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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sat, 10 Dec 2016 12:03:32 +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/2016/Dec/10/t1481367847pjunizksq8sokmt.htm/, Retrieved Mon, 06 May 2024 08:37:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298643, Retrieved Mon, 06 May 2024 08:37:00 +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] [] [2016-12-10 11:03:32] [037fdaa34a77b5f63489b3bcd360a80c] [Current]

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13	2	2	3	4
16	4	2	1	4
17	4	2	5	4
NA	4	3	4	4
NA	3	4	3	3
16	4	3	2	5
NA	1	4	4	4
NA	4	2	5	4
NA	3	NA	5	2
17	4	4	3	4
17	2	2	2	4
15	4	2	2	3
16	4	5	4	3
14	5	4	4	4
16	4	2	4	4
17	1	3	5	4
NA	2	1	2	5
NA	4	1	NA	NA
NA	4	3	2	4
NA	5	4	4	4
16	5	5	4	4
NA	4	5	4	4
16	1	1	5	4
NA	4	4	3	4
NA	2	2	4	4
NA	4	4	3	4
16	5	4	3	3
15	3	3	3	3
16	5	4	5	5
16	3	2	4	4
13	5	2	4	4
15	2	4	3	4
17	1	2	3	4
NA	NA	4	5	1
13	4	2	3	3
17	4	4	3	4
NA	3	3	3	4
14	5	3	5	5
14	4	4	3	4
18	NA	2	3	4
NA	4	3	3	4
17	2	2	4	3
13	3	4	3	4
16	1	2	1	5
15	3	2	4	4
15	3	3	4	3
NA	3	3	3	3
15	4	NA	4	5
13	4	4	4	4
NA	4	5	5	1
17	4	4	4	4
NA	4	4	4	4
NA	2	4	3	4
11	5	2	2	4
14	3	2	4	3
13	3	1	3	4
NA	4	3	3	3
17	4	4	3	4
16	4	3	4	2
NA	3	3	4	4
17	4	2	3	4
16	4	3	4	4
16	4	2	5	3
16	4	4	2	4
15	4	3	3	3
12	2	2	3	4
17	4	4	3	3
14	4	5	4	4
14	4	4	3	4
16	4	3	4	4
NA	4	2	3	4
NA	5	3	1	3
NA	3	4	4	3
NA	2	4	3	2
NA	4	4	2	4
15	5	5	3	5
16	4	4	3	4
14	5	4	4	5
15	5	4	5	2
17	2	3	3	4
NA	4	2	4	4
10	4	4	2	4
NA	4	4	2	4
17	3	4	2	5
NA	4	2	3	4
20	2	2	4	4
17	5	1	3	4
18	3	NA	5	4
NA	4	4	4	1
17	2	4	4	4
14	4	4	3	4
NA	3	3	4	3
17	3	4	3	4
NA	4	4	5	4
17	4	4	4	3
NA	4	2	4	3
16	3	4	3	4
18	4	4	4	5
18	3	1	1	3
16	3	4	4	4
NA	1	2	4	3
NA	4	3	4	4
15	3	3	4	5
13	3	4	4	3
NA	5	3	3	4
NA	5	4	5	4
NA	4	4	3	NA
NA	5	4	5	5
NA	4	4	4	4
16	4	5	4	4
NA	4	5	4	5
NA	4	2	4	3
NA	3	1	3	3
12	4	3	4	3
NA	3	3	3	4
16	4	1	3	4
16	2	4	3	4
NA	1	4	3	4
16	5	2	2	4
14	4	4	4	4
15	3	3	3	3
14	4	4	2	4
NA	4	4	4	5
15	4	2	4	4
NA	4	2	3	3
15	2	4	4	4
16	4	4	5	4
NA	4	2	4	3
NA	4	2	NA	3
NA	4	2	4	4
11	3	2	4	2
NA	4	5	4	4
18	5	2	5	3
NA	2	NA	2	4
11	5	2	4	4
NA	4	4	4	4
18	3	5	5	4
NA	NA	4	4	3
15	2	4	4	2
19	2	3	5	5
17	2	3	2	3
NA	4	1	4	4
14	4	4	5	4
NA	5	5	3	4
13	3	4	4	5
17	3	4	4	4
14	4	5	3	4
19	4	4	5	3
14	4	5	5	1
NA	4	5	3	4
NA	4	3	2	5
16	4	5	4	4
16	4	1	5	4
15	2	3	3	4
12	5	2	3	5
NA	4	2	4	4
17	4	NA	3	4
NA	4	4	2	4
NA	4	2	3	4
18	4	5	3	4
15	2	4	4	3
18	3	5	1	5
15	3	3	4	3
NA	4	2	3	4
NA	4	4	3	4
NA	4	2	2	5
16	4	3	3	4
NA	3	3	3	4
16	3	2	5	2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&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]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298643&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = + 14.9975 -0.416161IVHB1[t] + 0.141125IVHB2[t] + 0.15563IVHB3[t] + 0.230593IVHB4[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM[t] =  +  14.9975 -0.416161IVHB1[t] +  0.141125IVHB2[t] +  0.15563IVHB3[t] +  0.230593IVHB4[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM[t] =  +  14.9975 -0.416161IVHB1[t] +  0.141125IVHB2[t] +  0.15563IVHB3[t] +  0.230593IVHB4[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298643&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&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
TVDCSUM[t] = + 14.9975 -0.416161IVHB1[t] + 0.141125IVHB2[t] + 0.15563IVHB3[t] + 0.230593IVHB4[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+15	1.401	+1.0700e+01	5.905e-18	2.952e-18
IVHB1	-0.4162	0.1796	-2.3180e+00	0.02265	0.01132
IVHB2	+0.1411	0.1666	+8.4710e-01	0.3991	0.1995
IVHB3	+0.1556	0.1893	+8.2200e-01	0.4131	0.2066
IVHB4	+0.2306	0.2475	+9.3180e-01	0.3538	0.1769

\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) & +15 &  1.401 & +1.0700e+01 &  5.905e-18 &  2.952e-18 \tabularnewline
IVHB1 & -0.4162 &  0.1796 & -2.3180e+00 &  0.02265 &  0.01132 \tabularnewline
IVHB2 & +0.1411 &  0.1666 & +8.4710e-01 &  0.3991 &  0.1995 \tabularnewline
IVHB3 & +0.1556 &  0.1893 & +8.2200e-01 &  0.4131 &  0.2066 \tabularnewline
IVHB4 & +0.2306 &  0.2475 & +9.3180e-01 &  0.3538 &  0.1769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&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]+15[/C][C] 1.401[/C][C]+1.0700e+01[/C][C] 5.905e-18[/C][C] 2.952e-18[/C][/ROW]
[ROW][C]IVHB1[/C][C]-0.4162[/C][C] 0.1796[/C][C]-2.3180e+00[/C][C] 0.02265[/C][C] 0.01132[/C][/ROW]
[ROW][C]IVHB2[/C][C]+0.1411[/C][C] 0.1666[/C][C]+8.4710e-01[/C][C] 0.3991[/C][C] 0.1995[/C][/ROW]
[ROW][C]IVHB3[/C][C]+0.1556[/C][C] 0.1893[/C][C]+8.2200e-01[/C][C] 0.4131[/C][C] 0.2066[/C][/ROW]
[ROW][C]IVHB4[/C][C]+0.2306[/C][C] 0.2475[/C][C]+9.3180e-01[/C][C] 0.3538[/C][C] 0.1769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298643&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&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)	+15	1.401	+1.0700e+01	5.905e-18	2.952e-18
IVHB1	-0.4162	0.1796	-2.3180e+00	0.02265	0.01132
IVHB2	+0.1411	0.1666	+8.4710e-01	0.3991	0.1995
IVHB3	+0.1556	0.1893	+8.2200e-01	0.4131	0.2066
IVHB4	+0.2306	0.2475	+9.3180e-01	0.3538	0.1769

Multiple Linear Regression - Regression Statistics
Multiple R	0.258
R-squared	0.06656
Adjusted R-squared	0.02684
F-TEST (value)	1.676
F-TEST (DF numerator)	4
F-TEST (DF denominator)	94
p-value	0.1621
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.843
Sum Squared Residuals	319.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.258 \tabularnewline
R-squared &  0.06656 \tabularnewline
Adjusted R-squared &  0.02684 \tabularnewline
F-TEST (value) &  1.676 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value &  0.1621 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.843 \tabularnewline
Sum Squared Residuals &  319.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.258[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.06656[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.02684[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.676[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]94[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1621[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.843[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 319.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298643&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&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	0.258
R-squared	0.06656
Adjusted R-squared	0.02684
F-TEST (value)	1.676
F-TEST (DF numerator)	4
F-TEST (DF denominator)	94
p-value	0.1621
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.843
Sum Squared Residuals	319.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	15.84	-2.837
2	16	14.69	1.307
3	17	15.32	1.684
4	16	15.22	0.7795
5	17	15.29	1.713
6	17	15.68	1.319
7	15	14.62	0.3818
8	16	15.35	0.6472
9	14	15.03	-1.026
10	16	15.16	0.84
11	17	16.71	0.2947
12	16	15.17	0.8327
13	16	16.42	-0.423
14	16	14.64	1.36
15	15	15.33	-0.3311
16	16	15.41	0.5876
17	16	15.58	0.4238
18	13	14.74	-1.744
19	15	16.12	-1.119
20	17	16.25	0.7471
21	13	14.77	-1.774
22	17	15.29	1.713
23	14	15.27	-1.271
24	14	15.29	-1.287
25	17	15.76	1.238
26	13	15.7	-2.703
27	16	16.17	-0.1722
28	15	15.58	-0.5762
29	15	15.49	-0.4867
30	13	15.44	-2.442
31	17	15.44	1.558
32	11	14.43	-3.433
33	14	15.35	-1.346
34	13	15.28	-2.279
35	17	15.29	1.713
36	16	14.84	1.16
37	17	15	1.996
38	16	15.3	0.6988
39	16	15.09	0.9149
40	16	15.13	0.869
41	15	14.91	0.08505
42	12	15.84	-3.837
43	17	15.06	1.944
44	14	15.58	-1.583
45	14	15.29	-1.287
46	16	15.3	0.6988
47	15	15.24	-0.2422
48	16	15.29	0.7133
49	14	15.26	-1.257
50	15	14.72	0.2794
51	17	15.98	1.022
52	10	15.13	-5.131
53	17	15.78	1.222
54	20	15.99	4.008
55	17	14.45	2.553
56	17	16.27	0.7254
57	14	15.29	-1.287
58	17	15.7	1.297
59	17	15.21	1.788
60	16	15.7	0.2972
61	18	15.67	2.327
62	18	14.74	3.262
63	16	15.86	0.1415
64	15	15.95	-0.9479
65	13	15.63	-2.628
66	16	15.58	0.4166
67	12	15.07	-3.071
68	16	14.86	1.137
69	16	16.12	-0.119
70	16	14.43	1.567
71	14	15.44	-1.442
72	15	15.33	-0.3311
73	14	15.13	-1.131
74	15	15.16	-0.16
75	15	16.27	-1.275
76	16	15.6	0.4021
77	11	15.12	-4.115
78	18	14.67	3.331
79	11	14.74	-3.744
80	18	16.16	1.845
81	15	15.81	-0.8134
82	19	16.52	2.48
83	17	15.59	1.408
84	14	15.6	-1.598
85	13	16.09	-3.089
86	17	15.86	1.142
87	14	15.43	-1.428
88	19	15.37	3.633
89	14	15.05	-1.047
90	16	15.58	0.4166
91	16	15.17	0.8254
92	15	15.98	-0.9779
93	12	14.82	-2.819
94	18	15.43	2.572
95	15	16.04	-1.044
96	18	15.76	2.237
97	15	15.49	-0.4867
98	16	15.15	0.8545
99	16	15.27	0.7293

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  15.84 & -2.837 \tabularnewline
2 &  16 &  14.69 &  1.307 \tabularnewline
3 &  17 &  15.32 &  1.684 \tabularnewline
4 &  16 &  15.22 &  0.7795 \tabularnewline
5 &  17 &  15.29 &  1.713 \tabularnewline
6 &  17 &  15.68 &  1.319 \tabularnewline
7 &  15 &  14.62 &  0.3818 \tabularnewline
8 &  16 &  15.35 &  0.6472 \tabularnewline
9 &  14 &  15.03 & -1.026 \tabularnewline
10 &  16 &  15.16 &  0.84 \tabularnewline
11 &  17 &  16.71 &  0.2947 \tabularnewline
12 &  16 &  15.17 &  0.8327 \tabularnewline
13 &  16 &  16.42 & -0.423 \tabularnewline
14 &  16 &  14.64 &  1.36 \tabularnewline
15 &  15 &  15.33 & -0.3311 \tabularnewline
16 &  16 &  15.41 &  0.5876 \tabularnewline
17 &  16 &  15.58 &  0.4238 \tabularnewline
18 &  13 &  14.74 & -1.744 \tabularnewline
19 &  15 &  16.12 & -1.119 \tabularnewline
20 &  17 &  16.25 &  0.7471 \tabularnewline
21 &  13 &  14.77 & -1.774 \tabularnewline
22 &  17 &  15.29 &  1.713 \tabularnewline
23 &  14 &  15.27 & -1.271 \tabularnewline
24 &  14 &  15.29 & -1.287 \tabularnewline
25 &  17 &  15.76 &  1.238 \tabularnewline
26 &  13 &  15.7 & -2.703 \tabularnewline
27 &  16 &  16.17 & -0.1722 \tabularnewline
28 &  15 &  15.58 & -0.5762 \tabularnewline
29 &  15 &  15.49 & -0.4867 \tabularnewline
30 &  13 &  15.44 & -2.442 \tabularnewline
31 &  17 &  15.44 &  1.558 \tabularnewline
32 &  11 &  14.43 & -3.433 \tabularnewline
33 &  14 &  15.35 & -1.346 \tabularnewline
34 &  13 &  15.28 & -2.279 \tabularnewline
35 &  17 &  15.29 &  1.713 \tabularnewline
36 &  16 &  14.84 &  1.16 \tabularnewline
37 &  17 &  15 &  1.996 \tabularnewline
38 &  16 &  15.3 &  0.6988 \tabularnewline
39 &  16 &  15.09 &  0.9149 \tabularnewline
40 &  16 &  15.13 &  0.869 \tabularnewline
41 &  15 &  14.91 &  0.08505 \tabularnewline
42 &  12 &  15.84 & -3.837 \tabularnewline
43 &  17 &  15.06 &  1.944 \tabularnewline
44 &  14 &  15.58 & -1.583 \tabularnewline
45 &  14 &  15.29 & -1.287 \tabularnewline
46 &  16 &  15.3 &  0.6988 \tabularnewline
47 &  15 &  15.24 & -0.2422 \tabularnewline
48 &  16 &  15.29 &  0.7133 \tabularnewline
49 &  14 &  15.26 & -1.257 \tabularnewline
50 &  15 &  14.72 &  0.2794 \tabularnewline
51 &  17 &  15.98 &  1.022 \tabularnewline
52 &  10 &  15.13 & -5.131 \tabularnewline
53 &  17 &  15.78 &  1.222 \tabularnewline
54 &  20 &  15.99 &  4.008 \tabularnewline
55 &  17 &  14.45 &  2.553 \tabularnewline
56 &  17 &  16.27 &  0.7254 \tabularnewline
57 &  14 &  15.29 & -1.287 \tabularnewline
58 &  17 &  15.7 &  1.297 \tabularnewline
59 &  17 &  15.21 &  1.788 \tabularnewline
60 &  16 &  15.7 &  0.2972 \tabularnewline
61 &  18 &  15.67 &  2.327 \tabularnewline
62 &  18 &  14.74 &  3.262 \tabularnewline
63 &  16 &  15.86 &  0.1415 \tabularnewline
64 &  15 &  15.95 & -0.9479 \tabularnewline
65 &  13 &  15.63 & -2.628 \tabularnewline
66 &  16 &  15.58 &  0.4166 \tabularnewline
67 &  12 &  15.07 & -3.071 \tabularnewline
68 &  16 &  14.86 &  1.137 \tabularnewline
69 &  16 &  16.12 & -0.119 \tabularnewline
70 &  16 &  14.43 &  1.567 \tabularnewline
71 &  14 &  15.44 & -1.442 \tabularnewline
72 &  15 &  15.33 & -0.3311 \tabularnewline
73 &  14 &  15.13 & -1.131 \tabularnewline
74 &  15 &  15.16 & -0.16 \tabularnewline
75 &  15 &  16.27 & -1.275 \tabularnewline
76 &  16 &  15.6 &  0.4021 \tabularnewline
77 &  11 &  15.12 & -4.115 \tabularnewline
78 &  18 &  14.67 &  3.331 \tabularnewline
79 &  11 &  14.74 & -3.744 \tabularnewline
80 &  18 &  16.16 &  1.845 \tabularnewline
81 &  15 &  15.81 & -0.8134 \tabularnewline
82 &  19 &  16.52 &  2.48 \tabularnewline
83 &  17 &  15.59 &  1.408 \tabularnewline
84 &  14 &  15.6 & -1.598 \tabularnewline
85 &  13 &  16.09 & -3.089 \tabularnewline
86 &  17 &  15.86 &  1.142 \tabularnewline
87 &  14 &  15.43 & -1.428 \tabularnewline
88 &  19 &  15.37 &  3.633 \tabularnewline
89 &  14 &  15.05 & -1.047 \tabularnewline
90 &  16 &  15.58 &  0.4166 \tabularnewline
91 &  16 &  15.17 &  0.8254 \tabularnewline
92 &  15 &  15.98 & -0.9779 \tabularnewline
93 &  12 &  14.82 & -2.819 \tabularnewline
94 &  18 &  15.43 &  2.572 \tabularnewline
95 &  15 &  16.04 & -1.044 \tabularnewline
96 &  18 &  15.76 &  2.237 \tabularnewline
97 &  15 &  15.49 & -0.4867 \tabularnewline
98 &  16 &  15.15 &  0.8545 \tabularnewline
99 &  16 &  15.27 &  0.7293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 15.84[/C][C]-2.837[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 14.69[/C][C] 1.307[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.32[/C][C] 1.684[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.22[/C][C] 0.7795[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 15.29[/C][C] 1.713[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.68[/C][C] 1.319[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 14.62[/C][C] 0.3818[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.35[/C][C] 0.6472[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 15.03[/C][C]-1.026[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.16[/C][C] 0.84[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 16.71[/C][C] 0.2947[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.17[/C][C] 0.8327[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 16.42[/C][C]-0.423[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.64[/C][C] 1.36[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.33[/C][C]-0.3311[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 15.41[/C][C] 0.5876[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.58[/C][C] 0.4238[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.74[/C][C]-1.744[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 16.12[/C][C]-1.119[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.25[/C][C] 0.7471[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 14.77[/C][C]-1.774[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 15.29[/C][C] 1.713[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 15.27[/C][C]-1.271[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 15.29[/C][C]-1.287[/C][/ROW]
[ROW][C]25[/C][C] 17[/C][C] 15.76[/C][C] 1.238[/C][/ROW]
[ROW][C]26[/C][C] 13[/C][C] 15.7[/C][C]-2.703[/C][/ROW]
[ROW][C]27[/C][C] 16[/C][C] 16.17[/C][C]-0.1722[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 15.58[/C][C]-0.5762[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.49[/C][C]-0.4867[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 15.44[/C][C]-2.442[/C][/ROW]
[ROW][C]31[/C][C] 17[/C][C] 15.44[/C][C] 1.558[/C][/ROW]
[ROW][C]32[/C][C] 11[/C][C] 14.43[/C][C]-3.433[/C][/ROW]
[ROW][C]33[/C][C] 14[/C][C] 15.35[/C][C]-1.346[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 15.28[/C][C]-2.279[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 15.29[/C][C] 1.713[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 14.84[/C][C] 1.16[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15[/C][C] 1.996[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.3[/C][C] 0.6988[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 15.09[/C][C] 0.9149[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 15.13[/C][C] 0.869[/C][/ROW]
[ROW][C]41[/C][C] 15[/C][C] 14.91[/C][C] 0.08505[/C][/ROW]
[ROW][C]42[/C][C] 12[/C][C] 15.84[/C][C]-3.837[/C][/ROW]
[ROW][C]43[/C][C] 17[/C][C] 15.06[/C][C] 1.944[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 15.58[/C][C]-1.583[/C][/ROW]
[ROW][C]45[/C][C] 14[/C][C] 15.29[/C][C]-1.287[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.3[/C][C] 0.6988[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.24[/C][C]-0.2422[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 15.29[/C][C] 0.7133[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 15.26[/C][C]-1.257[/C][/ROW]
[ROW][C]50[/C][C] 15[/C][C] 14.72[/C][C] 0.2794[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 15.98[/C][C] 1.022[/C][/ROW]
[ROW][C]52[/C][C] 10[/C][C] 15.13[/C][C]-5.131[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 15.78[/C][C] 1.222[/C][/ROW]
[ROW][C]54[/C][C] 20[/C][C] 15.99[/C][C] 4.008[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 14.45[/C][C] 2.553[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 16.27[/C][C] 0.7254[/C][/ROW]
[ROW][C]57[/C][C] 14[/C][C] 15.29[/C][C]-1.287[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 15.7[/C][C] 1.297[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 15.21[/C][C] 1.788[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 15.7[/C][C] 0.2972[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 15.67[/C][C] 2.327[/C][/ROW]
[ROW][C]62[/C][C] 18[/C][C] 14.74[/C][C] 3.262[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 15.86[/C][C] 0.1415[/C][/ROW]
[ROW][C]64[/C][C] 15[/C][C] 15.95[/C][C]-0.9479[/C][/ROW]
[ROW][C]65[/C][C] 13[/C][C] 15.63[/C][C]-2.628[/C][/ROW]
[ROW][C]66[/C][C] 16[/C][C] 15.58[/C][C] 0.4166[/C][/ROW]
[ROW][C]67[/C][C] 12[/C][C] 15.07[/C][C]-3.071[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 14.86[/C][C] 1.137[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 16.12[/C][C]-0.119[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 14.43[/C][C] 1.567[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 15.44[/C][C]-1.442[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 15.33[/C][C]-0.3311[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 15.13[/C][C]-1.131[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.16[/C][C]-0.16[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 16.27[/C][C]-1.275[/C][/ROW]
[ROW][C]76[/C][C] 16[/C][C] 15.6[/C][C] 0.4021[/C][/ROW]
[ROW][C]77[/C][C] 11[/C][C] 15.12[/C][C]-4.115[/C][/ROW]
[ROW][C]78[/C][C] 18[/C][C] 14.67[/C][C] 3.331[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 14.74[/C][C]-3.744[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 16.16[/C][C] 1.845[/C][/ROW]
[ROW][C]81[/C][C] 15[/C][C] 15.81[/C][C]-0.8134[/C][/ROW]
[ROW][C]82[/C][C] 19[/C][C] 16.52[/C][C] 2.48[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.59[/C][C] 1.408[/C][/ROW]
[ROW][C]84[/C][C] 14[/C][C] 15.6[/C][C]-1.598[/C][/ROW]
[ROW][C]85[/C][C] 13[/C][C] 16.09[/C][C]-3.089[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 15.86[/C][C] 1.142[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 15.43[/C][C]-1.428[/C][/ROW]
[ROW][C]88[/C][C] 19[/C][C] 15.37[/C][C] 3.633[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 15.05[/C][C]-1.047[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 15.58[/C][C] 0.4166[/C][/ROW]
[ROW][C]91[/C][C] 16[/C][C] 15.17[/C][C] 0.8254[/C][/ROW]
[ROW][C]92[/C][C] 15[/C][C] 15.98[/C][C]-0.9779[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 14.82[/C][C]-2.819[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 15.43[/C][C] 2.572[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 16.04[/C][C]-1.044[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 15.76[/C][C] 2.237[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 15.49[/C][C]-0.4867[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 15.15[/C][C] 0.8545[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 15.27[/C][C] 0.7293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298643&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	15.84	-2.837
2	16	14.69	1.307
3	17	15.32	1.684
4	16	15.22	0.7795
5	17	15.29	1.713
6	17	15.68	1.319
7	15	14.62	0.3818
8	16	15.35	0.6472
9	14	15.03	-1.026
10	16	15.16	0.84
11	17	16.71	0.2947
12	16	15.17	0.8327
13	16	16.42	-0.423
14	16	14.64	1.36
15	15	15.33	-0.3311
16	16	15.41	0.5876
17	16	15.58	0.4238
18	13	14.74	-1.744
19	15	16.12	-1.119
20	17	16.25	0.7471
21	13	14.77	-1.774
22	17	15.29	1.713
23	14	15.27	-1.271
24	14	15.29	-1.287
25	17	15.76	1.238
26	13	15.7	-2.703
27	16	16.17	-0.1722
28	15	15.58	-0.5762
29	15	15.49	-0.4867
30	13	15.44	-2.442
31	17	15.44	1.558
32	11	14.43	-3.433
33	14	15.35	-1.346
34	13	15.28	-2.279
35	17	15.29	1.713
36	16	14.84	1.16
37	17	15	1.996
38	16	15.3	0.6988
39	16	15.09	0.9149
40	16	15.13	0.869
41	15	14.91	0.08505
42	12	15.84	-3.837
43	17	15.06	1.944
44	14	15.58	-1.583
45	14	15.29	-1.287
46	16	15.3	0.6988
47	15	15.24	-0.2422
48	16	15.29	0.7133
49	14	15.26	-1.257
50	15	14.72	0.2794
51	17	15.98	1.022
52	10	15.13	-5.131
53	17	15.78	1.222
54	20	15.99	4.008
55	17	14.45	2.553
56	17	16.27	0.7254
57	14	15.29	-1.287
58	17	15.7	1.297
59	17	15.21	1.788
60	16	15.7	0.2972
61	18	15.67	2.327
62	18	14.74	3.262
63	16	15.86	0.1415
64	15	15.95	-0.9479
65	13	15.63	-2.628
66	16	15.58	0.4166
67	12	15.07	-3.071
68	16	14.86	1.137
69	16	16.12	-0.119
70	16	14.43	1.567
71	14	15.44	-1.442
72	15	15.33	-0.3311
73	14	15.13	-1.131
74	15	15.16	-0.16
75	15	16.27	-1.275
76	16	15.6	0.4021
77	11	15.12	-4.115
78	18	14.67	3.331
79	11	14.74	-3.744
80	18	16.16	1.845
81	15	15.81	-0.8134
82	19	16.52	2.48
83	17	15.59	1.408
84	14	15.6	-1.598
85	13	16.09	-3.089
86	17	15.86	1.142
87	14	15.43	-1.428
88	19	15.37	3.633
89	14	15.05	-1.047
90	16	15.58	0.4166
91	16	15.17	0.8254
92	15	15.98	-0.9779
93	12	14.82	-2.819
94	18	15.43	2.572
95	15	16.04	-1.044
96	18	15.76	2.237
97	15	15.49	-0.4867
98	16	15.15	0.8545
99	16	15.27	0.7293

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.5804	0.8393	0.4196
9	0.6439	0.7121	0.3561
10	0.5066	0.9867	0.4934
11	0.4098	0.8197	0.5902
12	0.295	0.5901	0.705
13	0.2021	0.4042	0.7979
14	0.1391	0.2782	0.8609
15	0.09315	0.1863	0.9068
16	0.05773	0.1155	0.9423
17	0.03383	0.06765	0.9662
18	0.05557	0.1111	0.9444
19	0.04977	0.09954	0.9502
20	0.03554	0.07107	0.9645
21	0.04043	0.08085	0.9596
22	0.03267	0.06534	0.9673
23	0.03002	0.06004	0.97
24	0.03194	0.06389	0.9681
25	0.02746	0.05492	0.9725
26	0.0615	0.123	0.9385
27	0.04183	0.08367	0.9582
28	0.02885	0.0577	0.9711
29	0.01954	0.03907	0.9805
30	0.02955	0.05909	0.9705
31	0.02846	0.05692	0.9715
32	0.07344	0.1469	0.9266
33	0.06185	0.1237	0.9382
34	0.06601	0.132	0.934
35	0.06315	0.1263	0.9369
36	0.05172	0.1034	0.9483
37	0.06362	0.1272	0.9364
38	0.04862	0.09723	0.9514
39	0.03815	0.07629	0.9619
40	0.02852	0.05705	0.9715
41	0.01963	0.03926	0.9804
42	0.06465	0.1293	0.9354
43	0.06354	0.1271	0.9365
44	0.06355	0.1271	0.9365
45	0.05479	0.1096	0.9452
46	0.04204	0.08408	0.958
47	0.03011	0.06021	0.9699
48	0.02235	0.04471	0.9776
49	0.01748	0.03496	0.9825
50	0.01278	0.02555	0.9872
51	0.009733	0.01947	0.9903
52	0.09388	0.1878	0.9061
53	0.08297	0.1659	0.917
54	0.1904	0.3809	0.8096
55	0.2271	0.4542	0.7729
56	0.1895	0.379	0.8105
57	0.1697	0.3394	0.8303
58	0.1487	0.2973	0.8513
59	0.1428	0.2857	0.8572
60	0.1115	0.2229	0.8885
61	0.1264	0.2528	0.8736
62	0.2041	0.4082	0.7959
63	0.1629	0.3258	0.8371
64	0.1351	0.2703	0.8649
65	0.1661	0.3322	0.8339
66	0.1306	0.2611	0.8694
67	0.1845	0.3691	0.8155
68	0.1645	0.3291	0.8355
69	0.1272	0.2543	0.8728
70	0.1321	0.2642	0.8679
71	0.1179	0.2358	0.8821
72	0.08845	0.1769	0.9115
73	0.0686	0.1372	0.9314
74	0.04904	0.09809	0.951
75	0.04186	0.08372	0.9581
76	0.02858	0.05717	0.9714
77	0.07687	0.1537	0.9231
78	0.1823	0.3647	0.8177
79	0.2682	0.5364	0.7318
80	0.242	0.4841	0.758
81	0.2072	0.4144	0.7928
82	0.2789	0.5578	0.7211
83	0.22	0.44	0.78
84	0.1834	0.3668	0.8166
85	0.3409	0.6817	0.6591
86	0.2563	0.5127	0.7437
87	0.2819	0.5638	0.7181
88	0.4251	0.8501	0.5749
89	0.428	0.856	0.572
90	0.3277	0.6554	0.6723
91	0.8573	0.2855	0.1427

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.5804 &  0.8393 &  0.4196 \tabularnewline
9 &  0.6439 &  0.7121 &  0.3561 \tabularnewline
10 &  0.5066 &  0.9867 &  0.4934 \tabularnewline
11 &  0.4098 &  0.8197 &  0.5902 \tabularnewline
12 &  0.295 &  0.5901 &  0.705 \tabularnewline
13 &  0.2021 &  0.4042 &  0.7979 \tabularnewline
14 &  0.1391 &  0.2782 &  0.8609 \tabularnewline
15 &  0.09315 &  0.1863 &  0.9068 \tabularnewline
16 &  0.05773 &  0.1155 &  0.9423 \tabularnewline
17 &  0.03383 &  0.06765 &  0.9662 \tabularnewline
18 &  0.05557 &  0.1111 &  0.9444 \tabularnewline
19 &  0.04977 &  0.09954 &  0.9502 \tabularnewline
20 &  0.03554 &  0.07107 &  0.9645 \tabularnewline
21 &  0.04043 &  0.08085 &  0.9596 \tabularnewline
22 &  0.03267 &  0.06534 &  0.9673 \tabularnewline
23 &  0.03002 &  0.06004 &  0.97 \tabularnewline
24 &  0.03194 &  0.06389 &  0.9681 \tabularnewline
25 &  0.02746 &  0.05492 &  0.9725 \tabularnewline
26 &  0.0615 &  0.123 &  0.9385 \tabularnewline
27 &  0.04183 &  0.08367 &  0.9582 \tabularnewline
28 &  0.02885 &  0.0577 &  0.9711 \tabularnewline
29 &  0.01954 &  0.03907 &  0.9805 \tabularnewline
30 &  0.02955 &  0.05909 &  0.9705 \tabularnewline
31 &  0.02846 &  0.05692 &  0.9715 \tabularnewline
32 &  0.07344 &  0.1469 &  0.9266 \tabularnewline
33 &  0.06185 &  0.1237 &  0.9382 \tabularnewline
34 &  0.06601 &  0.132 &  0.934 \tabularnewline
35 &  0.06315 &  0.1263 &  0.9369 \tabularnewline
36 &  0.05172 &  0.1034 &  0.9483 \tabularnewline
37 &  0.06362 &  0.1272 &  0.9364 \tabularnewline
38 &  0.04862 &  0.09723 &  0.9514 \tabularnewline
39 &  0.03815 &  0.07629 &  0.9619 \tabularnewline
40 &  0.02852 &  0.05705 &  0.9715 \tabularnewline
41 &  0.01963 &  0.03926 &  0.9804 \tabularnewline
42 &  0.06465 &  0.1293 &  0.9354 \tabularnewline
43 &  0.06354 &  0.1271 &  0.9365 \tabularnewline
44 &  0.06355 &  0.1271 &  0.9365 \tabularnewline
45 &  0.05479 &  0.1096 &  0.9452 \tabularnewline
46 &  0.04204 &  0.08408 &  0.958 \tabularnewline
47 &  0.03011 &  0.06021 &  0.9699 \tabularnewline
48 &  0.02235 &  0.04471 &  0.9776 \tabularnewline
49 &  0.01748 &  0.03496 &  0.9825 \tabularnewline
50 &  0.01278 &  0.02555 &  0.9872 \tabularnewline
51 &  0.009733 &  0.01947 &  0.9903 \tabularnewline
52 &  0.09388 &  0.1878 &  0.9061 \tabularnewline
53 &  0.08297 &  0.1659 &  0.917 \tabularnewline
54 &  0.1904 &  0.3809 &  0.8096 \tabularnewline
55 &  0.2271 &  0.4542 &  0.7729 \tabularnewline
56 &  0.1895 &  0.379 &  0.8105 \tabularnewline
57 &  0.1697 &  0.3394 &  0.8303 \tabularnewline
58 &  0.1487 &  0.2973 &  0.8513 \tabularnewline
59 &  0.1428 &  0.2857 &  0.8572 \tabularnewline
60 &  0.1115 &  0.2229 &  0.8885 \tabularnewline
61 &  0.1264 &  0.2528 &  0.8736 \tabularnewline
62 &  0.2041 &  0.4082 &  0.7959 \tabularnewline
63 &  0.1629 &  0.3258 &  0.8371 \tabularnewline
64 &  0.1351 &  0.2703 &  0.8649 \tabularnewline
65 &  0.1661 &  0.3322 &  0.8339 \tabularnewline
66 &  0.1306 &  0.2611 &  0.8694 \tabularnewline
67 &  0.1845 &  0.3691 &  0.8155 \tabularnewline
68 &  0.1645 &  0.3291 &  0.8355 \tabularnewline
69 &  0.1272 &  0.2543 &  0.8728 \tabularnewline
70 &  0.1321 &  0.2642 &  0.8679 \tabularnewline
71 &  0.1179 &  0.2358 &  0.8821 \tabularnewline
72 &  0.08845 &  0.1769 &  0.9115 \tabularnewline
73 &  0.0686 &  0.1372 &  0.9314 \tabularnewline
74 &  0.04904 &  0.09809 &  0.951 \tabularnewline
75 &  0.04186 &  0.08372 &  0.9581 \tabularnewline
76 &  0.02858 &  0.05717 &  0.9714 \tabularnewline
77 &  0.07687 &  0.1537 &  0.9231 \tabularnewline
78 &  0.1823 &  0.3647 &  0.8177 \tabularnewline
79 &  0.2682 &  0.5364 &  0.7318 \tabularnewline
80 &  0.242 &  0.4841 &  0.758 \tabularnewline
81 &  0.2072 &  0.4144 &  0.7928 \tabularnewline
82 &  0.2789 &  0.5578 &  0.7211 \tabularnewline
83 &  0.22 &  0.44 &  0.78 \tabularnewline
84 &  0.1834 &  0.3668 &  0.8166 \tabularnewline
85 &  0.3409 &  0.6817 &  0.6591 \tabularnewline
86 &  0.2563 &  0.5127 &  0.7437 \tabularnewline
87 &  0.2819 &  0.5638 &  0.7181 \tabularnewline
88 &  0.4251 &  0.8501 &  0.5749 \tabularnewline
89 &  0.428 &  0.856 &  0.572 \tabularnewline
90 &  0.3277 &  0.6554 &  0.6723 \tabularnewline
91 &  0.8573 &  0.2855 &  0.1427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C] 0.5804[/C][C] 0.8393[/C][C] 0.4196[/C][/ROW]
[ROW][C]9[/C][C] 0.6439[/C][C] 0.7121[/C][C] 0.3561[/C][/ROW]
[ROW][C]10[/C][C] 0.5066[/C][C] 0.9867[/C][C] 0.4934[/C][/ROW]
[ROW][C]11[/C][C] 0.4098[/C][C] 0.8197[/C][C] 0.5902[/C][/ROW]
[ROW][C]12[/C][C] 0.295[/C][C] 0.5901[/C][C] 0.705[/C][/ROW]
[ROW][C]13[/C][C] 0.2021[/C][C] 0.4042[/C][C] 0.7979[/C][/ROW]
[ROW][C]14[/C][C] 0.1391[/C][C] 0.2782[/C][C] 0.8609[/C][/ROW]
[ROW][C]15[/C][C] 0.09315[/C][C] 0.1863[/C][C] 0.9068[/C][/ROW]
[ROW][C]16[/C][C] 0.05773[/C][C] 0.1155[/C][C] 0.9423[/C][/ROW]
[ROW][C]17[/C][C] 0.03383[/C][C] 0.06765[/C][C] 0.9662[/C][/ROW]
[ROW][C]18[/C][C] 0.05557[/C][C] 0.1111[/C][C] 0.9444[/C][/ROW]
[ROW][C]19[/C][C] 0.04977[/C][C] 0.09954[/C][C] 0.9502[/C][/ROW]
[ROW][C]20[/C][C] 0.03554[/C][C] 0.07107[/C][C] 0.9645[/C][/ROW]
[ROW][C]21[/C][C] 0.04043[/C][C] 0.08085[/C][C] 0.9596[/C][/ROW]
[ROW][C]22[/C][C] 0.03267[/C][C] 0.06534[/C][C] 0.9673[/C][/ROW]
[ROW][C]23[/C][C] 0.03002[/C][C] 0.06004[/C][C] 0.97[/C][/ROW]
[ROW][C]24[/C][C] 0.03194[/C][C] 0.06389[/C][C] 0.9681[/C][/ROW]
[ROW][C]25[/C][C] 0.02746[/C][C] 0.05492[/C][C] 0.9725[/C][/ROW]
[ROW][C]26[/C][C] 0.0615[/C][C] 0.123[/C][C] 0.9385[/C][/ROW]
[ROW][C]27[/C][C] 0.04183[/C][C] 0.08367[/C][C] 0.9582[/C][/ROW]
[ROW][C]28[/C][C] 0.02885[/C][C] 0.0577[/C][C] 0.9711[/C][/ROW]
[ROW][C]29[/C][C] 0.01954[/C][C] 0.03907[/C][C] 0.9805[/C][/ROW]
[ROW][C]30[/C][C] 0.02955[/C][C] 0.05909[/C][C] 0.9705[/C][/ROW]
[ROW][C]31[/C][C] 0.02846[/C][C] 0.05692[/C][C] 0.9715[/C][/ROW]
[ROW][C]32[/C][C] 0.07344[/C][C] 0.1469[/C][C] 0.9266[/C][/ROW]
[ROW][C]33[/C][C] 0.06185[/C][C] 0.1237[/C][C] 0.9382[/C][/ROW]
[ROW][C]34[/C][C] 0.06601[/C][C] 0.132[/C][C] 0.934[/C][/ROW]
[ROW][C]35[/C][C] 0.06315[/C][C] 0.1263[/C][C] 0.9369[/C][/ROW]
[ROW][C]36[/C][C] 0.05172[/C][C] 0.1034[/C][C] 0.9483[/C][/ROW]
[ROW][C]37[/C][C] 0.06362[/C][C] 0.1272[/C][C] 0.9364[/C][/ROW]
[ROW][C]38[/C][C] 0.04862[/C][C] 0.09723[/C][C] 0.9514[/C][/ROW]
[ROW][C]39[/C][C] 0.03815[/C][C] 0.07629[/C][C] 0.9619[/C][/ROW]
[ROW][C]40[/C][C] 0.02852[/C][C] 0.05705[/C][C] 0.9715[/C][/ROW]
[ROW][C]41[/C][C] 0.01963[/C][C] 0.03926[/C][C] 0.9804[/C][/ROW]
[ROW][C]42[/C][C] 0.06465[/C][C] 0.1293[/C][C] 0.9354[/C][/ROW]
[ROW][C]43[/C][C] 0.06354[/C][C] 0.1271[/C][C] 0.9365[/C][/ROW]
[ROW][C]44[/C][C] 0.06355[/C][C] 0.1271[/C][C] 0.9365[/C][/ROW]
[ROW][C]45[/C][C] 0.05479[/C][C] 0.1096[/C][C] 0.9452[/C][/ROW]
[ROW][C]46[/C][C] 0.04204[/C][C] 0.08408[/C][C] 0.958[/C][/ROW]
[ROW][C]47[/C][C] 0.03011[/C][C] 0.06021[/C][C] 0.9699[/C][/ROW]
[ROW][C]48[/C][C] 0.02235[/C][C] 0.04471[/C][C] 0.9776[/C][/ROW]
[ROW][C]49[/C][C] 0.01748[/C][C] 0.03496[/C][C] 0.9825[/C][/ROW]
[ROW][C]50[/C][C] 0.01278[/C][C] 0.02555[/C][C] 0.9872[/C][/ROW]
[ROW][C]51[/C][C] 0.009733[/C][C] 0.01947[/C][C] 0.9903[/C][/ROW]
[ROW][C]52[/C][C] 0.09388[/C][C] 0.1878[/C][C] 0.9061[/C][/ROW]
[ROW][C]53[/C][C] 0.08297[/C][C] 0.1659[/C][C] 0.917[/C][/ROW]
[ROW][C]54[/C][C] 0.1904[/C][C] 0.3809[/C][C] 0.8096[/C][/ROW]
[ROW][C]55[/C][C] 0.2271[/C][C] 0.4542[/C][C] 0.7729[/C][/ROW]
[ROW][C]56[/C][C] 0.1895[/C][C] 0.379[/C][C] 0.8105[/C][/ROW]
[ROW][C]57[/C][C] 0.1697[/C][C] 0.3394[/C][C] 0.8303[/C][/ROW]
[ROW][C]58[/C][C] 0.1487[/C][C] 0.2973[/C][C] 0.8513[/C][/ROW]
[ROW][C]59[/C][C] 0.1428[/C][C] 0.2857[/C][C] 0.8572[/C][/ROW]
[ROW][C]60[/C][C] 0.1115[/C][C] 0.2229[/C][C] 0.8885[/C][/ROW]
[ROW][C]61[/C][C] 0.1264[/C][C] 0.2528[/C][C] 0.8736[/C][/ROW]
[ROW][C]62[/C][C] 0.2041[/C][C] 0.4082[/C][C] 0.7959[/C][/ROW]
[ROW][C]63[/C][C] 0.1629[/C][C] 0.3258[/C][C] 0.8371[/C][/ROW]
[ROW][C]64[/C][C] 0.1351[/C][C] 0.2703[/C][C] 0.8649[/C][/ROW]
[ROW][C]65[/C][C] 0.1661[/C][C] 0.3322[/C][C] 0.8339[/C][/ROW]
[ROW][C]66[/C][C] 0.1306[/C][C] 0.2611[/C][C] 0.8694[/C][/ROW]
[ROW][C]67[/C][C] 0.1845[/C][C] 0.3691[/C][C] 0.8155[/C][/ROW]
[ROW][C]68[/C][C] 0.1645[/C][C] 0.3291[/C][C] 0.8355[/C][/ROW]
[ROW][C]69[/C][C] 0.1272[/C][C] 0.2543[/C][C] 0.8728[/C][/ROW]
[ROW][C]70[/C][C] 0.1321[/C][C] 0.2642[/C][C] 0.8679[/C][/ROW]
[ROW][C]71[/C][C] 0.1179[/C][C] 0.2358[/C][C] 0.8821[/C][/ROW]
[ROW][C]72[/C][C] 0.08845[/C][C] 0.1769[/C][C] 0.9115[/C][/ROW]
[ROW][C]73[/C][C] 0.0686[/C][C] 0.1372[/C][C] 0.9314[/C][/ROW]
[ROW][C]74[/C][C] 0.04904[/C][C] 0.09809[/C][C] 0.951[/C][/ROW]
[ROW][C]75[/C][C] 0.04186[/C][C] 0.08372[/C][C] 0.9581[/C][/ROW]
[ROW][C]76[/C][C] 0.02858[/C][C] 0.05717[/C][C] 0.9714[/C][/ROW]
[ROW][C]77[/C][C] 0.07687[/C][C] 0.1537[/C][C] 0.9231[/C][/ROW]
[ROW][C]78[/C][C] 0.1823[/C][C] 0.3647[/C][C] 0.8177[/C][/ROW]
[ROW][C]79[/C][C] 0.2682[/C][C] 0.5364[/C][C] 0.7318[/C][/ROW]
[ROW][C]80[/C][C] 0.242[/C][C] 0.4841[/C][C] 0.758[/C][/ROW]
[ROW][C]81[/C][C] 0.2072[/C][C] 0.4144[/C][C] 0.7928[/C][/ROW]
[ROW][C]82[/C][C] 0.2789[/C][C] 0.5578[/C][C] 0.7211[/C][/ROW]
[ROW][C]83[/C][C] 0.22[/C][C] 0.44[/C][C] 0.78[/C][/ROW]
[ROW][C]84[/C][C] 0.1834[/C][C] 0.3668[/C][C] 0.8166[/C][/ROW]
[ROW][C]85[/C][C] 0.3409[/C][C] 0.6817[/C][C] 0.6591[/C][/ROW]
[ROW][C]86[/C][C] 0.2563[/C][C] 0.5127[/C][C] 0.7437[/C][/ROW]
[ROW][C]87[/C][C] 0.2819[/C][C] 0.5638[/C][C] 0.7181[/C][/ROW]
[ROW][C]88[/C][C] 0.4251[/C][C] 0.8501[/C][C] 0.5749[/C][/ROW]
[ROW][C]89[/C][C] 0.428[/C][C] 0.856[/C][C] 0.572[/C][/ROW]
[ROW][C]90[/C][C] 0.3277[/C][C] 0.6554[/C][C] 0.6723[/C][/ROW]
[ROW][C]91[/C][C] 0.8573[/C][C] 0.2855[/C][C] 0.1427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298643&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.5804	0.8393	0.4196
9	0.6439	0.7121	0.3561
10	0.5066	0.9867	0.4934
11	0.4098	0.8197	0.5902
12	0.295	0.5901	0.705
13	0.2021	0.4042	0.7979
14	0.1391	0.2782	0.8609
15	0.09315	0.1863	0.9068
16	0.05773	0.1155	0.9423
17	0.03383	0.06765	0.9662
18	0.05557	0.1111	0.9444
19	0.04977	0.09954	0.9502
20	0.03554	0.07107	0.9645
21	0.04043	0.08085	0.9596
22	0.03267	0.06534	0.9673
23	0.03002	0.06004	0.97
24	0.03194	0.06389	0.9681
25	0.02746	0.05492	0.9725
26	0.0615	0.123	0.9385
27	0.04183	0.08367	0.9582
28	0.02885	0.0577	0.9711
29	0.01954	0.03907	0.9805
30	0.02955	0.05909	0.9705
31	0.02846	0.05692	0.9715
32	0.07344	0.1469	0.9266
33	0.06185	0.1237	0.9382
34	0.06601	0.132	0.934
35	0.06315	0.1263	0.9369
36	0.05172	0.1034	0.9483
37	0.06362	0.1272	0.9364
38	0.04862	0.09723	0.9514
39	0.03815	0.07629	0.9619
40	0.02852	0.05705	0.9715
41	0.01963	0.03926	0.9804
42	0.06465	0.1293	0.9354
43	0.06354	0.1271	0.9365
44	0.06355	0.1271	0.9365
45	0.05479	0.1096	0.9452
46	0.04204	0.08408	0.958
47	0.03011	0.06021	0.9699
48	0.02235	0.04471	0.9776
49	0.01748	0.03496	0.9825
50	0.01278	0.02555	0.9872
51	0.009733	0.01947	0.9903
52	0.09388	0.1878	0.9061
53	0.08297	0.1659	0.917
54	0.1904	0.3809	0.8096
55	0.2271	0.4542	0.7729
56	0.1895	0.379	0.8105
57	0.1697	0.3394	0.8303
58	0.1487	0.2973	0.8513
59	0.1428	0.2857	0.8572
60	0.1115	0.2229	0.8885
61	0.1264	0.2528	0.8736
62	0.2041	0.4082	0.7959
63	0.1629	0.3258	0.8371
64	0.1351	0.2703	0.8649
65	0.1661	0.3322	0.8339
66	0.1306	0.2611	0.8694
67	0.1845	0.3691	0.8155
68	0.1645	0.3291	0.8355
69	0.1272	0.2543	0.8728
70	0.1321	0.2642	0.8679
71	0.1179	0.2358	0.8821
72	0.08845	0.1769	0.9115
73	0.0686	0.1372	0.9314
74	0.04904	0.09809	0.951
75	0.04186	0.08372	0.9581
76	0.02858	0.05717	0.9714
77	0.07687	0.1537	0.9231
78	0.1823	0.3647	0.8177
79	0.2682	0.5364	0.7318
80	0.242	0.4841	0.758
81	0.2072	0.4144	0.7928
82	0.2789	0.5578	0.7211
83	0.22	0.44	0.78
84	0.1834	0.3668	0.8166
85	0.3409	0.6817	0.6591
86	0.2563	0.5127	0.7437
87	0.2819	0.5638	0.7181
88	0.4251	0.8501	0.5749
89	0.428	0.856	0.572
90	0.3277	0.6554	0.6723
91	0.8573	0.2855	0.1427

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	6	0.0714286	NOK
10% type I error level	26	0.309524	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 6 & 0.0714286 & NOK \tabularnewline
10% type I error level & 26 & 0.309524 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.0714286[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.309524[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298643&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	6	0.0714286	NOK
10% type I error level	26	0.309524	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.31852, df1 = 2, df2 = 92, p-value = 0.728
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4997, df1 = 8, df2 = 86, p-value = 0.1692
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.38035, df1 = 2, df2 = 92, p-value = 0.6847

\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 = 0.31852, df1 = 2, df2 = 92, p-value = 0.728
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.4997, df1 = 8, df2 = 86, p-value = 0.1692
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.38035, df1 = 2, df2 = 92, p-value = 0.6847
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&T=7

[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 = 0.31852, df1 = 2, df2 = 92, p-value = 0.728
[/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 = 1.4997, df1 = 8, df2 = 86, p-value = 0.1692
[/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 = 0.38035, df1 = 2, df2 = 92, p-value = 0.6847
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298643&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&T=7

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 = 0.31852, df1 = 2, df2 = 92, p-value = 0.728
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4997, df1 = 8, df2 = 86, p-value = 0.1692
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.38035, df1 = 2, df2 = 92, p-value = 0.6847

Variance Inflation Factors (Multicollinearity)
> vif IVHB1 IVHB2 IVHB3 IVHB4 1.032480 1.038649 1.063048 1.055761

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   IVHB1    IVHB2    IVHB3    IVHB4 
1.032480 1.038649 1.063048 1.055761 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298643&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   IVHB1    IVHB2    IVHB3    IVHB4 
1.032480 1.038649 1.063048 1.055761 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298643&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298643&T=8

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 IVHB1 IVHB2 IVHB3 IVHB4 1.032480 1.038649 1.063048 1.055761

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):

Parameters (R input):

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

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 <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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