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

Sat, 10 Dec 2016 10:38:38 +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/10/t1481362788ihgah7h341n6j1q.htm/, Retrieved Fri, 17 May 2024 15:37:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298614, Retrieved Fri, 17 May 2024 15:37:51 +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] [6 vragen] [2016-12-10 09:38:38] [ca14e1566745fb922befb698831e7d61] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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

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=298614&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=298614&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&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] = + 6.11764 + 0.559765V1[t] + 1.12123V2[t] + 0.114326V3[t] + 0.312904V4[t] + 0.248219V5[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.11764 +  0.559765V1[t] +  1.12123V2[t] +  0.114326V3[t] +  0.312904V4[t] +  0.248219V5[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298614&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.11764 +  0.559765V1[t] +  1.12123V2[t] +  0.114326V3[t] +  0.312904V4[t] +  0.248219V5[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298614&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&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] = + 6.11764 + 0.559765V1[t] + 1.12123V2[t] + 0.114326V3[t] + 0.312904V4[t] + 0.248219V5[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)	+6.118	1.321	+4.6320e+00	7.42e-06	3.71e-06
V1	+0.5598	0.1565	+3.5780e+00	0.0004582	0.0002291
V2	+1.121	0.1934	+5.7980e+00	3.444e-08	1.722e-08
V3	+0.1143	0.1401	+8.1630e-01	0.4155	0.2078
V4	+0.3129	0.1888	+1.6580e+00	0.09934	0.04967
V5	+0.2482	0.1804	+1.3760e+00	0.1708	0.08542

\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) & +6.118 &  1.321 & +4.6320e+00 &  7.42e-06 &  3.71e-06 \tabularnewline
V1 & +0.5598 &  0.1565 & +3.5780e+00 &  0.0004582 &  0.0002291 \tabularnewline
V2 & +1.121 &  0.1934 & +5.7980e+00 &  3.444e-08 &  1.722e-08 \tabularnewline
V3 & +0.1143 &  0.1401 & +8.1630e-01 &  0.4155 &  0.2078 \tabularnewline
V4 & +0.3129 &  0.1888 & +1.6580e+00 &  0.09934 &  0.04967 \tabularnewline
V5 & +0.2482 &  0.1804 & +1.3760e+00 &  0.1708 &  0.08542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298614&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]+6.118[/C][C] 1.321[/C][C]+4.6320e+00[/C][C] 7.42e-06[/C][C] 3.71e-06[/C][/ROW]
[ROW][C]V1[/C][C]+0.5598[/C][C] 0.1565[/C][C]+3.5780e+00[/C][C] 0.0004582[/C][C] 0.0002291[/C][/ROW]
[ROW][C]V2[/C][C]+1.121[/C][C] 0.1934[/C][C]+5.7980e+00[/C][C] 3.444e-08[/C][C] 1.722e-08[/C][/ROW]
[ROW][C]V3[/C][C]+0.1143[/C][C] 0.1401[/C][C]+8.1630e-01[/C][C] 0.4155[/C][C] 0.2078[/C][/ROW]
[ROW][C]V4[/C][C]+0.3129[/C][C] 0.1888[/C][C]+1.6580e+00[/C][C] 0.09934[/C][C] 0.04967[/C][/ROW]
[ROW][C]V5[/C][C]+0.2482[/C][C] 0.1804[/C][C]+1.3760e+00[/C][C] 0.1708[/C][C] 0.08542[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298614&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&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)	+6.118	1.321	+4.6320e+00	7.42e-06	3.71e-06
V1	+0.5598	0.1565	+3.5780e+00	0.0004582	0.0002291
V2	+1.121	0.1934	+5.7980e+00	3.444e-08	1.722e-08
V3	+0.1143	0.1401	+8.1630e-01	0.4155	0.2078
V4	+0.3129	0.1888	+1.6580e+00	0.09934	0.04967
V5	+0.2482	0.1804	+1.3760e+00	0.1708	0.08542

Multiple Linear Regression - Regression Statistics
Multiple R	0.5747
R-squared	0.3302
Adjusted R-squared	0.3094
F-TEST (value)	15.88
F-TEST (DF numerator)	5
F-TEST (DF denominator)	161
p-value	1.063e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.414
Sum Squared Residuals	321.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5747 \tabularnewline
R-squared &  0.3302 \tabularnewline
Adjusted R-squared &  0.3094 \tabularnewline
F-TEST (value) &  15.88 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 161 \tabularnewline
p-value &  1.063e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.414 \tabularnewline
Sum Squared Residuals &  321.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298614&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5747[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3302[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3094[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 15.88[/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] 1.063e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.414[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 321.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298614&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&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	0.5747
R-squared	0.3302
Adjusted R-squared	0.3094
F-TEST (value)	15.88
F-TEST (DF numerator)	5
F-TEST (DF denominator)	161
p-value	1.063e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.414
Sum Squared Residuals	321.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.24	-0.2363
2	16	15.12	0.8842
3	17	15.91	1.094
4	15	14.56	0.4436
5	16	15.91	0.09405
6	16	15.23	0.7681
7	16	14.42	1.577
8	16	15.1	0.902
9	17	16.91	0.08715
10	17	17.03	-0.02718
11	17	15.56	1.437
12	15	15.59	-0.593
13	16	15.23	0.7695
14	14	13.98	0.02326
15	16	15.16	0.8387
16	17	15.1	1.902
17	16	15.1	0.902
18	14	16.91	-2.911
19	17	15.79	1.208
20	16	14.79	1.215
21	15	15.79	-0.7916
22	16	15.66	0.3423
23	15	15.66	-0.6577
24	17	15.66	1.342
25	14	14.98	-0.9836
26	16	14.92	1.081
27	15	15.54	-0.5434
28	16	14.79	1.214
29	16	16.1	-0.1032
30	13	14.54	-1.536
31	15	17.03	-2.027
32	17	16.22	0.7825
33	15	14.78	0.2153
34	13	13.67	-0.6652
35	17	16.44	0.564
36	15	15.1	-0.09797
37	14	14.22	-0.2236
38	14	14.31	-0.3079
39	18	15.66	2.342
40	15	16.1	-1.103
41	17	17.03	-0.02718
42	13	13.86	-0.8624
43	16	17.27	-1.274
44	15	15.9	-0.9046
45	15	15.23	-0.2305
46	16	15.54	0.4566
47	15	15.66	-0.6581
48	13	15.79	-2.792
49	12	12.93	-0.9251
50	17	16.78	0.221
51	18	17.24	0.756
52	17	17.34	-0.3387
53	11	14.78	-3.785
54	14	14.11	-0.1093
55	13	15.66	-2.658
56	15	14.42	0.5775
57	17	14.98	2.016
58	16	15.48	0.5213
59	15	15.79	-0.7916
60	17	17.36	-0.3583
61	16	14.67	1.328
62	16	15.79	0.2084
63	16	14.49	1.513
64	15	15.91	-0.906
65	12	13.3	-1.301
66	17	15.48	1.521
67	14	15.48	-1.479
68	14	15.66	-1.656
69	16	14.92	1.081
70	15	14.67	0.3293
71	15	17.34	-2.34
72	13	15.43	-2.429
73	13	15.54	-2.543
74	17	16.1	0.8955
75	15	14.87	0.1307
76	16	15.79	0.2084
77	14	15.03	-1.033
78	15	13.98	1.023
79	17	14.54	2.463
80	16	15.66	0.3423
81	10	13.75	-3.748
82	16	15.79	0.2084
83	17	15.68	1.323
84	17	15.79	1.208
85	20	16.1	3.897
86	17	16.3	0.6983
87	18	15.91	2.094
88	15	15.1	-0.09797
89	17	14.98	2.016
90	14	12.87	1.126
91	15	15.66	-0.6577
92	17	15.66	1.342
93	16	15.79	0.2084
94	17	16.91	0.08715
95	15	14.92	0.08104
96	16	15.66	0.3423
97	18	15.99	2.011
98	18	16.53	1.47
99	16	16.91	-0.9129
100	16	15.1	0.902
101	17	15.23	1.77
102	15	15.66	-0.6577
103	13	16.1	-3.103
104	15	14.67	0.3293
105	17	16.66	0.3357
106	16	15.34	0.6552
107	16	15.12	0.8838
108	15	15.66	-0.6577
109	16	15.66	0.3423
110	16	15.35	0.6538
111	13	15.54	-2.543
112	15	15.23	-0.2305
113	12	13.8	-1.798
114	18	15.23	2.769
115	16	15.1	0.902
116	16	15.41	0.5905
117	17	15.53	1.473
118	16	16.47	-0.4657
119	14	15.54	-1.543
120	15	15.34	-0.3448
121	14	14.67	-0.6707
122	16	15.54	0.4566
123	15	15.79	-0.7916
124	17	16.55	0.4497
125	15	14.98	0.01636
126	16	15.23	0.7695
127	16	15.54	0.4566
128	15	14.56	0.4436
129	15	15.23	-0.2305
130	11	12.12	-1.115
131	16	15.48	0.5213
132	18	16.04	1.962
133	13	13.86	-0.8628
134	11	13.86	-2.862
135	16	15.23	0.7695
136	18	17.47	0.5274
137	15	16.78	-1.779
138	19	17.9	1.1
139	17	17.03	-0.02718
140	13	15.23	-2.231
141	14	15.35	-1.346
142	16	15.66	0.3423
143	13	15.31	-2.315
144	17	15.68	1.323
145	14	15.79	-1.792
146	19	16.15	2.847
147	14	14.54	-0.5365
148	16	15.66	0.3423
149	12	13.32	-1.321
150	16	16.78	-0.779
151	16	15.23	0.7695
152	15	15.54	-0.5434
153	12	15.03	-3.033
154	15	15.66	-0.6577
155	17	16.47	0.5343
156	13	15.41	-2.41
157	15	13.42	1.583
158	18	15.54	2.457
159	15	14.36	0.6425
160	18	15.54	2.457
161	15	17.09	-2.092
162	15	15.99	-0.9888
163	16	15.79	0.2097
164	13	13.77	-0.7677
165	16	15.54	0.4566
166	13	15.79	-2.792
167	16	13.86	2.136

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.24 & -0.2363 \tabularnewline
2 &  16 &  15.12 &  0.8842 \tabularnewline
3 &  17 &  15.91 &  1.094 \tabularnewline
4 &  15 &  14.56 &  0.4436 \tabularnewline
5 &  16 &  15.91 &  0.09405 \tabularnewline
6 &  16 &  15.23 &  0.7681 \tabularnewline
7 &  16 &  14.42 &  1.577 \tabularnewline
8 &  16 &  15.1 &  0.902 \tabularnewline
9 &  17 &  16.91 &  0.08715 \tabularnewline
10 &  17 &  17.03 & -0.02718 \tabularnewline
11 &  17 &  15.56 &  1.437 \tabularnewline
12 &  15 &  15.59 & -0.593 \tabularnewline
13 &  16 &  15.23 &  0.7695 \tabularnewline
14 &  14 &  13.98 &  0.02326 \tabularnewline
15 &  16 &  15.16 &  0.8387 \tabularnewline
16 &  17 &  15.1 &  1.902 \tabularnewline
17 &  16 &  15.1 &  0.902 \tabularnewline
18 &  14 &  16.91 & -2.911 \tabularnewline
19 &  17 &  15.79 &  1.208 \tabularnewline
20 &  16 &  14.79 &  1.215 \tabularnewline
21 &  15 &  15.79 & -0.7916 \tabularnewline
22 &  16 &  15.66 &  0.3423 \tabularnewline
23 &  15 &  15.66 & -0.6577 \tabularnewline
24 &  17 &  15.66 &  1.342 \tabularnewline
25 &  14 &  14.98 & -0.9836 \tabularnewline
26 &  16 &  14.92 &  1.081 \tabularnewline
27 &  15 &  15.54 & -0.5434 \tabularnewline
28 &  16 &  14.79 &  1.214 \tabularnewline
29 &  16 &  16.1 & -0.1032 \tabularnewline
30 &  13 &  14.54 & -1.536 \tabularnewline
31 &  15 &  17.03 & -2.027 \tabularnewline
32 &  17 &  16.22 &  0.7825 \tabularnewline
33 &  15 &  14.78 &  0.2153 \tabularnewline
34 &  13 &  13.67 & -0.6652 \tabularnewline
35 &  17 &  16.44 &  0.564 \tabularnewline
36 &  15 &  15.1 & -0.09797 \tabularnewline
37 &  14 &  14.22 & -0.2236 \tabularnewline
38 &  14 &  14.31 & -0.3079 \tabularnewline
39 &  18 &  15.66 &  2.342 \tabularnewline
40 &  15 &  16.1 & -1.103 \tabularnewline
41 &  17 &  17.03 & -0.02718 \tabularnewline
42 &  13 &  13.86 & -0.8624 \tabularnewline
43 &  16 &  17.27 & -1.274 \tabularnewline
44 &  15 &  15.9 & -0.9046 \tabularnewline
45 &  15 &  15.23 & -0.2305 \tabularnewline
46 &  16 &  15.54 &  0.4566 \tabularnewline
47 &  15 &  15.66 & -0.6581 \tabularnewline
48 &  13 &  15.79 & -2.792 \tabularnewline
49 &  12 &  12.93 & -0.9251 \tabularnewline
50 &  17 &  16.78 &  0.221 \tabularnewline
51 &  18 &  17.24 &  0.756 \tabularnewline
52 &  17 &  17.34 & -0.3387 \tabularnewline
53 &  11 &  14.78 & -3.785 \tabularnewline
54 &  14 &  14.11 & -0.1093 \tabularnewline
55 &  13 &  15.66 & -2.658 \tabularnewline
56 &  15 &  14.42 &  0.5775 \tabularnewline
57 &  17 &  14.98 &  2.016 \tabularnewline
58 &  16 &  15.48 &  0.5213 \tabularnewline
59 &  15 &  15.79 & -0.7916 \tabularnewline
60 &  17 &  17.36 & -0.3583 \tabularnewline
61 &  16 &  14.67 &  1.328 \tabularnewline
62 &  16 &  15.79 &  0.2084 \tabularnewline
63 &  16 &  14.49 &  1.513 \tabularnewline
64 &  15 &  15.91 & -0.906 \tabularnewline
65 &  12 &  13.3 & -1.301 \tabularnewline
66 &  17 &  15.48 &  1.521 \tabularnewline
67 &  14 &  15.48 & -1.479 \tabularnewline
68 &  14 &  15.66 & -1.656 \tabularnewline
69 &  16 &  14.92 &  1.081 \tabularnewline
70 &  15 &  14.67 &  0.3293 \tabularnewline
71 &  15 &  17.34 & -2.34 \tabularnewline
72 &  13 &  15.43 & -2.429 \tabularnewline
73 &  13 &  15.54 & -2.543 \tabularnewline
74 &  17 &  16.1 &  0.8955 \tabularnewline
75 &  15 &  14.87 &  0.1307 \tabularnewline
76 &  16 &  15.79 &  0.2084 \tabularnewline
77 &  14 &  15.03 & -1.033 \tabularnewline
78 &  15 &  13.98 &  1.023 \tabularnewline
79 &  17 &  14.54 &  2.463 \tabularnewline
80 &  16 &  15.66 &  0.3423 \tabularnewline
81 &  10 &  13.75 & -3.748 \tabularnewline
82 &  16 &  15.79 &  0.2084 \tabularnewline
83 &  17 &  15.68 &  1.323 \tabularnewline
84 &  17 &  15.79 &  1.208 \tabularnewline
85 &  20 &  16.1 &  3.897 \tabularnewline
86 &  17 &  16.3 &  0.6983 \tabularnewline
87 &  18 &  15.91 &  2.094 \tabularnewline
88 &  15 &  15.1 & -0.09797 \tabularnewline
89 &  17 &  14.98 &  2.016 \tabularnewline
90 &  14 &  12.87 &  1.126 \tabularnewline
91 &  15 &  15.66 & -0.6577 \tabularnewline
92 &  17 &  15.66 &  1.342 \tabularnewline
93 &  16 &  15.79 &  0.2084 \tabularnewline
94 &  17 &  16.91 &  0.08715 \tabularnewline
95 &  15 &  14.92 &  0.08104 \tabularnewline
96 &  16 &  15.66 &  0.3423 \tabularnewline
97 &  18 &  15.99 &  2.011 \tabularnewline
98 &  18 &  16.53 &  1.47 \tabularnewline
99 &  16 &  16.91 & -0.9129 \tabularnewline
100 &  16 &  15.1 &  0.902 \tabularnewline
101 &  17 &  15.23 &  1.77 \tabularnewline
102 &  15 &  15.66 & -0.6577 \tabularnewline
103 &  13 &  16.1 & -3.103 \tabularnewline
104 &  15 &  14.67 &  0.3293 \tabularnewline
105 &  17 &  16.66 &  0.3357 \tabularnewline
106 &  16 &  15.34 &  0.6552 \tabularnewline
107 &  16 &  15.12 &  0.8838 \tabularnewline
108 &  15 &  15.66 & -0.6577 \tabularnewline
109 &  16 &  15.66 &  0.3423 \tabularnewline
110 &  16 &  15.35 &  0.6538 \tabularnewline
111 &  13 &  15.54 & -2.543 \tabularnewline
112 &  15 &  15.23 & -0.2305 \tabularnewline
113 &  12 &  13.8 & -1.798 \tabularnewline
114 &  18 &  15.23 &  2.769 \tabularnewline
115 &  16 &  15.1 &  0.902 \tabularnewline
116 &  16 &  15.41 &  0.5905 \tabularnewline
117 &  17 &  15.53 &  1.473 \tabularnewline
118 &  16 &  16.47 & -0.4657 \tabularnewline
119 &  14 &  15.54 & -1.543 \tabularnewline
120 &  15 &  15.34 & -0.3448 \tabularnewline
121 &  14 &  14.67 & -0.6707 \tabularnewline
122 &  16 &  15.54 &  0.4566 \tabularnewline
123 &  15 &  15.79 & -0.7916 \tabularnewline
124 &  17 &  16.55 &  0.4497 \tabularnewline
125 &  15 &  14.98 &  0.01636 \tabularnewline
126 &  16 &  15.23 &  0.7695 \tabularnewline
127 &  16 &  15.54 &  0.4566 \tabularnewline
128 &  15 &  14.56 &  0.4436 \tabularnewline
129 &  15 &  15.23 & -0.2305 \tabularnewline
130 &  11 &  12.12 & -1.115 \tabularnewline
131 &  16 &  15.48 &  0.5213 \tabularnewline
132 &  18 &  16.04 &  1.962 \tabularnewline
133 &  13 &  13.86 & -0.8628 \tabularnewline
134 &  11 &  13.86 & -2.862 \tabularnewline
135 &  16 &  15.23 &  0.7695 \tabularnewline
136 &  18 &  17.47 &  0.5274 \tabularnewline
137 &  15 &  16.78 & -1.779 \tabularnewline
138 &  19 &  17.9 &  1.1 \tabularnewline
139 &  17 &  17.03 & -0.02718 \tabularnewline
140 &  13 &  15.23 & -2.231 \tabularnewline
141 &  14 &  15.35 & -1.346 \tabularnewline
142 &  16 &  15.66 &  0.3423 \tabularnewline
143 &  13 &  15.31 & -2.315 \tabularnewline
144 &  17 &  15.68 &  1.323 \tabularnewline
145 &  14 &  15.79 & -1.792 \tabularnewline
146 &  19 &  16.15 &  2.847 \tabularnewline
147 &  14 &  14.54 & -0.5365 \tabularnewline
148 &  16 &  15.66 &  0.3423 \tabularnewline
149 &  12 &  13.32 & -1.321 \tabularnewline
150 &  16 &  16.78 & -0.779 \tabularnewline
151 &  16 &  15.23 &  0.7695 \tabularnewline
152 &  15 &  15.54 & -0.5434 \tabularnewline
153 &  12 &  15.03 & -3.033 \tabularnewline
154 &  15 &  15.66 & -0.6577 \tabularnewline
155 &  17 &  16.47 &  0.5343 \tabularnewline
156 &  13 &  15.41 & -2.41 \tabularnewline
157 &  15 &  13.42 &  1.583 \tabularnewline
158 &  18 &  15.54 &  2.457 \tabularnewline
159 &  15 &  14.36 &  0.6425 \tabularnewline
160 &  18 &  15.54 &  2.457 \tabularnewline
161 &  15 &  17.09 & -2.092 \tabularnewline
162 &  15 &  15.99 & -0.9888 \tabularnewline
163 &  16 &  15.79 &  0.2097 \tabularnewline
164 &  13 &  13.77 & -0.7677 \tabularnewline
165 &  16 &  15.54 &  0.4566 \tabularnewline
166 &  13 &  15.79 & -2.792 \tabularnewline
167 &  16 &  13.86 &  2.136 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298614&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.24[/C][C]-0.2363[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.12[/C][C] 0.8842[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.91[/C][C] 1.094[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.56[/C][C] 0.4436[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.91[/C][C] 0.09405[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.23[/C][C] 0.7681[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 14.42[/C][C] 1.577[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.1[/C][C] 0.902[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.91[/C][C] 0.08715[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 17.03[/C][C]-0.02718[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.56[/C][C] 1.437[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.59[/C][C]-0.593[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.23[/C][C] 0.7695[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 13.98[/C][C] 0.02326[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.16[/C][C] 0.8387[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.1[/C][C] 1.902[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.1[/C][C] 0.902[/C][/ROW]
[ROW][C]18[/C][C] 14[/C][C] 16.91[/C][C]-2.911[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.79[/C][C] 1.208[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.79[/C][C] 1.215[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.79[/C][C]-0.7916[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.66[/C][C]-0.6577[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.66[/C][C] 1.342[/C][/ROW]
[ROW][C]25[/C][C] 14[/C][C] 14.98[/C][C]-0.9836[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.92[/C][C] 1.081[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.54[/C][C]-0.5434[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.79[/C][C] 1.214[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.1[/C][C]-0.1032[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.54[/C][C]-1.536[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 17.03[/C][C]-2.027[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.22[/C][C] 0.7825[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.78[/C][C] 0.2153[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.67[/C][C]-0.6652[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.44[/C][C] 0.564[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.1[/C][C]-0.09797[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.22[/C][C]-0.2236[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.31[/C][C]-0.3079[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.66[/C][C] 2.342[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.1[/C][C]-1.103[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 17.03[/C][C]-0.02718[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.86[/C][C]-0.8624[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 17.27[/C][C]-1.274[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.9[/C][C]-0.9046[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.23[/C][C]-0.2305[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.54[/C][C] 0.4566[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.66[/C][C]-0.6581[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.79[/C][C]-2.792[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 12.93[/C][C]-0.9251[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 16.78[/C][C] 0.221[/C][/ROW]
[ROW][C]51[/C][C] 18[/C][C] 17.24[/C][C] 0.756[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 17.34[/C][C]-0.3387[/C][/ROW]
[ROW][C]53[/C][C] 11[/C][C] 14.78[/C][C]-3.785[/C][/ROW]
[ROW][C]54[/C][C] 14[/C][C] 14.11[/C][C]-0.1093[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 15.66[/C][C]-2.658[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 14.42[/C][C] 0.5775[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 14.98[/C][C] 2.016[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 15.48[/C][C] 0.5213[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 15.79[/C][C]-0.7916[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 17.36[/C][C]-0.3583[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 14.67[/C][C] 1.328[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.79[/C][C] 0.2084[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 14.49[/C][C] 1.513[/C][/ROW]
[ROW][C]64[/C][C] 15[/C][C] 15.91[/C][C]-0.906[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 13.3[/C][C]-1.301[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 15.48[/C][C] 1.521[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.48[/C][C]-1.479[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 15.66[/C][C]-1.656[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 14.92[/C][C] 1.081[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 14.67[/C][C] 0.3293[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 17.34[/C][C]-2.34[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 15.43[/C][C]-2.429[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 15.54[/C][C]-2.543[/C][/ROW]
[ROW][C]74[/C][C] 17[/C][C] 16.1[/C][C] 0.8955[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 14.87[/C][C] 0.1307[/C][/ROW]
[ROW][C]76[/C][C] 16[/C][C] 15.79[/C][C] 0.2084[/C][/ROW]
[ROW][C]77[/C][C] 14[/C][C] 15.03[/C][C]-1.033[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 13.98[/C][C] 1.023[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 14.54[/C][C] 2.463[/C][/ROW]
[ROW][C]80[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]81[/C][C] 10[/C][C] 13.75[/C][C]-3.748[/C][/ROW]
[ROW][C]82[/C][C] 16[/C][C] 15.79[/C][C] 0.2084[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.68[/C][C] 1.323[/C][/ROW]
[ROW][C]84[/C][C] 17[/C][C] 15.79[/C][C] 1.208[/C][/ROW]
[ROW][C]85[/C][C] 20[/C][C] 16.1[/C][C] 3.897[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 16.3[/C][C] 0.6983[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 15.91[/C][C] 2.094[/C][/ROW]
[ROW][C]88[/C][C] 15[/C][C] 15.1[/C][C]-0.09797[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 14.98[/C][C] 2.016[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 12.87[/C][C] 1.126[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.66[/C][C]-0.6577[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 15.66[/C][C] 1.342[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.79[/C][C] 0.2084[/C][/ROW]
[ROW][C]94[/C][C] 17[/C][C] 16.91[/C][C] 0.08715[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 14.92[/C][C] 0.08104[/C][/ROW]
[ROW][C]96[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 15.99[/C][C] 2.011[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 16.53[/C][C] 1.47[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 16.91[/C][C]-0.9129[/C][/ROW]
[ROW][C]100[/C][C] 16[/C][C] 15.1[/C][C] 0.902[/C][/ROW]
[ROW][C]101[/C][C] 17[/C][C] 15.23[/C][C] 1.77[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 15.66[/C][C]-0.6577[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 16.1[/C][C]-3.103[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 14.67[/C][C] 0.3293[/C][/ROW]
[ROW][C]105[/C][C] 17[/C][C] 16.66[/C][C] 0.3357[/C][/ROW]
[ROW][C]106[/C][C] 16[/C][C] 15.34[/C][C] 0.6552[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.12[/C][C] 0.8838[/C][/ROW]
[ROW][C]108[/C][C] 15[/C][C] 15.66[/C][C]-0.6577[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]110[/C][C] 16[/C][C] 15.35[/C][C] 0.6538[/C][/ROW]
[ROW][C]111[/C][C] 13[/C][C] 15.54[/C][C]-2.543[/C][/ROW]
[ROW][C]112[/C][C] 15[/C][C] 15.23[/C][C]-0.2305[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 13.8[/C][C]-1.798[/C][/ROW]
[ROW][C]114[/C][C] 18[/C][C] 15.23[/C][C] 2.769[/C][/ROW]
[ROW][C]115[/C][C] 16[/C][C] 15.1[/C][C] 0.902[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 15.41[/C][C] 0.5905[/C][/ROW]
[ROW][C]117[/C][C] 17[/C][C] 15.53[/C][C] 1.473[/C][/ROW]
[ROW][C]118[/C][C] 16[/C][C] 16.47[/C][C]-0.4657[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 15.54[/C][C]-1.543[/C][/ROW]
[ROW][C]120[/C][C] 15[/C][C] 15.34[/C][C]-0.3448[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 14.67[/C][C]-0.6707[/C][/ROW]
[ROW][C]122[/C][C] 16[/C][C] 15.54[/C][C] 0.4566[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.79[/C][C]-0.7916[/C][/ROW]
[ROW][C]124[/C][C] 17[/C][C] 16.55[/C][C] 0.4497[/C][/ROW]
[ROW][C]125[/C][C] 15[/C][C] 14.98[/C][C] 0.01636[/C][/ROW]
[ROW][C]126[/C][C] 16[/C][C] 15.23[/C][C] 0.7695[/C][/ROW]
[ROW][C]127[/C][C] 16[/C][C] 15.54[/C][C] 0.4566[/C][/ROW]
[ROW][C]128[/C][C] 15[/C][C] 14.56[/C][C] 0.4436[/C][/ROW]
[ROW][C]129[/C][C] 15[/C][C] 15.23[/C][C]-0.2305[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 12.12[/C][C]-1.115[/C][/ROW]
[ROW][C]131[/C][C] 16[/C][C] 15.48[/C][C] 0.5213[/C][/ROW]
[ROW][C]132[/C][C] 18[/C][C] 16.04[/C][C] 1.962[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 13.86[/C][C]-0.8628[/C][/ROW]
[ROW][C]134[/C][C] 11[/C][C] 13.86[/C][C]-2.862[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 15.23[/C][C] 0.7695[/C][/ROW]
[ROW][C]136[/C][C] 18[/C][C] 17.47[/C][C] 0.5274[/C][/ROW]
[ROW][C]137[/C][C] 15[/C][C] 16.78[/C][C]-1.779[/C][/ROW]
[ROW][C]138[/C][C] 19[/C][C] 17.9[/C][C] 1.1[/C][/ROW]
[ROW][C]139[/C][C] 17[/C][C] 17.03[/C][C]-0.02718[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 15.23[/C][C]-2.231[/C][/ROW]
[ROW][C]141[/C][C] 14[/C][C] 15.35[/C][C]-1.346[/C][/ROW]
[ROW][C]142[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]143[/C][C] 13[/C][C] 15.31[/C][C]-2.315[/C][/ROW]
[ROW][C]144[/C][C] 17[/C][C] 15.68[/C][C] 1.323[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 15.79[/C][C]-1.792[/C][/ROW]
[ROW][C]146[/C][C] 19[/C][C] 16.15[/C][C] 2.847[/C][/ROW]
[ROW][C]147[/C][C] 14[/C][C] 14.54[/C][C]-0.5365[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 15.66[/C][C] 0.3423[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 13.32[/C][C]-1.321[/C][/ROW]
[ROW][C]150[/C][C] 16[/C][C] 16.78[/C][C]-0.779[/C][/ROW]
[ROW][C]151[/C][C] 16[/C][C] 15.23[/C][C] 0.7695[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 15.54[/C][C]-0.5434[/C][/ROW]
[ROW][C]153[/C][C] 12[/C][C] 15.03[/C][C]-3.033[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 15.66[/C][C]-0.6577[/C][/ROW]
[ROW][C]155[/C][C] 17[/C][C] 16.47[/C][C] 0.5343[/C][/ROW]
[ROW][C]156[/C][C] 13[/C][C] 15.41[/C][C]-2.41[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 13.42[/C][C] 1.583[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 15.54[/C][C] 2.457[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 14.36[/C][C] 0.6425[/C][/ROW]
[ROW][C]160[/C][C] 18[/C][C] 15.54[/C][C] 2.457[/C][/ROW]
[ROW][C]161[/C][C] 15[/C][C] 17.09[/C][C]-2.092[/C][/ROW]
[ROW][C]162[/C][C] 15[/C][C] 15.99[/C][C]-0.9888[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.79[/C][C] 0.2097[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 13.77[/C][C]-0.7677[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 15.54[/C][C] 0.4566[/C][/ROW]
[ROW][C]166[/C][C] 13[/C][C] 15.79[/C][C]-2.792[/C][/ROW]
[ROW][C]167[/C][C] 16[/C][C] 13.86[/C][C] 2.136[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298614&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.24	-0.2363
2	16	15.12	0.8842
3	17	15.91	1.094
4	15	14.56	0.4436
5	16	15.91	0.09405
6	16	15.23	0.7681
7	16	14.42	1.577
8	16	15.1	0.902
9	17	16.91	0.08715
10	17	17.03	-0.02718
11	17	15.56	1.437
12	15	15.59	-0.593
13	16	15.23	0.7695
14	14	13.98	0.02326
15	16	15.16	0.8387
16	17	15.1	1.902
17	16	15.1	0.902
18	14	16.91	-2.911
19	17	15.79	1.208
20	16	14.79	1.215
21	15	15.79	-0.7916
22	16	15.66	0.3423
23	15	15.66	-0.6577
24	17	15.66	1.342
25	14	14.98	-0.9836
26	16	14.92	1.081
27	15	15.54	-0.5434
28	16	14.79	1.214
29	16	16.1	-0.1032
30	13	14.54	-1.536
31	15	17.03	-2.027
32	17	16.22	0.7825
33	15	14.78	0.2153
34	13	13.67	-0.6652
35	17	16.44	0.564
36	15	15.1	-0.09797
37	14	14.22	-0.2236
38	14	14.31	-0.3079
39	18	15.66	2.342
40	15	16.1	-1.103
41	17	17.03	-0.02718
42	13	13.86	-0.8624
43	16	17.27	-1.274
44	15	15.9	-0.9046
45	15	15.23	-0.2305
46	16	15.54	0.4566
47	15	15.66	-0.6581
48	13	15.79	-2.792
49	12	12.93	-0.9251
50	17	16.78	0.221
51	18	17.24	0.756
52	17	17.34	-0.3387
53	11	14.78	-3.785
54	14	14.11	-0.1093
55	13	15.66	-2.658
56	15	14.42	0.5775
57	17	14.98	2.016
58	16	15.48	0.5213
59	15	15.79	-0.7916
60	17	17.36	-0.3583
61	16	14.67	1.328
62	16	15.79	0.2084
63	16	14.49	1.513
64	15	15.91	-0.906
65	12	13.3	-1.301
66	17	15.48	1.521
67	14	15.48	-1.479
68	14	15.66	-1.656
69	16	14.92	1.081
70	15	14.67	0.3293
71	15	17.34	-2.34
72	13	15.43	-2.429
73	13	15.54	-2.543
74	17	16.1	0.8955
75	15	14.87	0.1307
76	16	15.79	0.2084
77	14	15.03	-1.033
78	15	13.98	1.023
79	17	14.54	2.463
80	16	15.66	0.3423
81	10	13.75	-3.748
82	16	15.79	0.2084
83	17	15.68	1.323
84	17	15.79	1.208
85	20	16.1	3.897
86	17	16.3	0.6983
87	18	15.91	2.094
88	15	15.1	-0.09797
89	17	14.98	2.016
90	14	12.87	1.126
91	15	15.66	-0.6577
92	17	15.66	1.342
93	16	15.79	0.2084
94	17	16.91	0.08715
95	15	14.92	0.08104
96	16	15.66	0.3423
97	18	15.99	2.011
98	18	16.53	1.47
99	16	16.91	-0.9129
100	16	15.1	0.902
101	17	15.23	1.77
102	15	15.66	-0.6577
103	13	16.1	-3.103
104	15	14.67	0.3293
105	17	16.66	0.3357
106	16	15.34	0.6552
107	16	15.12	0.8838
108	15	15.66	-0.6577
109	16	15.66	0.3423
110	16	15.35	0.6538
111	13	15.54	-2.543
112	15	15.23	-0.2305
113	12	13.8	-1.798
114	18	15.23	2.769
115	16	15.1	0.902
116	16	15.41	0.5905
117	17	15.53	1.473
118	16	16.47	-0.4657
119	14	15.54	-1.543
120	15	15.34	-0.3448
121	14	14.67	-0.6707
122	16	15.54	0.4566
123	15	15.79	-0.7916
124	17	16.55	0.4497
125	15	14.98	0.01636
126	16	15.23	0.7695
127	16	15.54	0.4566
128	15	14.56	0.4436
129	15	15.23	-0.2305
130	11	12.12	-1.115
131	16	15.48	0.5213
132	18	16.04	1.962
133	13	13.86	-0.8628
134	11	13.86	-2.862
135	16	15.23	0.7695
136	18	17.47	0.5274
137	15	16.78	-1.779
138	19	17.9	1.1
139	17	17.03	-0.02718
140	13	15.23	-2.231
141	14	15.35	-1.346
142	16	15.66	0.3423
143	13	15.31	-2.315
144	17	15.68	1.323
145	14	15.79	-1.792
146	19	16.15	2.847
147	14	14.54	-0.5365
148	16	15.66	0.3423
149	12	13.32	-1.321
150	16	16.78	-0.779
151	16	15.23	0.7695
152	15	15.54	-0.5434
153	12	15.03	-3.033
154	15	15.66	-0.6577
155	17	16.47	0.5343
156	13	15.41	-2.41
157	15	13.42	1.583
158	18	15.54	2.457
159	15	14.36	0.6425
160	18	15.54	2.457
161	15	17.09	-2.092
162	15	15.99	-0.9888
163	16	15.79	0.2097
164	13	13.77	-0.7677
165	16	15.54	0.4566
166	13	15.79	-2.792
167	16	13.86	2.136

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.07187	0.1437	0.9281
10	0.0241	0.0482	0.9759
11	0.01	0.02001	0.99
12	0.003191	0.006381	0.9968
13	0.001099	0.002198	0.9989
14	0.002006	0.004012	0.998
15	0.001554	0.003107	0.9984
16	0.003336	0.006672	0.9967
17	0.001359	0.002718	0.9986
18	0.02282	0.04564	0.9772
19	0.01573	0.03147	0.9843
20	0.0145	0.029	0.9855
21	0.01717	0.03433	0.9828
22	0.00978	0.01956	0.9902
23	0.008084	0.01617	0.9919
24	0.00791	0.01582	0.9921
25	0.03027	0.06054	0.9697
26	0.02236	0.04472	0.9776
27	0.01858	0.03716	0.9814
28	0.01277	0.02555	0.9872
29	0.007996	0.01599	0.992
30	0.01499	0.02999	0.985
31	0.02391	0.04782	0.9761
32	0.02675	0.0535	0.9733
33	0.0181	0.0362	0.9819
34	0.02623	0.05245	0.9738
35	0.01831	0.03663	0.9817
36	0.01348	0.02697	0.9865
37	0.00902	0.01804	0.991
38	0.007484	0.01497	0.9925
39	0.01786	0.03572	0.9821
40	0.01556	0.03112	0.9844
41	0.01068	0.02136	0.9893
42	0.01229	0.02458	0.9877
43	0.009611	0.01922	0.9904
44	0.006906	0.01381	0.9931
45	0.00466	0.00932	0.9953
46	0.003132	0.006265	0.9969
47	0.002557	0.005114	0.9974
48	0.01175	0.0235	0.9882
49	0.009926	0.01985	0.9901
50	0.006933	0.01387	0.9931
51	0.005542	0.01108	0.9945
52	0.0038	0.0076	0.9962
53	0.03315	0.06629	0.9669
54	0.02493	0.04985	0.9751
55	0.05469	0.1094	0.9453
56	0.04328	0.08656	0.9567
57	0.0494	0.09881	0.9506
58	0.04245	0.08489	0.9576
59	0.03523	0.07047	0.9648
60	0.02699	0.05398	0.973
61	0.02372	0.04745	0.9763
62	0.0179	0.0358	0.9821
63	0.01724	0.03449	0.9828
64	0.01406	0.02812	0.9859
65	0.01398	0.02796	0.986
66	0.0177	0.03541	0.9823
67	0.01779	0.03559	0.9822
68	0.01775	0.0355	0.9822
69	0.01582	0.03163	0.9842
70	0.01213	0.02426	0.9879
71	0.02188	0.04377	0.9781
72	0.04656	0.09312	0.9534
73	0.08276	0.1655	0.9172
74	0.07455	0.1491	0.9254
75	0.06344	0.1269	0.9366
76	0.05111	0.1022	0.9489
77	0.04579	0.09159	0.9542
78	0.04069	0.08137	0.9593
79	0.07433	0.1487	0.9257
80	0.06092	0.1218	0.9391
81	0.2282	0.4565	0.7718
82	0.1972	0.3944	0.8028
83	0.195	0.39	0.805
84	0.1885	0.3769	0.8115
85	0.4596	0.9192	0.5404
86	0.4242	0.8483	0.5758
87	0.4761	0.9523	0.5239
88	0.4328	0.8655	0.5672
89	0.488	0.9761	0.512
90	0.4687	0.9374	0.5313
91	0.4325	0.865	0.5675
92	0.4294	0.8587	0.5706
93	0.3856	0.7713	0.6144
94	0.3431	0.6861	0.6569
95	0.3025	0.605	0.6975
96	0.2661	0.5322	0.7339
97	0.3065	0.613	0.6935
98	0.3102	0.6204	0.6898
99	0.2865	0.573	0.7135
100	0.2687	0.5374	0.7313
101	0.2892	0.5785	0.7108
102	0.2569	0.5138	0.7431
103	0.4089	0.8179	0.5911
104	0.37	0.74	0.63
105	0.3277	0.6554	0.6723
106	0.2963	0.5927	0.7037
107	0.2752	0.5503	0.7248
108	0.2434	0.4867	0.7566
109	0.2103	0.4206	0.7897
110	0.1863	0.3725	0.8137
111	0.2563	0.5126	0.7437
112	0.2193	0.4386	0.7807
113	0.2382	0.4763	0.7618
114	0.3638	0.7276	0.6362
115	0.35	0.7001	0.65
116	0.3264	0.6528	0.6736
117	0.3193	0.6385	0.6807
118	0.2899	0.5798	0.7101
119	0.2883	0.5766	0.7117
120	0.2475	0.495	0.7525
121	0.2134	0.4267	0.7866
122	0.184	0.368	0.816
123	0.1634	0.3267	0.8366
124	0.1462	0.2923	0.8538
125	0.1232	0.2463	0.8768
126	0.1095	0.219	0.8905
127	0.09154	0.1831	0.9085
128	0.08746	0.1749	0.9125
129	0.06861	0.1372	0.9314
130	0.06347	0.1269	0.9365
131	0.04949	0.09898	0.9505
132	0.05355	0.1071	0.9464
133	0.05123	0.1025	0.9488
134	0.1052	0.2104	0.8948
135	0.1048	0.2096	0.8952
136	0.08938	0.1788	0.9106
137	0.07611	0.1522	0.9239
138	0.06336	0.1267	0.9366
139	0.05306	0.1061	0.9469
140	0.05581	0.1116	0.9442
141	0.04755	0.0951	0.9525
142	0.03409	0.06819	0.9659
143	0.03509	0.07017	0.9649
144	0.03752	0.07504	0.9625
145	0.03831	0.07662	0.9617
146	0.09035	0.1807	0.9097
147	0.1099	0.2198	0.8901
148	0.07827	0.1565	0.9217
149	0.07066	0.1413	0.9293
150	0.06219	0.1244	0.9378
151	0.07262	0.1452	0.9274
152	0.04705	0.0941	0.953
153	0.04317	0.08634	0.9568
154	0.02798	0.05596	0.972
155	0.01649	0.03298	0.9835
156	0.1787	0.3575	0.8213
157	0.7654	0.4692	0.2346
158	0.7541	0.4918	0.2459

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.07187 &  0.1437 &  0.9281 \tabularnewline
10 &  0.0241 &  0.0482 &  0.9759 \tabularnewline
11 &  0.01 &  0.02001 &  0.99 \tabularnewline
12 &  0.003191 &  0.006381 &  0.9968 \tabularnewline
13 &  0.001099 &  0.002198 &  0.9989 \tabularnewline
14 &  0.002006 &  0.004012 &  0.998 \tabularnewline
15 &  0.001554 &  0.003107 &  0.9984 \tabularnewline
16 &  0.003336 &  0.006672 &  0.9967 \tabularnewline
17 &  0.001359 &  0.002718 &  0.9986 \tabularnewline
18 &  0.02282 &  0.04564 &  0.9772 \tabularnewline
19 &  0.01573 &  0.03147 &  0.9843 \tabularnewline
20 &  0.0145 &  0.029 &  0.9855 \tabularnewline
21 &  0.01717 &  0.03433 &  0.9828 \tabularnewline
22 &  0.00978 &  0.01956 &  0.9902 \tabularnewline
23 &  0.008084 &  0.01617 &  0.9919 \tabularnewline
24 &  0.00791 &  0.01582 &  0.9921 \tabularnewline
25 &  0.03027 &  0.06054 &  0.9697 \tabularnewline
26 &  0.02236 &  0.04472 &  0.9776 \tabularnewline
27 &  0.01858 &  0.03716 &  0.9814 \tabularnewline
28 &  0.01277 &  0.02555 &  0.9872 \tabularnewline
29 &  0.007996 &  0.01599 &  0.992 \tabularnewline
30 &  0.01499 &  0.02999 &  0.985 \tabularnewline
31 &  0.02391 &  0.04782 &  0.9761 \tabularnewline
32 &  0.02675 &  0.0535 &  0.9733 \tabularnewline
33 &  0.0181 &  0.0362 &  0.9819 \tabularnewline
34 &  0.02623 &  0.05245 &  0.9738 \tabularnewline
35 &  0.01831 &  0.03663 &  0.9817 \tabularnewline
36 &  0.01348 &  0.02697 &  0.9865 \tabularnewline
37 &  0.00902 &  0.01804 &  0.991 \tabularnewline
38 &  0.007484 &  0.01497 &  0.9925 \tabularnewline
39 &  0.01786 &  0.03572 &  0.9821 \tabularnewline
40 &  0.01556 &  0.03112 &  0.9844 \tabularnewline
41 &  0.01068 &  0.02136 &  0.9893 \tabularnewline
42 &  0.01229 &  0.02458 &  0.9877 \tabularnewline
43 &  0.009611 &  0.01922 &  0.9904 \tabularnewline
44 &  0.006906 &  0.01381 &  0.9931 \tabularnewline
45 &  0.00466 &  0.00932 &  0.9953 \tabularnewline
46 &  0.003132 &  0.006265 &  0.9969 \tabularnewline
47 &  0.002557 &  0.005114 &  0.9974 \tabularnewline
48 &  0.01175 &  0.0235 &  0.9882 \tabularnewline
49 &  0.009926 &  0.01985 &  0.9901 \tabularnewline
50 &  0.006933 &  0.01387 &  0.9931 \tabularnewline
51 &  0.005542 &  0.01108 &  0.9945 \tabularnewline
52 &  0.0038 &  0.0076 &  0.9962 \tabularnewline
53 &  0.03315 &  0.06629 &  0.9669 \tabularnewline
54 &  0.02493 &  0.04985 &  0.9751 \tabularnewline
55 &  0.05469 &  0.1094 &  0.9453 \tabularnewline
56 &  0.04328 &  0.08656 &  0.9567 \tabularnewline
57 &  0.0494 &  0.09881 &  0.9506 \tabularnewline
58 &  0.04245 &  0.08489 &  0.9576 \tabularnewline
59 &  0.03523 &  0.07047 &  0.9648 \tabularnewline
60 &  0.02699 &  0.05398 &  0.973 \tabularnewline
61 &  0.02372 &  0.04745 &  0.9763 \tabularnewline
62 &  0.0179 &  0.0358 &  0.9821 \tabularnewline
63 &  0.01724 &  0.03449 &  0.9828 \tabularnewline
64 &  0.01406 &  0.02812 &  0.9859 \tabularnewline
65 &  0.01398 &  0.02796 &  0.986 \tabularnewline
66 &  0.0177 &  0.03541 &  0.9823 \tabularnewline
67 &  0.01779 &  0.03559 &  0.9822 \tabularnewline
68 &  0.01775 &  0.0355 &  0.9822 \tabularnewline
69 &  0.01582 &  0.03163 &  0.9842 \tabularnewline
70 &  0.01213 &  0.02426 &  0.9879 \tabularnewline
71 &  0.02188 &  0.04377 &  0.9781 \tabularnewline
72 &  0.04656 &  0.09312 &  0.9534 \tabularnewline
73 &  0.08276 &  0.1655 &  0.9172 \tabularnewline
74 &  0.07455 &  0.1491 &  0.9254 \tabularnewline
75 &  0.06344 &  0.1269 &  0.9366 \tabularnewline
76 &  0.05111 &  0.1022 &  0.9489 \tabularnewline
77 &  0.04579 &  0.09159 &  0.9542 \tabularnewline
78 &  0.04069 &  0.08137 &  0.9593 \tabularnewline
79 &  0.07433 &  0.1487 &  0.9257 \tabularnewline
80 &  0.06092 &  0.1218 &  0.9391 \tabularnewline
81 &  0.2282 &  0.4565 &  0.7718 \tabularnewline
82 &  0.1972 &  0.3944 &  0.8028 \tabularnewline
83 &  0.195 &  0.39 &  0.805 \tabularnewline
84 &  0.1885 &  0.3769 &  0.8115 \tabularnewline
85 &  0.4596 &  0.9192 &  0.5404 \tabularnewline
86 &  0.4242 &  0.8483 &  0.5758 \tabularnewline
87 &  0.4761 &  0.9523 &  0.5239 \tabularnewline
88 &  0.4328 &  0.8655 &  0.5672 \tabularnewline
89 &  0.488 &  0.9761 &  0.512 \tabularnewline
90 &  0.4687 &  0.9374 &  0.5313 \tabularnewline
91 &  0.4325 &  0.865 &  0.5675 \tabularnewline
92 &  0.4294 &  0.8587 &  0.5706 \tabularnewline
93 &  0.3856 &  0.7713 &  0.6144 \tabularnewline
94 &  0.3431 &  0.6861 &  0.6569 \tabularnewline
95 &  0.3025 &  0.605 &  0.6975 \tabularnewline
96 &  0.2661 &  0.5322 &  0.7339 \tabularnewline
97 &  0.3065 &  0.613 &  0.6935 \tabularnewline
98 &  0.3102 &  0.6204 &  0.6898 \tabularnewline
99 &  0.2865 &  0.573 &  0.7135 \tabularnewline
100 &  0.2687 &  0.5374 &  0.7313 \tabularnewline
101 &  0.2892 &  0.5785 &  0.7108 \tabularnewline
102 &  0.2569 &  0.5138 &  0.7431 \tabularnewline
103 &  0.4089 &  0.8179 &  0.5911 \tabularnewline
104 &  0.37 &  0.74 &  0.63 \tabularnewline
105 &  0.3277 &  0.6554 &  0.6723 \tabularnewline
106 &  0.2963 &  0.5927 &  0.7037 \tabularnewline
107 &  0.2752 &  0.5503 &  0.7248 \tabularnewline
108 &  0.2434 &  0.4867 &  0.7566 \tabularnewline
109 &  0.2103 &  0.4206 &  0.7897 \tabularnewline
110 &  0.1863 &  0.3725 &  0.8137 \tabularnewline
111 &  0.2563 &  0.5126 &  0.7437 \tabularnewline
112 &  0.2193 &  0.4386 &  0.7807 \tabularnewline
113 &  0.2382 &  0.4763 &  0.7618 \tabularnewline
114 &  0.3638 &  0.7276 &  0.6362 \tabularnewline
115 &  0.35 &  0.7001 &  0.65 \tabularnewline
116 &  0.3264 &  0.6528 &  0.6736 \tabularnewline
117 &  0.3193 &  0.6385 &  0.6807 \tabularnewline
118 &  0.2899 &  0.5798 &  0.7101 \tabularnewline
119 &  0.2883 &  0.5766 &  0.7117 \tabularnewline
120 &  0.2475 &  0.495 &  0.7525 \tabularnewline
121 &  0.2134 &  0.4267 &  0.7866 \tabularnewline
122 &  0.184 &  0.368 &  0.816 \tabularnewline
123 &  0.1634 &  0.3267 &  0.8366 \tabularnewline
124 &  0.1462 &  0.2923 &  0.8538 \tabularnewline
125 &  0.1232 &  0.2463 &  0.8768 \tabularnewline
126 &  0.1095 &  0.219 &  0.8905 \tabularnewline
127 &  0.09154 &  0.1831 &  0.9085 \tabularnewline
128 &  0.08746 &  0.1749 &  0.9125 \tabularnewline
129 &  0.06861 &  0.1372 &  0.9314 \tabularnewline
130 &  0.06347 &  0.1269 &  0.9365 \tabularnewline
131 &  0.04949 &  0.09898 &  0.9505 \tabularnewline
132 &  0.05355 &  0.1071 &  0.9464 \tabularnewline
133 &  0.05123 &  0.1025 &  0.9488 \tabularnewline
134 &  0.1052 &  0.2104 &  0.8948 \tabularnewline
135 &  0.1048 &  0.2096 &  0.8952 \tabularnewline
136 &  0.08938 &  0.1788 &  0.9106 \tabularnewline
137 &  0.07611 &  0.1522 &  0.9239 \tabularnewline
138 &  0.06336 &  0.1267 &  0.9366 \tabularnewline
139 &  0.05306 &  0.1061 &  0.9469 \tabularnewline
140 &  0.05581 &  0.1116 &  0.9442 \tabularnewline
141 &  0.04755 &  0.0951 &  0.9525 \tabularnewline
142 &  0.03409 &  0.06819 &  0.9659 \tabularnewline
143 &  0.03509 &  0.07017 &  0.9649 \tabularnewline
144 &  0.03752 &  0.07504 &  0.9625 \tabularnewline
145 &  0.03831 &  0.07662 &  0.9617 \tabularnewline
146 &  0.09035 &  0.1807 &  0.9097 \tabularnewline
147 &  0.1099 &  0.2198 &  0.8901 \tabularnewline
148 &  0.07827 &  0.1565 &  0.9217 \tabularnewline
149 &  0.07066 &  0.1413 &  0.9293 \tabularnewline
150 &  0.06219 &  0.1244 &  0.9378 \tabularnewline
151 &  0.07262 &  0.1452 &  0.9274 \tabularnewline
152 &  0.04705 &  0.0941 &  0.953 \tabularnewline
153 &  0.04317 &  0.08634 &  0.9568 \tabularnewline
154 &  0.02798 &  0.05596 &  0.972 \tabularnewline
155 &  0.01649 &  0.03298 &  0.9835 \tabularnewline
156 &  0.1787 &  0.3575 &  0.8213 \tabularnewline
157 &  0.7654 &  0.4692 &  0.2346 \tabularnewline
158 &  0.7541 &  0.4918 &  0.2459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298614&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] 0.07187[/C][C] 0.1437[/C][C] 0.9281[/C][/ROW]
[ROW][C]10[/C][C] 0.0241[/C][C] 0.0482[/C][C] 0.9759[/C][/ROW]
[ROW][C]11[/C][C] 0.01[/C][C] 0.02001[/C][C] 0.99[/C][/ROW]
[ROW][C]12[/C][C] 0.003191[/C][C] 0.006381[/C][C] 0.9968[/C][/ROW]
[ROW][C]13[/C][C] 0.001099[/C][C] 0.002198[/C][C] 0.9989[/C][/ROW]
[ROW][C]14[/C][C] 0.002006[/C][C] 0.004012[/C][C] 0.998[/C][/ROW]
[ROW][C]15[/C][C] 0.001554[/C][C] 0.003107[/C][C] 0.9984[/C][/ROW]
[ROW][C]16[/C][C] 0.003336[/C][C] 0.006672[/C][C] 0.9967[/C][/ROW]
[ROW][C]17[/C][C] 0.001359[/C][C] 0.002718[/C][C] 0.9986[/C][/ROW]
[ROW][C]18[/C][C] 0.02282[/C][C] 0.04564[/C][C] 0.9772[/C][/ROW]
[ROW][C]19[/C][C] 0.01573[/C][C] 0.03147[/C][C] 0.9843[/C][/ROW]
[ROW][C]20[/C][C] 0.0145[/C][C] 0.029[/C][C] 0.9855[/C][/ROW]
[ROW][C]21[/C][C] 0.01717[/C][C] 0.03433[/C][C] 0.9828[/C][/ROW]
[ROW][C]22[/C][C] 0.00978[/C][C] 0.01956[/C][C] 0.9902[/C][/ROW]
[ROW][C]23[/C][C] 0.008084[/C][C] 0.01617[/C][C] 0.9919[/C][/ROW]
[ROW][C]24[/C][C] 0.00791[/C][C] 0.01582[/C][C] 0.9921[/C][/ROW]
[ROW][C]25[/C][C] 0.03027[/C][C] 0.06054[/C][C] 0.9697[/C][/ROW]
[ROW][C]26[/C][C] 0.02236[/C][C] 0.04472[/C][C] 0.9776[/C][/ROW]
[ROW][C]27[/C][C] 0.01858[/C][C] 0.03716[/C][C] 0.9814[/C][/ROW]
[ROW][C]28[/C][C] 0.01277[/C][C] 0.02555[/C][C] 0.9872[/C][/ROW]
[ROW][C]29[/C][C] 0.007996[/C][C] 0.01599[/C][C] 0.992[/C][/ROW]
[ROW][C]30[/C][C] 0.01499[/C][C] 0.02999[/C][C] 0.985[/C][/ROW]
[ROW][C]31[/C][C] 0.02391[/C][C] 0.04782[/C][C] 0.9761[/C][/ROW]
[ROW][C]32[/C][C] 0.02675[/C][C] 0.0535[/C][C] 0.9733[/C][/ROW]
[ROW][C]33[/C][C] 0.0181[/C][C] 0.0362[/C][C] 0.9819[/C][/ROW]
[ROW][C]34[/C][C] 0.02623[/C][C] 0.05245[/C][C] 0.9738[/C][/ROW]
[ROW][C]35[/C][C] 0.01831[/C][C] 0.03663[/C][C] 0.9817[/C][/ROW]
[ROW][C]36[/C][C] 0.01348[/C][C] 0.02697[/C][C] 0.9865[/C][/ROW]
[ROW][C]37[/C][C] 0.00902[/C][C] 0.01804[/C][C] 0.991[/C][/ROW]
[ROW][C]38[/C][C] 0.007484[/C][C] 0.01497[/C][C] 0.9925[/C][/ROW]
[ROW][C]39[/C][C] 0.01786[/C][C] 0.03572[/C][C] 0.9821[/C][/ROW]
[ROW][C]40[/C][C] 0.01556[/C][C] 0.03112[/C][C] 0.9844[/C][/ROW]
[ROW][C]41[/C][C] 0.01068[/C][C] 0.02136[/C][C] 0.9893[/C][/ROW]
[ROW][C]42[/C][C] 0.01229[/C][C] 0.02458[/C][C] 0.9877[/C][/ROW]
[ROW][C]43[/C][C] 0.009611[/C][C] 0.01922[/C][C] 0.9904[/C][/ROW]
[ROW][C]44[/C][C] 0.006906[/C][C] 0.01381[/C][C] 0.9931[/C][/ROW]
[ROW][C]45[/C][C] 0.00466[/C][C] 0.00932[/C][C] 0.9953[/C][/ROW]
[ROW][C]46[/C][C] 0.003132[/C][C] 0.006265[/C][C] 0.9969[/C][/ROW]
[ROW][C]47[/C][C] 0.002557[/C][C] 0.005114[/C][C] 0.9974[/C][/ROW]
[ROW][C]48[/C][C] 0.01175[/C][C] 0.0235[/C][C] 0.9882[/C][/ROW]
[ROW][C]49[/C][C] 0.009926[/C][C] 0.01985[/C][C] 0.9901[/C][/ROW]
[ROW][C]50[/C][C] 0.006933[/C][C] 0.01387[/C][C] 0.9931[/C][/ROW]
[ROW][C]51[/C][C] 0.005542[/C][C] 0.01108[/C][C] 0.9945[/C][/ROW]
[ROW][C]52[/C][C] 0.0038[/C][C] 0.0076[/C][C] 0.9962[/C][/ROW]
[ROW][C]53[/C][C] 0.03315[/C][C] 0.06629[/C][C] 0.9669[/C][/ROW]
[ROW][C]54[/C][C] 0.02493[/C][C] 0.04985[/C][C] 0.9751[/C][/ROW]
[ROW][C]55[/C][C] 0.05469[/C][C] 0.1094[/C][C] 0.9453[/C][/ROW]
[ROW][C]56[/C][C] 0.04328[/C][C] 0.08656[/C][C] 0.9567[/C][/ROW]
[ROW][C]57[/C][C] 0.0494[/C][C] 0.09881[/C][C] 0.9506[/C][/ROW]
[ROW][C]58[/C][C] 0.04245[/C][C] 0.08489[/C][C] 0.9576[/C][/ROW]
[ROW][C]59[/C][C] 0.03523[/C][C] 0.07047[/C][C] 0.9648[/C][/ROW]
[ROW][C]60[/C][C] 0.02699[/C][C] 0.05398[/C][C] 0.973[/C][/ROW]
[ROW][C]61[/C][C] 0.02372[/C][C] 0.04745[/C][C] 0.9763[/C][/ROW]
[ROW][C]62[/C][C] 0.0179[/C][C] 0.0358[/C][C] 0.9821[/C][/ROW]
[ROW][C]63[/C][C] 0.01724[/C][C] 0.03449[/C][C] 0.9828[/C][/ROW]
[ROW][C]64[/C][C] 0.01406[/C][C] 0.02812[/C][C] 0.9859[/C][/ROW]
[ROW][C]65[/C][C] 0.01398[/C][C] 0.02796[/C][C] 0.986[/C][/ROW]
[ROW][C]66[/C][C] 0.0177[/C][C] 0.03541[/C][C] 0.9823[/C][/ROW]
[ROW][C]67[/C][C] 0.01779[/C][C] 0.03559[/C][C] 0.9822[/C][/ROW]
[ROW][C]68[/C][C] 0.01775[/C][C] 0.0355[/C][C] 0.9822[/C][/ROW]
[ROW][C]69[/C][C] 0.01582[/C][C] 0.03163[/C][C] 0.9842[/C][/ROW]
[ROW][C]70[/C][C] 0.01213[/C][C] 0.02426[/C][C] 0.9879[/C][/ROW]
[ROW][C]71[/C][C] 0.02188[/C][C] 0.04377[/C][C] 0.9781[/C][/ROW]
[ROW][C]72[/C][C] 0.04656[/C][C] 0.09312[/C][C] 0.9534[/C][/ROW]
[ROW][C]73[/C][C] 0.08276[/C][C] 0.1655[/C][C] 0.9172[/C][/ROW]
[ROW][C]74[/C][C] 0.07455[/C][C] 0.1491[/C][C] 0.9254[/C][/ROW]
[ROW][C]75[/C][C] 0.06344[/C][C] 0.1269[/C][C] 0.9366[/C][/ROW]
[ROW][C]76[/C][C] 0.05111[/C][C] 0.1022[/C][C] 0.9489[/C][/ROW]
[ROW][C]77[/C][C] 0.04579[/C][C] 0.09159[/C][C] 0.9542[/C][/ROW]
[ROW][C]78[/C][C] 0.04069[/C][C] 0.08137[/C][C] 0.9593[/C][/ROW]
[ROW][C]79[/C][C] 0.07433[/C][C] 0.1487[/C][C] 0.9257[/C][/ROW]
[ROW][C]80[/C][C] 0.06092[/C][C] 0.1218[/C][C] 0.9391[/C][/ROW]
[ROW][C]81[/C][C] 0.2282[/C][C] 0.4565[/C][C] 0.7718[/C][/ROW]
[ROW][C]82[/C][C] 0.1972[/C][C] 0.3944[/C][C] 0.8028[/C][/ROW]
[ROW][C]83[/C][C] 0.195[/C][C] 0.39[/C][C] 0.805[/C][/ROW]
[ROW][C]84[/C][C] 0.1885[/C][C] 0.3769[/C][C] 0.8115[/C][/ROW]
[ROW][C]85[/C][C] 0.4596[/C][C] 0.9192[/C][C] 0.5404[/C][/ROW]
[ROW][C]86[/C][C] 0.4242[/C][C] 0.8483[/C][C] 0.5758[/C][/ROW]
[ROW][C]87[/C][C] 0.4761[/C][C] 0.9523[/C][C] 0.5239[/C][/ROW]
[ROW][C]88[/C][C] 0.4328[/C][C] 0.8655[/C][C] 0.5672[/C][/ROW]
[ROW][C]89[/C][C] 0.488[/C][C] 0.9761[/C][C] 0.512[/C][/ROW]
[ROW][C]90[/C][C] 0.4687[/C][C] 0.9374[/C][C] 0.5313[/C][/ROW]
[ROW][C]91[/C][C] 0.4325[/C][C] 0.865[/C][C] 0.5675[/C][/ROW]
[ROW][C]92[/C][C] 0.4294[/C][C] 0.8587[/C][C] 0.5706[/C][/ROW]
[ROW][C]93[/C][C] 0.3856[/C][C] 0.7713[/C][C] 0.6144[/C][/ROW]
[ROW][C]94[/C][C] 0.3431[/C][C] 0.6861[/C][C] 0.6569[/C][/ROW]
[ROW][C]95[/C][C] 0.3025[/C][C] 0.605[/C][C] 0.6975[/C][/ROW]
[ROW][C]96[/C][C] 0.2661[/C][C] 0.5322[/C][C] 0.7339[/C][/ROW]
[ROW][C]97[/C][C] 0.3065[/C][C] 0.613[/C][C] 0.6935[/C][/ROW]
[ROW][C]98[/C][C] 0.3102[/C][C] 0.6204[/C][C] 0.6898[/C][/ROW]
[ROW][C]99[/C][C] 0.2865[/C][C] 0.573[/C][C] 0.7135[/C][/ROW]
[ROW][C]100[/C][C] 0.2687[/C][C] 0.5374[/C][C] 0.7313[/C][/ROW]
[ROW][C]101[/C][C] 0.2892[/C][C] 0.5785[/C][C] 0.7108[/C][/ROW]
[ROW][C]102[/C][C] 0.2569[/C][C] 0.5138[/C][C] 0.7431[/C][/ROW]
[ROW][C]103[/C][C] 0.4089[/C][C] 0.8179[/C][C] 0.5911[/C][/ROW]
[ROW][C]104[/C][C] 0.37[/C][C] 0.74[/C][C] 0.63[/C][/ROW]
[ROW][C]105[/C][C] 0.3277[/C][C] 0.6554[/C][C] 0.6723[/C][/ROW]
[ROW][C]106[/C][C] 0.2963[/C][C] 0.5927[/C][C] 0.7037[/C][/ROW]
[ROW][C]107[/C][C] 0.2752[/C][C] 0.5503[/C][C] 0.7248[/C][/ROW]
[ROW][C]108[/C][C] 0.2434[/C][C] 0.4867[/C][C] 0.7566[/C][/ROW]
[ROW][C]109[/C][C] 0.2103[/C][C] 0.4206[/C][C] 0.7897[/C][/ROW]
[ROW][C]110[/C][C] 0.1863[/C][C] 0.3725[/C][C] 0.8137[/C][/ROW]
[ROW][C]111[/C][C] 0.2563[/C][C] 0.5126[/C][C] 0.7437[/C][/ROW]
[ROW][C]112[/C][C] 0.2193[/C][C] 0.4386[/C][C] 0.7807[/C][/ROW]
[ROW][C]113[/C][C] 0.2382[/C][C] 0.4763[/C][C] 0.7618[/C][/ROW]
[ROW][C]114[/C][C] 0.3638[/C][C] 0.7276[/C][C] 0.6362[/C][/ROW]
[ROW][C]115[/C][C] 0.35[/C][C] 0.7001[/C][C] 0.65[/C][/ROW]
[ROW][C]116[/C][C] 0.3264[/C][C] 0.6528[/C][C] 0.6736[/C][/ROW]
[ROW][C]117[/C][C] 0.3193[/C][C] 0.6385[/C][C] 0.6807[/C][/ROW]
[ROW][C]118[/C][C] 0.2899[/C][C] 0.5798[/C][C] 0.7101[/C][/ROW]
[ROW][C]119[/C][C] 0.2883[/C][C] 0.5766[/C][C] 0.7117[/C][/ROW]
[ROW][C]120[/C][C] 0.2475[/C][C] 0.495[/C][C] 0.7525[/C][/ROW]
[ROW][C]121[/C][C] 0.2134[/C][C] 0.4267[/C][C] 0.7866[/C][/ROW]
[ROW][C]122[/C][C] 0.184[/C][C] 0.368[/C][C] 0.816[/C][/ROW]
[ROW][C]123[/C][C] 0.1634[/C][C] 0.3267[/C][C] 0.8366[/C][/ROW]
[ROW][C]124[/C][C] 0.1462[/C][C] 0.2923[/C][C] 0.8538[/C][/ROW]
[ROW][C]125[/C][C] 0.1232[/C][C] 0.2463[/C][C] 0.8768[/C][/ROW]
[ROW][C]126[/C][C] 0.1095[/C][C] 0.219[/C][C] 0.8905[/C][/ROW]
[ROW][C]127[/C][C] 0.09154[/C][C] 0.1831[/C][C] 0.9085[/C][/ROW]
[ROW][C]128[/C][C] 0.08746[/C][C] 0.1749[/C][C] 0.9125[/C][/ROW]
[ROW][C]129[/C][C] 0.06861[/C][C] 0.1372[/C][C] 0.9314[/C][/ROW]
[ROW][C]130[/C][C] 0.06347[/C][C] 0.1269[/C][C] 0.9365[/C][/ROW]
[ROW][C]131[/C][C] 0.04949[/C][C] 0.09898[/C][C] 0.9505[/C][/ROW]
[ROW][C]132[/C][C] 0.05355[/C][C] 0.1071[/C][C] 0.9464[/C][/ROW]
[ROW][C]133[/C][C] 0.05123[/C][C] 0.1025[/C][C] 0.9488[/C][/ROW]
[ROW][C]134[/C][C] 0.1052[/C][C] 0.2104[/C][C] 0.8948[/C][/ROW]
[ROW][C]135[/C][C] 0.1048[/C][C] 0.2096[/C][C] 0.8952[/C][/ROW]
[ROW][C]136[/C][C] 0.08938[/C][C] 0.1788[/C][C] 0.9106[/C][/ROW]
[ROW][C]137[/C][C] 0.07611[/C][C] 0.1522[/C][C] 0.9239[/C][/ROW]
[ROW][C]138[/C][C] 0.06336[/C][C] 0.1267[/C][C] 0.9366[/C][/ROW]
[ROW][C]139[/C][C] 0.05306[/C][C] 0.1061[/C][C] 0.9469[/C][/ROW]
[ROW][C]140[/C][C] 0.05581[/C][C] 0.1116[/C][C] 0.9442[/C][/ROW]
[ROW][C]141[/C][C] 0.04755[/C][C] 0.0951[/C][C] 0.9525[/C][/ROW]
[ROW][C]142[/C][C] 0.03409[/C][C] 0.06819[/C][C] 0.9659[/C][/ROW]
[ROW][C]143[/C][C] 0.03509[/C][C] 0.07017[/C][C] 0.9649[/C][/ROW]
[ROW][C]144[/C][C] 0.03752[/C][C] 0.07504[/C][C] 0.9625[/C][/ROW]
[ROW][C]145[/C][C] 0.03831[/C][C] 0.07662[/C][C] 0.9617[/C][/ROW]
[ROW][C]146[/C][C] 0.09035[/C][C] 0.1807[/C][C] 0.9097[/C][/ROW]
[ROW][C]147[/C][C] 0.1099[/C][C] 0.2198[/C][C] 0.8901[/C][/ROW]
[ROW][C]148[/C][C] 0.07827[/C][C] 0.1565[/C][C] 0.9217[/C][/ROW]
[ROW][C]149[/C][C] 0.07066[/C][C] 0.1413[/C][C] 0.9293[/C][/ROW]
[ROW][C]150[/C][C] 0.06219[/C][C] 0.1244[/C][C] 0.9378[/C][/ROW]
[ROW][C]151[/C][C] 0.07262[/C][C] 0.1452[/C][C] 0.9274[/C][/ROW]
[ROW][C]152[/C][C] 0.04705[/C][C] 0.0941[/C][C] 0.953[/C][/ROW]
[ROW][C]153[/C][C] 0.04317[/C][C] 0.08634[/C][C] 0.9568[/C][/ROW]
[ROW][C]154[/C][C] 0.02798[/C][C] 0.05596[/C][C] 0.972[/C][/ROW]
[ROW][C]155[/C][C] 0.01649[/C][C] 0.03298[/C][C] 0.9835[/C][/ROW]
[ROW][C]156[/C][C] 0.1787[/C][C] 0.3575[/C][C] 0.8213[/C][/ROW]
[ROW][C]157[/C][C] 0.7654[/C][C] 0.4692[/C][C] 0.2346[/C][/ROW]
[ROW][C]158[/C][C] 0.7541[/C][C] 0.4918[/C][C] 0.2459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298614&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&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	0.07187	0.1437	0.9281
10	0.0241	0.0482	0.9759
11	0.01	0.02001	0.99
12	0.003191	0.006381	0.9968
13	0.001099	0.002198	0.9989
14	0.002006	0.004012	0.998
15	0.001554	0.003107	0.9984
16	0.003336	0.006672	0.9967
17	0.001359	0.002718	0.9986
18	0.02282	0.04564	0.9772
19	0.01573	0.03147	0.9843
20	0.0145	0.029	0.9855
21	0.01717	0.03433	0.9828
22	0.00978	0.01956	0.9902
23	0.008084	0.01617	0.9919
24	0.00791	0.01582	0.9921
25	0.03027	0.06054	0.9697
26	0.02236	0.04472	0.9776
27	0.01858	0.03716	0.9814
28	0.01277	0.02555	0.9872
29	0.007996	0.01599	0.992
30	0.01499	0.02999	0.985
31	0.02391	0.04782	0.9761
32	0.02675	0.0535	0.9733
33	0.0181	0.0362	0.9819
34	0.02623	0.05245	0.9738
35	0.01831	0.03663	0.9817
36	0.01348	0.02697	0.9865
37	0.00902	0.01804	0.991
38	0.007484	0.01497	0.9925
39	0.01786	0.03572	0.9821
40	0.01556	0.03112	0.9844
41	0.01068	0.02136	0.9893
42	0.01229	0.02458	0.9877
43	0.009611	0.01922	0.9904
44	0.006906	0.01381	0.9931
45	0.00466	0.00932	0.9953
46	0.003132	0.006265	0.9969
47	0.002557	0.005114	0.9974
48	0.01175	0.0235	0.9882
49	0.009926	0.01985	0.9901
50	0.006933	0.01387	0.9931
51	0.005542	0.01108	0.9945
52	0.0038	0.0076	0.9962
53	0.03315	0.06629	0.9669
54	0.02493	0.04985	0.9751
55	0.05469	0.1094	0.9453
56	0.04328	0.08656	0.9567
57	0.0494	0.09881	0.9506
58	0.04245	0.08489	0.9576
59	0.03523	0.07047	0.9648
60	0.02699	0.05398	0.973
61	0.02372	0.04745	0.9763
62	0.0179	0.0358	0.9821
63	0.01724	0.03449	0.9828
64	0.01406	0.02812	0.9859
65	0.01398	0.02796	0.986
66	0.0177	0.03541	0.9823
67	0.01779	0.03559	0.9822
68	0.01775	0.0355	0.9822
69	0.01582	0.03163	0.9842
70	0.01213	0.02426	0.9879
71	0.02188	0.04377	0.9781
72	0.04656	0.09312	0.9534
73	0.08276	0.1655	0.9172
74	0.07455	0.1491	0.9254
75	0.06344	0.1269	0.9366
76	0.05111	0.1022	0.9489
77	0.04579	0.09159	0.9542
78	0.04069	0.08137	0.9593
79	0.07433	0.1487	0.9257
80	0.06092	0.1218	0.9391
81	0.2282	0.4565	0.7718
82	0.1972	0.3944	0.8028
83	0.195	0.39	0.805
84	0.1885	0.3769	0.8115
85	0.4596	0.9192	0.5404
86	0.4242	0.8483	0.5758
87	0.4761	0.9523	0.5239
88	0.4328	0.8655	0.5672
89	0.488	0.9761	0.512
90	0.4687	0.9374	0.5313
91	0.4325	0.865	0.5675
92	0.4294	0.8587	0.5706
93	0.3856	0.7713	0.6144
94	0.3431	0.6861	0.6569
95	0.3025	0.605	0.6975
96	0.2661	0.5322	0.7339
97	0.3065	0.613	0.6935
98	0.3102	0.6204	0.6898
99	0.2865	0.573	0.7135
100	0.2687	0.5374	0.7313
101	0.2892	0.5785	0.7108
102	0.2569	0.5138	0.7431
103	0.4089	0.8179	0.5911
104	0.37	0.74	0.63
105	0.3277	0.6554	0.6723
106	0.2963	0.5927	0.7037
107	0.2752	0.5503	0.7248
108	0.2434	0.4867	0.7566
109	0.2103	0.4206	0.7897
110	0.1863	0.3725	0.8137
111	0.2563	0.5126	0.7437
112	0.2193	0.4386	0.7807
113	0.2382	0.4763	0.7618
114	0.3638	0.7276	0.6362
115	0.35	0.7001	0.65
116	0.3264	0.6528	0.6736
117	0.3193	0.6385	0.6807
118	0.2899	0.5798	0.7101
119	0.2883	0.5766	0.7117
120	0.2475	0.495	0.7525
121	0.2134	0.4267	0.7866
122	0.184	0.368	0.816
123	0.1634	0.3267	0.8366
124	0.1462	0.2923	0.8538
125	0.1232	0.2463	0.8768
126	0.1095	0.219	0.8905
127	0.09154	0.1831	0.9085
128	0.08746	0.1749	0.9125
129	0.06861	0.1372	0.9314
130	0.06347	0.1269	0.9365
131	0.04949	0.09898	0.9505
132	0.05355	0.1071	0.9464
133	0.05123	0.1025	0.9488
134	0.1052	0.2104	0.8948
135	0.1048	0.2096	0.8952
136	0.08938	0.1788	0.9106
137	0.07611	0.1522	0.9239
138	0.06336	0.1267	0.9366
139	0.05306	0.1061	0.9469
140	0.05581	0.1116	0.9442
141	0.04755	0.0951	0.9525
142	0.03409	0.06819	0.9659
143	0.03509	0.07017	0.9649
144	0.03752	0.07504	0.9625
145	0.03831	0.07662	0.9617
146	0.09035	0.1807	0.9097
147	0.1099	0.2198	0.8901
148	0.07827	0.1565	0.9217
149	0.07066	0.1413	0.9293
150	0.06219	0.1244	0.9378
151	0.07262	0.1452	0.9274
152	0.04705	0.0941	0.953
153	0.04317	0.08634	0.9568
154	0.02798	0.05596	0.972
155	0.01649	0.03298	0.9835
156	0.1787	0.3575	0.8213
157	0.7654	0.4692	0.2346
158	0.7541	0.4918	0.2459

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	10	0.06667	NOK
5% type I error level	53	0.353333	NOK
10% type I error level	74	0.493333	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 & 10 &  0.06667 & NOK \tabularnewline
5% type I error level & 53 & 0.353333 & NOK \tabularnewline
10% type I error level & 74 & 0.493333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298614&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]10[/C][C] 0.06667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]53[/C][C]0.353333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]74[/C][C]0.493333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298614&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&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	10	0.06667	NOK
5% type I error level	53	0.353333	NOK
10% type I error level	74	0.493333	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.0564, df1 = 2, df2 = 159, p-value = 0.1313
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.469, df1 = 10, df2 = 151, p-value = 0.1561
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.7195, df1 = 2, df2 = 159, p-value = 0.1825

\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 = 2.0564, df1 = 2, df2 = 159, p-value = 0.1313
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.469, df1 = 10, df2 = 151, p-value = 0.1561
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.7195, df1 = 2, df2 = 159, p-value = 0.1825
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298614&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 = 2.0564, df1 = 2, df2 = 159, p-value = 0.1313
[/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 = 1.469, df1 = 10, df2 = 151, p-value = 0.1561
[/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 = 1.7195, df1 = 2, df2 = 159, p-value = 0.1825
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298614&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&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 = 2.0564, df1 = 2, df2 = 159, p-value = 0.1313
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.469, df1 = 10, df2 = 151, p-value = 0.1561
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.7195, df1 = 2, df2 = 159, p-value = 0.1825

Variance Inflation Factors (Multicollinearity)
> vif V1 V2 V3 V4 V5 1.101238 1.131667 1.024346 1.049123 1.018325

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      V1       V2       V3       V4       V5 
1.101238 1.131667 1.024346 1.049123 1.018325 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298614&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      V1       V2       V3       V4       V5 
1.101238 1.131667 1.024346 1.049123 1.018325 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298614&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298614&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif V1 V2 V3 V4 V5 1.101238 1.131667 1.024346 1.049123 1.018325

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 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 6 ; 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