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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 09 Dec 2016 15:16:24 +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/09/t1481292999gkkw6quc0iqct39.htm/, Retrieved Fri, 01 Nov 2024 03:45:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298554, Retrieved Fri, 01 Nov 2024 03:45:47 +0000

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

-       [Multiple Regression] [multiple regressi...] [2016-12-09 14:16:24] [36884fbde1107444791dd71ee0072a5a] [Current]

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3	4	3	4	5	4	4	4
5	5	5	4	5	NA	4	4
5	4	4	4	4	3	3	2
5	4	4	4	4	3	3	3
4	4	3	4	5	4	4	3
5	5	5	5	5	3	4	3
5	4	3	3	5	4	2	3
5	5	5	4	5	4	2	4
5	5	4	1	5	2	2	4
5	4	3	3	5	1	2	4
5	5	5	4	4	4	3	2
NA	4	5	3	5	4	3	2
5	5	5	5	5	4	5	4
5	5	4	4	5	5	4	5
4	4	3	4	4	4	3	4
3	4	4	3	5	1	4	4
5	5	5	5	3	4	4	2
NA	NA	NA	NA	5	4	NA	NA
5	4	3	4	5	2	NA	2
5	3	3	5	5	3	4	5
4	4	4	4	5	3	NA	4
2	5	1	2	NA	2	3	1
5	5	4	5	3	1	3	5
5	5	4	5	4	3	2	3
5	5	4	2	4	2	2	4
4	4	4	3	4	NA	3	4
4	5	5	4	5	4	3	2
4	5	4	4	4	4	3	4
5	5	4	5	5	2	4	2
5	5	4	3	4	3	4	3
4	NA	4	2	5	4	3	4
5	5	4	5	4	4	4	4
5	5	5	5	4	4	3	4
1	1	1	2	4	3	4	4
5	5	4	5	5	4	3	4
4	5	4	3	5	4	3	4
4	4	4	3	5	4	3	5
4	4	4	4	5	4	3	4
5	5	4	4	2	3	2	4
4	4	5	3	4	3	5	3
4	4	4	3	4	4	3	4
5	4	4	4	4	2	1	4
3	3	4	NA	5	3	2	3
5	5	5	5	5	4	2	2
5	5	5	4	5	4	3	5
2	2	1	2	4	3	2	4
3	3	3	4	4	2	3	3
4	4	3	5	5	3	5	4
4	5	3	4	5	3	4	4
NA	NA	NA	4	5	4	5	4
5	5	4	4	4	3	2	3
5	5	5	3	4	3	4	4
4	4	4	4	5	3	3	4
5	5	3	4	5	3	3	4
5	5	5	4	5	3	2	4
4	4	4	4	4	5	3	5
5	5	4	5	5	4	2	4
4	5	3	1	5	NA	4	2
4	4	4	4	4	3	NA	4
3	4	3	3	4	4	3	5
4	4	3	1	5	4	1	2
4	5	4	4	5	1	1	3
5	4	4	4	4	4	3	4
4	5	4	4	4	3	NA	3
4	5	4	3	5	3	2	4
4	4	4	4	3	4	3	4
4	3	3	4	3	2	4	4
4	4	4	4	5	4	3	5
2	4	4	3	4	5	4	3
4	5	4	3	4	4	4	4
4	4	3	3	5	4	3	4
5	5	5	5	5	4	4	4
3	3	3	3	4	NA	4	4
3	4	3	3	5	4	3	4
5	4	5	4	4	2	3	4
4	3	3	4	4	4	5	4
5	5	5	4	4	2	2	4
4	5	4	5	5	5	4	4
4	3	3	4	4	5	3	3
5	5	3	5	4	2	3	3
5	5	5	4	4	4	3	2
5	4	3	3	4	3	4	2
4	4	3	3	4	3	4	2
5	4	4	4	2	3	NA	3
5	5	5	4	4	4	5	4
2	5	4	2	4	4	3	4
5	4	5	5	5	3	4	4
5	5	4	4	4	3	3	4
5	5	5	5	5	4	5	4
5	4	4	2	4	4	4	4
4	4	4	3	4	2	4	4
4	4	4	3	3	3	4	2
5	5	5	5	4	3	4	3
4	4	4	3	2	3	2	2
5	5	5	4	4	4	3	3
5	5	4	4	5	4	4	4
5	4	5	4	3	4	3	5
4	4	4	3	4	4	3	4
5	5	5	5	5	5	5	5
5	5	5	2	2	4	3	3
3	4	2	3	5	3	1	5
5	4	5	4	5	4	3	4
5	5	5	4	5	4	4	5
5	5	5	5	4	2	2	2
4	3	NA	3	4	3	3	3
4	4	5	4	5	3	4	4
4	4	4	3	5	3	4	5
4	4	4	4	4	4	4	4
5	5	5	3	4	4	4	5
5	5	4	4	5	4	NA	5
4	4	2	4	5	4	4	5
3	4	4	4	5	3	3	4
3	4	3	2	4	3	3	4
4	4	5	4	5	3	3	4
4	4	3	3	4	2	NA	4
5	5	4	4	5	3	4	4
5	4	4	4	4	2	2	4
4	4	5	4	5	4	5	5
5	5	5	5	5	5	2	5
5	4	4	3	4	3	2	5
4	4	3	3	4	3	2	4
4	4	3	4	4	3	3	4
5	5	4	4	5	2	3	4
5	5	5	5	5	3	4	5
5	5	3	4	4	3	NA	4
5	5	3	4	4	3	4	4
4	5	4	4	5	4	3	4
5	4	4	4	5	4	4	4
3	4	4	4	4	3	4	2
5	5	4	3	4	4	3	4
5	4	5	4	4	1	3	2
4	5	4	4	4	5	5	4
5	5	5	5	5	4	4	3
4	4	4	3	5	3	3	5
4	4	4	4	4	5	3	2
4	4	4	3	NA	4	3	4
4	4	5	5	4	3	3	3
2	3	2	4	4	NA	NA	NA
4	4	4	3	3	4	3	3
5	4	5	4	4	4	2	4
5	5	5	5	5	3	4	5
5	5	5	4	4	2	4	3
4	4	4	2	4	4	4	2
4	5	4	3	5	3	5	5
5	4	4	2	3	3	2	4
5	4	4	4	4	4	2	4
5	4	5	4	1	2	3	2
5	5	5	5	5	3	3	5
5	3	5	4	4	4	2	3
5	4	5	4	5	4	4	3
4	4	4	3	3	3	2	3
5	4	4	3	4	4	3	4
3	3	3	2	4	4	NA	4
3	4	4	4	4	3	3	4
4	5	4	5	4	2	3	4
4	5	4	4	5	4	4	4
3	5	3	5	5	2	2	4
3	4	3	2	5	3	5	5
5	5	5	4	5	4	4	3
5	5	4	4	4	3	3	NA
5	4	4	2	5	2	5	4
5	4	4	4	5	4	2	4
5	5	5	4	4	1	4	5
5	4	5	4	3	5	4	3
5	5	5	4	4	4	4	4
5	4	5	2	4	3	3	2
4	4	4	4	5	4	5	5
4	4	5	3	4	4	3	4
2	4	5	3	4	3	3	3

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298554&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
ITH1[t] = + 1.65059 + 0.339771ITH2[t] + 0.330146ITH3[t] + 0.158708ITH4[t] -0.0764435KVDD1[t] -0.048136KVDD2[t] -0.0776348KVDD3[t] + 0.0111557KVDD4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITH1[t] =  +  1.65059 +  0.339771ITH2[t] +  0.330146ITH3[t] +  0.158708ITH4[t] -0.0764435KVDD1[t] -0.048136KVDD2[t] -0.0776348KVDD3[t] +  0.0111557KVDD4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITH1[t] =  +  1.65059 +  0.339771ITH2[t] +  0.330146ITH3[t] +  0.158708ITH4[t] -0.0764435KVDD1[t] -0.048136KVDD2[t] -0.0776348KVDD3[t] +  0.0111557KVDD4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298554&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&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
ITH1[t] = + 1.65059 + 0.339771ITH2[t] + 0.330146ITH3[t] + 0.158708ITH4[t] -0.0764435KVDD1[t] -0.048136KVDD2[t] -0.0776348KVDD3[t] + 0.0111557KVDD4[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)	+1.651	0.5116	+3.2260e+00	0.001566	0.0007828
ITH2	+0.3398	0.09782	+3.4740e+00	0.0006866	0.0003433
ITH3	+0.3301	0.07887	+4.1860e+00	5.028e-05	2.514e-05
ITH4	+0.1587	0.06496	+2.4430e+00	0.01583	0.007914
KVDD1	-0.07644	0.07911	-9.6630e-01	0.3356	0.1678
KVDD2	-0.04814	0.0592	-8.1310e-01	0.4175	0.2088
KVDD3	-0.07763	0.05865	-1.3240e+00	0.1878	0.09391
KVDD4	+0.01116	0.06378	+1.7490e-01	0.8614	0.4307

\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) & +1.651 &  0.5116 & +3.2260e+00 &  0.001566 &  0.0007828 \tabularnewline
ITH2 & +0.3398 &  0.09782 & +3.4740e+00 &  0.0006866 &  0.0003433 \tabularnewline
ITH3 & +0.3301 &  0.07887 & +4.1860e+00 &  5.028e-05 &  2.514e-05 \tabularnewline
ITH4 & +0.1587 &  0.06496 & +2.4430e+00 &  0.01583 &  0.007914 \tabularnewline
KVDD1 & -0.07644 &  0.07911 & -9.6630e-01 &  0.3356 &  0.1678 \tabularnewline
KVDD2 & -0.04814 &  0.0592 & -8.1310e-01 &  0.4175 &  0.2088 \tabularnewline
KVDD3 & -0.07763 &  0.05865 & -1.3240e+00 &  0.1878 &  0.09391 \tabularnewline
KVDD4 & +0.01116 &  0.06378 & +1.7490e-01 &  0.8614 &  0.4307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&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]+1.651[/C][C] 0.5116[/C][C]+3.2260e+00[/C][C] 0.001566[/C][C] 0.0007828[/C][/ROW]
[ROW][C]ITH2[/C][C]+0.3398[/C][C] 0.09782[/C][C]+3.4740e+00[/C][C] 0.0006866[/C][C] 0.0003433[/C][/ROW]
[ROW][C]ITH3[/C][C]+0.3301[/C][C] 0.07887[/C][C]+4.1860e+00[/C][C] 5.028e-05[/C][C] 2.514e-05[/C][/ROW]
[ROW][C]ITH4[/C][C]+0.1587[/C][C] 0.06496[/C][C]+2.4430e+00[/C][C] 0.01583[/C][C] 0.007914[/C][/ROW]
[ROW][C]KVDD1[/C][C]-0.07644[/C][C] 0.07911[/C][C]-9.6630e-01[/C][C] 0.3356[/C][C] 0.1678[/C][/ROW]
[ROW][C]KVDD2[/C][C]-0.04814[/C][C] 0.0592[/C][C]-8.1310e-01[/C][C] 0.4175[/C][C] 0.2088[/C][/ROW]
[ROW][C]KVDD3[/C][C]-0.07763[/C][C] 0.05865[/C][C]-1.3240e+00[/C][C] 0.1878[/C][C] 0.09391[/C][/ROW]
[ROW][C]KVDD4[/C][C]+0.01116[/C][C] 0.06378[/C][C]+1.7490e-01[/C][C] 0.8614[/C][C] 0.4307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298554&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&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)	+1.651	0.5116	+3.2260e+00	0.001566	0.0007828
ITH2	+0.3398	0.09782	+3.4740e+00	0.0006866	0.0003433
ITH3	+0.3301	0.07887	+4.1860e+00	5.028e-05	2.514e-05
ITH4	+0.1587	0.06496	+2.4430e+00	0.01583	0.007914
KVDD1	-0.07644	0.07911	-9.6630e-01	0.3356	0.1678
KVDD2	-0.04814	0.0592	-8.1310e-01	0.4175	0.2088
KVDD3	-0.07763	0.05865	-1.3240e+00	0.1878	0.09391
KVDD4	+0.01116	0.06378	+1.7490e-01	0.8614	0.4307

Multiple Linear Regression - Regression Statistics
Multiple R	0.6258
R-squared	0.3917
Adjusted R-squared	0.3608
F-TEST (value)	12.69
F-TEST (DF numerator)	7
F-TEST (DF denominator)	138
p-value	1.643e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.6455
Sum Squared Residuals	57.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6258 \tabularnewline
R-squared &  0.3917 \tabularnewline
Adjusted R-squared &  0.3608 \tabularnewline
F-TEST (value) &  12.69 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value &  1.643e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.6455 \tabularnewline
Sum Squared Residuals &  57.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6258[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3917[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3608[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 12.69[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C] 1.643e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.6455[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 57.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298554&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&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	0.6258
R-squared	0.3917
Adjusted R-squared	0.3608
F-TEST (value)	12.69
F-TEST (DF numerator)	7
F-TEST (DF denominator)	138
p-value	1.643e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.6455
Sum Squared Residuals	57.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	3	3.794	-0.7943
2	5	4.304	0.6957
3	5	4.315	0.6845
4	4	3.783	0.2169
5	5	4.99	0.009987
6	5	3.78	1.22
7	5	4.95	0.05041
8	5	4.24	0.7604
9	5	3.935	1.065
10	5	4.926	0.07391
11	5	4.875	0.1246
12	5	4.427	0.5728
13	4	3.948	0.05166
14	3	4.11	-1.11
15	5	5.084	-0.08361
16	5	3.672	1.328
17	5	5.009	-0.008973
18	5	4.892	0.1084
19	5	4.475	0.5253
20	4	4.85	-0.8496
21	4	4.618	-0.6183
22	5	4.697	0.3032
23	5	4.419	0.5811
24	5	4.699	0.3007
25	5	5.107	-0.1071
26	1	1.922	-0.9218
27	5	4.701	0.2995
28	4	4.383	-0.3831
29	4	4.054	-0.05449
30	4	4.202	-0.202
31	5	4.897	0.1031
32	4	4.332	-0.3316
33	4	4.12	-0.1198
34	5	4.53	0.47
35	5	5.086	-0.08599
36	5	4.883	0.1169
37	2	2.417	-0.4169
38	3	3.694	-0.6937
39	4	3.923	0.07653
40	4	4.182	-0.1822
41	5	4.733	0.2671
42	5	4.76	0.2398
43	4	4.25	-0.2502
44	5	4.26	0.7402
45	5	4.998	0.002269
46	4	4.242	-0.2415
47	5	4.778	0.2218
48	3	3.801	-0.8008
49	4	3.529	0.4713
50	4	4.83	-0.8303
51	5	4.278	0.7215
52	4	4.509	-0.5089
53	4	4.355	-0.3549
54	4	3.704	0.2963
55	4	4.213	-0.2132
56	2	3.983	-1.983
57	4	4.382	-0.3819
58	4	3.713	0.2868
59	5	4.953	0.04697
60	3	3.713	-0.7132
61	5	4.705	0.2951
62	4	3.453	0.5467
63	5	5.122	-0.1223
64	4	4.575	-0.5748
65	4	3.549	0.4507
66	5	4.532	0.4681
67	5	4.926	0.07391
68	5	3.738	1.262
69	4	3.738	0.2622
70	5	4.793	0.2069
71	2	4.301	-2.301
72	5	4.661	0.3386
73	5	4.666	0.3336
74	5	4.875	0.1246
75	5	3.883	1.117
76	4	4.138	-0.1384
77	4	4.144	-0.1444
78	5	5.066	-0.06646
79	4	4.376	-0.3761
80	5	4.937	0.06275
81	5	4.464	0.5358
82	5	4.696	0.3038
83	4	4.12	-0.1198
84	5	4.838	0.1616
85	5	4.773	0.2273
86	3	3.598	-0.5976
87	5	4.532	0.4678
88	5	4.805	0.1945
89	5	5.259	-0.2587
90	4	4.503	-0.5027
91	4	4.025	-0.02499
92	4	4.201	-0.2009
93	5	4.723	0.2768
94	4	3.475	0.5247
95	3	4.25	-1.25
96	3	3.679	-0.6791
97	4	4.58	-0.5803
98	5	4.512	0.4877
99	5	4.452	0.5476
100	4	4.388	-0.3881
101	5	5.071	-0.07132
102	5	4.257	0.7433
103	4	3.915	0.0846
104	4	3.996	0.003522
105	5	4.638	0.3619
106	5	5.012	-0.01232
107	5	4.259	0.7414
108	4	4.542	-0.5418
109	5	4.124	0.8756
110	3	4.227	-1.227
111	5	4.46	0.5404
112	5	4.731	0.2693
113	4	4.415	-0.4149
114	5	4.942	0.05812
115	4	4.103	-0.1026
116	4	4.208	-0.208
117	4	4.804	-0.8043
118	4	4.185	-0.1851
119	5	4.686	0.3137
120	5	5.012	-0.01232
121	5	4.956	0.04412
122	4	3.861	0.1389
123	4	4.287	-0.2871
124	5	4.163	0.8367
125	5	4.356	0.6439
126	5	4.912	0.08808
127	5	5.09	-0.08996
128	5	4.335	0.6647
129	5	4.443	0.5566
130	4	4.311	-0.3108
131	5	4.12	0.8802
132	3	4.327	-1.327
133	4	4.873	-0.8732
134	4	4.464	-0.4642
135	3	4.544	-1.544
136	3	3.458	-0.4585
137	5	4.783	0.2168
138	5	3.826	1.174
139	5	4.28	0.7203
140	5	5.026	-0.02633
141	5	4.548	0.4518
142	5	4.871	0.1292
143	5	4.317	0.683
144	4	4.058	-0.05793
145	4	4.45	-0.4499
146	2	4.487	-2.487

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  3 &  3.794 & -0.7943 \tabularnewline
2 &  5 &  4.304 &  0.6957 \tabularnewline
3 &  5 &  4.315 &  0.6845 \tabularnewline
4 &  4 &  3.783 &  0.2169 \tabularnewline
5 &  5 &  4.99 &  0.009987 \tabularnewline
6 &  5 &  3.78 &  1.22 \tabularnewline
7 &  5 &  4.95 &  0.05041 \tabularnewline
8 &  5 &  4.24 &  0.7604 \tabularnewline
9 &  5 &  3.935 &  1.065 \tabularnewline
10 &  5 &  4.926 &  0.07391 \tabularnewline
11 &  5 &  4.875 &  0.1246 \tabularnewline
12 &  5 &  4.427 &  0.5728 \tabularnewline
13 &  4 &  3.948 &  0.05166 \tabularnewline
14 &  3 &  4.11 & -1.11 \tabularnewline
15 &  5 &  5.084 & -0.08361 \tabularnewline
16 &  5 &  3.672 &  1.328 \tabularnewline
17 &  5 &  5.009 & -0.008973 \tabularnewline
18 &  5 &  4.892 &  0.1084 \tabularnewline
19 &  5 &  4.475 &  0.5253 \tabularnewline
20 &  4 &  4.85 & -0.8496 \tabularnewline
21 &  4 &  4.618 & -0.6183 \tabularnewline
22 &  5 &  4.697 &  0.3032 \tabularnewline
23 &  5 &  4.419 &  0.5811 \tabularnewline
24 &  5 &  4.699 &  0.3007 \tabularnewline
25 &  5 &  5.107 & -0.1071 \tabularnewline
26 &  1 &  1.922 & -0.9218 \tabularnewline
27 &  5 &  4.701 &  0.2995 \tabularnewline
28 &  4 &  4.383 & -0.3831 \tabularnewline
29 &  4 &  4.054 & -0.05449 \tabularnewline
30 &  4 &  4.202 & -0.202 \tabularnewline
31 &  5 &  4.897 &  0.1031 \tabularnewline
32 &  4 &  4.332 & -0.3316 \tabularnewline
33 &  4 &  4.12 & -0.1198 \tabularnewline
34 &  5 &  4.53 &  0.47 \tabularnewline
35 &  5 &  5.086 & -0.08599 \tabularnewline
36 &  5 &  4.883 &  0.1169 \tabularnewline
37 &  2 &  2.417 & -0.4169 \tabularnewline
38 &  3 &  3.694 & -0.6937 \tabularnewline
39 &  4 &  3.923 &  0.07653 \tabularnewline
40 &  4 &  4.182 & -0.1822 \tabularnewline
41 &  5 &  4.733 &  0.2671 \tabularnewline
42 &  5 &  4.76 &  0.2398 \tabularnewline
43 &  4 &  4.25 & -0.2502 \tabularnewline
44 &  5 &  4.26 &  0.7402 \tabularnewline
45 &  5 &  4.998 &  0.002269 \tabularnewline
46 &  4 &  4.242 & -0.2415 \tabularnewline
47 &  5 &  4.778 &  0.2218 \tabularnewline
48 &  3 &  3.801 & -0.8008 \tabularnewline
49 &  4 &  3.529 &  0.4713 \tabularnewline
50 &  4 &  4.83 & -0.8303 \tabularnewline
51 &  5 &  4.278 &  0.7215 \tabularnewline
52 &  4 &  4.509 & -0.5089 \tabularnewline
53 &  4 &  4.355 & -0.3549 \tabularnewline
54 &  4 &  3.704 &  0.2963 \tabularnewline
55 &  4 &  4.213 & -0.2132 \tabularnewline
56 &  2 &  3.983 & -1.983 \tabularnewline
57 &  4 &  4.382 & -0.3819 \tabularnewline
58 &  4 &  3.713 &  0.2868 \tabularnewline
59 &  5 &  4.953 &  0.04697 \tabularnewline
60 &  3 &  3.713 & -0.7132 \tabularnewline
61 &  5 &  4.705 &  0.2951 \tabularnewline
62 &  4 &  3.453 &  0.5467 \tabularnewline
63 &  5 &  5.122 & -0.1223 \tabularnewline
64 &  4 &  4.575 & -0.5748 \tabularnewline
65 &  4 &  3.549 &  0.4507 \tabularnewline
66 &  5 &  4.532 &  0.4681 \tabularnewline
67 &  5 &  4.926 &  0.07391 \tabularnewline
68 &  5 &  3.738 &  1.262 \tabularnewline
69 &  4 &  3.738 &  0.2622 \tabularnewline
70 &  5 &  4.793 &  0.2069 \tabularnewline
71 &  2 &  4.301 & -2.301 \tabularnewline
72 &  5 &  4.661 &  0.3386 \tabularnewline
73 &  5 &  4.666 &  0.3336 \tabularnewline
74 &  5 &  4.875 &  0.1246 \tabularnewline
75 &  5 &  3.883 &  1.117 \tabularnewline
76 &  4 &  4.138 & -0.1384 \tabularnewline
77 &  4 &  4.144 & -0.1444 \tabularnewline
78 &  5 &  5.066 & -0.06646 \tabularnewline
79 &  4 &  4.376 & -0.3761 \tabularnewline
80 &  5 &  4.937 &  0.06275 \tabularnewline
81 &  5 &  4.464 &  0.5358 \tabularnewline
82 &  5 &  4.696 &  0.3038 \tabularnewline
83 &  4 &  4.12 & -0.1198 \tabularnewline
84 &  5 &  4.838 &  0.1616 \tabularnewline
85 &  5 &  4.773 &  0.2273 \tabularnewline
86 &  3 &  3.598 & -0.5976 \tabularnewline
87 &  5 &  4.532 &  0.4678 \tabularnewline
88 &  5 &  4.805 &  0.1945 \tabularnewline
89 &  5 &  5.259 & -0.2587 \tabularnewline
90 &  4 &  4.503 & -0.5027 \tabularnewline
91 &  4 &  4.025 & -0.02499 \tabularnewline
92 &  4 &  4.201 & -0.2009 \tabularnewline
93 &  5 &  4.723 &  0.2768 \tabularnewline
94 &  4 &  3.475 &  0.5247 \tabularnewline
95 &  3 &  4.25 & -1.25 \tabularnewline
96 &  3 &  3.679 & -0.6791 \tabularnewline
97 &  4 &  4.58 & -0.5803 \tabularnewline
98 &  5 &  4.512 &  0.4877 \tabularnewline
99 &  5 &  4.452 &  0.5476 \tabularnewline
100 &  4 &  4.388 & -0.3881 \tabularnewline
101 &  5 &  5.071 & -0.07132 \tabularnewline
102 &  5 &  4.257 &  0.7433 \tabularnewline
103 &  4 &  3.915 &  0.0846 \tabularnewline
104 &  4 &  3.996 &  0.003522 \tabularnewline
105 &  5 &  4.638 &  0.3619 \tabularnewline
106 &  5 &  5.012 & -0.01232 \tabularnewline
107 &  5 &  4.259 &  0.7414 \tabularnewline
108 &  4 &  4.542 & -0.5418 \tabularnewline
109 &  5 &  4.124 &  0.8756 \tabularnewline
110 &  3 &  4.227 & -1.227 \tabularnewline
111 &  5 &  4.46 &  0.5404 \tabularnewline
112 &  5 &  4.731 &  0.2693 \tabularnewline
113 &  4 &  4.415 & -0.4149 \tabularnewline
114 &  5 &  4.942 &  0.05812 \tabularnewline
115 &  4 &  4.103 & -0.1026 \tabularnewline
116 &  4 &  4.208 & -0.208 \tabularnewline
117 &  4 &  4.804 & -0.8043 \tabularnewline
118 &  4 &  4.185 & -0.1851 \tabularnewline
119 &  5 &  4.686 &  0.3137 \tabularnewline
120 &  5 &  5.012 & -0.01232 \tabularnewline
121 &  5 &  4.956 &  0.04412 \tabularnewline
122 &  4 &  3.861 &  0.1389 \tabularnewline
123 &  4 &  4.287 & -0.2871 \tabularnewline
124 &  5 &  4.163 &  0.8367 \tabularnewline
125 &  5 &  4.356 &  0.6439 \tabularnewline
126 &  5 &  4.912 &  0.08808 \tabularnewline
127 &  5 &  5.09 & -0.08996 \tabularnewline
128 &  5 &  4.335 &  0.6647 \tabularnewline
129 &  5 &  4.443 &  0.5566 \tabularnewline
130 &  4 &  4.311 & -0.3108 \tabularnewline
131 &  5 &  4.12 &  0.8802 \tabularnewline
132 &  3 &  4.327 & -1.327 \tabularnewline
133 &  4 &  4.873 & -0.8732 \tabularnewline
134 &  4 &  4.464 & -0.4642 \tabularnewline
135 &  3 &  4.544 & -1.544 \tabularnewline
136 &  3 &  3.458 & -0.4585 \tabularnewline
137 &  5 &  4.783 &  0.2168 \tabularnewline
138 &  5 &  3.826 &  1.174 \tabularnewline
139 &  5 &  4.28 &  0.7203 \tabularnewline
140 &  5 &  5.026 & -0.02633 \tabularnewline
141 &  5 &  4.548 &  0.4518 \tabularnewline
142 &  5 &  4.871 &  0.1292 \tabularnewline
143 &  5 &  4.317 &  0.683 \tabularnewline
144 &  4 &  4.058 & -0.05793 \tabularnewline
145 &  4 &  4.45 & -0.4499 \tabularnewline
146 &  2 &  4.487 & -2.487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&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] 3[/C][C] 3.794[/C][C]-0.7943[/C][/ROW]
[ROW][C]2[/C][C] 5[/C][C] 4.304[/C][C] 0.6957[/C][/ROW]
[ROW][C]3[/C][C] 5[/C][C] 4.315[/C][C] 0.6845[/C][/ROW]
[ROW][C]4[/C][C] 4[/C][C] 3.783[/C][C] 0.2169[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 4.99[/C][C] 0.009987[/C][/ROW]
[ROW][C]6[/C][C] 5[/C][C] 3.78[/C][C] 1.22[/C][/ROW]
[ROW][C]7[/C][C] 5[/C][C] 4.95[/C][C] 0.05041[/C][/ROW]
[ROW][C]8[/C][C] 5[/C][C] 4.24[/C][C] 0.7604[/C][/ROW]
[ROW][C]9[/C][C] 5[/C][C] 3.935[/C][C] 1.065[/C][/ROW]
[ROW][C]10[/C][C] 5[/C][C] 4.926[/C][C] 0.07391[/C][/ROW]
[ROW][C]11[/C][C] 5[/C][C] 4.875[/C][C] 0.1246[/C][/ROW]
[ROW][C]12[/C][C] 5[/C][C] 4.427[/C][C] 0.5728[/C][/ROW]
[ROW][C]13[/C][C] 4[/C][C] 3.948[/C][C] 0.05166[/C][/ROW]
[ROW][C]14[/C][C] 3[/C][C] 4.11[/C][C]-1.11[/C][/ROW]
[ROW][C]15[/C][C] 5[/C][C] 5.084[/C][C]-0.08361[/C][/ROW]
[ROW][C]16[/C][C] 5[/C][C] 3.672[/C][C] 1.328[/C][/ROW]
[ROW][C]17[/C][C] 5[/C][C] 5.009[/C][C]-0.008973[/C][/ROW]
[ROW][C]18[/C][C] 5[/C][C] 4.892[/C][C] 0.1084[/C][/ROW]
[ROW][C]19[/C][C] 5[/C][C] 4.475[/C][C] 0.5253[/C][/ROW]
[ROW][C]20[/C][C] 4[/C][C] 4.85[/C][C]-0.8496[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 4.618[/C][C]-0.6183[/C][/ROW]
[ROW][C]22[/C][C] 5[/C][C] 4.697[/C][C] 0.3032[/C][/ROW]
[ROW][C]23[/C][C] 5[/C][C] 4.419[/C][C] 0.5811[/C][/ROW]
[ROW][C]24[/C][C] 5[/C][C] 4.699[/C][C] 0.3007[/C][/ROW]
[ROW][C]25[/C][C] 5[/C][C] 5.107[/C][C]-0.1071[/C][/ROW]
[ROW][C]26[/C][C] 1[/C][C] 1.922[/C][C]-0.9218[/C][/ROW]
[ROW][C]27[/C][C] 5[/C][C] 4.701[/C][C] 0.2995[/C][/ROW]
[ROW][C]28[/C][C] 4[/C][C] 4.383[/C][C]-0.3831[/C][/ROW]
[ROW][C]29[/C][C] 4[/C][C] 4.054[/C][C]-0.05449[/C][/ROW]
[ROW][C]30[/C][C] 4[/C][C] 4.202[/C][C]-0.202[/C][/ROW]
[ROW][C]31[/C][C] 5[/C][C] 4.897[/C][C] 0.1031[/C][/ROW]
[ROW][C]32[/C][C] 4[/C][C] 4.332[/C][C]-0.3316[/C][/ROW]
[ROW][C]33[/C][C] 4[/C][C] 4.12[/C][C]-0.1198[/C][/ROW]
[ROW][C]34[/C][C] 5[/C][C] 4.53[/C][C] 0.47[/C][/ROW]
[ROW][C]35[/C][C] 5[/C][C] 5.086[/C][C]-0.08599[/C][/ROW]
[ROW][C]36[/C][C] 5[/C][C] 4.883[/C][C] 0.1169[/C][/ROW]
[ROW][C]37[/C][C] 2[/C][C] 2.417[/C][C]-0.4169[/C][/ROW]
[ROW][C]38[/C][C] 3[/C][C] 3.694[/C][C]-0.6937[/C][/ROW]
[ROW][C]39[/C][C] 4[/C][C] 3.923[/C][C] 0.07653[/C][/ROW]
[ROW][C]40[/C][C] 4[/C][C] 4.182[/C][C]-0.1822[/C][/ROW]
[ROW][C]41[/C][C] 5[/C][C] 4.733[/C][C] 0.2671[/C][/ROW]
[ROW][C]42[/C][C] 5[/C][C] 4.76[/C][C] 0.2398[/C][/ROW]
[ROW][C]43[/C][C] 4[/C][C] 4.25[/C][C]-0.2502[/C][/ROW]
[ROW][C]44[/C][C] 5[/C][C] 4.26[/C][C] 0.7402[/C][/ROW]
[ROW][C]45[/C][C] 5[/C][C] 4.998[/C][C] 0.002269[/C][/ROW]
[ROW][C]46[/C][C] 4[/C][C] 4.242[/C][C]-0.2415[/C][/ROW]
[ROW][C]47[/C][C] 5[/C][C] 4.778[/C][C] 0.2218[/C][/ROW]
[ROW][C]48[/C][C] 3[/C][C] 3.801[/C][C]-0.8008[/C][/ROW]
[ROW][C]49[/C][C] 4[/C][C] 3.529[/C][C] 0.4713[/C][/ROW]
[ROW][C]50[/C][C] 4[/C][C] 4.83[/C][C]-0.8303[/C][/ROW]
[ROW][C]51[/C][C] 5[/C][C] 4.278[/C][C] 0.7215[/C][/ROW]
[ROW][C]52[/C][C] 4[/C][C] 4.509[/C][C]-0.5089[/C][/ROW]
[ROW][C]53[/C][C] 4[/C][C] 4.355[/C][C]-0.3549[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 3.704[/C][C] 0.2963[/C][/ROW]
[ROW][C]55[/C][C] 4[/C][C] 4.213[/C][C]-0.2132[/C][/ROW]
[ROW][C]56[/C][C] 2[/C][C] 3.983[/C][C]-1.983[/C][/ROW]
[ROW][C]57[/C][C] 4[/C][C] 4.382[/C][C]-0.3819[/C][/ROW]
[ROW][C]58[/C][C] 4[/C][C] 3.713[/C][C] 0.2868[/C][/ROW]
[ROW][C]59[/C][C] 5[/C][C] 4.953[/C][C] 0.04697[/C][/ROW]
[ROW][C]60[/C][C] 3[/C][C] 3.713[/C][C]-0.7132[/C][/ROW]
[ROW][C]61[/C][C] 5[/C][C] 4.705[/C][C] 0.2951[/C][/ROW]
[ROW][C]62[/C][C] 4[/C][C] 3.453[/C][C] 0.5467[/C][/ROW]
[ROW][C]63[/C][C] 5[/C][C] 5.122[/C][C]-0.1223[/C][/ROW]
[ROW][C]64[/C][C] 4[/C][C] 4.575[/C][C]-0.5748[/C][/ROW]
[ROW][C]65[/C][C] 4[/C][C] 3.549[/C][C] 0.4507[/C][/ROW]
[ROW][C]66[/C][C] 5[/C][C] 4.532[/C][C] 0.4681[/C][/ROW]
[ROW][C]67[/C][C] 5[/C][C] 4.926[/C][C] 0.07391[/C][/ROW]
[ROW][C]68[/C][C] 5[/C][C] 3.738[/C][C] 1.262[/C][/ROW]
[ROW][C]69[/C][C] 4[/C][C] 3.738[/C][C] 0.2622[/C][/ROW]
[ROW][C]70[/C][C] 5[/C][C] 4.793[/C][C] 0.2069[/C][/ROW]
[ROW][C]71[/C][C] 2[/C][C] 4.301[/C][C]-2.301[/C][/ROW]
[ROW][C]72[/C][C] 5[/C][C] 4.661[/C][C] 0.3386[/C][/ROW]
[ROW][C]73[/C][C] 5[/C][C] 4.666[/C][C] 0.3336[/C][/ROW]
[ROW][C]74[/C][C] 5[/C][C] 4.875[/C][C] 0.1246[/C][/ROW]
[ROW][C]75[/C][C] 5[/C][C] 3.883[/C][C] 1.117[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 4.138[/C][C]-0.1384[/C][/ROW]
[ROW][C]77[/C][C] 4[/C][C] 4.144[/C][C]-0.1444[/C][/ROW]
[ROW][C]78[/C][C] 5[/C][C] 5.066[/C][C]-0.06646[/C][/ROW]
[ROW][C]79[/C][C] 4[/C][C] 4.376[/C][C]-0.3761[/C][/ROW]
[ROW][C]80[/C][C] 5[/C][C] 4.937[/C][C] 0.06275[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 4.464[/C][C] 0.5358[/C][/ROW]
[ROW][C]82[/C][C] 5[/C][C] 4.696[/C][C] 0.3038[/C][/ROW]
[ROW][C]83[/C][C] 4[/C][C] 4.12[/C][C]-0.1198[/C][/ROW]
[ROW][C]84[/C][C] 5[/C][C] 4.838[/C][C] 0.1616[/C][/ROW]
[ROW][C]85[/C][C] 5[/C][C] 4.773[/C][C] 0.2273[/C][/ROW]
[ROW][C]86[/C][C] 3[/C][C] 3.598[/C][C]-0.5976[/C][/ROW]
[ROW][C]87[/C][C] 5[/C][C] 4.532[/C][C] 0.4678[/C][/ROW]
[ROW][C]88[/C][C] 5[/C][C] 4.805[/C][C] 0.1945[/C][/ROW]
[ROW][C]89[/C][C] 5[/C][C] 5.259[/C][C]-0.2587[/C][/ROW]
[ROW][C]90[/C][C] 4[/C][C] 4.503[/C][C]-0.5027[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 4.025[/C][C]-0.02499[/C][/ROW]
[ROW][C]92[/C][C] 4[/C][C] 4.201[/C][C]-0.2009[/C][/ROW]
[ROW][C]93[/C][C] 5[/C][C] 4.723[/C][C] 0.2768[/C][/ROW]
[ROW][C]94[/C][C] 4[/C][C] 3.475[/C][C] 0.5247[/C][/ROW]
[ROW][C]95[/C][C] 3[/C][C] 4.25[/C][C]-1.25[/C][/ROW]
[ROW][C]96[/C][C] 3[/C][C] 3.679[/C][C]-0.6791[/C][/ROW]
[ROW][C]97[/C][C] 4[/C][C] 4.58[/C][C]-0.5803[/C][/ROW]
[ROW][C]98[/C][C] 5[/C][C] 4.512[/C][C] 0.4877[/C][/ROW]
[ROW][C]99[/C][C] 5[/C][C] 4.452[/C][C] 0.5476[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 4.388[/C][C]-0.3881[/C][/ROW]
[ROW][C]101[/C][C] 5[/C][C] 5.071[/C][C]-0.07132[/C][/ROW]
[ROW][C]102[/C][C] 5[/C][C] 4.257[/C][C] 0.7433[/C][/ROW]
[ROW][C]103[/C][C] 4[/C][C] 3.915[/C][C] 0.0846[/C][/ROW]
[ROW][C]104[/C][C] 4[/C][C] 3.996[/C][C] 0.003522[/C][/ROW]
[ROW][C]105[/C][C] 5[/C][C] 4.638[/C][C] 0.3619[/C][/ROW]
[ROW][C]106[/C][C] 5[/C][C] 5.012[/C][C]-0.01232[/C][/ROW]
[ROW][C]107[/C][C] 5[/C][C] 4.259[/C][C] 0.7414[/C][/ROW]
[ROW][C]108[/C][C] 4[/C][C] 4.542[/C][C]-0.5418[/C][/ROW]
[ROW][C]109[/C][C] 5[/C][C] 4.124[/C][C] 0.8756[/C][/ROW]
[ROW][C]110[/C][C] 3[/C][C] 4.227[/C][C]-1.227[/C][/ROW]
[ROW][C]111[/C][C] 5[/C][C] 4.46[/C][C] 0.5404[/C][/ROW]
[ROW][C]112[/C][C] 5[/C][C] 4.731[/C][C] 0.2693[/C][/ROW]
[ROW][C]113[/C][C] 4[/C][C] 4.415[/C][C]-0.4149[/C][/ROW]
[ROW][C]114[/C][C] 5[/C][C] 4.942[/C][C] 0.05812[/C][/ROW]
[ROW][C]115[/C][C] 4[/C][C] 4.103[/C][C]-0.1026[/C][/ROW]
[ROW][C]116[/C][C] 4[/C][C] 4.208[/C][C]-0.208[/C][/ROW]
[ROW][C]117[/C][C] 4[/C][C] 4.804[/C][C]-0.8043[/C][/ROW]
[ROW][C]118[/C][C] 4[/C][C] 4.185[/C][C]-0.1851[/C][/ROW]
[ROW][C]119[/C][C] 5[/C][C] 4.686[/C][C] 0.3137[/C][/ROW]
[ROW][C]120[/C][C] 5[/C][C] 5.012[/C][C]-0.01232[/C][/ROW]
[ROW][C]121[/C][C] 5[/C][C] 4.956[/C][C] 0.04412[/C][/ROW]
[ROW][C]122[/C][C] 4[/C][C] 3.861[/C][C] 0.1389[/C][/ROW]
[ROW][C]123[/C][C] 4[/C][C] 4.287[/C][C]-0.2871[/C][/ROW]
[ROW][C]124[/C][C] 5[/C][C] 4.163[/C][C] 0.8367[/C][/ROW]
[ROW][C]125[/C][C] 5[/C][C] 4.356[/C][C] 0.6439[/C][/ROW]
[ROW][C]126[/C][C] 5[/C][C] 4.912[/C][C] 0.08808[/C][/ROW]
[ROW][C]127[/C][C] 5[/C][C] 5.09[/C][C]-0.08996[/C][/ROW]
[ROW][C]128[/C][C] 5[/C][C] 4.335[/C][C] 0.6647[/C][/ROW]
[ROW][C]129[/C][C] 5[/C][C] 4.443[/C][C] 0.5566[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 4.311[/C][C]-0.3108[/C][/ROW]
[ROW][C]131[/C][C] 5[/C][C] 4.12[/C][C] 0.8802[/C][/ROW]
[ROW][C]132[/C][C] 3[/C][C] 4.327[/C][C]-1.327[/C][/ROW]
[ROW][C]133[/C][C] 4[/C][C] 4.873[/C][C]-0.8732[/C][/ROW]
[ROW][C]134[/C][C] 4[/C][C] 4.464[/C][C]-0.4642[/C][/ROW]
[ROW][C]135[/C][C] 3[/C][C] 4.544[/C][C]-1.544[/C][/ROW]
[ROW][C]136[/C][C] 3[/C][C] 3.458[/C][C]-0.4585[/C][/ROW]
[ROW][C]137[/C][C] 5[/C][C] 4.783[/C][C] 0.2168[/C][/ROW]
[ROW][C]138[/C][C] 5[/C][C] 3.826[/C][C] 1.174[/C][/ROW]
[ROW][C]139[/C][C] 5[/C][C] 4.28[/C][C] 0.7203[/C][/ROW]
[ROW][C]140[/C][C] 5[/C][C] 5.026[/C][C]-0.02633[/C][/ROW]
[ROW][C]141[/C][C] 5[/C][C] 4.548[/C][C] 0.4518[/C][/ROW]
[ROW][C]142[/C][C] 5[/C][C] 4.871[/C][C] 0.1292[/C][/ROW]
[ROW][C]143[/C][C] 5[/C][C] 4.317[/C][C] 0.683[/C][/ROW]
[ROW][C]144[/C][C] 4[/C][C] 4.058[/C][C]-0.05793[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 4.45[/C][C]-0.4499[/C][/ROW]
[ROW][C]146[/C][C] 2[/C][C] 4.487[/C][C]-2.487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298554&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&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	3	3.794	-0.7943
2	5	4.304	0.6957
3	5	4.315	0.6845
4	4	3.783	0.2169
5	5	4.99	0.009987
6	5	3.78	1.22
7	5	4.95	0.05041
8	5	4.24	0.7604
9	5	3.935	1.065
10	5	4.926	0.07391
11	5	4.875	0.1246
12	5	4.427	0.5728
13	4	3.948	0.05166
14	3	4.11	-1.11
15	5	5.084	-0.08361
16	5	3.672	1.328
17	5	5.009	-0.008973
18	5	4.892	0.1084
19	5	4.475	0.5253
20	4	4.85	-0.8496
21	4	4.618	-0.6183
22	5	4.697	0.3032
23	5	4.419	0.5811
24	5	4.699	0.3007
25	5	5.107	-0.1071
26	1	1.922	-0.9218
27	5	4.701	0.2995
28	4	4.383	-0.3831
29	4	4.054	-0.05449
30	4	4.202	-0.202
31	5	4.897	0.1031
32	4	4.332	-0.3316
33	4	4.12	-0.1198
34	5	4.53	0.47
35	5	5.086	-0.08599
36	5	4.883	0.1169
37	2	2.417	-0.4169
38	3	3.694	-0.6937
39	4	3.923	0.07653
40	4	4.182	-0.1822
41	5	4.733	0.2671
42	5	4.76	0.2398
43	4	4.25	-0.2502
44	5	4.26	0.7402
45	5	4.998	0.002269
46	4	4.242	-0.2415
47	5	4.778	0.2218
48	3	3.801	-0.8008
49	4	3.529	0.4713
50	4	4.83	-0.8303
51	5	4.278	0.7215
52	4	4.509	-0.5089
53	4	4.355	-0.3549
54	4	3.704	0.2963
55	4	4.213	-0.2132
56	2	3.983	-1.983
57	4	4.382	-0.3819
58	4	3.713	0.2868
59	5	4.953	0.04697
60	3	3.713	-0.7132
61	5	4.705	0.2951
62	4	3.453	0.5467
63	5	5.122	-0.1223
64	4	4.575	-0.5748
65	4	3.549	0.4507
66	5	4.532	0.4681
67	5	4.926	0.07391
68	5	3.738	1.262
69	4	3.738	0.2622
70	5	4.793	0.2069
71	2	4.301	-2.301
72	5	4.661	0.3386
73	5	4.666	0.3336
74	5	4.875	0.1246
75	5	3.883	1.117
76	4	4.138	-0.1384
77	4	4.144	-0.1444
78	5	5.066	-0.06646
79	4	4.376	-0.3761
80	5	4.937	0.06275
81	5	4.464	0.5358
82	5	4.696	0.3038
83	4	4.12	-0.1198
84	5	4.838	0.1616
85	5	4.773	0.2273
86	3	3.598	-0.5976
87	5	4.532	0.4678
88	5	4.805	0.1945
89	5	5.259	-0.2587
90	4	4.503	-0.5027
91	4	4.025	-0.02499
92	4	4.201	-0.2009
93	5	4.723	0.2768
94	4	3.475	0.5247
95	3	4.25	-1.25
96	3	3.679	-0.6791
97	4	4.58	-0.5803
98	5	4.512	0.4877
99	5	4.452	0.5476
100	4	4.388	-0.3881
101	5	5.071	-0.07132
102	5	4.257	0.7433
103	4	3.915	0.0846
104	4	3.996	0.003522
105	5	4.638	0.3619
106	5	5.012	-0.01232
107	5	4.259	0.7414
108	4	4.542	-0.5418
109	5	4.124	0.8756
110	3	4.227	-1.227
111	5	4.46	0.5404
112	5	4.731	0.2693
113	4	4.415	-0.4149
114	5	4.942	0.05812
115	4	4.103	-0.1026
116	4	4.208	-0.208
117	4	4.804	-0.8043
118	4	4.185	-0.1851
119	5	4.686	0.3137
120	5	5.012	-0.01232
121	5	4.956	0.04412
122	4	3.861	0.1389
123	4	4.287	-0.2871
124	5	4.163	0.8367
125	5	4.356	0.6439
126	5	4.912	0.08808
127	5	5.09	-0.08996
128	5	4.335	0.6647
129	5	4.443	0.5566
130	4	4.311	-0.3108
131	5	4.12	0.8802
132	3	4.327	-1.327
133	4	4.873	-0.8732
134	4	4.464	-0.4642
135	3	4.544	-1.544
136	3	3.458	-0.4585
137	5	4.783	0.2168
138	5	3.826	1.174
139	5	4.28	0.7203
140	5	5.026	-0.02633
141	5	4.548	0.4518
142	5	4.871	0.1292
143	5	4.317	0.683
144	4	4.058	-0.05793
145	4	4.45	-0.4499
146	2	4.487	-2.487

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.5189	0.9621	0.4811
12	0.6976	0.6048	0.3024
13	0.6048	0.7903	0.3952
14	0.6794	0.6412	0.3206
15	0.5961	0.8077	0.4039
16	0.7934	0.4132	0.2066
17	0.7183	0.5634	0.2817
18	0.6669	0.6661	0.3331
19	0.5931	0.8137	0.4069
20	0.6593	0.6815	0.3407
21	0.6927	0.6146	0.3073
22	0.6876	0.6248	0.3124
23	0.6958	0.6084	0.3042
24	0.6403	0.7195	0.3597
25	0.5701	0.8598	0.4299
26	0.6987	0.6026	0.3013
27	0.6429	0.7141	0.3571
28	0.6268	0.7463	0.3732
29	0.5607	0.8786	0.4393
30	0.507	0.9859	0.493
31	0.4424	0.8847	0.5576
32	0.3986	0.7972	0.6014
33	0.3377	0.6754	0.6623
34	0.29	0.58	0.71
35	0.2563	0.5126	0.7437
36	0.2097	0.4193	0.7903
37	0.2025	0.4049	0.7975
38	0.2118	0.4237	0.7882
39	0.1706	0.3412	0.8294
40	0.1537	0.3074	0.8463
41	0.1242	0.2484	0.8758
42	0.1043	0.2086	0.8957
43	0.08631	0.1726	0.9137
44	0.07887	0.1577	0.9211
45	0.06298	0.126	0.937
46	0.04816	0.09631	0.9518
47	0.03763	0.07526	0.9624
48	0.04669	0.09338	0.9533
49	0.03982	0.07964	0.9602
50	0.08315	0.1663	0.9169
51	0.09377	0.1875	0.9062
52	0.094	0.188	0.906
53	0.0783	0.1566	0.9217
54	0.06765	0.1353	0.9323
55	0.05325	0.1065	0.9468
56	0.2638	0.5275	0.7362
57	0.2319	0.4637	0.7681
58	0.2022	0.4043	0.7978
59	0.1683	0.3366	0.8317
60	0.1747	0.3494	0.8253
61	0.152	0.3039	0.848
62	0.1559	0.3117	0.8441
63	0.1311	0.2622	0.8689
64	0.1264	0.2528	0.8736
65	0.1163	0.2326	0.8837
66	0.1049	0.2097	0.8951
67	0.08499	0.17	0.915
68	0.1587	0.3174	0.8413
69	0.1365	0.2729	0.8635
70	0.116	0.2321	0.884
71	0.5811	0.8378	0.4189
72	0.553	0.894	0.447
73	0.5198	0.9605	0.4802
74	0.4737	0.9474	0.5263
75	0.5892	0.8216	0.4108
76	0.5415	0.9171	0.4585
77	0.4926	0.9852	0.5074
78	0.4465	0.8929	0.5535
79	0.4087	0.8174	0.5913
80	0.3618	0.7236	0.6382
81	0.35	0.6999	0.65
82	0.318	0.6361	0.682
83	0.2781	0.5562	0.7219
84	0.24	0.48	0.76
85	0.2153	0.4307	0.7847
86	0.2084	0.4168	0.7916
87	0.1902	0.3804	0.8098
88	0.1596	0.3192	0.8404
89	0.1387	0.2774	0.8613
90	0.1254	0.2507	0.8746
91	0.1013	0.2026	0.8987
92	0.08185	0.1637	0.9182
93	0.06655	0.1331	0.9335
94	0.06548	0.131	0.9345
95	0.1088	0.2175	0.8912
96	0.1185	0.237	0.8815
97	0.1127	0.2254	0.8873
98	0.1064	0.2128	0.8936
99	0.1021	0.2042	0.8979
100	0.09147	0.1829	0.9085
101	0.07223	0.1445	0.9278
102	0.07001	0.14	0.93
103	0.05399	0.108	0.946
104	0.0425	0.08501	0.9575
105	0.03953	0.07906	0.9605
106	0.02936	0.05873	0.9706
107	0.05382	0.1076	0.9462
108	0.04531	0.09062	0.9547
109	0.0636	0.1272	0.9364
110	0.07993	0.1599	0.9201
111	0.07396	0.1479	0.926
112	0.06567	0.1313	0.9343
113	0.05352	0.107	0.9465
114	0.04082	0.08164	0.9592
115	0.02967	0.05933	0.9703
116	0.02062	0.04123	0.9794
117	0.01886	0.03773	0.9811
118	0.01315	0.0263	0.9869
119	0.008975	0.01795	0.991
120	0.00587	0.01174	0.9941
121	0.004216	0.008432	0.9958
122	0.002609	0.005218	0.9974
123	0.001606	0.003212	0.9984
124	0.001688	0.003376	0.9983
125	0.001943	0.003886	0.9981
126	0.001518	0.003036	0.9985
127	0.0008309	0.001662	0.9992
128	0.0007458	0.001492	0.9993
129	0.0005264	0.001053	0.9995
130	0.0002702	0.0005405	0.9997
131	0.0005799	0.00116	0.9994
132	0.0005373	0.001075	0.9995
133	0.0002672	0.0005345	0.9997
134	0.0001234	0.0002469	0.9999
135	0.0001844	0.0003688	0.9998

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.5189 &  0.9621 &  0.4811 \tabularnewline
12 &  0.6976 &  0.6048 &  0.3024 \tabularnewline
13 &  0.6048 &  0.7903 &  0.3952 \tabularnewline
14 &  0.6794 &  0.6412 &  0.3206 \tabularnewline
15 &  0.5961 &  0.8077 &  0.4039 \tabularnewline
16 &  0.7934 &  0.4132 &  0.2066 \tabularnewline
17 &  0.7183 &  0.5634 &  0.2817 \tabularnewline
18 &  0.6669 &  0.6661 &  0.3331 \tabularnewline
19 &  0.5931 &  0.8137 &  0.4069 \tabularnewline
20 &  0.6593 &  0.6815 &  0.3407 \tabularnewline
21 &  0.6927 &  0.6146 &  0.3073 \tabularnewline
22 &  0.6876 &  0.6248 &  0.3124 \tabularnewline
23 &  0.6958 &  0.6084 &  0.3042 \tabularnewline
24 &  0.6403 &  0.7195 &  0.3597 \tabularnewline
25 &  0.5701 &  0.8598 &  0.4299 \tabularnewline
26 &  0.6987 &  0.6026 &  0.3013 \tabularnewline
27 &  0.6429 &  0.7141 &  0.3571 \tabularnewline
28 &  0.6268 &  0.7463 &  0.3732 \tabularnewline
29 &  0.5607 &  0.8786 &  0.4393 \tabularnewline
30 &  0.507 &  0.9859 &  0.493 \tabularnewline
31 &  0.4424 &  0.8847 &  0.5576 \tabularnewline
32 &  0.3986 &  0.7972 &  0.6014 \tabularnewline
33 &  0.3377 &  0.6754 &  0.6623 \tabularnewline
34 &  0.29 &  0.58 &  0.71 \tabularnewline
35 &  0.2563 &  0.5126 &  0.7437 \tabularnewline
36 &  0.2097 &  0.4193 &  0.7903 \tabularnewline
37 &  0.2025 &  0.4049 &  0.7975 \tabularnewline
38 &  0.2118 &  0.4237 &  0.7882 \tabularnewline
39 &  0.1706 &  0.3412 &  0.8294 \tabularnewline
40 &  0.1537 &  0.3074 &  0.8463 \tabularnewline
41 &  0.1242 &  0.2484 &  0.8758 \tabularnewline
42 &  0.1043 &  0.2086 &  0.8957 \tabularnewline
43 &  0.08631 &  0.1726 &  0.9137 \tabularnewline
44 &  0.07887 &  0.1577 &  0.9211 \tabularnewline
45 &  0.06298 &  0.126 &  0.937 \tabularnewline
46 &  0.04816 &  0.09631 &  0.9518 \tabularnewline
47 &  0.03763 &  0.07526 &  0.9624 \tabularnewline
48 &  0.04669 &  0.09338 &  0.9533 \tabularnewline
49 &  0.03982 &  0.07964 &  0.9602 \tabularnewline
50 &  0.08315 &  0.1663 &  0.9169 \tabularnewline
51 &  0.09377 &  0.1875 &  0.9062 \tabularnewline
52 &  0.094 &  0.188 &  0.906 \tabularnewline
53 &  0.0783 &  0.1566 &  0.9217 \tabularnewline
54 &  0.06765 &  0.1353 &  0.9323 \tabularnewline
55 &  0.05325 &  0.1065 &  0.9468 \tabularnewline
56 &  0.2638 &  0.5275 &  0.7362 \tabularnewline
57 &  0.2319 &  0.4637 &  0.7681 \tabularnewline
58 &  0.2022 &  0.4043 &  0.7978 \tabularnewline
59 &  0.1683 &  0.3366 &  0.8317 \tabularnewline
60 &  0.1747 &  0.3494 &  0.8253 \tabularnewline
61 &  0.152 &  0.3039 &  0.848 \tabularnewline
62 &  0.1559 &  0.3117 &  0.8441 \tabularnewline
63 &  0.1311 &  0.2622 &  0.8689 \tabularnewline
64 &  0.1264 &  0.2528 &  0.8736 \tabularnewline
65 &  0.1163 &  0.2326 &  0.8837 \tabularnewline
66 &  0.1049 &  0.2097 &  0.8951 \tabularnewline
67 &  0.08499 &  0.17 &  0.915 \tabularnewline
68 &  0.1587 &  0.3174 &  0.8413 \tabularnewline
69 &  0.1365 &  0.2729 &  0.8635 \tabularnewline
70 &  0.116 &  0.2321 &  0.884 \tabularnewline
71 &  0.5811 &  0.8378 &  0.4189 \tabularnewline
72 &  0.553 &  0.894 &  0.447 \tabularnewline
73 &  0.5198 &  0.9605 &  0.4802 \tabularnewline
74 &  0.4737 &  0.9474 &  0.5263 \tabularnewline
75 &  0.5892 &  0.8216 &  0.4108 \tabularnewline
76 &  0.5415 &  0.9171 &  0.4585 \tabularnewline
77 &  0.4926 &  0.9852 &  0.5074 \tabularnewline
78 &  0.4465 &  0.8929 &  0.5535 \tabularnewline
79 &  0.4087 &  0.8174 &  0.5913 \tabularnewline
80 &  0.3618 &  0.7236 &  0.6382 \tabularnewline
81 &  0.35 &  0.6999 &  0.65 \tabularnewline
82 &  0.318 &  0.6361 &  0.682 \tabularnewline
83 &  0.2781 &  0.5562 &  0.7219 \tabularnewline
84 &  0.24 &  0.48 &  0.76 \tabularnewline
85 &  0.2153 &  0.4307 &  0.7847 \tabularnewline
86 &  0.2084 &  0.4168 &  0.7916 \tabularnewline
87 &  0.1902 &  0.3804 &  0.8098 \tabularnewline
88 &  0.1596 &  0.3192 &  0.8404 \tabularnewline
89 &  0.1387 &  0.2774 &  0.8613 \tabularnewline
90 &  0.1254 &  0.2507 &  0.8746 \tabularnewline
91 &  0.1013 &  0.2026 &  0.8987 \tabularnewline
92 &  0.08185 &  0.1637 &  0.9182 \tabularnewline
93 &  0.06655 &  0.1331 &  0.9335 \tabularnewline
94 &  0.06548 &  0.131 &  0.9345 \tabularnewline
95 &  0.1088 &  0.2175 &  0.8912 \tabularnewline
96 &  0.1185 &  0.237 &  0.8815 \tabularnewline
97 &  0.1127 &  0.2254 &  0.8873 \tabularnewline
98 &  0.1064 &  0.2128 &  0.8936 \tabularnewline
99 &  0.1021 &  0.2042 &  0.8979 \tabularnewline
100 &  0.09147 &  0.1829 &  0.9085 \tabularnewline
101 &  0.07223 &  0.1445 &  0.9278 \tabularnewline
102 &  0.07001 &  0.14 &  0.93 \tabularnewline
103 &  0.05399 &  0.108 &  0.946 \tabularnewline
104 &  0.0425 &  0.08501 &  0.9575 \tabularnewline
105 &  0.03953 &  0.07906 &  0.9605 \tabularnewline
106 &  0.02936 &  0.05873 &  0.9706 \tabularnewline
107 &  0.05382 &  0.1076 &  0.9462 \tabularnewline
108 &  0.04531 &  0.09062 &  0.9547 \tabularnewline
109 &  0.0636 &  0.1272 &  0.9364 \tabularnewline
110 &  0.07993 &  0.1599 &  0.9201 \tabularnewline
111 &  0.07396 &  0.1479 &  0.926 \tabularnewline
112 &  0.06567 &  0.1313 &  0.9343 \tabularnewline
113 &  0.05352 &  0.107 &  0.9465 \tabularnewline
114 &  0.04082 &  0.08164 &  0.9592 \tabularnewline
115 &  0.02967 &  0.05933 &  0.9703 \tabularnewline
116 &  0.02062 &  0.04123 &  0.9794 \tabularnewline
117 &  0.01886 &  0.03773 &  0.9811 \tabularnewline
118 &  0.01315 &  0.0263 &  0.9869 \tabularnewline
119 &  0.008975 &  0.01795 &  0.991 \tabularnewline
120 &  0.00587 &  0.01174 &  0.9941 \tabularnewline
121 &  0.004216 &  0.008432 &  0.9958 \tabularnewline
122 &  0.002609 &  0.005218 &  0.9974 \tabularnewline
123 &  0.001606 &  0.003212 &  0.9984 \tabularnewline
124 &  0.001688 &  0.003376 &  0.9983 \tabularnewline
125 &  0.001943 &  0.003886 &  0.9981 \tabularnewline
126 &  0.001518 &  0.003036 &  0.9985 \tabularnewline
127 &  0.0008309 &  0.001662 &  0.9992 \tabularnewline
128 &  0.0007458 &  0.001492 &  0.9993 \tabularnewline
129 &  0.0005264 &  0.001053 &  0.9995 \tabularnewline
130 &  0.0002702 &  0.0005405 &  0.9997 \tabularnewline
131 &  0.0005799 &  0.00116 &  0.9994 \tabularnewline
132 &  0.0005373 &  0.001075 &  0.9995 \tabularnewline
133 &  0.0002672 &  0.0005345 &  0.9997 \tabularnewline
134 &  0.0001234 &  0.0002469 &  0.9999 \tabularnewline
135 &  0.0001844 &  0.0003688 &  0.9998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&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]11[/C][C] 0.5189[/C][C] 0.9621[/C][C] 0.4811[/C][/ROW]
[ROW][C]12[/C][C] 0.6976[/C][C] 0.6048[/C][C] 0.3024[/C][/ROW]
[ROW][C]13[/C][C] 0.6048[/C][C] 0.7903[/C][C] 0.3952[/C][/ROW]
[ROW][C]14[/C][C] 0.6794[/C][C] 0.6412[/C][C] 0.3206[/C][/ROW]
[ROW][C]15[/C][C] 0.5961[/C][C] 0.8077[/C][C] 0.4039[/C][/ROW]
[ROW][C]16[/C][C] 0.7934[/C][C] 0.4132[/C][C] 0.2066[/C][/ROW]
[ROW][C]17[/C][C] 0.7183[/C][C] 0.5634[/C][C] 0.2817[/C][/ROW]
[ROW][C]18[/C][C] 0.6669[/C][C] 0.6661[/C][C] 0.3331[/C][/ROW]
[ROW][C]19[/C][C] 0.5931[/C][C] 0.8137[/C][C] 0.4069[/C][/ROW]
[ROW][C]20[/C][C] 0.6593[/C][C] 0.6815[/C][C] 0.3407[/C][/ROW]
[ROW][C]21[/C][C] 0.6927[/C][C] 0.6146[/C][C] 0.3073[/C][/ROW]
[ROW][C]22[/C][C] 0.6876[/C][C] 0.6248[/C][C] 0.3124[/C][/ROW]
[ROW][C]23[/C][C] 0.6958[/C][C] 0.6084[/C][C] 0.3042[/C][/ROW]
[ROW][C]24[/C][C] 0.6403[/C][C] 0.7195[/C][C] 0.3597[/C][/ROW]
[ROW][C]25[/C][C] 0.5701[/C][C] 0.8598[/C][C] 0.4299[/C][/ROW]
[ROW][C]26[/C][C] 0.6987[/C][C] 0.6026[/C][C] 0.3013[/C][/ROW]
[ROW][C]27[/C][C] 0.6429[/C][C] 0.7141[/C][C] 0.3571[/C][/ROW]
[ROW][C]28[/C][C] 0.6268[/C][C] 0.7463[/C][C] 0.3732[/C][/ROW]
[ROW][C]29[/C][C] 0.5607[/C][C] 0.8786[/C][C] 0.4393[/C][/ROW]
[ROW][C]30[/C][C] 0.507[/C][C] 0.9859[/C][C] 0.493[/C][/ROW]
[ROW][C]31[/C][C] 0.4424[/C][C] 0.8847[/C][C] 0.5576[/C][/ROW]
[ROW][C]32[/C][C] 0.3986[/C][C] 0.7972[/C][C] 0.6014[/C][/ROW]
[ROW][C]33[/C][C] 0.3377[/C][C] 0.6754[/C][C] 0.6623[/C][/ROW]
[ROW][C]34[/C][C] 0.29[/C][C] 0.58[/C][C] 0.71[/C][/ROW]
[ROW][C]35[/C][C] 0.2563[/C][C] 0.5126[/C][C] 0.7437[/C][/ROW]
[ROW][C]36[/C][C] 0.2097[/C][C] 0.4193[/C][C] 0.7903[/C][/ROW]
[ROW][C]37[/C][C] 0.2025[/C][C] 0.4049[/C][C] 0.7975[/C][/ROW]
[ROW][C]38[/C][C] 0.2118[/C][C] 0.4237[/C][C] 0.7882[/C][/ROW]
[ROW][C]39[/C][C] 0.1706[/C][C] 0.3412[/C][C] 0.8294[/C][/ROW]
[ROW][C]40[/C][C] 0.1537[/C][C] 0.3074[/C][C] 0.8463[/C][/ROW]
[ROW][C]41[/C][C] 0.1242[/C][C] 0.2484[/C][C] 0.8758[/C][/ROW]
[ROW][C]42[/C][C] 0.1043[/C][C] 0.2086[/C][C] 0.8957[/C][/ROW]
[ROW][C]43[/C][C] 0.08631[/C][C] 0.1726[/C][C] 0.9137[/C][/ROW]
[ROW][C]44[/C][C] 0.07887[/C][C] 0.1577[/C][C] 0.9211[/C][/ROW]
[ROW][C]45[/C][C] 0.06298[/C][C] 0.126[/C][C] 0.937[/C][/ROW]
[ROW][C]46[/C][C] 0.04816[/C][C] 0.09631[/C][C] 0.9518[/C][/ROW]
[ROW][C]47[/C][C] 0.03763[/C][C] 0.07526[/C][C] 0.9624[/C][/ROW]
[ROW][C]48[/C][C] 0.04669[/C][C] 0.09338[/C][C] 0.9533[/C][/ROW]
[ROW][C]49[/C][C] 0.03982[/C][C] 0.07964[/C][C] 0.9602[/C][/ROW]
[ROW][C]50[/C][C] 0.08315[/C][C] 0.1663[/C][C] 0.9169[/C][/ROW]
[ROW][C]51[/C][C] 0.09377[/C][C] 0.1875[/C][C] 0.9062[/C][/ROW]
[ROW][C]52[/C][C] 0.094[/C][C] 0.188[/C][C] 0.906[/C][/ROW]
[ROW][C]53[/C][C] 0.0783[/C][C] 0.1566[/C][C] 0.9217[/C][/ROW]
[ROW][C]54[/C][C] 0.06765[/C][C] 0.1353[/C][C] 0.9323[/C][/ROW]
[ROW][C]55[/C][C] 0.05325[/C][C] 0.1065[/C][C] 0.9468[/C][/ROW]
[ROW][C]56[/C][C] 0.2638[/C][C] 0.5275[/C][C] 0.7362[/C][/ROW]
[ROW][C]57[/C][C] 0.2319[/C][C] 0.4637[/C][C] 0.7681[/C][/ROW]
[ROW][C]58[/C][C] 0.2022[/C][C] 0.4043[/C][C] 0.7978[/C][/ROW]
[ROW][C]59[/C][C] 0.1683[/C][C] 0.3366[/C][C] 0.8317[/C][/ROW]
[ROW][C]60[/C][C] 0.1747[/C][C] 0.3494[/C][C] 0.8253[/C][/ROW]
[ROW][C]61[/C][C] 0.152[/C][C] 0.3039[/C][C] 0.848[/C][/ROW]
[ROW][C]62[/C][C] 0.1559[/C][C] 0.3117[/C][C] 0.8441[/C][/ROW]
[ROW][C]63[/C][C] 0.1311[/C][C] 0.2622[/C][C] 0.8689[/C][/ROW]
[ROW][C]64[/C][C] 0.1264[/C][C] 0.2528[/C][C] 0.8736[/C][/ROW]
[ROW][C]65[/C][C] 0.1163[/C][C] 0.2326[/C][C] 0.8837[/C][/ROW]
[ROW][C]66[/C][C] 0.1049[/C][C] 0.2097[/C][C] 0.8951[/C][/ROW]
[ROW][C]67[/C][C] 0.08499[/C][C] 0.17[/C][C] 0.915[/C][/ROW]
[ROW][C]68[/C][C] 0.1587[/C][C] 0.3174[/C][C] 0.8413[/C][/ROW]
[ROW][C]69[/C][C] 0.1365[/C][C] 0.2729[/C][C] 0.8635[/C][/ROW]
[ROW][C]70[/C][C] 0.116[/C][C] 0.2321[/C][C] 0.884[/C][/ROW]
[ROW][C]71[/C][C] 0.5811[/C][C] 0.8378[/C][C] 0.4189[/C][/ROW]
[ROW][C]72[/C][C] 0.553[/C][C] 0.894[/C][C] 0.447[/C][/ROW]
[ROW][C]73[/C][C] 0.5198[/C][C] 0.9605[/C][C] 0.4802[/C][/ROW]
[ROW][C]74[/C][C] 0.4737[/C][C] 0.9474[/C][C] 0.5263[/C][/ROW]
[ROW][C]75[/C][C] 0.5892[/C][C] 0.8216[/C][C] 0.4108[/C][/ROW]
[ROW][C]76[/C][C] 0.5415[/C][C] 0.9171[/C][C] 0.4585[/C][/ROW]
[ROW][C]77[/C][C] 0.4926[/C][C] 0.9852[/C][C] 0.5074[/C][/ROW]
[ROW][C]78[/C][C] 0.4465[/C][C] 0.8929[/C][C] 0.5535[/C][/ROW]
[ROW][C]79[/C][C] 0.4087[/C][C] 0.8174[/C][C] 0.5913[/C][/ROW]
[ROW][C]80[/C][C] 0.3618[/C][C] 0.7236[/C][C] 0.6382[/C][/ROW]
[ROW][C]81[/C][C] 0.35[/C][C] 0.6999[/C][C] 0.65[/C][/ROW]
[ROW][C]82[/C][C] 0.318[/C][C] 0.6361[/C][C] 0.682[/C][/ROW]
[ROW][C]83[/C][C] 0.2781[/C][C] 0.5562[/C][C] 0.7219[/C][/ROW]
[ROW][C]84[/C][C] 0.24[/C][C] 0.48[/C][C] 0.76[/C][/ROW]
[ROW][C]85[/C][C] 0.2153[/C][C] 0.4307[/C][C] 0.7847[/C][/ROW]
[ROW][C]86[/C][C] 0.2084[/C][C] 0.4168[/C][C] 0.7916[/C][/ROW]
[ROW][C]87[/C][C] 0.1902[/C][C] 0.3804[/C][C] 0.8098[/C][/ROW]
[ROW][C]88[/C][C] 0.1596[/C][C] 0.3192[/C][C] 0.8404[/C][/ROW]
[ROW][C]89[/C][C] 0.1387[/C][C] 0.2774[/C][C] 0.8613[/C][/ROW]
[ROW][C]90[/C][C] 0.1254[/C][C] 0.2507[/C][C] 0.8746[/C][/ROW]
[ROW][C]91[/C][C] 0.1013[/C][C] 0.2026[/C][C] 0.8987[/C][/ROW]
[ROW][C]92[/C][C] 0.08185[/C][C] 0.1637[/C][C] 0.9182[/C][/ROW]
[ROW][C]93[/C][C] 0.06655[/C][C] 0.1331[/C][C] 0.9335[/C][/ROW]
[ROW][C]94[/C][C] 0.06548[/C][C] 0.131[/C][C] 0.9345[/C][/ROW]
[ROW][C]95[/C][C] 0.1088[/C][C] 0.2175[/C][C] 0.8912[/C][/ROW]
[ROW][C]96[/C][C] 0.1185[/C][C] 0.237[/C][C] 0.8815[/C][/ROW]
[ROW][C]97[/C][C] 0.1127[/C][C] 0.2254[/C][C] 0.8873[/C][/ROW]
[ROW][C]98[/C][C] 0.1064[/C][C] 0.2128[/C][C] 0.8936[/C][/ROW]
[ROW][C]99[/C][C] 0.1021[/C][C] 0.2042[/C][C] 0.8979[/C][/ROW]
[ROW][C]100[/C][C] 0.09147[/C][C] 0.1829[/C][C] 0.9085[/C][/ROW]
[ROW][C]101[/C][C] 0.07223[/C][C] 0.1445[/C][C] 0.9278[/C][/ROW]
[ROW][C]102[/C][C] 0.07001[/C][C] 0.14[/C][C] 0.93[/C][/ROW]
[ROW][C]103[/C][C] 0.05399[/C][C] 0.108[/C][C] 0.946[/C][/ROW]
[ROW][C]104[/C][C] 0.0425[/C][C] 0.08501[/C][C] 0.9575[/C][/ROW]
[ROW][C]105[/C][C] 0.03953[/C][C] 0.07906[/C][C] 0.9605[/C][/ROW]
[ROW][C]106[/C][C] 0.02936[/C][C] 0.05873[/C][C] 0.9706[/C][/ROW]
[ROW][C]107[/C][C] 0.05382[/C][C] 0.1076[/C][C] 0.9462[/C][/ROW]
[ROW][C]108[/C][C] 0.04531[/C][C] 0.09062[/C][C] 0.9547[/C][/ROW]
[ROW][C]109[/C][C] 0.0636[/C][C] 0.1272[/C][C] 0.9364[/C][/ROW]
[ROW][C]110[/C][C] 0.07993[/C][C] 0.1599[/C][C] 0.9201[/C][/ROW]
[ROW][C]111[/C][C] 0.07396[/C][C] 0.1479[/C][C] 0.926[/C][/ROW]
[ROW][C]112[/C][C] 0.06567[/C][C] 0.1313[/C][C] 0.9343[/C][/ROW]
[ROW][C]113[/C][C] 0.05352[/C][C] 0.107[/C][C] 0.9465[/C][/ROW]
[ROW][C]114[/C][C] 0.04082[/C][C] 0.08164[/C][C] 0.9592[/C][/ROW]
[ROW][C]115[/C][C] 0.02967[/C][C] 0.05933[/C][C] 0.9703[/C][/ROW]
[ROW][C]116[/C][C] 0.02062[/C][C] 0.04123[/C][C] 0.9794[/C][/ROW]
[ROW][C]117[/C][C] 0.01886[/C][C] 0.03773[/C][C] 0.9811[/C][/ROW]
[ROW][C]118[/C][C] 0.01315[/C][C] 0.0263[/C][C] 0.9869[/C][/ROW]
[ROW][C]119[/C][C] 0.008975[/C][C] 0.01795[/C][C] 0.991[/C][/ROW]
[ROW][C]120[/C][C] 0.00587[/C][C] 0.01174[/C][C] 0.9941[/C][/ROW]
[ROW][C]121[/C][C] 0.004216[/C][C] 0.008432[/C][C] 0.9958[/C][/ROW]
[ROW][C]122[/C][C] 0.002609[/C][C] 0.005218[/C][C] 0.9974[/C][/ROW]
[ROW][C]123[/C][C] 0.001606[/C][C] 0.003212[/C][C] 0.9984[/C][/ROW]
[ROW][C]124[/C][C] 0.001688[/C][C] 0.003376[/C][C] 0.9983[/C][/ROW]
[ROW][C]125[/C][C] 0.001943[/C][C] 0.003886[/C][C] 0.9981[/C][/ROW]
[ROW][C]126[/C][C] 0.001518[/C][C] 0.003036[/C][C] 0.9985[/C][/ROW]
[ROW][C]127[/C][C] 0.0008309[/C][C] 0.001662[/C][C] 0.9992[/C][/ROW]
[ROW][C]128[/C][C] 0.0007458[/C][C] 0.001492[/C][C] 0.9993[/C][/ROW]
[ROW][C]129[/C][C] 0.0005264[/C][C] 0.001053[/C][C] 0.9995[/C][/ROW]
[ROW][C]130[/C][C] 0.0002702[/C][C] 0.0005405[/C][C] 0.9997[/C][/ROW]
[ROW][C]131[/C][C] 0.0005799[/C][C] 0.00116[/C][C] 0.9994[/C][/ROW]
[ROW][C]132[/C][C] 0.0005373[/C][C] 0.001075[/C][C] 0.9995[/C][/ROW]
[ROW][C]133[/C][C] 0.0002672[/C][C] 0.0005345[/C][C] 0.9997[/C][/ROW]
[ROW][C]134[/C][C] 0.0001234[/C][C] 0.0002469[/C][C] 0.9999[/C][/ROW]
[ROW][C]135[/C][C] 0.0001844[/C][C] 0.0003688[/C][C] 0.9998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298554&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&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
11	0.5189	0.9621	0.4811
12	0.6976	0.6048	0.3024
13	0.6048	0.7903	0.3952
14	0.6794	0.6412	0.3206
15	0.5961	0.8077	0.4039
16	0.7934	0.4132	0.2066
17	0.7183	0.5634	0.2817
18	0.6669	0.6661	0.3331
19	0.5931	0.8137	0.4069
20	0.6593	0.6815	0.3407
21	0.6927	0.6146	0.3073
22	0.6876	0.6248	0.3124
23	0.6958	0.6084	0.3042
24	0.6403	0.7195	0.3597
25	0.5701	0.8598	0.4299
26	0.6987	0.6026	0.3013
27	0.6429	0.7141	0.3571
28	0.6268	0.7463	0.3732
29	0.5607	0.8786	0.4393
30	0.507	0.9859	0.493
31	0.4424	0.8847	0.5576
32	0.3986	0.7972	0.6014
33	0.3377	0.6754	0.6623
34	0.29	0.58	0.71
35	0.2563	0.5126	0.7437
36	0.2097	0.4193	0.7903
37	0.2025	0.4049	0.7975
38	0.2118	0.4237	0.7882
39	0.1706	0.3412	0.8294
40	0.1537	0.3074	0.8463
41	0.1242	0.2484	0.8758
42	0.1043	0.2086	0.8957
43	0.08631	0.1726	0.9137
44	0.07887	0.1577	0.9211
45	0.06298	0.126	0.937
46	0.04816	0.09631	0.9518
47	0.03763	0.07526	0.9624
48	0.04669	0.09338	0.9533
49	0.03982	0.07964	0.9602
50	0.08315	0.1663	0.9169
51	0.09377	0.1875	0.9062
52	0.094	0.188	0.906
53	0.0783	0.1566	0.9217
54	0.06765	0.1353	0.9323
55	0.05325	0.1065	0.9468
56	0.2638	0.5275	0.7362
57	0.2319	0.4637	0.7681
58	0.2022	0.4043	0.7978
59	0.1683	0.3366	0.8317
60	0.1747	0.3494	0.8253
61	0.152	0.3039	0.848
62	0.1559	0.3117	0.8441
63	0.1311	0.2622	0.8689
64	0.1264	0.2528	0.8736
65	0.1163	0.2326	0.8837
66	0.1049	0.2097	0.8951
67	0.08499	0.17	0.915
68	0.1587	0.3174	0.8413
69	0.1365	0.2729	0.8635
70	0.116	0.2321	0.884
71	0.5811	0.8378	0.4189
72	0.553	0.894	0.447
73	0.5198	0.9605	0.4802
74	0.4737	0.9474	0.5263
75	0.5892	0.8216	0.4108
76	0.5415	0.9171	0.4585
77	0.4926	0.9852	0.5074
78	0.4465	0.8929	0.5535
79	0.4087	0.8174	0.5913
80	0.3618	0.7236	0.6382
81	0.35	0.6999	0.65
82	0.318	0.6361	0.682
83	0.2781	0.5562	0.7219
84	0.24	0.48	0.76
85	0.2153	0.4307	0.7847
86	0.2084	0.4168	0.7916
87	0.1902	0.3804	0.8098
88	0.1596	0.3192	0.8404
89	0.1387	0.2774	0.8613
90	0.1254	0.2507	0.8746
91	0.1013	0.2026	0.8987
92	0.08185	0.1637	0.9182
93	0.06655	0.1331	0.9335
94	0.06548	0.131	0.9345
95	0.1088	0.2175	0.8912
96	0.1185	0.237	0.8815
97	0.1127	0.2254	0.8873
98	0.1064	0.2128	0.8936
99	0.1021	0.2042	0.8979
100	0.09147	0.1829	0.9085
101	0.07223	0.1445	0.9278
102	0.07001	0.14	0.93
103	0.05399	0.108	0.946
104	0.0425	0.08501	0.9575
105	0.03953	0.07906	0.9605
106	0.02936	0.05873	0.9706
107	0.05382	0.1076	0.9462
108	0.04531	0.09062	0.9547
109	0.0636	0.1272	0.9364
110	0.07993	0.1599	0.9201
111	0.07396	0.1479	0.926
112	0.06567	0.1313	0.9343
113	0.05352	0.107	0.9465
114	0.04082	0.08164	0.9592
115	0.02967	0.05933	0.9703
116	0.02062	0.04123	0.9794
117	0.01886	0.03773	0.9811
118	0.01315	0.0263	0.9869
119	0.008975	0.01795	0.991
120	0.00587	0.01174	0.9941
121	0.004216	0.008432	0.9958
122	0.002609	0.005218	0.9974
123	0.001606	0.003212	0.9984
124	0.001688	0.003376	0.9983
125	0.001943	0.003886	0.9981
126	0.001518	0.003036	0.9985
127	0.0008309	0.001662	0.9992
128	0.0007458	0.001492	0.9993
129	0.0005264	0.001053	0.9995
130	0.0002702	0.0005405	0.9997
131	0.0005799	0.00116	0.9994
132	0.0005373	0.001075	0.9995
133	0.0002672	0.0005345	0.9997
134	0.0001234	0.0002469	0.9999
135	0.0001844	0.0003688	0.9998

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.12	NOK
5% type I error level	20	0.16	NOK
10% type I error level	30	0.24	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 & 15 &  0.12 & NOK \tabularnewline
5% type I error level & 20 & 0.16 & NOK \tabularnewline
10% type I error level & 30 & 0.24 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&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]15[/C][C] 0.12[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.16[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.24[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298554&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&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	15	0.12	NOK
5% type I error level	20	0.16	NOK
10% type I error level	30	0.24	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8013, df1 = 2, df2 = 136, p-value = 0.169
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.78184, df1 = 14, df2 = 124, p-value = 0.687
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.26287, df1 = 2, df2 = 136, p-value = 0.7692

\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 = 1.8013, df1 = 2, df2 = 136, p-value = 0.169
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.78184, df1 = 14, df2 = 124, p-value = 0.687
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.26287, df1 = 2, df2 = 136, p-value = 0.7692
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&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 = 1.8013, df1 = 2, df2 = 136, p-value = 0.169
[/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 = 0.78184, df1 = 14, df2 = 124, p-value = 0.687
[/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 = 0.26287, df1 = 2, df2 = 136, p-value = 0.7692
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298554&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&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 = 1.8013, df1 = 2, df2 = 136, p-value = 0.169
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.78184, df1 = 14, df2 = 124, p-value = 0.687
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.26287, df1 = 2, df2 = 136, p-value = 0.7692

Variance Inflation Factors (Multicollinearity)
> vif ITH2 ITH3 ITH4 KVDD1 KVDD2 KVDD3 KVDD4 1.476106 1.502601 1.221563 1.224273 1.057727 1.090796 1.153010

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    ITH2     ITH3     ITH4    KVDD1    KVDD2    KVDD3    KVDD4 
1.476106 1.502601 1.221563 1.224273 1.057727 1.090796 1.153010 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298554&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    ITH2     ITH3     ITH4    KVDD1    KVDD2    KVDD3    KVDD4 
1.476106 1.502601 1.221563 1.224273 1.057727 1.090796 1.153010 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298554&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298554&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 ITH2 ITH3 ITH4 KVDD1 KVDD2 KVDD3 KVDD4 1.476106 1.502601 1.221563 1.224273 1.057727 1.090796 1.153010

Figure 1

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Figure 10

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Parameters (Session):

Parameters (R input):

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

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

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