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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 23 Jan 2017 10:36:48 +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/2017/Jan/23/t1485164482tzt1gcjfyhdbbky.htm/, Retrieved Wed, 15 May 2024 02:55:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304564, Retrieved Wed, 15 May 2024 02:55:59 +0000

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-       [Multiple Regression] [Vraag 11] [2017-01-23 09:36:48] [3146b6c9a81fba6ba78c11f749c05198] [Current]

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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=304564&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=304564&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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
ITHSUM[t] = + 17.5662 + 0.13258Bevr_Leeftijd[t] + 0.448847SKEOU1[t] -0.0709055`ITHSUM(t-1)`[t] -0.120619`ITHSUM(t-2)`[t] -0.0806693`ITHSUM(t-3)`[t] -0.0465023`ITHSUM(t-4)`[t] -0.0320619`ITHSUM(t-5)`[t] -0.0430093`ITHSUM(t-6)`[t] + 0.000148374`ITHSUM(t-7)`[t] + 0.0453525`ITHSUM(t-8)`[t] -0.0250929`ITHSUM(t-9)`[t] -0.0462852`ITHSUM(t-10)`[t] + 0.168037`ITHSUM(t-11)`[t] -0.0917857`ITHSUM(t-12)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  17.5662 +  0.13258Bevr_Leeftijd[t] +  0.448847SKEOU1[t] -0.0709055`ITHSUM(t-1)`[t] -0.120619`ITHSUM(t-2)`[t] -0.0806693`ITHSUM(t-3)`[t] -0.0465023`ITHSUM(t-4)`[t] -0.0320619`ITHSUM(t-5)`[t] -0.0430093`ITHSUM(t-6)`[t] +  0.000148374`ITHSUM(t-7)`[t] +  0.0453525`ITHSUM(t-8)`[t] -0.0250929`ITHSUM(t-9)`[t] -0.0462852`ITHSUM(t-10)`[t] +  0.168037`ITHSUM(t-11)`[t] -0.0917857`ITHSUM(t-12)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304564&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  17.5662 +  0.13258Bevr_Leeftijd[t] +  0.448847SKEOU1[t] -0.0709055`ITHSUM(t-1)`[t] -0.120619`ITHSUM(t-2)`[t] -0.0806693`ITHSUM(t-3)`[t] -0.0465023`ITHSUM(t-4)`[t] -0.0320619`ITHSUM(t-5)`[t] -0.0430093`ITHSUM(t-6)`[t] +  0.000148374`ITHSUM(t-7)`[t] +  0.0453525`ITHSUM(t-8)`[t] -0.0250929`ITHSUM(t-9)`[t] -0.0462852`ITHSUM(t-10)`[t] +  0.168037`ITHSUM(t-11)`[t] -0.0917857`ITHSUM(t-12)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304564&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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
ITHSUM[t] = + 17.5662 + 0.13258Bevr_Leeftijd[t] + 0.448847SKEOU1[t] -0.0709055`ITHSUM(t-1)`[t] -0.120619`ITHSUM(t-2)`[t] -0.0806693`ITHSUM(t-3)`[t] -0.0465023`ITHSUM(t-4)`[t] -0.0320619`ITHSUM(t-5)`[t] -0.0430093`ITHSUM(t-6)`[t] + 0.000148374`ITHSUM(t-7)`[t] + 0.0453525`ITHSUM(t-8)`[t] -0.0250929`ITHSUM(t-9)`[t] -0.0462852`ITHSUM(t-10)`[t] + 0.168037`ITHSUM(t-11)`[t] -0.0917857`ITHSUM(t-12)`[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)	+17.57	6.347	+2.7680e+00	0.006439	0.00322
Bevr_Leeftijd	+0.1326	0.1191	+1.1140e+00	0.2674	0.1337
SKEOU1	+0.4489	0.2852	+1.5740e+00	0.1179	0.05895
`ITHSUM(t-1)`	-0.0709	0.085	-8.3410e-01	0.4057	0.2028
`ITHSUM(t-2)`	-0.1206	0.08259	-1.4600e+00	0.1465	0.07325
`ITHSUM(t-3)`	-0.08067	0.08333	-9.6810e-01	0.3347	0.1674
`ITHSUM(t-4)`	-0.0465	0.08407	-5.5310e-01	0.5811	0.2905
`ITHSUM(t-5)`	-0.03206	0.08383	-3.8250e-01	0.7027	0.3514
`ITHSUM(t-6)`	-0.04301	0.08366	-5.1410e-01	0.608	0.304
`ITHSUM(t-7)`	+0.0001484	0.08399	+1.7670e-03	0.9986	0.4993
`ITHSUM(t-8)`	+0.04535	0.08337	+5.4400e-01	0.5873	0.2937
`ITHSUM(t-9)`	-0.02509	0.08386	-2.9920e-01	0.7652	0.3826
`ITHSUM(t-10)`	-0.04628	0.08352	-5.5420e-01	0.5804	0.2902
`ITHSUM(t-11)`	+0.168	0.08312	+2.0220e+00	0.04519	0.02259
`ITHSUM(t-12)`	-0.09179	0.08401	-1.0930e+00	0.2765	0.1383

\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) & +17.57 &  6.347 & +2.7680e+00 &  0.006439 &  0.00322 \tabularnewline
Bevr_Leeftijd & +0.1326 &  0.1191 & +1.1140e+00 &  0.2674 &  0.1337 \tabularnewline
SKEOU1 & +0.4489 &  0.2852 & +1.5740e+00 &  0.1179 &  0.05895 \tabularnewline
`ITHSUM(t-1)` & -0.0709 &  0.085 & -8.3410e-01 &  0.4057 &  0.2028 \tabularnewline
`ITHSUM(t-2)` & -0.1206 &  0.08259 & -1.4600e+00 &  0.1465 &  0.07325 \tabularnewline
`ITHSUM(t-3)` & -0.08067 &  0.08333 & -9.6810e-01 &  0.3347 &  0.1674 \tabularnewline
`ITHSUM(t-4)` & -0.0465 &  0.08407 & -5.5310e-01 &  0.5811 &  0.2905 \tabularnewline
`ITHSUM(t-5)` & -0.03206 &  0.08383 & -3.8250e-01 &  0.7027 &  0.3514 \tabularnewline
`ITHSUM(t-6)` & -0.04301 &  0.08366 & -5.1410e-01 &  0.608 &  0.304 \tabularnewline
`ITHSUM(t-7)` & +0.0001484 &  0.08399 & +1.7670e-03 &  0.9986 &  0.4993 \tabularnewline
`ITHSUM(t-8)` & +0.04535 &  0.08337 & +5.4400e-01 &  0.5873 &  0.2937 \tabularnewline
`ITHSUM(t-9)` & -0.02509 &  0.08386 & -2.9920e-01 &  0.7652 &  0.3826 \tabularnewline
`ITHSUM(t-10)` & -0.04628 &  0.08352 & -5.5420e-01 &  0.5804 &  0.2902 \tabularnewline
`ITHSUM(t-11)` & +0.168 &  0.08312 & +2.0220e+00 &  0.04519 &  0.02259 \tabularnewline
`ITHSUM(t-12)` & -0.09179 &  0.08401 & -1.0930e+00 &  0.2765 &  0.1383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304564&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]+17.57[/C][C] 6.347[/C][C]+2.7680e+00[/C][C] 0.006439[/C][C] 0.00322[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.1326[/C][C] 0.1191[/C][C]+1.1140e+00[/C][C] 0.2674[/C][C] 0.1337[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.4489[/C][C] 0.2852[/C][C]+1.5740e+00[/C][C] 0.1179[/C][C] 0.05895[/C][/ROW]
[ROW][C]`ITHSUM(t-1)`[/C][C]-0.0709[/C][C] 0.085[/C][C]-8.3410e-01[/C][C] 0.4057[/C][C] 0.2028[/C][/ROW]
[ROW][C]`ITHSUM(t-2)`[/C][C]-0.1206[/C][C] 0.08259[/C][C]-1.4600e+00[/C][C] 0.1465[/C][C] 0.07325[/C][/ROW]
[ROW][C]`ITHSUM(t-3)`[/C][C]-0.08067[/C][C] 0.08333[/C][C]-9.6810e-01[/C][C] 0.3347[/C][C] 0.1674[/C][/ROW]
[ROW][C]`ITHSUM(t-4)`[/C][C]-0.0465[/C][C] 0.08407[/C][C]-5.5310e-01[/C][C] 0.5811[/C][C] 0.2905[/C][/ROW]
[ROW][C]`ITHSUM(t-5)`[/C][C]-0.03206[/C][C] 0.08383[/C][C]-3.8250e-01[/C][C] 0.7027[/C][C] 0.3514[/C][/ROW]
[ROW][C]`ITHSUM(t-6)`[/C][C]-0.04301[/C][C] 0.08366[/C][C]-5.1410e-01[/C][C] 0.608[/C][C] 0.304[/C][/ROW]
[ROW][C]`ITHSUM(t-7)`[/C][C]+0.0001484[/C][C] 0.08399[/C][C]+1.7670e-03[/C][C] 0.9986[/C][C] 0.4993[/C][/ROW]
[ROW][C]`ITHSUM(t-8)`[/C][C]+0.04535[/C][C] 0.08337[/C][C]+5.4400e-01[/C][C] 0.5873[/C][C] 0.2937[/C][/ROW]
[ROW][C]`ITHSUM(t-9)`[/C][C]-0.02509[/C][C] 0.08386[/C][C]-2.9920e-01[/C][C] 0.7652[/C][C] 0.3826[/C][/ROW]
[ROW][C]`ITHSUM(t-10)`[/C][C]-0.04628[/C][C] 0.08352[/C][C]-5.5420e-01[/C][C] 0.5804[/C][C] 0.2902[/C][/ROW]
[ROW][C]`ITHSUM(t-11)`[/C][C]+0.168[/C][C] 0.08312[/C][C]+2.0220e+00[/C][C] 0.04519[/C][C] 0.02259[/C][/ROW]
[ROW][C]`ITHSUM(t-12)`[/C][C]-0.09179[/C][C] 0.08401[/C][C]-1.0930e+00[/C][C] 0.2765[/C][C] 0.1383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304564&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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)	+17.57	6.347	+2.7680e+00	0.006439	0.00322
Bevr_Leeftijd	+0.1326	0.1191	+1.1140e+00	0.2674	0.1337
SKEOU1	+0.4489	0.2852	+1.5740e+00	0.1179	0.05895
`ITHSUM(t-1)`	-0.0709	0.085	-8.3410e-01	0.4057	0.2028
`ITHSUM(t-2)`	-0.1206	0.08259	-1.4600e+00	0.1465	0.07325
`ITHSUM(t-3)`	-0.08067	0.08333	-9.6810e-01	0.3347	0.1674
`ITHSUM(t-4)`	-0.0465	0.08407	-5.5310e-01	0.5811	0.2905
`ITHSUM(t-5)`	-0.03206	0.08383	-3.8250e-01	0.7027	0.3514
`ITHSUM(t-6)`	-0.04301	0.08366	-5.1410e-01	0.608	0.304
`ITHSUM(t-7)`	+0.0001484	0.08399	+1.7670e-03	0.9986	0.4993
`ITHSUM(t-8)`	+0.04535	0.08337	+5.4400e-01	0.5873	0.2937
`ITHSUM(t-9)`	-0.02509	0.08386	-2.9920e-01	0.7652	0.3826
`ITHSUM(t-10)`	-0.04628	0.08352	-5.5420e-01	0.5804	0.2902
`ITHSUM(t-11)`	+0.168	0.08312	+2.0220e+00	0.04519	0.02259
`ITHSUM(t-12)`	-0.09179	0.08401	-1.0930e+00	0.2765	0.1383

Multiple Linear Regression - Regression Statistics
Multiple R	0.307
R-squared	0.09423
Adjusted R-squared	0.0002974
F-TEST (value)	1.003
F-TEST (DF numerator)	14
F-TEST (DF denominator)	135
p-value	0.4539
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.488
Sum Squared Residuals	835.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.307 \tabularnewline
R-squared &  0.09423 \tabularnewline
Adjusted R-squared &  0.0002974 \tabularnewline
F-TEST (value) &  1.003 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 135 \tabularnewline
p-value &  0.4539 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.488 \tabularnewline
Sum Squared Residuals &  835.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304564&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.307[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.09423[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.0002974[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.003[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]135[/C][/ROW]
[ROW][C]p-value[/C][C] 0.4539[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.488[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 835.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304564&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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	0.307
R-squared	0.09423
Adjusted R-squared	0.0002974
F-TEST (value)	1.003
F-TEST (DF numerator)	14
F-TEST (DF denominator)	135
p-value	0.4539
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.488
Sum Squared Residuals	835.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	18	16.2	1.797
2	15	15.79	-0.7874
3	14	15.76	-1.755
4	20	15.91	4.087
5	16	17.48	-1.478
6	16	15.45	0.5532
7	16	16.72	-0.7229
8	10	16.15	-6.149
9	19	16.94	2.058
10	19	17.44	1.562
11	16	17.2	-1.198
12	15	16.12	-1.125
13	18	15.46	2.54
14	17	15.96	1.042
15	19	16.42	2.583
16	17	15.88	1.116
17	19	16.1	2.901
18	20	16.8	3.204
19	5	14.18	-9.18
20	19	16.49	2.506
21	16	17.49	-1.487
22	15	16.43	-1.435
23	16	16.32	-0.32
24	18	16.9	1.1
25	16	16.66	-0.6592
26	15	16.85	-1.849
27	17	15.88	1.119
28	20	17.87	2.128
29	19	17.15	1.855
30	7	13.57	-6.569
31	13	18.26	-5.262
32	16	16.68	-0.6803
33	16	17.34	-1.343
34	18	16.73	1.267
35	18	17.4	0.6005
36	16	16.95	-0.9548
37	17	15.93	1.067
38	19	15.92	3.083
39	16	16.69	-0.6923
40	19	16.11	2.894
41	13	13.7	-0.7017
42	16	16.48	-0.4786
43	13	17.08	-4.081
44	12	17.81	-5.807
45	17	16.47	0.5333
46	17	17.54	-0.5357
47	17	16.3	0.7024
48	16	16.69	-0.6937
49	16	17.13	-1.127
50	14	16.46	-2.457
51	16	17.31	-1.311
52	13	16.51	-3.514
53	16	17.09	-1.094
54	14	16.36	-2.359
55	20	16.81	3.191
56	12	17.56	-5.564
57	13	16.34	-3.342
58	18	17.63	0.3707
59	14	16.71	-2.714
60	19	16.89	2.106
61	18	15.9	2.096
62	14	16.27	-2.268
63	18	16.46	1.54
64	19	16.51	2.486
65	15	15.52	-0.5154
66	14	17.31	-3.306
67	17	15.25	1.749
68	19	16.74	2.258
69	13	17.51	-4.512
70	19	16.07	2.933
71	18	17.62	0.3792
72	20	16.36	3.64
73	15	14.57	0.4297
74	15	16.09	-1.089
75	15	16.66	-1.658
76	20	16.57	3.431
77	15	16.03	-1.032
78	19	16.39	2.614
79	18	16.74	1.258
80	18	15.46	2.537
81	15	17.11	-2.11
82	20	16.34	3.656
83	17	16.7	0.3039
84	12	15.16	-3.161
85	18	16.66	1.338
86	19	16.7	2.296
87	20	18.19	1.806
88	17	15.91	1.093
89	15	15.99	-0.9915
90	16	16.55	-0.5549
91	18	16.33	1.666
92	18	15.54	2.456
93	14	17.1	-3.101
94	15	16.45	-1.452
95	12	15.74	-3.737
96	17	17.39	-0.3854
97	14	17.28	-3.284
98	18	16.95	1.053
99	17	17.31	-0.3102
100	17	16.85	0.1453
101	20	16.57	3.427
102	16	15.94	0.05534
103	14	15.89	-1.887
104	15	15.4	-0.3967
105	18	16.52	1.477
106	20	16.27	3.733
107	17	16.75	0.2487
108	17	15.23	1.768
109	17	17.19	-0.1898
110	17	16.94	0.06229
111	15	15.2	-0.2047
112	17	16.46	0.5418
113	18	16.16	1.843
114	17	15.92	1.075
115	20	16.81	3.188
116	15	15.13	-0.1287
117	16	16.22	-0.2236
118	15	16.07	-1.07
119	18	16.64	1.358
120	15	16.61	-1.606
121	18	17.32	0.6759
122	20	16.38	3.619
123	19	16.2	2.795
124	14	15.16	-1.159
125	16	15.79	0.214
126	15	17.16	-2.158
127	17	16.11	0.8896
128	18	16.44	1.564
129	20	16.97	3.025
130	17	16.92	0.08316
131	18	15.78	2.221
132	15	15.74	-0.7448
133	16	16.51	-0.5071
134	11	16.4	-5.402
135	15	15.85	-0.8511
136	18	16.71	1.286
137	17	16.76	0.2359
138	16	16.99	-0.9917
139	12	16.7	-4.701
140	19	16.86	2.145
141	18	16.32	1.679
142	15	16.28	-1.283
143	17	15.85	1.154
144	19	17.23	1.767
145	18	16.16	1.836
146	19	16.54	2.458
147	16	16	-0.004538
148	16	16.22	-0.2209
149	16	16.29	-0.2859
150	14	14.65	-0.6473

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  18 &  16.2 &  1.797 \tabularnewline
2 &  15 &  15.79 & -0.7874 \tabularnewline
3 &  14 &  15.76 & -1.755 \tabularnewline
4 &  20 &  15.91 &  4.087 \tabularnewline
5 &  16 &  17.48 & -1.478 \tabularnewline
6 &  16 &  15.45 &  0.5532 \tabularnewline
7 &  16 &  16.72 & -0.7229 \tabularnewline
8 &  10 &  16.15 & -6.149 \tabularnewline
9 &  19 &  16.94 &  2.058 \tabularnewline
10 &  19 &  17.44 &  1.562 \tabularnewline
11 &  16 &  17.2 & -1.198 \tabularnewline
12 &  15 &  16.12 & -1.125 \tabularnewline
13 &  18 &  15.46 &  2.54 \tabularnewline
14 &  17 &  15.96 &  1.042 \tabularnewline
15 &  19 &  16.42 &  2.583 \tabularnewline
16 &  17 &  15.88 &  1.116 \tabularnewline
17 &  19 &  16.1 &  2.901 \tabularnewline
18 &  20 &  16.8 &  3.204 \tabularnewline
19 &  5 &  14.18 & -9.18 \tabularnewline
20 &  19 &  16.49 &  2.506 \tabularnewline
21 &  16 &  17.49 & -1.487 \tabularnewline
22 &  15 &  16.43 & -1.435 \tabularnewline
23 &  16 &  16.32 & -0.32 \tabularnewline
24 &  18 &  16.9 &  1.1 \tabularnewline
25 &  16 &  16.66 & -0.6592 \tabularnewline
26 &  15 &  16.85 & -1.849 \tabularnewline
27 &  17 &  15.88 &  1.119 \tabularnewline
28 &  20 &  17.87 &  2.128 \tabularnewline
29 &  19 &  17.15 &  1.855 \tabularnewline
30 &  7 &  13.57 & -6.569 \tabularnewline
31 &  13 &  18.26 & -5.262 \tabularnewline
32 &  16 &  16.68 & -0.6803 \tabularnewline
33 &  16 &  17.34 & -1.343 \tabularnewline
34 &  18 &  16.73 &  1.267 \tabularnewline
35 &  18 &  17.4 &  0.6005 \tabularnewline
36 &  16 &  16.95 & -0.9548 \tabularnewline
37 &  17 &  15.93 &  1.067 \tabularnewline
38 &  19 &  15.92 &  3.083 \tabularnewline
39 &  16 &  16.69 & -0.6923 \tabularnewline
40 &  19 &  16.11 &  2.894 \tabularnewline
41 &  13 &  13.7 & -0.7017 \tabularnewline
42 &  16 &  16.48 & -0.4786 \tabularnewline
43 &  13 &  17.08 & -4.081 \tabularnewline
44 &  12 &  17.81 & -5.807 \tabularnewline
45 &  17 &  16.47 &  0.5333 \tabularnewline
46 &  17 &  17.54 & -0.5357 \tabularnewline
47 &  17 &  16.3 &  0.7024 \tabularnewline
48 &  16 &  16.69 & -0.6937 \tabularnewline
49 &  16 &  17.13 & -1.127 \tabularnewline
50 &  14 &  16.46 & -2.457 \tabularnewline
51 &  16 &  17.31 & -1.311 \tabularnewline
52 &  13 &  16.51 & -3.514 \tabularnewline
53 &  16 &  17.09 & -1.094 \tabularnewline
54 &  14 &  16.36 & -2.359 \tabularnewline
55 &  20 &  16.81 &  3.191 \tabularnewline
56 &  12 &  17.56 & -5.564 \tabularnewline
57 &  13 &  16.34 & -3.342 \tabularnewline
58 &  18 &  17.63 &  0.3707 \tabularnewline
59 &  14 &  16.71 & -2.714 \tabularnewline
60 &  19 &  16.89 &  2.106 \tabularnewline
61 &  18 &  15.9 &  2.096 \tabularnewline
62 &  14 &  16.27 & -2.268 \tabularnewline
63 &  18 &  16.46 &  1.54 \tabularnewline
64 &  19 &  16.51 &  2.486 \tabularnewline
65 &  15 &  15.52 & -0.5154 \tabularnewline
66 &  14 &  17.31 & -3.306 \tabularnewline
67 &  17 &  15.25 &  1.749 \tabularnewline
68 &  19 &  16.74 &  2.258 \tabularnewline
69 &  13 &  17.51 & -4.512 \tabularnewline
70 &  19 &  16.07 &  2.933 \tabularnewline
71 &  18 &  17.62 &  0.3792 \tabularnewline
72 &  20 &  16.36 &  3.64 \tabularnewline
73 &  15 &  14.57 &  0.4297 \tabularnewline
74 &  15 &  16.09 & -1.089 \tabularnewline
75 &  15 &  16.66 & -1.658 \tabularnewline
76 &  20 &  16.57 &  3.431 \tabularnewline
77 &  15 &  16.03 & -1.032 \tabularnewline
78 &  19 &  16.39 &  2.614 \tabularnewline
79 &  18 &  16.74 &  1.258 \tabularnewline
80 &  18 &  15.46 &  2.537 \tabularnewline
81 &  15 &  17.11 & -2.11 \tabularnewline
82 &  20 &  16.34 &  3.656 \tabularnewline
83 &  17 &  16.7 &  0.3039 \tabularnewline
84 &  12 &  15.16 & -3.161 \tabularnewline
85 &  18 &  16.66 &  1.338 \tabularnewline
86 &  19 &  16.7 &  2.296 \tabularnewline
87 &  20 &  18.19 &  1.806 \tabularnewline
88 &  17 &  15.91 &  1.093 \tabularnewline
89 &  15 &  15.99 & -0.9915 \tabularnewline
90 &  16 &  16.55 & -0.5549 \tabularnewline
91 &  18 &  16.33 &  1.666 \tabularnewline
92 &  18 &  15.54 &  2.456 \tabularnewline
93 &  14 &  17.1 & -3.101 \tabularnewline
94 &  15 &  16.45 & -1.452 \tabularnewline
95 &  12 &  15.74 & -3.737 \tabularnewline
96 &  17 &  17.39 & -0.3854 \tabularnewline
97 &  14 &  17.28 & -3.284 \tabularnewline
98 &  18 &  16.95 &  1.053 \tabularnewline
99 &  17 &  17.31 & -0.3102 \tabularnewline
100 &  17 &  16.85 &  0.1453 \tabularnewline
101 &  20 &  16.57 &  3.427 \tabularnewline
102 &  16 &  15.94 &  0.05534 \tabularnewline
103 &  14 &  15.89 & -1.887 \tabularnewline
104 &  15 &  15.4 & -0.3967 \tabularnewline
105 &  18 &  16.52 &  1.477 \tabularnewline
106 &  20 &  16.27 &  3.733 \tabularnewline
107 &  17 &  16.75 &  0.2487 \tabularnewline
108 &  17 &  15.23 &  1.768 \tabularnewline
109 &  17 &  17.19 & -0.1898 \tabularnewline
110 &  17 &  16.94 &  0.06229 \tabularnewline
111 &  15 &  15.2 & -0.2047 \tabularnewline
112 &  17 &  16.46 &  0.5418 \tabularnewline
113 &  18 &  16.16 &  1.843 \tabularnewline
114 &  17 &  15.92 &  1.075 \tabularnewline
115 &  20 &  16.81 &  3.188 \tabularnewline
116 &  15 &  15.13 & -0.1287 \tabularnewline
117 &  16 &  16.22 & -0.2236 \tabularnewline
118 &  15 &  16.07 & -1.07 \tabularnewline
119 &  18 &  16.64 &  1.358 \tabularnewline
120 &  15 &  16.61 & -1.606 \tabularnewline
121 &  18 &  17.32 &  0.6759 \tabularnewline
122 &  20 &  16.38 &  3.619 \tabularnewline
123 &  19 &  16.2 &  2.795 \tabularnewline
124 &  14 &  15.16 & -1.159 \tabularnewline
125 &  16 &  15.79 &  0.214 \tabularnewline
126 &  15 &  17.16 & -2.158 \tabularnewline
127 &  17 &  16.11 &  0.8896 \tabularnewline
128 &  18 &  16.44 &  1.564 \tabularnewline
129 &  20 &  16.97 &  3.025 \tabularnewline
130 &  17 &  16.92 &  0.08316 \tabularnewline
131 &  18 &  15.78 &  2.221 \tabularnewline
132 &  15 &  15.74 & -0.7448 \tabularnewline
133 &  16 &  16.51 & -0.5071 \tabularnewline
134 &  11 &  16.4 & -5.402 \tabularnewline
135 &  15 &  15.85 & -0.8511 \tabularnewline
136 &  18 &  16.71 &  1.286 \tabularnewline
137 &  17 &  16.76 &  0.2359 \tabularnewline
138 &  16 &  16.99 & -0.9917 \tabularnewline
139 &  12 &  16.7 & -4.701 \tabularnewline
140 &  19 &  16.86 &  2.145 \tabularnewline
141 &  18 &  16.32 &  1.679 \tabularnewline
142 &  15 &  16.28 & -1.283 \tabularnewline
143 &  17 &  15.85 &  1.154 \tabularnewline
144 &  19 &  17.23 &  1.767 \tabularnewline
145 &  18 &  16.16 &  1.836 \tabularnewline
146 &  19 &  16.54 &  2.458 \tabularnewline
147 &  16 &  16 & -0.004538 \tabularnewline
148 &  16 &  16.22 & -0.2209 \tabularnewline
149 &  16 &  16.29 & -0.2859 \tabularnewline
150 &  14 &  14.65 & -0.6473 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304564&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] 18[/C][C] 16.2[/C][C] 1.797[/C][/ROW]
[ROW][C]2[/C][C] 15[/C][C] 15.79[/C][C]-0.7874[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 15.76[/C][C]-1.755[/C][/ROW]
[ROW][C]4[/C][C] 20[/C][C] 15.91[/C][C] 4.087[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 17.48[/C][C]-1.478[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.45[/C][C] 0.5532[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 16.72[/C][C]-0.7229[/C][/ROW]
[ROW][C]8[/C][C] 10[/C][C] 16.15[/C][C]-6.149[/C][/ROW]
[ROW][C]9[/C][C] 19[/C][C] 16.94[/C][C] 2.058[/C][/ROW]
[ROW][C]10[/C][C] 19[/C][C] 17.44[/C][C] 1.562[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 17.2[/C][C]-1.198[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 16.12[/C][C]-1.125[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 15.46[/C][C] 2.54[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 15.96[/C][C] 1.042[/C][/ROW]
[ROW][C]15[/C][C] 19[/C][C] 16.42[/C][C] 2.583[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.88[/C][C] 1.116[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 16.1[/C][C] 2.901[/C][/ROW]
[ROW][C]18[/C][C] 20[/C][C] 16.8[/C][C] 3.204[/C][/ROW]
[ROW][C]19[/C][C] 5[/C][C] 14.18[/C][C]-9.18[/C][/ROW]
[ROW][C]20[/C][C] 19[/C][C] 16.49[/C][C] 2.506[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 17.49[/C][C]-1.487[/C][/ROW]
[ROW][C]22[/C][C] 15[/C][C] 16.43[/C][C]-1.435[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 16.32[/C][C]-0.32[/C][/ROW]
[ROW][C]24[/C][C] 18[/C][C] 16.9[/C][C] 1.1[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 16.66[/C][C]-0.6592[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 16.85[/C][C]-1.849[/C][/ROW]
[ROW][C]27[/C][C] 17[/C][C] 15.88[/C][C] 1.119[/C][/ROW]
[ROW][C]28[/C][C] 20[/C][C] 17.87[/C][C] 2.128[/C][/ROW]
[ROW][C]29[/C][C] 19[/C][C] 17.15[/C][C] 1.855[/C][/ROW]
[ROW][C]30[/C][C] 7[/C][C] 13.57[/C][C]-6.569[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 18.26[/C][C]-5.262[/C][/ROW]
[ROW][C]32[/C][C] 16[/C][C] 16.68[/C][C]-0.6803[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 17.34[/C][C]-1.343[/C][/ROW]
[ROW][C]34[/C][C] 18[/C][C] 16.73[/C][C] 1.267[/C][/ROW]
[ROW][C]35[/C][C] 18[/C][C] 17.4[/C][C] 0.6005[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 16.95[/C][C]-0.9548[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15.93[/C][C] 1.067[/C][/ROW]
[ROW][C]38[/C][C] 19[/C][C] 15.92[/C][C] 3.083[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 16.69[/C][C]-0.6923[/C][/ROW]
[ROW][C]40[/C][C] 19[/C][C] 16.11[/C][C] 2.894[/C][/ROW]
[ROW][C]41[/C][C] 13[/C][C] 13.7[/C][C]-0.7017[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 16.48[/C][C]-0.4786[/C][/ROW]
[ROW][C]43[/C][C] 13[/C][C] 17.08[/C][C]-4.081[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 17.81[/C][C]-5.807[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 16.47[/C][C] 0.5333[/C][/ROW]
[ROW][C]46[/C][C] 17[/C][C] 17.54[/C][C]-0.5357[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 16.3[/C][C] 0.7024[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 16.69[/C][C]-0.6937[/C][/ROW]
[ROW][C]49[/C][C] 16[/C][C] 17.13[/C][C]-1.127[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 16.46[/C][C]-2.457[/C][/ROW]
[ROW][C]51[/C][C] 16[/C][C] 17.31[/C][C]-1.311[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 16.51[/C][C]-3.514[/C][/ROW]
[ROW][C]53[/C][C] 16[/C][C] 17.09[/C][C]-1.094[/C][/ROW]
[ROW][C]54[/C][C] 14[/C][C] 16.36[/C][C]-2.359[/C][/ROW]
[ROW][C]55[/C][C] 20[/C][C] 16.81[/C][C] 3.191[/C][/ROW]
[ROW][C]56[/C][C] 12[/C][C] 17.56[/C][C]-5.564[/C][/ROW]
[ROW][C]57[/C][C] 13[/C][C] 16.34[/C][C]-3.342[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 17.63[/C][C] 0.3707[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 16.71[/C][C]-2.714[/C][/ROW]
[ROW][C]60[/C][C] 19[/C][C] 16.89[/C][C] 2.106[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 15.9[/C][C] 2.096[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 16.27[/C][C]-2.268[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 16.46[/C][C] 1.54[/C][/ROW]
[ROW][C]64[/C][C] 19[/C][C] 16.51[/C][C] 2.486[/C][/ROW]
[ROW][C]65[/C][C] 15[/C][C] 15.52[/C][C]-0.5154[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 17.31[/C][C]-3.306[/C][/ROW]
[ROW][C]67[/C][C] 17[/C][C] 15.25[/C][C] 1.749[/C][/ROW]
[ROW][C]68[/C][C] 19[/C][C] 16.74[/C][C] 2.258[/C][/ROW]
[ROW][C]69[/C][C] 13[/C][C] 17.51[/C][C]-4.512[/C][/ROW]
[ROW][C]70[/C][C] 19[/C][C] 16.07[/C][C] 2.933[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 17.62[/C][C] 0.3792[/C][/ROW]
[ROW][C]72[/C][C] 20[/C][C] 16.36[/C][C] 3.64[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 14.57[/C][C] 0.4297[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 16.09[/C][C]-1.089[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 16.66[/C][C]-1.658[/C][/ROW]
[ROW][C]76[/C][C] 20[/C][C] 16.57[/C][C] 3.431[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 16.03[/C][C]-1.032[/C][/ROW]
[ROW][C]78[/C][C] 19[/C][C] 16.39[/C][C] 2.614[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 16.74[/C][C] 1.258[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 15.46[/C][C] 2.537[/C][/ROW]
[ROW][C]81[/C][C] 15[/C][C] 17.11[/C][C]-2.11[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 16.34[/C][C] 3.656[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 16.7[/C][C] 0.3039[/C][/ROW]
[ROW][C]84[/C][C] 12[/C][C] 15.16[/C][C]-3.161[/C][/ROW]
[ROW][C]85[/C][C] 18[/C][C] 16.66[/C][C] 1.338[/C][/ROW]
[ROW][C]86[/C][C] 19[/C][C] 16.7[/C][C] 2.296[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 18.19[/C][C] 1.806[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.91[/C][C] 1.093[/C][/ROW]
[ROW][C]89[/C][C] 15[/C][C] 15.99[/C][C]-0.9915[/C][/ROW]
[ROW][C]90[/C][C] 16[/C][C] 16.55[/C][C]-0.5549[/C][/ROW]
[ROW][C]91[/C][C] 18[/C][C] 16.33[/C][C] 1.666[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 15.54[/C][C] 2.456[/C][/ROW]
[ROW][C]93[/C][C] 14[/C][C] 17.1[/C][C]-3.101[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 16.45[/C][C]-1.452[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 15.74[/C][C]-3.737[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 17.39[/C][C]-0.3854[/C][/ROW]
[ROW][C]97[/C][C] 14[/C][C] 17.28[/C][C]-3.284[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 16.95[/C][C] 1.053[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 17.31[/C][C]-0.3102[/C][/ROW]
[ROW][C]100[/C][C] 17[/C][C] 16.85[/C][C] 0.1453[/C][/ROW]
[ROW][C]101[/C][C] 20[/C][C] 16.57[/C][C] 3.427[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 15.94[/C][C] 0.05534[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 15.89[/C][C]-1.887[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 15.4[/C][C]-0.3967[/C][/ROW]
[ROW][C]105[/C][C] 18[/C][C] 16.52[/C][C] 1.477[/C][/ROW]
[ROW][C]106[/C][C] 20[/C][C] 16.27[/C][C] 3.733[/C][/ROW]
[ROW][C]107[/C][C] 17[/C][C] 16.75[/C][C] 0.2487[/C][/ROW]
[ROW][C]108[/C][C] 17[/C][C] 15.23[/C][C] 1.768[/C][/ROW]
[ROW][C]109[/C][C] 17[/C][C] 17.19[/C][C]-0.1898[/C][/ROW]
[ROW][C]110[/C][C] 17[/C][C] 16.94[/C][C] 0.06229[/C][/ROW]
[ROW][C]111[/C][C] 15[/C][C] 15.2[/C][C]-0.2047[/C][/ROW]
[ROW][C]112[/C][C] 17[/C][C] 16.46[/C][C] 0.5418[/C][/ROW]
[ROW][C]113[/C][C] 18[/C][C] 16.16[/C][C] 1.843[/C][/ROW]
[ROW][C]114[/C][C] 17[/C][C] 15.92[/C][C] 1.075[/C][/ROW]
[ROW][C]115[/C][C] 20[/C][C] 16.81[/C][C] 3.188[/C][/ROW]
[ROW][C]116[/C][C] 15[/C][C] 15.13[/C][C]-0.1287[/C][/ROW]
[ROW][C]117[/C][C] 16[/C][C] 16.22[/C][C]-0.2236[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 16.07[/C][C]-1.07[/C][/ROW]
[ROW][C]119[/C][C] 18[/C][C] 16.64[/C][C] 1.358[/C][/ROW]
[ROW][C]120[/C][C] 15[/C][C] 16.61[/C][C]-1.606[/C][/ROW]
[ROW][C]121[/C][C] 18[/C][C] 17.32[/C][C] 0.6759[/C][/ROW]
[ROW][C]122[/C][C] 20[/C][C] 16.38[/C][C] 3.619[/C][/ROW]
[ROW][C]123[/C][C] 19[/C][C] 16.2[/C][C] 2.795[/C][/ROW]
[ROW][C]124[/C][C] 14[/C][C] 15.16[/C][C]-1.159[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 15.79[/C][C] 0.214[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 17.16[/C][C]-2.158[/C][/ROW]
[ROW][C]127[/C][C] 17[/C][C] 16.11[/C][C] 0.8896[/C][/ROW]
[ROW][C]128[/C][C] 18[/C][C] 16.44[/C][C] 1.564[/C][/ROW]
[ROW][C]129[/C][C] 20[/C][C] 16.97[/C][C] 3.025[/C][/ROW]
[ROW][C]130[/C][C] 17[/C][C] 16.92[/C][C] 0.08316[/C][/ROW]
[ROW][C]131[/C][C] 18[/C][C] 15.78[/C][C] 2.221[/C][/ROW]
[ROW][C]132[/C][C] 15[/C][C] 15.74[/C][C]-0.7448[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 16.51[/C][C]-0.5071[/C][/ROW]
[ROW][C]134[/C][C] 11[/C][C] 16.4[/C][C]-5.402[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 15.85[/C][C]-0.8511[/C][/ROW]
[ROW][C]136[/C][C] 18[/C][C] 16.71[/C][C] 1.286[/C][/ROW]
[ROW][C]137[/C][C] 17[/C][C] 16.76[/C][C] 0.2359[/C][/ROW]
[ROW][C]138[/C][C] 16[/C][C] 16.99[/C][C]-0.9917[/C][/ROW]
[ROW][C]139[/C][C] 12[/C][C] 16.7[/C][C]-4.701[/C][/ROW]
[ROW][C]140[/C][C] 19[/C][C] 16.86[/C][C] 2.145[/C][/ROW]
[ROW][C]141[/C][C] 18[/C][C] 16.32[/C][C] 1.679[/C][/ROW]
[ROW][C]142[/C][C] 15[/C][C] 16.28[/C][C]-1.283[/C][/ROW]
[ROW][C]143[/C][C] 17[/C][C] 15.85[/C][C] 1.154[/C][/ROW]
[ROW][C]144[/C][C] 19[/C][C] 17.23[/C][C] 1.767[/C][/ROW]
[ROW][C]145[/C][C] 18[/C][C] 16.16[/C][C] 1.836[/C][/ROW]
[ROW][C]146[/C][C] 19[/C][C] 16.54[/C][C] 2.458[/C][/ROW]
[ROW][C]147[/C][C] 16[/C][C] 16[/C][C]-0.004538[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 16.22[/C][C]-0.2209[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 16.29[/C][C]-0.2859[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 14.65[/C][C]-0.6473[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304564&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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	18	16.2	1.797
2	15	15.79	-0.7874
3	14	15.76	-1.755
4	20	15.91	4.087
5	16	17.48	-1.478
6	16	15.45	0.5532
7	16	16.72	-0.7229
8	10	16.15	-6.149
9	19	16.94	2.058
10	19	17.44	1.562
11	16	17.2	-1.198
12	15	16.12	-1.125
13	18	15.46	2.54
14	17	15.96	1.042
15	19	16.42	2.583
16	17	15.88	1.116
17	19	16.1	2.901
18	20	16.8	3.204
19	5	14.18	-9.18
20	19	16.49	2.506
21	16	17.49	-1.487
22	15	16.43	-1.435
23	16	16.32	-0.32
24	18	16.9	1.1
25	16	16.66	-0.6592
26	15	16.85	-1.849
27	17	15.88	1.119
28	20	17.87	2.128
29	19	17.15	1.855
30	7	13.57	-6.569
31	13	18.26	-5.262
32	16	16.68	-0.6803
33	16	17.34	-1.343
34	18	16.73	1.267
35	18	17.4	0.6005
36	16	16.95	-0.9548
37	17	15.93	1.067
38	19	15.92	3.083
39	16	16.69	-0.6923
40	19	16.11	2.894
41	13	13.7	-0.7017
42	16	16.48	-0.4786
43	13	17.08	-4.081
44	12	17.81	-5.807
45	17	16.47	0.5333
46	17	17.54	-0.5357
47	17	16.3	0.7024
48	16	16.69	-0.6937
49	16	17.13	-1.127
50	14	16.46	-2.457
51	16	17.31	-1.311
52	13	16.51	-3.514
53	16	17.09	-1.094
54	14	16.36	-2.359
55	20	16.81	3.191
56	12	17.56	-5.564
57	13	16.34	-3.342
58	18	17.63	0.3707
59	14	16.71	-2.714
60	19	16.89	2.106
61	18	15.9	2.096
62	14	16.27	-2.268
63	18	16.46	1.54
64	19	16.51	2.486
65	15	15.52	-0.5154
66	14	17.31	-3.306
67	17	15.25	1.749
68	19	16.74	2.258
69	13	17.51	-4.512
70	19	16.07	2.933
71	18	17.62	0.3792
72	20	16.36	3.64
73	15	14.57	0.4297
74	15	16.09	-1.089
75	15	16.66	-1.658
76	20	16.57	3.431
77	15	16.03	-1.032
78	19	16.39	2.614
79	18	16.74	1.258
80	18	15.46	2.537
81	15	17.11	-2.11
82	20	16.34	3.656
83	17	16.7	0.3039
84	12	15.16	-3.161
85	18	16.66	1.338
86	19	16.7	2.296
87	20	18.19	1.806
88	17	15.91	1.093
89	15	15.99	-0.9915
90	16	16.55	-0.5549
91	18	16.33	1.666
92	18	15.54	2.456
93	14	17.1	-3.101
94	15	16.45	-1.452
95	12	15.74	-3.737
96	17	17.39	-0.3854
97	14	17.28	-3.284
98	18	16.95	1.053
99	17	17.31	-0.3102
100	17	16.85	0.1453
101	20	16.57	3.427
102	16	15.94	0.05534
103	14	15.89	-1.887
104	15	15.4	-0.3967
105	18	16.52	1.477
106	20	16.27	3.733
107	17	16.75	0.2487
108	17	15.23	1.768
109	17	17.19	-0.1898
110	17	16.94	0.06229
111	15	15.2	-0.2047
112	17	16.46	0.5418
113	18	16.16	1.843
114	17	15.92	1.075
115	20	16.81	3.188
116	15	15.13	-0.1287
117	16	16.22	-0.2236
118	15	16.07	-1.07
119	18	16.64	1.358
120	15	16.61	-1.606
121	18	17.32	0.6759
122	20	16.38	3.619
123	19	16.2	2.795
124	14	15.16	-1.159
125	16	15.79	0.214
126	15	17.16	-2.158
127	17	16.11	0.8896
128	18	16.44	1.564
129	20	16.97	3.025
130	17	16.92	0.08316
131	18	15.78	2.221
132	15	15.74	-0.7448
133	16	16.51	-0.5071
134	11	16.4	-5.402
135	15	15.85	-0.8511
136	18	16.71	1.286
137	17	16.76	0.2359
138	16	16.99	-0.9917
139	12	16.7	-4.701
140	19	16.86	2.145
141	18	16.32	1.679
142	15	16.28	-1.283
143	17	15.85	1.154
144	19	17.23	1.767
145	18	16.16	1.836
146	19	16.54	2.458
147	16	16	-0.004538
148	16	16.22	-0.2209
149	16	16.29	-0.2859
150	14	14.65	-0.6473

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.8459	0.3082	0.1541
19	0.9919	0.01613	0.008066
20	0.9914	0.01724	0.008622
21	0.9908	0.01847	0.009237
22	0.9837	0.03262	0.01631
23	0.9719	0.0562	0.0281
24	0.964	0.07197	0.03599
25	0.9429	0.1143	0.05714
26	0.9195	0.161	0.08048
27	0.8901	0.2198	0.1099
28	0.9198	0.1604	0.08022
29	0.8876	0.2249	0.1124
30	0.9453	0.1094	0.05469
31	0.9961	0.00772	0.00386
32	0.996	0.007991	0.003995
33	0.9937	0.01255	0.006277
34	0.9911	0.01782	0.008911
35	0.9871	0.02582	0.01291
36	0.9813	0.03736	0.01868
37	0.975	0.04995	0.02497
38	0.9716	0.0569	0.02845
39	0.9759	0.04819	0.02409
40	0.9736	0.05278	0.02639
41	0.972	0.05594	0.02797
42	0.9665	0.06702	0.03351
43	0.9726	0.05478	0.02739
44	0.9872	0.02564	0.01282
45	0.9855	0.02908	0.01454
46	0.9793	0.04133	0.02067
47	0.9717	0.05666	0.02833
48	0.962	0.07606	0.03803
49	0.9532	0.0936	0.0468
50	0.954	0.09205	0.04603
51	0.948	0.1041	0.05203
52	0.9612	0.0775	0.03875
53	0.9504	0.09911	0.04956
54	0.9453	0.1093	0.05465
55	0.9758	0.04837	0.02419
56	0.9918	0.01641	0.008206
57	0.9961	0.007773	0.003886
58	0.9947	0.01067	0.005334
59	0.9962	0.007694	0.003847
60	0.9957	0.00866	0.00433
61	0.9952	0.009519	0.004759
62	0.9962	0.007572	0.003786
63	0.9962	0.007585	0.003793
64	0.9961	0.007705	0.003852
65	0.9957	0.008537	0.004268
66	0.9962	0.007644	0.003822
67	0.9952	0.00969	0.004845
68	0.9955	0.008977	0.004488
69	0.998	0.003976	0.001988
70	0.9985	0.003036	0.001518
71	0.9978	0.00434	0.00217
72	0.9989	0.002231	0.001116
73	0.9984	0.003293	0.001646
74	0.9979	0.00412	0.00206
75	0.9975	0.005086	0.002543
76	0.9985	0.003032	0.001516
77	0.9984	0.003265	0.001633
78	0.9985	0.002928	0.001464
79	0.9984	0.003179	0.001589
80	0.9983	0.003424	0.001712
81	0.9982	0.003531	0.001765
82	0.9991	0.001871	0.0009353
83	0.9986	0.002859	0.00143
84	0.9989	0.002105	0.001052
85	0.9986	0.002787	0.001393
86	0.9984	0.003219	0.00161
87	0.998	0.004	0.002
88	0.9973	0.0054	0.0027
89	0.9961	0.00781	0.003905
90	0.9943	0.01147	0.005736
91	0.9951	0.009811	0.004905
92	0.9945	0.01095	0.005474
93	0.997	0.006098	0.003049
94	0.9955	0.00895	0.004475
95	0.9989	0.002271	0.001135
96	0.9985	0.002958	0.001479
97	0.999	0.00203	0.001015
98	0.9988	0.002497	0.001248
99	0.998	0.003984	0.001992
100	0.9973	0.005451	0.002726
101	0.9977	0.004644	0.002322
102	0.9963	0.007375	0.003688
103	0.9952	0.009578	0.004789
104	0.9931	0.01379	0.006895
105	0.9898	0.02036	0.01018
106	0.9883	0.02346	0.01173
107	0.9828	0.03431	0.01716
108	0.9761	0.04783	0.02392
109	0.9704	0.05916	0.02958
110	0.9594	0.08117	0.04059
111	0.943	0.1141	0.05705
112	0.944	0.112	0.056
113	0.9362	0.1276	0.06382
114	0.9118	0.1765	0.08824
115	0.9006	0.1988	0.09939
116	0.865	0.2699	0.135
117	0.8256	0.3488	0.1744
118	0.7735	0.4529	0.2265
119	0.768	0.464	0.232
120	0.7718	0.4565	0.2282
121	0.7074	0.5852	0.2926
122	0.7747	0.4507	0.2253
123	0.7976	0.4047	0.2024
124	0.7865	0.427	0.2135
125	0.7511	0.4978	0.2489
126	0.6808	0.6384	0.3192
127	0.5928	0.8144	0.4072
128	0.5119	0.9761	0.4881
129	0.5036	0.9928	0.4964
130	0.3773	0.7545	0.6227
131	0.529	0.942	0.471
132	0.3805	0.761	0.6195

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 &  0.8459 &  0.3082 &  0.1541 \tabularnewline
19 &  0.9919 &  0.01613 &  0.008066 \tabularnewline
20 &  0.9914 &  0.01724 &  0.008622 \tabularnewline
21 &  0.9908 &  0.01847 &  0.009237 \tabularnewline
22 &  0.9837 &  0.03262 &  0.01631 \tabularnewline
23 &  0.9719 &  0.0562 &  0.0281 \tabularnewline
24 &  0.964 &  0.07197 &  0.03599 \tabularnewline
25 &  0.9429 &  0.1143 &  0.05714 \tabularnewline
26 &  0.9195 &  0.161 &  0.08048 \tabularnewline
27 &  0.8901 &  0.2198 &  0.1099 \tabularnewline
28 &  0.9198 &  0.1604 &  0.08022 \tabularnewline
29 &  0.8876 &  0.2249 &  0.1124 \tabularnewline
30 &  0.9453 &  0.1094 &  0.05469 \tabularnewline
31 &  0.9961 &  0.00772 &  0.00386 \tabularnewline
32 &  0.996 &  0.007991 &  0.003995 \tabularnewline
33 &  0.9937 &  0.01255 &  0.006277 \tabularnewline
34 &  0.9911 &  0.01782 &  0.008911 \tabularnewline
35 &  0.9871 &  0.02582 &  0.01291 \tabularnewline
36 &  0.9813 &  0.03736 &  0.01868 \tabularnewline
37 &  0.975 &  0.04995 &  0.02497 \tabularnewline
38 &  0.9716 &  0.0569 &  0.02845 \tabularnewline
39 &  0.9759 &  0.04819 &  0.02409 \tabularnewline
40 &  0.9736 &  0.05278 &  0.02639 \tabularnewline
41 &  0.972 &  0.05594 &  0.02797 \tabularnewline
42 &  0.9665 &  0.06702 &  0.03351 \tabularnewline
43 &  0.9726 &  0.05478 &  0.02739 \tabularnewline
44 &  0.9872 &  0.02564 &  0.01282 \tabularnewline
45 &  0.9855 &  0.02908 &  0.01454 \tabularnewline
46 &  0.9793 &  0.04133 &  0.02067 \tabularnewline
47 &  0.9717 &  0.05666 &  0.02833 \tabularnewline
48 &  0.962 &  0.07606 &  0.03803 \tabularnewline
49 &  0.9532 &  0.0936 &  0.0468 \tabularnewline
50 &  0.954 &  0.09205 &  0.04603 \tabularnewline
51 &  0.948 &  0.1041 &  0.05203 \tabularnewline
52 &  0.9612 &  0.0775 &  0.03875 \tabularnewline
53 &  0.9504 &  0.09911 &  0.04956 \tabularnewline
54 &  0.9453 &  0.1093 &  0.05465 \tabularnewline
55 &  0.9758 &  0.04837 &  0.02419 \tabularnewline
56 &  0.9918 &  0.01641 &  0.008206 \tabularnewline
57 &  0.9961 &  0.007773 &  0.003886 \tabularnewline
58 &  0.9947 &  0.01067 &  0.005334 \tabularnewline
59 &  0.9962 &  0.007694 &  0.003847 \tabularnewline
60 &  0.9957 &  0.00866 &  0.00433 \tabularnewline
61 &  0.9952 &  0.009519 &  0.004759 \tabularnewline
62 &  0.9962 &  0.007572 &  0.003786 \tabularnewline
63 &  0.9962 &  0.007585 &  0.003793 \tabularnewline
64 &  0.9961 &  0.007705 &  0.003852 \tabularnewline
65 &  0.9957 &  0.008537 &  0.004268 \tabularnewline
66 &  0.9962 &  0.007644 &  0.003822 \tabularnewline
67 &  0.9952 &  0.00969 &  0.004845 \tabularnewline
68 &  0.9955 &  0.008977 &  0.004488 \tabularnewline
69 &  0.998 &  0.003976 &  0.001988 \tabularnewline
70 &  0.9985 &  0.003036 &  0.001518 \tabularnewline
71 &  0.9978 &  0.00434 &  0.00217 \tabularnewline
72 &  0.9989 &  0.002231 &  0.001116 \tabularnewline
73 &  0.9984 &  0.003293 &  0.001646 \tabularnewline
74 &  0.9979 &  0.00412 &  0.00206 \tabularnewline
75 &  0.9975 &  0.005086 &  0.002543 \tabularnewline
76 &  0.9985 &  0.003032 &  0.001516 \tabularnewline
77 &  0.9984 &  0.003265 &  0.001633 \tabularnewline
78 &  0.9985 &  0.002928 &  0.001464 \tabularnewline
79 &  0.9984 &  0.003179 &  0.001589 \tabularnewline
80 &  0.9983 &  0.003424 &  0.001712 \tabularnewline
81 &  0.9982 &  0.003531 &  0.001765 \tabularnewline
82 &  0.9991 &  0.001871 &  0.0009353 \tabularnewline
83 &  0.9986 &  0.002859 &  0.00143 \tabularnewline
84 &  0.9989 &  0.002105 &  0.001052 \tabularnewline
85 &  0.9986 &  0.002787 &  0.001393 \tabularnewline
86 &  0.9984 &  0.003219 &  0.00161 \tabularnewline
87 &  0.998 &  0.004 &  0.002 \tabularnewline
88 &  0.9973 &  0.0054 &  0.0027 \tabularnewline
89 &  0.9961 &  0.00781 &  0.003905 \tabularnewline
90 &  0.9943 &  0.01147 &  0.005736 \tabularnewline
91 &  0.9951 &  0.009811 &  0.004905 \tabularnewline
92 &  0.9945 &  0.01095 &  0.005474 \tabularnewline
93 &  0.997 &  0.006098 &  0.003049 \tabularnewline
94 &  0.9955 &  0.00895 &  0.004475 \tabularnewline
95 &  0.9989 &  0.002271 &  0.001135 \tabularnewline
96 &  0.9985 &  0.002958 &  0.001479 \tabularnewline
97 &  0.999 &  0.00203 &  0.001015 \tabularnewline
98 &  0.9988 &  0.002497 &  0.001248 \tabularnewline
99 &  0.998 &  0.003984 &  0.001992 \tabularnewline
100 &  0.9973 &  0.005451 &  0.002726 \tabularnewline
101 &  0.9977 &  0.004644 &  0.002322 \tabularnewline
102 &  0.9963 &  0.007375 &  0.003688 \tabularnewline
103 &  0.9952 &  0.009578 &  0.004789 \tabularnewline
104 &  0.9931 &  0.01379 &  0.006895 \tabularnewline
105 &  0.9898 &  0.02036 &  0.01018 \tabularnewline
106 &  0.9883 &  0.02346 &  0.01173 \tabularnewline
107 &  0.9828 &  0.03431 &  0.01716 \tabularnewline
108 &  0.9761 &  0.04783 &  0.02392 \tabularnewline
109 &  0.9704 &  0.05916 &  0.02958 \tabularnewline
110 &  0.9594 &  0.08117 &  0.04059 \tabularnewline
111 &  0.943 &  0.1141 &  0.05705 \tabularnewline
112 &  0.944 &  0.112 &  0.056 \tabularnewline
113 &  0.9362 &  0.1276 &  0.06382 \tabularnewline
114 &  0.9118 &  0.1765 &  0.08824 \tabularnewline
115 &  0.9006 &  0.1988 &  0.09939 \tabularnewline
116 &  0.865 &  0.2699 &  0.135 \tabularnewline
117 &  0.8256 &  0.3488 &  0.1744 \tabularnewline
118 &  0.7735 &  0.4529 &  0.2265 \tabularnewline
119 &  0.768 &  0.464 &  0.232 \tabularnewline
120 &  0.7718 &  0.4565 &  0.2282 \tabularnewline
121 &  0.7074 &  0.5852 &  0.2926 \tabularnewline
122 &  0.7747 &  0.4507 &  0.2253 \tabularnewline
123 &  0.7976 &  0.4047 &  0.2024 \tabularnewline
124 &  0.7865 &  0.427 &  0.2135 \tabularnewline
125 &  0.7511 &  0.4978 &  0.2489 \tabularnewline
126 &  0.6808 &  0.6384 &  0.3192 \tabularnewline
127 &  0.5928 &  0.8144 &  0.4072 \tabularnewline
128 &  0.5119 &  0.9761 &  0.4881 \tabularnewline
129 &  0.5036 &  0.9928 &  0.4964 \tabularnewline
130 &  0.3773 &  0.7545 &  0.6227 \tabularnewline
131 &  0.529 &  0.942 &  0.471 \tabularnewline
132 &  0.3805 &  0.761 &  0.6195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304564&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]18[/C][C] 0.8459[/C][C] 0.3082[/C][C] 0.1541[/C][/ROW]
[ROW][C]19[/C][C] 0.9919[/C][C] 0.01613[/C][C] 0.008066[/C][/ROW]
[ROW][C]20[/C][C] 0.9914[/C][C] 0.01724[/C][C] 0.008622[/C][/ROW]
[ROW][C]21[/C][C] 0.9908[/C][C] 0.01847[/C][C] 0.009237[/C][/ROW]
[ROW][C]22[/C][C] 0.9837[/C][C] 0.03262[/C][C] 0.01631[/C][/ROW]
[ROW][C]23[/C][C] 0.9719[/C][C] 0.0562[/C][C] 0.0281[/C][/ROW]
[ROW][C]24[/C][C] 0.964[/C][C] 0.07197[/C][C] 0.03599[/C][/ROW]
[ROW][C]25[/C][C] 0.9429[/C][C] 0.1143[/C][C] 0.05714[/C][/ROW]
[ROW][C]26[/C][C] 0.9195[/C][C] 0.161[/C][C] 0.08048[/C][/ROW]
[ROW][C]27[/C][C] 0.8901[/C][C] 0.2198[/C][C] 0.1099[/C][/ROW]
[ROW][C]28[/C][C] 0.9198[/C][C] 0.1604[/C][C] 0.08022[/C][/ROW]
[ROW][C]29[/C][C] 0.8876[/C][C] 0.2249[/C][C] 0.1124[/C][/ROW]
[ROW][C]30[/C][C] 0.9453[/C][C] 0.1094[/C][C] 0.05469[/C][/ROW]
[ROW][C]31[/C][C] 0.9961[/C][C] 0.00772[/C][C] 0.00386[/C][/ROW]
[ROW][C]32[/C][C] 0.996[/C][C] 0.007991[/C][C] 0.003995[/C][/ROW]
[ROW][C]33[/C][C] 0.9937[/C][C] 0.01255[/C][C] 0.006277[/C][/ROW]
[ROW][C]34[/C][C] 0.9911[/C][C] 0.01782[/C][C] 0.008911[/C][/ROW]
[ROW][C]35[/C][C] 0.9871[/C][C] 0.02582[/C][C] 0.01291[/C][/ROW]
[ROW][C]36[/C][C] 0.9813[/C][C] 0.03736[/C][C] 0.01868[/C][/ROW]
[ROW][C]37[/C][C] 0.975[/C][C] 0.04995[/C][C] 0.02497[/C][/ROW]
[ROW][C]38[/C][C] 0.9716[/C][C] 0.0569[/C][C] 0.02845[/C][/ROW]
[ROW][C]39[/C][C] 0.9759[/C][C] 0.04819[/C][C] 0.02409[/C][/ROW]
[ROW][C]40[/C][C] 0.9736[/C][C] 0.05278[/C][C] 0.02639[/C][/ROW]
[ROW][C]41[/C][C] 0.972[/C][C] 0.05594[/C][C] 0.02797[/C][/ROW]
[ROW][C]42[/C][C] 0.9665[/C][C] 0.06702[/C][C] 0.03351[/C][/ROW]
[ROW][C]43[/C][C] 0.9726[/C][C] 0.05478[/C][C] 0.02739[/C][/ROW]
[ROW][C]44[/C][C] 0.9872[/C][C] 0.02564[/C][C] 0.01282[/C][/ROW]
[ROW][C]45[/C][C] 0.9855[/C][C] 0.02908[/C][C] 0.01454[/C][/ROW]
[ROW][C]46[/C][C] 0.9793[/C][C] 0.04133[/C][C] 0.02067[/C][/ROW]
[ROW][C]47[/C][C] 0.9717[/C][C] 0.05666[/C][C] 0.02833[/C][/ROW]
[ROW][C]48[/C][C] 0.962[/C][C] 0.07606[/C][C] 0.03803[/C][/ROW]
[ROW][C]49[/C][C] 0.9532[/C][C] 0.0936[/C][C] 0.0468[/C][/ROW]
[ROW][C]50[/C][C] 0.954[/C][C] 0.09205[/C][C] 0.04603[/C][/ROW]
[ROW][C]51[/C][C] 0.948[/C][C] 0.1041[/C][C] 0.05203[/C][/ROW]
[ROW][C]52[/C][C] 0.9612[/C][C] 0.0775[/C][C] 0.03875[/C][/ROW]
[ROW][C]53[/C][C] 0.9504[/C][C] 0.09911[/C][C] 0.04956[/C][/ROW]
[ROW][C]54[/C][C] 0.9453[/C][C] 0.1093[/C][C] 0.05465[/C][/ROW]
[ROW][C]55[/C][C] 0.9758[/C][C] 0.04837[/C][C] 0.02419[/C][/ROW]
[ROW][C]56[/C][C] 0.9918[/C][C] 0.01641[/C][C] 0.008206[/C][/ROW]
[ROW][C]57[/C][C] 0.9961[/C][C] 0.007773[/C][C] 0.003886[/C][/ROW]
[ROW][C]58[/C][C] 0.9947[/C][C] 0.01067[/C][C] 0.005334[/C][/ROW]
[ROW][C]59[/C][C] 0.9962[/C][C] 0.007694[/C][C] 0.003847[/C][/ROW]
[ROW][C]60[/C][C] 0.9957[/C][C] 0.00866[/C][C] 0.00433[/C][/ROW]
[ROW][C]61[/C][C] 0.9952[/C][C] 0.009519[/C][C] 0.004759[/C][/ROW]
[ROW][C]62[/C][C] 0.9962[/C][C] 0.007572[/C][C] 0.003786[/C][/ROW]
[ROW][C]63[/C][C] 0.9962[/C][C] 0.007585[/C][C] 0.003793[/C][/ROW]
[ROW][C]64[/C][C] 0.9961[/C][C] 0.007705[/C][C] 0.003852[/C][/ROW]
[ROW][C]65[/C][C] 0.9957[/C][C] 0.008537[/C][C] 0.004268[/C][/ROW]
[ROW][C]66[/C][C] 0.9962[/C][C] 0.007644[/C][C] 0.003822[/C][/ROW]
[ROW][C]67[/C][C] 0.9952[/C][C] 0.00969[/C][C] 0.004845[/C][/ROW]
[ROW][C]68[/C][C] 0.9955[/C][C] 0.008977[/C][C] 0.004488[/C][/ROW]
[ROW][C]69[/C][C] 0.998[/C][C] 0.003976[/C][C] 0.001988[/C][/ROW]
[ROW][C]70[/C][C] 0.9985[/C][C] 0.003036[/C][C] 0.001518[/C][/ROW]
[ROW][C]71[/C][C] 0.9978[/C][C] 0.00434[/C][C] 0.00217[/C][/ROW]
[ROW][C]72[/C][C] 0.9989[/C][C] 0.002231[/C][C] 0.001116[/C][/ROW]
[ROW][C]73[/C][C] 0.9984[/C][C] 0.003293[/C][C] 0.001646[/C][/ROW]
[ROW][C]74[/C][C] 0.9979[/C][C] 0.00412[/C][C] 0.00206[/C][/ROW]
[ROW][C]75[/C][C] 0.9975[/C][C] 0.005086[/C][C] 0.002543[/C][/ROW]
[ROW][C]76[/C][C] 0.9985[/C][C] 0.003032[/C][C] 0.001516[/C][/ROW]
[ROW][C]77[/C][C] 0.9984[/C][C] 0.003265[/C][C] 0.001633[/C][/ROW]
[ROW][C]78[/C][C] 0.9985[/C][C] 0.002928[/C][C] 0.001464[/C][/ROW]
[ROW][C]79[/C][C] 0.9984[/C][C] 0.003179[/C][C] 0.001589[/C][/ROW]
[ROW][C]80[/C][C] 0.9983[/C][C] 0.003424[/C][C] 0.001712[/C][/ROW]
[ROW][C]81[/C][C] 0.9982[/C][C] 0.003531[/C][C] 0.001765[/C][/ROW]
[ROW][C]82[/C][C] 0.9991[/C][C] 0.001871[/C][C] 0.0009353[/C][/ROW]
[ROW][C]83[/C][C] 0.9986[/C][C] 0.002859[/C][C] 0.00143[/C][/ROW]
[ROW][C]84[/C][C] 0.9989[/C][C] 0.002105[/C][C] 0.001052[/C][/ROW]
[ROW][C]85[/C][C] 0.9986[/C][C] 0.002787[/C][C] 0.001393[/C][/ROW]
[ROW][C]86[/C][C] 0.9984[/C][C] 0.003219[/C][C] 0.00161[/C][/ROW]
[ROW][C]87[/C][C] 0.998[/C][C] 0.004[/C][C] 0.002[/C][/ROW]
[ROW][C]88[/C][C] 0.9973[/C][C] 0.0054[/C][C] 0.0027[/C][/ROW]
[ROW][C]89[/C][C] 0.9961[/C][C] 0.00781[/C][C] 0.003905[/C][/ROW]
[ROW][C]90[/C][C] 0.9943[/C][C] 0.01147[/C][C] 0.005736[/C][/ROW]
[ROW][C]91[/C][C] 0.9951[/C][C] 0.009811[/C][C] 0.004905[/C][/ROW]
[ROW][C]92[/C][C] 0.9945[/C][C] 0.01095[/C][C] 0.005474[/C][/ROW]
[ROW][C]93[/C][C] 0.997[/C][C] 0.006098[/C][C] 0.003049[/C][/ROW]
[ROW][C]94[/C][C] 0.9955[/C][C] 0.00895[/C][C] 0.004475[/C][/ROW]
[ROW][C]95[/C][C] 0.9989[/C][C] 0.002271[/C][C] 0.001135[/C][/ROW]
[ROW][C]96[/C][C] 0.9985[/C][C] 0.002958[/C][C] 0.001479[/C][/ROW]
[ROW][C]97[/C][C] 0.999[/C][C] 0.00203[/C][C] 0.001015[/C][/ROW]
[ROW][C]98[/C][C] 0.9988[/C][C] 0.002497[/C][C] 0.001248[/C][/ROW]
[ROW][C]99[/C][C] 0.998[/C][C] 0.003984[/C][C] 0.001992[/C][/ROW]
[ROW][C]100[/C][C] 0.9973[/C][C] 0.005451[/C][C] 0.002726[/C][/ROW]
[ROW][C]101[/C][C] 0.9977[/C][C] 0.004644[/C][C] 0.002322[/C][/ROW]
[ROW][C]102[/C][C] 0.9963[/C][C] 0.007375[/C][C] 0.003688[/C][/ROW]
[ROW][C]103[/C][C] 0.9952[/C][C] 0.009578[/C][C] 0.004789[/C][/ROW]
[ROW][C]104[/C][C] 0.9931[/C][C] 0.01379[/C][C] 0.006895[/C][/ROW]
[ROW][C]105[/C][C] 0.9898[/C][C] 0.02036[/C][C] 0.01018[/C][/ROW]
[ROW][C]106[/C][C] 0.9883[/C][C] 0.02346[/C][C] 0.01173[/C][/ROW]
[ROW][C]107[/C][C] 0.9828[/C][C] 0.03431[/C][C] 0.01716[/C][/ROW]
[ROW][C]108[/C][C] 0.9761[/C][C] 0.04783[/C][C] 0.02392[/C][/ROW]
[ROW][C]109[/C][C] 0.9704[/C][C] 0.05916[/C][C] 0.02958[/C][/ROW]
[ROW][C]110[/C][C] 0.9594[/C][C] 0.08117[/C][C] 0.04059[/C][/ROW]
[ROW][C]111[/C][C] 0.943[/C][C] 0.1141[/C][C] 0.05705[/C][/ROW]
[ROW][C]112[/C][C] 0.944[/C][C] 0.112[/C][C] 0.056[/C][/ROW]
[ROW][C]113[/C][C] 0.9362[/C][C] 0.1276[/C][C] 0.06382[/C][/ROW]
[ROW][C]114[/C][C] 0.9118[/C][C] 0.1765[/C][C] 0.08824[/C][/ROW]
[ROW][C]115[/C][C] 0.9006[/C][C] 0.1988[/C][C] 0.09939[/C][/ROW]
[ROW][C]116[/C][C] 0.865[/C][C] 0.2699[/C][C] 0.135[/C][/ROW]
[ROW][C]117[/C][C] 0.8256[/C][C] 0.3488[/C][C] 0.1744[/C][/ROW]
[ROW][C]118[/C][C] 0.7735[/C][C] 0.4529[/C][C] 0.2265[/C][/ROW]
[ROW][C]119[/C][C] 0.768[/C][C] 0.464[/C][C] 0.232[/C][/ROW]
[ROW][C]120[/C][C] 0.7718[/C][C] 0.4565[/C][C] 0.2282[/C][/ROW]
[ROW][C]121[/C][C] 0.7074[/C][C] 0.5852[/C][C] 0.2926[/C][/ROW]
[ROW][C]122[/C][C] 0.7747[/C][C] 0.4507[/C][C] 0.2253[/C][/ROW]
[ROW][C]123[/C][C] 0.7976[/C][C] 0.4047[/C][C] 0.2024[/C][/ROW]
[ROW][C]124[/C][C] 0.7865[/C][C] 0.427[/C][C] 0.2135[/C][/ROW]
[ROW][C]125[/C][C] 0.7511[/C][C] 0.4978[/C][C] 0.2489[/C][/ROW]
[ROW][C]126[/C][C] 0.6808[/C][C] 0.6384[/C][C] 0.3192[/C][/ROW]
[ROW][C]127[/C][C] 0.5928[/C][C] 0.8144[/C][C] 0.4072[/C][/ROW]
[ROW][C]128[/C][C] 0.5119[/C][C] 0.9761[/C][C] 0.4881[/C][/ROW]
[ROW][C]129[/C][C] 0.5036[/C][C] 0.9928[/C][C] 0.4964[/C][/ROW]
[ROW][C]130[/C][C] 0.3773[/C][C] 0.7545[/C][C] 0.6227[/C][/ROW]
[ROW][C]131[/C][C] 0.529[/C][C] 0.942[/C][C] 0.471[/C][/ROW]
[ROW][C]132[/C][C] 0.3805[/C][C] 0.761[/C][C] 0.6195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304564&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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
18	0.8459	0.3082	0.1541
19	0.9919	0.01613	0.008066
20	0.9914	0.01724	0.008622
21	0.9908	0.01847	0.009237
22	0.9837	0.03262	0.01631
23	0.9719	0.0562	0.0281
24	0.964	0.07197	0.03599
25	0.9429	0.1143	0.05714
26	0.9195	0.161	0.08048
27	0.8901	0.2198	0.1099
28	0.9198	0.1604	0.08022
29	0.8876	0.2249	0.1124
30	0.9453	0.1094	0.05469
31	0.9961	0.00772	0.00386
32	0.996	0.007991	0.003995
33	0.9937	0.01255	0.006277
34	0.9911	0.01782	0.008911
35	0.9871	0.02582	0.01291
36	0.9813	0.03736	0.01868
37	0.975	0.04995	0.02497
38	0.9716	0.0569	0.02845
39	0.9759	0.04819	0.02409
40	0.9736	0.05278	0.02639
41	0.972	0.05594	0.02797
42	0.9665	0.06702	0.03351
43	0.9726	0.05478	0.02739
44	0.9872	0.02564	0.01282
45	0.9855	0.02908	0.01454
46	0.9793	0.04133	0.02067
47	0.9717	0.05666	0.02833
48	0.962	0.07606	0.03803
49	0.9532	0.0936	0.0468
50	0.954	0.09205	0.04603
51	0.948	0.1041	0.05203
52	0.9612	0.0775	0.03875
53	0.9504	0.09911	0.04956
54	0.9453	0.1093	0.05465
55	0.9758	0.04837	0.02419
56	0.9918	0.01641	0.008206
57	0.9961	0.007773	0.003886
58	0.9947	0.01067	0.005334
59	0.9962	0.007694	0.003847
60	0.9957	0.00866	0.00433
61	0.9952	0.009519	0.004759
62	0.9962	0.007572	0.003786
63	0.9962	0.007585	0.003793
64	0.9961	0.007705	0.003852
65	0.9957	0.008537	0.004268
66	0.9962	0.007644	0.003822
67	0.9952	0.00969	0.004845
68	0.9955	0.008977	0.004488
69	0.998	0.003976	0.001988
70	0.9985	0.003036	0.001518
71	0.9978	0.00434	0.00217
72	0.9989	0.002231	0.001116
73	0.9984	0.003293	0.001646
74	0.9979	0.00412	0.00206
75	0.9975	0.005086	0.002543
76	0.9985	0.003032	0.001516
77	0.9984	0.003265	0.001633
78	0.9985	0.002928	0.001464
79	0.9984	0.003179	0.001589
80	0.9983	0.003424	0.001712
81	0.9982	0.003531	0.001765
82	0.9991	0.001871	0.0009353
83	0.9986	0.002859	0.00143
84	0.9989	0.002105	0.001052
85	0.9986	0.002787	0.001393
86	0.9984	0.003219	0.00161
87	0.998	0.004	0.002
88	0.9973	0.0054	0.0027
89	0.9961	0.00781	0.003905
90	0.9943	0.01147	0.005736
91	0.9951	0.009811	0.004905
92	0.9945	0.01095	0.005474
93	0.997	0.006098	0.003049
94	0.9955	0.00895	0.004475
95	0.9989	0.002271	0.001135
96	0.9985	0.002958	0.001479
97	0.999	0.00203	0.001015
98	0.9988	0.002497	0.001248
99	0.998	0.003984	0.001992
100	0.9973	0.005451	0.002726
101	0.9977	0.004644	0.002322
102	0.9963	0.007375	0.003688
103	0.9952	0.009578	0.004789
104	0.9931	0.01379	0.006895
105	0.9898	0.02036	0.01018
106	0.9883	0.02346	0.01173
107	0.9828	0.03431	0.01716
108	0.9761	0.04783	0.02392
109	0.9704	0.05916	0.02958
110	0.9594	0.08117	0.04059
111	0.943	0.1141	0.05705
112	0.944	0.112	0.056
113	0.9362	0.1276	0.06382
114	0.9118	0.1765	0.08824
115	0.9006	0.1988	0.09939
116	0.865	0.2699	0.135
117	0.8256	0.3488	0.1744
118	0.7735	0.4529	0.2265
119	0.768	0.464	0.232
120	0.7718	0.4565	0.2282
121	0.7074	0.5852	0.2926
122	0.7747	0.4507	0.2253
123	0.7976	0.4047	0.2024
124	0.7865	0.427	0.2135
125	0.7511	0.4978	0.2489
126	0.6808	0.6384	0.3192
127	0.5928	0.8144	0.4072
128	0.5119	0.9761	0.4881
129	0.5036	0.9928	0.4964
130	0.3773	0.7545	0.6227
131	0.529	0.942	0.471
132	0.3805	0.761	0.6195

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	46	0.4	NOK
5% type I error level	69	0.6	NOK
10% type I error level	84	0.730435	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 & 46 &  0.4 & NOK \tabularnewline
5% type I error level & 69 & 0.6 & NOK \tabularnewline
10% type I error level & 84 & 0.730435 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304564&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]46[/C][C] 0.4[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]69[/C][C]0.6[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]84[/C][C]0.730435[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304564&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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	46	0.4	NOK
5% type I error level	69	0.6	NOK
10% type I error level	84	0.730435	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 13.713, df1 = 2, df2 = 133, p-value = 3.849e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3928, df1 = 28, df2 = 107, p-value = 0.1166
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.691, df1 = 2, df2 = 133, p-value = 0.1883

\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 = 13.713, df1 = 2, df2 = 133, p-value = 3.849e-06
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3928, df1 = 28, df2 = 107, p-value = 0.1166
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.691, df1 = 2, df2 = 133, p-value = 0.1883
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304564&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 = 13.713, df1 = 2, df2 = 133, p-value = 3.849e-06
[/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 = 1.3928, df1 = 28, df2 = 107, p-value = 0.1166
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.691, df1 = 2, df2 = 133, p-value = 0.1883
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304564&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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 = 13.713, df1 = 2, df2 = 133, p-value = 3.849e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3928, df1 = 28, df2 = 107, p-value = 0.1166
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.691, df1 = 2, df2 = 133, p-value = 0.1883

Variance Inflation Factors (Multicollinearity)

> vif
 Bevr_Leeftijd         SKEOU1  `ITHSUM(t-1)`  `ITHSUM(t-2)`  `ITHSUM(t-3)` 
      1.061245       1.058833       1.084630       1.030652       1.051304 
 `ITHSUM(t-4)`  `ITHSUM(t-5)`  `ITHSUM(t-6)`  `ITHSUM(t-7)`  `ITHSUM(t-8)` 
      1.072337       1.066282       1.061652       1.077003       1.063230 
 `ITHSUM(t-9)` `ITHSUM(t-10)` `ITHSUM(t-11)` `ITHSUM(t-12)` 
      1.073633       1.062782       1.052455       1.059372

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
 Bevr_Leeftijd         SKEOU1  `ITHSUM(t-1)`  `ITHSUM(t-2)`  `ITHSUM(t-3)` 
      1.061245       1.058833       1.084630       1.030652       1.051304 
 `ITHSUM(t-4)`  `ITHSUM(t-5)`  `ITHSUM(t-6)`  `ITHSUM(t-7)`  `ITHSUM(t-8)` 
      1.072337       1.066282       1.061652       1.077003       1.063230 
 `ITHSUM(t-9)` `ITHSUM(t-10)` `ITHSUM(t-11)` `ITHSUM(t-12)` 
      1.073633       1.062782       1.052455       1.059372 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304564&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
 Bevr_Leeftijd         SKEOU1  `ITHSUM(t-1)`  `ITHSUM(t-2)`  `ITHSUM(t-3)` 
      1.061245       1.058833       1.084630       1.030652       1.051304 
 `ITHSUM(t-4)`  `ITHSUM(t-5)`  `ITHSUM(t-6)`  `ITHSUM(t-7)`  `ITHSUM(t-8)` 
      1.072337       1.066282       1.061652       1.077003       1.063230 
 `ITHSUM(t-9)` `ITHSUM(t-10)` `ITHSUM(t-11)` `ITHSUM(t-12)` 
      1.073633       1.062782       1.052455       1.059372 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304564&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304564&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
 Bevr_Leeftijd         SKEOU1  `ITHSUM(t-1)`  `ITHSUM(t-2)`  `ITHSUM(t-3)` 
      1.061245       1.058833       1.084630       1.030652       1.051304 
 `ITHSUM(t-4)`  `ITHSUM(t-5)`  `ITHSUM(t-6)`  `ITHSUM(t-7)`  `ITHSUM(t-8)` 
      1.072337       1.066282       1.061652       1.077003       1.063230 
 `ITHSUM(t-9)` `ITHSUM(t-10)` `ITHSUM(t-11)` `ITHSUM(t-12)` 
      1.073633       1.062782       1.052455       1.059372

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 12 ; par2 = Triple ; par3 = additive ; par4 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Triple ; par3 = additive ; par4 = 12 ; par5 = ;

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

par5 <- ''
par4 <- '12'
par3 <- 'additive'
par2 <- 'Triple'
par1 <- '2'
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