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 18:18:03 +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/t1481390412zjjkkr8yadsq8el.htm/, Retrieved Mon, 06 May 2024 02:57:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298734, Retrieved Mon, 06 May 2024 02:57:18 +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] [Multiple regressi...] [2016-12-10 17:18:03] [2e11ca31a00cf8de75c33c1af2d59434] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

13	4	2	3	5	1	1
16	5	3	4	5	2	1
17	4	4	4	5	2	1
15	3	4	3	4	2	1
16	4	4	4	5	1	1
16	3	4	4	5	2	1
17	3	4	3	3	2	1
16	3	4	4	4	2	1
17	4	5	4	5	1	0
17	4	5	4	5	1	1
17	4	4	4	5	1	1
15	4	4	3	5	1	0
16	4	4	3	4	1	0
14	3	3	4	4	1	1
16	4	4	4	2	1	1
17	3	4	4	4	2	1
16	3	4	4	4	1	1
NA	5	5	5	5	2	0
15	5	5	3	4	2	1
17	4	4	4	5	2	1
16	3	4	3	4	2	1
15	4	4	4	5	1	1
16	4	4	4	4	2	1
15	4	4	4	4	2	1
17	4	4	4	4	1	1
15	3	4	4	4	1	1
16	3	4	3	5	1	0
15	4	4	4	4	1	0
16	2	4	4	5	1	1
16	5	4	4	4	1	1
13	4	3	4	4	1	1
15	4	5	4	5	2	0
17	5	4	4	4	1	1
15	4	3	4	4	1	0
13	2	3	4	5	2	1
17	4	5	4	4	1	1
15	3	4	4	4	1	1
14	4	3	3	4	2	0
14	4	3	4	4	1	0
18	4	4	4	4	1	0
15	5	4	4	4	2	0
17	4	5	4	5	2	1
13	3	3	4	4	2	0
16	5	5	3	5	2	1
15	5	4	3	4	2	0
15	4	4	3	4	2	1
16	4	4	4	4	2	1
15	3	5	3	3	2	0
13	4	4	4	5	2	1
NA	2	3	2	3	1	1
17	4	5	4	4	2	0
17	5	5	4	5	1	0
17	5	5	4	4	2	0
11	4	3	4	5	2	0
14	4	3	3	4	1	1
13	4	4	4	4	2	1
15	3	4	3	3	1	1
17	3	4	4	4	1	0
16	4	4	3	5	2	0
15	4	4	4	5	1	1
17	5	5	4	5	2	1
16	2	4	4	5	2	0
16	4	4	4	5	2	1
16	3	4	4	2	2	1
15	4	4	4	5	1	1
12	4	2	4	4	2	1
17	4	4	3	5	1	1
14	4	4	3	5	2	1
14	5	4	3	3	1	1
16	3	4	3	5	2	1
15	3	4	3	4	2	1
15	4	5	5	5	2	1
13	4	4	4	4	1	1
13	4	4	4	4	2	0
17	4	4	5	5	1	1
15	3	4	4	4	2	1
16	4	4	4	5	1	1
14	3	4	3	5	2	1
15	3	3	4	4	1	1
17	4	3	4	4	2	1
16	4	4	4	4	2	1
12	3	3	4	4	1	1
16	4	4	4	5	1	1
17	4	4	4	5	1	0
17	4	4	4	5	2	1
20	5	4	4	4	2	1
17	5	4	5	4	2	1
18	4	4	4	5	2	1
15	3	4	4	4	2	1
17	3	4	4	4	1	1
14	4	2	3	4	2	1
15	4	4	4	4	1	0
17	4	4	4	4	1	1
16	4	4	4	5	1	1
17	4	5	4	5	1	0
15	3	4	3	5	2	1
16	4	4	4	4	2	1
18	5	4	4	4	2	1
18	5	4	5	4	1	1
16	4	5	4	5	2	1
NA	3	4	4	4	2	1
17	5	3	4	5	1	1
15	4	4	4	4	1	1
13	5	4	4	4	2	1
15	3	4	3	4	2	1
17	5	4	5	5	2	1
16	4	4	3	4	2	0
16	4	4	3	4	1	1
15	4	4	4	4	1	1
16	4	4	4	4	1	1
16	3	4	4	5	2	1
13	4	4	4	4	2	1
15	4	4	3	4	2	0
12	3	3	3	5	1	1
19	4	4	3	4	1	1
16	3	4	4	4	2	1
16	4	4	4	3	1	1
17	5	4	5	5	1	1
16	5	4	4	5	2	0
14	4	4	4	4	1	0
15	4	4	3	4	1	0
14	3	4	3	4	1	1
16	4	4	4	4	2	0
15	4	4	4	5	2	1
17	4	5	4	4	2	1
15	3	4	4	4	2	1
16	4	4	3	4	2	1
16	4	4	4	4	1	1
15	3	4	3	4	1	0
15	4	4	3	4	2	0
11	3	2	2	4	2	1
16	4	4	3	5	2	0
18	5	4	3	5	1	1
13	2	4	3	3	2	0
11	3	3	4	4	1	1
16	4	4	3	4	1	1
18	5	5	4	5	1	0
NA	NA	NA	NA	NA	1	1
15	4	5	4	4	2	1
19	5	5	5	5	1	1
17	4	5	4	5	1	1
13	4	4	3	4	1	0
14	3	4	4	5	1	0
16	4	4	4	4	2	1
13	4	4	4	4	1	1
17	4	4	4	5	2	1
14	4	4	4	5	2	1
19	5	4	3	5	2	1
14	4	3	4	4	2	1
16	4	4	4	4	1	1
12	3	3	3	4	1	1
16	4	5	4	4	1	1
16	4	4	3	4	1	1
15	4	4	4	4	2	0
12	3	4	3	5	2	0
15	4	4	4	4	2	1
17	5	4	4	5	2	0
13	4	4	4	3	1	1
15	2	3	4	4	2	1
18	4	4	4	4	1	0
15	4	3	3	5	2	1
18	4	4	4	4	1	0
15	4	5	5	4	1	1
15	5	4	4	4	1	1
16	5	4	3	4	1	1
13	3	3	4	5	1	1
16	4	4	4	4	2	1
13	4	4	4	5	2	1
16	2	3	5	5	1	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&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]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298734&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVSUM[t] = + 7.14991 + 0.494707SK1[t] + 1.13836SK2[t] + 0.314517SK4[t] + 0.190641SK5[t] -0.189145ALG4[t] + 0.255395ALG2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVSUM[t] =  +  7.14991 +  0.494707SK1[t] +  1.13836SK2[t] +  0.314517SK4[t] +  0.190641SK5[t] -0.189145ALG4[t] +  0.255395ALG2[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVSUM[t] =  +  7.14991 +  0.494707SK1[t] +  1.13836SK2[t] +  0.314517SK4[t] +  0.190641SK5[t] -0.189145ALG4[t] +  0.255395ALG2[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298734&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&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
TVSUM[t] = + 7.14991 + 0.494707SK1[t] + 1.13836SK2[t] + 0.314517SK4[t] + 0.190641SK5[t] -0.189145ALG4[t] + 0.255395ALG2[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)	+7.15	1.253	+5.7060e+00	5.555e-08	2.778e-08
SK1	+0.4947	0.1582	+3.1280e+00	0.002096	0.001048
SK2	+1.138	0.1918	+5.9350e+00	1.803e-08	9.017e-09
SK4	+0.3145	0.2088	+1.5060e+00	0.134	0.06702
SK5	+0.1906	0.1807	+1.0550e+00	0.2931	0.1466
ALG4	-0.1891	0.2196	-8.6120e-01	0.3904	0.1952
ALG2	+0.2554	0.2534	+1.0080e+00	0.3151	0.1576

\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) & +7.15 &  1.253 & +5.7060e+00 &  5.555e-08 &  2.778e-08 \tabularnewline
SK1 & +0.4947 &  0.1582 & +3.1280e+00 &  0.002096 &  0.001048 \tabularnewline
SK2 & +1.138 &  0.1918 & +5.9350e+00 &  1.803e-08 &  9.017e-09 \tabularnewline
SK4 & +0.3145 &  0.2088 & +1.5060e+00 &  0.134 &  0.06702 \tabularnewline
SK5 & +0.1906 &  0.1807 & +1.0550e+00 &  0.2931 &  0.1466 \tabularnewline
ALG4 & -0.1891 &  0.2196 & -8.6120e-01 &  0.3904 &  0.1952 \tabularnewline
ALG2 & +0.2554 &  0.2534 & +1.0080e+00 &  0.3151 &  0.1576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&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]+7.15[/C][C] 1.253[/C][C]+5.7060e+00[/C][C] 5.555e-08[/C][C] 2.778e-08[/C][/ROW]
[ROW][C]SK1[/C][C]+0.4947[/C][C] 0.1582[/C][C]+3.1280e+00[/C][C] 0.002096[/C][C] 0.001048[/C][/ROW]
[ROW][C]SK2[/C][C]+1.138[/C][C] 0.1918[/C][C]+5.9350e+00[/C][C] 1.803e-08[/C][C] 9.017e-09[/C][/ROW]
[ROW][C]SK4[/C][C]+0.3145[/C][C] 0.2088[/C][C]+1.5060e+00[/C][C] 0.134[/C][C] 0.06702[/C][/ROW]
[ROW][C]SK5[/C][C]+0.1906[/C][C] 0.1807[/C][C]+1.0550e+00[/C][C] 0.2931[/C][C] 0.1466[/C][/ROW]
[ROW][C]ALG4[/C][C]-0.1891[/C][C] 0.2196[/C][C]-8.6120e-01[/C][C] 0.3904[/C][C] 0.1952[/C][/ROW]
[ROW][C]ALG2[/C][C]+0.2554[/C][C] 0.2534[/C][C]+1.0080e+00[/C][C] 0.3151[/C][C] 0.1576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298734&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&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)	+7.15	1.253	+5.7060e+00	5.555e-08	2.778e-08
SK1	+0.4947	0.1582	+3.1280e+00	0.002096	0.001048
SK2	+1.138	0.1918	+5.9350e+00	1.803e-08	9.017e-09
SK4	+0.3145	0.2088	+1.5060e+00	0.134	0.06702
SK5	+0.1906	0.1807	+1.0550e+00	0.2931	0.1466
ALG4	-0.1891	0.2196	-8.6120e-01	0.3904	0.1952
ALG2	+0.2554	0.2534	+1.0080e+00	0.3151	0.1576

Multiple Linear Regression - Regression Statistics
Multiple R	0.5629
R-squared	0.3168
Adjusted R-squared	0.2909
F-TEST (value)	12.21
F-TEST (DF numerator)	6
F-TEST (DF denominator)	158
p-value	2.907e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.397
Sum Squared Residuals	308.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5629 \tabularnewline
R-squared &  0.3168 \tabularnewline
Adjusted R-squared &  0.2909 \tabularnewline
F-TEST (value) &  12.21 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value &  2.907e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.397 \tabularnewline
Sum Squared Residuals &  308.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5629[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3168[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2909[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 12.21[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C] 2.907e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.397[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 308.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298734&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&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	0.5629
R-squared	0.3168
Adjusted R-squared	0.2909
F-TEST (value)	12.21
F-TEST (DF numerator)	6
F-TEST (DF denominator)	158
p-value	2.907e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.397
Sum Squared Residuals	308.2

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.37	-0.3685
2	16	15.13	0.8731
3	17	15.77	1.229
4	15	14.77	0.2293
5	16	15.96	0.04028
6	16	15.28	0.7241
7	17	14.58	2.42
8	16	15.09	0.9148
9	17	16.84	0.1573
10	17	17.1	-0.09808
11	17	15.96	1.04
12	15	15.39	-0.3898
13	16	15.2	0.8008
14	14	14.14	-0.136
15	16	15.39	0.6122
16	17	15.09	1.915
17	16	15.27	0.7256
18	15	16.9	-1.898
19	17	15.77	1.229
20	16	14.77	1.229
21	15	15.96	-0.9597
22	16	15.58	0.4201
23	15	15.58	-0.5799
24	17	15.77	1.231
25	15	15.27	-0.2744
26	16	14.9	1.105
27	15	15.51	-0.5137
28	16	14.97	1.03
29	16	16.26	-0.2638
30	13	14.63	-1.631
31	15	16.65	-1.654
32	17	16.26	0.7362
33	15	14.38	0.6247
34	13	13.64	-0.6428
35	17	16.91	0.09256
36	15	15.27	-0.2744
37	14	13.87	0.1283
38	14	14.38	-0.3753
39	18	15.51	2.486
40	15	15.82	-0.8192
41	17	16.91	0.09107
42	13	13.69	-0.6915
43	16	17.09	-1.089
44	15	15.5	-0.5047
45	15	15.27	-0.2654
46	16	15.58	0.4201
47	15	15.46	-0.463
48	13	15.77	-2.771
49	17	16.46	0.5371
50	17	17.34	-0.3374
51	17	16.96	0.0424
52	11	14.38	-3.377
53	14	14.32	-0.3162
54	13	15.58	-2.58
55	15	14.77	0.2308
56	17	15.02	1.981
57	16	15.2	0.7993
58	15	15.96	-0.9597
59	17	17.4	-0.4036
60	16	14.53	1.474
61	16	15.77	0.2294
62	16	14.7	1.296
63	15	15.96	-0.9597
64	12	13.3	-1.303
65	17	15.65	1.355
66	14	15.46	-1.456
67	14	15.76	-1.759
68	16	14.96	1.039
69	15	14.77	0.2293
70	15	17.22	-2.223
71	13	15.77	-2.769
72	13	15.32	-2.325
73	17	16.27	0.7258
74	15	15.09	-0.08522
75	16	15.96	0.04028
76	14	14.96	-0.9613
77	15	14.14	0.864
78	17	14.44	2.558
79	16	15.58	0.4201
80	12	14.14	-2.136
81	16	15.96	0.04028
82	17	15.7	1.296
83	17	15.77	1.229
84	20	16.07	3.925
85	17	16.39	0.6108
86	18	15.77	2.229
87	15	15.09	-0.08522
88	17	15.27	1.726
89	14	12.99	1.011
90	15	15.51	-0.5137
91	17	15.77	1.231
92	16	15.96	0.04028
93	17	16.84	0.1573
94	15	14.96	0.03865
95	16	15.58	0.4201
96	18	16.07	1.925
97	18	16.58	1.422
98	16	16.91	-0.9089
99	17	15.32	1.684
100	15	15.77	-0.7691
101	13	16.07	-3.075
102	15	14.77	0.2293
103	17	16.58	0.4202
104	16	15.01	0.99
105	16	15.45	0.5454
106	15	15.77	-0.7691
107	16	15.77	0.2309
108	16	15.28	0.7241
109	13	15.58	-2.58
110	15	15.01	-0.01002
111	12	14.01	-2.012
112	19	15.45	3.545
113	16	15.09	0.9148
114	16	15.58	0.4216
115	17	16.77	0.2311
116	16	16.01	-0.009883
117	14	15.51	-1.514
118	15	15.2	-0.1992
119	14	14.96	-0.9598
120	16	15.32	0.6755
121	15	15.77	-0.7706
122	17	16.72	0.2817
123	15	15.09	-0.08522
124	16	15.27	0.7346
125	16	15.77	0.2309
126	15	14.7	0.2955
127	15	15.01	-0.01002
128	11	12.18	-1.179
129	16	15.2	0.7993
130	18	16.14	1.86
131	13	13.83	-0.83
132	11	14.14	-3.136
133	16	15.45	0.5454
134	18	17.34	0.6626
135	15	16.72	-1.718
136	19	17.91	1.093
137	17	17.1	-0.09808
138	13	15.2	-2.199
139	14	15.21	-1.21
140	16	15.58	0.4201
141	13	15.77	-2.769
142	17	15.77	1.229
143	14	15.77	-1.771
144	19	15.95	3.049
145	14	14.44	-0.4416
146	16	15.77	0.2309
147	12	13.82	-1.821
148	16	16.91	-0.9074
149	16	15.45	0.5454
150	15	15.32	-0.3245
151	12	14.71	-2.706
152	15	15.58	-0.5799
153	17	16.01	0.9901
154	13	15.58	-2.578
155	15	13.45	1.548
156	18	15.51	2.486
157	15	14.32	0.6823
158	18	15.51	2.486
159	15	17.22	-2.222
160	15	16.26	-1.264
161	16	15.95	0.05074
162	13	14.33	-1.327
163	16	15.58	0.4201
164	13	15.77	-2.771
165	16	14.15	1.854

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.37 & -0.3685 \tabularnewline
2 &  16 &  15.13 &  0.8731 \tabularnewline
3 &  17 &  15.77 &  1.229 \tabularnewline
4 &  15 &  14.77 &  0.2293 \tabularnewline
5 &  16 &  15.96 &  0.04028 \tabularnewline
6 &  16 &  15.28 &  0.7241 \tabularnewline
7 &  17 &  14.58 &  2.42 \tabularnewline
8 &  16 &  15.09 &  0.9148 \tabularnewline
9 &  17 &  16.84 &  0.1573 \tabularnewline
10 &  17 &  17.1 & -0.09808 \tabularnewline
11 &  17 &  15.96 &  1.04 \tabularnewline
12 &  15 &  15.39 & -0.3898 \tabularnewline
13 &  16 &  15.2 &  0.8008 \tabularnewline
14 &  14 &  14.14 & -0.136 \tabularnewline
15 &  16 &  15.39 &  0.6122 \tabularnewline
16 &  17 &  15.09 &  1.915 \tabularnewline
17 &  16 &  15.27 &  0.7256 \tabularnewline
18 &  15 &  16.9 & -1.898 \tabularnewline
19 &  17 &  15.77 &  1.229 \tabularnewline
20 &  16 &  14.77 &  1.229 \tabularnewline
21 &  15 &  15.96 & -0.9597 \tabularnewline
22 &  16 &  15.58 &  0.4201 \tabularnewline
23 &  15 &  15.58 & -0.5799 \tabularnewline
24 &  17 &  15.77 &  1.231 \tabularnewline
25 &  15 &  15.27 & -0.2744 \tabularnewline
26 &  16 &  14.9 &  1.105 \tabularnewline
27 &  15 &  15.51 & -0.5137 \tabularnewline
28 &  16 &  14.97 &  1.03 \tabularnewline
29 &  16 &  16.26 & -0.2638 \tabularnewline
30 &  13 &  14.63 & -1.631 \tabularnewline
31 &  15 &  16.65 & -1.654 \tabularnewline
32 &  17 &  16.26 &  0.7362 \tabularnewline
33 &  15 &  14.38 &  0.6247 \tabularnewline
34 &  13 &  13.64 & -0.6428 \tabularnewline
35 &  17 &  16.91 &  0.09256 \tabularnewline
36 &  15 &  15.27 & -0.2744 \tabularnewline
37 &  14 &  13.87 &  0.1283 \tabularnewline
38 &  14 &  14.38 & -0.3753 \tabularnewline
39 &  18 &  15.51 &  2.486 \tabularnewline
40 &  15 &  15.82 & -0.8192 \tabularnewline
41 &  17 &  16.91 &  0.09107 \tabularnewline
42 &  13 &  13.69 & -0.6915 \tabularnewline
43 &  16 &  17.09 & -1.089 \tabularnewline
44 &  15 &  15.5 & -0.5047 \tabularnewline
45 &  15 &  15.27 & -0.2654 \tabularnewline
46 &  16 &  15.58 &  0.4201 \tabularnewline
47 &  15 &  15.46 & -0.463 \tabularnewline
48 &  13 &  15.77 & -2.771 \tabularnewline
49 &  17 &  16.46 &  0.5371 \tabularnewline
50 &  17 &  17.34 & -0.3374 \tabularnewline
51 &  17 &  16.96 &  0.0424 \tabularnewline
52 &  11 &  14.38 & -3.377 \tabularnewline
53 &  14 &  14.32 & -0.3162 \tabularnewline
54 &  13 &  15.58 & -2.58 \tabularnewline
55 &  15 &  14.77 &  0.2308 \tabularnewline
56 &  17 &  15.02 &  1.981 \tabularnewline
57 &  16 &  15.2 &  0.7993 \tabularnewline
58 &  15 &  15.96 & -0.9597 \tabularnewline
59 &  17 &  17.4 & -0.4036 \tabularnewline
60 &  16 &  14.53 &  1.474 \tabularnewline
61 &  16 &  15.77 &  0.2294 \tabularnewline
62 &  16 &  14.7 &  1.296 \tabularnewline
63 &  15 &  15.96 & -0.9597 \tabularnewline
64 &  12 &  13.3 & -1.303 \tabularnewline
65 &  17 &  15.65 &  1.355 \tabularnewline
66 &  14 &  15.46 & -1.456 \tabularnewline
67 &  14 &  15.76 & -1.759 \tabularnewline
68 &  16 &  14.96 &  1.039 \tabularnewline
69 &  15 &  14.77 &  0.2293 \tabularnewline
70 &  15 &  17.22 & -2.223 \tabularnewline
71 &  13 &  15.77 & -2.769 \tabularnewline
72 &  13 &  15.32 & -2.325 \tabularnewline
73 &  17 &  16.27 &  0.7258 \tabularnewline
74 &  15 &  15.09 & -0.08522 \tabularnewline
75 &  16 &  15.96 &  0.04028 \tabularnewline
76 &  14 &  14.96 & -0.9613 \tabularnewline
77 &  15 &  14.14 &  0.864 \tabularnewline
78 &  17 &  14.44 &  2.558 \tabularnewline
79 &  16 &  15.58 &  0.4201 \tabularnewline
80 &  12 &  14.14 & -2.136 \tabularnewline
81 &  16 &  15.96 &  0.04028 \tabularnewline
82 &  17 &  15.7 &  1.296 \tabularnewline
83 &  17 &  15.77 &  1.229 \tabularnewline
84 &  20 &  16.07 &  3.925 \tabularnewline
85 &  17 &  16.39 &  0.6108 \tabularnewline
86 &  18 &  15.77 &  2.229 \tabularnewline
87 &  15 &  15.09 & -0.08522 \tabularnewline
88 &  17 &  15.27 &  1.726 \tabularnewline
89 &  14 &  12.99 &  1.011 \tabularnewline
90 &  15 &  15.51 & -0.5137 \tabularnewline
91 &  17 &  15.77 &  1.231 \tabularnewline
92 &  16 &  15.96 &  0.04028 \tabularnewline
93 &  17 &  16.84 &  0.1573 \tabularnewline
94 &  15 &  14.96 &  0.03865 \tabularnewline
95 &  16 &  15.58 &  0.4201 \tabularnewline
96 &  18 &  16.07 &  1.925 \tabularnewline
97 &  18 &  16.58 &  1.422 \tabularnewline
98 &  16 &  16.91 & -0.9089 \tabularnewline
99 &  17 &  15.32 &  1.684 \tabularnewline
100 &  15 &  15.77 & -0.7691 \tabularnewline
101 &  13 &  16.07 & -3.075 \tabularnewline
102 &  15 &  14.77 &  0.2293 \tabularnewline
103 &  17 &  16.58 &  0.4202 \tabularnewline
104 &  16 &  15.01 &  0.99 \tabularnewline
105 &  16 &  15.45 &  0.5454 \tabularnewline
106 &  15 &  15.77 & -0.7691 \tabularnewline
107 &  16 &  15.77 &  0.2309 \tabularnewline
108 &  16 &  15.28 &  0.7241 \tabularnewline
109 &  13 &  15.58 & -2.58 \tabularnewline
110 &  15 &  15.01 & -0.01002 \tabularnewline
111 &  12 &  14.01 & -2.012 \tabularnewline
112 &  19 &  15.45 &  3.545 \tabularnewline
113 &  16 &  15.09 &  0.9148 \tabularnewline
114 &  16 &  15.58 &  0.4216 \tabularnewline
115 &  17 &  16.77 &  0.2311 \tabularnewline
116 &  16 &  16.01 & -0.009883 \tabularnewline
117 &  14 &  15.51 & -1.514 \tabularnewline
118 &  15 &  15.2 & -0.1992 \tabularnewline
119 &  14 &  14.96 & -0.9598 \tabularnewline
120 &  16 &  15.32 &  0.6755 \tabularnewline
121 &  15 &  15.77 & -0.7706 \tabularnewline
122 &  17 &  16.72 &  0.2817 \tabularnewline
123 &  15 &  15.09 & -0.08522 \tabularnewline
124 &  16 &  15.27 &  0.7346 \tabularnewline
125 &  16 &  15.77 &  0.2309 \tabularnewline
126 &  15 &  14.7 &  0.2955 \tabularnewline
127 &  15 &  15.01 & -0.01002 \tabularnewline
128 &  11 &  12.18 & -1.179 \tabularnewline
129 &  16 &  15.2 &  0.7993 \tabularnewline
130 &  18 &  16.14 &  1.86 \tabularnewline
131 &  13 &  13.83 & -0.83 \tabularnewline
132 &  11 &  14.14 & -3.136 \tabularnewline
133 &  16 &  15.45 &  0.5454 \tabularnewline
134 &  18 &  17.34 &  0.6626 \tabularnewline
135 &  15 &  16.72 & -1.718 \tabularnewline
136 &  19 &  17.91 &  1.093 \tabularnewline
137 &  17 &  17.1 & -0.09808 \tabularnewline
138 &  13 &  15.2 & -2.199 \tabularnewline
139 &  14 &  15.21 & -1.21 \tabularnewline
140 &  16 &  15.58 &  0.4201 \tabularnewline
141 &  13 &  15.77 & -2.769 \tabularnewline
142 &  17 &  15.77 &  1.229 \tabularnewline
143 &  14 &  15.77 & -1.771 \tabularnewline
144 &  19 &  15.95 &  3.049 \tabularnewline
145 &  14 &  14.44 & -0.4416 \tabularnewline
146 &  16 &  15.77 &  0.2309 \tabularnewline
147 &  12 &  13.82 & -1.821 \tabularnewline
148 &  16 &  16.91 & -0.9074 \tabularnewline
149 &  16 &  15.45 &  0.5454 \tabularnewline
150 &  15 &  15.32 & -0.3245 \tabularnewline
151 &  12 &  14.71 & -2.706 \tabularnewline
152 &  15 &  15.58 & -0.5799 \tabularnewline
153 &  17 &  16.01 &  0.9901 \tabularnewline
154 &  13 &  15.58 & -2.578 \tabularnewline
155 &  15 &  13.45 &  1.548 \tabularnewline
156 &  18 &  15.51 &  2.486 \tabularnewline
157 &  15 &  14.32 &  0.6823 \tabularnewline
158 &  18 &  15.51 &  2.486 \tabularnewline
159 &  15 &  17.22 & -2.222 \tabularnewline
160 &  15 &  16.26 & -1.264 \tabularnewline
161 &  16 &  15.95 &  0.05074 \tabularnewline
162 &  13 &  14.33 & -1.327 \tabularnewline
163 &  16 &  15.58 &  0.4201 \tabularnewline
164 &  13 &  15.77 & -2.771 \tabularnewline
165 &  16 &  14.15 &  1.854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&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] 13.37[/C][C]-0.3685[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.13[/C][C] 0.8731[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.77[/C][C] 1.229[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.77[/C][C] 0.2293[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.96[/C][C] 0.04028[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.28[/C][C] 0.7241[/C][/ROW]
[ROW][C]7[/C][C] 17[/C][C] 14.58[/C][C] 2.42[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.09[/C][C] 0.9148[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.84[/C][C] 0.1573[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 17.1[/C][C]-0.09808[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.96[/C][C] 1.04[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.39[/C][C]-0.3898[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.2[/C][C] 0.8008[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.14[/C][C]-0.136[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.39[/C][C] 0.6122[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.09[/C][C] 1.915[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.27[/C][C] 0.7256[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 16.9[/C][C]-1.898[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.77[/C][C] 1.229[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.77[/C][C] 1.229[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.96[/C][C]-0.9597[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.58[/C][C] 0.4201[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.58[/C][C]-0.5799[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.77[/C][C] 1.231[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 15.27[/C][C]-0.2744[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.9[/C][C] 1.105[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.51[/C][C]-0.5137[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.97[/C][C] 1.03[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.26[/C][C]-0.2638[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.63[/C][C]-1.631[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.65[/C][C]-1.654[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.26[/C][C] 0.7362[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.38[/C][C] 0.6247[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.64[/C][C]-0.6428[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.91[/C][C] 0.09256[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.27[/C][C]-0.2744[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 13.87[/C][C] 0.1283[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.38[/C][C]-0.3753[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.51[/C][C] 2.486[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 15.82[/C][C]-0.8192[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 16.91[/C][C] 0.09107[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.69[/C][C]-0.6915[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 17.09[/C][C]-1.089[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.5[/C][C]-0.5047[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.27[/C][C]-0.2654[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.58[/C][C] 0.4201[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.46[/C][C]-0.463[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.77[/C][C]-2.771[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 16.46[/C][C] 0.5371[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 17.34[/C][C]-0.3374[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 16.96[/C][C] 0.0424[/C][/ROW]
[ROW][C]52[/C][C] 11[/C][C] 14.38[/C][C]-3.377[/C][/ROW]
[ROW][C]53[/C][C] 14[/C][C] 14.32[/C][C]-0.3162[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 15.58[/C][C]-2.58[/C][/ROW]
[ROW][C]55[/C][C] 15[/C][C] 14.77[/C][C] 0.2308[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 15.02[/C][C] 1.981[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 15.2[/C][C] 0.7993[/C][/ROW]
[ROW][C]58[/C][C] 15[/C][C] 15.96[/C][C]-0.9597[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 17.4[/C][C]-0.4036[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 14.53[/C][C] 1.474[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.77[/C][C] 0.2294[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 14.7[/C][C] 1.296[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.96[/C][C]-0.9597[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.3[/C][C]-1.303[/C][/ROW]
[ROW][C]65[/C][C] 17[/C][C] 15.65[/C][C] 1.355[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.46[/C][C]-1.456[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.76[/C][C]-1.759[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 14.96[/C][C] 1.039[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.77[/C][C] 0.2293[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 17.22[/C][C]-2.223[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 15.77[/C][C]-2.769[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 15.32[/C][C]-2.325[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.27[/C][C] 0.7258[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.09[/C][C]-0.08522[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.96[/C][C] 0.04028[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 14.96[/C][C]-0.9613[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 14.14[/C][C] 0.864[/C][/ROW]
[ROW][C]78[/C][C] 17[/C][C] 14.44[/C][C] 2.558[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.58[/C][C] 0.4201[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 14.14[/C][C]-2.136[/C][/ROW]
[ROW][C]81[/C][C] 16[/C][C] 15.96[/C][C] 0.04028[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 15.7[/C][C] 1.296[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.77[/C][C] 1.229[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 16.07[/C][C] 3.925[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.39[/C][C] 0.6108[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 15.77[/C][C] 2.229[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.09[/C][C]-0.08522[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.27[/C][C] 1.726[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 12.99[/C][C] 1.011[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 15.51[/C][C]-0.5137[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 15.77[/C][C] 1.231[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.96[/C][C] 0.04028[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.84[/C][C] 0.1573[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.96[/C][C] 0.03865[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 15.58[/C][C] 0.4201[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 16.07[/C][C] 1.925[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.58[/C][C] 1.422[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.91[/C][C]-0.9089[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 15.32[/C][C] 1.684[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 15.77[/C][C]-0.7691[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 16.07[/C][C]-3.075[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.77[/C][C] 0.2293[/C][/ROW]
[ROW][C]103[/C][C] 17[/C][C] 16.58[/C][C] 0.4202[/C][/ROW]
[ROW][C]104[/C][C] 16[/C][C] 15.01[/C][C] 0.99[/C][/ROW]
[ROW][C]105[/C][C] 16[/C][C] 15.45[/C][C] 0.5454[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 15.77[/C][C]-0.7691[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.77[/C][C] 0.2309[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.28[/C][C] 0.7241[/C][/ROW]
[ROW][C]109[/C][C] 13[/C][C] 15.58[/C][C]-2.58[/C][/ROW]
[ROW][C]110[/C][C] 15[/C][C] 15.01[/C][C]-0.01002[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 14.01[/C][C]-2.012[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 15.45[/C][C] 3.545[/C][/ROW]
[ROW][C]113[/C][C] 16[/C][C] 15.09[/C][C] 0.9148[/C][/ROW]
[ROW][C]114[/C][C] 16[/C][C] 15.58[/C][C] 0.4216[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 16.77[/C][C] 0.2311[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 16.01[/C][C]-0.009883[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 15.51[/C][C]-1.514[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.2[/C][C]-0.1992[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 14.96[/C][C]-0.9598[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.32[/C][C] 0.6755[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 15.77[/C][C]-0.7706[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.72[/C][C] 0.2817[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.09[/C][C]-0.08522[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.27[/C][C] 0.7346[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 15.77[/C][C] 0.2309[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 14.7[/C][C] 0.2955[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 15.01[/C][C]-0.01002[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 12.18[/C][C]-1.179[/C][/ROW]
[ROW][C]129[/C][C] 16[/C][C] 15.2[/C][C] 0.7993[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 16.14[/C][C] 1.86[/C][/ROW]
[ROW][C]131[/C][C] 13[/C][C] 13.83[/C][C]-0.83[/C][/ROW]
[ROW][C]132[/C][C] 11[/C][C] 14.14[/C][C]-3.136[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 15.45[/C][C] 0.5454[/C][/ROW]
[ROW][C]134[/C][C] 18[/C][C] 17.34[/C][C] 0.6626[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 16.72[/C][C]-1.718[/C][/ROW]
[ROW][C]136[/C][C] 19[/C][C] 17.91[/C][C] 1.093[/C][/ROW]
[ROW][C]137[/C][C] 17[/C][C] 17.1[/C][C]-0.09808[/C][/ROW]
[ROW][C]138[/C][C] 13[/C][C] 15.2[/C][C]-2.199[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 15.21[/C][C]-1.21[/C][/ROW]
[ROW][C]140[/C][C] 16[/C][C] 15.58[/C][C] 0.4201[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 15.77[/C][C]-2.769[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 15.77[/C][C] 1.229[/C][/ROW]
[ROW][C]143[/C][C] 14[/C][C] 15.77[/C][C]-1.771[/C][/ROW]
[ROW][C]144[/C][C] 19[/C][C] 15.95[/C][C] 3.049[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 14.44[/C][C]-0.4416[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 15.77[/C][C] 0.2309[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 13.82[/C][C]-1.821[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 16.91[/C][C]-0.9074[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 15.45[/C][C] 0.5454[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 15.32[/C][C]-0.3245[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 14.71[/C][C]-2.706[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 15.58[/C][C]-0.5799[/C][/ROW]
[ROW][C]153[/C][C] 17[/C][C] 16.01[/C][C] 0.9901[/C][/ROW]
[ROW][C]154[/C][C] 13[/C][C] 15.58[/C][C]-2.578[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.45[/C][C] 1.548[/C][/ROW]
[ROW][C]156[/C][C] 18[/C][C] 15.51[/C][C] 2.486[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 14.32[/C][C] 0.6823[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 15.51[/C][C] 2.486[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 17.22[/C][C]-2.222[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 16.26[/C][C]-1.264[/C][/ROW]
[ROW][C]161[/C][C] 16[/C][C] 15.95[/C][C] 0.05074[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.33[/C][C]-1.327[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.58[/C][C] 0.4201[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 15.77[/C][C]-2.771[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 14.15[/C][C] 1.854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298734&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&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	13.37	-0.3685
2	16	15.13	0.8731
3	17	15.77	1.229
4	15	14.77	0.2293
5	16	15.96	0.04028
6	16	15.28	0.7241
7	17	14.58	2.42
8	16	15.09	0.9148
9	17	16.84	0.1573
10	17	17.1	-0.09808
11	17	15.96	1.04
12	15	15.39	-0.3898
13	16	15.2	0.8008
14	14	14.14	-0.136
15	16	15.39	0.6122
16	17	15.09	1.915
17	16	15.27	0.7256
18	15	16.9	-1.898
19	17	15.77	1.229
20	16	14.77	1.229
21	15	15.96	-0.9597
22	16	15.58	0.4201
23	15	15.58	-0.5799
24	17	15.77	1.231
25	15	15.27	-0.2744
26	16	14.9	1.105
27	15	15.51	-0.5137
28	16	14.97	1.03
29	16	16.26	-0.2638
30	13	14.63	-1.631
31	15	16.65	-1.654
32	17	16.26	0.7362
33	15	14.38	0.6247
34	13	13.64	-0.6428
35	17	16.91	0.09256
36	15	15.27	-0.2744
37	14	13.87	0.1283
38	14	14.38	-0.3753
39	18	15.51	2.486
40	15	15.82	-0.8192
41	17	16.91	0.09107
42	13	13.69	-0.6915
43	16	17.09	-1.089
44	15	15.5	-0.5047
45	15	15.27	-0.2654
46	16	15.58	0.4201
47	15	15.46	-0.463
48	13	15.77	-2.771
49	17	16.46	0.5371
50	17	17.34	-0.3374
51	17	16.96	0.0424
52	11	14.38	-3.377
53	14	14.32	-0.3162
54	13	15.58	-2.58
55	15	14.77	0.2308
56	17	15.02	1.981
57	16	15.2	0.7993
58	15	15.96	-0.9597
59	17	17.4	-0.4036
60	16	14.53	1.474
61	16	15.77	0.2294
62	16	14.7	1.296
63	15	15.96	-0.9597
64	12	13.3	-1.303
65	17	15.65	1.355
66	14	15.46	-1.456
67	14	15.76	-1.759
68	16	14.96	1.039
69	15	14.77	0.2293
70	15	17.22	-2.223
71	13	15.77	-2.769
72	13	15.32	-2.325
73	17	16.27	0.7258
74	15	15.09	-0.08522
75	16	15.96	0.04028
76	14	14.96	-0.9613
77	15	14.14	0.864
78	17	14.44	2.558
79	16	15.58	0.4201
80	12	14.14	-2.136
81	16	15.96	0.04028
82	17	15.7	1.296
83	17	15.77	1.229
84	20	16.07	3.925
85	17	16.39	0.6108
86	18	15.77	2.229
87	15	15.09	-0.08522
88	17	15.27	1.726
89	14	12.99	1.011
90	15	15.51	-0.5137
91	17	15.77	1.231
92	16	15.96	0.04028
93	17	16.84	0.1573
94	15	14.96	0.03865
95	16	15.58	0.4201
96	18	16.07	1.925
97	18	16.58	1.422
98	16	16.91	-0.9089
99	17	15.32	1.684
100	15	15.77	-0.7691
101	13	16.07	-3.075
102	15	14.77	0.2293
103	17	16.58	0.4202
104	16	15.01	0.99
105	16	15.45	0.5454
106	15	15.77	-0.7691
107	16	15.77	0.2309
108	16	15.28	0.7241
109	13	15.58	-2.58
110	15	15.01	-0.01002
111	12	14.01	-2.012
112	19	15.45	3.545
113	16	15.09	0.9148
114	16	15.58	0.4216
115	17	16.77	0.2311
116	16	16.01	-0.009883
117	14	15.51	-1.514
118	15	15.2	-0.1992
119	14	14.96	-0.9598
120	16	15.32	0.6755
121	15	15.77	-0.7706
122	17	16.72	0.2817
123	15	15.09	-0.08522
124	16	15.27	0.7346
125	16	15.77	0.2309
126	15	14.7	0.2955
127	15	15.01	-0.01002
128	11	12.18	-1.179
129	16	15.2	0.7993
130	18	16.14	1.86
131	13	13.83	-0.83
132	11	14.14	-3.136
133	16	15.45	0.5454
134	18	17.34	0.6626
135	15	16.72	-1.718
136	19	17.91	1.093
137	17	17.1	-0.09808
138	13	15.2	-2.199
139	14	15.21	-1.21
140	16	15.58	0.4201
141	13	15.77	-2.769
142	17	15.77	1.229
143	14	15.77	-1.771
144	19	15.95	3.049
145	14	14.44	-0.4416
146	16	15.77	0.2309
147	12	13.82	-1.821
148	16	16.91	-0.9074
149	16	15.45	0.5454
150	15	15.32	-0.3245
151	12	14.71	-2.706
152	15	15.58	-0.5799
153	17	16.01	0.9901
154	13	15.58	-2.578
155	15	13.45	1.548
156	18	15.51	2.486
157	15	14.32	0.6823
158	18	15.51	2.486
159	15	17.22	-2.222
160	15	16.26	-1.264
161	16	15.95	0.05074
162	13	14.33	-1.327
163	16	15.58	0.4201
164	13	15.77	-2.771
165	16	14.15	1.854

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1218	0.2436	0.8782
11	0.0955	0.191	0.9045
12	0.04047	0.08095	0.9595
13	0.01603	0.03205	0.984
14	0.01111	0.02223	0.9889
15	0.01165	0.02331	0.9883
16	0.008816	0.01763	0.9912
17	0.004503	0.009006	0.9955
18	0.03369	0.06738	0.9663
19	0.02119	0.04238	0.9788
20	0.01337	0.02674	0.9866
21	0.01043	0.02086	0.9896
22	0.006782	0.01356	0.9932
23	0.01025	0.02049	0.9898
24	0.01063	0.02126	0.9894
25	0.009207	0.01841	0.9908
26	0.005871	0.01174	0.9941
27	0.005946	0.01189	0.9941
28	0.003556	0.007112	0.9964
29	0.002071	0.004142	0.9979
30	0.004592	0.009185	0.9954
31	0.01246	0.02492	0.9875
32	0.01251	0.02502	0.9875
33	0.008712	0.01742	0.9913
34	0.0138	0.02759	0.9862
35	0.00901	0.01802	0.991
36	0.006883	0.01377	0.9931
37	0.004411	0.008822	0.9956
38	0.00286	0.005721	0.9971
39	0.01009	0.02018	0.9899
40	0.008102	0.01621	0.9919
41	0.005318	0.01064	0.9947
42	0.004879	0.009757	0.9951
43	0.004057	0.008114	0.9959
44	0.002696	0.005393	0.9973
45	0.001818	0.003637	0.9982
46	0.001164	0.002328	0.9988
47	0.000965	0.00193	0.999
48	0.005021	0.01004	0.995
49	0.003665	0.007331	0.9963
50	0.002446	0.004892	0.9976
51	0.001641	0.003282	0.9984
52	0.01002	0.02003	0.99
53	0.007379	0.01476	0.9926
54	0.01957	0.03915	0.9804
55	0.01612	0.03223	0.9839
56	0.01951	0.03902	0.9805
57	0.01785	0.03571	0.9821
58	0.01522	0.03043	0.9848
59	0.01136	0.02273	0.9886
60	0.01089	0.02178	0.9891
61	0.008234	0.01647	0.9918
62	0.007564	0.01513	0.9924
63	0.006376	0.01275	0.9936
64	0.0057	0.0114	0.9943
65	0.005803	0.01161	0.9942
66	0.005998	0.012	0.994
67	0.007662	0.01532	0.9923
68	0.006522	0.01304	0.9935
69	0.004778	0.009556	0.9952
70	0.007629	0.01526	0.9924
71	0.02167	0.04334	0.9783
72	0.03554	0.07109	0.9645
73	0.03186	0.06372	0.9681
74	0.02468	0.04936	0.9753
75	0.01868	0.03736	0.9813
76	0.01703	0.03405	0.983
77	0.01421	0.02842	0.9858
78	0.03672	0.07344	0.9633
79	0.02968	0.05935	0.9703
80	0.04706	0.09411	0.9529
81	0.03689	0.07378	0.9631
82	0.03606	0.07212	0.9639
83	0.03647	0.07293	0.9635
84	0.1856	0.3713	0.8144
85	0.1642	0.3283	0.8358
86	0.2129	0.4257	0.7871
87	0.1841	0.3681	0.8159
88	0.2083	0.4166	0.7917
89	0.192	0.384	0.808
90	0.1658	0.3315	0.8342
91	0.1614	0.3229	0.8386
92	0.1347	0.2693	0.8653
93	0.1114	0.2228	0.8886
94	0.09154	0.1831	0.9085
95	0.07608	0.1522	0.9239
96	0.09277	0.1855	0.9072
97	0.09439	0.1888	0.9056
98	0.08339	0.1668	0.9166
99	0.09043	0.1809	0.9096
100	0.07754	0.1551	0.9225
101	0.1602	0.3204	0.8398
102	0.1358	0.2716	0.8642
103	0.1135	0.2269	0.8865
104	0.102	0.204	0.898
105	0.08587	0.1717	0.9141
106	0.07271	0.1454	0.9273
107	0.05882	0.1176	0.9412
108	0.05077	0.1015	0.9492
109	0.08289	0.1658	0.9171
110	0.06589	0.1318	0.9341
111	0.07873	0.1575	0.9213
112	0.2371	0.4743	0.7629
113	0.2309	0.4619	0.7691
114	0.2135	0.427	0.7865
115	0.1794	0.3588	0.8206
116	0.1571	0.3142	0.8429
117	0.161	0.322	0.839
118	0.1321	0.2642	0.8679
119	0.1131	0.2262	0.8869
120	0.09329	0.1866	0.9067
121	0.07999	0.16	0.92
122	0.0665	0.133	0.9335
123	0.05448	0.109	0.9455
124	0.04904	0.09808	0.951
125	0.039	0.078	0.961
126	0.03191	0.06382	0.9681
127	0.02332	0.04664	0.9767
128	0.01925	0.0385	0.9808
129	0.01448	0.02895	0.9855
130	0.01629	0.03258	0.9837
131	0.01381	0.02762	0.9862
132	0.03529	0.07058	0.9647
133	0.03315	0.0663	0.9668
134	0.02443	0.04885	0.9756
135	0.01978	0.03957	0.9802
136	0.01569	0.03138	0.9843
137	0.01322	0.02644	0.9868
138	0.01698	0.03397	0.983
139	0.0144	0.0288	0.9856
140	0.01097	0.02195	0.989
141	0.01956	0.03912	0.9804
142	0.02027	0.04055	0.9797
143	0.01803	0.03606	0.982
144	0.1148	0.2296	0.8852
145	0.09345	0.1869	0.9065
146	0.0691	0.1382	0.9309
147	0.09835	0.1967	0.9016
148	0.1035	0.207	0.8965
149	0.1419	0.2837	0.8581
150	0.162	0.324	0.838
151	0.3419	0.6839	0.6581
152	0.2488	0.4976	0.7512
153	0.1773	0.3547	0.8227
154	0.4269	0.8538	0.5731
155	0.5361	0.9278	0.4639

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1218 &  0.2436 &  0.8782 \tabularnewline
11 &  0.0955 &  0.191 &  0.9045 \tabularnewline
12 &  0.04047 &  0.08095 &  0.9595 \tabularnewline
13 &  0.01603 &  0.03205 &  0.984 \tabularnewline
14 &  0.01111 &  0.02223 &  0.9889 \tabularnewline
15 &  0.01165 &  0.02331 &  0.9883 \tabularnewline
16 &  0.008816 &  0.01763 &  0.9912 \tabularnewline
17 &  0.004503 &  0.009006 &  0.9955 \tabularnewline
18 &  0.03369 &  0.06738 &  0.9663 \tabularnewline
19 &  0.02119 &  0.04238 &  0.9788 \tabularnewline
20 &  0.01337 &  0.02674 &  0.9866 \tabularnewline
21 &  0.01043 &  0.02086 &  0.9896 \tabularnewline
22 &  0.006782 &  0.01356 &  0.9932 \tabularnewline
23 &  0.01025 &  0.02049 &  0.9898 \tabularnewline
24 &  0.01063 &  0.02126 &  0.9894 \tabularnewline
25 &  0.009207 &  0.01841 &  0.9908 \tabularnewline
26 &  0.005871 &  0.01174 &  0.9941 \tabularnewline
27 &  0.005946 &  0.01189 &  0.9941 \tabularnewline
28 &  0.003556 &  0.007112 &  0.9964 \tabularnewline
29 &  0.002071 &  0.004142 &  0.9979 \tabularnewline
30 &  0.004592 &  0.009185 &  0.9954 \tabularnewline
31 &  0.01246 &  0.02492 &  0.9875 \tabularnewline
32 &  0.01251 &  0.02502 &  0.9875 \tabularnewline
33 &  0.008712 &  0.01742 &  0.9913 \tabularnewline
34 &  0.0138 &  0.02759 &  0.9862 \tabularnewline
35 &  0.00901 &  0.01802 &  0.991 \tabularnewline
36 &  0.006883 &  0.01377 &  0.9931 \tabularnewline
37 &  0.004411 &  0.008822 &  0.9956 \tabularnewline
38 &  0.00286 &  0.005721 &  0.9971 \tabularnewline
39 &  0.01009 &  0.02018 &  0.9899 \tabularnewline
40 &  0.008102 &  0.01621 &  0.9919 \tabularnewline
41 &  0.005318 &  0.01064 &  0.9947 \tabularnewline
42 &  0.004879 &  0.009757 &  0.9951 \tabularnewline
43 &  0.004057 &  0.008114 &  0.9959 \tabularnewline
44 &  0.002696 &  0.005393 &  0.9973 \tabularnewline
45 &  0.001818 &  0.003637 &  0.9982 \tabularnewline
46 &  0.001164 &  0.002328 &  0.9988 \tabularnewline
47 &  0.000965 &  0.00193 &  0.999 \tabularnewline
48 &  0.005021 &  0.01004 &  0.995 \tabularnewline
49 &  0.003665 &  0.007331 &  0.9963 \tabularnewline
50 &  0.002446 &  0.004892 &  0.9976 \tabularnewline
51 &  0.001641 &  0.003282 &  0.9984 \tabularnewline
52 &  0.01002 &  0.02003 &  0.99 \tabularnewline
53 &  0.007379 &  0.01476 &  0.9926 \tabularnewline
54 &  0.01957 &  0.03915 &  0.9804 \tabularnewline
55 &  0.01612 &  0.03223 &  0.9839 \tabularnewline
56 &  0.01951 &  0.03902 &  0.9805 \tabularnewline
57 &  0.01785 &  0.03571 &  0.9821 \tabularnewline
58 &  0.01522 &  0.03043 &  0.9848 \tabularnewline
59 &  0.01136 &  0.02273 &  0.9886 \tabularnewline
60 &  0.01089 &  0.02178 &  0.9891 \tabularnewline
61 &  0.008234 &  0.01647 &  0.9918 \tabularnewline
62 &  0.007564 &  0.01513 &  0.9924 \tabularnewline
63 &  0.006376 &  0.01275 &  0.9936 \tabularnewline
64 &  0.0057 &  0.0114 &  0.9943 \tabularnewline
65 &  0.005803 &  0.01161 &  0.9942 \tabularnewline
66 &  0.005998 &  0.012 &  0.994 \tabularnewline
67 &  0.007662 &  0.01532 &  0.9923 \tabularnewline
68 &  0.006522 &  0.01304 &  0.9935 \tabularnewline
69 &  0.004778 &  0.009556 &  0.9952 \tabularnewline
70 &  0.007629 &  0.01526 &  0.9924 \tabularnewline
71 &  0.02167 &  0.04334 &  0.9783 \tabularnewline
72 &  0.03554 &  0.07109 &  0.9645 \tabularnewline
73 &  0.03186 &  0.06372 &  0.9681 \tabularnewline
74 &  0.02468 &  0.04936 &  0.9753 \tabularnewline
75 &  0.01868 &  0.03736 &  0.9813 \tabularnewline
76 &  0.01703 &  0.03405 &  0.983 \tabularnewline
77 &  0.01421 &  0.02842 &  0.9858 \tabularnewline
78 &  0.03672 &  0.07344 &  0.9633 \tabularnewline
79 &  0.02968 &  0.05935 &  0.9703 \tabularnewline
80 &  0.04706 &  0.09411 &  0.9529 \tabularnewline
81 &  0.03689 &  0.07378 &  0.9631 \tabularnewline
82 &  0.03606 &  0.07212 &  0.9639 \tabularnewline
83 &  0.03647 &  0.07293 &  0.9635 \tabularnewline
84 &  0.1856 &  0.3713 &  0.8144 \tabularnewline
85 &  0.1642 &  0.3283 &  0.8358 \tabularnewline
86 &  0.2129 &  0.4257 &  0.7871 \tabularnewline
87 &  0.1841 &  0.3681 &  0.8159 \tabularnewline
88 &  0.2083 &  0.4166 &  0.7917 \tabularnewline
89 &  0.192 &  0.384 &  0.808 \tabularnewline
90 &  0.1658 &  0.3315 &  0.8342 \tabularnewline
91 &  0.1614 &  0.3229 &  0.8386 \tabularnewline
92 &  0.1347 &  0.2693 &  0.8653 \tabularnewline
93 &  0.1114 &  0.2228 &  0.8886 \tabularnewline
94 &  0.09154 &  0.1831 &  0.9085 \tabularnewline
95 &  0.07608 &  0.1522 &  0.9239 \tabularnewline
96 &  0.09277 &  0.1855 &  0.9072 \tabularnewline
97 &  0.09439 &  0.1888 &  0.9056 \tabularnewline
98 &  0.08339 &  0.1668 &  0.9166 \tabularnewline
99 &  0.09043 &  0.1809 &  0.9096 \tabularnewline
100 &  0.07754 &  0.1551 &  0.9225 \tabularnewline
101 &  0.1602 &  0.3204 &  0.8398 \tabularnewline
102 &  0.1358 &  0.2716 &  0.8642 \tabularnewline
103 &  0.1135 &  0.2269 &  0.8865 \tabularnewline
104 &  0.102 &  0.204 &  0.898 \tabularnewline
105 &  0.08587 &  0.1717 &  0.9141 \tabularnewline
106 &  0.07271 &  0.1454 &  0.9273 \tabularnewline
107 &  0.05882 &  0.1176 &  0.9412 \tabularnewline
108 &  0.05077 &  0.1015 &  0.9492 \tabularnewline
109 &  0.08289 &  0.1658 &  0.9171 \tabularnewline
110 &  0.06589 &  0.1318 &  0.9341 \tabularnewline
111 &  0.07873 &  0.1575 &  0.9213 \tabularnewline
112 &  0.2371 &  0.4743 &  0.7629 \tabularnewline
113 &  0.2309 &  0.4619 &  0.7691 \tabularnewline
114 &  0.2135 &  0.427 &  0.7865 \tabularnewline
115 &  0.1794 &  0.3588 &  0.8206 \tabularnewline
116 &  0.1571 &  0.3142 &  0.8429 \tabularnewline
117 &  0.161 &  0.322 &  0.839 \tabularnewline
118 &  0.1321 &  0.2642 &  0.8679 \tabularnewline
119 &  0.1131 &  0.2262 &  0.8869 \tabularnewline
120 &  0.09329 &  0.1866 &  0.9067 \tabularnewline
121 &  0.07999 &  0.16 &  0.92 \tabularnewline
122 &  0.0665 &  0.133 &  0.9335 \tabularnewline
123 &  0.05448 &  0.109 &  0.9455 \tabularnewline
124 &  0.04904 &  0.09808 &  0.951 \tabularnewline
125 &  0.039 &  0.078 &  0.961 \tabularnewline
126 &  0.03191 &  0.06382 &  0.9681 \tabularnewline
127 &  0.02332 &  0.04664 &  0.9767 \tabularnewline
128 &  0.01925 &  0.0385 &  0.9808 \tabularnewline
129 &  0.01448 &  0.02895 &  0.9855 \tabularnewline
130 &  0.01629 &  0.03258 &  0.9837 \tabularnewline
131 &  0.01381 &  0.02762 &  0.9862 \tabularnewline
132 &  0.03529 &  0.07058 &  0.9647 \tabularnewline
133 &  0.03315 &  0.0663 &  0.9668 \tabularnewline
134 &  0.02443 &  0.04885 &  0.9756 \tabularnewline
135 &  0.01978 &  0.03957 &  0.9802 \tabularnewline
136 &  0.01569 &  0.03138 &  0.9843 \tabularnewline
137 &  0.01322 &  0.02644 &  0.9868 \tabularnewline
138 &  0.01698 &  0.03397 &  0.983 \tabularnewline
139 &  0.0144 &  0.0288 &  0.9856 \tabularnewline
140 &  0.01097 &  0.02195 &  0.989 \tabularnewline
141 &  0.01956 &  0.03912 &  0.9804 \tabularnewline
142 &  0.02027 &  0.04055 &  0.9797 \tabularnewline
143 &  0.01803 &  0.03606 &  0.982 \tabularnewline
144 &  0.1148 &  0.2296 &  0.8852 \tabularnewline
145 &  0.09345 &  0.1869 &  0.9065 \tabularnewline
146 &  0.0691 &  0.1382 &  0.9309 \tabularnewline
147 &  0.09835 &  0.1967 &  0.9016 \tabularnewline
148 &  0.1035 &  0.207 &  0.8965 \tabularnewline
149 &  0.1419 &  0.2837 &  0.8581 \tabularnewline
150 &  0.162 &  0.324 &  0.838 \tabularnewline
151 &  0.3419 &  0.6839 &  0.6581 \tabularnewline
152 &  0.2488 &  0.4976 &  0.7512 \tabularnewline
153 &  0.1773 &  0.3547 &  0.8227 \tabularnewline
154 &  0.4269 &  0.8538 &  0.5731 \tabularnewline
155 &  0.5361 &  0.9278 &  0.4639 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&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] 0.1218[/C][C] 0.2436[/C][C] 0.8782[/C][/ROW]
[ROW][C]11[/C][C] 0.0955[/C][C] 0.191[/C][C] 0.9045[/C][/ROW]
[ROW][C]12[/C][C] 0.04047[/C][C] 0.08095[/C][C] 0.9595[/C][/ROW]
[ROW][C]13[/C][C] 0.01603[/C][C] 0.03205[/C][C] 0.984[/C][/ROW]
[ROW][C]14[/C][C] 0.01111[/C][C] 0.02223[/C][C] 0.9889[/C][/ROW]
[ROW][C]15[/C][C] 0.01165[/C][C] 0.02331[/C][C] 0.9883[/C][/ROW]
[ROW][C]16[/C][C] 0.008816[/C][C] 0.01763[/C][C] 0.9912[/C][/ROW]
[ROW][C]17[/C][C] 0.004503[/C][C] 0.009006[/C][C] 0.9955[/C][/ROW]
[ROW][C]18[/C][C] 0.03369[/C][C] 0.06738[/C][C] 0.9663[/C][/ROW]
[ROW][C]19[/C][C] 0.02119[/C][C] 0.04238[/C][C] 0.9788[/C][/ROW]
[ROW][C]20[/C][C] 0.01337[/C][C] 0.02674[/C][C] 0.9866[/C][/ROW]
[ROW][C]21[/C][C] 0.01043[/C][C] 0.02086[/C][C] 0.9896[/C][/ROW]
[ROW][C]22[/C][C] 0.006782[/C][C] 0.01356[/C][C] 0.9932[/C][/ROW]
[ROW][C]23[/C][C] 0.01025[/C][C] 0.02049[/C][C] 0.9898[/C][/ROW]
[ROW][C]24[/C][C] 0.01063[/C][C] 0.02126[/C][C] 0.9894[/C][/ROW]
[ROW][C]25[/C][C] 0.009207[/C][C] 0.01841[/C][C] 0.9908[/C][/ROW]
[ROW][C]26[/C][C] 0.005871[/C][C] 0.01174[/C][C] 0.9941[/C][/ROW]
[ROW][C]27[/C][C] 0.005946[/C][C] 0.01189[/C][C] 0.9941[/C][/ROW]
[ROW][C]28[/C][C] 0.003556[/C][C] 0.007112[/C][C] 0.9964[/C][/ROW]
[ROW][C]29[/C][C] 0.002071[/C][C] 0.004142[/C][C] 0.9979[/C][/ROW]
[ROW][C]30[/C][C] 0.004592[/C][C] 0.009185[/C][C] 0.9954[/C][/ROW]
[ROW][C]31[/C][C] 0.01246[/C][C] 0.02492[/C][C] 0.9875[/C][/ROW]
[ROW][C]32[/C][C] 0.01251[/C][C] 0.02502[/C][C] 0.9875[/C][/ROW]
[ROW][C]33[/C][C] 0.008712[/C][C] 0.01742[/C][C] 0.9913[/C][/ROW]
[ROW][C]34[/C][C] 0.0138[/C][C] 0.02759[/C][C] 0.9862[/C][/ROW]
[ROW][C]35[/C][C] 0.00901[/C][C] 0.01802[/C][C] 0.991[/C][/ROW]
[ROW][C]36[/C][C] 0.006883[/C][C] 0.01377[/C][C] 0.9931[/C][/ROW]
[ROW][C]37[/C][C] 0.004411[/C][C] 0.008822[/C][C] 0.9956[/C][/ROW]
[ROW][C]38[/C][C] 0.00286[/C][C] 0.005721[/C][C] 0.9971[/C][/ROW]
[ROW][C]39[/C][C] 0.01009[/C][C] 0.02018[/C][C] 0.9899[/C][/ROW]
[ROW][C]40[/C][C] 0.008102[/C][C] 0.01621[/C][C] 0.9919[/C][/ROW]
[ROW][C]41[/C][C] 0.005318[/C][C] 0.01064[/C][C] 0.9947[/C][/ROW]
[ROW][C]42[/C][C] 0.004879[/C][C] 0.009757[/C][C] 0.9951[/C][/ROW]
[ROW][C]43[/C][C] 0.004057[/C][C] 0.008114[/C][C] 0.9959[/C][/ROW]
[ROW][C]44[/C][C] 0.002696[/C][C] 0.005393[/C][C] 0.9973[/C][/ROW]
[ROW][C]45[/C][C] 0.001818[/C][C] 0.003637[/C][C] 0.9982[/C][/ROW]
[ROW][C]46[/C][C] 0.001164[/C][C] 0.002328[/C][C] 0.9988[/C][/ROW]
[ROW][C]47[/C][C] 0.000965[/C][C] 0.00193[/C][C] 0.999[/C][/ROW]
[ROW][C]48[/C][C] 0.005021[/C][C] 0.01004[/C][C] 0.995[/C][/ROW]
[ROW][C]49[/C][C] 0.003665[/C][C] 0.007331[/C][C] 0.9963[/C][/ROW]
[ROW][C]50[/C][C] 0.002446[/C][C] 0.004892[/C][C] 0.9976[/C][/ROW]
[ROW][C]51[/C][C] 0.001641[/C][C] 0.003282[/C][C] 0.9984[/C][/ROW]
[ROW][C]52[/C][C] 0.01002[/C][C] 0.02003[/C][C] 0.99[/C][/ROW]
[ROW][C]53[/C][C] 0.007379[/C][C] 0.01476[/C][C] 0.9926[/C][/ROW]
[ROW][C]54[/C][C] 0.01957[/C][C] 0.03915[/C][C] 0.9804[/C][/ROW]
[ROW][C]55[/C][C] 0.01612[/C][C] 0.03223[/C][C] 0.9839[/C][/ROW]
[ROW][C]56[/C][C] 0.01951[/C][C] 0.03902[/C][C] 0.9805[/C][/ROW]
[ROW][C]57[/C][C] 0.01785[/C][C] 0.03571[/C][C] 0.9821[/C][/ROW]
[ROW][C]58[/C][C] 0.01522[/C][C] 0.03043[/C][C] 0.9848[/C][/ROW]
[ROW][C]59[/C][C] 0.01136[/C][C] 0.02273[/C][C] 0.9886[/C][/ROW]
[ROW][C]60[/C][C] 0.01089[/C][C] 0.02178[/C][C] 0.9891[/C][/ROW]
[ROW][C]61[/C][C] 0.008234[/C][C] 0.01647[/C][C] 0.9918[/C][/ROW]
[ROW][C]62[/C][C] 0.007564[/C][C] 0.01513[/C][C] 0.9924[/C][/ROW]
[ROW][C]63[/C][C] 0.006376[/C][C] 0.01275[/C][C] 0.9936[/C][/ROW]
[ROW][C]64[/C][C] 0.0057[/C][C] 0.0114[/C][C] 0.9943[/C][/ROW]
[ROW][C]65[/C][C] 0.005803[/C][C] 0.01161[/C][C] 0.9942[/C][/ROW]
[ROW][C]66[/C][C] 0.005998[/C][C] 0.012[/C][C] 0.994[/C][/ROW]
[ROW][C]67[/C][C] 0.007662[/C][C] 0.01532[/C][C] 0.9923[/C][/ROW]
[ROW][C]68[/C][C] 0.006522[/C][C] 0.01304[/C][C] 0.9935[/C][/ROW]
[ROW][C]69[/C][C] 0.004778[/C][C] 0.009556[/C][C] 0.9952[/C][/ROW]
[ROW][C]70[/C][C] 0.007629[/C][C] 0.01526[/C][C] 0.9924[/C][/ROW]
[ROW][C]71[/C][C] 0.02167[/C][C] 0.04334[/C][C] 0.9783[/C][/ROW]
[ROW][C]72[/C][C] 0.03554[/C][C] 0.07109[/C][C] 0.9645[/C][/ROW]
[ROW][C]73[/C][C] 0.03186[/C][C] 0.06372[/C][C] 0.9681[/C][/ROW]
[ROW][C]74[/C][C] 0.02468[/C][C] 0.04936[/C][C] 0.9753[/C][/ROW]
[ROW][C]75[/C][C] 0.01868[/C][C] 0.03736[/C][C] 0.9813[/C][/ROW]
[ROW][C]76[/C][C] 0.01703[/C][C] 0.03405[/C][C] 0.983[/C][/ROW]
[ROW][C]77[/C][C] 0.01421[/C][C] 0.02842[/C][C] 0.9858[/C][/ROW]
[ROW][C]78[/C][C] 0.03672[/C][C] 0.07344[/C][C] 0.9633[/C][/ROW]
[ROW][C]79[/C][C] 0.02968[/C][C] 0.05935[/C][C] 0.9703[/C][/ROW]
[ROW][C]80[/C][C] 0.04706[/C][C] 0.09411[/C][C] 0.9529[/C][/ROW]
[ROW][C]81[/C][C] 0.03689[/C][C] 0.07378[/C][C] 0.9631[/C][/ROW]
[ROW][C]82[/C][C] 0.03606[/C][C] 0.07212[/C][C] 0.9639[/C][/ROW]
[ROW][C]83[/C][C] 0.03647[/C][C] 0.07293[/C][C] 0.9635[/C][/ROW]
[ROW][C]84[/C][C] 0.1856[/C][C] 0.3713[/C][C] 0.8144[/C][/ROW]
[ROW][C]85[/C][C] 0.1642[/C][C] 0.3283[/C][C] 0.8358[/C][/ROW]
[ROW][C]86[/C][C] 0.2129[/C][C] 0.4257[/C][C] 0.7871[/C][/ROW]
[ROW][C]87[/C][C] 0.1841[/C][C] 0.3681[/C][C] 0.8159[/C][/ROW]
[ROW][C]88[/C][C] 0.2083[/C][C] 0.4166[/C][C] 0.7917[/C][/ROW]
[ROW][C]89[/C][C] 0.192[/C][C] 0.384[/C][C] 0.808[/C][/ROW]
[ROW][C]90[/C][C] 0.1658[/C][C] 0.3315[/C][C] 0.8342[/C][/ROW]
[ROW][C]91[/C][C] 0.1614[/C][C] 0.3229[/C][C] 0.8386[/C][/ROW]
[ROW][C]92[/C][C] 0.1347[/C][C] 0.2693[/C][C] 0.8653[/C][/ROW]
[ROW][C]93[/C][C] 0.1114[/C][C] 0.2228[/C][C] 0.8886[/C][/ROW]
[ROW][C]94[/C][C] 0.09154[/C][C] 0.1831[/C][C] 0.9085[/C][/ROW]
[ROW][C]95[/C][C] 0.07608[/C][C] 0.1522[/C][C] 0.9239[/C][/ROW]
[ROW][C]96[/C][C] 0.09277[/C][C] 0.1855[/C][C] 0.9072[/C][/ROW]
[ROW][C]97[/C][C] 0.09439[/C][C] 0.1888[/C][C] 0.9056[/C][/ROW]
[ROW][C]98[/C][C] 0.08339[/C][C] 0.1668[/C][C] 0.9166[/C][/ROW]
[ROW][C]99[/C][C] 0.09043[/C][C] 0.1809[/C][C] 0.9096[/C][/ROW]
[ROW][C]100[/C][C] 0.07754[/C][C] 0.1551[/C][C] 0.9225[/C][/ROW]
[ROW][C]101[/C][C] 0.1602[/C][C] 0.3204[/C][C] 0.8398[/C][/ROW]
[ROW][C]102[/C][C] 0.1358[/C][C] 0.2716[/C][C] 0.8642[/C][/ROW]
[ROW][C]103[/C][C] 0.1135[/C][C] 0.2269[/C][C] 0.8865[/C][/ROW]
[ROW][C]104[/C][C] 0.102[/C][C] 0.204[/C][C] 0.898[/C][/ROW]
[ROW][C]105[/C][C] 0.08587[/C][C] 0.1717[/C][C] 0.9141[/C][/ROW]
[ROW][C]106[/C][C] 0.07271[/C][C] 0.1454[/C][C] 0.9273[/C][/ROW]
[ROW][C]107[/C][C] 0.05882[/C][C] 0.1176[/C][C] 0.9412[/C][/ROW]
[ROW][C]108[/C][C] 0.05077[/C][C] 0.1015[/C][C] 0.9492[/C][/ROW]
[ROW][C]109[/C][C] 0.08289[/C][C] 0.1658[/C][C] 0.9171[/C][/ROW]
[ROW][C]110[/C][C] 0.06589[/C][C] 0.1318[/C][C] 0.9341[/C][/ROW]
[ROW][C]111[/C][C] 0.07873[/C][C] 0.1575[/C][C] 0.9213[/C][/ROW]
[ROW][C]112[/C][C] 0.2371[/C][C] 0.4743[/C][C] 0.7629[/C][/ROW]
[ROW][C]113[/C][C] 0.2309[/C][C] 0.4619[/C][C] 0.7691[/C][/ROW]
[ROW][C]114[/C][C] 0.2135[/C][C] 0.427[/C][C] 0.7865[/C][/ROW]
[ROW][C]115[/C][C] 0.1794[/C][C] 0.3588[/C][C] 0.8206[/C][/ROW]
[ROW][C]116[/C][C] 0.1571[/C][C] 0.3142[/C][C] 0.8429[/C][/ROW]
[ROW][C]117[/C][C] 0.161[/C][C] 0.322[/C][C] 0.839[/C][/ROW]
[ROW][C]118[/C][C] 0.1321[/C][C] 0.2642[/C][C] 0.8679[/C][/ROW]
[ROW][C]119[/C][C] 0.1131[/C][C] 0.2262[/C][C] 0.8869[/C][/ROW]
[ROW][C]120[/C][C] 0.09329[/C][C] 0.1866[/C][C] 0.9067[/C][/ROW]
[ROW][C]121[/C][C] 0.07999[/C][C] 0.16[/C][C] 0.92[/C][/ROW]
[ROW][C]122[/C][C] 0.0665[/C][C] 0.133[/C][C] 0.9335[/C][/ROW]
[ROW][C]123[/C][C] 0.05448[/C][C] 0.109[/C][C] 0.9455[/C][/ROW]
[ROW][C]124[/C][C] 0.04904[/C][C] 0.09808[/C][C] 0.951[/C][/ROW]
[ROW][C]125[/C][C] 0.039[/C][C] 0.078[/C][C] 0.961[/C][/ROW]
[ROW][C]126[/C][C] 0.03191[/C][C] 0.06382[/C][C] 0.9681[/C][/ROW]
[ROW][C]127[/C][C] 0.02332[/C][C] 0.04664[/C][C] 0.9767[/C][/ROW]
[ROW][C]128[/C][C] 0.01925[/C][C] 0.0385[/C][C] 0.9808[/C][/ROW]
[ROW][C]129[/C][C] 0.01448[/C][C] 0.02895[/C][C] 0.9855[/C][/ROW]
[ROW][C]130[/C][C] 0.01629[/C][C] 0.03258[/C][C] 0.9837[/C][/ROW]
[ROW][C]131[/C][C] 0.01381[/C][C] 0.02762[/C][C] 0.9862[/C][/ROW]
[ROW][C]132[/C][C] 0.03529[/C][C] 0.07058[/C][C] 0.9647[/C][/ROW]
[ROW][C]133[/C][C] 0.03315[/C][C] 0.0663[/C][C] 0.9668[/C][/ROW]
[ROW][C]134[/C][C] 0.02443[/C][C] 0.04885[/C][C] 0.9756[/C][/ROW]
[ROW][C]135[/C][C] 0.01978[/C][C] 0.03957[/C][C] 0.9802[/C][/ROW]
[ROW][C]136[/C][C] 0.01569[/C][C] 0.03138[/C][C] 0.9843[/C][/ROW]
[ROW][C]137[/C][C] 0.01322[/C][C] 0.02644[/C][C] 0.9868[/C][/ROW]
[ROW][C]138[/C][C] 0.01698[/C][C] 0.03397[/C][C] 0.983[/C][/ROW]
[ROW][C]139[/C][C] 0.0144[/C][C] 0.0288[/C][C] 0.9856[/C][/ROW]
[ROW][C]140[/C][C] 0.01097[/C][C] 0.02195[/C][C] 0.989[/C][/ROW]
[ROW][C]141[/C][C] 0.01956[/C][C] 0.03912[/C][C] 0.9804[/C][/ROW]
[ROW][C]142[/C][C] 0.02027[/C][C] 0.04055[/C][C] 0.9797[/C][/ROW]
[ROW][C]143[/C][C] 0.01803[/C][C] 0.03606[/C][C] 0.982[/C][/ROW]
[ROW][C]144[/C][C] 0.1148[/C][C] 0.2296[/C][C] 0.8852[/C][/ROW]
[ROW][C]145[/C][C] 0.09345[/C][C] 0.1869[/C][C] 0.9065[/C][/ROW]
[ROW][C]146[/C][C] 0.0691[/C][C] 0.1382[/C][C] 0.9309[/C][/ROW]
[ROW][C]147[/C][C] 0.09835[/C][C] 0.1967[/C][C] 0.9016[/C][/ROW]
[ROW][C]148[/C][C] 0.1035[/C][C] 0.207[/C][C] 0.8965[/C][/ROW]
[ROW][C]149[/C][C] 0.1419[/C][C] 0.2837[/C][C] 0.8581[/C][/ROW]
[ROW][C]150[/C][C] 0.162[/C][C] 0.324[/C][C] 0.838[/C][/ROW]
[ROW][C]151[/C][C] 0.3419[/C][C] 0.6839[/C][C] 0.6581[/C][/ROW]
[ROW][C]152[/C][C] 0.2488[/C][C] 0.4976[/C][C] 0.7512[/C][/ROW]
[ROW][C]153[/C][C] 0.1773[/C][C] 0.3547[/C][C] 0.8227[/C][/ROW]
[ROW][C]154[/C][C] 0.4269[/C][C] 0.8538[/C][C] 0.5731[/C][/ROW]
[ROW][C]155[/C][C] 0.5361[/C][C] 0.9278[/C][C] 0.4639[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298734&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&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	0.1218	0.2436	0.8782
11	0.0955	0.191	0.9045
12	0.04047	0.08095	0.9595
13	0.01603	0.03205	0.984
14	0.01111	0.02223	0.9889
15	0.01165	0.02331	0.9883
16	0.008816	0.01763	0.9912
17	0.004503	0.009006	0.9955
18	0.03369	0.06738	0.9663
19	0.02119	0.04238	0.9788
20	0.01337	0.02674	0.9866
21	0.01043	0.02086	0.9896
22	0.006782	0.01356	0.9932
23	0.01025	0.02049	0.9898
24	0.01063	0.02126	0.9894
25	0.009207	0.01841	0.9908
26	0.005871	0.01174	0.9941
27	0.005946	0.01189	0.9941
28	0.003556	0.007112	0.9964
29	0.002071	0.004142	0.9979
30	0.004592	0.009185	0.9954
31	0.01246	0.02492	0.9875
32	0.01251	0.02502	0.9875
33	0.008712	0.01742	0.9913
34	0.0138	0.02759	0.9862
35	0.00901	0.01802	0.991
36	0.006883	0.01377	0.9931
37	0.004411	0.008822	0.9956
38	0.00286	0.005721	0.9971
39	0.01009	0.02018	0.9899
40	0.008102	0.01621	0.9919
41	0.005318	0.01064	0.9947
42	0.004879	0.009757	0.9951
43	0.004057	0.008114	0.9959
44	0.002696	0.005393	0.9973
45	0.001818	0.003637	0.9982
46	0.001164	0.002328	0.9988
47	0.000965	0.00193	0.999
48	0.005021	0.01004	0.995
49	0.003665	0.007331	0.9963
50	0.002446	0.004892	0.9976
51	0.001641	0.003282	0.9984
52	0.01002	0.02003	0.99
53	0.007379	0.01476	0.9926
54	0.01957	0.03915	0.9804
55	0.01612	0.03223	0.9839
56	0.01951	0.03902	0.9805
57	0.01785	0.03571	0.9821
58	0.01522	0.03043	0.9848
59	0.01136	0.02273	0.9886
60	0.01089	0.02178	0.9891
61	0.008234	0.01647	0.9918
62	0.007564	0.01513	0.9924
63	0.006376	0.01275	0.9936
64	0.0057	0.0114	0.9943
65	0.005803	0.01161	0.9942
66	0.005998	0.012	0.994
67	0.007662	0.01532	0.9923
68	0.006522	0.01304	0.9935
69	0.004778	0.009556	0.9952
70	0.007629	0.01526	0.9924
71	0.02167	0.04334	0.9783
72	0.03554	0.07109	0.9645
73	0.03186	0.06372	0.9681
74	0.02468	0.04936	0.9753
75	0.01868	0.03736	0.9813
76	0.01703	0.03405	0.983
77	0.01421	0.02842	0.9858
78	0.03672	0.07344	0.9633
79	0.02968	0.05935	0.9703
80	0.04706	0.09411	0.9529
81	0.03689	0.07378	0.9631
82	0.03606	0.07212	0.9639
83	0.03647	0.07293	0.9635
84	0.1856	0.3713	0.8144
85	0.1642	0.3283	0.8358
86	0.2129	0.4257	0.7871
87	0.1841	0.3681	0.8159
88	0.2083	0.4166	0.7917
89	0.192	0.384	0.808
90	0.1658	0.3315	0.8342
91	0.1614	0.3229	0.8386
92	0.1347	0.2693	0.8653
93	0.1114	0.2228	0.8886
94	0.09154	0.1831	0.9085
95	0.07608	0.1522	0.9239
96	0.09277	0.1855	0.9072
97	0.09439	0.1888	0.9056
98	0.08339	0.1668	0.9166
99	0.09043	0.1809	0.9096
100	0.07754	0.1551	0.9225
101	0.1602	0.3204	0.8398
102	0.1358	0.2716	0.8642
103	0.1135	0.2269	0.8865
104	0.102	0.204	0.898
105	0.08587	0.1717	0.9141
106	0.07271	0.1454	0.9273
107	0.05882	0.1176	0.9412
108	0.05077	0.1015	0.9492
109	0.08289	0.1658	0.9171
110	0.06589	0.1318	0.9341
111	0.07873	0.1575	0.9213
112	0.2371	0.4743	0.7629
113	0.2309	0.4619	0.7691
114	0.2135	0.427	0.7865
115	0.1794	0.3588	0.8206
116	0.1571	0.3142	0.8429
117	0.161	0.322	0.839
118	0.1321	0.2642	0.8679
119	0.1131	0.2262	0.8869
120	0.09329	0.1866	0.9067
121	0.07999	0.16	0.92
122	0.0665	0.133	0.9335
123	0.05448	0.109	0.9455
124	0.04904	0.09808	0.951
125	0.039	0.078	0.961
126	0.03191	0.06382	0.9681
127	0.02332	0.04664	0.9767
128	0.01925	0.0385	0.9808
129	0.01448	0.02895	0.9855
130	0.01629	0.03258	0.9837
131	0.01381	0.02762	0.9862
132	0.03529	0.07058	0.9647
133	0.03315	0.0663	0.9668
134	0.02443	0.04885	0.9756
135	0.01978	0.03957	0.9802
136	0.01569	0.03138	0.9843
137	0.01322	0.02644	0.9868
138	0.01698	0.03397	0.983
139	0.0144	0.0288	0.9856
140	0.01097	0.02195	0.989
141	0.01956	0.03912	0.9804
142	0.02027	0.04055	0.9797
143	0.01803	0.03606	0.982
144	0.1148	0.2296	0.8852
145	0.09345	0.1869	0.9065
146	0.0691	0.1382	0.9309
147	0.09835	0.1967	0.9016
148	0.1035	0.207	0.8965
149	0.1419	0.2837	0.8581
150	0.162	0.324	0.838
151	0.3419	0.6839	0.6581
152	0.2488	0.4976	0.7512
153	0.1773	0.3547	0.8227
154	0.4269	0.8538	0.5731
155	0.5361	0.9278	0.4639

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	16	0.1096	NOK
5% type I error level	77	0.527397	NOK
10% type I error level	92	0.630137	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 & 16 &  0.1096 & NOK \tabularnewline
5% type I error level & 77 & 0.527397 & NOK \tabularnewline
10% type I error level & 92 & 0.630137 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&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]16[/C][C] 0.1096[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]77[/C][C]0.527397[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]92[/C][C]0.630137[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298734&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&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	16	0.1096	NOK
5% type I error level	77	0.527397	NOK
10% type I error level	92	0.630137	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.5549, df1 = 2, df2 = 156, p-value = 0.2145
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1453, df1 = 12, df2 = 146, p-value = 0.3285
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.51398, df1 = 2, df2 = 156, p-value = 0.5991

\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 = 1.5549, df1 = 2, df2 = 156, p-value = 0.2145
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1453, df1 = 12, df2 = 146, p-value = 0.3285
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.51398, df1 = 2, df2 = 156, p-value = 0.5991
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&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 = 1.5549, df1 = 2, df2 = 156, p-value = 0.2145
[/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 = 1.1453, df1 = 12, df2 = 146, p-value = 0.3285
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.51398, df1 = 2, df2 = 156, p-value = 0.5991
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298734&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&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 = 1.5549, df1 = 2, df2 = 156, p-value = 0.2145
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1453, df1 = 12, df2 = 146, p-value = 0.3285
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.51398, df1 = 2, df2 = 156, p-value = 0.5991

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK4 SK5 ALG4 ALG2 1.100311 1.124616 1.113117 1.031838 1.019182 1.047036

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK4      SK5     ALG4     ALG2 
1.100311 1.124616 1.113117 1.031838 1.019182 1.047036 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298734&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK4      SK5     ALG4     ALG2 
1.100311 1.124616 1.113117 1.031838 1.019182 1.047036 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298734&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298734&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK4 SK5 ALG4 ALG2 1.100311 1.124616 1.113117 1.031838 1.019182 1.047036

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 = 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):

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