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

Fri, 23 Dec 2016 15:55:42 +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/23/t1482505043y4yk48sugw1ednf.htm/, Retrieved Tue, 07 May 2024 08:25:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302972, Retrieved Tue, 07 May 2024 08:25:34 +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

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

-       [Multiple Regression] [] [2016-12-23 14:55:42] [361c8dad91b3f1ef2e651cd04783c23b] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

10	4	2	3	5	4
13	5	3	4	5	4
14	4	4	4	5	4
12	3	4	3	4	4
12	4	4	4	5	4
13	3	4	4	5	5
13	3	4	3	3	4
13	3	4	4	4	4
13	4	5	4	5	5
14	4	5	4	5	5
14	4	4	4	5	4
12	4	4	3	5	4
12	4	4	3	4	5
11	3	3	4	4	5
12	4	4	4	2	5
14	3	4	4	4	5
12	3	4	4	4	5
11	5	5	3	4	4
13	4	4	4	5	4
13	3	4	3	4	5
12	4	4	4	5	5
13	4	4	4	4	5
12	4	4	4	4	4
13	4	4	4	4	5
11	3	4	4	4	4
12	3	4	3	5	5
12	4	4	4	4	4
13	2	4	4	5	5
13	5	4	4	4	4
10	4	3	4	4	4
12	4	5	4	5	5
13	5	4	4	4	5
13	4	3	4	5	5
10	2	3	4	5	4
14	4	5	4	4	4
12	3	4	4	4	4
10	4	3	3	4	5
10	4	3	4	4	4
14	4	4	4	4	4
12	5	4	4	4	4
14	4	5	4	5	5
10	3	3	4	4	4
13	5	5	3	5	5
12	5	4	3	4	4
12	4	4	3	4	5
13	4	4	4	4	4
12	3	5	3	3	4
10	4	4	4	5	4
9	2	3	2	4	4
14	4	5	4	4	4
15	5	5	4	5	4
14	5	5	4	4	4
8	4	3	4	5	5
11	4	3	3	4	5
10	4	4	4	4	4
12	3	4	3	3	4
14	3	4	4	4	3
12	4	4	3	5	4
12	4	4	4	5	4
14	5	5	4	5	5
13	2	4	4	5	5
13	4	4	4	5	5
13	3	4	4	2	4
12	4	4	4	5	5
10	4	2	4	4	4
14	4	4	3	5	3
11	4	4	3	5	4
10	5	4	3	3	5
13	3	4	3	5	5
12	3	4	3	4	5
12	4	5	5	5	4
11	4	4	4	4	4
10	4	4	4	4	4
14	4	4	5	5	4
12	3	4	4	4	4
13	4	4	4	5	4
11	3	4	3	5	5
10	3	3	4	4	5
14	4	3	4	4	4
13	4	4	4	4	5
7	3	3	4	4	4
13	4	4	4	5	4
13	4	4	4	5	5
13	4	4	4	5	5
15	5	4	4	4	4
13	5	4	5	4	5
14	4	4	4	5	5
12	3	4	4	4	5
13	3	4	4	4	4
11	4	2	3	4	4
12	4	4	4	4	3
14	4	4	4	4	5
13	4	4	4	5	4
14	4	5	4	5	3
12	3	4	3	5	5
12	4	4	4	4	5
13	5	4	4	4	5
14	5	4	5	4	5
13	4	5	4	5	5
12	3	4	4	4	5
13	5	3	4	5	5
12	4	4	4	4	5
10	5	4	4	4	5
12	3	4	3	4	4
13	5	4	5	5	5
12	4	4	3	5	5
13	4	4	3	4	3
12	4	4	4	4	4
12	4	4	4	4	4
12	3	4	4	5	3
11	4	4	4	4	4
12	4	4	3	4	5
9	3	3	3	5	5
14	4	4	3	4	4
12	3	4	4	4	4
13	4	4	4	3	4
13	5	4	1	5	5
13	5	4	4	5	5
11	4	4	4	4	3
12	4	4	3	4	4
11	3	4	3	4	5
12	4	4	4	4	4
12	4	4	4	5	4
13	4	5	4	4	4
12	3	4	4	4	4
13	4	4	3	4	4
13	4	4	4	4	5
12	3	4	3	4	4
12	4	4	3	4	3
8	3	2	2	4	4
12	4	4	3	5	4
13	5	4	3	5	4
10	2	4	3	3	5
8	3	3	4	4	4
12	4	4	3	4	4
13	5	5	4	5	4
12	4	5	4	4	4
15	5	5	5	5	4
14	4	5	4	5	5
10	4	4	3	4	5
11	3	4	4	5	4
12	4	4	4	4	4
10	4	4	4	4	4
14	4	4	4	5	5
10	4	4	4	5	5
15	5	4	3	5	4
11	4	3	4	4	4
12	4	4	4	4	4
9	3	3	3	4	4
12	4	5	4	4	3
13	4	4	3	4	4
12	4	4	4	4	5
9	3	4	3	5	5
12	4	4	4	4	5
14	5	4	4	5	4
11	4	4	4	3	4
12	2	3	4	4	4
14	4	4	4	4	5
12	4	3	3	5	5
15	4	4	4	4	3
11	4	5	5	4	4
12	5	4	4	4	4
12	5	4	3	4	4
10	3	3	4	5	5
12	4	4	4	4	5
11	4	4	4	5	4
11	2	3	5	5	4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=302972&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 4.40619 + 0.407956SK1[t] + 1.09952SK2[t] + 0.267735SK4[t] + 0.251121SK5[t] -0.0639611SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  4.40619 +  0.407956SK1[t] +  1.09952SK2[t] +  0.267735SK4[t] +  0.251121SK5[t] -0.0639611SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  4.40619 +  0.407956SK1[t] +  1.09952SK2[t] +  0.267735SK4[t] +  0.251121SK5[t] -0.0639611SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302972&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&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
TVDC[t] = + 4.40619 + 0.407956SK1[t] + 1.09952SK2[t] + 0.267735SK4[t] + 0.251121SK5[t] -0.0639611SK6[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)	+4.406	1.219	+3.6160e+00	0.0004	0.0002
SK1	+0.408	0.1367	+2.9850e+00	0.003283	0.001641
SK2	+1.099	0.1676	+6.5590e+00	7.023e-10	3.512e-10
SK4	+0.2677	0.1647	+1.6250e+00	0.1061	0.05303
SK5	+0.2511	0.1592	+1.5770e+00	0.1166	0.05832
SK6	-0.06396	0.1639	-3.9020e-01	0.6969	0.3485

\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) & +4.406 &  1.219 & +3.6160e+00 &  0.0004 &  0.0002 \tabularnewline
SK1 & +0.408 &  0.1367 & +2.9850e+00 &  0.003283 &  0.001641 \tabularnewline
SK2 & +1.099 &  0.1676 & +6.5590e+00 &  7.023e-10 &  3.512e-10 \tabularnewline
SK4 & +0.2677 &  0.1647 & +1.6250e+00 &  0.1061 &  0.05303 \tabularnewline
SK5 & +0.2511 &  0.1592 & +1.5770e+00 &  0.1166 &  0.05832 \tabularnewline
SK6 & -0.06396 &  0.1639 & -3.9020e-01 &  0.6969 &  0.3485 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&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]+4.406[/C][C] 1.219[/C][C]+3.6160e+00[/C][C] 0.0004[/C][C] 0.0002[/C][/ROW]
[ROW][C]SK1[/C][C]+0.408[/C][C] 0.1367[/C][C]+2.9850e+00[/C][C] 0.003283[/C][C] 0.001641[/C][/ROW]
[ROW][C]SK2[/C][C]+1.099[/C][C] 0.1676[/C][C]+6.5590e+00[/C][C] 7.023e-10[/C][C] 3.512e-10[/C][/ROW]
[ROW][C]SK4[/C][C]+0.2677[/C][C] 0.1647[/C][C]+1.6250e+00[/C][C] 0.1061[/C][C] 0.05303[/C][/ROW]
[ROW][C]SK5[/C][C]+0.2511[/C][C] 0.1592[/C][C]+1.5770e+00[/C][C] 0.1166[/C][C] 0.05832[/C][/ROW]
[ROW][C]SK6[/C][C]-0.06396[/C][C] 0.1639[/C][C]-3.9020e-01[/C][C] 0.6969[/C][C] 0.3485[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302972&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&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)	+4.406	1.219	+3.6160e+00	0.0004	0.0002
SK1	+0.408	0.1367	+2.9850e+00	0.003283	0.001641
SK2	+1.099	0.1676	+6.5590e+00	7.023e-10	3.512e-10
SK4	+0.2677	0.1647	+1.6250e+00	0.1061	0.05303
SK5	+0.2511	0.1592	+1.5770e+00	0.1166	0.05832
SK6	-0.06396	0.1639	-3.9020e-01	0.6969	0.3485

Multiple Linear Regression - Regression Statistics
Multiple R	0.5862
R-squared	0.3437
Adjusted R-squared	0.3233
F-TEST (value)	16.86
F-TEST (DF numerator)	5
F-TEST (DF denominator)	161
p-value	2.205e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.234
Sum Squared Residuals	245.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5862 \tabularnewline
R-squared &  0.3437 \tabularnewline
Adjusted R-squared &  0.3233 \tabularnewline
F-TEST (value) &  16.86 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 161 \tabularnewline
p-value &  2.205e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.234 \tabularnewline
Sum Squared Residuals &  245.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5862[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3437[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3233[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 16.86[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]161[/C][/ROW]
[ROW][C]p-value[/C][C] 2.205e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.234[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 245.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302972&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&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	0.5862
R-squared	0.3437
Adjusted R-squared	0.3233
F-TEST (value)	16.86
F-TEST (DF numerator)	5
F-TEST (DF denominator)	161
p-value	2.205e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.234
Sum Squared Residuals	245.2

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	10	10.04	-0.04003
2	13	11.82	1.185
3	14	12.51	1.493
4	12	11.58	0.42
5	12	12.51	-0.5068
6	13	12.03	0.9651
7	13	11.33	1.671
8	13	11.85	1.152
9	13	13.54	-0.5424
10	14	13.54	0.4576
11	14	12.51	1.493
12	12	12.24	-0.2391
13	12	11.92	0.07601
14	11	10.68	0.3158
15	12	11.69	0.3105
16	14	11.78	2.216
17	12	11.78	0.2162
18	11	13.5	-2.495
19	13	12.51	0.4932
20	13	11.52	1.484
21	12	12.44	-0.4428
22	13	12.19	0.8083
23	12	12.26	-0.2557
24	13	12.19	0.8083
25	11	11.85	-0.8477
26	12	11.77	0.2328
27	12	12.26	-0.2557
28	13	11.63	1.373
29	13	12.66	0.3364
30	10	11.16	-1.156
31	12	13.54	-1.542
32	13	12.6	0.4003
33	13	11.34	1.657
34	10	10.59	-0.5914
35	14	13.36	0.6448
36	12	11.85	0.1523
37	10	10.82	-0.8245
38	10	11.16	-1.156
39	14	12.26	1.744
40	12	12.66	-0.6636
41	14	13.54	0.4576
42	10	10.75	-0.7482
43	13	13.68	-0.6826
44	12	12.4	-0.3959
45	12	11.92	0.07601
46	13	12.26	0.7443
47	12	12.43	-0.4284
48	10	12.51	-2.507
49	9	9.805	-0.8048
50	14	13.36	0.6448
51	15	14.01	0.9857
52	14	13.76	0.2368
53	8	11.34	-3.343
54	11	10.82	0.1755
55	10	12.26	-2.256
56	12	11.33	0.6711
57	14	11.91	2.088
58	12	12.24	-0.2391
59	12	12.51	-0.5068
60	14	13.95	0.04967
61	13	11.63	1.373
62	13	12.44	0.5572
63	13	11.35	1.655
64	12	12.44	-0.4428
65	10	10.06	-0.05664
66	14	12.3	1.697
67	11	12.24	-1.239
68	10	12.08	-2.081
69	13	11.77	1.233
70	12	11.52	0.484
71	12	13.87	-1.874
72	11	12.26	-1.256
73	10	12.26	-2.256
74	14	12.77	1.225
75	12	11.85	0.1523
76	13	12.51	0.4932
77	11	11.77	-0.7672
78	10	10.68	-0.6842
79	14	11.16	2.844
80	13	12.19	0.8083
81	7	10.75	-3.748
82	13	12.51	0.4932
83	13	12.44	0.5572
84	13	12.44	0.5572
85	15	12.66	2.336
86	13	12.87	0.1326
87	14	12.44	1.557
88	12	11.78	0.2162
89	13	11.85	1.152
90	11	9.789	1.211
91	12	12.32	-0.3196
92	14	12.19	1.808
93	13	12.51	0.4932
94	14	13.67	0.3297
95	12	11.77	0.2328
96	12	12.19	-0.1917
97	13	12.6	0.4003
98	14	12.87	1.133
99	13	13.54	-0.5424
100	12	11.78	0.2162
101	13	11.75	1.249
102	12	12.19	-0.1917
103	10	12.6	-2.6
104	12	11.58	0.42
105	13	13.12	-0.1185
106	12	12.18	-0.1751
107	13	12.05	0.9481
108	12	12.26	-0.2557
109	12	12.26	-0.2557
110	12	12.16	-0.1628
111	11	12.26	-1.256
112	12	11.92	0.07601
113	9	10.67	-1.668
114	14	11.99	2.012
115	12	11.85	0.1523
116	13	12	0.9954
117	13	12.05	0.9524
118	13	12.85	0.1492
119	11	12.32	-1.32
120	12	11.99	0.01205
121	11	11.52	-0.516
122	12	12.26	-0.2557
123	12	12.51	-0.5068
124	13	13.36	-0.3552
125	12	11.85	0.1523
126	13	11.99	1.012
127	13	12.19	0.8083
128	12	11.58	0.42
129	12	12.05	-0.05191
130	8	9.113	-1.113
131	12	12.24	-0.2391
132	13	12.65	0.353
133	10	10.86	-0.857
134	8	10.75	-2.748
135	12	11.99	0.01205
136	13	14.01	-1.014
137	12	13.36	-1.355
138	15	14.28	0.718
139	14	13.54	0.4576
140	10	11.92	-1.924
141	11	12.1	-1.099
142	12	12.26	-0.2557
143	10	12.26	-2.256
144	14	12.44	1.557
145	10	12.44	-2.443
146	15	12.65	2.353
147	11	11.16	-0.1562
148	12	12.26	-0.2557
149	9	10.48	-1.48
150	12	13.42	-1.419
151	13	11.99	1.012
152	12	12.19	-0.1917
153	9	11.77	-2.767
154	12	12.19	-0.1917
155	14	12.91	1.085
156	11	12	-1.005
157	12	10.34	1.66
158	14	12.19	1.808
159	12	11.08	0.9244
160	15	12.32	2.68
161	11	13.62	-2.623
162	12	12.66	-0.6636
163	12	12.4	-0.3959
164	10	10.94	-0.9354
165	12	12.19	-0.1917
166	11	12.51	-1.507
167	11	10.86	0.1409

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  10.04 & -0.04003 \tabularnewline
2 &  13 &  11.82 &  1.185 \tabularnewline
3 &  14 &  12.51 &  1.493 \tabularnewline
4 &  12 &  11.58 &  0.42 \tabularnewline
5 &  12 &  12.51 & -0.5068 \tabularnewline
6 &  13 &  12.03 &  0.9651 \tabularnewline
7 &  13 &  11.33 &  1.671 \tabularnewline
8 &  13 &  11.85 &  1.152 \tabularnewline
9 &  13 &  13.54 & -0.5424 \tabularnewline
10 &  14 &  13.54 &  0.4576 \tabularnewline
11 &  14 &  12.51 &  1.493 \tabularnewline
12 &  12 &  12.24 & -0.2391 \tabularnewline
13 &  12 &  11.92 &  0.07601 \tabularnewline
14 &  11 &  10.68 &  0.3158 \tabularnewline
15 &  12 &  11.69 &  0.3105 \tabularnewline
16 &  14 &  11.78 &  2.216 \tabularnewline
17 &  12 &  11.78 &  0.2162 \tabularnewline
18 &  11 &  13.5 & -2.495 \tabularnewline
19 &  13 &  12.51 &  0.4932 \tabularnewline
20 &  13 &  11.52 &  1.484 \tabularnewline
21 &  12 &  12.44 & -0.4428 \tabularnewline
22 &  13 &  12.19 &  0.8083 \tabularnewline
23 &  12 &  12.26 & -0.2557 \tabularnewline
24 &  13 &  12.19 &  0.8083 \tabularnewline
25 &  11 &  11.85 & -0.8477 \tabularnewline
26 &  12 &  11.77 &  0.2328 \tabularnewline
27 &  12 &  12.26 & -0.2557 \tabularnewline
28 &  13 &  11.63 &  1.373 \tabularnewline
29 &  13 &  12.66 &  0.3364 \tabularnewline
30 &  10 &  11.16 & -1.156 \tabularnewline
31 &  12 &  13.54 & -1.542 \tabularnewline
32 &  13 &  12.6 &  0.4003 \tabularnewline
33 &  13 &  11.34 &  1.657 \tabularnewline
34 &  10 &  10.59 & -0.5914 \tabularnewline
35 &  14 &  13.36 &  0.6448 \tabularnewline
36 &  12 &  11.85 &  0.1523 \tabularnewline
37 &  10 &  10.82 & -0.8245 \tabularnewline
38 &  10 &  11.16 & -1.156 \tabularnewline
39 &  14 &  12.26 &  1.744 \tabularnewline
40 &  12 &  12.66 & -0.6636 \tabularnewline
41 &  14 &  13.54 &  0.4576 \tabularnewline
42 &  10 &  10.75 & -0.7482 \tabularnewline
43 &  13 &  13.68 & -0.6826 \tabularnewline
44 &  12 &  12.4 & -0.3959 \tabularnewline
45 &  12 &  11.92 &  0.07601 \tabularnewline
46 &  13 &  12.26 &  0.7443 \tabularnewline
47 &  12 &  12.43 & -0.4284 \tabularnewline
48 &  10 &  12.51 & -2.507 \tabularnewline
49 &  9 &  9.805 & -0.8048 \tabularnewline
50 &  14 &  13.36 &  0.6448 \tabularnewline
51 &  15 &  14.01 &  0.9857 \tabularnewline
52 &  14 &  13.76 &  0.2368 \tabularnewline
53 &  8 &  11.34 & -3.343 \tabularnewline
54 &  11 &  10.82 &  0.1755 \tabularnewline
55 &  10 &  12.26 & -2.256 \tabularnewline
56 &  12 &  11.33 &  0.6711 \tabularnewline
57 &  14 &  11.91 &  2.088 \tabularnewline
58 &  12 &  12.24 & -0.2391 \tabularnewline
59 &  12 &  12.51 & -0.5068 \tabularnewline
60 &  14 &  13.95 &  0.04967 \tabularnewline
61 &  13 &  11.63 &  1.373 \tabularnewline
62 &  13 &  12.44 &  0.5572 \tabularnewline
63 &  13 &  11.35 &  1.655 \tabularnewline
64 &  12 &  12.44 & -0.4428 \tabularnewline
65 &  10 &  10.06 & -0.05664 \tabularnewline
66 &  14 &  12.3 &  1.697 \tabularnewline
67 &  11 &  12.24 & -1.239 \tabularnewline
68 &  10 &  12.08 & -2.081 \tabularnewline
69 &  13 &  11.77 &  1.233 \tabularnewline
70 &  12 &  11.52 &  0.484 \tabularnewline
71 &  12 &  13.87 & -1.874 \tabularnewline
72 &  11 &  12.26 & -1.256 \tabularnewline
73 &  10 &  12.26 & -2.256 \tabularnewline
74 &  14 &  12.77 &  1.225 \tabularnewline
75 &  12 &  11.85 &  0.1523 \tabularnewline
76 &  13 &  12.51 &  0.4932 \tabularnewline
77 &  11 &  11.77 & -0.7672 \tabularnewline
78 &  10 &  10.68 & -0.6842 \tabularnewline
79 &  14 &  11.16 &  2.844 \tabularnewline
80 &  13 &  12.19 &  0.8083 \tabularnewline
81 &  7 &  10.75 & -3.748 \tabularnewline
82 &  13 &  12.51 &  0.4932 \tabularnewline
83 &  13 &  12.44 &  0.5572 \tabularnewline
84 &  13 &  12.44 &  0.5572 \tabularnewline
85 &  15 &  12.66 &  2.336 \tabularnewline
86 &  13 &  12.87 &  0.1326 \tabularnewline
87 &  14 &  12.44 &  1.557 \tabularnewline
88 &  12 &  11.78 &  0.2162 \tabularnewline
89 &  13 &  11.85 &  1.152 \tabularnewline
90 &  11 &  9.789 &  1.211 \tabularnewline
91 &  12 &  12.32 & -0.3196 \tabularnewline
92 &  14 &  12.19 &  1.808 \tabularnewline
93 &  13 &  12.51 &  0.4932 \tabularnewline
94 &  14 &  13.67 &  0.3297 \tabularnewline
95 &  12 &  11.77 &  0.2328 \tabularnewline
96 &  12 &  12.19 & -0.1917 \tabularnewline
97 &  13 &  12.6 &  0.4003 \tabularnewline
98 &  14 &  12.87 &  1.133 \tabularnewline
99 &  13 &  13.54 & -0.5424 \tabularnewline
100 &  12 &  11.78 &  0.2162 \tabularnewline
101 &  13 &  11.75 &  1.249 \tabularnewline
102 &  12 &  12.19 & -0.1917 \tabularnewline
103 &  10 &  12.6 & -2.6 \tabularnewline
104 &  12 &  11.58 &  0.42 \tabularnewline
105 &  13 &  13.12 & -0.1185 \tabularnewline
106 &  12 &  12.18 & -0.1751 \tabularnewline
107 &  13 &  12.05 &  0.9481 \tabularnewline
108 &  12 &  12.26 & -0.2557 \tabularnewline
109 &  12 &  12.26 & -0.2557 \tabularnewline
110 &  12 &  12.16 & -0.1628 \tabularnewline
111 &  11 &  12.26 & -1.256 \tabularnewline
112 &  12 &  11.92 &  0.07601 \tabularnewline
113 &  9 &  10.67 & -1.668 \tabularnewline
114 &  14 &  11.99 &  2.012 \tabularnewline
115 &  12 &  11.85 &  0.1523 \tabularnewline
116 &  13 &  12 &  0.9954 \tabularnewline
117 &  13 &  12.05 &  0.9524 \tabularnewline
118 &  13 &  12.85 &  0.1492 \tabularnewline
119 &  11 &  12.32 & -1.32 \tabularnewline
120 &  12 &  11.99 &  0.01205 \tabularnewline
121 &  11 &  11.52 & -0.516 \tabularnewline
122 &  12 &  12.26 & -0.2557 \tabularnewline
123 &  12 &  12.51 & -0.5068 \tabularnewline
124 &  13 &  13.36 & -0.3552 \tabularnewline
125 &  12 &  11.85 &  0.1523 \tabularnewline
126 &  13 &  11.99 &  1.012 \tabularnewline
127 &  13 &  12.19 &  0.8083 \tabularnewline
128 &  12 &  11.58 &  0.42 \tabularnewline
129 &  12 &  12.05 & -0.05191 \tabularnewline
130 &  8 &  9.113 & -1.113 \tabularnewline
131 &  12 &  12.24 & -0.2391 \tabularnewline
132 &  13 &  12.65 &  0.353 \tabularnewline
133 &  10 &  10.86 & -0.857 \tabularnewline
134 &  8 &  10.75 & -2.748 \tabularnewline
135 &  12 &  11.99 &  0.01205 \tabularnewline
136 &  13 &  14.01 & -1.014 \tabularnewline
137 &  12 &  13.36 & -1.355 \tabularnewline
138 &  15 &  14.28 &  0.718 \tabularnewline
139 &  14 &  13.54 &  0.4576 \tabularnewline
140 &  10 &  11.92 & -1.924 \tabularnewline
141 &  11 &  12.1 & -1.099 \tabularnewline
142 &  12 &  12.26 & -0.2557 \tabularnewline
143 &  10 &  12.26 & -2.256 \tabularnewline
144 &  14 &  12.44 &  1.557 \tabularnewline
145 &  10 &  12.44 & -2.443 \tabularnewline
146 &  15 &  12.65 &  2.353 \tabularnewline
147 &  11 &  11.16 & -0.1562 \tabularnewline
148 &  12 &  12.26 & -0.2557 \tabularnewline
149 &  9 &  10.48 & -1.48 \tabularnewline
150 &  12 &  13.42 & -1.419 \tabularnewline
151 &  13 &  11.99 &  1.012 \tabularnewline
152 &  12 &  12.19 & -0.1917 \tabularnewline
153 &  9 &  11.77 & -2.767 \tabularnewline
154 &  12 &  12.19 & -0.1917 \tabularnewline
155 &  14 &  12.91 &  1.085 \tabularnewline
156 &  11 &  12 & -1.005 \tabularnewline
157 &  12 &  10.34 &  1.66 \tabularnewline
158 &  14 &  12.19 &  1.808 \tabularnewline
159 &  12 &  11.08 &  0.9244 \tabularnewline
160 &  15 &  12.32 &  2.68 \tabularnewline
161 &  11 &  13.62 & -2.623 \tabularnewline
162 &  12 &  12.66 & -0.6636 \tabularnewline
163 &  12 &  12.4 & -0.3959 \tabularnewline
164 &  10 &  10.94 & -0.9354 \tabularnewline
165 &  12 &  12.19 & -0.1917 \tabularnewline
166 &  11 &  12.51 & -1.507 \tabularnewline
167 &  11 &  10.86 &  0.1409 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&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] 10[/C][C] 10.04[/C][C]-0.04003[/C][/ROW]
[ROW][C]2[/C][C] 13[/C][C] 11.82[/C][C] 1.185[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 12.51[/C][C] 1.493[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 11.58[/C][C] 0.42[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 12.51[/C][C]-0.5068[/C][/ROW]
[ROW][C]6[/C][C] 13[/C][C] 12.03[/C][C] 0.9651[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 11.33[/C][C] 1.671[/C][/ROW]
[ROW][C]8[/C][C] 13[/C][C] 11.85[/C][C] 1.152[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 13.54[/C][C]-0.5424[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 13.54[/C][C] 0.4576[/C][/ROW]
[ROW][C]11[/C][C] 14[/C][C] 12.51[/C][C] 1.493[/C][/ROW]
[ROW][C]12[/C][C] 12[/C][C] 12.24[/C][C]-0.2391[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 11.92[/C][C] 0.07601[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 10.68[/C][C] 0.3158[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 11.69[/C][C] 0.3105[/C][/ROW]
[ROW][C]16[/C][C] 14[/C][C] 11.78[/C][C] 2.216[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 11.78[/C][C] 0.2162[/C][/ROW]
[ROW][C]18[/C][C] 11[/C][C] 13.5[/C][C]-2.495[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 12.51[/C][C] 0.4932[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 11.52[/C][C] 1.484[/C][/ROW]
[ROW][C]21[/C][C] 12[/C][C] 12.44[/C][C]-0.4428[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 12.19[/C][C] 0.8083[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 12.26[/C][C]-0.2557[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 12.19[/C][C] 0.8083[/C][/ROW]
[ROW][C]25[/C][C] 11[/C][C] 11.85[/C][C]-0.8477[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 11.77[/C][C] 0.2328[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 12.26[/C][C]-0.2557[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 11.63[/C][C] 1.373[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 12.66[/C][C] 0.3364[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 11.16[/C][C]-1.156[/C][/ROW]
[ROW][C]31[/C][C] 12[/C][C] 13.54[/C][C]-1.542[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 12.6[/C][C] 0.4003[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 11.34[/C][C] 1.657[/C][/ROW]
[ROW][C]34[/C][C] 10[/C][C] 10.59[/C][C]-0.5914[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 13.36[/C][C] 0.6448[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 11.85[/C][C] 0.1523[/C][/ROW]
[ROW][C]37[/C][C] 10[/C][C] 10.82[/C][C]-0.8245[/C][/ROW]
[ROW][C]38[/C][C] 10[/C][C] 11.16[/C][C]-1.156[/C][/ROW]
[ROW][C]39[/C][C] 14[/C][C] 12.26[/C][C] 1.744[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 12.66[/C][C]-0.6636[/C][/ROW]
[ROW][C]41[/C][C] 14[/C][C] 13.54[/C][C] 0.4576[/C][/ROW]
[ROW][C]42[/C][C] 10[/C][C] 10.75[/C][C]-0.7482[/C][/ROW]
[ROW][C]43[/C][C] 13[/C][C] 13.68[/C][C]-0.6826[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 12.4[/C][C]-0.3959[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 11.92[/C][C] 0.07601[/C][/ROW]
[ROW][C]46[/C][C] 13[/C][C] 12.26[/C][C] 0.7443[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 12.43[/C][C]-0.4284[/C][/ROW]
[ROW][C]48[/C][C] 10[/C][C] 12.51[/C][C]-2.507[/C][/ROW]
[ROW][C]49[/C][C] 9[/C][C] 9.805[/C][C]-0.8048[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 13.36[/C][C] 0.6448[/C][/ROW]
[ROW][C]51[/C][C] 15[/C][C] 14.01[/C][C] 0.9857[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 13.76[/C][C] 0.2368[/C][/ROW]
[ROW][C]53[/C][C] 8[/C][C] 11.34[/C][C]-3.343[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 10.82[/C][C] 0.1755[/C][/ROW]
[ROW][C]55[/C][C] 10[/C][C] 12.26[/C][C]-2.256[/C][/ROW]
[ROW][C]56[/C][C] 12[/C][C] 11.33[/C][C] 0.6711[/C][/ROW]
[ROW][C]57[/C][C] 14[/C][C] 11.91[/C][C] 2.088[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 12.24[/C][C]-0.2391[/C][/ROW]
[ROW][C]59[/C][C] 12[/C][C] 12.51[/C][C]-0.5068[/C][/ROW]
[ROW][C]60[/C][C] 14[/C][C] 13.95[/C][C] 0.04967[/C][/ROW]
[ROW][C]61[/C][C] 13[/C][C] 11.63[/C][C] 1.373[/C][/ROW]
[ROW][C]62[/C][C] 13[/C][C] 12.44[/C][C] 0.5572[/C][/ROW]
[ROW][C]63[/C][C] 13[/C][C] 11.35[/C][C] 1.655[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 12.44[/C][C]-0.4428[/C][/ROW]
[ROW][C]65[/C][C] 10[/C][C] 10.06[/C][C]-0.05664[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 12.3[/C][C] 1.697[/C][/ROW]
[ROW][C]67[/C][C] 11[/C][C] 12.24[/C][C]-1.239[/C][/ROW]
[ROW][C]68[/C][C] 10[/C][C] 12.08[/C][C]-2.081[/C][/ROW]
[ROW][C]69[/C][C] 13[/C][C] 11.77[/C][C] 1.233[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 11.52[/C][C] 0.484[/C][/ROW]
[ROW][C]71[/C][C] 12[/C][C] 13.87[/C][C]-1.874[/C][/ROW]
[ROW][C]72[/C][C] 11[/C][C] 12.26[/C][C]-1.256[/C][/ROW]
[ROW][C]73[/C][C] 10[/C][C] 12.26[/C][C]-2.256[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 12.77[/C][C] 1.225[/C][/ROW]
[ROW][C]75[/C][C] 12[/C][C] 11.85[/C][C] 0.1523[/C][/ROW]
[ROW][C]76[/C][C] 13[/C][C] 12.51[/C][C] 0.4932[/C][/ROW]
[ROW][C]77[/C][C] 11[/C][C] 11.77[/C][C]-0.7672[/C][/ROW]
[ROW][C]78[/C][C] 10[/C][C] 10.68[/C][C]-0.6842[/C][/ROW]
[ROW][C]79[/C][C] 14[/C][C] 11.16[/C][C] 2.844[/C][/ROW]
[ROW][C]80[/C][C] 13[/C][C] 12.19[/C][C] 0.8083[/C][/ROW]
[ROW][C]81[/C][C] 7[/C][C] 10.75[/C][C]-3.748[/C][/ROW]
[ROW][C]82[/C][C] 13[/C][C] 12.51[/C][C] 0.4932[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 12.44[/C][C] 0.5572[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 12.44[/C][C] 0.5572[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 12.66[/C][C] 2.336[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 12.87[/C][C] 0.1326[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 12.44[/C][C] 1.557[/C][/ROW]
[ROW][C]88[/C][C] 12[/C][C] 11.78[/C][C] 0.2162[/C][/ROW]
[ROW][C]89[/C][C] 13[/C][C] 11.85[/C][C] 1.152[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 9.789[/C][C] 1.211[/C][/ROW]
[ROW][C]91[/C][C] 12[/C][C] 12.32[/C][C]-0.3196[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 12.19[/C][C] 1.808[/C][/ROW]
[ROW][C]93[/C][C] 13[/C][C] 12.51[/C][C] 0.4932[/C][/ROW]
[ROW][C]94[/C][C] 14[/C][C] 13.67[/C][C] 0.3297[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 11.77[/C][C] 0.2328[/C][/ROW]
[ROW][C]96[/C][C] 12[/C][C] 12.19[/C][C]-0.1917[/C][/ROW]
[ROW][C]97[/C][C] 13[/C][C] 12.6[/C][C] 0.4003[/C][/ROW]
[ROW][C]98[/C][C] 14[/C][C] 12.87[/C][C] 1.133[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 13.54[/C][C]-0.5424[/C][/ROW]
[ROW][C]100[/C][C] 12[/C][C] 11.78[/C][C] 0.2162[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 11.75[/C][C] 1.249[/C][/ROW]
[ROW][C]102[/C][C] 12[/C][C] 12.19[/C][C]-0.1917[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 12.6[/C][C]-2.6[/C][/ROW]
[ROW][C]104[/C][C] 12[/C][C] 11.58[/C][C] 0.42[/C][/ROW]
[ROW][C]105[/C][C] 13[/C][C] 13.12[/C][C]-0.1185[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 12.18[/C][C]-0.1751[/C][/ROW]
[ROW][C]107[/C][C] 13[/C][C] 12.05[/C][C] 0.9481[/C][/ROW]
[ROW][C]108[/C][C] 12[/C][C] 12.26[/C][C]-0.2557[/C][/ROW]
[ROW][C]109[/C][C] 12[/C][C] 12.26[/C][C]-0.2557[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 12.16[/C][C]-0.1628[/C][/ROW]
[ROW][C]111[/C][C] 11[/C][C] 12.26[/C][C]-1.256[/C][/ROW]
[ROW][C]112[/C][C] 12[/C][C] 11.92[/C][C] 0.07601[/C][/ROW]
[ROW][C]113[/C][C] 9[/C][C] 10.67[/C][C]-1.668[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 11.99[/C][C] 2.012[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 11.85[/C][C] 0.1523[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 12[/C][C] 0.9954[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 12.05[/C][C] 0.9524[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 12.85[/C][C] 0.1492[/C][/ROW]
[ROW][C]119[/C][C] 11[/C][C] 12.32[/C][C]-1.32[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 11.99[/C][C] 0.01205[/C][/ROW]
[ROW][C]121[/C][C] 11[/C][C] 11.52[/C][C]-0.516[/C][/ROW]
[ROW][C]122[/C][C] 12[/C][C] 12.26[/C][C]-0.2557[/C][/ROW]
[ROW][C]123[/C][C] 12[/C][C] 12.51[/C][C]-0.5068[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 13.36[/C][C]-0.3552[/C][/ROW]
[ROW][C]125[/C][C] 12[/C][C] 11.85[/C][C] 0.1523[/C][/ROW]
[ROW][C]126[/C][C] 13[/C][C] 11.99[/C][C] 1.012[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 12.19[/C][C] 0.8083[/C][/ROW]
[ROW][C]128[/C][C] 12[/C][C] 11.58[/C][C] 0.42[/C][/ROW]
[ROW][C]129[/C][C] 12[/C][C] 12.05[/C][C]-0.05191[/C][/ROW]
[ROW][C]130[/C][C] 8[/C][C] 9.113[/C][C]-1.113[/C][/ROW]
[ROW][C]131[/C][C] 12[/C][C] 12.24[/C][C]-0.2391[/C][/ROW]
[ROW][C]132[/C][C] 13[/C][C] 12.65[/C][C] 0.353[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 10.86[/C][C]-0.857[/C][/ROW]
[ROW][C]134[/C][C] 8[/C][C] 10.75[/C][C]-2.748[/C][/ROW]
[ROW][C]135[/C][C] 12[/C][C] 11.99[/C][C] 0.01205[/C][/ROW]
[ROW][C]136[/C][C] 13[/C][C] 14.01[/C][C]-1.014[/C][/ROW]
[ROW][C]137[/C][C] 12[/C][C] 13.36[/C][C]-1.355[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 14.28[/C][C] 0.718[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 13.54[/C][C] 0.4576[/C][/ROW]
[ROW][C]140[/C][C] 10[/C][C] 11.92[/C][C]-1.924[/C][/ROW]
[ROW][C]141[/C][C] 11[/C][C] 12.1[/C][C]-1.099[/C][/ROW]
[ROW][C]142[/C][C] 12[/C][C] 12.26[/C][C]-0.2557[/C][/ROW]
[ROW][C]143[/C][C] 10[/C][C] 12.26[/C][C]-2.256[/C][/ROW]
[ROW][C]144[/C][C] 14[/C][C] 12.44[/C][C] 1.557[/C][/ROW]
[ROW][C]145[/C][C] 10[/C][C] 12.44[/C][C]-2.443[/C][/ROW]
[ROW][C]146[/C][C] 15[/C][C] 12.65[/C][C] 2.353[/C][/ROW]
[ROW][C]147[/C][C] 11[/C][C] 11.16[/C][C]-0.1562[/C][/ROW]
[ROW][C]148[/C][C] 12[/C][C] 12.26[/C][C]-0.2557[/C][/ROW]
[ROW][C]149[/C][C] 9[/C][C] 10.48[/C][C]-1.48[/C][/ROW]
[ROW][C]150[/C][C] 12[/C][C] 13.42[/C][C]-1.419[/C][/ROW]
[ROW][C]151[/C][C] 13[/C][C] 11.99[/C][C] 1.012[/C][/ROW]
[ROW][C]152[/C][C] 12[/C][C] 12.19[/C][C]-0.1917[/C][/ROW]
[ROW][C]153[/C][C] 9[/C][C] 11.77[/C][C]-2.767[/C][/ROW]
[ROW][C]154[/C][C] 12[/C][C] 12.19[/C][C]-0.1917[/C][/ROW]
[ROW][C]155[/C][C] 14[/C][C] 12.91[/C][C] 1.085[/C][/ROW]
[ROW][C]156[/C][C] 11[/C][C] 12[/C][C]-1.005[/C][/ROW]
[ROW][C]157[/C][C] 12[/C][C] 10.34[/C][C] 1.66[/C][/ROW]
[ROW][C]158[/C][C] 14[/C][C] 12.19[/C][C] 1.808[/C][/ROW]
[ROW][C]159[/C][C] 12[/C][C] 11.08[/C][C] 0.9244[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 12.32[/C][C] 2.68[/C][/ROW]
[ROW][C]161[/C][C] 11[/C][C] 13.62[/C][C]-2.623[/C][/ROW]
[ROW][C]162[/C][C] 12[/C][C] 12.66[/C][C]-0.6636[/C][/ROW]
[ROW][C]163[/C][C] 12[/C][C] 12.4[/C][C]-0.3959[/C][/ROW]
[ROW][C]164[/C][C] 10[/C][C] 10.94[/C][C]-0.9354[/C][/ROW]
[ROW][C]165[/C][C] 12[/C][C] 12.19[/C][C]-0.1917[/C][/ROW]
[ROW][C]166[/C][C] 11[/C][C] 12.51[/C][C]-1.507[/C][/ROW]
[ROW][C]167[/C][C] 11[/C][C] 10.86[/C][C] 0.1409[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302972&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&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	10	10.04	-0.04003
2	13	11.82	1.185
3	14	12.51	1.493
4	12	11.58	0.42
5	12	12.51	-0.5068
6	13	12.03	0.9651
7	13	11.33	1.671
8	13	11.85	1.152
9	13	13.54	-0.5424
10	14	13.54	0.4576
11	14	12.51	1.493
12	12	12.24	-0.2391
13	12	11.92	0.07601
14	11	10.68	0.3158
15	12	11.69	0.3105
16	14	11.78	2.216
17	12	11.78	0.2162
18	11	13.5	-2.495
19	13	12.51	0.4932
20	13	11.52	1.484
21	12	12.44	-0.4428
22	13	12.19	0.8083
23	12	12.26	-0.2557
24	13	12.19	0.8083
25	11	11.85	-0.8477
26	12	11.77	0.2328
27	12	12.26	-0.2557
28	13	11.63	1.373
29	13	12.66	0.3364
30	10	11.16	-1.156
31	12	13.54	-1.542
32	13	12.6	0.4003
33	13	11.34	1.657
34	10	10.59	-0.5914
35	14	13.36	0.6448
36	12	11.85	0.1523
37	10	10.82	-0.8245
38	10	11.16	-1.156
39	14	12.26	1.744
40	12	12.66	-0.6636
41	14	13.54	0.4576
42	10	10.75	-0.7482
43	13	13.68	-0.6826
44	12	12.4	-0.3959
45	12	11.92	0.07601
46	13	12.26	0.7443
47	12	12.43	-0.4284
48	10	12.51	-2.507
49	9	9.805	-0.8048
50	14	13.36	0.6448
51	15	14.01	0.9857
52	14	13.76	0.2368
53	8	11.34	-3.343
54	11	10.82	0.1755
55	10	12.26	-2.256
56	12	11.33	0.6711
57	14	11.91	2.088
58	12	12.24	-0.2391
59	12	12.51	-0.5068
60	14	13.95	0.04967
61	13	11.63	1.373
62	13	12.44	0.5572
63	13	11.35	1.655
64	12	12.44	-0.4428
65	10	10.06	-0.05664
66	14	12.3	1.697
67	11	12.24	-1.239
68	10	12.08	-2.081
69	13	11.77	1.233
70	12	11.52	0.484
71	12	13.87	-1.874
72	11	12.26	-1.256
73	10	12.26	-2.256
74	14	12.77	1.225
75	12	11.85	0.1523
76	13	12.51	0.4932
77	11	11.77	-0.7672
78	10	10.68	-0.6842
79	14	11.16	2.844
80	13	12.19	0.8083
81	7	10.75	-3.748
82	13	12.51	0.4932
83	13	12.44	0.5572
84	13	12.44	0.5572
85	15	12.66	2.336
86	13	12.87	0.1326
87	14	12.44	1.557
88	12	11.78	0.2162
89	13	11.85	1.152
90	11	9.789	1.211
91	12	12.32	-0.3196
92	14	12.19	1.808
93	13	12.51	0.4932
94	14	13.67	0.3297
95	12	11.77	0.2328
96	12	12.19	-0.1917
97	13	12.6	0.4003
98	14	12.87	1.133
99	13	13.54	-0.5424
100	12	11.78	0.2162
101	13	11.75	1.249
102	12	12.19	-0.1917
103	10	12.6	-2.6
104	12	11.58	0.42
105	13	13.12	-0.1185
106	12	12.18	-0.1751
107	13	12.05	0.9481
108	12	12.26	-0.2557
109	12	12.26	-0.2557
110	12	12.16	-0.1628
111	11	12.26	-1.256
112	12	11.92	0.07601
113	9	10.67	-1.668
114	14	11.99	2.012
115	12	11.85	0.1523
116	13	12	0.9954
117	13	12.05	0.9524
118	13	12.85	0.1492
119	11	12.32	-1.32
120	12	11.99	0.01205
121	11	11.52	-0.516
122	12	12.26	-0.2557
123	12	12.51	-0.5068
124	13	13.36	-0.3552
125	12	11.85	0.1523
126	13	11.99	1.012
127	13	12.19	0.8083
128	12	11.58	0.42
129	12	12.05	-0.05191
130	8	9.113	-1.113
131	12	12.24	-0.2391
132	13	12.65	0.353
133	10	10.86	-0.857
134	8	10.75	-2.748
135	12	11.99	0.01205
136	13	14.01	-1.014
137	12	13.36	-1.355
138	15	14.28	0.718
139	14	13.54	0.4576
140	10	11.92	-1.924
141	11	12.1	-1.099
142	12	12.26	-0.2557
143	10	12.26	-2.256
144	14	12.44	1.557
145	10	12.44	-2.443
146	15	12.65	2.353
147	11	11.16	-0.1562
148	12	12.26	-0.2557
149	9	10.48	-1.48
150	12	13.42	-1.419
151	13	11.99	1.012
152	12	12.19	-0.1917
153	9	11.77	-2.767
154	12	12.19	-0.1917
155	14	12.91	1.085
156	11	12	-1.005
157	12	10.34	1.66
158	14	12.19	1.808
159	12	11.08	0.9244
160	15	12.32	2.68
161	11	13.62	-2.623
162	12	12.66	-0.6636
163	12	12.4	-0.3959
164	10	10.94	-0.9354
165	12	12.19	-0.1917
166	11	12.51	-1.507
167	11	10.86	0.1409

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.3871	0.7742	0.6129
10	0.2515	0.503	0.7485
11	0.2256	0.4511	0.7744
12	0.1351	0.2701	0.8649
13	0.07536	0.1507	0.9246
14	0.06229	0.1246	0.9377
15	0.05181	0.1036	0.9482
16	0.09278	0.1856	0.9072
17	0.0696	0.1392	0.9304
18	0.1438	0.2875	0.8562
19	0.09972	0.1994	0.9003
20	0.1093	0.2186	0.8907
21	0.0866	0.1732	0.9134
22	0.06493	0.1299	0.9351
23	0.05466	0.1093	0.9453
24	0.04001	0.08002	0.96
25	0.08893	0.1779	0.9111
26	0.06465	0.1293	0.9354
27	0.04847	0.09694	0.9515
28	0.0361	0.07219	0.9639
29	0.02724	0.05447	0.9728
30	0.04681	0.09363	0.9532
31	0.06352	0.127	0.9365
32	0.0505	0.101	0.9495
33	0.04783	0.09566	0.9522
34	0.07354	0.1471	0.9265
35	0.06494	0.1299	0.9351
36	0.04851	0.09702	0.9515
37	0.05011	0.1002	0.9499
38	0.0592	0.1184	0.9408
39	0.07979	0.1596	0.9202
40	0.06424	0.1285	0.9358
41	0.04917	0.09833	0.9508
42	0.05046	0.1009	0.9495
43	0.03908	0.07816	0.9609
44	0.02921	0.05841	0.9708
45	0.0211	0.0422	0.9789
46	0.01687	0.03375	0.9831
47	0.01252	0.02503	0.9875
48	0.04236	0.08471	0.9576
49	0.03536	0.07071	0.9646
50	0.02906	0.05811	0.9709
51	0.02981	0.05963	0.9702
52	0.02248	0.04495	0.9775
53	0.1527	0.3053	0.8473
54	0.1273	0.2546	0.8727
55	0.2104	0.4209	0.7896
56	0.1851	0.3703	0.8149
57	0.2403	0.4807	0.7597
58	0.2049	0.4097	0.7951
59	0.1775	0.355	0.8225
60	0.1479	0.2958	0.8521
61	0.1456	0.2912	0.8544
62	0.1244	0.2488	0.8756
63	0.1357	0.2715	0.8643
64	0.1152	0.2304	0.8848
65	0.09469	0.1894	0.9053
66	0.1261	0.2522	0.8739
67	0.1242	0.2484	0.8758
68	0.1593	0.3185	0.8407
69	0.1646	0.3292	0.8354
70	0.1439	0.2878	0.8561
71	0.2083	0.4165	0.7917
72	0.2132	0.4264	0.7868
73	0.3076	0.6151	0.6924
74	0.3047	0.6093	0.6953
75	0.2716	0.5432	0.7284
76	0.241	0.4819	0.759
77	0.2209	0.4418	0.7791
78	0.2016	0.4033	0.7984
79	0.3694	0.7388	0.6306
80	0.3469	0.6939	0.6531
81	0.722	0.5559	0.278
82	0.689	0.622	0.311
83	0.6575	0.685	0.3425
84	0.625	0.75	0.375
85	0.7264	0.5471	0.2736
86	0.6873	0.6255	0.3127
87	0.7151	0.5698	0.2849
88	0.6836	0.6327	0.3164
89	0.6893	0.6214	0.3107
90	0.6851	0.6298	0.3149
91	0.6471	0.7058	0.3529
92	0.7058	0.5885	0.2942
93	0.6726	0.6547	0.3274
94	0.635	0.73	0.365
95	0.6005	0.799	0.3995
96	0.5573	0.8853	0.4427
97	0.5159	0.9681	0.4841
98	0.5126	0.9748	0.4874
99	0.4732	0.9465	0.5268
100	0.4456	0.8912	0.5544
101	0.4471	0.8943	0.5529
102	0.4055	0.811	0.5945
103	0.5571	0.8859	0.4429
104	0.5246	0.9508	0.4754
105	0.4779	0.9558	0.5221
106	0.4308	0.8616	0.5692
107	0.4089	0.8179	0.5911
108	0.3651	0.7302	0.6349
109	0.3228	0.6455	0.6772
110	0.2864	0.5727	0.7136
111	0.2832	0.5664	0.7168
112	0.2444	0.4888	0.7556
113	0.2596	0.5192	0.7404
114	0.336	0.672	0.664
115	0.3052	0.6104	0.6948
116	0.302	0.6041	0.698
117	0.28	0.5599	0.72
118	0.2397	0.4793	0.7603
119	0.2391	0.4783	0.7609
120	0.2023	0.4046	0.7977
121	0.1726	0.3453	0.8274
122	0.1425	0.285	0.8575
123	0.1185	0.2371	0.8815
124	0.09628	0.1926	0.9037
125	0.08171	0.1634	0.9183
126	0.07971	0.1594	0.9203
127	0.07568	0.1514	0.9243
128	0.07072	0.1414	0.9293
129	0.05457	0.1091	0.9454
130	0.05209	0.1042	0.9479
131	0.03913	0.07825	0.9609
132	0.02912	0.05825	0.9709
133	0.02868	0.05736	0.9713
134	0.07596	0.1519	0.924
135	0.05895	0.1179	0.9411
136	0.05034	0.1007	0.9497
137	0.0406	0.08121	0.9594
138	0.03175	0.06351	0.9682
139	0.04366	0.08732	0.9563
140	0.04313	0.08626	0.9569
141	0.03261	0.06521	0.9674
142	0.02255	0.0451	0.9775
143	0.03849	0.07699	0.9615
144	0.06805	0.1361	0.932
145	0.08892	0.1778	0.9111
146	0.1488	0.2977	0.8512
147	0.1607	0.3213	0.8393
148	0.1188	0.2375	0.8812
149	0.2481	0.4961	0.7519
150	0.193	0.3861	0.807
151	0.1718	0.3437	0.8282
152	0.1242	0.2484	0.8758
153	0.1205	0.2411	0.8795
154	0.08046	0.1609	0.9195
155	0.09224	0.1845	0.9078
156	0.1797	0.3595	0.8203
157	0.2364	0.4728	0.7636
158	0.43	0.8599	0.57

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.3871 &  0.7742 &  0.6129 \tabularnewline
10 &  0.2515 &  0.503 &  0.7485 \tabularnewline
11 &  0.2256 &  0.4511 &  0.7744 \tabularnewline
12 &  0.1351 &  0.2701 &  0.8649 \tabularnewline
13 &  0.07536 &  0.1507 &  0.9246 \tabularnewline
14 &  0.06229 &  0.1246 &  0.9377 \tabularnewline
15 &  0.05181 &  0.1036 &  0.9482 \tabularnewline
16 &  0.09278 &  0.1856 &  0.9072 \tabularnewline
17 &  0.0696 &  0.1392 &  0.9304 \tabularnewline
18 &  0.1438 &  0.2875 &  0.8562 \tabularnewline
19 &  0.09972 &  0.1994 &  0.9003 \tabularnewline
20 &  0.1093 &  0.2186 &  0.8907 \tabularnewline
21 &  0.0866 &  0.1732 &  0.9134 \tabularnewline
22 &  0.06493 &  0.1299 &  0.9351 \tabularnewline
23 &  0.05466 &  0.1093 &  0.9453 \tabularnewline
24 &  0.04001 &  0.08002 &  0.96 \tabularnewline
25 &  0.08893 &  0.1779 &  0.9111 \tabularnewline
26 &  0.06465 &  0.1293 &  0.9354 \tabularnewline
27 &  0.04847 &  0.09694 &  0.9515 \tabularnewline
28 &  0.0361 &  0.07219 &  0.9639 \tabularnewline
29 &  0.02724 &  0.05447 &  0.9728 \tabularnewline
30 &  0.04681 &  0.09363 &  0.9532 \tabularnewline
31 &  0.06352 &  0.127 &  0.9365 \tabularnewline
32 &  0.0505 &  0.101 &  0.9495 \tabularnewline
33 &  0.04783 &  0.09566 &  0.9522 \tabularnewline
34 &  0.07354 &  0.1471 &  0.9265 \tabularnewline
35 &  0.06494 &  0.1299 &  0.9351 \tabularnewline
36 &  0.04851 &  0.09702 &  0.9515 \tabularnewline
37 &  0.05011 &  0.1002 &  0.9499 \tabularnewline
38 &  0.0592 &  0.1184 &  0.9408 \tabularnewline
39 &  0.07979 &  0.1596 &  0.9202 \tabularnewline
40 &  0.06424 &  0.1285 &  0.9358 \tabularnewline
41 &  0.04917 &  0.09833 &  0.9508 \tabularnewline
42 &  0.05046 &  0.1009 &  0.9495 \tabularnewline
43 &  0.03908 &  0.07816 &  0.9609 \tabularnewline
44 &  0.02921 &  0.05841 &  0.9708 \tabularnewline
45 &  0.0211 &  0.0422 &  0.9789 \tabularnewline
46 &  0.01687 &  0.03375 &  0.9831 \tabularnewline
47 &  0.01252 &  0.02503 &  0.9875 \tabularnewline
48 &  0.04236 &  0.08471 &  0.9576 \tabularnewline
49 &  0.03536 &  0.07071 &  0.9646 \tabularnewline
50 &  0.02906 &  0.05811 &  0.9709 \tabularnewline
51 &  0.02981 &  0.05963 &  0.9702 \tabularnewline
52 &  0.02248 &  0.04495 &  0.9775 \tabularnewline
53 &  0.1527 &  0.3053 &  0.8473 \tabularnewline
54 &  0.1273 &  0.2546 &  0.8727 \tabularnewline
55 &  0.2104 &  0.4209 &  0.7896 \tabularnewline
56 &  0.1851 &  0.3703 &  0.8149 \tabularnewline
57 &  0.2403 &  0.4807 &  0.7597 \tabularnewline
58 &  0.2049 &  0.4097 &  0.7951 \tabularnewline
59 &  0.1775 &  0.355 &  0.8225 \tabularnewline
60 &  0.1479 &  0.2958 &  0.8521 \tabularnewline
61 &  0.1456 &  0.2912 &  0.8544 \tabularnewline
62 &  0.1244 &  0.2488 &  0.8756 \tabularnewline
63 &  0.1357 &  0.2715 &  0.8643 \tabularnewline
64 &  0.1152 &  0.2304 &  0.8848 \tabularnewline
65 &  0.09469 &  0.1894 &  0.9053 \tabularnewline
66 &  0.1261 &  0.2522 &  0.8739 \tabularnewline
67 &  0.1242 &  0.2484 &  0.8758 \tabularnewline
68 &  0.1593 &  0.3185 &  0.8407 \tabularnewline
69 &  0.1646 &  0.3292 &  0.8354 \tabularnewline
70 &  0.1439 &  0.2878 &  0.8561 \tabularnewline
71 &  0.2083 &  0.4165 &  0.7917 \tabularnewline
72 &  0.2132 &  0.4264 &  0.7868 \tabularnewline
73 &  0.3076 &  0.6151 &  0.6924 \tabularnewline
74 &  0.3047 &  0.6093 &  0.6953 \tabularnewline
75 &  0.2716 &  0.5432 &  0.7284 \tabularnewline
76 &  0.241 &  0.4819 &  0.759 \tabularnewline
77 &  0.2209 &  0.4418 &  0.7791 \tabularnewline
78 &  0.2016 &  0.4033 &  0.7984 \tabularnewline
79 &  0.3694 &  0.7388 &  0.6306 \tabularnewline
80 &  0.3469 &  0.6939 &  0.6531 \tabularnewline
81 &  0.722 &  0.5559 &  0.278 \tabularnewline
82 &  0.689 &  0.622 &  0.311 \tabularnewline
83 &  0.6575 &  0.685 &  0.3425 \tabularnewline
84 &  0.625 &  0.75 &  0.375 \tabularnewline
85 &  0.7264 &  0.5471 &  0.2736 \tabularnewline
86 &  0.6873 &  0.6255 &  0.3127 \tabularnewline
87 &  0.7151 &  0.5698 &  0.2849 \tabularnewline
88 &  0.6836 &  0.6327 &  0.3164 \tabularnewline
89 &  0.6893 &  0.6214 &  0.3107 \tabularnewline
90 &  0.6851 &  0.6298 &  0.3149 \tabularnewline
91 &  0.6471 &  0.7058 &  0.3529 \tabularnewline
92 &  0.7058 &  0.5885 &  0.2942 \tabularnewline
93 &  0.6726 &  0.6547 &  0.3274 \tabularnewline
94 &  0.635 &  0.73 &  0.365 \tabularnewline
95 &  0.6005 &  0.799 &  0.3995 \tabularnewline
96 &  0.5573 &  0.8853 &  0.4427 \tabularnewline
97 &  0.5159 &  0.9681 &  0.4841 \tabularnewline
98 &  0.5126 &  0.9748 &  0.4874 \tabularnewline
99 &  0.4732 &  0.9465 &  0.5268 \tabularnewline
100 &  0.4456 &  0.8912 &  0.5544 \tabularnewline
101 &  0.4471 &  0.8943 &  0.5529 \tabularnewline
102 &  0.4055 &  0.811 &  0.5945 \tabularnewline
103 &  0.5571 &  0.8859 &  0.4429 \tabularnewline
104 &  0.5246 &  0.9508 &  0.4754 \tabularnewline
105 &  0.4779 &  0.9558 &  0.5221 \tabularnewline
106 &  0.4308 &  0.8616 &  0.5692 \tabularnewline
107 &  0.4089 &  0.8179 &  0.5911 \tabularnewline
108 &  0.3651 &  0.7302 &  0.6349 \tabularnewline
109 &  0.3228 &  0.6455 &  0.6772 \tabularnewline
110 &  0.2864 &  0.5727 &  0.7136 \tabularnewline
111 &  0.2832 &  0.5664 &  0.7168 \tabularnewline
112 &  0.2444 &  0.4888 &  0.7556 \tabularnewline
113 &  0.2596 &  0.5192 &  0.7404 \tabularnewline
114 &  0.336 &  0.672 &  0.664 \tabularnewline
115 &  0.3052 &  0.6104 &  0.6948 \tabularnewline
116 &  0.302 &  0.6041 &  0.698 \tabularnewline
117 &  0.28 &  0.5599 &  0.72 \tabularnewline
118 &  0.2397 &  0.4793 &  0.7603 \tabularnewline
119 &  0.2391 &  0.4783 &  0.7609 \tabularnewline
120 &  0.2023 &  0.4046 &  0.7977 \tabularnewline
121 &  0.1726 &  0.3453 &  0.8274 \tabularnewline
122 &  0.1425 &  0.285 &  0.8575 \tabularnewline
123 &  0.1185 &  0.2371 &  0.8815 \tabularnewline
124 &  0.09628 &  0.1926 &  0.9037 \tabularnewline
125 &  0.08171 &  0.1634 &  0.9183 \tabularnewline
126 &  0.07971 &  0.1594 &  0.9203 \tabularnewline
127 &  0.07568 &  0.1514 &  0.9243 \tabularnewline
128 &  0.07072 &  0.1414 &  0.9293 \tabularnewline
129 &  0.05457 &  0.1091 &  0.9454 \tabularnewline
130 &  0.05209 &  0.1042 &  0.9479 \tabularnewline
131 &  0.03913 &  0.07825 &  0.9609 \tabularnewline
132 &  0.02912 &  0.05825 &  0.9709 \tabularnewline
133 &  0.02868 &  0.05736 &  0.9713 \tabularnewline
134 &  0.07596 &  0.1519 &  0.924 \tabularnewline
135 &  0.05895 &  0.1179 &  0.9411 \tabularnewline
136 &  0.05034 &  0.1007 &  0.9497 \tabularnewline
137 &  0.0406 &  0.08121 &  0.9594 \tabularnewline
138 &  0.03175 &  0.06351 &  0.9682 \tabularnewline
139 &  0.04366 &  0.08732 &  0.9563 \tabularnewline
140 &  0.04313 &  0.08626 &  0.9569 \tabularnewline
141 &  0.03261 &  0.06521 &  0.9674 \tabularnewline
142 &  0.02255 &  0.0451 &  0.9775 \tabularnewline
143 &  0.03849 &  0.07699 &  0.9615 \tabularnewline
144 &  0.06805 &  0.1361 &  0.932 \tabularnewline
145 &  0.08892 &  0.1778 &  0.9111 \tabularnewline
146 &  0.1488 &  0.2977 &  0.8512 \tabularnewline
147 &  0.1607 &  0.3213 &  0.8393 \tabularnewline
148 &  0.1188 &  0.2375 &  0.8812 \tabularnewline
149 &  0.2481 &  0.4961 &  0.7519 \tabularnewline
150 &  0.193 &  0.3861 &  0.807 \tabularnewline
151 &  0.1718 &  0.3437 &  0.8282 \tabularnewline
152 &  0.1242 &  0.2484 &  0.8758 \tabularnewline
153 &  0.1205 &  0.2411 &  0.8795 \tabularnewline
154 &  0.08046 &  0.1609 &  0.9195 \tabularnewline
155 &  0.09224 &  0.1845 &  0.9078 \tabularnewline
156 &  0.1797 &  0.3595 &  0.8203 \tabularnewline
157 &  0.2364 &  0.4728 &  0.7636 \tabularnewline
158 &  0.43 &  0.8599 &  0.57 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C] 0.3871[/C][C] 0.7742[/C][C] 0.6129[/C][/ROW]
[ROW][C]10[/C][C] 0.2515[/C][C] 0.503[/C][C] 0.7485[/C][/ROW]
[ROW][C]11[/C][C] 0.2256[/C][C] 0.4511[/C][C] 0.7744[/C][/ROW]
[ROW][C]12[/C][C] 0.1351[/C][C] 0.2701[/C][C] 0.8649[/C][/ROW]
[ROW][C]13[/C][C] 0.07536[/C][C] 0.1507[/C][C] 0.9246[/C][/ROW]
[ROW][C]14[/C][C] 0.06229[/C][C] 0.1246[/C][C] 0.9377[/C][/ROW]
[ROW][C]15[/C][C] 0.05181[/C][C] 0.1036[/C][C] 0.9482[/C][/ROW]
[ROW][C]16[/C][C] 0.09278[/C][C] 0.1856[/C][C] 0.9072[/C][/ROW]
[ROW][C]17[/C][C] 0.0696[/C][C] 0.1392[/C][C] 0.9304[/C][/ROW]
[ROW][C]18[/C][C] 0.1438[/C][C] 0.2875[/C][C] 0.8562[/C][/ROW]
[ROW][C]19[/C][C] 0.09972[/C][C] 0.1994[/C][C] 0.9003[/C][/ROW]
[ROW][C]20[/C][C] 0.1093[/C][C] 0.2186[/C][C] 0.8907[/C][/ROW]
[ROW][C]21[/C][C] 0.0866[/C][C] 0.1732[/C][C] 0.9134[/C][/ROW]
[ROW][C]22[/C][C] 0.06493[/C][C] 0.1299[/C][C] 0.9351[/C][/ROW]
[ROW][C]23[/C][C] 0.05466[/C][C] 0.1093[/C][C] 0.9453[/C][/ROW]
[ROW][C]24[/C][C] 0.04001[/C][C] 0.08002[/C][C] 0.96[/C][/ROW]
[ROW][C]25[/C][C] 0.08893[/C][C] 0.1779[/C][C] 0.9111[/C][/ROW]
[ROW][C]26[/C][C] 0.06465[/C][C] 0.1293[/C][C] 0.9354[/C][/ROW]
[ROW][C]27[/C][C] 0.04847[/C][C] 0.09694[/C][C] 0.9515[/C][/ROW]
[ROW][C]28[/C][C] 0.0361[/C][C] 0.07219[/C][C] 0.9639[/C][/ROW]
[ROW][C]29[/C][C] 0.02724[/C][C] 0.05447[/C][C] 0.9728[/C][/ROW]
[ROW][C]30[/C][C] 0.04681[/C][C] 0.09363[/C][C] 0.9532[/C][/ROW]
[ROW][C]31[/C][C] 0.06352[/C][C] 0.127[/C][C] 0.9365[/C][/ROW]
[ROW][C]32[/C][C] 0.0505[/C][C] 0.101[/C][C] 0.9495[/C][/ROW]
[ROW][C]33[/C][C] 0.04783[/C][C] 0.09566[/C][C] 0.9522[/C][/ROW]
[ROW][C]34[/C][C] 0.07354[/C][C] 0.1471[/C][C] 0.9265[/C][/ROW]
[ROW][C]35[/C][C] 0.06494[/C][C] 0.1299[/C][C] 0.9351[/C][/ROW]
[ROW][C]36[/C][C] 0.04851[/C][C] 0.09702[/C][C] 0.9515[/C][/ROW]
[ROW][C]37[/C][C] 0.05011[/C][C] 0.1002[/C][C] 0.9499[/C][/ROW]
[ROW][C]38[/C][C] 0.0592[/C][C] 0.1184[/C][C] 0.9408[/C][/ROW]
[ROW][C]39[/C][C] 0.07979[/C][C] 0.1596[/C][C] 0.9202[/C][/ROW]
[ROW][C]40[/C][C] 0.06424[/C][C] 0.1285[/C][C] 0.9358[/C][/ROW]
[ROW][C]41[/C][C] 0.04917[/C][C] 0.09833[/C][C] 0.9508[/C][/ROW]
[ROW][C]42[/C][C] 0.05046[/C][C] 0.1009[/C][C] 0.9495[/C][/ROW]
[ROW][C]43[/C][C] 0.03908[/C][C] 0.07816[/C][C] 0.9609[/C][/ROW]
[ROW][C]44[/C][C] 0.02921[/C][C] 0.05841[/C][C] 0.9708[/C][/ROW]
[ROW][C]45[/C][C] 0.0211[/C][C] 0.0422[/C][C] 0.9789[/C][/ROW]
[ROW][C]46[/C][C] 0.01687[/C][C] 0.03375[/C][C] 0.9831[/C][/ROW]
[ROW][C]47[/C][C] 0.01252[/C][C] 0.02503[/C][C] 0.9875[/C][/ROW]
[ROW][C]48[/C][C] 0.04236[/C][C] 0.08471[/C][C] 0.9576[/C][/ROW]
[ROW][C]49[/C][C] 0.03536[/C][C] 0.07071[/C][C] 0.9646[/C][/ROW]
[ROW][C]50[/C][C] 0.02906[/C][C] 0.05811[/C][C] 0.9709[/C][/ROW]
[ROW][C]51[/C][C] 0.02981[/C][C] 0.05963[/C][C] 0.9702[/C][/ROW]
[ROW][C]52[/C][C] 0.02248[/C][C] 0.04495[/C][C] 0.9775[/C][/ROW]
[ROW][C]53[/C][C] 0.1527[/C][C] 0.3053[/C][C] 0.8473[/C][/ROW]
[ROW][C]54[/C][C] 0.1273[/C][C] 0.2546[/C][C] 0.8727[/C][/ROW]
[ROW][C]55[/C][C] 0.2104[/C][C] 0.4209[/C][C] 0.7896[/C][/ROW]
[ROW][C]56[/C][C] 0.1851[/C][C] 0.3703[/C][C] 0.8149[/C][/ROW]
[ROW][C]57[/C][C] 0.2403[/C][C] 0.4807[/C][C] 0.7597[/C][/ROW]
[ROW][C]58[/C][C] 0.2049[/C][C] 0.4097[/C][C] 0.7951[/C][/ROW]
[ROW][C]59[/C][C] 0.1775[/C][C] 0.355[/C][C] 0.8225[/C][/ROW]
[ROW][C]60[/C][C] 0.1479[/C][C] 0.2958[/C][C] 0.8521[/C][/ROW]
[ROW][C]61[/C][C] 0.1456[/C][C] 0.2912[/C][C] 0.8544[/C][/ROW]
[ROW][C]62[/C][C] 0.1244[/C][C] 0.2488[/C][C] 0.8756[/C][/ROW]
[ROW][C]63[/C][C] 0.1357[/C][C] 0.2715[/C][C] 0.8643[/C][/ROW]
[ROW][C]64[/C][C] 0.1152[/C][C] 0.2304[/C][C] 0.8848[/C][/ROW]
[ROW][C]65[/C][C] 0.09469[/C][C] 0.1894[/C][C] 0.9053[/C][/ROW]
[ROW][C]66[/C][C] 0.1261[/C][C] 0.2522[/C][C] 0.8739[/C][/ROW]
[ROW][C]67[/C][C] 0.1242[/C][C] 0.2484[/C][C] 0.8758[/C][/ROW]
[ROW][C]68[/C][C] 0.1593[/C][C] 0.3185[/C][C] 0.8407[/C][/ROW]
[ROW][C]69[/C][C] 0.1646[/C][C] 0.3292[/C][C] 0.8354[/C][/ROW]
[ROW][C]70[/C][C] 0.1439[/C][C] 0.2878[/C][C] 0.8561[/C][/ROW]
[ROW][C]71[/C][C] 0.2083[/C][C] 0.4165[/C][C] 0.7917[/C][/ROW]
[ROW][C]72[/C][C] 0.2132[/C][C] 0.4264[/C][C] 0.7868[/C][/ROW]
[ROW][C]73[/C][C] 0.3076[/C][C] 0.6151[/C][C] 0.6924[/C][/ROW]
[ROW][C]74[/C][C] 0.3047[/C][C] 0.6093[/C][C] 0.6953[/C][/ROW]
[ROW][C]75[/C][C] 0.2716[/C][C] 0.5432[/C][C] 0.7284[/C][/ROW]
[ROW][C]76[/C][C] 0.241[/C][C] 0.4819[/C][C] 0.759[/C][/ROW]
[ROW][C]77[/C][C] 0.2209[/C][C] 0.4418[/C][C] 0.7791[/C][/ROW]
[ROW][C]78[/C][C] 0.2016[/C][C] 0.4033[/C][C] 0.7984[/C][/ROW]
[ROW][C]79[/C][C] 0.3694[/C][C] 0.7388[/C][C] 0.6306[/C][/ROW]
[ROW][C]80[/C][C] 0.3469[/C][C] 0.6939[/C][C] 0.6531[/C][/ROW]
[ROW][C]81[/C][C] 0.722[/C][C] 0.5559[/C][C] 0.278[/C][/ROW]
[ROW][C]82[/C][C] 0.689[/C][C] 0.622[/C][C] 0.311[/C][/ROW]
[ROW][C]83[/C][C] 0.6575[/C][C] 0.685[/C][C] 0.3425[/C][/ROW]
[ROW][C]84[/C][C] 0.625[/C][C] 0.75[/C][C] 0.375[/C][/ROW]
[ROW][C]85[/C][C] 0.7264[/C][C] 0.5471[/C][C] 0.2736[/C][/ROW]
[ROW][C]86[/C][C] 0.6873[/C][C] 0.6255[/C][C] 0.3127[/C][/ROW]
[ROW][C]87[/C][C] 0.7151[/C][C] 0.5698[/C][C] 0.2849[/C][/ROW]
[ROW][C]88[/C][C] 0.6836[/C][C] 0.6327[/C][C] 0.3164[/C][/ROW]
[ROW][C]89[/C][C] 0.6893[/C][C] 0.6214[/C][C] 0.3107[/C][/ROW]
[ROW][C]90[/C][C] 0.6851[/C][C] 0.6298[/C][C] 0.3149[/C][/ROW]
[ROW][C]91[/C][C] 0.6471[/C][C] 0.7058[/C][C] 0.3529[/C][/ROW]
[ROW][C]92[/C][C] 0.7058[/C][C] 0.5885[/C][C] 0.2942[/C][/ROW]
[ROW][C]93[/C][C] 0.6726[/C][C] 0.6547[/C][C] 0.3274[/C][/ROW]
[ROW][C]94[/C][C] 0.635[/C][C] 0.73[/C][C] 0.365[/C][/ROW]
[ROW][C]95[/C][C] 0.6005[/C][C] 0.799[/C][C] 0.3995[/C][/ROW]
[ROW][C]96[/C][C] 0.5573[/C][C] 0.8853[/C][C] 0.4427[/C][/ROW]
[ROW][C]97[/C][C] 0.5159[/C][C] 0.9681[/C][C] 0.4841[/C][/ROW]
[ROW][C]98[/C][C] 0.5126[/C][C] 0.9748[/C][C] 0.4874[/C][/ROW]
[ROW][C]99[/C][C] 0.4732[/C][C] 0.9465[/C][C] 0.5268[/C][/ROW]
[ROW][C]100[/C][C] 0.4456[/C][C] 0.8912[/C][C] 0.5544[/C][/ROW]
[ROW][C]101[/C][C] 0.4471[/C][C] 0.8943[/C][C] 0.5529[/C][/ROW]
[ROW][C]102[/C][C] 0.4055[/C][C] 0.811[/C][C] 0.5945[/C][/ROW]
[ROW][C]103[/C][C] 0.5571[/C][C] 0.8859[/C][C] 0.4429[/C][/ROW]
[ROW][C]104[/C][C] 0.5246[/C][C] 0.9508[/C][C] 0.4754[/C][/ROW]
[ROW][C]105[/C][C] 0.4779[/C][C] 0.9558[/C][C] 0.5221[/C][/ROW]
[ROW][C]106[/C][C] 0.4308[/C][C] 0.8616[/C][C] 0.5692[/C][/ROW]
[ROW][C]107[/C][C] 0.4089[/C][C] 0.8179[/C][C] 0.5911[/C][/ROW]
[ROW][C]108[/C][C] 0.3651[/C][C] 0.7302[/C][C] 0.6349[/C][/ROW]
[ROW][C]109[/C][C] 0.3228[/C][C] 0.6455[/C][C] 0.6772[/C][/ROW]
[ROW][C]110[/C][C] 0.2864[/C][C] 0.5727[/C][C] 0.7136[/C][/ROW]
[ROW][C]111[/C][C] 0.2832[/C][C] 0.5664[/C][C] 0.7168[/C][/ROW]
[ROW][C]112[/C][C] 0.2444[/C][C] 0.4888[/C][C] 0.7556[/C][/ROW]
[ROW][C]113[/C][C] 0.2596[/C][C] 0.5192[/C][C] 0.7404[/C][/ROW]
[ROW][C]114[/C][C] 0.336[/C][C] 0.672[/C][C] 0.664[/C][/ROW]
[ROW][C]115[/C][C] 0.3052[/C][C] 0.6104[/C][C] 0.6948[/C][/ROW]
[ROW][C]116[/C][C] 0.302[/C][C] 0.6041[/C][C] 0.698[/C][/ROW]
[ROW][C]117[/C][C] 0.28[/C][C] 0.5599[/C][C] 0.72[/C][/ROW]
[ROW][C]118[/C][C] 0.2397[/C][C] 0.4793[/C][C] 0.7603[/C][/ROW]
[ROW][C]119[/C][C] 0.2391[/C][C] 0.4783[/C][C] 0.7609[/C][/ROW]
[ROW][C]120[/C][C] 0.2023[/C][C] 0.4046[/C][C] 0.7977[/C][/ROW]
[ROW][C]121[/C][C] 0.1726[/C][C] 0.3453[/C][C] 0.8274[/C][/ROW]
[ROW][C]122[/C][C] 0.1425[/C][C] 0.285[/C][C] 0.8575[/C][/ROW]
[ROW][C]123[/C][C] 0.1185[/C][C] 0.2371[/C][C] 0.8815[/C][/ROW]
[ROW][C]124[/C][C] 0.09628[/C][C] 0.1926[/C][C] 0.9037[/C][/ROW]
[ROW][C]125[/C][C] 0.08171[/C][C] 0.1634[/C][C] 0.9183[/C][/ROW]
[ROW][C]126[/C][C] 0.07971[/C][C] 0.1594[/C][C] 0.9203[/C][/ROW]
[ROW][C]127[/C][C] 0.07568[/C][C] 0.1514[/C][C] 0.9243[/C][/ROW]
[ROW][C]128[/C][C] 0.07072[/C][C] 0.1414[/C][C] 0.9293[/C][/ROW]
[ROW][C]129[/C][C] 0.05457[/C][C] 0.1091[/C][C] 0.9454[/C][/ROW]
[ROW][C]130[/C][C] 0.05209[/C][C] 0.1042[/C][C] 0.9479[/C][/ROW]
[ROW][C]131[/C][C] 0.03913[/C][C] 0.07825[/C][C] 0.9609[/C][/ROW]
[ROW][C]132[/C][C] 0.02912[/C][C] 0.05825[/C][C] 0.9709[/C][/ROW]
[ROW][C]133[/C][C] 0.02868[/C][C] 0.05736[/C][C] 0.9713[/C][/ROW]
[ROW][C]134[/C][C] 0.07596[/C][C] 0.1519[/C][C] 0.924[/C][/ROW]
[ROW][C]135[/C][C] 0.05895[/C][C] 0.1179[/C][C] 0.9411[/C][/ROW]
[ROW][C]136[/C][C] 0.05034[/C][C] 0.1007[/C][C] 0.9497[/C][/ROW]
[ROW][C]137[/C][C] 0.0406[/C][C] 0.08121[/C][C] 0.9594[/C][/ROW]
[ROW][C]138[/C][C] 0.03175[/C][C] 0.06351[/C][C] 0.9682[/C][/ROW]
[ROW][C]139[/C][C] 0.04366[/C][C] 0.08732[/C][C] 0.9563[/C][/ROW]
[ROW][C]140[/C][C] 0.04313[/C][C] 0.08626[/C][C] 0.9569[/C][/ROW]
[ROW][C]141[/C][C] 0.03261[/C][C] 0.06521[/C][C] 0.9674[/C][/ROW]
[ROW][C]142[/C][C] 0.02255[/C][C] 0.0451[/C][C] 0.9775[/C][/ROW]
[ROW][C]143[/C][C] 0.03849[/C][C] 0.07699[/C][C] 0.9615[/C][/ROW]
[ROW][C]144[/C][C] 0.06805[/C][C] 0.1361[/C][C] 0.932[/C][/ROW]
[ROW][C]145[/C][C] 0.08892[/C][C] 0.1778[/C][C] 0.9111[/C][/ROW]
[ROW][C]146[/C][C] 0.1488[/C][C] 0.2977[/C][C] 0.8512[/C][/ROW]
[ROW][C]147[/C][C] 0.1607[/C][C] 0.3213[/C][C] 0.8393[/C][/ROW]
[ROW][C]148[/C][C] 0.1188[/C][C] 0.2375[/C][C] 0.8812[/C][/ROW]
[ROW][C]149[/C][C] 0.2481[/C][C] 0.4961[/C][C] 0.7519[/C][/ROW]
[ROW][C]150[/C][C] 0.193[/C][C] 0.3861[/C][C] 0.807[/C][/ROW]
[ROW][C]151[/C][C] 0.1718[/C][C] 0.3437[/C][C] 0.8282[/C][/ROW]
[ROW][C]152[/C][C] 0.1242[/C][C] 0.2484[/C][C] 0.8758[/C][/ROW]
[ROW][C]153[/C][C] 0.1205[/C][C] 0.2411[/C][C] 0.8795[/C][/ROW]
[ROW][C]154[/C][C] 0.08046[/C][C] 0.1609[/C][C] 0.9195[/C][/ROW]
[ROW][C]155[/C][C] 0.09224[/C][C] 0.1845[/C][C] 0.9078[/C][/ROW]
[ROW][C]156[/C][C] 0.1797[/C][C] 0.3595[/C][C] 0.8203[/C][/ROW]
[ROW][C]157[/C][C] 0.2364[/C][C] 0.4728[/C][C] 0.7636[/C][/ROW]
[ROW][C]158[/C][C] 0.43[/C][C] 0.8599[/C][C] 0.57[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302972&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.3871	0.7742	0.6129
10	0.2515	0.503	0.7485
11	0.2256	0.4511	0.7744
12	0.1351	0.2701	0.8649
13	0.07536	0.1507	0.9246
14	0.06229	0.1246	0.9377
15	0.05181	0.1036	0.9482
16	0.09278	0.1856	0.9072
17	0.0696	0.1392	0.9304
18	0.1438	0.2875	0.8562
19	0.09972	0.1994	0.9003
20	0.1093	0.2186	0.8907
21	0.0866	0.1732	0.9134
22	0.06493	0.1299	0.9351
23	0.05466	0.1093	0.9453
24	0.04001	0.08002	0.96
25	0.08893	0.1779	0.9111
26	0.06465	0.1293	0.9354
27	0.04847	0.09694	0.9515
28	0.0361	0.07219	0.9639
29	0.02724	0.05447	0.9728
30	0.04681	0.09363	0.9532
31	0.06352	0.127	0.9365
32	0.0505	0.101	0.9495
33	0.04783	0.09566	0.9522
34	0.07354	0.1471	0.9265
35	0.06494	0.1299	0.9351
36	0.04851	0.09702	0.9515
37	0.05011	0.1002	0.9499
38	0.0592	0.1184	0.9408
39	0.07979	0.1596	0.9202
40	0.06424	0.1285	0.9358
41	0.04917	0.09833	0.9508
42	0.05046	0.1009	0.9495
43	0.03908	0.07816	0.9609
44	0.02921	0.05841	0.9708
45	0.0211	0.0422	0.9789
46	0.01687	0.03375	0.9831
47	0.01252	0.02503	0.9875
48	0.04236	0.08471	0.9576
49	0.03536	0.07071	0.9646
50	0.02906	0.05811	0.9709
51	0.02981	0.05963	0.9702
52	0.02248	0.04495	0.9775
53	0.1527	0.3053	0.8473
54	0.1273	0.2546	0.8727
55	0.2104	0.4209	0.7896
56	0.1851	0.3703	0.8149
57	0.2403	0.4807	0.7597
58	0.2049	0.4097	0.7951
59	0.1775	0.355	0.8225
60	0.1479	0.2958	0.8521
61	0.1456	0.2912	0.8544
62	0.1244	0.2488	0.8756
63	0.1357	0.2715	0.8643
64	0.1152	0.2304	0.8848
65	0.09469	0.1894	0.9053
66	0.1261	0.2522	0.8739
67	0.1242	0.2484	0.8758
68	0.1593	0.3185	0.8407
69	0.1646	0.3292	0.8354
70	0.1439	0.2878	0.8561
71	0.2083	0.4165	0.7917
72	0.2132	0.4264	0.7868
73	0.3076	0.6151	0.6924
74	0.3047	0.6093	0.6953
75	0.2716	0.5432	0.7284
76	0.241	0.4819	0.759
77	0.2209	0.4418	0.7791
78	0.2016	0.4033	0.7984
79	0.3694	0.7388	0.6306
80	0.3469	0.6939	0.6531
81	0.722	0.5559	0.278
82	0.689	0.622	0.311
83	0.6575	0.685	0.3425
84	0.625	0.75	0.375
85	0.7264	0.5471	0.2736
86	0.6873	0.6255	0.3127
87	0.7151	0.5698	0.2849
88	0.6836	0.6327	0.3164
89	0.6893	0.6214	0.3107
90	0.6851	0.6298	0.3149
91	0.6471	0.7058	0.3529
92	0.7058	0.5885	0.2942
93	0.6726	0.6547	0.3274
94	0.635	0.73	0.365
95	0.6005	0.799	0.3995
96	0.5573	0.8853	0.4427
97	0.5159	0.9681	0.4841
98	0.5126	0.9748	0.4874
99	0.4732	0.9465	0.5268
100	0.4456	0.8912	0.5544
101	0.4471	0.8943	0.5529
102	0.4055	0.811	0.5945
103	0.5571	0.8859	0.4429
104	0.5246	0.9508	0.4754
105	0.4779	0.9558	0.5221
106	0.4308	0.8616	0.5692
107	0.4089	0.8179	0.5911
108	0.3651	0.7302	0.6349
109	0.3228	0.6455	0.6772
110	0.2864	0.5727	0.7136
111	0.2832	0.5664	0.7168
112	0.2444	0.4888	0.7556
113	0.2596	0.5192	0.7404
114	0.336	0.672	0.664
115	0.3052	0.6104	0.6948
116	0.302	0.6041	0.698
117	0.28	0.5599	0.72
118	0.2397	0.4793	0.7603
119	0.2391	0.4783	0.7609
120	0.2023	0.4046	0.7977
121	0.1726	0.3453	0.8274
122	0.1425	0.285	0.8575
123	0.1185	0.2371	0.8815
124	0.09628	0.1926	0.9037
125	0.08171	0.1634	0.9183
126	0.07971	0.1594	0.9203
127	0.07568	0.1514	0.9243
128	0.07072	0.1414	0.9293
129	0.05457	0.1091	0.9454
130	0.05209	0.1042	0.9479
131	0.03913	0.07825	0.9609
132	0.02912	0.05825	0.9709
133	0.02868	0.05736	0.9713
134	0.07596	0.1519	0.924
135	0.05895	0.1179	0.9411
136	0.05034	0.1007	0.9497
137	0.0406	0.08121	0.9594
138	0.03175	0.06351	0.9682
139	0.04366	0.08732	0.9563
140	0.04313	0.08626	0.9569
141	0.03261	0.06521	0.9674
142	0.02255	0.0451	0.9775
143	0.03849	0.07699	0.9615
144	0.06805	0.1361	0.932
145	0.08892	0.1778	0.9111
146	0.1488	0.2977	0.8512
147	0.1607	0.3213	0.8393
148	0.1188	0.2375	0.8812
149	0.2481	0.4961	0.7519
150	0.193	0.3861	0.807
151	0.1718	0.3437	0.8282
152	0.1242	0.2484	0.8758
153	0.1205	0.2411	0.8795
154	0.08046	0.1609	0.9195
155	0.09224	0.1845	0.9078
156	0.1797	0.3595	0.8203
157	0.2364	0.4728	0.7636
158	0.43	0.8599	0.57

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	5	0.0333333	OK
10% type I error level	28	0.186667	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 & 5 & 0.0333333 & OK \tabularnewline
10% type I error level & 28 & 0.186667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&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]5[/C][C]0.0333333[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.186667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302972&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&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	5	0.0333333	OK
10% type I error level	28	0.186667	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.1245, df1 = 2, df2 = 159, p-value = 0.1229
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4452, df1 = 10, df2 = 151, p-value = 0.1657
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.72336, df1 = 2, df2 = 159, p-value = 0.4867

\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 = 2.1245, df1 = 2, df2 = 159, p-value = 0.1229
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.4452, df1 = 10, df2 = 151, p-value = 0.1657
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.72336, df1 = 2, df2 = 159, p-value = 0.4867
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&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 = 2.1245, df1 = 2, df2 = 159, p-value = 0.1229
[/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 = 1.4452, df1 = 10, df2 = 151, p-value = 0.1657
[/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 = 0.72336, df1 = 2, df2 = 159, p-value = 0.4867
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302972&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&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 = 2.1245, df1 = 2, df2 = 159, p-value = 0.1229
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4452, df1 = 10, df2 = 151, p-value = 0.1657
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.72336, df1 = 2, df2 = 159, p-value = 0.4867

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK4 SK5 SK6 1.103463 1.116462 1.048985 1.040552 1.020741

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK4      SK5      SK6 
1.103463 1.116462 1.048985 1.040552 1.020741 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302972&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK4      SK5      SK6 
1.103463 1.116462 1.048985 1.040552 1.020741 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302972&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302972&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 SK1 SK2 SK4 SK5 SK6 1.103463 1.116462 1.048985 1.040552 1.020741

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):

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):

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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