnewline
83 &  6.847e-09 &  1.369e-08 &  1 \tabularnewline
84 &  7.762e-09 &  1.552e-08 &  1 \tabularnewline
85 &  4.124e-09 &  8.248e-09 &  1 \tabularnewline
86 &  3.25e-09 &  6.501e-09 &  1 \tabularnewline
87 &  2.766e-09 &  5.532e-09 &  1 \tabularnewline
88 &  1.468e-09 &  2.937e-09 &  1 \tabularnewline
89 &  2.59e-09 &  5.179e-09 &  1 \tabularnewline
90 &  3.14e-09 &  6.28e-09 &  1 \tabularnewline
91 &  1.93e-09 &  3.861e-09 &  1 \tabularnewline
92 &  4.062e-09 &  8.125e-09 &  1 \tabularnewline
93 &  1.672e-08 &  3.344e-08 &  1 \tabularnewline
94 &  1.414e-08 &  2.827e-08 &  1 \tabularnewline
95 &  7.541e-09 &  1.508e-08 &  1 \tabularnewline
96 &  4.086e-09 &  8.173e-09 &  1 \tabularnewline
97 &  3.333e-09 &  6.667e-09 &  1 \tabularnewline
98 &  2.563e-09 &  5.126e-09 &  1 \tabularnewline
99 &  1.697e-09 &  3.393e-09 &  1 \tabularnewline
100 &  2.029e-08 &  4.058e-08 &  1 \tabularnewline
101 &  1.258e-08 &  2.517e-08 &  1 \tabularnewline
102 &  8.416e-09 &  1.683e-08 &  1 \tabularnewline
103 &  7.006e-09 &  1.401e-08 &  1 \tabularnewline
104 &  3.845e-09 &  7.689e-09 &  1 \tabularnewline
105 &  8.182e-09 &  1.636e-08 &  1 \tabularnewline
106 &  9.684e-09 &  1.937e-08 &  1 \tabularnewline
107 &  7.406e-09 &  1.481e-08 &  1 \tabularnewline
108 &  3.932e-09 &  7.864e-09 &  1 \tabularnewline
109 &  2.436e-09 &  4.873e-09 &  1 \tabularnewline
110 &  3.237e-09 &  6.475e-09 &  1 \tabularnewline
111 &  1.806e-09 &  3.612e-09 &  1 \tabularnewline
112 &  1.131e-09 &  2.262e-09 &  1 \tabularnewline
113 &  7.218e-10 &  1.444e-09 &  1 \tabularnewline
114 &  3.955e-10 &  7.91e-10 &  1 \tabularnewline
115 &  2.045e-10 &  4.09e-10 &  1 \tabularnewline
116 &  1.045e-10 &  2.09e-10 &  1 \tabularnewline
117 &  5.262e-11 &  1.052e-10 &  1 \tabularnewline
118 &  3.531e-11 &  7.062e-11 &  1 \tabularnewline
119 &  1.785e-11 &  3.569e-11 &  1 \tabularnewline
120 &  2.521e-08 &  5.042e-08 &  1 \tabularnewline
121 &  0.0001782 &  0.0003563 &  0.9998 \tabularnewline
122 &  0.0001551 &  0.0003103 &  0.9998 \tabularnewline
123 &  0.0001096 &  0.0002192 &  0.9999 \tabularnewline
124 &  7.211e-05 &  0.0001442 &  0.9999 \tabularnewline
125 &  4.63e-05 &  9.259e-05 &  1 \tabularnewline
126 &  4.326e-05 &  8.653e-05 &  1 \tabularnewline
127 &  2.756e-05 &  5.513e-05 &  1 \tabularnewline
128 &  3.519e-05 &  7.039e-05 &  1 \tabularnewline
129 &  2.285e-05 &  4.57e-05 &  1 \tabularnewline
130 &  2.054e-05 &  4.108e-05 &  1 \tabularnewline
131 &  1.47e-05 &  2.94e-05 &  1 \tabularnewline
132 &  1.059e-05 &  2.119e-05 &  1 \tabularnewline
133 &  0.0001912 &  0.0003824 &  0.9998 \tabularnewline
134 &  0.002026 &  0.004052 &  0.998 \tabularnewline
135 &  0.00302 &  0.00604 &  0.997 \tabularnewline
136 &  0.01032 &  0.02064 &  0.9897 \tabularnewline
137 &  0.009072 &  0.01814 &  0.9909 \tabularnewline
138 &  0.006747 &  0.01349 &  0.9933 \tabularnewline
139 &  0.005193 &  0.01039 &  0.9948 \tabularnewline
140 &  0.01173 &  0.02346 &  0.9883 \tabularnewline
141 &  0.0111 &  0.02219 &  0.9889 \tabularnewline
142 &  0.00805 &  0.0161 &  0.9919 \tabularnewline
143 &  0.02742 &  0.05483 &  0.9726 \tabularnewline
144 &  0.04095 &  0.0819 &  0.959 \tabularnewline
145 &  0.03504 &  0.07008 &  0.965 \tabularnewline
146 &  0.03962 &  0.07924 &  0.9604 \tabularnewline
147 &  0.03762 &  0.07524 &  0.9624 \tabularnewline
148 &  0.03309 &  0.06619 &  0.9669 \tabularnewline
149 &  0.0255 &  0.05101 &  0.9745 \tabularnewline
150 &  0.01887 &  0.03775 &  0.9811 \tabularnewline
151 &  0.01839 &  0.03678 &  0.9816 \tabularnewline
152 &  0.01505 &  0.03009 &  0.985 \tabularnewline
153 &  0.01657 &  0.03314 &  0.9834 \tabularnewline
154 &  0.01175 &  0.02351 &  0.9882 \tabularnewline
155 &  0.05266 &  0.1053 &  0.9473 \tabularnewline
156 &  0.0403 &  0.0806 &  0.9597 \tabularnewline
157 &  0.04316 &  0.08632 &  0.9568 \tabularnewline
158 &  0.06004 &  0.1201 &  0.94 \tabularnewline
159 &  0.04626 &  0.09251 &  0.9537 \tabularnewline
160 &  0.0379 &  0.07581 &  0.9621 \tabularnewline
161 &  0.02862 &  0.05724 &  0.9714 \tabularnewline
162 &  0.02033 &  0.04066 &  0.9797 \tabularnewline
163 &  0.03551 &  0.07101 &  0.9645 \tabularnewline
164 &  0.02572 &  0.05144 &  0.9743 \tabularnewline
165 &  0.01892 &  0.03784 &  0.9811 \tabularnewline
166 &  0.01602 &  0.03203 &  0.984 \tabularnewline
167 &  0.1109 &  0.2219 &  0.8891 \tabularnewline
168 &  0.1345 &  0.2691 &  0.8655 \tabularnewline
169 &  0.2835 &  0.5669 &  0.7165 \tabularnewline
170 &  0.2231 &  0.4463 &  0.7769 \tabularnewline
171 &  0.6488 &  0.7023 &  0.3512 \tabularnewline
172 &  0.5617 &  0.8767 &  0.4383 \tabularnewline
173 &  0.6439 &  0.7122 &  0.3561 \tabularnewline
174 &  0.6267 &  0.7467 &  0.3733 \tabularnewline
175 &  0.782 &  0.436 &  0.218 \tabularnewline
176 &  0.7147 &  0.5707 &  0.2853 \tabularnewline
177 &  0.7275 &  0.545 &  0.2725 \tabularnewline
178 &  0.9465 &  0.1071 &  0.05353 \tabularnewline
179 &  0.9185 &  0.1629 &  0.08145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310133&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] 0.2028[/C][C] 0.4056[/C][C] 0.7972[/C][/ROW]
[ROW][C]8[/C][C] 0.1017[/C][C] 0.2034[/C][C] 0.8983[/C][/ROW]
[ROW][C]9[/C][C] 0.04373[/C][C] 0.08745[/C][C] 0.9563[/C][/ROW]
[ROW][C]10[/C][C] 0.0173[/C][C] 0.03461[/C][C] 0.9827[/C][/ROW]
[ROW][C]11[/C][C] 0.01286[/C][C] 0.02572[/C][C] 0.9871[/C][/ROW]
[ROW][C]12[/C][C] 0.005951[/C][C] 0.0119[/C][C] 0.994[/C][/ROW]
[ROW][C]13[/C][C] 0.03374[/C][C] 0.06747[/C][C] 0.9663[/C][/ROW]
[ROW][C]14[/C][C] 0.0198[/C][C] 0.03959[/C][C] 0.9802[/C][/ROW]
[ROW][C]15[/C][C] 0.009846[/C][C] 0.01969[/C][C] 0.9902[/C][/ROW]
[ROW][C]16[/C][C] 0.004708[/C][C] 0.009417[/C][C] 0.9953[/C][/ROW]
[ROW][C]17[/C][C] 0.003583[/C][C] 0.007166[/C][C] 0.9964[/C][/ROW]
[ROW][C]18[/C][C] 0.001822[/C][C] 0.003644[/C][C] 0.9982[/C][/ROW]
[ROW][C]19[/C][C] 0.0008795[/C][C] 0.001759[/C][C] 0.9991[/C][/ROW]
[ROW][C]20[/C][C] 0.0004214[/C][C] 0.0008428[/C][C] 0.9996[/C][/ROW]
[ROW][C]21[/C][C] 0.0005292[/C][C] 0.001058[/C][C] 0.9995[/C][/ROW]
[ROW][C]22[/C][C] 0.0007202[/C][C] 0.00144[/C][C] 0.9993[/C][/ROW]
[ROW][C]23[/C][C] 0.000401[/C][C] 0.000802[/C][C] 0.9996[/C][/ROW]
[ROW][C]24[/C][C] 0.0008723[/C][C] 0.001745[/C][C] 0.9991[/C][/ROW]
[ROW][C]25[/C][C] 0.0006068[/C][C] 0.001214[/C][C] 0.9994[/C][/ROW]
[ROW][C]26[/C][C] 0.0009276[/C][C] 0.001855[/C][C] 0.9991[/C][/ROW]
[ROW][C]27[/C][C] 0.0004884[/C][C] 0.0009768[/C][C] 0.9995[/C][/ROW]
[ROW][C]28[/C][C] 0.0003373[/C][C] 0.0006746[/C][C] 0.9997[/C][/ROW]
[ROW][C]29[/C][C] 0.0002171[/C][C] 0.0004342[/C][C] 0.9998[/C][/ROW]
[ROW][C]30[/C][C] 0.001099[/C][C] 0.002197[/C][C] 0.9989[/C][/ROW]
[ROW][C]31[/C][C] 0.0006623[/C][C] 0.001325[/C][C] 0.9993[/C][/ROW]
[ROW][C]32[/C][C] 0.0003977[/C][C] 0.0007954[/C][C] 0.9996[/C][/ROW]
[ROW][C]33[/C][C] 0.001677[/C][C] 0.003353[/C][C] 0.9983[/C][/ROW]
[ROW][C]34[/C][C] 0.003227[/C][C] 0.006454[/C][C] 0.9968[/C][/ROW]
[ROW][C]35[/C][C] 0.006712[/C][C] 0.01342[/C][C] 0.9933[/C][/ROW]
[ROW][C]36[/C][C] 0.005976[/C][C] 0.01195[/C][C] 0.994[/C][/ROW]
[ROW][C]37[/C][C] 0.004968[/C][C] 0.009936[/C][C] 0.995[/C][/ROW]
[ROW][C]38[/C][C] 0.003356[/C][C] 0.006712[/C][C] 0.9966[/C][/ROW]
[ROW][C]39[/C][C] 0.002156[/C][C] 0.004313[/C][C] 0.9978[/C][/ROW]
[ROW][C]40[/C][C] 0.001346[/C][C] 0.002693[/C][C] 0.9987[/C][/ROW]
[ROW][C]41[/C][C] 0.0008339[/C][C] 0.001668[/C][C] 0.9992[/C][/ROW]
[ROW][C]42[/C][C] 0.0006155[/C][C] 0.001231[/C][C] 0.9994[/C][/ROW]
[ROW][C]43[/C][C] 0.0003701[/C][C] 0.0007403[/C][C] 0.9996[/C][/ROW]
[ROW][C]44[/C][C] 0.0002192[/C][C] 0.0004384[/C][C] 0.9998[/C][/ROW]
[ROW][C]45[/C][C] 0.0001291[/C][C] 0.0002581[/C][C] 0.9999[/C][/ROW]
[ROW][C]46[/C][C] 0.0001213[/C][C] 0.0002426[/C][C] 0.9999[/C][/ROW]
[ROW][C]47[/C][C] 8.255e-05[/C][C] 0.0001651[/C][C] 0.9999[/C][/ROW]
[ROW][C]48[/C][C] 4.758e-05[/C][C] 9.515e-05[/C][C] 1[/C][/ROW]
[ROW][C]49[/C][C] 2.903e-05[/C][C] 5.807e-05[/C][C] 1[/C][/ROW]
[ROW][C]50[/C][C] 2.707e-05[/C][C] 5.414e-05[/C][C] 1[/C][/ROW]
[ROW][C]51[/C][C] 2.617e-05[/C][C] 5.234e-05[/C][C] 1[/C][/ROW]
[ROW][C]52[/C][C] 1.87e-05[/C][C] 3.74e-05[/C][C] 1[/C][/ROW]
[ROW][C]53[/C][C] 1.205e-05[/C][C] 2.41e-05[/C][C] 1[/C][/ROW]
[ROW][C]54[/C][C] 1.038e-05[/C][C] 2.075e-05[/C][C] 1[/C][/ROW]
[ROW][C]55[/C][C] 5.812e-06[/C][C] 1.162e-05[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 4.027e-06[/C][C] 8.054e-06[/C][C] 1[/C][/ROW]
[ROW][C]57[/C][C] 2.561e-06[/C][C] 5.122e-06[/C][C] 1[/C][/ROW]
[ROW][C]58[/C][C] 1.616e-06[/C][C] 3.231e-06[/C][C] 1[/C][/ROW]
[ROW][C]59[/C][C] 8.845e-07[/C][C] 1.769e-06[/C][C] 1[/C][/ROW]
[ROW][C]60[/C][C] 4.704e-07[/C][C] 9.409e-07[/C][C] 1[/C][/ROW]
[ROW][C]61[/C][C] 4.063e-07[/C][C] 8.127e-07[/C][C] 1[/C][/ROW]
[ROW][C]62[/C][C] 7.346e-07[/C][C] 1.469e-06[/C][C] 1[/C][/ROW]
[ROW][C]63[/C][C] 3.949e-07[/C][C] 7.898e-07[/C][C] 1[/C][/ROW]
[ROW][C]64[/C][C] 2.208e-07[/C][C] 4.415e-07[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 4.55e-07[/C][C] 9.1e-07[/C][C] 1[/C][/ROW]
[ROW][C]66[/C][C] 4.806e-07[/C][C] 9.611e-07[/C][C] 1[/C][/ROW]
[ROW][C]67[/C][C] 2.972e-07[/C][C] 5.944e-07[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 1.684e-07[/C][C] 3.369e-07[/C][C] 1[/C][/ROW]
[ROW][C]69[/C][C] 1.705e-07[/C][C] 3.409e-07[/C][C] 1[/C][/ROW]
[ROW][C]70[/C][C] 4.377e-07[/C][C] 8.754e-07[/C][C] 1[/C][/ROW]
[ROW][C]71[/C][C] 2.424e-07[/C][C] 4.847e-07[/C][C] 1[/C][/ROW]
[ROW][C]72[/C][C] 1.873e-07[/C][C] 3.747e-07[/C][C] 1[/C][/ROW]
[ROW][C]73[/C][C] 1.904e-07[/C][C] 3.808e-07[/C][C] 1[/C][/ROW]
[ROW][C]74[/C][C] 1.025e-07[/C][C] 2.05e-07[/C][C] 1[/C][/ROW]
[ROW][C]75[/C][C] 5.456e-08[/C][C] 1.091e-07[/C][C] 1[/C][/ROW]
[ROW][C]76[/C][C] 5.626e-08[/C][C] 1.125e-07[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 6.783e-08[/C][C] 1.357e-07[/C][C] 1[/C][/ROW]
[ROW][C]78[/C][C] 3.606e-08[/C][C] 7.211e-08[/C][C] 1[/C][/ROW]
[ROW][C]79[/C][C] 1.896e-08[/C][C] 3.791e-08[/C][C] 1[/C][/ROW]
[ROW][C]80[/C][C] 9.848e-09[/C][C] 1.97e-08[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 5.706e-09[/C][C] 1.141e-08[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 4.668e-09[/C][C] 9.335e-09[/C][C] 1[/C][/ROW]
[ROW][C]83[/C][C] 6.847e-09[/C][C] 1.369e-08[/C][C] 1[/C][/ROW]
[ROW][C]84[/C][C] 7.762e-09[/C][C] 1.552e-08[/C][C] 1[/C][/ROW]
[ROW][C]85[/C][C] 4.124e-09[/C][C] 8.248e-09[/C][C] 1[/C][/ROW]
[ROW][C]86[/C][C] 3.25e-09[/C][C] 6.501e-09[/C][C] 1[/C][/ROW]
[ROW][C]87[/C][C] 2.766e-09[/C][C] 5.532e-09[/C][C] 1[/C][/ROW]
[ROW][C]88[/C][C] 1.468e-09[/C][C] 2.937e-09[/C][C] 1[/C][/ROW]
[ROW][C]89[/C][C] 2.59e-09[/C][C] 5.179e-09[/C][C] 1[/C][/ROW]
[ROW][C]90[/C][C] 3.14e-09[/C][C] 6.28e-09[/C][C] 1[/C][/ROW]
[ROW][C]91[/C][C] 1.93e-09[/C][C] 3.861e-09[/C][C] 1[/C][/ROW]
[ROW][C]92[/C][C] 4.062e-09[/C][C] 8.125e-09[/C][C] 1[/C][/ROW]
[ROW][C]93[/C][C] 1.672e-08[/C][C] 3.344e-08[/C][C] 1[/C][/ROW]
[ROW][C]94[/C][C] 1.414e-08[/C][C] 2.827e-08[/C][C] 1[/C][/ROW]
[ROW][C]95[/C][C] 7.541e-09[/C][C] 1.508e-08[/C][C] 1[/C][/ROW]
[ROW][C]96[/C][C] 4.086e-09[/C][C] 8.173e-09[/C][C] 1[/C][/ROW]
[ROW][C]97[/C][C] 3.333e-09[/C][C] 6.667e-09[/C][C] 1[/C][/ROW]
[ROW][C]98[/C][C] 2.563e-09[/C][C] 5.126e-09[/C][C] 1[/C][/ROW]
[ROW][C]99[/C][C] 1.697e-09[/C][C] 3.393e-09[/C][C] 1[/C][/ROW]
[ROW][C]100[/C][C] 2.029e-08[/C][C] 4.058e-08[/C][C] 1[/C][/ROW]
[ROW][C]101[/C][C] 1.258e-08[/C][C] 2.517e-08[/C][C] 1[/C][/ROW]
[ROW][C]102[/C][C] 8.416e-09[/C][C] 1.683e-08[/C][C] 1[/C][/ROW]
[ROW][C]103[/C][C] 7.006e-09[/C][C] 1.401e-08[/C][C] 1[/C][/ROW]
[ROW][C]104[/C][C] 3.845e-09[/C][C] 7.689e-09[/C][C] 1[/C][/ROW]
[ROW][C]105[/C][C] 8.182e-09[/C][C] 1.636e-08[/C][C] 1[/C][/ROW]
[ROW][C]106[/C][C] 9.684e-09[/C][C] 1.937e-08[/C][C] 1[/C][/ROW]
[ROW][C]107[/C][C] 7.406e-09[/C][C] 1.481e-08[/C][C] 1[/C][/ROW]
[ROW][C]108[/C][C] 3.932e-09[/C][C] 7.864e-09[/C][C] 1[/C][/ROW]
[ROW][C]109[/C][C] 2.436e-09[/C][C] 4.873e-09[/C][C] 1[/C][/ROW]
[ROW][C]110[/C][C] 3.237e-09[/C][C] 6.475e-09[/C][C] 1[/C][/ROW]
[ROW][C]111[/C][C] 1.806e-09[/C][C] 3.612e-09[/C][C] 1[/C][/ROW]
[ROW][C]112[/C][C] 1.131e-09[/C][C] 2.262e-09[/C][C] 1[/C][/ROW]
[ROW][C]113[/C][C] 7.218e-10[/C][C] 1.444e-09[/C][C] 1[/C][/ROW]
[ROW][C]114[/C][C] 3.955e-10[/C][C] 7.91e-10[/C][C] 1[/C][/ROW]
[ROW][C]115[/C][C] 2.045e-10[/C][C] 4.09e-10[/C][C] 1[/C][/ROW]
[ROW][C]116[/C][C] 1.045e-10[/C][C] 2.09e-10[/C][C] 1[/C][/ROW]
[ROW][C]117[/C][C] 5.262e-11[/C][C] 1.052e-10[/C][C] 1[/C][/ROW]
[ROW][C]118[/C][C] 3.531e-11[/C][C] 7.062e-11[/C][C] 1[/C][/ROW]
[ROW][C]119[/C][C] 1.785e-11[/C][C] 3.569e-11[/C][C] 1[/C][/ROW]
[ROW][C]120[/C][C] 2.521e-08[/C][C] 5.042e-08[/C][C] 1[/C][/ROW]
[ROW][C]121[/C][C] 0.0001782[/C][C] 0.0003563[/C][C] 0.9998[/C][/ROW]
[ROW][C]122[/C][C] 0.0001551[/C][C] 0.0003103[/C][C] 0.9998[/C][/ROW]
[ROW][C]123[/C][C] 0.0001096[/C][C] 0.0002192[/C][C] 0.9999[/C][/ROW]
[ROW][C]124[/C][C] 7.211e-05[/C][C] 0.0001442[/C][C] 0.9999[/C][/ROW]
[ROW][C]125[/C][C] 4.63e-05[/C][C] 9.259e-05[/C][C] 1[/C][/ROW]
[ROW][C]126[/C][C] 4.326e-05[/C][C] 8.653e-05[/C][C] 1[/C][/ROW]
[ROW][C]127[/C][C] 2.756e-05[/C][C] 5.513e-05[/C][C] 1[/C][/ROW]
[ROW][C]128[/C][C] 3.519e-05[/C][C] 7.039e-05[/C][C] 1[/C][/ROW]
[ROW][C]129[/C][C] 2.285e-05[/C][C] 4.57e-05[/C][C] 1[/C][/ROW]
[ROW][C]130[/C][C] 2.054e-05[/C][C] 4.108e-05[/C][C] 1[/C][/ROW]
[ROW][C]131[/C][C] 1.47e-05[/C][C] 2.94e-05[/C][C] 1[/C][/ROW]
[ROW][C]132[/C][C] 1.059e-05[/C][C] 2.119e-05[/C][C] 1[/C][/ROW]
[ROW][C]133[/C][C] 0.0001912[/C][C] 0.0003824[/C][C] 0.9998[/C][/ROW]
[ROW][C]134[/C][C] 0.002026[/C][C] 0.004052[/C][C] 0.998[/C][/ROW]
[ROW][C]135[/C][C] 0.00302[/C][C] 0.00604[/C][C] 0.997[/C][/ROW]
[ROW][C]136[/C][C] 0.01032[/C][C] 0.02064[/C][C] 0.9897[/C][/ROW]
[ROW][C]137[/C][C] 0.009072[/C][C] 0.01814[/C][C] 0.9909[/C][/ROW]
[ROW][C]138[/C][C] 0.006747[/C][C] 0.01349[/C][C] 0.9933[/C][/ROW]
[ROW][C]139[/C][C] 0.005193[/C][C] 0.01039[/C][C] 0.9948[/C][/ROW]
[ROW][C]140[/C][C] 0.01173[/C][C] 0.02346[/C][C] 0.9883[/C][/ROW]
[ROW][C]141[/C][C] 0.0111[/C][C] 0.02219[/C][C] 0.9889[/C][/ROW]
[ROW][C]142[/C][C] 0.00805[/C][C] 0.0161[/C][C] 0.9919[/C][/ROW]
[ROW][C]143[/C][C] 0.02742[/C][C] 0.05483[/C][C] 0.9726[/C][/ROW]
[ROW][C]144[/C][C] 0.04095[/C][C] 0.0819[/C][C] 0.959[/C][/ROW]
[ROW][C]145[/C][C] 0.03504[/C][C] 0.07008[/C][C] 0.965[/C][/ROW]
[ROW][C]146[/C][C] 0.03962[/C][C] 0.07924[/C][C] 0.9604[/C][/ROW]
[ROW][C]147[/C][C] 0.03762[/C][C] 0.07524[/C][C] 0.9624[/C][/ROW]
[ROW][C]148[/C][C] 0.03309[/C][C] 0.06619[/C][C] 0.9669[/C][/ROW]
[ROW][C]149[/C][C] 0.0255[/C][C] 0.05101[/C][C] 0.9745[/C][/ROW]
[ROW][C]150[/C][C] 0.01887[/C][C] 0.03775[/C][C] 0.9811[/C][/ROW]
[ROW][C]151[/C][C] 0.01839[/C][C] 0.03678[/C][C] 0.9816[/C][/ROW]
[ROW][C]152[/C][C] 0.01505[/C][C] 0.03009[/C][C] 0.985[/C][/ROW]
[ROW][C]153[/C][C] 0.01657[/C][C] 0.03314[/C][C] 0.9834[/C][/ROW]
[ROW][C]154[/C][C] 0.01175[/C][C] 0.02351[/C][C] 0.9882[/C][/ROW]
[ROW][C]155[/C][C] 0.05266[/C][C] 0.1053[/C][C] 0.9473[/C][/ROW]
[ROW][C]156[/C][C] 0.0403[/C][C] 0.0806[/C][C] 0.9597[/C][/ROW]
[ROW][C]157[/C][C] 0.04316[/C][C] 0.08632[/C][C] 0.9568[/C][/ROW]
[ROW][C]158[/C][C] 0.06004[/C][C] 0.1201[/C][C] 0.94[/C][/ROW]
[ROW][C]159[/C][C] 0.04626[/C][C] 0.09251[/C][C] 0.9537[/C][/ROW]
[ROW][C]160[/C][C] 0.0379[/C][C] 0.07581[/C][C] 0.9621[/C][/ROW]
[ROW][C]161[/C][C] 0.02862[/C][C] 0.05724[/C][C] 0.9714[/C][/ROW]
[ROW][C]162[/C][C] 0.02033[/C][C] 0.04066[/C][C] 0.9797[/C][/ROW]
[ROW][C]163[/C][C] 0.03551[/C][C] 0.07101[/C][C] 0.9645[/C][/ROW]
[ROW][C]164[/C][C] 0.02572[/C][C] 0.05144[/C][C] 0.9743[/C][/ROW]
[ROW][C]165[/C][C] 0.01892[/C][C] 0.03784[/C][C] 0.9811[/C][/ROW]
[ROW][C]166[/C][C] 0.01602[/C][C] 0.03203[/C][C] 0.984[/C][/ROW]
[ROW][C]167[/C][C] 0.1109[/C][C] 0.2219[/C][C] 0.8891[/C][/ROW]
[ROW][C]168[/C][C] 0.1345[/C][C] 0.2691[/C][C] 0.8655[/C][/ROW]
[ROW][C]169[/C][C] 0.2835[/C][C] 0.5669[/C][C] 0.7165[/C][/ROW]
[ROW][C]170[/C][C] 0.2231[/C][C] 0.4463[/C][C] 0.7769[/C][/ROW]
[ROW][C]171[/C][C] 0.6488[/C][C] 0.7023[/C][C] 0.3512[/C][/ROW]
[ROW][C]172[/C][C] 0.5617[/C][C] 0.8767[/C][C] 0.4383[/C][/ROW]
[ROW][C]173[/C][C] 0.6439[/C][C] 0.7122[/C][C] 0.3561[/C][/ROW]
[ROW][C]174[/C][C] 0.6267[/C][C] 0.7467[/C][C] 0.3733[/C][/ROW]
[ROW][C]175[/C][C] 0.782[/C][C] 0.436[/C][C] 0.218[/C][/ROW]
[ROW][C]176[/C][C] 0.7147[/C][C] 0.5707[/C][C] 0.2853[/C][/ROW]
[ROW][C]177[/C][C] 0.7275[/C][C] 0.545[/C][C] 0.2725[/C][/ROW]
[ROW][C]178[/C][C] 0.9465[/C][C] 0.1071[/C][C] 0.05353[/C][/ROW]
[ROW][C]179[/C][C] 0.9185[/C][C] 0.1629[/C][C] 0.08145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310133&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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	0.2028	0.4056	0.7972
8	0.1017	0.2034	0.8983
9	0.04373	0.08745	0.9563
10	0.0173	0.03461	0.9827
11	0.01286	0.02572	0.9871
12	0.005951	0.0119	0.994
13	0.03374	0.06747	0.9663
14	0.0198	0.03959	0.9802
15	0.009846	0.01969	0.9902
16	0.004708	0.009417	0.9953
17	0.003583	0.007166	0.9964
18	0.001822	0.003644	0.9982
19	0.0008795	0.001759	0.9991
20	0.0004214	0.0008428	0.9996
21	0.0005292	0.001058	0.9995
22	0.0007202	0.00144	0.9993
23	0.000401	0.000802	0.9996
24	0.0008723	0.001745	0.9991
25	0.0006068	0.001214	0.9994
26	0.0009276	0.001855	0.9991
27	0.0004884	0.0009768	0.9995
28	0.0003373	0.0006746	0.9997
29	0.0002171	0.0004342	0.9998
30	0.001099	0.002197	0.9989
31	0.0006623	0.001325	0.9993
32	0.0003977	0.0007954	0.9996
33	0.001677	0.003353	0.9983
34	0.003227	0.006454	0.9968
35	0.006712	0.01342	0.9933
36	0.005976	0.01195	0.994
37	0.004968	0.009936	0.995
38	0.003356	0.006712	0.9966
39	0.002156	0.004313	0.9978
40	0.001346	0.002693	0.9987
41	0.0008339	0.001668	0.9992
42	0.0006155	0.001231	0.9994
43	0.0003701	0.0007403	0.9996
44	0.0002192	0.0004384	0.9998
45	0.0001291	0.0002581	0.9999
46	0.0001213	0.0002426	0.9999
47	8.255e-05	0.0001651	0.9999
48	4.758e-05	9.515e-05	1
49	2.903e-05	5.807e-05	1
50	2.707e-05	5.414e-05	1
51	2.617e-05	5.234e-05	1
52	1.87e-05	3.74e-05	1
53	1.205e-05	2.41e-05	1
54	1.038e-05	2.075e-05	1
55	5.812e-06	1.162e-05	1
56	4.027e-06	8.054e-06	1
57	2.561e-06	5.122e-06	1
58	1.616e-06	3.231e-06	1
59	8.845e-07	1.769e-06	1
60	4.704e-07	9.409e-07	1
61	4.063e-07	8.127e-07	1
62	7.346e-07	1.469e-06	1
63	3.949e-07	7.898e-07	1
64	2.208e-07	4.415e-07	1
65	4.55e-07	9.1e-07	1
66	4.806e-07	9.611e-07	1
67	2.972e-07	5.944e-07	1
68	1.684e-07	3.369e-07	1
69	1.705e-07	3.409e-07	1
70	4.377e-07	8.754e-07	1
71	2.424e-07	4.847e-07	1
72	1.873e-07	3.747e-07	1
73	1.904e-07	3.808e-07	1
74	1.025e-07	2.05e-07	1
75	5.456e-08	1.091e-07	1
76	5.626e-08	1.125e-07	1
77	6.783e-08	1.357e-07	1
78	3.606e-08	7.211e-08	1
79	1.896e-08	3.791e-08	1
80	9.848e-09	1.97e-08	1
81	5.706e-09	1.141e-08	1
82	4.668e-09	9.335e-09	1
83	6.847e-09	1.369e-08	1
84	7.762e-09	1.552e-08	1
85	4.124e-09	8.248e-09	1
86	3.25e-09	6.501e-09	1
87	2.766e-09	5.532e-09	1
88	1.468e-09	2.937e-09	1
89	2.59e-09	5.179e-09	1
90	3.14e-09	6.28e-09	1
91	1.93e-09	3.861e-09	1
92	4.062e-09	8.125e-09	1
93	1.672e-08	3.344e-08	1
94	1.414e-08	2.827e-08	1
95	7.541e-09	1.508e-08	1
96	4.086e-09	8.173e-09	1
97	3.333e-09	6.667e-09	1
98	2.563e-09	5.126e-09	1
99	1.697e-09	3.393e-09	1
100	2.029e-08	4.058e-08	1
101	1.258e-08	2.517e-08	1
102	8.416e-09	1.683e-08	1
103	7.006e-09	1.401e-08	1
104	3.845e-09	7.689e-09	1
105	8.182e-09	1.636e-08	1
106	9.684e-09	1.937e-08	1
107	7.406e-09	1.481e-08	1
108	3.932e-09	7.864e-09	1
109	2.436e-09	4.873e-09	1
110	3.237e-09	6.475e-09	1
111	1.806e-09	3.612e-09	1
112	1.131e-09	2.262e-09	1
113	7.218e-10	1.444e-09	1
114	3.955e-10	7.91e-10	1
115	2.045e-10	4.09e-10	1
116	1.045e-10	2.09e-10	1
117	5.262e-11	1.052e-10	1
118	3.531e-11	7.062e-11	1
119	1.785e-11	3.569e-11	1
120	2.521e-08	5.042e-08	1
121	0.0001782	0.0003563	0.9998
122	0.0001551	0.0003103	0.9998
123	0.0001096	0.0002192	0.9999
124	7.211e-05	0.0001442	0.9999
125	4.63e-05	9.259e-05	1
126	4.326e-05	8.653e-05	1
127	2.756e-05	5.513e-05	1
128	3.519e-05	7.039e-05	1
129	2.285e-05	4.57e-05	1
130	2.054e-05	4.108e-05	1
131	1.47e-05	2.94e-05	1
132	1.059e-05	2.119e-05	1
133	0.0001912	0.0003824	0.9998
134	0.002026	0.004052	0.998
135	0.00302	0.00604	0.997
136	0.01032	0.02064	0.9897
137	0.009072	0.01814	0.9909
138	0.006747	0.01349	0.9933
139	0.005193	0.01039	0.9948
140	0.01173	0.02346	0.9883
141	0.0111	0.02219	0.9889
142	0.00805	0.0161	0.9919
143	0.02742	0.05483	0.9726
144	0.04095	0.0819	0.959
145	0.03504	0.07008	0.965
146	0.03962	0.07924	0.9604
147	0.03762	0.07524	0.9624
148	0.03309	0.06619	0.9669
149	0.0255	0.05101	0.9745
150	0.01887	0.03775	0.9811
151	0.01839	0.03678	0.9816
152	0.01505	0.03009	0.985
153	0.01657	0.03314	0.9834
154	0.01175	0.02351	0.9882
155	0.05266	0.1053	0.9473
156	0.0403	0.0806	0.9597
157	0.04316	0.08632	0.9568
158	0.06004	0.1201	0.94
159	0.04626	0.09251	0.9537
160	0.0379	0.07581	0.9621
161	0.02862	0.05724	0.9714
162	0.02033	0.04066	0.9797
163	0.03551	0.07101	0.9645
164	0.02572	0.05144	0.9743
165	0.01892	0.03784	0.9811
166	0.01602	0.03203	0.984
167	0.1109	0.2219	0.8891
168	0.1345	0.2691	0.8655
169	0.2835	0.5669	0.7165
170	0.2231	0.4463	0.7769
171	0.6488	0.7023	0.3512
172	0.5617	0.8767	0.4383
173	0.6439	0.7122	0.3561
174	0.6267	0.7467	0.3733
175	0.782	0.436	0.218
176	0.7147	0.5707	0.2853
177	0.7275	0.545	0.2725
178	0.9465	0.1071	0.05353
179	0.9185	0.1629	0.08145

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	118	0.6821	NOK
5% type I error level	140	0.809249	NOK
10% type I error level	156	0.901734	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 & 118 &  0.6821 & NOK \tabularnewline
5% type I error level & 140 & 0.809249 & NOK \tabularnewline
10% type I error level & 156 & 0.901734 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310133&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]118[/C][C] 0.6821[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]140[/C][C]0.809249[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]156[/C][C]0.901734[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310133&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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	118	0.6821	NOK
5% type I error level	140	0.809249	NOK
10% type I error level	156	0.901734	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 3.8628, df1 = 2, df2 = 180, p-value = 0.02277
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 3.4533, df1 = 6, df2 = 176, p-value = 0.002996
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 7.9784, df1 = 2, df2 = 180, p-value = 0.0004787

\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 = 3.8628, df1 = 2, df2 = 180, p-value = 0.02277
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.4533, df1 = 6, df2 = 176, p-value = 0.002996
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 7.9784, df1 = 2, df2 = 180, p-value = 0.0004787
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310133&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 = 3.8628, df1 = 2, df2 = 180, p-value = 0.02277
[/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 = 3.4533, df1 = 6, df2 = 176, p-value = 0.002996
[/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 = 7.9784, df1 = 2, df2 = 180, p-value = 0.0004787
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310133&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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 = 3.8628, df1 = 2, df2 = 180, p-value = 0.02277
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 3.4533, df1 = 6, df2 = 176, p-value = 0.002996
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 7.9784, df1 = 2, df2 = 180, p-value = 0.0004787

Variance Inflation Factors (Multicollinearity)
> vif `(1-Bs)(1-B)Totind` `(1-Bs)(1-B)EnergySupply(t-1)` 1.039911 1.022633 `(1-Bs)(1-B)EnergySupply(t-1s)` 1.043587

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
            `(1-Bs)(1-B)Totind`  `(1-Bs)(1-B)EnergySupply(t-1)` 
                       1.039911                        1.022633 
`(1-Bs)(1-B)EnergySupply(t-1s)` 
                       1.043587 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310133&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
            `(1-Bs)(1-B)Totind`  `(1-Bs)(1-B)EnergySupply(t-1)` 
                       1.039911                        1.022633 
`(1-Bs)(1-B)EnergySupply(t-1s)` 
                       1.043587 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310133&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310133&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 `(1-Bs)(1-B)Totind` `(1-Bs)(1-B)EnergySupply(t-1)` 1.039911 1.022633 `(1-Bs)(1-B)EnergySupply(t-1s)` 1.043587

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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PDF link

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; 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