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*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 19 Dec 2017 12:13:56 +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/19/t1513682948vrjtlew1iksa7er.htm/, Retrieved Wed, 15 May 2024 06:16:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310289, Retrieved Wed, 15 May 2024 06:16:48 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

121

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

-       [Multiple Regression] [Multiple regression] [2017-12-19 11:13:56] [f44dd4af88e8b85f25b182ab83c3a44e] [Current]

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337	0,22	61
338	0,23	61
339	0,3	55
340	0,23	56
342	0,22	61
344	0,31	54
345	0,2	62
345	0,32	58
348	0,3	54
2815	0,9	56
2815	0,95	60
2815	0,89	59
2863	0,72	57
2863	0,72	60
2863	0,71	55
2864	0,81	57
2865	0,83	58
2865	0,73	55
2866	0,56	56
2866	0,56	55
2866	0,71	55
3154	0,73	56
3154	0,77	55
3154	0,7	58
3154	0,7	55
3154	0,95	66
3333	0,71	58
3333	0,93	60
3334	0,9	62
3334	0,9	62
3334	0,78	58
3471	0,8	56
3471	0,73	55
3471	0,7	57
3471	0,7	55
3471	0,73	61
3763	0,91	56
3763	0,91	59
3763	0,7	57
3763	0,78	56
3763	0,92	56
3871	0,96	57
3871	0,9	57
3871	0,96	54
3871	1,01	57
3871	0,96	61
3872	0,9	57
3872	1,1	56
3872	0,74	53,4
3872	0,9	58
7318	1,2	55
7319	1,55	56
7319	1,37	61
7320	1,52	60
7320	1,21	58
14340	2	57
14341	2,7	56
14341	1,61	55
14341	2,02	59
14344	1,53	60
14348	2,25	56
14350	2,02	58
772	0,31	56
772	0,31	57
772	0,41	59
772	0,35	58
772	0,35	56
772	0,35	57
772	0,41	59
772	0,4	59
776	0,3	56
776	0,3	57
776	0,3	59
776	0,3	55
776	0,3	58
776	0,3	55
776	0,3	59
776	0,3	57
378	0,3	57
378	0,3	55
378	0,3	60
379	0,28	57
379	0,31	56
890	0,32	57
890	0,31	56
890	0,4	57
890	0,4	53
890	0,4	56
891	0,38	56
2559	0,7	64
2560	0,6	58
2560	0,7	56
2560	0,7	59
2560	0,74	58
2561	0,78	54
2561	0,73	57
17877	2,01	59
17882	2,02	57
17887	2,02	59
17888	1,76	59
1890	0,55	55
1890	0,6	56
1890	0,56	54
1890	0,55	56
1890	0,7	58
1890	0,7	58
1890	0,5	65
1890	0,7	58
1891	0,5	57
1891	0,59	56
1892	0,54	56
1892	0,54	56
1892	0,54	57
1892	0,54	55
2757	0,7	59
2757	0,72	59
2757	0,72	57
2757	0,72	55
2757	0,7	60
2777	0,7	58

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310289&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Price[t] = -1943.65 + 8046.28Carat[t] -9.91939Table[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Price[t] =  -1943.65 +  8046.28Carat[t] -9.91939Table[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Price[t] =  -1943.65 +  8046.28Carat[t] -9.91939Table[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310289&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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
Price[t] = -1943.65 + 8046.28Carat[t] -9.91939Table[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)	-1944	3265	-5.9520e-01	0.5528	0.2764
Carat	+8046	273.3	+2.9440e+01	6.288e-56	3.144e-56
Table	-9.919	57.04	-1.7390e-01	0.8623	0.4311

\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) & -1944 &  3265 & -5.9520e-01 &  0.5528 &  0.2764 \tabularnewline
Carat & +8046 &  273.3 & +2.9440e+01 &  6.288e-56 &  3.144e-56 \tabularnewline
Table & -9.919 &  57.04 & -1.7390e-01 &  0.8623 &  0.4311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&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]-1944[/C][C] 3265[/C][C]-5.9520e-01[/C][C] 0.5528[/C][C] 0.2764[/C][/ROW]
[ROW][C]Carat[/C][C]+8046[/C][C] 273.3[/C][C]+2.9440e+01[/C][C] 6.288e-56[/C][C] 3.144e-56[/C][/ROW]
[ROW][C]Table[/C][C]-9.919[/C][C] 57.04[/C][C]-1.7390e-01[/C][C] 0.8623[/C][C] 0.4311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310289&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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)	-1944	3265	-5.9520e-01	0.5528	0.2764
Carat	+8046	273.3	+2.9440e+01	6.288e-56	3.144e-56
Table	-9.919	57.04	-1.7390e-01	0.8623	0.4311

Multiple Linear Regression - Regression Statistics
Multiple R	0.939
R-squared	0.8817
Adjusted R-squared	0.8797
F-TEST (value)	436
F-TEST (DF numerator)	2
F-TEST (DF denominator)	117
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1448
Sum Squared Residuals	2.453e+08

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.939 \tabularnewline
R-squared &  0.8817 \tabularnewline
Adjusted R-squared &  0.8797 \tabularnewline
F-TEST (value) &  436 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 117 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1448 \tabularnewline
Sum Squared Residuals &  2.453e+08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.939[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8817[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8797[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 436[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]117[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1448[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.453e+08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310289&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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	0.939
R-squared	0.8817
Adjusted R-squared	0.8797
F-TEST (value)	436
F-TEST (DF numerator)	2
F-TEST (DF denominator)	117
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1448
Sum Squared Residuals	2.453e+08

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=310289&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=310289&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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	337	-778.6	1116
2	338	-698.1	1036
3	339	-75.33	414.3
4	340	-648.5	988.5
5	342	-778.6	1121
6	344	15.05	329
7	345	-949.4	1294
8	345	55.83	289.2
9	348	-65.41	413.4
10	2815	4743	-1928
11	2815	5105	-2290
12	2815	4632	-1817
13	2863	3284	-421.3
14	2863	3255	-391.5
15	2863	3224	-360.6
16	2864	4008	-1144
17	2865	4159	-1294
18	2865	3385	-519.6
19	2866	2007	859.2
20	2866	2017	849.3
21	2866	3224	-357.6
22	3154	3375	-220.6
23	3154	3706	-552.4
24	3154	3113	40.58
25	3154	3143	10.82
26	3154	5046	-1892
27	3333	3194	139.1
28	3333	4944	-1611
29	3334	4683	-1349
30	3334	4683	-1349
31	3334	3757	-423.1
32	3471	3938	-466.9
33	3471	3385	86.43
34	3471	3123	347.7
35	3471	3143	327.8
36	3471	3325	145.9
37	3763	4823	-1060
38	3763	4793	-1030
39	3763	3123	639.7
40	3763	3777	-13.96
41	3763	4903	-1140
42	3871	5215	-1344
43	3871	4733	-861.6
44	3871	5245	-1374
45	3871	5618	-1747
46	3871	5176	-1305
47	3872	4733	-860.6
48	3872	6352	-2480
49	3872	3481	391.1
50	3872	4723	-850.7
51	7318	7166	151.7
52	7319	9973	-2654
53	7319	8475	-1156
54	7320	9692	-2372
55	7320	7217	103
56	1.434e+04	1.358e+04	756.5
57	1.434e+04	1.923e+04	-4885
58	1.434e+04	1.047e+04	3876
59	1.434e+04	1.372e+04	616.4
60	1.434e+04	9772	4572
61	1.435e+04	1.56e+04	-1257
62	1.435e+04	1.373e+04	615.5
63	772	-4.79	776.8
64	772	-14.71	786.7
65	772	770.1	1.92
66	772	297.2	474.8
67	772	317.1	454.9
68	772	307.1	464.9
69	772	770.1	1.92
70	772	689.6	82.38
71	776	-85.25	861.3
72	776	-95.17	871.2
73	776	-115	891
74	776	-75.33	851.3
75	776	-105.1	881.1
76	776	-75.33	851.3
77	776	-115	891
78	776	-95.17	871.2
79	378	-95.17	473.2
80	378	-75.33	453.3
81	378	-124.9	502.9
82	379	-256.1	635.1
83	379	-4.79	383.8
84	890	65.75	824.2
85	890	-4.79	894.8
86	890	709.5	180.5
87	890	749.1	140.9
88	890	719.4	170.6
89	891	558.5	332.6
90	2559	3054	-494.9
91	2560	2309	251.2
92	2560	3133	-573.3
93	2560	3104	-543.5
94	2560	3435	-875.3
95	2561	3797	-1236
96	2561	3365	-803.7
97	1.788e+04	1.364e+04	4233
98	1.788e+04	1.374e+04	4138
99	1.789e+04	1.372e+04	4162
100	1.789e+04	1.163e+04	6255
101	1890	1936	-46.24
102	1890	2329	-438.6
103	1890	2027	-136.6
104	1890	1926	-36.32
105	1890	3113	-1223
106	1890	3113	-1223
107	1890	1435	455.3
108	1890	3113	-1223
109	1891	1514	376.9
110	1891	2248	-357.2
111	1892	1846	46.14
112	1892	1846	46.14
113	1892	1836	56.06
114	1892	1856	36.23
115	2757	3104	-346.5
116	2757	3264	-507.4
117	2757	3284	-527.3
118	2757	3304	-547.1
119	2757	3094	-336.6
120	2777	3113	-336.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  337 & -778.6 &  1116 \tabularnewline
2 &  338 & -698.1 &  1036 \tabularnewline
3 &  339 & -75.33 &  414.3 \tabularnewline
4 &  340 & -648.5 &  988.5 \tabularnewline
5 &  342 & -778.6 &  1121 \tabularnewline
6 &  344 &  15.05 &  329 \tabularnewline
7 &  345 & -949.4 &  1294 \tabularnewline
8 &  345 &  55.83 &  289.2 \tabularnewline
9 &  348 & -65.41 &  413.4 \tabularnewline
10 &  2815 &  4743 & -1928 \tabularnewline
11 &  2815 &  5105 & -2290 \tabularnewline
12 &  2815 &  4632 & -1817 \tabularnewline
13 &  2863 &  3284 & -421.3 \tabularnewline
14 &  2863 &  3255 & -391.5 \tabularnewline
15 &  2863 &  3224 & -360.6 \tabularnewline
16 &  2864 &  4008 & -1144 \tabularnewline
17 &  2865 &  4159 & -1294 \tabularnewline
18 &  2865 &  3385 & -519.6 \tabularnewline
19 &  2866 &  2007 &  859.2 \tabularnewline
20 &  2866 &  2017 &  849.3 \tabularnewline
21 &  2866 &  3224 & -357.6 \tabularnewline
22 &  3154 &  3375 & -220.6 \tabularnewline
23 &  3154 &  3706 & -552.4 \tabularnewline
24 &  3154 &  3113 &  40.58 \tabularnewline
25 &  3154 &  3143 &  10.82 \tabularnewline
26 &  3154 &  5046 & -1892 \tabularnewline
27 &  3333 &  3194 &  139.1 \tabularnewline
28 &  3333 &  4944 & -1611 \tabularnewline
29 &  3334 &  4683 & -1349 \tabularnewline
30 &  3334 &  4683 & -1349 \tabularnewline
31 &  3334 &  3757 & -423.1 \tabularnewline
32 &  3471 &  3938 & -466.9 \tabularnewline
33 &  3471 &  3385 &  86.43 \tabularnewline
34 &  3471 &  3123 &  347.7 \tabularnewline
35 &  3471 &  3143 &  327.8 \tabularnewline
36 &  3471 &  3325 &  145.9 \tabularnewline
37 &  3763 &  4823 & -1060 \tabularnewline
38 &  3763 &  4793 & -1030 \tabularnewline
39 &  3763 &  3123 &  639.7 \tabularnewline
40 &  3763 &  3777 & -13.96 \tabularnewline
41 &  3763 &  4903 & -1140 \tabularnewline
42 &  3871 &  5215 & -1344 \tabularnewline
43 &  3871 &  4733 & -861.6 \tabularnewline
44 &  3871 &  5245 & -1374 \tabularnewline
45 &  3871 &  5618 & -1747 \tabularnewline
46 &  3871 &  5176 & -1305 \tabularnewline
47 &  3872 &  4733 & -860.6 \tabularnewline
48 &  3872 &  6352 & -2480 \tabularnewline
49 &  3872 &  3481 &  391.1 \tabularnewline
50 &  3872 &  4723 & -850.7 \tabularnewline
51 &  7318 &  7166 &  151.7 \tabularnewline
52 &  7319 &  9973 & -2654 \tabularnewline
53 &  7319 &  8475 & -1156 \tabularnewline
54 &  7320 &  9692 & -2372 \tabularnewline
55 &  7320 &  7217 &  103 \tabularnewline
56 &  1.434e+04 &  1.358e+04 &  756.5 \tabularnewline
57 &  1.434e+04 &  1.923e+04 & -4885 \tabularnewline
58 &  1.434e+04 &  1.047e+04 &  3876 \tabularnewline
59 &  1.434e+04 &  1.372e+04 &  616.4 \tabularnewline
60 &  1.434e+04 &  9772 &  4572 \tabularnewline
61 &  1.435e+04 &  1.56e+04 & -1257 \tabularnewline
62 &  1.435e+04 &  1.373e+04 &  615.5 \tabularnewline
63 &  772 & -4.79 &  776.8 \tabularnewline
64 &  772 & -14.71 &  786.7 \tabularnewline
65 &  772 &  770.1 &  1.92 \tabularnewline
66 &  772 &  297.2 &  474.8 \tabularnewline
67 &  772 &  317.1 &  454.9 \tabularnewline
68 &  772 &  307.1 &  464.9 \tabularnewline
69 &  772 &  770.1 &  1.92 \tabularnewline
70 &  772 &  689.6 &  82.38 \tabularnewline
71 &  776 & -85.25 &  861.3 \tabularnewline
72 &  776 & -95.17 &  871.2 \tabularnewline
73 &  776 & -115 &  891 \tabularnewline
74 &  776 & -75.33 &  851.3 \tabularnewline
75 &  776 & -105.1 &  881.1 \tabularnewline
76 &  776 & -75.33 &  851.3 \tabularnewline
77 &  776 & -115 &  891 \tabularnewline
78 &  776 & -95.17 &  871.2 \tabularnewline
79 &  378 & -95.17 &  473.2 \tabularnewline
80 &  378 & -75.33 &  453.3 \tabularnewline
81 &  378 & -124.9 &  502.9 \tabularnewline
82 &  379 & -256.1 &  635.1 \tabularnewline
83 &  379 & -4.79 &  383.8 \tabularnewline
84 &  890 &  65.75 &  824.2 \tabularnewline
85 &  890 & -4.79 &  894.8 \tabularnewline
86 &  890 &  709.5 &  180.5 \tabularnewline
87 &  890 &  749.1 &  140.9 \tabularnewline
88 &  890 &  719.4 &  170.6 \tabularnewline
89 &  891 &  558.5 &  332.6 \tabularnewline
90 &  2559 &  3054 & -494.9 \tabularnewline
91 &  2560 &  2309 &  251.2 \tabularnewline
92 &  2560 &  3133 & -573.3 \tabularnewline
93 &  2560 &  3104 & -543.5 \tabularnewline
94 &  2560 &  3435 & -875.3 \tabularnewline
95 &  2561 &  3797 & -1236 \tabularnewline
96 &  2561 &  3365 & -803.7 \tabularnewline
97 &  1.788e+04 &  1.364e+04 &  4233 \tabularnewline
98 &  1.788e+04 &  1.374e+04 &  4138 \tabularnewline
99 &  1.789e+04 &  1.372e+04 &  4162 \tabularnewline
100 &  1.789e+04 &  1.163e+04 &  6255 \tabularnewline
101 &  1890 &  1936 & -46.24 \tabularnewline
102 &  1890 &  2329 & -438.6 \tabularnewline
103 &  1890 &  2027 & -136.6 \tabularnewline
104 &  1890 &  1926 & -36.32 \tabularnewline
105 &  1890 &  3113 & -1223 \tabularnewline
106 &  1890 &  3113 & -1223 \tabularnewline
107 &  1890 &  1435 &  455.3 \tabularnewline
108 &  1890 &  3113 & -1223 \tabularnewline
109 &  1891 &  1514 &  376.9 \tabularnewline
110 &  1891 &  2248 & -357.2 \tabularnewline
111 &  1892 &  1846 &  46.14 \tabularnewline
112 &  1892 &  1846 &  46.14 \tabularnewline
113 &  1892 &  1836 &  56.06 \tabularnewline
114 &  1892 &  1856 &  36.23 \tabularnewline
115 &  2757 &  3104 & -346.5 \tabularnewline
116 &  2757 &  3264 & -507.4 \tabularnewline
117 &  2757 &  3284 & -527.3 \tabularnewline
118 &  2757 &  3304 & -547.1 \tabularnewline
119 &  2757 &  3094 & -336.6 \tabularnewline
120 &  2777 &  3113 & -336.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&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] 337[/C][C]-778.6[/C][C] 1116[/C][/ROW]
[ROW][C]2[/C][C] 338[/C][C]-698.1[/C][C] 1036[/C][/ROW]
[ROW][C]3[/C][C] 339[/C][C]-75.33[/C][C] 414.3[/C][/ROW]
[ROW][C]4[/C][C] 340[/C][C]-648.5[/C][C] 988.5[/C][/ROW]
[ROW][C]5[/C][C] 342[/C][C]-778.6[/C][C] 1121[/C][/ROW]
[ROW][C]6[/C][C] 344[/C][C] 15.05[/C][C] 329[/C][/ROW]
[ROW][C]7[/C][C] 345[/C][C]-949.4[/C][C] 1294[/C][/ROW]
[ROW][C]8[/C][C] 345[/C][C] 55.83[/C][C] 289.2[/C][/ROW]
[ROW][C]9[/C][C] 348[/C][C]-65.41[/C][C] 413.4[/C][/ROW]
[ROW][C]10[/C][C] 2815[/C][C] 4743[/C][C]-1928[/C][/ROW]
[ROW][C]11[/C][C] 2815[/C][C] 5105[/C][C]-2290[/C][/ROW]
[ROW][C]12[/C][C] 2815[/C][C] 4632[/C][C]-1817[/C][/ROW]
[ROW][C]13[/C][C] 2863[/C][C] 3284[/C][C]-421.3[/C][/ROW]
[ROW][C]14[/C][C] 2863[/C][C] 3255[/C][C]-391.5[/C][/ROW]
[ROW][C]15[/C][C] 2863[/C][C] 3224[/C][C]-360.6[/C][/ROW]
[ROW][C]16[/C][C] 2864[/C][C] 4008[/C][C]-1144[/C][/ROW]
[ROW][C]17[/C][C] 2865[/C][C] 4159[/C][C]-1294[/C][/ROW]
[ROW][C]18[/C][C] 2865[/C][C] 3385[/C][C]-519.6[/C][/ROW]
[ROW][C]19[/C][C] 2866[/C][C] 2007[/C][C] 859.2[/C][/ROW]
[ROW][C]20[/C][C] 2866[/C][C] 2017[/C][C] 849.3[/C][/ROW]
[ROW][C]21[/C][C] 2866[/C][C] 3224[/C][C]-357.6[/C][/ROW]
[ROW][C]22[/C][C] 3154[/C][C] 3375[/C][C]-220.6[/C][/ROW]
[ROW][C]23[/C][C] 3154[/C][C] 3706[/C][C]-552.4[/C][/ROW]
[ROW][C]24[/C][C] 3154[/C][C] 3113[/C][C] 40.58[/C][/ROW]
[ROW][C]25[/C][C] 3154[/C][C] 3143[/C][C] 10.82[/C][/ROW]
[ROW][C]26[/C][C] 3154[/C][C] 5046[/C][C]-1892[/C][/ROW]
[ROW][C]27[/C][C] 3333[/C][C] 3194[/C][C] 139.1[/C][/ROW]
[ROW][C]28[/C][C] 3333[/C][C] 4944[/C][C]-1611[/C][/ROW]
[ROW][C]29[/C][C] 3334[/C][C] 4683[/C][C]-1349[/C][/ROW]
[ROW][C]30[/C][C] 3334[/C][C] 4683[/C][C]-1349[/C][/ROW]
[ROW][C]31[/C][C] 3334[/C][C] 3757[/C][C]-423.1[/C][/ROW]
[ROW][C]32[/C][C] 3471[/C][C] 3938[/C][C]-466.9[/C][/ROW]
[ROW][C]33[/C][C] 3471[/C][C] 3385[/C][C] 86.43[/C][/ROW]
[ROW][C]34[/C][C] 3471[/C][C] 3123[/C][C] 347.7[/C][/ROW]
[ROW][C]35[/C][C] 3471[/C][C] 3143[/C][C] 327.8[/C][/ROW]
[ROW][C]36[/C][C] 3471[/C][C] 3325[/C][C] 145.9[/C][/ROW]
[ROW][C]37[/C][C] 3763[/C][C] 4823[/C][C]-1060[/C][/ROW]
[ROW][C]38[/C][C] 3763[/C][C] 4793[/C][C]-1030[/C][/ROW]
[ROW][C]39[/C][C] 3763[/C][C] 3123[/C][C] 639.7[/C][/ROW]
[ROW][C]40[/C][C] 3763[/C][C] 3777[/C][C]-13.96[/C][/ROW]
[ROW][C]41[/C][C] 3763[/C][C] 4903[/C][C]-1140[/C][/ROW]
[ROW][C]42[/C][C] 3871[/C][C] 5215[/C][C]-1344[/C][/ROW]
[ROW][C]43[/C][C] 3871[/C][C] 4733[/C][C]-861.6[/C][/ROW]
[ROW][C]44[/C][C] 3871[/C][C] 5245[/C][C]-1374[/C][/ROW]
[ROW][C]45[/C][C] 3871[/C][C] 5618[/C][C]-1747[/C][/ROW]
[ROW][C]46[/C][C] 3871[/C][C] 5176[/C][C]-1305[/C][/ROW]
[ROW][C]47[/C][C] 3872[/C][C] 4733[/C][C]-860.6[/C][/ROW]
[ROW][C]48[/C][C] 3872[/C][C] 6352[/C][C]-2480[/C][/ROW]
[ROW][C]49[/C][C] 3872[/C][C] 3481[/C][C] 391.1[/C][/ROW]
[ROW][C]50[/C][C] 3872[/C][C] 4723[/C][C]-850.7[/C][/ROW]
[ROW][C]51[/C][C] 7318[/C][C] 7166[/C][C] 151.7[/C][/ROW]
[ROW][C]52[/C][C] 7319[/C][C] 9973[/C][C]-2654[/C][/ROW]
[ROW][C]53[/C][C] 7319[/C][C] 8475[/C][C]-1156[/C][/ROW]
[ROW][C]54[/C][C] 7320[/C][C] 9692[/C][C]-2372[/C][/ROW]
[ROW][C]55[/C][C] 7320[/C][C] 7217[/C][C] 103[/C][/ROW]
[ROW][C]56[/C][C] 1.434e+04[/C][C] 1.358e+04[/C][C] 756.5[/C][/ROW]
[ROW][C]57[/C][C] 1.434e+04[/C][C] 1.923e+04[/C][C]-4885[/C][/ROW]
[ROW][C]58[/C][C] 1.434e+04[/C][C] 1.047e+04[/C][C] 3876[/C][/ROW]
[ROW][C]59[/C][C] 1.434e+04[/C][C] 1.372e+04[/C][C] 616.4[/C][/ROW]
[ROW][C]60[/C][C] 1.434e+04[/C][C] 9772[/C][C] 4572[/C][/ROW]
[ROW][C]61[/C][C] 1.435e+04[/C][C] 1.56e+04[/C][C]-1257[/C][/ROW]
[ROW][C]62[/C][C] 1.435e+04[/C][C] 1.373e+04[/C][C] 615.5[/C][/ROW]
[ROW][C]63[/C][C] 772[/C][C]-4.79[/C][C] 776.8[/C][/ROW]
[ROW][C]64[/C][C] 772[/C][C]-14.71[/C][C] 786.7[/C][/ROW]
[ROW][C]65[/C][C] 772[/C][C] 770.1[/C][C] 1.92[/C][/ROW]
[ROW][C]66[/C][C] 772[/C][C] 297.2[/C][C] 474.8[/C][/ROW]
[ROW][C]67[/C][C] 772[/C][C] 317.1[/C][C] 454.9[/C][/ROW]
[ROW][C]68[/C][C] 772[/C][C] 307.1[/C][C] 464.9[/C][/ROW]
[ROW][C]69[/C][C] 772[/C][C] 770.1[/C][C] 1.92[/C][/ROW]
[ROW][C]70[/C][C] 772[/C][C] 689.6[/C][C] 82.38[/C][/ROW]
[ROW][C]71[/C][C] 776[/C][C]-85.25[/C][C] 861.3[/C][/ROW]
[ROW][C]72[/C][C] 776[/C][C]-95.17[/C][C] 871.2[/C][/ROW]
[ROW][C]73[/C][C] 776[/C][C]-115[/C][C] 891[/C][/ROW]
[ROW][C]74[/C][C] 776[/C][C]-75.33[/C][C] 851.3[/C][/ROW]
[ROW][C]75[/C][C] 776[/C][C]-105.1[/C][C] 881.1[/C][/ROW]
[ROW][C]76[/C][C] 776[/C][C]-75.33[/C][C] 851.3[/C][/ROW]
[ROW][C]77[/C][C] 776[/C][C]-115[/C][C] 891[/C][/ROW]
[ROW][C]78[/C][C] 776[/C][C]-95.17[/C][C] 871.2[/C][/ROW]
[ROW][C]79[/C][C] 378[/C][C]-95.17[/C][C] 473.2[/C][/ROW]
[ROW][C]80[/C][C] 378[/C][C]-75.33[/C][C] 453.3[/C][/ROW]
[ROW][C]81[/C][C] 378[/C][C]-124.9[/C][C] 502.9[/C][/ROW]
[ROW][C]82[/C][C] 379[/C][C]-256.1[/C][C] 635.1[/C][/ROW]
[ROW][C]83[/C][C] 379[/C][C]-4.79[/C][C] 383.8[/C][/ROW]
[ROW][C]84[/C][C] 890[/C][C] 65.75[/C][C] 824.2[/C][/ROW]
[ROW][C]85[/C][C] 890[/C][C]-4.79[/C][C] 894.8[/C][/ROW]
[ROW][C]86[/C][C] 890[/C][C] 709.5[/C][C] 180.5[/C][/ROW]
[ROW][C]87[/C][C] 890[/C][C] 749.1[/C][C] 140.9[/C][/ROW]
[ROW][C]88[/C][C] 890[/C][C] 719.4[/C][C] 170.6[/C][/ROW]
[ROW][C]89[/C][C] 891[/C][C] 558.5[/C][C] 332.6[/C][/ROW]
[ROW][C]90[/C][C] 2559[/C][C] 3054[/C][C]-494.9[/C][/ROW]
[ROW][C]91[/C][C] 2560[/C][C] 2309[/C][C] 251.2[/C][/ROW]
[ROW][C]92[/C][C] 2560[/C][C] 3133[/C][C]-573.3[/C][/ROW]
[ROW][C]93[/C][C] 2560[/C][C] 3104[/C][C]-543.5[/C][/ROW]
[ROW][C]94[/C][C] 2560[/C][C] 3435[/C][C]-875.3[/C][/ROW]
[ROW][C]95[/C][C] 2561[/C][C] 3797[/C][C]-1236[/C][/ROW]
[ROW][C]96[/C][C] 2561[/C][C] 3365[/C][C]-803.7[/C][/ROW]
[ROW][C]97[/C][C] 1.788e+04[/C][C] 1.364e+04[/C][C] 4233[/C][/ROW]
[ROW][C]98[/C][C] 1.788e+04[/C][C] 1.374e+04[/C][C] 4138[/C][/ROW]
[ROW][C]99[/C][C] 1.789e+04[/C][C] 1.372e+04[/C][C] 4162[/C][/ROW]
[ROW][C]100[/C][C] 1.789e+04[/C][C] 1.163e+04[/C][C] 6255[/C][/ROW]
[ROW][C]101[/C][C] 1890[/C][C] 1936[/C][C]-46.24[/C][/ROW]
[ROW][C]102[/C][C] 1890[/C][C] 2329[/C][C]-438.6[/C][/ROW]
[ROW][C]103[/C][C] 1890[/C][C] 2027[/C][C]-136.6[/C][/ROW]
[ROW][C]104[/C][C] 1890[/C][C] 1926[/C][C]-36.32[/C][/ROW]
[ROW][C]105[/C][C] 1890[/C][C] 3113[/C][C]-1223[/C][/ROW]
[ROW][C]106[/C][C] 1890[/C][C] 3113[/C][C]-1223[/C][/ROW]
[ROW][C]107[/C][C] 1890[/C][C] 1435[/C][C] 455.3[/C][/ROW]
[ROW][C]108[/C][C] 1890[/C][C] 3113[/C][C]-1223[/C][/ROW]
[ROW][C]109[/C][C] 1891[/C][C] 1514[/C][C] 376.9[/C][/ROW]
[ROW][C]110[/C][C] 1891[/C][C] 2248[/C][C]-357.2[/C][/ROW]
[ROW][C]111[/C][C] 1892[/C][C] 1846[/C][C] 46.14[/C][/ROW]
[ROW][C]112[/C][C] 1892[/C][C] 1846[/C][C] 46.14[/C][/ROW]
[ROW][C]113[/C][C] 1892[/C][C] 1836[/C][C] 56.06[/C][/ROW]
[ROW][C]114[/C][C] 1892[/C][C] 1856[/C][C] 36.23[/C][/ROW]
[ROW][C]115[/C][C] 2757[/C][C] 3104[/C][C]-346.5[/C][/ROW]
[ROW][C]116[/C][C] 2757[/C][C] 3264[/C][C]-507.4[/C][/ROW]
[ROW][C]117[/C][C] 2757[/C][C] 3284[/C][C]-527.3[/C][/ROW]
[ROW][C]118[/C][C] 2757[/C][C] 3304[/C][C]-547.1[/C][/ROW]
[ROW][C]119[/C][C] 2757[/C][C] 3094[/C][C]-336.6[/C][/ROW]
[ROW][C]120[/C][C] 2777[/C][C] 3113[/C][C]-336.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310289&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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	337	-778.6	1116
2	338	-698.1	1036
3	339	-75.33	414.3
4	340	-648.5	988.5
5	342	-778.6	1121
6	344	15.05	329
7	345	-949.4	1294
8	345	55.83	289.2
9	348	-65.41	413.4
10	2815	4743	-1928
11	2815	5105	-2290
12	2815	4632	-1817
13	2863	3284	-421.3
14	2863	3255	-391.5
15	2863	3224	-360.6
16	2864	4008	-1144
17	2865	4159	-1294
18	2865	3385	-519.6
19	2866	2007	859.2
20	2866	2017	849.3
21	2866	3224	-357.6
22	3154	3375	-220.6
23	3154	3706	-552.4
24	3154	3113	40.58
25	3154	3143	10.82
26	3154	5046	-1892
27	3333	3194	139.1
28	3333	4944	-1611
29	3334	4683	-1349
30	3334	4683	-1349
31	3334	3757	-423.1
32	3471	3938	-466.9
33	3471	3385	86.43
34	3471	3123	347.7
35	3471	3143	327.8
36	3471	3325	145.9
37	3763	4823	-1060
38	3763	4793	-1030
39	3763	3123	639.7
40	3763	3777	-13.96
41	3763	4903	-1140
42	3871	5215	-1344
43	3871	4733	-861.6
44	3871	5245	-1374
45	3871	5618	-1747
46	3871	5176	-1305
47	3872	4733	-860.6
48	3872	6352	-2480
49	3872	3481	391.1
50	3872	4723	-850.7
51	7318	7166	151.7
52	7319	9973	-2654
53	7319	8475	-1156
54	7320	9692	-2372
55	7320	7217	103
56	1.434e+04	1.358e+04	756.5
57	1.434e+04	1.923e+04	-4885
58	1.434e+04	1.047e+04	3876
59	1.434e+04	1.372e+04	616.4
60	1.434e+04	9772	4572
61	1.435e+04	1.56e+04	-1257
62	1.435e+04	1.373e+04	615.5
63	772	-4.79	776.8
64	772	-14.71	786.7
65	772	770.1	1.92
66	772	297.2	474.8
67	772	317.1	454.9
68	772	307.1	464.9
69	772	770.1	1.92
70	772	689.6	82.38
71	776	-85.25	861.3
72	776	-95.17	871.2
73	776	-115	891
74	776	-75.33	851.3
75	776	-105.1	881.1
76	776	-75.33	851.3
77	776	-115	891
78	776	-95.17	871.2
79	378	-95.17	473.2
80	378	-75.33	453.3
81	378	-124.9	502.9
82	379	-256.1	635.1
83	379	-4.79	383.8
84	890	65.75	824.2
85	890	-4.79	894.8
86	890	709.5	180.5
87	890	749.1	140.9
88	890	719.4	170.6
89	891	558.5	332.6
90	2559	3054	-494.9
91	2560	2309	251.2
92	2560	3133	-573.3
93	2560	3104	-543.5
94	2560	3435	-875.3
95	2561	3797	-1236
96	2561	3365	-803.7
97	1.788e+04	1.364e+04	4233
98	1.788e+04	1.374e+04	4138
99	1.789e+04	1.372e+04	4162
100	1.789e+04	1.163e+04	6255
101	1890	1936	-46.24
102	1890	2329	-438.6
103	1890	2027	-136.6
104	1890	1926	-36.32
105	1890	3113	-1223
106	1890	3113	-1223
107	1890	1435	455.3
108	1890	3113	-1223
109	1891	1514	376.9
110	1891	2248	-357.2
111	1892	1846	46.14
112	1892	1846	46.14
113	1892	1836	56.06
114	1892	1856	36.23
115	2757	3104	-346.5
116	2757	3264	-507.4
117	2757	3284	-527.3
118	2757	3304	-547.1
119	2757	3094	-336.6
120	2777	3113	-336.4

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	1.056e-08	2.112e-08	1
7	8.26e-11	1.652e-10	1
8	2.391e-13	4.782e-13	1
9	1.612e-15	3.224e-15	1
10	1.916e-07	3.832e-07	1
11	5.926e-08	1.185e-07	1
12	7.205e-09	1.441e-08	1
13	1.299e-06	2.597e-06	1
14	1.507e-06	3.014e-06	1
15	2.403e-06	4.807e-06	1
16	6.134e-07	1.227e-06	1
17	1.415e-07	2.831e-07	1
18	8.023e-08	1.605e-07	1
19	9.667e-07	1.933e-06	1
20	2.61e-06	5.22e-06	1
21	9.739e-07	1.948e-06	1
22	4.897e-07	9.793e-07	1
23	1.725e-07	3.45e-07	1
24	1.21e-07	2.42e-07	1
25	6.048e-08	1.21e-07	1
26	2.242e-08	4.483e-08	1
27	1.978e-08	3.956e-08	1
28	7.395e-09	1.479e-08	1
29	2.585e-09	5.169e-09	1
30	9.121e-10	1.824e-09	1
31	3.845e-10	7.691e-10	1
32	1.441e-10	2.883e-10	1
33	8.457e-11	1.691e-10	1
34	8.938e-11	1.788e-10	1
35	6.003e-11	1.201e-10	1
36	6.779e-11	1.356e-10	1
37	2.239e-11	4.479e-11	1
38	7.914e-12	1.583e-11	1
39	1.515e-11	3.029e-11	1
40	7.766e-12	1.553e-11	1
41	2.725e-12	5.449e-12	1
42	1.022e-12	2.044e-12	1
43	3.451e-13	6.903e-13	1
44	1.423e-13	2.846e-13	1
45	7.458e-14	1.492e-13	1
46	3.043e-14	6.086e-14	1
47	1.045e-14	2.09e-14	1
48	2.193e-14	4.386e-14	1
49	1.74e-14	3.481e-14	1
50	6.745e-15	1.349e-14	1
51	1.039e-12	2.077e-12	1
52	1.201e-12	2.403e-12	1
53	6.622e-12	1.324e-11	1
54	1.201e-11	2.401e-11	1
55	1.541e-10	3.082e-10	1
56	1.292e-06	2.584e-06	1
57	0.0003716	0.0007433	0.9996
58	0.1482	0.2964	0.8518
59	0.2619	0.5239	0.7381
60	0.8389	0.3222	0.1611
61	0.9472	0.1056	0.05282
62	0.9742	0.05169	0.02585
63	0.9697	0.0606	0.0303
64	0.9651	0.06986	0.03493
65	0.9533	0.09344	0.04672
66	0.9417	0.1165	0.05826
67	0.9276	0.1447	0.07235
68	0.9115	0.177	0.08849
69	0.8876	0.2248	0.1124
70	0.8597	0.2807	0.1403
71	0.8503	0.2995	0.1497
72	0.8428	0.3144	0.1572
73	0.8392	0.3216	0.1608
74	0.8332	0.3335	0.1668
75	0.8331	0.3337	0.1669
76	0.833	0.334	0.167
77	0.8408	0.3183	0.1592
78	0.8506	0.2989	0.1494
79	0.8426	0.3147	0.1574
80	0.8355	0.329	0.1645
81	0.8382	0.3236	0.1618
82	0.8555	0.289	0.1445
83	0.8566	0.2868	0.1434
84	0.8918	0.2164	0.1082
85	0.9343	0.1315	0.06575
86	0.932	0.1361	0.06803
87	0.9301	0.1398	0.06991
88	0.933	0.1339	0.06697
89	0.9497	0.1006	0.0503
90	0.9372	0.1257	0.06283
91	0.9234	0.1532	0.07662
92	0.901	0.1979	0.09897
93	0.8764	0.2473	0.1236
94	0.8697	0.2605	0.1303
95	0.8921	0.2158	0.1079
96	0.8834	0.2332	0.1166
97	0.9446	0.1107	0.05535
98	0.9669	0.06611	0.03305
99	0.986	0.02797	0.01398
100	1	8.523e-10	4.262e-10
101	1	4.607e-09	2.304e-09
102	1	2.041e-08	1.02e-08
103	1	1.039e-07	5.195e-08
104	1	5.097e-07	2.548e-07
105	1	4.768e-07	2.384e-07
106	1	1.613e-07	8.063e-08
107	1	1.021e-06	5.104e-07
108	1	1.054e-09	5.272e-10
109	1	3.598e-09	1.799e-09
110	1	1.668e-10	8.342e-11
111	1	6.338e-09	3.169e-09
112	1	2.228e-07	1.114e-07
113	1	5.854e-06	2.927e-06
114	1	5.603e-07	2.802e-07

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  1.056e-08 &  2.112e-08 &  1 \tabularnewline
7 &  8.26e-11 &  1.652e-10 &  1 \tabularnewline
8 &  2.391e-13 &  4.782e-13 &  1 \tabularnewline
9 &  1.612e-15 &  3.224e-15 &  1 \tabularnewline
10 &  1.916e-07 &  3.832e-07 &  1 \tabularnewline
11 &  5.926e-08 &  1.185e-07 &  1 \tabularnewline
12 &  7.205e-09 &  1.441e-08 &  1 \tabularnewline
13 &  1.299e-06 &  2.597e-06 &  1 \tabularnewline
14 &  1.507e-06 &  3.014e-06 &  1 \tabularnewline
15 &  2.403e-06 &  4.807e-06 &  1 \tabularnewline
16 &  6.134e-07 &  1.227e-06 &  1 \tabularnewline
17 &  1.415e-07 &  2.831e-07 &  1 \tabularnewline
18 &  8.023e-08 &  1.605e-07 &  1 \tabularnewline
19 &  9.667e-07 &  1.933e-06 &  1 \tabularnewline
20 &  2.61e-06 &  5.22e-06 &  1 \tabularnewline
21 &  9.739e-07 &  1.948e-06 &  1 \tabularnewline
22 &  4.897e-07 &  9.793e-07 &  1 \tabularnewline
23 &  1.725e-07 &  3.45e-07 &  1 \tabularnewline
24 &  1.21e-07 &  2.42e-07 &  1 \tabularnewline
25 &  6.048e-08 &  1.21e-07 &  1 \tabularnewline
26 &  2.242e-08 &  4.483e-08 &  1 \tabularnewline
27 &  1.978e-08 &  3.956e-08 &  1 \tabularnewline
28 &  7.395e-09 &  1.479e-08 &  1 \tabularnewline
29 &  2.585e-09 &  5.169e-09 &  1 \tabularnewline
30 &  9.121e-10 &  1.824e-09 &  1 \tabularnewline
31 &  3.845e-10 &  7.691e-10 &  1 \tabularnewline
32 &  1.441e-10 &  2.883e-10 &  1 \tabularnewline
33 &  8.457e-11 &  1.691e-10 &  1 \tabularnewline
34 &  8.938e-11 &  1.788e-10 &  1 \tabularnewline
35 &  6.003e-11 &  1.201e-10 &  1 \tabularnewline
36 &  6.779e-11 &  1.356e-10 &  1 \tabularnewline
37 &  2.239e-11 &  4.479e-11 &  1 \tabularnewline
38 &  7.914e-12 &  1.583e-11 &  1 \tabularnewline
39 &  1.515e-11 &  3.029e-11 &  1 \tabularnewline
40 &  7.766e-12 &  1.553e-11 &  1 \tabularnewline
41 &  2.725e-12 &  5.449e-12 &  1 \tabularnewline
42 &  1.022e-12 &  2.044e-12 &  1 \tabularnewline
43 &  3.451e-13 &  6.903e-13 &  1 \tabularnewline
44 &  1.423e-13 &  2.846e-13 &  1 \tabularnewline
45 &  7.458e-14 &  1.492e-13 &  1 \tabularnewline
46 &  3.043e-14 &  6.086e-14 &  1 \tabularnewline
47 &  1.045e-14 &  2.09e-14 &  1 \tabularnewline
48 &  2.193e-14 &  4.386e-14 &  1 \tabularnewline
49 &  1.74e-14 &  3.481e-14 &  1 \tabularnewline
50 &  6.745e-15 &  1.349e-14 &  1 \tabularnewline
51 &  1.039e-12 &  2.077e-12 &  1 \tabularnewline
52 &  1.201e-12 &  2.403e-12 &  1 \tabularnewline
53 &  6.622e-12 &  1.324e-11 &  1 \tabularnewline
54 &  1.201e-11 &  2.401e-11 &  1 \tabularnewline
55 &  1.541e-10 &  3.082e-10 &  1 \tabularnewline
56 &  1.292e-06 &  2.584e-06 &  1 \tabularnewline
57 &  0.0003716 &  0.0007433 &  0.9996 \tabularnewline
58 &  0.1482 &  0.2964 &  0.8518 \tabularnewline
59 &  0.2619 &  0.5239 &  0.7381 \tabularnewline
60 &  0.8389 &  0.3222 &  0.1611 \tabularnewline
61 &  0.9472 &  0.1056 &  0.05282 \tabularnewline
62 &  0.9742 &  0.05169 &  0.02585 \tabularnewline
63 &  0.9697 &  0.0606 &  0.0303 \tabularnewline
64 &  0.9651 &  0.06986 &  0.03493 \tabularnewline
65 &  0.9533 &  0.09344 &  0.04672 \tabularnewline
66 &  0.9417 &  0.1165 &  0.05826 \tabularnewline
67 &  0.9276 &  0.1447 &  0.07235 \tabularnewline
68 &  0.9115 &  0.177 &  0.08849 \tabularnewline
69 &  0.8876 &  0.2248 &  0.1124 \tabularnewline
70 &  0.8597 &  0.2807 &  0.1403 \tabularnewline
71 &  0.8503 &  0.2995 &  0.1497 \tabularnewline
72 &  0.8428 &  0.3144 &  0.1572 \tabularnewline
73 &  0.8392 &  0.3216 &  0.1608 \tabularnewline
74 &  0.8332 &  0.3335 &  0.1668 \tabularnewline
75 &  0.8331 &  0.3337 &  0.1669 \tabularnewline
76 &  0.833 &  0.334 &  0.167 \tabularnewline
77 &  0.8408 &  0.3183 &  0.1592 \tabularnewline
78 &  0.8506 &  0.2989 &  0.1494 \tabularnewline
79 &  0.8426 &  0.3147 &  0.1574 \tabularnewline
80 &  0.8355 &  0.329 &  0.1645 \tabularnewline
81 &  0.8382 &  0.3236 &  0.1618 \tabularnewline
82 &  0.8555 &  0.289 &  0.1445 \tabularnewline
83 &  0.8566 &  0.2868 &  0.1434 \tabularnewline
84 &  0.8918 &  0.2164 &  0.1082 \tabularnewline
85 &  0.9343 &  0.1315 &  0.06575 \tabularnewline
86 &  0.932 &  0.1361 &  0.06803 \tabularnewline
87 &  0.9301 &  0.1398 &  0.06991 \tabularnewline
88 &  0.933 &  0.1339 &  0.06697 \tabularnewline
89 &  0.9497 &  0.1006 &  0.0503 \tabularnewline
90 &  0.9372 &  0.1257 &  0.06283 \tabularnewline
91 &  0.9234 &  0.1532 &  0.07662 \tabularnewline
92 &  0.901 &  0.1979 &  0.09897 \tabularnewline
93 &  0.8764 &  0.2473 &  0.1236 \tabularnewline
94 &  0.8697 &  0.2605 &  0.1303 \tabularnewline
95 &  0.8921 &  0.2158 &  0.1079 \tabularnewline
96 &  0.8834 &  0.2332 &  0.1166 \tabularnewline
97 &  0.9446 &  0.1107 &  0.05535 \tabularnewline
98 &  0.9669 &  0.06611 &  0.03305 \tabularnewline
99 &  0.986 &  0.02797 &  0.01398 \tabularnewline
100 &  1 &  8.523e-10 &  4.262e-10 \tabularnewline
101 &  1 &  4.607e-09 &  2.304e-09 \tabularnewline
102 &  1 &  2.041e-08 &  1.02e-08 \tabularnewline
103 &  1 &  1.039e-07 &  5.195e-08 \tabularnewline
104 &  1 &  5.097e-07 &  2.548e-07 \tabularnewline
105 &  1 &  4.768e-07 &  2.384e-07 \tabularnewline
106 &  1 &  1.613e-07 &  8.063e-08 \tabularnewline
107 &  1 &  1.021e-06 &  5.104e-07 \tabularnewline
108 &  1 &  1.054e-09 &  5.272e-10 \tabularnewline
109 &  1 &  3.598e-09 &  1.799e-09 \tabularnewline
110 &  1 &  1.668e-10 &  8.342e-11 \tabularnewline
111 &  1 &  6.338e-09 &  3.169e-09 \tabularnewline
112 &  1 &  2.228e-07 &  1.114e-07 \tabularnewline
113 &  1 &  5.854e-06 &  2.927e-06 \tabularnewline
114 &  1 &  5.603e-07 &  2.802e-07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&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]6[/C][C] 1.056e-08[/C][C] 2.112e-08[/C][C] 1[/C][/ROW]
[ROW][C]7[/C][C] 8.26e-11[/C][C] 1.652e-10[/C][C] 1[/C][/ROW]
[ROW][C]8[/C][C] 2.391e-13[/C][C] 4.782e-13[/C][C] 1[/C][/ROW]
[ROW][C]9[/C][C] 1.612e-15[/C][C] 3.224e-15[/C][C] 1[/C][/ROW]
[ROW][C]10[/C][C] 1.916e-07[/C][C] 3.832e-07[/C][C] 1[/C][/ROW]
[ROW][C]11[/C][C] 5.926e-08[/C][C] 1.185e-07[/C][C] 1[/C][/ROW]
[ROW][C]12[/C][C] 7.205e-09[/C][C] 1.441e-08[/C][C] 1[/C][/ROW]
[ROW][C]13[/C][C] 1.299e-06[/C][C] 2.597e-06[/C][C] 1[/C][/ROW]
[ROW][C]14[/C][C] 1.507e-06[/C][C] 3.014e-06[/C][C] 1[/C][/ROW]
[ROW][C]15[/C][C] 2.403e-06[/C][C] 4.807e-06[/C][C] 1[/C][/ROW]
[ROW][C]16[/C][C] 6.134e-07[/C][C] 1.227e-06[/C][C] 1[/C][/ROW]
[ROW][C]17[/C][C] 1.415e-07[/C][C] 2.831e-07[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 8.023e-08[/C][C] 1.605e-07[/C][C] 1[/C][/ROW]
[ROW][C]19[/C][C] 9.667e-07[/C][C] 1.933e-06[/C][C] 1[/C][/ROW]
[ROW][C]20[/C][C] 2.61e-06[/C][C] 5.22e-06[/C][C] 1[/C][/ROW]
[ROW][C]21[/C][C] 9.739e-07[/C][C] 1.948e-06[/C][C] 1[/C][/ROW]
[ROW][C]22[/C][C] 4.897e-07[/C][C] 9.793e-07[/C][C] 1[/C][/ROW]
[ROW][C]23[/C][C] 1.725e-07[/C][C] 3.45e-07[/C][C] 1[/C][/ROW]
[ROW][C]24[/C][C] 1.21e-07[/C][C] 2.42e-07[/C][C] 1[/C][/ROW]
[ROW][C]25[/C][C] 6.048e-08[/C][C] 1.21e-07[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 2.242e-08[/C][C] 4.483e-08[/C][C] 1[/C][/ROW]
[ROW][C]27[/C][C] 1.978e-08[/C][C] 3.956e-08[/C][C] 1[/C][/ROW]
[ROW][C]28[/C][C] 7.395e-09[/C][C] 1.479e-08[/C][C] 1[/C][/ROW]
[ROW][C]29[/C][C] 2.585e-09[/C][C] 5.169e-09[/C][C] 1[/C][/ROW]
[ROW][C]30[/C][C] 9.121e-10[/C][C] 1.824e-09[/C][C] 1[/C][/ROW]
[ROW][C]31[/C][C] 3.845e-10[/C][C] 7.691e-10[/C][C] 1[/C][/ROW]
[ROW][C]32[/C][C] 1.441e-10[/C][C] 2.883e-10[/C][C] 1[/C][/ROW]
[ROW][C]33[/C][C] 8.457e-11[/C][C] 1.691e-10[/C][C] 1[/C][/ROW]
[ROW][C]34[/C][C] 8.938e-11[/C][C] 1.788e-10[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 6.003e-11[/C][C] 1.201e-10[/C][C] 1[/C][/ROW]
[ROW][C]36[/C][C] 6.779e-11[/C][C] 1.356e-10[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 2.239e-11[/C][C] 4.479e-11[/C][C] 1[/C][/ROW]
[ROW][C]38[/C][C] 7.914e-12[/C][C] 1.583e-11[/C][C] 1[/C][/ROW]
[ROW][C]39[/C][C] 1.515e-11[/C][C] 3.029e-11[/C][C] 1[/C][/ROW]
[ROW][C]40[/C][C] 7.766e-12[/C][C] 1.553e-11[/C][C] 1[/C][/ROW]
[ROW][C]41[/C][C] 2.725e-12[/C][C] 5.449e-12[/C][C] 1[/C][/ROW]
[ROW][C]42[/C][C] 1.022e-12[/C][C] 2.044e-12[/C][C] 1[/C][/ROW]
[ROW][C]43[/C][C] 3.451e-13[/C][C] 6.903e-13[/C][C] 1[/C][/ROW]
[ROW][C]44[/C][C] 1.423e-13[/C][C] 2.846e-13[/C][C] 1[/C][/ROW]
[ROW][C]45[/C][C] 7.458e-14[/C][C] 1.492e-13[/C][C] 1[/C][/ROW]
[ROW][C]46[/C][C] 3.043e-14[/C][C] 6.086e-14[/C][C] 1[/C][/ROW]
[ROW][C]47[/C][C] 1.045e-14[/C][C] 2.09e-14[/C][C] 1[/C][/ROW]
[ROW][C]48[/C][C] 2.193e-14[/C][C] 4.386e-14[/C][C] 1[/C][/ROW]
[ROW][C]49[/C][C] 1.74e-14[/C][C] 3.481e-14[/C][C] 1[/C][/ROW]
[ROW][C]50[/C][C] 6.745e-15[/C][C] 1.349e-14[/C][C] 1[/C][/ROW]
[ROW][C]51[/C][C] 1.039e-12[/C][C] 2.077e-12[/C][C] 1[/C][/ROW]
[ROW][C]52[/C][C] 1.201e-12[/C][C] 2.403e-12[/C][C] 1[/C][/ROW]
[ROW][C]53[/C][C] 6.622e-12[/C][C] 1.324e-11[/C][C] 1[/C][/ROW]
[ROW][C]54[/C][C] 1.201e-11[/C][C] 2.401e-11[/C][C] 1[/C][/ROW]
[ROW][C]55[/C][C] 1.541e-10[/C][C] 3.082e-10[/C][C] 1[/C][/ROW]
[ROW][C]56[/C][C] 1.292e-06[/C][C] 2.584e-06[/C][C] 1[/C][/ROW]
[ROW][C]57[/C][C] 0.0003716[/C][C] 0.0007433[/C][C] 0.9996[/C][/ROW]
[ROW][C]58[/C][C] 0.1482[/C][C] 0.2964[/C][C] 0.8518[/C][/ROW]
[ROW][C]59[/C][C] 0.2619[/C][C] 0.5239[/C][C] 0.7381[/C][/ROW]
[ROW][C]60[/C][C] 0.8389[/C][C] 0.3222[/C][C] 0.1611[/C][/ROW]
[ROW][C]61[/C][C] 0.9472[/C][C] 0.1056[/C][C] 0.05282[/C][/ROW]
[ROW][C]62[/C][C] 0.9742[/C][C] 0.05169[/C][C] 0.02585[/C][/ROW]
[ROW][C]63[/C][C] 0.9697[/C][C] 0.0606[/C][C] 0.0303[/C][/ROW]
[ROW][C]64[/C][C] 0.9651[/C][C] 0.06986[/C][C] 0.03493[/C][/ROW]
[ROW][C]65[/C][C] 0.9533[/C][C] 0.09344[/C][C] 0.04672[/C][/ROW]
[ROW][C]66[/C][C] 0.9417[/C][C] 0.1165[/C][C] 0.05826[/C][/ROW]
[ROW][C]67[/C][C] 0.9276[/C][C] 0.1447[/C][C] 0.07235[/C][/ROW]
[ROW][C]68[/C][C] 0.9115[/C][C] 0.177[/C][C] 0.08849[/C][/ROW]
[ROW][C]69[/C][C] 0.8876[/C][C] 0.2248[/C][C] 0.1124[/C][/ROW]
[ROW][C]70[/C][C] 0.8597[/C][C] 0.2807[/C][C] 0.1403[/C][/ROW]
[ROW][C]71[/C][C] 0.8503[/C][C] 0.2995[/C][C] 0.1497[/C][/ROW]
[ROW][C]72[/C][C] 0.8428[/C][C] 0.3144[/C][C] 0.1572[/C][/ROW]
[ROW][C]73[/C][C] 0.8392[/C][C] 0.3216[/C][C] 0.1608[/C][/ROW]
[ROW][C]74[/C][C] 0.8332[/C][C] 0.3335[/C][C] 0.1668[/C][/ROW]
[ROW][C]75[/C][C] 0.8331[/C][C] 0.3337[/C][C] 0.1669[/C][/ROW]
[ROW][C]76[/C][C] 0.833[/C][C] 0.334[/C][C] 0.167[/C][/ROW]
[ROW][C]77[/C][C] 0.8408[/C][C] 0.3183[/C][C] 0.1592[/C][/ROW]
[ROW][C]78[/C][C] 0.8506[/C][C] 0.2989[/C][C] 0.1494[/C][/ROW]
[ROW][C]79[/C][C] 0.8426[/C][C] 0.3147[/C][C] 0.1574[/C][/ROW]
[ROW][C]80[/C][C] 0.8355[/C][C] 0.329[/C][C] 0.1645[/C][/ROW]
[ROW][C]81[/C][C] 0.8382[/C][C] 0.3236[/C][C] 0.1618[/C][/ROW]
[ROW][C]82[/C][C] 0.8555[/C][C] 0.289[/C][C] 0.1445[/C][/ROW]
[ROW][C]83[/C][C] 0.8566[/C][C] 0.2868[/C][C] 0.1434[/C][/ROW]
[ROW][C]84[/C][C] 0.8918[/C][C] 0.2164[/C][C] 0.1082[/C][/ROW]
[ROW][C]85[/C][C] 0.9343[/C][C] 0.1315[/C][C] 0.06575[/C][/ROW]
[ROW][C]86[/C][C] 0.932[/C][C] 0.1361[/C][C] 0.06803[/C][/ROW]
[ROW][C]87[/C][C] 0.9301[/C][C] 0.1398[/C][C] 0.06991[/C][/ROW]
[ROW][C]88[/C][C] 0.933[/C][C] 0.1339[/C][C] 0.06697[/C][/ROW]
[ROW][C]89[/C][C] 0.9497[/C][C] 0.1006[/C][C] 0.0503[/C][/ROW]
[ROW][C]90[/C][C] 0.9372[/C][C] 0.1257[/C][C] 0.06283[/C][/ROW]
[ROW][C]91[/C][C] 0.9234[/C][C] 0.1532[/C][C] 0.07662[/C][/ROW]
[ROW][C]92[/C][C] 0.901[/C][C] 0.1979[/C][C] 0.09897[/C][/ROW]
[ROW][C]93[/C][C] 0.8764[/C][C] 0.2473[/C][C] 0.1236[/C][/ROW]
[ROW][C]94[/C][C] 0.8697[/C][C] 0.2605[/C][C] 0.1303[/C][/ROW]
[ROW][C]95[/C][C] 0.8921[/C][C] 0.2158[/C][C] 0.1079[/C][/ROW]
[ROW][C]96[/C][C] 0.8834[/C][C] 0.2332[/C][C] 0.1166[/C][/ROW]
[ROW][C]97[/C][C] 0.9446[/C][C] 0.1107[/C][C] 0.05535[/C][/ROW]
[ROW][C]98[/C][C] 0.9669[/C][C] 0.06611[/C][C] 0.03305[/C][/ROW]
[ROW][C]99[/C][C] 0.986[/C][C] 0.02797[/C][C] 0.01398[/C][/ROW]
[ROW][C]100[/C][C] 1[/C][C] 8.523e-10[/C][C] 4.262e-10[/C][/ROW]
[ROW][C]101[/C][C] 1[/C][C] 4.607e-09[/C][C] 2.304e-09[/C][/ROW]
[ROW][C]102[/C][C] 1[/C][C] 2.041e-08[/C][C] 1.02e-08[/C][/ROW]
[ROW][C]103[/C][C] 1[/C][C] 1.039e-07[/C][C] 5.195e-08[/C][/ROW]
[ROW][C]104[/C][C] 1[/C][C] 5.097e-07[/C][C] 2.548e-07[/C][/ROW]
[ROW][C]105[/C][C] 1[/C][C] 4.768e-07[/C][C] 2.384e-07[/C][/ROW]
[ROW][C]106[/C][C] 1[/C][C] 1.613e-07[/C][C] 8.063e-08[/C][/ROW]
[ROW][C]107[/C][C] 1[/C][C] 1.021e-06[/C][C] 5.104e-07[/C][/ROW]
[ROW][C]108[/C][C] 1[/C][C] 1.054e-09[/C][C] 5.272e-10[/C][/ROW]
[ROW][C]109[/C][C] 1[/C][C] 3.598e-09[/C][C] 1.799e-09[/C][/ROW]
[ROW][C]110[/C][C] 1[/C][C] 1.668e-10[/C][C] 8.342e-11[/C][/ROW]
[ROW][C]111[/C][C] 1[/C][C] 6.338e-09[/C][C] 3.169e-09[/C][/ROW]
[ROW][C]112[/C][C] 1[/C][C] 2.228e-07[/C][C] 1.114e-07[/C][/ROW]
[ROW][C]113[/C][C] 1[/C][C] 5.854e-06[/C][C] 2.927e-06[/C][/ROW]
[ROW][C]114[/C][C] 1[/C][C] 5.603e-07[/C][C] 2.802e-07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310289&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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
6	1.056e-08	2.112e-08	1
7	8.26e-11	1.652e-10	1
8	2.391e-13	4.782e-13	1
9	1.612e-15	3.224e-15	1
10	1.916e-07	3.832e-07	1
11	5.926e-08	1.185e-07	1
12	7.205e-09	1.441e-08	1
13	1.299e-06	2.597e-06	1
14	1.507e-06	3.014e-06	1
15	2.403e-06	4.807e-06	1
16	6.134e-07	1.227e-06	1
17	1.415e-07	2.831e-07	1
18	8.023e-08	1.605e-07	1
19	9.667e-07	1.933e-06	1
20	2.61e-06	5.22e-06	1
21	9.739e-07	1.948e-06	1
22	4.897e-07	9.793e-07	1
23	1.725e-07	3.45e-07	1
24	1.21e-07	2.42e-07	1
25	6.048e-08	1.21e-07	1
26	2.242e-08	4.483e-08	1
27	1.978e-08	3.956e-08	1
28	7.395e-09	1.479e-08	1
29	2.585e-09	5.169e-09	1
30	9.121e-10	1.824e-09	1
31	3.845e-10	7.691e-10	1
32	1.441e-10	2.883e-10	1
33	8.457e-11	1.691e-10	1
34	8.938e-11	1.788e-10	1
35	6.003e-11	1.201e-10	1
36	6.779e-11	1.356e-10	1
37	2.239e-11	4.479e-11	1
38	7.914e-12	1.583e-11	1
39	1.515e-11	3.029e-11	1
40	7.766e-12	1.553e-11	1
41	2.725e-12	5.449e-12	1
42	1.022e-12	2.044e-12	1
43	3.451e-13	6.903e-13	1
44	1.423e-13	2.846e-13	1
45	7.458e-14	1.492e-13	1
46	3.043e-14	6.086e-14	1
47	1.045e-14	2.09e-14	1
48	2.193e-14	4.386e-14	1
49	1.74e-14	3.481e-14	1
50	6.745e-15	1.349e-14	1
51	1.039e-12	2.077e-12	1
52	1.201e-12	2.403e-12	1
53	6.622e-12	1.324e-11	1
54	1.201e-11	2.401e-11	1
55	1.541e-10	3.082e-10	1
56	1.292e-06	2.584e-06	1
57	0.0003716	0.0007433	0.9996
58	0.1482	0.2964	0.8518
59	0.2619	0.5239	0.7381
60	0.8389	0.3222	0.1611
61	0.9472	0.1056	0.05282
62	0.9742	0.05169	0.02585
63	0.9697	0.0606	0.0303
64	0.9651	0.06986	0.03493
65	0.9533	0.09344	0.04672
66	0.9417	0.1165	0.05826
67	0.9276	0.1447	0.07235
68	0.9115	0.177	0.08849
69	0.8876	0.2248	0.1124
70	0.8597	0.2807	0.1403
71	0.8503	0.2995	0.1497
72	0.8428	0.3144	0.1572
73	0.8392	0.3216	0.1608
74	0.8332	0.3335	0.1668
75	0.8331	0.3337	0.1669
76	0.833	0.334	0.167
77	0.8408	0.3183	0.1592
78	0.8506	0.2989	0.1494
79	0.8426	0.3147	0.1574
80	0.8355	0.329	0.1645
81	0.8382	0.3236	0.1618
82	0.8555	0.289	0.1445
83	0.8566	0.2868	0.1434
84	0.8918	0.2164	0.1082
85	0.9343	0.1315	0.06575
86	0.932	0.1361	0.06803
87	0.9301	0.1398	0.06991
88	0.933	0.1339	0.06697
89	0.9497	0.1006	0.0503
90	0.9372	0.1257	0.06283
91	0.9234	0.1532	0.07662
92	0.901	0.1979	0.09897
93	0.8764	0.2473	0.1236
94	0.8697	0.2605	0.1303
95	0.8921	0.2158	0.1079
96	0.8834	0.2332	0.1166
97	0.9446	0.1107	0.05535
98	0.9669	0.06611	0.03305
99	0.986	0.02797	0.01398
100	1	8.523e-10	4.262e-10
101	1	4.607e-09	2.304e-09
102	1	2.041e-08	1.02e-08
103	1	1.039e-07	5.195e-08
104	1	5.097e-07	2.548e-07
105	1	4.768e-07	2.384e-07
106	1	1.613e-07	8.063e-08
107	1	1.021e-06	5.104e-07
108	1	1.054e-09	5.272e-10
109	1	3.598e-09	1.799e-09
110	1	1.668e-10	8.342e-11
111	1	6.338e-09	3.169e-09
112	1	2.228e-07	1.114e-07
113	1	5.854e-06	2.927e-06
114	1	5.603e-07	2.802e-07

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	67	0.6147	NOK
5% type I error level	68	0.623853	NOK
10% type I error level	73	0.669725	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 & 67 &  0.6147 & NOK \tabularnewline
5% type I error level & 68 & 0.623853 & NOK \tabularnewline
10% type I error level & 73 & 0.669725 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&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]67[/C][C] 0.6147[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]68[/C][C]0.623853[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]73[/C][C]0.669725[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310289&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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	67	0.6147	NOK
5% type I error level	68	0.623853	NOK
10% type I error level	73	0.669725	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 46.306, df1 = 2, df2 = 115, p-value = 1.771e-15
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 22.708, df1 = 4, df2 = 113, p-value = 8.767e-14
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.0302, df1 = 2, df2 = 115, p-value = 0.3602

\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 = 46.306, df1 = 2, df2 = 115, p-value = 1.771e-15
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 22.708, df1 = 4, df2 = 113, p-value = 8.767e-14
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.0302, df1 = 2, df2 = 115, p-value = 0.3602
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&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 = 46.306, df1 = 2, df2 = 115, p-value = 1.771e-15
[/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 = 22.708, df1 = 4, df2 = 113, p-value = 8.767e-14
[/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 = 1.0302, df1 = 2, df2 = 115, p-value = 0.3602
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310289&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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 = 46.306, df1 = 2, df2 = 115, p-value = 1.771e-15
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 22.708, df1 = 4, df2 = 113, p-value = 8.767e-14
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.0302, df1 = 2, df2 = 115, p-value = 0.3602

Variance Inflation Factors (Multicollinearity)
> vif Carat Table 1.007093 1.007093

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   Carat    Table 
1.007093 1.007093 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310289&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   Carat    Table 
1.007093 1.007093 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310289&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310289&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 Carat Table 1.007093 1.007093

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; 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