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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 06 Dec 2016 16:47:46 +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/t1481039282wkfas7ty2gsqsdi.htm/, Retrieved Sat, 04 May 2024 11:12:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297852, Retrieved Sat, 04 May 2024 11:12:09 +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] [] [2016-12-06 15:47:46] [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=297852&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=297852&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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
PWSUM[t] = + 10.1393 -0.23353V1[t] + 0.425823V2[t] + 0.424058V3[t] -0.0870506V4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PWSUM[t] =  +  10.1393 -0.23353V1[t] +  0.425823V2[t] +  0.424058V3[t] -0.0870506V4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297852&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PWSUM[t] =  +  10.1393 -0.23353V1[t] +  0.425823V2[t] +  0.424058V3[t] -0.0870506V4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297852&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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
PWSUM[t] = + 10.1393 -0.23353V1[t] + 0.425823V2[t] + 0.424058V3[t] -0.0870506V4[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+10.14	1.209	+8.3850e+00	6.537e-13	3.269e-13
V1	-0.2335	0.135	-1.7300e+00	0.08714	0.04357
V2	+0.4258	0.1285	+3.3150e+00	0.001324	0.0006618
V3	+0.4241	0.1501	+2.8250e+00	0.005816	0.002908
V4	-0.08705	0.2245	-3.8780e-01	0.6991	0.3495

\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) & +10.14 &  1.209 & +8.3850e+00 &  6.537e-13 &  3.269e-13 \tabularnewline
V1 & -0.2335 &  0.135 & -1.7300e+00 &  0.08714 &  0.04357 \tabularnewline
V2 & +0.4258 &  0.1285 & +3.3150e+00 &  0.001324 &  0.0006618 \tabularnewline
V3 & +0.4241 &  0.1501 & +2.8250e+00 &  0.005816 &  0.002908 \tabularnewline
V4 & -0.08705 &  0.2245 & -3.8780e-01 &  0.6991 &  0.3495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297852&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]+10.14[/C][C] 1.209[/C][C]+8.3850e+00[/C][C] 6.537e-13[/C][C] 3.269e-13[/C][/ROW]
[ROW][C]V1[/C][C]-0.2335[/C][C] 0.135[/C][C]-1.7300e+00[/C][C] 0.08714[/C][C] 0.04357[/C][/ROW]
[ROW][C]V2[/C][C]+0.4258[/C][C] 0.1285[/C][C]+3.3150e+00[/C][C] 0.001324[/C][C] 0.0006618[/C][/ROW]
[ROW][C]V3[/C][C]+0.4241[/C][C] 0.1501[/C][C]+2.8250e+00[/C][C] 0.005816[/C][C] 0.002908[/C][/ROW]
[ROW][C]V4[/C][C]-0.08705[/C][C] 0.2245[/C][C]-3.8780e-01[/C][C] 0.6991[/C][C] 0.3495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297852&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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)	+10.14	1.209	+8.3850e+00	6.537e-13	3.269e-13
V1	-0.2335	0.135	-1.7300e+00	0.08714	0.04357
V2	+0.4258	0.1285	+3.3150e+00	0.001324	0.0006618
V3	+0.4241	0.1501	+2.8250e+00	0.005816	0.002908
V4	-0.08705	0.2245	-3.8780e-01	0.6991	0.3495

Multiple Linear Regression - Regression Statistics
Multiple R	0.4353
R-squared	0.1895
Adjusted R-squared	0.1535
F-TEST (value)	5.261
F-TEST (DF numerator)	4
F-TEST (DF denominator)	90
p-value	0.0007463
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.412
Sum Squared Residuals	179.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.4353 \tabularnewline
R-squared &  0.1895 \tabularnewline
Adjusted R-squared &  0.1535 \tabularnewline
F-TEST (value) &  5.261 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 90 \tabularnewline
p-value &  0.0007463 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.412 \tabularnewline
Sum Squared Residuals &  179.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297852&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.4353[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1895[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.1535[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 5.261[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]90[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0007463[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.412[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 179.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297852&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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	0.4353
R-squared	0.1895
Adjusted R-squared	0.1535
F-TEST (value)	5.261
F-TEST (DF numerator)	4
F-TEST (DF denominator)	90
p-value	0.0007463
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.412
Sum Squared Residuals	179.4

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	11.45	-0.4479
2	9	10.13	-1.133
3	12	11.83	0.1711
4	12	10.9	1.104
5	12	12.96	-0.9571
6	11	11.83	-0.8325
7	12	11.02	0.9762
8	12	10.64	1.356
9	15	12.77	2.231
10	13	12.02	0.977
11	12	11.4	0.5951
12	11	12.96	-1.955
13	9	10.98	-1.983
14	11	12.45	-1.449
15	12	12.1	-0.1037
16	12	11.69	0.314
17	12	11.73	0.2728
18	14	12.36	1.64
19	12	11.17	0.8287
20	9	12.3	-3.3
21	13	11.68	1.319
22	13	11.07	1.932
23	12	11.83	0.1675
24	12	11.93	0.06583
25	12	11.83	0.1675
26	12	11.96	0.04102
27	11	12.07	-1.066
28	13	10.75	2.254
29	13	11.64	1.362
30	10	12.26	-2.257
31	13	12.26	0.7435
32	5	10.32	-5.323
33	10	10.79	-0.7885
34	15	11.83	3.168
35	13	12	0.9952
36	12	10.98	1.019
37	13	11.83	1.169
38	13	11.92	1.084
39	11	11.41	-0.4084
40	12	11.92	0.08049
41	12	12.68	-0.6823
42	13	11.83	1.168
43	14	11.83	2.169
44	12	11.94	0.0623
45	12	11.83	0.1675
46	10	11.94	-1.936
47	12	12.62	-0.6211
48	12	11.87	0.1263
49	12	11.41	0.5916
50	13	11.55	1.445
51	14	11.87	2.128
52	10	10.32	-0.3215
53	13	12.72	0.2764
54	11	11.83	-0.8325
55	12	12.07	-0.06599
56	12	12.34	-0.3436
57	13	11.49	1.508
58	12	12.07	-0.06599
59	9	12.17	-3.169
60	12	12.49	-0.49
61	14	11.83	2.169
62	11	12.58	-1.577
63	12	12.68	-0.6823
64	12	11.92	0.08226
65	9	10.55	-1.555
66	13	12.3	0.7005
67	10	10.32	-0.3232
68	14	12.26	1.743
69	10	11.73	-1.727
70	12	11.41	0.5916
71	11	11.4	-0.4049
72	14	12.72	1.276
73	13	12.68	0.3194
74	12	11.49	0.5081
75	10	11.81	-1.812
76	12	11.68	0.3176
77	12	11.17	0.8287
78	15	13.34	1.66
79	12	12.63	-0.6348
80	12	11.54	0.4633
81	10	10.98	-0.979
82	12	12.68	-0.6806
83	12	12.02	-0.02475
84	12	12.49	-0.49
85	11	12.26	-1.258
86	13	12.77	0.2324
87	13	12.68	0.3177
88	11	11.4	-0.4031
89	10	11.87	-1.874
90	9	10.66	-1.66
91	13	12.26	0.7417
92	10	12.81	-2.811
93	13	11.56	1.443
94	12	11.41	0.5934
95	12	12.24	-0.2366

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  11.45 & -0.4479 \tabularnewline
2 &  9 &  10.13 & -1.133 \tabularnewline
3 &  12 &  11.83 &  0.1711 \tabularnewline
4 &  12 &  10.9 &  1.104 \tabularnewline
5 &  12 &  12.96 & -0.9571 \tabularnewline
6 &  11 &  11.83 & -0.8325 \tabularnewline
7 &  12 &  11.02 &  0.9762 \tabularnewline
8 &  12 &  10.64 &  1.356 \tabularnewline
9 &  15 &  12.77 &  2.231 \tabularnewline
10 &  13 &  12.02 &  0.977 \tabularnewline
11 &  12 &  11.4 &  0.5951 \tabularnewline
12 &  11 &  12.96 & -1.955 \tabularnewline
13 &  9 &  10.98 & -1.983 \tabularnewline
14 &  11 &  12.45 & -1.449 \tabularnewline
15 &  12 &  12.1 & -0.1037 \tabularnewline
16 &  12 &  11.69 &  0.314 \tabularnewline
17 &  12 &  11.73 &  0.2728 \tabularnewline
18 &  14 &  12.36 &  1.64 \tabularnewline
19 &  12 &  11.17 &  0.8287 \tabularnewline
20 &  9 &  12.3 & -3.3 \tabularnewline
21 &  13 &  11.68 &  1.319 \tabularnewline
22 &  13 &  11.07 &  1.932 \tabularnewline
23 &  12 &  11.83 &  0.1675 \tabularnewline
24 &  12 &  11.93 &  0.06583 \tabularnewline
25 &  12 &  11.83 &  0.1675 \tabularnewline
26 &  12 &  11.96 &  0.04102 \tabularnewline
27 &  11 &  12.07 & -1.066 \tabularnewline
28 &  13 &  10.75 &  2.254 \tabularnewline
29 &  13 &  11.64 &  1.362 \tabularnewline
30 &  10 &  12.26 & -2.257 \tabularnewline
31 &  13 &  12.26 &  0.7435 \tabularnewline
32 &  5 &  10.32 & -5.323 \tabularnewline
33 &  10 &  10.79 & -0.7885 \tabularnewline
34 &  15 &  11.83 &  3.168 \tabularnewline
35 &  13 &  12 &  0.9952 \tabularnewline
36 &  12 &  10.98 &  1.019 \tabularnewline
37 &  13 &  11.83 &  1.169 \tabularnewline
38 &  13 &  11.92 &  1.084 \tabularnewline
39 &  11 &  11.41 & -0.4084 \tabularnewline
40 &  12 &  11.92 &  0.08049 \tabularnewline
41 &  12 &  12.68 & -0.6823 \tabularnewline
42 &  13 &  11.83 &  1.168 \tabularnewline
43 &  14 &  11.83 &  2.169 \tabularnewline
44 &  12 &  11.94 &  0.0623 \tabularnewline
45 &  12 &  11.83 &  0.1675 \tabularnewline
46 &  10 &  11.94 & -1.936 \tabularnewline
47 &  12 &  12.62 & -0.6211 \tabularnewline
48 &  12 &  11.87 &  0.1263 \tabularnewline
49 &  12 &  11.41 &  0.5916 \tabularnewline
50 &  13 &  11.55 &  1.445 \tabularnewline
51 &  14 &  11.87 &  2.128 \tabularnewline
52 &  10 &  10.32 & -0.3215 \tabularnewline
53 &  13 &  12.72 &  0.2764 \tabularnewline
54 &  11 &  11.83 & -0.8325 \tabularnewline
55 &  12 &  12.07 & -0.06599 \tabularnewline
56 &  12 &  12.34 & -0.3436 \tabularnewline
57 &  13 &  11.49 &  1.508 \tabularnewline
58 &  12 &  12.07 & -0.06599 \tabularnewline
59 &  9 &  12.17 & -3.169 \tabularnewline
60 &  12 &  12.49 & -0.49 \tabularnewline
61 &  14 &  11.83 &  2.169 \tabularnewline
62 &  11 &  12.58 & -1.577 \tabularnewline
63 &  12 &  12.68 & -0.6823 \tabularnewline
64 &  12 &  11.92 &  0.08226 \tabularnewline
65 &  9 &  10.55 & -1.555 \tabularnewline
66 &  13 &  12.3 &  0.7005 \tabularnewline
67 &  10 &  10.32 & -0.3232 \tabularnewline
68 &  14 &  12.26 &  1.743 \tabularnewline
69 &  10 &  11.73 & -1.727 \tabularnewline
70 &  12 &  11.41 &  0.5916 \tabularnewline
71 &  11 &  11.4 & -0.4049 \tabularnewline
72 &  14 &  12.72 &  1.276 \tabularnewline
73 &  13 &  12.68 &  0.3194 \tabularnewline
74 &  12 &  11.49 &  0.5081 \tabularnewline
75 &  10 &  11.81 & -1.812 \tabularnewline
76 &  12 &  11.68 &  0.3176 \tabularnewline
77 &  12 &  11.17 &  0.8287 \tabularnewline
78 &  15 &  13.34 &  1.66 \tabularnewline
79 &  12 &  12.63 & -0.6348 \tabularnewline
80 &  12 &  11.54 &  0.4633 \tabularnewline
81 &  10 &  10.98 & -0.979 \tabularnewline
82 &  12 &  12.68 & -0.6806 \tabularnewline
83 &  12 &  12.02 & -0.02475 \tabularnewline
84 &  12 &  12.49 & -0.49 \tabularnewline
85 &  11 &  12.26 & -1.258 \tabularnewline
86 &  13 &  12.77 &  0.2324 \tabularnewline
87 &  13 &  12.68 &  0.3177 \tabularnewline
88 &  11 &  11.4 & -0.4031 \tabularnewline
89 &  10 &  11.87 & -1.874 \tabularnewline
90 &  9 &  10.66 & -1.66 \tabularnewline
91 &  13 &  12.26 &  0.7417 \tabularnewline
92 &  10 &  12.81 & -2.811 \tabularnewline
93 &  13 &  11.56 &  1.443 \tabularnewline
94 &  12 &  11.41 &  0.5934 \tabularnewline
95 &  12 &  12.24 & -0.2366 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297852&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] 11.45[/C][C]-0.4479[/C][/ROW]
[ROW][C]2[/C][C] 9[/C][C] 10.13[/C][C]-1.133[/C][/ROW]
[ROW][C]3[/C][C] 12[/C][C] 11.83[/C][C] 0.1711[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 10.9[/C][C] 1.104[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 12.96[/C][C]-0.9571[/C][/ROW]
[ROW][C]6[/C][C] 11[/C][C] 11.83[/C][C]-0.8325[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 11.02[/C][C] 0.9762[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 10.64[/C][C] 1.356[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 12.77[/C][C] 2.231[/C][/ROW]
[ROW][C]10[/C][C] 13[/C][C] 12.02[/C][C] 0.977[/C][/ROW]
[ROW][C]11[/C][C] 12[/C][C] 11.4[/C][C] 0.5951[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 12.96[/C][C]-1.955[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 10.98[/C][C]-1.983[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 12.45[/C][C]-1.449[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 12.1[/C][C]-0.1037[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 11.69[/C][C] 0.314[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 11.73[/C][C] 0.2728[/C][/ROW]
[ROW][C]18[/C][C] 14[/C][C] 12.36[/C][C] 1.64[/C][/ROW]
[ROW][C]19[/C][C] 12[/C][C] 11.17[/C][C] 0.8287[/C][/ROW]
[ROW][C]20[/C][C] 9[/C][C] 12.3[/C][C]-3.3[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 11.68[/C][C] 1.319[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 11.07[/C][C] 1.932[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 11.83[/C][C] 0.1675[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 11.93[/C][C] 0.06583[/C][/ROW]
[ROW][C]25[/C][C] 12[/C][C] 11.83[/C][C] 0.1675[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 11.96[/C][C] 0.04102[/C][/ROW]
[ROW][C]27[/C][C] 11[/C][C] 12.07[/C][C]-1.066[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 10.75[/C][C] 2.254[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 11.64[/C][C] 1.362[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 12.26[/C][C]-2.257[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 12.26[/C][C] 0.7435[/C][/ROW]
[ROW][C]32[/C][C] 5[/C][C] 10.32[/C][C]-5.323[/C][/ROW]
[ROW][C]33[/C][C] 10[/C][C] 10.79[/C][C]-0.7885[/C][/ROW]
[ROW][C]34[/C][C] 15[/C][C] 11.83[/C][C] 3.168[/C][/ROW]
[ROW][C]35[/C][C] 13[/C][C] 12[/C][C] 0.9952[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 10.98[/C][C] 1.019[/C][/ROW]
[ROW][C]37[/C][C] 13[/C][C] 11.83[/C][C] 1.169[/C][/ROW]
[ROW][C]38[/C][C] 13[/C][C] 11.92[/C][C] 1.084[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 11.41[/C][C]-0.4084[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 11.92[/C][C] 0.08049[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 12.68[/C][C]-0.6823[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 11.83[/C][C] 1.168[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 11.83[/C][C] 2.169[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 11.94[/C][C] 0.0623[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 11.83[/C][C] 0.1675[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 11.94[/C][C]-1.936[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 12.62[/C][C]-0.6211[/C][/ROW]
[ROW][C]48[/C][C] 12[/C][C] 11.87[/C][C] 0.1263[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 11.41[/C][C] 0.5916[/C][/ROW]
[ROW][C]50[/C][C] 13[/C][C] 11.55[/C][C] 1.445[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 11.87[/C][C] 2.128[/C][/ROW]
[ROW][C]52[/C][C] 10[/C][C] 10.32[/C][C]-0.3215[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 12.72[/C][C] 0.2764[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 11.83[/C][C]-0.8325[/C][/ROW]
[ROW][C]55[/C][C] 12[/C][C] 12.07[/C][C]-0.06599[/C][/ROW]
[ROW][C]56[/C][C] 12[/C][C] 12.34[/C][C]-0.3436[/C][/ROW]
[ROW][C]57[/C][C] 13[/C][C] 11.49[/C][C] 1.508[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 12.07[/C][C]-0.06599[/C][/ROW]
[ROW][C]59[/C][C] 9[/C][C] 12.17[/C][C]-3.169[/C][/ROW]
[ROW][C]60[/C][C] 12[/C][C] 12.49[/C][C]-0.49[/C][/ROW]
[ROW][C]61[/C][C] 14[/C][C] 11.83[/C][C] 2.169[/C][/ROW]
[ROW][C]62[/C][C] 11[/C][C] 12.58[/C][C]-1.577[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 12.68[/C][C]-0.6823[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 11.92[/C][C] 0.08226[/C][/ROW]
[ROW][C]65[/C][C] 9[/C][C] 10.55[/C][C]-1.555[/C][/ROW]
[ROW][C]66[/C][C] 13[/C][C] 12.3[/C][C] 0.7005[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 10.32[/C][C]-0.3232[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 12.26[/C][C] 1.743[/C][/ROW]
[ROW][C]69[/C][C] 10[/C][C] 11.73[/C][C]-1.727[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 11.41[/C][C] 0.5916[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 11.4[/C][C]-0.4049[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 12.72[/C][C] 1.276[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 12.68[/C][C] 0.3194[/C][/ROW]
[ROW][C]74[/C][C] 12[/C][C] 11.49[/C][C] 0.5081[/C][/ROW]
[ROW][C]75[/C][C] 10[/C][C] 11.81[/C][C]-1.812[/C][/ROW]
[ROW][C]76[/C][C] 12[/C][C] 11.68[/C][C] 0.3176[/C][/ROW]
[ROW][C]77[/C][C] 12[/C][C] 11.17[/C][C] 0.8287[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 13.34[/C][C] 1.66[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 12.63[/C][C]-0.6348[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 11.54[/C][C] 0.4633[/C][/ROW]
[ROW][C]81[/C][C] 10[/C][C] 10.98[/C][C]-0.979[/C][/ROW]
[ROW][C]82[/C][C] 12[/C][C] 12.68[/C][C]-0.6806[/C][/ROW]
[ROW][C]83[/C][C] 12[/C][C] 12.02[/C][C]-0.02475[/C][/ROW]
[ROW][C]84[/C][C] 12[/C][C] 12.49[/C][C]-0.49[/C][/ROW]
[ROW][C]85[/C][C] 11[/C][C] 12.26[/C][C]-1.258[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 12.77[/C][C] 0.2324[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 12.68[/C][C] 0.3177[/C][/ROW]
[ROW][C]88[/C][C] 11[/C][C] 11.4[/C][C]-0.4031[/C][/ROW]
[ROW][C]89[/C][C] 10[/C][C] 11.87[/C][C]-1.874[/C][/ROW]
[ROW][C]90[/C][C] 9[/C][C] 10.66[/C][C]-1.66[/C][/ROW]
[ROW][C]91[/C][C] 13[/C][C] 12.26[/C][C] 0.7417[/C][/ROW]
[ROW][C]92[/C][C] 10[/C][C] 12.81[/C][C]-2.811[/C][/ROW]
[ROW][C]93[/C][C] 13[/C][C] 11.56[/C][C] 1.443[/C][/ROW]
[ROW][C]94[/C][C] 12[/C][C] 11.41[/C][C] 0.5934[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 12.24[/C][C]-0.2366[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297852&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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	11.45	-0.4479
2	9	10.13	-1.133
3	12	11.83	0.1711
4	12	10.9	1.104
5	12	12.96	-0.9571
6	11	11.83	-0.8325
7	12	11.02	0.9762
8	12	10.64	1.356
9	15	12.77	2.231
10	13	12.02	0.977
11	12	11.4	0.5951
12	11	12.96	-1.955
13	9	10.98	-1.983
14	11	12.45	-1.449
15	12	12.1	-0.1037
16	12	11.69	0.314
17	12	11.73	0.2728
18	14	12.36	1.64
19	12	11.17	0.8287
20	9	12.3	-3.3
21	13	11.68	1.319
22	13	11.07	1.932
23	12	11.83	0.1675
24	12	11.93	0.06583
25	12	11.83	0.1675
26	12	11.96	0.04102
27	11	12.07	-1.066
28	13	10.75	2.254
29	13	11.64	1.362
30	10	12.26	-2.257
31	13	12.26	0.7435
32	5	10.32	-5.323
33	10	10.79	-0.7885
34	15	11.83	3.168
35	13	12	0.9952
36	12	10.98	1.019
37	13	11.83	1.169
38	13	11.92	1.084
39	11	11.41	-0.4084
40	12	11.92	0.08049
41	12	12.68	-0.6823
42	13	11.83	1.168
43	14	11.83	2.169
44	12	11.94	0.0623
45	12	11.83	0.1675
46	10	11.94	-1.936
47	12	12.62	-0.6211
48	12	11.87	0.1263
49	12	11.41	0.5916
50	13	11.55	1.445
51	14	11.87	2.128
52	10	10.32	-0.3215
53	13	12.72	0.2764
54	11	11.83	-0.8325
55	12	12.07	-0.06599
56	12	12.34	-0.3436
57	13	11.49	1.508
58	12	12.07	-0.06599
59	9	12.17	-3.169
60	12	12.49	-0.49
61	14	11.83	2.169
62	11	12.58	-1.577
63	12	12.68	-0.6823
64	12	11.92	0.08226
65	9	10.55	-1.555
66	13	12.3	0.7005
67	10	10.32	-0.3232
68	14	12.26	1.743
69	10	11.73	-1.727
70	12	11.41	0.5916
71	11	11.4	-0.4049
72	14	12.72	1.276
73	13	12.68	0.3194
74	12	11.49	0.5081
75	10	11.81	-1.812
76	12	11.68	0.3176
77	12	11.17	0.8287
78	15	13.34	1.66
79	12	12.63	-0.6348
80	12	11.54	0.4633
81	10	10.98	-0.979
82	12	12.68	-0.6806
83	12	12.02	-0.02475
84	12	12.49	-0.49
85	11	12.26	-1.258
86	13	12.77	0.2324
87	13	12.68	0.3177
88	11	11.4	-0.4031
89	10	11.87	-1.874
90	9	10.66	-1.66
91	13	12.26	0.7417
92	10	12.81	-2.811
93	13	11.56	1.443
94	12	11.41	0.5934
95	12	12.24	-0.2366

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.5627	0.8745	0.4373
9	0.6183	0.7635	0.3817
10	0.4779	0.9558	0.5221
11	0.3499	0.6999	0.6501
12	0.3345	0.669	0.6655
13	0.5306	0.9389	0.4694
14	0.575	0.85	0.425
15	0.4792	0.9584	0.5208
16	0.3974	0.7948	0.6026
17	0.3113	0.6226	0.6887
18	0.3464	0.6929	0.6536
19	0.2742	0.5483	0.7258
20	0.4498	0.8996	0.5502
21	0.535	0.93	0.465
22	0.5184	0.9632	0.4816
23	0.4471	0.8941	0.5529
24	0.3786	0.7572	0.6214
25	0.3142	0.6284	0.6858
26	0.256	0.512	0.744
27	0.2139	0.4278	0.7861
28	0.4044	0.8088	0.5956
29	0.3771	0.7542	0.6229
30	0.4628	0.9256	0.5372
31	0.4238	0.8476	0.5762
32	0.98	0.03991	0.01995
33	0.9746	0.05074	0.02537
34	0.9946	0.0108	0.005401
35	0.9928	0.01431	0.007153
36	0.9909	0.01812	0.009062
37	0.9894	0.0213	0.01065
38	0.9871	0.02581	0.0129
39	0.9814	0.03715	0.01857
40	0.9731	0.05372	0.02686
41	0.9644	0.07128	0.03564
42	0.9605	0.0789	0.03945
43	0.9752	0.04962	0.02481
44	0.9649	0.07017	0.03509
45	0.9514	0.09719	0.04859
46	0.9629	0.07418	0.03709
47	0.9536	0.09275	0.04638
48	0.9368	0.1265	0.06323
49	0.9197	0.1606	0.08031
50	0.9227	0.1547	0.07734
51	0.9571	0.08572	0.04286
52	0.9422	0.1157	0.05783
53	0.925	0.15	0.07501
54	0.9098	0.1805	0.09024
55	0.8812	0.2376	0.1188
56	0.8502	0.2997	0.1498
57	0.8673	0.2655	0.1327
58	0.8296	0.3408	0.1704
59	0.9601	0.07979	0.0399
60	0.9458	0.1083	0.05416
61	0.9724	0.05527	0.02764
62	0.9748	0.05049	0.02524
63	0.9718	0.05636	0.02818
64	0.9594	0.08128	0.04064
65	0.9551	0.08989	0.04494
66	0.9464	0.1071	0.05355
67	0.9241	0.1518	0.07588
68	0.9365	0.1269	0.06345
69	0.939	0.122	0.06101
70	0.9171	0.1659	0.08294
71	0.8844	0.2312	0.1156
72	0.9056	0.1889	0.09443
73	0.8687	0.2625	0.1313
74	0.8459	0.3082	0.1541
75	0.8382	0.3235	0.1618
76	0.7874	0.4252	0.2126
77	0.7705	0.459	0.2295
78	0.8458	0.3083	0.1542
79	0.8347	0.3307	0.1653
80	0.7879	0.4242	0.2121
81	0.7094	0.5812	0.2906
82	0.6117	0.7767	0.3883
83	0.5362	0.9276	0.4638
84	0.4361	0.8721	0.5639
85	0.5059	0.9882	0.4941
86	0.3656	0.7312	0.6344
87	0.2393	0.4785	0.7607

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.5627 &  0.8745 &  0.4373 \tabularnewline
9 &  0.6183 &  0.7635 &  0.3817 \tabularnewline
10 &  0.4779 &  0.9558 &  0.5221 \tabularnewline
11 &  0.3499 &  0.6999 &  0.6501 \tabularnewline
12 &  0.3345 &  0.669 &  0.6655 \tabularnewline
13 &  0.5306 &  0.9389 &  0.4694 \tabularnewline
14 &  0.575 &  0.85 &  0.425 \tabularnewline
15 &  0.4792 &  0.9584 &  0.5208 \tabularnewline
16 &  0.3974 &  0.7948 &  0.6026 \tabularnewline
17 &  0.3113 &  0.6226 &  0.6887 \tabularnewline
18 &  0.3464 &  0.6929 &  0.6536 \tabularnewline
19 &  0.2742 &  0.5483 &  0.7258 \tabularnewline
20 &  0.4498 &  0.8996 &  0.5502 \tabularnewline
21 &  0.535 &  0.93 &  0.465 \tabularnewline
22 &  0.5184 &  0.9632 &  0.4816 \tabularnewline
23 &  0.4471 &  0.8941 &  0.5529 \tabularnewline
24 &  0.3786 &  0.7572 &  0.6214 \tabularnewline
25 &  0.3142 &  0.6284 &  0.6858 \tabularnewline
26 &  0.256 &  0.512 &  0.744 \tabularnewline
27 &  0.2139 &  0.4278 &  0.7861 \tabularnewline
28 &  0.4044 &  0.8088 &  0.5956 \tabularnewline
29 &  0.3771 &  0.7542 &  0.6229 \tabularnewline
30 &  0.4628 &  0.9256 &  0.5372 \tabularnewline
31 &  0.4238 &  0.8476 &  0.5762 \tabularnewline
32 &  0.98 &  0.03991 &  0.01995 \tabularnewline
33 &  0.9746 &  0.05074 &  0.02537 \tabularnewline
34 &  0.9946 &  0.0108 &  0.005401 \tabularnewline
35 &  0.9928 &  0.01431 &  0.007153 \tabularnewline
36 &  0.9909 &  0.01812 &  0.009062 \tabularnewline
37 &  0.9894 &  0.0213 &  0.01065 \tabularnewline
38 &  0.9871 &  0.02581 &  0.0129 \tabularnewline
39 &  0.9814 &  0.03715 &  0.01857 \tabularnewline
40 &  0.9731 &  0.05372 &  0.02686 \tabularnewline
41 &  0.9644 &  0.07128 &  0.03564 \tabularnewline
42 &  0.9605 &  0.0789 &  0.03945 \tabularnewline
43 &  0.9752 &  0.04962 &  0.02481 \tabularnewline
44 &  0.9649 &  0.07017 &  0.03509 \tabularnewline
45 &  0.9514 &  0.09719 &  0.04859 \tabularnewline
46 &  0.9629 &  0.07418 &  0.03709 \tabularnewline
47 &  0.9536 &  0.09275 &  0.04638 \tabularnewline
48 &  0.9368 &  0.1265 &  0.06323 \tabularnewline
49 &  0.9197 &  0.1606 &  0.08031 \tabularnewline
50 &  0.9227 &  0.1547 &  0.07734 \tabularnewline
51 &  0.9571 &  0.08572 &  0.04286 \tabularnewline
52 &  0.9422 &  0.1157 &  0.05783 \tabularnewline
53 &  0.925 &  0.15 &  0.07501 \tabularnewline
54 &  0.9098 &  0.1805 &  0.09024 \tabularnewline
55 &  0.8812 &  0.2376 &  0.1188 \tabularnewline
56 &  0.8502 &  0.2997 &  0.1498 \tabularnewline
57 &  0.8673 &  0.2655 &  0.1327 \tabularnewline
58 &  0.8296 &  0.3408 &  0.1704 \tabularnewline
59 &  0.9601 &  0.07979 &  0.0399 \tabularnewline
60 &  0.9458 &  0.1083 &  0.05416 \tabularnewline
61 &  0.9724 &  0.05527 &  0.02764 \tabularnewline
62 &  0.9748 &  0.05049 &  0.02524 \tabularnewline
63 &  0.9718 &  0.05636 &  0.02818 \tabularnewline
64 &  0.9594 &  0.08128 &  0.04064 \tabularnewline
65 &  0.9551 &  0.08989 &  0.04494 \tabularnewline
66 &  0.9464 &  0.1071 &  0.05355 \tabularnewline
67 &  0.9241 &  0.1518 &  0.07588 \tabularnewline
68 &  0.9365 &  0.1269 &  0.06345 \tabularnewline
69 &  0.939 &  0.122 &  0.06101 \tabularnewline
70 &  0.9171 &  0.1659 &  0.08294 \tabularnewline
71 &  0.8844 &  0.2312 &  0.1156 \tabularnewline
72 &  0.9056 &  0.1889 &  0.09443 \tabularnewline
73 &  0.8687 &  0.2625 &  0.1313 \tabularnewline
74 &  0.8459 &  0.3082 &  0.1541 \tabularnewline
75 &  0.8382 &  0.3235 &  0.1618 \tabularnewline
76 &  0.7874 &  0.4252 &  0.2126 \tabularnewline
77 &  0.7705 &  0.459 &  0.2295 \tabularnewline
78 &  0.8458 &  0.3083 &  0.1542 \tabularnewline
79 &  0.8347 &  0.3307 &  0.1653 \tabularnewline
80 &  0.7879 &  0.4242 &  0.2121 \tabularnewline
81 &  0.7094 &  0.5812 &  0.2906 \tabularnewline
82 &  0.6117 &  0.7767 &  0.3883 \tabularnewline
83 &  0.5362 &  0.9276 &  0.4638 \tabularnewline
84 &  0.4361 &  0.8721 &  0.5639 \tabularnewline
85 &  0.5059 &  0.9882 &  0.4941 \tabularnewline
86 &  0.3656 &  0.7312 &  0.6344 \tabularnewline
87 &  0.2393 &  0.4785 &  0.7607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297852&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] 0.5627[/C][C] 0.8745[/C][C] 0.4373[/C][/ROW]
[ROW][C]9[/C][C] 0.6183[/C][C] 0.7635[/C][C] 0.3817[/C][/ROW]
[ROW][C]10[/C][C] 0.4779[/C][C] 0.9558[/C][C] 0.5221[/C][/ROW]
[ROW][C]11[/C][C] 0.3499[/C][C] 0.6999[/C][C] 0.6501[/C][/ROW]
[ROW][C]12[/C][C] 0.3345[/C][C] 0.669[/C][C] 0.6655[/C][/ROW]
[ROW][C]13[/C][C] 0.5306[/C][C] 0.9389[/C][C] 0.4694[/C][/ROW]
[ROW][C]14[/C][C] 0.575[/C][C] 0.85[/C][C] 0.425[/C][/ROW]
[ROW][C]15[/C][C] 0.4792[/C][C] 0.9584[/C][C] 0.5208[/C][/ROW]
[ROW][C]16[/C][C] 0.3974[/C][C] 0.7948[/C][C] 0.6026[/C][/ROW]
[ROW][C]17[/C][C] 0.3113[/C][C] 0.6226[/C][C] 0.6887[/C][/ROW]
[ROW][C]18[/C][C] 0.3464[/C][C] 0.6929[/C][C] 0.6536[/C][/ROW]
[ROW][C]19[/C][C] 0.2742[/C][C] 0.5483[/C][C] 0.7258[/C][/ROW]
[ROW][C]20[/C][C] 0.4498[/C][C] 0.8996[/C][C] 0.5502[/C][/ROW]
[ROW][C]21[/C][C] 0.535[/C][C] 0.93[/C][C] 0.465[/C][/ROW]
[ROW][C]22[/C][C] 0.5184[/C][C] 0.9632[/C][C] 0.4816[/C][/ROW]
[ROW][C]23[/C][C] 0.4471[/C][C] 0.8941[/C][C] 0.5529[/C][/ROW]
[ROW][C]24[/C][C] 0.3786[/C][C] 0.7572[/C][C] 0.6214[/C][/ROW]
[ROW][C]25[/C][C] 0.3142[/C][C] 0.6284[/C][C] 0.6858[/C][/ROW]
[ROW][C]26[/C][C] 0.256[/C][C] 0.512[/C][C] 0.744[/C][/ROW]
[ROW][C]27[/C][C] 0.2139[/C][C] 0.4278[/C][C] 0.7861[/C][/ROW]
[ROW][C]28[/C][C] 0.4044[/C][C] 0.8088[/C][C] 0.5956[/C][/ROW]
[ROW][C]29[/C][C] 0.3771[/C][C] 0.7542[/C][C] 0.6229[/C][/ROW]
[ROW][C]30[/C][C] 0.4628[/C][C] 0.9256[/C][C] 0.5372[/C][/ROW]
[ROW][C]31[/C][C] 0.4238[/C][C] 0.8476[/C][C] 0.5762[/C][/ROW]
[ROW][C]32[/C][C] 0.98[/C][C] 0.03991[/C][C] 0.01995[/C][/ROW]
[ROW][C]33[/C][C] 0.9746[/C][C] 0.05074[/C][C] 0.02537[/C][/ROW]
[ROW][C]34[/C][C] 0.9946[/C][C] 0.0108[/C][C] 0.005401[/C][/ROW]
[ROW][C]35[/C][C] 0.9928[/C][C] 0.01431[/C][C] 0.007153[/C][/ROW]
[ROW][C]36[/C][C] 0.9909[/C][C] 0.01812[/C][C] 0.009062[/C][/ROW]
[ROW][C]37[/C][C] 0.9894[/C][C] 0.0213[/C][C] 0.01065[/C][/ROW]
[ROW][C]38[/C][C] 0.9871[/C][C] 0.02581[/C][C] 0.0129[/C][/ROW]
[ROW][C]39[/C][C] 0.9814[/C][C] 0.03715[/C][C] 0.01857[/C][/ROW]
[ROW][C]40[/C][C] 0.9731[/C][C] 0.05372[/C][C] 0.02686[/C][/ROW]
[ROW][C]41[/C][C] 0.9644[/C][C] 0.07128[/C][C] 0.03564[/C][/ROW]
[ROW][C]42[/C][C] 0.9605[/C][C] 0.0789[/C][C] 0.03945[/C][/ROW]
[ROW][C]43[/C][C] 0.9752[/C][C] 0.04962[/C][C] 0.02481[/C][/ROW]
[ROW][C]44[/C][C] 0.9649[/C][C] 0.07017[/C][C] 0.03509[/C][/ROW]
[ROW][C]45[/C][C] 0.9514[/C][C] 0.09719[/C][C] 0.04859[/C][/ROW]
[ROW][C]46[/C][C] 0.9629[/C][C] 0.07418[/C][C] 0.03709[/C][/ROW]
[ROW][C]47[/C][C] 0.9536[/C][C] 0.09275[/C][C] 0.04638[/C][/ROW]
[ROW][C]48[/C][C] 0.9368[/C][C] 0.1265[/C][C] 0.06323[/C][/ROW]
[ROW][C]49[/C][C] 0.9197[/C][C] 0.1606[/C][C] 0.08031[/C][/ROW]
[ROW][C]50[/C][C] 0.9227[/C][C] 0.1547[/C][C] 0.07734[/C][/ROW]
[ROW][C]51[/C][C] 0.9571[/C][C] 0.08572[/C][C] 0.04286[/C][/ROW]
[ROW][C]52[/C][C] 0.9422[/C][C] 0.1157[/C][C] 0.05783[/C][/ROW]
[ROW][C]53[/C][C] 0.925[/C][C] 0.15[/C][C] 0.07501[/C][/ROW]
[ROW][C]54[/C][C] 0.9098[/C][C] 0.1805[/C][C] 0.09024[/C][/ROW]
[ROW][C]55[/C][C] 0.8812[/C][C] 0.2376[/C][C] 0.1188[/C][/ROW]
[ROW][C]56[/C][C] 0.8502[/C][C] 0.2997[/C][C] 0.1498[/C][/ROW]
[ROW][C]57[/C][C] 0.8673[/C][C] 0.2655[/C][C] 0.1327[/C][/ROW]
[ROW][C]58[/C][C] 0.8296[/C][C] 0.3408[/C][C] 0.1704[/C][/ROW]
[ROW][C]59[/C][C] 0.9601[/C][C] 0.07979[/C][C] 0.0399[/C][/ROW]
[ROW][C]60[/C][C] 0.9458[/C][C] 0.1083[/C][C] 0.05416[/C][/ROW]
[ROW][C]61[/C][C] 0.9724[/C][C] 0.05527[/C][C] 0.02764[/C][/ROW]
[ROW][C]62[/C][C] 0.9748[/C][C] 0.05049[/C][C] 0.02524[/C][/ROW]
[ROW][C]63[/C][C] 0.9718[/C][C] 0.05636[/C][C] 0.02818[/C][/ROW]
[ROW][C]64[/C][C] 0.9594[/C][C] 0.08128[/C][C] 0.04064[/C][/ROW]
[ROW][C]65[/C][C] 0.9551[/C][C] 0.08989[/C][C] 0.04494[/C][/ROW]
[ROW][C]66[/C][C] 0.9464[/C][C] 0.1071[/C][C] 0.05355[/C][/ROW]
[ROW][C]67[/C][C] 0.9241[/C][C] 0.1518[/C][C] 0.07588[/C][/ROW]
[ROW][C]68[/C][C] 0.9365[/C][C] 0.1269[/C][C] 0.06345[/C][/ROW]
[ROW][C]69[/C][C] 0.939[/C][C] 0.122[/C][C] 0.06101[/C][/ROW]
[ROW][C]70[/C][C] 0.9171[/C][C] 0.1659[/C][C] 0.08294[/C][/ROW]
[ROW][C]71[/C][C] 0.8844[/C][C] 0.2312[/C][C] 0.1156[/C][/ROW]
[ROW][C]72[/C][C] 0.9056[/C][C] 0.1889[/C][C] 0.09443[/C][/ROW]
[ROW][C]73[/C][C] 0.8687[/C][C] 0.2625[/C][C] 0.1313[/C][/ROW]
[ROW][C]74[/C][C] 0.8459[/C][C] 0.3082[/C][C] 0.1541[/C][/ROW]
[ROW][C]75[/C][C] 0.8382[/C][C] 0.3235[/C][C] 0.1618[/C][/ROW]
[ROW][C]76[/C][C] 0.7874[/C][C] 0.4252[/C][C] 0.2126[/C][/ROW]
[ROW][C]77[/C][C] 0.7705[/C][C] 0.459[/C][C] 0.2295[/C][/ROW]
[ROW][C]78[/C][C] 0.8458[/C][C] 0.3083[/C][C] 0.1542[/C][/ROW]
[ROW][C]79[/C][C] 0.8347[/C][C] 0.3307[/C][C] 0.1653[/C][/ROW]
[ROW][C]80[/C][C] 0.7879[/C][C] 0.4242[/C][C] 0.2121[/C][/ROW]
[ROW][C]81[/C][C] 0.7094[/C][C] 0.5812[/C][C] 0.2906[/C][/ROW]
[ROW][C]82[/C][C] 0.6117[/C][C] 0.7767[/C][C] 0.3883[/C][/ROW]
[ROW][C]83[/C][C] 0.5362[/C][C] 0.9276[/C][C] 0.4638[/C][/ROW]
[ROW][C]84[/C][C] 0.4361[/C][C] 0.8721[/C][C] 0.5639[/C][/ROW]
[ROW][C]85[/C][C] 0.5059[/C][C] 0.9882[/C][C] 0.4941[/C][/ROW]
[ROW][C]86[/C][C] 0.3656[/C][C] 0.7312[/C][C] 0.6344[/C][/ROW]
[ROW][C]87[/C][C] 0.2393[/C][C] 0.4785[/C][C] 0.7607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297852&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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	0.5627	0.8745	0.4373
9	0.6183	0.7635	0.3817
10	0.4779	0.9558	0.5221
11	0.3499	0.6999	0.6501
12	0.3345	0.669	0.6655
13	0.5306	0.9389	0.4694
14	0.575	0.85	0.425
15	0.4792	0.9584	0.5208
16	0.3974	0.7948	0.6026
17	0.3113	0.6226	0.6887
18	0.3464	0.6929	0.6536
19	0.2742	0.5483	0.7258
20	0.4498	0.8996	0.5502
21	0.535	0.93	0.465
22	0.5184	0.9632	0.4816
23	0.4471	0.8941	0.5529
24	0.3786	0.7572	0.6214
25	0.3142	0.6284	0.6858
26	0.256	0.512	0.744
27	0.2139	0.4278	0.7861
28	0.4044	0.8088	0.5956
29	0.3771	0.7542	0.6229
30	0.4628	0.9256	0.5372
31	0.4238	0.8476	0.5762
32	0.98	0.03991	0.01995
33	0.9746	0.05074	0.02537
34	0.9946	0.0108	0.005401
35	0.9928	0.01431	0.007153
36	0.9909	0.01812	0.009062
37	0.9894	0.0213	0.01065
38	0.9871	0.02581	0.0129
39	0.9814	0.03715	0.01857
40	0.9731	0.05372	0.02686
41	0.9644	0.07128	0.03564
42	0.9605	0.0789	0.03945
43	0.9752	0.04962	0.02481
44	0.9649	0.07017	0.03509
45	0.9514	0.09719	0.04859
46	0.9629	0.07418	0.03709
47	0.9536	0.09275	0.04638
48	0.9368	0.1265	0.06323
49	0.9197	0.1606	0.08031
50	0.9227	0.1547	0.07734
51	0.9571	0.08572	0.04286
52	0.9422	0.1157	0.05783
53	0.925	0.15	0.07501
54	0.9098	0.1805	0.09024
55	0.8812	0.2376	0.1188
56	0.8502	0.2997	0.1498
57	0.8673	0.2655	0.1327
58	0.8296	0.3408	0.1704
59	0.9601	0.07979	0.0399
60	0.9458	0.1083	0.05416
61	0.9724	0.05527	0.02764
62	0.9748	0.05049	0.02524
63	0.9718	0.05636	0.02818
64	0.9594	0.08128	0.04064
65	0.9551	0.08989	0.04494
66	0.9464	0.1071	0.05355
67	0.9241	0.1518	0.07588
68	0.9365	0.1269	0.06345
69	0.939	0.122	0.06101
70	0.9171	0.1659	0.08294
71	0.8844	0.2312	0.1156
72	0.9056	0.1889	0.09443
73	0.8687	0.2625	0.1313
74	0.8459	0.3082	0.1541
75	0.8382	0.3235	0.1618
76	0.7874	0.4252	0.2126
77	0.7705	0.459	0.2295
78	0.8458	0.3083	0.1542
79	0.8347	0.3307	0.1653
80	0.7879	0.4242	0.2121
81	0.7094	0.5812	0.2906
82	0.6117	0.7767	0.3883
83	0.5362	0.9276	0.4638
84	0.4361	0.8721	0.5639
85	0.5059	0.9882	0.4941
86	0.3656	0.7312	0.6344
87	0.2393	0.4785	0.7607

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	8	0.1	NOK
10% type I error level	23	0.2875	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 & 8 & 0.1 & NOK \tabularnewline
10% type I error level & 23 & 0.2875 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297852&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]8[/C][C]0.1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.2875[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297852&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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	8	0.1	NOK
10% type I error level	23	0.2875	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 7.1741, df1 = 2, df2 = 88, p-value = 0.001299
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0552, df1 = 8, df2 = 82, p-value = 0.4025
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.42124, df1 = 2, df2 = 88, p-value = 0.6575

\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 = 7.1741, df1 = 2, df2 = 88, p-value = 0.001299
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0552, df1 = 8, df2 = 82, p-value = 0.4025
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.42124, df1 = 2, df2 = 88, p-value = 0.6575
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297852&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 = 7.1741, df1 = 2, df2 = 88, p-value = 0.001299
[/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 = 1.0552, df1 = 8, df2 = 82, p-value = 0.4025
[/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 = 0.42124, df1 = 2, df2 = 88, p-value = 0.6575
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297852&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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 = 7.1741, df1 = 2, df2 = 88, p-value = 0.001299
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0552, df1 = 8, df2 = 82, p-value = 0.4025
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.42124, df1 = 2, df2 = 88, p-value = 0.6575

Variance Inflation Factors (Multicollinearity)
> vif V1 V2 V3 V4 1.016860 1.034345 1.059619 1.077623

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      V1       V2       V3       V4 
1.016860 1.034345 1.059619 1.077623 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297852&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      V1       V2       V3       V4 
1.016860 1.034345 1.059619 1.077623 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297852&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297852&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 1.016860 1.034345 1.059619 1.077623

Figure 1

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Parameters (Session):

par1 = TRUE ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

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

par5 <- ''
par4 <- ''
par3 <- 'No 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