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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 10 Dec 2016 18:23:10 +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/10/t148139060858c6pldr5v3lvpx.htm/, Retrieved Fri, 01 Nov 2024 03:30:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298736, Retrieved Fri, 01 Nov 2024 03:30:31 +0000

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Estimated Impact

101

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 17:23:10] [2e11ca31a00cf8de75c33c1af2d59434] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298736&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVSUM[t] = + 6.84284 + 0.499442SK1[t] + 1.12986SK2[t] + 0.335972SK4[t] + 0.181257SK5[t] + 0.24936ALG2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVSUM[t] =  +  6.84284 +  0.499442SK1[t] +  1.12986SK2[t] +  0.335972SK4[t] +  0.181257SK5[t] +  0.24936ALG2[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVSUM[t] =  +  6.84284 +  0.499442SK1[t] +  1.12986SK2[t] +  0.335972SK4[t] +  0.181257SK5[t] +  0.24936ALG2[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298736&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&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] = + 6.84284 + 0.499442SK1[t] + 1.12986SK2[t] + 0.335972SK4[t] + 0.181257SK5[t] + 0.24936ALG2[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)	+6.843	1.2	+5.7010e+00	5.641e-08	2.821e-08
SK1	+0.4994	0.1579	+3.1620e+00	0.001875	0.0009373
SK2	+1.13	0.1914	+5.9030e+00	2.091e-08	1.045e-08
SK4	+0.336	0.2072	+1.6220e+00	0.1068	0.05342
SK5	+0.1813	0.1802	+1.0060e+00	0.3162	0.1581
ALG2	+0.2494	0.2531	+9.8510e-01	0.3261	0.163

\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) & +6.843 &  1.2 & +5.7010e+00 &  5.641e-08 &  2.821e-08 \tabularnewline
SK1 & +0.4994 &  0.1579 & +3.1620e+00 &  0.001875 &  0.0009373 \tabularnewline
SK2 & +1.13 &  0.1914 & +5.9030e+00 &  2.091e-08 &  1.045e-08 \tabularnewline
SK4 & +0.336 &  0.2072 & +1.6220e+00 &  0.1068 &  0.05342 \tabularnewline
SK5 & +0.1813 &  0.1802 & +1.0060e+00 &  0.3162 &  0.1581 \tabularnewline
ALG2 & +0.2494 &  0.2531 & +9.8510e-01 &  0.3261 &  0.163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&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]+6.843[/C][C] 1.2[/C][C]+5.7010e+00[/C][C] 5.641e-08[/C][C] 2.821e-08[/C][/ROW]
[ROW][C]SK1[/C][C]+0.4994[/C][C] 0.1579[/C][C]+3.1620e+00[/C][C] 0.001875[/C][C] 0.0009373[/C][/ROW]
[ROW][C]SK2[/C][C]+1.13[/C][C] 0.1914[/C][C]+5.9030e+00[/C][C] 2.091e-08[/C][C] 1.045e-08[/C][/ROW]
[ROW][C]SK4[/C][C]+0.336[/C][C] 0.2072[/C][C]+1.6220e+00[/C][C] 0.1068[/C][C] 0.05342[/C][/ROW]
[ROW][C]SK5[/C][C]+0.1813[/C][C] 0.1802[/C][C]+1.0060e+00[/C][C] 0.3162[/C][C] 0.1581[/C][/ROW]
[ROW][C]ALG2[/C][C]+0.2494[/C][C] 0.2531[/C][C]+9.8510e-01[/C][C] 0.3261[/C][C] 0.163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298736&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&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)	+6.843	1.2	+5.7010e+00	5.641e-08	2.821e-08
SK1	+0.4994	0.1579	+3.1620e+00	0.001875	0.0009373
SK2	+1.13	0.1914	+5.9030e+00	2.091e-08	1.045e-08
SK4	+0.336	0.2072	+1.6220e+00	0.1068	0.05342
SK5	+0.1813	0.1802	+1.0060e+00	0.3162	0.1581
ALG2	+0.2494	0.2531	+9.8510e-01	0.3261	0.163

Multiple Linear Regression - Regression Statistics
Multiple R	0.56
R-squared	0.3136
Adjusted R-squared	0.292
F-TEST (value)	14.53
F-TEST (DF numerator)	5
F-TEST (DF denominator)	159
p-value	1.014e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.395
Sum Squared Residuals	309.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.56 \tabularnewline
R-squared &  0.3136 \tabularnewline
Adjusted R-squared &  0.292 \tabularnewline
F-TEST (value) &  14.53 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value &  1.014e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.395 \tabularnewline
Sum Squared Residuals &  309.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.56[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3136[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.292[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 14.53[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C] 1.014e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.395[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 309.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298736&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&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	0.56
R-squared	0.3136
Adjusted R-squared	0.292
F-TEST (value)	14.53
F-TEST (DF numerator)	5
F-TEST (DF denominator)	159
p-value	1.014e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.395
Sum Squared Residuals	309.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.26	-0.2639
2	16	15.23	0.7708
3	17	15.86	1.14
4	15	14.84	0.1571
5	16	15.86	0.1404
6	16	15.36	0.6398
7	17	14.66	2.338
8	16	15.18	0.8211
9	17	16.74	0.2599
10	17	16.99	0.01054
11	17	15.86	1.14
12	15	15.27	-0.2743
13	16	15.09	0.907
14	14	14.05	-0.04904
15	16	15.32	0.6842
16	17	15.18	1.821
17	16	15.18	0.8211
18	15	16.97	-1.972
19	17	15.86	1.14
20	16	14.84	1.157
21	15	15.86	-0.8596
22	16	15.68	0.3217
23	15	15.68	-0.6783
24	17	15.68	1.322
25	15	15.18	-0.1789
26	16	14.77	1.225
27	15	15.43	-0.429
28	16	14.86	1.139
29	16	16.18	-0.1778
30	13	14.55	-1.548
31	15	16.74	-1.74
32	17	16.18	0.8222
33	15	14.3	0.7009
34	13	13.73	-0.7309
35	17	16.81	0.1918
36	15	15.18	-0.1789
37	14	13.96	0.03686
38	14	14.3	-0.2991
39	18	15.43	2.571
40	15	15.93	-0.9284
41	17	16.99	0.01054
42	13	13.8	-0.7997
43	16	17.15	-1.153
44	15	15.59	-0.5924
45	15	15.34	-0.3424
46	16	15.68	0.3217
47	15	15.54	-0.5422
48	13	15.86	-2.86
49	17	16.56	0.4412
50	17	17.24	-0.2395
51	17	17.06	-0.05828
52	11	14.48	-3.48
53	14	14.21	-0.2125
54	13	15.68	-2.678
55	15	14.66	0.3383
56	17	14.93	2.07
57	16	15.27	0.7257
58	15	15.86	-0.8596
59	17	17.49	-0.4889
60	16	14.61	1.389
61	16	15.86	0.1404
62	16	14.82	1.184
63	15	15.86	-0.8596
64	12	13.42	-1.419
65	17	15.52	1.476
66	14	15.52	-1.524
67	14	15.66	-1.661
68	16	15.02	0.9758
69	15	14.84	0.1571
70	15	17.33	-2.325
71	13	15.68	-2.678
72	13	15.43	-2.429
73	17	16.2	0.8044
74	15	15.18	-0.1789
75	16	15.86	0.1404
76	14	15.02	-1.024
77	15	14.05	0.951
78	17	14.55	2.452
79	16	15.68	0.3217
80	12	14.05	-2.049
81	16	15.86	0.1404
82	17	15.61	1.39
83	17	15.86	1.14
84	20	16.18	3.822
85	17	16.51	0.4862
86	18	15.86	2.14
87	15	15.18	-0.1789
88	17	15.18	1.821
89	14	13.08	0.9174
90	15	15.43	-0.429
91	17	15.68	1.322
92	16	15.86	0.1404
93	17	16.74	0.2599
94	15	15.02	-0.02418
95	16	15.68	0.3217
96	18	16.18	1.822
97	18	16.51	1.486
98	16	16.99	-0.9895
99	17	15.23	1.771
100	15	15.68	-0.6783
101	13	16.18	-3.178
102	15	14.84	0.1571
103	17	16.7	0.305
104	16	15.09	0.907
105	16	15.34	0.6576
106	15	15.68	-0.6783
107	16	15.68	0.3217
108	16	15.36	0.6398
109	13	15.68	-2.678
110	15	15.09	-0.09301
111	12	13.89	-1.894
112	19	15.34	3.658
113	16	15.18	0.8211
114	16	15.5	0.5029
115	17	16.7	0.305
116	16	16.11	-0.1097
117	14	15.43	-1.429
118	15	15.09	-0.09301
119	14	14.84	-0.8429
120	16	15.43	0.571
121	15	15.86	-0.8596
122	17	16.81	0.1918
123	15	15.18	-0.1789
124	16	15.34	0.6576
125	16	15.68	0.3217
126	15	14.59	0.4064
127	15	15.09	-0.09301
128	11	12.25	-1.247
129	16	15.27	0.7257
130	18	16.02	1.977
131	13	13.91	-0.9129
132	11	14.05	-3.049
133	16	15.34	0.6576
134	18	17.24	0.7605
135	15	16.81	-1.808
136	19	17.82	1.175
137	17	16.99	0.01054
138	13	15.09	-2.093
139	14	15.11	-1.111
140	16	15.68	0.3217
141	13	15.68	-2.678
142	17	15.86	1.14
143	14	15.86	-1.86
144	19	16.02	2.977
145	14	14.55	-0.5485
146	16	15.68	0.3217
147	12	13.71	-1.713
148	16	16.81	-0.8082
149	16	15.34	0.6576
150	15	15.43	-0.429
151	12	14.77	-2.775
152	15	15.68	-0.6783
153	17	16.11	0.8903
154	13	15.5	-2.497
155	15	13.55	1.45
156	18	15.43	2.571
157	15	14.39	0.6062
158	18	15.43	2.571
159	15	17.14	-2.144
160	15	16.18	-1.178
161	16	15.84	0.1582
162	13	14.23	-1.23
163	16	15.68	0.3217
164	13	15.86	-2.86
165	16	14.07	1.933

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.26 & -0.2639 \tabularnewline
2 &  16 &  15.23 &  0.7708 \tabularnewline
3 &  17 &  15.86 &  1.14 \tabularnewline
4 &  15 &  14.84 &  0.1571 \tabularnewline
5 &  16 &  15.86 &  0.1404 \tabularnewline
6 &  16 &  15.36 &  0.6398 \tabularnewline
7 &  17 &  14.66 &  2.338 \tabularnewline
8 &  16 &  15.18 &  0.8211 \tabularnewline
9 &  17 &  16.74 &  0.2599 \tabularnewline
10 &  17 &  16.99 &  0.01054 \tabularnewline
11 &  17 &  15.86 &  1.14 \tabularnewline
12 &  15 &  15.27 & -0.2743 \tabularnewline
13 &  16 &  15.09 &  0.907 \tabularnewline
14 &  14 &  14.05 & -0.04904 \tabularnewline
15 &  16 &  15.32 &  0.6842 \tabularnewline
16 &  17 &  15.18 &  1.821 \tabularnewline
17 &  16 &  15.18 &  0.8211 \tabularnewline
18 &  15 &  16.97 & -1.972 \tabularnewline
19 &  17 &  15.86 &  1.14 \tabularnewline
20 &  16 &  14.84 &  1.157 \tabularnewline
21 &  15 &  15.86 & -0.8596 \tabularnewline
22 &  16 &  15.68 &  0.3217 \tabularnewline
23 &  15 &  15.68 & -0.6783 \tabularnewline
24 &  17 &  15.68 &  1.322 \tabularnewline
25 &  15 &  15.18 & -0.1789 \tabularnewline
26 &  16 &  14.77 &  1.225 \tabularnewline
27 &  15 &  15.43 & -0.429 \tabularnewline
28 &  16 &  14.86 &  1.139 \tabularnewline
29 &  16 &  16.18 & -0.1778 \tabularnewline
30 &  13 &  14.55 & -1.548 \tabularnewline
31 &  15 &  16.74 & -1.74 \tabularnewline
32 &  17 &  16.18 &  0.8222 \tabularnewline
33 &  15 &  14.3 &  0.7009 \tabularnewline
34 &  13 &  13.73 & -0.7309 \tabularnewline
35 &  17 &  16.81 &  0.1918 \tabularnewline
36 &  15 &  15.18 & -0.1789 \tabularnewline
37 &  14 &  13.96 &  0.03686 \tabularnewline
38 &  14 &  14.3 & -0.2991 \tabularnewline
39 &  18 &  15.43 &  2.571 \tabularnewline
40 &  15 &  15.93 & -0.9284 \tabularnewline
41 &  17 &  16.99 &  0.01054 \tabularnewline
42 &  13 &  13.8 & -0.7997 \tabularnewline
43 &  16 &  17.15 & -1.153 \tabularnewline
44 &  15 &  15.59 & -0.5924 \tabularnewline
45 &  15 &  15.34 & -0.3424 \tabularnewline
46 &  16 &  15.68 &  0.3217 \tabularnewline
47 &  15 &  15.54 & -0.5422 \tabularnewline
48 &  13 &  15.86 & -2.86 \tabularnewline
49 &  17 &  16.56 &  0.4412 \tabularnewline
50 &  17 &  17.24 & -0.2395 \tabularnewline
51 &  17 &  17.06 & -0.05828 \tabularnewline
52 &  11 &  14.48 & -3.48 \tabularnewline
53 &  14 &  14.21 & -0.2125 \tabularnewline
54 &  13 &  15.68 & -2.678 \tabularnewline
55 &  15 &  14.66 &  0.3383 \tabularnewline
56 &  17 &  14.93 &  2.07 \tabularnewline
57 &  16 &  15.27 &  0.7257 \tabularnewline
58 &  15 &  15.86 & -0.8596 \tabularnewline
59 &  17 &  17.49 & -0.4889 \tabularnewline
60 &  16 &  14.61 &  1.389 \tabularnewline
61 &  16 &  15.86 &  0.1404 \tabularnewline
62 &  16 &  14.82 &  1.184 \tabularnewline
63 &  15 &  15.86 & -0.8596 \tabularnewline
64 &  12 &  13.42 & -1.419 \tabularnewline
65 &  17 &  15.52 &  1.476 \tabularnewline
66 &  14 &  15.52 & -1.524 \tabularnewline
67 &  14 &  15.66 & -1.661 \tabularnewline
68 &  16 &  15.02 &  0.9758 \tabularnewline
69 &  15 &  14.84 &  0.1571 \tabularnewline
70 &  15 &  17.33 & -2.325 \tabularnewline
71 &  13 &  15.68 & -2.678 \tabularnewline
72 &  13 &  15.43 & -2.429 \tabularnewline
73 &  17 &  16.2 &  0.8044 \tabularnewline
74 &  15 &  15.18 & -0.1789 \tabularnewline
75 &  16 &  15.86 &  0.1404 \tabularnewline
76 &  14 &  15.02 & -1.024 \tabularnewline
77 &  15 &  14.05 &  0.951 \tabularnewline
78 &  17 &  14.55 &  2.452 \tabularnewline
79 &  16 &  15.68 &  0.3217 \tabularnewline
80 &  12 &  14.05 & -2.049 \tabularnewline
81 &  16 &  15.86 &  0.1404 \tabularnewline
82 &  17 &  15.61 &  1.39 \tabularnewline
83 &  17 &  15.86 &  1.14 \tabularnewline
84 &  20 &  16.18 &  3.822 \tabularnewline
85 &  17 &  16.51 &  0.4862 \tabularnewline
86 &  18 &  15.86 &  2.14 \tabularnewline
87 &  15 &  15.18 & -0.1789 \tabularnewline
88 &  17 &  15.18 &  1.821 \tabularnewline
89 &  14 &  13.08 &  0.9174 \tabularnewline
90 &  15 &  15.43 & -0.429 \tabularnewline
91 &  17 &  15.68 &  1.322 \tabularnewline
92 &  16 &  15.86 &  0.1404 \tabularnewline
93 &  17 &  16.74 &  0.2599 \tabularnewline
94 &  15 &  15.02 & -0.02418 \tabularnewline
95 &  16 &  15.68 &  0.3217 \tabularnewline
96 &  18 &  16.18 &  1.822 \tabularnewline
97 &  18 &  16.51 &  1.486 \tabularnewline
98 &  16 &  16.99 & -0.9895 \tabularnewline
99 &  17 &  15.23 &  1.771 \tabularnewline
100 &  15 &  15.68 & -0.6783 \tabularnewline
101 &  13 &  16.18 & -3.178 \tabularnewline
102 &  15 &  14.84 &  0.1571 \tabularnewline
103 &  17 &  16.7 &  0.305 \tabularnewline
104 &  16 &  15.09 &  0.907 \tabularnewline
105 &  16 &  15.34 &  0.6576 \tabularnewline
106 &  15 &  15.68 & -0.6783 \tabularnewline
107 &  16 &  15.68 &  0.3217 \tabularnewline
108 &  16 &  15.36 &  0.6398 \tabularnewline
109 &  13 &  15.68 & -2.678 \tabularnewline
110 &  15 &  15.09 & -0.09301 \tabularnewline
111 &  12 &  13.89 & -1.894 \tabularnewline
112 &  19 &  15.34 &  3.658 \tabularnewline
113 &  16 &  15.18 &  0.8211 \tabularnewline
114 &  16 &  15.5 &  0.5029 \tabularnewline
115 &  17 &  16.7 &  0.305 \tabularnewline
116 &  16 &  16.11 & -0.1097 \tabularnewline
117 &  14 &  15.43 & -1.429 \tabularnewline
118 &  15 &  15.09 & -0.09301 \tabularnewline
119 &  14 &  14.84 & -0.8429 \tabularnewline
120 &  16 &  15.43 &  0.571 \tabularnewline
121 &  15 &  15.86 & -0.8596 \tabularnewline
122 &  17 &  16.81 &  0.1918 \tabularnewline
123 &  15 &  15.18 & -0.1789 \tabularnewline
124 &  16 &  15.34 &  0.6576 \tabularnewline
125 &  16 &  15.68 &  0.3217 \tabularnewline
126 &  15 &  14.59 &  0.4064 \tabularnewline
127 &  15 &  15.09 & -0.09301 \tabularnewline
128 &  11 &  12.25 & -1.247 \tabularnewline
129 &  16 &  15.27 &  0.7257 \tabularnewline
130 &  18 &  16.02 &  1.977 \tabularnewline
131 &  13 &  13.91 & -0.9129 \tabularnewline
132 &  11 &  14.05 & -3.049 \tabularnewline
133 &  16 &  15.34 &  0.6576 \tabularnewline
134 &  18 &  17.24 &  0.7605 \tabularnewline
135 &  15 &  16.81 & -1.808 \tabularnewline
136 &  19 &  17.82 &  1.175 \tabularnewline
137 &  17 &  16.99 &  0.01054 \tabularnewline
138 &  13 &  15.09 & -2.093 \tabularnewline
139 &  14 &  15.11 & -1.111 \tabularnewline
140 &  16 &  15.68 &  0.3217 \tabularnewline
141 &  13 &  15.68 & -2.678 \tabularnewline
142 &  17 &  15.86 &  1.14 \tabularnewline
143 &  14 &  15.86 & -1.86 \tabularnewline
144 &  19 &  16.02 &  2.977 \tabularnewline
145 &  14 &  14.55 & -0.5485 \tabularnewline
146 &  16 &  15.68 &  0.3217 \tabularnewline
147 &  12 &  13.71 & -1.713 \tabularnewline
148 &  16 &  16.81 & -0.8082 \tabularnewline
149 &  16 &  15.34 &  0.6576 \tabularnewline
150 &  15 &  15.43 & -0.429 \tabularnewline
151 &  12 &  14.77 & -2.775 \tabularnewline
152 &  15 &  15.68 & -0.6783 \tabularnewline
153 &  17 &  16.11 &  0.8903 \tabularnewline
154 &  13 &  15.5 & -2.497 \tabularnewline
155 &  15 &  13.55 &  1.45 \tabularnewline
156 &  18 &  15.43 &  2.571 \tabularnewline
157 &  15 &  14.39 &  0.6062 \tabularnewline
158 &  18 &  15.43 &  2.571 \tabularnewline
159 &  15 &  17.14 & -2.144 \tabularnewline
160 &  15 &  16.18 & -1.178 \tabularnewline
161 &  16 &  15.84 &  0.1582 \tabularnewline
162 &  13 &  14.23 & -1.23 \tabularnewline
163 &  16 &  15.68 &  0.3217 \tabularnewline
164 &  13 &  15.86 & -2.86 \tabularnewline
165 &  16 &  14.07 &  1.933 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&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] 13.26[/C][C]-0.2639[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.23[/C][C] 0.7708[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.86[/C][C] 1.14[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.84[/C][C] 0.1571[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.86[/C][C] 0.1404[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.36[/C][C] 0.6398[/C][/ROW]
[ROW][C]7[/C][C] 17[/C][C] 14.66[/C][C] 2.338[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.18[/C][C] 0.8211[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.74[/C][C] 0.2599[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 16.99[/C][C] 0.01054[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.86[/C][C] 1.14[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.27[/C][C]-0.2743[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.09[/C][C] 0.907[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.05[/C][C]-0.04904[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.32[/C][C] 0.6842[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.18[/C][C] 1.821[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.18[/C][C] 0.8211[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 16.97[/C][C]-1.972[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.86[/C][C] 1.14[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.84[/C][C] 1.157[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.86[/C][C]-0.8596[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.68[/C][C]-0.6783[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.68[/C][C] 1.322[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 15.18[/C][C]-0.1789[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.77[/C][C] 1.225[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.43[/C][C]-0.429[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.86[/C][C] 1.139[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.18[/C][C]-0.1778[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.55[/C][C]-1.548[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.74[/C][C]-1.74[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.18[/C][C] 0.8222[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.3[/C][C] 0.7009[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.73[/C][C]-0.7309[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.81[/C][C] 0.1918[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.18[/C][C]-0.1789[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 13.96[/C][C] 0.03686[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.3[/C][C]-0.2991[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.43[/C][C] 2.571[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 15.93[/C][C]-0.9284[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 16.99[/C][C] 0.01054[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.8[/C][C]-0.7997[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 17.15[/C][C]-1.153[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.59[/C][C]-0.5924[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.34[/C][C]-0.3424[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.54[/C][C]-0.5422[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.86[/C][C]-2.86[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 16.56[/C][C] 0.4412[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 17.24[/C][C]-0.2395[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 17.06[/C][C]-0.05828[/C][/ROW]
[ROW][C]52[/C][C] 11[/C][C] 14.48[/C][C]-3.48[/C][/ROW]
[ROW][C]53[/C][C] 14[/C][C] 14.21[/C][C]-0.2125[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 15.68[/C][C]-2.678[/C][/ROW]
[ROW][C]55[/C][C] 15[/C][C] 14.66[/C][C] 0.3383[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 14.93[/C][C] 2.07[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 15.27[/C][C] 0.7257[/C][/ROW]
[ROW][C]58[/C][C] 15[/C][C] 15.86[/C][C]-0.8596[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 17.49[/C][C]-0.4889[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 14.61[/C][C] 1.389[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.86[/C][C] 0.1404[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 14.82[/C][C] 1.184[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.86[/C][C]-0.8596[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.42[/C][C]-1.419[/C][/ROW]
[ROW][C]65[/C][C] 17[/C][C] 15.52[/C][C] 1.476[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.52[/C][C]-1.524[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.66[/C][C]-1.661[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.02[/C][C] 0.9758[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.84[/C][C] 0.1571[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 17.33[/C][C]-2.325[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 15.68[/C][C]-2.678[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 15.43[/C][C]-2.429[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.2[/C][C] 0.8044[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.18[/C][C]-0.1789[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.86[/C][C] 0.1404[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 15.02[/C][C]-1.024[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 14.05[/C][C] 0.951[/C][/ROW]
[ROW][C]78[/C][C] 17[/C][C] 14.55[/C][C] 2.452[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 14.05[/C][C]-2.049[/C][/ROW]
[ROW][C]81[/C][C] 16[/C][C] 15.86[/C][C] 0.1404[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 15.61[/C][C] 1.39[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.86[/C][C] 1.14[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 16.18[/C][C] 3.822[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.51[/C][C] 0.4862[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 15.86[/C][C] 2.14[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.18[/C][C]-0.1789[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.18[/C][C] 1.821[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 13.08[/C][C] 0.9174[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 15.43[/C][C]-0.429[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 15.68[/C][C] 1.322[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.86[/C][C] 0.1404[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.74[/C][C] 0.2599[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 15.02[/C][C]-0.02418[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 16.18[/C][C] 1.822[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.51[/C][C] 1.486[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.99[/C][C]-0.9895[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 15.23[/C][C] 1.771[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 15.68[/C][C]-0.6783[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 16.18[/C][C]-3.178[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.84[/C][C] 0.1571[/C][/ROW]
[ROW][C]103[/C][C] 17[/C][C] 16.7[/C][C] 0.305[/C][/ROW]
[ROW][C]104[/C][C] 16[/C][C] 15.09[/C][C] 0.907[/C][/ROW]
[ROW][C]105[/C][C] 16[/C][C] 15.34[/C][C] 0.6576[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 15.68[/C][C]-0.6783[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.36[/C][C] 0.6398[/C][/ROW]
[ROW][C]109[/C][C] 13[/C][C] 15.68[/C][C]-2.678[/C][/ROW]
[ROW][C]110[/C][C] 15[/C][C] 15.09[/C][C]-0.09301[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 13.89[/C][C]-1.894[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 15.34[/C][C] 3.658[/C][/ROW]
[ROW][C]113[/C][C] 16[/C][C] 15.18[/C][C] 0.8211[/C][/ROW]
[ROW][C]114[/C][C] 16[/C][C] 15.5[/C][C] 0.5029[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 16.7[/C][C] 0.305[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 16.11[/C][C]-0.1097[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 15.43[/C][C]-1.429[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.09[/C][C]-0.09301[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 14.84[/C][C]-0.8429[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.43[/C][C] 0.571[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 15.86[/C][C]-0.8596[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.81[/C][C] 0.1918[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.18[/C][C]-0.1789[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.34[/C][C] 0.6576[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 14.59[/C][C] 0.4064[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 15.09[/C][C]-0.09301[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 12.25[/C][C]-1.247[/C][/ROW]
[ROW][C]129[/C][C] 16[/C][C] 15.27[/C][C] 0.7257[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 16.02[/C][C] 1.977[/C][/ROW]
[ROW][C]131[/C][C] 13[/C][C] 13.91[/C][C]-0.9129[/C][/ROW]
[ROW][C]132[/C][C] 11[/C][C] 14.05[/C][C]-3.049[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 15.34[/C][C] 0.6576[/C][/ROW]
[ROW][C]134[/C][C] 18[/C][C] 17.24[/C][C] 0.7605[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 16.81[/C][C]-1.808[/C][/ROW]
[ROW][C]136[/C][C] 19[/C][C] 17.82[/C][C] 1.175[/C][/ROW]
[ROW][C]137[/C][C] 17[/C][C] 16.99[/C][C] 0.01054[/C][/ROW]
[ROW][C]138[/C][C] 13[/C][C] 15.09[/C][C]-2.093[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 15.11[/C][C]-1.111[/C][/ROW]
[ROW][C]140[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 15.68[/C][C]-2.678[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 15.86[/C][C] 1.14[/C][/ROW]
[ROW][C]143[/C][C] 14[/C][C] 15.86[/C][C]-1.86[/C][/ROW]
[ROW][C]144[/C][C] 19[/C][C] 16.02[/C][C] 2.977[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 14.55[/C][C]-0.5485[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 13.71[/C][C]-1.713[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 16.81[/C][C]-0.8082[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 15.34[/C][C] 0.6576[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 15.43[/C][C]-0.429[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 14.77[/C][C]-2.775[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 15.68[/C][C]-0.6783[/C][/ROW]
[ROW][C]153[/C][C] 17[/C][C] 16.11[/C][C] 0.8903[/C][/ROW]
[ROW][C]154[/C][C] 13[/C][C] 15.5[/C][C]-2.497[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.55[/C][C] 1.45[/C][/ROW]
[ROW][C]156[/C][C] 18[/C][C] 15.43[/C][C] 2.571[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 14.39[/C][C] 0.6062[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 15.43[/C][C] 2.571[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 17.14[/C][C]-2.144[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 16.18[/C][C]-1.178[/C][/ROW]
[ROW][C]161[/C][C] 16[/C][C] 15.84[/C][C] 0.1582[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.23[/C][C]-1.23[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.68[/C][C] 0.3217[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 15.86[/C][C]-2.86[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 14.07[/C][C] 1.933[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298736&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&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	13.26	-0.2639
2	16	15.23	0.7708
3	17	15.86	1.14
4	15	14.84	0.1571
5	16	15.86	0.1404
6	16	15.36	0.6398
7	17	14.66	2.338
8	16	15.18	0.8211
9	17	16.74	0.2599
10	17	16.99	0.01054
11	17	15.86	1.14
12	15	15.27	-0.2743
13	16	15.09	0.907
14	14	14.05	-0.04904
15	16	15.32	0.6842
16	17	15.18	1.821
17	16	15.18	0.8211
18	15	16.97	-1.972
19	17	15.86	1.14
20	16	14.84	1.157
21	15	15.86	-0.8596
22	16	15.68	0.3217
23	15	15.68	-0.6783
24	17	15.68	1.322
25	15	15.18	-0.1789
26	16	14.77	1.225
27	15	15.43	-0.429
28	16	14.86	1.139
29	16	16.18	-0.1778
30	13	14.55	-1.548
31	15	16.74	-1.74
32	17	16.18	0.8222
33	15	14.3	0.7009
34	13	13.73	-0.7309
35	17	16.81	0.1918
36	15	15.18	-0.1789
37	14	13.96	0.03686
38	14	14.3	-0.2991
39	18	15.43	2.571
40	15	15.93	-0.9284
41	17	16.99	0.01054
42	13	13.8	-0.7997
43	16	17.15	-1.153
44	15	15.59	-0.5924
45	15	15.34	-0.3424
46	16	15.68	0.3217
47	15	15.54	-0.5422
48	13	15.86	-2.86
49	17	16.56	0.4412
50	17	17.24	-0.2395
51	17	17.06	-0.05828
52	11	14.48	-3.48
53	14	14.21	-0.2125
54	13	15.68	-2.678
55	15	14.66	0.3383
56	17	14.93	2.07
57	16	15.27	0.7257
58	15	15.86	-0.8596
59	17	17.49	-0.4889
60	16	14.61	1.389
61	16	15.86	0.1404
62	16	14.82	1.184
63	15	15.86	-0.8596
64	12	13.42	-1.419
65	17	15.52	1.476
66	14	15.52	-1.524
67	14	15.66	-1.661
68	16	15.02	0.9758
69	15	14.84	0.1571
70	15	17.33	-2.325
71	13	15.68	-2.678
72	13	15.43	-2.429
73	17	16.2	0.8044
74	15	15.18	-0.1789
75	16	15.86	0.1404
76	14	15.02	-1.024
77	15	14.05	0.951
78	17	14.55	2.452
79	16	15.68	0.3217
80	12	14.05	-2.049
81	16	15.86	0.1404
82	17	15.61	1.39
83	17	15.86	1.14
84	20	16.18	3.822
85	17	16.51	0.4862
86	18	15.86	2.14
87	15	15.18	-0.1789
88	17	15.18	1.821
89	14	13.08	0.9174
90	15	15.43	-0.429
91	17	15.68	1.322
92	16	15.86	0.1404
93	17	16.74	0.2599
94	15	15.02	-0.02418
95	16	15.68	0.3217
96	18	16.18	1.822
97	18	16.51	1.486
98	16	16.99	-0.9895
99	17	15.23	1.771
100	15	15.68	-0.6783
101	13	16.18	-3.178
102	15	14.84	0.1571
103	17	16.7	0.305
104	16	15.09	0.907
105	16	15.34	0.6576
106	15	15.68	-0.6783
107	16	15.68	0.3217
108	16	15.36	0.6398
109	13	15.68	-2.678
110	15	15.09	-0.09301
111	12	13.89	-1.894
112	19	15.34	3.658
113	16	15.18	0.8211
114	16	15.5	0.5029
115	17	16.7	0.305
116	16	16.11	-0.1097
117	14	15.43	-1.429
118	15	15.09	-0.09301
119	14	14.84	-0.8429
120	16	15.43	0.571
121	15	15.86	-0.8596
122	17	16.81	0.1918
123	15	15.18	-0.1789
124	16	15.34	0.6576
125	16	15.68	0.3217
126	15	14.59	0.4064
127	15	15.09	-0.09301
128	11	12.25	-1.247
129	16	15.27	0.7257
130	18	16.02	1.977
131	13	13.91	-0.9129
132	11	14.05	-3.049
133	16	15.34	0.6576
134	18	17.24	0.7605
135	15	16.81	-1.808
136	19	17.82	1.175
137	17	16.99	0.01054
138	13	15.09	-2.093
139	14	15.11	-1.111
140	16	15.68	0.3217
141	13	15.68	-2.678
142	17	15.86	1.14
143	14	15.86	-1.86
144	19	16.02	2.977
145	14	14.55	-0.5485
146	16	15.68	0.3217
147	12	13.71	-1.713
148	16	16.81	-0.8082
149	16	15.34	0.6576
150	15	15.43	-0.429
151	12	14.77	-2.775
152	15	15.68	-0.6783
153	17	16.11	0.8903
154	13	15.5	-2.497
155	15	13.55	1.45
156	18	15.43	2.571
157	15	14.39	0.6062
158	18	15.43	2.571
159	15	17.14	-2.144
160	15	16.18	-1.178
161	16	15.84	0.1582
162	13	14.23	-1.23
163	16	15.68	0.3217
164	13	15.86	-2.86
165	16	14.07	1.933

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.1405	0.281	0.8595
10	0.06798	0.136	0.932
11	0.04305	0.0861	0.9569
12	0.01743	0.03486	0.9826
13	0.00651	0.01302	0.9935
14	0.00923	0.01846	0.9908
15	0.0171	0.03419	0.9829
16	0.0182	0.03641	0.9818
17	0.009079	0.01816	0.9909
18	0.03233	0.06467	0.9677
19	0.02439	0.04877	0.9756
20	0.01649	0.03299	0.9835
21	0.01801	0.03601	0.982
22	0.01046	0.02093	0.9895
23	0.01037	0.02074	0.9896
24	0.008727	0.01745	0.9913
25	0.009399	0.0188	0.9906
26	0.006058	0.01212	0.9939
27	0.005386	0.01077	0.9946
28	0.003407	0.006815	0.9966
29	0.001927	0.003855	0.9981
30	0.004896	0.009792	0.9951
31	0.008914	0.01783	0.9911
32	0.008657	0.01731	0.9913
33	0.006065	0.01213	0.9939
34	0.009647	0.01929	0.9904
35	0.006218	0.01244	0.9938
36	0.004732	0.009464	0.9953
37	0.002988	0.005976	0.997
38	0.001911	0.003821	0.9981
39	0.007366	0.01473	0.9926
40	0.00607	0.01214	0.9939
41	0.003947	0.007894	0.9961
42	0.003949	0.007898	0.9961
43	0.003409	0.006818	0.9966
44	0.002311	0.004622	0.9977
45	0.001591	0.003182	0.9984
46	0.001004	0.002008	0.999
47	0.0008791	0.001758	0.9991
48	0.005786	0.01157	0.9942
49	0.004057	0.008115	0.9959
50	0.002764	0.005528	0.9972
51	0.001825	0.003651	0.9982
52	0.01498	0.02996	0.985
53	0.01081	0.02161	0.9892
54	0.03283	0.06567	0.9672
55	0.0258	0.0516	0.9742
56	0.03363	0.06727	0.9664
57	0.02897	0.05795	0.971
58	0.02361	0.04722	0.9764
59	0.01786	0.03573	0.9821
60	0.01618	0.03237	0.9838
61	0.01203	0.02407	0.988
62	0.01087	0.02174	0.9891
63	0.008746	0.01749	0.9913
64	0.008582	0.01716	0.9914
65	0.00994	0.01988	0.9901
66	0.01101	0.02202	0.989
67	0.01212	0.02424	0.9879
68	0.01006	0.02013	0.9899
69	0.007615	0.01523	0.9924
70	0.01336	0.02672	0.9866
71	0.02977	0.05954	0.9702
72	0.05237	0.1047	0.9476
73	0.04968	0.09937	0.9503
74	0.04019	0.08039	0.9598
75	0.03175	0.06351	0.9682
76	0.03034	0.06068	0.9697
77	0.0265	0.053	0.9735
78	0.05206	0.1041	0.9479
79	0.04176	0.08351	0.9582
80	0.05906	0.1181	0.9409
81	0.04741	0.09483	0.9526
82	0.04879	0.09759	0.9512
83	0.04674	0.09347	0.9533
84	0.1977	0.3954	0.8023
85	0.1727	0.3454	0.8273
86	0.2156	0.4311	0.7844
87	0.1869	0.3738	0.8131
88	0.2181	0.4362	0.7819
89	0.1983	0.3965	0.8017
90	0.1704	0.3407	0.8296
91	0.1699	0.3399	0.8301
92	0.1427	0.2853	0.8573
93	0.1191	0.2382	0.8809
94	0.09843	0.1969	0.9016
95	0.0814	0.1628	0.9186
96	0.09435	0.1887	0.9056
97	0.09828	0.1966	0.9017
98	0.0888	0.1776	0.9112
99	0.1	0.2	0.9
100	0.08478	0.1696	0.9152
101	0.1817	0.3634	0.8183
102	0.1552	0.3104	0.8448
103	0.1297	0.2594	0.8703
104	0.1155	0.2311	0.8845
105	0.09923	0.1985	0.9008
106	0.08344	0.1669	0.9166
107	0.06834	0.1367	0.9317
108	0.05873	0.1175	0.9413
109	0.09857	0.1971	0.9014
110	0.07926	0.1585	0.9207
111	0.09083	0.1817	0.9092
112	0.273	0.546	0.727
113	0.2663	0.5325	0.7337
114	0.248	0.4959	0.752
115	0.2116	0.4231	0.7884
116	0.1862	0.3725	0.8138
117	0.1886	0.3771	0.8114
118	0.1564	0.3127	0.8436
119	0.1333	0.2665	0.8668
120	0.1104	0.2208	0.8896
121	0.0962	0.1924	0.9038
122	0.08068	0.1614	0.9193
123	0.06675	0.1335	0.9332
124	0.05934	0.1187	0.9407
125	0.04818	0.09635	0.9518
126	0.04043	0.08085	0.9596
127	0.03002	0.06003	0.97
128	0.02547	0.05094	0.9745
129	0.01923	0.03845	0.9808
130	0.02249	0.04497	0.9775
131	0.01945	0.0389	0.9805
132	0.04421	0.08842	0.9558
133	0.04333	0.08665	0.9567
134	0.033	0.06599	0.967
135	0.02782	0.05563	0.9722
136	0.02283	0.04566	0.9772
137	0.01937	0.03874	0.9806
138	0.02417	0.04835	0.9758
139	0.02025	0.04049	0.9798
140	0.0151	0.0302	0.9849
141	0.02412	0.04825	0.9759
142	0.0233	0.04659	0.9767
143	0.02272	0.04543	0.9773
144	0.1141	0.2282	0.8859
145	0.1063	0.2126	0.8937
146	0.08278	0.1656	0.9172
147	0.0982	0.1964	0.9018
148	0.1158	0.2316	0.8842
149	0.1891	0.3781	0.8109
150	0.22	0.4401	0.78
151	0.4395	0.8789	0.5605
152	0.3399	0.6797	0.6601
153	0.2616	0.5232	0.7384
154	0.5667	0.8666	0.4333
155	0.6792	0.6416	0.3208
156	0.5206	0.9587	0.4794

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.1405 &  0.281 &  0.8595 \tabularnewline
10 &  0.06798 &  0.136 &  0.932 \tabularnewline
11 &  0.04305 &  0.0861 &  0.9569 \tabularnewline
12 &  0.01743 &  0.03486 &  0.9826 \tabularnewline
13 &  0.00651 &  0.01302 &  0.9935 \tabularnewline
14 &  0.00923 &  0.01846 &  0.9908 \tabularnewline
15 &  0.0171 &  0.03419 &  0.9829 \tabularnewline
16 &  0.0182 &  0.03641 &  0.9818 \tabularnewline
17 &  0.009079 &  0.01816 &  0.9909 \tabularnewline
18 &  0.03233 &  0.06467 &  0.9677 \tabularnewline
19 &  0.02439 &  0.04877 &  0.9756 \tabularnewline
20 &  0.01649 &  0.03299 &  0.9835 \tabularnewline
21 &  0.01801 &  0.03601 &  0.982 \tabularnewline
22 &  0.01046 &  0.02093 &  0.9895 \tabularnewline
23 &  0.01037 &  0.02074 &  0.9896 \tabularnewline
24 &  0.008727 &  0.01745 &  0.9913 \tabularnewline
25 &  0.009399 &  0.0188 &  0.9906 \tabularnewline
26 &  0.006058 &  0.01212 &  0.9939 \tabularnewline
27 &  0.005386 &  0.01077 &  0.9946 \tabularnewline
28 &  0.003407 &  0.006815 &  0.9966 \tabularnewline
29 &  0.001927 &  0.003855 &  0.9981 \tabularnewline
30 &  0.004896 &  0.009792 &  0.9951 \tabularnewline
31 &  0.008914 &  0.01783 &  0.9911 \tabularnewline
32 &  0.008657 &  0.01731 &  0.9913 \tabularnewline
33 &  0.006065 &  0.01213 &  0.9939 \tabularnewline
34 &  0.009647 &  0.01929 &  0.9904 \tabularnewline
35 &  0.006218 &  0.01244 &  0.9938 \tabularnewline
36 &  0.004732 &  0.009464 &  0.9953 \tabularnewline
37 &  0.002988 &  0.005976 &  0.997 \tabularnewline
38 &  0.001911 &  0.003821 &  0.9981 \tabularnewline
39 &  0.007366 &  0.01473 &  0.9926 \tabularnewline
40 &  0.00607 &  0.01214 &  0.9939 \tabularnewline
41 &  0.003947 &  0.007894 &  0.9961 \tabularnewline
42 &  0.003949 &  0.007898 &  0.9961 \tabularnewline
43 &  0.003409 &  0.006818 &  0.9966 \tabularnewline
44 &  0.002311 &  0.004622 &  0.9977 \tabularnewline
45 &  0.001591 &  0.003182 &  0.9984 \tabularnewline
46 &  0.001004 &  0.002008 &  0.999 \tabularnewline
47 &  0.0008791 &  0.001758 &  0.9991 \tabularnewline
48 &  0.005786 &  0.01157 &  0.9942 \tabularnewline
49 &  0.004057 &  0.008115 &  0.9959 \tabularnewline
50 &  0.002764 &  0.005528 &  0.9972 \tabularnewline
51 &  0.001825 &  0.003651 &  0.9982 \tabularnewline
52 &  0.01498 &  0.02996 &  0.985 \tabularnewline
53 &  0.01081 &  0.02161 &  0.9892 \tabularnewline
54 &  0.03283 &  0.06567 &  0.9672 \tabularnewline
55 &  0.0258 &  0.0516 &  0.9742 \tabularnewline
56 &  0.03363 &  0.06727 &  0.9664 \tabularnewline
57 &  0.02897 &  0.05795 &  0.971 \tabularnewline
58 &  0.02361 &  0.04722 &  0.9764 \tabularnewline
59 &  0.01786 &  0.03573 &  0.9821 \tabularnewline
60 &  0.01618 &  0.03237 &  0.9838 \tabularnewline
61 &  0.01203 &  0.02407 &  0.988 \tabularnewline
62 &  0.01087 &  0.02174 &  0.9891 \tabularnewline
63 &  0.008746 &  0.01749 &  0.9913 \tabularnewline
64 &  0.008582 &  0.01716 &  0.9914 \tabularnewline
65 &  0.00994 &  0.01988 &  0.9901 \tabularnewline
66 &  0.01101 &  0.02202 &  0.989 \tabularnewline
67 &  0.01212 &  0.02424 &  0.9879 \tabularnewline
68 &  0.01006 &  0.02013 &  0.9899 \tabularnewline
69 &  0.007615 &  0.01523 &  0.9924 \tabularnewline
70 &  0.01336 &  0.02672 &  0.9866 \tabularnewline
71 &  0.02977 &  0.05954 &  0.9702 \tabularnewline
72 &  0.05237 &  0.1047 &  0.9476 \tabularnewline
73 &  0.04968 &  0.09937 &  0.9503 \tabularnewline
74 &  0.04019 &  0.08039 &  0.9598 \tabularnewline
75 &  0.03175 &  0.06351 &  0.9682 \tabularnewline
76 &  0.03034 &  0.06068 &  0.9697 \tabularnewline
77 &  0.0265 &  0.053 &  0.9735 \tabularnewline
78 &  0.05206 &  0.1041 &  0.9479 \tabularnewline
79 &  0.04176 &  0.08351 &  0.9582 \tabularnewline
80 &  0.05906 &  0.1181 &  0.9409 \tabularnewline
81 &  0.04741 &  0.09483 &  0.9526 \tabularnewline
82 &  0.04879 &  0.09759 &  0.9512 \tabularnewline
83 &  0.04674 &  0.09347 &  0.9533 \tabularnewline
84 &  0.1977 &  0.3954 &  0.8023 \tabularnewline
85 &  0.1727 &  0.3454 &  0.8273 \tabularnewline
86 &  0.2156 &  0.4311 &  0.7844 \tabularnewline
87 &  0.1869 &  0.3738 &  0.8131 \tabularnewline
88 &  0.2181 &  0.4362 &  0.7819 \tabularnewline
89 &  0.1983 &  0.3965 &  0.8017 \tabularnewline
90 &  0.1704 &  0.3407 &  0.8296 \tabularnewline
91 &  0.1699 &  0.3399 &  0.8301 \tabularnewline
92 &  0.1427 &  0.2853 &  0.8573 \tabularnewline
93 &  0.1191 &  0.2382 &  0.8809 \tabularnewline
94 &  0.09843 &  0.1969 &  0.9016 \tabularnewline
95 &  0.0814 &  0.1628 &  0.9186 \tabularnewline
96 &  0.09435 &  0.1887 &  0.9056 \tabularnewline
97 &  0.09828 &  0.1966 &  0.9017 \tabularnewline
98 &  0.0888 &  0.1776 &  0.9112 \tabularnewline
99 &  0.1 &  0.2 &  0.9 \tabularnewline
100 &  0.08478 &  0.1696 &  0.9152 \tabularnewline
101 &  0.1817 &  0.3634 &  0.8183 \tabularnewline
102 &  0.1552 &  0.3104 &  0.8448 \tabularnewline
103 &  0.1297 &  0.2594 &  0.8703 \tabularnewline
104 &  0.1155 &  0.2311 &  0.8845 \tabularnewline
105 &  0.09923 &  0.1985 &  0.9008 \tabularnewline
106 &  0.08344 &  0.1669 &  0.9166 \tabularnewline
107 &  0.06834 &  0.1367 &  0.9317 \tabularnewline
108 &  0.05873 &  0.1175 &  0.9413 \tabularnewline
109 &  0.09857 &  0.1971 &  0.9014 \tabularnewline
110 &  0.07926 &  0.1585 &  0.9207 \tabularnewline
111 &  0.09083 &  0.1817 &  0.9092 \tabularnewline
112 &  0.273 &  0.546 &  0.727 \tabularnewline
113 &  0.2663 &  0.5325 &  0.7337 \tabularnewline
114 &  0.248 &  0.4959 &  0.752 \tabularnewline
115 &  0.2116 &  0.4231 &  0.7884 \tabularnewline
116 &  0.1862 &  0.3725 &  0.8138 \tabularnewline
117 &  0.1886 &  0.3771 &  0.8114 \tabularnewline
118 &  0.1564 &  0.3127 &  0.8436 \tabularnewline
119 &  0.1333 &  0.2665 &  0.8668 \tabularnewline
120 &  0.1104 &  0.2208 &  0.8896 \tabularnewline
121 &  0.0962 &  0.1924 &  0.9038 \tabularnewline
122 &  0.08068 &  0.1614 &  0.9193 \tabularnewline
123 &  0.06675 &  0.1335 &  0.9332 \tabularnewline
124 &  0.05934 &  0.1187 &  0.9407 \tabularnewline
125 &  0.04818 &  0.09635 &  0.9518 \tabularnewline
126 &  0.04043 &  0.08085 &  0.9596 \tabularnewline
127 &  0.03002 &  0.06003 &  0.97 \tabularnewline
128 &  0.02547 &  0.05094 &  0.9745 \tabularnewline
129 &  0.01923 &  0.03845 &  0.9808 \tabularnewline
130 &  0.02249 &  0.04497 &  0.9775 \tabularnewline
131 &  0.01945 &  0.0389 &  0.9805 \tabularnewline
132 &  0.04421 &  0.08842 &  0.9558 \tabularnewline
133 &  0.04333 &  0.08665 &  0.9567 \tabularnewline
134 &  0.033 &  0.06599 &  0.967 \tabularnewline
135 &  0.02782 &  0.05563 &  0.9722 \tabularnewline
136 &  0.02283 &  0.04566 &  0.9772 \tabularnewline
137 &  0.01937 &  0.03874 &  0.9806 \tabularnewline
138 &  0.02417 &  0.04835 &  0.9758 \tabularnewline
139 &  0.02025 &  0.04049 &  0.9798 \tabularnewline
140 &  0.0151 &  0.0302 &  0.9849 \tabularnewline
141 &  0.02412 &  0.04825 &  0.9759 \tabularnewline
142 &  0.0233 &  0.04659 &  0.9767 \tabularnewline
143 &  0.02272 &  0.04543 &  0.9773 \tabularnewline
144 &  0.1141 &  0.2282 &  0.8859 \tabularnewline
145 &  0.1063 &  0.2126 &  0.8937 \tabularnewline
146 &  0.08278 &  0.1656 &  0.9172 \tabularnewline
147 &  0.0982 &  0.1964 &  0.9018 \tabularnewline
148 &  0.1158 &  0.2316 &  0.8842 \tabularnewline
149 &  0.1891 &  0.3781 &  0.8109 \tabularnewline
150 &  0.22 &  0.4401 &  0.78 \tabularnewline
151 &  0.4395 &  0.8789 &  0.5605 \tabularnewline
152 &  0.3399 &  0.6797 &  0.6601 \tabularnewline
153 &  0.2616 &  0.5232 &  0.7384 \tabularnewline
154 &  0.5667 &  0.8666 &  0.4333 \tabularnewline
155 &  0.6792 &  0.6416 &  0.3208 \tabularnewline
156 &  0.5206 &  0.9587 &  0.4794 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C] 0.1405[/C][C] 0.281[/C][C] 0.8595[/C][/ROW]
[ROW][C]10[/C][C] 0.06798[/C][C] 0.136[/C][C] 0.932[/C][/ROW]
[ROW][C]11[/C][C] 0.04305[/C][C] 0.0861[/C][C] 0.9569[/C][/ROW]
[ROW][C]12[/C][C] 0.01743[/C][C] 0.03486[/C][C] 0.9826[/C][/ROW]
[ROW][C]13[/C][C] 0.00651[/C][C] 0.01302[/C][C] 0.9935[/C][/ROW]
[ROW][C]14[/C][C] 0.00923[/C][C] 0.01846[/C][C] 0.9908[/C][/ROW]
[ROW][C]15[/C][C] 0.0171[/C][C] 0.03419[/C][C] 0.9829[/C][/ROW]
[ROW][C]16[/C][C] 0.0182[/C][C] 0.03641[/C][C] 0.9818[/C][/ROW]
[ROW][C]17[/C][C] 0.009079[/C][C] 0.01816[/C][C] 0.9909[/C][/ROW]
[ROW][C]18[/C][C] 0.03233[/C][C] 0.06467[/C][C] 0.9677[/C][/ROW]
[ROW][C]19[/C][C] 0.02439[/C][C] 0.04877[/C][C] 0.9756[/C][/ROW]
[ROW][C]20[/C][C] 0.01649[/C][C] 0.03299[/C][C] 0.9835[/C][/ROW]
[ROW][C]21[/C][C] 0.01801[/C][C] 0.03601[/C][C] 0.982[/C][/ROW]
[ROW][C]22[/C][C] 0.01046[/C][C] 0.02093[/C][C] 0.9895[/C][/ROW]
[ROW][C]23[/C][C] 0.01037[/C][C] 0.02074[/C][C] 0.9896[/C][/ROW]
[ROW][C]24[/C][C] 0.008727[/C][C] 0.01745[/C][C] 0.9913[/C][/ROW]
[ROW][C]25[/C][C] 0.009399[/C][C] 0.0188[/C][C] 0.9906[/C][/ROW]
[ROW][C]26[/C][C] 0.006058[/C][C] 0.01212[/C][C] 0.9939[/C][/ROW]
[ROW][C]27[/C][C] 0.005386[/C][C] 0.01077[/C][C] 0.9946[/C][/ROW]
[ROW][C]28[/C][C] 0.003407[/C][C] 0.006815[/C][C] 0.9966[/C][/ROW]
[ROW][C]29[/C][C] 0.001927[/C][C] 0.003855[/C][C] 0.9981[/C][/ROW]
[ROW][C]30[/C][C] 0.004896[/C][C] 0.009792[/C][C] 0.9951[/C][/ROW]
[ROW][C]31[/C][C] 0.008914[/C][C] 0.01783[/C][C] 0.9911[/C][/ROW]
[ROW][C]32[/C][C] 0.008657[/C][C] 0.01731[/C][C] 0.9913[/C][/ROW]
[ROW][C]33[/C][C] 0.006065[/C][C] 0.01213[/C][C] 0.9939[/C][/ROW]
[ROW][C]34[/C][C] 0.009647[/C][C] 0.01929[/C][C] 0.9904[/C][/ROW]
[ROW][C]35[/C][C] 0.006218[/C][C] 0.01244[/C][C] 0.9938[/C][/ROW]
[ROW][C]36[/C][C] 0.004732[/C][C] 0.009464[/C][C] 0.9953[/C][/ROW]
[ROW][C]37[/C][C] 0.002988[/C][C] 0.005976[/C][C] 0.997[/C][/ROW]
[ROW][C]38[/C][C] 0.001911[/C][C] 0.003821[/C][C] 0.9981[/C][/ROW]
[ROW][C]39[/C][C] 0.007366[/C][C] 0.01473[/C][C] 0.9926[/C][/ROW]
[ROW][C]40[/C][C] 0.00607[/C][C] 0.01214[/C][C] 0.9939[/C][/ROW]
[ROW][C]41[/C][C] 0.003947[/C][C] 0.007894[/C][C] 0.9961[/C][/ROW]
[ROW][C]42[/C][C] 0.003949[/C][C] 0.007898[/C][C] 0.9961[/C][/ROW]
[ROW][C]43[/C][C] 0.003409[/C][C] 0.006818[/C][C] 0.9966[/C][/ROW]
[ROW][C]44[/C][C] 0.002311[/C][C] 0.004622[/C][C] 0.9977[/C][/ROW]
[ROW][C]45[/C][C] 0.001591[/C][C] 0.003182[/C][C] 0.9984[/C][/ROW]
[ROW][C]46[/C][C] 0.001004[/C][C] 0.002008[/C][C] 0.999[/C][/ROW]
[ROW][C]47[/C][C] 0.0008791[/C][C] 0.001758[/C][C] 0.9991[/C][/ROW]
[ROW][C]48[/C][C] 0.005786[/C][C] 0.01157[/C][C] 0.9942[/C][/ROW]
[ROW][C]49[/C][C] 0.004057[/C][C] 0.008115[/C][C] 0.9959[/C][/ROW]
[ROW][C]50[/C][C] 0.002764[/C][C] 0.005528[/C][C] 0.9972[/C][/ROW]
[ROW][C]51[/C][C] 0.001825[/C][C] 0.003651[/C][C] 0.9982[/C][/ROW]
[ROW][C]52[/C][C] 0.01498[/C][C] 0.02996[/C][C] 0.985[/C][/ROW]
[ROW][C]53[/C][C] 0.01081[/C][C] 0.02161[/C][C] 0.9892[/C][/ROW]
[ROW][C]54[/C][C] 0.03283[/C][C] 0.06567[/C][C] 0.9672[/C][/ROW]
[ROW][C]55[/C][C] 0.0258[/C][C] 0.0516[/C][C] 0.9742[/C][/ROW]
[ROW][C]56[/C][C] 0.03363[/C][C] 0.06727[/C][C] 0.9664[/C][/ROW]
[ROW][C]57[/C][C] 0.02897[/C][C] 0.05795[/C][C] 0.971[/C][/ROW]
[ROW][C]58[/C][C] 0.02361[/C][C] 0.04722[/C][C] 0.9764[/C][/ROW]
[ROW][C]59[/C][C] 0.01786[/C][C] 0.03573[/C][C] 0.9821[/C][/ROW]
[ROW][C]60[/C][C] 0.01618[/C][C] 0.03237[/C][C] 0.9838[/C][/ROW]
[ROW][C]61[/C][C] 0.01203[/C][C] 0.02407[/C][C] 0.988[/C][/ROW]
[ROW][C]62[/C][C] 0.01087[/C][C] 0.02174[/C][C] 0.9891[/C][/ROW]
[ROW][C]63[/C][C] 0.008746[/C][C] 0.01749[/C][C] 0.9913[/C][/ROW]
[ROW][C]64[/C][C] 0.008582[/C][C] 0.01716[/C][C] 0.9914[/C][/ROW]
[ROW][C]65[/C][C] 0.00994[/C][C] 0.01988[/C][C] 0.9901[/C][/ROW]
[ROW][C]66[/C][C] 0.01101[/C][C] 0.02202[/C][C] 0.989[/C][/ROW]
[ROW][C]67[/C][C] 0.01212[/C][C] 0.02424[/C][C] 0.9879[/C][/ROW]
[ROW][C]68[/C][C] 0.01006[/C][C] 0.02013[/C][C] 0.9899[/C][/ROW]
[ROW][C]69[/C][C] 0.007615[/C][C] 0.01523[/C][C] 0.9924[/C][/ROW]
[ROW][C]70[/C][C] 0.01336[/C][C] 0.02672[/C][C] 0.9866[/C][/ROW]
[ROW][C]71[/C][C] 0.02977[/C][C] 0.05954[/C][C] 0.9702[/C][/ROW]
[ROW][C]72[/C][C] 0.05237[/C][C] 0.1047[/C][C] 0.9476[/C][/ROW]
[ROW][C]73[/C][C] 0.04968[/C][C] 0.09937[/C][C] 0.9503[/C][/ROW]
[ROW][C]74[/C][C] 0.04019[/C][C] 0.08039[/C][C] 0.9598[/C][/ROW]
[ROW][C]75[/C][C] 0.03175[/C][C] 0.06351[/C][C] 0.9682[/C][/ROW]
[ROW][C]76[/C][C] 0.03034[/C][C] 0.06068[/C][C] 0.9697[/C][/ROW]
[ROW][C]77[/C][C] 0.0265[/C][C] 0.053[/C][C] 0.9735[/C][/ROW]
[ROW][C]78[/C][C] 0.05206[/C][C] 0.1041[/C][C] 0.9479[/C][/ROW]
[ROW][C]79[/C][C] 0.04176[/C][C] 0.08351[/C][C] 0.9582[/C][/ROW]
[ROW][C]80[/C][C] 0.05906[/C][C] 0.1181[/C][C] 0.9409[/C][/ROW]
[ROW][C]81[/C][C] 0.04741[/C][C] 0.09483[/C][C] 0.9526[/C][/ROW]
[ROW][C]82[/C][C] 0.04879[/C][C] 0.09759[/C][C] 0.9512[/C][/ROW]
[ROW][C]83[/C][C] 0.04674[/C][C] 0.09347[/C][C] 0.9533[/C][/ROW]
[ROW][C]84[/C][C] 0.1977[/C][C] 0.3954[/C][C] 0.8023[/C][/ROW]
[ROW][C]85[/C][C] 0.1727[/C][C] 0.3454[/C][C] 0.8273[/C][/ROW]
[ROW][C]86[/C][C] 0.2156[/C][C] 0.4311[/C][C] 0.7844[/C][/ROW]
[ROW][C]87[/C][C] 0.1869[/C][C] 0.3738[/C][C] 0.8131[/C][/ROW]
[ROW][C]88[/C][C] 0.2181[/C][C] 0.4362[/C][C] 0.7819[/C][/ROW]
[ROW][C]89[/C][C] 0.1983[/C][C] 0.3965[/C][C] 0.8017[/C][/ROW]
[ROW][C]90[/C][C] 0.1704[/C][C] 0.3407[/C][C] 0.8296[/C][/ROW]
[ROW][C]91[/C][C] 0.1699[/C][C] 0.3399[/C][C] 0.8301[/C][/ROW]
[ROW][C]92[/C][C] 0.1427[/C][C] 0.2853[/C][C] 0.8573[/C][/ROW]
[ROW][C]93[/C][C] 0.1191[/C][C] 0.2382[/C][C] 0.8809[/C][/ROW]
[ROW][C]94[/C][C] 0.09843[/C][C] 0.1969[/C][C] 0.9016[/C][/ROW]
[ROW][C]95[/C][C] 0.0814[/C][C] 0.1628[/C][C] 0.9186[/C][/ROW]
[ROW][C]96[/C][C] 0.09435[/C][C] 0.1887[/C][C] 0.9056[/C][/ROW]
[ROW][C]97[/C][C] 0.09828[/C][C] 0.1966[/C][C] 0.9017[/C][/ROW]
[ROW][C]98[/C][C] 0.0888[/C][C] 0.1776[/C][C] 0.9112[/C][/ROW]
[ROW][C]99[/C][C] 0.1[/C][C] 0.2[/C][C] 0.9[/C][/ROW]
[ROW][C]100[/C][C] 0.08478[/C][C] 0.1696[/C][C] 0.9152[/C][/ROW]
[ROW][C]101[/C][C] 0.1817[/C][C] 0.3634[/C][C] 0.8183[/C][/ROW]
[ROW][C]102[/C][C] 0.1552[/C][C] 0.3104[/C][C] 0.8448[/C][/ROW]
[ROW][C]103[/C][C] 0.1297[/C][C] 0.2594[/C][C] 0.8703[/C][/ROW]
[ROW][C]104[/C][C] 0.1155[/C][C] 0.2311[/C][C] 0.8845[/C][/ROW]
[ROW][C]105[/C][C] 0.09923[/C][C] 0.1985[/C][C] 0.9008[/C][/ROW]
[ROW][C]106[/C][C] 0.08344[/C][C] 0.1669[/C][C] 0.9166[/C][/ROW]
[ROW][C]107[/C][C] 0.06834[/C][C] 0.1367[/C][C] 0.9317[/C][/ROW]
[ROW][C]108[/C][C] 0.05873[/C][C] 0.1175[/C][C] 0.9413[/C][/ROW]
[ROW][C]109[/C][C] 0.09857[/C][C] 0.1971[/C][C] 0.9014[/C][/ROW]
[ROW][C]110[/C][C] 0.07926[/C][C] 0.1585[/C][C] 0.9207[/C][/ROW]
[ROW][C]111[/C][C] 0.09083[/C][C] 0.1817[/C][C] 0.9092[/C][/ROW]
[ROW][C]112[/C][C] 0.273[/C][C] 0.546[/C][C] 0.727[/C][/ROW]
[ROW][C]113[/C][C] 0.2663[/C][C] 0.5325[/C][C] 0.7337[/C][/ROW]
[ROW][C]114[/C][C] 0.248[/C][C] 0.4959[/C][C] 0.752[/C][/ROW]
[ROW][C]115[/C][C] 0.2116[/C][C] 0.4231[/C][C] 0.7884[/C][/ROW]
[ROW][C]116[/C][C] 0.1862[/C][C] 0.3725[/C][C] 0.8138[/C][/ROW]
[ROW][C]117[/C][C] 0.1886[/C][C] 0.3771[/C][C] 0.8114[/C][/ROW]
[ROW][C]118[/C][C] 0.1564[/C][C] 0.3127[/C][C] 0.8436[/C][/ROW]
[ROW][C]119[/C][C] 0.1333[/C][C] 0.2665[/C][C] 0.8668[/C][/ROW]
[ROW][C]120[/C][C] 0.1104[/C][C] 0.2208[/C][C] 0.8896[/C][/ROW]
[ROW][C]121[/C][C] 0.0962[/C][C] 0.1924[/C][C] 0.9038[/C][/ROW]
[ROW][C]122[/C][C] 0.08068[/C][C] 0.1614[/C][C] 0.9193[/C][/ROW]
[ROW][C]123[/C][C] 0.06675[/C][C] 0.1335[/C][C] 0.9332[/C][/ROW]
[ROW][C]124[/C][C] 0.05934[/C][C] 0.1187[/C][C] 0.9407[/C][/ROW]
[ROW][C]125[/C][C] 0.04818[/C][C] 0.09635[/C][C] 0.9518[/C][/ROW]
[ROW][C]126[/C][C] 0.04043[/C][C] 0.08085[/C][C] 0.9596[/C][/ROW]
[ROW][C]127[/C][C] 0.03002[/C][C] 0.06003[/C][C] 0.97[/C][/ROW]
[ROW][C]128[/C][C] 0.02547[/C][C] 0.05094[/C][C] 0.9745[/C][/ROW]
[ROW][C]129[/C][C] 0.01923[/C][C] 0.03845[/C][C] 0.9808[/C][/ROW]
[ROW][C]130[/C][C] 0.02249[/C][C] 0.04497[/C][C] 0.9775[/C][/ROW]
[ROW][C]131[/C][C] 0.01945[/C][C] 0.0389[/C][C] 0.9805[/C][/ROW]
[ROW][C]132[/C][C] 0.04421[/C][C] 0.08842[/C][C] 0.9558[/C][/ROW]
[ROW][C]133[/C][C] 0.04333[/C][C] 0.08665[/C][C] 0.9567[/C][/ROW]
[ROW][C]134[/C][C] 0.033[/C][C] 0.06599[/C][C] 0.967[/C][/ROW]
[ROW][C]135[/C][C] 0.02782[/C][C] 0.05563[/C][C] 0.9722[/C][/ROW]
[ROW][C]136[/C][C] 0.02283[/C][C] 0.04566[/C][C] 0.9772[/C][/ROW]
[ROW][C]137[/C][C] 0.01937[/C][C] 0.03874[/C][C] 0.9806[/C][/ROW]
[ROW][C]138[/C][C] 0.02417[/C][C] 0.04835[/C][C] 0.9758[/C][/ROW]
[ROW][C]139[/C][C] 0.02025[/C][C] 0.04049[/C][C] 0.9798[/C][/ROW]
[ROW][C]140[/C][C] 0.0151[/C][C] 0.0302[/C][C] 0.9849[/C][/ROW]
[ROW][C]141[/C][C] 0.02412[/C][C] 0.04825[/C][C] 0.9759[/C][/ROW]
[ROW][C]142[/C][C] 0.0233[/C][C] 0.04659[/C][C] 0.9767[/C][/ROW]
[ROW][C]143[/C][C] 0.02272[/C][C] 0.04543[/C][C] 0.9773[/C][/ROW]
[ROW][C]144[/C][C] 0.1141[/C][C] 0.2282[/C][C] 0.8859[/C][/ROW]
[ROW][C]145[/C][C] 0.1063[/C][C] 0.2126[/C][C] 0.8937[/C][/ROW]
[ROW][C]146[/C][C] 0.08278[/C][C] 0.1656[/C][C] 0.9172[/C][/ROW]
[ROW][C]147[/C][C] 0.0982[/C][C] 0.1964[/C][C] 0.9018[/C][/ROW]
[ROW][C]148[/C][C] 0.1158[/C][C] 0.2316[/C][C] 0.8842[/C][/ROW]
[ROW][C]149[/C][C] 0.1891[/C][C] 0.3781[/C][C] 0.8109[/C][/ROW]
[ROW][C]150[/C][C] 0.22[/C][C] 0.4401[/C][C] 0.78[/C][/ROW]
[ROW][C]151[/C][C] 0.4395[/C][C] 0.8789[/C][C] 0.5605[/C][/ROW]
[ROW][C]152[/C][C] 0.3399[/C][C] 0.6797[/C][C] 0.6601[/C][/ROW]
[ROW][C]153[/C][C] 0.2616[/C][C] 0.5232[/C][C] 0.7384[/C][/ROW]
[ROW][C]154[/C][C] 0.5667[/C][C] 0.8666[/C][C] 0.4333[/C][/ROW]
[ROW][C]155[/C][C] 0.6792[/C][C] 0.6416[/C][C] 0.3208[/C][/ROW]
[ROW][C]156[/C][C] 0.5206[/C][C] 0.9587[/C][C] 0.4794[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298736&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.1405	0.281	0.8595
10	0.06798	0.136	0.932
11	0.04305	0.0861	0.9569
12	0.01743	0.03486	0.9826
13	0.00651	0.01302	0.9935
14	0.00923	0.01846	0.9908
15	0.0171	0.03419	0.9829
16	0.0182	0.03641	0.9818
17	0.009079	0.01816	0.9909
18	0.03233	0.06467	0.9677
19	0.02439	0.04877	0.9756
20	0.01649	0.03299	0.9835
21	0.01801	0.03601	0.982
22	0.01046	0.02093	0.9895
23	0.01037	0.02074	0.9896
24	0.008727	0.01745	0.9913
25	0.009399	0.0188	0.9906
26	0.006058	0.01212	0.9939
27	0.005386	0.01077	0.9946
28	0.003407	0.006815	0.9966
29	0.001927	0.003855	0.9981
30	0.004896	0.009792	0.9951
31	0.008914	0.01783	0.9911
32	0.008657	0.01731	0.9913
33	0.006065	0.01213	0.9939
34	0.009647	0.01929	0.9904
35	0.006218	0.01244	0.9938
36	0.004732	0.009464	0.9953
37	0.002988	0.005976	0.997
38	0.001911	0.003821	0.9981
39	0.007366	0.01473	0.9926
40	0.00607	0.01214	0.9939
41	0.003947	0.007894	0.9961
42	0.003949	0.007898	0.9961
43	0.003409	0.006818	0.9966
44	0.002311	0.004622	0.9977
45	0.001591	0.003182	0.9984
46	0.001004	0.002008	0.999
47	0.0008791	0.001758	0.9991
48	0.005786	0.01157	0.9942
49	0.004057	0.008115	0.9959
50	0.002764	0.005528	0.9972
51	0.001825	0.003651	0.9982
52	0.01498	0.02996	0.985
53	0.01081	0.02161	0.9892
54	0.03283	0.06567	0.9672
55	0.0258	0.0516	0.9742
56	0.03363	0.06727	0.9664
57	0.02897	0.05795	0.971
58	0.02361	0.04722	0.9764
59	0.01786	0.03573	0.9821
60	0.01618	0.03237	0.9838
61	0.01203	0.02407	0.988
62	0.01087	0.02174	0.9891
63	0.008746	0.01749	0.9913
64	0.008582	0.01716	0.9914
65	0.00994	0.01988	0.9901
66	0.01101	0.02202	0.989
67	0.01212	0.02424	0.9879
68	0.01006	0.02013	0.9899
69	0.007615	0.01523	0.9924
70	0.01336	0.02672	0.9866
71	0.02977	0.05954	0.9702
72	0.05237	0.1047	0.9476
73	0.04968	0.09937	0.9503
74	0.04019	0.08039	0.9598
75	0.03175	0.06351	0.9682
76	0.03034	0.06068	0.9697
77	0.0265	0.053	0.9735
78	0.05206	0.1041	0.9479
79	0.04176	0.08351	0.9582
80	0.05906	0.1181	0.9409
81	0.04741	0.09483	0.9526
82	0.04879	0.09759	0.9512
83	0.04674	0.09347	0.9533
84	0.1977	0.3954	0.8023
85	0.1727	0.3454	0.8273
86	0.2156	0.4311	0.7844
87	0.1869	0.3738	0.8131
88	0.2181	0.4362	0.7819
89	0.1983	0.3965	0.8017
90	0.1704	0.3407	0.8296
91	0.1699	0.3399	0.8301
92	0.1427	0.2853	0.8573
93	0.1191	0.2382	0.8809
94	0.09843	0.1969	0.9016
95	0.0814	0.1628	0.9186
96	0.09435	0.1887	0.9056
97	0.09828	0.1966	0.9017
98	0.0888	0.1776	0.9112
99	0.1	0.2	0.9
100	0.08478	0.1696	0.9152
101	0.1817	0.3634	0.8183
102	0.1552	0.3104	0.8448
103	0.1297	0.2594	0.8703
104	0.1155	0.2311	0.8845
105	0.09923	0.1985	0.9008
106	0.08344	0.1669	0.9166
107	0.06834	0.1367	0.9317
108	0.05873	0.1175	0.9413
109	0.09857	0.1971	0.9014
110	0.07926	0.1585	0.9207
111	0.09083	0.1817	0.9092
112	0.273	0.546	0.727
113	0.2663	0.5325	0.7337
114	0.248	0.4959	0.752
115	0.2116	0.4231	0.7884
116	0.1862	0.3725	0.8138
117	0.1886	0.3771	0.8114
118	0.1564	0.3127	0.8436
119	0.1333	0.2665	0.8668
120	0.1104	0.2208	0.8896
121	0.0962	0.1924	0.9038
122	0.08068	0.1614	0.9193
123	0.06675	0.1335	0.9332
124	0.05934	0.1187	0.9407
125	0.04818	0.09635	0.9518
126	0.04043	0.08085	0.9596
127	0.03002	0.06003	0.97
128	0.02547	0.05094	0.9745
129	0.01923	0.03845	0.9808
130	0.02249	0.04497	0.9775
131	0.01945	0.0389	0.9805
132	0.04421	0.08842	0.9558
133	0.04333	0.08665	0.9567
134	0.033	0.06599	0.967
135	0.02782	0.05563	0.9722
136	0.02283	0.04566	0.9772
137	0.01937	0.03874	0.9806
138	0.02417	0.04835	0.9758
139	0.02025	0.04049	0.9798
140	0.0151	0.0302	0.9849
141	0.02412	0.04825	0.9759
142	0.0233	0.04659	0.9767
143	0.02272	0.04543	0.9773
144	0.1141	0.2282	0.8859
145	0.1063	0.2126	0.8937
146	0.08278	0.1656	0.9172
147	0.0982	0.1964	0.9018
148	0.1158	0.2316	0.8842
149	0.1891	0.3781	0.8109
150	0.22	0.4401	0.78
151	0.4395	0.8789	0.5605
152	0.3399	0.6797	0.6601
153	0.2616	0.5232	0.7384
154	0.5667	0.8666	0.4333
155	0.6792	0.6416	0.3208
156	0.5206	0.9587	0.4794

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	16	0.1081	NOK
5% type I error level	65	0.439189	NOK
10% type I error level	89	0.601351	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 &  0.1081 & NOK \tabularnewline
5% type I error level & 65 & 0.439189 & NOK \tabularnewline
10% type I error level & 89 & 0.601351 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&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] 0.1081[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]65[/C][C]0.439189[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]89[/C][C]0.601351[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298736&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&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	0.1081	NOK
5% type I error level	65	0.439189	NOK
10% type I error level	89	0.601351	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.0596, df1 = 2, df2 = 157, p-value = 0.1309
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3373, df1 = 10, df2 = 149, p-value = 0.2156
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.57315, df1 = 2, df2 = 157, p-value = 0.5649

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.0596, df1 = 2, df2 = 157, p-value = 0.1309
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3373, df1 = 10, df2 = 149, p-value = 0.2156
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.57315, df1 = 2, df2 = 157, p-value = 0.5649
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.0596, df1 = 2, df2 = 157, p-value = 0.1309
[/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 = 1.3373, df1 = 10, df2 = 149, p-value = 0.2156
[/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 = 0.57315, df1 = 2, df2 = 157, p-value = 0.5649
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298736&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.0596, df1 = 2, df2 = 157, p-value = 0.1309
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3373, df1 = 10, df2 = 149, p-value = 0.2156
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.57315, df1 = 2, df2 = 157, p-value = 0.5649

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK4 SK5 ALG2 1.098982 1.121640 1.097277 1.028088 1.046235

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK4      SK5     ALG2 
1.098982 1.121640 1.097277 1.028088 1.046235 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298736&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK4      SK5     ALG2 
1.098982 1.121640 1.097277 1.028088 1.046235 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298736&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298736&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 ALG2 1.098982 1.121640 1.097277 1.028088 1.046235

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):

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