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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 22 Dec 2016 18:56:41 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/22/t1482429638z0fbixeto7b0kl0.htm/, Retrieved Sun, 28 Apr 2024 23:20:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302600, Retrieved Sun, 28 Apr 2024 23:20:21 +0000

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-       [Multiple Regression] [] [2016-12-22 17:56:41] [361c8dad91b3f1ef2e651cd04783c23b] [Current]

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13	4	2	4	3	5	4
16	5	3	3	4	5	4
17	4	4	5	4	5	4
15	3	4	3	3	4	4
16	4	4	5	4	5	4
16	3	4	4	4	5	5
18	3	4	4	3	3	4
16	3	4	5	4	4	4
17	4	5	4	4	5	5
17	4	5	5	4	5	5
17	4	4	2	4	5	4
15	4	4	5	3	5	4
16	4	4	4	3	4	5
14	3	3	5	4	4	5
16	4	4	5	4	2	5
17	3	4	5	4	4	5
16	3	4	5	4	4	5
15	5	5	4	3	4	4
17	4	4	4	4	5	4
16	3	4	5	3	4	5
15	4	4	4	4	5	5
16	4	4	5	4	4	5
15	4	4	5	4	4	4
17	4	4	5	4	4	5
14	3	4	4	4	4	4
16	3	4	4	3	5	5
15	4	4	4	4	4	4
16	2	4	5	4	5	5
16	5	4	4	4	4	4
13	4	3	5	4	4	4
15	4	5	5	4	5	5
17	5	4	5	4	4	5
15	4	3	5	4	5	5
13	2	3	5	4	5	4
17	4	5	2	4	4	4
15	3	4	5	4	4	4
14	4	3	5	3	4	5
14	4	3	3	4	4	4
18	4	4	5	4	4	4
15	5	4	4	4	4	4
17	4	5	5	4	5	5
13	3	3	4	4	4	4
16	5	5	5	3	5	5
15	5	4	5	3	4	4
15	4	4	4	3	4	5
16	4	4	4	4	4	4
15	3	5	5	3	3	4
13	4	4	4	4	5	4
12	2	3	4	2	4	4
17	4	5	5	4	4	4
18	5	5	2	4	5	4
18	5	5	5	4	4	4
11	4	3	5	4	5	5
14	4	3	4	3	4	5
13	4	4	5	4	4	4
15	3	4	4	3	3	4
17	3	4	4	4	4	3
16	4	4	4	3	5	4
15	4	4	4	4	5	4
17	5	5	3	4	5	5
16	2	4	4	4	5	5
16	4	4	4	4	5	5
16	3	4	4	4	2	4
15	4	4	5	4	5	5
12	4	2	4	4	4	4
17	4	4	4	3	5	3
14	4	4	4	3	5	4
14	5	4	5	3	3	5
16	3	4	4	3	5	5
15	3	4	4	3	4	5
15	4	5	5	5	5	4
14	4	4	3	4	4	4
13	4	4	4	4	4	4
18	4	4	4	5	5	4
15	3	4	3	4	4	4
16	4	4	4	4	5	4
14	3	4	5	3	5	5
15	3	3	5	4	4	5
17	4	3	5	4	4	4
16	4	4	5	4	4	5
10	3	3	3	4	4	4
16	4	4	4	4	5	4
17	4	4	3	4	5	5
17	4	4	4	4	5	5
20	5	4	4	4	4	4
17	5	4	3	5	4	5
18	4	4	5	4	5	5
15	3	4	5	4	4	5
17	3	4	4	4	4	4
14	4	2	3	3	4	4
15	4	4	5	4	4	3
17	4	4	5	4	4	5
16	4	4	4	4	5	4
17	4	5	4	4	5	3
15	3	4	4	3	5	5
16	4	4	5	4	4	5
18	5	4	3	4	4	5
18	5	4	5	5	4	5
16	4	5	4	4	5	5
16	3	4	5	4	4	5
17	5	3	4	4	5	5
15	4	4	5	4	4	5
13	5	4	4	4	4	5
15	3	4	4	3	4	4
17	5	4	4	5	5	5
16	4	4	5	3	5	5
16	4	4	3	3	4	3
15	4	4	5	4	4	4
16	4	4	5	4	4	4
16	3	4	5	4	5	3
14	4	4	4	4	4	4
15	4	4	4	3	4	5
12	3	3	4	3	5	5
18	4	4	4	3	4	4
16	3	4	5	4	4	4
16	4	4	5	4	3	4
17	5	4	5	1	5	5
16	5	4	5	4	5	5
14	4	4	4	4	4	3
15	4	4	5	3	4	4
14	3	4	4	3	4	5
16	4	4	4	4	4	4
15	4	4	4	4	5	4
17	4	5	3	4	4	4
15	3	4	4	4	4	4
16	4	4	4	3	4	4
16	4	4	4	4	4	5
15	3	4	3	3	4	4
15	4	4	4	3	4	3
11	3	2	4	2	4	4
16	4	4	4	3	5	4
18	5	4	4	3	5	4
13	2	4	4	3	3	5
11	3	3	4	4	4	4
16	4	4	4	3	4	4
18	5	5	4	4	5	4
15	4	5	5	4	4	4
19	5	5	5	5	5	4
17	4	5	5	4	5	5
13	4	4	4	3	4	5
14	3	4	5	4	5	4
16	4	4	5	4	4	4
13	4	4	2	4	4	4
17	4	4	3	4	5	5
14	4	4	4	4	5	5
19	5	4	5	3	5	4
14	4	3	5	4	4	4
16	4	4	5	4	4	4
12	3	3	2	3	4	4
16	4	5	5	4	4	3
16	4	4	4	3	4	4
15	4	4	4	4	4	5
12	3	4	5	3	5	5
15	4	4	5	4	4	5
17	5	4	5	4	5	4
14	4	4	5	4	3	4
15	2	3	5	4	4	4
18	4	4	4	4	4	5
15	4	3	4	3	5	5
18	4	4	4	4	4	3
15	4	5	5	5	4	4
15	5	4	3	4	4	4
16	5	4	4	3	4	4
13	3	3	1	4	5	5
16	4	4	4	4	4	5
14	4	4	4	4	5	4
16	2	3	4	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=302600&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=302600&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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
TVC[t] = + 6.31271 + 0.564878SK1[t] + 1.16208SK2[t] + 0.0964604SK3[t] + 0.311706SK4[t] + 0.203867SK5[t] -0.0115225SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVC[t] =  +  6.31271 +  0.564878SK1[t] +  1.16208SK2[t] +  0.0964604SK3[t] +  0.311706SK4[t] +  0.203867SK5[t] -0.0115225SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302600&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVC[t] =  +  6.31271 +  0.564878SK1[t] +  1.16208SK2[t] +  0.0964604SK3[t] +  0.311706SK4[t] +  0.203867SK5[t] -0.0115225SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302600&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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
TVC[t] = + 6.31271 + 0.564878SK1[t] + 1.16208SK2[t] + 0.0964604SK3[t] + 0.311706SK4[t] + 0.203867SK5[t] -0.0115225SK6[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)	+6.313	1.45	+4.3540e+00	2.375e-05	1.187e-05
SK1	+0.5649	0.155	+3.6430e+00	0.000363	0.0001815
SK2	+1.162	0.1913	+6.0740e+00	8.78e-09	4.39e-09
SK3	+0.09646	0.1399	+6.8970e-01	0.4914	0.2457
SK4	+0.3117	0.1869	+1.6680e+00	0.0973	0.04865
SK5	+0.2039	0.181	+1.1260e+00	0.2618	0.1309
SK6	-0.01152	0.1877	-6.1390e-02	0.9511	0.4756

\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) & +6.313 &  1.45 & +4.3540e+00 &  2.375e-05 &  1.187e-05 \tabularnewline
SK1 & +0.5649 &  0.155 & +3.6430e+00 &  0.000363 &  0.0001815 \tabularnewline
SK2 & +1.162 &  0.1913 & +6.0740e+00 &  8.78e-09 &  4.39e-09 \tabularnewline
SK3 & +0.09646 &  0.1399 & +6.8970e-01 &  0.4914 &  0.2457 \tabularnewline
SK4 & +0.3117 &  0.1869 & +1.6680e+00 &  0.0973 &  0.04865 \tabularnewline
SK5 & +0.2039 &  0.181 & +1.1260e+00 &  0.2618 &  0.1309 \tabularnewline
SK6 & -0.01152 &  0.1877 & -6.1390e-02 &  0.9511 &  0.4756 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302600&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]+6.313[/C][C] 1.45[/C][C]+4.3540e+00[/C][C] 2.375e-05[/C][C] 1.187e-05[/C][/ROW]
[ROW][C]SK1[/C][C]+0.5649[/C][C] 0.155[/C][C]+3.6430e+00[/C][C] 0.000363[/C][C] 0.0001815[/C][/ROW]
[ROW][C]SK2[/C][C]+1.162[/C][C] 0.1913[/C][C]+6.0740e+00[/C][C] 8.78e-09[/C][C] 4.39e-09[/C][/ROW]
[ROW][C]SK3[/C][C]+0.09646[/C][C] 0.1399[/C][C]+6.8970e-01[/C][C] 0.4914[/C][C] 0.2457[/C][/ROW]
[ROW][C]SK4[/C][C]+0.3117[/C][C] 0.1869[/C][C]+1.6680e+00[/C][C] 0.0973[/C][C] 0.04865[/C][/ROW]
[ROW][C]SK5[/C][C]+0.2039[/C][C] 0.181[/C][C]+1.1260e+00[/C][C] 0.2618[/C][C] 0.1309[/C][/ROW]
[ROW][C]SK6[/C][C]-0.01152[/C][C] 0.1877[/C][C]-6.1390e-02[/C][C] 0.9511[/C][C] 0.4756[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302600&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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)	+6.313	1.45	+4.3540e+00	2.375e-05	1.187e-05
SK1	+0.5649	0.155	+3.6430e+00	0.000363	0.0001815
SK2	+1.162	0.1913	+6.0740e+00	8.78e-09	4.39e-09
SK3	+0.09646	0.1399	+6.8970e-01	0.4914	0.2457
SK4	+0.3117	0.1869	+1.6680e+00	0.0973	0.04865
SK5	+0.2039	0.181	+1.1260e+00	0.2618	0.1309
SK6	-0.01152	0.1877	-6.1390e-02	0.9511	0.4756

Multiple Linear Regression - Regression Statistics
Multiple R	0.5875
R-squared	0.3451
Adjusted R-squared	0.3206
F-TEST (value)	14.05
F-TEST (DF numerator)	6
F-TEST (DF denominator)	160
p-value	8.133e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.398
Sum Squared Residuals	312.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5875 \tabularnewline
R-squared &  0.3451 \tabularnewline
Adjusted R-squared &  0.3206 \tabularnewline
F-TEST (value) &  14.05 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value &  8.133e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.398 \tabularnewline
Sum Squared Residuals &  312.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302600&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5875[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3451[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3206[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 14.05[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C] 8.133e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.398[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 312.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302600&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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	0.5875
R-squared	0.3451
Adjusted R-squared	0.3206
F-TEST (value)	14.05
F-TEST (DF numerator)	6
F-TEST (DF denominator)	160
p-value	8.133e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.398
Sum Squared Residuals	312.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.19	-0.1906
2	16	15.13	0.8672
3	17	15.92	1.077
4	15	14.65	0.3504
5	16	15.92	0.07708
6	16	15.25	0.7499
7	18	14.54	3.458
8	16	15.15	0.8458
9	17	16.98	0.02298
10	17	17.07	-0.07348
11	17	15.63	1.366
12	15	15.61	-0.6112
13	16	15.3	0.7006
14	14	13.98	0.01943
15	16	15.3	0.7002
16	17	15.14	1.857
17	16	15.14	0.8573
18	15	17.04	-2.038
19	17	15.83	1.174
20	16	14.83	1.169
21	15	15.81	-0.8149
22	16	15.71	0.2925
23	15	15.72	-0.7191
24	17	15.71	1.292
25	14	15.06	-1.058
26	16	14.94	1.062
27	15	15.62	-0.6226
28	16	14.78	1.218
29	16	16.19	-0.1875
30	13	14.56	-1.557
31	15	17.07	-2.073
32	17	16.27	0.7276
33	15	14.75	0.2507
34	13	13.63	-0.6311
35	17	16.59	0.4082
36	15	15.15	-0.1542
37	14	14.23	-0.2337
38	14	14.36	-0.3641
39	18	15.72	2.281
40	15	16.19	-1.187
41	17	17.07	-0.07348
42	13	13.9	-0.8956
43	16	17.33	-1.327
44	15	15.97	-0.9722
45	15	15.3	-0.2994
46	16	15.62	0.3774
47	15	15.8	-0.8007
48	13	15.83	-2.826
49	12	12.71	-0.7073
50	17	16.88	0.1189
51	18	17.36	0.6395
52	18	17.45	0.554
53	11	14.75	-3.749
54	14	14.14	-0.1373
55	13	15.72	-2.719
56	15	14.54	0.4579
57	17	15.07	1.931
58	16	15.51	0.4852
59	15	15.83	-0.8265
60	17	17.45	-0.4454
61	16	14.69	1.315
62	16	15.81	0.1851
63	16	14.65	1.35
64	15	15.91	-0.9114
65	12	13.3	-1.298
66	17	15.53	1.474
67	14	15.51	-1.515
68	14	15.76	-1.757
69	16	14.94	1.062
70	15	14.73	0.2655
71	15	17.4	-2.397
72	14	15.53	-1.526
73	13	15.62	-2.623
74	18	16.14	1.862
75	15	14.96	0.03874
76	16	15.83	0.1735
77	14	15.03	-1.035
78	15	13.98	1.019
79	17	14.56	2.443
80	16	15.71	0.2925
81	10	13.8	-3.799
82	16	15.83	0.1735
83	17	15.72	1.282
84	17	15.81	1.185
85	20	16.19	3.813
86	17	16.39	0.6088
87	18	15.91	2.089
88	15	15.14	-0.1427
89	17	15.06	1.942
90	14	12.89	1.11
91	15	15.73	-0.7306
92	17	15.71	1.292
93	16	15.83	0.1735
94	17	17	-6.667e-05
95	15	14.94	0.06164
96	16	15.71	0.2925
97	18	16.08	1.921
98	18	16.58	1.416
99	16	16.98	-0.977
100	16	15.14	0.8573
101	17	15.22	1.782
102	15	15.71	-0.7075
103	13	16.18	-3.176
104	15	14.75	0.254
105	17	16.69	0.3085
106	16	15.6	0.4003
107	16	15.23	0.774
108	15	15.72	-0.7191
109	16	15.72	0.2809
110	16	15.37	0.6304
111	14	15.62	-1.623
112	15	15.3	-0.2994
113	12	13.78	-1.776
114	18	15.31	2.689
115	16	15.15	0.8458
116	16	15.52	0.4848
117	17	15.54	1.459
118	16	16.48	-0.4763
119	14	15.63	-1.634
120	15	15.41	-0.4073
121	14	14.73	-0.7345
122	16	15.62	0.3774
123	15	15.83	-0.8265
124	17	16.69	0.3118
125	15	15.06	-0.05772
126	16	15.31	0.6891
127	16	15.61	0.3889
128	15	14.65	0.3504
129	15	15.32	-0.3224
130	11	12.11	-1.11
131	16	15.51	0.4852
132	18	16.08	1.92
133	13	13.97	-0.9657
134	11	13.9	-2.896
135	16	15.31	0.6891
136	18	17.55	0.4466
137	15	16.88	-1.881
138	19	17.96	1.038
139	17	17.07	-0.07348
140	13	15.3	-2.299
141	14	15.36	-1.358
142	16	15.72	0.2809
143	13	15.43	-2.43
144	17	15.72	1.282
145	14	15.81	-1.815
146	19	16.18	2.824
147	14	14.56	-0.557
148	16	15.72	0.2809
149	12	13.39	-1.391
150	16	16.89	-0.8927
151	16	15.31	0.6891
152	15	15.61	-0.6111
153	12	15.03	-3.035
154	15	15.71	-0.7075
155	17	16.49	0.5122
156	14	15.52	-1.515
157	15	13.43	1.573
158	18	15.61	2.389
159	15	14.34	0.6588
160	18	15.63	2.366
161	15	17.19	-2.193
162	15	16.09	-1.091
163	16	15.88	0.1242
164	13	13.8	-0.7986
165	16	15.61	0.3889
166	14	15.83	-1.826
167	16	13.85	2.154

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.19 & -0.1906 \tabularnewline
2 &  16 &  15.13 &  0.8672 \tabularnewline
3 &  17 &  15.92 &  1.077 \tabularnewline
4 &  15 &  14.65 &  0.3504 \tabularnewline
5 &  16 &  15.92 &  0.07708 \tabularnewline
6 &  16 &  15.25 &  0.7499 \tabularnewline
7 &  18 &  14.54 &  3.458 \tabularnewline
8 &  16 &  15.15 &  0.8458 \tabularnewline
9 &  17 &  16.98 &  0.02298 \tabularnewline
10 &  17 &  17.07 & -0.07348 \tabularnewline
11 &  17 &  15.63 &  1.366 \tabularnewline
12 &  15 &  15.61 & -0.6112 \tabularnewline
13 &  16 &  15.3 &  0.7006 \tabularnewline
14 &  14 &  13.98 &  0.01943 \tabularnewline
15 &  16 &  15.3 &  0.7002 \tabularnewline
16 &  17 &  15.14 &  1.857 \tabularnewline
17 &  16 &  15.14 &  0.8573 \tabularnewline
18 &  15 &  17.04 & -2.038 \tabularnewline
19 &  17 &  15.83 &  1.174 \tabularnewline
20 &  16 &  14.83 &  1.169 \tabularnewline
21 &  15 &  15.81 & -0.8149 \tabularnewline
22 &  16 &  15.71 &  0.2925 \tabularnewline
23 &  15 &  15.72 & -0.7191 \tabularnewline
24 &  17 &  15.71 &  1.292 \tabularnewline
25 &  14 &  15.06 & -1.058 \tabularnewline
26 &  16 &  14.94 &  1.062 \tabularnewline
27 &  15 &  15.62 & -0.6226 \tabularnewline
28 &  16 &  14.78 &  1.218 \tabularnewline
29 &  16 &  16.19 & -0.1875 \tabularnewline
30 &  13 &  14.56 & -1.557 \tabularnewline
31 &  15 &  17.07 & -2.073 \tabularnewline
32 &  17 &  16.27 &  0.7276 \tabularnewline
33 &  15 &  14.75 &  0.2507 \tabularnewline
34 &  13 &  13.63 & -0.6311 \tabularnewline
35 &  17 &  16.59 &  0.4082 \tabularnewline
36 &  15 &  15.15 & -0.1542 \tabularnewline
37 &  14 &  14.23 & -0.2337 \tabularnewline
38 &  14 &  14.36 & -0.3641 \tabularnewline
39 &  18 &  15.72 &  2.281 \tabularnewline
40 &  15 &  16.19 & -1.187 \tabularnewline
41 &  17 &  17.07 & -0.07348 \tabularnewline
42 &  13 &  13.9 & -0.8956 \tabularnewline
43 &  16 &  17.33 & -1.327 \tabularnewline
44 &  15 &  15.97 & -0.9722 \tabularnewline
45 &  15 &  15.3 & -0.2994 \tabularnewline
46 &  16 &  15.62 &  0.3774 \tabularnewline
47 &  15 &  15.8 & -0.8007 \tabularnewline
48 &  13 &  15.83 & -2.826 \tabularnewline
49 &  12 &  12.71 & -0.7073 \tabularnewline
50 &  17 &  16.88 &  0.1189 \tabularnewline
51 &  18 &  17.36 &  0.6395 \tabularnewline
52 &  18 &  17.45 &  0.554 \tabularnewline
53 &  11 &  14.75 & -3.749 \tabularnewline
54 &  14 &  14.14 & -0.1373 \tabularnewline
55 &  13 &  15.72 & -2.719 \tabularnewline
56 &  15 &  14.54 &  0.4579 \tabularnewline
57 &  17 &  15.07 &  1.931 \tabularnewline
58 &  16 &  15.51 &  0.4852 \tabularnewline
59 &  15 &  15.83 & -0.8265 \tabularnewline
60 &  17 &  17.45 & -0.4454 \tabularnewline
61 &  16 &  14.69 &  1.315 \tabularnewline
62 &  16 &  15.81 &  0.1851 \tabularnewline
63 &  16 &  14.65 &  1.35 \tabularnewline
64 &  15 &  15.91 & -0.9114 \tabularnewline
65 &  12 &  13.3 & -1.298 \tabularnewline
66 &  17 &  15.53 &  1.474 \tabularnewline
67 &  14 &  15.51 & -1.515 \tabularnewline
68 &  14 &  15.76 & -1.757 \tabularnewline
69 &  16 &  14.94 &  1.062 \tabularnewline
70 &  15 &  14.73 &  0.2655 \tabularnewline
71 &  15 &  17.4 & -2.397 \tabularnewline
72 &  14 &  15.53 & -1.526 \tabularnewline
73 &  13 &  15.62 & -2.623 \tabularnewline
74 &  18 &  16.14 &  1.862 \tabularnewline
75 &  15 &  14.96 &  0.03874 \tabularnewline
76 &  16 &  15.83 &  0.1735 \tabularnewline
77 &  14 &  15.03 & -1.035 \tabularnewline
78 &  15 &  13.98 &  1.019 \tabularnewline
79 &  17 &  14.56 &  2.443 \tabularnewline
80 &  16 &  15.71 &  0.2925 \tabularnewline
81 &  10 &  13.8 & -3.799 \tabularnewline
82 &  16 &  15.83 &  0.1735 \tabularnewline
83 &  17 &  15.72 &  1.282 \tabularnewline
84 &  17 &  15.81 &  1.185 \tabularnewline
85 &  20 &  16.19 &  3.813 \tabularnewline
86 &  17 &  16.39 &  0.6088 \tabularnewline
87 &  18 &  15.91 &  2.089 \tabularnewline
88 &  15 &  15.14 & -0.1427 \tabularnewline
89 &  17 &  15.06 &  1.942 \tabularnewline
90 &  14 &  12.89 &  1.11 \tabularnewline
91 &  15 &  15.73 & -0.7306 \tabularnewline
92 &  17 &  15.71 &  1.292 \tabularnewline
93 &  16 &  15.83 &  0.1735 \tabularnewline
94 &  17 &  17 & -6.667e-05 \tabularnewline
95 &  15 &  14.94 &  0.06164 \tabularnewline
96 &  16 &  15.71 &  0.2925 \tabularnewline
97 &  18 &  16.08 &  1.921 \tabularnewline
98 &  18 &  16.58 &  1.416 \tabularnewline
99 &  16 &  16.98 & -0.977 \tabularnewline
100 &  16 &  15.14 &  0.8573 \tabularnewline
101 &  17 &  15.22 &  1.782 \tabularnewline
102 &  15 &  15.71 & -0.7075 \tabularnewline
103 &  13 &  16.18 & -3.176 \tabularnewline
104 &  15 &  14.75 &  0.254 \tabularnewline
105 &  17 &  16.69 &  0.3085 \tabularnewline
106 &  16 &  15.6 &  0.4003 \tabularnewline
107 &  16 &  15.23 &  0.774 \tabularnewline
108 &  15 &  15.72 & -0.7191 \tabularnewline
109 &  16 &  15.72 &  0.2809 \tabularnewline
110 &  16 &  15.37 &  0.6304 \tabularnewline
111 &  14 &  15.62 & -1.623 \tabularnewline
112 &  15 &  15.3 & -0.2994 \tabularnewline
113 &  12 &  13.78 & -1.776 \tabularnewline
114 &  18 &  15.31 &  2.689 \tabularnewline
115 &  16 &  15.15 &  0.8458 \tabularnewline
116 &  16 &  15.52 &  0.4848 \tabularnewline
117 &  17 &  15.54 &  1.459 \tabularnewline
118 &  16 &  16.48 & -0.4763 \tabularnewline
119 &  14 &  15.63 & -1.634 \tabularnewline
120 &  15 &  15.41 & -0.4073 \tabularnewline
121 &  14 &  14.73 & -0.7345 \tabularnewline
122 &  16 &  15.62 &  0.3774 \tabularnewline
123 &  15 &  15.83 & -0.8265 \tabularnewline
124 &  17 &  16.69 &  0.3118 \tabularnewline
125 &  15 &  15.06 & -0.05772 \tabularnewline
126 &  16 &  15.31 &  0.6891 \tabularnewline
127 &  16 &  15.61 &  0.3889 \tabularnewline
128 &  15 &  14.65 &  0.3504 \tabularnewline
129 &  15 &  15.32 & -0.3224 \tabularnewline
130 &  11 &  12.11 & -1.11 \tabularnewline
131 &  16 &  15.51 &  0.4852 \tabularnewline
132 &  18 &  16.08 &  1.92 \tabularnewline
133 &  13 &  13.97 & -0.9657 \tabularnewline
134 &  11 &  13.9 & -2.896 \tabularnewline
135 &  16 &  15.31 &  0.6891 \tabularnewline
136 &  18 &  17.55 &  0.4466 \tabularnewline
137 &  15 &  16.88 & -1.881 \tabularnewline
138 &  19 &  17.96 &  1.038 \tabularnewline
139 &  17 &  17.07 & -0.07348 \tabularnewline
140 &  13 &  15.3 & -2.299 \tabularnewline
141 &  14 &  15.36 & -1.358 \tabularnewline
142 &  16 &  15.72 &  0.2809 \tabularnewline
143 &  13 &  15.43 & -2.43 \tabularnewline
144 &  17 &  15.72 &  1.282 \tabularnewline
145 &  14 &  15.81 & -1.815 \tabularnewline
146 &  19 &  16.18 &  2.824 \tabularnewline
147 &  14 &  14.56 & -0.557 \tabularnewline
148 &  16 &  15.72 &  0.2809 \tabularnewline
149 &  12 &  13.39 & -1.391 \tabularnewline
150 &  16 &  16.89 & -0.8927 \tabularnewline
151 &  16 &  15.31 &  0.6891 \tabularnewline
152 &  15 &  15.61 & -0.6111 \tabularnewline
153 &  12 &  15.03 & -3.035 \tabularnewline
154 &  15 &  15.71 & -0.7075 \tabularnewline
155 &  17 &  16.49 &  0.5122 \tabularnewline
156 &  14 &  15.52 & -1.515 \tabularnewline
157 &  15 &  13.43 &  1.573 \tabularnewline
158 &  18 &  15.61 &  2.389 \tabularnewline
159 &  15 &  14.34 &  0.6588 \tabularnewline
160 &  18 &  15.63 &  2.366 \tabularnewline
161 &  15 &  17.19 & -2.193 \tabularnewline
162 &  15 &  16.09 & -1.091 \tabularnewline
163 &  16 &  15.88 &  0.1242 \tabularnewline
164 &  13 &  13.8 & -0.7986 \tabularnewline
165 &  16 &  15.61 &  0.3889 \tabularnewline
166 &  14 &  15.83 & -1.826 \tabularnewline
167 &  16 &  13.85 &  2.154 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302600&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] 13.19[/C][C]-0.1906[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.13[/C][C] 0.8672[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.92[/C][C] 1.077[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.65[/C][C] 0.3504[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.92[/C][C] 0.07708[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.25[/C][C] 0.7499[/C][/ROW]
[ROW][C]7[/C][C] 18[/C][C] 14.54[/C][C] 3.458[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.15[/C][C] 0.8458[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.98[/C][C] 0.02298[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 17.07[/C][C]-0.07348[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.63[/C][C] 1.366[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.61[/C][C]-0.6112[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.3[/C][C] 0.7006[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 13.98[/C][C] 0.01943[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.3[/C][C] 0.7002[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.14[/C][C] 1.857[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.14[/C][C] 0.8573[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 17.04[/C][C]-2.038[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.83[/C][C] 1.174[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.83[/C][C] 1.169[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.81[/C][C]-0.8149[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.71[/C][C] 0.2925[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.72[/C][C]-0.7191[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.71[/C][C] 1.292[/C][/ROW]
[ROW][C]25[/C][C] 14[/C][C] 15.06[/C][C]-1.058[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.94[/C][C] 1.062[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.62[/C][C]-0.6226[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.78[/C][C] 1.218[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.19[/C][C]-0.1875[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.56[/C][C]-1.557[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 17.07[/C][C]-2.073[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.27[/C][C] 0.7276[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.75[/C][C] 0.2507[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.63[/C][C]-0.6311[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.59[/C][C] 0.4082[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.15[/C][C]-0.1542[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.23[/C][C]-0.2337[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.36[/C][C]-0.3641[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.72[/C][C] 2.281[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.19[/C][C]-1.187[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 17.07[/C][C]-0.07348[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.9[/C][C]-0.8956[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 17.33[/C][C]-1.327[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.97[/C][C]-0.9722[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.3[/C][C]-0.2994[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.62[/C][C] 0.3774[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.8[/C][C]-0.8007[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.83[/C][C]-2.826[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 12.71[/C][C]-0.7073[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 16.88[/C][C] 0.1189[/C][/ROW]
[ROW][C]51[/C][C] 18[/C][C] 17.36[/C][C] 0.6395[/C][/ROW]
[ROW][C]52[/C][C] 18[/C][C] 17.45[/C][C] 0.554[/C][/ROW]
[ROW][C]53[/C][C] 11[/C][C] 14.75[/C][C]-3.749[/C][/ROW]
[ROW][C]54[/C][C] 14[/C][C] 14.14[/C][C]-0.1373[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 15.72[/C][C]-2.719[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 14.54[/C][C] 0.4579[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.07[/C][C] 1.931[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 15.51[/C][C] 0.4852[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 15.83[/C][C]-0.8265[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 17.45[/C][C]-0.4454[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 14.69[/C][C] 1.315[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.81[/C][C] 0.1851[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 14.65[/C][C] 1.35[/C][/ROW]
[ROW][C]64[/C][C] 15[/C][C] 15.91[/C][C]-0.9114[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 13.3[/C][C]-1.298[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 15.53[/C][C] 1.474[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.51[/C][C]-1.515[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 15.76[/C][C]-1.757[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 14.94[/C][C] 1.062[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 14.73[/C][C] 0.2655[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 17.4[/C][C]-2.397[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 15.53[/C][C]-1.526[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 15.62[/C][C]-2.623[/C][/ROW]
[ROW][C]74[/C][C] 18[/C][C] 16.14[/C][C] 1.862[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 14.96[/C][C] 0.03874[/C][/ROW]
[ROW][C]76[/C][C] 16[/C][C] 15.83[/C][C] 0.1735[/C][/ROW]
[ROW][C]77[/C][C] 14[/C][C] 15.03[/C][C]-1.035[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 13.98[/C][C] 1.019[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 14.56[/C][C] 2.443[/C][/ROW]
[ROW][C]80[/C][C] 16[/C][C] 15.71[/C][C] 0.2925[/C][/ROW]
[ROW][C]81[/C][C] 10[/C][C] 13.8[/C][C]-3.799[/C][/ROW]
[ROW][C]82[/C][C] 16[/C][C] 15.83[/C][C] 0.1735[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.72[/C][C] 1.282[/C][/ROW]
[ROW][C]84[/C][C] 17[/C][C] 15.81[/C][C] 1.185[/C][/ROW]
[ROW][C]85[/C][C] 20[/C][C] 16.19[/C][C] 3.813[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 16.39[/C][C] 0.6088[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 15.91[/C][C] 2.089[/C][/ROW]
[ROW][C]88[/C][C] 15[/C][C] 15.14[/C][C]-0.1427[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 15.06[/C][C] 1.942[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 12.89[/C][C] 1.11[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.73[/C][C]-0.7306[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 15.71[/C][C] 1.292[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.83[/C][C] 0.1735[/C][/ROW]
[ROW][C]94[/C][C] 17[/C][C] 17[/C][C]-6.667e-05[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 14.94[/C][C] 0.06164[/C][/ROW]
[ROW][C]96[/C][C] 16[/C][C] 15.71[/C][C] 0.2925[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.08[/C][C] 1.921[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 16.58[/C][C] 1.416[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 16.98[/C][C]-0.977[/C][/ROW]
[ROW][C]100[/C][C] 16[/C][C] 15.14[/C][C] 0.8573[/C][/ROW]
[ROW][C]101[/C][C] 17[/C][C] 15.22[/C][C] 1.782[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 15.71[/C][C]-0.7075[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 16.18[/C][C]-3.176[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 14.75[/C][C] 0.254[/C][/ROW]
[ROW][C]105[/C][C] 17[/C][C] 16.69[/C][C] 0.3085[/C][/ROW]
[ROW][C]106[/C][C] 16[/C][C] 15.6[/C][C] 0.4003[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.23[/C][C] 0.774[/C][/ROW]
[ROW][C]108[/C][C] 15[/C][C] 15.72[/C][C]-0.7191[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 15.72[/C][C] 0.2809[/C][/ROW]
[ROW][C]110[/C][C] 16[/C][C] 15.37[/C][C] 0.6304[/C][/ROW]
[ROW][C]111[/C][C] 14[/C][C] 15.62[/C][C]-1.623[/C][/ROW]
[ROW][C]112[/C][C] 15[/C][C] 15.3[/C][C]-0.2994[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 13.78[/C][C]-1.776[/C][/ROW]
[ROW][C]114[/C][C] 18[/C][C] 15.31[/C][C] 2.689[/C][/ROW]
[ROW][C]115[/C][C] 16[/C][C] 15.15[/C][C] 0.8458[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 15.52[/C][C] 0.4848[/C][/ROW]
[ROW][C]117[/C][C] 17[/C][C] 15.54[/C][C] 1.459[/C][/ROW]
[ROW][C]118[/C][C] 16[/C][C] 16.48[/C][C]-0.4763[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 15.63[/C][C]-1.634[/C][/ROW]
[ROW][C]120[/C][C] 15[/C][C] 15.41[/C][C]-0.4073[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 14.73[/C][C]-0.7345[/C][/ROW]
[ROW][C]122[/C][C] 16[/C][C] 15.62[/C][C] 0.3774[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.83[/C][C]-0.8265[/C][/ROW]
[ROW][C]124[/C][C] 17[/C][C] 16.69[/C][C] 0.3118[/C][/ROW]
[ROW][C]125[/C][C] 15[/C][C] 15.06[/C][C]-0.05772[/C][/ROW]
[ROW][C]126[/C][C] 16[/C][C] 15.31[/C][C] 0.6891[/C][/ROW]
[ROW][C]127[/C][C] 16[/C][C] 15.61[/C][C] 0.3889[/C][/ROW]
[ROW][C]128[/C][C] 15[/C][C] 14.65[/C][C] 0.3504[/C][/ROW]
[ROW][C]129[/C][C] 15[/C][C] 15.32[/C][C]-0.3224[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 12.11[/C][C]-1.11[/C][/ROW]
[ROW][C]131[/C][C] 16[/C][C] 15.51[/C][C] 0.4852[/C][/ROW]
[ROW][C]132[/C][C] 18[/C][C] 16.08[/C][C] 1.92[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 13.97[/C][C]-0.9657[/C][/ROW]
[ROW][C]134[/C][C] 11[/C][C] 13.9[/C][C]-2.896[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 15.31[/C][C] 0.6891[/C][/ROW]
[ROW][C]136[/C][C] 18[/C][C] 17.55[/C][C] 0.4466[/C][/ROW]
[ROW][C]137[/C][C] 15[/C][C] 16.88[/C][C]-1.881[/C][/ROW]
[ROW][C]138[/C][C] 19[/C][C] 17.96[/C][C] 1.038[/C][/ROW]
[ROW][C]139[/C][C] 17[/C][C] 17.07[/C][C]-0.07348[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 15.3[/C][C]-2.299[/C][/ROW]
[ROW][C]141[/C][C] 14[/C][C] 15.36[/C][C]-1.358[/C][/ROW]
[ROW][C]142[/C][C] 16[/C][C] 15.72[/C][C] 0.2809[/C][/ROW]
[ROW][C]143[/C][C] 13[/C][C] 15.43[/C][C]-2.43[/C][/ROW]
[ROW][C]144[/C][C] 17[/C][C] 15.72[/C][C] 1.282[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 15.81[/C][C]-1.815[/C][/ROW]
[ROW][C]146[/C][C] 19[/C][C] 16.18[/C][C] 2.824[/C][/ROW]
[ROW][C]147[/C][C] 14[/C][C] 14.56[/C][C]-0.557[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 15.72[/C][C] 0.2809[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 13.39[/C][C]-1.391[/C][/ROW]
[ROW][C]150[/C][C] 16[/C][C] 16.89[/C][C]-0.8927[/C][/ROW]
[ROW][C]151[/C][C] 16[/C][C] 15.31[/C][C] 0.6891[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 15.61[/C][C]-0.6111[/C][/ROW]
[ROW][C]153[/C][C] 12[/C][C] 15.03[/C][C]-3.035[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 15.71[/C][C]-0.7075[/C][/ROW]
[ROW][C]155[/C][C] 17[/C][C] 16.49[/C][C] 0.5122[/C][/ROW]
[ROW][C]156[/C][C] 14[/C][C] 15.52[/C][C]-1.515[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 13.43[/C][C] 1.573[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 15.61[/C][C] 2.389[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 14.34[/C][C] 0.6588[/C][/ROW]
[ROW][C]160[/C][C] 18[/C][C] 15.63[/C][C] 2.366[/C][/ROW]
[ROW][C]161[/C][C] 15[/C][C] 17.19[/C][C]-2.193[/C][/ROW]
[ROW][C]162[/C][C] 15[/C][C] 16.09[/C][C]-1.091[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.88[/C][C] 0.1242[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 13.8[/C][C]-0.7986[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 15.61[/C][C] 0.3889[/C][/ROW]
[ROW][C]166[/C][C] 14[/C][C] 15.83[/C][C]-1.826[/C][/ROW]
[ROW][C]167[/C][C] 16[/C][C] 13.85[/C][C] 2.154[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302600&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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	13.19	-0.1906
2	16	15.13	0.8672
3	17	15.92	1.077
4	15	14.65	0.3504
5	16	15.92	0.07708
6	16	15.25	0.7499
7	18	14.54	3.458
8	16	15.15	0.8458
9	17	16.98	0.02298
10	17	17.07	-0.07348
11	17	15.63	1.366
12	15	15.61	-0.6112
13	16	15.3	0.7006
14	14	13.98	0.01943
15	16	15.3	0.7002
16	17	15.14	1.857
17	16	15.14	0.8573
18	15	17.04	-2.038
19	17	15.83	1.174
20	16	14.83	1.169
21	15	15.81	-0.8149
22	16	15.71	0.2925
23	15	15.72	-0.7191
24	17	15.71	1.292
25	14	15.06	-1.058
26	16	14.94	1.062
27	15	15.62	-0.6226
28	16	14.78	1.218
29	16	16.19	-0.1875
30	13	14.56	-1.557
31	15	17.07	-2.073
32	17	16.27	0.7276
33	15	14.75	0.2507
34	13	13.63	-0.6311
35	17	16.59	0.4082
36	15	15.15	-0.1542
37	14	14.23	-0.2337
38	14	14.36	-0.3641
39	18	15.72	2.281
40	15	16.19	-1.187
41	17	17.07	-0.07348
42	13	13.9	-0.8956
43	16	17.33	-1.327
44	15	15.97	-0.9722
45	15	15.3	-0.2994
46	16	15.62	0.3774
47	15	15.8	-0.8007
48	13	15.83	-2.826
49	12	12.71	-0.7073
50	17	16.88	0.1189
51	18	17.36	0.6395
52	18	17.45	0.554
53	11	14.75	-3.749
54	14	14.14	-0.1373
55	13	15.72	-2.719
56	15	14.54	0.4579
57	17	15.07	1.931
58	16	15.51	0.4852
59	15	15.83	-0.8265
60	17	17.45	-0.4454
61	16	14.69	1.315
62	16	15.81	0.1851
63	16	14.65	1.35
64	15	15.91	-0.9114
65	12	13.3	-1.298
66	17	15.53	1.474
67	14	15.51	-1.515
68	14	15.76	-1.757
69	16	14.94	1.062
70	15	14.73	0.2655
71	15	17.4	-2.397
72	14	15.53	-1.526
73	13	15.62	-2.623
74	18	16.14	1.862
75	15	14.96	0.03874
76	16	15.83	0.1735
77	14	15.03	-1.035
78	15	13.98	1.019
79	17	14.56	2.443
80	16	15.71	0.2925
81	10	13.8	-3.799
82	16	15.83	0.1735
83	17	15.72	1.282
84	17	15.81	1.185
85	20	16.19	3.813
86	17	16.39	0.6088
87	18	15.91	2.089
88	15	15.14	-0.1427
89	17	15.06	1.942
90	14	12.89	1.11
91	15	15.73	-0.7306
92	17	15.71	1.292
93	16	15.83	0.1735
94	17	17	-6.667e-05
95	15	14.94	0.06164
96	16	15.71	0.2925
97	18	16.08	1.921
98	18	16.58	1.416
99	16	16.98	-0.977
100	16	15.14	0.8573
101	17	15.22	1.782
102	15	15.71	-0.7075
103	13	16.18	-3.176
104	15	14.75	0.254
105	17	16.69	0.3085
106	16	15.6	0.4003
107	16	15.23	0.774
108	15	15.72	-0.7191
109	16	15.72	0.2809
110	16	15.37	0.6304
111	14	15.62	-1.623
112	15	15.3	-0.2994
113	12	13.78	-1.776
114	18	15.31	2.689
115	16	15.15	0.8458
116	16	15.52	0.4848
117	17	15.54	1.459
118	16	16.48	-0.4763
119	14	15.63	-1.634
120	15	15.41	-0.4073
121	14	14.73	-0.7345
122	16	15.62	0.3774
123	15	15.83	-0.8265
124	17	16.69	0.3118
125	15	15.06	-0.05772
126	16	15.31	0.6891
127	16	15.61	0.3889
128	15	14.65	0.3504
129	15	15.32	-0.3224
130	11	12.11	-1.11
131	16	15.51	0.4852
132	18	16.08	1.92
133	13	13.97	-0.9657
134	11	13.9	-2.896
135	16	15.31	0.6891
136	18	17.55	0.4466
137	15	16.88	-1.881
138	19	17.96	1.038
139	17	17.07	-0.07348
140	13	15.3	-2.299
141	14	15.36	-1.358
142	16	15.72	0.2809
143	13	15.43	-2.43
144	17	15.72	1.282
145	14	15.81	-1.815
146	19	16.18	2.824
147	14	14.56	-0.557
148	16	15.72	0.2809
149	12	13.39	-1.391
150	16	16.89	-0.8927
151	16	15.31	0.6891
152	15	15.61	-0.6111
153	12	15.03	-3.035
154	15	15.71	-0.7075
155	17	16.49	0.5122
156	14	15.52	-1.515
157	15	13.43	1.573
158	18	15.61	2.389
159	15	14.34	0.6588
160	18	15.63	2.366
161	15	17.19	-2.193
162	15	16.09	-1.091
163	16	15.88	0.1242
164	13	13.8	-0.7986
165	16	15.61	0.3889
166	14	15.83	-1.826
167	16	13.85	2.154

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.2889	0.5778	0.7111
11	0.2119	0.4238	0.7881
12	0.1142	0.2284	0.8858
13	0.06204	0.1241	0.938
14	0.05887	0.1177	0.9411
15	0.08099	0.162	0.919
16	0.08972	0.1794	0.9103
17	0.05357	0.1071	0.9464
18	0.1128	0.2256	0.8872
19	0.08143	0.1629	0.9186
20	0.06413	0.1283	0.9359
21	0.05385	0.1077	0.9461
22	0.03402	0.06804	0.966
23	0.03567	0.07135	0.9643
24	0.03524	0.07047	0.9648
25	0.1045	0.209	0.8955
26	0.07749	0.155	0.9225
27	0.06743	0.1349	0.9326
28	0.04919	0.09837	0.9508
29	0.03357	0.06714	0.9664
30	0.04499	0.08998	0.955
31	0.06629	0.1326	0.9337
32	0.06363	0.1273	0.9364
33	0.04567	0.09135	0.9543
34	0.04488	0.08975	0.9551
35	0.03296	0.06592	0.967
36	0.02334	0.04667	0.9767
37	0.01816	0.03632	0.9818
38	0.01551	0.03101	0.9845
39	0.04026	0.08052	0.9597
40	0.03535	0.0707	0.9647
41	0.02531	0.05063	0.9747
42	0.02723	0.05446	0.9728
43	0.02358	0.04716	0.9764
44	0.0178	0.0356	0.9822
45	0.01387	0.02773	0.9861
46	0.009793	0.01959	0.9902
47	0.008854	0.01771	0.9911
48	0.02657	0.05314	0.9734
49	0.02407	0.04815	0.9759
50	0.01801	0.03603	0.982
51	0.01436	0.02873	0.9856
52	0.01263	0.02525	0.9874
53	0.08295	0.1659	0.917
54	0.06529	0.1306	0.9347
55	0.1193	0.2386	0.8807
56	0.09857	0.1971	0.9014
57	0.12	0.2401	0.88
58	0.1059	0.2118	0.8941
59	0.09049	0.181	0.9095
60	0.07422	0.1484	0.9258
61	0.06698	0.134	0.933
62	0.05273	0.1055	0.9473
63	0.05088	0.1018	0.9491
64	0.04328	0.08655	0.9567
65	0.04294	0.08589	0.9571
66	0.05282	0.1056	0.9472
67	0.05447	0.1089	0.9455
68	0.05755	0.1151	0.9424
69	0.05187	0.1037	0.9481
70	0.0423	0.0846	0.9577
71	0.06789	0.1358	0.9321
72	0.07838	0.1568	0.9216
73	0.1368	0.2736	0.8632
74	0.167	0.334	0.833
75	0.1481	0.2961	0.8519
76	0.1244	0.2487	0.8756
77	0.1139	0.2279	0.8861
78	0.1032	0.2064	0.8968
79	0.1663	0.3326	0.8337
80	0.141	0.282	0.859
81	0.4067	0.8134	0.5933
82	0.3653	0.7306	0.6347
83	0.3585	0.7169	0.6415
84	0.347	0.6939	0.653
85	0.6409	0.7182	0.3591
86	0.6043	0.7914	0.3957
87	0.656	0.6879	0.344
88	0.6164	0.7671	0.3836
89	0.6684	0.6632	0.3316
90	0.6473	0.7053	0.3527
91	0.6169	0.7661	0.3831
92	0.6154	0.7693	0.3846
93	0.5712	0.8576	0.4288
94	0.5258	0.9484	0.4742
95	0.4823	0.9645	0.5177
96	0.4403	0.8807	0.5597
97	0.4889	0.9777	0.5111
98	0.4984	0.9969	0.5016
99	0.471	0.9419	0.529
100	0.463	0.9261	0.537
101	0.4917	0.9834	0.5083
102	0.4529	0.9057	0.5471
103	0.6237	0.7526	0.3763
104	0.5832	0.8336	0.4168
105	0.5389	0.9222	0.4611
106	0.4969	0.9938	0.5031
107	0.4612	0.9224	0.5388
108	0.4236	0.8472	0.5764
109	0.3797	0.7593	0.6203
110	0.342	0.6839	0.658
111	0.3509	0.7018	0.6491
112	0.3076	0.6152	0.6924
113	0.328	0.656	0.672
114	0.4657	0.9314	0.5343
115	0.4483	0.8966	0.5517
116	0.4199	0.8398	0.5801
117	0.4148	0.8296	0.5852
118	0.3788	0.7576	0.6212
119	0.3961	0.7923	0.6039
120	0.3492	0.6983	0.6508
121	0.311	0.622	0.689
122	0.2719	0.5438	0.7281
123	0.2503	0.5006	0.7497
124	0.2251	0.4501	0.7749
125	0.1919	0.3838	0.8081
126	0.1714	0.3427	0.8286
127	0.1507	0.3015	0.8493
128	0.1419	0.2838	0.8581
129	0.1135	0.227	0.8865
130	0.1113	0.2226	0.8887
131	0.08823	0.1765	0.9118
132	0.0916	0.1832	0.9084
133	0.09596	0.1919	0.904
134	0.2072	0.4144	0.7928
135	0.1989	0.3979	0.8011
136	0.1701	0.3402	0.8299
137	0.1503	0.3005	0.8497
138	0.1259	0.2518	0.8741
139	0.1177	0.2354	0.8823
140	0.1162	0.2323	0.8838
141	0.1068	0.2135	0.8932
142	0.07932	0.1586	0.9207
143	0.09008	0.1802	0.9099
144	0.1107	0.2214	0.8893
145	0.1027	0.2053	0.8973
146	0.1944	0.3888	0.8056
147	0.2843	0.5685	0.7157
148	0.219	0.438	0.781
149	0.2446	0.4891	0.7554
150	0.1882	0.3764	0.8118
151	0.1813	0.3625	0.8187
152	0.1252	0.2505	0.8748
153	0.1195	0.239	0.8805
154	0.08013	0.1603	0.9199
155	0.04756	0.09511	0.9524
156	0.1379	0.2757	0.8621
157	0.5589	0.8822	0.4411

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.2889 &  0.5778 &  0.7111 \tabularnewline
11 &  0.2119 &  0.4238 &  0.7881 \tabularnewline
12 &  0.1142 &  0.2284 &  0.8858 \tabularnewline
13 &  0.06204 &  0.1241 &  0.938 \tabularnewline
14 &  0.05887 &  0.1177 &  0.9411 \tabularnewline
15 &  0.08099 &  0.162 &  0.919 \tabularnewline
16 &  0.08972 &  0.1794 &  0.9103 \tabularnewline
17 &  0.05357 &  0.1071 &  0.9464 \tabularnewline
18 &  0.1128 &  0.2256 &  0.8872 \tabularnewline
19 &  0.08143 &  0.1629 &  0.9186 \tabularnewline
20 &  0.06413 &  0.1283 &  0.9359 \tabularnewline
21 &  0.05385 &  0.1077 &  0.9461 \tabularnewline
22 &  0.03402 &  0.06804 &  0.966 \tabularnewline
23 &  0.03567 &  0.07135 &  0.9643 \tabularnewline
24 &  0.03524 &  0.07047 &  0.9648 \tabularnewline
25 &  0.1045 &  0.209 &  0.8955 \tabularnewline
26 &  0.07749 &  0.155 &  0.9225 \tabularnewline
27 &  0.06743 &  0.1349 &  0.9326 \tabularnewline
28 &  0.04919 &  0.09837 &  0.9508 \tabularnewline
29 &  0.03357 &  0.06714 &  0.9664 \tabularnewline
30 &  0.04499 &  0.08998 &  0.955 \tabularnewline
31 &  0.06629 &  0.1326 &  0.9337 \tabularnewline
32 &  0.06363 &  0.1273 &  0.9364 \tabularnewline
33 &  0.04567 &  0.09135 &  0.9543 \tabularnewline
34 &  0.04488 &  0.08975 &  0.9551 \tabularnewline
35 &  0.03296 &  0.06592 &  0.967 \tabularnewline
36 &  0.02334 &  0.04667 &  0.9767 \tabularnewline
37 &  0.01816 &  0.03632 &  0.9818 \tabularnewline
38 &  0.01551 &  0.03101 &  0.9845 \tabularnewline
39 &  0.04026 &  0.08052 &  0.9597 \tabularnewline
40 &  0.03535 &  0.0707 &  0.9647 \tabularnewline
41 &  0.02531 &  0.05063 &  0.9747 \tabularnewline
42 &  0.02723 &  0.05446 &  0.9728 \tabularnewline
43 &  0.02358 &  0.04716 &  0.9764 \tabularnewline
44 &  0.0178 &  0.0356 &  0.9822 \tabularnewline
45 &  0.01387 &  0.02773 &  0.9861 \tabularnewline
46 &  0.009793 &  0.01959 &  0.9902 \tabularnewline
47 &  0.008854 &  0.01771 &  0.9911 \tabularnewline
48 &  0.02657 &  0.05314 &  0.9734 \tabularnewline
49 &  0.02407 &  0.04815 &  0.9759 \tabularnewline
50 &  0.01801 &  0.03603 &  0.982 \tabularnewline
51 &  0.01436 &  0.02873 &  0.9856 \tabularnewline
52 &  0.01263 &  0.02525 &  0.9874 \tabularnewline
53 &  0.08295 &  0.1659 &  0.917 \tabularnewline
54 &  0.06529 &  0.1306 &  0.9347 \tabularnewline
55 &  0.1193 &  0.2386 &  0.8807 \tabularnewline
56 &  0.09857 &  0.1971 &  0.9014 \tabularnewline
57 &  0.12 &  0.2401 &  0.88 \tabularnewline
58 &  0.1059 &  0.2118 &  0.8941 \tabularnewline
59 &  0.09049 &  0.181 &  0.9095 \tabularnewline
60 &  0.07422 &  0.1484 &  0.9258 \tabularnewline
61 &  0.06698 &  0.134 &  0.933 \tabularnewline
62 &  0.05273 &  0.1055 &  0.9473 \tabularnewline
63 &  0.05088 &  0.1018 &  0.9491 \tabularnewline
64 &  0.04328 &  0.08655 &  0.9567 \tabularnewline
65 &  0.04294 &  0.08589 &  0.9571 \tabularnewline
66 &  0.05282 &  0.1056 &  0.9472 \tabularnewline
67 &  0.05447 &  0.1089 &  0.9455 \tabularnewline
68 &  0.05755 &  0.1151 &  0.9424 \tabularnewline
69 &  0.05187 &  0.1037 &  0.9481 \tabularnewline
70 &  0.0423 &  0.0846 &  0.9577 \tabularnewline
71 &  0.06789 &  0.1358 &  0.9321 \tabularnewline
72 &  0.07838 &  0.1568 &  0.9216 \tabularnewline
73 &  0.1368 &  0.2736 &  0.8632 \tabularnewline
74 &  0.167 &  0.334 &  0.833 \tabularnewline
75 &  0.1481 &  0.2961 &  0.8519 \tabularnewline
76 &  0.1244 &  0.2487 &  0.8756 \tabularnewline
77 &  0.1139 &  0.2279 &  0.8861 \tabularnewline
78 &  0.1032 &  0.2064 &  0.8968 \tabularnewline
79 &  0.1663 &  0.3326 &  0.8337 \tabularnewline
80 &  0.141 &  0.282 &  0.859 \tabularnewline
81 &  0.4067 &  0.8134 &  0.5933 \tabularnewline
82 &  0.3653 &  0.7306 &  0.6347 \tabularnewline
83 &  0.3585 &  0.7169 &  0.6415 \tabularnewline
84 &  0.347 &  0.6939 &  0.653 \tabularnewline
85 &  0.6409 &  0.7182 &  0.3591 \tabularnewline
86 &  0.6043 &  0.7914 &  0.3957 \tabularnewline
87 &  0.656 &  0.6879 &  0.344 \tabularnewline
88 &  0.6164 &  0.7671 &  0.3836 \tabularnewline
89 &  0.6684 &  0.6632 &  0.3316 \tabularnewline
90 &  0.6473 &  0.7053 &  0.3527 \tabularnewline
91 &  0.6169 &  0.7661 &  0.3831 \tabularnewline
92 &  0.6154 &  0.7693 &  0.3846 \tabularnewline
93 &  0.5712 &  0.8576 &  0.4288 \tabularnewline
94 &  0.5258 &  0.9484 &  0.4742 \tabularnewline
95 &  0.4823 &  0.9645 &  0.5177 \tabularnewline
96 &  0.4403 &  0.8807 &  0.5597 \tabularnewline
97 &  0.4889 &  0.9777 &  0.5111 \tabularnewline
98 &  0.4984 &  0.9969 &  0.5016 \tabularnewline
99 &  0.471 &  0.9419 &  0.529 \tabularnewline
100 &  0.463 &  0.9261 &  0.537 \tabularnewline
101 &  0.4917 &  0.9834 &  0.5083 \tabularnewline
102 &  0.4529 &  0.9057 &  0.5471 \tabularnewline
103 &  0.6237 &  0.7526 &  0.3763 \tabularnewline
104 &  0.5832 &  0.8336 &  0.4168 \tabularnewline
105 &  0.5389 &  0.9222 &  0.4611 \tabularnewline
106 &  0.4969 &  0.9938 &  0.5031 \tabularnewline
107 &  0.4612 &  0.9224 &  0.5388 \tabularnewline
108 &  0.4236 &  0.8472 &  0.5764 \tabularnewline
109 &  0.3797 &  0.7593 &  0.6203 \tabularnewline
110 &  0.342 &  0.6839 &  0.658 \tabularnewline
111 &  0.3509 &  0.7018 &  0.6491 \tabularnewline
112 &  0.3076 &  0.6152 &  0.6924 \tabularnewline
113 &  0.328 &  0.656 &  0.672 \tabularnewline
114 &  0.4657 &  0.9314 &  0.5343 \tabularnewline
115 &  0.4483 &  0.8966 &  0.5517 \tabularnewline
116 &  0.4199 &  0.8398 &  0.5801 \tabularnewline
117 &  0.4148 &  0.8296 &  0.5852 \tabularnewline
118 &  0.3788 &  0.7576 &  0.6212 \tabularnewline
119 &  0.3961 &  0.7923 &  0.6039 \tabularnewline
120 &  0.3492 &  0.6983 &  0.6508 \tabularnewline
121 &  0.311 &  0.622 &  0.689 \tabularnewline
122 &  0.2719 &  0.5438 &  0.7281 \tabularnewline
123 &  0.2503 &  0.5006 &  0.7497 \tabularnewline
124 &  0.2251 &  0.4501 &  0.7749 \tabularnewline
125 &  0.1919 &  0.3838 &  0.8081 \tabularnewline
126 &  0.1714 &  0.3427 &  0.8286 \tabularnewline
127 &  0.1507 &  0.3015 &  0.8493 \tabularnewline
128 &  0.1419 &  0.2838 &  0.8581 \tabularnewline
129 &  0.1135 &  0.227 &  0.8865 \tabularnewline
130 &  0.1113 &  0.2226 &  0.8887 \tabularnewline
131 &  0.08823 &  0.1765 &  0.9118 \tabularnewline
132 &  0.0916 &  0.1832 &  0.9084 \tabularnewline
133 &  0.09596 &  0.1919 &  0.904 \tabularnewline
134 &  0.2072 &  0.4144 &  0.7928 \tabularnewline
135 &  0.1989 &  0.3979 &  0.8011 \tabularnewline
136 &  0.1701 &  0.3402 &  0.8299 \tabularnewline
137 &  0.1503 &  0.3005 &  0.8497 \tabularnewline
138 &  0.1259 &  0.2518 &  0.8741 \tabularnewline
139 &  0.1177 &  0.2354 &  0.8823 \tabularnewline
140 &  0.1162 &  0.2323 &  0.8838 \tabularnewline
141 &  0.1068 &  0.2135 &  0.8932 \tabularnewline
142 &  0.07932 &  0.1586 &  0.9207 \tabularnewline
143 &  0.09008 &  0.1802 &  0.9099 \tabularnewline
144 &  0.1107 &  0.2214 &  0.8893 \tabularnewline
145 &  0.1027 &  0.2053 &  0.8973 \tabularnewline
146 &  0.1944 &  0.3888 &  0.8056 \tabularnewline
147 &  0.2843 &  0.5685 &  0.7157 \tabularnewline
148 &  0.219 &  0.438 &  0.781 \tabularnewline
149 &  0.2446 &  0.4891 &  0.7554 \tabularnewline
150 &  0.1882 &  0.3764 &  0.8118 \tabularnewline
151 &  0.1813 &  0.3625 &  0.8187 \tabularnewline
152 &  0.1252 &  0.2505 &  0.8748 \tabularnewline
153 &  0.1195 &  0.239 &  0.8805 \tabularnewline
154 &  0.08013 &  0.1603 &  0.9199 \tabularnewline
155 &  0.04756 &  0.09511 &  0.9524 \tabularnewline
156 &  0.1379 &  0.2757 &  0.8621 \tabularnewline
157 &  0.5589 &  0.8822 &  0.4411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302600&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]10[/C][C] 0.2889[/C][C] 0.5778[/C][C] 0.7111[/C][/ROW]
[ROW][C]11[/C][C] 0.2119[/C][C] 0.4238[/C][C] 0.7881[/C][/ROW]
[ROW][C]12[/C][C] 0.1142[/C][C] 0.2284[/C][C] 0.8858[/C][/ROW]
[ROW][C]13[/C][C] 0.06204[/C][C] 0.1241[/C][C] 0.938[/C][/ROW]
[ROW][C]14[/C][C] 0.05887[/C][C] 0.1177[/C][C] 0.9411[/C][/ROW]
[ROW][C]15[/C][C] 0.08099[/C][C] 0.162[/C][C] 0.919[/C][/ROW]
[ROW][C]16[/C][C] 0.08972[/C][C] 0.1794[/C][C] 0.9103[/C][/ROW]
[ROW][C]17[/C][C] 0.05357[/C][C] 0.1071[/C][C] 0.9464[/C][/ROW]
[ROW][C]18[/C][C] 0.1128[/C][C] 0.2256[/C][C] 0.8872[/C][/ROW]
[ROW][C]19[/C][C] 0.08143[/C][C] 0.1629[/C][C] 0.9186[/C][/ROW]
[ROW][C]20[/C][C] 0.06413[/C][C] 0.1283[/C][C] 0.9359[/C][/ROW]
[ROW][C]21[/C][C] 0.05385[/C][C] 0.1077[/C][C] 0.9461[/C][/ROW]
[ROW][C]22[/C][C] 0.03402[/C][C] 0.06804[/C][C] 0.966[/C][/ROW]
[ROW][C]23[/C][C] 0.03567[/C][C] 0.07135[/C][C] 0.9643[/C][/ROW]
[ROW][C]24[/C][C] 0.03524[/C][C] 0.07047[/C][C] 0.9648[/C][/ROW]
[ROW][C]25[/C][C] 0.1045[/C][C] 0.209[/C][C] 0.8955[/C][/ROW]
[ROW][C]26[/C][C] 0.07749[/C][C] 0.155[/C][C] 0.9225[/C][/ROW]
[ROW][C]27[/C][C] 0.06743[/C][C] 0.1349[/C][C] 0.9326[/C][/ROW]
[ROW][C]28[/C][C] 0.04919[/C][C] 0.09837[/C][C] 0.9508[/C][/ROW]
[ROW][C]29[/C][C] 0.03357[/C][C] 0.06714[/C][C] 0.9664[/C][/ROW]
[ROW][C]30[/C][C] 0.04499[/C][C] 0.08998[/C][C] 0.955[/C][/ROW]
[ROW][C]31[/C][C] 0.06629[/C][C] 0.1326[/C][C] 0.9337[/C][/ROW]
[ROW][C]32[/C][C] 0.06363[/C][C] 0.1273[/C][C] 0.9364[/C][/ROW]
[ROW][C]33[/C][C] 0.04567[/C][C] 0.09135[/C][C] 0.9543[/C][/ROW]
[ROW][C]34[/C][C] 0.04488[/C][C] 0.08975[/C][C] 0.9551[/C][/ROW]
[ROW][C]35[/C][C] 0.03296[/C][C] 0.06592[/C][C] 0.967[/C][/ROW]
[ROW][C]36[/C][C] 0.02334[/C][C] 0.04667[/C][C] 0.9767[/C][/ROW]
[ROW][C]37[/C][C] 0.01816[/C][C] 0.03632[/C][C] 0.9818[/C][/ROW]
[ROW][C]38[/C][C] 0.01551[/C][C] 0.03101[/C][C] 0.9845[/C][/ROW]
[ROW][C]39[/C][C] 0.04026[/C][C] 0.08052[/C][C] 0.9597[/C][/ROW]
[ROW][C]40[/C][C] 0.03535[/C][C] 0.0707[/C][C] 0.9647[/C][/ROW]
[ROW][C]41[/C][C] 0.02531[/C][C] 0.05063[/C][C] 0.9747[/C][/ROW]
[ROW][C]42[/C][C] 0.02723[/C][C] 0.05446[/C][C] 0.9728[/C][/ROW]
[ROW][C]43[/C][C] 0.02358[/C][C] 0.04716[/C][C] 0.9764[/C][/ROW]
[ROW][C]44[/C][C] 0.0178[/C][C] 0.0356[/C][C] 0.9822[/C][/ROW]
[ROW][C]45[/C][C] 0.01387[/C][C] 0.02773[/C][C] 0.9861[/C][/ROW]
[ROW][C]46[/C][C] 0.009793[/C][C] 0.01959[/C][C] 0.9902[/C][/ROW]
[ROW][C]47[/C][C] 0.008854[/C][C] 0.01771[/C][C] 0.9911[/C][/ROW]
[ROW][C]48[/C][C] 0.02657[/C][C] 0.05314[/C][C] 0.9734[/C][/ROW]
[ROW][C]49[/C][C] 0.02407[/C][C] 0.04815[/C][C] 0.9759[/C][/ROW]
[ROW][C]50[/C][C] 0.01801[/C][C] 0.03603[/C][C] 0.982[/C][/ROW]
[ROW][C]51[/C][C] 0.01436[/C][C] 0.02873[/C][C] 0.9856[/C][/ROW]
[ROW][C]52[/C][C] 0.01263[/C][C] 0.02525[/C][C] 0.9874[/C][/ROW]
[ROW][C]53[/C][C] 0.08295[/C][C] 0.1659[/C][C] 0.917[/C][/ROW]
[ROW][C]54[/C][C] 0.06529[/C][C] 0.1306[/C][C] 0.9347[/C][/ROW]
[ROW][C]55[/C][C] 0.1193[/C][C] 0.2386[/C][C] 0.8807[/C][/ROW]
[ROW][C]56[/C][C] 0.09857[/C][C] 0.1971[/C][C] 0.9014[/C][/ROW]
[ROW][C]57[/C][C] 0.12[/C][C] 0.2401[/C][C] 0.88[/C][/ROW]
[ROW][C]58[/C][C] 0.1059[/C][C] 0.2118[/C][C] 0.8941[/C][/ROW]
[ROW][C]59[/C][C] 0.09049[/C][C] 0.181[/C][C] 0.9095[/C][/ROW]
[ROW][C]60[/C][C] 0.07422[/C][C] 0.1484[/C][C] 0.9258[/C][/ROW]
[ROW][C]61[/C][C] 0.06698[/C][C] 0.134[/C][C] 0.933[/C][/ROW]
[ROW][C]62[/C][C] 0.05273[/C][C] 0.1055[/C][C] 0.9473[/C][/ROW]
[ROW][C]63[/C][C] 0.05088[/C][C] 0.1018[/C][C] 0.9491[/C][/ROW]
[ROW][C]64[/C][C] 0.04328[/C][C] 0.08655[/C][C] 0.9567[/C][/ROW]
[ROW][C]65[/C][C] 0.04294[/C][C] 0.08589[/C][C] 0.9571[/C][/ROW]
[ROW][C]66[/C][C] 0.05282[/C][C] 0.1056[/C][C] 0.9472[/C][/ROW]
[ROW][C]67[/C][C] 0.05447[/C][C] 0.1089[/C][C] 0.9455[/C][/ROW]
[ROW][C]68[/C][C] 0.05755[/C][C] 0.1151[/C][C] 0.9424[/C][/ROW]
[ROW][C]69[/C][C] 0.05187[/C][C] 0.1037[/C][C] 0.9481[/C][/ROW]
[ROW][C]70[/C][C] 0.0423[/C][C] 0.0846[/C][C] 0.9577[/C][/ROW]
[ROW][C]71[/C][C] 0.06789[/C][C] 0.1358[/C][C] 0.9321[/C][/ROW]
[ROW][C]72[/C][C] 0.07838[/C][C] 0.1568[/C][C] 0.9216[/C][/ROW]
[ROW][C]73[/C][C] 0.1368[/C][C] 0.2736[/C][C] 0.8632[/C][/ROW]
[ROW][C]74[/C][C] 0.167[/C][C] 0.334[/C][C] 0.833[/C][/ROW]
[ROW][C]75[/C][C] 0.1481[/C][C] 0.2961[/C][C] 0.8519[/C][/ROW]
[ROW][C]76[/C][C] 0.1244[/C][C] 0.2487[/C][C] 0.8756[/C][/ROW]
[ROW][C]77[/C][C] 0.1139[/C][C] 0.2279[/C][C] 0.8861[/C][/ROW]
[ROW][C]78[/C][C] 0.1032[/C][C] 0.2064[/C][C] 0.8968[/C][/ROW]
[ROW][C]79[/C][C] 0.1663[/C][C] 0.3326[/C][C] 0.8337[/C][/ROW]
[ROW][C]80[/C][C] 0.141[/C][C] 0.282[/C][C] 0.859[/C][/ROW]
[ROW][C]81[/C][C] 0.4067[/C][C] 0.8134[/C][C] 0.5933[/C][/ROW]
[ROW][C]82[/C][C] 0.3653[/C][C] 0.7306[/C][C] 0.6347[/C][/ROW]
[ROW][C]83[/C][C] 0.3585[/C][C] 0.7169[/C][C] 0.6415[/C][/ROW]
[ROW][C]84[/C][C] 0.347[/C][C] 0.6939[/C][C] 0.653[/C][/ROW]
[ROW][C]85[/C][C] 0.6409[/C][C] 0.7182[/C][C] 0.3591[/C][/ROW]
[ROW][C]86[/C][C] 0.6043[/C][C] 0.7914[/C][C] 0.3957[/C][/ROW]
[ROW][C]87[/C][C] 0.656[/C][C] 0.6879[/C][C] 0.344[/C][/ROW]
[ROW][C]88[/C][C] 0.6164[/C][C] 0.7671[/C][C] 0.3836[/C][/ROW]
[ROW][C]89[/C][C] 0.6684[/C][C] 0.6632[/C][C] 0.3316[/C][/ROW]
[ROW][C]90[/C][C] 0.6473[/C][C] 0.7053[/C][C] 0.3527[/C][/ROW]
[ROW][C]91[/C][C] 0.6169[/C][C] 0.7661[/C][C] 0.3831[/C][/ROW]
[ROW][C]92[/C][C] 0.6154[/C][C] 0.7693[/C][C] 0.3846[/C][/ROW]
[ROW][C]93[/C][C] 0.5712[/C][C] 0.8576[/C][C] 0.4288[/C][/ROW]
[ROW][C]94[/C][C] 0.5258[/C][C] 0.9484[/C][C] 0.4742[/C][/ROW]
[ROW][C]95[/C][C] 0.4823[/C][C] 0.9645[/C][C] 0.5177[/C][/ROW]
[ROW][C]96[/C][C] 0.4403[/C][C] 0.8807[/C][C] 0.5597[/C][/ROW]
[ROW][C]97[/C][C] 0.4889[/C][C] 0.9777[/C][C] 0.5111[/C][/ROW]
[ROW][C]98[/C][C] 0.4984[/C][C] 0.9969[/C][C] 0.5016[/C][/ROW]
[ROW][C]99[/C][C] 0.471[/C][C] 0.9419[/C][C] 0.529[/C][/ROW]
[ROW][C]100[/C][C] 0.463[/C][C] 0.9261[/C][C] 0.537[/C][/ROW]
[ROW][C]101[/C][C] 0.4917[/C][C] 0.9834[/C][C] 0.5083[/C][/ROW]
[ROW][C]102[/C][C] 0.4529[/C][C] 0.9057[/C][C] 0.5471[/C][/ROW]
[ROW][C]103[/C][C] 0.6237[/C][C] 0.7526[/C][C] 0.3763[/C][/ROW]
[ROW][C]104[/C][C] 0.5832[/C][C] 0.8336[/C][C] 0.4168[/C][/ROW]
[ROW][C]105[/C][C] 0.5389[/C][C] 0.9222[/C][C] 0.4611[/C][/ROW]
[ROW][C]106[/C][C] 0.4969[/C][C] 0.9938[/C][C] 0.5031[/C][/ROW]
[ROW][C]107[/C][C] 0.4612[/C][C] 0.9224[/C][C] 0.5388[/C][/ROW]
[ROW][C]108[/C][C] 0.4236[/C][C] 0.8472[/C][C] 0.5764[/C][/ROW]
[ROW][C]109[/C][C] 0.3797[/C][C] 0.7593[/C][C] 0.6203[/C][/ROW]
[ROW][C]110[/C][C] 0.342[/C][C] 0.6839[/C][C] 0.658[/C][/ROW]
[ROW][C]111[/C][C] 0.3509[/C][C] 0.7018[/C][C] 0.6491[/C][/ROW]
[ROW][C]112[/C][C] 0.3076[/C][C] 0.6152[/C][C] 0.6924[/C][/ROW]
[ROW][C]113[/C][C] 0.328[/C][C] 0.656[/C][C] 0.672[/C][/ROW]
[ROW][C]114[/C][C] 0.4657[/C][C] 0.9314[/C][C] 0.5343[/C][/ROW]
[ROW][C]115[/C][C] 0.4483[/C][C] 0.8966[/C][C] 0.5517[/C][/ROW]
[ROW][C]116[/C][C] 0.4199[/C][C] 0.8398[/C][C] 0.5801[/C][/ROW]
[ROW][C]117[/C][C] 0.4148[/C][C] 0.8296[/C][C] 0.5852[/C][/ROW]
[ROW][C]118[/C][C] 0.3788[/C][C] 0.7576[/C][C] 0.6212[/C][/ROW]
[ROW][C]119[/C][C] 0.3961[/C][C] 0.7923[/C][C] 0.6039[/C][/ROW]
[ROW][C]120[/C][C] 0.3492[/C][C] 0.6983[/C][C] 0.6508[/C][/ROW]
[ROW][C]121[/C][C] 0.311[/C][C] 0.622[/C][C] 0.689[/C][/ROW]
[ROW][C]122[/C][C] 0.2719[/C][C] 0.5438[/C][C] 0.7281[/C][/ROW]
[ROW][C]123[/C][C] 0.2503[/C][C] 0.5006[/C][C] 0.7497[/C][/ROW]
[ROW][C]124[/C][C] 0.2251[/C][C] 0.4501[/C][C] 0.7749[/C][/ROW]
[ROW][C]125[/C][C] 0.1919[/C][C] 0.3838[/C][C] 0.8081[/C][/ROW]
[ROW][C]126[/C][C] 0.1714[/C][C] 0.3427[/C][C] 0.8286[/C][/ROW]
[ROW][C]127[/C][C] 0.1507[/C][C] 0.3015[/C][C] 0.8493[/C][/ROW]
[ROW][C]128[/C][C] 0.1419[/C][C] 0.2838[/C][C] 0.8581[/C][/ROW]
[ROW][C]129[/C][C] 0.1135[/C][C] 0.227[/C][C] 0.8865[/C][/ROW]
[ROW][C]130[/C][C] 0.1113[/C][C] 0.2226[/C][C] 0.8887[/C][/ROW]
[ROW][C]131[/C][C] 0.08823[/C][C] 0.1765[/C][C] 0.9118[/C][/ROW]
[ROW][C]132[/C][C] 0.0916[/C][C] 0.1832[/C][C] 0.9084[/C][/ROW]
[ROW][C]133[/C][C] 0.09596[/C][C] 0.1919[/C][C] 0.904[/C][/ROW]
[ROW][C]134[/C][C] 0.2072[/C][C] 0.4144[/C][C] 0.7928[/C][/ROW]
[ROW][C]135[/C][C] 0.1989[/C][C] 0.3979[/C][C] 0.8011[/C][/ROW]
[ROW][C]136[/C][C] 0.1701[/C][C] 0.3402[/C][C] 0.8299[/C][/ROW]
[ROW][C]137[/C][C] 0.1503[/C][C] 0.3005[/C][C] 0.8497[/C][/ROW]
[ROW][C]138[/C][C] 0.1259[/C][C] 0.2518[/C][C] 0.8741[/C][/ROW]
[ROW][C]139[/C][C] 0.1177[/C][C] 0.2354[/C][C] 0.8823[/C][/ROW]
[ROW][C]140[/C][C] 0.1162[/C][C] 0.2323[/C][C] 0.8838[/C][/ROW]
[ROW][C]141[/C][C] 0.1068[/C][C] 0.2135[/C][C] 0.8932[/C][/ROW]
[ROW][C]142[/C][C] 0.07932[/C][C] 0.1586[/C][C] 0.9207[/C][/ROW]
[ROW][C]143[/C][C] 0.09008[/C][C] 0.1802[/C][C] 0.9099[/C][/ROW]
[ROW][C]144[/C][C] 0.1107[/C][C] 0.2214[/C][C] 0.8893[/C][/ROW]
[ROW][C]145[/C][C] 0.1027[/C][C] 0.2053[/C][C] 0.8973[/C][/ROW]
[ROW][C]146[/C][C] 0.1944[/C][C] 0.3888[/C][C] 0.8056[/C][/ROW]
[ROW][C]147[/C][C] 0.2843[/C][C] 0.5685[/C][C] 0.7157[/C][/ROW]
[ROW][C]148[/C][C] 0.219[/C][C] 0.438[/C][C] 0.781[/C][/ROW]
[ROW][C]149[/C][C] 0.2446[/C][C] 0.4891[/C][C] 0.7554[/C][/ROW]
[ROW][C]150[/C][C] 0.1882[/C][C] 0.3764[/C][C] 0.8118[/C][/ROW]
[ROW][C]151[/C][C] 0.1813[/C][C] 0.3625[/C][C] 0.8187[/C][/ROW]
[ROW][C]152[/C][C] 0.1252[/C][C] 0.2505[/C][C] 0.8748[/C][/ROW]
[ROW][C]153[/C][C] 0.1195[/C][C] 0.239[/C][C] 0.8805[/C][/ROW]
[ROW][C]154[/C][C] 0.08013[/C][C] 0.1603[/C][C] 0.9199[/C][/ROW]
[ROW][C]155[/C][C] 0.04756[/C][C] 0.09511[/C][C] 0.9524[/C][/ROW]
[ROW][C]156[/C][C] 0.1379[/C][C] 0.2757[/C][C] 0.8621[/C][/ROW]
[ROW][C]157[/C][C] 0.5589[/C][C] 0.8822[/C][C] 0.4411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302600&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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
10	0.2889	0.5778	0.7111
11	0.2119	0.4238	0.7881
12	0.1142	0.2284	0.8858
13	0.06204	0.1241	0.938
14	0.05887	0.1177	0.9411
15	0.08099	0.162	0.919
16	0.08972	0.1794	0.9103
17	0.05357	0.1071	0.9464
18	0.1128	0.2256	0.8872
19	0.08143	0.1629	0.9186
20	0.06413	0.1283	0.9359
21	0.05385	0.1077	0.9461
22	0.03402	0.06804	0.966
23	0.03567	0.07135	0.9643
24	0.03524	0.07047	0.9648
25	0.1045	0.209	0.8955
26	0.07749	0.155	0.9225
27	0.06743	0.1349	0.9326
28	0.04919	0.09837	0.9508
29	0.03357	0.06714	0.9664
30	0.04499	0.08998	0.955
31	0.06629	0.1326	0.9337
32	0.06363	0.1273	0.9364
33	0.04567	0.09135	0.9543
34	0.04488	0.08975	0.9551
35	0.03296	0.06592	0.967
36	0.02334	0.04667	0.9767
37	0.01816	0.03632	0.9818
38	0.01551	0.03101	0.9845
39	0.04026	0.08052	0.9597
40	0.03535	0.0707	0.9647
41	0.02531	0.05063	0.9747
42	0.02723	0.05446	0.9728
43	0.02358	0.04716	0.9764
44	0.0178	0.0356	0.9822
45	0.01387	0.02773	0.9861
46	0.009793	0.01959	0.9902
47	0.008854	0.01771	0.9911
48	0.02657	0.05314	0.9734
49	0.02407	0.04815	0.9759
50	0.01801	0.03603	0.982
51	0.01436	0.02873	0.9856
52	0.01263	0.02525	0.9874
53	0.08295	0.1659	0.917
54	0.06529	0.1306	0.9347
55	0.1193	0.2386	0.8807
56	0.09857	0.1971	0.9014
57	0.12	0.2401	0.88
58	0.1059	0.2118	0.8941
59	0.09049	0.181	0.9095
60	0.07422	0.1484	0.9258
61	0.06698	0.134	0.933
62	0.05273	0.1055	0.9473
63	0.05088	0.1018	0.9491
64	0.04328	0.08655	0.9567
65	0.04294	0.08589	0.9571
66	0.05282	0.1056	0.9472
67	0.05447	0.1089	0.9455
68	0.05755	0.1151	0.9424
69	0.05187	0.1037	0.9481
70	0.0423	0.0846	0.9577
71	0.06789	0.1358	0.9321
72	0.07838	0.1568	0.9216
73	0.1368	0.2736	0.8632
74	0.167	0.334	0.833
75	0.1481	0.2961	0.8519
76	0.1244	0.2487	0.8756
77	0.1139	0.2279	0.8861
78	0.1032	0.2064	0.8968
79	0.1663	0.3326	0.8337
80	0.141	0.282	0.859
81	0.4067	0.8134	0.5933
82	0.3653	0.7306	0.6347
83	0.3585	0.7169	0.6415
84	0.347	0.6939	0.653
85	0.6409	0.7182	0.3591
86	0.6043	0.7914	0.3957
87	0.656	0.6879	0.344
88	0.6164	0.7671	0.3836
89	0.6684	0.6632	0.3316
90	0.6473	0.7053	0.3527
91	0.6169	0.7661	0.3831
92	0.6154	0.7693	0.3846
93	0.5712	0.8576	0.4288
94	0.5258	0.9484	0.4742
95	0.4823	0.9645	0.5177
96	0.4403	0.8807	0.5597
97	0.4889	0.9777	0.5111
98	0.4984	0.9969	0.5016
99	0.471	0.9419	0.529
100	0.463	0.9261	0.537
101	0.4917	0.9834	0.5083
102	0.4529	0.9057	0.5471
103	0.6237	0.7526	0.3763
104	0.5832	0.8336	0.4168
105	0.5389	0.9222	0.4611
106	0.4969	0.9938	0.5031
107	0.4612	0.9224	0.5388
108	0.4236	0.8472	0.5764
109	0.3797	0.7593	0.6203
110	0.342	0.6839	0.658
111	0.3509	0.7018	0.6491
112	0.3076	0.6152	0.6924
113	0.328	0.656	0.672
114	0.4657	0.9314	0.5343
115	0.4483	0.8966	0.5517
116	0.4199	0.8398	0.5801
117	0.4148	0.8296	0.5852
118	0.3788	0.7576	0.6212
119	0.3961	0.7923	0.6039
120	0.3492	0.6983	0.6508
121	0.311	0.622	0.689
122	0.2719	0.5438	0.7281
123	0.2503	0.5006	0.7497
124	0.2251	0.4501	0.7749
125	0.1919	0.3838	0.8081
126	0.1714	0.3427	0.8286
127	0.1507	0.3015	0.8493
128	0.1419	0.2838	0.8581
129	0.1135	0.227	0.8865
130	0.1113	0.2226	0.8887
131	0.08823	0.1765	0.9118
132	0.0916	0.1832	0.9084
133	0.09596	0.1919	0.904
134	0.2072	0.4144	0.7928
135	0.1989	0.3979	0.8011
136	0.1701	0.3402	0.8299
137	0.1503	0.3005	0.8497
138	0.1259	0.2518	0.8741
139	0.1177	0.2354	0.8823
140	0.1162	0.2323	0.8838
141	0.1068	0.2135	0.8932
142	0.07932	0.1586	0.9207
143	0.09008	0.1802	0.9099
144	0.1107	0.2214	0.8893
145	0.1027	0.2053	0.8973
146	0.1944	0.3888	0.8056
147	0.2843	0.5685	0.7157
148	0.219	0.438	0.781
149	0.2446	0.4891	0.7554
150	0.1882	0.3764	0.8118
151	0.1813	0.3625	0.8187
152	0.1252	0.2505	0.8748
153	0.1195	0.239	0.8805
154	0.08013	0.1603	0.9199
155	0.04756	0.09511	0.9524
156	0.1379	0.2757	0.8621
157	0.5589	0.8822	0.4411

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	12	0.0810811	NOK
10% type I error level	30	0.202703	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 & 12 & 0.0810811 & NOK \tabularnewline
10% type I error level & 30 & 0.202703 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302600&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]12[/C][C]0.0810811[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.202703[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302600&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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	12	0.0810811	NOK
10% type I error level	30	0.202703	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.9429, df1 = 2, df2 = 158, p-value = 0.1467
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2895, df1 = 12, df2 = 148, p-value = 0.2303
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.9404, df1 = 2, df2 = 158, p-value = 0.1471

\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 = 1.9429, df1 = 2, df2 = 158, p-value = 0.1467
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2895, df1 = 12, df2 = 148, p-value = 0.2303
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.9404, df1 = 2, df2 = 158, p-value = 0.1471
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302600&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 = 1.9429, df1 = 2, df2 = 158, p-value = 0.1467
[/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 = 1.2895, df1 = 12, df2 = 148, p-value = 0.2303
[/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 = 1.9404, df1 = 2, df2 = 158, p-value = 0.1471
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302600&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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 = 1.9429, df1 = 2, df2 = 158, p-value = 0.1467
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2895, df1 = 12, df2 = 148, p-value = 0.2303
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.9404, df1 = 2, df2 = 158, p-value = 0.1471

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.105996 1.133158 1.044730 1.051857 1.048411 1.042723

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.105996 1.133158 1.044730 1.051857 1.048411 1.042723 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302600&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.105996 1.133158 1.044730 1.051857 1.048411 1.042723 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302600&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302600&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 SK3 SK4 SK5 SK6 1.105996 1.133158 1.044730 1.051857 1.048411 1.042723

Figure 1

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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

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

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