Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 13 Dec 2016 10:51:15 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/13/t1481622837mzzus2nz6j4hjyw.htm/, Retrieved Sun, 05 May 2024 05:01:39 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299025, Retrieved Sun, 05 May 2024 05:01:39 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

109

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

-       [Multiple Regression] [Multiple Regressi...] [2016-12-13 09:51:15] [94c1b173d9287822f5e2740a4a602bdd] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

14	4	3	3	3
19	5	4	4	3
17	4	5	5	3
17	NA	4	4	3
15	NA	4	4	4
20	5	3	5	3
15	5	3	5	NA
19	NA	4	5	3
15	NA	4	5	4
15	5	4	5	3
19	5	4	5	3
16	4	4	4	3
20	4	4	4	4
18	4	3	4	3
15	4	4	4	4
14	5	4	5	3
20	NA	4	4	4
16	3	4	4	NA
16	NA	4	5	4
16	5	4	4	3
10	NA	4	4	3
19	5	4	4	3
19	NA	4	4	3
16	NA	4	5	4
15	NA	3	5	3
18	4	4	4	4
17	4	4	4	3
19	4	4	5	3
17	4	4	5	3
14	3	4	3	3
19	4	3	5	3
20	5	4	4	4
5	NA	4	5	2
19	4	2	4	3
16	5	4	5	3
15	NA	4	4	3
16	3	3	4	4
18	2	4	4	4
16	5	4	5	4
15	NA	4	4	3
17	5	4	5	3
15	4	3	3	3
19	4	4	5	3
17	4	4	4	3
9	3	4	5	3
14	NA	4	5	3
15	4	4	4	3
16	3	4	3	3
18	5	4	5	3
18	NA	5	5	3
16	NA	5	4	4
17	2	3	3	3
19	3	4	4	3
16	2	4	4	3
19	NA	4	4	3
13	5	5	4	3
16	4	4	4	4
13	NA	4	4	3
12	5	4	5	3
17	5	4	4	3
17	4	5	4	3
17	5	4	4	3
16	4	4	4	3
16	4	2	4	2
14	5	4	5	3
16	3	4	4	3
13	2	4	4	4
16	5	4	4	3
14	NA	4	4	3
20	NA	4	4	3
12	NA	4	3	3
13	NA	3	4	3
18	NA	5	4	4
14	4	4	4	3
19	5	3	5	3
18	3	4	4	3
14	2	4	4	5
18	5	4	5	3
19	NA	4	5	3
15	1	3	3	3
14	NA	4	5	3
17	5	4	4	4
19	NA	4	5	4
13	5	5	5	5
19	4	4	5	4
18	5	4	5	4
20	NA	4	4	3
15	5	4	4	4
15	5	4	2	3
15	NA	4	4	3
20	4	5	5	3
15	NA	4	5	3
19	4	5	5	3
18	NA	4	4	3
18	4	4	4	4
15	4	5	4	5
20	5	4	5	4
17	5	4	4	3
12	NA	4	NA	NA
18	NA	4	5	4
19	4	4	4	3
20	2	4	4	3
13	NA	4	4	3
17	NA	4	5	4
15	NA	4	4	4
16	NA	4	5	3
18	NA	4	4	3
18	4	4	4	4
14	NA	4	4	4
15	NA	4	3	3
12	NA	4	4	3
17	3	3	3	3
14	5	4	5	NA
18	4	4	4	4
17	5	4	4	3
17	NA	4	5	4
20	5	4	4	3
16	3	4	4	3
14	4	4	4	3
15	3	4	4	3
18	NA	4	4	4
20	4	4	4	3
17	NA	4	5	4
17	4	4	4	3
17	5	4	4	3
17	NA	4	5	3
15	NA	4	4	3
17	NA	4	4	3
18	2	3	3	3
17	4	4	4	NA
20	4	5	4	5
15	NA	3	4	3
16	2	3	3	3
15	NA	4	4	NA
18	4	4	5	5
11	NA	NA	3	NA
15	4	4	4	3
18	5	5	5	4
20	4	5	5	3
19	NA	3	4	3
14	3	4	4	3
16	4	4	4	NA
15	3	4	3	3
17	4	5	5	3
18	2	4	4	4
20	5	5	5	4
17	4	3	4	3
18	NA	4	4	4
15	NA	3	3	3
16	4	4	4	4
11	5	4	4	3
15	4	4	4	3
18	2	4	3	3
17	NA	4	4	3
16	5	4	5	3
12	NA	4	3	3
19	NA	4	4	3
18	5	4	5	4
15	4	4	4	3
17	5	5	5	3
19	3	4	4	4
18	NA	4	4	3
19	4	NA	4	4
16	NA	3	4	3
16	4	4	4	4
16	NA	4	3	3
14	3	4	4	5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSOM[t] = + 14.3292 + 0.0424531TVDC1[t] -0.282698TVDC2[t] + 0.680656TVDC3[t] + 0.155867TVDC4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSOM[t] =  +  14.3292 +  0.0424531TVDC1[t] -0.282698TVDC2[t] +  0.680656TVDC3[t] +  0.155867TVDC4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSOM[t] =  +  14.3292 +  0.0424531TVDC1[t] -0.282698TVDC2[t] +  0.680656TVDC3[t] +  0.155867TVDC4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&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
ITHSOM[t] = + 14.3292 + 0.0424531TVDC1[t] -0.282698TVDC2[t] + 0.680656TVDC3[t] + 0.155867TVDC4[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)	+14.33	1.936	+7.4030e+00	4.65e-11	2.325e-11
TVDC1	+0.04245	0.2542	+1.6700e-01	0.8677	0.4338
TVDC2	-0.2827	0.4187	-6.7510e-01	0.5012	0.2506
TVDC3	+0.6807	0.3955	+1.7210e+00	0.08842	0.04421
TVDC4	+0.1559	0.3771	+4.1340e-01	0.6803	0.3401

\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) & +14.33 &  1.936 & +7.4030e+00 &  4.65e-11 &  2.325e-11 \tabularnewline
TVDC1 & +0.04245 &  0.2542 & +1.6700e-01 &  0.8677 &  0.4338 \tabularnewline
TVDC2 & -0.2827 &  0.4187 & -6.7510e-01 &  0.5012 &  0.2506 \tabularnewline
TVDC3 & +0.6807 &  0.3955 & +1.7210e+00 &  0.08842 &  0.04421 \tabularnewline
TVDC4 & +0.1559 &  0.3771 & +4.1340e-01 &  0.6803 &  0.3401 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&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]+14.33[/C][C] 1.936[/C][C]+7.4030e+00[/C][C] 4.65e-11[/C][C] 2.325e-11[/C][/ROW]
[ROW][C]TVDC1[/C][C]+0.04245[/C][C] 0.2542[/C][C]+1.6700e-01[/C][C] 0.8677[/C][C] 0.4338[/C][/ROW]
[ROW][C]TVDC2[/C][C]-0.2827[/C][C] 0.4187[/C][C]-6.7510e-01[/C][C] 0.5012[/C][C] 0.2506[/C][/ROW]
[ROW][C]TVDC3[/C][C]+0.6807[/C][C] 0.3955[/C][C]+1.7210e+00[/C][C] 0.08842[/C][C] 0.04421[/C][/ROW]
[ROW][C]TVDC4[/C][C]+0.1559[/C][C] 0.3771[/C][C]+4.1340e-01[/C][C] 0.6803[/C][C] 0.3401[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299025&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&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)	+14.33	1.936	+7.4030e+00	4.65e-11	2.325e-11
TVDC1	+0.04245	0.2542	+1.6700e-01	0.8677	0.4338
TVDC2	-0.2827	0.4187	-6.7510e-01	0.5012	0.2506
TVDC3	+0.6807	0.3955	+1.7210e+00	0.08842	0.04421
TVDC4	+0.1559	0.3771	+4.1340e-01	0.6803	0.3401

Multiple Linear Regression - Regression Statistics
Multiple R	0.2044
R-squared	0.04176
Adjusted R-squared	0.002651
F-TEST (value)	1.068
F-TEST (DF numerator)	4
F-TEST (DF denominator)	98
p-value	0.3767
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.174
Sum Squared Residuals	463.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2044 \tabularnewline
R-squared &  0.04176 \tabularnewline
Adjusted R-squared &  0.002651 \tabularnewline
F-TEST (value) &  1.068 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 98 \tabularnewline
p-value &  0.3767 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.174 \tabularnewline
Sum Squared Residuals &  463.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2044[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.04176[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.002651[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.068[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]98[/C][/ROW]
[ROW][C]p-value[/C][C] 0.3767[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.174[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 463.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299025&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&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	0.2044
R-squared	0.04176
Adjusted R-squared	0.002651
F-TEST (value)	1.068
F-TEST (DF numerator)	4
F-TEST (DF denominator)	98
p-value	0.3767
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.174
Sum Squared Residuals	463.2

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.16	-2.161
2	19	16.6	2.399
3	17	16.96	0.04356
4	20	17.56	2.436
5	15	17.28	-2.282
6	19	17.28	1.718
7	16	16.56	-0.5585
8	20	16.71	3.286
9	18	16.84	1.159
10	15	16.71	-1.714
11	14	17.28	-3.282
12	16	16.6	-0.6009
13	19	16.6	2.399
14	18	16.71	1.286
15	17	16.56	0.4415
16	19	17.24	1.761
17	17	17.24	-0.2391
18	14	15.84	-1.835
19	19	17.52	1.478
20	20	16.76	3.243
21	19	17.12	1.876
22	16	17.28	-1.282
23	16	16.95	-0.9546
24	18	16.63	1.371
25	16	17.44	-1.437
26	17	17.28	-0.2816
27	15	16.16	-1.161
28	19	17.24	1.761
29	17	16.56	0.4415
30	9	17.2	-8.197
31	15	16.56	-1.558
32	16	15.84	0.1646
33	18	17.28	0.7184
34	17	16.08	0.9244
35	19	16.52	2.484
36	16	16.47	-0.4736
37	13	16.32	-3.318
38	16	16.71	-0.7144
39	12	17.28	-5.282
40	17	16.6	0.3991
41	17	16.28	0.7242
42	17	16.6	0.3991
43	16	16.56	-0.5585
44	16	16.97	-0.968
45	14	17.28	-3.282
46	16	16.52	-0.516
47	13	16.63	-3.629
48	16	16.6	-0.6009
49	14	16.56	-2.558
50	19	17.56	1.436
51	18	16.52	1.484
52	14	16.79	-2.785
53	18	17.28	0.7184
54	15	16.03	-1.033
55	17	16.76	0.2432
56	13	17.31	-4.311
57	19	17.39	1.605
58	18	17.44	0.5625
59	15	16.76	-1.757
60	15	15.24	-0.2396
61	20	16.96	3.044
62	19	16.96	2.044
63	18	16.71	1.286
64	15	16.59	-1.588
65	20	17.44	2.563
66	17	16.6	0.3991
67	19	16.56	2.442
68	20	16.47	3.526
69	18	16.71	1.286
70	17	16.12	0.8819
71	18	16.71	1.286
72	17	16.6	0.3991
73	20	16.6	3.399
74	16	16.52	-0.516
75	14	16.56	-2.558
76	15	16.52	-1.516
77	20	16.56	3.442
78	17	16.56	0.4415
79	17	16.6	0.3991
80	18	16.08	1.924
81	20	16.59	3.412
82	16	16.08	-0.07562
83	18	17.55	0.4491
84	15	16.56	-1.558
85	18	17.15	0.8452
86	20	16.96	3.044
87	14	16.52	-2.516
88	15	15.84	-0.8354
89	17	16.96	0.04356
90	18	16.63	1.371
91	20	17.15	2.845
92	17	16.84	0.1588
93	16	16.71	-0.7144
94	11	16.6	-5.601
95	15	16.56	-1.558
96	18	15.79	2.207
97	16	17.28	-1.282
98	18	17.44	0.5625
99	15	16.56	-1.558
100	17	17	0.001105
101	19	16.67	2.328
102	16	16.71	-0.7144
103	14	16.83	-2.828

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.16 & -2.161 \tabularnewline
2 &  19 &  16.6 &  2.399 \tabularnewline
3 &  17 &  16.96 &  0.04356 \tabularnewline
4 &  20 &  17.56 &  2.436 \tabularnewline
5 &  15 &  17.28 & -2.282 \tabularnewline
6 &  19 &  17.28 &  1.718 \tabularnewline
7 &  16 &  16.56 & -0.5585 \tabularnewline
8 &  20 &  16.71 &  3.286 \tabularnewline
9 &  18 &  16.84 &  1.159 \tabularnewline
10 &  15 &  16.71 & -1.714 \tabularnewline
11 &  14 &  17.28 & -3.282 \tabularnewline
12 &  16 &  16.6 & -0.6009 \tabularnewline
13 &  19 &  16.6 &  2.399 \tabularnewline
14 &  18 &  16.71 &  1.286 \tabularnewline
15 &  17 &  16.56 &  0.4415 \tabularnewline
16 &  19 &  17.24 &  1.761 \tabularnewline
17 &  17 &  17.24 & -0.2391 \tabularnewline
18 &  14 &  15.84 & -1.835 \tabularnewline
19 &  19 &  17.52 &  1.478 \tabularnewline
20 &  20 &  16.76 &  3.243 \tabularnewline
21 &  19 &  17.12 &  1.876 \tabularnewline
22 &  16 &  17.28 & -1.282 \tabularnewline
23 &  16 &  16.95 & -0.9546 \tabularnewline
24 &  18 &  16.63 &  1.371 \tabularnewline
25 &  16 &  17.44 & -1.437 \tabularnewline
26 &  17 &  17.28 & -0.2816 \tabularnewline
27 &  15 &  16.16 & -1.161 \tabularnewline
28 &  19 &  17.24 &  1.761 \tabularnewline
29 &  17 &  16.56 &  0.4415 \tabularnewline
30 &  9 &  17.2 & -8.197 \tabularnewline
31 &  15 &  16.56 & -1.558 \tabularnewline
32 &  16 &  15.84 &  0.1646 \tabularnewline
33 &  18 &  17.28 &  0.7184 \tabularnewline
34 &  17 &  16.08 &  0.9244 \tabularnewline
35 &  19 &  16.52 &  2.484 \tabularnewline
36 &  16 &  16.47 & -0.4736 \tabularnewline
37 &  13 &  16.32 & -3.318 \tabularnewline
38 &  16 &  16.71 & -0.7144 \tabularnewline
39 &  12 &  17.28 & -5.282 \tabularnewline
40 &  17 &  16.6 &  0.3991 \tabularnewline
41 &  17 &  16.28 &  0.7242 \tabularnewline
42 &  17 &  16.6 &  0.3991 \tabularnewline
43 &  16 &  16.56 & -0.5585 \tabularnewline
44 &  16 &  16.97 & -0.968 \tabularnewline
45 &  14 &  17.28 & -3.282 \tabularnewline
46 &  16 &  16.52 & -0.516 \tabularnewline
47 &  13 &  16.63 & -3.629 \tabularnewline
48 &  16 &  16.6 & -0.6009 \tabularnewline
49 &  14 &  16.56 & -2.558 \tabularnewline
50 &  19 &  17.56 &  1.436 \tabularnewline
51 &  18 &  16.52 &  1.484 \tabularnewline
52 &  14 &  16.79 & -2.785 \tabularnewline
53 &  18 &  17.28 &  0.7184 \tabularnewline
54 &  15 &  16.03 & -1.033 \tabularnewline
55 &  17 &  16.76 &  0.2432 \tabularnewline
56 &  13 &  17.31 & -4.311 \tabularnewline
57 &  19 &  17.39 &  1.605 \tabularnewline
58 &  18 &  17.44 &  0.5625 \tabularnewline
59 &  15 &  16.76 & -1.757 \tabularnewline
60 &  15 &  15.24 & -0.2396 \tabularnewline
61 &  20 &  16.96 &  3.044 \tabularnewline
62 &  19 &  16.96 &  2.044 \tabularnewline
63 &  18 &  16.71 &  1.286 \tabularnewline
64 &  15 &  16.59 & -1.588 \tabularnewline
65 &  20 &  17.44 &  2.563 \tabularnewline
66 &  17 &  16.6 &  0.3991 \tabularnewline
67 &  19 &  16.56 &  2.442 \tabularnewline
68 &  20 &  16.47 &  3.526 \tabularnewline
69 &  18 &  16.71 &  1.286 \tabularnewline
70 &  17 &  16.12 &  0.8819 \tabularnewline
71 &  18 &  16.71 &  1.286 \tabularnewline
72 &  17 &  16.6 &  0.3991 \tabularnewline
73 &  20 &  16.6 &  3.399 \tabularnewline
74 &  16 &  16.52 & -0.516 \tabularnewline
75 &  14 &  16.56 & -2.558 \tabularnewline
76 &  15 &  16.52 & -1.516 \tabularnewline
77 &  20 &  16.56 &  3.442 \tabularnewline
78 &  17 &  16.56 &  0.4415 \tabularnewline
79 &  17 &  16.6 &  0.3991 \tabularnewline
80 &  18 &  16.08 &  1.924 \tabularnewline
81 &  20 &  16.59 &  3.412 \tabularnewline
82 &  16 &  16.08 & -0.07562 \tabularnewline
83 &  18 &  17.55 &  0.4491 \tabularnewline
84 &  15 &  16.56 & -1.558 \tabularnewline
85 &  18 &  17.15 &  0.8452 \tabularnewline
86 &  20 &  16.96 &  3.044 \tabularnewline
87 &  14 &  16.52 & -2.516 \tabularnewline
88 &  15 &  15.84 & -0.8354 \tabularnewline
89 &  17 &  16.96 &  0.04356 \tabularnewline
90 &  18 &  16.63 &  1.371 \tabularnewline
91 &  20 &  17.15 &  2.845 \tabularnewline
92 &  17 &  16.84 &  0.1588 \tabularnewline
93 &  16 &  16.71 & -0.7144 \tabularnewline
94 &  11 &  16.6 & -5.601 \tabularnewline
95 &  15 &  16.56 & -1.558 \tabularnewline
96 &  18 &  15.79 &  2.207 \tabularnewline
97 &  16 &  17.28 & -1.282 \tabularnewline
98 &  18 &  17.44 &  0.5625 \tabularnewline
99 &  15 &  16.56 & -1.558 \tabularnewline
100 &  17 &  17 &  0.001105 \tabularnewline
101 &  19 &  16.67 &  2.328 \tabularnewline
102 &  16 &  16.71 & -0.7144 \tabularnewline
103 &  14 &  16.83 & -2.828 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&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] 14[/C][C] 16.16[/C][C]-2.161[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 16.6[/C][C] 2.399[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.96[/C][C] 0.04356[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 17.56[/C][C] 2.436[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 17.28[/C][C]-2.282[/C][/ROW]
[ROW][C]6[/C][C] 19[/C][C] 17.28[/C][C] 1.718[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 16.56[/C][C]-0.5585[/C][/ROW]
[ROW][C]8[/C][C] 20[/C][C] 16.71[/C][C] 3.286[/C][/ROW]
[ROW][C]9[/C][C] 18[/C][C] 16.84[/C][C] 1.159[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 16.71[/C][C]-1.714[/C][/ROW]
[ROW][C]11[/C][C] 14[/C][C] 17.28[/C][C]-3.282[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 16.6[/C][C]-0.6009[/C][/ROW]
[ROW][C]13[/C][C] 19[/C][C] 16.6[/C][C] 2.399[/C][/ROW]
[ROW][C]14[/C][C] 18[/C][C] 16.71[/C][C] 1.286[/C][/ROW]
[ROW][C]15[/C][C] 17[/C][C] 16.56[/C][C] 0.4415[/C][/ROW]
[ROW][C]16[/C][C] 19[/C][C] 17.24[/C][C] 1.761[/C][/ROW]
[ROW][C]17[/C][C] 17[/C][C] 17.24[/C][C]-0.2391[/C][/ROW]
[ROW][C]18[/C][C] 14[/C][C] 15.84[/C][C]-1.835[/C][/ROW]
[ROW][C]19[/C][C] 19[/C][C] 17.52[/C][C] 1.478[/C][/ROW]
[ROW][C]20[/C][C] 20[/C][C] 16.76[/C][C] 3.243[/C][/ROW]
[ROW][C]21[/C][C] 19[/C][C] 17.12[/C][C] 1.876[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 17.28[/C][C]-1.282[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 16.95[/C][C]-0.9546[/C][/ROW]
[ROW][C]24[/C][C] 18[/C][C] 16.63[/C][C] 1.371[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 17.44[/C][C]-1.437[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 17.28[/C][C]-0.2816[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 16.16[/C][C]-1.161[/C][/ROW]
[ROW][C]28[/C][C] 19[/C][C] 17.24[/C][C] 1.761[/C][/ROW]
[ROW][C]29[/C][C] 17[/C][C] 16.56[/C][C] 0.4415[/C][/ROW]
[ROW][C]30[/C][C] 9[/C][C] 17.2[/C][C]-8.197[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.56[/C][C]-1.558[/C][/ROW]
[ROW][C]32[/C][C] 16[/C][C] 15.84[/C][C] 0.1646[/C][/ROW]
[ROW][C]33[/C][C] 18[/C][C] 17.28[/C][C] 0.7184[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 16.08[/C][C] 0.9244[/C][/ROW]
[ROW][C]35[/C][C] 19[/C][C] 16.52[/C][C] 2.484[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 16.47[/C][C]-0.4736[/C][/ROW]
[ROW][C]37[/C][C] 13[/C][C] 16.32[/C][C]-3.318[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 16.71[/C][C]-0.7144[/C][/ROW]
[ROW][C]39[/C][C] 12[/C][C] 17.28[/C][C]-5.282[/C][/ROW]
[ROW][C]40[/C][C] 17[/C][C] 16.6[/C][C] 0.3991[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 16.28[/C][C] 0.7242[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 16.6[/C][C] 0.3991[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.56[/C][C]-0.5585[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 16.97[/C][C]-0.968[/C][/ROW]
[ROW][C]45[/C][C] 14[/C][C] 17.28[/C][C]-3.282[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 16.52[/C][C]-0.516[/C][/ROW]
[ROW][C]47[/C][C] 13[/C][C] 16.63[/C][C]-3.629[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 16.6[/C][C]-0.6009[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 16.56[/C][C]-2.558[/C][/ROW]
[ROW][C]50[/C][C] 19[/C][C] 17.56[/C][C] 1.436[/C][/ROW]
[ROW][C]51[/C][C] 18[/C][C] 16.52[/C][C] 1.484[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 16.79[/C][C]-2.785[/C][/ROW]
[ROW][C]53[/C][C] 18[/C][C] 17.28[/C][C] 0.7184[/C][/ROW]
[ROW][C]54[/C][C] 15[/C][C] 16.03[/C][C]-1.033[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 16.76[/C][C] 0.2432[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 17.31[/C][C]-4.311[/C][/ROW]
[ROW][C]57[/C][C] 19[/C][C] 17.39[/C][C] 1.605[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 17.44[/C][C] 0.5625[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 16.76[/C][C]-1.757[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 15.24[/C][C]-0.2396[/C][/ROW]
[ROW][C]61[/C][C] 20[/C][C] 16.96[/C][C] 3.044[/C][/ROW]
[ROW][C]62[/C][C] 19[/C][C] 16.96[/C][C] 2.044[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 16.71[/C][C] 1.286[/C][/ROW]
[ROW][C]64[/C][C] 15[/C][C] 16.59[/C][C]-1.588[/C][/ROW]
[ROW][C]65[/C][C] 20[/C][C] 17.44[/C][C] 2.563[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 16.6[/C][C] 0.3991[/C][/ROW]
[ROW][C]67[/C][C] 19[/C][C] 16.56[/C][C] 2.442[/C][/ROW]
[ROW][C]68[/C][C] 20[/C][C] 16.47[/C][C] 3.526[/C][/ROW]
[ROW][C]69[/C][C] 18[/C][C] 16.71[/C][C] 1.286[/C][/ROW]
[ROW][C]70[/C][C] 17[/C][C] 16.12[/C][C] 0.8819[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 16.71[/C][C] 1.286[/C][/ROW]
[ROW][C]72[/C][C] 17[/C][C] 16.6[/C][C] 0.3991[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 16.6[/C][C] 3.399[/C][/ROW]
[ROW][C]74[/C][C] 16[/C][C] 16.52[/C][C]-0.516[/C][/ROW]
[ROW][C]75[/C][C] 14[/C][C] 16.56[/C][C]-2.558[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 16.52[/C][C]-1.516[/C][/ROW]
[ROW][C]77[/C][C] 20[/C][C] 16.56[/C][C] 3.442[/C][/ROW]
[ROW][C]78[/C][C] 17[/C][C] 16.56[/C][C] 0.4415[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 16.6[/C][C] 0.3991[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 16.08[/C][C] 1.924[/C][/ROW]
[ROW][C]81[/C][C] 20[/C][C] 16.59[/C][C] 3.412[/C][/ROW]
[ROW][C]82[/C][C] 16[/C][C] 16.08[/C][C]-0.07562[/C][/ROW]
[ROW][C]83[/C][C] 18[/C][C] 17.55[/C][C] 0.4491[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 16.56[/C][C]-1.558[/C][/ROW]
[ROW][C]85[/C][C] 18[/C][C] 17.15[/C][C] 0.8452[/C][/ROW]
[ROW][C]86[/C][C] 20[/C][C] 16.96[/C][C] 3.044[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 16.52[/C][C]-2.516[/C][/ROW]
[ROW][C]88[/C][C] 15[/C][C] 15.84[/C][C]-0.8354[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 16.96[/C][C] 0.04356[/C][/ROW]
[ROW][C]90[/C][C] 18[/C][C] 16.63[/C][C] 1.371[/C][/ROW]
[ROW][C]91[/C][C] 20[/C][C] 17.15[/C][C] 2.845[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 16.84[/C][C] 0.1588[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 16.71[/C][C]-0.7144[/C][/ROW]
[ROW][C]94[/C][C] 11[/C][C] 16.6[/C][C]-5.601[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 16.56[/C][C]-1.558[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 15.79[/C][C] 2.207[/C][/ROW]
[ROW][C]97[/C][C] 16[/C][C] 17.28[/C][C]-1.282[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 17.44[/C][C] 0.5625[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 16.56[/C][C]-1.558[/C][/ROW]
[ROW][C]100[/C][C] 17[/C][C] 17[/C][C] 0.001105[/C][/ROW]
[ROW][C]101[/C][C] 19[/C][C] 16.67[/C][C] 2.328[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 16.71[/C][C]-0.7144[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 16.83[/C][C]-2.828[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299025&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&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	14	16.16	-2.161
2	19	16.6	2.399
3	17	16.96	0.04356
4	20	17.56	2.436
5	15	17.28	-2.282
6	19	17.28	1.718
7	16	16.56	-0.5585
8	20	16.71	3.286
9	18	16.84	1.159
10	15	16.71	-1.714
11	14	17.28	-3.282
12	16	16.6	-0.6009
13	19	16.6	2.399
14	18	16.71	1.286
15	17	16.56	0.4415
16	19	17.24	1.761
17	17	17.24	-0.2391
18	14	15.84	-1.835
19	19	17.52	1.478
20	20	16.76	3.243
21	19	17.12	1.876
22	16	17.28	-1.282
23	16	16.95	-0.9546
24	18	16.63	1.371
25	16	17.44	-1.437
26	17	17.28	-0.2816
27	15	16.16	-1.161
28	19	17.24	1.761
29	17	16.56	0.4415
30	9	17.2	-8.197
31	15	16.56	-1.558
32	16	15.84	0.1646
33	18	17.28	0.7184
34	17	16.08	0.9244
35	19	16.52	2.484
36	16	16.47	-0.4736
37	13	16.32	-3.318
38	16	16.71	-0.7144
39	12	17.28	-5.282
40	17	16.6	0.3991
41	17	16.28	0.7242
42	17	16.6	0.3991
43	16	16.56	-0.5585
44	16	16.97	-0.968
45	14	17.28	-3.282
46	16	16.52	-0.516
47	13	16.63	-3.629
48	16	16.6	-0.6009
49	14	16.56	-2.558
50	19	17.56	1.436
51	18	16.52	1.484
52	14	16.79	-2.785
53	18	17.28	0.7184
54	15	16.03	-1.033
55	17	16.76	0.2432
56	13	17.31	-4.311
57	19	17.39	1.605
58	18	17.44	0.5625
59	15	16.76	-1.757
60	15	15.24	-0.2396
61	20	16.96	3.044
62	19	16.96	2.044
63	18	16.71	1.286
64	15	16.59	-1.588
65	20	17.44	2.563
66	17	16.6	0.3991
67	19	16.56	2.442
68	20	16.47	3.526
69	18	16.71	1.286
70	17	16.12	0.8819
71	18	16.71	1.286
72	17	16.6	0.3991
73	20	16.6	3.399
74	16	16.52	-0.516
75	14	16.56	-2.558
76	15	16.52	-1.516
77	20	16.56	3.442
78	17	16.56	0.4415
79	17	16.6	0.3991
80	18	16.08	1.924
81	20	16.59	3.412
82	16	16.08	-0.07562
83	18	17.55	0.4491
84	15	16.56	-1.558
85	18	17.15	0.8452
86	20	16.96	3.044
87	14	16.52	-2.516
88	15	15.84	-0.8354
89	17	16.96	0.04356
90	18	16.63	1.371
91	20	17.15	2.845
92	17	16.84	0.1588
93	16	16.71	-0.7144
94	11	16.6	-5.601
95	15	16.56	-1.558
96	18	15.79	2.207
97	16	17.28	-1.282
98	18	17.44	0.5625
99	15	16.56	-1.558
100	17	17	0.001105
101	19	16.67	2.328
102	16	16.71	-0.7144
103	14	16.83	-2.828

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.6978	0.6044	0.3022
9	0.6145	0.7709	0.3855
10	0.7519	0.4961	0.2481
11	0.8633	0.2734	0.1367
12	0.7932	0.4136	0.2068
13	0.7898	0.4204	0.2102
14	0.7151	0.5698	0.2849
15	0.6372	0.7257	0.3628
16	0.5997	0.8006	0.4003
17	0.5137	0.9727	0.4863
18	0.446	0.8921	0.554
19	0.3767	0.7534	0.6233
20	0.3601	0.7201	0.6399
21	0.3081	0.6162	0.6919
22	0.2876	0.5751	0.7124
23	0.2812	0.5624	0.7188
24	0.2523	0.5045	0.7477
25	0.2863	0.5727	0.7137
26	0.2298	0.4595	0.7702
27	0.196	0.392	0.804
28	0.1788	0.3575	0.8212
29	0.139	0.278	0.861
30	0.7864	0.4272	0.2136
31	0.7521	0.4959	0.2479
32	0.7104	0.5792	0.2896
33	0.658	0.684	0.342
34	0.635	0.7301	0.365
35	0.6848	0.6304	0.3152
36	0.6353	0.7295	0.3647
37	0.6956	0.6089	0.3044
38	0.6529	0.6941	0.3471
39	0.8593	0.2814	0.1407
40	0.8245	0.351	0.1755
41	0.7992	0.4016	0.2008
42	0.7559	0.4881	0.2441
43	0.7096	0.5808	0.2904
44	0.6637	0.6727	0.3363
45	0.7293	0.5414	0.2707
46	0.6834	0.6332	0.3166
47	0.7737	0.4527	0.2263
48	0.7311	0.5377	0.2689
49	0.7526	0.4949	0.2474
50	0.7209	0.5582	0.2791
51	0.7022	0.5955	0.2978
52	0.7355	0.529	0.2645
53	0.6919	0.6162	0.3081
54	0.6614	0.6773	0.3386
55	0.61	0.78	0.39
56	0.7643	0.4714	0.2357
57	0.749	0.502	0.251
58	0.7041	0.5917	0.2959
59	0.6819	0.6361	0.3181
60	0.6366	0.7267	0.3634
61	0.6813	0.6374	0.3187
62	0.6645	0.6709	0.3355
63	0.629	0.742	0.371
64	0.6303	0.7394	0.3697
65	0.6603	0.6794	0.3397
66	0.6066	0.7867	0.3934
67	0.6242	0.7517	0.3758
68	0.6923	0.6153	0.3077
69	0.6509	0.6983	0.3491
70	0.6135	0.773	0.3865
71	0.5699	0.8603	0.4301
72	0.5134	0.9732	0.4866
73	0.6792	0.6416	0.3208
74	0.6239	0.7522	0.3761
75	0.6286	0.7428	0.3714
76	0.6144	0.7712	0.3856
77	0.7535	0.4931	0.2465
78	0.7052	0.5896	0.2948
79	0.7075	0.5851	0.2925
80	0.7434	0.5133	0.2566
81	0.8026	0.3948	0.1974
82	0.7514	0.4971	0.2486
83	0.6837	0.6326	0.3163
84	0.6151	0.7699	0.3849
85	0.5384	0.9232	0.4616
86	0.5174	0.9651	0.4826
87	0.5962	0.8076	0.4038
88	0.5068	0.9863	0.4932
89	0.5039	0.9922	0.4961
90	0.5105	0.9789	0.4895
91	0.7075	0.585	0.2925
92	0.6046	0.7908	0.3954
93	0.5525	0.8951	0.4475
94	0.5006	0.9988	0.4994
95	0.3758	0.7516	0.6242

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.6978 &  0.6044 &  0.3022 \tabularnewline
9 &  0.6145 &  0.7709 &  0.3855 \tabularnewline
10 &  0.7519 &  0.4961 &  0.2481 \tabularnewline
11 &  0.8633 &  0.2734 &  0.1367 \tabularnewline
12 &  0.7932 &  0.4136 &  0.2068 \tabularnewline
13 &  0.7898 &  0.4204 &  0.2102 \tabularnewline
14 &  0.7151 &  0.5698 &  0.2849 \tabularnewline
15 &  0.6372 &  0.7257 &  0.3628 \tabularnewline
16 &  0.5997 &  0.8006 &  0.4003 \tabularnewline
17 &  0.5137 &  0.9727 &  0.4863 \tabularnewline
18 &  0.446 &  0.8921 &  0.554 \tabularnewline
19 &  0.3767 &  0.7534 &  0.6233 \tabularnewline
20 &  0.3601 &  0.7201 &  0.6399 \tabularnewline
21 &  0.3081 &  0.6162 &  0.6919 \tabularnewline
22 &  0.2876 &  0.5751 &  0.7124 \tabularnewline
23 &  0.2812 &  0.5624 &  0.7188 \tabularnewline
24 &  0.2523 &  0.5045 &  0.7477 \tabularnewline
25 &  0.2863 &  0.5727 &  0.7137 \tabularnewline
26 &  0.2298 &  0.4595 &  0.7702 \tabularnewline
27 &  0.196 &  0.392 &  0.804 \tabularnewline
28 &  0.1788 &  0.3575 &  0.8212 \tabularnewline
29 &  0.139 &  0.278 &  0.861 \tabularnewline
30 &  0.7864 &  0.4272 &  0.2136 \tabularnewline
31 &  0.7521 &  0.4959 &  0.2479 \tabularnewline
32 &  0.7104 &  0.5792 &  0.2896 \tabularnewline
33 &  0.658 &  0.684 &  0.342 \tabularnewline
34 &  0.635 &  0.7301 &  0.365 \tabularnewline
35 &  0.6848 &  0.6304 &  0.3152 \tabularnewline
36 &  0.6353 &  0.7295 &  0.3647 \tabularnewline
37 &  0.6956 &  0.6089 &  0.3044 \tabularnewline
38 &  0.6529 &  0.6941 &  0.3471 \tabularnewline
39 &  0.8593 &  0.2814 &  0.1407 \tabularnewline
40 &  0.8245 &  0.351 &  0.1755 \tabularnewline
41 &  0.7992 &  0.4016 &  0.2008 \tabularnewline
42 &  0.7559 &  0.4881 &  0.2441 \tabularnewline
43 &  0.7096 &  0.5808 &  0.2904 \tabularnewline
44 &  0.6637 &  0.6727 &  0.3363 \tabularnewline
45 &  0.7293 &  0.5414 &  0.2707 \tabularnewline
46 &  0.6834 &  0.6332 &  0.3166 \tabularnewline
47 &  0.7737 &  0.4527 &  0.2263 \tabularnewline
48 &  0.7311 &  0.5377 &  0.2689 \tabularnewline
49 &  0.7526 &  0.4949 &  0.2474 \tabularnewline
50 &  0.7209 &  0.5582 &  0.2791 \tabularnewline
51 &  0.7022 &  0.5955 &  0.2978 \tabularnewline
52 &  0.7355 &  0.529 &  0.2645 \tabularnewline
53 &  0.6919 &  0.6162 &  0.3081 \tabularnewline
54 &  0.6614 &  0.6773 &  0.3386 \tabularnewline
55 &  0.61 &  0.78 &  0.39 \tabularnewline
56 &  0.7643 &  0.4714 &  0.2357 \tabularnewline
57 &  0.749 &  0.502 &  0.251 \tabularnewline
58 &  0.7041 &  0.5917 &  0.2959 \tabularnewline
59 &  0.6819 &  0.6361 &  0.3181 \tabularnewline
60 &  0.6366 &  0.7267 &  0.3634 \tabularnewline
61 &  0.6813 &  0.6374 &  0.3187 \tabularnewline
62 &  0.6645 &  0.6709 &  0.3355 \tabularnewline
63 &  0.629 &  0.742 &  0.371 \tabularnewline
64 &  0.6303 &  0.7394 &  0.3697 \tabularnewline
65 &  0.6603 &  0.6794 &  0.3397 \tabularnewline
66 &  0.6066 &  0.7867 &  0.3934 \tabularnewline
67 &  0.6242 &  0.7517 &  0.3758 \tabularnewline
68 &  0.6923 &  0.6153 &  0.3077 \tabularnewline
69 &  0.6509 &  0.6983 &  0.3491 \tabularnewline
70 &  0.6135 &  0.773 &  0.3865 \tabularnewline
71 &  0.5699 &  0.8603 &  0.4301 \tabularnewline
72 &  0.5134 &  0.9732 &  0.4866 \tabularnewline
73 &  0.6792 &  0.6416 &  0.3208 \tabularnewline
74 &  0.6239 &  0.7522 &  0.3761 \tabularnewline
75 &  0.6286 &  0.7428 &  0.3714 \tabularnewline
76 &  0.6144 &  0.7712 &  0.3856 \tabularnewline
77 &  0.7535 &  0.4931 &  0.2465 \tabularnewline
78 &  0.7052 &  0.5896 &  0.2948 \tabularnewline
79 &  0.7075 &  0.5851 &  0.2925 \tabularnewline
80 &  0.7434 &  0.5133 &  0.2566 \tabularnewline
81 &  0.8026 &  0.3948 &  0.1974 \tabularnewline
82 &  0.7514 &  0.4971 &  0.2486 \tabularnewline
83 &  0.6837 &  0.6326 &  0.3163 \tabularnewline
84 &  0.6151 &  0.7699 &  0.3849 \tabularnewline
85 &  0.5384 &  0.9232 &  0.4616 \tabularnewline
86 &  0.5174 &  0.9651 &  0.4826 \tabularnewline
87 &  0.5962 &  0.8076 &  0.4038 \tabularnewline
88 &  0.5068 &  0.9863 &  0.4932 \tabularnewline
89 &  0.5039 &  0.9922 &  0.4961 \tabularnewline
90 &  0.5105 &  0.9789 &  0.4895 \tabularnewline
91 &  0.7075 &  0.585 &  0.2925 \tabularnewline
92 &  0.6046 &  0.7908 &  0.3954 \tabularnewline
93 &  0.5525 &  0.8951 &  0.4475 \tabularnewline
94 &  0.5006 &  0.9988 &  0.4994 \tabularnewline
95 &  0.3758 &  0.7516 &  0.6242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&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] 0.6978[/C][C] 0.6044[/C][C] 0.3022[/C][/ROW]
[ROW][C]9[/C][C] 0.6145[/C][C] 0.7709[/C][C] 0.3855[/C][/ROW]
[ROW][C]10[/C][C] 0.7519[/C][C] 0.4961[/C][C] 0.2481[/C][/ROW]
[ROW][C]11[/C][C] 0.8633[/C][C] 0.2734[/C][C] 0.1367[/C][/ROW]
[ROW][C]12[/C][C] 0.7932[/C][C] 0.4136[/C][C] 0.2068[/C][/ROW]
[ROW][C]13[/C][C] 0.7898[/C][C] 0.4204[/C][C] 0.2102[/C][/ROW]
[ROW][C]14[/C][C] 0.7151[/C][C] 0.5698[/C][C] 0.2849[/C][/ROW]
[ROW][C]15[/C][C] 0.6372[/C][C] 0.7257[/C][C] 0.3628[/C][/ROW]
[ROW][C]16[/C][C] 0.5997[/C][C] 0.8006[/C][C] 0.4003[/C][/ROW]
[ROW][C]17[/C][C] 0.5137[/C][C] 0.9727[/C][C] 0.4863[/C][/ROW]
[ROW][C]18[/C][C] 0.446[/C][C] 0.8921[/C][C] 0.554[/C][/ROW]
[ROW][C]19[/C][C] 0.3767[/C][C] 0.7534[/C][C] 0.6233[/C][/ROW]
[ROW][C]20[/C][C] 0.3601[/C][C] 0.7201[/C][C] 0.6399[/C][/ROW]
[ROW][C]21[/C][C] 0.3081[/C][C] 0.6162[/C][C] 0.6919[/C][/ROW]
[ROW][C]22[/C][C] 0.2876[/C][C] 0.5751[/C][C] 0.7124[/C][/ROW]
[ROW][C]23[/C][C] 0.2812[/C][C] 0.5624[/C][C] 0.7188[/C][/ROW]
[ROW][C]24[/C][C] 0.2523[/C][C] 0.5045[/C][C] 0.7477[/C][/ROW]
[ROW][C]25[/C][C] 0.2863[/C][C] 0.5727[/C][C] 0.7137[/C][/ROW]
[ROW][C]26[/C][C] 0.2298[/C][C] 0.4595[/C][C] 0.7702[/C][/ROW]
[ROW][C]27[/C][C] 0.196[/C][C] 0.392[/C][C] 0.804[/C][/ROW]
[ROW][C]28[/C][C] 0.1788[/C][C] 0.3575[/C][C] 0.8212[/C][/ROW]
[ROW][C]29[/C][C] 0.139[/C][C] 0.278[/C][C] 0.861[/C][/ROW]
[ROW][C]30[/C][C] 0.7864[/C][C] 0.4272[/C][C] 0.2136[/C][/ROW]
[ROW][C]31[/C][C] 0.7521[/C][C] 0.4959[/C][C] 0.2479[/C][/ROW]
[ROW][C]32[/C][C] 0.7104[/C][C] 0.5792[/C][C] 0.2896[/C][/ROW]
[ROW][C]33[/C][C] 0.658[/C][C] 0.684[/C][C] 0.342[/C][/ROW]
[ROW][C]34[/C][C] 0.635[/C][C] 0.7301[/C][C] 0.365[/C][/ROW]
[ROW][C]35[/C][C] 0.6848[/C][C] 0.6304[/C][C] 0.3152[/C][/ROW]
[ROW][C]36[/C][C] 0.6353[/C][C] 0.7295[/C][C] 0.3647[/C][/ROW]
[ROW][C]37[/C][C] 0.6956[/C][C] 0.6089[/C][C] 0.3044[/C][/ROW]
[ROW][C]38[/C][C] 0.6529[/C][C] 0.6941[/C][C] 0.3471[/C][/ROW]
[ROW][C]39[/C][C] 0.8593[/C][C] 0.2814[/C][C] 0.1407[/C][/ROW]
[ROW][C]40[/C][C] 0.8245[/C][C] 0.351[/C][C] 0.1755[/C][/ROW]
[ROW][C]41[/C][C] 0.7992[/C][C] 0.4016[/C][C] 0.2008[/C][/ROW]
[ROW][C]42[/C][C] 0.7559[/C][C] 0.4881[/C][C] 0.2441[/C][/ROW]
[ROW][C]43[/C][C] 0.7096[/C][C] 0.5808[/C][C] 0.2904[/C][/ROW]
[ROW][C]44[/C][C] 0.6637[/C][C] 0.6727[/C][C] 0.3363[/C][/ROW]
[ROW][C]45[/C][C] 0.7293[/C][C] 0.5414[/C][C] 0.2707[/C][/ROW]
[ROW][C]46[/C][C] 0.6834[/C][C] 0.6332[/C][C] 0.3166[/C][/ROW]
[ROW][C]47[/C][C] 0.7737[/C][C] 0.4527[/C][C] 0.2263[/C][/ROW]
[ROW][C]48[/C][C] 0.7311[/C][C] 0.5377[/C][C] 0.2689[/C][/ROW]
[ROW][C]49[/C][C] 0.7526[/C][C] 0.4949[/C][C] 0.2474[/C][/ROW]
[ROW][C]50[/C][C] 0.7209[/C][C] 0.5582[/C][C] 0.2791[/C][/ROW]
[ROW][C]51[/C][C] 0.7022[/C][C] 0.5955[/C][C] 0.2978[/C][/ROW]
[ROW][C]52[/C][C] 0.7355[/C][C] 0.529[/C][C] 0.2645[/C][/ROW]
[ROW][C]53[/C][C] 0.6919[/C][C] 0.6162[/C][C] 0.3081[/C][/ROW]
[ROW][C]54[/C][C] 0.6614[/C][C] 0.6773[/C][C] 0.3386[/C][/ROW]
[ROW][C]55[/C][C] 0.61[/C][C] 0.78[/C][C] 0.39[/C][/ROW]
[ROW][C]56[/C][C] 0.7643[/C][C] 0.4714[/C][C] 0.2357[/C][/ROW]
[ROW][C]57[/C][C] 0.749[/C][C] 0.502[/C][C] 0.251[/C][/ROW]
[ROW][C]58[/C][C] 0.7041[/C][C] 0.5917[/C][C] 0.2959[/C][/ROW]
[ROW][C]59[/C][C] 0.6819[/C][C] 0.6361[/C][C] 0.3181[/C][/ROW]
[ROW][C]60[/C][C] 0.6366[/C][C] 0.7267[/C][C] 0.3634[/C][/ROW]
[ROW][C]61[/C][C] 0.6813[/C][C] 0.6374[/C][C] 0.3187[/C][/ROW]
[ROW][C]62[/C][C] 0.6645[/C][C] 0.6709[/C][C] 0.3355[/C][/ROW]
[ROW][C]63[/C][C] 0.629[/C][C] 0.742[/C][C] 0.371[/C][/ROW]
[ROW][C]64[/C][C] 0.6303[/C][C] 0.7394[/C][C] 0.3697[/C][/ROW]
[ROW][C]65[/C][C] 0.6603[/C][C] 0.6794[/C][C] 0.3397[/C][/ROW]
[ROW][C]66[/C][C] 0.6066[/C][C] 0.7867[/C][C] 0.3934[/C][/ROW]
[ROW][C]67[/C][C] 0.6242[/C][C] 0.7517[/C][C] 0.3758[/C][/ROW]
[ROW][C]68[/C][C] 0.6923[/C][C] 0.6153[/C][C] 0.3077[/C][/ROW]
[ROW][C]69[/C][C] 0.6509[/C][C] 0.6983[/C][C] 0.3491[/C][/ROW]
[ROW][C]70[/C][C] 0.6135[/C][C] 0.773[/C][C] 0.3865[/C][/ROW]
[ROW][C]71[/C][C] 0.5699[/C][C] 0.8603[/C][C] 0.4301[/C][/ROW]
[ROW][C]72[/C][C] 0.5134[/C][C] 0.9732[/C][C] 0.4866[/C][/ROW]
[ROW][C]73[/C][C] 0.6792[/C][C] 0.6416[/C][C] 0.3208[/C][/ROW]
[ROW][C]74[/C][C] 0.6239[/C][C] 0.7522[/C][C] 0.3761[/C][/ROW]
[ROW][C]75[/C][C] 0.6286[/C][C] 0.7428[/C][C] 0.3714[/C][/ROW]
[ROW][C]76[/C][C] 0.6144[/C][C] 0.7712[/C][C] 0.3856[/C][/ROW]
[ROW][C]77[/C][C] 0.7535[/C][C] 0.4931[/C][C] 0.2465[/C][/ROW]
[ROW][C]78[/C][C] 0.7052[/C][C] 0.5896[/C][C] 0.2948[/C][/ROW]
[ROW][C]79[/C][C] 0.7075[/C][C] 0.5851[/C][C] 0.2925[/C][/ROW]
[ROW][C]80[/C][C] 0.7434[/C][C] 0.5133[/C][C] 0.2566[/C][/ROW]
[ROW][C]81[/C][C] 0.8026[/C][C] 0.3948[/C][C] 0.1974[/C][/ROW]
[ROW][C]82[/C][C] 0.7514[/C][C] 0.4971[/C][C] 0.2486[/C][/ROW]
[ROW][C]83[/C][C] 0.6837[/C][C] 0.6326[/C][C] 0.3163[/C][/ROW]
[ROW][C]84[/C][C] 0.6151[/C][C] 0.7699[/C][C] 0.3849[/C][/ROW]
[ROW][C]85[/C][C] 0.5384[/C][C] 0.9232[/C][C] 0.4616[/C][/ROW]
[ROW][C]86[/C][C] 0.5174[/C][C] 0.9651[/C][C] 0.4826[/C][/ROW]
[ROW][C]87[/C][C] 0.5962[/C][C] 0.8076[/C][C] 0.4038[/C][/ROW]
[ROW][C]88[/C][C] 0.5068[/C][C] 0.9863[/C][C] 0.4932[/C][/ROW]
[ROW][C]89[/C][C] 0.5039[/C][C] 0.9922[/C][C] 0.4961[/C][/ROW]
[ROW][C]90[/C][C] 0.5105[/C][C] 0.9789[/C][C] 0.4895[/C][/ROW]
[ROW][C]91[/C][C] 0.7075[/C][C] 0.585[/C][C] 0.2925[/C][/ROW]
[ROW][C]92[/C][C] 0.6046[/C][C] 0.7908[/C][C] 0.3954[/C][/ROW]
[ROW][C]93[/C][C] 0.5525[/C][C] 0.8951[/C][C] 0.4475[/C][/ROW]
[ROW][C]94[/C][C] 0.5006[/C][C] 0.9988[/C][C] 0.4994[/C][/ROW]
[ROW][C]95[/C][C] 0.3758[/C][C] 0.7516[/C][C] 0.6242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299025&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&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	0.6978	0.6044	0.3022
9	0.6145	0.7709	0.3855
10	0.7519	0.4961	0.2481
11	0.8633	0.2734	0.1367
12	0.7932	0.4136	0.2068
13	0.7898	0.4204	0.2102
14	0.7151	0.5698	0.2849
15	0.6372	0.7257	0.3628
16	0.5997	0.8006	0.4003
17	0.5137	0.9727	0.4863
18	0.446	0.8921	0.554
19	0.3767	0.7534	0.6233
20	0.3601	0.7201	0.6399
21	0.3081	0.6162	0.6919
22	0.2876	0.5751	0.7124
23	0.2812	0.5624	0.7188
24	0.2523	0.5045	0.7477
25	0.2863	0.5727	0.7137
26	0.2298	0.4595	0.7702
27	0.196	0.392	0.804
28	0.1788	0.3575	0.8212
29	0.139	0.278	0.861
30	0.7864	0.4272	0.2136
31	0.7521	0.4959	0.2479
32	0.7104	0.5792	0.2896
33	0.658	0.684	0.342
34	0.635	0.7301	0.365
35	0.6848	0.6304	0.3152
36	0.6353	0.7295	0.3647
37	0.6956	0.6089	0.3044
38	0.6529	0.6941	0.3471
39	0.8593	0.2814	0.1407
40	0.8245	0.351	0.1755
41	0.7992	0.4016	0.2008
42	0.7559	0.4881	0.2441
43	0.7096	0.5808	0.2904
44	0.6637	0.6727	0.3363
45	0.7293	0.5414	0.2707
46	0.6834	0.6332	0.3166
47	0.7737	0.4527	0.2263
48	0.7311	0.5377	0.2689
49	0.7526	0.4949	0.2474
50	0.7209	0.5582	0.2791
51	0.7022	0.5955	0.2978
52	0.7355	0.529	0.2645
53	0.6919	0.6162	0.3081
54	0.6614	0.6773	0.3386
55	0.61	0.78	0.39
56	0.7643	0.4714	0.2357
57	0.749	0.502	0.251
58	0.7041	0.5917	0.2959
59	0.6819	0.6361	0.3181
60	0.6366	0.7267	0.3634
61	0.6813	0.6374	0.3187
62	0.6645	0.6709	0.3355
63	0.629	0.742	0.371
64	0.6303	0.7394	0.3697
65	0.6603	0.6794	0.3397
66	0.6066	0.7867	0.3934
67	0.6242	0.7517	0.3758
68	0.6923	0.6153	0.3077
69	0.6509	0.6983	0.3491
70	0.6135	0.773	0.3865
71	0.5699	0.8603	0.4301
72	0.5134	0.9732	0.4866
73	0.6792	0.6416	0.3208
74	0.6239	0.7522	0.3761
75	0.6286	0.7428	0.3714
76	0.6144	0.7712	0.3856
77	0.7535	0.4931	0.2465
78	0.7052	0.5896	0.2948
79	0.7075	0.5851	0.2925
80	0.7434	0.5133	0.2566
81	0.8026	0.3948	0.1974
82	0.7514	0.4971	0.2486
83	0.6837	0.6326	0.3163
84	0.6151	0.7699	0.3849
85	0.5384	0.9232	0.4616
86	0.5174	0.9651	0.4826
87	0.5962	0.8076	0.4038
88	0.5068	0.9863	0.4932
89	0.5039	0.9922	0.4961
90	0.5105	0.9789	0.4895
91	0.7075	0.585	0.2925
92	0.6046	0.7908	0.3954
93	0.5525	0.8951	0.4475
94	0.5006	0.9988	0.4994
95	0.3758	0.7516	0.6242

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	0	0	OK
10% type I error level	0	0	OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299025&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&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	0	0	OK
10% type I error level	0	0	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.13369, df1 = 2, df2 = 96, p-value = 0.875
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1305, df1 = 8, df2 = 90, p-value = 0.3508
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.71391, df1 = 2, df2 = 96, p-value = 0.4923

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.13369, df1 = 2, df2 = 96, p-value = 0.875
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1305, df1 = 8, df2 = 90, p-value = 0.3508
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.71391, df1 = 2, df2 = 96, p-value = 0.4923
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.13369, df1 = 2, df2 = 96, p-value = 0.875
[/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 = 1.1305, df1 = 8, df2 = 90, p-value = 0.3508
[/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 = 0.71391, df1 = 2, df2 = 96, p-value = 0.4923
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299025&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.13369, df1 = 2, df2 = 96, p-value = 0.875
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1305, df1 = 8, df2 = 90, p-value = 0.3508
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.71391, df1 = 2, df2 = 96, p-value = 0.4923

Variance Inflation Factors (Multicollinearity)
> vif TVDC1 TVDC2 TVDC3 TVDC4 1.352849 1.255450 1.439439 1.116175

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   TVDC1    TVDC2    TVDC3    TVDC4 
1.352849 1.255450 1.439439 1.116175 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299025&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   TVDC1    TVDC2    TVDC3    TVDC4 
1.352849 1.255450 1.439439 1.116175 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299025&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299025&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 TVDC1 TVDC2 TVDC3 TVDC4 1.352849 1.255450 1.439439 1.116175

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 8 ; par2 = 0 ;

Parameters (R input):

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

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code