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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 06 Dec 2016 16:21:44 +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/06/t1481037798yv5yhlxk55hy2gb.htm/, Retrieved Fri, 01 Nov 2024 03:48:11 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297850, Retrieved Fri, 01 Nov 2024 03:48:11 +0000

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No (this computation is public)

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Estimated Impact

117

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

-       [Multiple Regression] [vragen imago en p...] [2016-12-06 15:21:44] [a5a1109b2531d70fe6d77f4f71bfe676] [Current]

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2	2	3	4	11
4	2	1	4	9
4	2	5	4	12
4	3	4	4	NA
3	4	3	3	NA
4	3	2	5	12
1	4	4	4	12
4	2	5	4	NA
3	NA	5	2	NA
4	4	3	4	11
2	2	2	4	12
4	2	2	3	12
4	5	4	3	15
5	4	4	4	13
4	2	4	4	12
1	3	5	4	11
2	1	2	5	NA
4	1	NA	NA	NA
4	3	2	4	9
5	4	4	4	NA
5	5	4	4	11
4	5	4	4	NA
1	1	5	4	12
4	4	3	4	NA
2	2	4	4	NA
4	4	3	4	NA
5	4	3	3	12
3	3	3	3	12
5	4	5	5	14
3	2	4	4	NA
5	2	4	4	12
2	4	3	4	9
1	2	3	4	13
NA	4	5	1	NA
4	2	3	3	13
4	4	3	4	12
3	3	3	4	NA
5	3	5	5	12
4	4	3	4	12
NA	2	3	4	12
4	3	3	4	NA
2	2	4	3	12
3	4	3	4	11
1	2	1	5	13
3	2	4	4	13
3	3	4	3	NA
3	3	3	3	NA
4	NA	4	5	13
4	4	4	4	10
4	5	5	1	NA
4	4	4	4	13
4	4	4	4	NA
2	4	3	4	NA
5	2	2	4	5
3	2	4	3	NA
3	1	3	4	10
4	3	3	3	NA
4	4	3	4	15
4	3	4	2	13
3	3	4	4	NA
4	2	3	4	12
4	3	4	4	13
4	2	5	3	13
4	4	2	4	11
4	3	3	3	NA
2	2	3	4	NA
4	4	3	3	12
4	5	4	4	12
4	4	3	4	13
4	3	4	4	14
4	2	3	4	NA
5	3	1	3	NA
3	4	4	3	NA
2	4	3	2	NA
4	4	2	4	NA
5	5	3	5	12
4	4	3	4	12
5	4	4	5	10
5	4	5	2	12
2	3	3	4	12
4	2	4	4	NA
4	4	2	4	NA
4	4	2	4	12
3	4	2	5	13
4	2	3	4	NA
2	2	4	4	14
5	1	3	4	10
3	NA	5	4	12
4	4	4	1	NA
2	4	4	4	13
4	4	3	4	11
3	3	4	3	NA
3	4	3	4	12
4	4	5	4	NA
4	4	4	3	12
4	2	4	3	13
3	4	3	4	12
4	4	4	5	9
3	1	1	3	NA
3	4	4	4	12
1	2	4	3	NA
4	3	4	4	14
3	3	4	5	NA
3	4	4	3	11
5	3	3	4	NA
5	4	5	4	NA
4	4	3	NA	NA
5	4	5	5	NA
4	4	4	4	NA
4	5	4	4	12
4	5	4	5	NA
4	2	4	3	NA
3	1	3	3	NA
4	3	4	3	12
3	3	3	4	NA
4	1	3	4	9
2	4	3	4	13
1	4	3	4	NA
5	2	2	4	10
4	4	4	4	14
3	3	3	3	10
4	4	2	4	12
4	4	4	5	NA
4	2	4	4	11
4	2	3	3	NA
2	4	4	4	14
4	4	5	4	13
4	2	4	3	12
4	2	NA	3	NA
4	2	4	4	NA
3	2	4	2	10
4	5	4	4	NA
5	2	5	3	12
2	NA	2	4	NA
5	2	4	4	12
4	4	4	4	NA
3	5	5	4	15
NA	4	4	3	NA
2	4	4	2	NA
2	3	5	5	12
2	3	2	3	12
4	1	4	4	10
4	4	5	4	12
5	5	3	4	12
3	4	4	5	NA
3	4	4	4	12
4	5	3	4	11
4	4	5	3	13
4	5	5	1	NA
4	5	3	4	NA
4	3	2	5	NA
4	5	4	4	13
4	1	5	4	11
2	3	3	4	10
5	2	3	5	9
4	2	4	4	NA
4	NA	3	4	12
4	4	2	4	NA
4	2	3	4	NA
4	5	3	4	13
2	4	4	3	10
3	5	1	5	13
3	3	4	3	NA
4	2	3	4	NA
4	4	3	4	NA
4	2	2	5	NA
4	3	3	4	12
3	3	3	4	NA
3	2	5	2	12

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 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 time6 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&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]

6 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297850&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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	6 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
V5[t] = + 10.2852 -0.230543V1[t] + 0.441032V2[t] + 0.442236V3[t] -0.099862V4[t] -0.00458843t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
V5[t] =  +  10.2852 -0.230543V1[t] +  0.441032V2[t] +  0.442236V3[t] -0.099862V4[t] -0.00458843t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]V5[t] =  +  10.2852 -0.230543V1[t] +  0.441032V2[t] +  0.442236V3[t] -0.099862V4[t] -0.00458843t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297850&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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
V5[t] = + 10.2852 -0.230543V1[t] + 0.441032V2[t] + 0.442236V3[t] -0.099862V4[t] -0.00458843t + 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)	+10.29	1.223	+8.4070e+00	6.315e-13	3.157e-13
V1	-0.2305	0.1353	-1.7040e+00	0.09184	0.04592
V2	+0.441	0.1299	+3.3950e+00	0.001028	0.000514
V3	+0.4422	0.1519	+2.9120e+00	0.004535	0.002267
V4	-0.09986	0.2253	-4.4320e-01	0.6587	0.3293
t	-0.004588	0.005421	-8.4640e-01	0.3996	0.1998

\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) & +10.29 &  1.223 & +8.4070e+00 &  6.315e-13 &  3.157e-13 \tabularnewline
V1 & -0.2305 &  0.1353 & -1.7040e+00 &  0.09184 &  0.04592 \tabularnewline
V2 & +0.441 &  0.1299 & +3.3950e+00 &  0.001028 &  0.000514 \tabularnewline
V3 & +0.4422 &  0.1519 & +2.9120e+00 &  0.004535 &  0.002267 \tabularnewline
V4 & -0.09986 &  0.2253 & -4.4320e-01 &  0.6587 &  0.3293 \tabularnewline
t & -0.004588 &  0.005421 & -8.4640e-01 &  0.3996 &  0.1998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&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]+10.29[/C][C] 1.223[/C][C]+8.4070e+00[/C][C] 6.315e-13[/C][C] 3.157e-13[/C][/ROW]
[ROW][C]V1[/C][C]-0.2305[/C][C] 0.1353[/C][C]-1.7040e+00[/C][C] 0.09184[/C][C] 0.04592[/C][/ROW]
[ROW][C]V2[/C][C]+0.441[/C][C] 0.1299[/C][C]+3.3950e+00[/C][C] 0.001028[/C][C] 0.000514[/C][/ROW]
[ROW][C]V3[/C][C]+0.4422[/C][C] 0.1519[/C][C]+2.9120e+00[/C][C] 0.004535[/C][C] 0.002267[/C][/ROW]
[ROW][C]V4[/C][C]-0.09986[/C][C] 0.2253[/C][C]-4.4320e-01[/C][C] 0.6587[/C][C] 0.3293[/C][/ROW]
[ROW][C]t[/C][C]-0.004588[/C][C] 0.005421[/C][C]-8.4640e-01[/C][C] 0.3996[/C][C] 0.1998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297850&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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)	+10.29	1.223	+8.4070e+00	6.315e-13	3.157e-13
V1	-0.2305	0.1353	-1.7040e+00	0.09184	0.04592
V2	+0.441	0.1299	+3.3950e+00	0.001028	0.000514
V3	+0.4422	0.1519	+2.9120e+00	0.004535	0.002267
V4	-0.09986	0.2253	-4.4320e-01	0.6587	0.3293
t	-0.004588	0.005421	-8.4640e-01	0.3996	0.1998

Multiple Linear Regression - Regression Statistics
Multiple R	0.4427
R-squared	0.196
Adjusted R-squared	0.1508
F-TEST (value)	4.338
F-TEST (DF numerator)	5
F-TEST (DF denominator)	89
p-value	0.001403
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.414
Sum Squared Residuals	177.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.4427 \tabularnewline
R-squared &  0.196 \tabularnewline
Adjusted R-squared &  0.1508 \tabularnewline
F-TEST (value) &  4.338 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 89 \tabularnewline
p-value &  0.001403 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.414 \tabularnewline
Sum Squared Residuals &  177.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.4427[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.196[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1508[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4.338[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]89[/C][/ROW]
[ROW][C]p-value[/C][C] 0.001403[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.414[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 177.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297850&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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	0.4427
R-squared	0.196
Adjusted R-squared	0.1508
F-TEST (value)	4.338
F-TEST (DF numerator)	5
F-TEST (DF denominator)	89
p-value	0.001403
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.414
Sum Squared Residuals	177.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	11.63	-0.6289
2	9	10.28	-1.279
3	12	12.04	-0.04308
4	12	11.05	0.947
5	12	13.17	-1.165
6	11	12.03	-1.027
7	12	11.16	0.8409
8	12	10.79	1.207
9	15	13	2.004
10	13	12.22	0.7798
11	12	11.56	0.4359
12	11	13.13	-2.134
13	9	11.11	-2.112
14	11	12.64	-1.643
15	12	12.24	-0.2386
16	12	11.85	0.1497
17	12	11.87	0.1342
18	14	12.53	1.474
19	12	11.3	0.7031
20	9	12.42	-3.424
21	13	11.77	1.232
22	13	11.17	1.829
23	12	11.95	0.0511
24	12	12.06	-0.05735
25	12	11.94	0.06027
26	12	12.06	-0.05626
27	11	12.16	-1.161
28	13	10.75	2.249
29	13	11.71	1.288
30	10	12.36	-2.359
31	13	12.35	0.6456
32	5	10.35	-5.353
33	10	10.81	-0.8105
34	15	11.9	3.102
35	13	12.09	0.9052
36	12	11.01	0.9928
37	13	11.89	1.114
38	13	11.98	1.018
39	11	11.43	-0.4333
40	12	11.97	0.02924
41	12	12.75	-0.7496
42	13	11.86	1.138
43	14	11.86	2.142
44	12	11.96	0.03683
45	12	11.85	0.152
46	10	11.96	-1.955
47	12	12.69	-0.6924
48	12	11.85	0.1458
49	12	11.39	0.6126
50	13	11.51	1.487
51	14	11.84	2.158
52	10	10.26	-0.2622
53	13	12.71	0.2854
54	11	11.81	-0.8067
55	12	12.03	-0.03262
56	12	12.34	-0.3396
57	13	11.45	1.547
58	12	12.02	-0.01885
59	9	12.13	-3.126
60	12	12.45	-0.4519
61	14	11.78	2.224
62	11	12.54	-1.543
63	12	12.65	-0.6486
64	12	11.86	0.1382
65	9	10.43	-1.433
66	13	12.21	0.7873
67	10	10.19	-0.1922
68	14	12.18	1.815
69	10	11.63	-1.627
70	12	11.29	0.709
71	11	11.29	-0.2888
72	14	12.63	1.373
73	13	12.6	0.396
74	12	11.37	0.6251
75	10	11.7	-1.701
76	12	11.58	0.4226
77	12	11.03	0.9692
78	15	13.25	1.747
79	12	12.5	-0.4966
80	12	11.37	0.635
81	10	10.8	-0.8019
82	12	12.56	-0.5627
83	12	11.88	0.1159
84	12	12.34	-0.3418
85	11	12.11	-1.105
86	13	12.64	0.3558
87	13	12.54	0.4615
88	11	11.21	-0.212
89	10	11.67	-1.666
90	9	10.43	-1.429
91	13	12.08	0.9221
92	10	12.64	-2.635
93	13	11.31	1.685
94	12	11.18	0.8179
95	12	12.05	-0.05121

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  11.63 & -0.6289 \tabularnewline
2 &  9 &  10.28 & -1.279 \tabularnewline
3 &  12 &  12.04 & -0.04308 \tabularnewline
4 &  12 &  11.05 &  0.947 \tabularnewline
5 &  12 &  13.17 & -1.165 \tabularnewline
6 &  11 &  12.03 & -1.027 \tabularnewline
7 &  12 &  11.16 &  0.8409 \tabularnewline
8 &  12 &  10.79 &  1.207 \tabularnewline
9 &  15 &  13 &  2.004 \tabularnewline
10 &  13 &  12.22 &  0.7798 \tabularnewline
11 &  12 &  11.56 &  0.4359 \tabularnewline
12 &  11 &  13.13 & -2.134 \tabularnewline
13 &  9 &  11.11 & -2.112 \tabularnewline
14 &  11 &  12.64 & -1.643 \tabularnewline
15 &  12 &  12.24 & -0.2386 \tabularnewline
16 &  12 &  11.85 &  0.1497 \tabularnewline
17 &  12 &  11.87 &  0.1342 \tabularnewline
18 &  14 &  12.53 &  1.474 \tabularnewline
19 &  12 &  11.3 &  0.7031 \tabularnewline
20 &  9 &  12.42 & -3.424 \tabularnewline
21 &  13 &  11.77 &  1.232 \tabularnewline
22 &  13 &  11.17 &  1.829 \tabularnewline
23 &  12 &  11.95 &  0.0511 \tabularnewline
24 &  12 &  12.06 & -0.05735 \tabularnewline
25 &  12 &  11.94 &  0.06027 \tabularnewline
26 &  12 &  12.06 & -0.05626 \tabularnewline
27 &  11 &  12.16 & -1.161 \tabularnewline
28 &  13 &  10.75 &  2.249 \tabularnewline
29 &  13 &  11.71 &  1.288 \tabularnewline
30 &  10 &  12.36 & -2.359 \tabularnewline
31 &  13 &  12.35 &  0.6456 \tabularnewline
32 &  5 &  10.35 & -5.353 \tabularnewline
33 &  10 &  10.81 & -0.8105 \tabularnewline
34 &  15 &  11.9 &  3.102 \tabularnewline
35 &  13 &  12.09 &  0.9052 \tabularnewline
36 &  12 &  11.01 &  0.9928 \tabularnewline
37 &  13 &  11.89 &  1.114 \tabularnewline
38 &  13 &  11.98 &  1.018 \tabularnewline
39 &  11 &  11.43 & -0.4333 \tabularnewline
40 &  12 &  11.97 &  0.02924 \tabularnewline
41 &  12 &  12.75 & -0.7496 \tabularnewline
42 &  13 &  11.86 &  1.138 \tabularnewline
43 &  14 &  11.86 &  2.142 \tabularnewline
44 &  12 &  11.96 &  0.03683 \tabularnewline
45 &  12 &  11.85 &  0.152 \tabularnewline
46 &  10 &  11.96 & -1.955 \tabularnewline
47 &  12 &  12.69 & -0.6924 \tabularnewline
48 &  12 &  11.85 &  0.1458 \tabularnewline
49 &  12 &  11.39 &  0.6126 \tabularnewline
50 &  13 &  11.51 &  1.487 \tabularnewline
51 &  14 &  11.84 &  2.158 \tabularnewline
52 &  10 &  10.26 & -0.2622 \tabularnewline
53 &  13 &  12.71 &  0.2854 \tabularnewline
54 &  11 &  11.81 & -0.8067 \tabularnewline
55 &  12 &  12.03 & -0.03262 \tabularnewline
56 &  12 &  12.34 & -0.3396 \tabularnewline
57 &  13 &  11.45 &  1.547 \tabularnewline
58 &  12 &  12.02 & -0.01885 \tabularnewline
59 &  9 &  12.13 & -3.126 \tabularnewline
60 &  12 &  12.45 & -0.4519 \tabularnewline
61 &  14 &  11.78 &  2.224 \tabularnewline
62 &  11 &  12.54 & -1.543 \tabularnewline
63 &  12 &  12.65 & -0.6486 \tabularnewline
64 &  12 &  11.86 &  0.1382 \tabularnewline
65 &  9 &  10.43 & -1.433 \tabularnewline
66 &  13 &  12.21 &  0.7873 \tabularnewline
67 &  10 &  10.19 & -0.1922 \tabularnewline
68 &  14 &  12.18 &  1.815 \tabularnewline
69 &  10 &  11.63 & -1.627 \tabularnewline
70 &  12 &  11.29 &  0.709 \tabularnewline
71 &  11 &  11.29 & -0.2888 \tabularnewline
72 &  14 &  12.63 &  1.373 \tabularnewline
73 &  13 &  12.6 &  0.396 \tabularnewline
74 &  12 &  11.37 &  0.6251 \tabularnewline
75 &  10 &  11.7 & -1.701 \tabularnewline
76 &  12 &  11.58 &  0.4226 \tabularnewline
77 &  12 &  11.03 &  0.9692 \tabularnewline
78 &  15 &  13.25 &  1.747 \tabularnewline
79 &  12 &  12.5 & -0.4966 \tabularnewline
80 &  12 &  11.37 &  0.635 \tabularnewline
81 &  10 &  10.8 & -0.8019 \tabularnewline
82 &  12 &  12.56 & -0.5627 \tabularnewline
83 &  12 &  11.88 &  0.1159 \tabularnewline
84 &  12 &  12.34 & -0.3418 \tabularnewline
85 &  11 &  12.11 & -1.105 \tabularnewline
86 &  13 &  12.64 &  0.3558 \tabularnewline
87 &  13 &  12.54 &  0.4615 \tabularnewline
88 &  11 &  11.21 & -0.212 \tabularnewline
89 &  10 &  11.67 & -1.666 \tabularnewline
90 &  9 &  10.43 & -1.429 \tabularnewline
91 &  13 &  12.08 &  0.9221 \tabularnewline
92 &  10 &  12.64 & -2.635 \tabularnewline
93 &  13 &  11.31 &  1.685 \tabularnewline
94 &  12 &  11.18 &  0.8179 \tabularnewline
95 &  12 &  12.05 & -0.05121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&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] 11[/C][C] 11.63[/C][C]-0.6289[/C][/ROW]
[ROW][C]2[/C][C] 9[/C][C] 10.28[/C][C]-1.279[/C][/ROW]
[ROW][C]3[/C][C] 12[/C][C] 12.04[/C][C]-0.04308[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 11.05[/C][C] 0.947[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 13.17[/C][C]-1.165[/C][/ROW]
[ROW][C]6[/C][C] 11[/C][C] 12.03[/C][C]-1.027[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 11.16[/C][C] 0.8409[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 10.79[/C][C] 1.207[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 13[/C][C] 2.004[/C][/ROW]
[ROW][C]10[/C][C] 13[/C][C] 12.22[/C][C] 0.7798[/C][/ROW]
[ROW][C]11[/C][C] 12[/C][C] 11.56[/C][C] 0.4359[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 13.13[/C][C]-2.134[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 11.11[/C][C]-2.112[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 12.64[/C][C]-1.643[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 12.24[/C][C]-0.2386[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 11.85[/C][C] 0.1497[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 11.87[/C][C] 0.1342[/C][/ROW]
[ROW][C]18[/C][C] 14[/C][C] 12.53[/C][C] 1.474[/C][/ROW]
[ROW][C]19[/C][C] 12[/C][C] 11.3[/C][C] 0.7031[/C][/ROW]
[ROW][C]20[/C][C] 9[/C][C] 12.42[/C][C]-3.424[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 11.77[/C][C] 1.232[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 11.17[/C][C] 1.829[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 11.95[/C][C] 0.0511[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 12.06[/C][C]-0.05735[/C][/ROW]
[ROW][C]25[/C][C] 12[/C][C] 11.94[/C][C] 0.06027[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 12.06[/C][C]-0.05626[/C][/ROW]
[ROW][C]27[/C][C] 11[/C][C] 12.16[/C][C]-1.161[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 10.75[/C][C] 2.249[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 11.71[/C][C] 1.288[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 12.36[/C][C]-2.359[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 12.35[/C][C] 0.6456[/C][/ROW]
[ROW][C]32[/C][C] 5[/C][C] 10.35[/C][C]-5.353[/C][/ROW]
[ROW][C]33[/C][C] 10[/C][C] 10.81[/C][C]-0.8105[/C][/ROW]
[ROW][C]34[/C][C] 15[/C][C] 11.9[/C][C] 3.102[/C][/ROW]
[ROW][C]35[/C][C] 13[/C][C] 12.09[/C][C] 0.9052[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 11.01[/C][C] 0.9928[/C][/ROW]
[ROW][C]37[/C][C] 13[/C][C] 11.89[/C][C] 1.114[/C][/ROW]
[ROW][C]38[/C][C] 13[/C][C] 11.98[/C][C] 1.018[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 11.43[/C][C]-0.4333[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 11.97[/C][C] 0.02924[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 12.75[/C][C]-0.7496[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 11.86[/C][C] 1.138[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 11.86[/C][C] 2.142[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 11.96[/C][C] 0.03683[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 11.85[/C][C] 0.152[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 11.96[/C][C]-1.955[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 12.69[/C][C]-0.6924[/C][/ROW]
[ROW][C]48[/C][C] 12[/C][C] 11.85[/C][C] 0.1458[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 11.39[/C][C] 0.6126[/C][/ROW]
[ROW][C]50[/C][C] 13[/C][C] 11.51[/C][C] 1.487[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 11.84[/C][C] 2.158[/C][/ROW]
[ROW][C]52[/C][C] 10[/C][C] 10.26[/C][C]-0.2622[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 12.71[/C][C] 0.2854[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 11.81[/C][C]-0.8067[/C][/ROW]
[ROW][C]55[/C][C] 12[/C][C] 12.03[/C][C]-0.03262[/C][/ROW]
[ROW][C]56[/C][C] 12[/C][C] 12.34[/C][C]-0.3396[/C][/ROW]
[ROW][C]57[/C][C] 13[/C][C] 11.45[/C][C] 1.547[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 12.02[/C][C]-0.01885[/C][/ROW]
[ROW][C]59[/C][C] 9[/C][C] 12.13[/C][C]-3.126[/C][/ROW]
[ROW][C]60[/C][C] 12[/C][C] 12.45[/C][C]-0.4519[/C][/ROW]
[ROW][C]61[/C][C] 14[/C][C] 11.78[/C][C] 2.224[/C][/ROW]
[ROW][C]62[/C][C] 11[/C][C] 12.54[/C][C]-1.543[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 12.65[/C][C]-0.6486[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 11.86[/C][C] 0.1382[/C][/ROW]
[ROW][C]65[/C][C] 9[/C][C] 10.43[/C][C]-1.433[/C][/ROW]
[ROW][C]66[/C][C] 13[/C][C] 12.21[/C][C] 0.7873[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 10.19[/C][C]-0.1922[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 12.18[/C][C] 1.815[/C][/ROW]
[ROW][C]69[/C][C] 10[/C][C] 11.63[/C][C]-1.627[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 11.29[/C][C] 0.709[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 11.29[/C][C]-0.2888[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 12.63[/C][C] 1.373[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 12.6[/C][C] 0.396[/C][/ROW]
[ROW][C]74[/C][C] 12[/C][C] 11.37[/C][C] 0.6251[/C][/ROW]
[ROW][C]75[/C][C] 10[/C][C] 11.7[/C][C]-1.701[/C][/ROW]
[ROW][C]76[/C][C] 12[/C][C] 11.58[/C][C] 0.4226[/C][/ROW]
[ROW][C]77[/C][C] 12[/C][C] 11.03[/C][C] 0.9692[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 13.25[/C][C] 1.747[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 12.5[/C][C]-0.4966[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 11.37[/C][C] 0.635[/C][/ROW]
[ROW][C]81[/C][C] 10[/C][C] 10.8[/C][C]-0.8019[/C][/ROW]
[ROW][C]82[/C][C] 12[/C][C] 12.56[/C][C]-0.5627[/C][/ROW]
[ROW][C]83[/C][C] 12[/C][C] 11.88[/C][C] 0.1159[/C][/ROW]
[ROW][C]84[/C][C] 12[/C][C] 12.34[/C][C]-0.3418[/C][/ROW]
[ROW][C]85[/C][C] 11[/C][C] 12.11[/C][C]-1.105[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 12.64[/C][C] 0.3558[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 12.54[/C][C] 0.4615[/C][/ROW]
[ROW][C]88[/C][C] 11[/C][C] 11.21[/C][C]-0.212[/C][/ROW]
[ROW][C]89[/C][C] 10[/C][C] 11.67[/C][C]-1.666[/C][/ROW]
[ROW][C]90[/C][C] 9[/C][C] 10.43[/C][C]-1.429[/C][/ROW]
[ROW][C]91[/C][C] 13[/C][C] 12.08[/C][C] 0.9221[/C][/ROW]
[ROW][C]92[/C][C] 10[/C][C] 12.64[/C][C]-2.635[/C][/ROW]
[ROW][C]93[/C][C] 13[/C][C] 11.31[/C][C] 1.685[/C][/ROW]
[ROW][C]94[/C][C] 12[/C][C] 11.18[/C][C] 0.8179[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 12.05[/C][C]-0.05121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297850&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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	11	11.63	-0.6289
2	9	10.28	-1.279
3	12	12.04	-0.04308
4	12	11.05	0.947
5	12	13.17	-1.165
6	11	12.03	-1.027
7	12	11.16	0.8409
8	12	10.79	1.207
9	15	13	2.004
10	13	12.22	0.7798
11	12	11.56	0.4359
12	11	13.13	-2.134
13	9	11.11	-2.112
14	11	12.64	-1.643
15	12	12.24	-0.2386
16	12	11.85	0.1497
17	12	11.87	0.1342
18	14	12.53	1.474
19	12	11.3	0.7031
20	9	12.42	-3.424
21	13	11.77	1.232
22	13	11.17	1.829
23	12	11.95	0.0511
24	12	12.06	-0.05735
25	12	11.94	0.06027
26	12	12.06	-0.05626
27	11	12.16	-1.161
28	13	10.75	2.249
29	13	11.71	1.288
30	10	12.36	-2.359
31	13	12.35	0.6456
32	5	10.35	-5.353
33	10	10.81	-0.8105
34	15	11.9	3.102
35	13	12.09	0.9052
36	12	11.01	0.9928
37	13	11.89	1.114
38	13	11.98	1.018
39	11	11.43	-0.4333
40	12	11.97	0.02924
41	12	12.75	-0.7496
42	13	11.86	1.138
43	14	11.86	2.142
44	12	11.96	0.03683
45	12	11.85	0.152
46	10	11.96	-1.955
47	12	12.69	-0.6924
48	12	11.85	0.1458
49	12	11.39	0.6126
50	13	11.51	1.487
51	14	11.84	2.158
52	10	10.26	-0.2622
53	13	12.71	0.2854
54	11	11.81	-0.8067
55	12	12.03	-0.03262
56	12	12.34	-0.3396
57	13	11.45	1.547
58	12	12.02	-0.01885
59	9	12.13	-3.126
60	12	12.45	-0.4519
61	14	11.78	2.224
62	11	12.54	-1.543
63	12	12.65	-0.6486
64	12	11.86	0.1382
65	9	10.43	-1.433
66	13	12.21	0.7873
67	10	10.19	-0.1922
68	14	12.18	1.815
69	10	11.63	-1.627
70	12	11.29	0.709
71	11	11.29	-0.2888
72	14	12.63	1.373
73	13	12.6	0.396
74	12	11.37	0.6251
75	10	11.7	-1.701
76	12	11.58	0.4226
77	12	11.03	0.9692
78	15	13.25	1.747
79	12	12.5	-0.4966
80	12	11.37	0.635
81	10	10.8	-0.8019
82	12	12.56	-0.5627
83	12	11.88	0.1159
84	12	12.34	-0.3418
85	11	12.11	-1.105
86	13	12.64	0.3558
87	13	12.54	0.4615
88	11	11.21	-0.212
89	10	11.67	-1.666
90	9	10.43	-1.429
91	13	12.08	0.9221
92	10	12.64	-2.635
93	13	11.31	1.685
94	12	11.18	0.8179
95	12	12.05	-0.05121

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.486	0.972	0.514
10	0.4025	0.8049	0.5975
11	0.3361	0.6723	0.6639
12	0.4269	0.8539	0.5731
13	0.5898	0.8204	0.4102
14	0.5872	0.8256	0.4128
15	0.5262	0.9475	0.4738
16	0.4263	0.8525	0.5737
17	0.3357	0.6715	0.6643
18	0.4165	0.8329	0.5835
19	0.3337	0.6674	0.6663
20	0.4584	0.9168	0.5416
21	0.6098	0.7804	0.3902
22	0.5847	0.8307	0.4153
23	0.5104	0.9791	0.4895
24	0.4398	0.8796	0.5602
25	0.3692	0.7384	0.6308
26	0.3082	0.6163	0.6918
27	0.2678	0.5357	0.7322
28	0.4264	0.8528	0.5736
29	0.3754	0.7508	0.6246
30	0.5125	0.9751	0.4875
31	0.4583	0.9167	0.5417
32	0.9881	0.0239	0.01195
33	0.9854	0.02917	0.01458
34	0.997	0.005989	0.002994
35	0.9956	0.008817	0.004409
36	0.9939	0.0123	0.00615
37	0.9919	0.01623	0.008113
38	0.989	0.022	0.011
39	0.9845	0.03091	0.01545
40	0.9774	0.04528	0.02264
41	0.9713	0.05736	0.02868
42	0.9654	0.06911	0.03456
43	0.9744	0.05123	0.02562
44	0.9635	0.07299	0.0365
45	0.9491	0.1018	0.05091
46	0.9665	0.06699	0.0335
47	0.96	0.08001	0.04
48	0.9445	0.1111	0.05555
49	0.9264	0.1473	0.07365
50	0.9218	0.1564	0.07821
51	0.9486	0.1027	0.05135
52	0.9334	0.1332	0.06659
53	0.9134	0.1733	0.08663
54	0.8996	0.2008	0.1004
55	0.8688	0.2624	0.1312
56	0.8381	0.3237	0.1619
57	0.8491	0.3018	0.1509
58	0.8084	0.3832	0.1916
59	0.9596	0.08076	0.04038
60	0.9484	0.1033	0.05163
61	0.9677	0.06467	0.03234
62	0.9741	0.05184	0.02592
63	0.9772	0.04556	0.02278
64	0.9658	0.06837	0.03419
65	0.9671	0.06577	0.03288
66	0.954	0.092	0.046
67	0.9367	0.1265	0.06325
68	0.9351	0.1298	0.06489
69	0.9521	0.09585	0.04793
70	0.931	0.1379	0.06897
71	0.9066	0.1867	0.09336
72	0.9007	0.1986	0.09931
73	0.8594	0.2813	0.1406
74	0.821	0.358	0.179
75	0.8396	0.3209	0.1604
76	0.7787	0.4426	0.2213
77	0.73	0.5399	0.27
78	0.7964	0.4071	0.2036
79	0.7801	0.4398	0.2199
80	0.7714	0.4572	0.2286
81	0.7307	0.5386	0.2693
82	0.6276	0.7447	0.3724
83	0.5073	0.9854	0.4927
84	0.4317	0.8635	0.5683
85	0.3746	0.7493	0.6254
86	0.238	0.476	0.762

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.486 &  0.972 &  0.514 \tabularnewline
10 &  0.4025 &  0.8049 &  0.5975 \tabularnewline
11 &  0.3361 &  0.6723 &  0.6639 \tabularnewline
12 &  0.4269 &  0.8539 &  0.5731 \tabularnewline
13 &  0.5898 &  0.8204 &  0.4102 \tabularnewline
14 &  0.5872 &  0.8256 &  0.4128 \tabularnewline
15 &  0.5262 &  0.9475 &  0.4738 \tabularnewline
16 &  0.4263 &  0.8525 &  0.5737 \tabularnewline
17 &  0.3357 &  0.6715 &  0.6643 \tabularnewline
18 &  0.4165 &  0.8329 &  0.5835 \tabularnewline
19 &  0.3337 &  0.6674 &  0.6663 \tabularnewline
20 &  0.4584 &  0.9168 &  0.5416 \tabularnewline
21 &  0.6098 &  0.7804 &  0.3902 \tabularnewline
22 &  0.5847 &  0.8307 &  0.4153 \tabularnewline
23 &  0.5104 &  0.9791 &  0.4895 \tabularnewline
24 &  0.4398 &  0.8796 &  0.5602 \tabularnewline
25 &  0.3692 &  0.7384 &  0.6308 \tabularnewline
26 &  0.3082 &  0.6163 &  0.6918 \tabularnewline
27 &  0.2678 &  0.5357 &  0.7322 \tabularnewline
28 &  0.4264 &  0.8528 &  0.5736 \tabularnewline
29 &  0.3754 &  0.7508 &  0.6246 \tabularnewline
30 &  0.5125 &  0.9751 &  0.4875 \tabularnewline
31 &  0.4583 &  0.9167 &  0.5417 \tabularnewline
32 &  0.9881 &  0.0239 &  0.01195 \tabularnewline
33 &  0.9854 &  0.02917 &  0.01458 \tabularnewline
34 &  0.997 &  0.005989 &  0.002994 \tabularnewline
35 &  0.9956 &  0.008817 &  0.004409 \tabularnewline
36 &  0.9939 &  0.0123 &  0.00615 \tabularnewline
37 &  0.9919 &  0.01623 &  0.008113 \tabularnewline
38 &  0.989 &  0.022 &  0.011 \tabularnewline
39 &  0.9845 &  0.03091 &  0.01545 \tabularnewline
40 &  0.9774 &  0.04528 &  0.02264 \tabularnewline
41 &  0.9713 &  0.05736 &  0.02868 \tabularnewline
42 &  0.9654 &  0.06911 &  0.03456 \tabularnewline
43 &  0.9744 &  0.05123 &  0.02562 \tabularnewline
44 &  0.9635 &  0.07299 &  0.0365 \tabularnewline
45 &  0.9491 &  0.1018 &  0.05091 \tabularnewline
46 &  0.9665 &  0.06699 &  0.0335 \tabularnewline
47 &  0.96 &  0.08001 &  0.04 \tabularnewline
48 &  0.9445 &  0.1111 &  0.05555 \tabularnewline
49 &  0.9264 &  0.1473 &  0.07365 \tabularnewline
50 &  0.9218 &  0.1564 &  0.07821 \tabularnewline
51 &  0.9486 &  0.1027 &  0.05135 \tabularnewline
52 &  0.9334 &  0.1332 &  0.06659 \tabularnewline
53 &  0.9134 &  0.1733 &  0.08663 \tabularnewline
54 &  0.8996 &  0.2008 &  0.1004 \tabularnewline
55 &  0.8688 &  0.2624 &  0.1312 \tabularnewline
56 &  0.8381 &  0.3237 &  0.1619 \tabularnewline
57 &  0.8491 &  0.3018 &  0.1509 \tabularnewline
58 &  0.8084 &  0.3832 &  0.1916 \tabularnewline
59 &  0.9596 &  0.08076 &  0.04038 \tabularnewline
60 &  0.9484 &  0.1033 &  0.05163 \tabularnewline
61 &  0.9677 &  0.06467 &  0.03234 \tabularnewline
62 &  0.9741 &  0.05184 &  0.02592 \tabularnewline
63 &  0.9772 &  0.04556 &  0.02278 \tabularnewline
64 &  0.9658 &  0.06837 &  0.03419 \tabularnewline
65 &  0.9671 &  0.06577 &  0.03288 \tabularnewline
66 &  0.954 &  0.092 &  0.046 \tabularnewline
67 &  0.9367 &  0.1265 &  0.06325 \tabularnewline
68 &  0.9351 &  0.1298 &  0.06489 \tabularnewline
69 &  0.9521 &  0.09585 &  0.04793 \tabularnewline
70 &  0.931 &  0.1379 &  0.06897 \tabularnewline
71 &  0.9066 &  0.1867 &  0.09336 \tabularnewline
72 &  0.9007 &  0.1986 &  0.09931 \tabularnewline
73 &  0.8594 &  0.2813 &  0.1406 \tabularnewline
74 &  0.821 &  0.358 &  0.179 \tabularnewline
75 &  0.8396 &  0.3209 &  0.1604 \tabularnewline
76 &  0.7787 &  0.4426 &  0.2213 \tabularnewline
77 &  0.73 &  0.5399 &  0.27 \tabularnewline
78 &  0.7964 &  0.4071 &  0.2036 \tabularnewline
79 &  0.7801 &  0.4398 &  0.2199 \tabularnewline
80 &  0.7714 &  0.4572 &  0.2286 \tabularnewline
81 &  0.7307 &  0.5386 &  0.2693 \tabularnewline
82 &  0.6276 &  0.7447 &  0.3724 \tabularnewline
83 &  0.5073 &  0.9854 &  0.4927 \tabularnewline
84 &  0.4317 &  0.8635 &  0.5683 \tabularnewline
85 &  0.3746 &  0.7493 &  0.6254 \tabularnewline
86 &  0.238 &  0.476 &  0.762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&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]9[/C][C] 0.486[/C][C] 0.972[/C][C] 0.514[/C][/ROW]
[ROW][C]10[/C][C] 0.4025[/C][C] 0.8049[/C][C] 0.5975[/C][/ROW]
[ROW][C]11[/C][C] 0.3361[/C][C] 0.6723[/C][C] 0.6639[/C][/ROW]
[ROW][C]12[/C][C] 0.4269[/C][C] 0.8539[/C][C] 0.5731[/C][/ROW]
[ROW][C]13[/C][C] 0.5898[/C][C] 0.8204[/C][C] 0.4102[/C][/ROW]
[ROW][C]14[/C][C] 0.5872[/C][C] 0.8256[/C][C] 0.4128[/C][/ROW]
[ROW][C]15[/C][C] 0.5262[/C][C] 0.9475[/C][C] 0.4738[/C][/ROW]
[ROW][C]16[/C][C] 0.4263[/C][C] 0.8525[/C][C] 0.5737[/C][/ROW]
[ROW][C]17[/C][C] 0.3357[/C][C] 0.6715[/C][C] 0.6643[/C][/ROW]
[ROW][C]18[/C][C] 0.4165[/C][C] 0.8329[/C][C] 0.5835[/C][/ROW]
[ROW][C]19[/C][C] 0.3337[/C][C] 0.6674[/C][C] 0.6663[/C][/ROW]
[ROW][C]20[/C][C] 0.4584[/C][C] 0.9168[/C][C] 0.5416[/C][/ROW]
[ROW][C]21[/C][C] 0.6098[/C][C] 0.7804[/C][C] 0.3902[/C][/ROW]
[ROW][C]22[/C][C] 0.5847[/C][C] 0.8307[/C][C] 0.4153[/C][/ROW]
[ROW][C]23[/C][C] 0.5104[/C][C] 0.9791[/C][C] 0.4895[/C][/ROW]
[ROW][C]24[/C][C] 0.4398[/C][C] 0.8796[/C][C] 0.5602[/C][/ROW]
[ROW][C]25[/C][C] 0.3692[/C][C] 0.7384[/C][C] 0.6308[/C][/ROW]
[ROW][C]26[/C][C] 0.3082[/C][C] 0.6163[/C][C] 0.6918[/C][/ROW]
[ROW][C]27[/C][C] 0.2678[/C][C] 0.5357[/C][C] 0.7322[/C][/ROW]
[ROW][C]28[/C][C] 0.4264[/C][C] 0.8528[/C][C] 0.5736[/C][/ROW]
[ROW][C]29[/C][C] 0.3754[/C][C] 0.7508[/C][C] 0.6246[/C][/ROW]
[ROW][C]30[/C][C] 0.5125[/C][C] 0.9751[/C][C] 0.4875[/C][/ROW]
[ROW][C]31[/C][C] 0.4583[/C][C] 0.9167[/C][C] 0.5417[/C][/ROW]
[ROW][C]32[/C][C] 0.9881[/C][C] 0.0239[/C][C] 0.01195[/C][/ROW]
[ROW][C]33[/C][C] 0.9854[/C][C] 0.02917[/C][C] 0.01458[/C][/ROW]
[ROW][C]34[/C][C] 0.997[/C][C] 0.005989[/C][C] 0.002994[/C][/ROW]
[ROW][C]35[/C][C] 0.9956[/C][C] 0.008817[/C][C] 0.004409[/C][/ROW]
[ROW][C]36[/C][C] 0.9939[/C][C] 0.0123[/C][C] 0.00615[/C][/ROW]
[ROW][C]37[/C][C] 0.9919[/C][C] 0.01623[/C][C] 0.008113[/C][/ROW]
[ROW][C]38[/C][C] 0.989[/C][C] 0.022[/C][C] 0.011[/C][/ROW]
[ROW][C]39[/C][C] 0.9845[/C][C] 0.03091[/C][C] 0.01545[/C][/ROW]
[ROW][C]40[/C][C] 0.9774[/C][C] 0.04528[/C][C] 0.02264[/C][/ROW]
[ROW][C]41[/C][C] 0.9713[/C][C] 0.05736[/C][C] 0.02868[/C][/ROW]
[ROW][C]42[/C][C] 0.9654[/C][C] 0.06911[/C][C] 0.03456[/C][/ROW]
[ROW][C]43[/C][C] 0.9744[/C][C] 0.05123[/C][C] 0.02562[/C][/ROW]
[ROW][C]44[/C][C] 0.9635[/C][C] 0.07299[/C][C] 0.0365[/C][/ROW]
[ROW][C]45[/C][C] 0.9491[/C][C] 0.1018[/C][C] 0.05091[/C][/ROW]
[ROW][C]46[/C][C] 0.9665[/C][C] 0.06699[/C][C] 0.0335[/C][/ROW]
[ROW][C]47[/C][C] 0.96[/C][C] 0.08001[/C][C] 0.04[/C][/ROW]
[ROW][C]48[/C][C] 0.9445[/C][C] 0.1111[/C][C] 0.05555[/C][/ROW]
[ROW][C]49[/C][C] 0.9264[/C][C] 0.1473[/C][C] 0.07365[/C][/ROW]
[ROW][C]50[/C][C] 0.9218[/C][C] 0.1564[/C][C] 0.07821[/C][/ROW]
[ROW][C]51[/C][C] 0.9486[/C][C] 0.1027[/C][C] 0.05135[/C][/ROW]
[ROW][C]52[/C][C] 0.9334[/C][C] 0.1332[/C][C] 0.06659[/C][/ROW]
[ROW][C]53[/C][C] 0.9134[/C][C] 0.1733[/C][C] 0.08663[/C][/ROW]
[ROW][C]54[/C][C] 0.8996[/C][C] 0.2008[/C][C] 0.1004[/C][/ROW]
[ROW][C]55[/C][C] 0.8688[/C][C] 0.2624[/C][C] 0.1312[/C][/ROW]
[ROW][C]56[/C][C] 0.8381[/C][C] 0.3237[/C][C] 0.1619[/C][/ROW]
[ROW][C]57[/C][C] 0.8491[/C][C] 0.3018[/C][C] 0.1509[/C][/ROW]
[ROW][C]58[/C][C] 0.8084[/C][C] 0.3832[/C][C] 0.1916[/C][/ROW]
[ROW][C]59[/C][C] 0.9596[/C][C] 0.08076[/C][C] 0.04038[/C][/ROW]
[ROW][C]60[/C][C] 0.9484[/C][C] 0.1033[/C][C] 0.05163[/C][/ROW]
[ROW][C]61[/C][C] 0.9677[/C][C] 0.06467[/C][C] 0.03234[/C][/ROW]
[ROW][C]62[/C][C] 0.9741[/C][C] 0.05184[/C][C] 0.02592[/C][/ROW]
[ROW][C]63[/C][C] 0.9772[/C][C] 0.04556[/C][C] 0.02278[/C][/ROW]
[ROW][C]64[/C][C] 0.9658[/C][C] 0.06837[/C][C] 0.03419[/C][/ROW]
[ROW][C]65[/C][C] 0.9671[/C][C] 0.06577[/C][C] 0.03288[/C][/ROW]
[ROW][C]66[/C][C] 0.954[/C][C] 0.092[/C][C] 0.046[/C][/ROW]
[ROW][C]67[/C][C] 0.9367[/C][C] 0.1265[/C][C] 0.06325[/C][/ROW]
[ROW][C]68[/C][C] 0.9351[/C][C] 0.1298[/C][C] 0.06489[/C][/ROW]
[ROW][C]69[/C][C] 0.9521[/C][C] 0.09585[/C][C] 0.04793[/C][/ROW]
[ROW][C]70[/C][C] 0.931[/C][C] 0.1379[/C][C] 0.06897[/C][/ROW]
[ROW][C]71[/C][C] 0.9066[/C][C] 0.1867[/C][C] 0.09336[/C][/ROW]
[ROW][C]72[/C][C] 0.9007[/C][C] 0.1986[/C][C] 0.09931[/C][/ROW]
[ROW][C]73[/C][C] 0.8594[/C][C] 0.2813[/C][C] 0.1406[/C][/ROW]
[ROW][C]74[/C][C] 0.821[/C][C] 0.358[/C][C] 0.179[/C][/ROW]
[ROW][C]75[/C][C] 0.8396[/C][C] 0.3209[/C][C] 0.1604[/C][/ROW]
[ROW][C]76[/C][C] 0.7787[/C][C] 0.4426[/C][C] 0.2213[/C][/ROW]
[ROW][C]77[/C][C] 0.73[/C][C] 0.5399[/C][C] 0.27[/C][/ROW]
[ROW][C]78[/C][C] 0.7964[/C][C] 0.4071[/C][C] 0.2036[/C][/ROW]
[ROW][C]79[/C][C] 0.7801[/C][C] 0.4398[/C][C] 0.2199[/C][/ROW]
[ROW][C]80[/C][C] 0.7714[/C][C] 0.4572[/C][C] 0.2286[/C][/ROW]
[ROW][C]81[/C][C] 0.7307[/C][C] 0.5386[/C][C] 0.2693[/C][/ROW]
[ROW][C]82[/C][C] 0.6276[/C][C] 0.7447[/C][C] 0.3724[/C][/ROW]
[ROW][C]83[/C][C] 0.5073[/C][C] 0.9854[/C][C] 0.4927[/C][/ROW]
[ROW][C]84[/C][C] 0.4317[/C][C] 0.8635[/C][C] 0.5683[/C][/ROW]
[ROW][C]85[/C][C] 0.3746[/C][C] 0.7493[/C][C] 0.6254[/C][/ROW]
[ROW][C]86[/C][C] 0.238[/C][C] 0.476[/C][C] 0.762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297850&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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
9	0.486	0.972	0.514
10	0.4025	0.8049	0.5975
11	0.3361	0.6723	0.6639
12	0.4269	0.8539	0.5731
13	0.5898	0.8204	0.4102
14	0.5872	0.8256	0.4128
15	0.5262	0.9475	0.4738
16	0.4263	0.8525	0.5737
17	0.3357	0.6715	0.6643
18	0.4165	0.8329	0.5835
19	0.3337	0.6674	0.6663
20	0.4584	0.9168	0.5416
21	0.6098	0.7804	0.3902
22	0.5847	0.8307	0.4153
23	0.5104	0.9791	0.4895
24	0.4398	0.8796	0.5602
25	0.3692	0.7384	0.6308
26	0.3082	0.6163	0.6918
27	0.2678	0.5357	0.7322
28	0.4264	0.8528	0.5736
29	0.3754	0.7508	0.6246
30	0.5125	0.9751	0.4875
31	0.4583	0.9167	0.5417
32	0.9881	0.0239	0.01195
33	0.9854	0.02917	0.01458
34	0.997	0.005989	0.002994
35	0.9956	0.008817	0.004409
36	0.9939	0.0123	0.00615
37	0.9919	0.01623	0.008113
38	0.989	0.022	0.011
39	0.9845	0.03091	0.01545
40	0.9774	0.04528	0.02264
41	0.9713	0.05736	0.02868
42	0.9654	0.06911	0.03456
43	0.9744	0.05123	0.02562
44	0.9635	0.07299	0.0365
45	0.9491	0.1018	0.05091
46	0.9665	0.06699	0.0335
47	0.96	0.08001	0.04
48	0.9445	0.1111	0.05555
49	0.9264	0.1473	0.07365
50	0.9218	0.1564	0.07821
51	0.9486	0.1027	0.05135
52	0.9334	0.1332	0.06659
53	0.9134	0.1733	0.08663
54	0.8996	0.2008	0.1004
55	0.8688	0.2624	0.1312
56	0.8381	0.3237	0.1619
57	0.8491	0.3018	0.1509
58	0.8084	0.3832	0.1916
59	0.9596	0.08076	0.04038
60	0.9484	0.1033	0.05163
61	0.9677	0.06467	0.03234
62	0.9741	0.05184	0.02592
63	0.9772	0.04556	0.02278
64	0.9658	0.06837	0.03419
65	0.9671	0.06577	0.03288
66	0.954	0.092	0.046
67	0.9367	0.1265	0.06325
68	0.9351	0.1298	0.06489
69	0.9521	0.09585	0.04793
70	0.931	0.1379	0.06897
71	0.9066	0.1867	0.09336
72	0.9007	0.1986	0.09931
73	0.8594	0.2813	0.1406
74	0.821	0.358	0.179
75	0.8396	0.3209	0.1604
76	0.7787	0.4426	0.2213
77	0.73	0.5399	0.27
78	0.7964	0.4071	0.2036
79	0.7801	0.4398	0.2199
80	0.7714	0.4572	0.2286
81	0.7307	0.5386	0.2693
82	0.6276	0.7447	0.3724
83	0.5073	0.9854	0.4927
84	0.4317	0.8635	0.5683
85	0.3746	0.7493	0.6254
86	0.238	0.476	0.762

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.02564	NOK
5% type I error level	10	0.128205	NOK
10% type I error level	23	0.294872	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 & 2 &  0.02564 & NOK \tabularnewline
5% type I error level & 10 & 0.128205 & NOK \tabularnewline
10% type I error level & 23 & 0.294872 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&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]2[/C][C] 0.02564[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.128205[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.294872[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297850&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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	2	0.02564	NOK
5% type I error level	10	0.128205	NOK
10% type I error level	23	0.294872	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 6.8132, df1 = 2, df2 = 87, p-value = 0.001783
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.055, df1 = 10, df2 = 79, p-value = 0.4067
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.35495, df1 = 2, df2 = 87, p-value = 0.7022

\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 = 6.8132, df1 = 2, df2 = 87, p-value = 0.001783
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.055, df1 = 10, df2 = 79, p-value = 0.4067
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.35495, df1 = 2, df2 = 87, p-value = 0.7022
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&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 = 6.8132, df1 = 2, df2 = 87, p-value = 0.001783
[/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 = 1.055, df1 = 10, df2 = 79, p-value = 0.4067
[/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 = 0.35495, df1 = 2, df2 = 87, p-value = 0.7022
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297850&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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 = 6.8132, df1 = 2, df2 = 87, p-value = 0.001783
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.055, df1 = 10, df2 = 79, p-value = 0.4067
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.35495, df1 = 2, df2 = 87, p-value = 0.7022

Variance Inflation Factors (Multicollinearity)
> vif V1 V2 V3 V4 t 1.017552 1.054518 1.081245 1.082508 1.050060

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      V1       V2       V3       V4        t 
1.017552 1.054518 1.081245 1.082508 1.050060 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297850&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      V1       V2       V3       V4        t 
1.017552 1.054518 1.081245 1.082508 1.050060 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297850&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297850&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 V1 V2 V3 V4 t 1.017552 1.054518 1.081245 1.082508 1.050060

Figure 1

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Parameters (Session):

par1 = TRUE ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ;

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

par5 <- '0'
par4 <- '0'
par3 <- 'Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '5'
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